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Mortality trends and disparities for coexisting chronic obstructive pulmonary disease and cardiovascular disease: A retrospective analysis of deaths in the United States from 1999–2020
Background Chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) greatly influence morbidity and mortality, with COPD patients frequently suffering from cardiovascular comorbidities like coronary heart disease and stroke. This study analyzes mortality trends and disparities among individuals in the United States (US) affected by both CVD and COPD. Methods This study analyzed death certificates from the CDC WONDER database for individuals aged 25 and older who died between 1999 and 2020 with both CVD (ICD I00-I99) and COPD (ICD J41-J44). Age-adjusted mortality rates (AAMRs) and annual percent change (APC) were calculated by year, sex, age group, race/ethnicity, geographic region, and urbanization status. Results Between 1999 and 2020, there were 3,590,124 reported deaths due to coexisting CVD and COPD, with overall AAMR slightly changing from 82.2 to 81.2 per 100,000 population, and a notable rise from 2018 to 2020 (APC: 5.28; 95% CI: 1.83 to 7.22) coinciding with the onset of COVID-19 pandemic. A similar surge in mortality was observed across multiple demographic subgroups, particularly among older adults. Disparities across age groups, sex, race, and geographic location were also observed in the mortality rates due to CVD and COPD. When analyzed by age group, older adults exhibited the highest AAMR at 824.1. Men had higher AAMRs than women (96.5 vs. 60.7). Ethnoracial analysis showed that non-Hispanic (NH) White individuals had the highest AAMRs (82.0), followed by NH American Indian or Alaska Native (74.5), NH Black (63.6), Hispanic (38.1), and NH Asian or Pacific Islander (25.1) individuals. Additionally, non-metropolitan areas had higher AAMRs compared to metropolitan areas (96.2 vs. 70.9). Conclusions The findings suggest that mortality rates for CVD and COPD have increased in recent years, coinciding with the onset of the COVID-19 pandemic, which may have exacerbated outcomes in vulnerable populations. The study highlights the need for targeted interventions to address the overlapping impacts of CVD and COPD, especially in high-risk groups.
Research on coupling evacuation of escalator and staircase in fire scenario
In order to improve mall evacuation efficiency, Pyrosim software and Anylogic software are used to study the coupled evacuation of mall personnel using escalators and stairs in a fire scenario. The available safe evacuation time of each evacuation exit was explored by analyzing the smoke transport, temperature, CO concentration and visibility of each floor of the mall under different fire source locations when the fire shutter was lowered to 1.8m from the ground. Then, the evacuation model of mall personnel under fire scenario was built by using Anylogic to compare and analyze the evacuation time of mall personnel using escalators coupled with stairs and stairs only, and analyze the evacuation time of personnel using escalators coupled with stairs when the fire shutter at the escalator is down to 1.8m from the ground and when the fire shutter is not down. The study shows that when the fire source is located on the top floor of the mall, the smoke does not affect the evacuation of people on other floors during the simulation time. Under the fire scenario, the escalator-coupled staircase evacuation can shorten the maximum evacuation time by 23.37% compared with the staircase only. The fire shutter descends to 1.8m from the ground and the evacuation efficiency is 6.41% higher than when the fire shutter is not descended. This research contributes to the enhancement of mall safety and has practical implications for future emergency management strategies in public spaces.
