95th Percentile Research Articles

Diagnostic performance of Mindray CL1200i high sensitivity cardiac troponin I assay compared to Abbott Alinity cardiac troponin I assay for the diagnosis of type 1 and 2 acute myocardial infarction in females and males: MERITnI study.

We examined the 0- and 2-h diagnostic performance of the Mindray high-sensitivity cardiac troponin I (hs-cTnI) assay using two predefined sex-specific 99th percentile upper reference limits (URL) in patients with normal electrocardiograms to aid in the diagnosis of myocardial infarction (MI). Consecutive emergency department patients undergoing serial high-sensitivity cardiac troponin I (hs-cTnI) testing on clinical indication were studied in the 'Mindray hs-cTnI Assay Analytical and Clinical Evaluation for the Diagnosis and RIsk Assessment of Myocardial InfarctIon' (MERITnI) trial (NCT05853042). Plasma hs-cTnI testing was performed using Mindray CL1200i (investigational) and Abbott Alinity (clinical) assays. In 1,556 patients (60.7 % male, 43.3 % White, 45.8 % Black, 34.8 % chest pain), 2.7 % had type 1 MI, 2.7 % type 2 MI, and 21.5 % non-MI myocardial injury. At 0 h for all MIs (n=86), using package insert URLs and Universal Sample Bank (USB) URLs, sensitivities were 83.7 and 93.0 %. At 0/2 h for all MIs with package insert and USB URLs, sensitivities were higher with serial testing, at 95.3 and 97.7 %. Negative predictive value (NPVs) were excellent and similar for both URLs, ranging from 98 to 100 %. Substantial hs-cTnI concentration differences were observed between sex and injury types. Alinity hs-cTnI diagnostic observations were similar for both package insert and USB URLs. The Mindray CL1200i hs-cTnI assay provides the relevant clinical diagnostic information to enable clinicians to deliver cost-effective care for patients to aid in the diagnosis of MI predicated on 0- and 2-h serial testing based on sex-specific 99th percentiles. Novel observations were observed for findings based on different URLs and for females and MI types.

Risk of first venous thrombosis by comparing different thrombin generation assay conditions: results from the MEGA case-control study.

Background: Hypercoagulability is a risk factor for venous thromboembolism (VTE). Thrombin generation (TG) is a global coagulation assay that measures an individual’s clotting tendency. We hypothesise that slow onset TG (achieved by using a low procoagulant stimulus or an inhibitor of coagulation) is optimal responsive to detect hypercoagulability. Objectives: To compare different TG assay conditions with respect to VTE risk and to assess the risk of first VTE. Methods: Basal TG at low tissue factor (TF) concentration and high TF concentration in the presence and absence of activated protein C (APC) were measured in plasma samples from 2,081 patients with first VTE and 2,908 healthy controls from the MEGA study. TG parameters and nAPCsr were categorised into quartiles as measured in the controls. We calculated odds ratios (ORs) with 95% confidence intervals of a first VTE for different TG categories. Results: Under all assay conditions the thrombin peak height was associated with VTE risk: peak height > 75th percentile, at low TF odds ratio (OR) 6.8 (95% confidence interval (CI) 5.5 – 8.3), at high TF, odds ratio 3.0 (95% CI 2.5 – 3.6) and at high TF + APC, OR 3.8 (95% CI 3.2 - 4.5), all compared with a peak height < 25th percentile obtained in controls. An increased nAPCsr (higher resistance to APC) was also associated with VTE risk, odds ratio 3.4 (95% CI 2.8 – 4.1). Conclusions: increased TG is associated with the risk of first VTE, particularly when triggered with a low procoagulant stimulus.

The Effect of Parental Weight and Genetics on the Body Mass Index of Very Low Birth Weight Infants as They Reach School Age.

Prematurely born individuals are usually of low or normal weight in childhood; in adulthood, however, their probability of being overweight is twice that of persons born at full term. There is not yet any way to predict the weight development of premature babies. A polygenic BMI score (BMI = body-mass index), calculated from the often very small individual effects of more than 2 million genetic variants, was recently described for adults. We studied the possible association of this score with the course of BMI in premature babies over time, from infancy up to the age of 10-14 years. 508 individuals were included in the study. At the age of 5-7 years, their mean body weight was 18.8 ± 3.3 kg. The difference between the highest and lowest deciles of the polygenic score was 3.3 kg. At age 10-14, the average body weight was 41.3 ± 11.3 kg, and the difference between the highest and lowest deciles had increased to 9.2 kg. In persons with birth weight under the 10th percentile (n = 68), the difference was 19.2 kg (30.9 kg vs. 50.1 kg). The polygenic BMI score was significantly associated with the BMI z-scores of the overall group and the subgroup of growth-retarded children. Extreme values of a polygenic BMI score are strongly associated with the weight development of preterm infants as they develop into children aged 10-14. The large effect size implies that this score may aid in the counseling of prematurely born children and their parents.

