Objective:To investigate the relationship between dietary behavior and sarcopenia in older adults aged ≥65 years in longevity areas of China based on latent class analysis.Methods:A total of 4 358 older adults aged ≥65 years were selected from the 2021 Healthy Aging and Biomarkers Cohort Study. The information about their demographic characteristics, lifestyles, and chronic disease histories were collected. A simplified food frequency questionnaire was used to collect information about their dietary intake in the last month. The food intake frequency and food category score were calculated, and the higher the food category score, the richer the dietary intake. Latent class analysis was used to identify the latent classes of the dietary behavior. Sarcopenia was diagnosed using the SARC-CalF. Multivariate logistic regression model was used to analyze the association of food category scores and different latent classes of the dietary behavior with the risk for sarcopenia.Results:In 4 358 older adults, 1 841 (42.24%) had sarcopenia. The frequencies of intakes of cereals and potatoes, vegetable and fruit, meat and bean products were lower in the sarcopenia group than in the non-sarcopenia group. The risk for sarcopenia decreased with the increase of food category score in older adults ( OR=0.850, 95% CI: 0.796-0.907). Latent class analysis identified 4 latent classes of the dietary behavior. Compared with those with class 1 (frequency of intake of all 5 food species was higher probability in T3 group), those with class 2 (frequency of intake of vegetables and fruits and energy-only foods were less likely to be in the T3 group) and class 3 (frequency of intake of all 5 food species was lower probability in T3 group) had significantly increased risk for sarcopenia ( OR=1.377, 95% CI: 1.131-1.676) and ( OR=1.354, 95% CI: 1.091-1.680), 37.7% and 35.4% increased risk for sarcopenia, respectively. Conclusion:Increasing dietary intake category and sufficient intake of various foods for a balanced dietary pattern can reduce the risk of sarcopenia in older adults.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

AIM:To appraise the bioequivalence and safety of the test preparation of ritonavir tab-lets and the reference preparation(trade name:Norvir?)in healthy adult subjects under fasting and postprandial conditions.METHODS:This study was a randomized,open-label,single-dose,four-period,fully repeated crossover design bioequivalence study protocol.Thirty-six healthy male and female volunteers were enrolled in the fasting and post-prandial conditions,and a single dose of the test preparation and reference preparation was orally administered.We used liquid chromatography-tan-dem mass spectrometry(LC-MS/MS)to finish the bioassay of the drug concentration of ritonavir in plasma.Pharmacokinetic parameters were statisti-cally analyzed using PhoenixWinNonlin8.1 software(Pharsight,USA)and a non-compartmental model.RESULTS:Under fasting conditions,the pharmacoki-netic parameters of the test and reference prepara-tions:Cmax(792.010±369.282)ng/mL and(856.939±394.427)ng/mL,AUC0-t(6 463.043±2 876.849)ng·mL-1·h and(6 907.690±3 046.132)ng·mL-1·h,AUC0-∞(6 603.617±2 916.352)ng·mL-1·h and(7 051.614±3 093.047)ng·mL-1·h.Here are the pharmacokinetic parameters for both the test prep-aration and the reference preparation in the post-prandial condition:Cmax(574.380±289.566)ng/mL and(615.796±297.382)ng/mL,AUC0-t(5 084.796±2 435.557)ng·mL-1·h and(5 414.167±2 416.952)ng·mL-1·h,AUC0-∞(5 219.144±2 487.793)ng·mL-1·h and(5 551.060±2 490.604)ng·mL-1·h.The 90%confidence interval of the geometric mean ratio of AUC0-t,AUC0-∞,and Cmax for the test preparation and reference preparation lied in the equivalent range of statistics.CONCLUSION:The tested preparation was bioequivalent to the reference preparation un-der fasting and postprandial conditions.

