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AbstractObjectiveTo provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission.DesignSystematic review.Data sourceOvid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019.Eligibility criteria for selecting studiesAll studies of predictive models for 28 day or 30 day hospital readmission that used EMR data.Outcome measuresCharacteristics of included studies, methods of prediction, predictive features, and performance of predictive models.ResultsOf 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77).ConclusionsOn average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

Over the past several decades, the overweight and obesity epidemic in the USA has resulted in a significant health and economic burden. Understanding current trends and future trajectories at both national and state levels is crucial for assessing the success of existing interventions and informing future health policy changes. We estimated the prevalence of overweight and obesity from 1990 to 2021 with forecasts to 2050 for children and adolescents (aged 5–24 years) and adults (aged ≥25 years) at the national level. Additionally, we derived state-specific estimates and projections for older adolescents (aged 15–24 years) and adults for all 50 states and Washington, DC. In this analysis, self-reported and measured anthropometric data were extracted from 134 unique sources, which included all major national surveillance survey data. Adjustments were made to correct for self-reporting bias. For individuals older than 18 years, overweight was defined as having a BMI of 25 kg/m2 to less than 30 kg/m2 and obesity was defined as a BMI of 30 kg/m2 or higher, and for individuals younger than 18 years definitions were based on International Obesity Task Force criteria. Historical trends of overweight and obesity prevalence from 1990 to 2021 were estimated using spatiotemporal Gaussian process regression models. A generalised ensemble modelling approach was then used to derive projected estimates up to 2050, assuming continuation of past trends and patterns. All estimates were calculated by age and sex at the national level, with estimates for older adolescents (aged 15–24 years) and adults aged (≥25 years) also calculated for 50 states and Washington, DC. 95% uncertainty intervals (UIs) were derived from the 2·5th and 97·5th percentiles of the posterior distributions of the respective estimates. In 2021, an estimated 15·1 million (95% UI 13·5–16·8) children and young adolescents (aged 5–14 years), 21·4 million (20·2–22·6) older adolescents (aged 15–24 years), and 172 million (169–174) adults (aged ≥25 years) had overweight or obesity in the USA. Texas had the highest age-standardised prevalence of overweight or obesity for male adolescents (aged 15–24 years), at 52·4% (47·4–57·6), whereas Mississippi had the highest for female adolescents (aged 15–24 years), at 63·0% (57·0–68·5). Among adults, the prevalence of overweight or obesity was highest in North Dakota for males, estimated at 80·6% (78·5–82·6), and in Mississippi for females at 79·9% (77·8–81·8). The prevalence of obesity has outpaced the increase in overweight over time, especially among adolescents. Between 1990 and 2021, the percentage change in the age-standardised prevalence of obesity increased by 158·4% (123·9–197·4) among male adolescents and 185·9% (139·4–237·1) among female adolescents (15–24 years). For adults, the percentage change in prevalence of obesity was 123·6% (112·4–136·4) in males and 99·9% (88·8–111·1) in females. Forecast results suggest that if past trends and patterns continue, an additional 3·33 million children and young adolescents (aged 5–14 years), 3·41 million older adolescents (aged 15–24 years), and 41·4 million adults (aged ≥25 years) will have overweight or obesity by 2050. By 2050, the total number of children and adolescents with overweight and obesity will reach 43·1 million (37·2–47·4) and the total number of adults with overweight and obesity will reach 213 million (202–221). In 2050, in most states, a projected one in three adolescents (aged 15–24 years) and two in three adults (≥25 years) will have obesity. Although southern states, such as Oklahoma, Mississippi, Alabama, Arkansas, West Virginia, and Kentucky, are forecast to continue to have a high prevalence of obesity, the highest percentage changes from 2021 are projected in states such as Utah for adolescents and Colorado for adults. Existing policies have failed to address overweight and obesity. Without major reform, the forecasted trends will be devastating at the individual and population level, and the associated disease burden and economic costs will continue to escalate. Stronger governance is needed to support and implement a multifaceted whole-system approach to disrupt the structural drivers of overweight and obesity at both national and local levels. Although clinical innovations should be leveraged to treat and manage existing obesity equitably, population-level prevention remains central to any intervention strategies, particularly for children and adolescents. Bill & Melinda Gates Foundation.

This study investigates the effects of Fe and Ag doping on the structure, surface properties, and electrochemical performance of a Ba-containing perovskite oxide for the oxygen evolution reaction (OER). Three catalysts, LBC, Fe-doped LBC (LBCF), and Ag-impregnated LBCF (LBCF-A), were synthesized using sol–gel and wet impregnation methods. Their physicochemical properties were characterized using XRD, FESEM, HRTEM, ICP, FTIR, and XPS. Electrochemical evaluations, including linear sweep voltammetry (LSV), Tafel analysis, and turnover frequency (TOF), were conducted to assess OER performance. Among these, LBCF-A exhibited the best OER activity, with an overpotential of 317 mV at 10 mA.cm −2 , a Tafel slope of 101 mV.dec −1 , and a significantly higher TOF. The enhanced performance is attributed to Fe-induced modulation of Co oxidation states, formation of oxygen vacancies, and Ag-mediated surface modifications that enriched surface hydroxyls (OH - ), O 2 , and H 2 O. These synergistic effects improved conductivity and charge transfer, making LBCF-A a promising candidate for efficient and cost-effective OER catalysis.

