A multi-omics approach to investigate characteristics of gut microbiota and metabolites in hypertension and diabetic nephropathy SPF rat models.

Background: Imbalance in intestinal microbiota caused by microbial species and proportions or metabolites derived from microbes are associated with hypertension, as well as diabetic nephropathy. However, the involvement of the intestinal microbiota and metabolites in hypertension and diabetic nephropathy comorbidities (HDN) remains to be elucidated. Methods: We investigated the effects of intestinal microbiota on HDN in a rat model and determined the abundance of the intestinal microbiota using 16S rRNA sequencing. Changes in fecal and serum metabolites were analyzed using ultra-high-performance liquid chromatography-mass spectrometry. Results: The results showed abundance of Proteobacteria and Verrucomicrobia was substantially higher, whereas that of Bacteroidetes was significant lower in the HDN group than in the sham group. Akkermansia, Bacteroides, Blautia, Turicibacter, Lactobacillus, Romboutsia, and Fusicatenibacter were the most abundant, and Prevotella, Lachnospiraceae_NK4A136_group, and Prevotella_9 were the least abundant in the HDN group. Further analysis with bile acid metabolites in serum showed that Blautia was negatively correlated with taurochenodeoxycholic acid, taurocholic acid, positively correlated with cholic acid and glycocholic acid in serum. Conclusions: These findings suggest that the gut microbiota and metabolites in feces and serum substantially differed between the HDN and sham groups. The F/B ratio was higher in the HDN group than in the sham group. Blautia is potentially associated with HDN that correlated with differentially expressed bile acid metabolites, which might regulate the pathogenesis of HDN via the microorganism-gut-metabolite axis.

Pulmonary mucinous adenocarcinoma: An overview of pathophysiology and advancements in treatment.

Pulmonary mucinous adenocarcinoma (PMA), a distinct subtype of non-small cell lung cancer (NSCLC), is characterized by an abundance of mucin-producing cells. Although this subtype comprises a relatively small fraction of lung adenocarcinomas, PMA stands apart due to its unique clinical, pathological, and molecular features. This review comprehensively discusses the pathophysiology and etiology, clinical features, diagnostic methods, treatment strategies, prognosis, and future directions for PMA, drawing from relevant literature and existing studies. Advances in PMA treatment includes surgical intervention, targeted therapy, immunotherapy, and adjuvant therapy. Particularly, we discussed factors influencing the prognosis of PMAs, such as molecular markers, pathological features, and the impact of the latest treatment advances on prognosis. Moreover, we intended this review to be a comprehensive reference for diagnosing, treating, and assessing the prognosis of PMA, providing valuable guidance for clinical practice.

Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults.

PURPOSE: The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs. MATERIALS AND METHODS: This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health examinations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to China Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS. RESULTS: The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05). CONCLUSION: The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.

Effect of psychosocial interventions for depression in adults with chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: People with chronic kidney disease (CKD) treated with dialysis are frequently affected by depression. Psychotherapy has been reported to decrease depressive symptoms in various chronic diseases and is a potential treatment option for depression. We aimed to perform a systematic review and meta-analysis to evaluate the effect of psychotherapy on depression in adults with CKD. METHODS: We searched MEDLINE, Embase, Web of Science, and Cochrane for published studies up to October 31, 2023. Two investigators independently reviewed the included studies and extracted relevant data. Randomized controlled trials (RCTs) assessing the impact of interventions that provide psychological, emotional, or social support without the use of pharmacological substances on depressive symptoms in people with CKD were included and summarized. Scores on different tools for depressive assessment and quality of life were pooled. RESULTS: A total of 19 RCTs published between 2004 and 2023 were included and analyzed. The weighted mean difference (WMD) for all included studies with regard to depression was - 2.32 (95%CI=-3.83, -0.80, P = 0.003). The WMD for Beck Depression Inventory (BDI) score of depression was - 3.27 (95%CI=-7.81, 1.27, P = 0.158) with significant heterogeneity (I2 = 95.1%). Significant WMD was detected for the Hospital Anxiety and Depression Scale (HADS) tool: WMD=-1.90, 95%CI=-2.91, -0.90, P < 0.001. The WMD for all included studies regarding quality of life was 1.21 (95%CI=-0.51, 2.93, P = 0.168). The WMD for Kidney Disease Quality of Life Short Form (KDQOL-SF) score was 4.55 (95%CI = 0.50, 8.60, P = 0.028). The WMD for SF-36 score was 0.02 (95%CI=-10.33, 10.36, P = 0.998). Significant difference on outcomes of S-PRT scale was observed (WMD = 2.42, 95%CI = 1.07, 3.76, P < 0.001). CONCLUSIONS: Psychosocial interventions probably reduce the depression level among CKD patients. Preliminary evidence suggests that psychosocial interventions might be beneficial for the quality of life in CKD patients. Our results provide medical facilities with an evidence-based basis for establishing psychosocial interventions in kidney care settings.

