The team started with the publicly available sequencing dataset GSE203206, downloaded from the Gene Expression Omnibus repository, which contains transcriptomic data from 39 Alzheimer’s disease samples and 8 normal control samples. Using t-tests implemented in the limma R Bioconductor package, they identified 958 differentially expressed genes, with a strikingly symmetric result of 479 genes upregulated and 479 genes downregulated in the diseased brains. That balance alone is noteworthy, because it suggests widespread, bidirectional remodeling of the transcriptome rather than a simple overshoot of one or two processes, and it gives researchers a rich catalogue of candidate players to investigate.

To make biological sense of that gene list, the researchers ran Gene Ontology and pathway enrichment analyses. The upregulated genes clustered mainly around response to stimulus, cytoplasmic functions, small molecule binding and signal transduction, painting a picture of a brain under persistent stress, actively reorganizing its signaling machinery. The downregulated genes, by contrast, were enriched for multicellular organism development, cell junction biology, ion binding and cardiac conduction, hinting that fundamental structural and developmental programs are being quietly dismantled as the disease progresses. These divergent functional signatures reinforce the idea that Alzheimer’s is not one pathway gone wrong but an entire coordinated system drifting out of tune.

The next step was network analysis. By mapping the differentially expressed genes onto a protein-protein interaction network, the team built a graph containing 4,886 nodes and 10,342 edges, an enormous molecular web from which they extracted the most connected hubs. Ten genes rose to the top: HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7. Several of these names will be familiar to neuroscientists. HSP90AA1 encodes a heat shock protein chaperone already flagged by proteomic studies in the entorhinal cortex of Alzheimer’s patients, while FN1, the fibronectin gene, has recently been linked through rare genetic variants to protection against the notorious APOE ε4 risk factor.

Beyond static expression patterns, the study probed the regulatory layers that might control these hub genes. Constructing a microRNA-hub gene regulatory network, the authors identified hsa-mir-545-3p and hsa-miR-548f-5p as microRNAs that could help orchestrate Alzheimer’s pathology by fine-tuning multiple hub genes simultaneously. Similarly, a transcription factor-hub gene network implicated PLAG1 and MEF2A as master regulators that may be involved in disease development. This kind of multi-level regulation matters because it suggests that upstream control points, rather than individual downstream genes, could offer the most efficient targets for intervention, and because non-coding RNAs and transcription factors are increasingly seen as versatile biomarkers in neurodegeneration.

Perhaps the most clinically intriguing part of the work is the drug-hub gene interaction analysis, which predicted four existing drug molecules as candidates for Alzheimer’s treatment: Sulindac, Infliximab, Norfloxacin and Gemcitabine. The logic of repurposing is simple and appealing. These compounds already have established safety profiles, known pharmacokinetics and, in some cases, mechanisms that touch inflammation or cell survival, processes central to Alzheimer’s biology. Sulindac is a nonsteroidal anti-inflammatory drug, Infliximab is an antibody targeting tumor necrosis factor, and both speak to the chronic inflammatory component that has shadowed Alzheimer’s research for decades. Gemcitabine and Norfloxacin add further molecular diversity to the candidate pool.

To test these predictions computationally, the researchers performed molecular docking between hub gene products and corresponding active molecules. The docking analysis revealed that Isocryptomerin and Macrophylloside D, natural product-derived compounds, showed strong binding activities to HSP90AA1 and FN1 respectively. Docking scores cannot substitute for wet-lab validation, but favorable computed binding poses provide a rational starting point for medicinal chemists and suggest that these molecules are worth experimental follow-up as potential modulators of the disease’s most central protein hubs.

The study also evaluated the clinical diagnostic potential of the hub genes using receiver operating characteristic curve analysis, a standard method for estimating how well a biomarker separates diseased from healthy samples. Strong diagnostic performance would mean that measuring these genes, or the proteins and microRNAs they encode, could one day help identify Alzheimer’s earlier and more reliably than current cognitive assessments and imaging alone. With disease-modifying therapies finally emerging, the window for meaningful intervention depends critically on early detection, making reliable molecular biomarkers one of the field’s most valuable goals.

