Miracle-dz/UniFAR-FTUs huggingface dataset:
Composition and diverse differences of intestinal microbiota in ulcerative colitis patients

Objective To explore the composition of the intestinal microbiota in ulcerative colitis (UC) patients and to identify differences in the microbiota between patients with active disease and those in remission. Methods Between September 2020 and June 2021, we enrolled into our study, and collected stool samples from, patients with active UC or in remission and healthy control subjects. The diagnosis of UC was based on clinical, endoscopic, radiological, and histological findings. The composition of the intestinal microbiota was determined by sequencing of the 16S rRNA V3–V4 region and by bioinformatic methods. The functional composition of the intestinal microbiota was predicted using PICRUSt 2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) software. Results We found that the intestinal flora was significantly less rich and diverse in UC patients than in healthy control subjects. Beta diversity analysis revealed notable differences in the intestinal flora compositions among the three groups, but there was no statistical difference in alpha diversity between UC patients with active disease and those in remission. At the phylum level, the relative abundances of Proteobacteria and Patescibacteria were significantly higher, and the relative abundances of Desulfobacterota and Verrucomicrobiota were lower, in UC patients with active disease than in the healthy control group. Higher levels of potential pathogens and lower levels of butyrate-producing bacteria were also detected in UC patients with active disease. Linear discriminant analysis Effect Size (LefSe) revealed that 71 bacterial taxa could serve as biomarkers, with 26 biomarkers at the genus level. In addition, network analysis showed that there was a positive correlation between Roseburia and Lachnospira. Functional predictions indicated that gene functions involving the metabolism of some substances, such as methane, lipopolysaccharide, geraniol, and ansamycins, were significantly different among the three groups. Conclusion The richness and diversity of the intestinal microbiota differed significantly among the three groups. Richness describes the state of being rich in number of intestinal bacteria, whereas diversity is the number of different species of intestinal bacteria. Different bacterial taxa could be used as biomarkers, expanding our understanding of the relationship between the intestinal microbiota microenvironment and UC in the future.
Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm

Background: Psoriasis is a chronic and immune-mediated skin disorder that currently has no cure. Pyroptosis has been proved to be involved in the pathogenesis and progression of psoriasis. However, the role pyroptosis plays in psoriasis remains elusive. Methods: RNA-sequencing data of psoriasis patients were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed pyroptosis-related genes (PRGs) between psoriasis patients and normal individuals were obtained. A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. PRG and immune cell correlation was also investigated. Subsequently, a novel diagnostic model comprising PRGs for psoriasis was constructed using a random forest algorithm (ntree = 400). A receiver operating characteristic (ROC) analysis was used to evaluate the classification performance through both internal and external validation. Consensus clustering analysis was used to investigate whether there was a difference in biological functions within PRG-based subtypes. Finally, the expression of the kernel PRGs were validated in vivo by qRT-PCR. Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. The process of T cell CD4 memory activated and mast cells resting were correlated with PRGs. Ten PRGs, IL-1β, AIM2, CASP5, DHX9, CASP4, CYCS, CASP1, GZMB, CHMP2B, and CASP8, were subsequently screened using a random forest diagnostic model. ROC analysis revealed that our model has good diagnostic performance in both internal validation (area under the curve [AUC] = 0.930 [95% CI 0.877–0.984]) and external validation (mean AUC = 0.852). PRG subtypes indicated differences in metabolic processes and the MAPK signaling pathway. Finally, the qRT-PCR results demonstrated the apparent dysregulation of PRGs in psoriasis, especially AIM2 and GZMB. Conclusion: Pyroptosis may play a crucial role in psoriasis and could provide new insights into the diagnosis and underlying mechanisms of psoriasis.
A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

Purpose The present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options. Methods 164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility. Results Elevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits. Conclusion In conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.
Prevalence of central obesity and associated factors in Ethiopia: A systematic review and meta-analysis

