Browse Articles

Alcohol consumption associated with suicidal ideation, and suicide attempts in substance users: A cross-sectional study of an addiction registry in western Iran

Background Suicide is recognized as a major problem worldwide and is particularly prevalent among specific groups, including individuals with substance use disorders (SUDs). The present study aimed to investigate alcohol consumption as a risk factor for suicidal ideation and attempts among those with substance use disorders (SUDs) in western Iran. Methods This is a cross-sectional study, involving 1,112 individuals with SUDs who sought treatment at Farabi Hospital in Kermanshah, Iran, between the years 2019 and 2021. These participants were included in the study through a convenient sampling method as part of an interview-based assessment study. Results The participant’s average age was 37.97 years, and 982 were male (94.7%). Overall, 285 (27.5%) individuals had a history of suicide attempts, 316 (30.5%) individuals reported suicidal ideation, and 463 (41.6%) were alcohol users. In individuals who consumed alcohol, the prevalence of suicidal ideation (172 (37.2%) individuals), and a history of suicide attempts (156 (33.8%) individuals) was significantly higher compared to non-alcohol users. There was a statistically significant relationship between alcohol consumption and a history of suicide attempts (p < 0.05). The probability of suicide attempted in people with a history of alcohol consumption was 1.5 times, and in patients with a history of simultaneous substance use, it was 1.4 times that of other patients (all Ps < 0.05). Conclusion Our study results revealed that alcohol consumption among individuals with SUDs is associated with increased rates of suicidal ideation, attempts, and death. Therefore, clinicians should consider it as a separate suicide risk factor.

Three-dimensional analysis using a dental model scanner: Morphological changes of occlusal appliances used for sleep bruxism under dry and wet conditions

Objective This study used a dental model scanner and best-fit alignment to analyze the deformation patterns of stabilized occlusal appliances (OcAs) used to treat sleep bruxism when stored in wet or dry conditions. Methods Eight OcAs were prepared using polymethyl methacrylate, stored in water at room temperature for 4 weeks (wet storage), and then in air for 4 weeks (dry storage). After being stored in water for one month for hydration, they were 3D-scanned and digitized on days 0, 28, and 56. 3D-deformation patterns were obtained by comparing the storage-D group data using a best-fit alignment program that aligns the nearest points of the corresponding images in a virtual space and calculates the difference between the two images. The maximum deviation and deformed area in the ± direction, and the volume of the wet (W) and the dry (D) condition groups were compared using Wilcoxon’s signed rank test. Results OcA showed no obvious deformities in the W group at 4 weeks. However, in the D Group, a typical deformation pattern was detected at 4 weeks, with the posterior margin of the molars shrinking anteriorly, the palatal side of the molars lifted, and the buccal side retracted inward. The Ⅾ group was significantly larger than the W groups in terms of maximum deviation in the ± direction, deformed area, and volume. Conclusions After 4 weeks of storage under dry conditions, OcA showed a typical deformation pattern of shrinkage toward the center, with in-center lifting toward the palatal side. Significantly greater surface deviations, deformation areas, and deformation volumes were observed under dry conditions than in wet conditions.

Trends, gender, and racial disparities in patients with mortality due to paroxysmal tachycardia: A nationwide analysis from 1999–2020

Background Paroxysmal tachycardia encompasses various heart rhythm disorders that cause rapid heart rates. Its episodic occurrence makes it difficult to identify and measure its prevalence and trends in the population. Additionally, there is limited data on disparities and trends in mortality due to paroxysmal tachycardia, which is essential for assessing current medical approaches and identifying at-risk populations. Methods Our study examined death certificates from 1999 to 2020 using the CDC WONDER Database to identify deaths caused by paroxysmal tachycardia in individuals aged 25 and older, using the ICD-10 code I47. Age-adjusted mortality rates (AAMRs) and annual percent changes (APC) were calculated by year, gender, age group, race/ethnicity, geographic location, and urbanization status. Trends in AAMRs were analyzed using the Joinpoint Regression Program to identify significant changes and inflection points in mortality trends throughout the study period. Results Between 1999 and 2020, 155,320 deaths were reported in patients with paroxysmal tachycardia. Overall, AAMR decreased from 4.8 to 3.7 per 100,000 population between 1999 and 2020, despite showing a significant increase from 2014 to 2020 (APC: 4.33; 95% CI: 3.53 to 5.56). Men had consistently higher AAMRs than women (4.7 vs. 2.2). Furthermore, we found that AAMRs were highest among Non-Hispanic (NH) Black or African Americans and lowest in NH Asian or Pacific Islanders (4 vs. 1.9). Nonmetropolitan areas had higher AAMRs than metropolitan areas (3.6 vs. 3.2). Conclusions Our analysis showed a significant decrease in mortality from paroxysmal tachycardia since 1999, although there has been a slight increase in recent years. However, disparities remain, with higher AAMRs among men, NH Black or African Americans, and residents of non-metropolitan areas. These findings call for immediate public health actions to curb the rising trends and reduce potential disparities.

Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga

Early detection of nuisance species is crucial for managing threatened ecosystems and preventing widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, often at minimal cost and effort. However, eDNA analyses are prone to errors that can complicate their use in management frameworks. To address this, eDNA studies must consider imperfect detections and estimate error rates. Detecting nuisance species at low abundances with minimal uncertainty is vital for successful containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world’s largest marine reserves. Chondria tumulosa is a cryptogenic red alga with invasive traits, forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantitative polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of C. tumulosa that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting C. tumulosa at occupied sites was at least 92% when multiple qPCR replicates were positive. False-positive rates were 3% or less and false-negative errors were 11% or less. The assay proved effective for routine monitoring at shallow sites (less than 10 m), even when C. tumulosa abundance was below 1%. Successful implementation of eDNA tools in conservation decision-making requires balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay’s high sensitivity to C. tumulosa, and we outline steps to infer ecological presence-absence from molecular data. This reliable, cost-effective tool enhances the detection of low-abundance species, and supports timely management interventions.

Exploration of different quantitative polymerase chain reaction-based genotyping methods to distinguish Apcmin/+ mice from wildtype mice

Various molecular methods have been established for genotyping single-nucleotide variants (SNVs). However, despite the widespread availability of quantitative polymerase chain reaction (qPCR) instruments in biomedical laboratories, the lack of professional analytical tools impedes the application of qPCR in genotyping. Apcmin/+ mice, which harbour a germline Apc mutation (g.2549T>A) associated with multiple intestinal neoplasms, are extensively employed in colorectal cancer research. In this study, we used Apc as a model and assessed the feasibility of different qPCR-based methods for SNV genotyping, considering approaches with and without genotyping analytical tools. We initially employed allele-specific PCR followed by electrophoresis to determine the genotypes of Apc in tail tissues from potential Apcmin/+ mice, and this method served as the benchmark for evaluating the performance of qPCR-based methods. Dye-based qPCR and melting curve assays exhibited distinct dissociation patterns that differentiated between synthesised wildtype (TT) and heterozygous mutant (TA) DNA and between TT and TA genotype mice based on analysis of tissue samples. This discrimination ability of these assays was unaffected by the use of different intercalating dyes (SYBR Green I or EvaGreen). Dual-probe qPCR assays were developed to simultaneously detect mutant and wildtype alleles using differently labelled probes. The genotyping module and delta cycle threshold method were used to facilitate the analysis of results. The qPCR-based methods displayed 100% agreement with the standard genotyping outcomes. When the PCR–electrophoresis method was used, approximately 15% of the samples required re-examination to obtain conclusive results. In contrast, when the qPCR methods were used, success rates exceeding 99% were achieved with a single test. Additionally, all qPCR-based methods determined mouse genotypes by analysis of stool samples, highlighting the applicability of these methods for non-invasive genotyping. Loss of heterozygosity in the Apc gene in intestinal polyps was detected using the dual-probe assay with delta cycle threshold method. In summary, this study successfully implemented intercalator-based and probe-based qPCR methods, with and without professional analytical modules, for characterising Apc in tissue and stool samples. Furthermore, these methods can be extended to allow genotyping of other SNVs and can facilitate non-invasive genotyping of transgenic animals.

Daily briefing: How scientists can help protect US federal research

Explainable Graph Spectral Clustering of text documents

Spectral clustering methods are known for their ability to represent clusters of diverse shapes, densities etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Therefore, there is an urgent need to elaborate methods for explaining the outcome of the clustering. We have constructed in this paper a theoretical bridge linking the clusters resulting from Graph Spectral Clustering and the actual document content, given that similarities between documents are computed as cosine measures in tf or tfidf representation. This link enables to provide with explanation of cluster membership in clusters produced by GSA. We present a proposal of explanation of the results of combinatorial and normalized Laplacian based graph spectral clustering. For this purpose, we show (approximate) equivalence of combinatorial Laplacian embedding and of K-embedding (proposed in this paper) and term vector space embedding. We performed an experimental study showing that K-embedding approximates well Laplacian embedding under favourable block matrix conditions and show that approximation is good enough under other conditions. We show also perfect equivalence of normalized Laplacian embedding and the M-embedding (proposed in this paper) and (weighted) term vector space embedding. Hence a bridge is constructed between the textual contents and the clustering results using both combinatorial and normalized Laplacian based Graph Spectral Clustering methods. We provide a theoretical background for our approach. An initial version of this paper is available at arXiv, (Starosta B 2023). The Reader may refer to that text to get acquainted with formal aspects of our method and find a detailed overview of motivation.

