A Novel Fault Diagnosis Method of High-Speed Train Based on Few-Shot Learning.

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy.

Regulation of NCOA4-mediated iron recycling ameliorates paraquat-induced lung injury by inhibiting ferroptosis.

Paraquat (PQ) is an irreplaceable insecticide in many countries for the advantage of fast-acting and broad-spectrum. However, PQ was classified as the most prevailing poisoning substance for suicide with no specific antidote. Therefore, it is imperative to develop more effective therapeutic agents for the treatment of PQ poisoning. In the present study, both the RNA-Seq and the application of various cell death inhibitors reflected that ferroptosis exerts a crucial regulatory role in PQ poisoning. Moreover, we found PQ strengthens lipid peroxidation as evidenced by different experimental approaches. Of note, pretreatment of iron chelation agent DFO could ameliorate the ferroptotic cell death and alleviate the ferroptosis-related events. Mechanistically, PQ treatment intensively impaired mitochondrial homeostasis, enhanced phosphorylation of AMPK, accelerated the autophagy flux and triggered the activation of Nuclear receptor coactivator 4-ferritin heavy chain (NCOA4-FTH) axis. Importantly, the activation of autophagy was observed prior to the degradation of ferritin, and inhibition of autophagy could inhibit the accumulation of iron caused by the ferritinophagy process. Genetic and pharmacological inhibition of ferritinophagy could alleviate the lethal oxidative events, and rescue the ferroptotic cell death. Excitingly, in the mouse models of PQ poisoning, both the administration of DFO and adeno-associated virus-mediated FTH overexpression significantly reduced PQ-induced ferroptosis and improved the pathological characteristics of pulmonary fibrosis. In summary, the current work provides an in-depth study on the mechanism of PQ intoxication, describes a framework for the further understanding of ferroptosis in PQ-associated biological processes, and demonstrates modulation of iron metabolism may act as a promising therapeutic agent for the management of PQ toxicity.

Protective effect of Inonotus obliquus polysaccharide on mice infected with Neospora caninum.

The study aimed to explore the protective effects of Inonotus obliquus polysaccharide (IOP) on Neospora caninum (N. caninum) infection. Our data showed that the survival rate of the mice was the highest and the survival time was the longest when the IOP was 2 mg/10 g. In agreement with these observations, IOP alleviated the pathological damage in the various organs and tissues of the mice. Compared with that in the Neosporidium infection model group, the content of N. caninum in the heart, liver, spleen, lung, kidney and brain, determined through HE staining, was significantly lower. In addition, IOP inhibited the levels of immunoglobulin G1 (IgG1) and immunoglobulin G2 (IgG2a) from the 21st to 42nd day of the administration group, whereas the levels of interleukin-12 (IL-12) and serum tumor necrosis factor alpha (TNF-α) were down-regulated at 7 d - 42 d. The production of CD4+ T lymphocytes was promoted, the number of CD8+ T lymphocytes were significantly lower and the CD4+/CD8+ ratio was significantly elevated. Furthermore, IOP effectively balanced the levels of hormones including gonadotropin-releasing hormone (GnRH), luteotropic hormone (LH) and testosterone (T) in male mice, and progesterone (PROG), estradiol (E2) and prolactin (PRL) in female mice. These findings demonstrate that IOP exerts protective effects against pathological damage caused by N. caninum infection in mice, and improve the immune function of the organism and regulate the secretion balance of sex hormones.

Rapid and sensitive detection of Staphylococcus aureus using a THz metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites.