As with any computational study, caveats apply. The findings are hypotheses generated from correlation in a modest sample of 39 disease and 8 control brains, and hub genes identified by network centrality do not automatically equal causal drivers. The predicted drug interactions and docking results require validation in cell and animal models before any clinical translation. Nevertheless, the pipeline used here mirrors successful approaches in other diseases and provides a transparent, reproducible map of where future experimental effort should be directed.

Taken together, the work offers new insights into Alzheimer’s pathogenesis by nominating ten hub genes, two microRNAs, two transcription factors and several repurposable compounds as candidate diagnostic and therapeutic markers. It exemplifies a broader trend in biomedical research: as public sequencing archives grow, computational biology can extract biomedical value from data that already exists, at a fraction of the cost of new clinical trials. For a disease that affects tens of millions of people worldwide and still defies a cure, every new molecular lead, however preliminary, matters. The hub genes, microRNAs and transcription factors identified here now join the growing arsenal of targets that researchers worldwide will test in the years ahead.

The dataset at the heart of this study, GSE203206, is one of a growing number of publicly deposited transcriptomic datasets in the Gene Expression Omnibus, a repository maintained by the National Center for Biotechnology Information. Because such archives are openly accessible, any laboratory with computational resources can reanalyze raw sequencing reads, apply its own statistical thresholds, and cross-check published conclusions. This transparency has become a cornerstone of modern genomics, allowing independent teams to validate biomarker candidates across independent cohorts, a step that will be essential before any of the ten hub genes identified here can be considered a robust diagnostic marker.

Several of the hub genes carry biological stories that extend well beyond Alzheimer’s research. YAP1, for instance, is the key effector of the Hippo signaling pathway, a conserved cascade best known for controlling organ size and cell proliferation, and it has been increasingly implicated in neural regeneration and glial responses to injury. CDK7 is a cyclin-dependent kinase that functions as part of the transcriptional machinery, phosphorylating the RNA polymerase II tail and thereby regulating the expression of broad gene programs, which makes its dysregulation potentially consequential for many downstream pathways at once. LSM2 and SKP1 participate in RNA processing and ubiquitin-mediated protein degradation respectively, both processes that intersect with the protein homeostasis failures characteristic of neurodegenerative disease.

The involvement of HSP90AA1 is particularly interesting from a therapeutic standpoint. Heat shock protein 90 acts as a molecular chaperone that stabilizes numerous client proteins, many of which are involved in signaling cascades, and chaperone overload has been proposed as a mechanism by which misfolded and aggregated proteins, such as tau and amyloid-beta species, persist in the aging brain. Inhibitors of this chaperone have been explored in oncology for years, meaning that a substantial body of pharmacological knowledge and chemical tool compounds already exists and could be adapted for neurodegeneration research.

The microRNA findings also fit into a broader literature. MicroRNAs are short non-coding RNAs that each typically regulate dozens to hundreds of messenger RNAs, so a single microRNA shift can ripple across entire pathways. Previous work has documented widespread microRNA alterations in Alzheimer’s brain tissue and even in circulating blood, fueling interest in these molecules as minimally invasive biomarkers detectable in plasma or cerebrospinal fluid. The specific candidates reported here, hsa-mir-545-3p and hsa-miR-548f-5p, now join that expanding catalogue and can be tested for reproducibility in independent sample sets.

Molecular docking itself deserves a note of context. The method models how a small molecule fits into the three-dimensional structure of a target protein and estimates binding strength computationally, often within hours and at negligible cost compared with laboratory screening. Its predictions, however, are only as good as the protein structures and scoring functions used, and docking affinities frequently fail to translate into cellular activity. Natural products such as Isocryptomerin, derived from coniferous plants, and Macrophylloside D offer chemical diversity that synthetic libraries sometimes lack, which is why they attract attention as starting scaffolds. The sensible next steps are biochemical binding assays, neuronal cell models, and ultimately animal studies to determine whether any of these computational leads survives contact with biological reality.

