Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT.

BACKGROUND: Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students. METHODS: A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT. RESULTS: After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research. CONCLUSION: The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.

Bioflavonoid combination attenuates diabetes-induced nephropathy in rats via modulation of MMP-9/TIMP-1, TGF-ß, and GLUT-4-associated pathways.

Background: Diabetic nephropathy represents a significant microvascular complication of diabetes, characterized by extracellular matrix accumulation, loss of cell-cell junctions, microalbuminuria, and diminished creatinine clearance. Despite its prevalence, therapeutic options dedicated to this condition are currently lacking. Natural products like bioflavonoids have garnered attention for their potential therapeutic benefits. The present study aimed to evaluate the efficacy of a bioflavonoid combination, including ginger extract, soy extract, and hesperetin, in a diabetic rat model. Methods: Diabetes was initiated in the rat pups via intraperitoneal injection of streptozotocin on the fifth postnatal day. After six weeks, rats exhibiting blood sugar levels exceeding 160 mg/dL were allocated into diabetic control and treatment groups, with eight animals each. A subset of rats received citrate buffer as a control. The treatment group received the bioflavonoid combination orally for twenty-four weeks. Various parameters, including glycemic levels, urinary parameters, antioxidant status, mRNA expression via Western blot, gel zymography, and immunohistochemistry, were assessed at the study's conclusion. Results: The bioflavonoid combination demonstrated significant reductions in hyperglycemia and various urinary parameters compared to controls. Notably, it modulated MMP-9/TIMP-1 expression, upregulated GLUT-4, and downregulated TGF-ß. Additionally, the combination enhanced total antioxidant capacity, indicating potential antioxidative benefits. Conclusions: This study highlights the therapeutic potential of a bioflavonoid combination (ginger extract, soy extract, and hesperetin) in improving renal function in diabetic nephropathy. By modulating key factors such as MMP-9/TIMP-1, TGF-ß, and GLUT-4, this combination presents a promising avenue for further exploration in managing diabetic nephropathy. These findings underscore the importance of natural products as potential therapeutic agents in addressing diabetic complications.

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality rates. Despite the existence of multiple studies on PPCs, a comprehensive knowledge base that can effectively integrate and visualize the diverse risk factors associated with PPCs is currently lacking. This study aims to develop an online knowledge platform on risk factors for PPCs (Postoperative Pulmonary Complications Risk Factor Knowledge Base, PPCRKB) that categorizes and presents the risk and protective factors associated with PPCs, as well as to facilitate the development of individualized prevention and management strategies for PPCs based on the needs of each investigator. The PPCRKB is a novel knowledge base that encompasses all investigated potential risk factors linked to PPCs, offering users a web-based platform to access these risk factors. The PPCRKB contains 2673 entries, 915 risk factors that have been categorized into 11 distinct groups. These categories include habit and behavior, surgical factors, anesthetic factors, auxiliary examination, environmental factors, clinical status, medicines and treatment, demographic characteristics, psychosocial factors, genetic factors and miscellaneous factors. The PPCRKB holds significant value for PPC research. The inclusion of both quantitative and qualitative data in the PPCRKB enhances the ability to uncover new insights and solutions related to PPCs. It could provide clinicians with a more comprehensive perspective on research related to PPCs in future. Database URL: http://sysbio.org.cn/PPCs.

The global burden of cataracts and its attributable risk factors in 204 countries and territories: a systematic analysis of the global burden of disease study.

