Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

BACKGROUND: Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. OBJECTIVE: This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. METHODS: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. RESULTS: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. IMPACT STATEMENT: We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.

BACKGROUND: High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. METHODS: This 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance. RESULTS: Analysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity. CONCLUSION: The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model.

Enhanced Fenton-like process over Z-scheme MoO3 surface decorated with Fe2O3 under visible light.

Photocatalysts consisting of Z-scheme heterojunctions are commonly used in wastewater treatment due to their exceptional reactivity in photocatalysis and highly efficient visible-light utilization. In this work, Fe2O3-decorated MoO3 rods were synthesized through a two-step method and their photodegradation of methylene blue (MB) was evaluated. The Fe2O3/MoO3 rods were characterized by XRD, SEM, micro-Raman, XPS, UV-Vis DRS, and PL to investigate their structural, morphological, and optical properties. The results indicate that the photodegradation efficiency of Fe2O3/MoO3 improved through a reduction in the gap energy and persistence of a 1D hexagonal prism structure. The degradation rate of MB was enhanced from 31.7 to 91.5% after irradiation for 180 min owing to electron-hole separation and Fenton-like process. Formation of the OH radical is a key factor in the photodegradation reaction and with the addition of H2O2 the efficiency can further improve via a Fenton-like mechanism. Furthermore, the Z-scheme mechanism concurrently delineated. The Fe2O3/MoO3 rod composites were also found to retain high photocatalytic efficiency after being reused five times, which may be useful for future applications.

Gut-lung axis and asthma: A historical review on mechanism and future perspective.

BACKGROUND: Gut microbiota are closely related to the development and regulation of the host immune system by regulating the maturation of immune cells and the resistance to pathogens, which affects the host immunity. Early use of antibiotics disrupts the homeostasis of gut microbiota and increases the risk of asthma. Gut microbiota actively interact with the host immune system via the gut-lung axis, a bidirectional communication pathway between the gut and lung. The manipulation of gut microbiota through probiotics, helminth therapy, and fecal microbiota transplantation (FMT) to combat asthma has become a hot research topic. BODY: This review mainly describes the current immune pathogenesis of asthma, gut microbiota and the role of the gut-lung axis in asthma. Moreover, the potential of manipulating the gut microbiota and its metabolites as a treatment strategy for asthma has been discussed. CONCLUSION: The gut-lung axis has a bidirectional effect on asthma. Gut microecology imbalance contributes to asthma through bacterial structural components and metabolites. Asthma, in turn, can also cause intestinal damage through inflammation throughout the body. The manipulation of gut microbiota through probiotics, helminth therapy, and FMT can inform the treatment strategies for asthma by regulating the maturation of immune cells and the resistance to pathogens.

Hidden danger: The long-term effect of ultrafine particles on mortality and its sociodemographic disparities in New York State.

Although previous studies have shown increased health risks of particulate matters, few have evaluated the long-term health impacts of ultrafine particles (UFPs or PM0.1, ≤ 0.1 µm in diameter). This study assessed the association between long-term exposure to UFPs and mortality in New York State (NYS), including total non-accidental and cause-specific mortalities, sociodemographic disparities and seasonal trends. Collecting data from a comprehensive chemical transport model and NYS Vital Records, we used the interquartile range (IQR) and high-level UFPs (≥75 % percentile) as indicators to link with mortalities. Our modified difference-in-difference model controlled for other pollutants, meteorological factors, spatial and temporal confounders. The findings indicate that long-term UFPs exposure significantly increases the risk of non-accidental mortality (RR=1.10, 95 % CI: 1.05, 1.17), cardiovascular mortality (RR=1.11, 95 % CI: 1.05, 1.18) particularly for cerebrovascular (RR=1.21, 95 % CI: 1.10, 1.35) and pulmonary heart diseases (RR=1.33, 95 % CI: 1.13, 1.57), and respiratory mortality (borderline significance, RR=1.09, 95 % CI: 1.00, 1.18). Hispanics (RR=1.13, 95 % CI: 1.00, 1.29) and non-Hispanic Blacks (RR=1.40, 95 % CI: 1.16, 1.68) experienced significantly higher mortality risk after exposure to UFPs, compared to non-Hispanic Whites. Children under five, older adults, non-NYC residents, and winter seasons are more susceptible to UFPs' effects.

