BACKGROUND: Semaglutide, a glucagon-like peptide 1 receptor agonist (GLP1RA), is available in both parenteral and oral preparations. Studies of injectable preparations have convincingly demonstrated its beneficial effect on major adverse cardiac events (MACE). This predictive analysis was undertaken to forecast early termination of the SOUL trial (oral semaglutide) as well as the primary events.
METHODS: SOUL is a multicenter, double-blind, placebo-controlled randomized controlled trial (RCT) evaluating the reduction in MACE associated with oral semaglutide versus placebo in patients with type 2 diabetes (T2D) and cardiovascular (CV) disease. A sample of 9642 participants will be followed for 5 years and 5 months. A random-effects model meta-analysis, pooling hazard ratios from previous RCTs, was conducted using R software to inform the predictive model. The background CV event rates from the placebo arms of previous RCTs with semaglutide were matched with the pre-adjudicated assumptions of the SOUL trial to create the predictive model. The truncated trial duration, MACE, and its individual components in the intervention and placebo arms were estimated. The predicted difference between the two groups was estimated using the chi-squared test.
RESULTS: A pooled analysis of 10,013 patients revealed a significant reduction in the number of MACEs associated with semaglutide (HR 0.79, 95% CI 0.69-0.91). Predictive analysis indicated that 1225 events would be achieved by 3.78 years, suggesting premature termination.
CONCLUSIONS: The mathematical model based on the meta-analysis predicts that the SOUL study on oral semaglutide will be terminated early, with oral semaglutide showing benefits in terms of MACE compared to placebo. If the SOUL study corroborates the findings of this model, it may not only form the basis for the calculation of power but also define the duration of such studies, reducing costs and easing the process of designing cardiovascular outcome trials (CVOTs).
BACKGROUND: INPLASY202460061.

The program code developed by others is appropriately cited in the text and listed in the references section. The raw and processed data on which study conclusions are based are not available. The statistical syntax needed to reproduce analyses in the article is available upon request. The methods section provides references for the materials described therein. We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study, and we follow APA Style Journal Article Reporting Standards. This study\'s design, hypotheses, and data analytic plan were not pre-registered. Prior research supports the need for elementary-aged students with reading difficulties (RD) to receive explicit systematic small group evidence-based reading instruction. Yet for many students, simply receiving an evidence-based reading instruction in a small group setting is insufficient to reach the progress milestones needed to meet grade level reading standards. The current study examined whether: (1) elementary school students with RD constitute a homogeneous or heterogeneous groups when considering their basic language and cognitive skills (using a latent profile analysis), and (2) if latent profiles are predictive of response to reading comprehension instruction (using a mixed modeling approach). The sample consisted of 335 students, including students with RD and typical students (n = 57). The results revealed heterogeneity within students with RD - there were two distinct profiles, with one having higher basic language (reading fluency and decoding) and cognitive (verbal domain productivity, cognitive flexibility, working memory) skills and lower attention skills, and the other having stronger attention skills and lower basic language and cognitive skills. The findings also suggested that latent profiles were predictive of response to reading comprehension instruction. Our results provide a convincing argument for leading the field in the direction of developing customized interventions. It is conceivable, but remains to be further examined, that researchers and educators could potentially improve reading outcomes through providing a customized reading intervention to a student based on their cognitive-language profile.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

We present a full Bayesian analysis of multiplicative double seasonal autoregressive (DSAR) models in a unified way, considering identification (best subset selection), estimation, and prediction problems. We assume that the DSAR model errors are normally distributed and introduce latent variables for the model lags, and then we embed the DSAR model in a hierarchical Bayes normal mixture structure. By employing the Bernoulli prior for each latent variable and the mixture normal and inverse gamma priors for the DSAR model coefficients and variance, respectively, we derive the full conditional posterior and predictive distributions in closed form. Using these derived conditional posterior and predictive distributions, we present the full Bayesian analysis of DSAR models by proposing the Gibbs sampling algorithm to approximate the posterior and predictive distributions and provide multi-step-ahead predictions. We evaluate the efficiency of the proposed full Bayesian analysis of DSAR models using an extensive simulation study, and we then apply our work to several real-world hourly electricity load time series datasets in 16 European countries.

