Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex molecular profile of DNA repair genes in epithelial ovarian carcinoma patients with different sensitivity to platinum-based therapy

Epithelial ovarian carcinoma (EOC) is known for high mortality due to diagnosis at advanced stages and frequent therapy resistance. Previous findings suggested that the DNA repair system is involved in the therapeutic response of cancer patients and DNA repair genes are promising targets for novel therapies. This study aimed to address complex inter-relations among gene expression levels, methylation profiles, and somatic mutations in DNA repair genes and EOC prognosis and therapy resistance status. We found significant associations of DUT expression with the presence of peritoneal metastases in EOC patients. The high-grade serous EOC subtype was enriched with TP53 mutations compared to other subtypes. Furthermore, somatic mutations in XPC and PRKDC were significantly associated with worse overall survival of EOC patients, and higher FAAP20 expression in platinum-resistant than platinum-sensitive patients was observed. We found higher methylation of RAD50 in platinum-resistant than in platinum-sensitive patients. Somatic mutations in BRCA1 and RAD9A were significantly associated with higher RBBP8 methylation in platinum-sensitive compared to platinum-resistant EOC patients. In conclusion, we discovered associations of several candidate genes from the DNA repair pathway with the prognosis and platinum resistance status of EOC patients, which deserve further validation as potential predictive biomarkers.
Viral infectious diseases severity: co-presence of transcriptionally active microbes (TAMs) can play an integral role for disease severity

Humans have been challenged by infectious diseases for all of their recorded history, and are continually being affected even today. Next-generation sequencing (NGS) has enabled identification of, i) culture independent microbes, ii) emerging disease-causing pathogens, and iii) understanding of the genome architecture. This, in turn, has highlighted that pathogen/s are not a monolith, and thereby allowing for the differentiation of the wide-ranging disease symptoms, albeit infected by a primary pathogen. The conventional ‘one disease - one pathogen’ paradigm has been positively revisited by considering limited yet important evidence of the co-presence of multiple transcriptionally active microbes (TAMs), potential pathogens, in various infectious diseases, including the COVID-19 pandemic. The ubiquitous microbiota presence inside humans gives reason to hypothesize that the microbiome, especially TAMs, contributes to disease etiology. Herein, we discuss current evidence and inferences on the co-infecting microbes particularly in the diseases caused by the RNA viruses - Influenza, Dengue, and the SARS-CoV-2. We have highlighted that the specific alterations in the microbial taxonomic abundances (dysbiosis) is functionally connected to the exposure of primary infecting pathogen/s. The microbial presence is intertwined with the differential host immune response modulating differential disease trajectories. The microbiota-host interactions have been shown to modulate the host immune responses to Influenza and SARS-CoV-2 infection, wherein the active commensal microbes are involved in the generation of virus-specific CD4 and CD8 T-cells following the influenza virus infection. Furthermore, COVID-19 dysbiosis causes an increase in inflammatory cytokines such as IL-6, TNF-α, and IL-1β, which might be one of the important predisposing factors for severe infection. Through this article, we aim to provide a comprehensive view of functional microbiomes that can have a significant regulatory impact on predicting disease severity (mild, moderate and severe), as well as clinical outcome (survival and mortality). This can offer fresh perspectives on the novel microbial biomarkers for stratifying patients for severe disease symptoms, disease prevention and augmenting treatment regimens.
New insights on the expression patterns of specific Arabinogalactan proteins in reproductive tissues of Arabidopsis thaliana

