The scale of the analysis is itself remarkable. Researchers led by Mengyao Sun, Yue Yin, and Zejun Jia of Zhongshan Hospital, Fudan University, searched the Web of Science Core Collection using an elaborate query that combined artificial intelligence terms, ranging from machine learning and deep learning to large language models such as ChatGPT and BioGPT, with cancer terminology and drug design vocabulary. After rigorous screening that excluded veterinary studies, publications not employing AI methods, and research unrelated to anticancer therapy, the final dataset comprised 12,906 original research articles and 2,648 review articles. Each publication was then dissected using a multi-tool analytical arsenal including Excel 2025, CiteSpace version 6.4.1, VOSviewer version 1.6.20, and the Bibliometrix R package, with the entire procedure reported in alignment with international bibliometric reporting guidelines.

The geographic distribution of this research output tells a story of shifting global power in science. China emerged as the undisputed leader in total publication volume, contributing 41.9 percent of all publications with 6,514 articles, followed by the United States with 2,802 publications and India with 812. In 2022, China overtook the United States to become the leading annual producer. Yet the picture is more nuanced than raw numbers suggest. Among the top ten contributing countries, China had the lowest proportion of internationally collaborative publications at just 12.9 percent, while the United Kingdom led with a striking 55.9 percent of its papers co-authored across borders. Perhaps more tellingly, developed countries demonstrated significantly higher average citation counts than their developing counterparts, a statistically significant disparity reflecting differences in journal provenance, research infrastructure, funding intensity, and the strength of international networks.

At the institutional level, Harvard University generated the highest number of publications, followed closely by the Chinese Academy of Sciences and the University of California System. Cluster analysis revealed two prominent global scientific cooperation networks, one centered on the United States and the other on China, effectively dividing the field into two gravitational spheres. The pharmaceutical industry is deeply embedded in this landscape, with Roche, Pfizer, Novartis, AstraZeneca, and Merck among the contributing corporations. Funding data show the National Natural Science Foundation of China and the United States Department of Health and Human Services as the foremost sponsors, while AstraZeneca, Pfizer, Novartis, and Roche lead corporate investment, focusing primarily on early-stage drug discovery rather than clinical validation.

Individual researchers have also left indelible marks on the field. Professor Alex Zhavoronkov was identified as the most prolific author with 55 publications, and notably, half of the top ten high-output authors hail from Insilico Medicine, underscoring the outsized influence of industry in this domain. Professor Michael Patrick Menden received the highest number of citations, and every highly cited author is an expert in either information science or medicine, confirming the deeply interdisciplinary character of AI-assisted drug design. Interestingly, the majority of highly cited scholars are concentrated in Europe, home to institutions such as the European Molecular Biology Laboratory-European Bioinformatics Institute and the German Helmholtz Association, which fostered early integration of biology, chemistry, and computational sciences. Europe also nurtured pioneering companies like Exscientia and catalyzed AlphaFold itself. By contrast, although Chinese researchers hold four of the top ten positions in publication volume, none appeared on the highly cited list, a gap the authors suggest reflects the nation’s status as a rising star that must now prioritize research quality over quantity.

The keyword analysis paints a vivid portrait of what scientists are actually studying. The terms artificial intelligence, immunotherapy, and breast cancer dominated, with breast, prostate, lung, and liver cancers attracting the greatest attention. This concentration is no accident. Breast and prostate cancers rank among the most prevalent malignancies in women and men across Europe and the United States, and their favorable five-year survival rates, exceeding 90 percent for breast cancer and 98 percent for prostate cancer, create substantial commercial incentives. Breast cancer alone accounts for 7.7 percent of the global economic cost of cancer, making it the third most economically burdensome malignancy. Both cancers also possess well-established molecular classification systems and clearly defined druggable driver targets, which make them ideal testing grounds for AI technologies. Across cancer types, drug development converges on a limited set of validated targets: HER2, estrogen receptor, and CDK4/6 in breast cancer; EGFR tyrosine kinase inhibitors in non-small cell lung cancer; the androgen receptor in prostate cancer; and immune checkpoint inhibitors in hepatocellular carcinoma. This pattern, the authors note, reflects a persistent me-too and me-better development paradigm, with genuine first-in-class innovation remaining scarce.

The technological evolution of the field reads like a history of machine learning itself. In the early period from 2011 to 2012, support vector machines reigned supreme, with studies concentrated on specific diseases and drugs such as breast cancer, aromatase inhibitors, and tamoxifen. Between 2012 and 2017, random forests and artificial neural networks gained prominence as machine learning became systematically integrated into drug design for property prediction and molecular modeling. From 2018 to 2022, big data, web servers, and convolutional neural networks emerged as dominant themes, marking a transition to deep learning applied to massive datasets and online services that lowered barriers to entry. More recently, the scope has broadened from traditional structure-based drug design toward predicting pharmacodynamic efficacy, and from small molecules to innovative biotherapeutics including tumor vaccines, therapeutic antibodies, and antibody-drug conjugates. Immunotherapy has become a leading focal point, with AI being applied to neoantigen prediction, antigenic peptide design, and the optimization of T cell, dendritic cell, and natural killer cell therapies. Emerging hotspots include Toll-like receptor agonists as vaccine adjuvants, macrophage polarization, neutrophil extracellular traps, and the transcription factor STAT3.

Despite the dazzling growth, the analysis unflinchingly documents the field’s structural weaknesses. Tumors are extraordinarily complex biological systems: high-grade gliomas exhibit intratumoral heterogeneity, immunosuppressive microenvironments, glioma stem cells, and the physical barrier of the blood-brain barrier, while phenotypic plasticity, now recognized as a hallmark of cancer, allows tumor cells to dedifferentiate, resist drugs, and even switch lineages, as when lung adenocarcinoma transforms into small cell lung cancer. Most AI models are trained on static, reductionist datasets such as molecular structures or in vitro assays, blind to the dynamic, adaptive nature of tumors in living patients. The field also suffers from a paper-driven rather than need-driven orientation: algorithmic publications proliferate because entry barriers are low and publication is fast, while clinical translation remains sparse, with the probability of market approval hovering at approximately 5 percent even after phase 1 trials. Data fragmentation compounds the problem, with models trained on public databases like ChEMBL and TCGA that suffer from batch effects, inconsistent standardization, and shallow clinical annotations, creating what the authors describe as a data archipelago. Between 2019 and 2024, pharmaceutical companies using AI in Europe, the United States, and the Asia-Pacific region faced significant data breaches, highlighting the urgent challenges of privacy, security, and regulatory compliance, particularly when human genetic resources are involved. The black-box nature of many sophisticated models further conflicts with regulatory demands for clear mechanisms of action.

The path forward, the authors argue, demands a fundamental reorientation toward clinically driven innovation. Priorities include integrating multi-omics data spanning genomics, proteomics, metabolomics, lipidomics, and spatial transcriptomics; building specialized disease cohort databases including patient-derived xenograft models and organoid biobanks; and adopting federated learning frameworks that allow collaborative model training while protecting privacy. Closed-loop validation systems that combine AI with active learning and high-throughput wet-lab platforms such as CRISPR screens and microfluidic organ chips could finally connect computational predictions to biological reality. AlphaFold-style protein structure prediction, geometric deep learning frameworks for RNA-ligand interactions, and generative AI for de novo protein design are expanding the druggable space beyond traditionally undruggable targets, while machine learning-enhanced nanoparticles, liposomes, extracellular vesicles, and even nanorobots promise precision drug delivery. AI has already demonstrated the ability to cut research and development costs by more than 40 percent and compress timelines from years to months, and AI-optimized anticancer drugs such as CV8102, PRT3789, ISM6331, and ISM5043 have reached clinical trials. If the field can marry its computational firepower with biological insight, explainable algorithms, and rigorous clinical validation, the vision of truly AI-designed cancer medicines may finally move from promise to prescription.

Subject of Research: Bibliometric analysis of global research trends in AI-driven anticancer drug design from 2011 to 2025

Article Title: Global research status and trends in the AI-driven anticancer drug design: a bibliometric analysis of 2011–2025

Article References: Sun, M., Yin, Y., & Jia, Z. (2026). Global research status and trends in the AI-driven anticancer drug design: a bibliometric analysis of 2011–2025. Clinical Cancer Bulletin, 5(1), Article 8. https://doi.org/10.1007/s44272-026-00060-8

Image Credits: AI Generated

DOI: 10.1007/s44272-026-00060-8

Keywords: artificial intelligence, anticancer drug design, bibliometric analysis, immunotherapy, machine learning, drug discovery, breast cancer, clinical translation, multi-omics, AlphaFold, tumor microenvironment, China

The study employed two complementary computational tools to dissect this vast literature. VOSviewer, a bibliometric network software, was used to construct maps based on keyword co-occurrence, bibliographic coupling, and co-citation relationships, revealing the field’s most influential journals, authors, and countries. SciMAT, a science mapping program, was then applied to track the longitudinal evolution of the field’s thematic structure across four consecutive periods: 1996–2008, 2009–2015, 2016–2020, and 2021–2024. To validate this segmentation, the team ran an exploratory piecewise linear regression on annual publication counts, comparing candidate breakpoint models with the Akaike Information Criterion, the Bayesian Information Criterion, and goodness-of-fit statistics. The optimal model identified publication-growth transitions in 2009, 2016, and 2020, closely matching the chosen intervals and providing quantitative evidence that the periodization reflects genuine shifts in research intensity rather than arbitrary divisions.