Introduction: The global distribution and trends in the attributable burden of cataract risk have rarely been systematically explored. To guide the development of targeted and accurate cataract screening and treatment strategies, we analyzed the burden of cataract disease attributable to known risk factors. Method: This study utilized detailed cataract data from the Global Burden of Disease e 2019, and we analyzed disability-adjusted life years (DALYs) e each risk factor from 1990 to 2019. Additionally, we calculated estimated annual percentage changes (EAPCs) during the study period. Results: The results revealed that from 1990-2019, the global age-standardized DALYs of e attributable to particulate matter pollution, smoking, high fasting glucose plasma and high BMI showed steady downward trends (1990-2009: EAPC = -0.21 [-0.57 -0.14]); 2000-2009: EAPC = -0.95 [-1.01 -0.89]; 2010-2019: EAPC = -1.41 [-1.8 -1.02]). The age-standardized DALYs and mortality caused by each risk factor were highest in the low-middle sociodemographic index (SDI) region (EAPC = -1.77[(-2.19--1.34)]). The overall disease burden of cataracts is lower in males than in females. When analyzing the EAPCs of cataract disease burden for each risk factor individually, we found that the age-standardized disability-adjusted life years caused by particulate matter pollution and smoking decreased (PMP1990-2009: EAPC = -0.53 [-0.9--0.16]; 2000-2009: EAPC = -1.39 [-1.45--1.32]; 2010-2019: EAPC = -2.27 [-2.75--1.79]; smoking 2000 to 2009: EAPC = -1.51 [-1.6--1.43], 2009 to 2019: EAPC = -1.34 [-1.68--1])), while high fasting plasma glucose and high body mass index increased annually (HFPG1990 to 1999: EAPC = 1.27 [0.89-1.65], 2000 to 2009: EAPC = 1.02 [0.82-1.22], 2010-2019: EAPC = 0.44 [0.19-0.68]; HBMI 1990 to 1999: EAPC = 1.65 [1.37-1.94], 2000 to 2009: EAPC = 1.56 [1.43-1.68], 2010-2019: EAPC = 1.47 [1.18-1.77]). Disscussion: The burden of cataracts caused by ambient particulate matter and smoking is increasing in low, low-middle SDI areas, and specific and effective measures are urgently needed. The results of this study suggest that reducing particulate matter pollution, quitting smoking, controlling blood glucose, and lowering BMI could play important roles in reducing the occurrence of cataracts, especially in older people.

Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study.

BACKGROUND: Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients. METHODS AND MATERIAL: The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA ∆vki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁ∆vk(sj)ǁ). Regression and classification analyses were performed on the raw data, MA ∆vki(sj), and ED ǁ∆vk(sj)ǁ. Different machine-learning algorithms were chosen for the STATISTICA and WEKA software. RESULTS: The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients of the RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0%. General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA ∆vki(sj) data. The GDA algorithm's total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA ∆vki(sj) data. CONCLUSIONS: An information fusion perturbation theory and machine learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.

CBD2: A functional biomarker database for colorectal cancer.

The rapidly evolving landscape of biomarkers for colorectal cancer (CRC) necessitates an integrative, updated repository. In response, we constructed the Colorectal Cancer Biomarker Database (CBD), which collected and displayed the curated biomedicine information for 870 CRC biomarkers in the previous study. Building on CBD, we have now developed CBD2, which includes information on 1569 newly reported biomarkers derived from different biological sources (DNA, RNA, protein, and others) and clinical applications (diagnosis, treatment, and prognosis). CBD2 also incorporates information on nonbiomarkers that have been identified as unsuitable for use as biomarkers in CRC. A key new feature of CBD2 is its network analysis function, by which users can investigate the visible and topological network between biomarkers and identify their relevant pathways. CBD2 also allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multidrug, multitarget, multipathway analyses, toward facilitating the design of polypharmacological treatments for CRC. CBD2 is freely available at http://www.eyeseeworld.com/cbd.

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application.