Mapping the future: The current landscape and future directions of evidence-based practice in Saudi radiology departments

Purpose To examine the current application of Evidence-Based Practice (EBP) among radiology professionals, including radiologists and radiographers, in Saudi Arabia and to identify challenges in order to propose suitable future improvement strategies if it is required. Method A mixed-method design was used in this study. A survey consisting of 23 questions addressing research activities and EBP within radiology departments was sent to radiology personnel. The sample size of the quantitative phase of the study was determined using a formula specific for an infinite or unknown population. The formula used was n = P(1-P)Z2/d2, which resulted in a required sample size of 384 participants. A total of 345 participants; however, 45 did not fully complete the questionnaire and were therefore excluded. The data were analyzed using SPSS version 27. Inferential statistics, including non-parametric tests such as the Mann-Whitney U Test and the Kruskal-Wallis Test, were used to assess the influence of demographic factors on perceptions and challenges related to the adoption of evidence-based practice (EBP) in research within Saudi Arabia. Additionally, 20 semi-structured interviews were conducted with radiology personnel across the country. The sampling technique for the qualitative phase of the study was guided by the study’s objectives and the unique characteristics of the research group. The participants were purposively sampled in order to include radiologists and radiographers who work in different types of hospitals (public, semi-public, private) in Saudi Arabia. Responses from the interviews were coded, and key themes were identified following Miles and Huberman’s framework. Results The findings revealed a positive attitude towards research and EBP among Saudi radiology personnel. Over half of the participants (74.3%) strongly agreed that they understood and were familiar with EBP. They also felt confident in their ability to conduct scientific research in radiology (Mean = 4.27) and believed that they should actively initiate projects (Mean = 4.10). Radiologists reported a higher level of agreement compared to radiographers regarding their familiarity with EBP and their ability to critically evaluate the quality of research (P-value = <0.05). However, participants indicated lower level of agreement about their ability to develop their current practice based on EBP and engage in discussions with colleagues about research evidence. Key challenges identified include a lack of training, insufficient support and limited autonomy, which may hinder EBP implementation. Conclusion This study underscores the need for comprehensive education, ongoing training and a supportive organisational culture to enhance EBP adaption.

Trends and socioeconomic inequalities of recommended antenatal care services utilization in Ethiopia: A decomposition analysis using Ethiopian nationwide Demographic Health Surveys 2011–2019

Background Antenatal care (ANC) services are essential to reduce maternal and newborn morbidity and mortality rates. However, the trends and socioeconomic inequality of utilizing recommended ANC services has not been well studied in Ethiopia. Therefore, this study aims to investigate the trends and socioeconomic disparities in receiving recommended ANC services among Ethiopian women. Methods This study used recent Ethiopian Demographic Health Surveys (EDHS) conducted in 2011, 2016, and 2019. Binary logistic regression model was employed to assess the association between receiving the recommended ANC services and explanatory variables and socioeconomic disparities were estimated through concentration index (CIX) analysis. Moreover, Wagstaff approach was used to decompose the relative CIX to the contribution of explanatory variables for the observed disparities. Results This study found that 37.37% (95%CI: 36.46–38.28%) of mothers utilized the recommended ANC services in Ethiopia. The trend in the coverage of recommended ANC services increased from ~ 30% in 2011 to 44.70% in 2019. Mother’s age and education, household wealth status, distance of the nearest health facility, and experiencing domestic abuse (i.e., wife beating) were significantly associated with utilization of recommended ANC services. The relative estimated CIX for wealth index, mothers education, Ethiopian administrative regions, and residence were 0.15 (P < 0.001), 0.14 (P < 0.001), 0.07(P < 0.001), and −0.11(P < 0.001), respectively. Wealth status of the households contributed for almost two-thirds (66.58%) of the observed disparity in recommended ANC service utilization across wealth categories. Conclusion The study revealed that Ethiopian women’s utilization of recommended ANC services was unequal by their socioeconomic classes, with better off women more likely to utilize the recommended ANC services than worse off women. Hence, the responsible body should improve the access and quality of antenatal care services for underprivileged women in Ethiopia.

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.