Clinical Efficacy and Safety of Massage for the Treatment of Restless Leg Syndrome in Hemodialysis Patients: A Meta-Analysis of 5 Randomized Controlled Trials.

Aim: We conducted this meta-analysis to evaluate the clinical efficacy and safety of massage for the treatment of hemodialysis patients with restless leg syndrome (RLS). Methods: A comprehensive literature search was performed using the PubMed database, EMBASE database (via OVID), and the Cochrane Library in order to identify eligible randomized controlled trials (RCTs) published before August 31, 2021. After extracted essential data and assessed risk of bias of each eligible study, we calculated the pooled estimate of RLS score and safety after treatment. Statistical analysis was performed by using Review Manager 5.3. Results: Five studies involving 369 hemodialysis patients with RLS were analyzed. The RLS score after treatment [mean difference (MD), -12.01; 95% confidence interval (CI), -14.91 to -9.11] and mean difference of RLS score at the beginning and end of treatment [mean difference (MD), -11.94; 95% confidence interval (CI), -15.45 to -8.43] in a massage group was significantly better than that in route care group. Subgroup analysis suggested that massage with lavender oil also significantly reduced the RLS score after treatment (MD, -14.22; 95% CI, -17.81 to -10.63) and mean difference of RLS score at the beginning and end of treatment (MD, -14.87; 95% CI, -18.29 to -11.45) compared with route care. Meanwhile, massage regime significantly relieved RLS severity compared with route care but did not increase adverse events. Conclusion: Massage may be a preferred treatment modality for hemodialysis patients with RLS because it effectively reduces RLS symptoms, relieves RLS severity, and does not increase the risk of adverse events. However, future study with a larger sample size is warranted due to the fact that only limited number of eligible studies with small sample size are enrolled.

DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method.

Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.

Protein acetylation regulates xylose metabolism during adaptation of Saccharomyces cerevisiae.

BACKGROUND: As the second most abundant polysaccharide in nature, hemicellulose can be degraded to xylose as the feedstock for bioconversion to fuels and chemicals. To enhance xylose conversion, the engineered Saccharomyces cerevisiae with xylose metabolic pathway is usually adapted with xylose as the carbon source in the laboratory. However, the mechanism under the adaptation phenomena of the engineered strain is still unclear. RESULTS: In this study, xylose-utilizing S. cerevisiae was constructed and used for the adaptation study. It was found that xylose consumption rate increased 1.24-fold in the second incubation of the yYST12 strain in synthetic complete-xylose medium compared with the first incubation. The study figured out that it was observed at the single-cell level that the stagnation time for xylose utilization was reduced after adaptation with xylose medium in the microfluidic device. Such transient memory of xylose metabolism after adaptation with xylose medium, named "xylose consumption memory", was observed in the strains with both xylose isomerase pathway and xylose reductase and xylitol dehydrogenase pathways. In further, the proteomic acetylation of the strains before and after adaptation was investigated, and it was revealed that H4K5 was one of the most differential acetylation sites related to xylose consumption memory of engineered S. cerevisiae. We tested 8 genes encoding acetylase or deacetylase, and it was found that the knockout of the GCN5 and HPA2 encoding acetylases enhanced the xylose consumption memory. CONCLUSIONS: The behavior of xylose consumption memory in engineered S. cerevisiae can be successfully induced with xylose in the adaptation. H4K5Ac and two genes of GCN5 and HPA2 are related to xylose consumption memory of engineered S. cerevisiae during adaptation. This study provides valuable insights into the xylose adaptation of engineered S. cerevisiae.

Drug rash with eosinophilia and systemic symptoms and severe renal injury induced by proton pump inhibitor therapy: A case report.

INTRODUCTION: Proton pump inhibitors (PPIs) are widely prescribed and generally well tolerated but can rarely cause severe allergic reactions, such as drug rash with eosinophilia and systemic symptoms (DRESS). We report a case of DRESS and renal injury induced by PPIs, and describe the therapeutic process. PATIENT CONCERNS: The patient was a 66-year-old female who complained of fever, pruritus, desquamation, erythema multiforme, and anuria caused by omeprazole taken for 2 weeks to treat abdominal distention. DIAGNOSIS: The clinical history revealed a similar episode of PPI-induced fever, eosinophilia, and acute kidney injury more than 1 year ago. The present laboratory tests revealed eosinophilia and oliguric renal failure. The renal biopsy was performed subsequently and proved the diagnosis of PPI-induced DRESS. INTERVENTIONS: After the suspected diagnosis of PPI-induced DRESS, omeprazole was discontinued and methylprednisolone infusion (40 mg qd) was initiated. Because of oliguric renal failure, the patient received intermittent hemodialysis. OUTCOMES: The patient initially responded to omeprazole discontinuation, hemodialysis, and glucocorticoids but later died from severe infection during the tapering of glucocorticoid therapy. CONCLUSION: Clinicians should remain on high alert for potential life-threatening complications when prescribing PPIs. If unexplained renal injury develops in a patient taking a PPI, renal biopsy may help in identifying the pathogenesis and might facilitate timely intervention.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy.

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important 'hub' genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Multiple genome pattern analysis and signature gene identification for the Caucasian lung adenocarcinoma patients with different tobacco exposure patterns.

