Major vascular injury during laparoscopic surgery is rare but potentially catastrophic. Existing literature is largely limited to case series and registry-based analyses without reliable denominators or validated measures of disease severity. Procedure-specific risk and overall population burden remain incompletely defined. This study aimed to determine the statewide incidence, severity profile, and procedure-specific risk of iatrogenic major vascular injury during inpatient laparoscopic surgery. We conducted a retrospective population-based cohort study using the 2024 Texas Inpatient and Outpatient Public Use Data File (PUDF). Adult patients (≥ 18years) undergoing inpatient laparoscopic procedures were identified using ICD-10-PCS approach codes. Iatrogenic major vascular injury was defined using ICD-10-CM diagnosis codes T81.71* and/or I97.5*. The primary outcome was statewide incidence per 10,000 inpatient laparoscopic procedures. Secondary outcomes included markers of clinical severity, including mortality, ICU admission, conversion to open surgery, transfusion, organ failure, and length of stay. Among 119,652 inpatient laparoscopic procedures, 84 cases of iatrogenic major vascular injury were identified, yielding an incidence of 7.02 per 10,000 procedures. (R3C3) These injuries were associated with high clinical severity, including an in-hospital mortality rate of 7.2% and ICU admission in 66.3% of cases. Surgical management often required escalation, with 55.4% of cases undergoing conversion to open surgery and 30.1% requiring blood transfusion. Acute kidney injury (26.5%) and respiratory failure (24.1%) were common. The median length of stay was 6days (IQR 3-11), compared with 3days (IQR 1-5) without injury. Diagnostic laparoscopy demonstrated the highest incidence (20.59 per 10,000), whereas colorectal resection had the lowest (0.89 per 10,000). Iatrogenic major vascular injury during inpatient laparoscopic surgery was rare but associated with substantial morbidity and mortality. The elevated incidence observed during diagnostic laparoscopy may suggest abdominal access as an important contributing mechanism of injury, reinforcing the need for continued emphasis on safe-entry techniques and heightened specialty-specific risk awareness. (R2C1).

This study aimed to evaluate the impact of antenatal corticosteroid (ACS) administration prior to delivery on neonatal outcomes in extreme preterm neonates delivered between 21 and 23 weeks of gestation. This retrospective cohort study used data from the National Center for Health Statistics Vital Statistics database. Linked birth and infant death data files from 2015 to 2021 were included in the analysis. The study included singleton, nonanomalous pregnancies that were delivered between 21 and 24 weeks of gestation. Analysis was limited to births with known ACS status. The outcomes of interest were infant mortality, 5-minute Apgar score < 6, and neonatal intensive care unit admission. Univariate analysis was performed to determine the association between exposure and outcome. Logistic regression analysis was performed to determine the association, adjusting for potential confounders. There were 50,671 births included in the analysis. In this group, 15,601 (31%) received ACS prior to delivery. ACS administration prior to delivery was associated with lower neonatal death rate between 21 and 24 weeks (32.9 vs. 56.1%, p < 0.0001, adjusted odds ratio [aOR]: 0.53 [0.51-0.56]). Sub-analysis based on delivery at each gestational week demonstrated a protective effect at 21 weeks (70.1 vs. 80.7%, p = 0.001, aOR: 0.56 [0.34-0.91]); at 22 weeks (54.1 vs. 75.9%, aOR: 0.40 [0.35-0.47]); at 23 weeks (39.1 vs. 50.9%, aOR: 0.65, aOR [0.61-0.70]); and at 24 weeks (24.6 vs. 30.1%, aOR: 0.78 [0.73-0.83]). ACS administration in extreme preterm neonates born between 21 and 24 weeks was associated with improved survival. · ACSs lowered neonatal death from 56.1 to 32.9% at 21 to 24 weeks.. · Greatest survival benefit was seen at 22 weeks with a 60% risk reduction.. · ACSs were linked to higher use of ventilation, surfactant, and antibiotics..

