Asthma remains a prevalent respiratory condition with exacerbations that challenge both patients and healthcare providers. A Mount Sinai team delved into the prognostic power of type 2 inflammatory biomarkers—specifically blood eosinophils (BEC) and exhaled nitric oxide (FeNO)—to predict future exacerbations in asthma patients under specialty care. These biomarkers reflect nuanced immune pathways; FeNO indicates type 2 airway inflammation and T-helper cell activity, whereas BEC quantifies circulating IL-5 and eosinophil effector cells critical in inflammatory responses. By leveraging real-world clinical data alongside genomic insights from the REGAIN cohort via advanced network analysis, the study provides a refined stratification strategy, potentially revolutionizing personalized asthma management.

In the realm of sleep-disordered breathing, the intersection of obstructive sleep apnea with neurodegenerative markers hints at intricate pathophysiological mechanisms. Mount Sinai researchers examined longitudinal changes in plasma beta-amyloid 42/40 ratios among cognitively normal older adults to assess whether the burden of sleep apnea accelerates amyloid deposition, a hallmark of Alzheimer’s disease. This longitudinal investigation integrates polysomnographic metrics with biomarker trajectories, shedding light on sleep’s role in neurocognitive decline and offering a potential avenue for early interventions targeting OSA to mitigate dementia risk.

The deployment of AI in critical care has transformed patient monitoring, yet real-world model performance over time requires vigilant assessment. Mount Sinai researchers evaluated MEWS++ (Modified Early Warning Score Plus Plus), an AI-driven clinical deterioration prediction model operationalized in a tertiary care center’s step-down units. Their analysis revealed insights into temporal model stability and performance drift, emphasizing the importance of longitudinal validation frameworks to ensure AI-driven decisions remain accurate and reliable in dynamic clinical environments, preventing adverse events through timely alerts.

A provocative shift in understanding chronic respiratory diseases was highlighted in an expert symposium challenging the rigid dichotomy between asthma and chronic obstructive pulmonary disease (COPD). Mount Sinai’s Chair of Medicine, Dr. Monica Kraft, presented emerging evidence on shared inflammatory pathways, airway remodeling processes, and immune mechanisms that blur clinical distinctions between these entities. She discussed recent biologic therapies, such as dupilumab, targeting type 2 inflammation that may benefit subsets of COPD patients with overlapping pathobiology, thereby advocating for integrated treatment guidelines informed by molecular phenotyping and imaging biomarkers.

The intricate relationship between OSA and cardiovascular outcomes remains an enigma, with prior randomized controlled trials failing to demonstrate the cardiovascular benefits of continuous positive airway pressure (CPAP) therapy. Novel analyses from the Mount Sinai team re-examined data from landmark studies including the SAVE and ISAACC trials, dissecting differential risks for myocardial infarction versus stroke and identifying phenotype-specific physiologic markers such as hypoxemia burden and autonomic dysfunction. By employing machine learning to parse heterogeneity in treatment effects, their work aims to delineate patient subpopulations most likely to derive cardiovascular protection from CPAP intervention.

Further refining cardiovascular risk prediction, Mount Sinai investigators utilized the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to develop parsimonious machine learning models targeting non-sleepy individuals with OSA—those frequently underdiagnosed due to minimal daytime symptoms. Their data-driven approach incorporates polysomnography-derived metrics beyond traditional Apnea-Hypopnea Index (AHI), enhancing early identification of patients at heightened cardiovascular risk and facilitating proactive clinical management strategies tailored to silent yet high-risk disease phenotypes.

On the frontier of digital health, another Mount Sinai study applied natural language processing (NLP) utilizing enhanced large language models (LLMs) fine-tuned for healthcare settings to extract clinically relevant symptomatology and cardiovascular event data from unstructured electronic health records (EHRs). This cutting-edge methodological advancement addresses the labor-intensive bottleneck of chart reviews and holds promise for scalable, real-time surveillance of disease trajectories, particularly within vulnerable populations such as those exposed to World Trade Center dust, thereby optimizing care pathways through granular longitudinal data extraction.

Indoor air quality’s impact on maternal and fetal health also garnered attention, with research tracking associations between indoor air pollutants and maternal blood pressure fluctuations throughout pregnancy. This urban prospective cohort study illuminates the cardiovascular risks imposed by environmental exposures during a critical physiological window, emphasizing the necessity for public health policies aimed at mitigating indoor pollution and safeguarding maternal and neonatal outcomes, an area often overshadowed by ambient outdoor pollution studies.