The numbers tell a dramatic story of scientific acceleration. During the formative 1996–2008 period, the field produced an average of just 3.23 publications per year, with erratic year-to-year fluctuations typical of an emerging discipline still defining its conceptual foundations. Output increased nearly twentyfold in 2009–2015, reaching 64.43 publications annually as geographic information systems, remote sensing, and spatial modelling entered the mainstream. The third period saw another surge to 307.60 publications per year, and the final interval peaked at an average of 697.50, topping out at 762 papers in 2022, which alone represented 15.8 percent of the entire dataset. This trajectory aligns temporally with landmark international initiatives, including the Millennium Ecosystem Assessment of 2005, The Economics of Ecosystems and Biodiversity in 2010, the creation of IPBES in 2012, and the adoption of the Sustainable Development Goals and the Paris Agreement in 2015, all of which elevated ecosystem services to a central position in global sustainability policy.

Keyword co-occurrence analysis, built on 88 retained author keywords connected by 1,295 links, partitioned the research landscape into seven thematic clusters. The largest, containing 32 terms, revolves around urban planning, green infrastructure, ecosystem services, and sustainability, with terms such as air pollution, urban heat island, and resilience frequently appearing together, reflecting a strong focus on how green infrastructure addresses urban environmental challenges. A second cluster of 21 terms centres on ecosystem service valuation, urbanization, and land-use change, where the prominence of China underscores that country’s outsized role in advancing valuation studies amid rapid land transformation. A third cluster is dominated by modelling frameworks, with the InVEST model emerging as the principal tool for quantifying and spatially mapping ecosystem services, while the PLUS model is primarily associated with land-use simulation and future scenario analysis. Remaining clusters cover remote sensing, geographic information systems, biodiversity conservation, ecological security patterns, and trade-off analysis, illustrating the methodological breadth of the discipline.

The co-citation structure of journals and publications reveals where the field’s intellectual roots lie. Ecological Indicators, Science of the Total Environment, and Landscape and Urban Planning stand out as the most influential sources, demonstrating that urban ecosystem service assessment is anchored at the intersection of ecological assessment, environmental sustainability, and urban planning. Among cited papers, three intellectual lineages emerge clearly: a red cluster of foundational conceptual and classification work by authors such as Rudolf de Groot, Benjamin Burkhard, and Gómez-Baggethun and Barton; a green cluster of valuation studies led by Robert Costanza and Gaodi Xie, including the landmark 1997 Nature paper valuing the world’s ecosystem services and natural capital; and a blue cluster examining urbanization, biodiversity, climate regulation, and modelling, with contributions from Jian Peng, Chunyang He, Foley, Grimm, Liu, and Nelson. Author co-citation analysis confirms three complementary research traditions, valuation and urban ecology, methodological development, and applied ecosystem management, with de Groot’s presence in two clusters highlighting his cross-cutting influence.

Geographically, the field remains strikingly concentrated. Bibliographic coupling at the country level, restricted to nations with at least 50 publications, identifies China, the United States, Germany, Italy, and England as the network’s centre of gravity, sharing overlapping cited literatures and conceptual foundations. Countries such as Brazil, India, Iran, and Mexico show substantial connections, indicating that research has expanded beyond traditionally dominant scientific communities, yet many countries, particularly across Africa, remain barely represented. The study’s authors flag this as a critical knowledge gap, noting that Africa is projected to experience the world’s fastest urban growth by 2050, precisely the regions where ecosystem service assessments are likely to become most essential for planning and policy.

The thematic evolution analysis is perhaps the study’s most revealing contribution. In the earliest period, conservation stood alone as the field’s motor theme, exhibiting high centrality and density and linking to nascent concepts like ecosystem services, resilience, land-use change, and sustainability. By 2009–2015, ecosystem services, their values, carbon storage and sequestration, urban planning, and landscape metrics had become motor themes, coinciding with the integration of GIS, LiDAR, and participatory planning. The 2016–2020 period brought diversification and operational maturity: land-use and land-cover change rose to prominence, blue-green infrastructure expanded from a conservation focus to a multifunctional approach addressing stormwater, economics, and spatial planning, and cultural ecosystem services entered the vocabulary. In the final period, ecosystem services itself surpassed land-use change as the most influential theme, absorbing earlier topics as sub-themes, while ecological risk evolved into a dominant focus treating whole ecosystems as integrated risk receptors, and valuation shifted from static assessments toward dynamic, coupled frameworks incorporating coupling-coordination models and bivariate spatial correlation.

An overlay analysis of keyword stability across periods shows a stability index climbing from 0.21 to 0.86, demonstrating that an increasing share of the field’s vocabulary persists across successive eras even as new terms continually appear. Rather than fragmenting, the field has consolidated while diversifying, with themes branching and recombining rather than replacing one another. Concepts have evolved in recognisable lineages: urban heat island research matured into ecosystem-based cooling service assessment, carbon storage studies expanded into blue-green infrastructure and nature-based solutions, and cultural ecosystem services progressed from descriptive valuation toward planning-oriented decision support. Yet the authors caution that diversification has been driven primarily by methodological innovation rather than fundamentally new conceptual domains, and that biophysical and environmental themes remain disproportionately dominant across all four periods.

This imbalance defines the field’s most pressing frontier. Socio-economic inequalities, governance dynamics, institutional frameworks, stakeholder behaviour, and environmental justice remain comparatively underexplored, even as themes like public participation and decision-making have gained some traction in recent years. The study also acknowledges its own limitations: the analysis covered only English-language articles in a single database, relied on author keywords rather than full text, and produced bibliometric linkages that represent scholarly association rather than causal relationships. The authors argue that future progress will depend on integrating grey literature from municipalities and planning agencies, expanding research in the rapidly urbanizing Global South, and embedding ecological, social, economic, and governance dimensions within holistic frameworks. For a discipline that has grown from three papers a year to more than 700, the next challenge is not measuring what nature gives cities, but ensuring that those measurements serve everyone who lives in them.

Subject of Research: Bibliometric and science mapping analysis of urban ecosystem service assessment research from 1996 to 2024

Article Title: Evaluating the evolution of urban ecosystem service assessment research through bibliometric and science mapping analysis

Article References: Dey, S., Niyogi, J. G., Das, D., & Majumdar, S. (2026). Evaluating the evolution of urban ecosystem service assessment research through bibliometric and science mapping analysis. Discover Cities, 3(1), Article 188. https://doi.org/10.1007/s44327-026-00369-y

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00369-y

Keywords: urban ecosystem services, ecosystem service assessment, bibliometric analysis, science mapping, VOSviewer, SciMAT, thematic evolution, green infrastructure, InVEST model, land-use change, urban sustainability, ecosystem service valuation

The team searched the SCOPUS database using a deliberately stringent four-domain strategy, requiring studies to simultaneously address seaweed or marine algae, specific crop types, yield and productivity parameters, and nutrient uptake or soil fertility. From more than 55,000 seaweed-related records and over a million crop-focused publications in the database, the intersection yielded just 156 non-redundant English-language journal articles. That narrow funnel was intentional, the authors explain, because it captured only research sitting squarely at the soil–crop–nutrient nexus rather than the broader universe of algal science. A parallel search in Web of Science returned 139 articles with roughly 89 percent overlap, lending confidence that the core literature was well captured.

Geographically, the field is dominated by Asia, which produced 84 of the 156 papers, with India alone contributing 41. Europe followed with 41 publications, led by Italy, Greece, and Portugal, while North America added 12. The authors attribute Asia’s leadership to abundant coastal biodiversity, long-standing cultural use of marine biomass in farming, and national programs encouraging bio-input adoption. Europe’s output, they note, has been stimulated by the EU Fertilising Products Regulation, which formally recognized biostimulants as a product category in 2019. Africa and South America remain strikingly underrepresented, a gap the researchers link to limited marine access, infrastructure, and research funding rather than any lack of agronomic potential.

What exactly makes seaweed so valuable to crops? The answer lies in a dense cocktail of bioactive compounds. Brown seaweeds such as Ascophyllum nodosum contain alginates, laminarin, and fucoidan; red species like Kappaphycus alvarezii and Gracilaria supply carrageenan and agar; green algae such as Ulva contribute their own sulfated polysaccharides. Layered on top are phytohormones including auxins, cytokinins, and gibberellins, along with betaines, phenolic antioxidants, amino acids, and micronutrients. Together, these molecules modulate plant physiology at multiple levels, from photosynthetic efficiency to the expression of nutrient transporter genes in root membranes.

