De-glutathionylases: The resilient underdogs to keep neurodegeneration at bay.

Proteins become S-glutathionylated as a result of the derivatization of their cysteine thiols with the thiolate anion derivative of glutathione; this process is frequently linked to diseases and protein misbehavior. Along with the other well-known oxidative modifications like S-nitrosylation, S-glutathionylation has quickly emerged as a major contributor to a number of diseases, with a focus on neurodegeneration. The immense clinical significance of S-glutathionylation in cell signaling and the genesis of diseases are progressively coming to light with advanced research, which is also creating new opportunities for prompt diagnostics that utilize this phenomenon. In-depth investigation in recent years has revealed other significant deglutathionylases in addition to glutaredoxin, necessitating the hunt for their specific substrates. The precise catalytic mechanisms of these enzymes must also be understood, along with how the intracellular environment affects their impact on protein conformation and function. These insights must then be extrapolated to the understanding of neurodegeneration and the introduction of novel and clever therapeutic approaches to clinics. Clarifying the importance of the functional overlap of glutaredoxin and other deglutathionylases and examining their complementary functions as defense systems in the face of stress are essential prerequisites for predicting and promoting cell survival under high oxidative/nitrosative stress.

Choosing the most appropriate existing type 2 diabetes risk assessment tool for use in the Philippines: a case-control study with an urban Filipino population.

BACKGROUND: As the prevalence of type 2 diabetes (T2DM) increases in low- to middle-income countries, the burden on individuals and health care systems also increases. The use of diabetes risk assessment tools could identify those at risk, leading to prevention or early detection of diabetes. The aim of this study was to evaluate the appropriateness of 6 existing T2DM risk screening tools in detecting dysglycemia in Zamboanga City, Philippines. METHODS: This study used a case-control design in an urban setting in the southern Philippines. There were 200 participants in two groups: 1) those diagnosed with diabetes (n = 50; recruited from diabetes clinics) and 2) those with no previous diagnosis of diabetes (n = 150; recruited from community locations). Participants completed six tools (the Finnish Diabetes Risk Score [FINDRISC], the Canadian Diabetes Risk Score [CANRISK], the Indian Diabetes Risk Score [IDRS], the American Diabetes Association [ADA] risk score, an Indonesian undiagnosed diabetes mellitus [UDDM] scoring system, and a Filipino tool). Scores were compared to fasting plasma glucose levels, which are recommended in Philippines clinical practice guidelines as a valid, available, and low cost option for T2DM diagnosis. Appropriateness of tools was determined through accuracy, sensitivity, specificity, positive/negative predictive value (PPV, NPV), and positive/negative likelihood ratios. RESULTS: The Filipino tool had the highest specificity (0.73) and PPV (0.27), but lowest sensitivity (0.68). The IDRS and Indonesian UDDM tool had the highest NPV at 0.96, but were not amongst the highest in other scores. The CANRISK tied for highest area under the receiver operating characteristic (ROC) curve (AUC), AUC (0.80), but other scores were not noteworthy. Overall, the FINDRISC was the most effective with highest sensitivity (0.94), tied for highest AUC (0.80), and with middle scores in other variables (specificity: 0.45, PPV: 0.20, NPV: 0.95), when using the published cut-off score of 9. When increasing the cut-off score to 11, specificity increased (0.71) and sensitivity was not greatly affected (0.86). CONCLUSIONS: Our results suggest that the FINDRISC is more suitable than other known diabetes risk assessment tools in an urban Filipino population; effectiveness increased with a higher cut-off score.

A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features.

Clustering is a challenging problem in machine learning in which one attempts to group N objects into K0 groups based on P features measured on each object. In this article, we examine the case where N ≪ P and K0 is not known. Clustering in such high dimensional, small sample size settings has numerous applications in biology, medicine, the social sciences, clinical trials, and other scientific and experimental fields. Whereas most existing clustering algorithms either require the number of clusters to be known a priori or are sensitive to the choice of tuning parameters, our method does not require the prior specification of K0 or any tuning parameters. This represents an important advantage for our method because training data are not available in the applications we consider (i.e., in unsupervised learning problems). Without training data, estimating K0 and other hyperparameters-and thus applying alternative clustering algorithms-can be difficult and lead to inaccurate results. Our method is based on a simple transformation of the Gram matrix and application of the strong law of large numbers to the transformed matrix. If the correlation between features decays as the number of features grows, we show that the transformed feature vectors concentrate tightly around their respective cluster expectations in a low-dimensional space. This result simplifies the detection and visualization of the unknown cluster configuration. We illustrate the algorithm by applying it to 32 benchmarked microarray datasets, each containing thousands of genomic features measured on a relatively small number of tissue samples. Compared to 21 other commonly used clustering methods, we find that the proposed algorithm is faster and twice as accurate in determining the "best" cluster configuration.

Raised Venous Lactate and Markers of Intestinal Translocation Are Associated With Mortality Among In-Patients With HIV-Associated TB in Rural South Africa.

INTRODUCTION: Case fatality among in-patients with HIV-associated tuberculosis (HIV-TB) in Africa is high. We investigated the factors associated with mortality in a rural South African hospital. METHODS: This was a prospective observational study of HIV-TB in-patients, with death by 8 weeks the endpoint. RESULTS: Of 99 patients (median CD4 count 72 cells/mm³), 32 (32%) died after median 8-day TB treatment. TB was diagnosed microbiologically in 75/99 and clinico-radiologically in 24, with no mortality difference between these groups [31% versus 38% (P = 0.53)]. Median venous lactate was 5.5 mmol/L (interquartile range 3.9-6.2) in those who died and 3.1 mmol/L (interquartile range 2.2-4.1) in survivors (P < 0.001). In multivariable analysis, lactate ≥4 mmol/L [adjusted odds ratio (aOR) 9.8, 95% confidence interval (CI): 3.0 to 32.2], Glasgow Coma Score <15 (aOR 6.6, 95% CI: 1.5 to 29.6), CD4 count <50 cells per cubic millimeter (aOR 5.5, 95% CI: 1.6 to 18.5), and age ≥50 (aOR 7.7, 95% CI: 1.2 to 46.9) independently predicted death. In a nested case-control study, comparing those who died versus CD4-matched survivors, median plasma lipopolysaccharide concentrations were 93 and 57 pg/mL (P = 0.026) and intestinal fatty acid-binding protein, 132 and 0 pg/mL (P = 0.002). CONCLUSIONS: Mortality was high and predicted by elevated lactate, likely reflecting a sepsis-syndrome secondary to TB or bacterial coinfection with intestinal barrier dysfunction appearing to contribute.