Objective To study the pharmacokinetic profile of zidovudine and lamivudine tablets(ZL)in Chinese healthy subjects and to evaluate its bioequivalence and safety.Methods A randomized,open,single-dose,two-sequence,four-cycle and fully replicated crossover bioequivalence trial was conducted in 32 healthy subjects both fasting and postprandial,and two preparations of ZL tablets were administered orally in each cycle,with a washout period of 5 days.The concentrations of zidovudine and lamivudine in plasma were determined using high performance liquid chromatography-tandem mass spectrometry.The pharmacokinetic evaluation index parameters were statistically analyzed using Phoenix WinNonlin version 8.1 data statistical software to evaluate bioequivalence.Results The Cmax of zidovudine under fasting and postprandial conditions between ZL and the reference drugs after a single dose were(3 782.499±1 921.649)vs.(3 543.164±1 946.076)ng·mL-1 and(1 585.827±914.246)vs.(1 667.595±862.945)ng·mL-1,respectively.And the AUC0-t for fasting and postprandial conditions of zidovudine was(3 177.091±819.538)vs.(3 071.375±972.145)h·ng·mL-1 and(2 437.999±478.147)vs.(2 402.725±477.792)h·ng·mL-1,respectively;while the AUC0-∞ were(3 225.674±825.131)vs.(3 093.448±972.340)h·ng·mL-1and(2 464.310±480.790)vs.(2 427.693±477.933)h·ng·mL-1,respectively.The Cmax of a single dose of lamivudine under fasting and postprandial conditions between ZL and the reference drugs were(1 923.329±490.572)vs.(1 830.570±476.947)ng·mL-1 and(1 922.711±589.130)vs.(1 881.857±527.577)ng·mL-1,respectively.The AUC0-t for preprandial and postprandial lamivudine was(7 598.265±1 376.774)vs.(7 283.422±1 356.146)h·ng·mL-1 and(7 554.169±958.379)vs.(7 329.376±924.075)h·ng·mL-1,respectively,whereas the AUC0-∞ were(7 734.038±1 326.907)vs.(7 405.088±1 340.036)h·ng·mL-1 and(7 660.916±958.694)vs.(7 435.102±930.448)h·ng·mL-1,in fasting and fed tests,the 90％confidence intervals(CI)of the geometric mean ratios of the main pharmacokinetic parameters between test and reference preparations were all within the range of 80％-125％,respectively.A total of 37 adverse events occurred during the trial period,including 21 in the fasting group and 16 in the postprandial group,and no serious adverse events occurred.Conclusion The test formulations of zidovudine and lamivudine tablets were bioequivalent and well tolerated in healthy Chinese subjects under fasting and fed conditions compared to the reference tablets.

AIM:To appraise the bioequivalence and safety of the test preparation of ritonavir tab-lets and the reference preparation(trade name:Norvir?)in healthy adult subjects under fasting and postprandial conditions.METHODS:This study was a randomized,open-label,single-dose,four-period,fully repeated crossover design bioequivalence study protocol.Thirty-six healthy male and female volunteers were enrolled in the fasting and post-prandial conditions,and a single dose of the test preparation and reference preparation was orally administered.We used liquid chromatography-tan-dem mass spectrometry(LC-MS/MS)to finish the bioassay of the drug concentration of ritonavir in plasma.Pharmacokinetic parameters were statisti-cally analyzed using PhoenixWinNonlin8.1 software(Pharsight,USA)and a non-compartmental model.RESULTS:Under fasting conditions,the pharmacoki-netic parameters of the test and reference prepara-tions:Cmax(792.010±369.282)ng/mL and(856.939±394.427)ng/mL,AUC0-t(6 463.043±2 876.849)ng·mL-1·h and(6 907.690±3 046.132)ng·mL-1·h,AUC0-∞(6 603.617±2 916.352)ng·mL-1·h and(7 051.614±3 093.047)ng·mL-1·h.Here are the pharmacokinetic parameters for both the test prep-aration and the reference preparation in the post-prandial condition:Cmax(574.380±289.566)ng/mL and(615.796±297.382)ng/mL,AUC0-t(5 084.796±2 435.557)ng·mL-1·h and(5 414.167±2 416.952)ng·mL-1·h,AUC0-∞(5 219.144±2 487.793)ng·mL-1·h and(5 551.060±2 490.604)ng·mL-1·h.The 90%confidence interval of the geometric mean ratio of AUC0-t,AUC0-∞,and Cmax for the test preparation and reference preparation lied in the equivalent range of statistics.CONCLUSION:The tested preparation was bioequivalent to the reference preparation un-der fasting and postprandial conditions.

