Version Of Database Research Articles

TranscriptDB: a transcript-centric database to study eukaryotic transcript conservation and evolution.

Eukaryotic genes can encode multiple distinct transcripts through the alternative splicing (AS) of genes. Interest in the AS mechanism and its evolution across different species has stimulated numerous studies, leading to several databases that provide information on AS and transcriptome data across multiple eukaryotic species. However, existing resources do not offer information on transcript conservation and evolution between genes of multiple species. Similarly to genes, identifying conserved transcripts-those from homologous genes that have retained a similar exon composition-is useful for determining transcript homology relationships, studying transcript functions and reconstructing transcript phylogenies. To address this gap, we have developed TranscriptDB, a database dedicated to studying the conservation and evolution of transcripts within gene families. TranscriptDB offers an extensive catalog of conserved transcripts and phylogenies for 317 annotated eukaryotic species, sourced from Ensembl database version 111. It serves multiple purposes, including the exploration of gene and transcript evolution. Users can access TranscriptDB through various browsing and querying tools, including a user-friendly web interface. The incorporated web servers enable users to retrieve information on transcript evolution using their own data as input. Additionally, a REST application programming interface is available for programmatic data retrieval. A data directory is also available for bulk downloads. TranscriptDB and its resources are freely accessible at https://transcriptdb.cobius.usherbrooke.ca.

Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.

People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an explainable machine learning(ML) model based on clinical features for early prediction of the risk of sepsis in TBI patients. We enrolled all patients with TBI in the Medical Information Mart for Intensive Care IV database from 2008 to 2019. All patients were randomly divided into a training set (70%) and a test set (30%). The univariate and multivariate regression analyses were used for feature selection. Six ML methods were applied to develop the model. The predictive performance of different models were determined based on the area under the curve (AUC) and calibration curves in the test cohort. In addition, we selected the eICU Collaborative Research Database version 1.2 as the external validation dataset. Finally, we used the Shapley additive interpretation to account for the effects of features attributed to the model. Of the 1555 patients enrolled in the final cohort, 834 (53.6%) patients developed sepsis after TBI. Six variables were associated with concomitant sepsis and were used to develop ML models. Of the 6 models constructed, the Extreme Gradient Boosting (XGB) model achieved the best performance with an AUC of 0.807 and an accuracy of 74.5% in the internal validation cohort, and an AUC of 0.762 for the external validation. Feature importance analysis revealed that use mechanical ventilation, SAPSII score, use intravenous pressors, blood transfusion on admission, history of diabetes, and presence of post-stroke sequelae were the top six most influential features of the XGB model. As shown in the study, the ML model could be used to predict the occurrence of sepsis in patients with TBI in the intensive care unit.

Identification and validation of autophagy-related genes in Hirschsprung's disease.

Hirschsprung's disease (HSCR) is a congenital disorder characterized by aganglionosis in the intermuscular and submucosal nerve plexuses of the gut, leading to impaired gastrointestinal function. Although the precise cause and pathophysiology of HSCR remain elusive, increasing evidence points to a significant role of autophagy in its development, warranting further investigation into its underlying mechanisms. This study utilized publicly available microarray expression profiling datasets, GSE96854 and GSE98502, from the Gene Expression Omnibus (GEO). The R software (version 4.2.0) was employed to identify autophagy-related genes potentially showing differential expression in HSCR. Subsequent analyses included correlation analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analysis using the STRING database (version 11.0) and Cytoscape software (version 3.8.2). Ultimately, HSCR samples were used to verify the mRNA levels of important genes by quantitative real-time polymerase chain reaction (qRT-PCR) in a laboratory setting. We have discovered 20 genes that are involved in autophagy and show variable expression. Among these genes, 15 are up-regulated and five are down-regulated. The enrichment analysis using the GO and KEGG pathways revealed a notable enrichment in pathways related to the control of autophagy. Nine hub genes were found via the investigation of the PPI network constructed from STRING database and module analysis using Cytoscape. Moreover, the concordance between SIRT1 expression in the HSCR model and the bioinformatics analysis of mRNA chip findings was validated using qRT-PCR. Utilizing bioinformatics analysis, we identified 20 potential genes associated with Hirschsprung's disease that play a role in autophagy. Notably, the upregulation of SIRT1 may profoundly influence the progression of HSCR by regulating autophagy-related pathways, offering a novel perspective on the disease's pathogenesis.

