Natural products for the treatment of age-related macular degeneration.

BACKGROUND: Age-related macular degeneration (AMD) is a chronic retinal disease that significantly influences the vision of the elderly. PURPOSE: There is no effective treatment and prevention method. The pathogenic process behind AMD is complex, including oxidative stress, inflammation, and neovascularization. It has been demonstrated that several natural products can be used to manage AMD, but systematic summaries are lacking. STUDY DESIGN AND METHODS: PubMed, Web of Science, and ClinicalTrials.gov were searched using the keywords "Biological Products" AND "Macular Degeneration" for studies published within the last decade until May 2023 to summarize the latest findings on the prevention and treatment of age-related macular degeneration through the herbal medicines and functional foods. RESULTS: The eligible studies were screened, and the relevant information about the therapeutic action and mechanism of natural products used to treat AMD was extracted. Our findings demonstrate that natural substances, including retinol, phenols, and other natural products, prevent the development of new blood vessels and protect the retina from oxidative stress in cells and animal models. However, they have barely been examined in clinical studies. CONCLUSION: Natural products could be highly prospective candidate drugs used to treat AMD, and further preclinical and clinical research is required to validate it to control the disease.

RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer.

BACKGROUND: Robot-assisted radical prostatectomy (RARP) has emerged as a pivotal surgical intervention for the treatment of prostate cancer (PCa). However, the complexity of clinical cases, heterogeneity of PCa, and limitations in physician expertise pose challenges to rational decision-making in RARP. To address these challenges, the authors aimed to organize the knowledge of previously complex cohorts and establish an online platform named the RARP knowledge base (RARPKB) to provide reference evidence for personalized treatment plans. MATERIALS AND METHODS: PubMed searches over the past two decades were conducted to identify publications describing RARP. The authors collected, classified, and structured surgical details, patient information, surgical data, and various statistical results from the literature. A knowledge-guided decision-support tool was established using MySQL, DataTable, ECharts, and JavaScript. ChatGPT-4 and two assessment scales were used to validate and compare the platform. RESULTS: The platform comprised 583 studies, 1589 cohorts, 1 911 968 patients, and 11 986 records, resulting in 54 834 data entries. The knowledge-guided decision support tool provide personalized surgical plan recommendations and potential complications on the basis of patients' baseline and surgical information. Compared with ChatGPT-4, RARPKB outperformed in authenticity (100% vs. 73%), matching (100% vs. 53%), personalized recommendations (100% vs. 20%), matching of patients (100% vs. 0%), and personalized recommendations for complications (100% vs. 20%). Postuse, the average System Usability Scale score was 88.88±15.03, and the Net Promoter Score of RARPKB was 85. The knowledge base is available at: http://rarpkb.bioinf.org.cn . CONCLUSIONS: The authors introduced the pioneering RARPKB, the first knowledge base for robot-assisted surgery, with an emphasis on PCa. RARPKB can assist in personalized and complex surgical planning for PCa to improve its efficacy. RARPKB provides a reference for the future applications of artificial intelligence in clinical practice.

Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses.

BACKGROUND: Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). METHODS: We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. RESULTS: ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. CONCLUSIONS: These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

From multi-omics approaches to personalized medicine in myocardial infarction.

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI.

Background: Deep learning methods are increasingly applied in the medical field; however, their lack of interpretability remains a challenge. Captum is a tool that can be used to interpret neural network models by computing feature importance weights. Although Captum is an interpretable model, it is rarely used to study medical problems, and there is a scarcity of data regarding MRI anatomical measurements for patients with prostate cancer after undergoing Robotic-Assisted Radical Prostatectomy (RARP). Consequently, predictive models for continence that use multiple types of anatomical MRI measurements are limited. Methods: We explored the energy efficiency of deep learning models for predicting continence by analyzing MRI measurements. We analyzed and compared various statistical models and provided reference examples for the clinical application of interpretable deep-learning models. Patients who underwent RARP at our institution between July 2019 and December 2020 were included in this study. A series of clinical MRI anatomical measurements from these patients was used to discover continence features, and their impact on continence was primarily evaluated using a series of statistical methods and computational models. Results: Age and six other anatomical measurements were identified as the top seven features of continence by the proposed model UINet7 with an accuracy of 0.97, and the first four of these features were also found by primary statistical analysis. Conclusions: This study fills the gaps in the in-depth investigation of continence features after RARP due to the limitations of clinical data and applicable models. We provide a pioneering example of the application of deep-learning models to clinical problems. The interpretability analysis of deep learning models has the potential for clinical applications.

Association Between Urinary Bisphenols and Body Composition Among American Adults: Cross-Sectional National Health and Nutrition Examination Survey Study.

BACKGROUND: Bisphenol A (BPA), bisphenol S (BPS), and bisphenol F (BPF) are widely used in various consumer products. They are environmental contaminants with estrogenic properties that have been linked to various health outcomes. Understanding their impact on body composition is crucial for identifying potential health risks and developing preventive strategies. However, most current studies have only focused on their relationship with BMI. OBJECTIVE: This study aimed to investigate the association between urinary levels of BPA, BPS, and BPF and body composition, including BMI, lean mass, and fat mass, in a large population-based sample. METHODS: We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey 2003-2016. Body composition data were assessed using dual-energy X-ray absorptiometry, which provided precise measurements of lean mass, fat mass, and other indicators. We used multivariate linear regression models to estimate the associations, adjusting for potential confounders such as age, gender, race, socioeconomic factors, and lifestyle variables. RESULTS: The results revealed significant associations between bisphenol exposure and body composition. After adjusting for covariates, BPS showed a positive association with BMI, with quartiles 3 and 4 having 0.91 (95% CI 0.34-1.48) and 1.15 (95% CI 0.55-1.74) higher BMI, respectively, compared with quartile 1 (P<.001). BPA was negatively associated with total lean mass (TLM) and appendicular lean mass, with quartiles 2, 3, and 4 having -7.85 (95% CI -11.44 to -4.25), -12.33 (95% CI -16.12 to -8.54), and -11.08 (95% CI -15.16 to -7.01) lower TLM, respectively, compared with quartile 1 (P<.001). BPS was negatively associated with TLM, with quartiles 3 (ß=-10.53, 95% CI -16.98 to -4.08) and 4 (ß=-11.14, 95% CI -17.83 to -4.45) having significantly lower TLM (P=.005). Both BPA and BPS showed a positive dose-response relationship with trunk fat (BPA: P=.002; BPS: P<.001) and total fat (BPA: P<.001; BPS: P=.01). No significant association was found between BPF and any body composition parameter. CONCLUSIONS: This large-sample study highlights the associations between urinary levels of BPA and BPS and alterations in body composition, including changes in lean mass, fat mass, and regional fat distribution. These findings underscore the importance of understanding the potential health risks associated with bisphenol exposure and emphasize the need for targeted interventions to mitigate adverse effects on body composition.

The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study.

BACKGROUND: Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. OBJECTIVE: We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. METHODS: The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC. RESULTS: The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. CONCLUSIONS: By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method.

BACKGROUND: Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer. METHODS: A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction. RESULTS: Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers. CONCLUSIONS: A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.