DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

LUX ARRHYTHMO links CBF pathway and jasmonic acid metabolism to regulate cold tolerance of tea plants.

Cold stress declines the quality and yield of tea, yet the molecular basis underlying cold tolerance of tea plants (Camellia sinensis) remains largely unknown. Here, we identified a circadian rhythm component LUX ARRHYTHMO (LUX) that potentially regulates cold tolerance of tea plants through a genome-wide association study and transcriptomic analysis. The expression of CsLUX phased with sunrise and sunset and was strongly induced by cold stress. Genetic assays indicated that CsLUX is a positive regulator of freezing tolerance in tea plants. CsLUX was directly activated by CsCBF1 and repressed the expression level of CsLOX2, which regulates the cold tolerance of tea plants through dynamically modulating jasmonic acid content. Furthermore, we showed that the CsLUX-CsJAZ1 complex attenuated the physical interaction of CsJAZ1 with CsICE1, liberating CsICE1 with transcriptional activities to withstand cold stress. Notably, a single-nucleotide variation of C-to-A in the coding region of CsLUX was functionally validated as the potential elite haplotype for cold response, which provided valuable molecular markers for future cold resistance breeding in tea plants.

Dynamic Mechanism of Cerebral Venous Disruption: Longitudinal Evidence From a Community-Based Cohort.

BACKGROUND: This study aims to investigate the temporal and spatial patterns of structural brain injury related to deep medullary veins (DMVs) damage. METHODS AND RESULTS: This is a longitudinal analysis of the population-based Shunyi cohort study. Baseline DMVs numbers were identified on susceptibility-weighted imaging. We assessed vertex-wise cortex maps and diffusion maps at both baseline and follow-up using FSL software and the longitudinal FreeSurfer analysis suite. We performed statistical analysis of global measurements and voxel/vertex-wise analysis to explore the relationship between DMVs number and brain structural measurements. A total of 977 participants were included in the baseline, of whom 544 completed the follow-up magnetic resonance imaging (age 54.97±7.83 years, 32% men, mean interval 5.56±0.47 years). A lower number of DMVs was associated with a faster disruption of white matter microstructural integrity, presented by increased mean diffusivity and radial diffusion (ß=0.0001 and SE=0.0001 for both, P=0.04 and 0.03, respectively), in extensive deep white matter (threshold-free cluster enhancement P<0.05, adjusted for age and sex). Of particular interest, we found a bidirectional trend association between DMVs number and change in brain volumes. Specifically, participants with mild DMVs disruption showed greater cortical enlargement, whereas those with severe disruption exhibited more significant brain atrophy, primarily involving clusters in the frontal and parietal lobes (multiple comparison corrected P<0.05, adjusted for age, sex, and total intracranial volume). CONCLUSIONS: Our findings posed the dynamic pattern of brain parenchymal lesions related to DMVs injury, shedding light on the interactions and chronological roles of various pathological mechanisms.

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.

Introduction: In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods: Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion: In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

Associations of sleep-related variables with reverse dipping patterns of blood pressure in α-synucleinopathies.

INTRODUCTION: The reverse dipping blood pressure (BP) pattern is very common in α-synucleinopathies. We aimed to explore the associations of sleep-related variables with abnormal BP circadian rhythms in Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS: A total of 126 patients, 76 with PD and 50 with MSA, were included. All participants underwent ambulatory BP monitoring and full-night polysomnography (PSG). We analyzed abnormal dipping patterns and sleep-related parameters, including moderate to severe obstructive sleep apnea (OSA), rapid eye movement behavior disorder (RBD), average oxygen saturation (SaO2%), lowest SaO2%, duration of SaO2% <90%, and apnea-hypopnea index (AHI). Binary logistic regression was performed to explore the associations between paraclinical variables, sleep-related variables, and reverse dipping patterns. RESULTS: Reverse dipping patterns were predominant in patients with PD (58.5 %) and MSA (68.0 %). Patients with MSA had higher AHI, RBD, and lower average SaO2% than those with PD. Taking both diseases together as a whole group of α-synucleinopathies, logistic regression analysis indicates the Hoehn-Yahr stage (odds ratio [OR] = 2.00 for reverse systolic and 2.34 for reverse diastolic dipping patterns), moderate to severe OSA (OR = 2.71 for reverse systolic and 2.53 for reverse diastolic dipping patterns), average SaO2% (OR = 1.35 for reverse systolic dipping patterns), and male sex (OR = 2.70 for reverse diastolic dipping patterns) were independently associated with reverse dipping patterns. CONCLUSIONS: Reverse dipping patterns were common in patients with PD and MSA. Hoehn-Yahr stage, moderate to severe OSA, average SaO2%, and male sex were associated with reverse dipping patterns in α-synucleinopathy.