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website. We divided the evaluation indicators into four categories, including (i) medical service quality; (ii) medical service efficiency; (iii) profitability and sustainability; (iv) case grouping ability. Then we carried out descriptive analysis and comprehensive scoring on outcome measurements performance, improvement strategy and specialty performance. At last, the DRGKB finally contains 297 entries. It was found that DRGs generally have a considerable impact on hospital operations, including average length of stay, medical quality and use of medical resources. At the same time, the current DRGs also have many deficiencies, including insufficient reimbursement rates and the ability to classify complex cases. We analyzed these underperforming parts by domain. In conclusion, this research innovatively constructed a knowledgebase to quantify the practice effects of DRGs, analyzed and visualized the development trends and area performance from a comprehensive perspective. This study provides a data-driven research paradigm for following DRGs-related work along with a proposed DRGs evolution model. Availability and implementation: DRGKB is freely available at http://www.sysbio.org.cn/drgkb/. Database URL: http://www.sysbio.org.cn/drgkb/.

Natural products act as game-changer potentially in treatment and management of sepsis-mediated inflammation: A clinical perspective.

BACKGROUND: Sepsis, a life-threatening condition resulting from uncontrolled host responses to infection, poses a global health challenge with limited therapeutic options. Due to high heterogeneity, sepsis lacks specific therapeutic drugs. Additionally, there remains a significant gap in the clinical management of sepsis regarding personalized and precise medicine. PURPOSE: This review critically examines the scientific landscape surrounding natural products in sepsis and sepsis-mediated inflammation, highlighting their clinical potential. METHODS: Following the PRISMA guidelines, we retrieved articles from PubMed to explore potential natural products with therapeutic effects in sepsis-mediated inflammation. RESULTS: 434 relevant in vitro and in vivo studies were identified and screened. Ultimately, 55 studies were obtained as the supporting resources for the present review. We divided the 55 natural products into three categories: those influencing the synthesis of inflammatory factors, those affecting surface receptors and modulatory factors, and those influencing signaling pathways and the inflammatory cascade. CONCLUSION: Natural products' potential as game-changers in sepsis-mediated inflammation management lies in their ability to modulate hallmarks in sepsis, including inflammation, immunity, and coagulopathy, which provides new therapeutic avenues that are readily accessible and capable of undergoing rapid clinical validation and deployment, offering a gift from nature to humanity. Innovative techniques like bioinformatics, metabolomics, and systems biology offer promising solutions to overcome these obstacles and facilitate the development of natural product-based therapeutics, holding promise for personalized and precise sepsis management and improving patient outcomes. However, standardization, bioavailability, and safety challenges arise during experimental validation and clinical trials of natural products.

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.

Pro- and anti-inflammatory cytokines are the game-changers in childhood obesity-associated metabolic disorders (diabetes and non-alcoholic fatty liver diseases).

Childhood obesity is a chronic inflammatory epidemic that affects children worldwide. Obesity affects approximately 1 in 5 children worldwide. Obesity in children can worsen weight gain and raise the risk of obesity-related comorbidities like diabetes and non-alcoholic fatty liver disease (NAFLD). It can also negatively impact the quality of life for these children. Obesity disrupts immune system function, influencing cytokine (interleukins) balance and expression levels, adipokines, and innate and adaptive immune cells. The altered expression of immune system mediators, including interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-17 (IL-17), interleukin-18 (IL-18), transforming growth factor (TGF), tumor necrosis factor (TNF), and others, caused inflammation, progression, and the development of pediatric obesity and linked illnesses such as diabetes and NAFLD. Furthermore, anti-inflammatory cytokines, including interleukin-2 (IL-2), have been shown to have anti-diabetes and IL-1 receptor antagonist (IL-1Ra) anti-diabetic and pro-NAFLFD properties, and interleukin-10 (IL-10) has been shown to have a dual role in managing diabetes and anti-NAFLD. In light of the substantial increase in childhood obesity-associated disorders such as diabetes and NAFLD and the absence of an effective pharmaceutical intervention to inhibit immune modulation factors, it is critical to consider the alteration of immune system components as a preventive and therapeutic approach. Thus, the current review focuses on the most recent information regarding the influence of pro- and anti-inflammatory cytokines (interleukins) and their molecular mechanisms on pediatric obesity-associated disorders (diabetes and NAFLD). Furthermore, we discussed the current therapeutic clinical trials in childhood obesity-associated diseases, diabetes, and NAFLD.