BACKGROUND: When considering therapies for lung adenocarcinoma (LUAD) patients, the carcinogenic mechanisms of smokers are believed to differ from those who have never smoked. The rising trend in the proportion of nonsmokers in LUAD urgently requires the understanding of such differences at a molecular level for the development of precision medicine. METHODS: Three independent LUAD tumor sample sets-TCGA, SPORE and EDRN-were used. Genome patterns of expression (GE), copy number variation (CNV) and methylation (ME) were reviewed to discover the differences between them for both smokers and nonsmokers. Tobacco-related signature genes distinguishing these two groups of LUAD were identified using the GE, ME and CNV values of the whole genome. To do this, a novel iterative multi-step selection method based on the partial least squares (PLS) algorithm was proposed to overcome the high variable dimension and high noise inherent in the data. This method can thoroughly evaluate the importance of genes according to their statistical differences, biological functions and contributions to the tobacco exposure classification model. The kernel partial least squares (KPLS) method was used to further optimize the accuracies of the classification models. RESULTS: Forty-three, forty-eight and seventy-five genes were identified as GE, ME and CNV signatures, respectively, to distinguish smokers from nonsmokers. Using only the gene expression values of these 43 GE signature genes, ME values of the 48 ME signature genes or copy numbers of the 75 CNV signature genes, the accuracies of TCGA training and SPORE/EDRN independent validation datasets all exceed 76%. More importantly, the focal amplicon in Telomerase Reverse Transcriptase in nonsmokers, the broad deletion in ChrY in male nonsmokers and the greater amplification of MDM2 in female nonsmokers may explain why nonsmokers of both genders tend to suffer LUAD. These pattern analysis results may have clear biological interpretation in the molecular mechanism of tumorigenesis. Meanwhile, the identified signature genes may serve as potential drug targets for the precision medicine of LUAD.

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma.

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

Association between predialysis hypermagnesaemia and morbidity of uraemic restless legs syndrome in maintenance haemodialysis patients: a retrospective observational study in Zhejiang, China.

OBJECTIVE: The aim of the present study was to determine whether the predialysis serum magnesium level was associated with morbidity of uraemic restless legs syndrome (RLS) in maintenance haemodialysis patients. DESIGN: A retrospective observational study of morbidity of uraemic RLS was conducted. SETTING: Patients on maintenance haemodialysis three times a week. PARTICIPANTS: We reviewed 578 patients receiving maintenance haemodialysis for >1 year as our cohort. OUTCOME MEASURES: Uraemic RLS was diagnosed according to International RLS Study Group criteria, and hypermagnesaemia was defined as serum magnesium level >1.02 mmol/L. RESULTS: The prevalence of uraemic RLS was 14.4% in our study cohort. Univariate analysis indicated that patients with uraemic RLS differed significantly from non-RLS ones in certain demographic and clinical characteristics, including younger age, longer dialysis duration, higher serum parathyroid hormone level and higher prevalence of predialysis hyperphosphataemia and hypermagnesaemia. Binary logistic-regression model analysis indicated that predialysis hypermagnesaemia was independently associated with uraemic RLS and conferred an increase in morbidity of the syndrome (OR=2.024; 95% CI 1.160 to 3.532; p=0.013). Moreover, we found that dialysis duration and predialysis hyperphosphataemia were independently associated with morbidity of uraemic RLS. CONCLUSIONS: Our data indicated that the predialysis serum magnesium level was associated with morbidity of uraemic RLS in maintenance haemodialysis patients and that predialysis hypermagnesaemia might serve as an independent risk factor for the syndrome.

Severe mental disorders following anti-retroviral treatment in a patient on peritoneal dialysis: A case report and literature review.

BACKGROUND: Antiviral drugs are widely used in populations with viral infection caused by immunologic inadequacy. Because these drugs are mainly metabolized by the kidneys, patients with renal failure undergoing renal replacement therapy are prone to drug adverse effects and poisoning. Severe neurotoxicity caused by antiviral drugs is a rare but life-threatening complication. CASE SUMMARY: This study reported one male patient on peritoneal dialysis who suffered from severe mental disorders after receiving an overdose of acyclovir and valacyclovir for the treatment of herpes zoster. The literature review suggested that hemodialysis is better than peritoneal dialysis to clear acyclovir from the circulation. The patient died after his consciousness deteriorated despite peritoneal dialysis and continuous blood purification. CONCLUSION: This case emphasizes cautiousness when using anti-retroviral drugs in patients with uremia. Hemodialysis is optimal method to remove the drugs.

Effect of individualized exercise during maintenance haemodialysis on exercise capacity and health-related quality of life in patients with uraemia.

OBJECTIVE: To investigate the effect of individualized exercise on exercise capacity and health-related quality of life (HRQoL) in uraemic patients during maintenance haemodialysis (MHD). METHODS: Patients receiving MHD were divided randomly into a test group, who underwent recumbent cycling exercise during dialysis, and a control group, who performed simple stretching exercises. The same dialysis protocol was used for all study participants. At study start and after 12 weeks, exercise capacity was measured using tests of physical ability; HRQoL was measured using the kidney disease quality of life score (KDQOL-SF™). RESULTS: A total of 65 patients were included in the study: 33 in the control group and 32 in the test group. There were no significant differences in patient characteristics between the two groups at baseline. After 12 weeks, there were significant improvements in exercise capacity and in many of the items of the KDQOL-SF™ in the test group compared with the control group. CONCLUSION: Individualized exercise during MHD significantly improved the exercise capacity and HRQoL for uraemic patients within a short time period, and could therefore be used as a simple, cost-effective therapeutic approach.