bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of m6A modification patterns and development of m6A–hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer
Authors: Shen Xi , Zhong Jianxin , He Jinlan , Han Jiaqi , Chen Nianyong 
Published: August 2022

BackgroundN6-methylation (m6A) modification of RNA has been found to have essential effects on aspects of the tumor microenvironment (TME) including hypoxia status and mobilization of immune cells. However, there are no studies to explore the combined effect of m6A modification and hypoxia on molecular heterogeneity and TME of triple-negative breast cancer (TNBC).MethodsWe collected The Cancer Genome Atlas (TCGA-TNBC, N=139), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC-TNBC, N=297), the GSE103091, GSE21653, and GSE135565 series from the Gene Expression Omnibus (GEO-TNBC, N=247), and FUSCCTNBC (N=245) for our study. The non-negative matrix factorization algorithm was used to cluster TNBC samples. Immune cell infiltration was analyzed by the CIBERSORT algorithm. The enrichment scores were calculated by single-sample gene set enrichment analysis(ssGSEA) to characterize TME in TNBC samples. Immunohistochemistry (IHC) and qRT-PCR were performed to detect the gene expression.ResultsBased on the expression of m6A-related genes, we identified three distinct m6A clusters (denoted A, B, and C) in TNBC samples. Comparing the TME characteristics among the three clusters, we observed that cluster C was strongly related to hypoxia status and immune suppression, whereas clusters A and B displayed more immune cell infiltration. Therefore, we combine m6A and hypoxia related genes to classify two m6A-hypoxia clusters of TNBC and screened six prognostic genes by LASSO-Cox regression to construct a m6A-hypoxia signature(MHPS), which divided TNBC samples into high- and low-risk groups. We identified different TME features, immune cell infiltration between the two groups, and a better immunotherapy response was observed in the low-risk group. A nomogram was constructed with tumor size, lymph node, and risk score to improve clinical application of MHPS.ConclusionWe identified distinct TME characteristics of TNBC based on three different m6A modification patterns. Then, we constructed a specific m6A–hypoxia signature for TNBC to evaluate risk and predict immunotherapy response of patients, to enable more accurate treatment in the future.
Gut microbiota on admission as predictive biomarker for acute necrotizing pancreatitis
Authors: Zou Menglian , Yang Zihan , Fan Yue , Gong Liang , Han Ziying , Ji Li , Hu Xiaomin , Wu Dong 
Published: August 2022

BackgroundAcute necrotizing pancreatitis (NP), a severe form of acute pancreatitis (AP), has higher mortality and worse outcome than non-necrotizing pancreatitis (non-NP). Infected NP is a devastating subgroup of NP. To date neither NP nor infected NP has robust prediction strategies, which may delay early recognition and timely intervention. Recent studies revealed correlations between disturbed gut microbiota and AP severity. Some features of intestinal microbiota have the potential to become biomarkers for NP prediction.MethodsWe performed 16S rRNA sequencing to analyze gut microbiota features in 20 healthy controls (HC), and 58 AP patients on hospital admission. The AP patients were later classified into NP and non-NP groups based on subsequent diagnostic imaging features. Random forest regression model and ROC curve were applied for NP and infected NP prediction. PIRCUSt2 was used for bacterial functional pathway prediction analysis.ResultsWe found that the three groups (HC, NP, and non-NP) had distinct microorganism composition. NP patients had reduced microbial diversity, higher abundance of Enterobacteriales, but lower abundance of Clostridiales and Bacteroidales compared with the non-NP group. Correlation analyses displayed that intestine bacterial taxonomic alterations were related to severity, ICU admission, and prognosis. By pathway prediction, species more abundant in NP patients had positive correlation with synthesis and degradation of ketone bodies, and benzoate degradation. Enterococcus faecium (ASV2) performed best in discriminating NP and non-NP patients. Finegoldia magna (ASV3) showed the maximal prediction capacity among all ASVs and had comparable accuracy with Balthazar CT to detect patients with infected NP.ConclusionsOur study suggests that NP patients have distinct intestinal microbiota on admission compared to non-NP patients. Dysbiosis of intestinal microbiota might influence NP progression through ketone body or benzoate metabolism. Enterococcus faecium and Finegoldia magna are potential predictors for NP and infected NP. Our findings explore biomarkers which may inform clinical decision-making in AP and shed light on further studies on NP pathophysiology and management.
Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021
Authors: Li Yuanyuan , Luo Zhuowei , Hao Yuwan , Zhang Yi , Yang Limin , Li Zhongqiu , Zhou Zhengbin , Li Shizhu 
Published: August 2022

BackgroundVisceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.Materials and methodsThe information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.ResultsA total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.ConclusionThe AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.
Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis
Authors: Xia Lin , Gong Ningji 
Published: August 2022

Background: Osteoarthritis (OA) is a major factor causing pain and disability. Studies performed to date have suggested that synovitis is possibly a critical OA-related pathological change. Ferroptosis represents a novel type of lipid peroxidation-induced iron-dependent cell death. However, its effect on OA remains largely unclear.Objective: This work focused on identifying and validating the possible ferroptosis-related genes (FRGs) involved in synovitis of OA through bioinformatics analysis.Materials and Methods: The microarray dataset GSE55235 was downloaded in the database Gene Expression Omnibus (GEO). By the Venn diagram and GEO2R, differentially expressed genes (DEGs) and ferroptosis DEGs (FDEGs) were detected. DEGs were screened by GO and KEGG enrichment analysis, as well as protein-protein interaction (PPI) analysis. Besides, the software Cytoscape and database STRING were utilized to construct hub gene networks. Moreover, this study used the database NetworkAnalyst to predict the target miRNAs of the hub genes. Finally, the hub genes were confirmed by analysis of the receiver operating characteristic (ROC) curve on the GSE12021 and GSE1919 databases. Considering the relationship between ferroptosis and immunity, this study applied CIBERSORTx to analyze the immune infiltration in OA in addition.Results: This work discovered seven genes, including ATF3, IL6, CDKN1A, IL1B, EGR1, JUN, and CD44, as the hub FDEGs. The ROC analysis demonstrated that almost all hub genes had good diagnostic properties in GSE12021 and GSE 1919.Conclusion: This study discovered seven FDEGs to be the possible diagnostic biomarkers and therapeutic targets of synovitis during OA, which sheds more light on the pathogenesis of OA at the transcriptome level.
Somatosensory evoked potentials and their relation to microstructural damage in patients with multiple sclerosis—A whole brain DTI study
Authors: Hamann Jan , Ettrich Barbara , Hoffman Karl Titus , Then Bergh Florian , Lobsien Donald 
Published: August 2022

IntroductionSomatosensory evoked potentials (SSEP) play a pivotal role in the diagnosis and disease monitoring of multiple sclerosis (MS). Delayed latencies are a surrogate for demyelination along the sensory afference. This study aimed to evaluate if SSEP latencies are representative of demyelination of the brain overall, by correlating with cerebral microstructural integrity as measured by Magnetic resonance (MR) diffusion tensor imaging (DTI). Analysis was performed in a hypothesis-free whole brain approach using tract-based spatial statistics (TBSS).Material and methodsA total of 46 patients with MS or clinically isolated syndrome were included in the study. Bilateral SSEPs of the median nerve measuring mean N20 latencies (mN20) and Central Conduction Time (CCT), were acquired. MRI scans were performed at 3T. DTI acquisition was done with a single-shot echoplanar imaging technique with 80 diffusion directions. The FSL software package was used to process the DTI datasets and to calculate maps of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). These maps were then further analyzed using the TBSS module. The mean N20 and CCT and the right- and left-sided N20 and CCT were separately correlated to FA, AD, and RD, controlled for age, gender, and EDSS as variables of non-interest.ResultsWidespread negative correlations of SSEP latencies with FA (p = 0.0005) and positive correlations with RD (p = 0.0003) were measured in distinct white matter tracts, especially the optic tracts, corpus callosum, and posterior corona radiata. No correlation with AD was found in any white matter tract.ConclusionHighly significant correlations of FA and RD to SSEPs suggest that their latency is representative of widespread microstructural change, and especially demyelination in patients suffering from MS, reaching beyond the classic somatosensory regions. This points to the usefulness of SSEPs as a non-invasive tool in the evaluation of microstructural damage to the brain.
EEG background frequency is associated with discharge outcomes in non-ICU hospitalized patients with COVID-19
Authors: Seibert Kaitlin M. , Lee Wonhee , Eid Alexandra , Espinal Amy E. , Klein Sara A. , Abumurad Sumayyah K. , Tao James X. , Issa Naoum P. 
Published: August 2022

