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Leishmaniasis is a neglected tropical disease (NTD) affecting millions worldwide. Current treatments have limitations, highlighting the need for new strategies for leishmaniasis drug discovery. Herein, we utilized a benchmarking-informed structure-based virtual screening (SBVS) strategy against Leishmania major folate pathway, especially via targeting pteridine reductase 1 (PTR1). Firstly, representative bioactive molecules against Lm-PTR1 were compiled. Secondly, a challenging DEKOIS 2.0 benchmark set was generated to assess the screening performance of three docking tools, FRED, AutoDock Vina, and PLANTS. Interestingly, FRED showed the best screening performance, with pROC-AUC of 0.84 and EF 1% of 12.5. Consequently, as an example, an ensemble VS of NANPDB using FRED against PTR1 was conducted. The results nominated three candidates for further investigations, namely Anastatin A, valoneic acid dilactone, and 1,6-di-O-galloyl glucose. To assess the binding stability of the candidates, four MD simulations for 500 ns including folic acid - PTR1 complex system as a reference were conducted. Consequently, MM-GBSA calculations and MD profiles confirmed the stable binding of valoneic dilactone and 1,6-di-O-galloyl glucose and ranked them superior to the reference folic acid. These results suggest the ability of both candidates to hinder the access of folic acid to the cofactor NADPH and hence modulate the catalytic function of Lm-PTR1. The identified candidates are recommended for subsequent in vitro evaluations in future investigations. Overall, this benchmarking strategy against Lm-PTR1 can be broadly applied to any accessible compound database for SBVS campaigns. The benchmark dataset for Lm-PTR1 will be made publicly accessible on www.dekois.com.

As compilations of natural products, Traditional Chinese medicines (TCMs) formulae constitute a rich resource for novel drug discovery. Nevertheless, their complex composition brought about significant challenges in identifying bioactive constituents. Chai-Gui Decoction (CGD) is a combination of two classic prescriptions commonly employed in gynecological therapeutics, and its active ingredients remain uncharacterized. This study aimed to establish a cell-based affinity mass spectrometry strategy for the rapid discovery of the active components from complex TCM formulae. This study chose CGD as an example and the G protein-coupled estrogen receptor (GPER) as a target. A stable HEK293-T cell line overexpressing GPER-1 were established and incubated with CGD to execute ligand screening. Cell membrane fragments were harvested, and the ligand-bound components were isolated and identified by using UPLC-QTOF/MS. The theoretical binding affinities of the fished ligands for GPER were calculated via molecular docking. The compounds exhibiting the highest theoretical affinity were further managed to binding validation using a cellular thermal shift assay (CETSA), and their activity was confirmed through cell-based experiments. A novel fishing assay was developed, which integrated ligand-receptor binding in live cells, cell membrane extraction and lysis, and liquid chromatography-mass spectrometry identification. Seven compounds from the CGD extract demonstrated significantly higher detection levels in GPER-overexpressing cells compared to vehicle control cells. Molecular docking analyses further indicated high binding affinities between these compounds and the GPER protein. Among them, the two compounds with the highest predicted affinity, 16α-Hydroxytrametenolic acid (16α-HTA) and Alisol A, were confirmed to bind GPER by altering its thermal stability in CETSA. Subsequent cellular investigations revealed that 16α-HTA and Alisol A reduced GPER expression at a low dose and suppressed AKT phosphorylation independently of EGFR. The present study proposes a feasible strategy integrating cell extraction, UPLC-QTOF/MS analysis, molecular docking simulation, CETSA and cell-based experiments, which enables the effective exploration and validation of ligands for protein targets within complex mixtures.

