Progress on mechanisms of antibiotic resistance in Clostridioides difficile.

Clostridioides difficile (CD) is one of the most common pathogens causing health-care-associated infectious diarrhea and is listed by the U.S. Centers for Disease Control and Prevention as an urgent antibiotic resistance (AR) threat. Many resistance genes can be transferred between different CD strains present in the clinical setting, community, and environment. The antimicrobial resistance (AMR) of CD continues to evolve with the emergence and acquisition of new drug resistance mechanisms. CD has developed diverse drug resistance mechanisms, such as drug alteration, modification of the target site, and extrusion of drugs via efflux pumps. Researches have provided comprehensive knowledge about resistance mechanisms of macrolides and quinolones in CD. However, the mechanisms of resistance for metronidazole, vancomycin, and other therapeutic antibiotics against Clostridioides difficile infection (CDI) are only beginning to be elucidated. Some previously unfound mechanisms, such as plasmid-mediated drug resistance in CD, may also play an important role. In this review, we summarize the research progress on drug resistance mechanisms of CD with antimicrobial drugs already used clinically, such as metronidazole, vancomycin, and fidaxomicin, thereby providing the references for the clinical treatment and prevention of CDI, as well as the development of new antibacterial drugs and detection kits for drug resistant bacteria.

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.

BACKGROUND: Gene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets. RESULTS: Meta-analysis of multi-data sets is an efficient method for tackling the above problem. To improve the performance of meta-analysis, we propose a novel meta-analysis framework. It consists of two parts: (1) a novel data augmentation strategy. Various cross-platform normalization methods exist, which can preserve original biological information of gene expression datasets from different angles and add different "perturbations" to the dataset. Using such perturbation, we provide a feasible means for gene expression data augmentation; (2) elastic data shared lasso (DSL-[Formula: see text]). The DSL-[Formula: see text] method spans the continuum between individual models for each dataset and one model for all datasets. It also overcomes the shortcomings of the data shared lasso method when dealing with highly correlated features. Comprehensive simulation experiment results show that the proposed method has high prediction and gene selection performance. We then apply the proposed method to non-small cell lung cancer (NSCLC) blood gene expression data in order to identify key tumor-related genes. The outcomes of our experiment indicate that the method could be used for identifying a set of robust disease-related gene signatures that may be used for NSCLC early diagnosis or prognosis or even targeting. CONCLUSION: We propose a novel and effective meta-analysis method for biological research, extrapolating and integrating information from multiple gene expression datasets.

Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method.

BACKGROUND/AIMS: One of the most important impacts of personalized medicine is the connection between patients' genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. METHODS: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. RESULTS: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient's in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient's gene-expression profile. CONCLUSION: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.

Update on antimicrobial resistance in Clostridium difficile.

Clostridium difficile is the leading cause of healthcare-associated diarrhea. Since 2002, the morbidity and mortality rates of C. difficile infection have increased dramatically in Europe and North America. The emergence of C. difficile strains that are resistant to multiple antimicrobial agents can complicate prevention programs and potential treatment. Although most clinical isolates are still susceptible to metronidazole and vancomycin, heteroresistance to metronidazole and increasing vancomycin MICs (minimum inhibitory concentrations) have been reported. The prevalence of resistance to other antimicrobial agents, including erythromycin and moxifloxacin, is highly variable in different countries and regions. The exact mechanism of reduced susceptibility to metronidazole or vancomycin is still not clear. The principal mechanism of erythromycin, fluoroquinolones and rifamycins resistance in C. difficile is determined by target alterations. This review will focus primarily on the antimicrobial susceptibility patterns and resistance mechanisms of C. difficile in order to provide an up-to-date review on the topic.

MUMA: a multi-omics meta-learning algorithm for data interpretation and classification.

Multi-omics data integration is a promising field combining various types of omics data, such as genomics, transcriptomics, and proteomics, to comprehensively understand the molecular mechanisms underlying life and disease. However, the inherent noise, heterogeneity, and high dimensionality of multi-omics data present challenges for existing methods to extract meaningful biological information without overfitting. This paper introduces a novel Multi-Omics Meta-learning Algorithm (MUMA) that employs self-adaptive sample weighting and interaction-based regularization for enhanced diagnostic performance and interpretability in multi-omics data analysis. Specifically, MUMA captures crucial biological processes across different omics layers by learning a flexible sample reweighting function adaptable to various noise scenarios. Additionally, MUMA incorporates an interaction-based regularization term, encouraging the model to learn from the relationships among different omics modalities. We evaluate MUMA using simulations and eighteen real datasets, demonstrating its superior performance compared to state-of-the-art methods in classifying biological samples (e.g., cancer subtypes) and selecting relevant biomarkers from noisy multi-omics data. As a powerful tool for multi-omics data integration, MUMA can assist researchers in achieving a deeper understanding of the biological systems involved. The source code for MUMA is available at https://github.com/bio-ai-source/MUMA.