Objective:To investigate the relationship between dietary behavior and sarcopenia in older adults aged ≥65 years in longevity areas of China based on latent class analysis.Methods:A total of 4 358 older adults aged ≥65 years were selected from the 2021 Healthy Aging and Biomarkers Cohort Study. The information about their demographic characteristics, lifestyles, and chronic disease histories were collected. A simplified food frequency questionnaire was used to collect information about their dietary intake in the last month. The food intake frequency and food category score were calculated, and the higher the food category score, the richer the dietary intake. Latent class analysis was used to identify the latent classes of the dietary behavior. Sarcopenia was diagnosed using the SARC-CalF. Multivariate logistic regression model was used to analyze the association of food category scores and different latent classes of the dietary behavior with the risk for sarcopenia.Results:In 4 358 older adults, 1 841 (42.24%) had sarcopenia. The frequencies of intakes of cereals and potatoes, vegetable and fruit, meat and bean products were lower in the sarcopenia group than in the non-sarcopenia group. The risk for sarcopenia decreased with the increase of food category score in older adults ( OR=0.850, 95% CI: 0.796-0.907). Latent class analysis identified 4 latent classes of the dietary behavior. Compared with those with class 1 (frequency of intake of all 5 food species was higher probability in T3 group), those with class 2 (frequency of intake of vegetables and fruits and energy-only foods were less likely to be in the T3 group) and class 3 (frequency of intake of all 5 food species was lower probability in T3 group) had significantly increased risk for sarcopenia ( OR=1.377, 95% CI: 1.131-1.676) and ( OR=1.354, 95% CI: 1.091-1.680), 37.7% and 35.4% increased risk for sarcopenia, respectively. Conclusion:Increasing dietary intake category and sufficient intake of various foods for a balanced dietary pattern can reduce the risk of sarcopenia in older adults.

Objective:To document any effect of combining transcranial magnetic stimulation (rTMS) with peripheral magnetic stimulation (rPMS) to relieve shoulder pain in hemiplegic stroke survivors.Methods:Sixty hemiplegic stroke survivors with shoulder pain were randomly divided into a Central Group ( n=20), a Peripheral Group ( n=20), and a Combined Group ( n=20). In addition to routine pharmacological intervention and rehabilitation training, the Central Group received 10Hz rTMS over the M1 area of the affected hemisphere, the Peripheral Group received 20Hz rPMS at Erb′s point of the affected limb, while the Combined Group received rTMS followed by rPMS. Before as well as after 2 and 4 weeks of treatment, all were assessed using a numerical pain intensity rating scale (NRS), for passive range of motion (PROM) of the shoulder joint, and using the Fugl-Meyer upper extremity assessment (FMA). Results:Before the treatment there were no significant differences among the 3 groups. After 2 weeks there was significant improvement in the average shoulder PROM and FMA scores, and a significant decrease in the average NRS ratings. After 4 weeks there was continued significant improvement. After 2 and 4 weeks, the Combined Group gave significantly lower NRS ratings than the others and had greater average shoulder PROM improvement. The average FMA score of the Combined Group (36.10±13.32) after two weeks was significantly better than those of the Central and Peripheral groups. However, no significant differences were found between the Central and Peripheral Groups in the other measurements.Conclusions:Both rTMS and rPMS can relieve shoulder pain and promote the recovery of motor function, with superior synergistic effects observed in their combined application.