Identification and characterization of a novel type II toxin-antitoxin system in Aeromonas veronii.

The bacterial type II toxin-antitoxin (TA) system is a rich genetic element that participates in various physiological processes. Aeromonas veronii is the main bacterial pathogen threatening the freshwater aquaculture industry. However, the distribution of type II TA system in A. veronii was seldom documented and its roles in the life activities of A. veronii were still unexplored. In this study, a novel type II TA system AvtA-AvtT was predicted in a fish pathogen Aeromonas veronii biovar sobria with multi-drug resistance using TADB 2.0. Through an Escherichia coli host killing and rescue assay, we demonstrated that AvtA and AvtT worked as a genuine TA system, and the predicted toxin AvtT actually functioned as an antitoxin while the predicted antitoxin AvtA actually functioned as a toxin. The binding ability of AvtA with AvtT proteins were confirmed by dot blotting analysis and co-immunoprecipitation assay. Furthermore, we found that the toxin and antitoxin labelled with fluorescent proteins were co-localized. In addition, it was found that the transcription of AvtAT bicistronic operon was repressed by the AvtAT protein complex. Deletion of avtA gene and avtT gene had no obvious effect on the drug susceptibility. This study provides first characterization of type II TA system AvtA-AvtT in aquatic pathogen A. veronii.

Alterations of gut microbiota are associated with blood pressure: a cross-sectional clinical trial in Northwestern China.

BACKGROUND: The human gut microbiota (GM) is involved in the pathogenesis of hypertension (HTN), and could be affected by various factors, including sex and geography. However, available data directly linking GM to HTN based on sex differences are limited. METHODS: This study investigated the GM characteristics in HTN subjects in Northwestern China, and evaluate the associations of GM with blood pressure levels based on sex differences. A total of 87 HTN subjects and 45 controls were recruited with demographic and clinical characteristics documented. Fecal samples were collected for 16S rRNA gene sequencing and metagenomic sequencing. RESULTS: GM diversity was observed higher in females compared to males, and principal coordinate analysis showed an obvious segregation of females and males. Four predominant phyla of fecal GM included Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. LEfSe analysis indicated that phylum unidentified_Bacteria was enriched in HTN females, while Leuconostocaceae, Weissella and Weissella_cibaria were enriched in control females (P < 0.05). Functionally, ROC analysis revealed that Cellular Processes (0.796, 95% CI 0.620 ~ 0.916), Human Diseases (0.773, 95% CI 0.595 ~ 0.900), Signal transduction (0.806, 95% CI 0.631 ~ 0.922) and Two-component system (0.806, 95% CI 0.631 ~ 0.922) could differentiate HTN females as effective functional classifiers, which were also positively correlated with systolic blood pressure levels. CONCLUSIONS: This work provides evidence of fecal GM characteristics in HTN females and males in a northwestern Chinese population, further supporting the notion that GM dysbiosis may participate in the pathogenesis of HTN, and the role of sex differences should be considered. Trial registration Chinese Clinical Trial Registry, ChiCTR1800019191. Registered 30 October 2018 - Retrospectively registered, http://www.chictr.org.cn/ .

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients.

