Assessment of left ventricular ejection fraction in artificial intelligence based on left ventricular opacification.

Background: Left ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF. Objectives: The aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF. Methods: This retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated. Results: In LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95). Conclusions: AI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.

Do the unusual resignations of independent directors affect auditors' professional judgment?

In Kangmei's first trial judgment, where the independent directors faced significant joint and several liabilities, it triggered a "wave" of resignations among independent directors of listed companies. Auditors auditing financial reports of these companies might consider this a significant signal, raising the question: does this signal influence their professional judgment? The study examines the relationship between the resignation of independent directors and auditors' professional judgment in A-share listed companies, following Kangmei's initial trial. This examination is conducted across three dimensions: audit pricing, audit input, and audit opinion.The findings indicate that the unusual resignations of independent directors prompt uditors to pay increased attention to the risk of material misstatements by clients, primarily reflected in audit pricing. However, these resignations do not significantly impact audit input or the judgment of audit opinions. Furthermore, this research enriches the existing literature on audit pricing and the role of independent directors, while also unveiling the specific pathways through which the departure of independent directors impacts auditors' professional judgment.

A new simplified risk assessment model enhances postoperative prophylaxis of venous thromboembolism in Chinese adult patients with inguinal hernia (CHAT-3): A prospective, multicenter, randomized controlled trial.

BACKGROUND: Venous thromboembolism (VTE) significantly affects the prognosis of surgical patients with inguinal hernia. The complex Caprini score, commonly used for postoperative VTE risk assessment, poses practical challenges for surgeons in clinical settings. METHODS: The CHAT-3 trial, a prospective, multicenter, randomized controlled trial, compared a simple three-factor model to assess VTE risk against routine practices in post-inguinal hernia surgery (IHS) patients. The patients were randomly assigned (1:1) to the intervention or control arm. The intervention group used the three-factor model to identify patients at moderate or high risk of VTE for subsequent prophylaxis according to clinical guidelines. Both groups were followed for four weeks, with randomization implemented using computer-generated sequences. The primary outcome measured was the rate of VTE prophylaxis. Secondary outcomes included time spent on VTE risk assessment (surgeon self-reported), postoperative D-dimer trends, perioperative VTE occurrence, bleeding events, and the net clinical benefit. RESULTS: Of the 1,109 participants, 508 in the experimental group and 601 in the control group completed follow-up. The three-factor model showed higher VTE prophylaxis rates in all patients (pharmacologic prophylaxis: 26.2% vs. 6.00%, P<0.001) and particularly in those at high risk (pharmacologic prophylaxis: 57.3% vs. 9.50%, P<0.001). The experimental group significantly reduced VTE risk assessment time compared to the Caprini score (1.39±0.55 min vs. 5.73±1.35 min, P<0.001). The experimental group had lower D-dimer levels (0.26±0.73 mg/L vs. 0.35±0.55 mg/L, P=0.028). In the experimental group, the patients did not experience an increased risk of VTE (0% vs. 1.66%, P=0.268) and bleeding (1.18% vs. 0.67%, P=0.558) compared to the controls. There was no significant difference in net clinical benefit, which combined VTE and bleeding events, between the experimental and control groups (1.18% vs. 0.83%, P=0.559). CONCLUSION: Applying the simple three-factor model in perioperative VTE management could quickly identify the patient with a high risk of VTE and improve the prophylaxis rate of perioperative VTE. TRIAL REGISTRATION: XXX. TRIAL REGISTRATION: ChiCTR2000033769.

Effect of out-of-school visual art activities on academic performance. The mediating role of socioeconomic status.

The application of visual art and other extracurricular activities to children's sustainable development is predominantly discussed in Western countries. Consequently, non-Western society could not cherish the benefit of visual art on their children's cognitive and non-cognitive skill development due to a lack of evidence that would revive the community, educators, and policy-makers' impressions about visual art activities, in addition to its amusement use. Thus, the present study adopted a cross-sectional study comprised of a large-scale survey (N = 1624) taken from the southwest part of China to assess the impact of out-of-school visual art activities on children's academic attainment across economically advantaged and disadvantaged children. Astonishingly, the study's findings shed light on current Chinese parents' dedication to purchasing out-of-school activities regardless of their social class difference; notwithstanding, lower-class parents ought to learn that spending time with their children during their activities is more beneficial. The study's implication calls for curriculum policy reform involving aesthetic education and expanding community youth centers for different extracurricular activities.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids.

