Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing a Clinical Study With Dietary Supplements: It's All in the Details

A successful randomized clinical trial of the effect of dietary supplements on a chosen endpoint begins with developing supporting data in preclinical studies while paying attention to easily overlooked details when planning the related clinical trial. In this perspective, we draw on our experience studying the effect of an ethanolic extract from Artemisia dracunculus L. (termed PMI-5011) on glucose homeostasis as a potential therapeutic option in providing resilience to metabolic syndrome (MetS). Decisions on experimental design related to issues ranging from choice of mouse model to dosing levels and route of administration in the preclinical studies will be discussed in terms of translation to the eventual human studies. The more complex considerations in planning the clinical studies present different challenges as these studies progress from testing the safety of the dietary supplement to assessing the effect of the dietary supplement on a predetermined clinical outcome. From the vantage point of hindsight, we will outline potential pitfalls when translating preclinical studies to clinical studies and point out details to address when designing clinical studies of dietary supplements.
Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease

Extramammary Paget’s disease (EMPD) is a rare, malignant cutaneous adenocarcinoma with a high recurrence rate after surgical resection. Early diagnosis of EMPD is critical as 15%–40% of cases progress into an invasive form and resulting in a dismal prognosis. However, EMPD can be a diagnostic challenge to pathologists, especially in the grassroots hospital, because of its low incidence and nonspecific clinical presentation. Although AI-enabled computer-aided diagnosis solutions have been extensively used in dermatological pathological image analysis to diagnose common skin cancers such as melanoma and basal cell carcinoma, these techniques have yet been applied to diagnose EMPD. Here, we developed and verified a deep learning method with five different deep convolutional neural networks, named ResNet34, ResNet50, MobileNetV2, GoogLeNet, and VGG16, in Asian EMPD pathological image screening to distinguish between Paget’s and normal cells. We further demonstrated that the results of the proposed method are quantitative, fast, and repeatable by a retrospective single-center study. The ResNet34 model achieved the best performance with an accuracy of 95.522% in pathological images collected at a magnification of ×40. We envision this method can potentially empower grassroots pathologists’ efficiency and accuracy as well as to ultimately provide better patient care.
Brain Connectivity and Symptom Changes After Transcranial Magnetic Stimulation in Patients With Borderline Personality Disorder

Objectives Repetitive transcranial magnetic stimulation (rTMS) is an innovative method in the treatment of borderline personality disorder (BPD). We hypothesized that prefrontal rTMS in patients with BPD leads to improved BPD symptoms and that these effects are associated with brain connectivity changes. Methods Fourteen patients with BPD received 15 sessions of individually navigated prefrontal rTMS over the right dorsolateral prefrontal cortex. Clinical effects were measured by the Borderline Symptom List 23, UPPS-P, the Difficulties in Emotion Regulation Scale (DERS), the Zung Self-Rating Anxiety Scale (SAS), and the Montgomery and Åsberg Depression Rating Scale (MADRS). Effects of rTMS on brain connectivity were observed with a seed correlation analysis on resting-state fMRI and with a beta series correlation analysis on Go/No Go tasks during fMRI. Assessments were made before and immediately after the treatment. Results The assessments after rTMS showed significant reductions in two subscales of UPPS-P, and in DERS, SAS, and MADRS. The brain connectivity analysis revealed significant decreases in amygdala and insula connectivity with nodes of the posterior default mode network (pDMN; precuneus, posterior cingulate cortex, parietal lobules). Connectivity changes were observed both in the resting state and during inhibition. The decrease of amygdala-pDMN connectivity was positively correlated with reduced depression and lack of premeditation after rTMS. Conclusions Despite the study limitations (open single-arm study in a small sample), our findings suggest a possible neural mechanism of rTMS effect in BPD, reduced amygdala connectivity with the pDMN network, which was positively associated with symptom reduction.
Gene Expression-Based Predication of RNA Pseudouridine Modification in Tumor Microenvironment and Prognosis of Glioma Patients