INTRODUCTION: Postmenopausal women with osteoporosis (PMOP) are prone to fragility fractures. Osteoporosis is associated with alterations in the levels of specific circulating metabolites. OBJECTIVES: To analyze the metabolic profile of individuals with PMOP and identify novel metabolites associated with bone mineral density (BMD). METHODS: We performed an unsupervised metabolomics analysis of plasma samples from participants with PMOP and of normal controls (NC) with normal bone mass. BMD values for the lumber spine and the proximal femur were determined using dual-energy X-ray absorptiometry. Principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were performed for metabolomic profile analyses. Metabolites with P < 0.05 in the t-test, VIP > 1 in the PLS-DA model, and SNR > 0.3 between the PMOP and NC groups were defined as differential abundant metabolites (DAMs). The SHapley additive explanations (SHAP) method was utilized to determine the importance of permutation of each DAM in the predictive model between the two groups. ROC analysis and correlation analysis of metabolite relative abundance and BMD/T-scores were conducted. KEGG pathway analysis was used for functional annotation of the candidate metabolites. RESULTS: Overall, 527 annotated molecular markers were extracted in the positive and negative total ion chromatogram (TIC) of each sample. The PMOP and NC groups could be differentiated using the PLS-DA model. Sixty-eight DAMs were identified, with most relative abundances decreasing in the PMOP samples. SHAP was used to identify 9 DAM metabolites as factors distinguishing PMOP from NC. The logistic regression model including Triethanolamine, Linoleic acid, and PC(18:1(9Z)/18:1(9Z)) metabolites demonstrated excellent discrimination performance (sensitivity = 97.0, specificity = 96.6, AUC = 0.993). The correlation analysis revealed that the abundances of Triethanolamine, PC(18:1(9Z)/18:1(9Z)), 16-Hydroxypalmitic acid, and Palmitic acid were significantly positively correlated with the BMD/T score (Pearson correlation coefficients > 0.5, P < 0.05). Most candidate metabolites were involved in lipid metabolism based on KEGG functional annotations. CONCLUSION: The plasma metabolomic signature of PMOP patients differed from that of healthy controls. Marker metabolites may help provide information for the diagnosis, therapy, and prevention of PMOP. We highlight the application of feature selection approaches in the analysis of high-dimensional biological data.

Plasma lipidomics analysis reveals altered lipids signature in patients with osteonecrosis of the femoral head.

INTRODUCTION: Although studies have established a link between lipid metabolism disorder and osteonecrosis of the femoral head (ONFH), the characteristics of the circulating lipidome signature of ONFH have not yet been investigated and need to be explored. OBJECTIVES: We aimed to explore the plasma lipidome signatures in patients with ONFH, and to identify specific lipid biomarkers of ONFH. METHODS: In this study, a comprehensive detection and analysis of plasma lipidomics was conducted in clinical human cohort, including 32 healthy normal control (NC) subjects and 91 ONFH patients in different subgroups [alcohol-induced ONFH (AONFH), steroid-induced ONFH (SONFH), and traumatic-induced ONFH (TONFH)] or at different disease stages (stage I, II, III and IV of ONFH) using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). RESULTS: Overall, the plasma lipidome profile differs between ONFH and NC samples. Lipidome signature including 22 common differentially expressed lipids (DELs) in all three subgroups (variable importance in projection > 1, P < 0.05, fold change > 1.5 or < 0.67, compared to the NC group) was identified. Besides, the subtype-specific lipidome profiles for each ONFH subgroup were also analyzed. Generally, the AONFH subgroup has the largest number of DELs, and the plasma levels of triacylglycerol lipid compounds increased obviously in the AONFH samples. In the subgroup of SONFH, the relative abundance of lipid 4-Aminobenzoic acid increased significantly with changes in the expression of several of its interactive genes. We have identified that 9 stage-positive and 2 stage-negative lipids may function as novel biomarkers predicting the progression of ONFH. CONCLUSION: Our study presents an overview of the phenotype-related plasma lipidome signature of patients with ONFH. The results will provide insight into the mechanisms underlying the metabolism of lipids in the pathogenesis and progression of ONFH and help identify novel lipids biomarkers or disease diagnosis and treatment targets.

A comprehensive analysis of intratumor microbiome in head and neck squamous cell carcinoma.

PURPOSE: Human microbiome has been considered as the second genome of our body. The intratissue/intratumor microbiome analysis is a relatively new field and deserves more attention. In this study, we conducted a comprehensive analysis of microbiome signatures of head and neck squamous cell carcinoma (HNSC). METHODS: The intratumor microbiome profiling and clinicopathological information about a total of 177 HNSC samples, including 155 tumors and 22 adjacent normal tissues, were obtained from The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA) databases. We identified the microbes that differed between tumors and normal tissues, and assessed their utility values as diagnostic biomarkers. The microbiome signatures under different conditions of clinicopathological parameters were also analyzed. RESULTS: The intratissue microbiome profiles differed between tumor and normal samples of HNSC. The composition of four, six, and six microbes changed in tumors compared to normal tissues at the phylum, order, and genus levels, respectively (P < 0.05). Eight of the differential microbes performed well in distinguishing tumors from normal tissues (AUC > 0.7, P ≤ 0.001). The microbiome signature was found to be associated with tumor clinicopathological characteristics such as host-gender, host-age, tumor stage, and neoplasm histologic grade. CONCLUSION: Overall, our results revealed an intratissue microbiome signature of HNSC. We concluded that the intratumor microbiome signature may also reflect human biology in both healthy and disease status, and provide novel perspective for microbiota research about their roles in tumors.