Association of Rare NOTCH3 Variants With Prevalent and Incident Stroke and Dementia in the General Population.

BACKGROUND: It is uncertain whether rare NOTCH3 variants are associated with stroke and dementia in the general population and whether they lead to alterations in cognitive function. This study aims to determine the associations of rare NOTCH3 variants with prevalent and incident stroke and dementia, as well as cognitive function changes. METHODS AND RESULTS: In the prospective community-based Shunyi Study, a total of 1007 participants were included in the baseline analysis. For the follow-up analysis, 1007 participants were included in the stroke analysis, and 870 participants in the dementia analysis. All participants underwent baseline brain magnetic resonance imaging, carotid ultrasound, and whole exome sequencing. Rare NOTCH3 variants were defined as variants with minor allele frequency <1%. A total of 137 rare NOTCH3 carriers were enrolled in the baseline study. At baseline, rare NOTCH3 variant carriers had higher rates of stroke (8.8% versus 5.6%) and dementia (2.9% versus 0.8%) compared with noncarriers. After adjustment for associated risk factors, the epidermal growth factor-like repeats (EGFr)-involving rare NOTCH3 variants were associated with a higher risk of prevalent stroke (odds ratio [OR], 2.697 [95% CI, 1.266-5.745]; P=0.040) and dementia (OR, 8.498 [95% CI, 1.727-41.812]; P=0.032). After 5 years of follow-up, we did not find that the rare NOTCH3 variants increased the risk of incident stroke and dementia. There was no statistical difference in the change in longitudinal cognitive scale scores. CONCLUSIONS: Rare NOTCH3 EGFr-involving variants are genetic risk factors for stroke and dementia in the general Chinese population.

Multiomics was used to clarify the mechanism by which air pollutants affect chronic obstructive pulmonary disease: A human cohort study.

Exposure to air pollutants has been associated with various adverse health outcomes, including chronic obstructive pulmonary disease (COPD). However, the precise underlying mechanism by which air pollution impacts COPD through remains insufficiently understood. To elucidated the molecular mechanism by which air pollutant exposure contributes to alterations in the gut microbiome and metabolism in AECOPD patients, we employed metagenomics and untargeted metabolomics to analyse the gut microbial, faecal, and serum metabolites. The correlations among air pollutants, gut microbes, serum metabolites, and blood biochemical markers were assessed using generalised additive mixed models and Spearman correlation analysis. The findings revealed that for every 10 µg/m3 increase in PM2.5 concentration, the α-diversity of the gut flora decreased by 2.16% (95% CI: 1.80%-2.53%). We found seven microorganisms that were significantly associated with air pollutants, of which Enterococcus faecium, Bacteroides fragilis, Ruthenibacterium lactatiformans, and Subdoligranulum sp.4_3_54A2FAA were primarily associated with glycolysis. We identified 13 serum metabolites and 17 faecal metabolites significantly linked to air pollutants. Seven of these metabolites, which were strongly associated with air pollutants and blood biochemical indices, were found in both serum and faecal samples. Some of these metabolites, such as 2,5-furandicarboxylic acid, C-8C1P and melatonin, were closely associated with disturbances in lipid and fatty acid metabolism in AECOPD patients. These findings underscore the impact of air pollutants on overall metabolism based on influencing gut microbes and metabolites in AECOPD patients. Moreover, these altered biomarkers establish the biologic connection between air pollutant exposure and AECOPD outcomes.The identification of pertinent biomarkers provides valuable insights for the development of precision COPD prevention strategies.

Pattern of Brain Parenchymal Damage Related to Cerebral Small Vessel Disease in Carriers of Rare NOTCH3 Variants.