Weak saliency ensemble network for person Re-identification using infrared light images

In recent years, person re-identification (re-id) has primarily been studied using visible light (VL) images. However, the challenges of employing VL images in nighttime environments have prompted research into using infrared light (IR) images. Yet, the utilization of both VL and IR images in person re-id has resulted in increased computational cost and processing time in multi-modality systems, leading to studies focusing solely on IR images. Nevertheless, IR images, lacking color and texture information, generally yield lower recognition performance in existing person re-id studies. In addition, previous studies have shown that person re-id performance suffers in the presence of complex background noise. To tackle these challenges, this study proposes a new weak saliency ensemble network (WSE-Net) for person re-id using IR images. WSE-Net incorporates a channel reduction of feature (CRF) method to reduce computational cost in the ensemble network, a technique for converting input images into group of patch images and feeding them into the ensemble model to enhance the reduced feature information, and a grouped convolution ensemble network (GCE-Net) that enables the fusion of features extracted from original and attention-guided ensemble models.The performance of person re-id using WSE-Net was evaluated on the Dongguk body-based person recognition database version 1 (DBPerson-Recog-DB1) and the Sun Yat-sen university multiple modality re-identification version 1 (SYSU-MM01). Experimental results demonstrated that on DBPerson-Recog-DB1, WSE-Net achieved 93.65% in rank 1, 95.28% in mean average precision (mAP), and 93.52% in the harmonic mean of precision and recall. Additionally, on SYSU-MM01, WSE-Net achieved 86.85% in rank 1, 44.58% in mAP, and 40.06% in the harmonic mean of precision and recall. Furthermore, the accuracy of WSE-Net on both datasets surpassed that of state-of-the-art methods.

Database of Candidate Targets for the LIFE Mission

We present the database of potential targets for the Large Interferometer For Exoplanets (LIFE), a space-based mid-infrared nulling interferometer mission proposed for the Voyage 2050 science program of the European Space Agency. The database features stars, their planets and disks, main astrophysical parameters, and ancillary observations. It allows users to create target lists based on various criteria to predict, for instance, exoplanet detection yields for the LIFE mission. As such, it enables mission design trade-offs, provides context for the analysis of data obtained by LIFE, and flags critical missing data. Work on the database is in progress, but given its relevance to LIFE and other space missions, including the Habitable Worlds Observatory, we present its main features here. A preliminary version of the LIFE database is publicly available on the German Astrophysical Virtual Observatory.

Spectral Performance of the Follow-up X-Ray Telescope on Board the EP Satellite

The Follow-up X-ray Telescope (FXT) is one of the two main scientific instruments on board the Einstein Probe astronomical satellite, which was launched in 2024 January. FXT focuses on the energy range of 0.3–10 keV and mainly conducts follow-up observations of transients and burst sources. It consists of two units of completely independent optical system and detector system (FXT-A and FXT-B). The focal plane detector adopts PNCCD provided by Max Planck Institute for Extraterrestrial Physics. FXT was designed to have three operating modes with different integration times and readout schemes, namely full-frame mode, partial-window mode and timing mode. We conducted a detailed calibration for PNCCD at the Institute of High Energy Physics before launch. Our results demonstrate that both FXT-A and FXT-B exhibit excellent spectral performance. The energy resolution (Full Width at Half Maximum) of FXT-A and FXT-B are both better than 85 eV at 1.487 keV. We determined a mean equivalent noise charge around 2.8 e− for FXT-A and FXT-B in three operating modes at −90°C ± 0.5°C, except for a few noisy pixels in full-frame mode. In addition, we measured the relation of charge transfer inefficiency as function of photon energy and confirmed the ability to detect photons in the energy range of 0.3–10 keV. These calibration results have been ingested into the initial version of calibration database and applied to the analysis of scientific data acquired by FXT.

