Open-access Database Research Articles

Multilocus sequence typing and phenotypic properties of Streptococcus mutans from Thai children with different caries statuses

BackgroundStreptococcus mutans is studied for its acidogenic and aciduric characteristics, notably its biofilm formation in the presence of sucrose, toward its role in the caries process. Variations in both genotype and phenotype have been reported among clinical isolates of S. mutans. This study aimed to examine genotypic and phenotypic characteristics of S. mutans obtained from Thai children with varying caries statuses.MethodsWe determined the presence of S. mutans and caries status in 395 children aged 3–4 years. From 325 children carrying S. mutans, we selected 90 with different caries statuses—caries-free (CF; n = 30), low severity of caries (LC; n = 30), or high severity of caries (HC; n = 30). Three isolates of S. mutans were taken from each child, thus, a total of 270 isolates were obtained. Multilocus sequence typing (MLST) was used to genotype the isolates and assess their clonal relationships. The properties, including biofilm formation, collagen binding, and acid production and tolerance were also evaluated.ResultsChildren with carious lesions showed a higher detection rate and number of S. mutans in saliva than those without caries. S. mutans from individuals with HC status showed the lowest biofilm formation ability, while this group had the highest detection rate of collagen-binding isolates. There was no difference in acid production or tolerance by caries status. Genotyping by MLST did not reveal any clone of S. mutans specific to CF status. This result remained even when we included MLST data from the open-access PubMLST database. MLST did identify clones containing only strains from caries-affected hosts, but tests of their phenotypic properties did not reveal any differences between S. mutans from these clones and clones that were from both caries-free and caries-affected children.ConclusionsThe clonal relationships of S. mutans indicated by MLST were not associated with the status of dental caries in the host.

Present and future distribution of the European pond turtle versus seven exotic freshwater turtles, with a focus on Eastern Europe

Freshwater turtles are often used as terrarium pets, especially juveniles of exotic species. At the adult stage they are often released by their owners into the wild despite their high invasion potential. In Europe these thermophilic potentially invasive alien species occupy the habitats of the native European pond turtle Emys orbicularis (Linnaeus, 1758), with new records from the wild being made specifically in Eastern Europe (Latvia and Ukraine) during recent decades. Assessing the potential of alien freshwater turtles to establish in new territories is of great concern for preventing invasion risks while preserving native biodiversity in the present context of climate change. We explored this issue by identifying the present and future (by 2050) suitable habitats of the European pond turtle and several potentially invasive alien species of freshwater turtle already settled in Europe, using a geographic information system (GIS) modelling approach based on datasets from CliMond for climate, Near-global environmental information (NGEI) for freshwater ecosystems (EarthEnv) and Maxent modelling using open-access databases, data from the literature and original field data. Modelling was performed for seven species of alien freshwater turtles occurring from the extreme northern to southern borders of the European range of E. orbicularis: the pond slider Trachemys scripta (Thunberg and Schoepff, 1792), the river cooter Pseudemys concinna (Le Conte, 1830), the Florida red-bellied cooter Pseudemys nelsoni (Carr, 1938), the false map turtle Graptemys pseudogeographica (Gray, 1831), the Chinese softshell turtle Pelodiscus sinensis (Wiegmann, 1835), the Caspian turtle Mauremys caspica (Gmelin, 1774) and the Balkan terrapin Mauremys rivulata (Valenciennes, 1833). In Ukraine, the most Eastern limit of E. orbicularis distribution, were previously reported northern American originated T. scripta, M. rivulata, M. caspica, whereas in Latvia, Emys’ most northern limit, were additionally reported P. concinna, P. nelsoni, G. pseudogeographica and Asia originated P. sinensis. The resulting Species Distribution Models (SDM) were of excellent performance (AUC > 0.8). Of these alien species, the most potentially successful in terms of range expansion throughout Europe were T. scripta (34.3% of potential range expansion), G. pseudogeographica (24.1%), and M. caspica (8.9%) and M. rivulata (4.3%) mainly in Eastern Europe, especially in the south of Ukraine (Odesa, Kherson, Zaporizhzhia regions, and Crimean Peninsula). Correlation between the built SDMs for the native E. orbicularis and the invasive alien T. scripta was reliably high, confirming the highly likely competition between these two species in places they cooccur. Moreover, a Multiple Regression Analysis revealed that by 2050, in most of Europe (from the western countries to Ukraine), the territory overlap between E. orbicularis and potentially invasive alien species of freshwater turtles will increase by 1.2 times, confirming higher competition in the future. Importantly, by 2050, Eastern Europe and Ukraine are predicted to be the areas with most suitable habitats for the European pond turtle yet with most limited overlap with the invasive alien species. We conclude that Eastern Europe and Ukraine are the most relevant priority conservation areas for the European pond turtle where it is now necessary to take protective measures to ensure safe habitat for this native species on the long-term.

