Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

The aryl hydrocarbon receptor (AhR) plays a crucial role in regulating many physiological processes. Activating the AhR-CYP1A1 axis has emerged as a novel therapeutic strategy against various inflammatory diseases. Here, a practical in situ cell-based fluorometric assay was constructed to screen AhR-CYP1A1 axis modulators, via functional sensing of CYP1A1 activities in live cells. Firstly, a cell-permeable, isoform-specific enzyme-activable fluorogenic substrate for CYP1A1 was rationally constructed for in-situ visualizing the dynamic changes of CYP1A1 function in living systems, which was subsequently used for discovering the efficacious modulators of the AhR-CYP1A1 axis. Following screening of a compound library, LAC-7 was identified as an efficacious activator of the AhR-CYP1A1 axis, which dose-dependently up-regulated the expression levels of both CYP1A1 and AhR in multiple cell lines. LAC-7 also suppressed macrophage M1 polarization and reduced the levels of inflammatory factors in LPS-induced bone marrow-derived macrophages. Animal tests showed that LAC-7 could significantly mitigate DSS-induced ulcerative colitis and LPS-induced acute lung injury in mice, and markedly reduced the levels of multiple inflammatory factors. Collectively, an optimized fluorometric cell-based assay was devised for in situ functional imaging of CYP1A1 activities in living systems, which strongly facilitated the discovery of efficacious modulators of the AhR-CYP1A1 axis as novel anti-inflammatory agents.

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

Objective:To investigate the risk factors and their diagnostic efficacy of perioperative lower limb deep vein thrombosis (DVT) in polytrauma patients with predominant severe limb trauma.Methods:A retrospective cohort study was conducted to analyze the clinical data of 155 polytrauma patients with predominant severe trauma who were admitted to Wuxi Ninth People′s Hospital from January 2021 to December 2024, including 64 males and 91 females, aged 13-95 years [(52.1±16.9)years]. Abbreviated injury scale (AIS) was 5-15 points [(7.4±2.1)points] and injury severity score (ISS) was 17-59 points [(21.3±6.5)points]. Based on the occurrence of DVT in the perioperative period, the patients were divided into preoperative DVT group with 17 patients (11.0%) and non-preoperative DVT group with 138 patients (89.0%) as well as postoperative DVT group with 24 patients (15.5%) and non-postoperative DVT group with 131 patients (84.5%). Basic clinical data were collected, including gender, age, body mass index (BMI), underlying diseases (hypertension, diabetes mellitus), hemoglobin level (Hb), platelet count (PLT), D-dimer, ISS, trauma site [cranial and brain trauma, thoracic and abdominal trauma, upper limb trauma, lower limb trauma (femoral fracture, patellar fracture, tibial or fibular fracture, foot fracture, vascular injury), and pelvic fracture], preoperative waiting time for surgery, surgical site (pelvis and lower limb, other areas), surgical protocols (pelvic and lower limb internal fixation, external fixation of lower limb, lower limb amputation), operation duration less or more than 2 hours, amount of intraoperative blood loss, intraoperative blood transfusion requirement, venous thromboembolism (VTE) prophylaxis (pharmacological and mechanical modalities) and length of hospital stay. Univariate analysis and multivariate binary Logistic regression analysis were conducted to investigate the correlation between the aforementioned indicators and incidence of perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma and determine the independent risk factors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) of the relevant risk factors were analyzed to evaluate and compare the diagnostic efficacy of the risk factors for perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma.Results:Univariate analysis results showed that age, history of hypertension, D-dimer, thoracic and abdominal trauma, pelvic fracture, preoperative waiting time for surgery, and length of hospital stay were significantly correlated with preoperative of DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), pelvic fracture ( OR=5.03, 95% CI 1.09, 23.20, P<0.05), preoperative waiting time for surgery ( OR=1.10, 95% CI 1.00, 1.22, P<0.05) and length of hospital stay ( OR=0.89,95% CI 0.81,0.98, P<0.05) were highly correlated with preoperative DVT of the lower limbs in the patients ( P<0.05). Univariate analysis results showed that age, D-dimer, ISS, foot fracture, and length of hospital stay were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.01, 1.08, P<0.01), D-dimer ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), ISS ( OR=1.09, 95% CI 1.01, 1.17, P<0.05), and foot fracture ( OR=3.51 , 95% CI 1.25 , 9.87 , P<0.05) were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of the ROC curve analysis indicated that preoperative waiting time for surgery (AUC=0.83, 95% CI 0.75, 0.91) had the highest diagnostic efficacy for preoperative DVT of the lower limbs in the patients, with the diagnostic efficacies of pelvic fracture (AUC=0.75, 95% CI 0.65, 0.85) and age (AUC=0.70, 95% CI 0.59, 0.82) decreasing successively. For postoperative DVT of the lower limbs in the patients, D-dimer (AUC=0.71, 95% CI 0.61, 0.81) exhibited the highest diagnostic efficacy, followed by age (AUC=0.70, 95% CI 0.59, 0.81), ISS (AUC=0.64, 95% CI 0.51, 0.76) and foot fracture (AUC=0.62, 95% CI 0.49, 0.74), with diagnostic efficacy decreased successively. Conclusions:For polytrauma patients with predominant severe limb trauma, age, pelvic fracture and preoperative waiting time for surgery are independent risk factors for preoperative DVT, while age, D-dimer, ISS and foot fracture are independent risk factors for postoperative DVT. Additionally, preoperative waiting time for surgery has the best diagnostic efficacy for preoperative DVT, followed by pelvic fracture and age. D-dimer has the best diagnostic efficacy for postoperative DVT, followed by age, ISS and foot fracture.

