Machine learning to predict completion of treatment for pancreatic cancer.

BACKGROUND: Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would complete pre- or postoperative chemotherapy through machine learning (ML). METHODS: Patients with resectable PC identified in our institutional pancreas database were grouped into two categories: those who completed all intended treatments (i.e., surgery plus either neoadjuvant or adjuvant chemotherapy), and those who did not. We applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction, and further examined it through bootstrapping for sensitivity. RESULTS: Among 208 patients, the median age was 69, with 49.5% female and 62% white participants. Most had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. The PC predominantly affected the pancreatic head. Neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Incomplete therapy was correlated with older age and lower ECOG status. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of PC, initial bilirubin levels, and tumor location in the pancreatic head. The models also flagged other factors, such as jaundice and specific cancer markers, impacting treatment completion. The predictive accuracy (area under the receiver operating characteristic curve) was 0.67 for both models, with performance expected to improve with larger datasets. CONCLUSIONS: Our findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.

Examining cognition and brain networks using magnetoencephalography in paediatric autoimmune encephalitis and acute disseminated encephalomyelitis: a preliminary study.

Paediatric autoimmune encephalitis, including acute disseminated encephalomyelitis, are inflammatory brain diseases presenting with cognitive deficits, psychiatric symptoms, seizures, MRI and EEG abnormalities. Despite improvements in disease recognition and early immunotherapy, long-term outcomes in paediatric autoimmune encephalitis remain poor. Our aim was to understand functional connectivity changes that could be associated with negative developmental outcomes across different types of paediatric autoimmune encephalitis using magnetoencephalography. Participants were children diagnosed with paediatric autoimmune encephalitis at least 18 months before testing and typically developing children. All completed magnetoencephalography recording at rest, T1 MRI scans and neuropsychology testing. Brain connectivity (specifically in delta and theta) was estimated with amplitude envelope correlation, and network efficiency was measured using graph measures (global efficiency, local efficiency and modularity). Twelve children with paediatric autoimmune encephalitis (11.2 ± 3.5 years, interquartile range 9 years; 5M:7F) and 12 typically developing controls (10.6 ± 3.2 years, interquartile range 7 years; 8M:4F) participated. Children with paediatric autoimmune encephalitis did not differ from controls in working memory (t(21) = 1.449; P = 0.162; d = 0.605) but had significantly lower processing speed (t(21) = 2.463; P = 0.023; Cohen's d = 1.028). Groups did not differ in theta network topology measures. The paediatric autoimmune encephalitis group had a significantly lower delta local efficiency across all thresholds tested (d = -1.60 at network threshold 14%). Theta modularity was associated with lower working memory (ß = -0.781; t(8) = -2.588, P = 0.032); this effect did not survive correction for multiple comparisons (P(corr) = 0.224). Magnetoencephalography was able to capture specific network alterations in paediatric autoimmune encephalitis patients. This preliminary study demonstrates that magnetoencephalography is an appropriate tool for assessing children with paediatric autoimmune encephalitis and could be associated with cognitive outcomes.

Comparative life cycle assessment of natural and recycled aggregate concrete: A review.

Recycled aggregate concrete (RAC), mainly made from recycled materials such as construction and demolition waste (CDW), has emerged as a sustainable alternative to natural aggregate concrete (NAC). While RAC offers potential benefits in waste reduction and resource conservation, a comprehensive understanding of its environmental impact and sustainability compared to NAC has been lacking. This study addresses this gap by conducting a thorough review and analysis of comparative Life Cycle Assessment (LCA) studies between RAC and NAC. This paper synthesizes current literature to evaluate the environmental impact of both materials throughout their life cycles, from raw material extraction to disposal. It examines key factors such as energy consumption, greenhouse gas emissions, and resource depletion to provide a thorough comprehension of the effects on the environment of each concrete type throughout their life cycles. Challenges in using RAC as a sustainable concrete option, such as sourcing and quality control, are also discussed, along with recommendations for future research and industry practices. The findings indicate that the environmental impact of RAC compared to NAC is significantly influenced by transport distances and modes. In addition, the choice of functional units in LCAs substantially affects the comparison between RAC and NAC, with strength reliability offering a clear benefit by addressing concrete property variability and better reflecting real-world conditions.

The Gaming Disorder Identification Test (GADIT) - A screening tool for Gaming Disorder based on ICD-11.

Background: Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment. Method: We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder. Results: Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets. Conclusion: The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.

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.

Trends in Atherosclerotic Cardiovascular Disease and lipid management. A population-level observational cohort study in Wales.

AIMS: European clinical guidelines recommend that patients with atherosclerotic cardiovascular disease (ASCVD), including ischaemic heart disease (IHD), stroke and peripheral arterial disease (PAD), are prescribed lipid lowering treatment (LLT) and treated to target low-density lipoprotein cholesterol (LDL-C) levels. This study aimed to document trends in ASCVD including treatment, monitoring, and achievement of target LDL-C. METHOD: A retrospective observational population study using linked health-care data (2010-22). RESULTS: Over the study period the number of patients with ASCVD increased from 181,153 to 207,747 (8882 to 9398 per 100,000). The proportion of patients prescribed LLT decreased from 75.3% in 2010 to 67.1% in 2022; high-intensity statin therapy increased from 9.4% to 25.2% and non-high-intensity statin therapy decreased from 59.6% to 38.2%. The prescribing of high-intensity statin therapy was consistently higher amongst patients with IHD (10.9% in 2010 increasing to 28.0% in 2022) than in patients with stroke (4.7% to 21.6%) or PAD (3.9% to 10.6%).The proportion of cases with documented LDL-C decreased from 58.0% in 2010 to 49.3% in 2022. Of those with documented LDL-C in 2022, 44.0% achieved LDL-C <1.8 mmol/L, including 45.2% of those with IHD, 42.0% of those with stroke and only 32.8% of those with PAD. CONCLUSION: Prescribing of LLT, including HI-statin therapy, documentation of LDL-C and achievement of target LDL-C levels was relatively low, especially in PAD patients. Although target achievement in "tested patients" increased over time, the proportion of patients undergoing lipid testing declined. More rigorous lipid management requires prioritisation, especially for PAD and stroke patients.

We analysed trends in the presentation of atherosclerotic cardiovascular disease and lipid management in a population between 2010 to 2022 The number of patients with atherosclerotic cardiovascular disease increased by 14% but the proportion receiving lipid lowering therapy decreased.Patients with ischaemic heart disease were more effectively managed than patients with stroke and patients with peripheral arterial disease were the least effectively managed.

Examining the Role of Sports Betting Marketing in Youth Problem Gambling.

BACKGROUND: Sports betting is becoming increasingly common among young people in the UK and Australia. There is a need to understand how the marketing of sports betting may influence risky and pathological gambling to inform policies aimed at reducing harm. This study examines whether sports betting advertising may predict problem gambling scores among young people, while accounting for non-marketing variables. METHODS: We recruited 567 participants (53.1% male) aged 18-24 years from an online research panel. Participants were eligible if they had an active betting account and regularly bet on sports. We conducted a hierarchical regression analysis to examine whether four marketing-related measures (exposure to advertising, ad-driven betting decisions, use of betting inducements, and perceived susceptibility to betting inducements) could predict PGSI scores. We controlled for several demographic, psychological, and behavioural variables, including gender, gambling participation, spend per session, participation in in-play betting, normative beliefs about sports betting, and impulsivity. RESULTS: The study revealed that sports betting marketing was positively associated with PGSI scores after controlling for non-marketing variables. Significant marketing predictors included ad-driven betting decisions and perceived influence from betting inducements. Other significant predictors included participation in non-sports betting gambling activities, spend per session, involvement in in-play betting, and the impulsivity trait of negative urgency. CONCLUSION: Sports betting marketing appears to be implicated in young people's gambling problems. Specifically, young people who have gambling problems may be more likely to bet in response to advertising, and betting incentives may contribute to an intensification of their gambling behaviour. This study supports the implementation of regulations and restrictions on advertising as a measure to protect young problem gamblers.

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.

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.

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.

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.

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.

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.

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.

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.