A nomogram to predict in-hospital mortality of gastrointestinal bleeding patients in the intensive care unit

BackgroundGastrointestinal bleeding (GIB) is a common condition in clinical practice, and predictive models for patients with GIB have been developed. However, assessments of in-hospital mortality due to GIB in the intensive care unit (ICU), especially in critically ill patients, are still lacking. This study was designed to screen out independent predictive factors affecting in-hospital mortality and thus establish a predictive model for clinical use.MethodsThis retrospective study included 1,442 patients with GIB who had been admitted to the ICU. They were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 1.0 database and divided into a training group and a validation group in a ratio of 7:3. The main outcome measure was in-hospital mortality. Least absolute shrinkage and section operator (LASSO) regression was used to screen out independent predictors and create a nomogram.ResultsLASSO regression picked out nine independent predictors: heart rate (HR), activated partial thromboplastin time (aPTT), acute physiology score III (APSIII), sequential organ failure assessment (SOFA), cerebrovascular disease, acute kidney injury (AKI), norepinephrine, vasopressin, and dopamine. Our model proved to have excellent predictive value with regard to in-hospital mortality (the area under the receiver operating characteristic curve was 0.906 and 0.881 in the training and validation groups, respectively), as well as a good outcome on a decision curve analysis to assess net benefit.ConclusionOur model effectively predicts in-hospital mortality in patients with GIB, indicating that it may prove to be a valuable tool in future clinical practice

基于最優電壓矢量的有源濾波器電流控制新方法

A novel hysteresis current control method for active power filter based on optimal voltage vector is suggested. The method jointly utilizes two sets of phase current difference comparators to determine the locations of the reference voltage vector and the current error vector. When the system reference voltage vector is changing rapidly and randomly which makes it difficult to estimate its location, the new method can fast detect its correct position by a single try-and-error process which is essential to the performance of active power filter. The related concept and formulation are presented. Computer simulation is conducted on a testing system and the results show that the method can fast determine optimal voltage vector, therefore effectively reduce current tracking error, and in the meanwhile apparently reduce switching frequency of voltage source inverter (VSI) and hence improve efficiency of active power filter noticeably. 提出了一種新的基于最優電壓矢量的有源濾波器滯環電流控制方法。該方法的特點是用一組滯環相間電流比較器和一組階梯式相間電流比較器相結合，快速、正確地判斷有源濾波器參考電壓空間矢量所在的區域，并由此決定最優電壓矢量及對有源濾波器實行滯環電流控制。用電磁暫態程序進行的計算機仿真結果表明，該方法能快速、正確地確定最優電壓矢量，從而可有效地降低開關頻率和提高有源濾波器的效率。其突出優點是在參考電壓空間矢量變化較快且難以預測的情況下，仍能快速跟蹤及確定其所在的區域，從而可有效地減少電流補償誤差，改善有源濾波器的性能

Recombinant mycobacterium tuberculosis fusion protein for diagnosis of mycobacterium tuberculosis infection: a short-term economic evaluation

ObjectivesRecombinant Mycobacterium tuberculosis fusion protein (EC) was anticipated to be used for the scale-up of clinical application for diagnosis of Mycobacterium tuberculosis infection in China, but it lacked a head-to-head economic evaluation based on the Chinese population. This study aimed to estimate the cost-utility and the cost-effectiveness of both EC and tuberculin pure protein derivative (TB-PPD) for diagnosis of Mycobacterium tuberculosis infection in the short term.MethodsFrom a Chinese societal perspective, both cost-utility analysis and cost-effectiveness analysis were performed to evaluate the economics of EC and TB-PPD for a one-year period based on clinical trials and decision tree model, with quality-adjusted life years (QALYs) as the utility-measured primary outcome and diagnostic performance (including the misdiagnosis rate, the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided) as the effective-measured secondary outcome. One-way and probabilistic sensitivity analyses were performed to validate the robustness of the base-case analysis, and a scenario analysis was conducted to evaluate the difference in the charging method between EC and TB-PPD.ResultsThe base-case analysis showed that, compared with TB-PPD, EC was the dominant strategy with an incremental cost-utility ratio (ICUR) of saving 192,043.60 CNY per QALY gained, and with an incremental cost-effectiveness ratio (ICER) of saving 7,263.53 CNY per misdiagnosis rate reduction. In addition, there was no statistical difference in terms of the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided, and EC was a similar cost-saving strategy with a lower test cost (98.00 CNY) than that of TB-PPD (136.78 CNY). The sensitivity analysis showed the robustness of cost-utility and cost-effectiveness analysis, and the scenario analysis indicated cost-utility in EC and cost-effectiveness in TB-PPD.ConclusionThis economic evaluation from a societal perspective showed that, compared to TB-PPD, EC was likely to be a cost-utility and cost-effective intervention in the short term in China

