Coronary artery calcification often appears a variety of complex lesions,increasing coronary intervention of the difficulty of treatment,especially the severe calcification lesions,usually cannot be fully dilated,resulting in a reduced success rate of surgery,an increased rate of acute stent thrombosis and restenosis,and even a serious impact on the prognosis of patients.Intravascular lithotripsy(IVL)is increasingly used in calcified lesions.There is more and more evidence of using in stable angina pectoris and unstable angina pectoris,but its use in acute ST-segment elevation myocardial infarction is limited,and only a few cases have been reported abroad.Moreover,the consensus of Chinese experts in the diagnosis and treatment of coronary artery calcification in 2021 edition lists thrombotic lesions as contraindications of shock wave balloon.This case is the first time in China to report the use of shock wave balloon in patients with acute ST elevation myocardial infarction complicated with thrombus.In this case,the patient with acute ST elevation myocardial infarction complicated with thrombus was severely under expanded stent after stent implantation,and obtain good curative effect using shockwave balloon at selected time in hospital after intensive anticoagulant therapy.

Objective:To explore the feasibility of shortening the time of long exercise test (LET) from 120 to 60 minutes by analyzing the positive rate within 60 minutes among periodic paralysis (PP) patients who were positive in 120-minute test.Methods:The data of patients undergoing 120-minute LET from January 2015 to October 2021 in Beijing Tiantan Hospital, Capital Medical University were retrospectively analyzed, with 30%, 33%, and 40% as diagnostic cut-off values, respectively. PP patients with positive results within 120 minutes after exercise were enrolled in the study. The positive rate within 30 minutes and 60 minutes after exercise was calculated. The change rates of compound muscle action potential (CMAP) amplitude and the sensitivity and specificity of LET at 30 minutes, 60 minutes, and 120 minutes after exercise were analyzed. The change rate of CMAP amplitude in PP patients who did not show positive results within 60 minutes was further calculated.Results:A total of 254 patients were examined, including 114 PP patients. With 30%, 33%, and 40% as diagnostic cut-off values, the results showed that there were 88, 88, and 82 positive PP patients, respectively. Under each diagnostic cut-off values, the age of positive PP patients was (32±10) years, with a male proportion of 98% (86/88), 98% (86/88), and 99% (81/82), respectively; the positive rate of PP patients within 30 minutes after exercise was 60% (53/88), 58% (51/88), and 41% (34/82), respectively; the positive rate of PP patients within 60 minutes after exercise was 91% (80/88), 86% (76/88), and 83% (68/82), respectively. At the cut-off values of 30%, 33% and 40%, the change rate of CMAP amplitude at 30 minutes [-36% (-49%, -23%), -36% (-49%, -23%), -37% (-51%, -24%)], 60 minutes [-51% (-66%, -40%), -51% (-66%, -40%), -53% (-66%, -42%)] and 120 minutes [-57% (-67%, -45%), -57% (-67%, -45%), -58% (-67%, -46%)] after exercise showed statistically significant difference among 3 time points ( H=57.764, 57.764, 59.616, respectively, all P<0.001); the further comparison between time points showed that there was statistically significant difference in the change rate of CMAP amplitude between 60 minutes ( Z=5.419, 5.419, 5.531, respectively, all P<0.001), 120 minutes ( Z=7.325, 7.325, 7.431, respectively, all P<0.001) and 30 minutes after exercise, but there was no statistically significant difference in the change rate of CMAP amplitude between 120 minutes and 60 minutes after exercise ( Z=1.906, 1.906, 1.899, respectively, all P>0.05); the sensitivity of LET for the diagnosis of PP at 60 minutes after exercise was 70.2% (80/114), 66.7% (76/114) and 59.6% (68/114), and the specificity of LET for the diagnosis of PP was 77.9% (109/140), 84.3% (118/140) and 91.4%(128/140), respectively. When 30%, 33% and 40% were used as the diagnostic cut-off values, and the change rate of CMAP amplitude at 60 minutes after exercise fell below these cut-off values but showed a decline of ≥20%, ≥22% and ≥24%, respectively, the detection time should be extended to 120 minutes. Conclusions:Whether using 30%, 33%, or 40% as diagnostic cut-off values, it is feasible to shorten the LET time from 120 minutes to 60 minutes. The 60-minute LET has good sensitivity and specificity for the diagnosis of PP. It is recommended to extend the detection time to 120 minutes for patients with a ≥20%, ≥22%, or ≥24% decline in CMAP amplitude at 60 minutes after exercise while falling short of corresponding diagnostic cut-off values when 30%, 33%, and 40% are used as diagnostic cut-off values. This method can not only improve the examination efficiency of LET, but also minimize the missed diagnosis as much as possible.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.