Comprehensive Exploration of the Neuroprotective Mechanisms of Ginkgo biloba Leaves in Treating Neurological Disorders.

Neurological disorders (NDs) are diseases that seriously affect the health of individuals worldwide, potentially leading to a significant reduction in the quality of life for patients and their families. Herbal medicines have been widely used in the treatment of NDs due to their multi-target and multi-pathway features. Ginkgo biloba leaves (GBLs), one of the most popular herbal medicines in the world, have been demonstrated to present therapeutic effects on NDs. However, the pharmacological mechanisms of GBLs in the treatment of neurological disorders have not been systematically summarized. This study aimed to summarize the molecular mechanism of GBLs in treating NDs from the cell models, animal models, and clinical trials of studies. Four databases, i.e., PubMed, Google Scholar, CNKI, and Web of Science were searched using the following keywords: "Ginkgo biloba", "Ginkgo biloba extract", "Ginkgo biloba leaves", "Ginkgo biloba leaves extract", "Neurological disorders", "Neurological diseases", and "Neurodegenerative diseases". All items meeting the inclusion criteria on the treatment of NDs with GBLs were extracted and summarized. Additionally, PRISMA 2020 was performed to independently evaluate the screening methods. Out of 1385 records in the database, 52 were screened in relation to the function of GBLs in the treatment of NDs; of these 52 records, 39 were preclinical trials and 13 were clinical studies. Analysis of pharmacological studies revealed that GBLs can improve memory, cognition, behavior, and psychopathology of NDs and that the most frequently associated GBLs are depression, followed by Alzheimer's disease, stroke, Huntington's disease, and Parkinson's disease. Additionally, the clinical studies of depression, AD, and stroke are the most common, and most of the remaining ND data are available from in vitro or in vivo animal studies. Moreover, the possible mechanisms of GBLs in treating NDs are mainly through free radical scavenging, anti-oxidant activity, anti-inflammatory response, mitochondrial protection, neurotransmitter regulation, and antagonism of PAF. This is the first paper to systematically and comprehensively investigate the pharmacological effects and neuroprotective mechanisms of GBLs in the treatment of NDs thus far. All findings contribute to a better understanding of the efficacy and complexity of GBLs in treating NDs, which is of great significance for the further clinical application of this herbal medicine.

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation.

Introduction: Large Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases. Methods: We fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI's Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named "GastroBot," which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information. Results: When evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results. Discussion: Research findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions.

Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and effectively. In this study, we apply a pre-trained language model to the domain of CPM. We have meticulously assembled, processed, and released the first CPMI dataset and fine-tuned the ChatGLM-6B base model, resulting in the development of CPMI-ChatGLM. We employed consumer-grade graphics cards for parameter-efficient fine-tuning and investigated the impact of LoRA and P-Tuning v2, as well as different data scales and instruction data settings on model performance. We evaluated CPMI-ChatGLM using BLEU, ROUGE, and BARTScore metrics. Our model achieved scores of 0.7641, 0.8188, 0.7738, 0.8107, and - 2.4786 on the BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L and BARTScore metrics, respectively. In comparison experiments and human evaluation with four large language models of similar parameter scales, CPMI-ChatGLM demonstrated state-of-the-art performance. CPMI-ChatGLM demonstrates commendable proficiency in CPM recommendations, making it a promising tool for auxiliary diagnosis and treatment. Furthermore, the various attributes in the CPMI dataset can be used for data mining and analysis, providing practical application value and research significance.

Observational study on the potential mechanism of Sanao decoction in the treatment of asthma based on network pharmacology and molecular docking.