Staphylococcus aureus (S. aureus) poses a serious threat to global public health, necessitating the establishment of rapid and simple tools for its accurate identification. Herein, we developed a terahertz (THz) metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites for quantitative S. aureus assays in different clinical samples. Fe3O4@Au@Cys@Apt has the dual advantages of magnetism and a high refractive index in the THz range and was used to rapidly separate and enrich target bacteria in a complex environmental solution. Furthermore, conjugation to the nanocomposites significantly increased the resonance frequency shift of the THz metamaterial after target loading. Our results showed that the shifts in the metamaterial resonance frequency were linearly related to S. aureus concentrations ranging from 1.0 × 103 to 1.0 × 107 CFU/mL, with a detection limit of 4.78 × 102 CFU/mL. The biosensor was further applied to S. aureus detection in spiked human urine and blood with satisfactory recoveries (82.4-109.6%). Our approach also demonstrated strong concordance with traditional plate counting (R2 = 0.99306) while significantly lowering the analysis time from 24 h to <1 h. In conclusion, the proposed biosensor can not only perform culture-free and extraction-free detection of target bacteria but can also be easily extended to the determination of other pathogenic bacteria, rendering it suitable for various bacteria-related disease diagnoses.

Immunotherapies of acute myeloid leukemia: Rationale, clinical evidence and perspective.

Acute myeloid leukemia (AML) is a prevalent hematological malignancy that exhibits a wide array of molecular abnormalities. Although traditional treatment modalities such as chemotherapy and allogeneic stem cell transplantation (HSCT) have become standard therapeutic approaches, a considerable number of patients continue to face relapse and encounter a bleak prognosis. The emergence of immune escape, immunosuppression, minimal residual disease (MRD), and other contributing factors collectively contribute to this challenge. Recent research has increasingly highlighted the notable distinctions between AML tumor microenvironments and those of healthy individuals. In order to investigate the potential therapeutic mechanisms, this study examines the intricate transformations occurring between leukemic cells and their surrounding cells within the tumor microenvironment (TME) of AML. This review classifies immunotherapies into four distinct categories: cancer vaccines, immune checkpoint inhibitors (ICIs), antibody-based immunotherapies, and adoptive T-cell therapies. The results of numerous clinical trials strongly indicate that the identification of optimal combinations of novel agents, either in conjunction with each other or with chemotherapy, represents a crucial advancement in this field. In this review, we aim to explore the current and emerging immunotherapeutic methodologies applicable to AML patients, identify promising targets, and emphasize the crucial requirement to augment patient outcomes. The application of these strategies presents substantial therapeutic prospects within the realm of precision medicine for AML, encompassing the potential to ameliorate patient outcomes.

Prediction models of surgical site infection after gastrointestinal surgery: a nationwide prospective cohort study.

OBJECTIVE: This study aimed to construct and validate a clinical prediction model for surgical site infection (SSI) risk 30 days after gastrointestinal surgery. MATERIALS AND METHODS: This multicentre study involving 57 units conducted a 30-day postoperative follow-up of 17 353 patients who underwent gastrointestinal surgery at the unit from 1 March 2021 to 28 February 2022. The authors collected a series of hospitalisation data, including demographic data, preoperative preparation, intraoperative procedures and postoperative care. The main outcome variable was SSI, defined according to the Centres for Disease Control and Prevention guidelines. This study used the least absolute shrinkage and selection operator (LASSO) algorithm to screen predictive variables and construct a prediction model. The receiver operating characteristic curve, calibration and clinical decision curves were used to evaluate the prediction performance of the prediction model. RESULTS: Overall, 17 353 patients were included in this study, and the incidence of SSI was 1.6%. The univariate analysis combined with LASSO analysis showed that 20 variables, namely, chronic liver disease, chronic kidney disease, steroid use, smoking history, C-reactive protein, blood urea nitrogen, creatinine, albumin, blood glucose, bowel preparation, surgical antibiotic prophylaxis, appendix surgery, colon surgery, approach, incision type, colostomy/ileostomy at the start of the surgery, colostomy/ileostomy at the end of the surgery, length of incision, surgical duration and blood loss were identified as predictors of SSI occurrence ( P <0.05). The area under the curve values of the model in the train and test groups were 0.7778 and 0.7868, respectively. The calibration curve and Hosmer-Lemeshow test results demonstrated that the model-predicted and actual risks were in good agreement, and the model forecast accuracy was high. CONCLUSIONS: The risk assessment system constructed in this study has good differentiation, calibration and clinical benefits and can be used as a reference tool for predicting SSI risk in patients.

DNA barcodes and morphology reveal new species within the Rheotanytarsus guineensis species group from China (Diptera: Chironomidae).

