Objective To compare the clinical efficacy and safety differences between the triple immunosuppressive regimen of mizoribine (MZR) or mycophenolate mofetil (MMF) combined with tacrolimus (Tac) and glucocorticoids of kidney transplant recipients in one year after transplantation. Methods A single-center retrospective cohort study design was adopted. The clinical data of 156 patients who underwent the first allogeneic kidney transplantation at the Third Hospital of Peking University from January 2022 to December 2024 were included. The patients were divided into the MZR group (78 cases) and the MMF group (78 cases) based on the initial immunosuppressive regimen after transplantation. The baseline data, medication use in one year after transplantation, blood routine indicators, adverse events and clinical outcomes were compared between the two groups. For the repeated-measurement longitudinal data, repeated-measurement analysis of variance was used to evaluate the main effects of the groups, time and their interaction effects. Results At the primary efficacy endpoint, there were no statistically significant differences in the one-year survival rate, graft survival rate and incidence of acute rejection between the two groups (all P > 0.05). In terms of medication patterns and laboratory test indicators, there were significant time main effects and "time × group" interaction effects for Tac dosage (all P < 0.05). The Tac dosage in the MZR group was lower than that in the MMF group from 2 to 3 months after transplantation, and it was higher in the MZR group than in the MMF group from 7 to 12 months and one year after transplantation. White blood cell count in the MZR group was higher at one month after transplantation, and the platelet count was higher one year after transplantation (all P < 0.05). In terms of infection, the incidence of novel coronavirus and Pneumocystis jirovecii pneumonia infections in the MZR group was lower (all P < 0.05). In terms of metabolic indicators, the post-transplantation uric acid level was better in the MZR group than in the MMF group, but the total cholesterol level was higher one year after transplantation (all P < 0.05). In terms of liver function, there was a brief and mild increase in transaminase in the early stage after transplantation in the MZR group. Conclusions During the one-year follow-up after transplantation, both the MZR and MMF regimens demonstrate comparable immunosuppressive efficacy and overall safety in Chinese kidney transplant recipients. The MZR regimen shows clear advantages in reducing specific infection risks, alleviating bone marrow suppression and improving uric acid metabolism, but its potential impact on blood lipids needs to be monitored. Therefore, the MZR regimen may be an effective and safe alternative immunosuppressive option for kidney transplant recipients, especially those with high infection risk, poor hematological tolerance, or comorbid hyperuricemia.

Quality marker (Q-Marker) is an innovative concept and model for quality control of Traditional Chinese medicines (TCMs), which will navigate the new direction of quality development of TCMs. Yet, how to characterize the overall quality attributes of TCMs and their biological effects is still debating. In view of this key scientific issue, this paper proposes a research method based on mass spectrometry imaging (MSI) technology for the discovery and confirmation of TCMs Q-Marker. MSI is powerful in investigating the spatial distribution of molecules in a variety of samples, and visualizing the information obtained from MS. On this basis, combine with the five principles of TCMs Q-Marker validation, i.e., specificity, transmission and traceability, testability, prescription compatibility, and validity, were applied to confirm the finalized Q-Marker. It will lead the new direction of quality development of TCMs.

With the rapid development of radiotherapy technology, the therapeutic outcomes of tumor patients have improved significantly, enabling effective disease control. However, during radiotherapy, the skin as the first barrier of the human body is inevitably exposed to radiation, leading to superficial skin injury. This injury often manifests as blistering, cracking, bleeding, and ulceration, resulting in wounds that are difficult to heal and potentially affecting the effectiveness of the treatment. At present, the therapeutic effect of drugs on radiation-induced skin injury remains limited, and the development of new drugs depends on the elucidation of the mechanisms. Therefore, it is crucial to investigate the mechanisms of radiation-induced skin injury. This article reviews these mechanisms, including DNA damage, oxidative stress, inflammatory response, and vascular damage and fibrosis, and summarizes the therapeutic drugs and targeted proteins in recent years, aiming to provide a reference for the further development and clinical application of drugs for radiation-induced skin injury.

With the rapid development of radiotherapy technology, the therapeutic outcomes of tumor patients have improved significantly, enabling effective disease control. However, during radiotherapy, the skin as the first barrier of the human body is inevitably exposed to radiation, leading to superficial skin injury. This injury often manifests as blistering, cracking, bleeding, and ulceration, resulting in wounds that are difficult to heal and potentially affecting the effectiveness of the treatment. At present, the therapeutic effect of drugs on radiation-induced skin injury remains limited, and the development of new drugs depends on the elucidation of the mechanisms. Therefore, it is crucial to investigate the mechanisms of radiation-induced skin injury. This article reviews these mechanisms, including DNA damage, oxidative stress, inflammatory response, and vascular damage and fibrosis, and summarizes the therapeutic drugs and targeted proteins in recent years, aiming to provide a reference for the further development and clinical application of drugs for radiation-induced skin injury.