Spatially varying stress regime in the southern Junggar Basin, NW China

The southern Junggar Basin in NW China is an important tectonic unit in the region of the Tibetan Plateau and has been the focus of considerable research into its tectonic processes and geodynamic setting. However, the relationship between deep structural deformation and stress in this region remains unclear. This study investigates the Gaoquan and Hutubi anticlines in the southern Junggar Basin using three-dimensional geophysical data and a finite-element numerical simulation to examine the crustal stress distribution and stress regime at depths of up to 7 km. Numerical simulation results indicate that the stress regime in the southern Junggar Basin changes from west to east. In the western part of the region, including the Gaoquan anticline at depths of 4900–6100 m, the maximum horizontal principal stress shows a peak of 140–200 MPa, the minimum horizontal principal stress is 110–170 MPa, and the vertical principal stress is 115–175 MPa, indicating a mixed stress regime incorporating both compression and strike-slip components. In the eastern part of the region, including the Hutubi anticline at depths of 5400–7800 m, the maximum horizontal principal stress shows a peak of 160–280 MPa, the minimum horizontal principal stress is 155–250 MPa, and the vertical principal stress is 125–215 MPa, indicating a compressive stress regime. The stress magnitude and orientation are affected by the presence of faults and depth in the crust. Combining these results with the regional tectonic setting, it is considered that the geometrical relationship between pre-existing faults and the current stress field is the main control on the west–east differentiation in the stress regime, with spatial variations in the mechanical parameters of the crust and the pressure coefficient being secondary factors. These results provide insights into the relationship between stress and deformation, and support the updated version of the World Stress Map database.

The triglyceride-glucose index dynamic trajectory reveals the association between the clinical subphenotype of insulin resistance and mortality in patients with sepsis

BackgroundThe relationship between the dynamic changes in insulin resistance (IR) and the prognosis of septic patients remains unclear. This study aims to investigate the correlation between the clinical subphenotype of IR represented by the triglyceride-glucose (TyG) index trajectory and the mortality rate among patients with sepsis.MethodsIn this retrospective cohort study, we utilized data from septic patients within the Medical Information Mart for Intensive Care (MIMIC)-IV database version 2.0 to construct trajectories of the TyG index over 72 h. Subsequently, we computed the similarity among various TyG index trajectories with the dynamic time warping (DTW) algorithm and utilized the hierarchical clustering (HC) algorithm to demarcate distinct cluster and identified subphenotypes according to the trajectory trend. Subsequently, we assessed the mortality risk between different subphenotypes using analyses such as survival analysis and validated the robustness of the results through propensity score matching (PSM) and various models.ResultsA total of 2350 patients were included in the study. Two trajectory trends: TyG index decreasing (n = 926) and TyG index increasing (n = 1424) were identified, which indicated corresponding to the clinical subphenotype of increased and alleviative IR respectively. The 28-day and in-hospital mortality for the increased IR group was 28.51% and 25.49% respectively. In comparison, patients in the alleviative IR group with a 28-day mortality of 23.54% and an in-hospital mortality of 21.60%. These subphenotypes exhibited distinct prognosis, time dependent Cox model showed the increased IR group with a higher 28-day mortality [hazard ratio (HR): 1.07, 95% confidence interval (CI): 1.02–1.12, P = 0.01] and in-hospital mortality [HR: 1.05, 95% CI: 1.00–1.11, P = 0.045] compared to the alleviative IR group. Sensitivity analyses with various models further validated the robustness of our findings.ConclusionDynamic increase in the TyG index trajectory is associated with elevated mortality risk among patients with sepsis, which suggests that dynamic increased IR exacerbates the risk of poor outcomes in patients.

Prognostic value of red blood cell distribution width-to-albumin ratio in ICU patients with coronary heart disease and diabetes mellitus.

The red blood cell distribution width (RDW)-to-albumin ratio (RAR) has emerged as a potentially valuable prognostic indicator in diverse medical conditions. However, the prognostic significance of RAR in intensive care unit (ICU) patients with coronary heart disease (CHD) and diabetes mellitus (DM) remains uncertain and requires further investigation. This study aims to investigate the prognostic significance of RAR in ICU patients with coexisting CHD and DM through a retrospective cohort analysis using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version 2.2). The study population included patients aged 18 years or older who were diagnosed with both CHD and DM. The primary endpoint was 1-year mortality, and the secondary endpoints included 30-day mortality, 90-day mortality, hospital length of stay (LOS), and ICU LOS. A total of 3416 patients, of whom 64.64% were male, were included in the study. The 30-day mortality, 90-day mortality, and 1-year mortality were 7.08%, 7.44%, and 7.49%, respectively. After adjusting for confounding factors, multivariate Cox proportional risk analysis demonstrated that high RAR levels were associated with an increased risk of 30-day mortality (HR, 1.53 [95% CI 1.17-2.07], P = 0.006), 90-day mortality (HR, 1.58 [95% CI 1.17-2.13], P = 0.003), and 1-year mortality (HR, 1.58 [95% CI 1.17-2.13], P = 0.003). Furthermore, the restricted cubic spline (RCS) model indicated a linear relationship between RAR and 1-year mortality. The results suggest that RAR holds potential as a valuable prognostic biomarker in ICU patients with both CHD and DM. Elevated RAR levels were found to be significantly associated with increased mortality during hospitalization, facilitating the identification of individuals at higher risk of adverse outcomes. These findings underscore the importance of incorporating RAR into risk stratification and overall management strategies for ICU patients with coexisting CHD and DM.