Ultrafine Particles and Hospital Visits for Chronic Lower Respiratory Diseases in New York State.

RATIONALE: Exposure to particulate matter is associated with various adverse health outcomes. Ultrafine particles are a unique public health challenge due to their size. However, limited studies have examined their impacts on human health, especially across seasons and demographics. OBJECTIVES: To evaluate the effect of ultrafine particle exposure on the risk of visiting the emergency department for a chronic lower respiratory disease in New York State NYS, 2013-2018. METHODS: We used a case-crossover design and conditional logistic regression to estimate how ultrafine particle exposure led to chronic lower respiratory disease-related emergency department visits. GEOS-Chem-APM, a state-of-the-art chemical transport model with a size-resolved particle microphysics model, generated air pollution simulation data. We then matched ultrafine particle exposure estimates to geocoded health records for asthma, bronchiectasis, chronic bronchitis, emphysema, unspecified bronchitis, and other chronic airway obstructions in NYS from 2013-2018. In addition, we assessed interactions with age, ethnicity, race, sex, meteorological factors, and season. MEASUREMENTS AND MAIN RESULTS: Each interquartile range increase in ultrafine particle exposure led to a 0.37% increased risk of a respiratory-related emergency department visit on lag 0-0 (95% CI: 0.23-0.52%) and a 1.81% increase on lag 0-6 (95% CI: 1.58-2.03%). The highest risk was in the subtype emphysema (lag 0-5: 4.18%, 95% CI: 0.16-8.37%), followed by asthma (lag 0-6: 2.00%), chronic bronchitis (lag 0-6: 1.78%), other chronic airway obstructions (lag 0-6: 1.60%), and unspecified bronchitis (lag 0-6: 1.49%). We also found significant interactions between UFP health impacts and season (fall, 3.29%), temperature (<90th percentile, 2.27%), relative humidity (>90th percentile, 4.63%), age (children <18, 3.19%), and sex (men, 2.06%) on lag 0-6. CONCLUSION: In this study, UFP exposure increased chronic lower respiratory disease-related emergency department visits across all seasons and demographics, yet these associations varied according to various factors, which requires more research.

A feasibility study of a handmade ultrasound-guided phantom for paracentesis.

BACKGROUND: Simulation-based training is effective for ultrasound (US)-guided procedures. However, commercially developed simulators are costly. This study aims to evaluate the feasibility of a hand-made phantom for US-guided paracentesis. METHODS: We described the recipe to prepare an agar phantom. We collected the US performance data of 50 novices, including 22 postgraduate-year (PGY) residents and 28 undergraduate-year (UGY) students, who used the phantom for training, as well as 12 emergency residents with prior US-guided experience. We obtained the feedback after using the phantom with the Likert 5-point scale. The data were presented with medians and interquartile ranges (IQRs) and analyzed by the Wilcoxon rank sum test. RESULTS: While emergency residents demonstrated superior performance compared to trainees, all trainees exhibited acceptable proficiency (global rating of ≥ 3, 50/50 vs. 12/12, p = 1.000) and comparable needle steadiness [5 (5) vs. 5 (5), p = 0.223]. No significant difference in performance was observed between PGYs [5 (4-5)] and UGYs [5 (4-5), p = 0.825]. No significant differences were observed in terms of image stimulation, puncture texture, needle visualization, drainage simulation, and endurance of the phantom between emergency residents and trainees. However, experienced residents rated puncture texture and draining fluid as "neutral" (3/5 on the Likert scale). The cost of the paracentesis phantom is US$16.00 for at least 30 simulations, reducing it to US$6.00 without a container. CONCLUSIONS: The paracentesis phantom proves to be a practical and cost-effective training tool. It enables novices to acquire paracentesis skills, enhances their US proficiency, and boosts their confidence. Nevertheless, further investigation is needed to assess its long-term impact on clinical performance in real patients. TRIAL REGISTRATION: NCT04792203 at the ClinicalTrials.gov.

Mysteriously rapid rise in Legionnaires' disease incidence correlates with declining atmospheric sulfur dioxide.