Following the publication of the above paper, it has been drawn to the Editor's attention by a concerned reader that, regarding the cell migration assay data shown in Fig. 3A on p. 738, data panels 'a' and 'c' for the 'Co‑cultured for 12 h' experiments (lower row of panels) exhibited overlapping sections, such that these data panels, which were intended to show the results of differently performed experiments, had apparently been derived from the same original source. Moreover, the 'Co‑cultured for 6 h'/'d' panel (on the top row) was also found to share overlapping data with the 'Co‑cultured for 12 h'/'b' panel on the lower row. In addition, it was noted that various of the β‑actin control blots shown in Figs. 1B and 6A on p. 737 and p. 740, respectively, were strikingly similar, where different experimental conditions were reported, suggesting that either or both of these figures may have been assembled incorrectly. Upon examining their original data, the authors have realized that data in these figures were inadvertently assembled incorrectly. Certain of the images for the 6 h and 12 h time points were inadvertently misused during figure compilation due to a folder selection error; specifically, the data erroneously shown in the 'Co‑cultured for 6 h'/'d' panel and in the 'Co‑cultured for 12 h'/'a' panel have been replaced with the correct data. Concerning the re‑use of the β‑actin control blots, those in Fig. 6 were included in error; the revised versions of Figs. 4 and 6 are shown on the next page. The authors confirm that the errors associated with these figures did not have any significant impact on either the results or the conclusions reported in this study, and all the authors agree with the publication of this Corrigendum. The authors are grateful to the Editor of International Journal of Molecular Medicine for allowing them the opportunity to publish this Corrigendum; furthermore, they apologize to the readership of the Journal for any inconvenience caused. [International Journal of Molecular Medicine 37: 734‑742, 2016; DOI: 10.3892/ijmm.2016.2473].

Food reformulation is frequently discussed as a potential strategy to address obesity and diet-related non-communicable diseases. However, evidence regarding its effectiveness and broader implications within globalised food systems remains limited. This study examined changes in the nutrient content of packaged foods commercialised in Uruguay between 2021 and 2025, a period characterised by limited domestic regulatory action but during which front-of-package (FOP) warning label policies were implemented in Argentina and Brazil, Uruguay's main trading partners. Nutrient information was obtained through systematic in-store data collection and compiled into an online database. Linear mixed-effects models were used to estimate changes in nutrient content, accounting for repeated product-level observations and testing interactions between year and country of origin. At the aggregate level, modest but statistically significant reductions were observed in total fat, saturated fat, and sodium, alongside a small increase in fibre content. However, significant interactions between year and country of origin revealed substantial heterogeneity in reformulation patterns. Products imported from Argentina showed the most pronounced reformulation, with significant reductions in total fat, saturated fat, and sodium, and increases in fibre content. In contrast, domestically produced products in Uruguay exhibited limited reformulation, with statistically significant changes observed only for saturated fat and of smaller magnitude. Products from Brazil and other countries showed minimal changes, with statistically significant increases limited to fibre content. Category-level analyses further indicated that reformulation effects were uneven across the packaged food supply. These findings are consistent with the possibility that the implementation of FOP warning labels in Argentina was associated with reformulation with spillover effects into the Uruguayan market. Differences in policy design, particularly the stringency of the underlying nutrient profile model, may help explain the divergent industry responses. The limited changes observed among domestic products in the period highlight the need for complementary policy instruments to promote more substantial reformulation.

How gram-negative bacteria coordinate the synthesis of their multilayered envelopes is a long-standing fundamental question. We compile protein and metabolite measurements obtained from Escherichia coli to eliminate mechanisms that do not coordinate envelope synthesis during steady-state growth. These measurements reveal that envelope synthesis pathway expression and envelope precursor concentrations are both stable across growth rates, thus eliminating enzyme levels and metabolite levels as coordination mechanisms. We propose instead that envelope assembly pathways are coordinated by post-translational mechanisms that control a small number of enzymes and transport proteins, which in turn control upstream synthesis pathways via classic negative feedback. We further hypothesize that many signals that have been proposed to directly regulate envelope synthesis pathways act indirectly via known negative feedback loops.

Escherichia coli encounters chemically diverse carbon sources, and the observed outputs of its transcriptional regulatory network (TRN) vary with substrate chemistry, metabolic entry route, and growth physiology. Here, we compiled PRECISE-NP881, an 881-condition transcriptome compendium comprising 346 RNA-seq profiles generated for this study during growth on 43 individual carbon sources, and used independent component analysis to quantify condition-specific activities of 137 iModulons, defined here as statistically independent gene-expression modules. We identified 25 carbon-catabolism iModulons and summarized their activity patterns across the 43 substrates into four activity-defined substrate groups. These activity patterns were associated with measured growth rates, substrate chemical classes, central-metabolic entry routes, carbon-normalized stoichiometric yield, and model-estimated proteome allocation. Faster-growing sugar conditions showed low CRP-linked iModulon activity, whereas slower-growing conditions showed elevated, condition-specific activity of CRP-linked and substrate-specific catabolic iModulons. TCA-entry and amino acid-associated conditions were linked with NtrC-1 and Propionate iModulon activities, with targeted knock-out assays supporting the conditional physiological relevance of selected propionyl-CoA-associated genes. A subset of nitrogen-containing, slower-growth conditions with predicted ammonium release induced the cryptic prophage-associated SgcABCEQX iModulon. Projection of an independent glucose starvation/refeeding time-course dataset revealed overlapping dynamics among selected carbon-catabolism iModulons and coordinated changes in growth- and stress-associated TRN outputs. Together, these results provide a systems-level atlas of observed carbon-responsive transcriptional states and systematize carbon physiology at scale.