Lung cancer diagnostics are likewise undergoing a paradigm shift with the incorporation of AI-based risk stratification tools. Mount Sinai investigators validated RADLogics, a deep learning framework employing multiple neural networks to analyze nodule characteristics from sequential CT imaging, enhancing predictive accuracy for malignancy beyond single timepoint assessments. This innovation supports more informed clinical decision-making regarding lung nodule surveillance and intervention timing, potentially reducing unnecessary invasive procedures and optimizing early cancer detection pathways.

In addition to diagnostic precision, disparities in post-imaging follow-up care for incidentally detected lung nodules were rigorously analyzed. The study found that socio-economic determinants such as race and insurance status significantly influence adherence to recommended outpatient assessments and diagnostic completeness, particularly in never-smoker populations where lung cancer risk is often underestimated. These findings underscore critical health equity concerns and call for targeted interventions to bridge gaps in cancer care delivery.

Amid these themes, the integration of AI and machine learning across pulmonary and cardiovascular domains remains a unifying thread in Mount Sinai’s submissions. Their research elucidates not merely the clinical phenotypes and pathophysiologic underpinnings of complex diseases, but also innovates on implementation science by managing heterogeneity in patient populations and enhancing real-world application and generalizability of predictive algorithms, thus bridging the gap between bench and bedside.

Mount Sinai Health System, renowned as one of the largest academic medical centers in the New York metro region, continues to lead at the confluence of clinical excellence, pioneering research, and technology-driven care. Their comprehensive approach, spanning seven hospitals and hundreds of research laboratories, harnesses AI and advanced informatics not only to elucidate disease mechanisms but also to elevate patient-centered outcomes. As lung and sleep medicine evolve, Mount Sinai’s work at ATS 2026 serves as a beacon illuminating future directions in respiratory science, inclusive of precision medicine, digital health, and health equity—catalyzing a transformation in diagnosis, treatment, and prevention strategies worldwide.

Subject of Research: Pulmonary medicine, asthma, obstructive sleep apnea, cardiovascular outcomes, artificial intelligence in healthcare, lung cancer diagnostics

Article Title: Mount Sinai Unveils Pioneering Respiratory Research at ATS 2026: From Biomarker-Driven Asthma Care to AI-Powered Cardiovascular Risk Prediction in Sleep Apnea

News Publication Date: May 19, 2026

Web References:
– https://conference.thoracic.org/
– https://ats2026.d365.events/education

References: Not provided in original content

Image Credits: Not provided in original content

Keywords: Respiratory system, lungs, airway, sleep apnea, sleep disorders, asthma, air pollution, cardiovascular risk, artificial intelligence, pulmonary medicine, lung cancer, health disparities

Composting, a time-honored technique for recycling organic waste into valuable soil amendments, faces significant challenges stemming from its potential to emit potent greenhouse gases such as methane (CH4) and nitrous oxide (N2O), as well as odorous and toxic substances including ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOCs). These emissions not only exacerbate air pollution and global warming but also degrade the nutrient content and agronomic efficacy of the final compost product. The newly published study in Environmental and Biogeochemical Processes advances our understanding of how strategic interventions during composting can suppress these emissions while boosting nutrient retention.

The extensive meta-analytical approach entailed analyzing worldwide datasets that describe the effects of various compost control measures, categorized into biological, chemical, physical, and mechanical interventions. Biological strategies comprised microbial inoculants designed to modulate microbial communities, whereas chemical measures included amendments such as biochar and gypsum that interact directly with the chemical environment of the compost. Physical methods involved enhanced aeration systems and the addition of bulking agents to optimize oxygen diffusion and moisture balance. Mechanical solutions focused on mixing techniques and novel electric field applications to disrupt emission pathways and accelerate decomposition.

Significantly, these interventions were shown to elevate composting temperatures by approximately 48 percent, a thermal increase that is pivotal for pathogen inactivation as well as for expediting the conversion of complex organic substrates into stable humic substances. Elevated temperatures also create less conducive conditions for methanogenic archaea, microbes responsible for methane generation under anaerobic pockets within compost piles. This thermal effect, combined with disciplinary strategies, resulted in remarkable reductions in emissions: methane levels fell by around 69 percent, nitrous oxide by 83 percent, ammonia by 78 percent, and carbon dioxide by 78 percent as well, reflecting an overall suppression of gaseous losses from the system.