Bronchial asthma (BA) is a chronic respiratory disease closely related to immune system dysregulation. Traditional Chinese medicine has long adopted the strategy of Sanao decoction in the treatment of bronchial asthma. However, due to the multi-target and multi-pathway characteristics of Chinese herbal medicine, we are still unclear about the specific mechanism of Sanao decoction in treating bronchial asthma. To investigate the mechanism of action of Sanao decoction in the treatment of BA using a network pharmacology approach and preliminary validation by molecular docking technology. Traditional Chinese medicine systems pharmacology database and analysis platform and UniProt databases were used to search the active ingredients and targets of Sanao decoction, and BA-related targets were screened according to GeneCards and online Mendelian inheritance in man database databases. The intersection targets were imported into the STRING database to construct a protein-protein interaction network, and Cytoscape 3.9.1 software was used to screen out hub genes. This study also constructed a "drug-ingredient-target" visual network diagram. Gene Ontology and Kyoto Encyclopedia of Genomes enrichment analysis was performed on targets in the protein-protein interaction network using the ClusterProfiler package in R, with a P value < .05. Autodock software was used for molecular docking to complete the preliminary verification of core components and targets. A total of 73 active compounds and 308 targets of Sanao decoction, including 1640 BA-related disease targets, were retrieved from mainstream databases. Gene Ontology analysis and Kyoto encyclopedia of genes and genomes enrichment analysis suggested that Sanao decoction plays a role in the treatment of BA through signaling pathways such as PI3K-Akt, MAPK, and IL-17 signaling pathway. The 9 core goals represent the main elements related to Sanao decoction in the treatment of BA. Subsequently, the molecular docking results showed that most of the active compounds of Sanao decoction have strong binding efficiency with the hub gene. Sanao decoction has a key impact on BA through multiple channels. In summary, this intricate network reflects the potential of Sanao decoction in treating BA, a multifactorial disease. In addition, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical application of Sanao decoction.

New insight into the mechanisms of Ginkgo biloba leaves in the treatment of cancer.

BACKGROUND: Ginkgo biloba leaves (GBLs), as an herbal dietary supplement and a traditional Chinese medicine, have been used in treating diseases for hundred years. Recently, increasing evidence reveals that the extracts and active ingredients of GBLs have anti-cancer (chemo-preventive) properties. However, the molecular mechanism of GBLs in anti-cancer has not been comprehensively summarized. PURPOSE: To systematically summarize the literatures for identifying the molecular mechanism of GBLs in cellular, animal models and clinical trials of cancers, as well as for critically evaluating the current evidence of efficacy and safety of GBLs for cancers. METHODS: Employing the search terms "Ginkgo biloba" and "cancer" till July 25, 2023, a comprehensive search was carried out in four electronic databases including Scopus, PubMed, Google Scholar and Web of Science. The articles not contained in the databases are performed by manual searches and all the literatures on anti-cancer research and mechanism of action of GBLs was extracted and summarized. The quality of methodology was assessed independently through PRISMA 2020. RESULTS: Among 84 records found in the database, 28 were systematic reviews related to GBLs, while the remaining 56 records were related to the anticancer effects of GBLs, which include studies on the anticancer activities and mechanisms of extracts or its components in GBLs at cellular, animal, and clinical levels. During these studies, the top six cancer types associated with GBLs are lung cancer, hepatocellular carcinoma, gastric cancer, breast cancer, colorectal cancer, and cervical cancer. Further analysis reveals that GBLs primarily exert their anticancer effects by stimulating cancer cell apoptosis, inhibiting cell proliferation, invasion and migration of cancers, exhibiting anti-inflammatory and antioxidant properties, and modulating signaling pathways. Besides, the pharmacology, toxicology, and clinical research on the anti-tumor activity of GBLs have also been discussed. CONCLUSIONS: This is the first paper to thoroughly investigate the pharmacology effect, toxicology, and the mechanisms of action of GBLs for anti-cancer properties. All the findings will reinforce the need to explore the new usage of GBLs in cancers and offer comprehensive reference data and recommendations for future research on this herbal medicine.

Association between systemic immune-inflammation index and diabetes: a population-based study from the NHANES.