Dataset of an actual motor vehicle insurance portfolio

AbstractAdvanced analytics plays a vital role in enhancing various aspects of business operations within the insurance sector by providing valuable insights that drive informed decision-making, primarily through effective database utilization. However, open access databases in the insurance industry are exceedingly rare, as they are the basis of the business, encapsulating all the risk structure of the company. This makes it challenging for researchers and practitioners to access comprehensive insurance datasets for analysis and assessing new approaches. This paper introduces an extensive database specifically tailored for non-life motor insurance, containing 105,555 rows and encompassing a wide array of 30 variables. The dataset comprises important date-related information, such as effective date, date of birth of the insured, and renewal date, essential for policy management and risk assessment. Additionally, it includes relevant economic variables, such as premiums and claim costs, for assessments of products’ financial profitability. Moreover, the database features an array of risk-related variables, such as vehicle size, economic value, power, and weight, which significantly contribute to understanding risk dynamics. By leveraging the statistical analysis of this rich database, researchers could identify novel risk profiles, reveal variables that influence insured claims behaviour, and contribute to the advancement of educational and research initiatives in the dynamic fields of economics and actuarial sciences. The availability of this comprehensive database opens new opportunities for research and teaching and empowers insurance professionals to enhance their risk assessment and decision-making processes.

Area-level factors associated with variation in involuntary psychiatric hospitalisation across England: a cross-sectional, ecological study.

Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England. The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22. There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs. Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.

RefXAS: an open access database of X-ray absorption spectra.

Under DAPHNE4NFDI, the X-ray absorption spectroscopy (XAS) reference database, RefXAS, has been set up. For this purpose, we developed a method to enable users to submit a raw dataset, with its associated metadata, via a dedicated website for inclusion in the database. Implementation of the database includes an upload of metadata to the scientific catalogue and an upload of files via object storage, with automated query capabilities through a web server and visualization of the data and files. Based on the mode of measurements, quality criteria have been formulated for the automated check of any uploaded data. In the present work, the significant metadata fields for reusability, as well as reproducibility of results (FAIR data principles), are discussed. Quality criteria for the data uploaded to the database have been formulated and assessed. Moreover, the usability and interoperability of available XAS data/file formats have been explored. The first version of the RefXAS database prototype is presented, which features a human verification procedure, currently being tested with a new user interface designed specifically for curators; a user-friendly landing page; a full list of datasets; advanced search capabilities; a streamlined upload process; and, finally, a server-side automatic authentication and (meta-) data storage via MongoDB, PostgreSQL and (data-) files via relevant APIs.

Public Availability of Data Management and Analysis Scripts of Studies Conducted on Open-Access Intensive Care Databases.

Intensive care units (ICUs) provide care for critical patients at high risk of morbidity and mortality, and require continuous monitoring of clinical, biological and, imaging parameters. Collaborative ventures have enabled the emergence of large open access databases for the secondary use of Electronic Health Records (EHRs). The objective of this work was to evaluate the availability of scripts and datasets in publications based on ICU open-access databases. We included 910 original articles based on four ICU open-access databases (Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, and Medical Information Mart for Intensive Care). The majority of the studies did not provide their data management scripts (n=839, 92.9%), neither the analysis script (n=843, 93.4%) in the article. Attempts to contact the 845 corresponding authors in question resulted in 89.11% (n=753) of our e-mail requests going unanswered over a two-month period. We received 51 automated messages (55.43%) indicating that emails have not been delivered, while 6 messages (6.52%) redirected to alternative email addresses. Only 20 corresponding authors (18.18%) answered, finally providing the requested materials. Despite scientific journals recommendations to share materials, our study unveils the absence of crucial components for the replication of studies by other research teams.