The molecular evidence is particularly compelling. Transcriptomic analyses show that seaweed extracts reprogram key metabolic pathways, including phenylpropanoid and flavonoid biosynthesis, and upregulate genes tied to photosynthesis, hormone signaling, and carbon, nitrogen, and sulfur metabolism. In rapeseed, Ascophyllum nodosum extract improved nitrogen and sulfur acquisition by boosting the transcription of root membrane transporters for those nutrients. Betaines in the extracts appear to inhibit chlorophyll degradation, preserving photosynthetic capacity, while enhanced activity of enzymes like Rubisco and carbonic anhydrase supports greater carbon assimilation and starch biosynthesis.

Field results back the laboratory findings. Seaweed sap applied as a foliar spray or soil drench has boosted yields in rice, wheat, maize, green gram, tomato, kiwifruit, and citrus across multiple studies. One trial reported a nearly 19 percent grain yield increase in boro rice with Ascophyllum-derived biostimulants compared to untreated controls. Nutrient uptake enhancements ranged from modest 7 to 11 percent gains in potato tubers to extraordinary responses exceeding 200 to 400 percent in sesame, though the authors caution that this variability reflects species-specific and application-dependent differences. Notably, most experimental evidence comes from India, where Kappaphycus alvarezii and Gracilaria edulis dominate the literature, raising generalisability concerns that the review flags explicitly.

Beyond the plant itself, seaweed amendments reshape the soil. Polysaccharides like alginate form gel-like matrices that improve soil aggregation, aeration, and moisture retention, while humic and fulvic acids in seaweed sap buffer soil pH. The amendments also feed rhizosphere microbial communities, including nitrogen-fixing bacteria and phosphorus-solubilizing fungi, accelerating organic matter decomposition and nutrient mineralization. Seaweeds even act as chelating agents, adsorbing heavy metals from contaminated soils and shielding crops from toxicity. Under stress conditions, seaweed extracts help plants maintain favorable potassium-to-sodium balances under salinity, preserve leaf turgor during drought, and activate salicylic acid and jasmonic acid signaling pathways that prime defenses against pathogens.

The thematic mapping revealed four dominant research clusters: algal physiology and bioactive mechanisms, seaweed-based inputs for yield and nutrition, soil health and organic amendments, and biofertilizers combined with stress adaptation strategies. Temporal clustering showed the field’s evolution from taxonomic and foundational algal studies in the early 2000s toward applied agronomic research after 2010, and finally toward integrated soil health and climate-smart agriculture in recent years. The post-2018 surge in soil health and biofertilizer research coincides with the EU regulation and the launch of the UN Decade of Ecosystem Restoration, suggesting policy is actively steering the science.

Economically, the picture is promising but incomplete. Commercial products such as Stimplex, Kelpak, Maxicrop, and Sagarika have achieved market penetration, and studies on rice and maize in India reported net returns exceeding 15 to 25 percent above conventional fertilizer-only treatments. Yet comprehensive techno-economic assessments for smallholder contexts in South Asia and sub-Saharan Africa remain scarce, and production costs hinge heavily on harvesting methods, extraction technology, and formulation type. Integrating seaweed processing into coastal biorefineries, where co-products like agar and carrageenan offset costs, offers one pathway to economic sustainability within a circular bioeconomy.

The review’s authors are candid about remaining gaps: standardized application protocols, multi-location field trials across diverse agroclimatic zones, multi-omics elucidation of molecular mechanisms, life-cycle assessments, and systematic screening of underutilized red and green seaweed species all demand attention. Still, the trajectory is unmistakable. As agriculture grapples with soil degradation, nutrient leaching, and climate volatility, seaweed-based amendments offer a rare combination of benefits, feeding crops, restoring soils, and supporting microbial life, all from a renewable marine resource. The evidence base is now consolidated; the challenge ahead is translating it into reproducible, affordable practice at farm scale.

Subject of Research: Bibliometric analysis of global research trends on seaweed-based soil amendments and biostimulants for crop productivity and soil health from 2000 to 2024

Article Title: Global research trends on seaweed-based amendments for soil health and crop productivity

Article References: Sarkar, S., Dutta, S., Dey, S., Dhar, A., Garai, S., Ghosh, S., Brahmachari, K., & Ghosh, A. (2026). Global research trends on seaweed-based amendments for soil health and crop productivity. Discover Agriculture, 4(1), Article 289. https://doi.org/10.1007/s44279-026-00741-x

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00741-x

Keywords: seaweed, biostimulants, soil health, crop yield, sustainable agriculture, macroalgae, biofertilizers, nutrient uptake, bibliometric analysis, Ascophyllum nodosum, Kappaphycus alvarezii, climate-smart agriculture

The analysis, covering publications from 2000 to 2025, drew on the Scopus database and followed a PRISMA-adapted screening workflow to construct a final analytical corpus of 1,746 articles and reviews spanning 432 journals and involving 11,277 unique authors. The field has expanded at a compound annual growth rate of 17.10%, accumulating 42,075 citations with an average of 24.10 citations per document. Growth accelerated sharply after 2018: annual output rose from 84 publications in 2018 to 259 in 2025, a trajectory the study attributes to increasing prioritization of antimicrobial resistance on the global research agenda and a post-pandemic surge in translational diagnostic research.

To frame the analysis, the study anchored itself in the World Health Organization’s bacterial priority pathogen lists. The WHO’s 2017 framework classified resistant bacteria into critical, high, and medium priority categories, and its 2024 update expanded the list to 24 pathogens across 15 bacterial families using multicriteria decision analysis that weighed mortality, incidence, resistance trends, transmissibility, preventability, treatability, and the state of the drug development pipeline. The critical tier now includes carbapenem-resistant Acinetobacter baumannii, carbapenem-resistant and third-generation cephalosporin-resistant Enterobacterales, and rifampicin-resistant Mycobacterium tuberculosis, while carbapenem-resistant Pseudomonas aeruginosa was moved from critical to high priority based on regional resistance trends and comparatively lower transmission capacity.

Against this backdrop, the bibliometric results reveal a field organized around distinct pathogen axes. Staphylococcus aureus dominated the corpus with 730 publications and 21,945 total citations, followed by Mycobacterium tuberculosis with 439 publications, Enterobacterales with 389, Pseudomonas aeruginosa with 309, Acinetobacter baumannii with 279, and Enterococcus faecium with 250. Growth over the past five years was strongest for Streptococcus pneumoniae at 27.79%, Pseudomonas aeruginosa at 26.35%, and Enterobacterales at 22.81%, signaling a decisive shift in research attention toward Gram-negative carbapenem resistance as the most urgent clinical frontier.

At the platform level, conventional PCR and nucleic acid amplification testing appeared in 61.51% of publications, while whole-genome sequencing featured in 55.44%, making these two technologies the twin pillars of the literature. Because platform categories were not mutually exclusive, many publications combined both approaches. Multiplex PCR stood out for impact, averaging 42.57 citations per article, reflecting the foundational role of early target-specific resistance detection. Emerging technologies, including nanopore sequencing, metagenomic sequencing, and CRISPR-based diagnostics, appeared at low frequencies but formed distinct and growing clusters, suggesting they represent innovation fronts that have not yet reached routine clinical integration.

Resistance marker analysis identified mecA/mecC, rpoB, blaNDM, katG, and vanA/vanB as the most frequently studied molecular targets. Methicillin resistance in staphylococci, epitomized by the mecA gene and its newer variant mecC, anchored the Gram-positive research tradition, while the tuberculosis markers rpoB, katG, and inhA defined a mature and specialized diagnostic axis. Carbapenemase genes, including blaKPC, blaNDM, and blaOXA-48, together with the mobile colistin resistance gene mcr and the fluoroquinolone targets gyrA and parC, showed the strongest recent growth, with gyrA and parC expanding at compound annual rates of 44.28% and 49.53% respectively over the last five years.

Thematic mapping of keyword co-occurrence networks revealed that the literature is structured around six interpretable clusters. Two emerged as mature core themes: PCR-based rapid antimicrobial resistance detection, organized around MRSA, multiplex PCR, and the mecA/mecC and vanA/vanB markers, and a whole-genome sequencing and genomic epidemiology theme spanning multiple pathogen groups. The tuberculosis resistance marker axis formed a strong but specialized mature theme, while the carbapenemase and Gram-negative resistance gene cluster, the metagenomic and nanopore clinical diagnostics cluster, and a general cross-pathogen antimicrobial resistance cluster were identified as emerging or niche research fronts.

Thematic evolution analysis across three time windows documented a clear conceptual restructuring. The early period from 2000 to 2010 centered on target-specific markers such as mecA, vancomycin resistance, and real-time PCR, reflecting an era of single-gene rapid tests. The middle period from 2011 to 2020 brought whole-genome sequencing, tuberculosis, multiplex PCR, and the Enterobacterales-carbapenemase axis to prominence. The recent period from 2021 to 2025 represents a more integrated antimicrobial resistance framework in which WGS, Staphylococcus aureus, and antibiotic resistance concepts dominate, demonstrating the field’s transition from individual marker detection to genomically integrated, translationally oriented diagnostics.