BACKGROUND AND OBJECTIVES: Previous studies reported that carriers of rare NOTCH3 variants comprised more than 10% of the general population and are susceptible to a heavy overall burden of cerebral small vessel disease while the injury patterns remain uncovered. This study aimed to investigate the imaging features in relation to rare NOTCH3 variants and the interaction between cortical atrophy and white matter lesions from a longitudinal view, with respect to spatial and dynamic patterns. METHODS: As part of a community-based cohort, we included participants with complete whole-exome sequencing and brain MRI in the baseline analysis. All participants were invited for a 5-year follow-up MRI, and those who did not complete the follow-up were excluded from the longitudinal analysis. NOTCH3 variants with minor allele frequency <1% in all 4 public population databases were defined as rare variants. We used general linear models to compare the volume of white matter hyperintensity (WMH) volume and brain parenchymal fraction between rare NOTCH3 variant carriers and noncarriers. In addition, we compared the WMH probability map and vertex-wise cortex maps at a voxel/vertex-wise level. RESULTS: A total of 1,054 participants were included in baseline analysis (13.56% carried rare NOTCH3 variants), among whom 661 had a follow-up brain MRI (13.76% carried rare NOTCH3 variants). Rare NOTCH3 variant carriers had a heavier white matter hyperintensity burden (1.65 vs 0.85 mL, p = 0.025) and had more extensive WMH distributed in the periventricular areas. We also found that rare NOTCH3 variant carriers were susceptible to worse cortical atrophy (ß = -0.004, SE = 0.002, p = 0.057, adjusted for age and sex). Cortical atrophy of multiple regions in the frontal and parietal lobes was related to white matter hyperintensity progression. DISCUSSION: Individuals with rare NOTCH3 variants have a distinct pattern of brain parenchymal damage related to CSVD. Our findings uncover the important genetic predisposition in age-related cerebral small vessel disease in the general population.

Develop adult extrapolation to pediatrics and pediatric dose optimization based on the physiological pharmacokinetic model of azithromycin.

Physiologically-based pharmacokinetic (PBPK) models are more frequently used for supporting pediatric dose selection in small-molecule drugs. Through literature research, drug parameters of azithromycin and clinical data from different studies were obtained. Through parameter optimization of the absorption and dissolution process, the adult intravenous model was extended to the adult oral model. The adult intravenous and oral PBPK models are precise to meet the AAFE<2 standard, and the pharmacokinetic parameters of the predicted values of the model are all within the mean standard deviation of the clinical observations. The values of plasma protein unbound fraction, renal clearance, and gastric juice pH between adults and pediatrics were changed by using the age-dependent pediatric organ maturity formula, and the adult model was extrapolated to the pediatric model. The final developed pediatric PBPK model was used to evaluate optimal dosing for children of different developmental ages. The relationship between the frist dose and age was as follows: 8.8 mg/kg/day from 0.5 to 2 years old, 9.2 mg/kg/day from 3 to 6 years old, 9.4 mg/kg/day from 7 to 12 years old, and 8.2 mg/kg/day from 13 to 18 years old, taken in half for 2-5 days. Simultaneously, the simulated exposures achieved with the dosing regimen proposed were comparable to adult plasma exposures for treatment of community-acquired pneumonia. A reasonable azithromycin pharmacokinetic-pharmacodynamic model for adults and pediatrics has been established, which can be demonstrated by the use of literature pediatric data to develop pediatric PBPK models, expanding the scope of this powerful modeling tool.

White matter and gray matter changes related to cognition in community populations.

Objective: Further studies are needed to improve the understanding of the pathological process underlying cognitive impairments. The purpose of this study is to investigate the global and topographic changes of white matter integrity and cortical structure related to cognitive impairments in a community-based population. Methods: A cross-sectional analysis was performed based on 995 subjects (aged 56.8 ± 9.1 years, 34.8% males) from the Shunyi study, a community-dwelling cohort. Cognitive status was accessed by a series of neurocognitive tests including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), category Verbal Fluency Test (VFT), Digit Span Test (DST), and Trail Making Tests A and B (TMT-A and TMT-B). Structural and diffusional MRI data were acquired. White matter integrity was assessed using fractional anisotropy (FA), mean diffusivity (MD), and peak width of skeletonized mean diffusivity (PSMD). Cortical surface area, thickness, and volume were measured using Freesurfer. Probabilistic tractography was further conducted to track the white matter fibers connecting to the cortical regions related to cognition. General linear models were used to investigate the association between brain structure and cognition. Results: Global mean FA and MD were significantly associated with performances in VFT (FA, ß 0.119, p < 0.001; MD, ß -0.128, p < 0.001). Global cortical surface area, thickness, and volume were not related to cognitive scores. In tract-based spatial statistics analysis, disruptive white matter integrity was related to cognition impairment, mainly in visuomotor processing speed, semantic memory, and executive function (TMT-A and VFT), rather than verbal short-term memory and working memory (DST). In the whole brain vertex-wise analysis, surface area in the left orbitofrontal cortex, right posterior-dorsal part of the cingulate gyrus, and left central sulcus were positively associated with MMSE and MoCA scores, and the association were independent of the connecting white matter tract. Conclusion: Disrupted white matter integrity and regional cortical surface area were related to cognition in community-dwelling populations. The associations of cortical surface area and cognition were independent of the connecting white matter tract.

