Sample Weight Research Articles

Machine learning in public health informatics: Evidence that complex sampling structures may not be needed for prediction models with imbalanced outcomes.

The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights. We evaluated the predictive performance of machine learning models on imbalanced outcomes from the US National Health and Nutrition Examination Survey (USNHANES) without using sampling weights. Four machine learning models (support vector machine, random forest, least absolute shrinkage and selection operator regression, and deep neural network) were compared with a logistic model that incorporates the survey's complex sampling design. Three resampling methods (oversampling, undersampling, and combined) were used to address class imbalance during the model training process. For all models, the balanced accuracy was similar (ranging from 0.72 to 0.76) and the specificity was smaller than sensitivity except for random forest. The support vector machine and neural networks performed best with sensitivity (ranging from 0.79 to 0.83), while the random forest had the largest specificity (ranging from 0.86 to 0.96), with one exception. PR-AUC scores and Brier scores were low ranging from 0.2529 to 0.3313 (lower scores worse) and 0.1005-0.3245 (lower scores better), respectively CONCLUSIONS: The machine learning models had overall similar predictive capacity to the recommended methods which integrate the complex sampling design for the prediction of osteoarthritis occurrence with USNHANES.

Unpacking the Mood States of Children and Youth in Saskatchewan, Canada, in the Context of the COVID-19 Pandemic: Insights from the "See Us, Hear Us 2.0" Study.

The COVID-19 pandemic created a growing need for insights into the mental health of children and youth and their use of coping mechanisms during this period. We assessed mood symptoms and related factors among children and youth in Saskatchewan. We examined if coping abilities mediated the relationship between risk factors and mood states. "See Us, Hear Us 2.0", a cross-sectional study of 563 child-parent dyads, provided the data. The dependent variable, current mood state, was measured by the CoRonavIruS health Impact Survey (CRISIS) scale. Independent variables included sociodemographics, behaviors, household conditions, and coping ability. Multiple linear regression and mediation analyses were conducted, ensuring sample representativeness with sampling weights and addressing missing data through multiple imputations. The participants reported mood symptoms ("moderate" to "extreme") ranging from 23% to 38% on the CRISIS scale. Factors such as older children, hybrid learning, disrupted activities, and increased screen time worsened moods. The ethnic minority groups (BIPOC) living in mid-sized cities/towns experienced more negative moods compared to Whites residing in cities. Coping ability mediated the relationship between extracurricular activities and mood states. Our results underscore the importance of tailored interventions, recognizing the diverse needs of specific age groups, gender identities, and ethnicities and addressing the adverse effects of the pandemic-related disruptions on the mental health and well-being of school children in Saskatchewan. Our study also suggests prioritizing the diverse needs of children and youth during the planning and implementation of mental health services in the province.

Geographic factors associated with SARS-CoV-2 prevalence during the first wave − 6 districts in Zambia, July 2020

BackgroundGeographical factors can affect infectious disease transmission, including SARS-CoV-2, a virus that is spread through respiratory secretions. Prioritization of surveillance and response activities during a pandemic can be informed by a pathogen’s geographical transmission patterns. We assessed the relationship between geographical factors and SARS-CoV-2 prevalence in Zambia.MethodsWe did a cross-sectional study of SARS-CoV-2 prevalence in six districts in July 2020, which was during the upslope of the first wave in Zambia. In each district, 16 Standard Enumeration Areas (SEAs) were randomly selected and 20 households from each SEA were sampled. The SEA PCR prevalence was calculated as the number of persons testing PCR positive for SARS-CoV-2 in the SEA times the individual sampling weight for the SEA divided by the SEA population. We analysed SEA geographical data for population density, socioeconomic status (SES) (with lower scores indicating reduced vulnerability), literacy, access to water, and sanitation, and hygiene (WASH) factors. Gaussian conditional autoregressive (CAR) models and Generalised estimating equations (GEE) were used to measure adjusted prevalence Ratios (aPRs) and 95% confidence intervals (CIs) for SARS-CoV-2 prevalence with geographical factors, after adjusting for clustering by district, in R.ResultsOverall, the median SARS-CoV-2 prevalence in the 96 SEAs was 41.7 (Interquartile range (IQR), 0.0-96.2) infections per 1000 persons. In the multivariable CAR analysis, increasing SES vulnerability was associated with lower SARS-CoV-2 prevalence (aPR) = 0.85, 95% CI: 0.78–0.94). Conversely, urban SEAs and poor access to WASH were associated with a higher SARS-CoV-2 prevalence (aPR = 1.73, 95% CI: 1.46–2.03, No soap: aPR = 1.47, 95% CI: 1.05–2.05, households without piped water: aPR = 1.32, 95% CI: 1.05–1.65, 30 min to fetch water: aPR = 23.39, 95% CI: 8.89–61.52). Findings were similar in the multivariable GEE analysis.ConclusionsSARS-CoV-2 prevalence was higher in wealthier, urban EAs, with poor access to WASH. As this study was conducted early in the first wave could have impacted our findings. Additional analyses from subsequent waves could confirm if these findings persist. During the beginning of a COVID-19 wave in Zambia, surveillance and response activities should be focused on urban population centres and improving access to WASH.