The study’s methodological rigor included a validation exercise in which 150 randomly selected records were blindly reassessed to test the rule-based text-matching system used to classify pathogens, platforms, markers, and clinical contexts. Concordance rates reached 100% for platform and resistance-marker labels, 91.3% for pathogen labels, and 86.7% for clinical-context labels, yielding an overall average agreement of 94.5%. The analysis also mapped the geography of the field: China led production with 263 publications, followed by the United States with 252, the United Kingdom with 130, and Germany with 127, though the United Kingdom and France showed higher rates of international collaboration and network centrality, revealing a divide between volume-based productivity and collaboration-intensive influence.

The findings carry important implications for clinical practice. Prior evidence shows that rapid diagnostic tests, when deployed alongside antimicrobial stewardship programs, reduce mortality in bloodstream infections compared with blood culture alone. The bibliometric structure documented here confirms that molecular diagnostics has evolved beyond answering whether a pathogen is present, into a multilayered data-generating enterprise that supports resistance prediction, monitoring of clonal spread, and clinical and public health decision-making. At the same time, the study acknowledges limitations: PCR panels and WGS report genetic content rather than physiological state, meaning phenomena such as bacterial persistence and tolerance fall largely outside the field’s marker-centered vocabulary, and future work integrating phenotypic, genomic, and virulence-layer data will be essential to close the gap between resistance prediction and treatment outcome.

Subject of Research: Bibliometric mapping of molecular diagnostic platforms and resistance markers for WHO priority bacterial pathogens

Article Title: Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends

Article References: Ünlü, S. (2026). Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends. MicrobiologyOpen, 15(5), Article e70394. https://doi.org/10.1002/mbo3.70394

Image Credits: AI Generated

DOI: 10.1002/mbo3.70394

Keywords: antimicrobial resistance, molecular diagnostics, WHO priority pathogens, whole-genome sequencing, PCR, bibliometric analysis, MRSA, carbapenemase genes, Mycobacterium tuberculosis, Enterobacterales, CRISPR diagnostics, genomic epidemiology

The research team, led by Yi Zheng, Qianrong Wang, Qiong Zhang, and Hong-Mei Zhang of the Department of Clinical Oncology at Xijing Hospital, The Fourth Military Medical University in Xi’an, China, retrieved publications from the Web of Science Core Collection covering January 1, 2017 through December 31, 2025. After rigorous screening that excluded purely technical papers, early-access items, and retracted publications, 979 studies—933 articles and 46 reviews—formed the analytical core. These publications, spread across 267 journals and authored by 7,509 researchers, collectively cited 33,183 references, a testament to the field’s rapid expansion and deep intellectual roots.

The analytical toolkit itself reflects the computational sophistication that single-cell biology now demands. The team employed Bibliometrix 5.0 to chart publication and citation trends, CiteSpace 6.1R6 for keyword evolution and interdisciplinary knowledge mapping, and VOSviewer 1.6.20 for constructing co-authorship and collaboration networks, supplemented by R and Python for visualization. Keyword synonyms were manually standardized—merging terms such as hepatocellular carcinoma, HCC, and liver cancer into unified categories—while dual-map overlays traced how knowledge flows between research domains, revealing citation trajectories from molecular biology and immunology into genetics, and from clinical medicine into health sciences.

The headline finding is a dramatic acceleration in output. Publications and citations rose steeply between 2017 and 2020 before continuing upward at a more moderate pace, a trajectory the authors characterize as typical of an emerging field transitioning from early adoption to maturity. Geographically, the landscape is strikingly concentrated: China leads in both publication volume and citations, amassing 12,576 citations compared with 2,689 for the United States and 1,073 for France. Even more remarkable, all ten of the most prolific institutions are Chinese, with Fudan University alone producing 287 articles—29.3 percent of the total—followed by Sun Yat-sen University with 192 and Zhejiang University with 139. The United States remains China’s primary international partner, anchoring a powerful trans-Pacific research axis, while secondary collaborations link Japan with Canada, Australia with Germany, and Spain with Italy.

Within China, the analysis uncovered a pronounced regional imbalance. Research activity clusters in the southeastern coastal and central regions—Shanghai, Guangdong, Zhejiang, and Hunan—while western and northeastern provinces contribute far fewer studies. The authors attribute this pattern to two interlocking factors: the endemicity of hepatitis B virus in southeastern China, which drives a correspondingly high liver cancer burden and attracts greater funding, and the uneven distribution of biomedical research infrastructure. This concentration underscores, they argue, the need for intranational collaborative networks that connect established hubs with high-burden regions that have historically lacked research capacity.

At the individual level, a small cadre of investigators anchors the field. Fan Jia emerges as the most prolific contributor with 25 publications, followed by Zhou Jian with 24 and Gao Qiang with 12, and these same researchers serve as central hubs in the co-authorship network, connecting multiple research groups. Among the most cited authors, Zhang Zemin leads with 3,187 citations, ahead of Zhang Mingqi with 3,138 and Hu Xuedan with 1,217. The journal landscape is equally revealing: Frontiers in Immunology published the most articles at 80, while Cell accumulated the highest citation count at 2,174, followed by the Journal of Hepatology with 2,016 and Nature with 1,495. Core journals fostering collaboration include the Journal of Hepatology, Nature Communications, and Frontiers in Immunology.

The intellectual foundations of the field trace back to a handful of landmark studies. The most cited publication, with 1,558 citations, is the 2017 Cell paper by Zheng and colleagues, ‘Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing,’ which established the first comprehensive single-cell transcriptomic atlas of T cells in hepatocellular carcinoma and exposed the profound heterogeneity and dysfunctional states of tumor-infiltrating immune cells. The second most cited work, Zhang and colleagues’ 2019 Cell study on the dynamic immune landscape of hepatocellular carcinoma, identified LAMP3-positive dendritic cells as migratory regulators of lymphocyte crosstalk. Aizarani and colleagues’ 2019 Nature paper, which constructed a comprehensive human liver cell atlas and discovered a TROP2-intermediate progenitor population with bipotent organoid-forming capacity, rounds out the foundational trio. Co-citation analysis further shows the field rests on a dual foundation: deep exploration of cancer-associated fibroblasts and stromal biology, exemplified by a heavily cited 2017 Annual Review of Pathology review, coupled with adoption of key computational tools such as SCENIC, the single-cell regulatory network inference and clustering method published in Nature Methods.

Perhaps the most compelling narrative emerges from the keyword evolution analysis, which documents a clear three-phase conceptual progression. The initial phase was descriptive, dominated by terms like gene expression, intratumor heterogeneity, and transcriptome, as researchers cataloged the complete cellular repertoire of liver tumors and identified previously unappreciated malignant subpopulations with stem-like properties. The second phase shifted to the tumor microenvironment, with surging interest in T cells, macrophages, cancer-associated fibroblasts, and immunosuppression—work that dismantled the simplistic M1/M2 macrophage dichotomy and revealed exhausted CD8-positive T cells and regulatory T cells as drivers of immune evasion. The current frontier, marked by burst keywords including cellular crosstalk, growth factors, extracellular vesicles, and gene regulatory networks, moves from static snapshots to dynamic models of intercellular communication, as computational tools infer ligand-receptor interactions and map the signaling circuits that orchestrate angiogenesis, metastasis, and drug resistance.

These insights are already flowing into clinical translation. Single-cell RNA sequencing has refined the understanding of immune checkpoints beyond PD-1, revealing co-inhibitory and co-stimulatory receptors on specific T cell subsets and paving the way for rational combination immunotherapies, such as TIGIT and PD-1 co-blockade. The identification of pro-tumorigenic myeloid populations, including TREM2-positive macrophages that suppress CD8-positive T cell infiltration after transarterial chemoembolization, has opened targets for myeloid-directed therapies such as CSF1R and CD47 blockers. Distinct cancer-associated fibroblast subtypes, some of which determine immunotherapy efficacy—such as POSTN-positive fibroblasts—can now be selectively targeted or reprogrammed, with FAP inhibitors advancing through clinical testing for advanced solid cancers. The technology has also exposed vulnerabilities in therapy-resistant subclones: targeting PPAR-gamma counteracts tumor adaptation to immune checkpoint blockade, while the SNRPB-CCNB1 axis, which promotes progression and cisplatin resistance through lipid metabolism reprogramming, offers a route to augment chemotherapy sensitivity. Cell-type-specific gene signatures derived from single-cell data can deconvolve bulk RNA sequencing from patient biopsies, yielding prognostic tools such as a senescence-related gene signature and a 57-gene matrix stiffness signature that aid patient stratification and personalized treatment planning.