Prior biological knowledge and phenotype information can help identify disease genes from whole genome/exome sequencing studies, but how best to incorporate external knowledge with variant data remains challenging. We developed a machine learning algorithm called RankVar to prioritize causative variants for rare diseases, based on clinical notes and genome/exome sequencing profiles. RankVar uses a random forest classifier trained on ~ 1 million variants from the 1000 Genomes Project with spiked-in pathogenic variants. For testing, we compiled sequencing data and phenotype information from several independent datasets: 260 subjects from the Children's Hospital of Philadelphia (CHOP) with positive genetic diagnosis of various Mendelian diseases, 135 subjects from Birth Defects Biorepository (BDB), as well as 356 and 97 subjects with candidate causal variants for autism spectrum disorders from the Simons Simplex Collection (SSC) and the Simons Foundation Powering Autism Research for Knowledge (SPARK), respectively. RankVar achieves a top 10 variant accuracy of 90.0%, 81.5%, 46.1%, and 76.3% for CHOP, BDB, SSC, and SPARK, respectively, with improved performance over existing approaches. Notably, RankVar successfully identified X-linked and Y-linked disease-causal variants, such as KDM6A (p.N915Kfs5*) and SRY (p.W98X), as the top candidate variants. Moreover, we evaluated RankVar for genomic reinterpretation of 130 unsolved CHOP cases with hearing loss and successfully identified 61 candidate causal variants after manual review. In summary, RankVar performed favorably relative to existing methods in our evaluation, accommodated different genetic models and X/Y chromosome variants, and may provide a useful framework for prioritizing variants in monogenic or oligogenic diseases. We anticipate that RankVar may aid in primary genetic diagnosis, genome reinterpretation of previously unsolved cases, and the discovery of novel disease genes.

The Japanese Healthcare-associated Infections Surveillance (JHAIS) Committee of the Japanese Society for Infection Prevention and Control (JSIPC) initiated a nationwide surveillance program for medical device-associated infections in 2009. This report is a summary of the data collected and reported by hospitals participating in the JHAIS Surveillance from January 2023 through December 2025. The wards covered by the surveillance program are intensive care units and acute-care general wards. We collected surveillance data on four major device-associated infections; central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP) and ventilator-associated events (VAE). Definitions and criteria of the targeted infections were based on the manual developed by the National Healthcare Safety Network (NHSN), a surveillance system in the United States. However, the JHAIS Surveillance Project also included clinically diagnosed sepsis in addition to laboratory-confirmed bloodstream infections, and infection rates were calculated for the overall as well as for each category. Over the three-year period, aggregate counts of ventilator-days and associated infection events were compiled for intensive care units; values other than these were collected for both intensive care units and acute general wards. CLABSI including clinical sepsis 3,434 events, CAUTI 5,617 events, VAP 540 events, Ventilator-associated Condition (VAC) 508 events, Infection-related Ventilator-Associated Complication (IVAC) 211 events, and Possible Ventilator-Associated Pneumonia (PVAP) 129 events. Device-days were 1,829,660 central line-days, 3,413,781 urinary catheter-days. The total number of ventilator-days was 246,728. Specifically, among facilities participating in VAP surveillance was 129,417 ventilator-days, whereas among facilities participating in VAE surveillance was 164,314 ventilator-days. Some facilities contributed to both surveillances. The cumulative infection rates per 1,000 device-days were: CLABSI1.88 per 1,000 central line-days; CAUTI 1.65 per 1,000 catheter-days; VAP 4.17 per 1,000 ventilator-days; VAC3.09 per 1,000 ventilator-days; IVAC 1.28 per 1,000 ventilator-days; and PVAP 0.78 per 1,000 ventilator-days. The highest incidence was observed for VAP, with a value exceeding that of PVAP by more than fivefold. Among device-associated infections, the infection rate of VAP was the highest, showing a marked difference from CLABSI and CAUTI. In addition, substantial discrepancies were observed between the infection rates of VAP and PVAP. Hospitals should use these data to guide local improvement efforts aimed at reducing infection rates as much as possible.