Background: Systemic Immune-Inflammation Index (SII) has been reported to be associated with diabetes. We aimed to assess possible links between SII and diabetes. Methods: Data were obtained from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) database. After removing missing data for SII and diabetes, we examined patients older than 20 years. Simultaneously, the relationship between SII and diabetes was examined using weighted multivariate regression analysis, subgroup analysis, and smooth curve fitting. Results: There were 7877 subjects in this study, the average SII was 524.91 ± 358.90, and the prevalence of diabetes was 16.07%. Weighted multivariate regression analysis found that SII was positively associated with diabetes, and in model 3, this positive association remained stable (OR = 1.04; 95% CI: 1.02-1.06; p = 0.0006), indicating that each additional unit of SII, the possibility of having diabetes increased by 4%. Gender, age, BMI, regular exercise, high blood pressure, and smoking did not significantly affect this positive link, according to the interaction test (p for trend>0.05). Discussion: Additional prospective studies are required to examine the precise connection between higher SII levels and diabetes, which may be associated with higher SII levels.

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy.

Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the automated diagnosis of DR, and in particular, multi-lesion segmentation tasks. In this paper, we propose a novel Transformer-based model for DR segmentation that incorporates hyperbolic embeddings and a spatial prior module. The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. Hyperbolic embeddings are used to classify feature matrices from the model at the pixel level. We evaluated the proposed model's performance on the publicly available datasets and compared it with other widely used DR segmentation models. The results show that our model outperforms these widely used DR segmentation models. The incorporation of hyperbolic embeddings and a spatial prior module into the Vision Transformer-based model significantly improves the accuracy of DR segmentation. The hyperbolic embeddings enable us to better capture the underlying geometric structure of the feature matrices, which is important for accurate segmentation. The spatial prior module improves the continuity of the features and helps to better distinguish between lesions and normal tissues. Overall, our proposed model has potential for clinical use in automated DR diagnosis, improving accuracy and speed of diagnosis. Our study shows that the integration of hyperbolic embeddings and a spatial prior module with a Vision Transformer-based model improves the performance of DR segmentation models. Future research can explore the application of our model to other medical imaging tasks, as well as further optimization and validation in real-world clinical settings.

Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment.

Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.

Systematic investigation of the mechanism of herbal medicines for the treatment of prostate cancer.

Due to various unpleasant side effects and general ineffectiveness of current treatments for prostate cancer (PCa), more and more people with PCa try to look for complementary and alternative medicine such as herbal medicine. However, since herbal medicine has multi-components, multi-targets and multi-pathways features, its underlying molecular mechanism of action is not yet known and still needs to be systematically explored. Presently, a comprehensive approach consisting of bibliometric analysis, pharmacokinetic assessment, target prediction and network construction is firstly performed to obtain PCa-related herbal medicines and their corresponding candidate compounds and potential targets. Subsequently, a total of 20 overlapping genes between DEGs in PCa patients and the target genes of the PCa-related herbs, as well as five hub genes, i.e., CCNA2, CDK2, CTH, DPP4 and SRC were determined employing bioinformatics analysis. Further, the roles of these hub genes in PCa were also investigated through survival analysis and tumour immunity analysis. Moreover, to validate the reliability of the C-T interactions and to further explore the binding modes between ingredients and their targets, the molecular dynamics (MD) simulations were carried out. Finally, based on the modularization of the biological network, four signaling pathways, i.e., PI3K-Akt, MAPK, p53 and cell cycle were integrated to further analyze the therapeutic mechanism of PCa-related herbal medicine. All the results show the mechanism of action of herbal medicines on treating PCa from the molecular to systematic levels, providing a reference for the treatment of complex diseases using TCM.

Altered microstructural pattern of the cortex and basal forebrain cholinergic system in wilson's disease: an automated fiber quantification tractography study.