Introduction Obesity is a global public health concern that is now on the rise, especially in low- and middle-income nations. Despite the fact that there are several studies reporting the prevalence of central obesity among adults in Ethiopia, there is a lack of a systematic review and meta-analysis synthesizing the existing observational studies. Therefore, this systematic review and meta-analysis aimed to determine the prevalence of central obesity and its associated factors in Ethiopia. Methods Online libraries such as PubMed, Google Scholar, Scopus, Science Direct, and Addis Ababa University were searched. Data were extracted using Microsoft Excel and analyzed using STATA statistical software (v. 16). Forest plots, Begg’s rank test, and Egger’s regression test were all used to check for publication bias. To look for heterogeneity, I2 was computed, and an overall estimated analysis was carried out. Subgroup analysis was done by region and study setting. In addition, the pooled odds ratio for related covariates was calculated. Results Out of 685 studies assessed, 20 met our criteria and were included in the study. A total of 12,603 people were included in the study. The prevalence of central obesity was estimated to be 37.31% [95% confidence interval (CI): 29.55–45.07]. According to subgroup analysis by study region and setting, the highest prevalence was observed in the Dire Dawa region (61.27%) and community-based studies (41.83%), respectively. Being a woman (AOR = 6.93; 95% CI: 3.02–10.85), having better socioeconomic class (AOR = 5.45; 95% CI: 0.56–10.34), being of age 55 and above (AOR = 5.23; 95% CI: 2.37–8.09), being physically inactive (AOR = 1.80; 95% CI: 1.37–2.24), being overweight (AOR = 4.00; 95% CI: 2.58–5.41), being obese (AOR = 6.82; 95% CI: 2.21–11.43), and having hypertension (AOR = 3.84; 95% CI: 1.29–6.40) were the factors associated with central obesity. Conclusion The prevalence of central obesity was high in Ethiopia. Being a woman, having a higher socioeconomic class, being older, being physically inactive, being overweight or obese, and having hypertension were all associated. Therefore, it is vital for the government and health organizations to design and implement preventive measures like early detection, close monitoring, and positive reversal of central obesity in all patients and the general population. High-quality investigations on the prevalence of central obesity in the Ethiopian people are required to better understand the status of central obesity in Ethiopia. Systematic review registration https://www.crd. york.ac. uk/PROSPERO, identifier: CRD42022329234.
ADT: The automatic weather station data tool

Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use.
Infrared and visible image fusion based on particle swarm optimization and dense block

Infrared and visible image fusion aims to preserve essential thermal information and crucial visible details from two types of input images to generate an informative fusion image for better visual perception. In recent years, several hybrid methods have been applied in the field of infrared and visible image fusion. In this paper, we proposed a novel image fusion method based on particle swarm optimization and dense block for visible and infrared images. Particle swarm optimization is utilized to optimize the weighting factors of the coefficients obtained by discrete wavelet transform, then the coefficients are fused with the optimum weight to obtain the initial fusion image. The final fusion image is created by integrating the first fused image with the input visible image using a deep learning model, in which dense block is utilized for better feature extraction ability. The results of comparison experiments demonstrate that our method produces fusion images with richer details and texture features, and the fused image reduces the artifacts and noise.
An optimized Factor H-Fc fusion protein against multidrug-resistant Neisseria gonorrhoeae

