Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation.

Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.

Reducing annotating load: Active learning with synthetic images in surgical instrument segmentation.

Accurate instrument segmentation in the endoscopic vision of minimally invasive surgery is challenging due to complex instruments and environments. Deep learning techniques have shown competitive performance in recent years. However, deep learning usually requires a large amount of labeled data to achieve accurate prediction, which poses a significant workload. To alleviate this workload, we propose an active learning-based framework to generate synthetic images for efficient neural network training. In each active learning iteration, a small number of informative unlabeled images are first queried by active learning and manually labeled. Next, synthetic images are generated based on these selected images. The instruments and backgrounds are cropped out and randomly combined with blending and fusion near the boundary. The proposed method leverages the advantage of both active learning and synthetic images. The effectiveness of the proposed method is validated on two sinus surgery datasets and one intraabdominal surgery dataset. The results indicate a considerable performance improvement, especially when the size of the annotated dataset is small. All the code is open-sourced at: https://github.com/HaonanPeng/active_syn_generator.

Development of the Saini-Hodgins Addiction Risk Potential of Games (SHARP-G) Scale: An International Delphi study.

Background and Objectives: As the gaming industry experiences exponential growth, concerns about gaming disorder (GD) also grow. It is crucial to understand the structural features of games that can interact with individual characteristics of gamers to promote GD. This research consolidates the views of an international body of panelists to create an assessment tool for gauging the addictive potential of distinct games. Methods: Utilizing the iterative and structured Delphi method, an international panel of researchers, clinicians, and people with lived experience were recruited to offer a multifaceted viewpoint on the addictive risk associated with specific structural elements in games. Two rounds of surveys facilitated consensus. Results: The panel initially included 40 members-ten from research, eight from clinical settings, and 22 with lived experiences. The second round included 27 panelists-seven from research, eight from clinical settings, and 11 with lived experiences. The study identified 25 structural features that contribute to potentially addictive gaming patterns. Discussion and Conclusions: Consensus was found for 25 features, which were distilled into a 23-item evaluation tool. The Saini-Hodgins Addiction Risk Potential of Games Scale (SHARP-G) consists of five overarching categories: 'Social,' 'Gambling-Like Features,' 'Personal Investment,' 'Accessibility,' and 'World Design.' SHARP-G yields a total score indicating level of addiction risk. A case study applying the scale to three games of differing perceived risk levels demonstrated that that score corresponded to game risk as expected. While the SHARP-G scale requires further validation, it provides significant promise for evaluating gaming experiences and products.

High energy laser propagation through natural convection of air: a benchmark for validation of numerical simulation.

The study of propagation medium effects on lasers continues to be an active area of research. High energy laser (HEL) propagation through planetary atmosphere is particularly nuanced as the beam generates its own flow field and suffers from additional degrading effects. Herein, we construct experimental setups conducive to probing the physics of the laser-atmosphere interaction and generating validation datasets for high fidelity predictive software. Measured and derived parameters are presented, and predictive models are generated utilizing random forest regression.

Enhancing the Preanesthesia Evaluation: Consensus Development of the Cannabis Use and Behaviors Assessment Tool (CUBAT).

PURPOSE: This project aimed to develop an evidence-based preanesthesia cannabis use assessment tool to acquire complete and accurate patient history and develop a best-informed, individualized anesthesia and analgesia care plan. DESIGN: Modified Delphi. METHODS: Using an evidence synthesis and multistage, modified Delphi process, eight experts from across the United States developed a consensus-based tool to aid in developing a best-informed, individualized plan for anesthesia and analgesia care. FINDINGS: Two survey rounds integrated informed evidence-based tool revisions. The final tool included instructions for use, a glossary of terms, and seven key assessment items aimed at gathering the most influential information regarding cannabis use. CONCLUSIONS: The Cannabis Use and Behaviors Assessment Tool is a first-of-its-kind tool providing an essential framework for preanesthesia cannabis use assessment.

