BACKGROUND:Studies have reported that alendronate intake significantly increases bone mineral density in patients with osteoporosis. OBJECTIVE:To analyze and compare the changes in metabolites before and after alendronate intervention in ovariectomized rats by chromatography-mass spectrometry,and to further explore the specific mechanism and target of alendronate in the treatment of osteoporosis. METHODS:A total of 36 female Sprague-Dawley rats were randomly divided into model group,alendronate sodium group and sham operation group.The osteoporosis model was established by ovariectomy in the first two groups.Four weeks after modeling,the rats in the alendronate group were intragastrically given alendronate sodium,while those in the sham operation group and model group were given equal volume of normal saline.After 12 weeks of continuous gavage,the metabolites of the lumbar spine were analyzed by chromatography-mass spectrometry,and the common differential metabolites were obtained,which were analyzed by bioinformatics such as Kyoto Gene and Genome Encyclopedia pathway. RESULTS AND CONCLUSION:Totally 17 different metabolites were obtained in the three groups.The enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes showed that alendronate sodium could regulate unsaturated fatty acid biosynthesis,linoleic acid metabolism and other pathways to protect ovariectomized rats.These results indicate that alendronate sodium may exert its anti-osteoporosis effect by interfering with unsaturated fatty acid bioanabolism and linoleic acid metabolism,so as to achieve the purpose of preventing osteoporosis

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

Houpo Sanwutang， included in the Catalogue of Ancient Classical Prescriptions （Second Batch）， was first recorded in the Synopsis of Golden Chamber written by ZHANG Zhongjing from the Eastern Han dynasty and was modified by successive generations of medical experts. A total of 37 pieces of effective data involving 37 ancient Chinese medical books were retrieved from different databases. Through literature mining， statistical analysis， and data processing， combined with modern articles， this study employed bibliometrics to investigate the historical origin， composition， decoction methods， clinical application， and other key information. The results showed that the medicinal origin of Houpo Sanwutang was clearly documented in classic books. Based on the conversion of the measurements from the Han Dynasty， it is recommended that 110.4 g Magnolia Officinalis Cortex， 55.2 g Rhei Radix et Rhizoma， and 72 g Aurantii Fructus Immaturus should be taken. Magnolia Officinalis Cortex and Aurantii Fructus Immaturus should be decocted with 2 400 mL water first， and 1 000 mL should be taken from the decocted liquid. Following this， Rhei Radix et Rhizoma should be added for further decoction， and then 600 mL should be taken from the decocted liquid. A single dose of administration is 200 mL， and the medication can be stopped when patients restore smooth bowel movement. Houpo Sanwutang has the effect of moving Qi， relieving stuffiness and fullness， removing food stagnation， and regulating bowels. It can be used in treating abdominal distending pain， guarding， constipation， and other diseases with the pathogenesis of stagnated heat and stagnated Qi in the stomach. The above results provide reference for the future development and research of Houpo Sanwutang.

OBJECTIVE To analyze the influencing factors of voriconazole trough concentration and adverse drug reactions （ADR） in renal transplant recipients. METHODS Data from inpatients who received voriconazole and therapeutic drug monitoring in our hospital between January 2022 and August 2023 were retrospectively analyzed. Patients were divided into renal transplant group and non-renal transplant group based on transplantation status. A 1∶1 propensity score matching （PSM） method was used to balance differences in baselines between the two groups. Voriconazole trough concentrations， target attainment rate， clinical efficacy， and ADR were compared between the two groups. Multiple linear regression （backward） was used to analyze the factors influencing voriconazole trough concentrations in the renal transplant group. Univariate analysis and binary Logistic regression were used to identify independent risk factors for ADR in the renal transplant group. RESULTS After PSM， 48 patients were included in each group. There were no statistically significant differences in the mean voriconazole trough concentration， target attainment rate or efficacy rate between the two groups （P＞0.05）. The total incidence of ADR was significantly higher in the renal transplant group than in the non-renal transplant group （P＜0.05）. Multiple linear regression analysis showed that age， average daily dose， pulmonary infection， total bilirubin during medication， day-1 loading dose， use of the original drug， concomitant immunosuppressant use， and the occurrence of ADR were factors influencing voriconazole trough concentration in renal transplant patients （P＜0.05）. Binary Logistic regression analysis showed that abnormal direct bilirubin during medication [OR＝7.747， 95%CI （1.334， 45.005）， P＝0.023] was an independent risk factor for ADR in renal transplant patients receiving voriconazole. CONCLUSIONS Age， average daily dose， pulmonary infection， use of the original drug， day-1 loading dose， total bilirubin during medication， concomitant immunosuppressant use， and the occurrence of ADR are the factors influencing voriconazole trough concentration in renal transplant patients. Furthermore， patients with abnormal direct bilirubin during medication are more susceptible to ADR.