Degradation Potential of Metakaolin-Based Geopolymer Composites Immersed in Real and Simulated Acidic Environments

This study investigates the degradation potential of metakaolin-based geopolymer (GP) composites when exposed to real and simulated acidic environments. Traditional OPC concrete, commonly used in wastewater treatment facilities, faces considerable deterioration due to the destructive chemical composition of municipal wastewater. This extensive investigation aims to assess the performance of GP composites as a sustainable alternative to such materials. The metakaolin-based GP mortar samples were prepared and subjected to immersion in a primary clarifier unit at a local wastewater treatment plant (real) and a laboratory-made acetic acid solution (simulation) for up to four weeks after curing. The analysis included measurements of % strength and % weight loss, as well as characterization techniques such as isothermal calorimetry, microstructure (SEM), and mercury porosimetry (MIP). The outcomes signified a cumulative heat generation of 534 J/g at three days, with an average compressive strength of 79.4 MPa past 28 days. Exposure to acetic acid led to a 13% decline in compressive strength and a 3.90% loss in sample weight, while exposure to real wastewater resulted in an 18% strength and a 5.60% weight loss. Observations from SEM revealed microstructural changes, including the formation of biofilms and air voids, indicating multifaceted interactions between the GP matrix and its surrounding environment. This research effectively highlights the potential of metakaolin-based geopolymer composites to improve durability against acidic conditions, suggesting future applications in the construction of infrastructure exposed to such harsh chemical environments.

The protective effect of frequent religious service attendance on pregnancy loss

BackgroundAccumulating evidence suggests that spirituality and religiosity may be associated with improved health outcomes. However, few studies have examined maternal religiosity as a protective factor for perinatal outcomes. We explored the association between maternal religious attendance and pregnancy loss.MethodsData were drawn from the Future Families & Child Wellbeing Study’s first and second waves and medical records (n = 1874). Religious attendance was a self-reported response to the question “About how often do you attend religious services?” Pregnancy loss was measured from responses to the second wave survey question, “Since focal child’s birth, have you had any miscarriages/abortions/stillbirths?” Logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for the association between maternal religious attendance frequency and pregnancy loss. Models were adjusted for sampling weights, religious preference, socioeconomic and behavioral factors.Results8% (n = 164) of mothers reported having a pregnancy loss. Of those with a pregnancy loss, 28% (n = 46) attended services hardly ever and 20% (n = 20) attended services once a week or more. Women who attended services more frequently had 58% increased odds of not experiencing a pregnancy loss (OR:1.58;95%CI:1.01,2.48) after adjusting for potential confounding. A post hoc analysis found no difference in pregnancy loss type or subsequent reproductive history based on attendance level.ConclusionsResults suggest that higher maternal religious attendance frequency may be a protective factor for pregnancy loss. Further research is needed to understand the association between maternal religious attendance and mechanisms for pregnancy loss.

Differences in US Adult Dietary Patterns by Cardiovascular Health and Socioeconomic Vulnerability.

