Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. METHODS: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. RESULTS: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. CONCLUSION: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Selection of Fermentation Supernatant from Probiotic Strains Exhibiting Intestinal Epithelial Barrier Protective Ability and Evaluation of Their Effects on Colitis Mouse and Weaned Piglet Models.

The intestinal epithelial barrier can prevent the invasion of pathogenic microorganisms and food antigens to maintain a consistent intestinal homeostasis. However, an imbalance in this barrier can result in various diseases, such as inflammatory bowel disease, malnutrition, and metabolic disease. Thus, the aim of this study was to select probiotic strains with epithelial barrier-enhancing ability in cell-based model and further investigate them for their improving effects on colitis mouse and weaned piglet models. The results showed that selected specific cell-free fermentation supernatants (CFSs) from Ligilactobacillus salivarius P1, Lactobacillus gasseri P12, and Limosilactobacillus reuteri G7 promoted intestinal epithelial cell growth and proliferation, strengthening the intestinal barrier in an intestinal epithelial cell line Caco-2 model. Further, the administration of CFSs of L. salivarius P1, L. gasseri P12, and L. reuteri G7 were found to ameliorate DSS-induced colitis in mice. Additionally, spray-dried powders of CFS from the three strains were examined in a weaned piglet model, only CFS powder of L. reuteri G7 could ameliorate the feed/gain ratio and serum levels of D-lactate and endotoxin. In conclusion, a new potential probiotic strain, L. reuteri G7, was selected and showed ameliorating effects in both colitis mouse and weaned piglet models.

Efficacy and Safety of Fillers for the Treatment of Nasolabial Folds: A Network meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Nasolabial fold formation is increasingly becoming a cause of concern for many people. However, no network meta-analysis has compared the efficacy of different fillers in treating nasolabial folds. This network meta-analysis simultaneously compared the efficacy and safety of various fillers. METHODS: We included randomized controlled trials (RCTs) that used fillers to treat nasolabial folds. We extracted data of Wrinkle Severity Rating Scale (WSRS), Global Esthetic Improvement Scale (GAIS, investigator) scores, GAIS scores (self-reported) and adverse events. RESULTS: We included 13 RCTs. WSRS scores at 6 months were higher in patients receiving HA than those receiving poly (L-lactic acid) (mean difference [MD] 0.630, 95% confidence interval [CI] 0.275, 0.985) but significantly lower in patients receiving HA than in those receiving bovine collagen (MD - 0.580, 95% CI - 0.777, - 0.383) and porcine collagen (MD - 0.525, 95% CI - 0.790, - 0.260). Regarding adverse events, HA was significantly less likely to cause nodule formation compared with bovine collagen (RR 0.593, 95% CI 0.438, 0.803). CONCLUSION: HA is a safe filler for correcting nasolabial folds, and poly (L-lactic acid) showed potential in treating nasolabial folds. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

U-shaped association between plasma C-peptide and sarcopenia: A cross-sectional study of elderly Chinese patients with diabetes mellitus.

Limited research exists regarding the relationship between fasting plasma C-peptide levels and sarcopenia. As a result, our study aimed to examine this association in elderly Chinese diabetic patients. This cross-sectional study included 288 elderly patients with diabetes mellitus from the Fourth People's Hospital in Guiyang who were enrolled prospectively between March 2020 and February 2023. The independent variable of interest was fasting plasma C-peptide, while the dependent variable was sarcopenia. Data on several covariates, including demographic factors, lifestyle habits, co-morbidities, anthropometric indicators, and laboratory indicators, were also collected. Of the 288 participants, 27.43% (79/288) had sarcopenia. After adjusting for potential confounding variables, we found a U-shaped association between fasting plasma C-peptide levels and sarcopenia, with inflection points identified at approximately 774 pmol/L and 939 mmol/L. Within the range of 50-744 pmol/L, each 100 pmol/L increase in CysC was associated with a 37% decrease in the odds of sarcopenia (odds ratio [OR], 0.63; 95% confidence interval [CI], 0.49 to 0.83; P < 0.001). Additionally, within the range of 939-1694 pmol/L, each 100 pmol/L increase in fasting plasma C-peptide was associated with a 76% increase in the odds of sarcopenia (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.11 to 2.81; P = 0.017). Our study revealed a U-shaped association between fasting plasma C-peptide levels and the likelihood of sarcopenia, with lower risk in the range of 774-939 pmol/L. These findings may assist in the development of more effective prevention and treatment strategies for sarcopenia in elderly diabetic patients.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Hydrochlorothiazide-induced glucose metabolism disorder is mediated by the gut microbiota via LPS-TLR4-related macrophage polarization.

