The incidence of orthopedic trauma-related diseases keeps rising annually, which brings an urgent need to optimize the diagnostic and treatment processes to enhance treatment efficiency and improve patients′ prognosis. Traditional diagnostic methods in traumatic orthopedics primarily rely on manual film interpretation and classification, resulting in a substantial workload for physicians and consequently a low efficiency. Furthermore, during multidisciplinary consultations and cross-hospital referrals for patients with orthopedic trauma, accessing medical records and facilitating information exchange can be challenging, leading to delays in surgical intervention. The rapid advancement of artificial intelligence (AI) technology, characterized by feature engineering, artificial neural networks, and deep learning, has transformed the landscape of rapid diagnosis and precision treatment for orthopedic trauma. Nonetheless, centralized storage during task training poses risks of privacy disclosure and security concerns that impede the widespread application of AI models. In contrast, the decentralized nature of blockchain technology offers a secure operational environment for AI-driven diagnostics and treatments and the integration of blockchain and AI can deliver more accurate, efficient, and safe services for patients with orthopedic trauma. Currently, challenges remain in the inter-institutional sharing of data, constant phenomenon of data silos and absence of standardized protocols for developing collaborative models in clinical settings. To address these challenges, the authors reviewed the research advancements in integrated application of blockchain technology and artificial intelligence in diagnosing and treating orthopedic trauma, aiming to provide insights into the development of a digital diagnostic system tailored to this field in China.

Objective To investigate the prognostic value of atherogenic index of plasma(AIP)for the occurrence of major adverse cardiovascular events(MACE)in elderly patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 355 elderly patients with acute STEMI who received coronary interventional therapy in Department of Cardiology,Affilia-ted Hospital of Hebei University from January to May 2023 were recruited retrospectively,and fi-nally 343 of them with complete telephone follow-up data were included in this study.According to their AIP quartile level,they were divided into A1 group(＜0.212,84 cases),A2 group(0.212-0.339,87 cases),A3 group(0.339-0.434,86 cases)and A4 group(≥0.434,86 cases).The incidences of cardiac death,nonfatal myocardial infarction,ischemia-driven target vessel re-modeling and heart failure re-hospitalization were observed during 1-year follow-up.Kaplan-Meier survival curve was plotted to compare the incidence of MACE in the 4 groups.ROC curve analysis was employed to determine the predictive value of AIP.Results During 1-year follow-up,signifi-cant differences were observed in the proportions of ischemia-driven target vessel revasculariza-tion,heart failure re-hospitalization and non-fatal acute myocardial infarction among the 4 groups(P＜0.05,P＜0.01),and such difference was also seen in the cumulative survival rate among them(log rankx2=8.528,P=0.036).Multivariate Cox proportional hazards regression analysis showed that gender,hypertension,atrial fibrillation,multi-vessel disease,left main artery disease,number of stents,SYNTAX score,Killip grade,BNP,HbA1c,TC,LDL-C and HDL-C levels,and AIP were independent predictors of MACE.The AUC value of AIP in predicting MACE in elderly patients with acute STEMI was 0.855(95％CI:0.776-0.933),with a sensitivity of 66.7％and a specificity of 93.0％.When the above indicators combined together,the AUC value was 0.907(95％CI:0.954-0.987),and the sensitivity and specificity was 100.0％and 90.7％,respectively.The AUC value of combined prediction was significantly better than that of single indicator(P＜0.05).Conclusion AIP is a powerful biomarker,and can be used to predict the prognosis of elderly acute STEMI after coronary interventional therapy,and it combined with Killip grade,SYNTAX score,HbA1c,and number of stents shows better predictive efficacy.