Difference in calcium ion precipitation between free and immobilized Halovibrio mesolongii HMY2.

Biomineralization has become a research focus in wastewater treatment due to its much lower costs compared to traditional methods. However, the low sodium chloride (NaCl)-tolerance of bacteria limits applications to only water with low NaCl concentrations. Here, calcium ions in hypersaline wastewater (10% NaCl) were precipitated by free and immobilized Halovibrio mesolongii HMY2 bacteria and the differences between them were determined. The results show that calcium ions can be transformed into several types of calcium carbonate with a range of morphologies, abundant organic functional groups (C-H, C-O-C, C=O, etc), protein secondary structures (ß-sheet, α-helix, 310 helix, and ß-turn), P=O and S-H indicated by P2p and S2p, and more negative δ13CPDB (‰) values (-16.8‰ to -18.4‰). The optimal conditions for the immobilized bacteria were determined by doing experiments with six factors and five levels and using response surface method. Under the action of two groups of immobilized bacteria prepared under the optimal conditions, by the 10th day, Ca2+ ion precipitation ratios had increased to 79%-89% and 80%-88% with changes in magnesium ion cencentrations. Magnesium ions can significantly inhibit the calcium ion precipitation, and this inhibitory effect can be decreased under the action of immobilized bacteria. Minerals induced by immobilized bacteria always aggregated together, had higher contents of Mg, P, and S, lower stable carbon isotope values and less well-developed protein secondary structures. This study demonstrates an economic and eco-friendly method for recycling calcium ions in hypersaline wastewater, providing an easy step in the process of desalination.

Lipid metabolism analysis for peripheral blood in patients with alcohol-induced and steroid-induced osteonecrosis of the femoral head. / é ’ç²¾æ€§å’Œæ¿€ç´ æ€§è‚¡éª¨å¤´åæ­»çš„å¤–å‘¨è¡€è„‚è´¨ç»„å­¦åˆ†æž.