Advancements and challenges in high-sensitivity cardiac troponin assays: diagnostic, pathophysiological, and clinical perspectives.

Although significant progress has been made in recent years, some important questions remain regarding the analytical performance, pathophysiological interpretation and clinical use of cardiac troponin I (cTnI) and T (cTnT) measurements. Several recent studies have shown that a progressive and continuous increase in circulating levels of cTnI and cTnT below the cut-off value (i.e. the 99th percentile upper reference limit) may play a relevant role in cardiovascular risk assessment both in the general population and in patients with cardiovascular or extra-cardiac disease. International guidelines recommend the use of standardized clinical algorithms based on temporal changes in circulating cTnI and cTnT levels measured by high-sensitivity (hs) methods to detect myocardial injury progressing to acute myocardial infarction. Some recent studies have shown that some point-of-care assays for cTnI with hs performance ensure a faster diagnostic turnaround time and thus significantly reduce the length of stay of patients admitted to emergency departments with chest pain. However, several confounding factors need to be considered in this setting. A novel approach may be the combined assessment of laboratory methods (including hs-cTn assay) and other clinical data, possibly using machine learning methods. In the present document of the Italian Study Group on Cardiac Biomarkers, the authors aimed to discuss these new trends regarding the analytical, pathophysiological and clinical issues related to the measurement of cardiac troponins using hs-cTnI and hs-cTnT methods.

Ecological and human health risks of potentially toxic elements (PTEs) in street dust of Al-Hillah City, Iraq using Monte Carlo simulation.

Street dust is a primary source of metal pollution in urban environments, posing a significant threat to human health through chronic exposure via inhalation, ingestion, and skin contact. This study used deterministic and Monte Carlo simulation to assess the health risks of potential toxic elements (PTEs) in the street dust of Al-Hillah City. The average concentrations of elements in the samples followed the order: Al>Fe>S>K>Sr>Mn>Cr>Ba > Zn>Ni>Pb>Cu>Co>As > Sn>Sb>Cd. In the study area, all the measured elements exceeded UCC values except for Al, Ba, Fe, and K. The results for the enrichment factor (EF), geo accumulation index (Igeo), and contamination factor (CF) revealed that the most sampled locations were polluted with sulfur (S), arsenic (As), and chromium (Cr). The highest values of the pollution load index were not for a solely land use class; they were identified at different sampling stations. According to the potential ecological risk rating, As and Cd pose a medium risk, while Cr, Cu, Ni, Pb, and Zn have low risks. The probabilistic Monte Carlo simulation highlighted the significant health risks from PTEs in street dust, especially for children, with HI values of 2.01, 3.24, and 5.26 at the 5th, 50th, and 95th percentiles, respectively. In comparison, HI values for adults were much lower at 0.29, 0.41, and 0.58, remaining within safe limits. Lifetime Cancer Risk (LTCR) estimates showed that 99.7% of adults and 97% of children exposed to levels exceeding the safe threshold 1E-4. Sensitivity analysis revealed that chromium (Cr) and nickel (Ni) were the main PTEs contributing to health risks in children and adults' groups.

Probabilistic and FEM hydro-mechanical coupling analysis of rainfall-induced slope instability

The random field theory coupled with a FEM hydro-mechanical model was employed to analyse slope responses under rainfall, with a particular focus on post-rainfall behaviour. This study uniquely analysed the heterogeneity of soil strength and permeability through the application of random field theory, providing a novel perspective on its impact on slope stability. Numerical modelling under varying rainfall intensities revealed that the factor of safety (FoS) decreased with prolonged rainfall, particularly at higher intensities. Post-rainfall behaviour varied for different rainfall events, that gentle and moderate rainfalls led to a gradual decline in FoS due to water infiltration, while heavy rainfall allowed recovery through drainage, and extremely heavy rainfall triggered immediate failure. A novel finding is that post-rainfall water seepage can govern slope stability under extreme rainfall conditions. At failure, the surface layer of a fully saturated slope lost clay suction and exhibited concentrated plastic shear strain at its base. Weak zones with interconnected high-permeability channels significantly influenced slope stability by facilitating water infiltration. Probabilistic analysis further showed that the FoS at the end of simulations typically ranged from 1.1 to 1.4, with a recommended 5th percentile value of 1.1 for slope designs.

BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098

Ongoing ecological research is concerned with analysing climate-induced changes in species distribution. For this purpose, the projection must have high-quality bioclimatic variables from historical and future climatic periods for the projection. To date, there are many global bioclimatic variables on this topic. Nevertheless, a consistent dataset with identical model variables from historic and projected periods is rare. We present 26 bioclimatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM simulations for 1971–2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bioclimatic variables. For projection periods we chose 20 year ranges between 2021–2098. Here, we offer two versions of them: (1) variables separated into RCP 2.6, 4.5 and 8.5, including percentiles among the realisations and within the RCPs; and (2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values.

A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions

In the face of escalating climate change, airports worldwide are finding themselves at the mercy of extreme weather events. This research paper presents a comprehensive system that models key indicators, aiding airport management during such challenging weather conditions. The system adopts an integrated approach, combining various machine learning models to provide a detailed projection of an airport's future state, drawing from past occurrences. The heart of the system is a model that focuses on the airport's peak service rate. This model meticulously correlates weather conditions and runway configurations with the 99th percentile of observed throughput from the training dataset. As such, the peak service rate model provides an estimate of the airport's capacity, which is essential for effective planning and resource allocation. Moreover, the system includes a predictive model that assesses the likelihood of air traffic flow management regulations based on weather data and calendar information. The robustness of this model against noise and uncertainty in the training dataset is fortified by the application of confident learning techniques and the inclusion of monotonic constraints. The system further enhances its capabilities by forecasting the potential entry rate of regulations, expressed in hourly arrivals, providing valuable insights that can guide proactive decision-making. By seamlessly integrating these three models, the system serves as an effective tool for airport operators and airlines. It enables operational optimisation and the development of strategic plans to mitigate the effects of increasing weather-related disruptions.

Microscopic and Biomechanical Analysis of PEEK Interspinous Spacers for Spinal Fusion Applications

Spinal fusion is a surgical intervention used to join two or more vertebrae in the spine. An often-used method involves the placement of intervertebral spacers. They are commonly composed of biocompatible materials like polyetheretherketone. It has strength, longevity, and the capacity to interact harmoniously with the human body. Standardized mechanical tests were performed on two distinct implants to assess their biomechanical characteristics. The studies were conducted at a velocity of 2 mm/min. The stopping criteria were determined based on the loads sustained by the 50th percentile. Furthermore, the chemical composition of the implants was assessed using Raman spectroscopy. The implant created via subtractive manufacturing has a significant change in its elastic region at a force of 1300 N, and it begins subsidence when vertebrae are subjected to a load of 1500 N. The integration of microscopic characterization techniques with the mechanical analysis of prostheses in numerous case studies facilitates the biomechanical evaluation of implants.

Co-occurring climate events and environmental justice in California, 2018-2019.

Climate change will increase the frequency of extreme weather events. This means climate-driven events like wildfires and power outages will likely co-occur more often, potentially magnifying their health risks. We characterized three types of climate-driven events-anomalously warm temperatures, wildfire burn zone disasters, and long power outages-in 58 California counties during 2018-2019. We defined county-day anomalously warm temperatures when daily average temperatures exceeded 24 °C and the 85th percentile of the long-term county average. We defined county-day wildfire burn zone disasters when an active wildfire burn zone intersected a county, burned 1+ structures, killed a civilian, or received a Federal Emergency Management Agency Fire Management Declaration, and overlapped with a community. For a subset of the 38 counties (66%), long power outage county days were identified using PowerOutage.us data when an outage affected >0.5% of county customers for 8+ h. Co-occurring events were when 2+ of these events occurred on the same county day. Using the CDC/ATSDR Social Vulnerability Index (SVI), we determined whether co-occurring events disproportionately affected vulnerable populations. Nearly every county (97%) experienced at least one day of anomalously warm temperatures, 57% had at least one wildfire burn zone disaster day, and 63% (24/38 counties with available data) had at least one long power outage day. The most common co-occurring events (anomalously warm temperatures and wildfire burn zone disasters) impacted 24 (41%) counties for 144 total county-days. We did not find a clear connection between co-occurring events and social vulnerability. We observed an inverse correlation between co-occurring wildfire burn zone disasters and long power outage days with SVI, and a positive correlation between co-occurring anomalously warm and long power outage days with SVI. This analysis can inform regional resource allocation and other state-wide planning and policy objectives to reduce the adverse effects of climate-driven events.