The authors are candid about the field’s remaining obstacles. Tissue dissociation of fibrotic livers can introduce transcriptional stress artifacts, and the loss of spatial context limits interpretation of cellular interactions. Batch effects across datasets and the sheer dimensionality of single-cell data complicate integration and noise discrimination, while high costs restrict large-scale cohort studies and the absence of standardized protocols hampers reproducibility. Emerging solutions—spatial transcriptomics, fixed-cell technologies, and machine learning-based batch correction—begin to address these limitations. The bibliometric analysis itself carries caveats: reliance on a single English-language database may underrepresent non-English contributions, and citation counts do not necessarily reflect clinical significance. Notably, the dual-map overlay revealed sparse representation from the physics, materials, and chemistry domains, suggesting untapped potential for interdisciplinary integration. Looking forward, the authors call for accelerated functional validation of single-cell-identified targets in preclinical models, standardized computational pipelines for inferring intercellular communication networks, and dedicated funding programs to broaden access. What began as cellular cartography, the analysis concludes, has evolved into a sophisticated discipline deconstructing the dynamic tumor ecosystem—one that offers a clear path toward more personalized and effective therapies for a disease that urgently needs them.

Subject of Research: Bibliometric analysis of single-cell RNA sequencing research in liver cancer

Article Title: Bibliometric analysis of research on liver cancer and single‑cell RNA sequencing: evolutionary trends, opportunities, and future perspectives

Article References: Zheng, Y., Wang, Q., Zhang, Q., & Zhang, H.-M. (2026). Bibliometric analysis of research on liver cancer and single‑cell RNA sequencing: evolutionary trends, opportunities, and future perspectives. Holistic Integrative Oncology, 5(1), Article 65. https://doi.org/10.1007/s44178-026-00285-6

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00285-6

Keywords: single-cell RNA sequencing, liver cancer, hepatocellular carcinoma, tumor microenvironment, bibliometric analysis, cancer-associated fibroblasts, tumor-associated macrophages, gene regulatory networks, immunotherapy, spatial transcriptomics, cellular crosstalk, biomarkers

The study, published in Discover Global Society, analyzed 294 scholarly publications indexed in the Scopus database between 2000 and 2025, all focused on UBIE research connected to Brazil, Russia, India, China, and South Africa. Using two widely respected bibliometric tools, VOSviewer and Biblioshiny, author Ravi Shankar Rai of C-DAC Noida charted publication trends, citation networks, keyword patterns, author productivity, and bibliographic coupling across the corpus. The results reveal a field that barely existed at the turn of the millennium but has since become a mature and rapidly expanding research program, with important lessons for scientists, university leaders, and policymakers alike.

The temporal trends alone tell a striking story. Between 2000 and 2012, fewer than five papers on university-based innovation ecosystems connected to BRICS countries appeared each year, signaling that the topic was still in its conceptual infancy. The decisive inflection point arrived in 2013 and 2014, coinciding with a surge of policy interest in knowledge-based development across the BRICS economies and a proliferation of Triple Helix-themed conferences and special journal issues. After steady growth through 2019, the field dipped during the pandemic years of 2019 to 2021, then rebounded sharply, reaching 39 papers in 2023 and 45 articles in 2024. That late surge, the study notes, coincides with the rising popularity of newer theoretical frameworks and a broader push toward open innovation and stakeholder engagement.

The disciplinary makeup of the literature is equally revealing. Social sciences account for roughly 28 percent of the papers, reflecting a focus on governance, policymaking, and institutions, while business, management, and accounting contribute about 26.8 percent, centered on entrepreneurial universities and commercialization. Economics, econometrics, and finance represent 11 percent, and smaller but growing shares come from decision sciences, environmental science, and computer science—the latter signaling the emergence of digital innovation as a research theme. Notably, engineering contributes only 4.2 percent and energy a mere 2.4 percent, suggesting that the applied technological dimensions of university-driven innovation remain understudied, particularly within BRICS countries where such questions carry enormous strategic weight.

Behind these aggregate patterns stand a relatively small set of influential scholars. Almeida M., Cai Y., Fischer B., and Schaeffer P.R. each authored six papers in the corpus, shaping the field’s agenda on institutional structures and policy processes, while Etzkowitz H. and Fischer B.B. published five each. The institutional diversity represented—universities spanning Brazil, South Korea, China, Finland, India, and South Africa—underscores the international character of the research, even as the concentration of productivity among a handful of institutions raises questions about who effectively sets the field’s intellectual direction.

Citation analysis adds a crucial layer of nuance. Etzkowitz’s 2004 article on the evolution of the entrepreneurial university stands as the most globally cited work, with 500 citations, reflecting the enduring power of the Triple Helix model of university–industry–government interaction. Yet its low local citation ratio suggests the field draws on the Triple Helix more in a citational than a critically engaged way. Meanwhile, Cheng and colleagues’ 2019 study of a Chinese nanotech innovation cluster recorded the highest intra-field influence, a hallmark of an emerging research stream that has not yet penetrated wider citation networks. Normalized citation metrics point to recent empirical work, such as Li’s 2020 study, gaining traction quickly within the community.

Perhaps the most policy-relevant finding concerns the gap between publication volume and citation impact at the country level. China leads the corpus in total citations with 1,071, followed by Brazil with 647—consistent with their roles as the bloc’s largest producers of academic output. But when citations are measured per article, a very different hierarchy emerges. Finland averages 70.2 citations per paper, nearly five times China’s 14.3, while Chile (65.5) and South Korea (59.2) also far outperform the BRICS group. India, despite its substantial research base, averages just 6.9 citations per article. The contrast suggests two distinct strategies: scale-driven output in China and Brazil versus smaller-volume, high-impact research in Finland, Chile, and Korea—a distinction with real implications for how nations should evaluate the returns on their innovation investments.

The co-citation analysis, which maps which scholars are cited together, exposes the field’s intellectual architecture in four main clusters: a dominant core around Etzkowitz and the Triple Helix; a cluster focused on regional innovation policy in Asia, especially China; a group studying university entrepreneurship and commercialization; and a cluster concerned with regional economic development. Strikingly, the architects of the Quadruple and Quintuple Helix frameworks—models that add civil society and environmental sustainability to the classic three-actor model—are nearly absent from the network’s key nodes. For a field studying innovation in societies marked by deep inequality and urgent environmental challenges, the study argues, this omission is both a theoretical weakness and a practical blind spot.

Keyword analysis reinforces the same diagnosis. The term ‘triple helix’ appears 81 times, ‘innovation’ 71 times, and ‘China’ 61 times, confirming both the framework’s dominance and the concentration of geographic attention. The notion of the ‘entrepreneurial university’ appears 17 times, marking a conceptual shift toward universities as active agents of economic development. Yet terms related to social equity, inclusive innovation, and sustainable development are conspicuously rare—an omission the study identifies as a significant conceptual gap given the socio-economic realities of BRICS nations.

The study also looks forward. The 2024–2025 expansion of BRICS to include Indonesia, Egypt, Ethiopia, Iran, and the UAE introduces a wave of new institutional and cultural contexts that existing UBIE scholarship, built around the original five members, has not yet absorbed. The author calls for comparative research on Quadruple and Quintuple Helix approaches, systematic examination of equity and inclusiveness within university innovation ecosystems, longitudinal studies tracking how ecosystems respond to political and economic shifts, and deeper investigation of digital technologies and artificial intelligence as forces reshaping university–industry–government collaboration. For policymakers, the message is equally clear: building successful innovation ecosystems requires more than increased research funding. It demands institutional arrangements that let universities act as genuine innovation partners, and attention to who actually benefits from the knowledge universities create.

Subject of Research: Bibliometric analysis of university-based innovation ecosystem research in BRICS countries from 2000 to 2025

Article Title: A bibliometric analysis of university based innovation ecosystem research in BRICS countries from 2000 to 2025

Article References: Rai, R. S. (2026). A bibliometric analysis of university based innovation ecosystem research in BRICS countries from 2000 to 2025. Discover Global Society, 4(1), Article 235. https://doi.org/10.1007/s44282-026-00491-7

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00491-7

Keywords: university-based innovation ecosystems, BRICS, bibliometric analysis, Triple Helix, Quadruple Helix, entrepreneurial university, innovation policy, higher education, university–industry collaboration, Scopus, emerging economies, knowledge-based development

The technical machinery behind the study is as important as its findings. Bibliometrics and scientometrics treat the scientific literature itself as a dataset: every paper becomes a node in a network, every citation a directed edge, every co-authorship a bond between institutions and nations. By systematically extracting these relationships and visualizing them with tools such as co-authorship mapping, the researchers could measure not only how much has been published about sustainability in higher education but who produces it, who cites it, and how tightly — or loosely — the global research community is woven together. This approach follows well-established methodological guidance in the field, including frameworks for conducting rigorous bibliometric reviews and the principles laid out in the Leiden Manifesto for responsible research metrics, which caution against reducing science to raw counts alone.