Impact of postoperative pancreatic fistula on outcomes in pancreatoduodenectomy: a comprehensive analysis of American College of Surgeons National Surgical Quality Improvement Program data.

BACKGROUND: Pancreatoduodenectomy (PD) is a major surgical procedure associated with significant risks, particularly postoperative pancreatic fistula (POPF). Studies have highlighted the importance of certain risk factors for POPF, which are crucial for surgical decision-making and the management of high-risk patients undergoing PD. This study aimed to assess the surgical outcomes of patients undergoing PD who met the International Study Group of Pancreatic Surgery - Class D (ISGPS-D) criteria. METHODS: This study analyzed American College of Surgeons National Surgical Quality Improvement Program data (2014-2021) for patients undergoing ISGPS-D PD, classified as having a soft pancreatic texture and a pancreatic duct of ≤3 mm. This study focused on mortality rates and the correlation between several factors and POPF (ISGPS grade B/C). RESULTS: From 5964 patients who underwent PD and met the ISGPS-D criteria, the 30-day mortality rate was 1.98%. Males had a higher incidence of POPF than females (57.42% vs 47.35%, respectively; P < .001). Patients with POPF experienced significantly higher rates of major postoperative complications (Clavien-Dindo grade ≥ IIIa), including thrombosis, pneumonia, sepsis, delayed gastric emptying, wound disruption, infections, and acute renal failure. There was a marked increase in the 30-day readmission and mortality rates in patients with POPF (30.0% vs 17.6% and 3.2% vs 1.4%, respectively; all P < .001). Multivariate analysis highlighted female sex as a protective factor against mortality (odds ratio [OR], 0.47; P < .001) and extended hospital stay (>10 days) as a predictor of increased mortality risk (OR, 2.37; P < .001). CONCLUSION: This study underscored the significant association between POPF and increased postoperative morbidity and mortality rates. Future efforts should concentrate on refining surgical techniques and improving preoperative assessments to mitigate the risks associated with POPF in patients undergoing PD.

Minorities Face Delays to Pancreatic Cancer Treatment Regardless of Diagnosis Setting.

INTRODUCTION: Our analysis was designed to characterize the demographics and disparities between the diagnosis of pancreas cancer during emergency presentation (EP) and the outpatient setting (OP) and to see the impact of our institutions pancreatic multidisciplinary clinic (PMDC) on these disparities. METHODS: Institutional review board-approved retrospective review of our institutional cancer registry and PMDC databases identified patients diagnosed/treated for pancreatic ductal adenocarcinoma between 2014 and 2022. Chi-square tests were used for categorical variables, and one-way ANOVA with a Bonferroni correction was used for continuous variables. Statistical significance was set at p < 0.05. RESULTS: A total of 286 patients met inclusion criteria. Eighty-nine patients (31.1%) were underrepresented minorities (URM). Fifty-seven (64.0%) URMs presented during an EP versus 100 (50.8%) non-URMs (p = 0.037). Forty-one (46.1%) URMs were reviewed at PMDC versus 71 (36.0%) non-URMs (p = 0.10). No differences in clinical and pathologic stage between the cohorts (p = 0.28) were present. URMs took 22 days longer on average to receive treatment (66.5 days vs. 44.8 days, p = 0.003) in the EP cohort and 18 days longer in OP cohort (58.0 days vs. 40.5 days, p < 0.001) compared with non-URMs. Pancreatic Multidisciplinary Clinic enrollment in EP cohort eliminated the difference in time to treatment between cohorts (48.3 days vs. 37.0 days; p = 0.151). RESULTS: Underrepresented minorities were more likely to be diagnosed via EP and showed delayed times to treatment compared with non-URM counterparts. Our PMDC alleviated some of these observed disparities. Future studies are required to elucidate the specific factors that resulted in these findings and to identify solutions.