Single-cell RNA sequencing and multiple bioinformatics methods to identify the immunity and ferroptosis-related biomarkers of SARS-CoV-2 infections to ischemic stroke.

BACKGROUND: Since December 2019, Coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant morbidity and mortality worldwide. There is an increased risk of ischemic stroke (IS) associated with COVID-19. However, few studies have been reported to explain the potential correlation between COVID-19 and IS. METHODS: We investigated the relationship and relevant mechanisms between COVID-19 and IS using single-cell RNA sequencing and multiple bioinformatics approaches. RESULTS: By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 73 COVID-19-related IS genes. According to the KEGG pathway analysis, the COVID-19-related IS disease genes were significantly enriched in the hematopoietic cell lineage pathway, ribosome pathway, COVID-19 pathway and primary immunodeficiency pathway. Finally, three genes associated with immunity (B4GALT5, CRISPLD2, F5) and two genes associated with ferroptosis (ACSL1, CREB5) were identified up-regulated in COVID-19-related IS. Significantly, it was found that all five genes were highly expressed in monocytes by single cell RNA sequencing. CONCLUSION: We believe these genes (B4GALT5, CRISPLD2, F5, ACSL1, CREB5) may regulate the immune response and ferroptosis of multiple immune cells, mainly including monocytes, which may contribute to the development of COVID-19-related IS. In addition, these genes may be potential targets for the treatment of COVID-19-related IS.

CircGNB1 facilitates the malignant phenotype of GSCs by regulating miR-515-5p/miR-582-3p-XPR1 axis.

Glioma is the most common and aggressive primary malignant brain tumor. Circular RNAs (circRNAs) and RNA-binding proteins (RBPs) have been verified to mediate diverse biological behaviors in various human cancers. Therefore, the aim of this study was to explore a novel circRNA termed circGNB1 and elucidate relative molecular mechanism in functional phenotypes, which might be a potential prognostic biomarker and therapeutic approach for glioma. CircGNB1 was upregulated in glioma and closely associated with the low poor prognosis. Functional assays demonstrated that circGNB1 overexpression promoted glioma stem cells (GSCs) viability proliferation, invasion, and neurosphere formation. Mechanistically, circGNB1 upregulated the expression of oncogene XPR1 via sponging miR-515-5p and miR-582-3p. The following experiments proved XPR1 could promote the malignant phenotype of GSCs via upregulating IL6 expression and activating JAK2/STAT3 signaling. Moreover, the RNA binding protein IGF2BP3 could bind to and maintain the stability of circGNB1, thus promoting the effects of circGNB1 on GSCs. Our study reveals that circGNB1 plays a crucial role in promoting tumorigenesis and malignant progression in glioma, which provides a promising cancer biomarker.

Bi-objective bus scheduling optimization with passenger perception in mind.