The Behavioral Health Emergency Assistance Response Division (B-HEARD) program is an emergency medical services (EMS)-based alternative to police-led responses for nonviolent mental health crises in New York City. This study aimed to examine B-HEARD's effects on nonviolent mental health-related EMS call rates. Data from the EMS Incident Dispatch Data file (January 2019-December 2024) were analyzed. The sample included 76 New York Police Department precincts, of which 31 adopted B-HEARD during the study period; the remaining 45 precincts did not adopt the program. A staggered-adoption, difference-in-differences design was used to compare monthly precinct-level rates of nonviolent mental health-related EMS calls between precincts that adopted B-HEARD and those that did not. Estimates were disaggregated by implementation wave and time since adoption. Estimated effects were heterogeneous over time and across implementation waves. When aggregated by implementation wave, early-adopting precincts did not show statistically significant changes. By contrast, statistically significant reductions in call rate were observed among B-HEARD-adopting precincts in March 2023. When aggregated by calendar month, statistically significant call-rate reductions were concentrated in certain months during 2024. Dynamic event-time estimates suggested that, when present, reductions emerged approximately 1 year after program implementation. The findings suggest that EMS-based, nonpolice crisis response programs may reduce nonviolent mental health-related EMS calls under specific operational and contextual conditions. The heterogeneous and delayed effects observed provide a benchmark for future evaluations aimed at identifying when and where these models translate into measurable, system-level change.

Examining the extent professionals explore pornography use in cases of harmful sexual behaviour: A retrospective case file analysis.

Introduction: Local anesthetics (LAs) are commonly used to provide regional insensitivity in dentistry, minor surgical procedures, and general anesthesia but may rarely cause allergic reactions. Methods: The file data of 101 patients who were referred to the Department of Allergy and Immunology, Ege University Faculty of Medicine, with a suspected LA allergy between February 2020 and August 2024 were retrospectively reviewed. Patients included in the study were contacted by phone and asked whether they had used LAs after the test and whether any reactions had developed. Results: Female patients comprised 81% (n = 82) of the study cohort. Patients were predominantly referred from the departments of dentistry (45% [n = 46]) and anesthesiology (22% [n = 22]). Patients were referred due to reactions related to LAs alone (n = 51) and both LAs and other drugs (n = 50). The severity of LA reactions was mild (n = 47), moderate (n = 36), or severe (n = 18), with dyspnea being the most common symptom (n = 47). Prick-intradermal (P-ID) and provocation tests were performed on all the patients (suspected LAs [n = 32], alternative LAs [n = 23], random [n = 46]). Only one patient tested positive in the P-ID test, and a provocation test was performed on this patient with an alternative agent, with a negative result. Of the 80 patients reached by phone, 41 received LAs based on test results and did not develop an allergic reaction, whereas the other 39 patients did not receive LAs. Conclusion: Immunologically mediated reactions to LAs were exceedingly rare in this study. These findings highlight that most suspected reactions are not attributable to confirmed allergic mechanisms and underscore the importance of a careful clinical history and guideline-based evaluation to avoid unnecessary drug avoidance.

Scientific confidence relies on the integrity and verifiability of primary observations, yet increasing experimental scales and computational complexities challenge traditional mechanisms of trust. In proteomics, community standards emphasize the public deposition of raw mass spectrometry (MS) data to enable reanalysis and reproducibility. However, recent advances in software capable of simulating MS data raise the possibility that datasets may be altered or generated entirely in silico and presented as experimentally acquired data. Here, we argue that current standards rarely distinguish between raw data formats in terms of their provenance guarantees or susceptibility to modification. To address this emerging vulnerability, we propose a hierarchy of evidence for MS-based reporting, analogous to established evidence hierarchies in medicine, in which confidence in reported findings increases with the traceability and verifiable provenance of raw data files. With this framework, we aim to support reviewers and readers in assessing the robustness of published claims and to stimulate discussion on strengthening data integrity safeguards in proteomics.

Voice phishing (vishing) has emerged as a major vector for social engineering, exploiting the emotional and persuasive aspects of speech to deceive victims. Existing public datasets are often monolingual and lack structured annotations of persuasive intent and interactional structure in telephony scenarios. VISHGUARD is a multilingual synthetic audio corpus designed to advance research on persuasion-sensitive vishing detection. It contains 3,000 simulated phone calls in English, French, and Modern Standard Arabic evenly distributed between fraudulent and legitimate categories. Each sample is annotated across several dimensions, including persuasion strategy, interactional markers, and emotional tone. The audio recordings were synthetically generated using text-to-speech synthesis and controlled noise mixing, with ambient sounds used to approximate office, street, and home-like acoustic variability rather than full telecommunication-channel effects. The dataset covers durations from 10 to 90 seconds and maintains balanced linguistic and class distributions. VISHGUARD provides a reproducible, script-conditioned synthetic telephony corpus with multidimensional annotations to support controlled research on persuasion-aware vishing detection in multilingual settings. All data files are publicly available to ensure transparency and reuse.