Naturally occurring dietary patterns, a major contributor to health, are not well described among those with cardiovascular disease (CVD) - particularly in light of socioeconomic vulnerability. We sought to identify major dietary patterns in the US and their distribution by CVD, social risk factors, and Supplemental Nutrition Assistance Program (SNAP) participation. This was a cross-sectional study among 32,498 noninstitutionalized adults from the National Health and Nutrition Examination Survey (2009-2020). We used principal component analysis to identify common dietary patterns. Individuals were assigned to the pattern for which they had the highest component score. Using multinomial logit regression, we estimated the percentage whose diets aligned with each pattern in population subgroups stratified by CVD, social risk factors, and SNAP. Analyses were adjusted for age, gender, race and ethnicity, total energy intake, and year, with sampling weights to provide nationally representative estimates. Four dietary patterns were identified among US adults: American (33.7%; high in solid fats, added sugars, and refined grains), Prudent (22.6%; high in vegetables, nuts/seeds, oils, seafood, and poultry), Legume (15.8%), and Fruit/Whole Grain/Dairy (27.9%), that together explained 29.2% of dietary variance. More adults with prevalent CVD (37.1%) than without (33.3%, p=0.005) aligned with the American Pattern, with no differences among other patterns. Each additional social risk factor associated with more adults aligned with American (2.5% absolute increase) and Legume (1.3%), and fewer aligned with Prudent (-1.9%) and Fruit/Whole Grain/Dairy (-1.9%) patterns (p<0.001 each). Analysis of dietary patterns across SNAP participation showed higher proportion of SNAP participants and income-eligible SNAP non-participants compared to non-eligible adults for the American (40.2% [38.1, 42.3%], 35.1% [32.7, 37.5%], 31.9% [31.0, 32.8%], respectively) and Legume patterns (17.2% [15.6, 18.8%], 17.8% [16.1, 19.5%]), 15.4% [14.6,16.1%], respectively) and less for Prudent (17.0% [15.5, 18.6%], 20.2% [18.2, 22.3%], 24.2% [23.3, 25.1%], respectively) and Fruit/Whole Grain/Dairy Patterns (25.6% [23.8%, 27.3%], 26.9%[27.6%,29.5%], 28.6% [27.6%, 29.5%], respectively). Empirical dietary patterns vary by CVD and socioeconomic vulnerability. Initiatives to improve nutrition in at-risk individuals should consider these naturally occurring dietary patterns and their variation in key subgroups.

Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging.

Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

Association between osteoarthritis and cognitive function: results from the NHANES 2011-2014 and Mendelian randomization study.

Previous meta-analyses have demonstrated osteoarthritis (OA) is associated with an increased risk of dementia, but these studies were prone to bias based on residual confounding factors and reverse causality. We aimed to investigate associations between OA and cognitive function using data from the National Health and Nutrition Examination Survey (NHANES) and to investigate the causality using Mendelian randomization (MR). This is a cross-sectional study and MR study. Data from the NHANES 2011-2014 were used. Multiple linear, logistic regressions and stratified analyses were used to determine the association between OA status and cognitive function. Sample weights were used to ensure result generalizability. Two-sample MR analysis was conducted to examine the association between OA and dementia. Mediation analyses were performed to investigate the mediating effects of depression. We did not demonstrate a significant association between OA and cognitive performance after adjusting for relevant covariates (p > 0.05), and the population of individuals with both OA and depression was associated with higher odds of low total word recall cognitive performance (odds ratio (OR) = 4.74, 95% confidence interval (CI): 1.09-20.63; p = 0.04). Genetically predicted specific-site OA was not significantly associated with the risk of dementia (OR = 1.12; 95% CI: 0.96-1.32; p = 0.16), Alzheimer's disease (OR = 0.95, 95% CI: 0.68-1.31, p = 0.74), vascular dementia (OR = 1.32, 95% CI: 0.82-2.13, p = 0.25) with accepted heterogeneity and no evidence of directional pleiotropy. Furthermore, major depression was found to mediate the pathway between OA and vascular dementia (β = 0.044, 95% CI: -0.391 to 0.479, p < 0.05). Our findings indicate that there is no significant association or causal relationship between OA and cognitive decline. However, depression may serve as an important factor influencing cognitive outcomes. Future research should further explore the bidirectional causal relationship and underlying mechanisms.

Association between the composite dietary antioxidant index and non-alcoholic fatty liver disease: evidence from National Health and Nutrition Examination Survey 2005-2016.