OBJECTIVES: Osteonecrosis of the femoral head (ONFH), also known as vascular necrosis of the femoral head, is combined with lipid metabolism disorders in most patients. This study aims to explore the lipid metabolism profiles in different subtypes of ONFH. METHODS: The subjects were divided into an alcohol-induced osteonecrosis of the femoral head (AONFH) group, a steroid-induced osteonecrosis of the femoral head (SONFH) group, and a normal control (NC) group (n=16, 29, and 32, respectively). Ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) was used to detect the lipidomics analysis in the peripheral blood samples of subjects and identify the underlying biomarkers. The samples were preprocessed, the partial least squares discriminant analysis (PLS-DA) was adopted, and the variable importance for the projection (VIP) values were calculated to measure the expression pattern of each lipid metabolite and observe the influence and explanatory power of the expression pattern of each lipid metabolite on the classification and discrimination between the different groups. The lipid metabolites with fold change (FC)>2, P<0.05 and VIP>1 in the different groups were screened as differential lipids. Among them, the differential lipids co-existing in the AONFH group and the SONFH group were regarded as common differential lipids for ONFH, and the differential lipids that exist separately were regarded as specific differential lipids in the AONFH group or the SONFH group. Binary logistic regression was used to evaluate the diagnostic value of differential lipid metabolites on the basis of the receiver operator characteristic (ROC) curve analysis. Based on the disease stage information, the correlation between the differential lipids and the disease stage was analyzed in the AONFH group and the SONFH group. RESULTS: In this study, 1 358 lipid metabolites were detected in each plasma sample. Compared with the NC group, there were significant difference in the expression patterns of lipid metabolism profiles in the AONFH group and the SONFH group. A total of 62 and 64 differential lipid metabolites were screened in the AONFH and SONFH patients (FC>2, P<0.05, VIP>1) respectively, and these differential lipids were mainly up-regulated in the disease samples. Nine differential lipid metabolites were further identified, which were shared by the AONFH group and the SONFH group; the area under the curve (AUC) in 6 kinds of lipid components was greater than 0.7, including 1-myristoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine, hypoxanthin, serotonin, PE (19:0/22:5), PE (19:0/22:5), and cholest-5-en-3-yl beta-D-glucopyranosiduronic acid. Fifty-three specific differential lipid metabolites were identified in the AONFH group, and 55 specific differential lipid metabolites were identified in the SONFH group. The AUC in 6 kinds of lipid components was greater than 0.9, including 1D-myo-Inositol 1,2-cyclic phosphate, L-pyroglutamic acid, DL-carnitine, 8-amino-7-oxononanoic acid, Clobetasol, and presqualene diphosphate. In the AONFH group, there were 9 differential lipid metabolites related to the disease stages, including LPG 18:1, serotonin, PC (22:4e/23:0), PC (19:2/18:5), hypoxanthin, PE (18:1/20:3), LPE 18:1, 1-stearoyl-2-arachidonoyl-sn-glycerol, and PE (16:0/18:1); with AONFH disease progresses from I/II stages to III/IV stages, the relative content of these 9 differential lipid metabolites was increased. In the SONFH group, 8 differential lipid metabolites were found to be related to the stage of the disease, including TM6076000, 4-(1,1-dimethylpropyl)phenol, D-617, asarone, phenylac-gln-OH, creatine, leu-pro, and 8-amino-7-oxononanoic acid; and with the SONFH progressed from stage I/II to stage III/IV, the content of these 8 differential lipid metabolites were gradually increased. CONCLUSIONS: This study analyzes the characteristics of the plasma lipid metabolism profile in the AONFH and SONFH patients, and which identifies the differential lipid metabolites related to disease diagnosis and evaluation. These results provide evidence for exploring lipid metabolism alterations and the mining of novel lipid biomarkers for the ONFH.

Identification and characterization of an apoptosis-inducing factor 1 involved in apoptosis and immune defense of oyster, Crassostrea gigas.

The apoptosis-inducing factor (AIF) is a phylogenetically old protein with classic function of inducing caspase-independent apoptosis, which extensively present in all primary kingdoms. In the present study, an AIF homologue (designated as CgAIF1) was identified from oyster Crassostrea gigas. The open reading frame of CgAIF1 cDNA was of 1836 bp encoding a peptide of 611 amino acid residues. There are a Pyr_redox_2 domain and an AIF_C domain in the predicted CgAIF1 protein. The deduced amino acid sequence of CgAIF1 shared 35.44%-79.22% similarity with AIF1s from other species. In the phylogenetic tree, CgAIF1 firstly clustered with mollusc AIF1s, and then with insect AIF1s, displaying separation from vertebrate AIF1s. The mRNA transcripts of CgAIF1 were constitutively distributed in all the tested oyster tissues, with the highest level in gills (12.98-fold of that in haemocytes, p < 0.05). After LPS and Poly (I:C) stimulation, the mRNA transcripts of CgAIF1 in gills were significantly increased at 6 h and 24 h (5.79-fold, p < 0.001, and 21.96-fold compared to the control group, p < 0.05), respectively. In immunocytochemical assay, the CgAIF1 positive signals were mainly distributed in the cytoplasm of haemocytes, while after Poly (I:C) stimulation, the increased CgAIF1 positive signals were observed in the nucleus. Moreover, in the HEK293T cells transfected with pcDNA3.1-CgAIF1 recombinant plasmid, green signal of CgAIF1 were observed in both the cytoplasm and nucleus. The cell mortality rate, cell shrinking and the phosphatidylserine (PS) ectropion (Annexin V+/PI- cells and Annexin V+/PI+ cells) of CgAIF1 transfected HEK293T cells were significantly increased, compared to the groups with or without pcDNA3.1 transfection. These results collectively suggested that CgAIF1 was a conserved AIF1 member in oysters, and participated in immune response by inducing cell apoptosis.

Resveratrol targets TyrRS acetylation to protect against radiation-induced damage.