Characteristics and Outcomes of Pediatric Trisomy 13 and 18 Inpatient Admissions in Texas.

Transcription factors can exhibit tissue-dependent chromatin-binding patterns. In Caenorhabditis elegans, genome-wide transcription factor binding profiles have primarily been generated from whole-animal samples using chromatin immunoprecipitation-based approaches, which represent aggregated signals from multiple tissues. Genome-wide datasets generated using Cleavage Under Targets and Tagmentation remain limited in this organism. UNC-130 is expressed in several tissues, but muscle-specific chromatin-binding data have not been available. In addition to providing muscle-resolved binding information, such datasets may also serve as useful references for optimizing CUT&Tag experimental and computational workflows in C. elegans. A Cre-loxP-based conditional tagging strategy was used to epitope-label endogenous UNC-130 specifically in muscle tissue. Cleavage Under Targets and Tagmentation was performed on embryonic cells to profile genome-wide chromatin occupancy. The dataset includes raw paired-end sequencing reads, processed alignment files, peak files, normalized coverage tracks, and genome-editing construct information. All data files and associated metadata are deposited in public repositories and provide a resource for examining muscle-specific chromatin occupancy of UNC-130.

Cervical cancer (CC) mortality in the Baltic countries remains high. We explored CC mortality trends and educational inequalities in CC mortality in the Baltics in the context of organized CC screening (introduced in 2004 in Lithuania, 2006 in Estonia and 2009 in Latvia) and compared the results with Finland (where screening started in 1963). Data for the Baltic countries came from longitudinal mortality follow-up studies of population censuses in 2000/2001 and 2011, and data for Finland from the longitudinal register-based population data file of Statistics Finland. CC deaths (ICD-10 code C53) were linked from national mortality registries. Information on education was census- or register-based. Overall and education-specific age-standardized mortality rates (ASMRs) and mortality rate ratios were calculated for 2000-2007 and 2008-2015 for women aged 30-49 and 50-64 years. The Baltic countries had 5-9 times higher overall ASMRs than Finland and much larger inequalities in CC mortality between low- and highly educated women. From 2000-2007 to 2008-2015 absolute inequalities in younger women reduced in all countries, except Latvia and relative inequalities increased in Estonia and Latvia. In older women, absolute inequalities increased in the Baltics but not in Finland; relative inequalities increased in all countries. The reduction in CC mortality and in absolute inequalities in younger women in Estonia and Lithuania may be associated with the introduction of organized screening. However, increasing CC mortality among older low-educated women in the Baltic countries is alarming, indicating that they have not benefitted equally from CC prevention.

Fair Workweek Laws (FWLs) aim to improve the predictability and stability of employee work schedules. They do so by requiring employers to provide work schedules with sufficient advance notice or to compensate workers when such notice is not provided. Despite the growing number of U.S. jurisdictions with active FWLs (currently more than 10), there is little empirical evidence of their effects on work schedules. This paper examines the impact of Los Angeles’s FWL on the predictability and stability of work schedules, using administrative data from seven independent retail chains operating in Los Angeles, CA. We find that Los Angeles’s FWL increased the predictability of work schedules: shift-level advance notice increased by 8.6% in the log specification, and the levels specification implies an increase of 1.31 days per shift. Furthermore, the share of shifts scheduled with less than 14 days’ notice decreased from 49.9% to 34.8%. However, we find that the law did not improve several commonly used measures of schedule stability, such as consistency in shift start times and hours worked. Our analysis also does not find evidence to support several commonly raised concerns about FWLs, including reduced work hours, increased employee turnover, reduced hiring, increased use of part-time workers, and reduced store operating hours. Overall, our results suggest that although FWLs can lead to some improvements in schedule predictability without these commonly raised negative consequences, they may have limited potential to increase schedule stability. This paper was accepted by Karan Girotra, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05622 .