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

BACKGROUND: The present study investigated the outcomes and possible predictive factors of autologous bone marrow cells (BMCs) therapy in patients with \"no-option\" critical limb ischaemia (CLI). It was focused on exploring the clinical background and prior statin and renin-angiotensin system (RAS)-acting agents pharmacotherapy related to the therapeutic efficacy of BMCs treatment.
METHODS: In the present study, we reviewed thirty-three patients (mean age 64.9 ± 10 years; 31 males) with advanced CLI after failed or impossible revascularisation, who were treated with 40 mL of autologous BMCs by local intramuscular application. Patients with limb salvage and wound healing (N = 22) were considered as responders to BMCs therapy, and patients with limb salvage and complete ischemic wound healing (N = 13) were defined as super-responders. Logistic regression models were used to screen and identify the prognostic factors, and a receiver operating characteristics (ROC) curve, a linear regression, and a survival curve were drawn to determine the predictive accuracy, the correlation between the candidate predictors, and the risk of major amputation.
RESULTS: Based on the univariate regression analysis, baseline C-reactive protein (CRP) and transcutaneous oxygen pressure (TcPO2) values were identified as prognostic factors of the responders, while CRP value, ankle-brachial index (ABI), and bone marrow-derived mononuclear cells (BM-MNCs) concentration were identified as prognostic factors of the super-responders. An area under the ROC curve of 0.768 indicated good discrimination for CRP > 8.1 mg/L before transplantation as a predictive factor for negative clinical response. Linear regression analysis revealed a significant dependence between the levels of baseline CRP and the concentration of BM-MNCs in transplanted bone marrow. Patients taking atorvastatin before BMCs treatment (N = 22) had significantly improved TcPO2 and reduced pain scale after BMCs transplant, compared to the non-atorvastatin group. Statin treatment was associated with reduced risk for major amputation. However, the difference was not statistically significant. Statin use was also associated with a significantly higher concentration of BM-MNCs in the transplanted bone marrow compared to patients without statin treatment. Patients treated with RAS-acting agents (N = 20) had significantly reduced pain scale after BMCs transplant, compared to the non-RAS-acting agents group. Similar results, reduced pain scale and improved TcPO2, were achieved in patients treated with atorvastatin and RAS-acting agents (N = 17) before BMCs treatment. Results of the Spearman correlation showed a significant positive correlation between CLI regression, responders, and previous therapy before BMCs transplant with RAS-acting agents alone or with atorvastatin.
CONCLUSIONS: CRP and TcPO2 were prognostic factors of the responders, while CRP value, ABI, and BM-MNCs concentration were identified as predictive factors of the super-responders. Atorvastatin treatment was associated with a significantly increased concentration of BM-MNCs in bone marrow concentrate and higher TcPO2 and lower pain scale after BMCs treatment in CLI patients. Similarly, reduced pain scales and improved TcPO2 were achieved in patients treated with atorvastatin and RAS-acting agents before BMCs treatment. Positive correlations between responders and previous treatment before BMCs transplant with RAS-acting agents alone or with atorvastatin were significant.

This research aims to identify the accessibility of the entire population, especially the slum population to existing healthcare facilities (HCF) as well as the slum neighborhoods having low geographic accessibility, and finally, to provide an analytical model for the people living in areas that are outside the coverage range of existing healthcare facilities (HCF) across the study area. Spatial data has been collected and used based on the road network, elevation, location of HCF, municipal boundary, slum point, and satellite images from various sources. Also, non-spatial data such as socioeconomic variables are collected from questionnaires survey within a particular period. The spatial analysis tool like as near, network analysis, and predictive analysis in the ArcGIS platform was used to examine geographic accessibility. The results of the spatial analysis show that the distribution of public healthcare facility centers in the study area has not been uniformly distributed. Across 84% of areas in the study area have sound spatial accessibility with traveling time coverage is about 12 min. However, 16% of areas have a traveling time of 12 to 30 min under low accessibility with existing slum neighborhoods. Therefore, the low spatial accessibility areas are demanding new healthcare facilities in the study area. The Analytical Hierarchy Process (AHP) is employed to find the most optimal and efficient locational suitability for building new healthcare facility centers. The finding of AHP analysis for site suitability of healthcare facilities revealed five major classes as most suitable (2%), suitable (5%), moderate (35%), poor (54%), and very poor (4%) in the study area. Moreover, the realistic framework of this study helps to measure geographic accessibility and suitability in any geographical area.