Textile circularity research requires product-level data on garment composition and component structure, but most available information is either aggregated at fibre level, derived from physical audits with limited market coverage, or not organized at the colour-variant level needed for sorting and recycling assessment. This data article presents a harmonized fast-fashion garment-variant dataset compiled from publicly accessible Hennes and Mauritz (H&M) and Uniqlo product pages serving the United Kingdom and Australia. Product web addresses (URLs) were collected from 24 to 26 March 2026, and product details were extracted between 24 March and 8 April 2026. Records were filtered, expanded to colour-specific variants where needed, harmonized into a common JSON Lines (JSONL) schema, normalized for material names and garment categories, parsed into component-level material-composition entries, and subjected to consistency checks. After filtering and public-release processing, the dataset contains 47,522 colour-specific garment-variant records. Each record includes source provenance, retailer and region, URL fields and timestamps, gender or section metadata, original category, product name, variant colour, assigned and normalized material-composition text, harmonized parent and detailed garment categories, and structured component-composition entries. The public release excludes product images, raw webpage captures, screenshots, review text, product ratings, review counts, and retailer-specific acquisition scripts. The released files include the final JSONL dataset, processing scripts, mapping tables, processing summaries, citation metadata, license files, and workflow figures. Validation and quality-control information is provided through record-flow summaries, exported mapping tables, assignment-type diagnostics, component percentage-sum flags, and removal counts for unresolved or inconsistent records. The dataset can be reused for sustainability and circularity applications including sorting-compatibility assessment, pre-processing-rule development, design-for-recycling evaluation, fibre-to-fibre recycling analysis, material-flow modelling, life-cycle inventory construction, and comparative studies of online garment information.

Prior research has shown that, among normal hearing college students, Hispanic-identifying participants experience higher levels of environmental noise and lower signal to noise ratios as compared with White non-Hispanic participants. The primary objective of this study was to examine whether these differences extend to cochlear implant (CI) users by using CI datalogging to quantify characteristics of the listeners' auditory environments. The authors further examined whether differences in auditory environments between groups persisted after controlling for demographic and socioeconomic factors. The primary socioeconomic variable of interest was population density, as it strongly correlates with other socioeconomic factors (e.g., education and income) and is more likely to directly influence auditory environments. A retrospective chart review of CI patients at a tertiary medical center in New York City identified 80 adults (38 Hispanic, 42 White non-Hispanic) for further review. Demographic variables were compiled, and home addresses were used to obtain population-based socioeconomic data via the U.S. Census. Datalogging information extracted from the CI speech processor included hours of total use and time spent in different auditory environments, classified by the CI software into sound levels (in dBA) and sound scenes ("noise," "quiet," "speech in noise," "speech in quiet," "music," and "other"). Despite similar levels of device usage, there was a statistically significant group difference in the percentage of time spent in each scene: Hispanic-identifying participants spent more time in "speech in noise," "music," and "noise"; White non-Hispanics spent more time in "quiet" and "other." The Hispanic participants lived in census tracts with higher population density, which correlated with higher sound levels (>70 dBA) in the environment. Group differences in auditory environments remained statistically significant after controlling for age, CI experience, and population density (median daily level difference ~2.4 dB). Even after accounting for demographic and socioeconomic factors, the two groups showed distinct auditory environments, indicating a possible cultural contribution to these differences. Audiologists counseling CI patients regarding auditory environments should be conscious of their patients' cultural background and may consider the impact of listening preferences when advising on which environments to seek out or avoid.