Subject of Research: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies

Article Title: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies

Article References: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies. (n.d.). https://doi.org/10.1007/s12126-026-09665-9

Image Credits: AI Generated

DOI: 10.1007/s12126-026-09665-9

Keywords: Identification, Candidate, Biomarkers, Signaling, Pathways, Associated, Alzheimer, Disease, Bioinformatics, Analysis, Next, Generation

PCOS is one of the most common endocrine disorders affecting women of reproductive age, characterized by irregular ovulation, clinical or biochemical signs of elevated androgens, and the presence of polycystic ovarian morphology. A hallmark of the condition is an excess of small, arrested follicles that fail to reach developmental maturity, a phenomenon known as follicular arrest. Anti-Müllerian hormone, or AMH, is often elevated in PCOS patients because of the abundance of small growing follicles, and it has become a valuable biomarker for diagnosis and disease severity. Yet the cellular reasons why these follicles stall remain incompletely understood. Because granulosa cells supply the developing follicle with energy, growth factors, and vascular signals, they represent a logical place to look for the roots of this arrest.

To investigate, the team isolated granulosa cells from women undergoing in vitro fertilization at a single center, applying the Rotterdam criteria to diagnose PCOS. The final cohort consisted of a control group of twelve women whose serum AMH levels fell within the normal range of 2 to 5 nanograms per milliliter, and a PCOS group of eleven women who met the Rotterdam criteria and displayed elevated AMH above 5 nanograms per milliliter. To control for the possibility that differences might simply reflect follicle size rather than disease, the researchers further subdivided cells from both groups according to follicular diameter, comparing cells from large follicles exceeding 14 millimeters with those from small follicles under 14 millimeters. All cells were cultured under standardized laboratory conditions, allowing the team to compare morphology, proliferation, mitochondrial activity, and secretory function directly.

The results were consistent across several independent lines of measurement. Under the microscope, PCOS-derived granulosa cells displayed abnormal morphology and an enlarged cell size compared with cells from healthy controls. When their capacity to divide was assessed, the PCOS cells proliferated significantly more slowly. This impaired growth is particularly consequential because granulosa cell proliferation drives follicle expansion during development; cells that cannot multiply properly cannot support a follicle’s progression toward ovulation. The finding suggests that the follicular arrest characteristic of PCOS may begin within the somatic compartment of the follicle rather than being solely an oocyte problem.

Deeper analysis revealed where the cellular failure likely originates: the mitochondria. These organelles serve as the cell’s power plants, generating adenosine triphosphate, or ATP, the chemical currency that fuels virtually every energy-demanding process, including cell division, protein synthesis, and secretion. The researchers found that both mitochondrial function and intracellular ATP levels were significantly reduced in PCOS granulosa cells. This energy deficit provides a coherent explanation for the observed proliferation defect, as cells with insufficient ATP cannot sustain the biosynthetic workload required to replicate. Mitochondrial dysfunction in granulosa cells has been suspected in PCOS before, but linking it quantitatively to both proliferative failure and secretory impairment in the same cohort strengthens the case that it is a central defect rather than an incidental finding.

Perhaps the most novel component of the study concerns angiogenesis, the formation of new blood vessels, which is essential for follicle development. A growing follicle depends on a rich vascular network to receive oxygen, nutrients, and hormones from the bloodstream. Granulosa cells contribute to building this network indirectly through paracrine signaling, releasing factors that stimulate nearby endothelial cells to organize into vessel structures. To test this function, the team collected conditioned media, essentially the liquid culture environment in which the granulosa cells had been growing, and applied it to human umbilical vein endothelial cells in a tube formation assay, a standard laboratory test of angiogenic capacity. The conditioned media from PCOS granulosa cells significantly impaired the ability of endothelial cells to form tubes, demonstrating that the angiogenic support normally provided by these ovarian cells was diminished in the disease state.

The effect was not uniform across follicle sizes. Granulosa cells harvested from larger follicles showed a more pronounced impairment in angiogenic support than those from smaller follicles, an observation that could help explain why larger follicles in PCOS ovaries so often fail to progress to ovulation despite reaching substantial size. At the molecular level, the researchers examined the expression of angiogenesis-related cytokines and found that three key pro-angiogenic chemokines, CXCL6, IL8, and MCP1, were consistently downregulated in PCOS granulosa cells. Interestingly, vascular endothelial growth factor A, or VEGF-A, the most famous angiogenic factor, showed a less consistent pattern, suggesting that the angiogenic deficit in PCOS is not simply a matter of reduced VEGF but rather a broader disruption of the chemokine-mediated signaling network that coordinates blood vessel formation.