Disease modeling with isogenic Induced Pluripotent Stem Cell (iPSC)-differentiated organoids serves as a powerful technique for studying disease mechanisms. Multiplexed coculture is crucial to mitigate batch effects when studying the genetic effects of disease-causing variants in differentiated iPSCs or organoids, and demultiplexing at the single-cell level can be conveniently achieved by assessing natural genetic barcodes. Here, to enable cost-efficient time-series experimental designs via multiplexed bulk and single-cell RNA-seq of hybrids, we introduce a computational method in our Vireo Suite, Vireo-bulk, to effectively deconvolve pooled bulk RNA-seq data by genotype reference, and thereby quantify donor abundance over the course of differentiation and identify differentially expressed genes among donors. Furthermore, with multiplexed scRNA-seq and bulk RNA-seq, we demonstrate the usefulness and necessity of a pooled design to reveal donor iPSC line heterogeneity during macrophage cell differentiation and to model rare WT1 mutation-driven kidney disease with chimeric organoids. Our work provides an experimental and analytic pipeline for dissecting disease mechanisms with chimeric organoids.

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model. METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles. FINDINGS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables. FUNDING: This study was supported by the National Key R&D Program of China.

Life cycle assessment of sewage sludge treatment and disposal technologies based on carbon emissions and environmental impacts.

This study aimed to create a comprehensive evaluation method for sewage sludge (SS) treatment and disposal technologies, considering carbon emission and environmental impacts. Life cycle assessment (LCA) were conducted on six SS treatment and disposal technologies in China. The assessments used the IPCC emission factor approach to calculate carbon emissions and the CML2001 method to determine environmental impact factors. Additionally, a colour-coded method was implemented to quantify the evaluation results. The study found that S1 (anaerobic digestion + land application) had the lowest carbon emissions and environmental impact, making it the optimal technology. The S1 scenario had carbon emissions of 669 kg CO2(t DS)-1 and environmental impacts of 5.20E-10. A sensitivity analysis was conducted to show the impacts of each unit in the six technologies on total carbon emissions and environmental impacts. The results showed that landfilling has a high sensitivity to carbon emissions and environmental impacts. Therefore, controlling greenhouse gases and toxic substances in sludge landfills is crucial for reducing carbon emissions and environmental pollution.

Bayesian compositional models for ordinal response.

Ordinal response is commonly found in medicine, biology, and other fields. In many situations, the predictors for this ordinal response are compositional, which means that the sum of predictors for each sample is fixed. Examples of compositional data include the relative abundance of species in microbiome data and the relative frequency of nutrition concentrations. Moreover, the predictors that are strongly correlated tend to have similar influence on the response outcome. Conventional cumulative logistic regression models for ordinal responses ignore the fixed-sum constraint on predictors and their associated interrelationships, and thus are not appropriate for analyzing compositional predictors.To solve this problem, we proposed Bayesian Compositional Models for Ordinal Response to analyze the relationship between compositional data and an ordinal response with a structured regularized horseshoe prior for the compositional coefficients and a soft sum-to-zero restriction on coefficients through the prior distribution. The method was implemented with R package rstan using efficient Hamiltonian Monte Carlo algorithm. We performed simulations to compare the proposed approach and existing methods for ordinal responses. Results revealed that our proposed method outperformed the existing methods in terms of parameter estimation and prediction. We also applied the proposed method to a microbiome study HMP2Data, to find microorganisms linked to ordinal inflammatory bowel disease levels. To make this work reproducible, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCO.

Using Pupil Diameter for Psychological Resilience Assessment in Medical Students Based on SVM and SHAP Model.