The Rheotanytarsus guineensis species group (Diptera: Chironomidae) is a species diverse and taxonomically difficult group. Using DNA barcodes, we found five new species within the R. guineensis species group and reviewed the species group based on adult males from China. Rheotanytarsus guoae Lin & Yao sp. n., Rheotanytarsus miaoae Lin & Yao sp. n., Rheotanytarsus qiangi Lin & Yao sp. n., Rheotanytarsus yueqingensis Lin & Yao sp. n., and Rheotanytarsus yui Lin & Yao sp. n. are all described and figured. A key to known adult males of the R. guineensis species group worldwide is provided for the first time.

Association between corticosteroid use and 28-day mortality in septic shock patients with gram-negative bacterial infection: a retrospective study.

Purpose: Although corticosteroids are recommended in the 2021 Surviving Sepsis Campaign (SSC) guidelines, evidence with respect to their effects on short-term mortality remains conflicting. We conducted this study to identify whether corticosteroids alter 28-day mortality in septic shock patients with gram-negative bacterial infection. Materials and methods: A total of 621 patients with septic shock and gram-negative bacterial culture results were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Propensity score matching (PSM) was performed, and Kaplan-Meier survival curve analyses with log-rank tests were used to determine the relationship between corticosteroid use and the risk of 28-day mortality. Subgroup analyses were conducted to assess whether the conclusions were stable and reliable. Results: Corticosteroid administration was associated with increased 28-day mortality in septic shock patients with gram-negative bacterial infection (log-rank test P = 0.028). The incidence of Stage 2 or 3 AKI and the rate of hospital mortality were higher among patients who received corticosteroids. The incidence of Stage 2 or 3 AKI in the early period significantly mediated the relationship between corticosteroid use and 28-day mortality [P =0.046 for the average causal mediation effect (ACME)]. Interaction tests indicated that the effect of corticosteroid use was maintained in patients with a neutrophil-to-lymphocyte ratio (NLR) of <20 (P-value for interaction = 0.027). Conclusion: Systemic corticosteroid use could be harmful in septic shock patients with gram-negative bacterial infection, especially in patients with relatively low NLR.

A Novel Optical Fiber Terahertz Biosensor Based on Anti-Resonance for The Rapid and Nondestructive Detection of Tumor Cells.

The sensitive and accurate detection of tumor cells is essential for successful cancer therapy and improving cancer survival rates. However, current tumor cell detection technologies have some limitations for clinical applications due to their complexity, low specificity, and high cost. Herein, we describe the design of a terahertz anti-resonance hollow core fiber (THz AR-HCF) biosensor that can be used for tumor cell detection. Through simulation and experimental comparisons, the low-loss property of the THz AR-HCF was verified, and the most suitable fiber out of multiple THz AR-HCFs was selected for biosensing applications. By measuring different cell numbers and different types of tumor cells, a good linear relationship between THz transmittance and the numbers of cells between 10 and 106 was found. Meanwhile, different types of tumor cells can be distinguished by comparing THz transmission spectra, indicating that the biosensor has high sensitivity and specificity for tumor cell detection. The biosensor only required a small amount of sample (as low as 100 µL), and it enables label-free and nondestructive quantitative detection. Our flow cytometry results showed that the cell viability was as high as 98.5 ± 0.26% after the whole assay process, and there was no statistically significant difference compared with the negative control. This study demonstrates that the proposed THz AR-HCF biosensor has great potential for the highly sensitive, label-free, and nondestructive detection of circulating tumor cells in clinical samples.