ALOX5AP is a new prognostic indicator in acute myeloid leukemia.

BACKGROUND: The overexpression of ALOX5AP has been observed in many types of cancer and has been identified as an oncogene. However, its role in acute myeloid leukemia (AML) has not been extensively studied. This study aimed to identify the expression and methylation patterns of ALOX5AP in bone marrow (BM) samples of AML patients, and further explore its clinical significance. METHODS: Eighty-two de novo AML patients and 20 healthy donors were included in the study. Meanwhile, seven public datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were included to confirm the alteration of ALOX5AP. Receiver operating characteristic (ROC) curve analysis was applied to determine the discriminative capacity of ALOX5AP expression to discriminate AML. The prognostic value of ALOX5AP was identified by the Kaplan-Meier method and log-rank test. It was further validated in four independent cohorts (n = 1186). Significantly different genes associated with ALOX5AP expression were subsequently compared by LinkedOmics, and Metascape database. RESULTS: The level of ALOX5AP expression was significantly increased in bone marrow cells of AML patients compared with healthy donors (P < 0.05). ROC curve analysis suggested that ALOX5AP expression might be a potential biomarker to discriminate AML from controls. ALOX5AP overexpression was associated with decreased overall survival (OS) in AML according to the TCGA data (P = 0.006), which was validated by other four independent cohorts. DNA methylation levels of ALOX5AP were significantly lower in AML patients compared to normal samples (P < 0.05), as confirmed in the Diseasemeth database and the independent cohort GSE63409. ALOX5AP level was positively associated with genes with proleukemic effects such as PAX2, HOX family, SOX11, H19, and microRNAs that act as oncogenes in leukemia, such as miR125b, miR-93, miR-494, miR-193b, while anti-leukemia-related genes and tumor suppressor microRNAs such as miR-582, miR-9 family and miR-205 were negatively correlated. CONCLUSION: ALOX5AP overexpression, associated with its hypomethylation, predicts poorer prognosis in AML.

Integrating molecular interactions and gene expression to identify biomarkers and network modules of chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) causes chronic obstructive conditions, chronic bronchitis, and emphysema, and is a major cause of death worldwide. Although several efforts for identifying biomarkers and pathways have been made, specific causal COPD mechanism remains unknown. OBJECTIVE: This study combined biological interaction data with gene expression data for a better understanding of the biological process and network module for COPD. METHODS: Using a sparse network-based method, we selected 49 genes from peripheral blood mononuclear cell expression data of 136 subjects, including 42 ex-smoking controls and 94 subjects with COPD. RESULTS: These 49 genes might influence biological processes and molecular functions related to COPD. For example, our result suggests that FoxO signaling may contribute to the atrophy of COPD peripheral muscle tissues via oxidative stress. CONCLUSIONS: Our approach enhances the existing understanding of COPD disease pathogenesis and predicts new genetic markers and pathways that may influence COPD pathogenesis.

Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.

BACKGROUND: Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning. OBJECTIVE: Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers. METHODS: In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients. RESULTS: We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment. CONCLUSIONS: Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.

Sparse principal component analysis based on genome network for correcting cell type heterogeneity in epigenome-wide association studies.

In epigenome-wide association studies (EWAS), the mixed methylation expression caused by the combination of different cell types may lead the researchers to find the false methylation site related to the phenotype of interest. To correct the EWAS false discovery, some non-reference models based on sparse principal component analysis (sparse PCA) have been proposed. These models assume that all methylation sites have the same priori probability in each PC load. However, it is known that there already has gene network structure corresponding to the methylation site. How to integrate this genome network knowledge into the sparse PCA models to enhance the performance of existing models is an open research problem. We introduce GN-ReFAEWAS, a non-reference analysis model which integrates the prior gene network structure into the PCA framework to control the false discovery in EWAS. We used one simulated data set, three real data sets, and three additional tests for experiments and compared with four existing models. Experimental results show that the GN-ReFAEWAS model is better than the existing model by 2-90% in the indicators of sensitivity, specificity, genomic control factor λ, and correlation coefficient factor cov with known cell phenotype ratio.

A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis.

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the Cox model. This strategy is included a new learning technique, self-paced learning (SPL), and a new gene selection method, SCAD-Net penalty. The SPL method is adopted to aid to build a more accurate prediction with its built-in mechanism of learning from easy samples first and adaptively learning from hard samples. The SCAD-Net penalty has fixed the problem of the SCAD method without an inherent mechanism to fuse the prior graphical information. We combined the SPL with the SCAD-Net penalty to the Cox model (SSNC). The simulation shows that the SSNC outperforms the benchmark in terms of prediction and gene selection. The analysis of a large-scale experiment across several cancer datasets shows that the SSNC method not only results in higher prediction accuracies but also identifies markers that satisfactory stability across another validation dataset. The demo code for the proposed method is provided in supplemental file.

