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

OBJECTIVE: To compare the therapeutic efficacy of portal and tail vein transplantation of bone marrow-derived mesenchymal stem cells (BMSCs) against cholestatic liver fibrosis in mice. METHODS: BMSCs were isolated and co-cultured with starvation-activated hepatic stellate cells (HSCs). HSC activation markers were identified using immunofluorescence and qRT-PCR. BMSCs were injected into the liver tissues of bile duct ligation (BDL) mice via the tail and portal veins. Histomorphology, liver function, inflammatory cytokines, and the expression of key proteins were all determined in the liver tissues. RESULTS: BMSCs inhibited HSC activation by reducing α-SMA and collagen I expression. Compared to tail vein injection, DIL-labeled BMSCs injected through the portal vein maintained a high homing rate in the liver. Moreover, BMSCs transplanted through the portal vein resulted in greater improvement in liver color, hardness, and gallbladder size than did those transplanted through the tail vein. Furthermore, BMSCs injected by portal vein, but not tail vein, markedly ameliorated liver function, reduced the secretion of inflammatory cytokines, including TNF-α, IL-6, and IL-1β, and decreased α-SMA + hepatic stellate cell (HSC) activation and collagen fiber formation. CONCLUSION The therapeutic effect of BMSCs on cholestatic liver fibrosis in mice via portal vein transplantation was superior to that of tail vein transplantation. This comparative study provides reference information for further BMSC studies focused on clinical cholestatic liver diseases.
Animals ; Mice ; Mesenchymal Stem Cell Transplantation ; Liver Cirrhosis/etiology* ; Male ; Cholestasis/therapy* ; Mice, Inbred C57BL ; Hepatic Stellate Cells ; Mesenchymal Stem Cells

Objective To explore the relationship between bedtime procrastination and fear of missing out and the intervention effect of aerobic exercise on bedtime procrastination,so as to provide a theoretical basis and practical reference for remedying bedtime procrastination.Methods Totally 988 students were selected through random sampling and then surveyed with the bedtime procrastination scale and the fear of missing out scale.Correlation and regression analyses were performed to explore the relationship between bedtime procrastination and fear of missing out.A total of 36 students were recruited from the 988 students to participate in the exercise intervention and they were assigned into an exercise group and a control group by the random number table method,with 18 students in each group.The exercise group performed aerobic exercise for 12 weeks,while the control group maintained daily activities.The participants' scores on the bedtime procrastination scale and the fear of missing out scale were recorded before and after the intervention and compared.Results The fear of missing out was positively correlated with bedtime procrastination among college students(r=0.214,P<0.001),and it was an important predictive factor for bedtime procrastination(β=0.241,P<0.001).After the intervention,the scores of bedtime procrastination scale decreased in the exercise group(t=2.277,P=0.036),while there was no significant difference in the scores of the control group before and after intervention(t=-0.787,P=0.442).Conclusions A high level of fear of missing out indicates severe bedtime procrastination.And 12-week exercise intervention could remedy bedtime procrastination.
Humans ; Fear ; Exercise ; Male ; Female ; Procrastination ; Young Adult ; Cross-Sectional Studies ; Students/psychology* ; Adult ; Surveys and Questionnaires ; Adolescent

Objective To establish a ultra-high performance liquid chromatography-tandem mass spectrometer(UPLC-MS/MS)method for determining the concentration of sotagliflozin in rat plasma and apply it to pharmacokinetic studies in rats.Methods Electrospray negative ion multi-reaction ion detection was used.Chromatographic column:EXT-C18(2.1 mm × 100.0 mm,2.7 μm);column temperature:45 ℃;mobile phase:5 mmol·L-1 ammonium acetate aqueous solution-acetonitrile;flow rate:0.35 mL·min-1;ion pairs:sotagliflozin m/z 483.3→315.1,dapagliflozin m/z 467.4→329.2;injection volume:6 μL,plasma samples were processed using methyl tert-butyl ether liquid-liquid extraction.Six male SD rats were administered a single oral dose of sogliflozin at 40 mg·kg-1,and detected the concentration of sogliflozin in plasma.Pharmacokinetic parameters were calculated using Drug And Statistics(DAS)2.1.1.Results Sotagliflozin showed good linearity within the range of 5-2 000 ng·mL-1,with intra-day and inter-day precision both less than 15％.The recovery rate,matrix effect,and stability were all within the specified range.Pharmacokinetic parameters:Cmax was(3 716.67±568.28)ng·mL-1,tmax was(1.00±0.32)h,t1/2 was(2.28±0.45)h,AUC0-t was(1.70 × 104±2 075.87)ng·mL-1·h.Conclusion This study established a method for determining the concentration of sotagliflozin in rat plasma,which is characterized by high sensitivity,rapid detection,and good repeatability.It is suitable for the determination of sotagliflozin concentration in plasma and pharmacokinetic studies.

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.