Resveratrol (RSV) has broad prospective applications as a radiation protection drug, but its mechanism of action is not yet clear. Here, we found that 5 µM RSV can effectively reduce the cell death caused by irradiation. Irradiation leads to G2/M phase arrest in the cell cycle, whereas RSV treatment increases S-phase cell cycle arrest, which is associated with sirtuin 1 (SIRT1) regulation. Meanwhile, RSV promotes DNA damage repair, mainly by accelerating the efficiency of homologous recombination repair. Under oxidative stress, tyrosyl-tRNA synthetase (TyrRS) is transported to the nucleus to protect against DNA damage. RSV can promote TyrRS acetylation, thus promoting TyrRS to enter the nucleus, where it regulates the relevant signaling proteins and reduces apoptosis and DNA damage. SIRT1 is a deacetylase, and SIRT1 knockdown or inhibition can increase TyrRS acetylation levels, further reducing radiation-induced apoptosis after RSV treatment. Our study revealed a new radiation protection mechanism for RSV, in which the acetylation of TyrRS and its translocation into the nucleus is promoted, and this mechanism may also represent a novel protective target against irradiation.-Gao, P., Li, N., Ji, K., Wang, Y., Xu, C., Liu, Y., Wang, Q., Wang, J., He, N., Sun, Z., Du, L., Liu, Q. Resveratrol targets TyrRS acetylation to protect against radiation-induced damage.

Serum anti-Müllerian hormone levels are associated with low bone mineral density in premenopausal women.

OBJECTIVE: To investigate the associations between anti-Müllerian hormone (AMH) and bone mineral density (BMD) induced by ovarian insufficiency in premenopausal women. METHODS: Subjects were consecutively enrolled from January 2015 to December 2018. Dual energy X-ray absorptiometry (DXA) examination was set as the gold standard, with T-scores less than -2.5/1 as thresholds for the definition of osteoporosis (OP)/osteopenia. RESULTS: A total of 87 subjects were included in the low BMD group, and 39 subjects were included in the control group. Serum AMH levels were decreased significantly in the low BMD group (p < 0.05) with a negative correlation between AMH and age. Strong positive correlations between AMH and BMD/T-score existed in all subjects and subjects with low BMD, and remained even after age adjustment. An exploratory multivariate regression model indicated that age and AMH remained predictive and might be independent risk factors with adjusted odds ratios (ORs) of 0.9 (p = 0.009) and 36 (p < 0.001), respectively. The receiver operating characteristic (ROC) curve analysis estimated that the sensitivity and specificity were 78.2 and 76.9%, respectively, for identifying low BMD subjects from controls when the cut-off value for AMH was set to 0.800 ng/mL. CONCLUSIONS: Serum AMH levels are associated with low BMD in premenopausal women with suspected ovarian insufficiency.

ACTN4 and the pathways associated with cell motility and adhesion contribute to the process of lung cancer metastasis to the brain.

BACKGROUND: The aim of this study was to identify critical gene pathways that are associated with lung cancer metastasis to the brain. METHODS: The RNA-Seq approach was used to establish the expression profiles of a primary lung cancer, adjacent benign tissue, and metastatic brain tumor from a single patient. The expression profiles of these three types of tissues were compared to define differentially expressed genes, followed by serial-cluster analysis, gene ontology analysis, pathway analysis, and knowledge-driven network analysis. Reverse transcription-polymerase chain reaction (RT-PCR) was used to validate the expression of essential candidate genes in tissues from ten additional patients. RESULTS: Differential gene expression among these three types of tissues was classified into multiple clusters according to the patterns of their alterations. Further bioinformatic analysis of these expression profile data showed that the network of the signal transduction pathways related to actin cytoskeleton reorganization, cell migration, and adhesion was associated with lung cancer metastasis to the brain. The expression of ACTN4 (actinin, alpha 4), a cytoskeleton protein gene essential for cytoskeleton organization and cell motility, was significantly elevated in the metastatic brain tumor but not in the primary lung cancer tissue. CONCLUSIONS: The signaling pathways involved in the regulation of cytoskeleton reorganization, cell motility, and focal adhesion play a role in the process of lung cancer metastasis to the brain. The contribution of ACTN4 to the process of lung cancer metastasis to the brain could be mainly through regulation of actin cytoskeleton reorganization, cell motility, and focal adhesion.