Malaria remains a major global health burden, with rising resistance to artemisinin and most current therapies, alongside emerging parasite species and genetic mutations that undermine disease control efforts. Identifying drug candidates with favorable physicochemical profiles is crucial for improving success rates in antimalarial drug discovery. A comprehensive dataset comprising 52 approved and clinical-stage antimalarial drugs and 1,708 antimalarial research compounds was compiled. Their physicochemical properties were analyzed to characterize distribution patterns and identify parameters that distinguish successful drugs from research compounds. Four key parameters-molecular weight (MW), calculated partition coefficient (cLogP), topological polar surface area (TPSA), and fraction of sp3-hybridized carbons (Fsp3)-showed significant differences between drugs and research compounds. These parameters enabled the definition of an antimalarial-specific physicochemical space described by 248.71 ≤ MW ≤ 535.51, 1.86 ≤ cLogP ≤ 5.21, 28.16 ≤ TPSA ≤ 100.52, and 0.11 ≤ Fsp3 ≤ 1. Approximately 75% of approved or clinical antimalarial drugs fall within this space, compared with 49% of research compounds and 46% of high-potency candidates. These findings highlight a distinct and data-driven physicochemical profile associated with successful antimalarial agents, underscoring limitations of general drug-likeness rules such as Lipinski's Rule of Five (Ro5). The proposed space enhances compound prioritization by focusing on property ranges linked to clinical success. However, the analysis is constrained by available datasets and may not fully reflect emerging chemotypes or novel therapeutic modalities. This study defines an antimalarial-specific physicochemical space that can support compound prioritization and guide optimization efforts during antimalarial drug discovery.

The K&#x3b2;/K&#x3b1; intensity ratios are critical parameters that quantitatively characterize atomic shell transition dynamics and radiative branching probabilities. In this study, we systematically evaluated the capability of machine learning (ML) algorithms to predict these ratios, as well as their advantages over traditional theoretical models (such as Scofield and semi-empirical calculations). A large dataset comprising 2124 experimental measurements compiled from the literature, covering elements with atomic numbers (Z) from 11 to 96, was structured to include more than ten variables, such as atomic number, sample form, excitation source, detector type, and energy resolution. Missing observations were imputed using the multivariate imputation by chained equations (MICE) method in the R programming language. Categorical variables were one-hot encoded, and the data were split into an 80% training set and a 20% test set. Seven heterogeneous individual models (RF, XGBoost, Cubist, SVR, GPR, BRNN, and GLMNET) were constructed, along with seven different stacking combinations derived from them. Following 10&#xd7;10-fold cross-validation, the highest accuracy was achieved by the stacked model using a BRNN meta-learner (RMSE&#x202f;=&#x202f;0.009; R2&#x202f;=&#x202f;0.973). This model reduced the test error of the Scofield theory by nearly 48% and performed significantly better according to the Diebold-Mariano test (p&#x202f;<&#x202f;0.001). SHAP analysis revealed that atomic number is the primary determinant, while sample purity and excitation source have secondary yet physically consistent effects. Furthermore, an online R/Shiny-based calculator enhances the practical applicability of the method by enabling users to input their experimental parameters and receive instantaneous K&#x3b2;/K&#x3b1; predictions. These results demonstrate that at the current stage of theoretical and experimental development, data-driven approaches provide significant advantages in both accuracy and interpretability over classical theories for complex atomic parameters such as the K&#x3b2;/&#x39a;&#x3b1; intensity ratio. Overall, this work constitutes a significant step toward reducing deviations in high-Z elements, improving detector calibration, and establishing new atomic databases.

The analysis of metabolic profiles using high resolution mass spectrometry (MS) data provides deep insights into biological processes. In metabolomics, MS analysis generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a metabolite family (mFam), evaluating MS features at the metabolite family level can help assigning functional roles to individual MS features. These data can help interpreting metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is important to have good quality, reliable, and comprehensive spectral libraries. We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from labs working in different fields, including metabolomics of animals, microorganisms, and plants. The mFam-MS/MS collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. The mFam collaboration used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories measured on 12 instrument types, including QTOF, Orbitrap, and Ion Mobility-QTOF systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.

Aboveground carbon (AGC) fluxes from deforestation and subsequent regrowth in tropical moist forest (TMF) are increasingly well characterized, but carbon losses and gains following partial disturbance are uncertain. We synthesized 146 studies quantifying postdisturbance AGC changes relative to undisturbed forests across TMF. Immediate AGC losses (mean&#xa0;&#xb1;&#xa0;1 SD; 2.5&#xa0;&#xb1;&#xa0;2.3 years after disturbance) following partial anthropogenic disturbances were greatest for forest fires (49&#xa0;&#xb1;&#xa0;26%), selective logging (34&#xa0;&#xb1;&#xa0;20%), and edge effects (31&#xa0;&#xb1;&#xa0;19%). Higher-frequency and -intensity disturbances significantly increased carbon loss. After 20 years of regeneration, AGC stock was higher in recovering degraded forests (41 to 117%) compared to secondary regrowth forests after complete deforestation (1 to 74%), indicating greater regeneration potential when forest structure is preserved. Our compiled database and associated meta-analysis improve accuracy and completeness for carbon inventory reporting and modeling. Substantial AGC losses and gains from distinct degradation and recovery processes are now better characterized, serving as an evidence base for policies to halt degradation and foster recovery for climate mitigation.