Automatic State Interaction with Large Localized Active Spaces for Multimetallic Systems.

The localized active space self-consistent field method factorizes a complete active space wave function into an antisymmetrized product of localized active space wave function fragments. Correlation between fragments is then reintroduced through localized active space state interaction (LASSI), in which the Hamiltonian is diagonalized in a model space of LAS states. However, the optimal procedure for defining the LAS fragments and LASSI model space is unknown. We here present an automated framework to explore systematically convergent sets of model spaces, which we call LASSI[r, q]. This method requires the user to select only r, the number of electron hops from one fragment to another, and q, the number of fragment basis functions per Hilbert space, which converges to CASCI in the limit of r, q → ∞. Numerical tests of this method on the trimetal oxo-centered complexes [Fe(III)Al(III)Fe(II)(µ3-O)(HCOO)6] and [Fe(III)2Fe(II)(µ3-O)(HCOO)6] show efficient convergence to the CASCI limit with 4-10 orders of magnitude fewer states than CASCI.

Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations.

In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.

Mapping Youth Awareness of Sports Betting Advertising During the 2022 FIFA World Cup.

Young people are known to be highly engaged in sports betting and therefore may be particularly susceptible to the effects of gambling-related advertising. The purpose of the present study was to examine young people's recall of sports betting advertising during the 2022 FIFA World Cup. The sample consisted of 190 UK residents aged 18-24 who had watched at least one 2022 World Cup match. A cross-sectional survey was conducted to collect data on participants' recall of sports betting advertisements across several media types and for different bets and betting offers, as well as their problem gambling scores. The findings indicated that young people were able to recall a high amount of advertising for various types of bets (95.6%) and betting inducements (89.5%). A high proportion of young people recalled advertising for risky bet types and promotions, such as 64.2% for in-play betting and 68.1% for sign-up offers. Overall, higher-risk gamblers recalled encountering more advertising than lower-risk gamblers. Participants recalled encountering sports betting advertisements on social media the most (10-14 ads per week), then on internet banners and television (5-9 ads per week, respectively). Less than half (46.3%) of respondents were aware of advertising for responsible gambling tools. This study underscores the need for policy measures that limit young people's exposure to gambling advertising, particularly for products that may contribute to gambling-related harm, and that increase the promotion of responsible gambling tools.

Divergent Bimetallic Mechanisms in Copper(II)-Mediated C-C, N-N, and O-O Oxidative Coupling Reactions.

Copper-catalyzed aerobic oxidative coupling of diaryl imines provides a route for conversion of ammonia to hydrazine. The present study uses experimental and density functional theory computational methods to investigate the mechanism of N-N bond formation, and the data support a mechanism involving bimolecular coupling of Cu-coordinated iminyl radicals. Computational analysis is extended to CuII-mediated C-C, N-N, and O-O coupling reactions involved in the formation of cyanogen (NC-CN) from HCN, 1,3-butadiyne from ethyne (i.e., Glaser coupling), hydrazine from ammonia, and hydrogen peroxide from water. The results reveal two different mechanistic pathways. Heteroatom ligands with an uncoordinated lone pair (iminyl, NH2, OH) undergo charge transfer to CuII, generating ligand-centered radicals that undergo facile bimolecular radical-radical coupling. Ligands lacking a lone pair (CN and CCH) form bridged binuclear diamond-core structures that undergo C-C coupling. This mechanistic bifurcation is rationalized by analysis of spin densities in key intermediates and transition states, as well as multiconfigurational calculations. Radical-radical coupling is especially favorable for N-N coupling owing to energetically favorable charge transfer in the intermediate and thermodynamically favorable product formation.

Red box, green box: A self-report behavioral frequency measurement approach for behavioral addictions research.