The potential role of ferroptosis in COVID-19-related cardiovascular injury.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a global health threat in 2019. An important feature of the disease is that multiorgan symptoms of SARS-CoV-2 infection persist after recovery. Evidence indicates that people who recovered from COVID-19, even those under the age of 65 years without cardiovascular risk factors such as smoking, obesity, hypertension, and diabetes, had a significantly increased risk of cardiovascular disease for up to one year after diagnosis. Therefore, it is important to closely monitor individuals who have recovered from COVID-19 for potential cardiovascular damage that may manifest at a later stage. Ferroptosis is an iron-dependent form of non-apoptotic cell death characterized by the production of reactive oxygen species (ROS) and increased lipid peroxide levels. Several studies have demonstrated that ferroptosis plays an important role in cancer, ischemia/reperfusion injury (I/RI), and other cardiovascular diseases. Altered iron metabolism, upregulation of reactive oxygen species, and glutathione peroxidase 4 inactivation are striking features of COVID-19-related cardiovascular injury. SARS-CoV-2 can cause cardiovascular ferroptosis, leading to cardiovascular damage. Understanding the mechanism of ferroptosis in COVID-19-related cardiovascular injuries will contribute to the development of treatment regimens for preventing or reducing COVID-19-related cardiovascular complications. In this article, we go over the pathophysiological underpinnings of SARS-CoV-2-induced acute and chronic cardiovascular injury, the function of ferroptosis, and prospective treatment approaches.

Carbohydrate antigen 125 in atrial fibrillation.

Atrial fibrillation is the most common arrhythmia in clinical settings. It is associated with an increased risk of death, stroke, and peripheral vascular embolism. Catheter ablation (CA) is an effective treatment for patients with AF. However, many studies have reported suboptimal outcomes, as AF recurrence rates often remain high after CA. Therefore, there is a need for early identification of patients who are prone to recurrence and require anti-inflammatory and/or antiarrhythmic treatment after CA. In recent years, Prediction of AF and AF recurrence after CA has become a hot topic in clinical practice. A lot of biomarkers (Such as B-type natriuretic peptide, N-terminal pro-BNP, high sensitivity C reactive protein etc.) have been identified as markers for predicting the risk of AF and AF recurrence after CA. Although these markers have been shown to predict AF and AF recurrence after CA, there are currently no relevant guidelines to indicate which of these markers have absolute predictive value. Therefore, Finding the appropriate indicators that can efficiently and accurately predict AF recurrence after AF ablation is important to provide the best treatment for each patient. These indicators still need exploration. Carbohydrate antigen 125 (CA-125) is a tumor marker suitable for the screening, diagnosis, and monitoring of ovarian malignant tumors. It has been widely studied in patients with heart failure. In recent years, the role of CA-125 in AF has been widely studied, and we provide a review in this article. It is wide availability and low cost provide additional advantages for its rapid implementation. This article reviews the role of CA-125 in patients with atrial fibrillation.

FAM3 family genes are associated with prognostic value of human cancer: a pan-cancer analysis.

Family with sequence similarity three member (FAM3) plays a crucial role in the malignant development of various cancers of human. However, there remains doubtful what specific role of FAM3 family genes in pan-cancer. Our study aimed to investigate the role of FAM3 family genes in prognosis, immune subtype, tumor immune microenvironment, stemness score, and anticancer drug sensitivity of pan-cancer. We obtained data from UCSC Xena GDC and CellMiner databases, and used them to study the correlation of the expression, survival, immune subtype, tumor microenvironment, stemness score, and anticancer drug sensitivity between FAM3 family genes with pan-cancer. Furthermore, we investigated the tumor cellular functions and clinical prognostic value FAMC3 in pancreatic cancer (PAAD) using cellular experiments and tissue microarray. Cell Counting Kit-8 (CCK-8), transwell invasion, wound-healing and apoptosis assays were performed to study the effect of FAM3C on SW1990 cells' proliferation, migration, invasion and apoptosis. Immunohistochemical staining was used to study the relationship between FAM3C expression and clinical characteristics of pancreatic cancer patients. The results revealed that FAM3 family genes are significantly differential expression in tumor and adjacent normal tissues in 7 cancers (CHOL, HNSC, KICH, LUAD, LUSC, READ, and STAD). The expression of FAM3 family genes were negatively related with the RNAss, and robust correlated with immune type, tumor immune microenvironment and drug sensitivity. The expression of FAM3 family genes in pan-cancers were significantly different in immune type C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-beta dominant). Meanwhile, overexpression FAM3C promoted SW1990 cells proliferation, migration, invasion and suppressed SW1990 cells apoptosis. While knockdown of FAM3C triggered opposite results. High FAM3C expression was associated with duodenal invasion, differentiation and liver metastasis. In summary, this study provided a new perspective on the potential therapeutic role of FAM3 family genes in pan-cancer. In particular, FAM3C may play an important role in the occurrence and progression of PAAD.