Tumor infiltrating lymphocytes (TILs) usually become exhausted and dysfunctional owing to chronic contact with tumor cells and overexpression of multiple inhibitor receptors. Activation of TILs by targeting the inhibitory and stimulatory checkpoints has emerged as one of the most promising immunotherapy prospectively. We investigated whether triggering of CD28, 4-1BB, and PD-1 checkpoints simultaneously or alone could enhance the immune response capacity of lymphocytes. In this regard, anti-PD-1, CD80-Fc, and 4-1BBL-Fc proteins were designed and produced in CHO-K1 cells as an expression host. Following confirmation of the Fc fusion proteins’ ability to bind to native targets expressed on engineered CHO-K1 cells (CHO-K1/hPD-1, CHO-K1/hCD28, CHO-K1/hCTLA4, and CHO-K1/h4-1BB), the effects of each protein, on its own and in various combinations, were assessed in vitro on T cell proliferation, cytotoxicity, and cytokines secretion using the Mixed lymphocyte reaction (MLR) assay, 7-AAD/CFSE cell-mediated cytotoxicity assay, and a LEGENDplex™ Human Th Cytokine Panel, respectively. MLR results demonstrated that T cell proliferation in the presence of the combinations of anti-PD-1/CD80-Fc, CD80-Fc/4-1BBL-Fc, and anti-PD-1/CD80-Fc/4-1BBL-Fc proteins was significantly higher than in the untreated condition (1.83-, 1.91-, and 2.02-fold respectively). Furthermore, anti-PD-1 (17%), 4-1BBL-Fc (19.2%), anti-PD-1/CD80-Fc (18.6%), anti-PD-1/4-1BBL-Fc (21%), CD80-Fc/4-1BBL-Fc (18.5%), and anti-PD-1/CD80-Fc/4-1BBL-Fc (17.3%) significantly enhanced cytotoxicity activity compared to untreated condition (7.8%). However, concerning the cytokine production, CD80-Fc and 4-1BBL-Fc alone or in combination significantly increased the secretion of IFN‐γ, TNF-α, and IL-2 compared with the untreated conditions. In conclusion, this research establishes that the various combinations of produced anti-PD-1, CD80-Fc, and 4-1BBL-Fc proteins can noticeably induce the immune response in vitro . Each of these combinations may be effective in killing or destroying cancer cells depending on the type and stage of cancer.

The Experimental Design was applied to optimize the electrocatalytic activity of La 0.8 Ba 0.2 CoO 3 perovskite oxide/Active Carbon composite material in the alkaline solution for the Oxygen Evolution Reaction. After the preparation of La 0.8 Ba 0.2 CoO 3 , and structural characterizations, the experimental design was utilized to determine the optimal amount of the composite material and testing conditions. The overpotential was defined as the response variable, and the mass ratio of perovskite/active carbon, Potassium hydroxide (KOH) concentration, and Poly(vinylidene fluoride) (PVDF) amount were considered effective parameters. The significance of model terms is demonstrated by P -values less than 0.0500. The proposed prediction model determined the optimal amounts of 0.665 mg of PVDF, a KOH concentration of 0.609 M, and A perovskite/Active Carbon mass ratio of 2.81 with 308.22 mV overpotential (2.27% greater than the actual overpotential). The stability test of the optimized electrode material over 24 h suggests that it could be a good candidate electrocatalyst for OER with reusability potential.

Physical mutagens such as low-dose gamma irradiation and cold plasma have recently emerged as eco-friendly tools for enhancing plant vigor, stress tolerance, and disease resistance. However, their impact on genetic stability remains insufficiently characterized. Here, we performed transcriptome-wide single-nucleotide polymorphism (SNP) discovery in Solanum lycopersicum seedlings infected with Tomato brown rugose fruit virus (ToBRFV; Tobamovirus fructirugosum ) and subjected to either 15 Gy gamma irradiation or cold plasma treatment. RNA-Seq analysis revealed distinct mutational footprints: gamma irradiation induced 82 high-confidence SNPs, whereas cold plasma generated 36, with only two SNPs shared between treatments. Chromosomal mapping indicated that gamma-induced SNPs were clustered on chromosomes 7, 12, and 9, while cold plasma-associated mutations were more evenly distributed, predominantly on chromosomes 6 and 11. Most SNPs were localized within protein-coding regions, resulting exclusively in nonsynonymous substitutions; however, the limited SNP dataset and transcriptome-based approach prevent robust inference of selection pressure. Functionally, gamma-induced SNPs were enriched in genes related to terpene biosynthesis, lipid metabolism, and secondary metabolite pathways, while cold plasma targeted genes associated with transcriptional regulation, redox signaling, and chloroplast function, which are closely linked to hormone-mediated signaling networks such as auxin pathways that coordinate plant stress responses and developmental adaptation. Protein modeling further highlighted amino acid substitutions in conserved domains of NB-LRR and regulatory proteins, suggesting possible contributions to plant stress and immune responses. Collectively, our results demonstrate that both treatments induce limited yet functionally relevant transcriptomic mutations within expressed genes, without evidence of widespread mutational disruption at the transcriptome level. However, because the analysis is based on RNA-Seq data, these findings reflect transcriptome-level stability rather than genome-wide genomic safety, and further validation using whole-genome sequencing would be required to assess genome-wide mutational effects.