Using type IV Pearson distribution to calculate the probabilities of underrun and overrun of lists of multiple cases.

BACKGROUND: An efficient operating room needs both little underutilised and overutilised time to achieve optimal cost efficiency. The probabilities of underrun and overrun of lists of cases can be estimated by a well defined duration distribution of the lists. OBJECTIVE: To propose a method of predicting the probabilities of underrun and overrun of lists of cases using Type IV Pearson distribution to support case scheduling. DESIGN: Six years of data were collected. The first 5 years of data were used to fit distributions and estimate parameters. The data from the last year were used as testing data to validate the proposed methods. The percentiles of the duration distribution of lists of cases were calculated by Type IV Pearson distribution and t-distribution. Monte Carlo simulation was conducted to verify the accuracy of percentiles defined by the proposed methods. SETTING: Operating rooms in John D. Dingell VA Medical Center, United States, from January 2005 to December 2011. MAIN OUTCOME MEASURES: Differences between the proportion of lists of cases that were completed within the percentiles of the proposed duration distribution of the lists and the corresponding percentiles. RESULTS: Compared with the t-distribution, the proposed new distribution is 8.31% (0.38) more accurate on average and 14.16% (0.19) more accurate in calculating the probabilities at the 10th and 90th percentiles of the distribution, which is a major concern of operating room schedulers. The absolute deviations between the percentiles defined by Type IV Pearson distribution and those from Monte Carlo simulation varied from 0.20  min (0.01) to 0.43  min (0.03). Operating room schedulers can rely on the most recent 10 cases with the same combination of surgeon and procedure(s) for distribution parameter estimation to plan lists of cases. Values are mean (SEM). CONCLUSION: The proposed Type IV Pearson distribution is more accurate than t-distribution to estimate the probabilities of underrun and overrun of lists of cases. However, as not all the individual case durations followed log-normal distributions, there was some deviation from the true duration distribution of the lists.

Intratumoral Microbiota: Metabolic Influences and Biomarker Potential in Gastrointestinal Cancer.

Gastrointestinal (GI) cancers impose a substantial global health burden, highlighting the necessity for deeper understanding of their intricate pathogenesis and treatment strategies. This review explores the interplay between intratumoral microbiota, tumor metabolism, and major types of GI cancers (including esophageal, gastric, liver, pancreatic, and colorectal cancers), summarizing recent studies and elucidating their clinical implications and future directions. Recent research revealed altered microbial signatures within GI tumors, impacting tumor progression, immune responses, and treatment outcomes. Dysbiosis-induced alterations in tumor metabolism, including glycolysis, fatty acid metabolism, and amino acid metabolism, play critical roles in cancer progression and therapeutic resistance. The integration of molecular mechanisms and potential biomarkers into this understanding further enhances the prognostic significance of intratumoral microbiota composition and therapeutic opportunities targeting microbiota-mediated tumor metabolism. Despite advancements, challenges remain in understanding the dynamic interactions within the tumor microenvironment (TME). Future research directions, including advanced omics technologies and prospective clinical studies, offer promising avenues for precision oncology and personalized treatment interventions in GI cancer. Overall, integrating microbiota-based approaches and molecular biomarkers into GI cancer management holds promise for improving patient outcomes and survival.

From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

Exploring the Interconnection between Metabolic Dysfunction and Gut Microbiome Dysbiosis in Osteoarthritis: A Narrative Review.

Osteoarthritis (OA) is a prevalent joint disorder and the most common form of arthritis, affecting approximately 500 million people worldwide, or about 7% of the global population. Its pathogenesis involves a complex interplay between metabolic dysfunction and gut microbiome (GM) alterations. This review explores the relationship between metabolic disorders-such as obesity, diabetes, and dyslipidemia-and OA, highlighting their shared risk factors, including aging, sedentary lifestyle, and dietary habits. We further explore the role of GM dysbiosis in OA, elucidating how systemic inflammation, oxidative stress, and immune dysregulation driven by metabolic dysfunction and altered microbial metabolites contribute to OA progression. Additionally, the concept of "leaky gut syndrome" is discussed, illustrating how compromised gut barrier function exacerbates systemic and local joint inflammation. Therapeutic strategies targeting metabolic dysfunction and GM composition, including lifestyle interventions, pharmacological and non-pharmacological factors, and microbiota-targeted therapies, are reviewed for their potential to mitigate OA progression. Future research directions emphasize the importance of identifying novel biomarkers for OA risk and treatment response, adopting personalized treatment approaches, and integrating multiomics data to enhance our understanding of the metabolic-GM-OA connection and advance precision medicine in OA management.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

Latest insights into the global epidemiological features, screening, early diagnosis and prognosis prediction of esophageal squamous cell carcinoma.