Ensemble learning-based gene signature and risk model for predicting prognosis of triple-negative breast cancer.

Although medical science has been fully developed, due to the high heterogeneity of triple-negative breast cancer (TNBC), it is still difficult to use reasonable and precise treatment. In this study, based on local optimization-feature screening and genomics screening strategy, we screened 25 feature genes. In multiple machine learning algorithms, feature genes have excellent discriminative diagnostic performance among samples composed of multiple large datasets. After screening at the single-cell level, we identified genes expressed substantially in myeloid cells (MCGs) that have a potential association with TNBC. Based on MCGs, we distinguished two types of TNBC patients who showed considerable differences in survival status and immune-related characteristics. Immune-related gene risk scores (IRGRS) were established, and their validity was verified using validation cohorts. A total of 25 feature genes were obtained, among which CXCL9, CXCL10, CCL7, SPHK1, and TREM1 were identified as the result after single-cell level analysis and screening. According to these entries, the cohort was divided into MCA and MCB subtypes, and the two subtypes had significant differences in survival status and tumor-immune microenvironment. After Lasso-Cox screening, IDO1, GNLY, IRF1, CTLA4, and CXCR6 were selected for constructing IRGRS. There were significant differences in drug sensitivity and immunotherapy sensitivity among high-IRGRS and low-IRGRS groups. We revealed the dynamic relationship between TNBC and TIME, identified a potential biomarker called Granulysin (GNLY) related to immunity, and developed a multi-process machine learning package called "MPMLearning 1.0" in Python.

Single-cell profiling identifies T cell subsets associated with control of tuberculosis dissemination.

To identify T cell subsets associated with control of tuberculosis, single-cell transcriptome and T cell receptor sequencing were performed on total T cells from patients with tuberculosis and healthy controls. Fourteen distinct subsets of T cells were identified by unbiased UMAP clustering. A GZMK-expressing CD8+ cytotoxic T cell cluster and a SOX4-expressing CD4+ central memory T cell cluster were depleted, while a MKI67-expressing proliferating CD3+ T cell cluster was expanded in patients with tuberculosis compared with healthy controls. The ratio of Granzyme K-expressing CD8+CD161-Ki-67- and CD8+Ki-67+ T cell subsets was significantly reduced and inversely correlated with the extent of TB lesions in patients with TB. In contrast, ratio of Granzyme B-expressing CD8+Ki-67+ and CD4+CD161+Ki-67- T cells and Granzyme A-expressing CD4+CD161+Ki-67- T cells were correlated with the extent of TB lesions. It is concluded that granzyme K-expressing CD8+ T cell subsets might contribute to protection against tuberculosis dissemination.

Tim-3 expression is induced by mycobacterial antigens and identifies tissue-resident subsets of MAIT cells from patients with tuberculosis.

