Dietary folate intake and all-cause mortality and cardiovascular mortality in American adults with non-alcoholic fatty liver disease: Data from NHANES 2003 to 2018.

Background: The relationship between dietary folate intake and prior mortality in adult patients with Non-alcoholic Fatty Liver Disease (NAFLD) has not been clearly studied. We aimed to examine the relationship between dietary folate intake and all-cause and cardiovascular (CVD) mortality in adult NAFLD patients in the US.

Methods: Using data from National Health and Nutrition Examination Survey (NHANES) 2003-2018 and associated mortality data we conducted a cohort study of US adult NAFLD subjects.

Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

Objective: The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations.

Materials And Methods: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset.

Efficacy of Chinese Medicine Treatment Based on Syndrome Differentiation for Primary Insomnia: A Randomized Placebo Controlled Triple-Blinded Trial.

Objective: To assess efficacy of Chinese medicine (CM) on insomnia considering characteristics of treatment based on syndrome differentiation.

Methods: A total of 116 participants aged 18 to 65 years with moderate and severe primary insomnia were randomized to the placebo (n=20) or the CM group (n=96) for a 4-week treatment and a 4-week follow-up. Three CM clinicians independently prescribed treatments for each patient based on syndromes differentiation.

Head-to-head comparison of Ga-FAPI-04 PET/CT and F-FDG PET/CT in the evaluation of primary digestive system cancer: a systematic review and meta-analysis.

Introduction: Althoug F-FDG positron emission tomography/computed tomography (PET/CT) is widely accepted as a diagnostic tool for detecting digestive cancers, Ga-FAPI-04 PET/CT may perform better in detecting gastrointestinal malignancies at an earlier stage. This study aimed to systematically review the diagnostic performance of Ga-FAPI-04 PET/CT compared with that of F-FDG PET/CT in primary digestive system cancers.

Methods: In this study, a comprehensive search using the PubMed, EMBASE, and Web of Science databases was performed to identify studies that met the eligibility criteria from the beginning of the databases to March 2023.

DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning.

Analysis of Chinese Medical Syndrome Features of Ischemic Stroke Based on Similarity of Symptoms Subgroup.

Objective: To derive the Chinese medicine (CM) syndrome classification and subgroup syndrome characteristics of ischemic stroke patients.

Methods: By extracting the CM clinical electronic medical records (EMRs) of 7,170 hospitalized patients with ischemic stroke from 2016 to 2018 at Weifang Hospital of Traditional Chinese Medicine, Shandong Province, China, a patient similarity network (PSN) was constructed based on the symptomatic phenotype of the patients. Thereafter the efficient community detection method BGLL was used to identify subgroups of patients.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g.

Using the Symptom Patient Similarity Network to Explore the Difference between the Chinese and Western Medicine Pathways of Ischemic Stroke and its Comorbidities.

Methods: Individualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed.

Diversity and molecular network patterns of symptom phenotypes.

Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science.

Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model.

Objective: To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes.

Methods: Based on the electronic medical record data of 3413 outpatient visits from 995 type 2 diabetes patients with comorbidities, descriptive statistical methods were used to analyze the basic characteristics of the population, the distribution characteristics of comorbidities, and TCM syndromes. A neural network model for the prediction of type 2 diabetic comorbidities based on TCM syndromes was constructed.

Study on the TCM Syndromes Evolution and Chinese Herbal Characteristics of Type 2 Diabetes Patients with Different Courses of Disease in TCM "Heat Stage": A Real-World Study.

Objective: The purpose of this study is to analyze and summarize the syndrome distribution, syndrome evolution, and Chinese herb medicine characteristics of T2D in heat stage.

Method: In this study, 228 heat-stage T2D patients were divided into three groups based on the course of disease. Group 1 (the course of disease ≤5 years) included 118 patients.

Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions.

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links.

Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books.

This study aims to explore the topological regularities of the character network of ancient traditional Chinese medicine (TCM) book. We applied the 2-gram model to construct language networks from ancient TCM books. Each text of the book was separated into sentences and a TCM book was generated as a directed network, in which nodes represent Chinese characters and links represent the sequential associations between Chinese characters in the sentences (the occurrence of identical sequential associations is considered as the weight of this link).

Disease phenotype synonymous prediction through network representation learning from PubMed database.

Synonym mapping between phenotype concepts from different terminologies is difficult because terminology databases have been developed largely independently. Existing maps of synonymous phenotype concepts from different terminology databases are highly incomplete, and manually mapping is time consuming and laborious. Therefore, building an automatic method for predictive mapping of synonymous phenotypes is of special importance.

Analysis of disease comorbidity patterns in a large-scale China population.

Background: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

Methods: We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China.

Treatment satisfaction in Chinese medicine outpatient care: a comparison of patients' and doctors' views.

Background: Both doctors' and patients' opinions are important in the process of treatment and healthcare of Chinese medicine. This study is to compare patients' and doctors' treatment satisfaction over the course of two visits in a Chinese medicine outpatient setting, and to explain their respective views.

Methods: Patients' chief complaints were collected prior to the outpatient encounter.

Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough.

Pediatric cough is a heterogeneous condition in terms of symptoms and the underlying disease mechanisms. Symptom phenotypes hold complicated interactions between each other to form an intricate network structure. This study aims to investigate whether the network structure of pediatric cough symptoms is associated with the prognosis and outcome of patients.

HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction.

The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease-related (e.

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations.

Heterogeneous network embedding for identifying symptom candidate genes.

Objective: Investigating the molecular mechanisms of symptoms is a vital task in precision medicine to refine disease taxonomy and improve the personalized management of chronic diseases. Although there are abundant experimental studies and computational efforts to obtain the candidate genes of diseases, the identification of symptom genes is rarely addressed. We curated a high-quality benchmark dataset of symptom-gene associations and proposed a heterogeneous network embedding for identifying symptom genes.

Framing Electronic Medical Records as Polylingual Documents in Query Expansion.

We present a study of electronic medical record (EMR) retrieval that emulates situations in which a doctor treats a new patient. Given a query consisting of a new patient's symptoms, the retrieval system returns the set of most relevant records of previously treated patients. However, due to semantic, functional, and treatment synonyms in medical terminology, queries are often incomplete and thus require enhancement.

Heterogeneous network propagation for herb target identification.

Background: Identifying targets of herbs is a primary step for investigating pharmacological mechanisms of herbal drugs in Traditional Chinese medicine (TCM). Experimental targets identification of herbs is a difficult and time-consuming work. Computational method for identifying herb targets is an efficient approach.

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways.

Background And Objective: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment.

Methods: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases.

Multistage analysis method for detection of effective herb prescription from clinical data.

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g.

[Effect of jianpi tongluo jiedu recipe on expression levels of COX-2, NF-kappaBp65, and Bcl-2 in gastric mucosa of patients with precancerous lesions of gastric cancer].

Objective: To explore the effect of Jianpi Tongluo Jiedu Recipe (JTJR) on protein expression levels of COX-2, NF-kappaBp65, Bcl-2, and Bax, mRNA expression levels of COX-2 and Bcl-2, and the apoptotic index (Al) in gastric mucosa of patients with precancerous lesions of gastric cancer (PL-GC).

Methods: Totally 65 PLGC patients were recruited and treated by JTJR (modified by syndrome typing), one dose per day for six successive months. Protein expression levels of COX-2, NF-KBp65, Bcl-2, and Bax were detected in 65 patients using immunohistochemical (IHC) assay before and after treatment.