Taken together, the findings sketch a coherent mechanistic framework for how PCOS disrupts follicle development. Mitochondrial dysfunction reduces ATP availability, which in turn limits cellular proliferation and dampens the secretion of angiogenic chemokines. Reduced angiogenic signaling compromises the vascular supply to developing follicles, depriving both the granulosa cells and the oocyte of the metabolic support needed for maturation. The authors describe this as a functional interplay between metabolic dysfunction and disrupted chemokine-mediated angiogenic signaling, a chain of causation that connects the metabolic phenotype of PCOS to its reproductive consequences. Because the chemokines CXCL6, IL8, and MCP1 emerged as consistently downregulated factors, they represent plausible targets for interventions aimed at restoring follicular vascular support in affected women.

The study carries practical implications for fertility medicine. Many women with PCOS require assisted reproductive technology to conceive, and the quality of the follicular environment is a determinant of oocyte competence and embryo development. If the granulosa cell dysfunction identified here proves to be modifiable, strategies to improve mitochondrial function or replenish angiogenic chemokine signaling could theoretically enhance follicle quality in PCOS patients undergoing IVF. Such approaches remain speculative, and the study is a relatively small observational analysis conducted at a single center, so the findings will need replication in larger and more diverse cohorts before they translate into clinical protocols. The authors note that the work provides potential targets for improving reproductive outcomes rather than an immediate treatment.

Beyond its clinical relevance, the study contributes to a growing appreciation of the ovary as a metabolically demanding organ in which cellular energy status and developmental signaling are tightly intertwined. The follicle is often studied primarily through its hormonal and genetic regulation, but this research underscores that the physical infrastructure of follicle growth, from mitochondrial ATP production to the surrounding vasculature, may be equally decisive. For the millions of women living with PCOS worldwide, a condition that remains among the leading causes of anovulatory infertility, understanding that their follicles may be starved of both energy and vascular support offers a new dimension to the search for causes and cures. As research continues to map the molecular pathways linking mitochondrial health, chemokine signaling, and folliculogenesis, the granulosa cell may well emerge as a key gateway through which future therapies for PCOS are delivered.

The study was conducted under ethical oversight at Cathay General Hospital in Taipei, with approval from the hospital’s Ethics Committee and written informed consent obtained from all participants, in accordance with the Declaration of Helsinki. The work received financial support from the National Science and Technology Council of Taiwan and from Cathay General Hospital, and the authors declared no competing interests.

Readers should note that the article was published as an accepted manuscript in open access form, released early to provide faster access to peer-reviewed research. This version is citable and carries a permanent DOI, though it remains subject to editorial revisions before the final Version of Record replaces it. The research is categorized under topics including endocrine reproductive disorders, fertility, and gonadal disorders, reflecting its position at the intersection of reproductive endocrinology and cellular metabolism research.

Subject of Research: Mitochondrial dysfunction in granulosa cells is associated with impaired proliferation and angiogenic support in women with polycystic ovarian syndrome and elevated AMH

Article Title: Mitochondrial dysfunction in granulosa cells is associated with impaired proliferation and angiogenic support in women with polycystic ovarian syndrome and elevated AMH

Article References: Hong, K.-J., Lin, J.-J., & Lai, T.-H. (2026). Mitochondrial dysfunction in granulosa cells is associated with impaired proliferation and angiogenic support in women with polycystic ovarian syndrome and elevated AMH. Journal of Ovarian Research. https://doi.org/10.1186/s13048-026-02264-x

Image Credits: AI Generated

DOI: 10.1186/s13048-026-02264-x

Keywords: Mitochondrial, dysfunction, granulosa, cells, associated, impaired, proliferation, angiogenic, support, women, polycystic, ovarian

The choice to define access as the use of at least one renewable source, including grid electricity, is methodologically significant. Because hydropower contributed 93.4 percent of Uganda’s grid generation in 2023 and roughly 92 percent of installed generating capacity is renewable, treating grid electricity as a proxy for renewable supply is defensible at the national level. However, the authors’ sensitivity analysis, which excluded grid electricity from the outcome and found a lower prevalence with broadly similar predictors, strengthens confidence in the robustness of the central findings. This dual approach acknowledges a persistent measurement challenge in energy research: households connected to a renewable-heavy grid experience clean energy consumption without any direct awareness of it, while off-grid solar users often have a more tangible relationship with the technology powering their homes.