Novel therapeutics against the global threat of multidrug-resistant Neisseria gonorrhoeae are urgently needed. Gonococci evade killing by complement by binding factor H (FH), a key inhibitor of the alternative pathway. FH comprises 20 short consensus repeat (SCR) domains organized as a single chain. Gonococci bind FH through domains 6 and 7, and C-terminal domains 18 through 20. Previously, we showed that a chimeric protein comprising (from the N- to C-terminus) FH domains 18-20 (containing a point mutation in domain 19 to prevent lysis of host cells) fused to human IgG1 Fc (called FH*/Fc1) killed gonococci in a complement-dependent manner and reduced the duration and bacterial burden in the mouse vaginal colonization model of gonorrhea. Considering the N. gonorrhoeae-binding FH domains 18-20 are C-terminal in native FH, we reasoned that positioning Fc N-terminal to FH* (Fc1/FH*) would improve binding and bactericidal activity. Although both molecules bound gonococci similarly, Fc1/FH* displayed a 5-fold lower IC50 (the concentration required for 50% killing in complement-dependent bactericidal assays) than FH*/Fc1. To further increase complement activation, we replaced human IgG1 Fc in Fc1/FH* with Fc from human IgG3, the most potent complement-activating IgG subclass, to obtain Fc3/FH*. Bactericidal activity was further increased ~2.3-fold in Fc3/FH* compared to Fc1/FH*. Fc3/FH* killed (defined by <50% survival) 45/45 (100%) diverse PorB1B-expessing gonococci, but only 2/15 PorB1A-expressing isolates, in a complement-dependent manner. Decreased Fc3/FH* binding accounted for the limited activity against PorB1A strains. Fc3/FH* was efficacious against all four tested PorB1B gonococcal strains in the mouse vaginal colonization model when administered at a dose of 5 µg intravaginally, daily. Furthermore, Fc3/FH* retained bactericidal activity when reconstituted following lyophilization or spray-drying, suggesting feasibility for formulation into intravaginal rings. In conclusion, Fc3/FH* represents a promising prophylactic immunotherapeutic against multidrug-resistant gonococci.
Exploring the diversity and structural response of sediment-associated microbiota communities to environmental pollution at the siangshan wetland in Taiwan using environmental DNA metagenomic approach

One of the most crucial struggles for the aquatic ecosystem and modern society is environmental pollution. New approaches, such as the environmental DNA Metagenomic approach, have become a powerful tool to investigate the abundance and diversity of sediment-associated microbiota communities. Nevertheless, information on the response of microbial populations to heavy metals (HMs) in the coastal sediments of Taiwan is scarce. 44 sediment samples were collected from the Siangshan wetland in Taiwan (Taiwan Strait) at two different depths (surface and sub-surface) to measure various environmental variables viz., heavy metals, total organic matters, and sediment composition. Moreover, the abundance and structure of sediment-associated microbiota were examined using high-throughput 16S rRNA gene (V3-V4) next-generation sequencing, to explore the response of the microbiota community to environmental variables. According to the computed pollution indices viz., sediment quality guidelines (SQGs), enrichment factor (EF), geo-accumulation index (Igeo), pollution index (PI), and pollution load index (PLI), the studied sites were classified as unpolluted, moderately polluted, and extremely polluted. Our results revealed that the majority of high-quality reads were assigned to bacteria (~ 95.05%), Archaea (~ 4.83%), and 0.13% were unclassified. Study sites were dominated largely by Proteobacteria with a total of 38.02% across all sediment samples, followed by Bacteroidota (7.18%), Desulfobacterota (6.97%), Actinobacterota (6.68%), Cyanobacteria (5.84%), Chloroflexota (4.18%), Planctomycetota (4.16%), and Firmicutes (3.69%). Beta diversity (Non-metric dimensional scaling analysis, nMDS) explained that surface and sub-surface groups had significantly different microbial community compositions (p = 0.01). Meanwhile, polluted sites exhibited more richness and diversity than unpolluted sites. Redundancy analysis (RDA) illustrated that Mn, Cu, Al, Co, Ni, Sand, and Cr had a sizable effect on the structure of microbiota communities (at the class level). This work highlights the potential responsibility of environmental variables in shaping the sedimentary microbiota of the Siangshan wetland via integrating various ecological variables with alteration of the microbiota composition.
Biological control and plant growth promotion properties of Streptomyces albidoflavus St-220 isolated from Salvia miltiorrhiza rhizosphere