The incidence of orthopedic trauma-related diseases keeps rising annually, which brings an urgent need to optimize the diagnostic and treatment processes to enhance treatment efficiency and improve patients′ prognosis. Traditional diagnostic methods in traumatic orthopedics primarily rely on manual film interpretation and classification, resulting in a substantial workload for physicians and consequently a low efficiency. Furthermore, during multidisciplinary consultations and cross-hospital referrals for patients with orthopedic trauma, accessing medical records and facilitating information exchange can be challenging, leading to delays in surgical intervention. The rapid advancement of artificial intelligence (AI) technology, characterized by feature engineering, artificial neural networks, and deep learning, has transformed the landscape of rapid diagnosis and precision treatment for orthopedic trauma. Nonetheless, centralized storage during task training poses risks of privacy disclosure and security concerns that impede the widespread application of AI models. In contrast, the decentralized nature of blockchain technology offers a secure operational environment for AI-driven diagnostics and treatments and the integration of blockchain and AI can deliver more accurate, efficient, and safe services for patients with orthopedic trauma. Currently, challenges remain in the inter-institutional sharing of data, constant phenomenon of data silos and absence of standardized protocols for developing collaborative models in clinical settings. To address these challenges, the authors reviewed the research advancements in integrated application of blockchain technology and artificial intelligence in diagnosing and treating orthopedic trauma, aiming to provide insights into the development of a digital diagnostic system tailored to this field in China.

Multiple injuries caused by trauma have high rates of disability and mortality and are difficult to treat, which have a negative impact on the patients, their families and the society. At present, the medical model of trauma treatment is still inadequate, and the treatment of trauma patients faces great challenges. Artificial intelligence (AI) is an intelligent technology based on machine learning, reinforcement learning and deep learning algorithm, and it has been applied to the treatment of patients with trauma. Its efficient and accurate computer vision, planning and decision-making, and big data statistical analysis not only improve the safety and efficiency in the treatment of trauma, but also reduce the workload of clinicians, which makes up for the deficiency of the traditional model of trauma care. After screening the recent studies of AI in trauma care, the authors review its application in emergency triage, diagnosis, treatment and prevention of war trauma, in order to introduce the latest research progress of AI in trauma care and provide references for future developments.

Objective: To investigate the surgical complications in the treatment of stage Ⅰ endometrial cancer by robotic-assisted laparoscopy, the risk degree of Clavein-Dindo complications and the main risk factors affecting the occurrence of surgical complications. Methods: A retrospective case-control study was conducted in the First Affiliated Hospital of Zhengzhou University from October 2014 to June 2019. The patients were divided into robotic-assisted laparoscopy group and traditional laparoscopy group according to the operation mode, including 131 cases in robot group and 290 cases in traditional laparoscopy group. To compare the complications during and after operation and the risk degree of complications between the two groups by Clavein-Dindo classification standard, the age, body mass index (BMI), comorbidities, past history of pelvic surgery, American Society of Anesthesiologists (ASA) grade, preoperative anemia, number of pelvic lymph node resection, number of abdominal aortic lymph node resection, the total number of lymph node resection, operation time, surgical methods (robot surgery or traditional laparoscopic surgery) and other clinicopathological data were analyzed by logistic regression analysis. Results: (1) Complications of operation: the incidence of operative complications (including intraoperative and postoperative complications) in robot group was significantly lower than that in traditional laparoscopy group [(20.6%, 27/131) vs (34.8%, 101/290); χ²=8.620, P=0.003)]. The incidence of intraoperative complications in robot group was lower than that in traditional laparoscopy group [1.5% (2/131) vs 6.2% (18/290); χ²=4.368, P=0.037]. The incidence of intraoperative vascular injury in robot group was significantly lower than that in traditional laparoscopy group [0.8% (1/131) vs 5.2% (15/290); χ²=4.798, P=0.022]. The incidence of postoperative complications in robot group was also lower than that in traditional laparoscopy group [19.1% (25/131) vs 28.6% (83/290); χ²=4.303, P=0.038], but the incidence of postoperative lymphatic leakage in robot group was higher than that in traditional laparoscopy group [10.7% (14/131) vs 5.2% (15/290); χ²=4.279, P=0.039]. (2) Clavein-Dindo classification: the incidence of Clavein-Dindo Ⅰ, Ⅲ, Ⅲ, Ⅳ and Ⅴ grade between two groups were respectively 3.8% (5/131) vs 11.0% (32/290), 13.7% (18/131) vs 14.5% (42/290), 3.1% (4/131) vs 8.6% (25/290), 0 (0/131) vs 0.3% (1/290), 0 (0/131) vs 0.3% (1/290), and the incidence of grade Ⅰ (χ²=5.684, P=0.015) and Ⅲ (χ²=4.361, P=0.037) complications were statistically significant. The incidence of severe complications in robot group (grade Ⅲ and above) was lower than that in traditional laparoscopy group [3.1% (4/131) vs 9.3% (27/290); χ²=5.179, P=0.023]. (3) Analysis of influencing factors of surgical complications: univariate analysis showed that BMI (χ²=15.801, P=0.000), preoperative anemia (χ²=14.299, P=0.000), total number of lymph node resection (χ²=10.425, P=0.001), surgical methods (χ²=8.620, P=0.003) were related to the occurrence of surgical complications of endometrial carcinoma. Multivariate analysis showed that BMI (OR=0.289, 95%CI: 0.097-0.864, P=0.026), preoperative anemia (OR=0.309, 95%CI: 0.129-0.740, P=0.008), the total number of lymph node resection (OR=0.624, 95%CI: 0.403-0.966, P=0.034) and surgical methods (OR=3.491, 95%CI: 1.030-11.840, P=0.045) were independent risk factors for surgical complications of endometrial carcinoma. Conclusions Compared with traditional laparoscopic surgery, robot-assisted laparoscopic surgery has fewer complications and lower incidence of severe complications. BMI, preoperative anemia, the total number of lymph node resection and surgical methods are independent risk factors for the occurrence of surgical complications of stage Ⅰ endometrial cancer.