Hydrochlorothiazide (HCTZ) is reported to impair glucose tolerance and may induce new onset of diabetes, but the pharmacomicrobiomics of the adverse effect for HCTZ remains unknown. Mice-fed HCTZ exhibited insulin resistance and impaired glucose tolerance. By using FMT and antibiotic cocktail models, we found that HCTZ-induced metabolic disorder was mediated by commensal microbiota. HCTZ consumption disturbed the structure of the intestinal microbiota, causing abnormal elevation of Gram-negative Enterobacteriaceae and lipopolysaccharide (LPS) then leading to intestinal barrier dysfunction. Additionally, HCTZ activated TLR4 signaling and induced macrophage polarization and inflammation in the liver. Furthermore, HCTZ-induced macrophage polarization and metabolic disorder were abrogated by blocking TLR4 signaling. HCTZ consumption caused a significant increase in Gram-negative Enterobacteriaceae, which elevated the levels of LPS, thereby activating LPS/TLR4 pathway, promoting inflammation and macrophage polarization, and resulting in metabolic disorders. These findings revealed that the gut microbiome is the key medium underlying HCTZ-induced metabolic disorder.

Anlotinib Combined With Toripalimab as First-Line Therapy for Unresectable Hepatocellular Carcinoma: A Prospective, Multicenter, Phase II Study.

BACKGROUND: For patients with unresectable hepatocellular carcinoma (HCC), the first-line therapeutic options are still relatively limited, and treatment outcomes remain poor. We aimed to assess the efficacy and safety of anlotinib combined with toripalimab as first-line therapy for unresectable HCC. METHODS: In this single-arm, multicenter, phase II study (ALTER-H-003), patients with advanced HCC without previous systemic anticancer therapy were recruited. Eligible patients were given anlotinib (12 mg on days 1-14) combined with toripalimab (240 mg on day 1) in a 3-week cycle. The primary endpoint was the objective response rate (ORR) by immune-related Response Evaluation Criteria in Solid Tumours (irRECIST)/RECIST v1.1 and modified RECIST (mRECIST). Secondary endpoints included disease control rate (DCR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety. RESULTS: Between January 2020 and Jul 2021, 31 eligible patients were treated and included in the full analysis set. At data cutoff (January 10, 2023), the ORR was 29.0% (95% CI: 12.1%-46.0%) by irRECIST/RECIST v1.1, and 32.3% (95% CI: 14.8%-49.7%) by mRECIST criteria, respectively. Confirmed DCR and median DoR by irRECIST/RECIST v1.1 and mRECIST criteria were 77.4 % (95% CI: 61.8%-93.0%) and not reached (range: 3.0-22.5+ months), respectively. Median PFS was 11.0 months (95% CI: 3.4-18.5 months) and median OS was 18.2 months (95% CI: 15.8-20.5 months). Of the 31 patients assessed for adverse events (AEs), the most common grade ≥ 3 treatment-related AEs were hand-foot syndrome (9.7%, 3/31), hypertension (9.7%, 3/31), arthralgia (9.7%, 3/31), abnormal liver function (6.5%, 2/31), and decreased neutrophil counts (6.5%, 2/31). CONCLUSIONS: Anlotinib combined with toripalimab showed promising efficacy and manageable safety in Chinese patients with unresectable HCC in the first-line setting. This combination therapy may offer a potential new therapeutic approach for patients with unresectable HCC.

Parabacteroides distasonis ameliorates hepatic fibrosis potentially via modulating intestinal bile acid metabolism and hepatocyte pyroptosis in male mice.