Hospitalization outcomes of long-term care facility (LTCF) residents aged 60 years or older who are admitted to rehabilitation hospitals may differ by diagnosis at admission. This study aimed to determine the relationship between principal admitting diagnoses and discharge destination and the role of length of stay (LoS) and comorbid conditions. This retrospective cohort study utilized the Texas Inpatient Public Use Data File from October 2022 to June 2024. The cohort included 3,446 patients admitted to rehabilitation hospitals from LTCFs. Discharge destination (five-level) was modeled with multinomial logistic regression, adjusting for age, gender, race/ethnicity, and health insurance status. Neurological disorders (20.0%) and myopathies (20.2%) were the most common principal admitting diagnoses. Compared with neurological disorders, myopathies had a higher likelihood of transferring to subacute or short-term hospital care (RRR = 2.18, 95% CI: 1.41-3.37) and home under care (RRR = 1.69, 95% CI: 1.21-2.36). General symptoms or functional impairments were similarly associated with a higher relative risk of discharge from acute to short-term care hospitals (RRR = 2.05, 95% CI: 1.13-3.72) and home under care (RRR = 1.73, 95% CI: 1.08-2.77). Post-surgical or orthopedic aftercare had a lower relative risk of discharge to skilled nursing rather than rehabilitation hospital facilities (RRR = 0.50, 95% CI: 0.28-0.88). Among patients admitted to rehabilitation hospitals from LTCFs, a neurological principal admitting diagnosis and extended LoS with comorbid conditions carry a higher likelihood of discharge to an intensive care facility. These findings suggest a need for proactive admission diagnosis-tailored discharge planning and attention for patients with extended LoS.

In June 2022, the Dobbs decision rescinded abortion rights in the US. Did this mobilize women's healthcare providers, whose jobs and patients' health were impacted? We measure mobilization in two ways: 1. Engagement in a physician-led get-out-the-vote (GOTV) drive; and 2. Turnout in the 2022 midterms. Using a novel dataset of 6,205 physicians involved in a healthcare-based GOTV campaign and voter file data, we utilize linear probability models, t-tests, and parametric regression discontinuity analysis to show that women's healthcare providers were more politically engaged than physicians in other specialties. In 2022, the percentage of GOTV participants specializing in women's health increased by 50% and the share of women's health provider participating in the GOTV program spiked by 4.8-6.7 percentage points (p < 0.05). Regarding turnout, 6.4% of women's health providers were first-time midterm voters, compared to 5.7% for other physicians. Overall, women's health providers were 3.3 percentage points more likely to vote (p < 0.05) in the first post-Dobbs election than other physicians. Among Democratic physicians, women's health providers were 6.3 percentage points more likely to vote than others. Our findings have implications for mobilization research and GOTV strategies by highlighting that individuals directly impacted by policy changes are motivated to engage politically.

Since publication of the FAIR Guiding Principles in 2016, the scientific community has increasingly sought to make experimental data findable, accessible, interoperable, and reusable. Operationalizing the FAIR principles in routine scientific workflows remains challenging without a standardized, workable infrastructure. With over 10,000 datasets from over 40 institutions, spanning more than 50 diverse assay types ranging from single-cell sequencing technologies to 2D and 3D spatial omics, the U.S. National Institutes of Health (NIH) Human Bio-Molecular Atlas Program (HuBMAP) consortium has been ideally situated to create a FAIR ecosystem. With the goal of achieving data “FAIRness,” HuBMAP developed and implemented well-defined, community-endorsed metadata reporting standards across the research lifecycle. These reporting standards include detailed schemas, harmonized across a multitude of assays, that define the metadata associated with a dataset and the organization of the corresponding data files. These standards ensure documentation of the data collection process, of the data themselves, and of the manner in which the data are packaged for sharing, while remaining compliant with the Health Insurance Portability and Accountability Act (HIPAA). The use of these reporting standards, in tandem with technology to foster adherence, allows HuBMAP to fulfill its goal of generating FAIR data for open dissemination through its Data Portal and Human Reference Atlas. The procedures and simple workflow adopted by HuBMAP investigators serve as a model for other scientific communities aiming to maximize the value of varied datasets addressing a shared research question. The HuBMAP end-to-end, metadata-centered workflow has been replicated and enhanced by the NIH Cellular Senescence Network (SenNet) consortium and is readily available through open-source technology for others to utilize.