Clinicopathological Characteristics, Prognosis, and Correlated Tumor Cell Function of Tropomodulin-3 in Pancreatic Adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor with a high mortality rate. Searching for novel biomarkers that can influence its prognosis may help patients. It has been shown that tropomodulin-3 (TMOD3) may influence tumor progression, but its role in pancreatic cancer is not clear. We aimed to explore the expression and prognostic value of TMOD3 in PAAD. METHODS: We used bioinformatics analysis to analyze the relationship between TMOD3 expression and clinicopathological features and prognosis and verified it with clinical data from tissue microarray. We also conducted in vitro cell experiments to explore the effects of TMOD3 on the function of PAAD cells. RESULTS: TMOD3 expression was found to be significantly higher in PAAD tissues than in matched paracancerous tissues (P < 0.05). Meanwhile, high TMOD3 expression was associated with significantly poorer overall survival (P < 0.05). Analysis of relevant clinicopathological characteristics data obtained from TCGA showed that high TMOD3 expression correlated with age, TNM stage, N stage, and M stage (P < 0.05). Analysis of correlation data obtained from tissue microarrays showed that high TMOD3 expression was associated with lymph node invasion, nerve invasion, macrovascular invasion, and TNM stage (P < 0.05). In addition, siRNA knockdown of TMOD3 significantly reduced the migration and invasion of PAAD cells. CONCLUSION: Our study shows that TMOD3 may be associated with the progression of PAAD cells, and that it is an independent risk factor for poor pathological features and prognosis of PAAD. It may be helpful as a prognostic indicator of clinical outcomes in PAAD patients.

Comprehensive analysis identifies CLEC1B as a potential prognostic biomarker in hepatocellular carcinoma.

BACKGROUND: C-type lectin domain family 1 member B (CLEC1B, encoding the CLEC-2 protein), a member of the C-type lectin superfamily, is a type II transmembrane receptor involved in platelet activation, angiogenesis, and immune and inflammatory responses. However, data regarding its function and clinical prognostic value in hepatocellular carcinoma (HCC) remain scarce. METHODS: The expression of CLEC1B was explored using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RT-qPCR, western blot, and immunohistochemistry assays were employed to validate the downregulation of CLEC1B. Univariate Cox regression and survival analyses were used to evaluate the prognostic value of CLEC1B. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential association between cancer hallmarks and CLEC1B expression. The TISIDB database was applied to search for the correlation between immune cell infiltration levels and CLEC1B expression. The association between CLEC1B and immunomodulators was conducted by Spearman correlation analysis based on the Sangerbox platform. Annexin V-FITC/PI apoptosis kit was used for the detection of cell apoptosis. RESULTS: The expression of CLEC1B was low in various tumors and exhibited a promising clinical prognostic value for HCC patients. The expression level of CLEC1B was tightly associated with the infiltration of various immune cells in the HCC tumor microenvironment (TME) and positively correlated with a bulk of immunomodulators. In addition, CLEC1B and its related genes or interacting proteins are implicated in multiple immune-related processes and signaling pathways. Moreover, overexpression of CLEC1B significantly influenced the treatment effects of sorafenib on HCC cells. CONCLUSIONS: Our results reveal that CLEC1B could serve as a potential prognostic biomarker and may be a novel immunoregulator for HCC. However, its function in immune regulation should be further explored.

Identification of Risk Factors and Phenotypes of Surgical Site Infection in Patients After Abdominal Surgery.