Application of MALDI-TOF mass spectrometry for identification of Nocardia species

BackgroundNocardiosis, despite its rarity and underreporting, is significant due to its severe impact, characterized by high morbidity and mortality rates. The development of a precise, reliable, rapid, and straightforward technique for identifying the pathogenic agent in clinical specimens is crucial to reduce fatality rates and facilitate timely antimicrobial treatment. In this study, we aimed to identify Nocardia spp. in clinical isolates, using MALDI-TOF MS as the primary method, with molecular methods as the gold standard. Clinical Nocardia isolates were identified using 16S rRNA/hsp65/gyrB/secA1/rpoB gene sequencing. Identification performance of the Bruker MALDI Biotyper 3.1 (V09.0.0.0_8468) and MBT Compass 4.1 (V11.0.0.0_10833) for Nocardia identification was evaluated.ResultsSeventy-six Nocardia isolates were classified into 12 species through gene sequencing. The MALDI Biotyper 3.1 (V09.0.0.0_8468) achieved 100% genus-level accuracy and 84.2% species accuracy (64/76). The MBT Compass 4.1 with the BDAL Database (V11.0.0.0_10833) improved species identification to 98.7% (75/76). The updated database enhanced species level identification with scores > 1.7, increasing from 77.6% (59/76) to 94.7% (72/76), a significant improvement (P = 0.001). The new and simplified extraction increased the proportion of strains identified to the species level with scores > 1.7 from 62.0% (18/29) to 86.2% (25/29) (P = 0.016). An in-house library construction ensured accurate species identification for all isolates.ConclusionsThe Bruker mass spectrometer can accurately identify Nocardia species, albeit with some variations observed between different database versions. The MALDI Biotyper 3.1 (V09.0.0.0_8468) has limitations in identifying Nocardia brasiliensis, with some strains only identifiable to the genus level. MBT Compass 4.1 (V11.0.0.0_10833) effectively addresses this shortfall, improving species identification accuracy to 98.7%, and offering quick and reliable identification of Nocardia. Both database versions incorrectly identified the clinically less common Nocardia sputorum as Nocardia araoensis. For laboratories that have not upgraded their databases and are unable to achieve satisfactory identification results for Nocardia, employing the new and simplified extraction method can provide a degree of improvement in identification outcomes.

PSIV-5 Prioritizing candidate genes for seminal traits and residual feed intake in cattle: Integrating QTLome Analysis and RNAseq Datasets