Epidemiology of high-energy trauma in Chile: An ecological analysis using public registries.

High-energy trauma is defined as severe organic injuries resulting from events that generate a large amount of kinetic, electrical, or thermal energy. It represents a significant public health concern, accounting for 10% of global mortality. This article aims to describe the epidemiology of high-energy trauma in Chile. Specifically, it seeks to compare the mortality rate per 100 000 inhabitants among member countries of the World Health Organization (WHO), provide a descriptive analysis of notifications under the Explicit Health Guarantees (GES) for the health issue of polytraumatized patients, and analyze the trend in the mortality rate due to external causes in Chile. This study employs an ecological design using three open-access databases. First, the WHO database on deaths from traffic accidents in 2019 was used. Then, the GES database was consulted for the "Polytraumatized" issue between 2018 and 2022. Finally, the Chilean Department of Health Statistics database on causes of death between 1997 and 2020 was utilized. In 2019, Chile ranked in the middle regarding the mortality rate per 100 000 inhabitants due to traffic accidents. GES notifications for polytrauma predominantly involved men aged 20 to 40 years and those affiliated with the public health system, highlighting a primary focus for prevention efforts. Mortality from accidents showed a decreasing trend, with significant structural changes identified in 2000 and 2007.

A comprehensive review of retinal disease diagnosis and open access datasets: Fundus and OCT images

The rapid advancements in deep learning algorithms and the availability of large, open-access databases of fundus and OCT (optical coherence tomography) images have contributed greatly to advancements in computer-assisted diagnostics and the localization of various disorders affecting the retina. This study offers a comprehensive examination of retinal diseases and various recent applications of deep learning strategies for categorising key retinal conditions, such as diabetic retinopathy, glaucoma, age-related macular degeneration, choroidal neovascularization, retinal detachment, media haze, myopia, and dry eyes. Open-access datasets continue to play a critical role in the advancement of digital health research and innovation within the field of ophthalmology. Thirty open-access databases containing fundus and OCT (optical coherence tomography) pictures, which are often utilised by researchers, were carefully examined in this work. A summary of these datasets was created, which includes the number of images, dataset size, and supplementary items in the dataset, as well as information on eye disease and country of origin. We also discussed challenges and limitations of novel deep learning models. Finally, in conclusion, we discussed some important insights and provided directions for future research opportunities.

Exploring gamification in the Administration context: a systematic literature review

Through a comprehensive review of studies reported in the literature on gamification – which is understood as the use of game design elements in non-game contexts, this work sought to present the results obtained from a Systematic Literature Review (SLR) of studies related to the use of gamification in management and business. This study was conducted based on the application of the Systematic Model for Research in Open Access Databases (SMROAD). The model applied involved the conduct of a comprehensive survey of research studies related to the subject matter investigated in a number of journals and the categorization of the data obtained into two aspects: i) general aspects; and ii) dimension of analysis and its categorizations according to the subject matter. For the literature review, we examined 20 journals, and 24 articles were selected out of a total of 4973 articles initially investigated. The results obtained showed that although research on gamification is incipient and is in need of theoretical and empirical deepening in specific contexts, such as in finance, the use of gamification as a management tool can provide us with relevant individual and organizational results.

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package “ConsensusClusterPlus” on the bulk RNA-seq data. Hub genes were discerned through the application of the R package “limma” and univariate regression analysis, with the calculation of risk scores carried out using the R package “survminer”. To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan–Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages “ESTIMATE” and “GSVA” were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs.