The multilevel modeling strategy deserves particular attention. By including a random intercept for cluster and applying cluster-robust standard errors, the analysis accounts for the reality that households within the same village or parish share circumstances, from local grid extension decisions to the presence of solar vendors and the influence of neighbors’ adoption choices. The adjusted intraclass correlation coefficient of 0.444 is strikingly high, indicating that nearly half of the variation in renewable energy access occurs at the cluster level rather than the household level. This finding carries substantial policy weight. It suggests that community-level interventions, such as siting service centers, supporting local entrepreneurs, and targeting awareness campaigns at the village scale, could be considerably more efficient than approaches that treat households as independent decision-makers operating in identical environments.

Among the household-level predictors, the association with primary education stands out for the magnitude of its effect. Households whose respondents had primary education had more than three times the odds of renewable energy access compared with those without it. This aligns with a consistent body of Ugandan evidence, including earlier work by Lee and colleagues on energy transition determinants, Aarakit and colleagues on solar photovoltaic adoption, and Murungi and colleagues on rural electricity access, all of which identified education as a central driver. Education likely operates through several channels simultaneously: greater literacy enables comprehension of product information and contracts, higher earning potential improves affordability, and schooling can increase familiarity with and trust in new technologies. The dose-response question, whether secondary or tertiary education confers still greater advantage, remains open but the direction of the gradient in prior literature suggests it would.

Equally notable is the negative association with older age. Respondents aged sixty years and above had roughly a quarter of the odds of renewable energy access compared with younger households. In a region where decades of conflict disrupted livelihoods and where older adults may be less mobile, less literate, and more dependent on familiar cooking practices, this pattern points to a risk that the energy transition could bypass exactly those households that spend the most time exposed to indoor air pollution from firewood and charcoal. The health stakes are considerable. Replacing open fires and traditional stoves with cleaner technologies has been estimated to save up to 7.6 million lives annually worldwide by reducing exposure to harmful indoor and outdoor air pollutants, and older household members are typically the most intensively exposed group.

The service-environment variables emerged as the strongest predictors in the model, and their interpretation may be the most actionable. Households lacking local availability of renewable energy services had 84 percent lower odds of access, and those unable to reach all energy options locally had 63 percent lower odds. Living far from energy sources reduced the odds by nearly 80 percent. These are not measures of household motivation or awareness; they are measures of supply-side presence. In other words, even within a single region, whether a household can adopt renewable energy depends heavily on whether technicians, spare parts, vendors, and installation services exist within practical reach. This resonates with a well-documented failure mode of off-grid solar programs across sub-Saharan Africa, where products are sold or donated but subsequently fall into disuse because no local repair capacity exists.

The training variable reinforces this supply-side interpretation. Households without any renewable energy training had 71 percent lower odds of access than those with training. Training in this context plausibly encompasses both formal awareness campaigns and practical instruction in installation, maintenance, and safe use. The finding suggests that knowledge transfer functions as a genuine bottleneck rather than a mere correlate, and it provides an empirical basis for programs that pair technology distribution with community education. It also connects to the education finding: where formal schooling is limited, structured training may substitute for some of the familiarity and confidence that education would otherwise provide.

The strongest single predictor was the absence of reported energy barriers, which quadrupled the odds of access. Households that perceived no obstacles to obtaining renewable energy were dramatically more likely to actually use it. This perception measure likely aggregates financial constraints, information gaps, distrust of vendors, and logistical difficulties into a single subjective assessment. Its dominance in the model implies that demand-side interventions aimed at reducing perceived barriers, through microfinance, subsidies, consumer protection, or demonstration projects, could yield outsized returns. It also cautions against purely technological approaches: a solar panel available in a district town accomplishes little for a household that believes it cannot afford, install, or maintain one.

The regional context sharpens the urgency of these findings. Northern Uganda is the country’s second-largest charcoal-producing region, responsible for 39.5 percent of national production, and its dependence on charcoal has driven measurable ecological damage, including a 90 percent decline in shea trees and increasingly erratic rainfall with longer, hotter dry seasons. Nationally, more than 500,000 acres of forest are cleared each year and 62.5 percent of forest cover has been lost over three decades, largely for fuelwood, with projections suggesting that nearly all forests outside protected areas could be gone by 2050. Against this backdrop, the 29.5 percent of surveyed households without any renewable energy source represent not only a development gap but an ongoing contribution to deforestation and greenhouse gas emissions.

