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Autism spectrum disorder (ASD) is a neurodevelopmental disorder and has a predilection for children. Its symptoms, such as lifelong social communication deficits and repetitive sensory-motor behaviors, put a huge burden on the patient’s family and society. Currently, there is no cure for ASD, and some medications that can improve its symptoms are often accompanied by adverse effects. Among many complementary and alternative medicine (CAM) therapies, acupuncture has shown promising application potential, but after years of practice, it has not been recognized as the preferred CAM therapy for ASD. Therefore, we analyzed and discussed the clinical study reports of acupuncture in the treatment of ASD in the past 15 years from the aspects of study subjects, group setting, intervention modalities, acupoint selection, outcome evaluation, and safety. The data accumulated at present are not sufficient to support the clinical effectiveness of acupuncture in ASD and to justify its use in clinical practice. They provide, however, initial evidence of possible effectiveness and encourage further investigation in order to reach firm conclusions. Based on a comprehensive analysis, we believed that following the Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) and Consolidated Standards of Reporting Trials (CONSORT), screening the optimal combination of acupoints applying a rigorous scientific study design, and performing the related functional experiments may be the effective way to convincingly test the hypothesis that acupuncture may be beneficial in ASD patients. The significance of this review is to provide a reference for researchers to carry out high-quality clinical trials of acupuncture in the treatment of ASD from the perspective of the combination of modern medicine and traditional Chinese medicine.

BackgroundThe role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.MethodsGene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm.ResultsThe value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group.ConclusionInflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.

A rolling bearing performance degradation assessment algorithm based on single ring theorem in the random matrix theory is proposed to solve the problem that the traditional feature extraction algorithm is not sufficient to extract the useful information of the bearing under the industrial big data environment, and the constructed index is difficult to effectively characterize the bearing degradation process. Firstly, through segmentation, randomization, amplification and dimensional reconstruction of the bearing sampling data, a high-dimensional random matrix of the bearing at the current moment is established; Secondly, given the good high-dimensional data processing ability of random matrix theory, the distribution of eigenvalues of bearing random matrix is investigated by using the single ring theorem. Finally, the average spectral radius and the distribution of eigenvalue discrete points are used as degradation indexes to describe the degradation history of rolling bearings. The application research of the proposed method was carried out through the IMS rolling bearing full life test data, the research results show that the two quantitative indexes established by using the average spectral radius and the number of discrete points proposed based on the single ring theorem, can effectively describe the degradation process of rolling bearings and are more sensitive to the early anomaly state of rolling bearing.

Background/purpose: Nasopharyngeal carcinoma (NPC) is a malignant neoplasm of the head and neck. This study aims to use integrated bioinformatics technologies to develop a predictive miRNA-signature correlated with the prognosis of NPC. Materials and methods: Initially, the differentially expressed miRNAs (DEMs) in NPC were identified, and then DEMs related to the prognosis of NPC were further screened. Subsequently, the relatively important DEMs identified by random forest algorithm were used to construct a predictive signature by multivariate COX regression analysis. Moreover, PCA, Kaplan–Meier analysis, time-dependent ROC analysis, and univariate and multivariate COX regression analysis were performed to evaluate the ability of the signature in risk identification and prognosis prediction in NPC. Results: Hsa-miR-29c, hsa-miR-30e and hsa-miR-93 were selected from DEMs to construct a signature, and their abnormal expression was significantly associated with poor prognosis of NPC. The average AUC values of 1- to 5-year OS, DFS and DMFS predicted by the signature were all above 0.7, and showed better clinical independence than other indexes. In addition, 295 differentially expressed mRNAs could be used as potential target genes of the 3 DEMs. Among them, 56 differentially expressed mRNAs were related to PFS. GO and KEGG enrichment analysis indicated that the poor prognosis of NPC was related to the abnormality of chromosomes, cytokines, and chemokines. Conclusion: We constructed a three-miRNA signature with good independent performance in predicting the prognosis for NPC. This study may lay the foundation for exploring new therapeutic targets and improving survival outcomes in NPC patients.