The intricate molecular environment of the native membrane profoundly influences every aspect of membrane protein (MP) biology. Despite this, the most prevalent method of studying MPs uses detergent-like molecules that disrupt and remove this vital local membrane context. This severely impedes our ability to quantitatively decipher the local molecular context and comprehend its regulatory role in the structure, function, and biogenesis of MPs. Using a library of membrane-active polymers we have developed a platform for the high-throughput analysis of the membrane proteome. The platform enables near-complete spatially resolved extraction of target MPs directly from their endogenous membranes into native nanodiscs that maintain the local membrane context. We accompany this advancement with an open-access database that quantifies the polymer-specific extraction variability for 2065 unique mammalian MPs and provides the most optimized condition for each of them. Our method enables rapid and near-complete extraction and purification of target MPs directly from their endogenous organellar membranes at physiological expression levels while maintaining the nanoscale local membrane environment. Going beyond the plasma membrane proteome, our platform enables extraction from any target organellar membrane including the endoplasmic reticulum, mitochondria, lysosome, Golgi, and even transient organelles such as the autophagosome. To further validate this platform, we took several independent MPs and demonstrated how our resource can enable rapid extraction and purification of target MPs from different organellar membranes with high efficiency and purity. Further, taking two synaptic vesicle MPs, we show how the database can be extended to capture multiprotein complexes between overexpressed MPs. We expect these publicly available resources to empower researchers across disciplines to efficiently capture membrane 'nano-scoops' containing a target MP and interface with structural, functional, and other bioanalytical approaches. We demonstrate an example of this by combining our extraction platform with single-molecule TIRF imaging to demonstrate how it can enable rapid determination of homo-oligomeric states of target MPs in native cell membranes.

DOCK-PET: database of CNS kinetic parameters in the healthy human brain for existing PET tracers.

Information about developed positron emission tomography (PET) tracers and obtained clinical PET images is publicly available in a database. However, findings regarding the kinetic parameters of PET tracers are yet to be summarized. Therefore, in this study, we created an open-access database of central nervous system (CNS) kinetic parameters in the healthy human brain for existing PET tracers (DOCK-PET). Our database includes information on the kinetic parameters and compounds of existing CNS-PET tracers. The kinetic parameter dataset comprises the analysis methods, VT, BPND, K parameters, relevant literature, and study details. The list of PET tracers and kinetic parameter information was compiled through keyword-based searches of PubMed and the Molecular Imaging and Contrast Agent Database (MICAD). The kinetic parameters obtained, including VT, BPND, and K parameters, were reorganized based on the defined brain anatomical regions. All data were rigorously double-checked before being summarized in Microsoft Excel and JavaScript Object Notation (JSON) formats. Of the 247 PET tracers identified through searches using the PubMed and MICAD websites, the kinetic parameters of 120 PET tracers were available. Among the 120 PET tracers, compound structures with chemical and physical properties were obtained from the PubChem website or the ChemDraw software. Furthermore, the affinity information of the 104 PET tracers was gathered from PubChem or extensive literature surveys of the 120 PET tracers. We developed a comprehensive open-access database, DOCK-PET, that includes both kinetic parameters of healthy humans and compound information for existing CNS-PET tracers.

Causal role of immune cells in uveitis: a Mendelian randomization study.

Uveitis refers to a group inflammation affecting the uvea, retina, retinal blood vessels as well as vitreous body, which is one of the common causes of blindness. There is growing evidence linking different types of immune cells to uveitis, although it remains uncertain if these associations imply causal relationships. Recent advancements in high-density genetic markers like SNPs or CNVs for genotyping, along with the progress in genome-wide association studies (GWAS) technologies, have improved our understanding of the immunological mechanisms involved in ocular diseases. Therefore, our objective was to investigate the potential causal link between immune cells and uveitis using a Mendelian randomization study. The exposure and outcome GWAS data for this study were sourced from an open-access database (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was utilized to evaluate the causal relationship between 731 immune cell features and uveitis. Various MR methods were employed to reduce bias and obtain dependable estimates of the causal link between the immune cell variables and the outcomes. Instrumental variable selection criteria were carefully chosen to enhance the accuracy and efficacy of the causal relationship between different immune cell types and the risk of uveitis. Using two-sample MR, IVW modeling showed that GAD had significant effect on immunophenotypes. CD3 levels on CD45RA- CD4+ T cells (OR = 1.087, 95%CI = 1.029 ~ 1.147, p = 0.003) and CD3 levels on CM CD4+ T cells (OR = 1.086, 95%CI = 1.033 ~ 1.141, p = 0.001) were found to be elevated in cases of uveitis. HLA DR levels in CD14- CD16+ monocyte cells (OR = 0.735, 95% CI = 0.635 ~ 0.850, p < 0.001) and HLA DR levels in NK cells (OR = 0.910, 95% CI = 0.851 ~ 0.972, p = 0.005) were observed to be reduced in individuals with uveitis. Furthermore, Two cells were identified to be significantly associated with uveitis risk: HLA DR on in NK cells (OR = 0.938, 95%CI = 0.899 ~ 0.979, p = 0.003), HLA DR on CD14- CD16+ monocytes (OR = 0.924, 95%CI = 0.878 ~ 0.972, p = 0.002). This study highlights the intricate relationship between immune cells and generalized anxiety disorder using genetic methods, offering valuable insights for future clinical investigations.