Objective To establish a ultra-high performance liquid chromatography-tandem mass spectrometer(UPLC-MS/MS)method for determining the concentration of sotagliflozin in rat plasma and apply it to pharmacokinetic studies in rats.Methods Electrospray negative ion multi-reaction ion detection was used.Chromatographic column:EXT-C18(2.1 mm × 100.0 mm,2.7 μm);column temperature:45 ℃;mobile phase:5 mmol·L-1 ammonium acetate aqueous solution-acetonitrile;flow rate:0.35 mL·min-1;ion pairs:sotagliflozin m/z 483.3→315.1,dapagliflozin m/z 467.4→329.2;injection volume:6 μL,plasma samples were processed using methyl tert-butyl ether liquid-liquid extraction.Six male SD rats were administered a single oral dose of sogliflozin at 40 mg·kg-1,and detected the concentration of sogliflozin in plasma.Pharmacokinetic parameters were calculated using Drug And Statistics(DAS)2.1.1.Results Sotagliflozin showed good linearity within the range of 5-2 000 ng·mL-1,with intra-day and inter-day precision both less than 15％.The recovery rate,matrix effect,and stability were all within the specified range.Pharmacokinetic parameters:Cmax was(3 716.67±568.28)ng·mL-1,tmax was(1.00±0.32)h,t1/2 was(2.28±0.45)h,AUC0-t was(1.70 × 104±2 075.87)ng·mL-1·h.Conclusion This study established a method for determining the concentration of sotagliflozin in rat plasma,which is characterized by high sensitivity,rapid detection,and good repeatability.It is suitable for the determination of sotagliflozin concentration in plasma and pharmacokinetic studies.

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.

Objective To evaluate the bioequivalence of oral sidenafil citrate tablets manufactured(100 mg)test preparations and reference preparations in healthy subjects under fasting and fed conditions.Methods Using a single-dose,randomized,open-lable,two-period,two-way crossover design,36 healthy subjects respectively for fasting and fed study were enrolled,and randomized into two groups to receive a single dose of test 100 mg with 7-day washout period.Plasma concentration of sidenafil and N-demethylsildenafil was determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS)method.The pharmacokinetic parameters were calculated by Analyst 1.6.3(AB Scie)using non-compartmental model,and bioequivalence evaluation was performed for the two preparations.Relevant safety evaluations were performed during the trial.Results The main pharmacokinetic parameters of sidenafil after a single oral dose of sidenafil citrate tablets under fasting condition for test and reference were as follows:Cmax were(494.69±230.94)and(558.78±289.83)ng·mL-1,AUC0-t were(1 336.21±509.78)and(1 410.82±625.99)h·ng·mL-1,AUC0-were(1 366.49±512.16)and(1 441.84±628.04)h·ng·mL-1,respectively.The main pharmacokinetic parameters of sidenafil under fed condition for T and R were as follows:Cmax were(381.89±126.53)and(432.47±175.91)ng·mL-1,AUC0-t were(1 366.34±366.99)and(1 412.76±420.37)h·ng·mL-1,AUC0-were(1 403.28±375.32)and(1 454.13±429.87)h·ng·mL-1,respectively.The results demonstrated the bioequivalence of sidenafil citrate tablets between T and R.The incidence of adverse events in fasting and fed tests were 33.33％and 25.00％,respectively.No serious adverse event was reported.Conclusion The test and reference formulation of sidenafil citrate tablets were equivalent and was safe.

Objective To investigate the clinical characteristics of female migraine patients with patent foramen ovale(PFO)and design a risk prediction model for PFO in female migraine patients(migraineur patients PFO risk prediction model,MPRPM).Methods Female migraine patients who visited Zhongshan Hospital,Fudan University from Jun 1,2019 to Dec 31,2022 were included.Preoperative information and follow-up results after discontinuation of medication were collected.Patients were divided into PFO-positive and PFO-negative groups based on transesophageal echocardiography results.A multivariate Logistic regression model and a random forest model were constructed,and the random forest model was validated multidimensionally.Key features were selected based on the mean decrease accuracy(MDA)to construct MPRPM.Results A total of 305 female patients were included in the study,with 204 patients in the PFO-positive group and 101 patients in the PFO-negative group.Multivariate Logistic regression analysis showed that age at migraine onset,attack frequency,severe impact on life during attacks,exercise-related headaches,menstruation-induced headaches,aura migraines,and a history of cryptogenic stroke were predictive factors for PFO positivity.The random forest model effectively predicted the incidence of PFO in female migraine patients,with an AUC of 0.895(95%CI:0.847-0.943).MPRPM demonstrated a sensitivity of 71.6%and specificity of 91.1%(AUC:0.862,95%CI:0.818-0.906,P<0.001).The optimal cut-off value was 2.5 points.Patients correctly classified by the model showed a higher rate of symptom improvement compared to incorrectly classified patients(94.3%vs.82.0%,P=0.023).Conclusion We identified predictive factors for PFO in migraine patients.MPRPM can provide guidance in the diagnostic process and therapeutic decision-making for female migraine patients,assist in patient triage,and reduce the healthcare burden.