Oxidative stress contributes to the progression of non-alcoholic fatty liver disease (NAFLD). Antioxidants from food can reduce NAFLD incidence, and the Composite Dietary Antioxidant Index (CDAI) measures total antioxidant capacity (TAC). However, the relationship between CDAI and NAFLD in the US adult population remains unclear. To assess whether CDAI is associated with NAFLD in US adults. This population-based cross-sectional study used data on US adults from the National Health and Nutrition Examination Survey (NHANES) 2005-2016 cycles. Data were analyzed from January to February 2024. CDAI obtained from the dietary intake questionnaire. The main outcome was NAFLD which defined by the US fatty liver score (USFLI) ≥30. Sampling weights were calculated according to NHANES guidelines. Among 9,746 adults included in this study [mean age, 48.3 years; 4,662 (47.6%) males], 3,324 (33.0%) were classified as having NAFLD using USFLI. In the fully adjusted of multivariable logistic regression, CDAI was negatively associated with NAFLD (odds ratio [OR], 0.95; 95% CI, 0.93-0.98). Furthermore, individuals in the highest quartile of CDAI were 34% less likely to have NAFLD compared to those in the lowest quartile (OR, 0.66; 95% CI, 0.52-0.85). In subgroup analyses, CDAI was inversely associated with NAFLD among participants with a BMI <25 (OR, 0.89; 95% CI, 0.83-0.95) and without metabolic syndrome (OR, 0.93; 95% CI, 0.91-0.96). The interaction tests revealed significant differences in these subgroups (P for interaction = 0.04 for BMI and 0.003 for metabolic syndrome). Sensitivity analyses confirmed this association using the hepatic steatosis index (HSI) to define NAFLD, applying unweighted logistic regression, adjusting for physical activity or after excluding non-Hispanic Black participants, and after excluding medications known for their potential hepatotoxic effects. In this cross-sectional study based on six cycles (2005-2016) of the NHANES, CDAI was negatively associated with NAFLD in US adult population. This association highlights the potential for dietary interventions to reduce NAFLD incidence and underscores the need for future research, including clinical trials and mechanistic studies, to further explore the role of dietary antioxidants in NAFLD prevention and management.

Pooling the complex survey data across the 64 lower and middle-income countries: A study on antibiotic usage in under-five children.

Antibiotic exposure for fever/cough has been rising alarmingly, especially among children under five in low- and middle-income countries (LMICs). This excessive use could result in higher healthcare costs, and antibiotic resistance is an alarming trend in developing countries. As a result, it's crucial to look at the variables that affect antibiotic exposure and highlight the subgroups among whom antibiotic abuse is the most prevalent. We used the most recent standard Demographic and Health Survey (DHS) data for 64 LMICs. Sample weights were employed in studies to ensure exact standard errors and estimate p-values. To analyze antibiotic exposures across countries, continents, economic levels, and the top and bottom ten countries with fever/cough, we conducted descriptive statistics. Additionally, we provided accompanying bar diagrams for each descriptive finding to enhance visual understanding. For geospatial analysis, we utilized ArcGIS, a powerful tool for mapping and spatial analysis. We used data from 141,018 children under five who were reported to have had fever/cough recently in 64 LMICs. Among them, 30.4% were exposed to antibiotics for fever/cough. Congo (68.7%), Egypt (65.5%), and Tajikistan (61.8%) had the highest prevalence of antibiotic consumption across LMICs. On the other hand, Cameroon (0.3%), Armenia (4.0%), and Mauritania (6.1%) had the lowest. In the final binary logistic regression, mothers that had any formal education (Primary: OR=1.28, 95% CI:1.07-1.53, Secondary: OR=1.38, 95% CI:1.14-1.67, Higher Education: OR=1.69, 95% CI:1.23-2.33) were more likely to expose their children to antibiotics for fever/cough than illiterate ones. Additionally, children in richer households (Richer: OR=1.38, 95% CI:1.15-1.66, Richest: OR=1.46, 95% CI:1.19-1.80) were more likely to receive antibiotics than those in poorer households. Antibiotic exposure for fever/cough in children under five was relatively moderate across 64 LMICs. However, the rate was incredibly high in some LMICs and incredibly low in others. The researchers recommend that the countries with high and low percentages of antibiotic exposure investigate either any possibility of antibiotic abuse or proper healthcare service at the national level.