Study on intestinal microbial communities of three different cattle populations on Qinghai-Tibet Plateau
The Tibetan cattle, indispensable·animals on the Qinghai-Tibet Plateau, have become a focal point for the region’s economic development. As such, the hybridization of these cattle has been recognized as a pivotal strategy to enhance the local cattle industry. However, research on the gut microbiota of Tibetan hybrid cattle remains scarce. Based on this, we conducted a comparative analysis of the gut microbiota and its functional implications across three distinct cattle populations: two the hybrid cattle populations (Tibetan local cattle × Holstein cattle, TH and Tibetan local cattle × Jersey cattle, TJ) and one the Tibetan locoal cattle population (BL). Bacteroidetes and Firmicutes dominate the gut microbiota across all populations at the phylum level. In addition, the predominant phyla in BL cattle were found to be Cyanobacteria, Verrucomicrobiota, and Actinobacteria, which may be one of the important reasons for the adaptability of Tibetan local cattle to the high-altitude environment of the Qinghai-Tibet Plateau. Further analysis identified specific biomarkers associated with the immune systems of BL cattle, including Bacteroidales_RF16, Coriobacterium, and Muribaculaceae. In contrast, TH cattle are primarily dominated by Oscillospiraceae and Clostridia_UCG_014, and TJ cattle are mainly dominated by Christensenellaceae and Gammaproteobacteria. KEGG enrichment analysis revealed that BL and TH cattle showed significant enrichment in the immune system, energy metabolism, and amino acid metabolism-related pathways compared with TJ cattle. Overall, these results suggest that BL and TH cattle demonstrate enhanced adaptability compared to TJ cattle, and indicate that intestinal microbiota of cattle at different altitudes and breeds have diverse structures and functions. Our study presents a new perspective on the role of the microbiome in the hybridization and enhancement of Tibetan cattle.
Grand Canyons on the Moon
Magnitude, risk factors and economic impacts of diabetic emergencies in developing countries: A systematic review
Background Diabetic ketoacidosis (DKA), hyperglycemic hyperosmolar syndrome (HHS) and severe hypoglycemia are considered as the life-threatening diabetic emergencies of diabetic patients worldwide. As the prevalence of diabetes grows in developing countries, so too does the impact of these costly human and economic complications. Noticeable scarcity of data concerning the magnitude, the cost expenditures as well as well unidentified predictors of these complications made the management more difficult in the resource limited health care settings. Thus, this systematic review aimed to assess the magnitude, risk factors and economic impacts of diabetes emergencies among diabetic patients in the developing countries. Methods Following PRISMA (2020) guidelines, databases of PubMed, EMBASE, Cochrane and Scopus were searched for studies reporting on prevalence, risk factors, and direct costs of diabetes emergencies published in English from 2000 to 2023. Forty eligible studies were extracted and retrieved using manual data extraction form and automation tools. Studies were analyzed and combined in a narrative synthesis. The estimations of direct cost expenditure were standardized to 2023 USD. Result A comprehensive examination was conducted on the 40 eligible studies, with the majority originating from African sources. The review shows the prevalence of diabetic emergencies; DKA episodes in the range of (3.8%-73.4%), HHS (0.9%-58%) and Severe hypoglycemia (3.3%-64.7%) per year in the developing countries. Infection, new onset of the diabetes, and non-compliance to medications and diets were reported as the most common risk factors of theses diabetic emergencies. Besides, the costs of hospitalization taken from the patients’ perspective, that were associated per one diabetic emergency event per patient was reported in the range of 105–230 USD in the developing countries. Conclusion The rising prevalence of diabetic emergencies in poor nations, where infections, non-compliance, and new onset of diabetes are major causes, highlighted the urgent need for preventative interventions. Identifying high-risk individuals is crucial for implementing tailored strategies to reduce emergency visits and hospital admissions. The significant economic burden of these emergencies exacerbates the strain on already limited healthcare resources. In order to enhance health outcomes and lessen the financial strain on healthcare systems in these areas, preventive strategies must be incorporated into diabetes management programs.
How are researchers using AI? Survey reveals pros and cons for science
The Serbian version of the Pandemic-Related Pregnancy Stress Scale (PREPS-SRB)–A validation study
Pregnancy is a sensitive period in a woman’s life when psychological distress can have negative consequences for the mother and fetus. Prolonged and intensified symptoms of anxiety and depression caused by the COVID-19 pandemic increase the risk of maternal and fetal health complications. The Pandemic-Related Pregnancy Stress Scale (PREPS) is a thoroughly designed tool that helps determine and analyze stress among pregnant women during pandemics in three domains: Preparedness in childbirth, (2) Infection, and (3) Positive Appraisal. A cross-sectional study included 189 pregnant women attending a community health center, “Dr Simo Milošević,” in Belgrade, Serbia, from January to February 2022. Pregnant women anonymously completed a questionnaire as part of the study. The mean scores for those three domains are as follows: Preparedness (2.4 ± 0.9), Infection stress (2.8 ± 1.1), and Positive Appraisal (3.7 ± 0.9). Internal consistency of the PREPS questionnaire for PREPS-Total (α = 0.867). An explanatory factor analysis of the PREPS showed that the Serbian version of the Pandemic-Related Pregnancy Stress Scale has good psychometric properties. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) was found to be 0.860, indicating a high degree of sampling adequacy. Additionally, Bartlett’s Test of Sphericity yielded a statistically significant result (χ2 = 1564.206, df = 105, p < 0.001). The CFA showed very good fit indices for the Serbian sample, confirming the factor structure of the original English version. The RMSEA value of 0.056 (0.036–0.075) and values for fit indices TLI (0.961) and CFI (0.974) were above the cut-off of ≥0.95, indicating an excellent fit. All standardized factor loadings were statistically significant and ranged from 0.50 to 0.85. The PREPS-SRB questionnaire serves as a valuable tool for Serbian healthcare professionals, allowing them to identify pregnant women experiencing significant stress related to the COVID-19 pandemic.