Clinical evaluation of oral levofloxacin 500 mg once-daily dosage for treatment of lower respiratory tract infections and urinary tract infections: a prospective multicenter study in China.

Levofloxacin (LVFX), a fluoroquinolone agent, has a broad spectrum that covers Gram-positive and -negative bacteria and atypical pathogens. It demonstrates good clinical efficacy in the treatment of various infections, including lower respiratory tract infections (LRTIs) and urinary tract infections (UTIs). To evaluate the efficacy and safety of oral LVFX 500 mg once daily, a large open-label clinical trial was conducted in 1266 patients (899 with LRTIs and 367 with UTIs) at 32 centers in China. In the per-protocol population, the clinical efficacy rate (cure or improvement) at 7 to 14 days after the end of treatment was 96.4% (666/691) for LRTIs and 95.7% (267/279) for UTIs. In 53 patients diagnosed with atypical pneumonia the treatment was effective. The bacteriological efficacy rate was 96.6% (256/265) for LRTIs and 93.3% (126/135) for UTIs. The eradication rate of the causative pathogens was 100% (33/33) for Haemophilus influenzae and 96.0% (24/25) for Streptococcus pneumoniae in LRTIs, and 94.1% (80/85) for Escherichia coli in UTIs. The overall efficacy rates were 89.3% (617/691) for LRTIs and 87.8% (245/279) for UTIs. The incidence of drug-related adverse events (ADRs) was 17.3% (215/1245), and the incidence of drug-related laboratory abnormalities was 15.7% (191/1213). Common ADRs were dizziness, nausea, and insomnia. Common laboratory abnormalities included "WBC decreased", "alanine aminotransferase (ALT) increased", "aspartate aminotransferase (AST) increased", and "lactate dehydrogenase (LDH) increased". All of these events were mentioned in the package inserts of fluoroquinolones including LVFX, and most events were mild and transient. Thirty-four patients (2.7%) were withdrawn from the study because of the ADRs. No new ADRs were found. This study concluded that the dosage regimen of LVFX 500 mg once daily was effective and tolerable for the treatment of LRTIs and UTIs.

Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning.

Blood-Brain-Barrier (BBB) is a strict permeability barrier for maintaining the Central Nervous System (CNS) homeostasis. One of the most important conditions to judge a CNS drug is to figure out whether it has BBB permeability or not. In the past 20 years, the existing prediction approaches are usually based on the data of the physical characteristics and chemical structure of drugs. However, these methods are usually only applicable to small molecule compounds based on passive diffusion through BBB. To deal this problem, one of the most famous methods is multi-core SVM method, which is based on clinical phenotypes about Drug Side Effects and Drug Indications to predict drug penetration of BBB. This paper proposed a Deep Learning method to predict the Blood-Brain-Barrier permeability based on the clinical phenotypes data. The validation result on three datasets proved that Deep Learning method achieves better performance than the other existing methods. The average accuracy of our method reaches 0.97, AUC reaches 0.98, and the F1 score is 0.92. The results proved that Deep Learning methods can significantly improve the prediction accuracy of drug BBB permeability and it can help researchers to reduce clinical trials and find new CNS drugs.

Hybrid L1/2† + 2 method for gene selection in the Cox proportional hazards model.

BACKGROUND AND OBJECTIVE: An important issue in genomic research is to identify the significant genes that related to survival from tens of thousands of genes. Although Cox proportional hazards model is a conventional survival analysis method, it does not induce the gene selection. METHODS: In this paper, we extend the hybrid L1/2  + 2 regularization (HLR) idea to the censored survival situation, a new edition of sparse Cox model based on the HLR method has been proposed. We develop two algorithms for solving the HLR penalized Cox model; one is the coordinate descent algorithm with HLR thresholding operator, the other is the weight iteration method. RESULTS: The proposed method was tested on six public mRNA data sets of serval kinds of cancers, AML, Breast cancer, Pancreatic cancer, DLBCL and Melanoma. The test results indicate that the method identified a small subset of genes but essential while giving best or equivalent predictive performance, as compared to some popular methods. CONCLUSIONS: The results of empirical and simulations imply that the proposed strategy is highly competitive in studying high dimensional survival data among several state-of-the-art methods.

A method to identify trace sulfated IgG N-glycans as biomarkers for rheumatoid arthritis.