The role of proinflammatory cytokines and CXC chemokines (CXCL1-CXCL16) in the progression of prostate cancer: insights on their therapeutic management.

Reproductive cancers are malignancies that develop in the reproductive organs. One of the leading cancers affecting the male reproductive system on a global scale is prostate cancer (PCa). The negative consequences of PCa metastases endure and are severe, significantly affecting mortality and life quality for those who are affected. The association between inflammation and PCa has captured interest for a while. Inflammatory cells, cytokines, CXC chemokines, signaling pathways, and other elements make up the tumor microenvironment (TME), which is characterized by inflammation. Inflammatory cytokines and CXC chemokines are especially crucial for PCa development and prognosis. Cytokines (interleukins) and CXC chemokines such as IL-1, IL-6, IL-7, IL-17, TGF-ß, TNF-α, CXCL1-CXCL6, and CXCL8-CXCL16 are thought to be responsible for the pleiotropic effects of PCa, which include inflammation, progression, angiogenesis, leukocyte infiltration in advanced PCa, and therapeutic resistance. The inflammatory cytokine and CXC chemokines systems are also promising candidates for PCa suppression and immunotherapy. Therefore, the purpose of this work is to provide insight on how the spectra of inflammatory cytokines and CXC chemokines evolve as PCa develops and spreads. We also discussed recent developments in our awareness of the diverse molecular signaling pathways of these circulating cytokines and CXC chemokines, as well as their associated receptors, which may one day serve as PCa-targeted therapies. Moreover, the current status and potential of theranostic PCa therapies based on cytokines, CXC chemokines, and CXC receptors (CXCRs) are examined.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes.

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions.

Neuroblastoma (NB) is the most frequent solid tumor in pediatric cases, contributing to around 15% of childhood cancer-related deaths. The wide-ranging genetic, morphological, and clinical diversity within NB complicates the success of current treatment methods. Acquiring an in-depth understanding of genetic alterations implicated in the development of NB is essential for creating safer and more efficient therapies for this severe condition. Several molecular signatures are being studied as potential targets for developing new treatments for NB patients. In this article, we have examined the molecular factors and genetic irregularities, including those within insulin gene enhancer binding protein 1 (ISL1), dihydropyrimidinase-like 3 (DPYSL3), receptor tyrosine kinase-like orphan receptor 1 (ROR1) and murine double minute 2-tumor protein 53 (MDM2-P53) that play an essential role in the development of NB. A thorough summary of the molecular targeted treatments currently being studied in pre-clinical and clinical trials has been described. Recent studies of immunotherapeutic agents used in NB are also studied in this article. Moreover, we explore potential future directions to discover new targets and treatments to enhance existing therapies and ultimately improve treatment outcomes and survival rates for NB patients.

A novel deep generative model for mRNA vaccine development: Designing 5' UTRs with N1-methyl-pseudouridine modification.

Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-5' UTRs rather than directly utilizing high-expression endogenous gene 5' UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ-5' UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5' UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5' UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5' UTRs.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Exploring the pathophysiological influence of heme oxygenase-1 on neuroinflammation and depression: A study of phytotherapeutic-based modulation.