With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs. Improving the classical Genetic Algorithm (GA) by adaptively determining the crossover probability and mutation probability of the algorithm. We use an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the Dual-CBSOM. Taking Qingdao city as an example for optimization, the constructed A_DPGA is compared with the classical GA and Adaptive Genetic Algorithm (AGA). By solving the arithmetic example, we get the optimal solution that can reduce the overall objective function value by 2.3%, improve the bus operation cost by 4.0%, and reduce the passenger travel cost by 6.3%. The conclusions show that the Dual_CBSOM built can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost. It is demonstrated that the A_DPGA built in this research has faster convergence and better optimization results.

Cre/lox-Mediated CRISPRi Library Reveals Core Genome of a Type I Methanotroph Methylotuvimicrobium buryatense 5GB1C.

Methanotrophs play key roles in global methane cycling and are promising platforms for methane bioconversion. However, major gaps existing in fundamental knowledge undermines understanding of these methane-consuming microorganisms. To associate genes with a phenotype at the genome-wide level, we developed a Cre/lox-mediated method for constructing a large-scale CRISPRi library in a model methanotroph Methylotuvimicrobium buryatense 5GB1C. The efficiency of this Cre mediated integration method was up to a level of 105 CFU/µg DNA. Targeting 4,100 predicted protein-coding genes, our CRISPRi pooled screening uncovered 788 core genes for the growth of strain 5GB1C using methane. The core genes are highly consistent with the gene knockout results, indicating the reliability of the CRISPRi screen. Insights from the core genes include that annotated isozymes generally exist in metabolic pathways and many core genes are hypothetical genes. This work not only provides functional genomic data for both fundamental research and metabolic engineering of methanotrophs, but also offers a method for CRISPRi library construction. IMPORTANCE Due to their key role in methane cycling and their industrial potential, methanotrophs have drawn increasing attention. Genome-wide experimental approaches for gene-phenotype mapping accelerate our understanding and engineering of a bacterium. However, these approaches are still unavailable in methanotrophs. This work has two significant implications. First, the core genes identified here provide functional genetic basics for complete reconstruction of the metabolic network and afford more clues for knowledge gaps. Second, the Cre-mediated knock-in method developed in this work enables large-scale DNA library construction in methanotrophs; the CRISPRi library can be used to screen the genes associated with special culture conditions.

White cell count trajectory and type 2 diabetes in health management population: a retrospective cohort study.

OBJECTIVE: The purposes of current research were to investigate the time-dependent dynamic alteration of white cell count (WCC) in health management populations and to analyse its association with the type 2 diabetes mellitus (T2DM). DESIGN: Retrospective cohort research. SETTING: Shengjing Hospital of China Medical University in China's Shenyang. PARTICIPANTS: The number of non-diabetic subjects (median age: 40 years) totaled 7924 in the present work. PRIMARY AND SECONDARY OUTCOME MEASURES: Assessment and grouping of changes in WCC between 2016 and 2019 were accomplished through group-based trajectory modelling. Newly diagnosed incidence associated with T2DM was estimated based on varying trajectories of WCC by the Kaplan-Meier method. Statistical differences in the incidences of new-onset T2DM among various WCC trajectory groups were examined through log-rank test. The influence of WCC trajectory alterations on the new-onset T2DM was evaluated with a multiparameter Cox (proportional-hazards) model. RESULTS: During a 3-year (2016-2019) period, the T2DM development was noted in 3.14% (249) of the subjects. The trajectory model of WCC change was divided into four groups. For participants with low stability (trajectory 1), medium stability (trajectory 2), medium stability (trajectory 3) and high stability (trajectory 4), the incidence of T2DM was separately 2.2%, 2.62%, 4.82% and 7.4%, respectively. After adjusting for the underlying confounders, new-onset T2DM HRs (95% CIs) for trajectories 3 and 4 were separately 1.94 (1.32 to 2.83) and 3.08 (1.82 to 5.21) compared with the trajectory 1, implying that the T2DM risks were statistically significantly high. CONCLUSION: The 3-year T2DM incidence was impacted independently by the WCC trajectory fluctuations. The grouping trajectory can reflect the dynamic change of WCC over time, which is more representative than the measured WCC at a single time point.