Basal forebrain (BF) cholinergic projection neurons form a highly extensive input to the cortex. Failure of BF cholinergic circuits is responsible for the cognitive impairment associated with Wilson's disease (WD), but whether and how the microstructural changes in fiber projections between the BF and cerebral cortex influence prospective memory (PM) remain poorly understood. We collected diffusion tensor imaging (DTI) data from 21 neurological WD individuals and 26 healthy controls (HCs). The experiment reconstructed the probabilistic streamlined tractography of 18 white matter tracts using an automated fiber quantification (AFQ) toolkit. Tract properties (FA, MD, RD, and AD) were computed for 100 points along each tract for each participant, and the differences between the groups were examined. Subsequently, correlation analysis was performed to evaluate whether abnormal microstructural white matter integrity measures correlate with PM performance. Additional investigations used a tract-based spatial statistics (TBSS) approach to identify regions with altered white matter structure between groups and verify the reliability of the AFQ results. The highest nonoverlapping DTI-related differences were detected in the anterior thalamic radiation (ATR), corticospinal tract (CST), corpus callosum, association fibers, and limbic system fibers. Additionally, PM parameters of the patient group were highly correlated with white matter microstructure changes in the inferior longitudinal fasciculus. Our study highlights that the performance of projections between cholinergic input and output areas-the cerebral cortex and BF-may serve as neural biomarkers of PM and disease prognosis.

Systematic investigation of the clinical significance and prognostic value of the CBXs in esophageal cancer.

Esophageal cancer (ESCA), one of the most aggressive malignant tumors, has been announced to be the ninth most common cancer and the sixth leading cause of cancer-related death in the world. Chromobox family members (CBXs) are important epigenetic regulators which are related with the transcription of target genes. The role of CBXs in carcinomas has been reported in many studies. However, the function and prognostic value of different CBXs in ESCA are still largely unknown. In this article, we first performed differential expression analysis through several methods including Oncomine and Gene Expression Profiling Interactive Analysis. The results led us to determine the differential expression of CBXs in pan-cancer, especially ESCA. Then we evaluated the prognostic value of different CBX messenger RNA (mRNA) expression in patients with ESCA through the Kaplan-Meier plotter and the Human Protein Atlas database. In addition, we used cBioPortal to explore all genetic alterations and mutations in the CBXs in ESCA. Simultaneously, the correlation between its expression and the level of immune infiltration of ESCA was visualized by TIMER. Finally, the biological function of CBXs in ESCA is obtained through Biological Enrichment Analysis including gene ontology and Kyoto Encyclopedia of Genes and Genomes. The expression levels of CBX3/4/5 and CBX8 in ESCA tissues increased significantly and the expression level of CBX7 decreased through differential expression analysis. Additionally, CBX1 is significantly related to the clinical cancer stage and disease-free survival of ESCA patients. The high mRNA expression of CBX4 is related to the short overall survival of patients with esophageal squamous cell carcinoma, and the high mRNA expression of CBX3/7/8 is related to the short overall survival of patients with esophageal adenocarcinoma, indicating that CBX1/3/4/7/8 may be a potential prognostic biomarker for the survival of ESCA patients. Besides, the expression of CBXs is significantly related to the infiltration of a variety of immune cells, including six types of CD4-positive T-lymphocytes, macrophages, neutrophils, bursindependentlymphocyte, CD8-positive T-lymphocytes cells and dendritic cells in ESCA. Moreover, we found that CBXs are mainly associated with the inhibition of cell cycle and apoptosis pathway. Further, enrichment analysis indicated that CBXs and correlated genes were enriched in mismatch repair, DNA replication, cancer pathways, and spliceosomes. Our research may provide new insights into the choice of prognosis biomarkers of the CBXs in ESCA.

A classification method of gastric cancer subtype based on residual graph convolution network.

