Objective To investigate parameter changes for skin aging in murine model induced by D-galactose. Methods Sixty 3-month-old female mice were randomly divided into three groups: normal control group,low dose (80 mg/kg)and high dose (1000 mg/kg) D-galactose groups. After subcutaneous administration for 6 weeks, the aging models were established. Then, histochemical standards relating to aging were measured and morphologic alterations of surplus dorsal skin were observed under microscope and analyzed. Results In contrast with the control group, high dose D-galactose group showed that the thickness of dermis (624.5 ?48.5) ?m was significantly reduced (P

BACKGROUND:Locking compression plate and dynamic hip screw are the two major extramedul ary fixations for the femoral intertrochanteric fractures, however, the comparison of the clinical efficacy between two methods is stil controversial. OBJECTIVE:To systematical y evaluate the clinical efficacy of locking compression plate versus dynamic hip screw in the treatment of femoral intertrochanteric fractures, and provide a theoretical basis for clinical application. METHODS:Authors searched for control ed studies on locking compression plate and dynamic hip screw in the treatment of femoral intertrochanteric fractures in PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP periodical database, Wanfang resource database, Chinese Biomedical Literature service systems published from January 1999 to April 2014. The inclusion and exclusion criteria were made, and the literature meeting the criteria was screened, and the methodological quality of the included studies was evaluated. Meta-analysis was carried out using the RevMan 5.2 software. RESULTS AND CONCLUSION:Ultimately 682 patients from 8 studies met the inclusion criteria, including 336 patients in the locking compression plate group and 346 patients in the dynamic hip screw group. Meta-analysis results showed that:there were no statistical y significant differences in operating time [MD=-12.07, 95%CI (-29.85, 5.71), P=0.18], peri-operative bleeding loss [MD=-15.01, 95%CI (-87.85, 57.83), P=0.69], post-operation drainage [MD=-13.62, 95%CI (-28.49, 1.26), P=0.07], ambulation time [MD=-0.14, 95%CI (-0.68, 0.41), P=0.63], length of hospitalization [MD=-0.74, 95%CI (-2.29, 0.82), P=0.35], bone union time [MD=-1.18, 95%CI (-2.78, 0.42), P=0.15] between locking compression plate and dynamic hip screw groups. The excellent and good rate of postoperative hip function reduction [OR=2.03, 95%CI (1.23, 3.36), P=0.006] was significantly higher in locking compression plate group than in the dynamic hip screw group. The incidence of coxa vara was lower in the locking compression plate group than in the dynamic hip screw group [OR=0.34, 95%CI (0.12, 0.96), P=0.04]. There were no significant differences in looseness, breakage, withdrawal of internal fixation [OR=1.20, 95%CI (0.59, 2.45), P=0.61] and the incidence of total complications [OR=0.55, 95%CI (0.24, 1.28), P=0.16] between locking compression plate and dynamic hip screw groups. However, the included studies have high possibility of selection bias and measurement bias, and wil affect proof strength of results. Therefore, more clinical randomized control ed studies with compact design are needed for verification.

BACKGROUND:Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury,but the specific targets and regulatory mechanisms are unclear. OBJECTIVE:A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu)receptor 3(KDELR3)as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis,to provide new ideas and methods for the treatment of osteoarthritis. METHODS:The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes.The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language,and osteoarthritis hypoxia genes were extracted for differential analysis.Differentially expressed genes were analyzed for GO function and KEGG signaling pathway.Weighted correlation network analysis(WGCNA)and machine learning were also used to screen osteoarthritis hypoxia signature genes,and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR. RESULTS AND CONCLUSION:(1)8492 osteoarthritis genes were obtained by batch correction and principal component analysis,mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting;200 hypoxia genes were also obtained,resulting in 41 osteoarthritis hypoxia differentially expressed genes.(2)GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids;cellular components such as lysosomal lumen and Golgi lumen;and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity.(3)KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt,FoxO,and microRNAs in cancer.(4)The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening.(5)The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium(P=0.014)but lower in the meniscus(P=0.024)after validation by gene microarray.(6)In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group(P=0.005),while KDELR3 was closely associated with Macrophages M0(P=0.014)and T cells follicular helper(P=0.014).Using a machine learning approach,we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia,expecting to provide a new direction for better treatment of osteoarthritis.

BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION:(1)12 548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.000 14).(5)In vitro chondrocyte assay showed that type Ⅱ collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes(P<0.05),while in osteoarthritis ferroptosis,mast cells activated was closely correlated with dendritic cells(r=0.99);KLF2 was closely correlated with natural killer cells(r=-1,P=0.017)and T cells follicular helper(r=-1,P=0.017).(6)The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2.

Transcranial direct current stimulation (tDCS) is a non-invasive low-current brain stimulation technique, which is mainly based on the different polarity of electrode stimulation to make the activation threshold of neurons different, thereby regulating the excitability of the cerebral cortex. In this paper, healthy subjects were randomly divided into three groups: anodal stimulation group, cathodal stimulation group and sham stimulation group, with 5 subjects in each group. Then, the performance data of the three groups of subjects were recorded before and after stimulation to test their mental rotation ability, and resting state and task state electroencephalogram (EEG) data were collected. Finally, through comparative analysis of the behavioral data and EEG data of the three groups of subjects, the effect of electrical stimulation of different polarities on the three-dimensional mental rotation ability was explored. The results of the study found that the correct response time/accuracy rate and the accuracy rate performance of the anodal stimulation group were higher than those of the cathodal stimulation and sham stimulation groups, and there was a significant difference (
Electric Stimulation ; Electroencephalography ; Frontal Lobe ; Humans ; Reaction Time ; Transcranial Direct Current Stimulation

BACKGROUND: Links between alterations in gut microbiota composition and amyotrophic lateral sclerosis (ALS) have previously been reported. This study aimed to examine the microbiota in the nasal cavity of ALS. METHODS: Sixty-six ALS patients and 40 healthy caregivers who live in close proximity with patients were enrolled. High throughput metagenomic sequencing of the 16S ribosomal deoxyribonucleic acid (rDNA) gene V3-V4 region of nasal microbiota was used to characterize the alpha and beta diversity and relative abundance of bacterial taxa, predict function, and conduct correlation analysis between specific taxa and clinical features. RESULTS: The nasal microbiome of ALS patients showed lower alpha diversity than that of corresponding healthy family members. Genera Gaiella , Sphingomonas , Polaribacter _1, Lachnospiraceae _NK4A136_group, Klebsiella , and Alistipes were differentially enriched in ALS patients compared to controls. Nasal microbiota composition in ALS patients significantly differed from that in healthy subjects (unweighted UniFrac P = 0.001), while Linear discriminant analysis Effect Size (LEfSe) analysis indicated that Bacteroidetes and Firmicutes dominated healthy nasal communities at the phylum level, whereas Actinobacteria was the predominant phylum and Thermoleophilia was the predominant class in ALS patients. Genus Faecalibacterium and Alistipes were positively correlated with ALS functional rating scale revised (ALSFRS-R; rs = 0.349, P = 0.020 and rs = 0.393, P = 0.008), while Prevotella -9 and Bacteroides operational taxonomic units (OTUs) were positively associated with lung function (FVC) in ALS patients ( rs = 0.304, P = 0.045, and rs = 0.300, P = 0.048, respectively). Prevotella -1 was positively correlated with white blood cell counts (WBC, rs = 0.347, P = 0.021), neutrophil percentage (Neu%, rs = 0.428, P = 0.004), and neutrophil-to-lymphocyte ratio (NLR, rs = 0.411, P = 0.006), but negatively correlated with lymphocyte percentage (Lym%, rs = -0.408, P = 0.006). In contrast, Streptococcus was negatively associated with Neu% ( rs = -0.445, P = 0.003) and NLR ( rs = -0.436, P = 0.003), while positively associated with Lym% ( rs = 0.437, P = 0.003). No significant differences in nasal microbiota richness and evenness were detected among the severe and mild ALS patients. CONCLUSIONS ALS is accompanied by altered nasal microbial community composition and diversity. The findings presented here highlight the need to understand how dysbiosis of nasal microbiota may contribute to the development of ALS.
Humans ; Amyotrophic Lateral Sclerosis/microbiology* ; Feces/microbiology* ; Microbiota/genetics* ; Gastrointestinal Microbiome/genetics* ; Bacteria/genetics* ; RNA, Ribosomal, 16S/genetics*

Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.
Cognition ; Cognition Disorders ; Humans ; Memory, Short-Term ; Neuropsychological Tests ; Prefrontal Cortex ; Transcranial Direct Current Stimulation