As one of the important paths for China to achieve the \"dual carbon\" strategy， developing hydrogen fuel cell vehicles is currently being promoted in various regions across the country， including passenger cars， coaches， and heavy-duty trucks. Quantifying the carbon reduction potential of hydrogen fuel cell vehicles for different vehicle types and regions has become a hot research topic. Using a life cycle assessment method that considers future vehicle fuel economy， power generation carbon emission factors， hydrogen production carbon emission factors， and regional differences in the scale and hydrogen production methods， this study quantitatively evaluated the life cycle carbon emissions of different types of vehicles， including fuel cell vehicles （FCV）， traditional fuel vehicles （ICEV）， and battery electric vehicles （BEV）. We compared and analyzed the carbon reduction potential of hydrogen fuel cell vehicles at different times and in different regions and conducted an uncertainty analysis on hydrogen consumption per hundred kilometers. The results showed that by 2025， the life cycle carbon emissions of hydrogen fuel cell coaches would decrease by 36.0% compared to that of traditional fuel coaches， but the reduction in carbon emissions for hydrogen fuel cell heavy-duty trucks was not significant. By 2035， as the hydrogen energy source structure in China continues to improve， the life cycle carbon emissions of hydrogen fuel cell heavy-duty trucks were predicted to decrease by 36.5% compared to that of traditional fuel heavy-duty trucks. The decarbonization potential was most significant for heavy-duty trucks compared to that of passenger cars and coaches. Taking the Beijing-Tianjin-Hebei demonstration group as an example in 2035， as the hydrogen consumption per hundred kilometers decreases by 20%， the carbon reduction potential of FCV passenger cars， coaches， and heavy-duty trucks would increase by 7.29%， 9.93%， and 19.57%， respectively. Therefore， it is recommended to prioritize the promotion of hydrogen fuel cell coaches in the short term， heavy-duty trucks in the long term， and passenger cars as a supplement. Promoting hydrogen fuel cell vehicles in different regions and stages will help advance the low-carbon development of the automotive industry in China.

Objective To analyze the trends of disease burden of cervical cancer,uterine cancer,and ovarian cancer among Chinese women from 1990 to 2019,and to provide a basis for formulating precise prevention and control measures in China. Methods The global disease burden data in 2019 were used to describe the changes in indicators such as incidence,mortality,years of life lost due to premature mortality(YLL),years lived with disability(YLD),and disability-adjusted life year(DALY) of cervical,uterine,and ovarian cancers in China from 1990 to 2019.Furthermore,the Bayesian age-period-cohort model was adopted to predict the incidence and mortality of the cancers from 2020 to 2030. Results From 1990 to 2019,the incidence rates and mortality of cervical,uterine,and ovarian cancers in Chinese women showed an upward trend,and the age-standardized incidence rate of ovarian cancer increased the most(0.78%).In 2019,the incidence of cervical cancer and uterine cancer concentrated in the women of 55-59 years old,and ovarian cancer mainly occurred in the women of 70-74 years old.The DALY,YLL,and YLD of cervical,uterine,and ovarian cancers all presented varying degrees of growth at all ages.The Bayesian age-period-cohort model predicted that from 2020 to 2030,the incidence and mortality of cervical cancer in China showed a decreasing trend,while those of uterine cancer and ovarian cancer showed an increasing trend.There was no significant change in the age with high incidence of the three cancers. Conclusions From 1990 to 2019,the overall disease burden of cervical,uterine,and ovarian cancers in China increased,while the disease burden of cervical cancer decreased after 2020.It is recommended that the efforts should be doubled for the prevention and control of cervical,uterine,and ovarian cancers.
目的 分析1990至2019年中国女性宫颈癌、子宫癌和卵巢癌的疾病负担变化趋势,为国家制订精准的防控策略提供依据。方法 基于2019年全球疾病负担数据,描述1990至2019年中国宫颈癌、子宫癌和卵巢癌的发病、死亡、早死亡损失寿命年(YLL)、伤残损失寿命年(YLD)、伤残调整生命年(DALY)等指标的变化情况,并使用贝叶斯年龄-时期-队列模型对2020至2030年发病及死亡情况进行预测。结果 1990至2019年中国女性宫颈癌、子宫癌和卵巢癌的发病率和死亡率均呈上升趋势,卵巢癌年龄标准化发病率增幅最高(0.78%)。2019年宫颈癌、子宫癌的高发年龄均为55~59岁,卵巢癌为70~74岁。宫颈癌、子宫癌和卵巢癌各年龄段的DALY、YLL、YLD均有不同程度的增长。贝叶斯年龄-时期-队列模型预测显示,2020至2030年中国宫颈癌的发病率和死亡率呈下降趋势,而子宫癌和卵巢癌的发病率和死亡率呈上升趋势,但3种肿瘤的高发年龄段没有明显变化。结论 1990至2019年中国宫颈癌、子宫癌和卵巢癌的疾病负担整体上升,2020年以后宫颈癌的疾病负担下降,建议进一步加强宫颈癌、子宫癌和卵巢癌的防控工作。.