Root rot disease caused by Fusarium oxysporum is a devastating disease of Salvia miltiorrhiza and dramatically affected the production and quality of Sa. miltiorrhiza. Besides the agricultural and chemical control, biocontrol agents can be utilized as an additional solution. In the present study, an actinomycete that highly inhibited F. oxysporum was isolated from rhizosphere soil and identified as based on morphological and molecular characteristics. Greenhouse assay proved that the strain had significant biological control effect against Sa. miltiorrhiza root rot disease and growth-promoting properties on Sa. miltiorrhiza seedlings. To elucidate the biocontrol and plant growth-promoting properties of St-220, we employed an analysis combining genome mining and metabolites detection. Our analyses based on genome sequence and bioassays revealed that the inhibitory activity of St-220 against F. oxysporum was associated with the production of enzymes targeting fungal cell wall and metabolites with antifungal activities. Strain St-220 possesses phosphate solubilization activity, nitrogen fixation activity, siderophore and indole-3-acetic acid production activity in vitro, which may promote the growth of Sa. miltiorrhiza seedlings. These results suggest that St. albidoflavus St-220 is a promising biocontrol agent and also a biofertilizer that could be used in the production of Sa. miltiorrhiza.
Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

Background and purpose This study aimed to effectively identify children with drug-resistant epilepsy (DRE) in the early stage of epilepsy, and take personalized interventions, to improve patients' prognosis, reduce serious comorbidity, and save social resources. Herein, we developed and validated a nomogram prediction model for children with DRE. Methods The training set was patients with epilepsy who visited the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) between January 2015 and December 2017. The independent risk factors for DRE were screened by univariate and multivariate logistic regression analyses using SPSS21 software. The nomogram was designed according to the regression coefficient. The nomogram was validated in the training and validation sets. Internal validation was conducted using bootstrapping analyses. We also externally validated this instrument in patients with epilepsy from the Children's Hospital of Soochow University (Gusu District, Jiangsu Province, China) and Yancheng Maternal and Child Health Hospital between January 2018 and December 2018. The nomogram's performance was assessed by concordance (C-index), calibration curves, as well as GiViTI calibration belts. Results Multivariate logistic regression analysis of 679 children with epilepsy from the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) showed that onset age<1, status epilepticus (SE), focal seizure, > 20 pre-treatment seizures, clear etiology (caused by genetic, structural, metabolic, or infectious), development and epileptic encephalopathy (DEE), and neurological abnormalities were all independent risk factors for DRE. The AUC of 0.92 for the training set compared to that of 0.91 for the validation set suggested a good discrimination ability of the prediction model. The C-index was 0.92 and 0.91 in the training and validation sets. Additionally, both good calibration curves and GiViTI calibration belts (P-value: 0.849 and 0.291, respectively) demonstrated that the predicted risks had strong consistency with the observed outcomes, suggesting that the prediction model in both groups was perfectly calibrated. Conclusion A nomogram prediction model for DRE was developed, with good discrimination and calibration in the training set and the validation set. Furthermore, the model demonstrated great accuracy, consistency, and prediction ability. Therefore, the nomogram prediction model can aid in the timely identification of DRE in children.
MRI radiomics predicts progression-free survival in prostate cancer

Objective To assess the predictive value of magnetic resonance imaging (MRI) radiomics for progression-free survival (PFS) in patients with prostate cancer (PCa). Methods 191 patients with prostate cancer confirmed by puncture biopsy or surgical pathology were included in this retrospective study, including 133 in the training group and 58 in the validation group. All patients underwent T2WI and DWI serial scans. Three radiomics models were constructed using univariate logistic regression and Gradient Boosting Decision Tree(GBDT) for feature screening, followed by Cox risk regression to construct a mixed model combining radiomics features and clinicopathological risk factors and to draw a nomogram. The performance of the models was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. The Kaplan-Meier method was applied for survival analysis. Results Compared with the radiomics model, the hybrid model consisting of a combination of radiomics features and clinical data performed the best in predicting PFS in PCa patients, with AUCs of 0.926 and 0.917 in the training and validation groups, respectively. Decision curve analysis showed that the radiomics nomogram had good clinical application and the calibration curve proved to have good stability. Survival curves showed that PFS was shorter in the high-risk group than in the low-risk group. Conclusion The hybrid model constructed from radiomics and clinical data showed excellent performance in predicting PFS in prostate cancer patients. The nomogram provides a non-invasive diagnostic tool for risk stratification of clinical patients.