Objective To compare and analyze the occurrence of delayed cerebral ischemia(DCI)after coiling and clipping of intracranial aneurysms, and explore the risk factors of DCI. Methods A total of 236 patients with aneurysms diagnosed by CT angiography (CTA) or digital subtraction angiography (DSA) in Department of Neurosurgery, Tianjin Medical University General Hospital were enrolled in this study from March 2011 to May 2014. Patients were divided into clipping group(n=135) and coiling group(n=101). The clinical characteristics were compared between two groups, including gender, age, medical history, GCS score, Hunt-Hess grade, Fisher grade, WFNS grade, aneurysm location, prognosis and incidence of DCI. Risk factors for DCI were investigated by Logistic regression analysis. Results DCI was occurred in 36 patients (26.7%) underwent clipping operation while in 11 patients (10.9%) underwent coiling operation. The incidence was significantly higher in clipping group compared with that of coiling group (P <0.01). The patients were followed up for 6 months. The poor prognosis rates were 17.0%and 25.7%in clipping group and coiling group, respectively (P>0.01). The overall mortality was 11.0%, the former had a lower mortality rate (5.9% vs. 17.8%, P <0.01). According to Logistic regression analysis, Fisher Grade 3-4, postoperative pulmonary infection and surgical procedure were independent risk factors for DCI (P<0.01). Conclusion DCI is one of the most significant factors for high fatality and morbidity of postoperative aneurysm patients. There is a low occurrence of DCI after coiling compared with that of clipping. If we pay more attention to risk factors associated with the DCI, it will improve the prognosis of postoperative aneurysm patients greatly.

A sequential extraction procedure has been proposed for the evaluation of the speciation of heavy metals including Cd,Cu,Pb and Zn in sediments from mariculture area,and the speciation of heavy metals was separated and defined as acid soluble fraction,reducible fraction,fraction bound organic matter,fraction bound sulfides and residual fraction. Matrix effects of high salinity on the determination of heavy metals in sediments were eliminated by matrix matching and internal standard methods when inductively couple plasma optical emission spectroscopy (ICP-OES) and mass spectroscopy (ICP-MS) were used,respectively. The results showed that the measured values of marine sediment reference materials were consistent with the standard values when the digestion solutions were determined after dilution. The extraction results of the prepositional extraction procedure and European Community Bureau of Reference Program (BCR) procedure were compared and the selectivity of extractants was investigated. The preliminary studies indicated that this sequential extraction procedure was applicable for evaluating the speciation of heavy metals in sediment with organic substances pollution and eutrophication,especially for fraction bound organic matter and fraction bound sulfides.