What the analysis found is a field that has expanded almost continuously since its modest beginnings in the mid-1970s. The early literature, emerging in the wake of the environmental movement and the concept of sustainable development, was sparse and largely concentrated in North America and Western Europe. Over subsequent decades, publication volumes climbed steadily, with particularly sharp acceleration after the United Nations launched its Decade of Education for Sustainable Development and, later, the 2030 Agenda and its seventeen Sustainable Development Goals. Universities responded to these policy signals by embedding sustainability into curricula, research agendas and institutional commitments, generating a corresponding wave of scholarly output that examined everything from key competencies for sustainable development to the greening of campus operations and the integration of the goals into formative academic offerings.

Yet the geography of this growth is deeply lopsided. Developed countries serve as the key drivers of scientific production in the field, accounting for the lion’s share of publications, citations and institutional leadership. Researchers in wealthy nations dominate the most-cited journals, convene the major conferences, and set the intellectual agenda for how sustainability should be taught. By contrast, the study identifies significant barriers limiting the participation of developing countries — most notably the scarcity of research funding and the lack of international cooperation. These constraints do not merely reduce publication counts; they adversely affect the global impact of scholarship produced in the Global South, where many of the most acute sustainability challenges — from climate vulnerability to biodiversity loss to energy poverty — are experienced most directly.

Perhaps the most technically revealing component of the study is its co-authorship network analysis, which exposes significant fragmentation in collaboration among researchers from different regions. In network terms, the global research community on sustainability in higher education is not a densely connected web but an archipelago of clusters, with strong ties within regions and institutions but comparatively weak bridges across them. This fragmentation matters for a very practical reason: sustainability is inherently a transdisciplinary enterprise, requiring the integration of insights from engineering, ecology, economics, pedagogy and the social sciences. When researchers from different regions and disciplines rarely co-publish, transdisciplinary approaches struggle to take root, and the field risks reproducing the same siloed thinking that sustainability education is meant to overcome.

The study also delivers a timely assessment of how the COVID-19 pandemic reshaped sustainability education. When campuses closed in 2020, universities were forced to migrate hands-on, experiential learning — a cornerstone of education for sustainable development — into digital environments. The analysis highlights the rapid adoption of hybrid pedagogical models that combine digital tools with practical approaches, a shift that began as emergency improvisation and has since evolved into a durable feature of the pedagogical landscape. Researchers who had long championed real-world learning opportunities, in which students move from the classroom into communities and workplaces to tackle live sustainability problems, found themselves redesigning these experiences for screens. The pandemic thus functioned as an unplanned stress test, accelerating digital transformation in ways that would otherwise have taken years.

The regional perspectives in the study are particularly valuable because they come from a research team rooted in Latin America and Europe rather than the traditional centers of bibliometric research in North America and Northern Europe. Latin American universities have been actively mapping their sustainability initiatives, from institutional declarations to curricular reforms, and scholarship from the region has documented both encouraging progress and persistent obstacles, including limited funding, heavy teaching loads and weak institutional incentives for sustainability research. Chilean universities, for example, have been the subject of bibliometric analyses of social responsibility, and Colombian institutions have been studied for holistic integration of sustainability. The new study situates these regional efforts within the global picture, showing how they connect — or fail to connect — to the wider network.

The authors close with recommendations aimed at strengthening international collaboration and consolidating regional research networks. Concretely, this means designing funding instruments that explicitly support North-South and South-South partnerships, reducing the administrative and financial barriers that keep researchers in developing countries out of international consortia, and building regional networks that can aggregate critical mass before plugging into global structures. It also means investing in the research infrastructure of the Global South — journal access, bibliometric visibility, open publishing pathways — so that locally generated knowledge about sustainability education can circulate globally rather than remaining invisible to citation indices, which are known to underrepresent journals from developing regions.

The stakes of these reforms could hardly be higher. Higher education institutions are widely regarded as critical actors in creating a sustainable future: they train the professionals who will design climate policy, manage ecosystems and transform industries, and they produce much of the research on which those decisions depend. If the knowledge base for sustainability education remains concentrated in a handful of wealthy countries, the solutions it generates will be shaped by a narrow band of experience and may fit poorly in the contexts that need them most. Conversely, a genuinely connected, well-funded and transdisciplinary global research community could accelerate the flow of good practice from wherever it emerges to wherever it is needed. The new analysis provides both a warning about the current state of the network and a quantitative roadmap for weaving it together.

Subject of Research: Bibliometric and scientometric analysis of global scientific production, collaboration networks and regional disparities in sustainability research within higher education since 1975.

Article Title: Global patterns in higher education and sustainability research: trends, collaboration networks and regional perspectives

Article References: Gunckel, T., Rubio, A., Guiu, R. F., & Moraga, H. (2026). Global patterns in higher education and sustainability research: trends, collaboration networks and regional perspectives. Journal of Environmental Studies and Sciences. https://doi.org/10.1007/s13412-026-01138-4

Image Credits: AI Generated

DOI: 10.1007/s13412-026-01138-4

Keywords: sustainability, higher education, bibliometric analysis, scientometrics, co-authorship networks, Sustainable Development Goals, international collaboration, developing countries, research funding, COVID-19, hybrid pedagogy, transdisciplinary research

The headline number is growth. Output in this niche is expanding at an annual rate of 21.9 percent, and publication volume surged sevenfold between 2020 and 2024. To put that in context, previous bibliometric work reported growth rates of 15.3 percent for sustainable agriculture research in Africa and 18.7 percent for global climate change adaptation studies, meaning this particular intersection of problems is outpacing both. Using a three-parameter logistic growth model fitted to the annual publication counts from 2004 to 2024, the team projects that the field will reach its productivity peak around 2031, with a coefficient of determination of 0.786. In other words, the literature is still in its steep growth phase, not yet mature, and roughly half of everything that will ever be published in this domain had appeared within just ten years of the field’s inception. The cumulative projections suggest 90 percent of the eventual corpus will exist by around 2040 and 99 percent by 2050.

The methodology behind these findings reflects a careful trade-off between precision and coverage. The authors deliberately crafted a narrow Scopus query combining the terms sub-Saharan Africa, climate resilience, and food security in titles, abstracts, and keywords, filtering for original research articles, reviews, and book chapters published in English. From 287 initial records, 63 duplicates were removed, leaving 224 documents, none of which were excluded at the title-abstract screening stage, a sign of the query’s precision. The team chose Scopus alone, rather than combining databases, because its standardised metadata is essential for the co-authorship, keyword co-occurrence, and citation network analyses performed with VOSviewer and the Bibliometrix R package. They are candid about the costs of this choice: excluding French-language publications likely underrepresents Francophone West and Central Africa, and the relatively small corpus means the network findings should be read as indicative rather than definitive.

Perhaps the most striking finding concerns the geography of collaboration. Scientific partnerships follow a pronounced hub-and-spoke architecture in which the United States and the United Kingdom act as central hubs, channeling connections primarily toward East Africa and Southern Africa. The United States leads production with 102 publications, followed by South Africa with 88 and the United Kingdom with 69; within the continent, Kenya and Ethiopia emerge as the major contributors, with Nigeria and Ghana close behind. Yet regional centres are rising: Kenya and South Africa now function as secondary hubs organizing their respective regions, and India and China are emerging as new partners with distinct strategies, India emphasizing links with West African countries such as Ghana and Senegal while China spreads connections more broadly. A concentrated corridor links the United States and Western Europe to East Africa, alongside a South Africa-Zimbabwe-Zambia axis and a nascent Ghana-Nigeria-India nexus.

That spatial pattern collides awkwardly with the map of actual food vulnerability. Collaborations cluster along the US-Europe-East Africa corridor while regions such as the Sahel and the Great Lakes remain thinly represented in the indexed literature, a gap the authors partly attribute to the exclusion of French-language research but which persists even accounting for that bias. They describe the result as an inverted geography of knowledge, in which the places generating the most research are not the places facing the greatest food insecurity. Kenya presents a particularly intriguing case, recording the second-highest total citations among the ten most-cited countries and the highest average citation rate per article at 43.2. The authors caution that this Kenyan anomaly could reflect genuine quality linked to CGIAR-affiliated research networks, the simple fact that older publications accumulate more citations, or a case-study effect in which foreign researchers using Kenyan sites inflate the country’s apparent impact, and they decline to distinguish between these explanations without age-normalised citation data.

Beneath the growth curves lies a deeper epistemological fault line. Multidimensional scaling of keyword co-occurrences reveals a tripolar conceptual structure split between a technicist paradigm centred on plant breeding, genetics, and productivity, and a systemic paradigm emphasising smallholder adaptation, livelihoods, and vulnerability. The semantic core of the field is anchored by three dominant terms, food security with 107 occurrences, climate change with 98, and sub-Saharan Africa with 73, but the conceptual distance between the biological and social clusters remains considerable, quantifying a divide that scholars of sustainability science have long described qualitatively. Interestingly, the geographic terms align more closely with the social cluster than the biotechnological one, suggesting that research about Africa is framed more by social-systemic thinking than by laboratory science.