The refugee-hosting dimension of Northern Uganda adds another layer of vulnerability. Districts in the region host large displaced populations with particularly limited access to clean cooking and renewable technologies, and energy needs in such settings often intensify pressure on surrounding forests. Any strategy to expand renewable access in Lira and Gulu, and in neighboring districts with similar profiles, will need to account for population displacement, land tenure uncertainty, and the specific constraints faced by refugee and host communities alike.

Several limitations inherent to the cross-sectional design warrant consideration when interpreting the associations reported. Because exposure and outcome were measured at a single point in time, the direction of causality cannot be established with certainty. It is possible, for instance, that households that adopt renewable technologies subsequently gain access to training or services rather than the reverse, although the plausibility and consistency of the findings with prior longitudinal and intervention studies lend credibility to the proposed causal pathways. Self-reported data on energy use may also be subject to recall or social desirability bias, and the two-district sampling frame, while purposively chosen for population density and sub-regional representation, means the results should be generalized to the rest of Northern Uganda with appropriate caution.

Nevertheless, the convergence of this study’s predictors with prior Ugandan and regional literature creates a coherent evidence base for policy. The picture that emerges is of an energy transition gated by three interlocking conditions: human capital, captured by education and training; physical and commercial infrastructure, captured by local service availability and proximity; and perceived feasibility, captured by the absence of barriers. Interventions that address only one of these conditions are likely to underperform. A subsidy program without local technicians will produce idle equipment; a training campaign without affordable products will produce awareness without adoption; a vendor network without community trust will produce sales without sustained use.

The findings also speak to Uganda’s national commitments. The country’s installed renewable capacity of 1,067 megawatts in 2020, roughly a quarter of total capacity, falls well short of the International Energy Agency’s 2030 benchmark of 71 percent renewable capacity, and Uganda ranks among the ten countries with the lowest access to clean cooking energy. Because household-level adoption is the unit at which energy poverty is ultimately experienced, studies like this one, which identify modifiable predictors at the household and community scale, complement national capacity statistics with the granular evidence needed for targeted implementation. The high clustering of access within communities suggests that geographically targeted programming, concentrating services, training, and barrier-reduction efforts in underserved parishes, could achieve rapid gains.

More broadly, the study illustrates a methodological contribution relevant beyond Uganda: the explicit modeling of between-cluster variation in energy access. The intraclass correlation of 0.444 quantifies something often asserted but rarely measured, namely that energy access is a fundamentally local phenomenon. Future household energy surveys in low- and middle-income settings would benefit from routinely reporting this statistic, as it directly informs sample size calculations for cluster-randomized evaluations and signals the potential effectiveness of community-level versus household-level interventions.

In sum, the evidence from 634 households in Lira and Gulu indicates that renewable energy access in Northern Uganda is neither randomly distributed nor solely a function of household wealth. It is structured by education, age, training, local service ecosystems, geographic proximity, and perceived barriers, with community context explaining nearly half of all variation. Expanding local service availability, embedding training in distribution programs, and systematically reducing the barriers households report would constitute an evidence-aligned pathway toward more equitable energy access in a region whose forests, health, and post-conflict recovery all depend on it.

Subject of Research: Factors associated with access to renewable energy in Northern Uganda: a cross-sectional community-based study

Article Title: Factors associated with access to renewable energy in Northern Uganda: a cross-sectional community-based study

Article References: Musinguzi, M., Kigongo, E., Emmanuel, I., Opido, J. O., Amito, F., Opio, I. O., Waziri, H., Kabunga, A., & Sam Opollo, M. (2026). Factors associated with access to renewable energy in Northern Uganda: a cross-sectional community-based study. BMC Environmental Science, 3(1), Article 23. https://doi.org/10.1186/s44329-026-00063-9

Image Credits: AI Generated

DOI: 10.1186/s44329-026-00063-9

Keywords: Factors, associated, access, renewable, energy, Northern, Uganda, cross-sectional, community-based, scientific research