OBJECTIVES: We aimed to determine the current incidence rate and risk factors for surgical site infection (SSI) after abdominal surgery in China and to further demonstrate the clinical features of patients with SSI. BACKGROUND: Contemporary epidemiology and clinical features of SSI after abdominal surgery remain poorly characterized. METHODS: A prospective multicenter cohort study was conducted from March 2021 to February 2022; the study included patients who underwent abdominal surgery at 42 hospitals in China. Multivariable logistic regression analysis was performed to identify risk factors for SSI. Latent class analysis (LCA) was used to explore the population characteristics of SSI. RESULTS: In total, 23,982 patients were included in the study, of whom 1.8% developed SSI. There was a higher SSI incidence in open surgery (5.0%) than in laparoscopic or robotic surgeries (0.9%). Multivariable logistic regression indicated that the independent risk factors for SSI after abdominal surgery were older age, chronic liver disease, mechanical bowel preparation, oral antibiotic bowel preparation, colon or pancreas surgery, contaminated or dirty wounds, open surgery, and colostomy/ileostomy. LCA revealed 4 subphenotypes in patients undergoing abdominal surgery. Types α and ß were mild subclasses with a lower SSI incidence; whereas types γ and δ were the critical subgroups with a higher SSI incidence, but their clinical features were different. CONCLUSIONS: LCA identified 4 subphenotypes in patients who underwent abdominal surgery. Types γ and δ were critical subgroups with a higher SSI incidence. This phenotype classification can be used to predict SSI after abdominal surgery.

Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning.

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.

Study on association of serum uric acid levels with bipolar disorder: systematic review and meta-analysis in Chinese patients.

BACKGROUND: The purine system represented by uric acid may be involved in the pathogenesis of bipolar disorder, This study intends to explore the association of serum uric acid levels with bipolar disorder in Chinese patients through meta-analysis. METHODS: Electronic databases, including PubMed, Embase, Web of Science, and China National Knowledge Infrastructure (CNKI), searching from inception to December 2022. Randomized Controlled Trials that reported serum uric acid levels and bipolar disorder were included. Two investigators independently extracted data and RevMan5.4 and Stata14.2 were used for statistical analyses. RESULTS: Twenty-eight studies with 4482 bipolar disorder, 1568 depression, 785 schizophrenia, and 2876 healthy control subjects were included in this meta-analysis. The results of the meta-analysis showed that serum uric acid levels in the bipolar disorder group were significantly higher than those in depression [SMD 0.53 (0.37, 0.70), p < 0.00001], schizophrenia [SMD 0.27 (0.05, 0.49), p = 0.02] and healthy control group [SMD 0.87 (0.67, 1.06), p < 0.00001]. Subgroup-analysis showed that in Chinese people with bipolar disorder, uric acid levels of the manic episode were higher than the depressed episode [SMD 0.31 (0.22, 0.41), p < 0.00001]. CONCLUSION: Our results indicated a strong association between serum uric acid levels and bipolar disorder in Chinese patients, but further studies about whether uric acid levels can be a biomarker for bipolar disorder still need to investigate.

Biallelic variants in CRIPT cause a Rothmund-Thomson-like syndrome with increased cellular senescence.

PURPOSE: Rothmund-Thomson syndrome (RTS) is characterized by poikiloderma, sparse hair, small stature, skeletal defects, cancer, and cataracts, resembling features of premature aging. RECQL4 and ANAPC1 are the 2 known disease genes associated with RTS in >70% of cases. We describe RTS-like features in 5 individuals with biallelic variants in CRIPT (OMIM 615789). METHODS: Two newly identified and 4 published individuals with CRIPT variants were systematically compared with those with RTS using clinical data, computational analysis of photographs, histologic analysis of skin, and cellular studies on fibroblasts. RESULTS: All CRIPT individuals fulfilled the diagnostic criteria for RTS and additionally had neurodevelopmental delay and seizures. Using computational gestalt analysis, CRIPT individuals showed greatest facial similarity with individuals with RTS. Skin biopsies revealed a high expression of senescence markers (p53/p16/p21) and the senescence-associated ß-galactosidase activity was elevated in CRIPT-deficient fibroblasts. RECQL4- and CRIPT-deficient fibroblasts showed an unremarkable mitotic progression and unremarkable number of mitotic errors and no or only mild sensitivity to genotoxic stress by ionizing radiation, mitomycin C, hydroxyurea, etoposide, and potassium bromate. CONCLUSION: CRIPT causes an RTS-like syndrome associated with neurodevelopmental delay and epilepsy. At the cellular level, RECQL4- and CRIPT-deficient cells display increased senescence, suggesting shared molecular mechanisms leading to the clinical phenotypes.