As a highly invasive carcinoma, esophageal cancer (EC) was the eighth most prevalent malignancy and the sixth leading cause of cancer-related death worldwide in 2020. Esophageal squamous cell carcinoma (ESCC) is the major histological subtype of EC, and its incidence and mortality rates are decreasing globally. Due to the lack of specific early symptoms, ESCC patients are usually diagnosed with advanced-stage disease with a poor prognosis, and the incidence and mortality rates are still high in many countries, especially in China. Therefore, enormous challenges still exist in the management of ESCC, and novel strategies are urgently needed to further decrease the incidence and mortality rates of ESCC. Although the key molecular mechanisms underlying ESCC pathogenesis have not been fully elucidated, certain promising biomarkers are being investigated to facilitate clinical decision-making. With the advent and advancement of high-throughput technologies, such as genomics, proteomics and metabolomics, valuable biomarkers with high sensitivity, specificity and stability could be identified for ESCC. Herein, we aimed to determine the epidemiological features of ESCC in different regions of the world, especially in China, and focused on novel molecular biomarkers associated with ESCC screening, early diagnosis and prognosis prediction.

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

A behavioral study of daily mean turnover times and first case of the day start tardiness.

BACKGROUND: Previous research has identified 2 psychological biases in operating room (OR) decisions on the day of surgery: risk attitude of the decision-maker at the OR control desk and decisions made by OR staff to increase clinical work per unit time during the hours they are assigned. Resulting decisions are worse than random chance at reducing overutilized time. To isolate the second bias from decisions at the OR control desk, previous studies of the second bias have analyzed decisions made in non-OR locations and on nights/weekends. Another way to isolate the second bias from decisions at the OR control desk is to study facilities with negligible overutilized OR time. We examined the second bias using data from such a facility. METHODS: One year of data was collected from a 5-OR hospital. Allocated OR time that minimized the inefficiency of use of OR time was determined first to confirm there was virtually no overutilized OR time. A structural equation model was then built to evaluate the relations among variables while controlling for other correlations. We tested the hypothesis that nonoperative times were no longer on days with little versus relatively large workload. RESULTS: The extra ORs were not cost efficient (i.e., the mean potential improvement varied among days from 21.1% ± 0.2% [SE] to 38.9% ± 0.2%), resulting in very little overutilized OR time. However, conditioned on the preceding tactical decision of running extra ORs, the allocated OR time during the studied period was that which minimized the inefficiency of use of OR time. As the preceding results showed that the facility was suitable for the behavioral study, the behavioral study was performed, and the hypothesized relation confirmed. Each 1-hour decrease in the daily estimated (total) duration of elective cases resulted in a managerially unimportant decrease in the mean turnover times (0.41 ± 0.21 minutes, P = 0.053). Excluding turnovers when there were >2 turnovers occurring simultaneously, there was no significant decrease (0.17 ± 0.24 minutes, P = 0.464) in the mean turnover times per each 1-hour decrease in the daily estimated (total) duration. Similarly, after excluding prolonged turnovers (>60 minutes), there was no significant decrease (0.16 ± 0.16 minutes, P = 0.315) in the mean turnover times per each 1-hour decrease in the daily estimated (total) duration. CONCLUSIONS: Previous experimental and observational studies found many clinicians maintained high clinical work per unit time during the hours to which they were assigned. We tested and confirmed a prediction of this bias as was applied during regularly scheduled OR hours among an entire surgical team. Overall, the staff worked just as quickly on days with few or many hours of cases. The OR staff did not slow down, thus filling the time. These results have important implications for the cost utility of information technologies to facilitate managerial decision-making on the day of surgery.