Association of distinct γ-glutamyltransferase trajectories with incident hyperglycemia using latent class growth mixture modeling: A longitudinal cohort study of Chinese adults.

AIMS: To elucidate the association between distinct latent γ-glutamyltransferase (GGT) increasing trajectories in life time and incident hyperglycemia. METHODS: 4547 subjects were followed up for 3 years (January 2016-December 2019), and data regarding fasting plasma glucose, HbA1c, GGT, and other indices were recorded. Latent class growth mixed modeling (LCGMM) was used to analyze GGT latent trajectories. RESULTS: A three-class quadratic model was selected as the best fit by LCGMM. Subjects were categorized into three latent classes: high-increasing (n = 98, 2.16%), low-increasing (n = 364, 8.01%), and stable (n = 4085, 89.83%) classes. Adjusted hazard ratios of hyperglycemia for the high-increasing and low-increasing classes were 1.341 (1.076-2.051) and 1.264 (1.048-1.525) when compared with stable class, respectively. Odds Ratios (ORs) of GGT slopes were confirmed in the 28-57-year age group, shaped like an "M", ranging from 1.144 (1.059, 1.237) to 2.502 (1.384-3.862). Significant differences in the associations between model-estimated GGT values and hyperglycemia incidence were observed from 28 to 51 years of age, with ORs ranging from 1.011 (1.011, 1.012) to 1.014 (1.012, 1.019). CONCLUSIONS: Our study demonstrated that subjects in GGT increasing classes exhibited higher risks of developing hyperglycemia. A steeper GGT slope is a more effective predictor of hyperglycemia than the GGT value.

Serum Cystatin C Trajectory Is a Marker Associated With Diabetic Kidney Disease.

Objective: To explore the association of the trajectory of serum Cystatin C (Cysc) with diabetic kidney disease (DKD), a retrospective cohort study of Chinese subjects was carried out. Method: A review of 2,928 diabetes mellitus (DM) patients admitted to the clinic and ward of the Endocrinology Department, Shengjing Hospital of China Medical University from January 1, 2014 to December 31, 2014 was performed. Subsequent visits to the hospital were followed until December 31, 2020. The primary endpoint was the incidence of DKD as diagnosed by urinary albumin/creatinine ratio ≥30 mg/g and/or estimated glomerular filtration rate <60 ml/min per 1.73 m2. Healthy control subjects were identified from a health checkup database in Shengjing Hospital from 2016 to 2019. The latent class growth mixed modeling (LCGMM) method was used to analyze latent classes of serum Cysc in healthy and DM subjects. Finally, the hazard ratios (HRs) of latent classes of Cysc in DM subjects were analyzed by Cox regression analysis. Results: A total of 805 type 2 diabetes mellitus (T2DM) and 349 healthy subjects were included in the trial. The HRs of quartiles of baseline Cysc in T2DM subjects were 7.15 [95% confidence interval (CI), 2.79 to 25.57], 2.30 (95% CI, 1.25 to 4.24), and 2.05 (95% CI, 1.14 to 3.70), respectively, for quartile 4 (Q4), Q3, and Q2 when compared with Q1. Through LCGMM, a 1-class linear model was selected for the Cysc latent class in healthy subjects. In contrast, a 3-class linear model was selected for that in DM subjects. The slopes of the three latent classes in T2DM subjects were larger than the slope in healthy subjects. The HRs of incident DKD were 3.43 (95% CI, 1.93 to 6.11) for the high-increasing class and 1.80 (95% CI, 1.17 to 2.77) for the middle-increasing class after adjusting for confounding variables. Conclusions: Patients with T2DM had a higher velocity of increase in Cysc than healthy subjects. Patients with high baseline Cysc values and high latent increasing velocity of Cysc had a higher risk of developing DKD in later life. More attention should be paid to patients with these high-risk factors.