Effectively assessing psychological resilience for medical students is vital for identifying at-risk individuals and developing tailored interventions. At present, few studies have combined physiological indexes of the human body and machine learning for psychological resilience assessment. This study presents a novel approach that employs pupil diameter features and machine learning to predict psychological resilience risk objectively. Firstly, we designed a stimulus paradigm (via auditory and visual stimuli) and collected pupil diameter data from participants using eye-tracking technology. Secondly, the pupil data was preprocessed, including linear interpolation, blink detection, and subtractive baseline correction. Thirdly, statistical metrics were extracted and optimal feature subsets were obtained by Recursive Feature Elimination with Cross-Validation (RFECV). Subsequently, the classification models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were trained. The experimental results show that the SVM model has the best performance, and its balance accuracy, recall, and AUC reach 0.906, 0.89, and 0.932, respectively. Finally, we leveraged the Shapley additive explanation (SHAP) model for interpretability analysis. It revealed auditory stimuli have a more significant effect than visual stimuli in psychological resilience assessment. These findings suggested that pupil diameter could be a vital metric for assessing psychological resilience.

Assessment of barriers to cancer screening and interventions implemented to overcome these barriers in 27 Latin American and Caribbean countries.

There is a gap in the understanding of the barriers to cancer screening participation and complying with downstream management in the Community of Latin American and Caribbean states (CELAC). Our study aimed to assess barriers across the cancer screening pathway from the health system perspective, and interventions in place to improve screening in CELAC. A standardized tool was used to collect information on the barriers across the screening pathway through engagement with the health authorities of 27 member states of CELAC. Barriers were organized in a framework adapted from the Tanahashi conceptual model and consisted of the following dimensions: availability of services, access (covering accessibility and affordability), acceptability, user-provider interaction, and effectiveness of services (which includes governance, protocols and guidelines, information system, and quality assurance). The tool also collected information of interventions in place, categorized in user-directed interventions to increase demand, user-directed interventions to increase access, provider-directed interventions, and policy and system-level interventions. All countries prioritized barriers related to the information systems, such as the population register not being accurate or complete (N = 19; 70.4%). All countries implemented some kind of intervention to improve cancer screening, group education being the most reported (N = 23; 85.2%). Training on screening delivery was the most referred provider-directed intervention (N = 19; 70.4%). The study has identified several barriers to the implementation of cancer screening in the region and interventions in place to overcome some of the barriers. Further analysis is required to evaluate the effectiveness of these interventions in achieving their objectives.

Compilation of a self-management assessment scale for postoperative patients with aortic dissection.

Objective: The aim of this research was to compile a self-management assessment scale for patients with aortic dissection (AD). The questionnaire is useful in making the patient aware of the need for post-operative care in order to contribute to improving the outcome and quality of life. Methods: The initial version of the "postoperative self-management assessment scale for patients with aortic dissection" was developed using the Delphi expert consultation method based on qualitative research results, consultation of self-management-related literature, reference to the existing self-management scale, and self-efficacy theory, combined with the disease characteristics of AD. By using the convenience sampling method, a total of 201 patients with AD who had undergone surgery were selected as the research participants. The initial version of the scale was used for follow-up investigation, and the scale entries were evaluated and exploratory factor analysis carried out to form the formal version of the "postoperative self-management assessment scale for patients with aortic dissection." A total of 214 patients with AD after surgery were selected as the research participants. The formal version of the scale was used for follow-up investigation, and its reliability and validity were evaluated. Results: The formal version of the scale had 6 dimensions and 35 entries. The Cronbach's α coefficient for the total scale was 0.908, the split-half reliability was 0.790, and the test-retest reliability after 2 weeks was 0.471. The content validity index of the total scale was 0.963. Exploratory factor analysis yielded six common factors, and the cumulative contribution rate of variance was 66.303%. Confirmatory factor analysis showed that except for the incremental fit index, Tucker-Lewis index, and comparative fit index >0.85, slightly lower than 0.90, χ 2/df <3, root mean square of approximation <0.08, parsimonious goodness-of-fit index, and parsimonious normed fit index >0.50; all other model fitting requirements were satisfied, indicating that the model fitting was acceptable. Conclusion: We compiled the postoperative self-management assessment scale for patients with AD, which has demonstrated excellent reliability and validity and can be used as a tool to evaluate the postoperative self-management level in patients with aortic dissection.

Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study.

INTRODUCTION: Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial. METHODS AND ANALYSIS: In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups. ETHICS AND DISSEMINATION: The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR2300069496.

Enterprise innovation and audit pricing: An evidence study from China's A-share listed companies.

Driven by innovation strategy, Chinese enterprises' innovation investment, and research and development capability have been continuously improved, and the audit risk caused by this has attracted widespread attention from the academic community. This study takes China's A-share listed companies from 2013 to 2021 as samples to empirically test the relationship between innovation input and audit pricing of Chinese enterprises. Research shows that the higher the innovation investment, the higher the audit cost. High-quality corporate governance, sufficient research and development personnel, research and development subsidies, and operating cash flow can all play a negative moderating role. A good innovation environment will weaken the positive influence between innovation input and audit fees. This study theoretically confirms the risk-oriented audit pricing mechanism, which is of great significance for optimizing enterprise innovation risk management and improving audit service levels.

Assessment of Nursing Practice Environment and Its Influencing Factors: A Cross-Sectional Study at Shandong Province, Jinan.

Objective: To investigate the current nursing practice environment in Jinan, Shandong Province, and to identify the factors influencing the practice environment. Methods: This study is a cross-sectional study for nurses. From October to December 2022, using the clustering and stratified sampling methods, 2426 nurses from internal Medicine, Surgery, Obstetrics and Gynecology, Outpatient Department and Intensive Care Department of the Provincial Hospital of Shandong Medical University were selected and then investigated and analyzed using the revised Nurse Practice Environment Assessment Scale. Results: The overall mean evaluation of the practice environment scored 75.13±19.87, with a minimum value of 59.74 and a maximum value of 95.82. The items with higher scores were "the hospital has systematic training for new nurses", "the work system is perfect", and "the hospital can provide continuing education for nurses in accordance with the needs of their positions". The items with lower scores were "nurses enjoy legal benefits", "nurses have the opportunity to participate in hospital management decisions", and "nurses have the opportunity to participate in hospital internal management". The results of the multiple linear regression analysis of the factors influencing nurses' practice environment showed that gender, education, position, and years of work were independent influences on nurses' practice environment scores (p < 0.05), and they explained 48.127% of the variation in the total scores of the nurses' practice environment scale. The estimated values (ß) of sex, education, cheif nurse, nurses staff, work experience (year), and whether the only child variables were 3.141, 3.237, 2.713, 5.471, 2.074 and 0.732, respectively. Conclusion: The nurse practice environment still needs to be improved, mainly in terms of hospital management participation, human resource allocation and salary distribution system.

Parents' acceptance attitudes towards the vaccination of children based on M-LSGDM approach in China: a cross-sectional study.

OBJECTIVES: Ensuring that children receive timely vaccinations is paramount for preventing infectious diseases, and parental attitude plays a pivotal role in this process. This study addresses this gap in the existing literature by examining parental attitudes towards vaccinating their children. DESIGN: A cross-sectional study. METHODS: An online survey including parents' sociodemographic characteristics, risk perception and attitudes towards child vaccination towards COVID-19 was conducted. The modified large-scale group decision-making approach for practicality and binary logistic regression was used to identify the predictors influencing parents' decision-making. RESULTS: Of the 1292 parents participated, 957 (74.1%) were willing to vaccinate their children, while 335 (25.9%) refused the vaccination. The study indicated that age, parental anxiety regarding child vaccination, concerns about the child's susceptibility to the disease, opinions towards vaccination benefits versus disadvantages, place of residence, average family income and children's health were significant predictors (p<0.05). CONCLUSIONS: While most parents supported childhood vaccination, some opposed it. Addressing persistent barriers is crucial to ensure widespread vaccination and child well-being.