Arabinogalactan proteins (AGPs) are hydroxyproline-rich glycoproteins containing a high proportion of carbohydrates, widely distributed in the plant kingdom and ubiquitously present in land plants. AGPs have long been suggested to play important roles in plant reproduction and there is already evidence that specific glycoproteins are essential for male and female gametophyte development, pollen tube growth and guidance, and successful fertilization. However, the functions of many of these proteins have yet to be uncovered, mainly due to the difficulty to study individual AGPs. In this work, we generated molecular tools to analyze the expression patterns of a subgroup of individual AGPs in different Arabidopsis tissues, focusing on reproductive processes. This study focused on six AGPs: four classical AGPs (AGP7, AGP25, AGP26, AGP27), one AG peptide (AGP24) and one chimeric AGP (AGP31). These AGPs were first selected based on their predicted expression patterns along the reproductive tissues from available RNA-seq data. Promoter analysis using β-glucuronidase fusions and qPCR in different Arabidopsis tissues allowed to confirm these predictions. AGP7 was mainly expressed in female reproductive tissues, more precisely in the style, funiculus, and integuments near the micropyle region. AGP25 was found to be expressed in the style, septum and ovules with higher expression in the chalaza and funiculus tissues. AGP26 was present in the ovules and pistil valves. AGP27 was expressed in the transmitting tissue, septum and funiculus during seed development. AGP24 was expressed in pollen grains, in mature embryo sacs, with highest expression at the chalazal pole and in the micropyle. AGP31 was expressed in the mature embryo sac with highest expression at the chalaza and, occasionally, in the micropyle. For all these AGPs a co-expression analysis was performed providing new hints on its possible functions. This work confirmed the detection in Arabidopsis male and female tissues of six AGPs never studied before regarding the reproductive process. These results provide novel evidence on the possible involvement of specific AGPs in plant reproduction, as strong candidates to participate in pollen-pistil interactions in an active way, which is significant for this field of study.
Characteristics and patterns of marine debris in the Chinese beach-sea continuum

The amounts of marine debris and plastic pollution have been growing dramatically in the environment. Floating marine debris can be transported between the sea and coastal zone by various processes. However, it remains unknown if marine debris in the sea and coastal zone are quantitatively related. This study collected time-series data on marine debris (including plastics) from China’s beaches and adjoining seas over a 10-year period (2012–2021). It aims to investigate (1) the characteristics of debris distribution in the beach-sea continuum, (2) the relationships among the density and accumulation density of marine debris on beaches, sea surfaces and seafloors. Our results show that the density of marine debris and plastics on beaches and seafloors in the pandemic period were 3–5 folds of those in the pre-pandemic period. The density (104682.4 ± 27793.0 items km–2) and accumulation density (1787.4 ± 220.0 kg km–2) of marine debris on beaches were significantly higher than those on both sea surfaces (3320.8 ± 446.0 items km–2 and 21.6 ± 5.7 kg km–2) and seafloors (2453.7 ± 868.4 items km–2 and 117.5 ± 70.4 kg km–2). The density of plastics on beaches (85373.0 ± 21633.8 items km–2) was significantly higher than that on sea surfaces (2915.4 ± 365.8 items km–2) and seafloors (2254.2 ± 717.1 items km–2). The density of small-to-medium (0–10cm) floating debris (3419.5 ± 418.2 items km–2) was more than 100 times higher than the density of large (> 10cm) floating debris (29.6 ± 3.1 items km–2). The proportion of plastics in marine debris on sea surfaces (83.3 ± 1.3%) was significantly higher than that on beaches (74.1 ± 2.5%) but not significantly different from that on seafloors (81.1 ± 3.0%). There are significant relationships between the density of debris on seafloors and that on beaches, between the density or accumulation density of debris on seafloors and that on sea surfaces. This study suggests that the density and accumulation density of marine debris on seafloors were closely associated with those on sea surfaces and beaches. The results can help to estimate the transportation, deposition and aggregation of marine debris from beaches and sea surfaces to seafloors.
The role of retinal Müller cells in diabetic retinopathy and related therapeutic advances

Diabetic retinopathy (DR) is a significant complication of diabetes. During the pathogenesis of retinal microangiopathy and neuronopathy, activated retinal Müller cells (RMCs) undergo morphological and structural changes such as increased expression of glial fibrillary acidic protein, disturbance of potassium and water transport regulation, and onset of production of a large number of inflammatory and vascular growth factors as well as chemokines. Evidently, activated RMCs are necessary for the pathogenesis of DR; therefore, exploring the role of RMCs in DR may provide a new target for the treatment thereof. This article reviews the mechanism of RMCs involvement in DR and the progress in related treatments.
Construction of a diagnostic signature and immune landscape of pulmonary arterial hypertension

Background Molecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective. Methods Two independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools. Results The WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells. Conclusion Our study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.
Von Willebrand factor in diagnostics and treatment of cardiovascular disease: Recent advances and prospects