Background: Clinical diagnosis and treatment of tumors are greatly complicated by their heterogeneity, and the subtype classification of cancer frequently plays a significant role in the subsequent treatment of tumors. Presently, the majority of studies rely far too heavily on gene expression data, omitting the enormous power of multi-omics fusion data and the potential for patient similarities. Method: In this study, we created a gastric cancer subtype classification model called RRGCN based on residual graph convolutional network (GCN) using multi-omics fusion data and patient similarity network. Given the multi-omics data's high dimensionality, we built an artificial neural network Autoencoder (AE) to reduce the dimensionality of the data and extract hidden layer features. The model is then built using the feature data. In addition, we computed the correlation between patients using the Pearson correlation coefficient, and this relationship between patients forms the edge of the graph structure. Four graph convolutional network layers and two residual networks with skip connections make up RRGCN, which reduces the amount of information lost during transmission between layers and prevents model degradation. Results: The results show that RRGCN significantly outperforms other classification methods with an accuracy as high as 0.87 when compared to four other traditional machine learning methods and deep learning models. Conclusion: In terms of subtype classification, RRGCN excels in all areas and has the potential to offer fresh perspectives on disease mechanisms and disease progression. It has the potential to be used for a broader range of disorders and to aid in clinical diagnosis.

The degeneration changes of basal forebrain are associated with prospective memory impairment in patients with Wilson's disease.

INTRODUCTION: Degeneration changes of the basal forebrain (BF) are suggested to play an important role in cognitive impairment and memory loss in patients with Alzheimer's disease and Parkinson's disease. However, little is known about if and how the structure and function of BF are abnormal in Wilson's disease (WD). METHODS: Here, we employed the structural and resting-state functional magnetic resonance imaging (fMRI) data from 19 WD individuals and 24 healthy controls (HC). Voxel-based morphometry (VBM) and functional connectivity analysis were applied to investigate the structural and functional degeneration changes of BF in WD. Moreover, the linear regression analyses were performed in the patient group to depict the correlations between the aberrant gray volume and functional connectivity of the BF and clinical performances, such as the prospective memory (PM) and mini-mental state examination (MMSE). RESULTS: VBM analysis showed that compared with HC, the volume of overlapping cell groups of BF termed CH1-3 and CH4 was significantly reduced in WD. Additionally, the decreased functional connectivity of the CH4 was distributed in the bilateral temporal-parietal junction (TPJ), right thalamus, orbitofrontal gyrus (ORB), and left middle cingulate cortex (MCC). The performances of the time-based prospective memory (TBPM) and event-based prospective memory (EBPM) were related to reduced functional connectivity between CH4 and right ORB. Besides, the functional connectivity of left TPJ was also significantly correlated with EBPM in WD. CONCLUSION: These findings indicated that the degenerative changes of BF may affect PM through the innervation brain function and may help to understand the neural mechanisms underlying cognitive impairment in WD.

Structural and Functional Changes Are Related to Cognitive Status in Wilson's Disease.

Patients with Wilson's disease (WD) suffer from prospective memory (PM) impairment, and some of patients develop cognitive impairment. However, very little is known about how brain structure and function changes effect PM in WD. Here, we employed multimodal neuroimaging data acquired from 22 WD patients and 26 healthy controls (HC) who underwent three-dimensional T1-weighted, diffusion tensor imaging (DTI), and resting state functional magnetic resonance imaging (RS-fMRI). We investigated gray matter (GM) volumes with voxel-based morphometry, DTI metrics using the fiber tractography method, and RS-fMRI using the seed-based functional connectivity method. Compared with HC, WD patients showed GM volume reductions in the basal ganglia (BG) and occipital fusiform gyrus, as well as volume increase in the visual association cortex. Moreover, whiter matter (WM) tracks of WD were widely impaired in association and limbic fibers. WM tracks in association fibers are significant related to PM in WD patients. Relative to HC, WD patients showed that the visual association cortex functionally connects to the thalamus and hippocampus, which is associated with global cognitive function in patients with WD. Together, these findings suggested that PM impairment in WD may be modulated by aberrant WM in association fibers, and that GM volume changes in the association cortex has no direct effect on cognitive status, but indirectly affect global cognitive function by its aberrant functional connectivity (FC) in patients with WD. Our findings may provide a new window to further study how WD develops into cognitive impairment, and deepen our understanding of the cognitive status and neuropathology of WD.