Comparison of bioelectrical impedance analysis and computed tomography for the assessment of muscle mass in patients with gastric cancer.

BACKGROUND: Low muscle mass was significantly correlated with poor clinical outcomes in cancer patients. This study aimed to compare the differences between bioelectrical impedance analysis (BIA) and computed tomography (CT) in measuring skeletal muscle mass and detecting low muscle mass in patients with gastric cancer (GC). METHOD: This cross-sectional study included a total of 302 consecutive patients diagnosed with GC at our institution from October 2021 to March 2023. CT images were analyzed at the L3 level to obtain the cross-sectional area of skeletal muscle, which was subsequently used for calculating whole-body skeletal muscle mass via the Shen equation and skeletal muscle tissue density. BIA was utilized to measure skeletal muscle mass using the manufacturer's proprietary algorithms. Skeletal muscle mass (kg) was divided by height squared (m2) to obtain skeletal muscle index (SMI, kg/m2). Pearson's correlation coefficient was performed to assess the correlation between SMI measured by BIA and CT. The agreement between the two methods was assessed using Bland-Altman analyses. The clinically acceptable agreement was defined as the 95% limits of agreement (LOA) for the percentage bias falling within ± 10%. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of BIA in identifying low muscle mass. RESULTS: A total of 59 patients (19.5%) were identified as having low muscle mass based on CT analysis, whereas only 19 patients (6.3%) met the criteria for low muscle mass according to BIA analysis. BIA-measured SMI showed a strong positive correlation with CT-measured SMI in all patients (r = 0.715, P < 0.001). With Bland-Altman analysis, there was a significant mean bias of 1.18 ± 0.96 kg/m2 (95% CI 1.07-1.29, P < 0.001) between SMI measured by BIA and CT. The 95% LOA for the percentage bias ranged from -7.98 to 33.92%, which exceeded the clinically acceptable range of ± 10%. A significant difference was observed in the mean bias of SMI measured by BIA and CT between patients with and without GLIM malnutrition (1.42 ± 0.91 kg/m2 versus 0.98 ± 0.96 kg/m2, P < 0.001). The cut-off values for BIA-measured SMI in identifying low muscle mass using CT as the reference were 10.11 kg/m2 for males and 8.71 kg/m2 for females (male: AUC = 0.840, 95% CI: 0.772-0.908; female: AUC = 0.721, 95% CI: 0.598-0.843). CONCLUSIONS: Despite a significant correlation, the values of skeletal muscle mass obtained BIA and CT cannot be used interchangeably. The BIA method may overestimate skeletal muscle mass in GC patients compared to CT, especially among those with GLIM malnutrition, leading to an underestimation of low muscle mass prevalence.

Prevalence of thalassemia-carrier couples and fertility risk assessment.

Thalassemia is a highly prevalent hematologic disease in Guizhou, China. This study aimed to determine the epidemiological characteristics of thalassemia in couples at childbearing age and assess the neonatal risk of thalassemia in this subpopulation. A cohort of 4481 couples at childbearing age were recruited for thalassemia carrier screening by both traditional hematological tests and next-generation sequencing. Of them, 1314 (14.66%) thalassemia carriers were identified, including 857 (9.76%) α-thalassemia, 391 (4.36%) ß-thalassemia, and 48 (0.54%) composite α and ß-thalassemia. A total of 12 α-globin gene alterations and 16 ß-globin mutations were detected, including four novel thalassemia mutations. SEA was the most common α-thalassemia genotype (26.86%), CD41-42 the most common ß-thalassemia genotype (36.57%), and αα/- α3.7 + CD41-42 the most common composite α- and ß-thalassemia genotype (18.75%). Ethnically, the Zhuang had the highest rate of thalassemia gene carriers among the ethnic groups. Geographically, Qiannan had the highest rate of thalassemia gene carriers. In addition, 38 of the 48 couples with composite α- and ß-thalassemia were high-risk thalassemia carriers, and 4 carrying the -SEA/αα gene needed fertility guidance.