Plastics are a growing environmental and health threat. Microplastics (MPs, <5&#x2009;mm) and nanoplastics (NPs, <1 &#x3bc;m) are pervasive environmental contaminants increasingly detected within human tissues, including placenta, cord blood, breast milk, and infant stool, highlighting chronic early-life exposure. Children represent a uniquely vulnerable population due to higher intake relative to body mass, immature detoxification and immune systems, and rapid organogenesis. MPs and NPs (MNPs) can traverse biological barriers, accumulate in multiple organs, and disrupt key developmental processes through oxidative stress, inflammation, barrier dysfunction, and microbiome dysbiosis. Evidence from in vitro, animal and human studies indicates systemic impacts across gastrointestinal, pulmonary, endocrine, reproductive, immune, and central nervous systems, including impaired intestinal barrier function, dysbiosis, metabolic dysregulation, altered lung morphogenesis, endocrine disruption, reproductive abnormalities, immune dysregulation, and neurocognitive deficits. In addition, many chemicals associated with plastics pose risks to human health due to their toxicity and ability to leach into the surrounding area. This review compiles current knowledge on the physicochemical properties, exposure pathways, and system-specific effects of MNPs and the additives associated with plastics in pediatric populations. It also discusses the need for comprehensive policies to reduce plastic pollution. IMPACT: The increased prevalence of microplastics and nanoplastics pose a threat to children's health. Microplastics and nanoplastics have been found in many organs including the placenta. Children are particularly vulnerable to the effects of microplastics and nanoplastics. The mechanism by which they increase health risks in children are discussed.

Early gastric cancer is characterised by subtle mucosal and colour changes that frequently lead to missed lesions during routine endoscopy, making detection difficult. Indigo carmine spraying is a classical chromoendoscopic method to enhance mucosal surface irregularities and has been believed to have the potential to facilitate the detection of early gastric neoplasia. This method is widely used in Japan; however, whether it improves gastric neoplasm detection remains unclear. In this prospective study, we aim to evaluate the usefulness of indigo carmine spraying for detecting gastric cancer and gastric adenoma during upper gastrointestinal endoscopy in patients at high risk of gastric cancer. This prospective multicentre observational study will include over 30 institutions. Patients undergoing upper gastrointestinal endoscopy for surveillance after endoscopic treatment or pretreatment screening will be enrolled. The age range has been set from 20 years to 95 years, and patients for whom a biopsy will not be feasible will be excluded. Gastric observation will consist of two steps: the first will use white light imaging, followed by a second-pass observation after spraying 20-40&#x2009;mL of indigo carmine at a concentration of 0.1-0.4%. The primary endpoint will be the proportion of patients with gastric cancer or adenoma lesions detected during the second-pass observation among those who undergo successful indigo carmine examination. A one-sided binomial test (&#x3b1;=0.05) will be used to compare the detection rate with a predefined threshold of 1.0%. We aim to enrol a total of 1050 patients to achieve 80% power. This study was approved by the Institutional Review Board of Kanagawa Cancer Center (approval number: 2025-92). Written informed consent will be obtained at the time of registration. Following completion of this research, the findings will be promptly compiled and published in appropriate academic conferences and peer-reviewed international journals. UMIN000059685.