Parabacteroides distasonis (P. distasonis) plays an important role in human health, including diabetes, colorectal cancer and inflammatory bowel disease. Here, we show that P. distasonis is decreased in patients with hepatic fibrosis, and that administration of P. distasonis to male mice improves thioacetamide (TAA)- and methionine and choline-deficient (MCD) diet-induced hepatic fibrosis. Administration of P. distasonis also leads to increased bile salt hydrolase (BSH) activity, inhibition of intestinal farnesoid X receptor (FXR) signaling and decreased taurochenodeoxycholic acid (TCDCA) levels in liver. TCDCA produces toxicity in mouse primary hepatic cells (HSCs) and induces mitochondrial permeability transition (MPT) and Caspase-11 pyroptosis in mice. The decrease of TCDCA by P. distasonis improves activation of HSCs through decreasing MPT-Caspase-11 pyroptosis in hepatocytes. Celastrol, a compound reported to increase P. distasonis abundance in mice, promotes the growth of P. distasonis with concomitant enhancement of bile acid excretion and improvement of hepatic fibrosis in male mice. These data suggest that supplementation of P. distasonis may be a promising means to ameliorate hepatic fibrosis.

Ten circumstances and solutions for finding the sample mean and standard deviation for meta-analysis.

A common problem in meta-analyses is the unavailability of mean and standard deviation (SD). Unfortunately, only having values of the median, interquartile range (IQR), or range cannot be directly utilized for meta-analysis. Although some estimation and conversion methods have been proposed in the past two decades, there were no published and user-friendly tools developed based on multiple scenarios of missing SD. Therefore, this study aimed to provide a collection of possible circumstances of missing sample means or SD with solutions for teaching and research. A total of 10 common circumstances of missing SD or mean could have available statistics of p value, t value, z score, confidence interval, standard error, median, IQR, and range. Teachers and investigators can use relevant formulas for finding the sample mean and SD according to the available circumstance. Due to the complicated computations, our team provides a free available spreadsheet. With ever-evolving statistical methods, some formulas may be further improved in the future; therefore, it is recommended to involve statisticians in evidence-based practice or systematic reviews.

The functional change of the P2X7R containing the Ala348 to Thr polymorphism is associated with the pathogenesis of gout.

Our previous study has shown that ATP action on P2X7R could be the second signal to induce the onset of gouty arthritis. However, the functional changes of P2X7R single nucleotide polymorphisms (SNPs) on the effects of ATP-P2X7R-IL-1ß signaling pathway and uric acid remained unknown. We aimed to investigate the association between the functional change of P2X7R containing the Ala348 to Thr polymorphisms (rs1718119) and the pathogenesis of gout. First, 270 gout patients and 70 hyperuricemic patients (without gout attack history in recent 5 years) were recruited for genotyping. In addition, the changes of ATP-induced pore formation were assessed in HEK-293T cells overexpressing different mutants in P2RX7, and the effects on P2X7R-NLRP3-IL-1ß pathway activation were explored in P2RX7 overexpression THP-1 cells. The risk allele for gout was A at rs1718119, and the AA and AG genotypes exhibited a higher risk of gout. Furthermore, Ala348 to Thr mutants increased P2X7-dependent ethidium+ bromide uptake, upregulated IL-1ß and NLRP3 levels as compared to the wild-type. We suggest that genetic polymorphisms of P2X7R containing the Ala348 to Thr are associated with the increased risk of gout, showing an enhanced gain-of-function effect on the development of this disease.

Effectiveness and Safety of Energy-Based Devices for Acne Scars: A Network Meta-Analysis of Randomized Controlled Trials.

Background: Acne vulgaris is an inflammatory disease of the pilosebaceous unit in teenagers. Acne-induced inflammation leads to acne scarring. Scholars have discussed acne scar treatments; however, energy-based devices with satisfactory outcomes remain unidentified. Objective: To measure quartile grading scale and visual analog scale (VAS) to study the difference between energy-based devices. Methods: We included randomized controlled trials that evaluated patients with acne scars. The primary outcomes were the quartile grading scale and VAS scores. We used Confidence in Network Meta-Analysis to evaluate indirectness, imprecision, heterogeneity, and incoherence. Results: A total of 26 studies met the inclusion criteria. The quartile grading scale results revealed that ablative fractional laser was significantly more effective than nonablative fractional laser (standard mean difference [SMD]: 0.516, confidence interval [95% CI]: 0.281-0.750) and radiofrequency treatment (SMD: 0.941, 95% CI: 0.540-1.342). Moreover, nonablative fractional laser was significantly more effective than radiofrequency treatment (SMD: 0.426, 95% CI: 0.049-0.802). No significant difference in VAS score was found among the devices. Conclusion: Ablative fractional laser is an effective treatment for acne scars although it is associated with more pain.

Invasive Ageratina adenophora can maintain its ecological advantages over time through releasing its autotoxicity by accumulating a bacterium Bacillus cereus.