There are, however, signs of convergence. Author productivity follows Lotka’s law with brutal clarity: 91.4 percent of the 613 authors in the corpus produced a single document, and only 8 percent produced two, indicating a field with high researcher turnover and fragile, unconsolidated teams. Yet three thematic clusters, plant genetics, agricultural systems and water management, and ecological and food-system resilience, have begun to forge inter-cluster collaborations, particularly after 2023, with figures such as Tafadzwanashe Mabhaudhi acting as connectors between communities. Emerging keywords including alternative agriculture, agroforestry, and terms focused on human dimensions signal a shift toward more integrated, systems-oriented approaches, echoing IPCC calls for nature-based solutions. The field, in short, appears to be edging toward a synthesis phase ahead of its projected 2031 maturity.

The publication landscape mirrors these dynamics. Open-access and interdisciplinary journals have become the dominant dissemination channels, with Frontiers in Sustainable Food Systems, Sustainability, and Environmental Research Letters topping the Bradford’s Law core, and the open-access share of this literature reaching 67 percent, well above the 35 percent seen in African agricultural research more generally. Foundational documents include Chivenge and colleagues’ 2015 paper on neglected and underutilised crop species, which leads with 396 citations, alongside influential works on maize breeding for climate resilience and on climate vulnerability broadly. Recent years show a rising cohort of African lead authors, including scholars such as El Bilali, Akinsemolu, and Kiribou, suggesting a gradual shift toward regional ownership of the research agenda even as structural dependence on Northern institutions endures.

The authors close with a strategic action plan calibrated to the window before the projected 2031 peak. They call for deliberate integration of technical and systemic approaches through interdisciplinary initiatives such as Living Labs, a redistribution of research funding toward the most vulnerable and underrepresented regions such as the Sahel and the Horn of Africa with African institutions leading funded consortia, and a shift from short three-year project grants to long-term institutional support of roughly ten years to stabilise the notoriously transient research community. They further recommend mandating African first authorship in half of supported international partnerships, funding African-led open-access journals and multilingual science communication, and reforming research evaluation to weight policy engagement, community involvement, and data sharing alongside publication counts. If the scientific community follows this evidence-backed roadmap, the coming decade of explosive growth could deliver not just more papers but measurably more resilient food systems for the hundreds of millions of people across sub-Saharan Africa whose harvests now hang on an increasingly erratic sky.

Subject of Research: Bibliometric mapping of climate resilience and food security research in sub-Saharan Africa from 2004 to 2025

Article Title: Scopus-based bibliometric mapping of climate resilience and food security research in sub-Saharan Africa from 2004 to 2025

Article References: Chimi, P. M., Etoundi, L. F. M., Yonga, G., Mfout, A. V., Menkamla, A. T., Maralossou, B., Bell, J. M., & Mala, W. A. (2026). Scopus-based bibliometric mapping of climate resilience and food security research in sub-Saharan Africa from 2004 to 2025. Discover Global Society, 4(1), Article 236. https://doi.org/10.1007/s44282-026-00598-x

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00598-x

Keywords: climate resilience, food security, sub-Saharan Africa, bibliometric analysis, scientific collaboration, climate change adaptation, smallholder farmers, agroforestry, VOSviewer, Scopus, research policy, sustainable agriculture

The team employed a hybrid bibliometric–systematic literature review methodology, guided by the PRISMA framework, combining quantitative science mapping with qualitative thematic synthesis. Starting from 162 records retrieved from Scopus on January 28, 2026, two independent reviewers screened titles and abstracts against predefined inclusion and exclusion criteria, ultimately retaining 135 publications across 52 academic sources. The field exhibits an annual growth rate of 14.35 percent, a strikingly young average document age of 2.49 years, and an average of 4.25 citations per document drawn from a cumulative base of 5,946 cited works. Authorship analysis revealed 521 contributing authors with an average of 4.62 co-authors per paper, reflecting the deeply interdisciplinary character of a research area that straddles computer science, marketing, and information systems.

The temporal picture is one of explosive acceleration. Before 2020, scholarly output on AI recommenders in marketplace contexts was sporadic, characteristic of an exploratory phase. A notable increase emerged in 2020, followed by a sharp surge from 2023 onward, with publication peaks of 44 documents in 2024 and 48 in 2025. The authors attribute this trajectory to structural shifts in consumer behavior during and after the COVID-19 pandemic, which accelerated digital adoption and pushed firms to prioritize scalable, automated personalization. As online platforms absorbed enormous volumes of behavioral data, machine learning and deep learning architectures became the default machinery for modeling user–item interactions, transforming marketplaces from transaction-oriented platforms into intelligence-driven ecosystems in which product discovery itself is algorithmically mediated.

Methodologically, the reviewed literature remains dominated by traditional machine learning approaches, prized for their accessibility and modest computational demands. Collaborative filtering and deep learning methods form the second tier, marking a clear shift toward representation learning and data-driven personalization. Deep models—ranging from session-based neural networks to stacked denoising autoencoders—capture nonlinear preference patterns that classical techniques miss, while hybrid architectures that blend multiple recommendation strategies show improved accuracy and resilience against persistent problems such as data sparsity and the cold-start dilemma. Sentiment analysis and natural language processing, including BERT-based frameworks, are increasingly woven into recommendation pipelines, allowing systems to incorporate the emotional and attitudinal signals embedded in reviews and ratings rather than relying solely on transaction histories.

Keyword co-occurrence mapping in VOSviewer revealed six thematic clusters that the authors interpret through a proposed multi-level framework linking AI architecture, consumer cognition, and marketplace implementation. Clusters one and three concern the technological core: recommendation techniques, natural language processing, and predictive analytics that forecast purchase behavior from classification algorithms, random forests, and recurrent neural networks. Clusters two and four address the human side—how personalization intensity, explanation interfaces, and adaptive content shape satisfaction, trust, and purchase intention. Clusters five and six concern platform environments and system integration: interface design, e-service quality, scalable backend architectures, real-time data pipelines, and the governance frameworks required to keep personalization lawful and reliable at industrial scale.

The behavioral analysis draws heavily on two theoretical pillars. The Stimulus–Organism–Response model treats algorithmic features—personalization depth, adaptive ranking, transparency cues—as external stimuli that shape internal cognitive and affective states, which in turn drive engagement and purchasing. The Theory of Planned Behavior explains how attitudes, subjective norms, and perceived behavioral control convert those internal states into intentions. Within this lens, explainable AI emerges as more than a compliance feature: studies show that attribute-based explanations raise user trust and lower algorithmic anxiety in utilitarian shopping contexts, while perceived fairness and privacy protection feed a multidimensional trust construct spanning the recommender itself, the platform, and the individual recommendations it delivers.

Citation analysis exposes the field’s intellectual DNA and its blind spots. The most cited work, a machine learning recommender built on association rule mining by Loukili and colleagues, exemplifies performance-oriented research that prizes predictive accuracy. The second most cited study advances deep neural collaborative filtering, capturing nonlinear preference structures. The third integrates multitask deep learning to predict buying behavior from affective signals in user-generated content. Together these milestones trace an evolution from rule-based optimization to neural architectures to sentiment-aware personalization—yet the authors note that academic recognition remains concentrated on methodological innovation, while trust sustainability, algorithmic bias, and long-term deployment outcomes are comparatively underexplored in the field’s most influential papers.

Perhaps the review’s most consequential finding is the persistent implementation gap between experimental prototypes and production-ready systems. Many algorithms achieve impressive predictive performance in benchmarks, but far fewer studies address infrastructure scalability, data governance, privacy compliance, interoperability, or integration with enterprise architectures. Federated learning approaches and compliance-aware backend designs point toward architectures that can personalize while respecting regulatory constraints, and API-driven integration with unified data normalization is identified as essential for delivering consistent personalization across channels. Geographically, research output is heavily concentrated in India, China, and the United States, with emerging contributions from Indonesia, Morocco, and Malaysia—a pattern the authors link to the maturity of digital market ecosystems and the dominance of fast-turnaround conference venues, which account for roughly 71 percent of the corpus.

The authors also acknowledge the limits of their synthesis. Reliance on a single database may have excluded relevant work indexed elsewhere; the conference-heavy dataset may overrepresent algorithmic advances relative to behavioral theory; and only English-language publications were included, potentially omitting studies from major e-commerce regions. The proposed multi-level framework is explicitly conceptual rather than statistically validated, intended to organize existing knowledge and guide future inquiry. Even so, the synthesis offers a structured roadmap organized around five directions: deeper theoretical integration between behavioral science and algorithm design, longitudinal studies of effects such as algorithm fatigue and over-personalization, ethical research on explainability and bias mitigation, technological work on context-aware and generative personalization, and managerial attention to deployment feasibility.

The overriding message is deceptively simple: a recommender system is only as effective as the weakest of its three interdependent layers. A model that is accurate but opaque erodes trust; a system that is trusted but unscalable never reaches production; an architecture that is scalable but psychologically tone-deaf fails to convert engagement into loyalty. As digital marketplaces pivot from static recommendation lists toward immersive, generative, and real-time personalization, the review argues that the next generation of AI commerce will be judged not by prediction accuracy alone, but by its capacity to be transparent, trustworthy, and deployable—a reframing with profound implications for the platforms that mediate billions of consumer decisions every day.