To identify whether there is a gender-based disparity in salary among sports medicine fellowship-trained academic orthopaedic surgeons. Deidentified faculty compensation data were obtained from the Association of American Medical Colleges, which are compiled after distributing surveys to 157 Liaison Committee on Medical Education-accredited medical schools through a deidentified internet-based survey application. Mean and median data for the 2023 calendar year was extracted from this dataset for male and female sports medicine fellowship-trained orthopaedic surgeons and stratified in a cross-sectional fashion by position, including assistant professor, associate professor, and professor. Independent sample t-tests and Cohen's d test were performed with Python and shown through a bar graph. A total of 312 orthopaedic surgeons were included in this analysis, with 268 (85.9%) male and 44 (14.1%) female surgeons. Sports medicine fellowship-trained female surgeons earn significantly lower salaries than their male counterparts at the positions of assistant professor ($504,994 vs $654,697; P&#x2009;<&#x2009;.001), associate professor ($617,612 vs 776,754; P&#x2009;<&#x2009;.001), and professor ($486,303 vs $820,406; P&#x2009;<&#x2009;.001). The effect size of the difference between male and female salaries is greatest at the position of professor (d&#x2009;=&#x2009;7.5), although there is a large difference in means between male and female assistant professors (d&#x2009;=&#x2009;4.2) and associate professors (d&#x2009;=&#x2009;3.4). Female sports medicine fellowship-trained academic orthopaedic surgeons earn significantly lower salaries at the assistant professor, associate professor, and professor levels. However, data were not able to be stratified based on additional variables that may influence salary among orthopaedic surgeons such as age, years in practice, geographic location, practice focus, and surgical volume. This study shows that gender-based disparities exist in compensation among sports medicine fellowship-trained academic orthopaedic surgeons.

Nontuberculous mycobacteria pulmonary disease (NTM PD) has been increasing in the United States, and New York City is an important hotspot, with a high burden of disease. We assessed how neighborhood-level risk factors influenced NTM PD prevalence in New York City. We used outpatient claims data from hospitals participating in the Patient-Centered Outcomes Research Institute network, compiled by the INSIGHT Clinical Research Network at Weill Cornell Medicine. NTM PD period prevalence (referred to here as 'prevalence') was estimated by New York City neighborhood for the study period 2012 through 2022. A case was defined as someone with at least one claim for NTM PD. De-identified NTM PD case data and demographic data were analyzed by neighborhood of residence at diagnosis. Using prevalence estimates, we detected high- and low-prevalence regions within New York City and associated these estimates with demographic, clinical, socioeconomic, and environmental neighborhood-level factors using Poisson regression and backward elimination of covariates. Overall, 6,169 NTM PD cases were identified among persons receiving care across 17 private New York City hospitals. The mean age was 67.5 years, 68.7% were female, and 57.3% were White. Over the study period, NTM PD prevalence increased throughout New York City, and median year built of housing units, median income, and median age of residents were significant neighborhood-level risk factors. The highest prevalence neighborhoods were in Manhattan, while the lowest prevalence neighborhoods were in Brooklyn and Staten Island. Our findings indicate that neighborhood-level access to care may explain the heterogeneity in NTM PD prevalence among New York City neighborhoods, as higher income, newer neighborhoods exhibited the highest NTM PD prevalences. Future studies should examine the extent of undetected NTM PD in New York City, particularly in low-income areas.

Accurate calculation of the compressive stiffness of micropiles ([Formula: see text]) is essential for forecasting load-displacement behavior and maintaining foundation serviceability in geotechnical structures. Conventional analytical and numerical methods frequently oversimplify soil-structure interaction and require substantial calibration, thereby limiting their applicability across diverse ground conditions. This paper presents a data-driven predictive approach that combines supervised machine learning techniques with a field-based micropile ([Formula: see text]) test database to address these limitations. A comprehensive dataset of 393 in-situ MP compression experiments was compiled after statistical preprocessing, including normalization, randomization, and outlier elimination based on the interquartile range criterion. Nine geotechnical and geometric characteristics were utilized as predictors of [Formula: see text]. Five ensemble learning models-Gradient Boosting ([Formula: see text]), Light Gradient Boosting ([Formula: see text]), Histogram-based Gradient Boosting ([Formula: see text]), Extreme Gradient Boosting ([Formula: see text]), and Categorical Boosting ([Formula: see text])-were created and refined with the Parrot Optimization Algorithm ([Formula: see text]) for hyperparameter optimization. The [Formula: see text] algorithm demonstrated the greatest prediction reliability. Comparative analyses demonstrated that [Formula: see text] decreased prediction error by 10-22% compared to other boosting models while ensuring enhanced convergence stability. The proposed [Formula: see text]-optimized boosting framework offers a precise, interpretable, and computationally efficient method for calculating [Formula: see text] directly from field data. This hybrid modeling methodology reconciles empirical testing with predictive analytics, providing a pragmatic solution for performance-oriented [Formula: see text] design and foundation system optimization in geotechnical engineering.