The Prognostic Value of Deficient Mismatch Repair in Stage II-IVa Nasopharyngeal Carcinoma in the Era of IMRT.

In the era of intensity-modulated radiotherapy (IMRT), it is important to analyse the prognostic value of deficient mismatch repair (dMMR) in nasopharyngeal carcinoma (NPC). In this study, in pretreatment biopsies of 69 patients with stage II-IVa NPC, the expression levels of MMR proteins, including MLH1, MSH2, MSH6 and PMS2, were assessed by immunohistochemistry (IHC). The median follow-up time was 37.5 months (3.1-87.4 months). 50.7% of cases (35/69) showed preserved expression of all 4 MMR proteins, which was interpreted as proficient mismatch repair (pMMR). Only 1.5% of cases (1/69) lost expression of all 4 MMR proteins, 26.1% of cases (18/69) have PMS2 loss alone and 21.7% of cases (15/69) lost expression of both PMS2 and MLH1. Thus, 49.3% of cases (34/69) lost expression of one or more MMR proteins, which was interpreted as dMMR. There was no significant difference (P > 0.05) in terms of sex, age, clinical stage, T category, N category or therapy regimens between the dMMR and pMMR groups. The multivariate Cox regression analysis revealed that dMMR was an independent significant prognostic factor for distant metastasis-free survival (DMFS) (dMMR vs pMMR: P = 0.01, HR = 0.25, 95% CI: 0.09~0.75). Therefore, NPC patients with dMMR had significantly superior DMFS compared with patients with pMMR. It can be expected that dMMR will become a new independent prognostic factor for NPC.

Comprehensive Analysis of CDCAs Methylation and Immune Infiltrates in Hepatocellular Carcinoma.

BACKGROUND: As essential components of cycle growth, the cell division cycle-associated family genes (CDCAs) have crucial roles in tumor development and progression, especially in hepatocellular carcinoma (HCC). However, due to the tumor heterogeneity of HCC, little is known about the methylation variability of CDCAs in mediating phenotypic changes (e.g., immune infiltrates) in HCC. Presently, we aim to comprehensively explore the expression and prognosis of CDCAs methylation with regard to immune infiltrates of HCC. METHODS: We first identified the correlating differentially expressed genes (co-DEGs) among 19 different types of cancer cohorts (a total of 7,783 patients) and then constructed the weighted gene co-expressed and co-methylated networks. Applying the clustering analysis, significant modules of DEGs including CDCAs were selected and their functional bioinformatics analyses were performed. Besides, using DiseaseMeth and TIMER, the correlation between the methylation levels of CDCAs and tumor immune infiltrates was also analyzed. In final, to assess the influence of CDCAs methylation on clinical prognosis, Kaplan-Meier and Cox regression analysis were carried out. RESULT: A total of 473 co-DEGs are successfully identified, while seven genes of CDCAs (CDCA1-3 and CDCA5-8) have significant over-expression in HCC. Co-expressed and co-methylated networks reveal the strong positive correlations in mRNA expression and methylation levels of CDCAs. Besides, the biological enrichment analysis of CDCAs demonstrates that they are significantly related to the immune function regulation of infiltrating immune cells in HCC. Also, the methylation analysis of CDCAs depicts the strong association with the tumor immunogenicity, i.e., low-methylation of CDCA1, CDCA2, and CDCA8 dramatically reduced the immune infiltrate levels of T cells and cytotoxic lymphocytes. Additionally, CDCA1-6 and CDCA8 with low-methylation levels significantly deteriorate the overall survival of patients in HCC. CONCLUSIONS: The co-expressed and co-methylated gene networks of CDCAs show a powerful association with immune function regulation. And the methylation levels of CDCAs suggesting the prognostic value and infiltrating immune differences could be a novel and predictive biomarker for the response of immunotherapy.