Development of Finite Element Human Body Model (IITDHBM) of the 50th Percentile Indian Male in the Standing Posture (IM50S) for Use in Extreme Loading Conditions

Development of Finite Element Human Body Model (IITDHBM) of the 50th Percentile Indian Male in the Standing Posture (IM50S) for Use in Extreme Loading Conditions

Addressing the "Black Hole" of Low Back Pain Care With Clinical Decision Support: User-Centered Design and Initial Usability Study.

Low back pain (LBP) is a highly prevalent problem causing substantial personal and societal burden. Although there are specific types of LBP, each with evidence-based treatment recommendations, most patients receive a nonspecific diagnosis that does not facilitate evidence-based and individualized care. We designed, developed, and initially tested the usability of a LBP diagnosis and treatment decision support tool based on the available evidence for use by clinicians who treat LBP, with an initial focus on chiropractic care. Our 3-step user-centered design approach consisted of identifying clinical requirements through the analysis of evidence reviews, iteratively identifying task-based user requirements and developing a working web-based prototype, and evaluating usability through scenario-based interviews and the System Usability Scale. The 5 participating users had an average of 18.5 years of practicing chiropractic medicine. Clinical requirements included 44 patient interview and examination items. Of these, 13 interview items were enabled for all patients and 13 were enabled conditional on other input items. One examination item was enabled for all patients and 16 were enabled conditional on other items. One item was a synthesis of interview and examination items. These items provided evidence of 12 possible working diagnoses of which 3 were macrodiagnoses and 9 were microdiagnoses. Each diagnosis had relevant treatment recommendations and corresponding patient educational materials. User requirements focused on tasks related to inputting data, and reviewing and selecting working diagnoses, treatments, and patient education. User input led to key refinements in the design, such as organizing the input questions by microdiagnosis, adding a patient summary screen that persists during data input and when reviewing output, adding more information buttons and graphics to input questions, and providing traceability by highlighting the input items used by the clinical logic to suggest a working diagnosis. Users believed that it would be important to have the tool accessible from within an electronic health record for adoption within their workflows. The System Usability Scale score for the prototype was 84.75 (range: 67.5-95), considered as the top 10th percentile. Users believed that the tool was easy to use although it would require training and practice on the clinical content to use it effectively. With such training and practice, users believed that it would improve care and shed light on the "black hole" of LBP diagnosis and treatment. Our systematic process of defining clinical requirements and eliciting user requirements to inform a clinician-facing decision support tool produced a prototype application that was viewed positively and with enthusiasm by clinical users. With further planned development, this tool has the potential to guide clinical evaluation, inform more specific diagnosis, and encourage patient education and individualized treatment planning for patients with LBP through the application of evidence at the point of care.

Clock Proteins Have the Potential to Improve Term Delivery Date Prediction: A Proof-of-Concept Study

Our ability to accurately predict the delivery date of term pregnancies is limited by shortcomings of modern-day clinical tools and due date estimation methods. The pregnancy clock is a series of coordinated and harmonized signals between mother, fetus, and placenta that regulate the length of gestation. Clock proteins are thought to be important mediators of these signals, yet few studies have investigated their potential utility as predictors of term delivery date. In this study, we performed a cross-sectional proteome analysis of 2648 serum samples collected between 18 and 28 weeks of gestation from mothers who delivered at term. The cohort included pregnancies both with and without complications. A total of 15 proteins of diverse functionalities were shown to have a direct association with time to birth (TTB), 11 of which have not been previously linked to gestational age. The protein A Distintegrin and Metalloproteinase 12 (ADA12) was one of the 15 proteins shown to have an association with TTB. Mothers who expressed the highest levels of ADA12 in the cohort (90th percentile) gave birth earlier than mothers who expressed the lowest levels of ADA12 (10th percentile) at a statistically significant rate (median gestational age at birth 390/7 weeks vs. 393/7 weeks, p < 0.001). Altogether, these findings suggest that ADA12, as well as potentially other clock proteins, have the potential to serve as clinical predictors of term delivery date in uncomplicated pregnancies and represent an important step towards characterizing the role(s) of clock proteins in mediating pregnancy length.