Spatial variation and determinant factors of alcohol consumption in Ethiopia: Spatial and multilevel analysis of Ethiopian demographic and health survey.

Alcohol consumption continues to be a public health problem in Ethiopia. Previous investigations have been conducted on alcohol consumption in Ethiopia; however, these investigations were limited to specific localities, which could not represent the existing alcohol consumption in different parts of Ethiopia. Besides, the spatial variation of alcohol consumption was not well investigated in the previous studies, which could hinder the implementation of effective intervention towards alcohol consumption. Hence, this study aimed to determine the spatial distribution and determinant factors of alcohol consumption in Ethiopia. Secondary data from the 2016 Ethiopian demographic health survey was used in this study. A total of 44,023 weight samples were included using a stratified two-stage cluster sampling technique. The spatial variation of alcohol consumption was analyzed using ArcGIS version 10.7.1. The statistical significance of alcohol consumption clusters were identified using Kuldorff's SaTScan version 10.2. A multi-level analysis was also conducted to identify factors associated with alcohol consumption using STATA version 14. In this study, 33.15% (95%CI: 32.5-33.8) of the study participants consumed alcohol with statistically significant spatial variation across regions of the country. Traditional religion (AOR = 13.7; 95%CI: 2.68-70.3), Regional variations (Amhara region-AOR = 3.56; 95%CI: 1.85-6.8, living in a low proportion of community literacy (AOR = 1.84; 95%CI: 1.1-3.18), cigarette smoking habit (AOR = 15.82; 95%CI: 4.31-58.1), and chewing Khat (AOR = 2.98; 95%CI: 1.22-7.27) were positively linked with alcohol consumption. Hot spot areas of alcohol consumption were found in Tigray, Amhara, and some parts of Oromia regions. The statistical significance of the primary clusters was also observed in Tigray and Amhara regions. We found that one-third of Ethiopia's population is consuming alcohol. Having a cigarette smoking habit, chewing khat, high proportion of community literacy, and traditional religion were associated factors for alcohol consumption. Therefore, the federal government of Ethiopia, and ministry of health, and other concerned bodies should work in collaboration to decrease the proportion of people consuming alcohol.

Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack.

Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can completely not access the structures and parameters of the target model, as well as the intermediate features and any training data associated with the model. To tackle this task, previous methods generate transferable adversarial examples from a transparent substitute model to the target model. However, we found that these works have the limitations of taking static substitute model structure for different targets, only using hard synthesized examples once, and still relying on data statistics of the target model. This may potentially harm the performance of attacking the target model. To this end, we propose a novel Dynamic Routing and Knowledge Re-Learning framework (DraKe) to effectively learn a dynamic substitute model from the target model. Specifically, given synthesized training samples, a dynamic substitute structure learning strategy is proposed to adaptively generate optimal substitute model structure via a policy network according to different target models and tasks. To facilitate the substitute training, we present a graph-based structure information learning to capture the structural knowledge learned from the target model. For the inherent limitation that online data generation can only be learned once, a dynamic knowledge re-learning strategy is proposed to adjust the weights of optimization objectives and re-learn hard samples. Extensive experiments on four public image classification datasets and one face recognition benchmark are conducted to evaluate the efficacy of our Drake. We can obtain significant improvement compared with state-of-the-art competitors. More importantly, our DraKe consistently achieves attack superiority for different target models (e.g., residual networks, and vision transformers), showing great potential for complex real-world applications.

Fat, protein content in Hermetia Illucens due to feeding papaya, moringa leaves, and snails

Abstract Indonesia is a tropical country rich in biological resources. However, the main problem currently facing Indonesia is the accumulation of waste, especially organic waste, which accounts for 40.7% of the waste generated by Indonesian society. This research provides innovation in organic waste processing by developing and utilizing maggots by feeding papaya leaves, moringa leaves and snails in reducing organic waste. The research approach used was a quantitative descriptive approach. The study was designed with three different treatments, as follows: Treatment A: Moringa leaves &amp; rice bran; Treatment B: papaya leaves &amp; rice bran; Treatment C: snail &amp; rice bran. Examination of protein content was carried out in three stages, namely deconstruction, distillation, and titration, which were then calculated using the basic method of calculating nitrogen content which was converted into protein content associated with the Kjeldahl method. Meanwhile, the examination of fat content was carried out with a general calculation to determine the fat content in a sample, namely by comparing the weight of the initial sample with the weight of fat that was successfully extracted. The results showed that the highest protein content was found in maggots fed with snail food (40.77%) and the highest fat content was found in maggots fed with Moringa leaves (22.03%). This research can increase the economic value of maggot as animal feed, farmers can choose the right type of feed to produce maggot with the required protein or fat content, and can encourage the development of the maggot industry in Indonesia, both for waste management and as an animal feed commodity.