BACKGROUND: The heme oxygenase (HO) system plays a significant role in neuroprotection and reduction of neuroinflammation and neurodegeneration. The system, via isoforms HO-1 and HO-2, regulates cellular redox balance. HO-1, an antioxidant defense enzyme, is highlighted due to its association with depression, characterized by heightened neuroinflammation and impaired oxidative stress responses. METHODOLOGY: We observed the pathophysiology of HO-1 and phytochemicals as its modulator. We explored Science Direct, Scopus, and PubMed for a comprehensive literature review. Bibliometric and temporal trend analysis were done using VOSviewer. RESULTS: Several phytochemicals can potentially alleviate neuroinflammation and oxidative stress-induced depressive symptoms. These effects result from inhibiting the MAPK and NK-κB pathways - both implicated in the overproduction of pro-inflammatory factors - and from the upregulation of HO-1 expression mediated by Nrf2. Bibliometric and temporal trend analysis further validates these associations. CONCLUSION: In summary, our findings suggest that antidepressant agents can mitigate neuroinflammation and depressive disorder pathogenesis via the upregulation of HO-1 expression. These agents suppress pro-inflammatory mediators and depressive-like symptoms, demonstrating that HO-1 plays a significant role in the neuroinflammatory process and the development of depression.

Unlocking the Immunomodulatory Potential of Rosmarinic Acid Isolated from Punica granatum L. Using Bioactivity-Guided Approach: In Silico, In Vitro, and In Vivo Approaches.

BACKGROUND: Punica granatum L. is well-known for its multifaceted therapeutic potential, including anti-inflammatory and immunomodulatory activities. AIM: This study aimed to characterize an immunomodulatory compound isolated from Punica granatum L. using a bioactivity-guided approach. METHODS: Chromatographic techniques were adopted for isolation and purification of secondary metabolites. In silico, in vitro, and in vivo methods were performed to characterize the therapeutic potential of the isolated compound. RESULTS: Using preparative thin-layer chromatography, rosmarinic acid was isolated from F4 (column chromatography product obtained from a butanolic fraction of the extract). The impact of rosmarinic acid was assessed in rats using the neutrophil adhesion test, DTH response, and phagocytic index. In immunized rats, rosmarinic acid demonstrated significant immunomodulatory potential. Computational experiments, like molecular docking and molecular dynamics, were also conducted against two targeted receptors, Cereblon (PDB ID: 8AOQ) and human CD22 (PDB ID: 5VKM). Computational studies suggested that an increase in phagocytic index by rosmarinic acid could be attributed to inhibiting Cereblon and CD22. Pharmacokinetics and toxicity prediction also suggested the drug-likeness of rosmarinic acid. CONCLUSION: Rosmarinic acid is a potential candidate, but extensive research needs to be done to translate this molecule from bench to bedside.

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.

Elevated incidence of somatic mutations at prevalent genetic sites.

The common loci represent a distinct set of the human genome sites that harbor genetic variants found in at least 1% of the population. Small somatic mutations occur at the common loci and non-common loci, i.e. csmVariants and ncsmVariants, are presumed with similar probabilities. However, our work revealed that within the coding region, common loci constituted only 1.03% of all loci, yet they accounted for 5.14% of TCGA somatic mutations. Furthermore, the small somatic mutation incidence rate at these common loci was 2.7 times that observed in the non-common. Notably, the csmVariants exhibited an impressive recurrent rate of 36.14%, which was 2.59 times of the ncsmVariants. The C-to-T transition at the CpG sites accounted for 32.41% of the csmVariants, which was 2.93 times for the ncsmVariants. Interestingly, the aging-related mutational signature contributed to 13.87% of the csmVariants, 5.5 times that of ncsmVariants. Moreover, 35.93% of the csmVariants contexts exhibited palindromic features, outperforming ncsmVariant contexts by 1.84 times. Notably, cancer patients with higher csmVariants rates had better progression-free survival. Furthermore, cancer patients with high-frequency csmVariants enriched with mismatch repair deficiency were also associated with better progression-free survival. The accumulation of csmVariants during cancerogenesis is a complex process influenced by various factors. These include the presence of a substantial percentage of palindromic sequences at csmVariants sites, the impact of aging and DNA mismatch repair deficiency. Together, these factors contribute to the higher somatic mutation incidence rates of common loci and the overall accumulation of csmVariants in cancer development.

Warfarin-A natural anticoagulant: A review of research trends for precision medication.

BACKGROUND: Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE: The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS: A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS: Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION: Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.