Aberrant expression of methyltransferases and demethylases may augment tumor initiation, proliferation and metastasis through RNA modification, such as m6A and m5C. However, activity of pseudouridine (Ψ) modification of RNA remains unknown in glioma, the most common malignant intracranial tumor. In this study, we explored the expression profiles of the Ψ synthase genes in glioma and constructed an efficient prediction model for glioma prognosis based on the CGGA and TCGA datasets. In addition, the risk-score signature was positively associated with malignancy of gliomas and the abundance of tumor-infiltrating immune cells such as macrophages M0 and regulatory T cells (Tregs), but negatively associated with the abundance of monocytes, NK cell activation and T cell CD4+ naive. In terms of mechanism, the risk-score signature was positively associated with the expression of inflammatory molecules such as S100A11 and CASP4 in glioma. Overall, this study provided evidence for the activity of RNA Ψ modification in glioma malignancy and local immunity.
Size Perception of a Sport Target as a Function of Practice Success Conditions

Superior motor task success has been correlated with participants’ self-reports of a larger-than-actual size of a sport-related target. In the present study, we examined whether a putting practice condition with greater success would differentially impact participants’ self-reported perceptions of the size of the putting hole during acquisition and retention. We randomly assigned participants to one of three different practice conditions (success-early, success-late, and self-controlled success) and had them self-report their perceived size of the putting hole upon completion of each required putting distance (25, 50, 75, 100, 125, 150, 175, 200 cm). Although there were no statistically significant differences between motor task success in the acquisition or retention period for the practice conditions, self-reported perceptions of target size were impacted by practice condition. During the acquisition period, participants in the self-controlled success and success-late conditions self-reported that the putting hole was larger than did participants in the success-early condition. In the retention period, participants in the self-controlled success condition perceived the target as larger than those in the success-early condition. These findings are the first to show that practice condition, independent of task success, differentially impacted self-reported perception of a target size.
Association Between H. pylori Infection and Colorectal Polyps: A Meta-Analysis of Observational Studies

Background It has been suggested that Helicobacter pylori (H. pylori) infection is associated with hypergastrinemia and proliferation of colorectal mucosa via direct stimulation, dysbiosis of the gut microbiome, and changes in the gut microbiome, all of which may lead to the formation of colorectal polyps. However, the consensus remains lacking regarding whether H. pylori infection is independently associated with colorectal polyps and whether the association differs according to histological type of colorectal polyps. To summarize the current evidence regarding the relationship between H. pylori infection and colorectal polyps, we conducted a meta-analysis of related observational studies according to the histological types of colorectal polyps. Methods Observational studies investigating the association between H. pylori infection and colorectal polyps using multivariate analyses were included by search of PubMed, Embase, and Web of Science. A random-effects model was adopted to combine the results. Results Seventeen studies that include 322,395 participants were analyzed. It was shown that H. pylori infection was independently associated with overall colorectal polyps (odds ratio [OR]: 1.67, 95% CI: 1.24–2.24, p < 0.001; I2 = 73%). According to the histological type of colorectal polyps, H. pylori infection was independently associated with adenomatous polyps (APs; OR: 1.71, 95% CI: 1.47–1.99, p < 0.001; I2 = 86%), advanced APs (OR: 2.06, 95% CI: 1.56–2.73, p < 0.001; I2 = 0%), and hyperplastic polyps (HPs; OR: 1.54, 95% CI: 1.02–2.30, p = 0.04; I2 = 78%). Evidence based on only one study showed that H. pylori infection was not associated with sessile serrated polyps (SSPs; OR: 1.00, 95% CI: 0.93–1.07, p = 0.99). Conclusions Current evidence from case-control and cross-sectional studies suggested that H. pylori infection was independently associated with colorectal APs, advanced APs, and HPs, but not with SSPs. These findings suggested H. pylori infection may be a possible risk factor of colorectal polyp, which is important for the prevention of colorectal polyp in the adult population.
Current Status of Acceptance of Disability and the Correlation With the Life Quality in Parkinson's Disease in Southwest China

Background Acceptance of disability (AOD) is a process that a patient must undergo to cope with altered abilities, but its effect in Parkinson's disease (PD) remains unclear. The present study aimed to determine the level of AOD, examine the influence of sociodemographic variables and disease characteristics on the AOD level, and evaluate the relation between AOD level and quality of life in a cohort of PD patients from Southwest of China. Methods A total of 336 PD patients were consecutively recruited from November 2018 to October 2020. At enrollment, demographic and clinical data were obtained using a questionnaire, and the Acceptance of Disability Scale-Revised (AODS-R) scale was used to measure the AOD level. Results The mean total score of AOD is 87.28, indicating a moderate level of disability acceptance in PD patients. Statistical analysis showed that understanding of PD, family support, and UPDRS-II score were major factors affecting AOD level, and patients with low AOD levels were more likely to have poorer quality of life. Conclusion AOD is a serious problem in PD patients in Southwest China, especially among individuals with insufficient family support and a lack of recognition of the disease. AOD was also associated with motor function and daily living ability, thus suggesting that evaluation of the AOD and promotion of the awareness may be helpful to improve the quality of life in patients with PD.
Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation

Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (CNNs). A new deep learning-based neural network method was proposed to automatically and efficiently identify commonly used standard echocardiographic views. A total of 367,571 echocardiographic image slices from 3,772 subjects were used to train and validate the proposed echocardiographic view recognition model where 23 standard echocardiographic views commonly used to diagnose CHDs in children were identified. The F1 scores of a majority of views were all ≥0.90, including subcostal sagittal/coronal view of the atrium septum, apical four-chamber view, apical five-chamber view, low parasternal four-chamber view, sax-mid, sax-basal, parasternal long-axis view of the left ventricle (PSLV), suprasternal long-axis view of the entire aortic arch, M-mode echocardiographic recording of the aortic (M-AO) and the left ventricle at the level of the papillary muscle (M-LV), Doppler recording from the mitral valve (DP-MV), the tricuspid valve (DP-TV), the ascending aorta (DP-AAO), the pulmonary valve (DP-PV), and the descending aorta (DP-DAO). This study provides a solid foundation for the subsequent use of artificial intelligence (AI) to identify CHDs in children.
Intrinsic Brain Activity Alterations in Patients With Mild Cognitive Impairment-to-Normal Reversion: A Resting-State Functional Magnetic Resonance Imaging Study From Voxel to Whole-Brain Level

Mild cognitive impairment (MCI) reversion refers to patients with MCI who revert from MCI to a normal cognitive state. Exploring the underlying neuromechanism of MCI reverters may contribute to providing new insights into the pathogenesis of Alzheimer’s disease and developing therapeutic interventions. Information on patients with MCI and healthy controls (HCs) was collected from the Alzheimer’s Disease Neuroimaging Initiative database. We redefined MCI reverters as patients with MCI whose logical memory scores changed from MCI to normal levels using the logical memory criteria. We explored intrinsic brain activity alterations in MCI reverters from voxel, regional, and whole-brain levels by comparing resting-state functional magnetic resonance imaging metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) between MCI reverters and HCs. Finally, partial correlation analyses were conducted between cognitive scale scores and resting-state functional magnetic resonance imaging metrics of brain regions, revealing significant group differences. Thirty-two patients with MCI from the Alzheimer’s Disease Neuroimaging Initiative database were identified as reverters. Thirty-seven age-, sex-, and education-matched healthy individuals were also enrolled. At the voxel level, compared with the HCs, MCI reverters had increased ALFF, fALFF, and PerAF in the frontal gyrus (including the bilateral orbital inferior frontal gyrus and left middle frontal gyrus), increased PerAF in the left fusiform gyrus, and decreased ALFF and fALFF in the right inferior cerebellum. Regarding regional and whole-brain levels, MCI reverters showed increased ReHo in the left fusiform gyrus and right median cingulate and paracingulate gyri; increased DC in the left inferior temporal gyrus and left medial superior frontal; decreased DC in the right inferior cerebellum and bilateral insular gyrus relative to HCs. Furthermore, significant correlations were found between cognitive performance and neuroimaging changes. These findings suggest that MCI reverters show significant intrinsic brain activity changes compared with HCs, potentially related to the cognitive reversion of patients with MCI. These results enhance our understanding of the underlying neuromechanism of MCI reverters and may contribute to further exploration of Alzheimer’s disease.
Diseases of Cannabis sativa Caused by Diverse Fusarium Species

The potential of species of Fusarium to cause significant economic losses in Cannabis sativa due to plant diseases and mycotoxin residues is the subject of this review. Sixteen species of Fusarium, reported as associated with cannabis production, are classified in six species complexes: Fusarium oxysporum, F. solani, F. incarnatum-equiseti, F. sambucinum, F. tricinctum, and F. fujikuroi. Taxonomy in this genus is the subject of debate, and removal of species in the F. solani Species Complex to the genus, Neocosmospora, has been proposed. Many species associated with C. sativa are also opportunistic pathogens of humans and animals. Species of Fusarium produce a myriad of mycotoxins, including at least three (deoxynivalenol, zearalenone, and fumonisin B) deemed the most important mycotoxins in human and animal foods. These chemicals vary from the very simple chemicals (moniliformin and butanolide) to the structurally complex depsipeptides (beauvericin and enniatin B) and trichothecenes (deoxynivalenol and its acetylated derivatives, diacetoxyscirpenol, and T-2-toxin). The robust strategies for disease management (e.g., exclusion of the pathogen, control of environment, and host resistance) are in development for this new crop and have primarily relied on management systems for other crops. Biopesticides have been labeled for use on C. sativa; however, few efficacy trials have been performed. Host resistance to these pathogens and transmission are also understudied. The new markets for C. sativa and its derivative products require knowledge for control of these important fungal diseases to provide safe products for human consumption.
Integrated Analysis of mRNA-Seq and MiRNA-Seq Reveals the Molecular Mechanism of the Intestinal Immune Response in Marsupenaeus japonicus Under Decapod Iridescent Virus 1 Infection