Behavior frequency measures in behavioral addictions research fail to account for how engagement in the activity relates to each respondent's personal circumstances. We propose a "Red Box, Green Box" method, an alternative to conventional self-report behavior questions. Participants report two distinct time-based values: (1) Green box: time spent engaged in the activity during 'free' time, and (2) Red box: engagement in the activity at times when the respondent should be doing something else (e.g., studying, working, sleeping, exercising, etc.). Some practical examples of the 'red box, green box' method are provided. This method may help to calibrate behavioral frequency for each respondent and yield clearer insights into displacement effects and risks associated with frequency of use. We suggest some future research directions to test the feasibility and utility of this approach in different implementations.

The longitudinal associations between internet addiction and ADHD symptoms among adolescents.

Background and aims: Internet addiction has been linked to ADHD-related symptoms. However, the direction of the relationship and its potential for reciprocal relations is not well understood. This study examined the potential reciprocal relations between the three components of ADHD and Internet addiction, as well as the moderating effects of gender on these relations. Methods: Using a longitudinal design, we collected data of 865 Chinese adolescents across three waves (Mage = 13.78, SD = 1.56 in wave 1), with a time interval of 6 months. Results: Cross-lagged analyses revealed bidirectional associations between hyperactivity, inattention, impulsivity, and Internet addiction over time. Multi-group analyses did not yield any significant gender differences in these relationships. Discussion and conclusions: These findings enhance our understanding of the complex link between ADHD components and Internet addiction and have implications for interventions aimed at reducing the prevalence of Internet addiction and ADHD.

The Scalable Variant Call Representation: Enabling Genetic Analysis Beyond One Million Genomes.

The Variant Call Format (VCF) is widely used in genome sequencing but scales poorly. For instance, we estimate a 150,000 genome VCF would occupy 900 TiB, making it both costly and complicated to produce and analyze. The issue stems from VCF's requirement to densely represent both reference-genotypes and allele-indexed arrays. These requirements lead to unnecessary data duplication and, ultimately, very large files. To address these challenges, we introduce the Scalable Variant Call Representation (SVCR). This representation reduces file sizes by ensuring they scale linearly with samples. SVCR achieves this by adopting reference blocks from the Genomic Variant Call Format (GVCF) and employing local allele indices. SVCR is also lossless and mergeable, allowing for N+1 and N+K incremental joint-calling. We present two implementations of SVCR: SVCR-VCF, which encodes SVCR in VCF format, and VDS, which uses Hail's native format. Our experiments confirm the linear scalability of SVCR-VCF and VDS, in contrast to the super-linear growth seen with standard VCF files. We also discuss the VDS Combiner, a scalable, open-source tool for producing a VDS from GVCFs and unique features of VDS which enable rapid data analysis. SVCR, and VDS in particular, ensure the scientific community can generate, analyze, and disseminate genetics datasets with millions of samples.

The role of stigma in cannabis use disclosure: an exploratory study.

BACKGROUND: Although cannabis use incidence, societal acceptance, and legislation all trend positively, cannabis remains federally illegal in the USA. Prior studies have revealed that patients are reluctant to disclose their cannabis use history in the healthcare system, which can negatively impact patient care. This study reports the frequency of cannabis use disclosure with special considerations for stigmatization. To better understand the limitations, providers face in providing collaborative, comprehensive, and informed care, this study evaluated four domains of stigma: perceived, anticipated, enacted, and internalized. METHODS: This study used a descriptive exploratory design. Data collection occurred using an anonymous, online national survey with a convenience sample in the USA. Recruitment relied on electronic media and occurred between July and December 2022. Participants were adults older than 21 years and self-identified as having used cannabis and accessed the healthcare system within the last five years. The survey measured demographic characteristics, cannabis use, and disclosure patterns. Stigma was measured using the Stigma Use Stigma Mechanism Scale (SU-SMS) and Substance Abuse Use Self-Stigma Scale (SASSS) with language modifications for cannabis. Ordinal logistic regression models were performed to evaluate associations between the frequency of cannabis use disclosure patterns and each stigma category. Associations were assessed using Chi-squared or Fisher's exact tests. RESULTS: Data were available for 249 respondents. Most participants (57.1%) reported initiating a conversation about cannabis with their healthcare provider; 27.8% of the time, cannabis is never discussed, and healthcare providers initiate only 15.1% of related discussions. Anticipated stigma [95% CI 1.045-1.164] and total stigma [95% CI 1.001-1.039] had statistically significant associations with nondisclosure. Annual household income (p = .04), chronicity of cannabis use (p = .03), frequency of cannabis use (p = .02), and a known amount of CBD in products consumed (p = .01) had statistically significant associations with the frequency of cannabis use disclosure. CONCLUSIONS: Patients who use cannabis experience stigmatization in the healthcare setting that may limit disclosure of cannabis use history. Future studies would be well served to explore anticipated stigma more deeply. Healthcare providers should be knowledgeable to lead such conversations relating to cannabis while maintaining an unbiased perspective.