Tissue-resident MAIT cells in tuberculous pleural effusions, the site of tuberculosis infection, were investigated in the study. Tim-3+CD69+CD103+ and CD39+CD69+CD103+ tissue-resident MAIT cell subsets were identified in tuberculous pleural effusions. Tim-3 expression in MAIT cells was greatly induced and CD39 expression was elevated following ex vivo stimulation with Mycobacterium tuberculosis antigens. Mycobacterial antigen-stimulated Tim-3+CD69+CD103+ tissue-resident MAIT cells had higher frequency of IFN-γ- and granzyme B-producing cells than Tim-3-CD69+CD103+ subset, while CD39+CD69+CD103+ MAIT cells had similar frequency of IFN-γ-positive cells but higher ratio of granzyme B-producing cells than CD39-CD69+CD103+ subset. Blocking of IL-2, IL-12p70 or IL-18 but not IL-15 led to significantly reduced expression of Tim-3 compared with isotype antibody control. In contrast, CD39 expression was not influenced by any of the cytokines tested. Tim-3+ MAIT cells had higher levels of lipid uptake and lipid content than Tim-3- cells. It is concluded that Tim-3+CD69+CD103+ tissue-resident MAIT cells were elevated in tuberculous pleural effusions and had higher capacity to produce effector molecules of IFN-γ and granzyme B.

A bibliometric and visualized analysis of liver fibrosis from 2002 to 2022.

Fibrosis of the liver is a degenerative alteration that occurs in the majority of chronic liver disorders. Further progression can lead to cirrhosis, liver failure, and hepatocellular carcinoma, which can seriously affect the health and lives of patients. The field of liver fibrosis research has flourished in the last 20 years, with approximately 9000 articles retrieved from the Web of Science Core Collection database alone. In order to identify future research hotspots and potential paths in a thorough and scientifically reliable manner, it is important to organize and visualize the research on this topic from a holistic and very general perspective. This study used bibliometric analysis with CiteSpace and VOSviewer software to provide a quantitative analysis, hotspot mining, and commentary of articles published in the field of liver fibrosis over the last 20 years. This bibliometric analysis contains a total of 8994 articles with 45667 authors from 6872 institutions in 97 countries, published in 1371 journals and citing 156 309 references. The literature volume has steadily increased over the last 20 years. Research has focused on gastroenterology and hepatology, pharmacology and pharmacy, and medicine, research, and experimental areas. We found that the pathological mechanisms, diagnostic and quantitative methods, etiology, and antifibrotic strategies constitute the knowledge structure of liver fibrosis. Finding mechanisms for liver fibrosis regression, identifying precise noninvasive diagnostic and prognostic biomarkers, and creating efficient liver fibrosis patient treatments are the main goals of current research.

Comparison of the second consensus statement with the movement disorder society criteria for multiple system atrophy: A single-center analysis.

INTRODUCTION: This study aimed at comparing the differences between the second consensus statement and Movement Disorder Society (MDS) criteria for Multiple System Atrophy (MSA) in a single Chinese cohort. METHODS: We retrospectively reviewed 73 patients with MSA over the past five years. They were categorized as patients with probable and possible MSA according to the second consensus statement in addition to clinically established and clinically probable MSA according to the MDS criteria. The core clinical, supportive clinical, and imaging features were analyzed and compared between the two MSA subtypes. RESULTS: A total of 40 patients with MSA-P and 33 patients with MSA-C were included in this study. Approximately 78.7% of the category of probable patients in the second consensus statement can be categorized as clinically established MSA in the MDS criteria and five patients with non-supporting features in the second consensus statement criteria can be diagnosed as clinically probable MSA in the MDS criteria. "Rapid progression" and "moderate to severe postural instability" within three years of motor onset dominated among the supportive features. Approximately 78.9% of patients possessed at least one imaging marker with predominant signal decrease of putamen on iron-sensitive sequences (38.0% of patients). Twenty-two patients could not be diagnosed as clinically established MSA mainly due to the lack of supportive or imaging features. CONCLUSIONS: A high degree of agreement was noticed between the two criteria sets. The supportive and imaging features played important role in the diagnosis of MSA and affected the diagnostic level in the current criteria.

Association between Anatomical Variations of the Circle of Willis and Covert Vascular Brain Injury in the General Population.