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement in technology, the implementation of artificial intelligence techniques is done for the reliability analysis of the structure. Designing the structure based on the probability of failure leads to an economical design. Machine learning models used for predicting the factor of safety of the wall are Emotional Neural Network, Multivariate Adaptive Regression Spline, and SOS–LSSVM. The First-Order Second Moment Method is used for calculating the reliability index of the wall. In addition, these models are assessed based on the results they produce, and the best model among these is concluded for extensive field study in the future. The overall performance evaluation through various accuracy quantification determined SOS–LSSVM as the best model. The obtained results show that the reliability index calculated by the AI methods differs from the reference values by less than 2%. These methodologies have made the problems facile by increasing the precision of the result. Artificial intelligence has removed the cumbersome calculations in almost all the acquainted fields and disciplines. The techniques used in this study are evolved versions of some older algorithms. This work aims to clarify the probabilistic approach toward designing the structures, using the artificial intelligence to simplify the practical evaluations.

Study designLongitudinal cohort study of privately insured beneficiaries with and without traumatic spinal cord injury (SCI).ObjectivesCompare the incidence of and adjusted hazards for psychological morbidities among adults with and without traumatic SCI, and examine the effect of chronic centralized and neuropathic pain on outcomes.SettingPrivately insured beneficiaries were included if they had an ICD-9-CM diagnostic code for traumatic SCI (n = 9081). Adults without SCI were also included (n = 1,474,232).MethodsIncidence of common psychological morbidities were compared at 5-years of enrollment. Survival models were used to quantify unadjusted and adjusted hazard ratios for incident psychological morbidities.ResultsAdults with SCI had a higher incidence of any psychological morbidity (59.1% vs. 30.9%) as compared to adults without SCI, and differences were to a clinically meaningful extent. Survival models demonstrated that adults with SCI had a greater hazard for any psychological morbidity (HR: 1.67; 95%CI: 1.61, 1.74), and all but one psychological disorder (impulse control disorders), and ranged from HR: 1.31 (1.24, 1.39) for insomnia to HR: 2.10 (1.77, 2.49) for post-traumatic stress disorder. Centralized and neuropathic pain was associated with all psychological disorders, and ranged from HR: 1.31 (1.23, 1.39) for dementia to HR: 3.83 (3.10, 3.68) for anxiety.ConclusionsAdults with SCI have a higher incidence of and risk for common psychological morbidities, as compared to adults without SCI. Efforts are needed to facilitate the development of early interventions to reduce risk of chronic centralized and neuropathic pain and psychological morbidity onset/progression in this higher risk population.

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

BackgroundThere is a paucity of evidence regarding the association between family physicians’ panel size and health outcomes of patients with hypertension in China.ObjectiveTo examine the association between family physicians’ panel size and health outcomes of patients with hypertension in urban China.DesignThis retrospective cohort study during 1 contract year from July 1, 2018, to June 31, 2019, was set in four community health centers (CHCs) in Xiamen City, China.ParticipantsA total of 18,119 adult patients (18+) diagnosed with hypertension and their 61 family physicians were included.Main MeasuresFamily physicians’ panel size was measured by the number of registered patients in the preceding 6 months. The outcome measures included blood pressure (BP) control rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) measured at each follow-up visit.Key ResultsEvery additional 100 patients to the panel size were associated with an average of 17% increase in BP control rate (95% confidence interval [CI] = 1.15 to 1.19), and decrease in SBP (− 0.3 mmHg, 95% CI: − 0.38 to − 0.30), DBP (− 0.4 mmHg, 95% CI: − 0.39 to − 0.34), and MAP (− 0.4 mmHg, 95% CI: − 0.38 to − 0.33). After entering the quadratic term of panel size in the model, the panel size was negatively associated with BP control rate and positively associated with SBP, DBP, and MAP, while for the quadratic term, the odds ratio for BP control rate was positive and the coefficients for SBP, DBP, and MAP were negative. A U-shape association was found between panel size and health outcomes of patients with hypertension, and the turning point was about 600 patients.ConclusionsThe panel size of family physicians was curvilinearly associated with health outcomes of patients with hypertension in urban China.