Relationship between the ApoB/ApoA-I ratio trajectory and risk of type 2 diabetes in China: a retrospective cohort study.

PURPOSE: Little research has investigated the correlation of changes in long-term apolipoprotein B/apolipoprotein A-I (ApoB/ApoA-I) ratio with risk of new-onset type 2 diabetes (T2D) among ordinary people. Therefore, the research took long-term ApoB/ApoA-I ratio trajectories as independent variables for exploring their association with the risk of newly diagnosed T2D. METHODS: Altogether 5362 non-diabetic participants with a median age of 49 were enrolled in the cohort study. Their ApoB/ApoA-I ratio trajectories from 2016 to 2019 were analyzed and grouped using group-based trajectory modeling. The Kaplan-Meier approach was employed for calculating the newly diagnosed T2D-related incidence with different ApoB/ApoA-I ratio trajectories. A log-rank test was conducted for testing the presence of statistical difference in new-onset T2D incidence among the different ApoB/ApoA-I ratio trajectory groups. A multivariate Cox proportional hazards regression model was adopted for analyzing how ApoB/ApoA-I ratio trajectory changes affected new-onset T2D. RESULTS: From 2016 to 2019, 199 patients developed T2D (3% in 3 years). The incidence of T2D was 2.0%, 3.28%, 5.86%, and 6.92% for low, middle, upper, and high ApoB/ApoA-I ratio trajectories, respectively. Following adjustment of underlying confounding factors, in contrast to low ApoB/ApoA-I ratio trajectory, new-onset T2D risk ratios and hazard ratio (HR) (95% confidence intervals [CI]) for the middle lower ApoB/ApoA-I ratio trajectory, and upper middle and high ApoB/ApoA-I ratio trajectories were [HR (95% CI)] 1.35(0.88-2.08), 1.98(1.27-3.09) and 2.42(1.35-4.34), respectively, indicating high and statistically significant risks of T2D. CONCLUSION: Variations of the ApoB/ApoA-I ratio trajectory exerted independent effects on the 3-year incidence of T2D. Long-term monitoring on the ApoB/ApoA-I ratio locus may help improve the identification on patients with T2D.

Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs. METHODS: This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree. RESULTS: The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level. CONCLUSION: Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC.

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

Association of cystatin C levels with metabolic syndrome incidence: a nested case-control study with propensity score matching.

OBJECTIVE: Metabolic syndrome (MetS) involves multiple metabolic disorders and seriously affects human health. Identification of key biological factors associated with MetS incidence is therefore important. We explored the association between MetS and the biochemical profiles of Chinese adults in Shenyang City in a nested case-control study. METHODS: We included adult participants who underwent physical examination at our hospital for 2 consecutive years. Participants' biochemical profiles and other MetS components were tested and monitored continuously. Propensity score matching was used to adjust confounding factors between participants with and without MetS. We analyzed the association between incidence of MetS and the biochemical profiles of participants. RESULTS: Of 5702 participants who underwent physical examination between 1 January 2017 and 1 December 2018, 538 had confirmed newly developed MetS. After successfully matching 436 pairs of participants, mean cystatin C (Cys-C) level was significantly higher in the MetS group than in the non-MetS group. Logistic regression analysis indicated that age (years) and γ-glutamate transpeptidase, creatinine, uric acid, and Cys-C levels were significantly associated with MetS incidence; among these, the odds ratio of Cys-C was highest (3.03; 95% confidence interval, 1.02-9.00). CONCLUSIONS: Cys-C levels were significantly associated with the incidence of MetS among Chinese adults.

The Operon Encoding Hydrolytic Dehalogenation of 4-Chlorobenzoate Is Transcriptionally Regulated by the TetR-Type Repressor FcbR and Its Ligand 4-Chlorobenzoyl Coenzyme A.