BACKGROUND AND PURPOSE: The circle of Willis (COW) is a circulatory anastomosis located at the base of the brain. Little is known about the association between covert vascular brain injury and COW configurations in the general population. We explored this relationship in a community-based Chinese sample. METHODS: A total of 1,055 patients (mean age, 54.8 ± 8.9 years; 36.0% men) without intracranial arterial stenosis were included in the analysis. Magnetic resonance imaging was performed to evaluate the presence of imaging markers of covert vascular brain injury, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), enlarged perivascular spaces, and brain atrophy. Magnetic resonance angiography was used to classify the COW configurations according to the completeness, symmetry, and presence of the fetal posterior cerebral artery (FTP). The association between vascular lesions and variations in COW was analyzed. RESULTS: Among the 1,055 patients, 104 (9.9%) had a complete COW. Completeness correlated with age (p = 0.001). Incomplete COW was positively associated with WMH severity (OR = 2.071; 95% CI, 1.004-4.270) and CMB presence (OR = 1.542; 95% CI, 1.012-2.348), independent of age and sex. The presence of FTP was associated with lacunes (OR = 1.878; 95% CI, 1.069-3.298), more severe WMHs (OR = 1.739; 95% CI, 1.064-2.842), and less severe enlarged perivascular spaces (OR = 0.562; 95% CI, 0.346-0.915). CONCLUSIONS: COW configuration was significantly related to various covert vascular brain injuries.

Evaluation of IFIT3 and ORM1 as Biomarkers for Discriminating Active Tuberculosis from Latent Infection.

OBJECTIVE: Current commercially available immunological tests cannot be used for discriminating active tuberculosis (TB) from latent TB infection. To evaluate the value of biomarker candidates in the diagnosis of active TB, this study aimed to identify differentially expressed genes in peripheral blood mononuclear cells (PBMCs) between patients with active TB and individuals with latent TB infection by transcriptome sequencing. METHODS: The differentially expressed genes in unstimulated PBMCs and in Mycobacterium tuberculosis (Mtb) antigen-stimulated PBMCs from patients with active TB and individuals with latent TB infection were identified by transcriptome sequencing. Selected candidate genes were evaluated in cohorts consisting of 110 patients with TB, 30 individuals with latent TB infections, and 50 healthy controls by quantitative real-time RT-PCR. Receiver operating characteristic (ROC) curve analysis was performed to calculate the diagnostic value of the biomarker candidates. RESULTS: Among the differentially expressed genes in PBMCs without Mtb antigen stimulation, interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) had the highest area under curve (AUC) value (0.918, 95% CI: 0.852-0.984, P<0.0001) in discriminating patients with active TB from individuals with latent TB infection, with a sensitivity of 91.86% and a specificity of 84.00%. In Mtb antigen-stimulated PBMCs, orosomucoid 1 (ORM1) had a high AUC value (0.833, 95% CI: 0.752-0.915, P<0.0001), with a sensitivity of 81.94% and a specificity of 70.00%. CONCLUSION: IFIT3 and ORM1 might be potential biomarkers for discriminating active TB from latent TB infection.

Artificial intelligence-driven prediction of multiple drug interactions.

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

[Expression of CD160 in natural killer (NK) cells from patients with active tuberculosis and its relationship with cell functions].

Objective To investigate the relationship between the CD160 expression and anti-tuberculosis immunity. Methods Fluorescence quantitative real-time PCR was used to detect the expression of CD160 in peripheral blood mononuclear cells (PBMCs). Flow cytometry was used to analyze the expression of CD160 on main subtypes of PBMCs, such as T cells, B cells, NK cells and monocytes. The relationship among CD160 and perforin, granzyme B, granulysin, CD69, CD107 and IFN-γ in NK cells was analyzed by flow cytometry. Results CD160 mRNAs in the PBMCs from patients with active tuberculosis was significantly down-regulated, and the levels of CD160 expression in Mycobacterium tuberculosis (MTB)-positive patients was significantly lower than in MTB-negative patients. The expression of CD160 on B cells and monocytes was lower in patients with active tuberculosis as compared with normal controls, while no significant difference was observed on CD3+ T cells. NK cells from patients with active tuberculosis had significantly lower CD160 expression than those from normal controls. In vitro culture with MTB antigens led to down-regulated expression of CD160 on NK cells. The activation marker CD69 on NK in patients with active tuberculosis was significantly lower than that in normal controls. The expression of perforin, granzyme B, granulysin, CD69 and CD107 in CD160+ NK cells was significantly higher than that of CD160- NK cells. However, the expression of IFN-γ in CD160+ NK cells was significantly lower than that of CD160- NK cells. Conclusion The mRNA and protein expression of CD160 was significantly down-regulated in patients with active tuberculosis. CD160 promotes the activation and degranulation of NK cells associated with tuberculosis antigens, but suppresses the expression of IFN-γ of NK cells. CD160 may become a new target for the diagnosis and treatment of tuberculosis.