OBJECTIVE To construct ensemble learning models for predicting high-use days of budesonide based on meteorological factors， thereby providing reference for hospital pharmacy management. METHODS Meteorological data for 2024 and outpatient budesonide usage data from the jurisdiction of Sanming Hospital of Integrated Traditional Chinese and Western Medicine were collected. High-use days were defined as the 75th percentile of outpatient budesonide usage， and a corresponding dataset was established. The prediction task was formulated as a classification problem， and three ensemble learning models were developed： Random Forest， Extreme Gradient Boosting （XGBoost）， and Histogram-based Gradient Boosting Classifier. Model performance was evaluated using accuracy， precision， recall， F1-score， and log-loss. Model interpretability was analyzed using Shapley Additive Explanations （SHAP）. RESULTS The Histogram-based Gradient Boosting Classifier achieved the best performance （accuracy＝0.75， F1-score＝0.48）， followed by XGBoost （accuracy＝0.74， F1-score＝0.43） and Random Forest （accuracy＝0.72， F1-score＝0.22）. SHAP results suggested that the prediction results of the last two models have the highest correction. CONCLUSIONS Ensemble learning models can effectively predict high-use days of budesonide， with the Histogram- based Gradient Boosting Classifier demonstrating the best predictive performance. Low temperature， high humidity， and low atmospheric pressure show significant positive impacts on the prediction of daily budesonide usage.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

BACKGROUND: Doxorubicin hydrochloride (DOX) is extensively used in the treatment of various tumors. However, its clinical application is limited due to dose-dependent cardiotoxicity. Currently, few effective strategies exist to mitigate or eliminate DOX-induced cardiomyopathy (DIC). Although ferroptosis is implicated in DIC and its inhibition partially alleviates the condition, the direct targets of DOX in the progression of cardiotoxicity remain unclear. This study aimed to discover the direct targets of DOX in ferroptosis-mediated DIC. METHODS: A DOX pulldown assay was performed to identify proteins specifically binding to DOX in murine hearts, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify candidate proteins. A cardiac injury mouse model was established by DOX treatment. Based on this, multiple ferroptosis biomarkers were detected by flow cytometry, quantitative real-time polymerase chain reaction, western blotting, immunochemistry, etc. Besides, specific activator and inhibitor of signaling pathways were applied to illuminate molecular mechanisms. RESULTS: Glutathione S-transferase P1 (GSTP1) was identified as a DOX target. GSTP1 activity was inhibited in DOX-treated cardiomyocytes, while its overexpression significantly alleviated DIC. Moreover, GSTP1 overexpression inhibited acyl-CoA synthetase long-chain family member 4 (ACSL4)-dependent ferroptosis. Mechanistically, GSTP1 overexpression suppressed c-Jun N-terminal kinase (JNK) phosphorylation, thereby reducing reactive oxygen species (ROS) production and inhibiting ferroptosis in DIC. CONCLUSIONS This study identifies the DOX/GSTP1/JNK axis as a critical pathway mediating ACSL4-dependent ferroptosis in DIC. GSTP1 is highlighted as a potential key mediator of ferroptosis and a promising therapeutic target for DIC.

Retrieving keyframes most relevant to text from small intestine videos with given labels can efficiently and accurately locate pathological regions. However, training directly on raw video data is extremely slow, while learning visual representations from image-text datasets leads to computational inconsistency. To tackle this challenge, a small bowel video keyframe retrieval based on multi-modal contrastive learning (KRCL) is proposed. This framework fully utilizes textual information from video category labels to learn video features closely related to text, while modeling temporal information within a pretrained image-text model. It transfers knowledge learned from image-text multimodal models to the video domain, enabling interaction among medical videos, images, and text data. Experimental results on the hyper-spectral and Kvasir dataset for gastrointestinal disease detection (Hyper-Kvasir) and the Microsoft Research video-to-text (MSR-VTT) retrieval dataset demonstrate the effectiveness and robustness of KRCL, with the proposed method achieving state-of-the-art performance across nearly all evaluation metrics.
Humans ; Video Recording ; Intestine, Small/diagnostic imaging* ; Machine Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms

OBJECTIVES: To investigate the effect of in vitro cultured calculus bovis (ICCB) on cerebral ischemia/reperfusion injury (CIRI) and its mechanism. METHODS: A CIRI rat model and a cell model were induced by middle cerebral artery occlusion (MCAO) in Sprague Dawley rats and oxygen glucose deprivation/reperfusion (OGD/R) in BV2 cells, respectively. The CIRI rat model was evaluated using the modified neurological severity score (mNSS), brain water content, and cerebral infarction volume after 1.5 h of ischemia followed by 72 h of reperfusion. Histopathological changes in the cortex and hippocampal CA1 region were observed with hematoxylin and eosin staining. Microglial polarization and NOD-like receptor thermal protein domain associated protein (NLRP) 3 inflammasome expression in the cortex were examined by immunofluorescence. BV2 cell viability was measured via MTT assay after treatment with ICCB and Nigericin. The expressions of NLRP3, ASC, caspase-1 proteins and inflammatory cytokines were detected with Western blotting in OGD/R treated BV2 cells (0.5 h OGD+24 h reperfusion) and in cells pretreated with Nigericin for 24 h. RESULTS: ICCB treatment significantly improved neurological function, reduced cerebral infarct volume and brain water content, and mitigated pathological damage in the cortical and hippocampal CA1 regions of rats subjected to CIRI (all P<0.05). ICCB promoted the transition of cortical microglia from M1 to M2 phenotypes and suppressed NLRP3 activation in microglial cells (all P<0.01). ICCB significantly down-regulated the expression of NLRP3, ASC, and caspase-1 proteins, and reduced the secretion of IL-18 and IL-1β in BV2 cells of OGD/R model (all P<0.01). In addition, Nigericin significantly reversed the salvage effect of ICCB on model cells (both P<0.01) and the modulation of inflammatory cytokines (P<0.05). CONCLUSIONS ICCB exerts a protective effect against CIRI by mitigating neuroinflammation, through the reduction of M1 microglial polarization, promotion of M2 conversion, and suppression of the NLRP3/ASC/caspase-1 signaling pathway.
Animals ; Rats, Sprague-Dawley ; Reperfusion Injury/prevention & control* ; Microglia/metabolism* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein ; Brain Ischemia/metabolism* ; Male