Legionnaires' disease (LD) is a severe form of pneumonia (∼10-25% fatality rate) caused by inhalation of aerosols containing Legionella, a pathogenic gram-negative bacteria. These bacteria can grow, spread, and aerosolize through building water systems. A recent dramatic increase in LD incidence has been observed globally, with a 9-fold increase in the United States from 2000 to 2018, and with disproportionately higher burden for socioeconomically vulnerable subgroups. Despite the focus of decades of research since the infamous 1976 outbreak, substantial knowledge gaps remain with regard to source of exposure and the reason(s) for the dramatic increase in LD incidence. Here, we rule out factors indicated in literature to contribute to its long-term increases and identify a hitherto unexplored explanatory factor. We also provide an epidemiological demonstration that the occurrence of LD is linked with exposure to cooling towers (CTs). Our results suggest that declining sulfur dioxide air pollution, which has many well-established health benefits, results in reduced acidity of aerosols emitted from CTs, which may prolong the survival duration of Legionella in contaminated CT droplets and contribute to the increase in LD incidence. Mechanistically associating decreasing aerosol acidity with this respiratory disease has implications for better understanding its transmission, predicting future risks, and informed design of preventive and interventional strategies that consider the complex impacts of continued sulfur dioxide changes.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 µg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 µg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Wolfberry genome database: integrated genomic datasets for studying molecular biology.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

Synergistic acceleration of machine learning and molecular docking for prostate-specific antigen ligand design.

Prostate-specific antigen (PSA) serves as a critical biomarker for the early detection and continuous monitoring of prostate cancer. However, commercial PSA detection methods primarily rely on antigen-antibody interactions, leading to issues such as high costs, stringent storage requirements, and potential cross-reactivity due to PSA variant sequence homology. This study is dedicated to the precise design and synthesis of molecular entities tailored for binding with PSA. By employing a million-level virtual screening to obtain potential PSA compounds and effectively guiding the synthesis using machine learning methods, the resulting lead compounds exhibit significantly improved binding affinity compared to those developed before by researchers using high-throughput screening for PSA, substantially reducing screening and development costs. Unlike antibody detection, the design of these small molecules offers promising avenues for advancing prostate cancer diagnostics. Furthermore, this study establishes a systematic framework for the rapid development of customized ligands that precisely target specific protein entities.

Efficacy and safety of cadonilimab in previously treated recurrent or metastatic nasopharyngeal carcinoma(COMPASSION-06): A phase II multicenter study.

OBJECTIVE: This study was designed to assess the efficacy and safety of cadonilimab monotherapy, a first-in-class, bi-specific PD-1/CTLA-4 antibody, in patients with previously treated recurrent or metastatic nasopharyngeal carcinoma (R/M-NPC). PATIENTS AND METHODS: This multicenter, open-label, single-arm, phase II clinical trial enrolled patients with R/M-NPC who had failed first-line platinum-based chemotherapy and second-line single agent or combined chemotherapy, and immunotherapy-naive. Patients received cadonilimab for 6 mg/kg once every 2 weeks (Q2W). The primary endpoint was objective response rate (ORR) in full analysis set (FAS) assessed by investigators according to RECIST v.1.1. The secondary endpoint included progression-free survival (PFS), overall survival (OS), duration of response (DoR), time to response (TTR) and safety. RESULTS: A total of 23 patients were assessed. The median time from first dose to data cutoff was 16.56 (range, 0.8-25.2) months. ORR was 26.1 % (95 %CI:10.2-48.4). The ORR were 44.4 % (95 %CI: 13.7-78.8) and 14.3 % (95 %CI:1.8-42.8) in patients with tumor PD-L1 expression ≥50 % and <50 %, respectively. ORR was achieved in 40.0 % (95 %CI:12.2-73.8) of patients with EBV-DNA level <4000 IU/ml (n = 10) and 15.4 % (95 %CI:1.9-45.4) of those with ≥4000 IU/ml. The median PFS was 3.71 months (95 %CI: 1.84-9.30). respectively. Median OS was not reached, and the 12-month OS rate was 79.7 % (95 % CI:54.5-91.9). Only two patients (8.3 %) experienced Grade ≥3 treatment-related adverse events (TRAEs) with hypothyroidism (30.4 %), rash (21.7 %) and pruritus (21.7 %) being the most prevalent TRAEs. CONCLUSION: Cadonilimab monotherapy demonstrated a promising efficacy and manageable toxicity in patients with previously treated R-M/NPC and provide an efficacious salvage treatment option.