A plasma 3-marker microRNA biosignature distinguishes spinal tuberculosis from other spinal destructive diseases and pulmonary tuberculosis.

Accurate spinal tuberculosis (TB) diagnosis is of utmost importance for adequately treating and managing the disease. Given the need for additional diagnostic tools, this study aimed to investigate the utility of host serum miRNA biomarkers for diagnosing and distinguishing spinal tuberculosis (STB) from pulmonary tuberculosis (PTB) and other spinal diseases of different origins (SDD). For a case-controlled investigation, a total of 423 subjects were voluntarily recruited, with 157 cases of STB, 83 cases of SDD, 30 cases of active PTB, and 153 cases of healthy controls (CONT) in 4 clinical centers. To discover the STB-specific miRNA biosignature, a high-throughput miRNA profiling study was performed in the pilot study with 12 cases of STB and 8 cases of CONT using the Exiqon miRNA PCR array platform. A bioinformatics study identified that the 3-plasma miRNA combination (hsa-miR-506-3p, hsa-miR-543, hsa-miR-195-5p) might serve as a candidate biomarker for STB. The subsequent training study developed the diagnostic model using multivariate logistic regression in training data sets, including CONT(n=100) and STB (n=100). Youden's J index determined the optimal classification threshold. Receiver Operating Characteristic (ROC) curve analysis showed that 3-plasma miRNA biomarker signatures have an area under the curve (AUC) = 0.87, sensitivity = 80.5%, and specificity = 80.0%. To explore the possible potential to distinguish spinal TB from PDB and other SDD, the diagnostic model with the same classification threshold was applied to the analysis of the independent validation data set, including CONT(n=45), STB(n=45), brucellosis spondylitis (BS, n=30), PTB (n=30), spinal tumor (ST, n=30) and pyogenic spondylitis (PS, n=23). The results showed diagnostic model based on three miRNA signatures could discriminate the STB from other SDD groups with sensitivity=80%, specificity=96%, Positive Predictive Value (PPV)=84%, Negative Predictive Value (NPV)=94%, the total accuracy rate of 92%. These results indicate that this 3-plasma miRNA biomarker signature could effectively discriminate the STB from other spinal destructive diseases and pulmonary tuberculosis. The present study shows that the diagnostic model based on 3-plasma miRNA biomarker signature (hsa-miR-506-3p, hsa-miR-543, hsa-miR-195-5p) may be used for medical guidance to discriminate the STB from other spinal destructive disease and pulmonary tuberculosis.

Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification.

Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks. Adversarial training (AT) is, so far, the only method that can guarantee the robustness of DNNs to adversarial attacks. However, the robustness generalization accuracy gain of AT is still far lower than the standard generalization accuracy of an undefended model, and there is known to be a trade-off between the standard generalization accuracy and the robustness generalization accuracy of an adversarially trained model. In order to improve the robustness generalization and the standard generalization performance trade-off of AT, we propose a novel defense algorithm called Between-Class Adversarial Training (BCAT) that combines Between-Class learning (BC-learning) with standard AT. Specifically, BCAT mixes two adversarial examples from different classes and uses the mixed between-class adversarial examples to train a model instead of original adversarial examples during AT. We further propose BCAT+ which adopts a more powerful mixing method. BCAT and BCAT+ impose effective regularization on the feature distribution of adversarial examples to enlarge between-class distance, thus improving the robustness generalization and the standard generalization performance of AT. The proposed algorithms do not introduce any hyperparameters into standard AT; therefore, the process of hyperparameters searching can be avoided. We evaluate the proposed algorithms under both white-box attacks and black-box attacks using a spectrum of perturbation values on CIFAR-10, CIFAR-100, and SVHN datasets. The research findings indicate that our algorithms achieve better global robustness generalization performance than the state-of-the-art adversarial defense methods.