To facilitate responsible biomedical data sharing, large-scale data harmonization, and cutting-edge artificial intelligence (AI) applications by augmenting controlled-access data dissemination models (DDMs) with Data-derived Identity Verification (DIVe). We developed a watermarking-based DIVe system that embeds a cryptographically verifiable and traceable identity within each data file. This system was deployed for the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Consortium and currently watermarks a 3.8TB multimodal dataset for each approved data request. Over 1.8 petabytes, spanning over 900 data copies and 160 million files, have been uniquely watermarked and disseminated. Private provenance tracing, a novel data sharing license, and educational materials extend trust and accountability beyond controlled environments. Typical controlled-access DDMs assume data remain at rest. Data-derived Identity Verification provides an organizing principle for augmenting DDMs such that data can move while maintaining protection from data leaks. Watermarking-based DIVe supports secure, traceable dissemination of sensitive data, supporting the development of AI technologies in biomedicine.

Large population databases frequently include complicated family structures that are amenable to modern biometric methods: allowing for intergenerational and extended pedigree analyses. To date, much of the latent potential of these resources remains untapped due to numerous complexities that arise in their analysis. Two difficult and critical problems are (1) finding independent extended families within larger population databases, and (2) determining coefficients of relatedness among all pairs of individuals within those extended families. If these problems were solved, researchers could more fully utilize data on extended families for biometric modeling. In this paper, we provide fast, computationally efficient algorithms for both of these problems and several more that are applicable to arbitrarily large and complex pedigrees. The algorithms rely solely on mother-child and father-child relationships that form the basis of many large population databases. These methods will be invaluable to any researcher trying to segment standard pedigree data files into independent extended family units, compute relatedness coefficients within extended families, and conduct intergenerational and other biometric modeling.

Association of Postoperative Fever and Antibiotic Use with Surgical Site Infection in Children with Complicated Appendicitis: A Retrospective Cohort Study.

The increasing sophistication of Monte Carlo (MC)-based simulations, such as TOPAS-nBio for radiation track structure and DNA damage, and MEDRAS-MC for subsequent DNA repair and cell survival, has ushered in an era ofin-silicosingle-cell radiobiology. However, these simulations remain computationally prohibitive. In this study, we introduce a novel method that significantly accelerates the integrated simulation process forin-silicosingle-cell radiobiology. Our approach centers on pre-calculating and constructing a single-particle-track (SPT) standard DNA damage (SDD) data library using TOPAS-nBio. Each SPT-SDD data entry in this library records the positions of various DNA damage types (e.g. base damage, single-strand breaks, and double-strand breaks) produced by a single particle track. The comprehensive library contains a large number of SPT-SDD data entries, covering a broad range of particle energies. This data library functions as a look-up table, allowing for rapid assembly of DNA damage data for any desired dose level. This is achieved by randomly fetching and superimposing many SPT-SDD data entries from the pre-calculated library. The resulting 'composite' SDD data file then serves as input for MEDRAS-MC to simulate and compute DNA damage outcomes, including chromosome aberrations and cell lethality for each individual cell. Furthermore, 'timestamps' can be added to the superimposed data to account for dose rate effects. This work presents the first integrated MC framework that bridges track-structure simulation (TOPAS-nBio) with mechanistic repair-misrepair modeling (MEDRAS-MC) to predict chromosome aberrations and cell lethality on a cell-by-cell basis. In this paper, we first describe this novel methodology and then apply it to compute radiation-induced outcomes for three previously reportedin vitroexperiments. These applications involve 280 kVpx-rays, protons, and alpha particles, covering a broad spectrum of linear energy transfer. Finally, we compare ourin silicoresults with experimental data and provide a detailed discussion of any observed discrepancies.

MotivationLarge scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding.ResultsTo facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine–a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex ‘cancer profiles’ built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency.AvailabilityThe Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601

Peru GDP real-time dataset (1994\u20132025): Tracking three decades of revisions