Assessing external exposome by implementing an Environmental Data Management System using Open Data

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017–2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Łódź for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Łódź the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions.

Minimizing measurement error in treatment outcome estimates: A meta-analysis comparing estimates between the gambling timeline followback and other self-report assessments of gambling behavior.

The aim of the current meta-analysis was to examine potential differences in posttreatment effect size estimates for gambling frequency (i.e., the number of days gambled) and gambling expenditure (i.e., the amount of money gambled) when using the gambling timeline followback (G-TLFB) versus other self-report assessments. Using an open-access meta-analysis database of studies on cognitive behavioral treatment for gambling disorder, 22 studies representing 2,824 participants were identified for inclusion. Hedges's g effect sizes representing posttreatment differences on gambling frequency and expenditure between cognitive behavioral treatment versus inactive and minimal treatment controls were calculated, and mixed-effect subgroup analyses examined the effect sizes for each outcome between studies using the G-TLFB versus other self-report assessments. Mixed-effect subgroup analyses indicated that the effect size for gambling frequency was significantly lower for studies using the G-TLFB (g = -0.15) than studies using other self-report assessments (g = -0.71). When examining whether the use of the G-TLFB was associated with the posttreatment effect size for gambling frequency in a random-effect metaregression model that controlled for study grant funding status, the use of the G-TLFB was not significantly associated with effect size. The effect size for gambling expenditure was not significantly different between studies using the G-TLFB (g = -0.22) versus studies using other self-report assessments (g = -0.38). The G-TLFB yields more conservative and precise effect size estimates of posttreatment gambling frequency, but not gambling expenditure, than other self-report assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification

The classification of Alzheimer's disease (AD) using deep learning models is hindered by the limited availability of data. Medical image datasets are scarce due to stringent regulations on patient privacy, preventing their widespread use in research. Moreover, although open-access databases such as the Open Access Series of Imaging Studies (OASIS) are available publicly for providing medical image data for research, they often suffer from imbalanced classes. Thus, to address the issue of insufficient data, this study proposes the integration of a generative adversarial network (GAN) that can achieve comparable accuracy with a reduced data requirement. GANs are unsupervised deep learning networks commonly used for data augmentation that generate high-quality synthetic data to overcome data scarcity. Experimental data from the OASIS database are used in this research to train the GAN model in generating synthetic MRI data before being included in a pretrained convolutional neural network (CNN) model for multistage AD classification. As a result, this study has demonstrated that a multistage AD classification accuracy above 80% can be achieved even with a reduced dataset. The exceptional performance of GANs positions them as a solution for overcoming the challenge of insufficient data in AD classification.

Vehicle Stock Numbers and Survival Functions for On-Road Exhaust Emissions Analysis in India: 1993–2018

Road transport plays a crucial role in sustaining all the personal and freight movement needs of residential, commercial, and industrial activities, and in Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads contribute to as much as 50% of particulate matter pollution in a year. Therefore, effective management of vehicle exhaust emissions is vital not only for improving current air quality but also for ensuring the long-term benefits from efforts to reduce air pollution. In the approved clean air action plans for 131 cities under the national clean air program (NCAP), more than 50% of the implementable actions are transport-centric. Having a reliable and replicable vehicle exhaust emissions inventory is essential for effective planning, which can help establish a baseline, support scenario analysis, and allow for tracking progress in the sector. This process begins with accessing accurate vehicle stock numbers, typically obtained from vehicle registration databases, traffic surveys, and other governmental records. Often, in low- and middle-income countries like India, these numbers require extensive data cleaning before they can be used for emissions and pollution analysis. This paper presents a cleaned, open-access vehicle stock database for India and outlines a methodology to build and maintain an in-use vehicle age-mix database for future years. The database covers the years 1993 to 2018 for the entire country and individual states, along with estimates of the age distribution of vehicles using survival functions. By offering a comprehensive and reliable data source, this paper aims to support sustainable national and urban air quality management efforts, helping policymakers and stakeholders make informed decisions to improve air quality and public health.

