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Tuberculous meningitis remains highly lethal. In this trial, an intensified regimen of levofloxacin and higher-dose rifampin added to standard therapy was compared with standard antituberculosis therapy alone. The intensified regimen did not result in a higher survival rate. Early treatment with antituberculosis chemotherapy and adjunctive treatment with glucocorticoids reduce the rate of death and disability from tuberculous meningitis, but the disease still kills or disables almost half the patients with the condition. 1 , 2 The current guidelines recommend treatment with four antituberculosis drugs for at least the first 2 months of therapy, followed by treatment with two drugs (rifampin and isoniazid) for an additional 7 to 10 months. 3 , 4 However, these recommendations are based on data from pulmonary tuberculosis and do not take into account the differential ability of antituberculosis drugs to penetrate the brain. Rifampin is considered to . . .

AbstractIn the course of bioassay-guided study for natural antidiabetic compounds from microorganisms, two steroids, i.e. compounds 1 and 2, were isolated in vitro from biomass of antidiabetic Streptomyces sp. strain TD-X10. Structures of the compounds 1 and 2 were elucidated on the basis of spectroscopic analysis of nuclear magnetic resonance and mass spectral data and comparison with reported literature data as β-sitosterol and β-sitosteryl ferulate, respectively. While compound 1 exhibited faintly inhibitory activity against protein tyrosine phosphatase 1B, a highly validated therapeutic target for diabetes mellitus, compound 2 displayed a moderate inhibitory effect to the enzyme with IC50 value of 40.05 ± 2.46 µM. The results highlight the antidiabetic potential of natural products from actinobacteria, particularly Streptomyces species.

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/ .

Aim of the study: To investigate the detection rate of respiratory viruses identified by multiplex real-time PCR (MPL real-time PCR) in respiratory specimens collected from hospitalized patients with acute lower respiratory tract infections (LRTI) over a five-year period (2020–2024), and to emphasize the importance of MPL real-time PCR testing in identifying respiratory viruses responsible for severe lower respiratory tract infections requiring hospitalization. Subjects and Methods: This cross-sectional retrospective study analyzed 15,936 respiratory specimens collected from hospitalized patients between 2020 and 2024. Seventeen respiratory viruses were detected using MPL real-time PCR. Statistical comparisons were performed using the chi-square test. Results and Discussion: The overall respiratory virus detection rate was 31.88% and was significantly higher in children than in adults (52.98% vs. 18.10%). The most frequently detected viruses were rhinovirus, influenza A, respiratory syncytial virus, and parainfluenza virus type 3, while influenza A and SARS-CoV-2 predominated in adults. During the peak of the COVID-19 pandemic in 2021, SARS-CoV-2 accounted for 78.92% of detected viruses, accompanied by marked suppression of other respiratory pathogens. Measles virus re-emerged in 2024, predominantly affecting children (17.65%). Most Respiratory virus-positive cases (82.8%) involved single-agent infections. Conclusions: Hospitalized acute LRTI cases often lack distinctive clinical signs to identify viral pathogens. MPL real-time PCR provides simultaneous multi-virus detection, enabling accurate etiological diagnosis and strengthening hospital-based viral surveillance, particularly in resource-limited settings.

Determining the best therapy for HIV-associated cryptococcal meningitis in resource-poor settings is controversial. In this trial in Vietnam, initial therapy with amphotericin B with flucytosine had better outcomes than amphotericin B alone or with fluconazole. There are approximately 1 million cases of cryptococcal meningitis annually and 625,000 deaths. 1 Treatment guidelines recommend induction therapy with amphotericin B deoxycholate (0.7 to 1 mg per kilogram of body weight per day) and flucytosine (100 mg per kilogram per day). 2 However, this treatment has not been shown to reduce mortality, as compared with amphotericin B monotherapy. 2 , 3 Flucytosine is frequently unavailable where the disease burden is greatest, and concerns about cost and side effects have limited its use in resource-poor settings. 4 Fluconazole is readily available, is associated with low rates of adverse events, and has good penetration into cerebrospinal . . .

The use of statistical models to predict pipe failures has become an important tool for proactive management of drinking water networks. This targeted review provides an overview of the evolution of existing statistical models, grouped into three categories: deterministic, probabilistic and machine learning. The main advantage of deterministic models is simplicity and relatively minimal data requirements. Deterministic models predicting failure rates for the network or large groups of pipes perform well. These models are also useful for shorter prediction intervals that describe the influences of seasonality. Probabilistic models can accommodate randomness and are useful for predicting time-to-failure, interarrival times and the probability of failure. Probability models are useful for individual pipe models. Generally, machine learning approaches describe large complex data more accurately and can improve predictions for individual pipe failure models yet is complex and requires expert knowledge. Non-parametric models are better suited to the non-linear relationships between pipe failure variables. Census data and socio-economic data require further research. Choosing the most appropriate statistical model requires careful consideration of the type of variables, prediction interval, spatial level, response type and level of inference required.

Background The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. Results To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments. Conclusions Considering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments.

Background A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. Method In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. Result We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models’ predictive capabilities. Conclusion We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.

SummaryUsing decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.IntroductionGenetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.MethodsThe study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.ResultsIn both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.ConclusionsThe addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.