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Risk factors for postoperative nausea and vomiting after endovascular interventional therapy: a case–control study
Association between food insecurity and mental health outcomes among a convenient sample of Lebanese pregnant women
Food insecurity (FI) is a pressing public health challenge, suggested to be associated with psychological distress and detrimental effects, especially in vulnerable populations such as pregnant women. To date, little is known in Lebanese pregnant women on the association between FI and emotional and behavioral outcomes. Thus, this study aimed to explore the association between FI, and emotional (anxiety, depression, distress) and behavioral (disordered eating, sleep quality) outcomes in a convenient sample of adult Lebanese pregnant women. A cross sectional study involving 146 pregnant women was conducted between 20 January 2023 and 16 September 2024. An online questionnaire was used to collect sociodemographic, financial, and medical characteristics. FI was assessed using the Arabic validated version of the Household Food Insecurity Access Scale, anxiety and depression using the Arabic validated version of the Patient Health Questionnaire, distress using the Beirut Distress Scale, disordered eating using the Arabic validated version of the Disordered Eating Attitudes in Pregnancy Scale, and sleep quality using the culturally-adapted Arabic version of the Pittsburgh Sleep Quality Index. Enter logistic regression models assessed the determinants of the dependent variables: anxiety, depression, distress, sleep quality, disordered eating. Findings showed that 66.4% of participants had FI, 50.7% had anxiety, 45% had depression, 83.6% reported high distress levels, 9.6% had disordered eating, and 57.5% had poor sleep quality. FI was associated with higher distress level, disordered eating, and poor sleep quality. No associations between FI and anxiety and depression were found. Given the high levels of FI and psychological distress in our sample, we emphasize the need for a comprehensive approach to support the physical and psychological health of pregnant women in Lebanon, with a focus on addressing underlying factors such as FI. Antenatal care must prioritize assessing food security and screening for and treating associated mental and behavioral health problems.
An enhancement of machine learning model performance in disease prediction with synthetic data generation
Prediction of damage evolution in carbonate building stones subjected to simulated acid rain using M5P model
Orientation selectivity properties for integrated affine quasi quadrature models of complex cells
This paper presents an analysis of the orientation selectivity properties of idealized models of complex cells in terms of affine quasi quadrature measures, which combine the responses of idealized models of simple cells in terms of affine Gaussian derivatives by (i) pointwise squaring, (ii) summation of responses for different orders of spatial derivation and (iii) spatial integration. Specifically, this paper explores the consequences of assuming that the family of spatial receptive fields should be covariant under spatial affine transformations, thereby implying that the receptive fields ought to span a variability over the degree of elongation. We investigate the theoretical properties of three main ways of defining idealized models of complex cells and compare the predictions from these models to neurophysiologically obtained receptive field histograms over the resultant of biological orientation selectivity curves. It is shown that the extended modelling mechanisms lead to more uniform behaviour and a wider span over the values of the resultant that are covered, compared to an earlier presented idealized model of complex cells without spatial integration. More generally, we propose to, based on the presented results: (i) include an explicit variability over the degree of elongation of the receptive fields in functional models of complex cells, and that (ii) the suggested methodology with comparisons to biological orientation selectivity curves and orientation selectivity histograms could be used as a new tool to evaluate other computational models of complex cells in relation to biological measurements.
Identifying influential assets in higher order interdependent infrastructure networks through population impact
Novel binary Ti-Zr, Ti-Ce, and Zr-Ce oxides as dual-function adsorbents and reaction accelerators for CO₂ capture and ethylene urea synthesis
Advancing person-centered care: Protocol for quality measurement and management (QM2) in the New York State system for opioid use disorder treatment
Introduction The United States is facing an opioid use disorder (OUD) epidemic, marked by unprecedented overdose death rates. In New York State, synthetic opioids significantly contribute to the increasing overdose deaths, disproportionately impacting Black and Latinx communities. There is an urgent need to address issues related to equitable access to and the quality of care provided by substance use disorder (SUD) treatment programs. In light of this, the Quality Measurement and Management Research Center (QM2-RC) brought together an academic-government partnership to develop a person-centered quality measurement system and to assess its impact on a statewide treatment system that serves approximately 180,000 individuals per year. Methods and analysis The QM2-RC encompasses three interconnected projects (Project 1, 2, and 3) aimed at developing a quality management strategy and evaluating its impact on system performance across New York State. This report specifically focuses on Project 3, which involves a stepped-wedge trial with 35 clinics receiving a quality management intervention that includes performance coaching. This intervention will be compared to a treatment-as-usual (TAU) condition for clinics not participating in the trial. Administrative data will be utilized to monitor outcomes over four years. The coaching intervention, guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) model, emphasizes interpreting quality measures and applying insights to enhance care. Coaches will provide support on data utilization, patient-centered care, harm reduction strategies, and the use of patient monitoring tools. The trial aims to evaluate clinic staff and leadership attitudes, experiences, and behaviors through surveys, semi-structured interviews, and external facilitator notes. Primary clinic outcomes will be assessed through adverse events, decreased clinic rates of substance use related emergency department visits and hospitalizations as well as mortality among patients within the first 12 months after admission to treatment after adjusting for individual and community level characteristics. This study is being developed over a multi-year period and will be informed by a mixed-methods approach incorporating multiple data sources, qualitative interviews, patient and clinic surveys. The study is being conducted in partnership with New York State Office of Addiction Services and Supports (OASAS) and will be informed by input from patient, providers, health insurers, family members and local governing units. Discussion Project 3 of the QM2 study specifically targets key barriers in measuring the quality of SUD treatment, including technological limitations, unvalidated measures, workforce data literacy, and concerns about fairness in assessing clinical complexity. Through the implementation of a stepped-wedge trial involving 35 clinics, the project aims to develop new quality measures, offer performance feedback, and engage clinic leadership and staff in efforts to improve practices. The ultimate goal of Project 3 is to overcome these barriers, promote person-centered care, and improve SUD treatment practices across New York State.
An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization
Change in platelet and leukocyte counts and hospital mortality in adults with acute kidney injury receiving continuous renal replacement therapy
Hatching-Box: Automated in situ monitoring of Drosophila melanogaster development in standard rearing vials
In this paper we propose the Hatching-Box, a novel in situ imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila melanogaster in standard rearing vials and during regular rearing routines, reducing the need for explicit experiments.This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled/empty pupae and flies over multiple days. Given the affordable and reproducible design of the Hatching-Box in combination with our generic client/server-based software, the system can easily be scaled to monitor an arbitrary amount of rearing vials simultaneously. We evaluated our system on a curated image dataset comprising nearly 416,000 annotated objects and performed several studies on real world experiments. We successfully reproduced results from well-established circadian experiments by comparing the eclosion periods of wild type flies to the clock mutants pershort, perlong and per0 without involvement of any manual labor. Furthermore we show, that the Hatching-Box is able to extract additional information about group behavior as well as population development and activity. These results not only demonstrate the applicability of our system for long-term experiments but also indicate its benefits for automated monitoring in the general cultivation process.
Differentiation of acute-phase AQP4-IgG+ optic neuritis from CNS inflammatory diseases using optic nerve head blood flow analysis
Evaluation of potential drug–drug interactions and its determinants among outpatient prescriptions in six community chain pharmacies in Asmara, Eritrea: a cross-sectional study
Correction: Probiotics attenuate valproate-induced liver steatosis and oxidative stress in mice
Potentiometric and spectroscopic characterization of uridine and its derivatives complexes
The association between frailty and recurrent pregnancy loss in reproductive-aged women: a cross-sectional study
Insulin resistance markers HOMA-IR, TyG and TyG-BMI index in relation to heart failure risk: NHANES 2011-2016
Background Insulin resistance (IR) is increasingly recognized as an important factor in the development of heart failure (HF). This study aimed to evaluate the association and predictive ability of three IR markers—HOMA-IR, TyG, and TyG-BMI index—with HF risk. Methods Data from 7,668 participants in the NHANES 2011–2016 survey were analyzed. Multivariable logistic regression was used to assess the relationship between HOMA-IR, TyG, and TyG-BMI with HF incidence, adjusting for potential confounders. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and restricted cubic spline (RCS) analysis were conducted to compare the predictive performance of the three markers. Results HOMA-IR (OR = 1.017, 95% CI: 1.006–1.027, P < 0.01), TyG (OR = 1.798, 95% CI: 1.453–2.225, P < 0.001), and TyG-BMI (OR = 1.006, 95% CI: 1.004–1.008, P < 0.001) were all significantly associated with HF risk, with TyG showing the strongest association. ROC curve analysis demonstrated that TyG (AUC = 0.61) and TyG-BMI (AUC = 0.62) had better predictive abilities than HOMA-IR (AUC = 0.6). In subgroup analyses, HOMA-IR showed higher sensitivity in the female population, while TyG-BMI provided a complementary role to TyG in individuals with diabetes. Conclusion TyG showed a stronger association with HF risk than HOMA-IR and TyG-BMI and outperformed HOMA-IR in predicting HF risk, particularly in specific subpopulations. These findings highlight the importance of further research into the clinical application of TyG for early identification and management of HF risk.