Prevalence of Mycobacterium bovis in deer in mainland China: a systematic review and meta-analysis

BackgroundDeer tuberculosis is a chronic zoonotic infectious disease, despite the existence of socio-economic and zoonotic risk factors, but at present, there has been no systematic review of deer tuberculosis prevalence in mainland China. The aim of this meta-analysis was to estimate the overall prevalence of deer TB in mainland China and to assess possible associations between potential risk factors and the prevalence of deer tuberculosis.MethodologyThis study was searched in six databases in Chinese and English, respectively (1981 to December 2023). Four authors independently reviewed the titles and abstracts of all retrieved articles to establish the inclusion exclusion criteria. Using the meta-analysis package estimated the combined effects. Cochran’s Q-statistic was used to analyze heterogeneity. Funnel plots (symmetry) and used the Egger’s test identifying publication bias. Trim-and-fill analysis methods were used for validation and sensitivity analysis. we also performed subgroup and meta-regression analyses.ResultsIn this study, we obtained 4,400 studies, 20 cross-sectional studies were screened and conducted a systematic review and meta-analysis. Results show: The overall prevalence of tuberculosis in deer in mainland China was 16.1% (95% confidence interval (CI):10.5 24.6; (Deer tuberculosis infected 5,367 out of 22,215 deer in mainland China) 5,367/22215; 1981 to 2023). The prevalence in Central China was the highest 17.5% (95% CI:14.0–21.9; 63/362), and among provinces, the prevalence in Heilongjiang was the highest at 26.5% (95% CI:13.2–53.0; 1557/4291). Elaphurus davidianus was the most commonly infected species, with a prevalence of 35.3% (95% CI:18.5–67.2; 6/17). We also assessed the association between geographic risk factors and the incidence of deer tuberculosis.ConclusionDeer tuberculosis is still present in some areas of China. Assessing the association between risk factors and the prevalence of deer tuberculosis showed that reasonable and scientific-based breeding methods, a suitable breeding environment, and rapid and accurate detection methods could effectively reduce the prevalence of deer tuberculosis. In addition, in the management and operation of the breeding base, improving the scientific feed nutrition standards and establishing comprehensive standards for disease prevention, immunization, quarantine, treatment, and disinfection according to the breeding varieties and scale, are suggested as ways to reduce the prevalence of deer tuberculosis

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data

Novel Biomarkers Distinguishing Active Tuberculosis from Latent Infection Identified by Gene Expression Profile of Peripheral Blood Mononuclear Cells

BACKGROUND: Humans infected with Mycobacterium tuberculosis (MTB) can delete the pathogen or otherwise become latent infection or active disease. However, the factors influencing the pathogen clearance and disease progression from latent infection are poorly understood. This study attempted to use a genome-wide transcriptome approach to identify immune factors associated with MTB infection and novel biomarkers that can distinguish active disease from latent infection. METHODOLOGY/PRINCIPAL FINDINGS: Using microarray analysis, we comprehensively determined the transcriptional difference in purified protein derivative (PPD) stimulated peripheral blood mononuclear cells (PBMCs) in 12 individuals divided into three groups: TB patients (TB), latent TB infection individuals (LTBI) and healthy controls (HC) (n = 4 per group). A transcriptional profiling of 506 differentially expressed genes could correctly group study individuals into three clusters. Moreover, 55- and 229-transcript signatures for tuberculosis infection (TB&LTBI) and active disease (TB) were identified, respectively. The validation study by quantitative real-time PCR (qPCR) performed in 83 individuals confirmed the expression patterns of 81% of the microarray identified genes. Decision tree analysis indicated that three genes of CXCL10, ATP10A and TLR6 could differentiate TB from LTBI subjects. Additional validation was performed to assess the diagnostic ability of the three biomarkers within 36 subjects, which yielded a sensitivity of 71% and specificity of 89%. CONCLUSIONS/SIGNIFICANCE: The transcription profiles of PBMCs induced by PPD identified distinctive gene expression patterns associated with different infectious status and provided new insights into human immune responses to MTB. Furthermore, this study indicated that a combination of CXCL10, ATP10A and TLR6 could be used as novel biomarkers for the discrimination of TB from LTBI

Human activity learning for assistive robotics using a classifier ensemble

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K-nearest neighbour and random forest - is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities