[Element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine].

This study aims to construct the element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine, providing basic technical support for the formation and transformation of the evidence chain of Chinese patent medicine and providing collection, induction, and summary schemes for massive and disorganized clinical data. Based on the elements of evidence-based PICOS, the conventional construction methods of knowledge graph were collected and summarized. Firstly, the data entities related to Chinese patent medicine were classified, and entity linking was performed(disambiguation). Secondly, the study associated and classified the attribute information of the data entity. Finally, the logical relationship between entities was constructed, and then the element relationship and extension path of the knowledge map conforming to the characteristics of clinical evidence of Chinese patent medicine were summarized. The construction of the clinical evidence knowledge map of Chinese patent medicine was mainly based on process design and logical structure, and the element relationship of the knowledge map was expressed according to the PICOS principle and evidence level. The extension path crossed three levels(model layer, data layer application, and new evidence application), and the study gradually explored the path from disease, core evaluation indicators, Chinese patent medicine, core prescriptions, syndrome and treatment rules, and medical case comparison(evolution law) to new drug research and development. In this study, the top-level design of the construction of the clinical evidence knowledge map of Chinese patent medicine has been clarified, but it still needs the joint efforts of interdisciplinary disciplines. With the continuous improvement of the map construction technology in line with the characteristics of TCM, the study can provide necessary basic technical support and reference for the development of the TCM discipline.

Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis.

Understanding how outdoor environments affect mental health outcomes is vital in today's fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between the outdoor environment and human perception. In a systematic review, we investigate how deep learning techniques can shed light on a better understanding of the influence of outdoor environments on human perceptions and emotions, with an emphasis on mental health outcomes. We have systematically reviewed 40 articles published in SCOPUS and the Web of Science databases which were the published papers between 2016 and 2023. The study presents and utilizes a novel topic modeling method to identify coherent keywords. By extracting the top words of each research topic, and identifying the current topics, we indicate that current studies are classified into three areas. The first topic was "Urban Perception and Environmental Factors" where the studies aimed to evaluate perceptions and mental health outcomes. Within this topic, the studies were divided based on human emotions, mood, stress, and urban features impacts. The second topic was titled "Data Analysis and Urban Imagery in Modeling" which focused on refining deep learning techniques, data collection methods, and participants' variability to understand human perceptions more accurately. The last topic was named "Greenery and visual exposure in urban spaces" which focused on the impact of the amount and the exposure of green features on mental health and perceptions. Upon reviewing the papers, this study provides a guide for subsequent research to enhance the view of using deep learning techniques to understand how urban environments influence mental health. It also provides various suggestions that should be taken into account when planning outdoor spaces.

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

[Data mining in traditional Chinese medicine product quality review].

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.

The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms.

BACKGROUND: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer. OBJECTIVE: This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care. METHODS: Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients' perceptions of these modalities. RESULTS: Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100% cancer types), anxiety and depression (9/10, 90%), and poor sleep (9/10, 90%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients' refusal of conventional treatment or delays in diagnosis and treatment. Doctors' ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation. CONCLUSIONS: Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients' perceptions of TCIM. Such information will help clarify patients' needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients.

Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis.

Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.

Data mining in traditional Chinese medicine product quality review / 中国中药杂志

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.

Integrated Solid-Phase Extraction, Ultra-High-Performance Liquid Chromatography-Quadrupole-Orbitrap High-Resolution Mass Spectrometry, and Multidimensional Data-Mining Techniques to Unravel the Metabolic Network of Dehydrocostus Lactone in Rats.

Dehydrocostus lactone (DL) is among the representative ingredients of traditional Chinese medicine (TCM), with excellent anticancer, antibacterial, and anti-inflammatory activities. In this study, an advanced strategy based on ultra-high-performance liquid chromatography-quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was integrated to comprehensively explore the metabolic fate of DL in rats. First, prior to data collection, all biological samples (plasma, urine, and feces) were concentrated and purified using solid-phase extraction (SPE) pre-treatment technology. Then, during data collection, in the full-scan (FS) data-dependent acquisition mode, FS-ddMS2 was intelligently combined with FS-parent ion list (PIL)-dynamic exclusion (DE) means for targeted monitoring and deeper capture of more low-abundance ions of interest. After data acquisition, data-mining techniques such as high-resolution extracted ion chromatograms (HREICs), multiple mass defect filters (MMDFs), diagnostic product ions (DPIs), and neutral loss fragments (NLFs) were incorporated to extensively screen and profile all the metabolites in multiple dimensions. As a result, a total of 71 metabolites of DL (parent drug included) were positively or tentatively identified. The results suggested that DL in vivo mainly underwent hydration, hydroxylation, dihydrodiolation, sulfonation, methylation, dehydrogenation, dehydration, N-acetylcysteine conjugation, cysteine conjugation, glutathione conjugation, glycine conjugation, taurine conjugation, etc. With these inferences, we successfully mapped the "stepwise radiation" metabolic network of DL in rats, where several drug metabolism clusters (DMCs) were discovered. In conclusion, not only did we provide a refined strategy for inhibiting matrix effects and fully screening major-to-trace metabolites, but also give substantial data reference for mechanism investigation, in vivo distribution visualization, and safety evaluation of DL.

HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats.

In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.

Trends in inflammatory bowel disease treatment in the past two decades-a high-level text mining analysis of PubMed publications.

AIM: Many therapeutic options for inflammatory bowel disease (IBD) emerged during the last 2 decades, along with the rise in disease prevalence and incidence. We aimed at assessing the published literature on different treatment options in that period. Special attention was attributed to specific medication mechanisms and geographic diversity. MATERIALS AND METHODS: We have queried PubMed for all available IBD-related entries published during 2000-2020. The following data were extracted for each entry: PubMed unique article ID (PMID), title, publishing journal, abstract text, keywords (if any), and authors' affiliations. Two gastrointestinal specialists decided in consensus on a list of terms to classify entries. The terms belonged to five treatment groups: medical, surgical, dietary, microbiome manipulation, and complementary medicine. The medical and complementary medicine groups were further sub-classified. Annual trends of publications for the years 2000-2020 were plotted for different treatment types. The slopes of publication trends were calculated by fitting regression lines to the annual number of publications. RESULTS: Overall, 77,505 IBD entries were published between 2000 and 2020. Medical treatment showed the highest number of total publications 21,540/77,505 (27.8%), followed by surgical 7605/77,505 (9.8%), microbiome research 5260/77,505 (6.8%), dietary 4819/77,505 (6.2%), and complementary medicine treatment 762/77,505 (1.0%). Interestingly, since 2012 there is a steep rise in microbiome publications that outnumbered surgery in the last 2 years. Trend analysis of medical treatment showed that biologics had the steepest slope (57.5, p < 0.001), followed by immunomodulators (4.9, p < 0.001), small molecules (3.9, p < 0.001), and 5-ASA (3.8, p < 0.001). CONCLUSION: According to our high-level publications trend analysis, the past 2 decades certainly deserve the reference as the "biologic era", as publications regarding biological therapy outnumbered all other treatment options. Interestingly, though very popular among patients, complementary medicine was not studied with correlation to its' acceptance among patients.

Current Status of Mining, Modification, and Application of Cellulases in Bioactive Substance Extraction.

Cellulases have been used to extract bioactive ingredients from medical plants; however, the poor enzymatic properties of current cellulases significantly limit their application. Two strategies are expected to address this concern: (1) new cellulase gene mining strategies have been promoted, optimized, and integrated, thanks to the improvement of gene sequencing, genomic data, and algorithm optimization, and (2) known cellulases are being modified, thanks to the development of protein engineering, crystal structure data, and computing power. Here, we focus on mining strategies and provide a systemic overview of two approaches based on sequencing and function. Strategies based on protein structure modification, such as introducing disulfide bonds, proline, salt bridges, N-glycosylation modification, and truncation of loop structures, have already been summarized. This review discusses four aspects of cellulase-assisted extraction. Initially, cellulase alone was used to extract bioactive substances, and later, mixed enzyme systems were developed. Physical methods such as ultrasound, microwave, and high hydrostatic pressure have assisted in improving extraction efficiency. Cellulase changes the structure of biomolecules during the extraction process to convert them into effective ingredients with better activity and bioavailability. The combination of cellulase with other enzymes and physical technologies is a promising strategy for future extraction applications.

Herb-drug interactions and toxicity: Underscoring potential mechanisms and forecasting clinically relevant interactions induced by common phytoconstituents via data mining and computational approaches.

Herbals in the form of medicine are employed extensively around the world. Herbal and conventional medicine combination is a potentially dangerous practice mainly in comorbid, hepato insufficient and frail patients leading to perilous herb-drug interactions (HDI) and toxicity. This study features potential HDI of 15 globally famous plant species through data mining and computational methods. Several plant species were found to mimic warfarin. Phytochemicals from M. charantia induced hypoglycemica. M. chamomila and G. biloba possessed anticoagulant activities. S. hispanica reduces postprandial glycemia. R. officinalis has been reported to inhibit the efflux of anticancer substrates while A. sativum can boost the clearance of anticancer agents. P. ginseng can alter blood coagulation. A cross link of the biological and in silico data revealed that a plethora of herbal metabolites such as ursolic and rosmarinic acid among others are possible/probable inhibitors of specific CYP450 enzymes. Consequently, plant species/metabolites with a given pharmacological property/metabolizing enzyme should not be mixed with drugs having the same pharmacological property/metabolizing enzyme. Even if combined with drugs, herbal medicines must be used at low doses for a short period of time and under the supervision of a healthcare professional to avoid potential adverse and toxic effects.

Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease.

This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

Mining of high throughput screening database reveals AP-1 and autophagy pathways as potential targets for COVID-19 therapeutics.

The recent global pandemic of the Coronavirus disease 2019 (COVID-19) caused by the new coronavirus SARS-CoV-2 presents an urgent need for the development of new therapeutic candidates. Many efforts have been devoted to screening existing drug libraries with the hope to repurpose approved drugs as potential treatments for COVID-19. However, the antiviral mechanisms of action of the drugs found active in these phenotypic screens remain largely unknown. In an effort to deconvolute the viral targets in pursuit of more effective anti-COVID-19 drug development, we mined our in-house database of approved drug screens against 994 assays and compared their activity profiles with the drug activity profile in a cytopathic effect (CPE) assay of SARS-CoV-2. We found that the autophagy and AP-1 signaling pathway activity profiles are significantly correlated with the anti-SARS-CoV-2 activity profile. In addition, a class of neurology/psychiatry drugs was found to be significantly enriched with anti-SARS-CoV-2 activity. Taken together, these results provide new insights into SARS-CoV-2 infection and potential targets for COVID-19 therapeutics, which can be further validated by in vivo animal studies and human clinical trials.

A neurodynamic optimization approach to supervised feature selection via fractional programming.

Feature selection is an important issue in machine learning and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some global feature selection methods based on unsupervised redundancy minimization can potentiate clustering performance improvements, their efficacy for classification may be limited. In this paper, a neurodynamics-based holistic feature selection approach is proposed via feature redundancy minimization and relevance maximization. An information-theoretic similarity coefficient matrix is defined based on multi-information and entropy to measure feature redundancy with respect to class labels. Supervised feature selection is formulated as a fractional programming problem based on the similarity coefficients. A neurodynamic approach based on two one-layer recurrent neural networks is developed for solving the formulated feature selection problem. Experimental results with eight benchmark datasets are discussed to demonstrate the global convergence of the neural networks and superiority of the proposed neurodynamic approach to several existing feature selection methods in terms of classification accuracy, precision, recall, and F-measure.

The effectiveness of acupoint herbal patching for functional constipation: Protocol for a meta-analysis and data mining.

BACKGROUND: Functional constipation is a common functional problem of the digestive system that has a negative impact on physical, mental health of patients and quality of life. At present, acupoint herbal patching as an adjuvant therapy is currently undergoing clinical trials in different medical centers. However, no relevant systematic review or meta-analysis has been designed to evaluate the effects of acupoint herbal patching on functional constipation. There is also a lack of systematic evaluation and analysis of acupoints and herbs. METHODS: We will search the following 8 databases from their inception to November 15, 2020, without language restrictions: the Cochrane Central Register of Controlled Trials, PubMed, Embase, the Web of Science, the Chinese Biomedical Literature Database, the Chinese Scientific Journal Database, the Wan-Fang Database and the China National Knowledge Infrastructure. The primary outcome measures will be clinical effective rate, functional outcomes, and quality of life. Data that meets the inclusion criteria will be extracted and analyzed using RevMan V.5.3 software. Two reviewers will evaluate the studies using the Cochrane Collaboration risk of bias tool. We will use the GRADE approach to assess the overall quality of evidence supporting the primary outcomes. We will also use Spass software (Version19.0) for complex network analysis to explore the potential core prescription of acupoint herbal patching for functional constipation. RESULTS: This study will analyze the clinical effective rate, functional outcomes, quality of life, improvement of clinical symptoms of functional constipation, and effective prescriptions of acupoint herbal patching for patients with functional constipation. CONCLUSION: Our findings will provide evidence for the effectiveness and potential treatment prescriptions of acupoint herbal patching for patients with functional constipation. PROSPERO REGISTRATION NUMBER: PROSPERO CRD 42020193489.

Lignocellulosic fibres surface interactions in enzymatic reaction using data-mining.

Lignocellulosic fibres modification focused so far essentially on the resulting material properties to create functional fibres instead of determining the reaction influencing parameters. Using a data-mining algorithm, surface chemical composition of the fibres after modifications was compared to multiple signals. A 24 h reaction at either 25 °C or 60 °C, pH5 was conducted in presence of trans-ferulic acid, laccase, and lignocellulosic fibres (flax, hemp, or cellulose) having different chemical surface composition. Dimers and trimers were detected in variable concentrations in the reaction filtrate and extractive. At 25 °C, crystalline cellulose, amorphous cellulose, xylans, mannans, and lignins were well correlated to specific reaction products while at 60 °C, only lignins and xylan were found correlated to reaction products. Fibres surface composition affected the extractive profile. Lignocellulosic surface composition influence on the product formed was unveiled using a data mining approach. This study presents a way to unveil non-evident chemical interface interaction in reactions.

An automated algorithm using free-text clinical notes to improve identification of transgender people.

Accurate identification of transgender persons is a critical first step in conducting transgender health studies. To develop an automated algorithm for identifying transgender individuals from electronic medical records (EMR) using free-text clinical notes. The development and validation of the algorithm was based on data from an integrated healthcare system that served as a participating site in the multicenter Study of Transition Outcomes and Gender. The training and test datasets each contained a total of 300 individuals identified between 2006 and 2014. Both datasets underwent a full medical record review by experienced research abstractors. The validated algorithm was then implemented to identify transgender individuals in the EMR using all clinical notes of patients that received care between January 1, 2015 and June 30, 2018. Validation of the algorithm against the full chart review demonstrated a high degree of accuracy with 97% sensitivity, 95% specificity, 94% positive predictive value, and 97% negative predictive value. The algorithm classified 7,409 individuals (3.5%) as "Definitely transgender" and 679 individuals (0.3%) as "Probably transgender" out of 212,138 candidates with a total of 378,641 clinical notes. The computerized NLP algorithm can support essential efforts to improve the health of transgender people.

Gene Mining and Sequence Analysis of Purine Nucleosidase Based on RNA-Seq.

Caterpillar fungus (Ophiocordyceps sinensis) is one of the most valued fungal Traditional Chinese medicine (TCM), and it contains plenty of active ingredients such as adenosine. Adenosine is considered as a biologically effective ingredient that has a variety of anti-tumor and immunomodulatory activities. In order to further elucidate the mechanism of purine nucleosidase (PN) in adenosine biosynthesis, a gene encoding PN was successfully mined and further analyzed based on the RNA-Seq database of caterpillar fungus. The full-length cDNA of PN was 855 bp, which encoded 284 amino acids. BLAST analysis showed the highest homology of 85.06% with nucleoside hydrolase in NCBI. ProtProm analysis showed that the relative molecular weight was 30.69 kDa and the isoelectric point was 11.55. The secondary structure of PN was predicted by Predict Protein; the results showed that alpha helix structure accounted for 28.17%, strand structure accounted for 11.97%, and loop structure accounted for 59.86%. Moreover, PN gene was further cloned from transcriptome and detected by agarose gel electrophoresis for verification. This study provides more sufficient scientific basis and new ideas for the genetic regulation of adenosine biosynthesis in fungal TCM.

LRRC19-A Bridge between Selenium Adjuvant Therapy and Renal Clear Cell Carcinoma: A Study Based on Datamining.

Kidney renal clear cell carcinoma (KIRC) is the most common and fatal subtype of renal cancer. Antagonistic associations between selenium and cancer have been reported in previous studies. Selenium compounds, as anti-cancer agents, have been reported and approved for clinical trials. The main active form of selenium in selenoproteins is selenocysteine (Sec). The process of Sec biosynthesis and incorporation into selenoproteins plays a significant role in biological processes, including anti-carcinogenesis. However, a comprehensive selenoprotein mRNA analysis in KIRC remains absent. In the present study, we examined all 25 selenoproteins and identified key selenoproteins, glutathione peroxidase 3 (GPX3) and type 1 iodothyronine deiodinase (DIO1), with the associated prognostic biomarker leucine-rich repeat containing 19 (LRRC19) in clear cell renal cell carcinoma cases from The Cancer Genome Atlas (TCGA) database. We performed validations for the key gene expression levels by two individual clear cell renal cell carcinoma cohorts, GSE781 and GSE6344, datasets from the Gene Expression Omnibus (GEO) database. Multivariate survival analysis demonstrated that low expression of LRRC19 was an independent risk factor for OS. Gene set enrichment analysis (GSEA) identified tyrosine metabolism, metabolic pathways, peroxisome, and fatty acid degradation as differentially enriched with the high LRRC19 expression in KIRC cases, which are involved in selenium therapy of clear cell renal cell carcinoma. In conclusion, low expression of LRRC19 was identified as an independent risk factor, which will advance our understanding concerning the selenium adjuvant therapy of clear cell renal cell carcinoma.