Longitudinal Changes in Functional Connectivity of the Caudate Is Associated With Recovery From Bell's Palsy.

Several studies have demonstrated through resting-state functional magnetic resonance imaging (fMRI) that functional connectivity changes are important in the recovery from Bell's palsy (BP); however, these studies have only focused on the cortico-cortical connectivity. It is unclear how corticostriatal connectivity relates to the recovery process of patients with BP. In the present study, we evaluated the relationship between longitudinal changes of caudate-based functional connectivity and longitudinal changes of facial performance in patients with intractable BP. Twenty-one patients with intractable BP underwent resting-state fMRI as well as facial behavioral assessments prior to treatment (PT) and at the middle stage of treatment (MT); and 21 age- and sex-matched healthy controls (HC) were recruited and received the same protocol. The caudate was divided into dorsal and ventral sub-regions and separate functional connectivity was calculated. Compared with HC, patients with intractable BP at the PT stage showed decreased functional connectivity of both the dorsal and ventral caudate mainly distributed in the somatosensory network, including the bilateral precentral gyrus (MI), left postcentral gyrus, media frontal gyrus, and superior temporal gyrus (STG). Alternatively, patients in the MT stage showed decreased functional connectivity primarily distributed in the executive network and somatosensory network, including the bilateral cingulate cortex (CC), left anterior cingulate cortex (LACC), inferior prefrontal gyrus (IFG), MI, STG, and paracentral lobe. The longitudinal changes in functional connectivity of both the dorsal and ventral caudate were mainly observed in the executive network, including the right ACC, left CC, and IFG. Functional connectivity changes in the right ACC and left IFG were significantly correlated with changes in facial behavioral performance. These findings indicated that corticostriatal connectivity changes are associated with recovery from BP.

Aberrant Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Wilson's Disease.

Both abnormalities of resting-state cerebral blood flow (CBF) and functional connectivity in Wilson's disease (WD) have been identified by several studies. Whether the coupling of CBF and functional connectivity is imbalanced in WD remains largely unknown. To assess this possibility, 27 patients with WD and 27 sex- and age-matched healthy controls were recruited to acquire functional MRI and arterial spin labeling imaging data. Functional connectivity strength (FCS) and CBF were calculated based on standard gray mask. Compared to healthy controls, the CBF-FCS correlations of patients with WD were significantly decreased in the basal ganglia and the cerebellum and slightly increased in the prefrontal cortex and thalamus. In contrast, decreased CBF of patients with WD occurred predominately in subcortical and cognitive- and emotion-related brain regions, including the basal ganglia, thalamus, insular, and inferior prefrontal cortex, whereas increased CBF occurred primarily in the temporal cortex. The FCS decrease in WD patients was predominately in the basal ganglia and thalamus, and the increase was primarily in the prefrontal cortex. These findings suggest that aberrant neurovascular coupling in the brain may be a possible neuropathological mechanism underlying WD.

Functional Connectivity Modulation by Acupuncture in Patients with Bell's Palsy.

Bell's palsy (BP), an acute unilateral facial paralysis, is frequently treated with acupuncture in many countries. However, the mechanism of treatment is not clear so far. In order to explore the potential mechanism, 22 healthy volunteers and 17 BP patients with different clinical duration were recruited. The resting-state functional magnetic resonance imaging scans were conducted before and after acupuncture at LI4 (Hegu), respectively. By comparing BP-induced functional connectivity (FC) changes with acupuncture-induced FC changes in the patients, the abnormal increased FC that could be reduced by acupuncture was selected. The FC strength of the selected FC at various stages was analyzed subsequently. Our results show that FC modulation of acupuncture is specific and consistent with the tendency of recovery. Therefore, we propose that FC modulation by acupuncture may be beneficial to recovery from the disease.