Abstract The QTLome represents the collection of QTLs (quantitative trait loci) documented for each trait within a specific species, detailing the position, effect, and mode of inheritance of each locus. Despite the abundance of known QTLs for productive traits in cattle, only a few genes have been identified as selection markers. This study aimed to prioritize genes within validated QTLs associated with crucial traits in cattle by integrating this information with RNAseq data. Initially, bovine QTLs (n = 161,730) were sourced from the AnimalQTLdb database (version 44), and both graphical and quantitative analyses were conducted using R software. Only annotations within the UMD 3.1 genome were considered, with overlapping QTLs from the same scientific report grouped together. Following quality control measures, the number of QTLs was reduced to 37,146. Validated QTLs were determined by identifying overlapping annotations from various studies or those within a distance of < 1 Mb for the same phenotypic trait, resulting in a total of 3,856 validated QTLs. For gene prioritization, validated QTLs were selected based on semen traits and residual feed intake (RFI). Transcriptomes corresponding to these traits were sourced from NCBI (PRJNA516089 and PRJEB7696). Sequence filtering was performed using the fastp program, and transcript counts were obtained with SALMON software. Sequences were aligned to the Ensembl UMD 3.1 reference genome (version 94), and differential expression analysis was conducted using the R package DESeq2, considering only the group factor (high/low) for each trait (fertility/RFI). Integration of QTLs and transcriptomes utilized the methodology of the cageminer R package, which prioritizes candidate genes within validated QTLs, identifies those grouped in co-expression modules enriched by reference genes, and correlates with the study phenotype (biserial r > 0.2). Reference genes were obtained from Ignatieva et al. (2021 PMID 34290736) and Chen et al. (2021; PMID 33664767). Among the 18 validated QTLs for semen traits, four candidate genes (TSPAN6, QSER1, ADRB2, AKAP4) were identified. Except for ADRB2, these genes exhibited significant differential expression (P < 0.05) in contrasting groups of Holstein cattle. They are associated with semen traits and perform functions such as regulation of spermatogenesis, development, activation, and cell growth. Regarding RFI, 127 validated QTLs yielded 205 prioritized genes, of which 123 showed significant differential expression (P < 0.05) in divergent groups of the Nelore breed. These genes are involved in the immune response and the polymerization and organization of actin filaments, processes that have been reported as biologically relevant to RFI. The implemented strategy was efficient for gene prioritization through the integration of QTLs and RNAseq.

Factors Affecting the Indoor Air Quality and Occupants’ Thermal Comfort in Urban Agglomeration Regions in the Hot and Humid Climate of Pakistan

The World Air Quality Index indicates that Pakistan ranks as the third most polluted country, regarding the average (Particulate Matter) PM2.5 concentration, which is 14.2 times higher than the World Health Organization’s annual air quality guideline. It is crucial to implement a program aimed at reducing PM2.5 levels in Pakistan’s urban areas. This review paper highlights the importance of indoor air pollution in urban regions such as Lahore, Faisalabad, Gujranwala, Rawalpindi, and Karachi, while also considering the effects of outdoor air temperature on occupants’ thermal comfort. The study aims to evaluate past methodological approaches to enhance indoor air quality in buildings. The main research question is to address whether there are statistical correlations between the PM2.5 and the operative air temperature and whether other indoor climatic variables have an impact on the thermal comfort assessment in densely built urban agglomeration regions in Pakistan. A systematic review analysis method was employed to investigate the effects of particulate matter (PM2.5), carbon oxides (COx), nitrogen oxides (NOx), sulfur oxides (SOx), and volatile organic compounds (VOCs) on residents’ health. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol guided the identification of key terms and the extraction of cited studies. The literature review incorporated a combination of descriptive research methods to inform the research context regarding both ambient and indoor air quality, providing a theoretical and methodological framework for understanding air pollution and its mitigation in various global contexts. The study found a marginally significant relationship between the PM2.5 operative air temperature and occupants’ overall temperature satisfaction, Ordinal Regression (OR) = 0.958 (95%—Confidence Interval (CI) [0.918, 1.000]), p = 0.050, Nagelkerke − Regression (R2) = 0.042. The study contributes to research on the development of an evidence-based thermal comfort assessment benchmark criteria for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database version 2.1.

STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature.

Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction subnetwork of the STRING database. This corpus comprises 1287 documents with ∼3500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model's capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database. We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BackgroundVariant interpretation is essential for identifying patients’ disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).ResultsThe Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.ConclusionsVIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at https://genomeinterpretation.org/vipdb

A New Critical Plane Multiaxial Fatigue Criterion with an Exponent to Account for High Mean Stress Effect

The mean stress effect remains a critical aspect in multiaxial fatigue analysis. This work presents a new criterion that, based on the classical Findley criterion, applies a material-dependent exponent to the mean normal stress term and includes the ultimate tensile stress as a fitting parameter. This way of considering the non-linear effect of the mean stress, with a material-dependent rather than a fixed exponent, is totally innovative among the multiaxial fatigue criteria found in the literature. In order to verify its accuracy, the new criterion has been checked against an extended version of the Papuga database of multiaxial experimental tests with 485 results, and compared with the criteria by Findley, Robert, and Papuga. The new criterion provides outstanding results for pure uniaxial cases, with multiaxial performance similar to the Robert criterion with a smaller range of error and a conservative trend, even surpassing the popular Papuga method in several relevant loading scenarios. These features enhance the applicability and versatility of the criterion for its use in the fatigue design of structural components.