Correction: A lightweight and robust authentication scheme for the healthcare system using public cloud server
The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe
The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to automate the diagnosis or screening of COVID-19, in large-scale. The gold standard for COVID-19 detection, quantitative-Real-Time-Polymerase-Chain-Reaction (qRT-PCR), is expensive and time-consuming. Alternatively, haematology-based detections were fast and near-accurate, although those were less explored. The external-validity of the haematology-based COVID-19-predictions on diverse populations are yet to be fully investigated. Here we report external-validity of machine learning-based prediction scores from haematological parameters recorded in different hospitals of Brazil, Italy, and Western Europe (raw sample size, 195554). The XGBoost classifier performed consistently better (out of seven ML classifiers) on all the datasets. The working models include a set of either four or fourteen haematological parameters. The internal performances of the XGBoost models (AUC scores range from 84% to 97%) were superior to ML models reported in the literature for some of these datasets (AUC scores range from 84% to 87%). The meta-validation on the external performances revealed the reliability of the performance (AUC score 86%) along with good accuracy of the probabilistic prediction (Brier score 14%), particularly when the model was trained and tested on fourteen haematological parameters from the same country (Brazil). The external performance was reduced when the model was trained on datasets from Italy and tested on Brazil (AUC score 69%) and Western Europe (AUC score 65%); presumably affected by factors, like, ethnicity, phenotype, immunity, reference ranges, across the populations. The state-of-the-art in the present study is the development of a COVID-19 prediction tool that is reliable and parsimonious, using a fewer number of hematological features, in comparison to the earlier study with meta-validation, based on sufficient sample size (n = 195554). Thus, current models can be applied at other demographic locations, preferably, with prior training of the model on the same population. Availability: https://covipred.bits-hyderabad.ac.in/home ; https://github.com/debashreebanerjee/CoviPred .
Drill, baby, drill? Trump policies will hurt climate ― but US green transition is under way
Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning
Detecting cracks in asphalt concrete slabs is challenging due to environmental factors like lighting changes, surface reflections, and weather conditions, which affect image quality and crack detection accuracy. This study introduces a novel deep learning-based anomaly model for effective crack detection. A large dataset of panel images was collected and processed using denoising, standardization, and data augmentation techniques, with crack areas labeled via LabelImg software. The core model is an improved Xception network, enhanced with an adaptive activation function, dynamic attention mechanism, and multi-level residual connections. These innovations optimize feature extraction, enhance feature weighting, and improve information transmission, significantly boosting accuracy and robustness. The improved model achieves a 97.6% accuracy and a Matthews correlation coefficient of 0.98, remaining stable under varying lighting conditions. This method not only provides a fresh approach to crack detection but also greatly enhances detection efficiency.
Analyzing the labor market and salary determinants for big data talent based on job advertisements in China
The demand for big data talent is rapidly increasing with the growth of the big data industry. However, there has been limited research on what employers seek in recruiting big data talent. This paper aims to apply labor market segmentation theories to the big data labor market and develop a theoretical framework to analyze the distribution of big data talent in different labor market segments. Furthermore, we develop a salary determination model to explain wage differentials. An empirical analysis is conducted using online job advertisements from a Chinese recruitment website to investigate the labor market for big data talent in China. Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. Different types of enterprises have different requirements for individual characteristics and offer various levels of big data job positions. Furthermore, our results reveal that individual, job-related and organizational characteristics are all significant predictors of salaries. These findings can provide particularly useful insights for organizations and managers in the big data industry.