The Consequences of Preterm Birth in the Children of Mothers with Inflammatory Bowel Disease: A Nationwide Cohort Study.

Mothers with inflammatory bowel disease (IBD) are at increased risk of delivering their infants preterm. In the general population, chronic diseases in adulthood as well as increased morbidity and mortality are associated with being born preterm. We aimed to examine whether this was true for preterm versus full-term infants born to mothers with IBD. This is an observational cohort study based on the Danish Health Registries. The study population comprised all live-born singleton children, born to mothers with IBD, during the study period of 1995 through 2016. We estimated the risk of selected chronic diseases in offspring born preterm to mothers with IBD. These included epilepsy, chronic lung disease and asthma, depression and bipolar affective disorder, autism spectrum disorders, schizophrenia/psychosis, attention-deficit hyperactivity disorder, and mental retardation/intellectual disabilities. We adjusted for relevant confounders. In all, 736 children were born preterm, and 9655 were born full-term to mothers with IBD. The median, 25th, and 75th percentiles of the follow-up time of the children were 11.1 (6.4, 16.3) years and 11.4 (7.3, 16.3) years for the full-term and preterm cohorts, respectively. There was a statistically significant increased risk of mental retardation/intellectual disabilities in preterm versus full-term children (aHR 2.15; 95% CI 1.07-4.36). We also found a non-significantly increased risk of epilepsy (1.11; 0.51-2.42), chronic lung disease and asthma (1.10; 0.76-1.60), schizophrenia/psychosis (1.17; 0.50-2.73), and attention-deficit/hyperactivity disorder 1.15 (0.83-1.60). Our study indicates the possibility of chronic health consequences in children born preterm to mothers with IBD.

Trends in Alzheimer's-Related Mortality Among Type 2 Diabetes Patients in the United States: 1999-2019.

Recent research has shown that type 2 diabetes mellitus (T2DM) has increased the burden of Alzheimer's disease (AD) in the US aging population. However, trends in mortality from this comorbidity among adults aged ≥ 65 years have not been investigated. This study examined trends and disparities in AD-related mortality among older US adults with T2DM from 1999 to 2019. Data from the CDC WONDER database were analysed to assess AD-related mortality in patients with T2DM aged ≥ 65 between 1999 and 2019. Age-adjusted mortality rates (AAMRs) per 100,000 people and annual percent change (APC) were calculated and stratified by year, sex, race/ethnicity, age, urbanisation and geographical region. From 1999 to 2019, there were 71,550 deaths with T2DM and AD among adults aged ≥ 65. AAMRs rose from 4.12 in 1999 to 11.65 in 2019, with the sharpest increase between 2014 and 2017 (APC: 10.81; 95% CI: -3.20 to 13.43). Women had slightly higher AAMRs than men, with rates increasing from 4.71 in 1999 to 11.61 in 2019 for women, and from 4.08 to 11.70 for men. Hispanic individuals saw the highest increase in AAMR (11.15), followed by non-Hispanic Black (9.30) and White populations (7.92). AAMRs were highest in the West (10.91) and the Midwest (9.62), while the Northeast (4.70) had the lowest. Nonmetropolitan areas had consistently higher AAMRs (10.74) than large metropolitan areas (6.68) and small/medium metropolitan areas (9.25). States in the top 90th percentile for T2DM-AD mortality included California, South Dakota and Kentucky, where rates were approximately eight times higher than in states in the lowest 10th percentile. This study reveals a significant rise in T2DM-AD comorbidity-related mortality among older adults, especially among Hispanics, women and rural residents. These findings underscore the need for targeted interventions to reduce the burden in vulnerable populations.

Prenatal exposure to heat and humidity and infant birth size in Ghana.