Platelet count as a prognostic marker for acute respiratory distress syndrome

BackgroundThis study aimed to evaluate the role of platelet count (PLT) in the prognosis of patients with acute respiratory distress syndrome (ARDS).MethodsThe data were extracted from the Medical Information Mart for Intensive Care database (version 2.2). Patients diagnosed with ARDS according to criteria from Berlin Definition and had the platelet count (PLT) measured within the first day after intensive care unit admission were analyzed. Based on PLT, ARDS patients were divided into four groups: PLT ≤ 100 × 109/L, PLT 101–200 × 109/L, PLT 201–300 × 109/L, and PLT > 300 × 109/L. The primary outcome was 28-day mortality. Survival probabilities were analyzed using Kaplan–Meier. Furthermore, the association between PLT and mortality in ARDS patients was assessed using a univariate and multivariable Cox proportional hazards model.ResultsOverall, the final analysis included 3,207 eligible participants with ARDS. According to the Kaplan–Meier curves for 28-day mortality of PLT, PLT ≤ 100 × 109/L was associated with a higher incidence of mortality (P = 0.001), the same trends were observed in the 60-day (P = 0.001) and 90‐day mortality (P = 0.001). In the multivariate model adjusted for the potential factors, the adjusted hazard ratio at PLT 101–200 × 109/L group, PLT 201–300 × 109/L, and PLT > 300 × 109/L was 0.681 [95% confidence interval (CI): 0.576–0.805, P < 0.001], 0.733 (95% CI: 0.604–0.889, P = 0.002), and 0.787 (95% CI: 0.624–0.994, P = 0.044) compared to the reference group (PLT ≤ 100 × 109/L), respectively. Similar relationships between the PLT ≤ 100 × 109/L group and 28-day mortality were obtained in most subgroups.ConclusionPLT appeared to be an independent predictor of mortality in critically ill patients with ARDS.

Bacteria, Fungi, and Scalp Psoriasis: Understanding the Role of the Microbiome in Disease Severity.

Background: Psoriasis is a chronic skin condition affected by genetic and environmental factors. Changes in the skin microbiome may affect the immune system and skin barrier functions, thereby contributing to the development and progression of psoriasis. The scalp, which is a common site for psoriasis, is often resistant to therapy. Although several studies have investigated the scalp microbiome, analyses focusing on both bacteria and fungi remain scarce. Methods: We examined the scalp microbiomes of 11 patients with psoriasis complicated with scalp lesions and categorized them according to their Psoriasis Area Severity Index (PASI) scores. The bacterial and fungal data were analyzed using QIIME2 pipeline version 2021.04 and the UNITE database version 8.3, respectively. Results: The Shannon indices for mild (2 patients), moderate (4 patients), and severe (5 patients) groups were 0.97, 1.38, and 1.88, respectively. A significant correlation was observed between increased mycobiome diversity and disease severity (p = 4.07 × 10-5, Spearman's correlation: 0.9269). Compared with the mild and moderate groups, the severe group exhibited a higher abundance of Malassezia globosa. Pseudomonas and Staphylococcus were, respectively, more prevalent in the moderate and severe groups than in the mild group. Conclusions: This study highlights the potential role of increased fungal diversity and specific microbial compositions in the severity of scalp psoriasis, suggesting a possible avenue for targeted therapeutic interventions.

Prevalence rates for ectodermal dysplasia syndromes.

