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.

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.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

RNA splicing factors in normal hematopoiesis and hematologic malignancies: novel therapeutic targets and strategies.

RNA splicing, a crucial transesterification-based process by which noncoding regions are removed from premature RNA to create mature mRNA, regulates various cellular functions, such as proliferation, survival, and differentiation. Clinical and functional studies over the past 10 y have confirmed that mutations in RNA splicing factors are among the most recurrent genetic abnormalities in hematologic neoplasms, including myeloid malignancies, chronic lymphocytic leukemia, mantle cell lymphoma, and clonal hematopoiesis. These findings indicate an important role for splicing factor mutations in the development of clonal hematopoietic disorders. Mutations in core or accessory components of the RNA spliceosome complex alter splicing sites in a manner of change of function. These changes can result in the dysregulation of cancer-associated gene expression and the generation of novel mRNA transcripts, some of which are not only critical to disease development but may be also serving as potential therapeutic targets. Furthermore, multiple studies have revealed that hematopoietic cells bearing mutations in splicing factors depend on the expression of the residual wild-type allele for survival, and these cells are more sensitive to reduced expression of wild-type splicing factors or chemical perturbations of the splicing machinery. These findings suggest a promising possibility for developing novel therapeutic opportunities in tumor cells based on mutations in splicing factors. Here, we combine current knowledge of the mechanistic and functional effects of frequently mutated splicing factors in normal hematopoiesis and the effects of their mutations in hematologic malignancies. Moreover, we discuss the development of potential therapeutic opportunities based on these mutations.

Identifying microbial signatures for patients with postmenopausal osteoporosis using gut microbiota analyses and feature selection approaches.

Osteoporosis (OP) is a metabolic bone disorder characterized by low bone mass and deterioration of micro-architectural bone tissue. The most common type of OP is postmenopausal osteoporosis (PMOP), with fragility fractures becoming a global burden for women. Recently, the gut microbiota has been connected to bone metabolism. The aim of this study was to characterize the gut microbiota signatures in PMOP patients and controls. Fecal samples from 21 PMOP patients and 37 controls were collected and analyzed using amplicon sequencing of the V3-V4 regions of the 16S rRNA gene. The bone mineral density (BMD) measurement and laboratory biochemical test were performed on all participants. Two feature selection algorithms, maximal information coefficient (MIC) and XGBoost, were employed to identify the PMOP-related microbial features. Results showed that the composition of gut microbiota changed in PMOP patients, and microbial abundances were more correlated with total hip BMD/T-score than lumbar spine BMD/T-score. Using the MIC and XGBoost methods, we identified a set of PMOP-related microbes; a logistic regression model revealed that two microbial markers (Fusobacteria and Lactobacillaceae) had significant abilities in disease classification between the PMOP and control groups. Taken together, the findings of this study provide new insights into the etiology of OP/PMOP, as well as modulating gut microbiota as a therapeutic target in the diseases. We also highlight the application of feature selection approaches in biological data mining and data analysis, which may improve the research in medical and life sciences.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data.

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SIGNIFICANCE: Functional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.