A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation.

Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.

Detection of dextran, maltodextrin and soluble starch in the adulterated Lycium barbarum polysaccharides (LBPs) using Fourier-transform infrared spectroscopy (FTIR) and machine learning models.

Due to the similar chemical structures and physicochemical properties, it is challenging to distinguish dextran, maltodextrin, and soluble starch from the polysaccharide products of plant origin, such as Lycium barbarum polysaccharides (LBPs). Using the first-order derivatives of Fourier-transformed infrared spectroscopy (FTIR, wave range 1800-400 cm-1), this study proposed a two-step pipeline to identify dextran, maltodextrin, and soluble starch from adulterated LBPs samples qualitatively and quantitatively. We applied principal component analysis (PCA) to reduce the dimensionality of FTIR features. For the qualitative step, a set of machine learning models, including logistic regression, support vector machine (SVM), Naïve Bayes, and partial least squares (PLS), were used to classify the adulterants. For the quantitative step, linear regression, LASSO, random forest, and PLS were used to predict the concentration of LBPs adulterants. The results showed that logistic regression and SVM are suitable for classifying adulterants, and random forests is superior for predicting adulterant concentrations. This would be the first attempt to discriminate the adulterants from the polysaccharide's product of plant origin. The proposed two-step methods can be easily extended to other applications for the quantitative and qualitative detection of samples from adulterants with similar chemical structures.

Biosynthesis of Isonitrile Lipopeptide Metallophores from Pathogenic Mycobacteria.

Isonitrile lipopeptides (INLPs) are known to be related to the virulence of pathogenic mycobacteria by mediating metal transport, but their biosynthesis remains obscure. In this work, we use in vitro biochemical assays, site-directed mutagenesis, chemical synthesis, and spectroscopy techniques to scrutinize the activity of core enzymes required for INLP biosynthesis in mycobacteria. Compared to environmental Streptomyces, pathogenic Mycobacterium employ a similar chemical logic and enzymatic machinery in INLP biosynthesis, differing mainly in the fatty-acyl chain length, which is controlled by multiple enzymes in the pathway. Our in-depth study on the non-heme iron(II) and α-ketoglutarate-dependent dioxygenase for isonitrile generation, including Rv0097 from Mycobacterium tuberculosis (Mtb), demonstrates that it recognizes a free-standing small molecule substrate, different from the recent hypothesis that a carrier protein is required for Rv0097 in Mtb. A key residue in Rv0097 is further identified to dictate the varied fatty-acyl chain length specificity between Streptomyces and Mycobacterium.

Machine learning approaches for electronic health records phenotyping: a methodical review.

OBJECTIVE: Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND METHODS: We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. RESULTS: Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION: Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. CONCLUSION: Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.

A rare case of gouty arthropathy in the spine complicated with acute cord compression.

Gout is one of the most common inflammatory arthropathies in the developed world. However, involvement of the spine is relatively rare, and other sinister differential diagnoses will need to be considered. We describe an unusual case of gouty tophi deposition within the spine in an elderly patient presenting with signs and symptoms of acute cord compression. Important differential diagnoses that need to be excluded include bony metastases from underlying malignancy and other infective/inflammatory causes. Early recognition of imaging findings can avoid delayed or inappropriate medical treatment.

Spatial and Temporal Distribution Characteristics and Potential Risks of Sulfonamides in the Shaanxi Section of the Weihe River.

The hazards of antibiotics as emerging contaminants to aquatic ecosystems and human health have received global attention. This study investigates the presence, concentration levels, spatial and temporal distribution patterns, and their potential risks to aquatic organisms and human health of sulfonamides (SAs) in the Shaanxi section of the Weihe River. The SA pollution in the Weihe River was relatively less than that in other rivers in China and abroad. The spatial and temporal distribution showed that the total concentrations of SAs in the Weihe River were highest in the main stream (ND−35.296 ng/L), followed by the south tributary (3.718−34.354 ng/L) and north tributary (5.476−9.302 ng/L) during the wet water period. Similarly, the order of concentration from highest to lowest during the flat water period was main stream (ND−3 ng/L), north tributary (ND−2.095 ng/L), and south tributary (ND−1.3 ng/L). In addition, the ecological risk assessment showed that the SAs other than sulfadiazine (SDZ) and sulfamethoxazole (SMZ) posed no significant risk (RQS < 0.01) to the corresponding sensitive species during both periods, with no significant risk to human health for different age groups, as suggested by the health risk assessment. The risk of the six SAs to both aquatic organisms and human health decreased significantly from 2016 to 2021.

[Analysis of spectrum-activity relationship among antioxidant parts of Lycii Fructus using three different chemometrics methods].

The chemical components of Lycii Fructus were analyzed by liquid chromatography( LC) and mass spectrometry( MS for the establishment of spectrum-activity relationship,on the basis of which its antioxidant active ingredients were determined. In this experiment,Lycii Fructus was extracted with different solvents and then separated into 80 samples by macroporous adsorption resin and reversed-phase chromatography,respectively. The antioxidant components were enriched into 11 samples and their scavenging abilities against DPPH free radical and ferric ion reducing antioxidant power( FRAP) were significantly stronger than those before the treatment( P<0. 05). The spectrum-activity relationship regarding the antioxidant activity in vitro of Lycii Fructus was established by Pearson correlation analysis,orthogonal partial least squares( OPLS) and elastic net regression. Six chromatographic peaks greatly contributing to the antioxidant activity in vitro of Lycii Fructus were identified as rutin( P6),quercetin( P35),scopoletin( P14),N-cis-feruloyl-4-O-ß-D-glucopyranosyl-tyramine or N-( 4-O-ß-D-glucopyranosyl-trans-feruloyl)-tyramine( P8), ferulic acid( P13) and1,3,5-dihydroxy-2-isoprenyl-3-xanthone( P23). The active components associated with free radical scavenging were rutin and quercetin both belonging to flavonoids. The reduction of Fe3+was based on phenylpropanoids such as ferulic acid,scopoletin,xanthone and phenolic amides. These results indicated that the antioxidant activity of Lycii Fructus was ascribed to the synergistic action of different products through different ways. Besides,the data analysis model should be chosen carefully for the establishment of spectrum-activity relationship,thus ensuring the reliability of results.

Fasting Plasma Glucose Indicates Reversibility of the Acute Insulin Response after Short-Term Intensive Insulin Therapy in Patients with Various Duration of Type 2 Diabetes.

Recovery of acute insulin response (AIR) is shown to be associated with long-term outcomes of patients with early type 2 diabetes treated with short-term intensive insulin therapy (SIIT). However, the complexity of measuring an AIR limits its utility in a real-world clinical setting. The aim of the study was to assess fasting indicators that may estimate recovery of the AIR after SIIT. We enrolled 62 patients with type 2 diabetes mellitus (T2DM) of varying disease duration who had poor glycemic control. Participants were treated with SIIT using insulin pumps to achieve near normoglycemia for 7 days. The AIR before and after the therapy were measured by intravenous glucose tolerance tests. After the therapy, AIR increased from -16.7 (-117.4, 52.4) pmol/L·min to 178.7 (31.8, 390.7) pmol/L·min (P < 0.001) while hyperglycemia was alleviated; this improvement was observed in all disease duration categories. AIR was almost absent when fasting plasma glucose (FPG) > 10 mmol/L, while both AIR (R = -0.53, P < 0.001) and its improvement from baseline (△AIR, R = -0.52, P < 0.001) were negatively associated with FPG after SIIT when FPG < 10 mmol/L. In multivariate analyses, FPG after SIIT and baseline fasting C peptide were independent indicators of both AIR after the therapy and ∆AIR; HDL-C after the therapy also predicted AIR after the therapy. We concluded that recovery of the AIR could be obtained in T2DM patients of varying disease duration by SIIT and it could be conveniently estimated using posttreatment fasting plasma glucose and other fasting indicators.