Von Willebrand factor (VWF) is a large multimeric glycoprotein involved in hemostasis. It is essential for platelet adhesion to the subendothelium of the damaged endothelial layer at high shear rates. Such shear rates occur in small-diameter arteries, especially at stenotic sites. Moreover, VWF carries coagulation factor VIII and protects it from proteolysis in the bloodstream. Deficiency or dysfunction of VWF predisposes to bleeding. In contrast, an increase in the concentration of high molecular weight multimers (HMWM) of VWF is closely associated with arterial thrombotic events. Severe aortic stenosis (AS) or hypertrophic obstructive cardiomyopathy (HOCM) can deplete HMWM of VWF and lead to cryptogenic, gastrointestinal, subcutaneous, and mucosal bleeding. Considering that VWF facilitates primary hemostasis and a local inflammatory response at high shear rates, its dysfunction may contribute to the development of coronary artery disease (CAD) and its complications. However, current diagnostic methods do not allow for an in-depth analysis of this contribution. The development of novel diagnostic techniques, primarily microfluidic, is underway. Such methods can provide physiologically relevant assessments of VWF function at high shear rates; however, they have not been introduced into clinical practice. The development and use of agents targeting VWF interaction with the vessel wall and/or platelets may be reasonable in prevention of CAD and its complications, given the prominent role of VWF in arterial thrombosis.
Gut-microbiome-based predictive model for ST-elevation myocardial infarction in young male patients

Background ST-segment elevation myocardial infarction (STEMI) in young male patients accounts for a significant proportion of total heart attack events. Therefore, clinical awareness and screening for acute myocardial infarction (AMI) in asymptomatic patients at a young age is required. The gut microbiome is potentially involved in the pathogenesis of STEMI. The aim of the current study is to develop an early risk prediction model based on the gut microbiome and clinical parameters for this population. Methods A total of 81 young males (age < 44 years) were enrolled in this study. Forty-one young males with STEMI were included in the case group, and the control group included 40 young non-coronary artery disease (CAD) males. To identify the differences in gut microbiome markers between these two groups, 16S rRNA-based gut microbiome sequencing was performed using the Illumina MiSeq platform. Further, a nomogram and corresponding web page were constructed. The diagnostic efficacy and practicability of the model were analyzed using K-fold cross-validation, calibration curves, and decision curve analysis (DCA). Results Compared to the control group, a significant decrease in tendency regarding α and β diversity was observed in patients in the case group and identified as a significantly altered gut microbiome represented by Streptococcus and Prevotella. Regarding clinical parameters, compared to the control group, the patients in the case group had a higher body mass index (BMI), systolic blood pressure (SBP), triglyceride (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and low blood urea nitrogen (BUN). Additionally, BMI and SBP were significantly (p<0.05) positively correlated with Streptococcus and [Ruminococcus]. Further, BMI and SBP were significantly (p<0.05) negatively correlated with Prevotella and Megasphaera. A significant negative correlation was only observed between Prevotella and AST (p < 0.05). Finally, an early predictive nomogram and corresponding web page were constructed based on the gut microbiome and clinical parameters with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.877 and a C-index of 0.911. For the internal validation, the stratified K-fold cross-validation (K = 3) was as follows: AUC value of 0.934. The calibration curves of the model showed good consistency between the actual and predicted probabilities. The DCA results showed that the model had a high net clinical benefit for use in the clinical setting. Conclusion In this study, we combined the gut microbiome and common clinical parameters to construct a prediction model. Our analysis shows that the constructed model is a non-invasive tool with potential clinical application in predicting STEMI in the young males.
A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.
An NLP tool for data extraction from electronic health records: COVID-19 mortalities and comorbidities