Learning with semantic ambiguity for unbiased scene graph generation.

Scene graph generation (SGG) aims to identify and extract objects from images and elucidate their interrelations. This task faces two primary challenges. Firstly, the long-tail distribution of relation categories causes SGG models to favor high-frequency relations, such as "on" and "in". Secondly, some subject-object pairs may have multiple reasonable relations, which often possess a certain degree of semantic similarity. However, the use of one-hot ground-truth relation labels does not effectively represent the semantic similarities and distinctions among relations. In response to these challenges, we propose a model-agnostic method named Mixup and Balanced Relation Learning (MBRL). This method assigns soft labels to samples exhibiting semantic ambiguities and optimizes model training by adjusting the loss weights for fine-grained and low-frequency relation samples. Its model-agnostic design facilitates seamless integration with diverse SGG models, enhancing their performance across various relation categories. Our approach is evaluated on widely-used datasets, including Visual Genome and Generalized Question Answering, both with over 100,000 images, providing rich visual contexts for scene graph model evaluation. Experimental results show that our method outperforms state-of-the-art approaches on multiple scene graph generation tasks, demonstrating significant improvements in both relation prediction accuracy and the handling of imbalanced data distributions.

Menstrual cycle effects on thermoregulation while exercising in the heat.

Women may be challenged to maintain thermoregulation due to hormonal changes associated with the menstrual cycle. The purpose of this study was to assess the effect of the menstrual cycle phase on core temperature, hydration status, and perceived exertion while exercising under uncompensable heat gain. Eleven eumenorrheic women (24.4±1.1yrs, 65.7±2.4kg, 22.7±1.5% body fat) walked for two 180-min trials in a heat chamber (35°C and 30% relative humidity) during early-follicular (EF) and mid-luteal (ML) phases. Subjects completed three intervals of 50min of exercise at 50% VO2max. Physiological strain index (PSI), core temperature (TC), perceived heat (PH), and rating of perceived exertion (RPE) were measured throughout both trials. Nude body weight (NBW) and blood samples were collected pre- and post-trial. Blood samples were analyzed for hematocrit (Hct), hemoglobin (Hb), serum estrogen, progesterone, and aldosterone. NBW showed a main effect of time (p=0.002, ηp2=0.62). Aldosterone showed main effect of time (p=0.004, ηp2=0.59) and phase (p=0.014, ηp2=0.47), peaking post exercise in both EF and ML (527.6.1±89.0pg·mL-1 vs 827.4±129.5pgmL-1 respectively, p=0.014). Estradiol and progesterone showed main effects of phase (p=0.007, ηp2=0.53; p=0.045, ηp2=0.30) but not time (p=0.68, p=0.32). TC showed main effect of time (p<0.001, ηp2=0.89) and phase, peaking at 170min (EF: 37.8±0.1°C vs. ML: 38.0±0.1°C, p=0.032, ηp2=0.38). Main effect of time was seen for PSI (p=0.002, ηp2=0.88), PH (p=0.004, ηp2=0.66), and RPE (p=0.026, ηp2=0.80). Sweat rate, Hct, Hb, and percent dehydration were not different between the phases. In conclusion, subjects demonstrated elevated Tc and basal aldosterone in ML corresponding with elevations in estrogen and progesterone. Aldosterone significantly increased following exercise in the heat but remained elevated in ML. These results indicate that elevated Tc during ML is maintained during exercise in the heat despite similar perceived heat and effort between phases.

Rat Fecal Metabolomics-Based Analysis.