UNASSIGNED: An increase in deaths has been perceived during the pandemic, which cannot be explained only by COVID-19. The actual number of deaths far exceeds the recorded data on deaths directly related to SARS-CoV-2 infection. Data from early and short-lived pandemic studies show a dramatic shift in cardiovascular mortality. Grounded in the post-pandemic era, macroscopic big data on cardiovascular mortality during the pandemic need to be further reviewed and studied, which is crucial for cardiovascular disease prevention and control.
UNASSIGNED: We retrieved and collected data associated with cardiovascular disease mortality from the National Vital Statistic System from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform based on the ICD-10 codes. We applied regression analysis to characterize overall cardiovascular disease mortality trends from 2010 to 2023 and built a time series model to predict mortality for 2020-2023 based on mortality data from 2010 to 2019 in order to affirm the existence of the excess deaths by evaluating observed vs. predicted mortality. We also conducted subgroup analyses by sex, age and race/ethnicity for the purpose of obtaining more specific sociodemographic information.
UNASSIGNED: All-cause age-standardised mortality rates (ASMRs) for CVD dramatically increased between 2019 and 2021[annual percentage change (APC) 11.27%, p < 0.01], and then decreased in the following 2021-2023(APC: -7.0%, p < 0.01). Subgroup analyses found that the ASMR change was most pronounced in Alaska Indians/Native American people (APC: 16.5% in 2019-2021, -12.5% in 2021-2023, both p < 0.01), Hispanics (APC: 12.1% in 2019-2021, -12.2% in 2021-2023, both p < 0.05) and non-Hispanic Black people (APC:11.8% in 2019-2021, -10.3% in 2021-2023, both p < 0.01)whether during the increasing or declining phase. Similarly, the ASMR change was particularly dramatic for the 25-44 age group (APC:19.8% in 2019-2021, -15.4% in 2021-2023, both p < 0.01) and males (APC: 11.5% in 2019-2021, -7.6% in 2021-2023, both p < 0.01). By the end of 2023, the proportion of COVID-related excess death remained high among the elderly (22.4%), males (42.8%) and Alaska Indians/Native American people(39.7%). In addition, we did not find the presence of excess deaths in the young (25-44) and middle-aged cohort (45-64) in 2023, while excess deaths remained persistent in the elderly.
UNASSIGNED: All-cause ASMRs for CVD increased notably during the initial two years of the COVID-19 pandemic and then witnessed a decline in 2021-2023. The cohorts (the young, males and minorities) with the steepest rise in mortality decreased at the fastest rate instead. Previous initiatives to promote cardiovascular health were effective, but further research on cardiovascular healthcare for the elderly and racial disparities should be attached to priority considering the presence of sociodemographic differences in CVD death.