Cognitive impairment is a kind of senile disease that leads to the decline of personality and behavior ability of the elderly,which seriously affects the quality of daily life of patients.The prevalence rate of the disease increases year by year with the acceleration of the aging process of the society,and its incidence is affected by many risk factors.At this stage,the curative effect for middle and advanced patients is poor.So early identification and intervention to delay the progression of cognitive impairment have become the focus of relevant research.Blood pressure variability can lead to damage of target organs such as heart,brain tissue and kidney,which is closely related to cognitive impairment.In order to expand a new perspective of early intervention in cognitive impairment,this paper reviews the effects of blood pressure variability on different cognitive impairment and its possible pathogenic mechanism.

Objective:To investigate the accuracy of resting energy expenditure(REE)prediction formulas and to develop a new REE prediction formula suitable for hospitalized older adults.Methods:Older adults hospitalized in the Department of Geriatrics from October 1, 2022, to November 31, 2022, were included in the study.The predicted values of REE(pREE)were estimated using 13 commonly employed formulas that incorporate parameters related to resting energy expenditure, such as gender, age, body mass index(BMI), and body weight.Indirect calorimetry measurements(mREE)served as the gold standard for comparison.Group differences between pREE and mREE, the coefficient of concordance( ICC), and accuracy(defined as±10% of the mREE values)were utilized to evaluate the performance of the formulas.The ten-fold cross-validation method was employed to identify valid variables and to construct a new prediction formula.The performance of this new formula was compared to mREE, the Harris-Benedict formula, the European Society of Clinical Nutrition and Metabolism(ESPEN)formula, and the Chinese Society of Clinical Nutrition and Metabolism(CSPEN)formula. Results:A total of 223 hospitalized participants aged 60 to 98 years(mean age 79.5±8.2 years)were included in the study.Among these participants, 49.3%(110 cases)were male, and the prevalence of frailty was approximately 84.3%(188 cases).The median difference between pREE and mREE ranged from 9.1 to 232.1 kcal/d.The predictions from the Harris-Benedict, ESPEN, and CSPEN equations differed significantly from mREE(all P<0.05), with respective accuracies of 30.9%, 31.4%, and 24.7%.A new equation was developed: pREE=794.847+ 8.661×body weight -7.976 × age+ 14.757 ×grip strength+ 5.037 × heart rate, with an ICC of 0.6(95% CI: 0.5-0.7), and the accuracy reached 56.3%. Conclusions:Existing equations demonstrate low accuracy in predicting REE among hospitalized older adults.The newly developed equation shows improved performance compared to previous models and can serve as a reference method for predicting REE in this demographic.

Objective:To investigate the accuracy of resting energy expenditure(REE)prediction formulas and to develop a new REE prediction formula suitable for hospitalized older adults.Methods:Older adults hospitalized in the Department of Geriatrics from October 1, 2022, to November 31, 2022, were included in the study.The predicted values of REE(pREE)were estimated using 13 commonly employed formulas that incorporate parameters related to resting energy expenditure, such as gender, age, body mass index(BMI), and body weight.Indirect calorimetry measurements(mREE)served as the gold standard for comparison.Group differences between pREE and mREE, the coefficient of concordance( ICC), and accuracy(defined as±10% of the mREE values)were utilized to evaluate the performance of the formulas.The ten-fold cross-validation method was employed to identify valid variables and to construct a new prediction formula.The performance of this new formula was compared to mREE, the Harris-Benedict formula, the European Society of Clinical Nutrition and Metabolism(ESPEN)formula, and the Chinese Society of Clinical Nutrition and Metabolism(CSPEN)formula. Results:A total of 223 hospitalized participants aged 60 to 98 years(mean age 79.5±8.2 years)were included in the study.Among these participants, 49.3%(110 cases)were male, and the prevalence of frailty was approximately 84.3%(188 cases).The median difference between pREE and mREE ranged from 9.1 to 232.1 kcal/d.The predictions from the Harris-Benedict, ESPEN, and CSPEN equations differed significantly from mREE(all P<0.05), with respective accuracies of 30.9%, 31.4%, and 24.7%.A new equation was developed: pREE=794.847+ 8.661×body weight -7.976 × age+ 14.757 ×grip strength+ 5.037 × heart rate, with an ICC of 0.6(95% CI: 0.5-0.7), and the accuracy reached 56.3%. Conclusions:Existing equations demonstrate low accuracy in predicting REE among hospitalized older adults.The newly developed equation shows improved performance compared to previous models and can serve as a reference method for predicting REE in this demographic.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers

[This corrects the article DOI: 10.1016/j.apsb.2022.09.003.].