The intestine is not only an important digestive organ but also an important immune organ for shrimp; it plays a key role in maintaining homeostasis. Decapod iridescent virus 1 (DIV1) is a new type of shrimp-lethal virus that has received extensive attention in recent years. To date, most studies of the shrimp intestinal immune response under viral infections have relied on single omics analyses; there is a lack of systematic multi-omics research. In the current study, intestinal mRNA-seq and microRNA (miRNA)-seq analyses of Marsupenaeus japonicus under DIV1 infection were performed. A total of 1,976 differentially expressed genes (DEGs) and 32 differentially expressed miRNAs (DEMs) were identified. Among them, 21 DEMs were negatively correlated with 194 DEGs from a total of 223 correlations. Functional annotation analysis revealed that M. japonicus can regulate glycosaminoglycan biosynthesis (chondroitin sulfate, dermatan sulfate, and keratan sulfate), vitamin metabolism (retinol metabolism and ascorbate and aldarate metabolism), immune pathway activation (Toll and IMD signaling pathways, Wnt signaling pathway, IL-17 signaling pathway, and Hippo signaling pathway), immunity enzyme activity promotion (triose-phosphate isomerase), antimicrobial peptide (AMP) expression, reactive oxygen species (ROS) production, and cell apoptosis through miRNAs to participate in the host’s antiviral immune response, while DIV1 can influence Warburg effect-related pathways (pyruvate metabolism, glycolysis/gluconeogenesis, and citrate cycle), glycosphingolipid biosynthesis-related pathways (glycosphingolipid biosynthesis—globo and isoglobo series and glycosphingolipid biosynthesis—lacto and neolacto series), and the tight junction and adhesion junction of the intestinal mucosal epithelium through the host’s miRNAs and mRNA to promote its own invasion and replication. These results indicate that intestinal miRNAs play important roles in the shrimp immune response against DIV1 infection. This study provides a basis for further study of the shrimp intestinal antiviral immune response and for the formulation of effective new strategies for the prevention and treatment of DIV1 infection.
A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis

A Bayesian-model-averaging Copula (i.e., BMAC) approach was proposed for correlation analysis of monthly rainfall and runoff in Xiangxi River watershed, China. The BMAC approach was formulated by incorporating existing Bayesian model averaging (i.e., BMA) method and Archimedean Copula techniques (e.g., Gumbel-Hougaard, Clayton and Frank Copulas) within a general bivariate hydrologic correlation analysis framework. In this paper, the BMA method was applied to determine the marginal distribution functions of variables, and the Copula method was used to analyze the correlation. Results showed that: 1) the BMA method could improve the representation of the marginal distribution of hydrological variables with smaller corresponding errors; 2) the predictive joint distributions of monthly rainfall and runoff was much better calibrated by the Gumbel Copula according to criteria of the root mean square error (i.e., RMSE), Akaike Information Criterion (i.e., AIC) values, Anderson-Darling test (i.e., AD test), and Cramer-von Mises test (i.e., CM test); and 3) the bivariate joint probability and return periods of rainfall and runoff based on the optimal Copula function was characterized and the monthly rainfall and runoff presented a strong positive correlation based on Kendall and Spearman’s rank correlation coefficients. Therefore, the BMAC approach performed reasonably well and can be further used to simulate runoff values according to the historical and predicted rainfall data. Highlights: 1) A Bayesian-model-averaging Copula method is proposed for correlation analysis; 2) the monthly rainfall and runoff in Xiangxi River watershed has a positive correlation. 3) Gumbel Copula is the best in modelling the joint distributions in the Xiangxi River watershed.
A Hybrid Feature Selection and Ensemble Approach to Identify Depressed Users in Online Social Media

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research.