ObjectiveTo assess risk factors for encephalopathy in non-ICU hospitalized patients with COVID-19 and the effect of encephalopathy on short-term outcomes.MethodsWe collected clinical and electrophysiological characteristics of fifty patients with COVID-19 infection admitted to a ward service and who had an electroencephalogram (EEG) performed. Associations with short-term outcomes including hospital length of stay and discharge disposition were determined from univariate and multivariate statistical analysis.ResultsClinical delirium was associated with encephalopathy on EEG, cefepime use was associated with increased length of stay, and of all factors analyzed, background frequency on EEG alone was correlated with discharge disposition.ConclusionEncephalopathy is one of the major determinants of short-term outcomes in hospitalized non-ICU patients with COVID-19.
Modulating the gut microbiota ameliorates spontaneous seizures and cognitive deficits in rats with kainic acid-induced status epilepticus by inhibiting inflammation and oxidative stress
Authors: Wang Xue , Yang Chunyu , Yang Liu , Zhang Yongbo 
Published: August 2022

IntroductionEpilepsy is a highly prevalent neurological disease whose treatment has always been challenging. Hence, it is crucial to explore the molecular mechanisms underlying epilepsy inhibition. Inflammation and oxidative stress are important pathophysiological changes in epilepsy that contribute to the development of spontaneous seizures and cognitive deficits. In recent years, altered gut microbiota composition was found to be involved in epilepsy, but the underlying mechanism remains unclear. Modulation of the gut microbiota showed a positive impact on the brain by regulating oxidative stress and inflammation. Hence, this study evaluated the effect of modulating gut dysbiosis by treating epileptic rats with prebiotics, probiotics, and synbiotics and investigated the underlying molecular mechanism.Materials and methodsEpileptic rat models were established by injecting 1 μl of kainic acid (KA, 0.4 μg/μl) into the right amygdalae. The rats were divided into Sham, KA, KA+prebiotic [inulin:1 g/kg body weight (bw)/day], KA+probiotics (10 × 109cfu of each bacteria/kg, bw/day), and KA+synbiotic groups (1:1 mixture of prebiotics and probiotics). Seizures were monitored, and cognitive function was assessed in all rats. Biochemical indicators, namely, oxidative stress, DNA damage, glutamate levels, and inflammation markers, were also determined.ResultsThe KA-induced status epilepticus (SE) rats exhibited spontaneous seizures and cognitive deficits. This was accompanied by the activation of glial cells, the inflammatory response (IL-1 β, IL-6, and TNF-α), lipid peroxidation (MDA), DNA damage (8-OHdG), the release of glutamate, and a decline in total antioxidant ability (GSH). These changes were alleviated by partial treatment with prebiotics, probiotics, and synbiotics.ConclusionModulating gut dysbiosis ameliorates spontaneous seizures and cognitive deficits in rats with KA-induced status epilepticus. The underlying mechanism may potentially involve the inhibition of inflammation and oxidative stress.
A new selenium source from Se-enriched Cardamine violifolia improves growth performance, anti-oxidative capacity and meat quality in broilers
Authors: Xu Xiao , Wei Yu , Zhang Yue , Jing Xiaoqing , Cong Xin , Gao Qingyu , Cheng Shuiyuan , Zhu Zhenzhou , Zhu Huiling , Zhao Jiangchao , Liu Yulan 
Published: August 2022

BackgroundCardamine violifolia (Cv) is a kind of selenium-enriched plant which contains high levels of organic selenium (Se) such as selenocysteine and methylselenocysteine. This study was conducted to investigate the effects of this new source of Se on the growth performance, anti-oxidative capacity and meat quality in broilers compared with other frequently-used Se sources.MethodsA total of 240 broilers were allotted into 4 treatments: (1) Control group (Se free diets, CON); (2) Sodium selenite group (0.3 mg/kg Se sourced from Na2SeO3 diets, SeNa); (3) Selenium yeast group (0.3 mg/kg Se sourced from Se-Yeast diets, SeY); (4) Plant Se group (0.3 mg/kg Se sourced from Cv diets, SeCv). The whole study lasted 42 days and was divided into 2 stages (1–21 d as earlier stage and 22–42 d as later stage).ResultsThe results showed that the broilers fed SeCv diets had improved average daily gain and the ratio of feed to gain compared to the broilers fed SeNa and SeY diets during the earlier stage. However, there was no significant difference in growth performance of broilers fed these 3 sources of Se diets during the whole period. The broilers fed SeCv diets had improved intestinal mucosal morphology on d 21 and 42. Enhanced liver total anti-oxidative capacity was observed from the broilers fed SeCv diets compared with the other 2 Se sources diets on d 21. Furthermore, lower liver malondialdehyde contents were determined from the broilers fed SeCv and SeY diets compared with SeNa diets. At last, the broilers fed SeCv had increased redness in thigh muscle and decreased cooking loss in both breast and thigh muscle compared with the boilers fed SeNa diets. However, the broilers had similar meat quality between SeCv group and SeY group.ConclusionIn conclusion, these results demonstrated that SeCv was a well-organic Se source for broilers.
GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion
Authors: Shi Xu , Wang Long , Li Yu , Wu Jian , Huang Hong 
Published: August 2022