The gut's symbiome, a hidden metabolic organ, has gained scientific interest for its crucial role in human health. Acting as a biochemical factory, the gut microbiome produces numerous small molecules that significantly impact host metabolism. Metabolic profiling facilitates the exploration of its influence on human health and disease through the symbiotic relationship. Fecal metabolomics-based analysis is an indisputably valuable tool for elucidating the biochemistry of digestion and absorption in the gastrointestinal system, serving as the most suitable specimen to study the symbiotic relationship between the host and the intestinal microbiota. It is well-established that the balance of the intestinal microbiota changes in response to various stimuli, both physiological, such as gender, age, diet, and exercise, and pathological, such as gastrointestinal and hepatic diseases. Fecal samples have been analyzed using widely adopted analytical techniques, including NMR spectroscopy, GC-MS, and LC-MS/MS. Rat fecal samples are frequently used and particularly useful substrates for metabolomics-based studies in related fields.The complexity and diversity of fecal samples necessitate careful and skillful handling to extract metabolites, while avoiding their deterioration, effectively and quantitatively. Several determinative factors, such as the fecal sample weight to extraction solvent solution volume, the nature and pH value of the extraction solvent, and the homogenization process, play crucial roles in achieving optimal extraction for obtaining high-quality metabolic fingerprints, whether for untargeted or targeted metabolomics.

Sex- and Age-Specific Analysis of Mountain Biking Injuries: A 10-Year Review of National Injury Data.

Mountain biking (MTB) is a quickly growing sport, with many athletes sustaining high-impact injuries. Current literature has not analyzed the most recent MTB-related national emergency department (ED) injury data. It was hypothesized that (1) the total number of injuries presenting to US EDs would significantly increase over the study period, (2) male patients would experience higher rates of shoulder injuries and airborne injury mechanisms than female patients, and (3) youths would present more frequently with injuries of the head and face than adults. Descriptive epidemiology study. All data were extracted from the National Electronic Injury Surveillance System (NEISS), a public database representing approximately 100 US EDs. The NEISS was queried for MTB-related injuries between January 1, 2013, and December 31, 2022. A total of 2543 MTB-related cases presented to US EDs between 2013 and 2022, which extrapolated to a national estimate (NE) of 109,558 injuries using NEISS sample weights. MTB-related injuries increased significantly between 2013 and 2022 (P = .009), including a 64% increase between 2019 and 2020. The most common injury mechanism was falling off the bike (62.6%; NE = 68,536), and the upper extremity was the most frequently injured body region (22.4%; NE = 24,511). Male patients presented with significantly more shoulder (21.1% vs 7.4%, respectively; P < .001) and trunk (21.4% vs 19.8%, respectively; P < .001) injuries than female patients as well as more airborne injury mechanisms (15.7% vs 10.8%, respectively; P < .001). Female patients sustained significantly more concussions (7.2% vs 3.5%, respectively; P < .001) than male patients. Children presented with higher rates of head/face injuries (34.1%), concussions (6.0%), and airborne mechanisms of injury (19.4%) than any other age group. MTB injuries increased significantly between 2013 and 2022 likely because of the popularity of outdoor sports during COVID-19. The upper extremity was the most frequently injured body region, and falling off the bike was the most common mechanism. Leading injuries and mechanisms varied significantly by sex and age. This study recommends additional MTB safety measures such as shoulder support for male riders, better coaching on front brake control for youth riders, and an increased emphasis on concussion protocols for female riders.

Increasing Incidence of Pickleball Injuries Presenting to US Emergency Departments: A 10-year Epidemiologic Analysis of Mechanisms and Trends.