In South China, karst groundwater is an important water resource for industrial, agricultural, and drinking purposes. However, karst aquifers are highly vulnerable to pollution, leading to deteriorating karst groundwater quality and posing potential health risks to local residents. In this study, 22 groundwater samples were collected from a karst aquifer in the southwestern part of Hubei Province. The hydrogeochemical characteristics and their controlling factors were examined, and the potential health risks associated with groundwater pollutant concentrations in karst groundwater were assessed. The results showed that the groundwater is slightly alkaline with low chemical oxygen demand values, indicating good water quality. The groundwater facies type was identified as HCO3‐Ca at most sample spots, showing low total dissolved solids concentrations. Substantial spatial variations in Na+, CO32−, and NO2− concentrations were found, whereas spatial variations in the K+, Ca2+, Cl−, HCO3−, and F− concentrations were small. In addition, the dissolution of gypsum deposits and magnesium carbonate sedimentary rocks at sampling sites resulted in groundwater facies types of HCO3•SO4‐Ca and HCO3‐Ca•Mg, with low total dissolved solids concentrations. The karst groundwater chemistry in the study area was mainly controlled by water–rock interactions, as well as by the dissolution of gypsum deposits and magnesium carbonate sedimentary rocks at specific groundwater sampling sites. The groundwater Cl− concentrations were mainly affected by atmospheric precipitation. NO3− was mainly derived from atmospheric precipitation, domestic sewage, septic tanks, and industrial activities, whereas SO42− was derived from atmospheric precipitation, sulfate rock dissolution, and sulfide mineral oxidation. These results highlight the absence of potential human health risks of NO3− and F− to infants, children, and adults, as their concentrations are below the corresponding regional background values. In contrast, the potential health risks of Cl− cannot be ignored, particularly for infants. This study offers scientific guidelines for protecting and allocating local groundwater resources. Practitioner Points: The dissolution of gypsum deposits and magnesium carbonate sedimentary rocks resulted in groundwater facies types of HCO3 + SO4‐Ca and HCO3‐Ca·Mg.The karst groundwater chemistry in the study area was mainly controlled by water–rock interaction.The groundwater Cl− concentrations were mainly affected by atmospheric precipitation.The potential health risks of Cl− were not negligible, particularly for infants. [ABSTRACT FROM AUTHOR]

Trueperella pyogenes is an important opportunistic pathogenic bacterium widely distributed in the environment. Pyolysin (PLO) is a primary virulence factor of T. pyogenes and capable of lysing many different cells. PLO is a member of the cholesterol-dependent cytolysin (CDC) family of which the primary structure only presents a low level of homology with other members from 31% to 45%. By deeply studying PLO, we can understand the overall pathogenic mechanism of CDC family proteins. This study established a mouse muscle tissue model infected with recombinant PLO (rPLO) and its single-point mutations, rPLO N139K and rPLO F240A, and explored its mechanism of causing inflammatory damage. The inflammatory injury abilities of rPLO N139K and rPLO F240A are significantly reduced compared to rPLO. This study elaborated on the inflammatory mechanism of PLO by examining its unit point mutations in detail. Our data also provide a theoretical basis and practical significance for future research on toxins and bacteria. [ABSTRACT FROM AUTHOR]

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

BackgroundInduction chemotherapy (IC) can alleviate locoregionally advanced nasopharyngeal carcinoma (LA-NPC), but effectiveness differs between patients, toxicity is problematic, and effective blood-based IC efficacy predictors are lacking. Here, we aimed to identify biomarkers for early identification of IC beneficiaries.MethodsSixty-four pairs of matched plasma samples collected before and after IC from LA-NPC patients including 34 responders and 30 non-responders, as well as 50 plasma samples of healthy individuals, were tested using data-independent acquisition mass spectrometry. The proteins associated with clinical traits or IC benefits were investigated by weighted gene co-expression network analysis (WGCNA) and soft cluster analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional annotations were performed to determine the potential function of the identified proteins. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of candidate biomarkers in predicting IC beneficiaries.ResultsCompared with healthy individuals, 1027 differentially expressed proteins (DEPs) were found in the plasma of LA-NPC patients. Based on feedback from IC outcomes, 463 DEPs were identified in the pre-IC plasma between responders and non-responders. A total of 1212 DEPs represented the proteomic changes before and after IC in responders, while 276 DEPs were identified in post-IC plasma between responders and non-responders. WGCNA identified nine protein co-expression modules correlated with clinical traits. Soft cluster analysis identified four IC benefits-related protein clusters. Functional enrichment analysis showed that these proteins may play a role in IC via immunity, complement, coagulation, glycosaminoglycan and serine. Four proteins differentially expressed in all group comparisons, paraoxonase/arylesterase 1 (PON1), insulin-like growth factor-binding protein 3 (IGFBP-3), rheumatoid factor D5 light chain (v-kappa-3) and RNA helicase (DDX55), were associated with clinical traits or IC benefits. A four-protein model accurately identified potential IC beneficiaries (AUC=0.95) while diagnosing LA-NPC (AUC=0.92), and the prediction performance was verified using the models to confirm the effective IC (AUC=0.97) and evaluate IC outcome (AUC=0.94).ConclusionThe plasma protein profiles among IC responders and non-responders were different. PON1, IGFBP3, v-kappa-3 and DDX55 could serve as potential biomarkers for early identification of IC beneficiaries for individualised treatment of LA-NPC.

Lipidomics profiling workflow. (a) Plasma lipidomics extraction was accomplished by fractionating samples into pools of species with similar physicochemical properties using appropriate combinations of organic solvents, after the addition of an internal standard. (b) UPLC-MS/MS-based platforms were used to perform optimal profiling of plasma lipidomics. Data preprocessing generated a list of chromatographic peak areas for the lipidomics detected in each sample injection. Inter- and intra-batch normalization was carried out with quality control (QC) calibration and internal standard correction. (c) Once normalized, univariate and multivariate data analyses were performed. (d) Selection of the best candidate biomarkers for the early diagnosis and efficacy evaluation of TB. (e) The results of this experiment provide four ideas for the research and development of potential antituberculotic medications, so as to achieve the purpose of antituberculosis treatment.