Plant invasive success is attributed to invaders' ecological advantages over their native neighbors. However, increasing evidence suggests that these advantages are expected to attenuate over time because of natural enemy accumulation, ecological evolution of native species and autotoxicity. We determined how an invasive Ageratina adenophora could remain its competitive advantages over time by avoiding its autotoxicity. Our results highlighted that the autotoxicity of A. adenophora in its invaded soil was reduced by some microbes. Moreover, an autotoxic allelochemical, 2-coumaric acid glucoside, detected in the invaded soil, demonstrated distinctly autotoxic effects on its seed germination and seedling growth. However, the autotoxic effects were greatly alleviated by a bacterium Bacillus cereus, accumulated by A. adenophora. Furthermore, the allelochemical could be almost completely degraded by B. cereus within 96 h. Accordingly, we speculate that A. adenophora could aggregate B. cereus to release its autotoxicity maintaining its competitive advantages over time.

Panax notoginseng Alleviates Colitis via the Regulation of Gut Microbiota.

Gut microbiota are significantly associated with the occurrence and development of inflammatory bowel disease (IBD). Panax notoginseng saponins (PNS) could be used for colitis and to modulate gut microbiota. However, the mechanism behind the effects of PNS on anti-colitis that are pertinent to gut microbiota is largely unknown. This study aimed to evaluate the anti-colitis effects of PNS and explore the involved mechanism as it is related to gut microbiota. Results showed that PNS significantly alleviated dextran sulfate sodium (DSS)-induced colitis. Meanwhile, after PNS treatment, the tight junction proteins were enhanced and proinflammatory cytokines, such as TNF-[Formula: see text], IL-6, IL-1[Formula: see text], and IL-17, were decreased. Furthermore, Bacteroides spp. were significantly increased after modeling, while PNS reduced their abundance and significantly increased the amount of Akkermansia spp. in vivo. Importantly, Akkermansia spp. and Bacteroides spp. were correlated with the IBD disease indicators. Moreover, fecal microbiota transplantation (FMT) experiments confirmed that PNS-reshaped gut microbiota significantly alleviated DSS-induced colitis, while A. muciniphila significantly reduced the levels of the LPS-induced cellular inflammatory factors IL-1[Formula: see text] and TNF-[Formula: see text]. In conclusion, PNS alleviated colitis pertinent to the upregulation of Akkermania spp. and downregulation of Bacteroides spp. in the gut.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Effect of ginsenoside compound K on alleviating colitis via modulating gut microbiota.

BACKGROUND: Ginsenoside compound K (GC-K) potentially alleviates ulcerative colitis involved in gut microbiota, which is significantly associated with the occurrence and development of colitis. However, the effect and mechanism of GC-K on anti-colitis in relation to gut microbiota are not clear. This study focused on the prevention and mechanism of GC-K on Dextran sulfate sodium (DSS)-induced colitis of mice pertinent to gut microbiota. METHODS: DSS was used to establish a chronic colitis mouse model. Body weight analysis, colon length measurement, HE staining, and inflammatory factors levels were processed in animal experiments. Flow cytometry was employed to analyze Th17/Treg cells in the mouse spleen and blood. 16S rRNA sequencing was utilized to analyze gut microbiota. Fecal microbiota transplantation (FMT) experiment was employed to verify the anti-colitis efficacy of GC-K by reshaping gut microbiota. RESULTS: GC-K significantly relieved colitis-related symptoms due to decreased disease activity index (DAI) scores, spleen weight, and increased colon length. Additionally, the tight junction proteins were increased, and the pro-inflammatory cytokines, such as TNF-α, IL-6, IL-1ß and IL-17, were decreased after GC-K treatment. Furthermore, Bacteroides spp. significantly increased after modeling. Moreover, FMT experiments confirmed that GC-K-driven gut microbiota greatly relieved DSS-induced colitis. CONCLUSION: GC-K alleviated colitis via the modulation of gut microbiota.

LncRNA weighted gene co-expression network analysis reveals novel biomarkers related to prostate cancer metastasis.