Ectodermal dysplasias (EDs) are a heterogeneous group of genetic conditions affecting the development and/or homeostasis of two or more ectodermal derivatives, including hair, teeth, nails, and certain glands. There are currently 49 recognized EDs with molecularly confirmed etiology. The EDs are very rare disorders, individually and in aggregate. Very little is published regarding the prevalence of these rare disorders. As a result of the genomics revolution, rare diseases have emerged as a global health priority. The various disabilities arising from rare disorders, as well as diagnostic and treatment uncertainty, have been demonstrated to have detrimental effects on the health, psychosocial, and economic aspects of families affected by rare disorders. Contemporary research methodologies and databases can address what have been historic challenges encountered when conducting research on rare diseases. In this study, we aim to ascertain period prevalence rates for several of the more common ectodermal dysplasia syndromes, by querying a large multicenter database of electronic health records, Oracle Real-World Data. For each of the included ectodermal dysplasia syndromes a clinical definition was developed by a committee of international experts with interests in EDs. The clinical definitions were based upon a combination of clinical features and designated by ICD-9 and ICD-10 codes. The January 2023 version of the Oracle Real-World Data database was queried for medical records that coincided with the clinical definitions. For our study, there were 64,523,460 individual medical records queried. Period prevalence rates were calculated for the following ED disorders: hypohidrotic ectodermal dysplasia, found to be 2.99 per 100,000; ectodermal dysplasia and immunodeficiency 1, 0.23 per 100,000; Clouston syndrome, 0.15 per 100,000; ectrodactyly ectodermal dysplasia and cleft lip/palate syndrome, 0.61 per 100,000; ankyloblepharon-ectodermal defects-cleft lip/palate syndrome, 0.36 per 100,000; focal dermal hypoplasia, 0.10 per 100,000; and incontinentia pigmenti, 0.88 per 100,000. This study established estimated period prevalence rates for several of the ectodermal dysplasia syndromes, and it demonstrated the feasibility of utilizing large multicenter databases of electronic health records, such as Oracle Real World Data.

The updated BOSZ synthetic stellar spectral library

Context. The modeling of stellar spectra of flux standards observed by the Hubble and James Webb space telescopes requires a large synthetic spectral library that covers a wide atmospheric parameter range. Aims. The aim of this paper is to present and describe the calculation methods behind the updated version of the BOSZ synthetic spectral database, which was originally designed to fit the CALSPEC flux standards. These new local thermodynamic equilibrium (LTE) models incorporate both MARCS and ATLAS9 model atmospheres, updated continuous opacities, and 23 new molecular line lists. Methods. The new grid was calculated with Synspec using the LTE approximation and covers metallicities [M/H] from −2.5 to 0.75 dex, [α/M] from −0.25 to 0.5 dex, and [C/M] from −0.75 to 0.5 dex, providing spectra for 336 unique compositions. Calculations for stars between 2800 and 8000 K use MARCS model atmospheres, and ATLAS9 is used between 7500 and 16 000 K. Results. The new BOSZ grid includes 628 620 synthetic spectra from 50 nm to 32 µm with models for 495 Teff−log 𝑔 parameter pairs per composition and per microturbulent velocity. Each spectrum has eight different resolutions spanning a range from R = 500 to 50 000 as well as the original resolution of the synthesis. The microturbulent velocities are 0, 1, 2, and 4 km s−1. Conclusions. The new BOSZ grid extends the temperature range to cooler temperatures compared to the original grid because the updated molecular line lists make modeling possible for cooler stars. A publicly available and consistently calculated database of model spectra is important for many astrophysical analyses, for example spectroscopic surveys and the determination of stellar elemental compositions.

Environmental Impact of Footwear Using Life Cycle Assessment—Case Study of Professional Footwear

Life cycle assessment (LCA) is a method for assessing the environmental impact of a product, activity, or system across all the stages of its life cycle. LCA can identify the activities with a major impact on the environment throughout the life cycle of a product. To analyze the environmental implications of footwear, the LCA was applied to a pair of shoes designed for professional use. In this paper, the impact of a single pair of shoes was studied. Every year, footwear production worldwide is over 22 billion pairs, which has a significant impact on the environment. In this case study, the “cradle-to-grave” approach was used, which refers to all the activities involved in the life cycle of a footwear product, starting from raw material extraction, manufacturing, use, maintenance, and, in the end, disposal. The LCA was conducted using the SimaPro software. The environmental impact assessment of the analyzed shoe needed the acquisition of two crucial datasets. Background inventory data were sourced from the Ecoinvent database (version 3.3). The impact was quantified using the Global Warming Potential (GWP) metric, which calculates the contribution of emissions to global warming over a 100-year time limit according to the established values provided by the Intergovernmental Panel on Climate Change (IPCC). The impact of greenhouse gas (GHG) emissions was measured in relative carbon dioxide equivalents (kg CO2eq) to facilitate a standardized comparison. The results show that the total carbon footprint for a pair of safety boots is 18.65 kg of CO2eq with the “component manufacture” stage as a major contributor accumulating almost 80%.