Palliative Medicine and End of Life Care Between Races in an Academic Intensive Care Unit.

Background: Although palliative medicine (PM) is more commonly being integrated into the intensive care unit (ICU), research on racial disparities in this area is lacking. Our objectives were to (a) identify racial disparities in utilization of PM consultation for patients who received ICU care and (b) determine if there were differences in the use of code status or PM consultation over time based on race. Materials and Methods: Retrospective analysis of 571 patients, 18 years and above, at a tertiary care institution who received ICU care and died during their hospital stay. We analyzed two timeframes, 2008-2009 and 2018-2019. Univariate analysis was utilized to evaluate baseline characteristics. A multivariate logistic regression model and interaction P values were employed to assess for differential use of PM consultation, do not resuscitate (DNR) orders, and comfort care (CC) orders between races in aggregate and for changes over time. Results: There was a notable increase in Black/African-American (AA) (54% to 61%) and Hispanic/Latino (2% to 3%) patients over time in our population. Compared to White patients, we found no differences between PM consultation and CC orders. There was a lower probability of DNR orders for Black/AA (adjusted odds ratio [aOR] 0.569; P = .049; confidence interval [CI]: 0.324-0.997) and other/unknown/multiracial patients (aOR: 0.389; P = .273; CI: 0.169-0.900). Comparing our earlier time period to the later time period, we found an increased usage of PM for all patients. Interaction P values suggest there were no differences between races regarding PM, DNR, and CC orders. Conclusions: PM use has increased over time at our institution. Contrary to the previous literature, there were no differences in the frequency of utilization of PM consultation between races. Further analysis to evaluate the usage of PM in the ICU setting in varying populations and geographic locations is warranted.

Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling.

The scarcity of malignant Hodgkin and Reed-Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1-4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed-Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.

Impaired control and gaming-related harm in relation to gaming Disorder.

The concept of impaired control (IC) over gaming is an important element of assessment and interventions for problem gaming and gaming-related harm. Past studies have reported that gaming disorder (GD) is associated with various negative consequences, but there is limited research on the relationship between IC over gaming and negative outcomes. To address this gap, the study investigated the relationship between impaired control and gaming-related harm among individuals with self-identified gaming disorder. It was hypothesized that IC would be positively associated with gaming-related harm and harm severity. In addition, it was predicted that IC would be a significant predictor of harm when controlling for age, gender, psychological distress, and gaming urges. The current study recruited 513 participants through an online survey platform. The Impaired Control Over Gaming Scale (ICOGS) was used to measure IC, and modified items from Browne et al.'s taxonomy of gambling harms were used to assess gaming harm severity. The logistic regression results showed that IC was positively related to all forms of harm, after controlling for other variables. The predictive value of IC was similar across financial, psychological, relationship, social and work/school domains. These results supported the importance of IC as a mechanism that contributes to the experience of gaming-related harm, and the need to target IC in interventions for GD.