Pickleball is one of the fastest-growing sports in the United States. It is popular among seniors but has recently grown across all age groups. As pickleball has gained interest, its corresponding injury burden has also increased. The purpose of this study was to identify pickleball-related injury trends and underlying mechanisms across different age groups presenting to US emergency departments. It was hypothesized that (1) pickleball-related injuries would increase significantly between 2013 and 2022, (2) older age groups would have the greatest frequency of injury, and (3) falls would be the most common mechanism of injury. Descriptive epidemiology study. The National Electronic Injury Surveillance System (NEISS) was queried for pickleball injuries between January 1, 2013, and December 31, 2022. The demographic information, injury site, and diagnosis were recorded, and case narratives were reviewed to identify the injury mechanism. The statistical sample weight assigned for NEISS cases by hospital was used to calculate national estimates (NEs). Injury trends over time were evaluated by linear regression. A total of 1110 NEISS cases representing 66,350 nationally estimated pickleball-related injuries were included. The mean age was 64 ± 14.7 years (range, 4-93 years). Most injuries occurred in those aged 65 to 80 years (NEISS cases = 643; NE = 40,507 [61.1%]), followed by those aged 35 to 64 years (NEISS cases = 349; NE = 20,785 [31.3%]). Falls were the most common injury mechanism overall (NEISS cases = 699; NE = 43,434 [65.5%]). However, being hit with a paddle and a twist/inversion were the most common mechanisms for ages 0 to 17 and 18 to 34 years, respectively. Fractures were the most common diagnosis (NEISS cases = 363; NE = 21,703 [32.7%]), followed by strains/sprains (NEISS cases = 327; NE = 20,419 [30.8%]). Also, 69.1% of all fractures occurred in female players. The wrist was the most injured, accounting for 12.7% of all injuries. Significant increases in pickleball injuries were seen over time (P < .001) and across all age groups (P < .05). There was a significant increase in pickleball-related injuries presenting between 2013 and 2022, with most injuries arising from a fall. Notably, injuries decreased by 14% between 2019 and 2020 but rose again by 41% between 2020 and 2021 during the coronavirus 2019 pandemic. Older players disproportionately presented to the emergency department with pickleball-related injuries. Increased awareness and comprehensive injury prevention initiatives may be warranted to address the considerable prevalence of fractures and overuse/strain injuries.

A Cost-Effective Nonaqueous Reversed-Phase High-Performance Liquid Chromatography Method to Measure Vitamin D3 in Hen's Egg Yolk.

The objective of this study is to develop an HPLC-UV method for the cost-effective and quantitative determination of vitamin D3 in food, even in the presence of vitamin D2, with a specific focus on egg yolk. During method development, the performance of three stationary phases in resolving the peak of vitamin D2 from that of vitamin D3 was investigated. The physicochemical properties of these phases differed particularly in the extent of hydrophobicity and silanophilic activity, including a GraceSmart RP C18 column without silanol endcapping, a Robusta RP C18 column with silanol endcapping, and a Waters Xbridge RP C18 column with ethylene-bridged hybrid (BEH) particle technology. The Xbridge C18 stationary phase exhibited the most favorable performance, leading to an RS of 1.6 under the following nonaqueous reversed-phase (NARP) experimental conditions: mobile phase, acetonitrile, methanol, and trifluoroacetic acid in a (99/1/0.1, v/v/v) ratio; column temperature, 15°C. The developed chromatographic method does not require preanalytical purification steps and is also compatible with mass spectrometry. The identity of the vitamin D3 peak observed in the HPLC analysis was verified via GC-MS. The NARP-HPLC-UV method was partially validated, demonstrating satisfactory linearity, precision, accuracy, limit of quantification, and robustness. The HPLC method was then successfully applied to the analysis of real egg yolk samples, revealing average concentrations of vitamin D3 of 4-5µg/g of wet weight sample.

Risk Factors for Adults with Chronic Obstructive Pulmonary Disease in the United States, Utilizing State-Based Surveillance

Chronic Obstructive Pulmonary Disease (COPD) is a complex and heterogeneous condition. Exposure to tobacco smoke and air pollutants are key risk factors for COPD development; however, other risk factors include race/ethnicity, sex of adults, a history of asthma, occupational exposures, and chronic respiratory infections. Data for the current study were from the 2022 Behavioral Risk Factor Surveillance Survey. Chi-squares and multinomial logistic regression analyses, adjusted with the survey’s sampling weight, were used to examine how critical health indicators impacted a COPD diagnosis. Participants (N = 311,175) were adults aged 45 years and older. Adjusted multinomial regression analyses showed adults who reported asthma, current and former smoking, poor physical health, depression, less physical activity, and fatigue were more likely to report COPD. Those with COPD were more likely to be male than female. Moreover, those with COPD reported higher rates of health insurance coverage, and yet had lower income and more financial difficulty affording a doctor for health services. In a follow up regression analysis, examining racial differences in COPD for participants, American Indian adults had a higher odds of reporting COPD than the “other” race groups. Because COPD remains a leading cause of death and disability in the U.S., and racial disparities persist in respiratory outcomes, continuing to identify risk factors for vulnerable groups could assist health program planners with development of successful health messaging.