BACKGROUND: Most prostate cancer patients die from metastasis and lack accurate efficacious biomarkers to monitor the disease behavior, optimize treatment and assess prognosis. Herein, we aimed to identify meaningful lncRNA biomarkers associated with prostate cancer metastatic progression. METHODS: By repurposing microarray probes, 11,624 lncRNAs in prostate cancer were obtained from Gene Expression Omnibus  database (GSE46691, N = 545; GSE29079, N = 235; GSE94767, N = 130). Weighted gene co-expression network analysis was applied to determine the co-expression lncRNA network pertinent to metastasis. Hub lncRNAs were screened. RNA-seq and clinical data from the Cancer Genome Atlas prostate cancer (TCGA-PRAD) cohort (N = 531) were analyzed. Transwell assay and bioinformatic analysis were performed for mechanism research. RESULTS: The high expression levels of nine hub lncRNAs (FTX, AC005261.1, NORAD, LINC01578, AC004542.2, ZFAS1, EBLN3P, THUMPD3-AS1, GAS5) were significantly associated with Gleason score and increased probability of metastatic progression. Among these lncRNAs, ZFAS1 had the consistent trends of expression in all of the analysis from different cohorts, and the Kaplan-Meier survival analyses showed higher expression of ZFAS1 was associated with shorter relapse free survival. In-vitro studies confirmed that downregulation of ZFAS1 decreased prostate cancer cell migration. CONCLUSION: We offered some new insights into discovering lncRNA markers correlated with metastatic progression of prostate cancer using the WGCNA. Some may serve as potential prognostic biomarkers and therapeutic targets for advanced metastatic prostate cancer.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Background and objective: Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. Results: All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. Conclusion: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Effects of apolipoprotein E on regulating insulin sensitivity via regulating insulin receptor signalosome in caveolae.

AIMS: Although impaired insulin signaling at a post-receptor level was a well-established key driver of insulin resistance, the role of surface abnormal insulin receptor (INSR) location in insulin resistance pathogenesis tended to be ignored and its molecular mechanisms remained obscure. Herein, this study aimed to investigate hepatic apolipoprotein E (APOE) impaired cellular insulin action via reducing cell surface INSR, especially in caveolae. KEY FINDINGS: Downregulation of APOE enhanced the caveolae translocation of INSR and glucose transporter 2 (GLUT2), and improved hepatic cells' sensitivity to insulin. Consistently, mice with selective suppression of liver tissue APOE showed lower fasting insulin and fasting glucose levels, better homeostatic model assessment (HOMA) index (HOMA-IS, HOMA-IR, HOMA-ß) and quantitative insulin sensitivity check index (QUICKI). Furthermore, the co-localization of INSR and CAV1 in the liver of these mice were more substantial than controls. SIGNIFICANCE: APOE might adversely set the basal gain of INSR signaling implied that APOE could be a new endogenous INSR regulator.

Effectiveness of Botulinum Toxin Type A Injection on Scars: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Scars can cause pain, long-term physical dysfunction, and psychological harm. Botulinum toxin type A (BoNT-A) is one treatment choice for scars, but further evidence is needed to confirm its efficacy. METHODS: This systematic review included randomized controlled trials that investigated the effectiveness of BoNT-A on scars. The mean and standard deviation for the Vancouver Scar Scale, Stony Brook Scar Evaluation Scale, visual analog scale for appearance evaluation, visual analog scale for scar pain evaluation, and scar width were extracted for subgroup analysis. RESULTS: Twenty-one randomized controlled trials were included. The BoNT-A group had a lower Vancouver Scar Scale score than the saline group (standardized mean difference, -0.73; 95 percent CI, -1.12 to -0.35; p = 0.0002) but a higher score than the steroid group (standardized mean difference, 0.85; 95 percent CI, 0.27 to 1.43; p = 0.004). The BoNT-A group exhibited a higher Stony Brook Scar Evaluation Scale grade than the saline group (standardized mean difference, 1.42; 95 percent CI, 0.83 to 2.00; p < 0.00001). The visual analog scale for appearance evaluation revealed higher scores in the BoNT-A group than in the saline group (standardized mean difference, 1.14; 95 percent CI, 0.69 to 1.60; p < 0.00001). As for pain evaluation, the BoNT-A group had a lower visual analog scale score than the steroid group (standardized mean difference, -2.57; 95 percent CI, -4.40 to -0.74; p = 0.006). Furthermore, scar width was significantly shorter in the BoNT-A group than in the control group (standardized mean difference, -1.11; 95 percent CI, -1.38 to -0.83; p < 0.00001). CONCLUSIONS: BoNT-A is more effective in treating scars than saline, although steroids may exhibit higher potency. Therefore, it can be considered an alternative in patients not amenable to steroid treatment. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.