Objective:To construct a predictive model for postoperative delirium (POD) in patients undergoing gastrointestinal surgery using machine learning.Methods:This retrospective study used clinical data from patients who underwent gastrointestinal surgery at Subei People′s Hospital between September 2022 and April 2024. The entire dataset was randomly divided into the training and validation sets in an 8∶2 ratio. Multivariate logistic regression analysis was conducted to identify the factors influencing POD. Eleven machine learning models were established and compared. The performance of the models was validated using metrics, including accuracy, precision, recall, Youden′s index, F1 score, Matthews′ correlation coefficient, Kappa coefficient, log loss, and Brier score. Receiver operating characteristic and calibration curves were plotted to assess the discrimination and consistency of the model. Shapley additive explanations were used in Python for interpretative analysis of the model with the best predictive performance, and the importance of the feature parameters was ranked.Results:A total of 1, 785 patients were ultimately included, of which 833 (46.67%) experienced POD. The results of multivariate logistic regression analysis revealed that advanced age, lower preoperative serum calcium ion concentration, postoperative pulmonary infection, and higher preoperative systolic blood pressure were independent risk factors for POD in patients undergoing gastrointestinal surgery, while laparoscopic surgery was a protective factor ( P<0.05). Among the 11 machine learning models, the categorical feature gradient boosting model exhibited the best performance, with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval 0.77-0.87). The ranking of feature importance indicated that age had the greatest contribution in predicting POD. Conclusions:The predictive model for POD established based on the categorical boosting algorithm has higher predictive efficacy and clinical application value in patients undergoing gastrointestinal surgery.

Good endometrial receptivity is an essential factor for embryo implantation,and gene expression in endometrial tissue during the window of implantation(WOI)is closely related to receptivity.Transcriptome sequencing technology enables the identification of gene expression profiles of endometrium during different menstrual phases,as well as microRNAs and long-chain non-coding RNA sequences involved in regulating gene expression.Combining this technology with bioinformatics analysis provides a better understanding of specific gene expression during the receptive period and offers technical support for studying its regulatory mechanism.Moreover,gene expression profiles of the endometrium during different menstrual phases hold significant clinical application value for accurately assessing endometrium receptivity in infertility patients and those with repeated implantation failure,thereby guiding individualized embryo transfer strategies.This review summarizes the progress of transcriptome sequencing in evaluating human endometrial receptivity and discusses future research directions.This review aims to understand the complex molecular mechanisms of endometrial receptivity formation and regulation from the transcriptional level,in order to improve the implantation rate of embryos in assisted reproductive technology and reduce the abortion rate.

Transient perivascular inflammation of the carotid artery(TIPIC)syndrome is a relatively rare disease,and ultrasound is the first screening method for initial diagnosis of the disease.Contrast-enhanced ultrasound(CEUS)has unique advantages in the follow-up of patients with TIPIC syndrome.This paper reports a patient with TIPIC syndrome who was treated with acute left neck pain.The inflammation was significantly re-lieved and subsided after treatment with non-steroidal anti-inflammatory drugs.The ultrasound changes of carotid artery lesions in this patient during follow-up were analyzed,and the application value of CEUS in the follow-up diagnosis of this disease was summarized,in the hope of providing clinical reference.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.