BackgroundAnalysis of histopathological slices of gastric cancer is the gold standard for diagnosing gastric cancer, while manual identification is time-consuming and highly relies on the experience of pathologists. Artificial intelligence methods, particularly deep learning, can assist pathologists in finding cancerous tissues and realizing automated detection. However, due to the variety of shapes and sizes of gastric cancer lesions, as well as many interfering factors, GCHIs have a high level of complexity and difficulty in accurately finding the lesion region. Traditional deep learning methods cannot effectively extract discriminative features because of their simple decoding method so they cannot detect lesions accurately, and there is less research dedicated to detecting gastric cancer lesions.MethodsWe propose a gastric cancer lesion detection network (GCLDNet). At first, GCLDNet designs a level feature aggregation structure in decoder, which can effectively fuse deep and shallow features of GCHIs. Second, an attention feature fusion module is introduced to accurately locate the lesion area, which merges attention features of different scales and obtains rich discriminative information focusing on lesion. Finally, focal Tversky loss (FTL) is employed as a loss function to depress false-negative predictions and mine difficult samples.ResultsExperimental results on two GCHI datasets of SEED and BOT show that DSCs of the GCLDNet are 0.8265 and 0.8991, ACCs are 0.8827 and 0.8949, JIs are 0.7092 and 0.8182, and PREs are 0.7820 and 0.8763, respectively.ConclusionsExperimental results demonstrate the effectiveness of GCLDNet in the detection of gastric cancer lesions. Compared with other state-of-the-art (SOTA) detection methods, the GCLDNet obtains a more satisfactory performance. This research can provide good auxiliary support for pathologists in clinical diagnosis.
Integration of healthy volunteers in early phase clinical trials with immuno-oncological compounds
Authors: Radanovic Igor , Klarenbeek Naomi , Rissmann Robert , Groeneveld Geert Jan , van Brummelen Emilie M. J. , Moerland Matthijs , Bosch Jacobus J. 
Published: August 2022

AimTraditionally, early phase clinical trials in oncology have been performed in patients based on safety risk-benefit assessment. Therapeutic transition to immuno-oncology may open new opportunities for studies in healthy volunteers, which are conducted faster and are less susceptible to confounders. Aim of this study was to investigate to what extent this approach is utilized and whether pharmacodynamic endpoints are evaluated in these early phase trials. We conducted a comprehensive review of clinical trials with healthy volunteers using immunotherapies potentially relevant for oncology.MethodsLiterature searches according to PRISMA guidelines and after registration in PROSPERO were conducted in PubMed, Embase, Web of Science and Cochrane databases with the cut-off date 20 October 2020, using search terms of relevant targets in immuno-oncology. Articles describing clinical trials with immunotherapeutics in healthy volunteers with a mechanism relevant for oncology were included. “Immunotherapeutic” was defined as compounds exhibiting effects through immunological targets. Data including study design and endpoints were extracted, with specific attention to pharmacodynamic endpoints and safety.ResultsIn total, we found 38 relevant immunotherapeutic compounds tested in HVs, with 86% of studies investigating safety, 82% investigating the pharmacokinetics (PK) and 57% including at least one pharmacodynamic (PD) endpoint. Most of the observed adverse events (AEs) were Grade 1 and 2, consisting mostly of gastrointestinal, cutaneous and flu-like symptoms. Severe AEs were leukopenia, asthenia, syncope, headache, flu-like reaction and liver enzymes increase. PD endpoints investigated comprised of cytokines, immune and inflammatory biomarkers, cell counts, phenotyping circulating immune cells and ex vivo challenge assays.DiscussionHealthy volunteer studies with immuno-oncology compounds have been performed, although not to a large extent. The integration of healthy volunteers in well-designed proof-of-mechanism oriented drug development programs has advantages and could be pursued more in the future, since integrative clinical trial protocols may facilitate early dose selection and prevent cancer patients to be exposed to non-therapeutic dosing regimens.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=210861, identifier CRD42020210861