Subject of Research: A systematic review of AI-based recommender systems in online marketplaces, integrating algorithmic architectures, consumer behavior, and personalization

Article Title: Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization

Article References: Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization. (n.d.). https://doi.org/10.1007/s44163-026-02182-3

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02182-3

Keywords: artificial intelligence, recommender systems, online marketplaces, personalization, consumer behavior, e-commerce, machine learning, deep learning, collaborative filtering, explainable AI, bibliometric analysis, digital commerce

The dominance of China, with 85 publications, and Italy, with 25, reflects complementary strengths rather than simple redundancy. Chinese groups have historically driven much of the fundamental materials chemistry of layered hydroxides, including refinements in co-precipitation, hydrothermal, and reconstruction methods that control layer charge density, interlayer spacing, and particle size, all of which govern how much antibiotic can be hosted and how quickly it escapes. Italian contributions, by contrast, have been closely associated with the biomedical translation of anionic clays, particularly in antitumoral and anti-inflammatory delivery, and that translational orientation naturally extends to antimicrobial applications where controlled release can reduce dosing frequency and limit the emergence of resistance at infection sites. The concentration of output in journals such as Applied Clay Science, the International Journal of Nanomedicine, and the International Journal of Biological Macromolecules illustrates the interdisciplinary position of the field: it sits at the intersection of clay mineralogy, nanomedicine, and macromolecular therapeutics, and authors must choose venues according to whether the novelty lies in the material synthesis or in the biological outcome.

The keyword co-occurrence findings, in which LDH, sustained release, and drug delivery emerge as central nodes, confirm that the community defines itself around the release kinetics problem rather than around any single antibiotic class. This is consistent with the underlying chemistry. The general formula of these materials, in which divalent and trivalent metal cations form positively charged hydroxide sheets balanced by interlayer anions and water, allows antibiotics bearing carboxylate, phosphate, or other anionic groups to be intercalated electrostatically. Once intercalated, release is governed by a combination of anion exchange with physiological counter-ions, partial dissolution of the layers, and diffusion through the particle periphery. Because the layers dissolve more readily in acidic media, the resulting carriers exhibit pronounced pH sensitivity, a property that is valuable at infected sites and in intracellular compartments such as phagolysosomes, where pH falls below that of blood and can be used to trigger preferential drug liberation. This same alkaline degradation behavior in gastric media, noted in the source literature, is a double-edged characteristic: it complicates oral delivery of acid-labile payloads yet can be exploited for gastric-responsive formulations.

The comparative framing against other carrier families provides useful context for interpreting why LDHs attract sustained attention despite their younger bibliography. Polyvinyl alcohol hydrogels are appreciated for biocompatibility and water processing but suffer from mechanical weakness and excessive hydrophilicity that accelerate burst release. Metal-organic frameworks offer exceptional internal surface area and tunable pore chemistry, yet concerns over long-term structural stability in aqueous biological environments, biodegradation products, and scalable cost remain active research questions. Mesoporous silica materials are the most clinically mature of the three comparators, but their typical pore dimensions constrain the size of molecules that can be loaded efficiently, and pore architecture influences release in ways that are difficult to tune independently. LDHs occupy a distinctive niche among these alternatives because their loading capacity is not limited by rigid pore windows; instead, the interlayer gallery can expand to accommodate bulky anionic species, and the layer charge can be adjusted through the divalent to trivalent cation ratio, giving formulators a direct handle on loading density and exchange kinetics. Their documented high anion exchange capacity, colloidal stability, and low toxicity in the reviewed literature further support this positioning.

The bibliometric emphasis on citation trends and thematic evolution also illuminates how the field has responded to the clinical backdrop of antimicrobial resistance. The 2022 GLASS report cited in the source article documents high resistance rates among common bacterial pathogens, and the parallel slowdown in the discovery of genuinely new antibiotic scaffolds has shifted attention toward maximizing the performance of existing drugs. Sustained-release delivery is one of the few strategies that improves the pharmacodynamic profile of an established antibiotic without requiring new chemistry at the molecular level. By flattening the sharp plasma spikes and troughs characteristic of immediate-release formulations, steady delivery maintains concentrations within the therapeutic window for longer periods, reduces the frequency of subinhibitory exposure that selects for resistant subpopulations, and improves patient adherence through fewer doses. In this sense, the bibliometric growth of LDH antibiotic delivery research can be read as a materials-science response to a pharmacological and epidemiological problem.

The methodological apparatus of the review itself deserves comment, because bibliometric analysis is increasingly used to map emerging biomedical materials fields and its limitations should be understood when interpreting the results. Restricting the corpus to English-language publications indexed in SCI-Expanded and ESCI introduces a selection bias toward established journals and anglophone or internationally publishing groups, which may undercount contributions from regions with strong domestic journals. The exclusion terms applied to remove degradation and adsorption studies, while scientifically justified, may also have removed hybrid papers that examined both adsorption and release. Nevertheless, the use of two complementary tools, VOSviewer for network visualization and the Bibliometrix package in R for descriptive and thematic statistics, strengthens the reliability of the mapping, since agreement between independent platforms on core findings such as country productivity and keyword clusters reduces the likelihood of software-specific artifacts. The reporting of total link strength as a measure of interaction intensity between nodes follows standard practice in science-mapping studies and allows readers to gauge not just the presence of a collaboration or co-occurrence but its relative weight within the network.

The identification of prominent authors, highly cited works, and funding sources within the 217-document corpus serves a practical function for newcomers to the field. Highly cited papers typically cluster around foundational demonstrations of antibiotic intercalation and release profiling, and tracing the citation flow from these works toward more recent publications reveals a thematic migration: early studies emphasized proof of concept for loading and release, while later work increasingly incorporates biological evaluation, including minimum inhibitory concentration assays, biofilm models, and cytocompatibility testing. The three-field plot analysis, which links countries, institutions, and keywords, exposes where interdisciplinary gaps persist, and the review’s framing of these gaps as opportunities for collaboration is consistent with the observation that no single discipline currently owns the problem. Materials chemists can optimize synthesis and interlayer architecture, microbiologists can define clinically relevant resistance and biofilm challenges, pharmacologists can model release and dosing, and toxicologists can establish biocompatibility thresholds, yet the bibliometric evidence suggests these communities have not fully converged.

The connection drawn between LDH drug delivery research and the Sustainable Development Goals reflects a broader trend in which bibliometric studies situate technical fields within global health priorities. Antimicrobial resistance is explicitly recognized in international frameworks as a threat to sustainable development, and delivery technologies that extend the useful life of existing antibiotics contribute to that agenda without demanding new molecular discovery. The emphasis on relevance to Sustainable Development Goals in the keyword and funding analysis indicates that funding agencies and journals increasingly reward work framed in these terms, which may in turn shape the direction of future publications toward applications with clear health-system relevance, such as wound dressings, implant coatings, and oral formulations for persistent infections.

Looking forward, the bibliometric evidence points toward several strategic directions that follow logically from the identified themes. First, the prominence of sustained release as a keyword suggests that quantitative release modeling, rather than qualitative demonstration, will be the differentiating contribution in coming years, since regulatory translation requires reproducible kinetics under physiologically relevant conditions. Second, the presence of gene therapy, biosensing, and ocular applications in the broader LDH literature signals that antimicrobial researchers may borrow formulation strategies from these adjacent domains, for example exploiting the positive surface charge that prolongs corneal residence to design mucoadhesive antimicrobial films. Third, the geographic concentration of output in two countries implies substantial untapped collaborative capacity, particularly in regions with high antimicrobial resistance burden but lower publication visibility in the indexed corpus, and international partnerships could align material development with the clinical epidemiology of resistance. Finally, the absence of prior bibliometric treatment of this field, which the review establishes as its central novelty, means that the 2025 dataset will serve as a baseline against which future updates can measure whether the field grows in volume, diversifies in geography, or shifts thematically from synthesis-oriented to clinically validated studies, and such longitudinal comparison is precisely the kind of insight that systematic bibliometric monitoring is designed to provide.

Subject of Research: Layered double hydroxides in sustained antibiotic delivery: a bibliometric review

Article Title: Layered double hydroxides in sustained antibiotic delivery: a bibliometric review

Article References: Verma, S. S., Varadavenkatesan, T., Selvaraj, R., & Vinayagam, R. (2026). Layered double hydroxides in sustained antibiotic delivery: a bibliometric review. Journal of Saudi Chemical Society, 30(5), Article 65. https://doi.org/10.1007/s44442-026-00120-7

Image Credits: AI Generated

DOI: 10.1007/s44442-026-00120-7

Keywords: Layered, double, hydroxides, sustained, antibiotic, delivery, bibliometric, review, scientific research