Impact of cash transfers on the association between prenatal exposures to high temperatures and low birthweight: Retrospective analysis from the LEAP 1000 study.

OBJECTIVE: To explore the associations between prenatal temperature exposures and low birthweight (LBW) and modification by cash transfer (CT) receipt. DESIGN: Retrospective cohort study. SETTING: Five rural districts in Northern Ghana. POPULATION OR SAMPLE: A total of 3016 infants born to women interviewed as part of the Livelihood Empowerment Against Poverty (LEAP 1000) impact evaluation between 2015 and 2017. METHODS: Birthweight was collected using household surveys administered to LEAP 1000 eligible women. We used a UNICEF-developed multiple imputation approach to address missingness of birthweight and applied an empirical heaping correction to the multiply imputed birthweight data. Survey data were linked to the European Centre for Medium-Range Weather Forecasts Reanalysis 5-hourly temperature averaged to weeks for 2011-2017 using community centroids. Using distributed-lag nonlinear models, we explored the lag-specific associations between weekly average temperatures greater than 30°C and LBW, and stratified by LEAP 1000 treatment. MAIN OUTCOME MEASURES: Low birthweight (<2.5 kg). RESULTS: Twelve percent (n = 365) of infants were LBW; the mean ± SD birthweight was 3.02 ± 0.37 kg. Overall, increasing temperatures were associated with increased odds of LBW, with the greatest odds observed in the 3 weeks before birth (odds ratio 1.005-1.025). These positive associations were even larger among comparison infants and null among treatment infants. CONCLUSIONS: Our study found increased odds of LBW with high weekly average temperatures throughout pregnancy and the preconception period and demonstrate mitigated effects by the LEAP 1000 CT program. More evidence on the potential of CTs to serve as adaptation interventions in low- and middle-income countries is needed to protect pregnant persons and their infants from the impacts of climate change.

Probabilistic risk assessment for determining nonessential metals in commercial infant formula products in Taiwan.

During the early months of life, infant formula plays a crucial role as a primary source of both food and essential nutrients for infants, serving as a replacement for or supplement to breast milk. However, nonessential metals in infant formulas are a concern because infants are highly vulnerable to chemical exposure. The aim of this study was to investigate infant exposure to nonessential metals in infant formula products in Taiwan and assess the associated health risks. In this study, concentrations of arsenic (As), barium (Ba), cadmium (Cd), manganese (Mn), lead (Pb), and vanadium (V) in 45 formula products for 0-1-year-old infants were determined by inductively coupled plasma mass spectrometry. The mean As, Ba, Cd, Mn, Pb, and V concentrations were 6.42, 280, 3.72, 1425, 20.4, and 21.9 µg/kg, respectively. According to our probabilistic simulation of the estimated daily intake of metals, the proportion of hazard quotients exceeding one was 7.69% for As and 3.29% for Mn, and that of hazard index (HI) values exceeding 1 was >17% for metals. Arsenic had the largest HI contribution (46.9%), followed by Mn (22.3%) and Pb (12.7%). The nonessential metals content in infant formula raises potential noncarcinogenic health concerns for infants in Taiwan. Therefore, regulations for nonessential metals must be imposed on related food products in Taiwan, with a particular focus on As and Mn.

A Nickel Anchored Covalent Organic Framework as Unimolecular Metallaphotocatalyst for Visible Light Driver C-P Bond Coupling Reaction.