Death Risk Response of High-Altitude Resident Populations to COVID-19 Vaccine: A Retrospective Cohort Study.

Vásquez-Velásquez, Cinthya, Diego Fano-Sizgorich, and Gustavo F Gonzales. Death risk response of high-altitude resident populations to COVID-19 vaccine: Retrospective cohort study. High Alt Med Biol. 00:00-00, 2024. Background: Peru had one of the highest mortality rates caused by the coronavirus disease 2019 (COVID-19) pandemic worldwide. Vaccination significantly reduces mortality. However, the effectiveness of vaccination might differ at different altitudinal levels. The study aimed to evaluate the effect modification of altitude on the association between vaccination and COVID-19 mortality in Peru. Methodology: A retrospective cohort, using open access databases of deaths, COVID-19 cases, hospitalizations, and vaccination was obtained from the Peruvian Ministry of Health. Deaths due to COVID-19 were evaluated in vaccinated and nonvaccinated patients. Crude (RR) and adjusted relative risks (aRR) were calculated using generalized linear models of Poisson family with robust variances. Models were adjusted for age, sex, pandemic wave, and Human Development Index. To evaluate the interaction by altitude, a stratified analysis by this variable was performed. The variable altitude was categorized as, 0-499 m (828,298 cases), 500-1,499 m (64,735 cases), 1,500-2,499 m (106,572 cases), and ≥2,500 m (179,004 cases). The final sample studied included 1,362,350 cases. Results: The vaccine showed a considerable reduction of death risk with the second (aRR: 0.41, 95% confidence interval [CI]: 0.38-0.44) and third doses (aRR: 0.21, 95% CI: 0.20-0.23). In the adjusted and interaction model, it can be observed that medium and high altitude present a higher risk of death compared to sea level (aRR: 2.58 and 2.03, respectively). Likewise, the two doses' group presents an aRR:1.22 for medium altitude (1,500-2,499 m) and 1.6 for high altitude (≥2,500 m), compared with low-altitude population, suggesting that the action of vaccination at high altitude is altered by the effect of the altitude itself. Conclusions: Altitude might modify the protective effect of SARS-CoV-2 vaccine against COVID-19 death.

Evaluating the Relationship between Depression, Suicidal Thoughts, and Ulcerative Colitis in Acne Patients on Isotretinoin: A Case-Control Study.

Our case-control study investigated the association between isotretinoin use and the incidence of depression, suicidal thoughts, and ulcerative colitis among 14,365 acne patients using the All of Us open-access database. The findings revealed no statistically significant correlation between isotretinoin use and these conditions, supporting the safety profile of isotretinoin in dermatological treatments.

ETCNN: An ensemble transformer-convolutional neural network for automatic analysis of fetal heart rate

ObjectiveTraditional methods face challenges in accurately analyzing fetal heart rate (FHR) signals due to the complexity of accelerations and decelerations (Acc/Dec) and their cyclic definition relationship with baseline. We aim to develop a deep learning model, Ensemble Transformer-Convolutional Neural Network (ETCNN), to improve baseline/Acc/Dec determination accuracy and validate its generalization across multi-center and multi-device test datasets. MethodsWe proposed ETCNN as a solution, treating FHR analysis as a one-dimensional signal segmentation problem. ETCNN consists of four subnetworks (TCNNs), each equipped with convolutional kernel size of 21, 31, 61, and 81, respectively. Each subnetwork integrates Channel-Residual (C-Res) modules and Channel Cross fusion with Transformer (CCT) modules. C-Res modules dynamically prune irrelevant channels, focusing on critical FHR episodes, while CCT modules harness multi-scale features to narrow semantic gaps. ResultsTrained on Lille Catholic University’s open-access database (LCU-DB), ETCNN’s performance surpassed twelve traditional methods and three deep learning models across four independent multi-center and multi-device test datasets. Ablation experiments demonstrated the effectiveness of ensemble learning, multi-scale convolution, residual channel attention, channel cross fusion attention, and multi-head attention in improving performance. Conclusion and significanceETCNN shows promise for accurate and efficient FHR analysis, with successful generalization across various datasets. Its advancements hold potential for clinical applications in fetal monitoring.