Correction: The implementation and impact of non-invasive prenatal testing (NIPT) for Down’s syndrome into antenatal screening programmes: A systematic review and meta-analysis
Prediction of mechanical characteristics of shearer intelligent cables under bending conditions
The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN’s causal and dilated convolution operations to capture long-term sequential features, BiLSTM’s bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model’s performance is validated through comparisons with multiple other models, and the contributions of input features to the model’s predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.
Effects of high-intensity interval training on physical performance, systolic blood pressure, oxidative stress and inflammatory markers in skeletal muscle of spontaneously hypertensive rats
Aim To investigate whether high-intensity interval training (HIIT) improves physical performance, systolic blood pressure, and markers of oxidative stress and inflammation in skeletal muscle of spontaneously hypertensive rats (SHR). Methods Nineteen male SHR rats were randomly assigned to two groups: sedentary (SHRC) and trained (SHR+T). The SHR+T group trained five times a week for eight weeks on a treadmill, while the SHR group remained without any exercise stimulus throughout the experimental period. Maximum physical performance and systolic blood pressure (SBP) were assessed before and after the training period. The following variables were measured in the tibialis anterior (TA) muscle: gene expression of the NADPH oxidase complex (NOX2, NOX4, p22phox, p47phox) and the NF-kB pathway (NF-kB and Ik-B), lipid peroxidation (malonaldehyde; MDA), protein carbonylation, hydrophilic antioxidant capacity (HAC) and pro-inflammatory cytokines (IL-6 and TNF-α). Results SHR+T rats showed higher physical performance and levels of IL-6, and lower SBP and protein carbonylation (p<0.05), compared with SHRC rats. No significant differences (p>0.05) were observed in the other variables. Significance Our results indicate that HIIT is an effective non-pharmacologic strategy to improve physical performance, reduce SBP, and modulate the skeletal muscle oxidative damage and inflammation in hypertensive rats.
Correction for Wu et al., NR2B subunit of the NMDA glutamate receptor regulates appetite in the parabrachial nucleus
Correction: From colorblind to systemic racism: Emergence of a rhetorical shift in higher education discourse in response to the murder of George Floyd
Correction for He et al., Structural insights into the assembly and energy transfer of haptophyte photosystem I–light-harvesting supercomplex
Fecal bacteria transplantation replicates aerobic exercise to reshape the gut microbiota in mice to inhibit high-fat diet-induced atherosclerosis
Aerobic exercise exerts a significant impact on the gut microbiota imbalance and atherosclerosis induced by a high-fat diet. However, whether fecal microbiota transplantation, based on aerobic exercise, can improve atherosclerosis progression remains unexplored. In this study, we utilized male C57 mice to establish models of aerobic exercise and atherosclerosis, followed by fecal microbiota transplantation(Fig 1a). Firstly, we analyzed the body weight, somatotype, adipocyte area, and aortic HE images of the model mice. Our findings revealed that high-fat diet -induced atherosclerosis mice exhibited elevated lipid accumulation, larger adipocyte area, and more severe atherosclerosis progression. Additionally, we assessed plasma lipid levels, inflammatory factors, and gut microbiota composition in each group of mice. high-fat diet -induced atherosclerosis mice displayed dyslipidemia along with inflammatory responses and reduced gut microbiota diversity as well as abundance of beneficial bacteria. Subsequently performing fecal microbiota transplantation demonstrated that high-fat diet -induced atherosclerosis mice experienced weight loss accompanied by reduced lipid accumulation while normalizing their gut microbiota profile; furthermore it significantly improved blood lipids and inflammation markers thereby exhibiting notable anti- atherosclerosis effects. The findings suggest that aerobic exercise can modify gut microbiota composition and improve high-fat diet-induced atherosclerosis(Fig 1b). Moreover, these beneficial effects can be effectively transmitted through fecal microbiota transplantation, offering a promising therapeutic approach for managing atherosclerosis.