Previous studies - primarily in high income countries - have shown that high prenatal temperatures are associated with adverse birth outcomes. However, these studies are mostly focused on average exposure across the full gestational period or short-term exposure immediately prior to delivery and may miss important sensitive windows of exposure in utero. Further, nearly all use ambient air temperature data, which neglect physiologically important interactions between air temperature and humidity. The Ghana Randomized Air Pollution and Health Study (GRAPHS) recruited pregnant individuals from 2013 to 2015 from communities in the Kintampo North Municipality and Kintampo South District of Ghana. We estimated daily maximum shaded wet bulb globe temperature (WBGTmax) and heat index (HImax) during pregnancy and examined associations with birth weight, birth length, head circumference, and incidence of low birth weight, preterm birth, and small for gestational age. Using linear regression analyses, trimester average models identified that higher WBGTmax in the first trimester was associated with larger head circumference; second trimester was associated with shorter birth length, lower birth weight and higher odds of preterm birth, and third trimester was associated with shorter gestational age and larger head circumference. Time-varying analyses using distributed lag nonlinear models find that, compared to the median, lower WBGTmax and HImax (25th percentile) during the first half of pregnancy was associated with higher birth weight and longer birth length. Compared to the median, lower WBGTmax and HImax (25th percentile) in the second half of pregnancy was associated with smaller head circumference while higher wet bulb globe temperature (75th percentile) was associated with larger head circumference. Overall, our study identified that higher WBGTmax and HImax are associated with pregnancy duration and newborn size. Given the overall trend in our study area of rising temperatures, these data suggest that adaptation strategies are urgently needed to protect child health.

A combined wavelet analysis-quantile mapping (WA-QM) method for bias correction: capturing the intra-annual temporal patterns in climate model precipitation simulations and projections

Abstract Despite recent improvements, global climate models (GCMs) still have biases that prevent their direct application. Quantile mapping (QM) has been widely used in bias correction (BC) due to its effectiveness in aligning the cumulative density function of climate model data with observations. However, QM has a significant limitation: it fails to consider the temporal correspondence between the model data and observations, which restricts its ability to represent intra-annual temporal patterns. To address this issue, this study integrated wavelet analysis (WA) into QM to develop a combined method termed WA-QM, which aimed to preserve QM’s efficacy in overall BC and preservation of climate change signals, while capturing the intra-annual temporal patterns. The effectiveness of WA-QM was investigated using monthly precipitation data from five Coupled Model Intercomparison Project Phase 6 models in the Pan Third Pole region, which includes Central Asia (CA), Southeast Asia, and the Tibetan Plateau. Quantile delta mapping (QDM) and scaled distribution mapping (SDM) served as the benchmark methods for assessment. The results indicated that integrating WA into QDM or SDM did not compromise the ability of QDM or SDM to correct overall biases and preserve the model’s climate change signal. Furthermore, WA could be an effective tool to overcome QM’s limitation in capturing intra-annual temporal patterns. The WA approach employed discrete wavelet transformation to decompose GCM data into various frequency bands and then adjusted their standard deviations and signs to ensure that their relative relationships were consistent with those in observations. Compared to the standalone QM approach, WA-QM showed greater accuracy in monthly statistical metrics. In the CA region, WA-SDM reduced the monthly root mean square error of the mean by 25.2%, the standard deviation by 17.6%, and the 90th percentile by 26.7% compared to SDM. The effectiveness of WA-QM was demonstrated across different data periods, spatial areas, and GCMs.

Analysis of lipogenic and metabolic subtypes and survival outcome in patients with pancreatic adenocarcinoma.

775 Background: Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for Pancreatic Ductal Adenocarcinoma (PDAC). In previous studies, molecular profiling in tumor tissue has identified Cholesterogenic, Classical, or Exocrine-Like pathological subtypes associated with improved prognosis. Herein, in a clinical trial called Project Survival, we molecularly and clinically characterized a prospective PDAC biomarker cohort. This multicenter (n=6) clinical trial (NCT 02781012) of PDAC combined with high-fidelity longitudinal phenotypic characterization and multi-omic profiling was utilized to identify biomarkers with diagnostic and therapeutic utility. The study included assessment of plasma, serum, buffy coat, urine, saliva, and tumor tissue samples and followed 269 PDAC subjects for up to 7 years (Median survival was 700 days, with IQR of 822 days). Methods: We assessed plasma samples to identify circulating biomarkers using proteomics, metabolomics, and lipidomics profiling. Multi-omic comparison of patients with Overall Survival (OS) above the 75th percentile (improved survival) against those with OS below the 25th percentile (poor survival) was performed. Results: We identified distinct panels of proteins, metabolites, and lipids that were associated with patient survival outcomes. Protein biomarkers that were increased in improved survival outcomes were associated with cholesterol metabolism (7/14, p-value = 0.00085) and glycolysis/gluconeogenesis (5/12, p-value = 0.00089), however, protein markers associated with immune surveillance and inflammation were associated with poorer survival. For metabolite biomarkers, 19 metabolites were increased in poorer survivors of which 13/19 were carnitine metabolites, and 42 metabolites were increased in patients with improved survival. Interestingly, only two lipid molecular species of the diacylglyceride family were increased in patients with poorer survival. Conclusions: Project Survival, a prospective biomarker-driven clinical trial identified distinct metabolic and molecular subtypes of patients which characterize survival outcome in PDAC. These subtypes go beyond tumor genomic characterization and align with distinct cholesterogenic and glucogenic phenotypes associated with survival outcomes, which can potentially be leveraged for therapeutic intervention strategies.