Background The high infection rate, severe symptoms, and evolving aspects of the COVID-19 pandemic provide challenges for a variety of medical systems around the world. Automatic information retrieval from unstructured text is greatly aided by Natural Language Processing (NLP), the primary approach taken in this field. This study addresses COVID-19 mortality data from the intensive care unit (ICU) in Kuwait during the first 18 months of the pandemic. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. In addition, comorbid conditions or concurrent diseases were retrieved and analyzed in relation to a variety of causes of mortality. Method An NLP system using the Python programming language is constructed to automate the process of extracting primary and secondary causes of death, as well as comorbidities. The system is capable of handling inaccurate and messy data, this includes inadequate formats, spelling mistakes and mispositioned information. A machine learning decision trees method is used to classify the causes of death. Results For 54.8% of the 1691 ICU patients we studied, septic shock or sepsis-related multiorgan failure was the leading cause of mortality. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whether caused by ARDS or other causes. Conclusion We created an NLP system to automate the extraction of causes of death and comorbidities from EHRs. Our method processes messy and erroneous data and classifies the primary and intermediate causes of death of COVID-19 patients. We advocate arranging the EHR with well-defined sections and menu-driven options to reduce incorrect forms.
Stereotypes in the German Physics Olympiad - Hurdle or no Harm at all?

The German Physics Olympiad is a science competition in which students can compete to measure their Physics knowledge and skills with other students. Female participants are underrepresented and typically drop out of the competition earlier than their male counterparts. As the cause for this underrepresentation, social identity threat theory identifies a threat to women’s gender identity in the predominantly male environment. Stereotype threat theory adds negative stereotypes about women’s abilities in physics as a heightening factor. In this study, growth mindset and values affirmation interventions, as well as a combination of both methods, were integrated into a weekend seminar of Physics content to protect female participants from the harmful influences of stereotype and social identity threat. As female and male students’ sense of belonging and gender identification remained at equal levels, respectively, after the interventions, the results did not show any effects of stereotype threat or social identity threat for the female students. The results suggest that women who are highly interested and talented in physics and have taken first steps to pursue physics and to engage with the physics community beyond mandatory school education are not as susceptible to stereotypes and harmful cues in the environment as might previously have been assumed. Implications for future research and science competitions are discussed.
Extracellular vesicle DNA from human melanoma tissues contains cancer-specific mutations

Liquid biopsies are promising tools for early diagnosis and residual disease monitoring in patients with cancer, and circulating tumor DNA isolated from plasma has been extensively studied as it has been shown to contain tumor-specific mutations. Extracellular vesicles (EVs) present in tumor tissues carry tumor-derived molecules such as proteins and nucleic acids, and thus EVs can potentially represent a source of cancer-specific DNA. Here we identified the presence of tumor-specific DNA mutations in EVs isolated from six human melanoma metastatic tissues and compared the results with tumor tissue DNA and plasma DNA. Tumor tissue EVs were isolated using enzymatic treatment followed by ultracentrifugation and iodixanol density cushion isolation. A panel of 34 melanoma-related genes was investigated using ultra-sensitive sequencing (SiMSen-seq). We detected mutations in six genes in the EVs (BRAF, NRAS, CDKN2A, STK19, PPP6C, and RAC), and at least one mutation was detected in all melanoma EV samples. Interestingly, the mutant allele frequency was higher in DNA isolated from tumor-derived EVs compared to total DNA extracted directly from plasma DNA, supporting the potential role of tumor EVs as future biomarkers in melanoma.
Evaluating the impact of employees’ perception of identity threat on knowledge sharing behavior during COVID-19: The mediation and moderation effect of social capital and reward system

Quarantine policies introduced in the context of COVID-19 are affecting business operations and slowing down the flow rate of the overall economy. Different degrees and types of threats occur in both the living environment and the working environment during the epidemic prevention, which causes many additional uncertainties. The impact on employees is the identity threat from environment and organizations. This is different from the related research on the identification and impact of the threat before the occurrence of COVID-19. However, in the post-pandemic period, companies continue to strengthen important factors that can increase innovation and recovery, including the role of employee knowledge sharing. The organizational inequity and lack of organizational justice bring about the threat of internal identification in organizations. In order to ensure their own interests in organizations, employees may think twice when sharing knowledge. Therefore, this study explores the relationship among employees’ identity threat, social capital and knowledge sharing behavior from the perspective of organizational behavior. In this study, a sample of high-tech employees was conducted, and a total of 434 questionnaires were obtained. The research results show that employees’ perception of identity threat has a negative impact on knowledge sharing behavior and positively affects social capital; employees’ social capital positively affects their knowledge sharing behaviors; and organizational reward system moderates the relationship between identity threat and knowledge sharing behavior. Based on the comprehensive research findings, this research proposes corresponding theoretical and practical implications.