The bacterial hydrolytic dehalogenation of 4-chlorobenzoate (4CBA) is a coenzyme A (CoA)-activation-type catabolic pathway that is usually a common part of the microbial mineralization of chlorinated aromatic compounds. Previous studies have shown that the transport and dehalogenation genes for 4CBA are typically clustered as an fcbBAT1T2T3C operon and inducibly expressed in response to 4CBA. However, the associated molecular mechanism remains unknown. In this study, a gene (fcbR) adjacent to the fcb operon was predicted to encode a TetR-type transcriptional regulator in Comamonas sediminis strain CD-2. The fcbR knockout strain exhibited constitutive expression of the fcb cluster. In the host Escherichia coli, the expression of the Pfcb -fused green fluorescent protein (gfp) reporter was repressed by the introduction of the fcbR gene, and genetic studies combining various catabolic genes suggest that the ligand for FcbR may be an intermediate metabolite. Purified FcbR could bind to the Pfcb DNA probe in vitro, and the metabolite 4-chlorobenzyl-CoA (4CBA-CoA) prevented FcbR binding to the P fcb DNA probe. Isothermal titration calorimetry (ITC) measurements showed that 4CBA-CoA could bind to FcbR at a 1:1 molar ratio. DNase I footprinting showed that FcbR protected a 42-bp DNA motif (5'-GGAAATCAATAGGTCCATAGAAAATCTATTGACTAATCGAAT-3') that consists of two sequence repeats containing four pseudopalindromic sequences (5'-TCNATNGA-3'). This binding motif overlaps with the -35 box of Pfcb and was proposed to prevent the binding of RNA polymerase. This study characterizes a transcriptional repressor of the fcb operon, together with its ligand, thus identifying halogenated benzoyl-CoA as belonging to the class of ligands of transcriptional regulators.IMPORTANCE The bacterial hydrolytic dehalogenation of 4CBA is a special CoA-activation-type catabolic pathway that plays an important role in the biodegradation of polychlorinated biphenyls and some herbicides. With genetic and biochemical approaches, the present study identified the transcriptional repressor and its cognate effector of a 4CBA hydrolytic dehalogenation operon. This work extends halogenated benzoyl-CoA as a new member of CoA-derived effector compounds that mediate allosteric regulation of transcriptional regulators.

Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy.

The aim of this study is to evaluate the feasibility of using blood serum surface-enhanced Raman spectroscopy (SERS) to identify benign and malignant thyroid nodules. Blood serum samples collected from three different groups including healthy volunteers (n = 22), patients with benign nodules (n = 19) and malignant nodules (n = 22) were measured by SERS. The spectral analysis results demonstrate that biomolecules in serum, such as amino acids, adenine and nucleic acid bases, change differently due to the different progression of nodules. By further combining with partial least square analysis and linear discriminant analysis (PLS-LDA) method, diagnostic accuracies of 93.65% and 82.93%, sensitivities of 92.68% and 81.82% and specificities of 95.45% and 84.21% can be achieved for differentiating healthy versus thyroid nodular groups and benign versus malignant groups, respectively. The above results have suggested that the blood serum SERS technique is helpful for precise diagnosis and timely treatment for patients with thyroid nodules.

Evaluation of the Association between Obesity Markers and Type 2 Diabetes: A Cohort Study Based on a Physical Examination Population.

PURPOSE: To evaluate the predictive effect of different obesity markers on the risk of developing type 2 diabetes in a population of healthy individuals who underwent physical examination and to provide a reference for the early detection of individuals at risk of diabetes. METHODS: This retrospective cohort study included 15206 healthy subjects who underwent a physical examination (8307 men and 6899 women). Information on the study population was obtained from the Dryad Digital Repository. Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) of different obesity markers, including the lipid accumulation index (LAP), body mass index (BMI), waist-to-height ratio (WHtR), visceral adiposity index (VAI), and body roundness index (BRI) on the development of type 2 diabetes. The effectiveness of each obesity marker in predicting the risk of developing type 2 diabetes was analyzed using the receiver operating characteristic curve (ROC curve) and the area under the curve (AUC). RESULTS: After a mean follow-up of 5.4 years, there were 372 new cases of type 2 diabetes. After correcting for confounding factors such as age, sex, smoking, alcohol consumption, exercise, and blood pressure, Cox proportional risk model analysis showed that elevations in BMI, LAP, WHtR, VAI, and BRI increased the risk of developing type 2 diabetes. The ROC curve results showed that LAP was the best predictor of the risk of developing diabetes, with an AUC (95% CI) of 0.759 (0.752-0.766), an optimal cutoff value of 16.04, a sensitivity of 0.72, and a specificity of 0.69. CONCLUSION: An increase in the BMI, LAP, WHtR, VAI, and BRI can increase the risk of developing type 2 diabetes, with LAP being the best predictor of this risk.

Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model.

BACKGROUND: To predict and make an early diagnosis of diabetes is a critical approach in a population with high risk of diabetes, one of the devastating diseases globally. Traditional and conventional blood tests are recommended for screening the suspected patients; however, applying these tests could have health side effects and expensive cost. The goal of this study was to establish a simple and reliable predictive model based on the risk factors associated with diabetes using a decision tree algorithm. METHODS: A retrospective cross-sectional study was used in this study. A total of 10,436 participants who had a health check-up from January 2017 to July 2017 were recruited. With appropriate data mining approaches, 3454 participants remained in the final dataset for further analysis. Seventy percent of these participants (2420 cases) were then randomly allocated to either the training dataset for the construction of the decision tree or the testing dataset (30%, 1034 cases) for evaluation of the performance of the decision tree. For this purpose, the cost-sensitive J48 algorithm was used to develop the decision tree model. RESULTS: Utilizing all the key features of the dataset consisting of 14 input variables and two output variables, the constructed decision tree model identified several key factors that are closely linked to the development of diabetes and are also modifiable. Furthermore, our model achieved an accuracy of classification of 90.3% with a precision of 89.7% and a recall of 90.3%. CONCLUSION: By applying simple and cost-effective classification rules, our decision tree model estimates the development of diabetes in a high-risk adult Chinese population with strong potential for implementation of diabetes management.

Methylomonas rhizoryzae sp. nov., a type I methanotroph isolated from the rhizosphere soil of rice.

A gammaproteobacterial methanotroph, strain GJ1T, was isolated from a rhizosphere soil sample of rice in Nanjing, China. The cells were Gram-negative, motile rods with a single polar flagellum, and they contained type I intracytoplasmic membranes. The cells formed pink colonies. The strain possessed both the particulate methane monooxygenase enzyme (pMMO) and the soluble methane monooxygenase enzyme (sMMO). pxmABC, encoding a divergent methane monooxygenase (pXMO), and nifH, which encodes dinitrogenase reductase, were also present. Methane and methanol were utilized as sole carbon sources, while other carbon sources, including acetate, pyruvate, succinate, citrate, malate, glucose, urea, methylamine, ethanol and formate, could not be utilized by strain GJ1T. Cell grew optimally at 25-33 °C (range 16-37 °C), pH 6.0-8.0 (range 5.5-8.5) and 0-1.2% NaCl (no growth above 1.5% NaCl). Phylogenetic analyses based on the 16S rRNA gene, pmoA and nifH showed that the isolate belongs to the genus Methylomonas of the family Methylococcaceae within the class Gammaproteobacteria. The major quinone was determined to be MQ-8, and the major fatty acids were observed to be C16:1 and C14:0. The genome size of strain GJ1T is about 4.55 Mb, and the DNA G + C content of the strain was determined to be 53.67 mol% within the range of the genus Methylomonas (47-58 mol%) reported at present. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain GJ1T and Methylomonas koyamae Fw12E-YT among the genus Methylomonas were the highest, and they were only 74.66% and 21.40%, respectively. In consequence, results of phenotypic characterization and phylogenetic analyses support strain GJ1T as a novel species within the genus Methylomonas, namely, Methylomonas rhizoryzae sp. nov.. The type strain is GJ1T (= ACCC 61706).