Air pollution, temperature and mumps: A time-series study of independent and interaction effects.

Understanding the associations of air pollutants and temperature with the incidence of mumps, and exploring the interaction effects of heat index (HI) and air pollutants, are crucial for disease intervention under the context of climate change. This time-series study was conducted in Jining city using data on daily mumps cases, meteorological factors and air pollutants obtained from 2015 to 2021. Distributed lag models combined with quasi-Poisson regressions were applied to examine these associations. The findings indicated a heightened risk of mumps in correlation with elevated exposure to PM2.5, PM10, and NO2. Specifically, the precent changes in the risk of mumps were 0.6 % and 0.3 % associated with per 10 μg/m3 increment in PM2.5 and PM10 at lag0 day, respectively. The cumulative effects of temperature were significantly associated with mumps, showing a "U"-shape pattern. Compared to the minimum effect value of 4 ℃, relative risk (RRs) of mumps for high and low temperature (at the 90th and 10th percentiles) were 1.961 (1.106-3.477) and 1.058 (0.976-1.158), respectively. There were significant synergistic effects between particulate matters (PMs) and HI. Compared to the low concentrations of PMs and normal heat index (NHI), co-exposure to high PMs and high heat index (HHI) has the highest risk (RRPM2.5: 1.32 and, RRPM10: 1.30). Individuals aged over 5 years was susceptible to particulate matters (PMs). Our results suggested that mitigating both PM and high temperature exposure may bring more health benefits for mumps incidence.

Toward a Cohort Perspective of Climate Epidemiology: The Case of Examining Intergenerational Inequalities in Susceptibility to Non-Optimal Temperatures in Japan.

Younger generations are projected to experience more severe climate exposure impacts during their lifetimes than older generations as global warming progresses. Despite the increasing evidence of the recent temporal changes in heat-related mortality risks, there remains a lack of research exploring this association from a cohort perspective. Our objective was to quantify the variation in susceptibility to short-term effects of non-optimal temperature on mortality, across generations and over the life course of specific generations, using a novel age-period-cohort approach. An extended two-stage analytical approach was applied to a nationwide mortality dataset covering individuals born from 1866 to 2019 in all 47 prefectures in Japan. Daily mortality counts observed between 1972 and 2019 were aggregated into 5-year birth cohorts and corresponding age groups of the decedents. For each prefecture and birth cohort, the age-dependent association between ambient temperature and daily mortality was modeled using conditional quasi-Poisson regression. Then, the prefecture-specific associations were pooled across cohorts, separately for each age group, using a repeated-measure meta-regression. To model the intergenerational changes in risks, a nonlinear, continuous term for cohort was applied in the meta-analysis. A total of 29 million all-cause deaths were analyzed. The relative risk (RR) of heat-related mortality (99th temperature percentile compared to minimum mortality temperature) decreased across generations for elder adults (65-89 years of age), from [95% confidence interval (CI): 1.13, 1.23] for those born in 1901-1905 to (95% CI: 1.01, 1.07) for those born in 1926-1930 (). Similar to heat-related risk, the cold-related mortality risk (at first percentile) also decreased across the same cohorts (). The predicted continuous trends in heat- and cold-related mortality risks exhibited a nonlinear decline across generations. An inconsistent pattern was observed for middle-aged people (40-64 years of age) born between 1930 and 1960, with a slight increase in risks for cold and heat over generations. For cohort-specific risk trajectories, heat- and cold-related mortality risks generally increased with age, after 60 years old. This nationwide, individual-level study adopted a novel cohort perspective to investigate how population susceptibility to short-term non-optimal temperature exposure varies across generations. Our findings revealed disparities in susceptibility between generations, highlighting the importance for researchers and policymakers to consider cohort differences in efforts to promote future health advancements and reduce inequalities. https://doi.org/10.1289/EHP15226.