The urgent need to develop a sustainable and environmentally friendly method for synthesizing organophosphine compounds is underscored by their extensive applications in organic synthesis, coordination chemistry, medicinal chemistry, and photoelectric materials. Metalated covalent organic frameworks (MCOFs), which seamlessly integrate the inherent photo properties of COF with the catalytic capabilities of metal ions, offer an optimal material for efficient transformation of organics sustainably. In this study, we introduce a simple COF with nickel anchorages (Bpy-COF-NiCl2 ) as a unimolecular metallaphotocatalytic system for effective C-P bond formation. This heterogeneous photocatalyst exhibits superior catalytic performance, achieving yields of up to 95 %, and demonstrates broad substrate tolerance and functional group reactivity. Notably, the metallaphotocatalytic system has demonstrated the capability to process aryl bromides to produce the desired product, a feat not previously reported. Finally, the production and reusability test at the gram scale attests to its superior practicality for designing future organic cross-coupling reactions.

Maternal serum neonicotinoids during early-mid pregnancy and congenital heart diseases in offspring: An exploratory study.

Experimental evidence has indicated a correlation between in-utero exposure to neonicotinoid pesticides (NEOs) and adverse birth outcomes in mammals. However, the distribution of NEO exposure during human pregnancy, as well as its association with congenital heart diseases (CHDs), the most common birth defects, are unclear. Our purpose was to explore the distribution of and contributing factors to NEO exposure in pregnant women during early-mid pregnancy and to assess the associations between NEOs and CHDs. This nested case-control study was conducted within an ongoing prospective birth cohort study and enrolled 141 CHD singletons and their 282 individually matched controls. Six "parent" NEOs and three NEO metabolites were measured in maternal serum collected at an average gestational age of 16 weeks, using liquid chromatography-tandem mass spectrometry. Logistic regression was used to quantify the NEOs-CHDs associations and explore potential contributing factors to serum NEO levels in controls. N-desmethyl acetamiprid (N-dm-ACE) and imidacloprid (IMI) were the most frequently detected NEOs, found in 100% and 20% of maternal sera, respectively. We did not find a statistically significant association between total NEOs and overall CHDs. However, there was a trend towards a higher risk of septal defects with greater serum NEOs (ORs ranged from 1.80 to 2.36), especially nitro-containing NEOs represented by IMI. Pregnant women with lower education had elevated serum total NEOs compared to women with higher education (OR = 48.39, 95% CI: 23.48-99.72). Pregnant women were primarily exposed to N-dm-ACE and IMI during early-mid pregnancy. Gestational exposure to NEOs may be associated with an increased risk of septal defects, but the evidence is limited at present. Education is a potential contributing factor to NEO exposure in pregnant women. Larger and more precise studies with longitudinal biospecimen collection, are recommended to validate our exploratory findings.

Ambient heat and diabetes hospitalizations: Does the timing of heat exposure matter?

BACKGROUND: Although ambient heat exposure is linked with diabetes mortality, the impacts of heat exposure on diabetes-related hospitalizations remain controversial. Previous research did not examine the timing of heat-diabetes associations and relation with comorbidities/risk factors. OBJECTIVE: We examined the association between heat exposure and diabetes-related hospitalizations in the transitional and summer months and identified populations vulnerable to heat. METHODS: We conducted a time-stratified case-crossover study. Data on diabetes hospital admissions (primary diagnosis of type 1 and type 2 diabetes, 2013-2020) were collected by the New York State (NYS) Department of Health under the state legislative mandate. We treated temperature and air pollutants as continuous variables and defined the heat exposure as per interquartile range (IQR, a measure between the 25th and 75th percentiles) increase of daily mean temperature. Conditional logistic regressions were performed to quantify the heat-diabetes associations after controlling for air pollutants and time variant variables. Multiplicative-scale interactions between heat and demographics/comorbidities/risk factors on diabetes hospitalizations were investigated. RESULTS: Each IQR increase in temperature was associated with significantly increased risks for diabetes admissions that occurred immediately and lasted for an entire week during multi-day lags in the transitional month of May (ranges of excess risk: 3.1 %-4.8 %) but not in the summer (June-August) (ranges of excess risk: -0.3 %-1.3 %). The significant increases in the excess risk of diabetes were also found among diabetes patients with complications of neuronopathy (excess risk: 27.7 %) and hypoglycemia (excess risk: 19.1 %). Furthermore, the modification effects on the heat-diabetes association were significantly stronger in females, Medicaid enrollees, non-compliant patients, and individuals with comorbidities of atherosclerotic heart disease and old myocardial infarction. CONCLUSIONS: Ambient heat exposure significantly increased the burden of hospital admissions for diabetes in transitional rather than summer months indicating the importance of exposure timing. Vulnerability to heat varied by demographics and heart comorbidity.