N-linked glycans on immunoglobulin G (IgG) have been associated with pathogenesis of diseases and the therapeutic functions of antibody-based drugs; however, low-abundance species are difficult to detect. Here we show a glycomic approach to detect these species on human IgGs using a specialized microfluidic chip. We discover 20 sulfated and 4 acetylated N-glycans on IgGs. Using multiple reaction monitoring method, we precisely quantify these previously undetected low-abundance, trace and even ultra-trace N-glycans. From 277 patients with rheumatoid arthritis (RA) and 141 healthy individuals, we also identify N-glycan biomarkers for the classification of both rheumatoid factor (RF)-positive and negative RA patients, as well as anti-citrullinated protein antibodies (ACPA)-positive and negative RA patients. This approach may identify N-glycosylation-associated biomarkers for other autoimmune and infectious diseases and lead to the exploration of promising glycoforms for antibody therapeutics.Post-translational modifications can affect antibody function in health and disease, but identification of all variants is difficult using existing technologies. Here the authors develop a microfluidic method to identify and quantify low-abundance IgG N-glycans and show some of these IgGs can be used as biomarkers for rheumatoid arthritis.

Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L1/2 +2 Regularization.

Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In this paper, we presented a new hybrid L1/2 +2 regularization (HLR) function, a linear combination of L1/2 and L2 penalties, to select the relevant gene in the logistic regression. The HLR approach inherits some fascinating characteristics from L1/2 (sparsity) and L2 (grouping effect where highly correlated variables are in or out a model together) penalties. We also proposed a novel univariate HLR thresholding approach to update the estimated coefficients and developed the coordinate descent algorithm for the HLR penalized logistic regression model. The empirical results and simulations indicate that the proposed method is highly competitive amongst several state-of-the-art methods.

Network-Based Logistic Classification with an Enhanced L 1/2 Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer.

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L 1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L 1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L 1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L 1 regularization, the old L 1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L 1 regularization, the old L 1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.

Identification of 13 blood-based gene expression signatures to accurately distinguish tuberculosis from other pulmonary diseases and healthy controls.

Tuberculosis (TB), caused by infection with mycobacterium tuberculosis, is still a major threat to human health worldwide. Current diagnostic methods encounter some limitations, such as sample collection problem or unsatisfied sensitivity and specificity issue. Moreover, it is hard to identify TB from some of other lung diseases without invasive biopsy. In this paper, the logistic models with three representative regularization approaches including Lasso (the most popular regularization method), and L1/2 (the method that inclines to achieve more sparse solution than Lasso) and Elastic Net (the method that encourages a grouping effect of genes in the results) adopted together to select the common gene signatures in microarray data of peripheral blood cells. As the result, 13 common gene signatures were selected, and sequentially the classifier based on them is constructed by the SVM approach, which can accurately distinguish tuberculosis from other pulmonary diseases and healthy controls. In the test and validation datasets of the blood gene expression profiles, the generated classification model achieved 91.86% sensitivity and 93.48% specificity averagely. Its sensitivity is improved 6%, but only 26% gene signatures used compared to recent research results. These 13 gene signatures selected by our methods can be used as the basis of a blood-based test for the detection of TB from other pulmonary diseases and healthy controls.

Multimodal therapy for adult Wilms' tumour: an experience from one centre.

INTRODUCTION: Wilms' tumour (WT) is very rare in adults but very common in children. Treatment guidelines for adult patients with WT are still insufficient. Some study groups recommend that therapeutic protocols for adults with WT (AWT) should follow the guidelines that have been established for children. OBJECTIVE: To describe the clinical and pathological characteristics of AWT as well as the treatment protocols and outcomes for AWT at our treatment centre. MATERIAL AND METHODS: Seven patients (5 females and 2 males) were diagnosed with AWT in our hospital between 2002 and 2009. The tumours were staged and the patients were treated according to the paediatric regimen recommended by the National Wilms' Tumor Study Group. RESULTS: The median patient age at the time of diagnosis was 29 years (range, 16-37 years). Flank pain was the most common clinical presentation. One patient was in Stage I of disease development, two were in Stage II, two were in Stage III and two were in Stage IV. Anaplasia was present in 3 patients with Stage III or Stage IV disease. All of the patients but one underwent nephrectomy and 2 incomplete surgeries were performed. Seven patients received 2-drug or 3-drug chemotherapy (dactinomycin and vincristine and/or doxorubicin). Two patients with Stage III disease also received radiation therapy (a total dose of 3600 or 3960 cGy). Complete remission was achieved in 4 patients. Three patients (one with Stage III disease, 2 patients with Stage IV disease) died of their disease and those patients were all classified with an unfavourable histological type called anaplasia. With a median follow-up of 53.5 months (range, 40-102 months), the 3-year and 5-year overall survival rates were 57.1% (95% confidence interval, 20.4-93.8%). CONCLUSIONS: The results of this report suggest that histological anaplasia might be an adverse prognostic factor for AWT. Proper application of the diagnostic and therapeutic regimens established for children may improve the prognosis of adult patients with WT.