Nutrient dynamics, a critical factor determining the agronomic value of compost, were positively influenced by these management tactics. Retention of nitrogen, indispensable for plant growth, surged by nearly 89 percent, indicating that less nitrogen was lost as volatilized ammonia or denitrified nitrous oxide. Additionally, the humic acid content—an index of compost maturity and soil health benefits—increased by about 29 percent, signaling enhanced organic matter stabilization. The germination index, an assay reflecting phytotoxicity and compost stability, improved by 73 percent, underscoring the production of safer, more effective fertilizers through these optimized composting protocols.

Among all tested amendments, biochar—the carbonaceous residue obtained from pyrolyzing biomass—stood out as the most potent technology for harmonizing emission mitigation with nutrient preservation. Its intricate porous matrix acts as a physical adsorbent for ammonia and nitrous oxide while fostering microbial environments that favor nutrient stabilization. The study elucidated biochar’s capacity to balance compost chemistry by reducing gaseous nitrogen losses and promoting compost maturation, making it an indispensable tool for future organic waste recycling initiatives.

The researchers emphasize that the compost feedstock—whether manure, food waste, sewage sludge, or agricultural residues—significantly influences emission profiles and nutrient retention rates. Different substrates vary in carbon-to-nitrogen ratios, moisture content, and microbial consortia, necessitating tailored compost management schemes that optimize operational parameters for each type of input. This insight challenges the conventional one-size-fits-all approach and highlights the need for precision composting strategies that consider waste heterogeneity and local environmental conditions.

Crucially, the study not only underscores composting’s role in closing nutrient loops and improving soil fertility but also frames it as a strategic environmental technology capable of decoupling organic waste handling from climate change drivers. Organic waste streams worldwide are burgeoning, and without effective recycling pathways, they pose escalating threats to landfills, water bodies, and atmospheric quality. Enhanced composting practices thus emerge as indispensable for transforming waste liabilities into agronomic assets while curbing greenhouse gas emissions across the agricultural sector.

The synergistic potential of combining diverse mitigation approaches also emerged from the analysis. For example, coupling optimized aeration regimes with chemical amendments such as biochar and gypsum could amplify reductions in gas emissions and nutrient losses beyond levels achievable by individual interventions alone. Such integrative composting systems warrant further exploration to develop cost-effective, scalable solutions that accommodate varying climatic and operational contexts globally.

Looking forward, the authors advocate for broadening the scope of empirical studies to encompass diverse geographies and climatic zones. Such data expansion will enrich meta-analytical models and facilitate the development of globally applicable composting guidelines that remain sensitive to regional environmental and socio-economic realities. Moreover, exploring emerging technologies like electric field application offers promising avenues for innovation in reducing noxious emissions and improving compost quality.

By delivering a rigorous, evidence-based assessment of diverse composting management strategies, this study equips stakeholders with a scientifically vetted roadmap to enhance both environmental sustainability and agricultural productivity. Its revelations propel composting from a traditional waste management technique to a dynamic component of climate-smart agriculture, embodying the intertwined goals of emission reduction, resource efficiency, and soil health enhancement essential to global food security.

This meta-analytical research acts as a clarion call for the adoption of intelligent composting protocols that prioritize emission control without compromising nutrient cycling. As the world grapples with intensifying climate challenges and growing demands for sustainable agriculture, these findings highlight a pragmatic pathway to harness organic waste for ecological and economic benefit. Implementing these strategies has the potential to revolutionize organic fertilizer production, reducing the environmental footprint of farming operations while fostering resilient, fertile soils capable of sustaining future generations.

Subject of Research: Not applicable
Article Title: Synthesis of air pollution patterns and nutrient composition during organic fertilizer production: a meta-analytical study
News Publication Date: 27-Jan-2026
Web References: https://doi.org/10.48130/ebp-0025-0022
References: Abdellah YAY, Gao J, Shi Z, Shi X, Liu W, et al. 2026. Synthesis of air pollution patterns and nutrient composition during organic fertilizer production: a meta-analytical study. Environmental and Biogeochemical Processes 2: e005 doi: 10.48130/ebp-0025-0022
Image Credits: Yousif Abdelrahman Yousif Abdellah, Jianou Gao, Zhaoji Shi, Xiaofei Shi, Wei Liu, Chengmo Yang, Katharina Maria Keiblinger, Xinyue Zhao, Elsiddig A. E. Elsheikh, Shahid Iqbal, Shanshan Sun, Dong Liu, & Fuqiang Yu
Keywords: Air pollution, Additive effects, Fertilizers, Metaanalysis