A review of machine learning applications in life cycle assessment studies.

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.

Impact of heat on emergency hospital admissions related to kidney diseases in Texas: Uncovering racial disparities.

BACKGROUND AND OBJECTIVE: While impact of heat exposure on human health is well-documented, limited research exists on its effect on kidney disease hospital admissions especially in Texas, a state with diverse demographics and a high heat-related death rate. We aimed to explore the link between high temperatures and emergency kidney disease hospital admissions across 12 Texas Metropolitan Statistical Areas (MSAs) from 2004 to 2013, considering causes, age groups, and ethnic populations. METHODS: To investigate the correlation between high temperatures and emergency hospital admissions, we utilized MSA-level hospital admission and weather data. We employed a Generalized Additive Model to calculate the association specific to each MSA, and then performed a random effects meta-analysis to estimate the overall correlation. Analyses were stratified by age groups, admission causes, and racial/ethnic disparities. Sensitivity analysis involved lag modifications and ozone inclusion in the model. RESULTS: Our analysis found that each 1 °C increase in temperature was associated with a 1.73 % (95 % CI [1.43, 2.03]) increase in hospital admissions related to all types of kidney diseases. Besides, the effect estimates varied across different age groups and specific types of kidney diseases. We observed statistically significant associations between high temperatures and emergency hospital admissions for Acute Kidney Injury (AKI) (3.34 % (95 % CI [2.86, 3.82])), Kidney Stone (1.76 % (95 % CI [0.94, 2.60])), and Urinary Tract Infections (UTI) (1.06 % (95 % CI [0.61, 1.51])). Our research findings indicate disparities in certain Metropolitan Statistical Areas (MSAs). In Austin, Houston, San Antonio, and Dallas metropolitan areas, the estimated effects are more pronounced for African Americans when compared to the White population. Additionally, in Dallas, Houston, El Paso, and San Antonio, the estimated effects are greater for the Hispanic group compared to the Non-Hispanic group. CONCLUSIONS: This study finds a strong link between higher temperatures and kidney disease-related hospital admissions in Texas, especially for AKI. Public health actions are necessary to address these temperature-related health risks, including targeted kidney health initiatives. More research is needed to understand the mechanisms and address health disparities among racial/ethnic groups.

Interfacial Organization and Forces Arising from Epithelial-Cancerous Monolayer Interactions.

The interfacial interactions between epithelia and cancer cells have profound relevance for tumor development and metastasis. Through monolayer confrontation of MCF10A (nontumorigenic human breast epithelial cells) and MDA-MB-231 (human epithelial breast cancer cells) cells, we investigate the epithelial-cancerous interfacial interactions at the tissue level. We show that the monolayer interaction leads to competitive interfacial morphodynamics and drives an intricate spatial organization of MCF10A cells into multicellular finger-like structures, which further branch into multiple subfinger-like structures. These hierarchical interfacial structures penetrate the cancer monolayer and can spontaneously segregate or even envelop cancer cell clusters, consistent with our theoretical prediction. By tracking the substrate displacements via embedded fluorescent nanobeads and implementing nanomechanical modeling that combines atomic force microscopy and finite element simulations, we computed mechanical force patterns, including traction forces and monolayer stresses, caused by the monolayer interaction. It is found that the heterogeneous mechanical forces accumulated in the monolayers are able to squeeze cancer cells, leading to three-dimensional interfacial bulges or cell extrusion, initiating the p53 apoptosis signaling pathways of cancer cells. We reveal that intercellular E-cadherin and P-cadherin of epithelial cells differentially regulate the interfacial organization including migration speed, directionality, spatial correlation, F-actin alignment, and subcellular protrusions of MCF10A cells; whereas E-cadherin governs interfacial geometry that is relevant to force localization and cancer cell extrusion, P-cadherin maintains interfacial integrity that enables long-range force transmission. Our findings suggest that the collaborative molecular and mechanical behaviors are crucial for preventing epithelial tissues from undergoing tumor invasion.