OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Background Gestational weight gain is closely related to maternal and infant health outcomes. Pregnant women are simultaneously exposed to four metals—lithium (Li), vanadium (V), uranium (U), and bismuth (Bi)—through inhalation of fine particulate matter and consumption of contaminated food and water. Existing studies suggest that exposure to these metals may be associated with gestational weight gain. However, no study has yet explored the complex relationships between exposure to mixtures of these four metals and weight gain at different stages of pregnancy. Objective To investigate the associations between mixed exposure to Li, V, U, and Bi in early pregnancy and the average weekly gestational weight gain during both early pregnancy and mid-to-late pregnancy. Methods This prospective study recruited eligible women in early pregnancy from an obstetrics clinic of a tertiary hospital in Jinan, China, between September 2021 and July 2023. Pre-pregnancy weight, current weight (at 11⁺⁰ to 13⁺⁶ weeks of gestation), and spot urine samples (≥5.0 mL) were collected at enrollment. Urinary concentrations of Li, V, Bi, and U were determined using inductively coupled plasma mass spectrometry. Participants were followed up in late pregnancy (≥28 weeks of gestation) to collect information on physical activity via questionnaire; weight measurements at the last antenatal visit (35⁺⁰ to 37⁺⁶ weeks of gestation) were obtained from the hospital information system. After adjusting for covariates, multiple linear regression and generalized additive models were used to assess the associations of individual metals with weekly weight gain in early pregnancy and in mid-to-late pregnancy. Bayesian kernel machine regression (BKMR) and quantile-based g-computation (Qgcomp) were applied to evaluate the joint effects of the metal mixture exposure on weekly weight gain at the two gestational stages. Results A total of 313 pregnant women were included. The geometric means of urinary Li, V, U, and Bi concentrations were 37.07, 0.20, 0.06, and 0.04 μg·L⁻¹, respectively; after creatinine adjustment, the corresponding values were 46.82, 0.25, 0.07, and 0.05 μg·g⁻¹ (Cr). The mean weekly gestational weight gain was (0.19±0.25) kg in early pregnancy and (0.53 ± 0.18) kg in mid-to-late pregnancy. Both multiple linear regression and generalized additive models showed that urinary V concentration was positively associated with average weekly gestational weight gain in early pregnancy, while no significant associations were found for other metals or for gestational weight gain in mid-to-late pregnancy. In the BKMR model with early-pregnancy weight gain as the outcome, V had the strongest association [posterior inclusion probability (PIP)=0.773]. When other metals were fixed at their medians, V showed a positive non-linear association with the outcome. A significant single-metal effect of V and its interaction with Li were observed. Compared with the 50th percentile of the metal mixture, the average weekly weight gain in early pregnancy increased by 0.016 (95%CI: 0.003, 0.029) and 0.018 (95%CI: 0.001, 0.036) at the 60th and 65th percentiles, respectively; conversely, at the 25th percentile, it decreased by 0.026 (95%CI: 0.002, 0.050). Overall, the joint effect of the metal mixture on early- pregnancy weight gain showed an upward trend. In the BKMR model for mid-to-late pregnancy gestational weight gain, all PIPs were<0.5, and no significant single-metal effects, interactions, or joint effects were identified. Qgcomp results confirmed a positive association between the metal mixture and early-pregnancy weight gain (b=0.031, 95%CI: 0.010, 0.051; P<0.01), with V contributing the highest positive weight (0.71). No significant association was found for weight gain in mid-to-late pregnancy (b=0.007, P=0.339). Conclusion Higher levels of co-exposure to the Li, V, Bi, and U metal mixture during early pregnancy may be associated with increased average weekly weight gain in early pregnancy. Among these metals, V exhibits a predominant role and appears to interact with Li. No association is observed between early-pregnancy metal mixture exposure and average weekly gestational weight gain in mid-to-late pregnancy. These findings suggest that monitoring and managing metal exposure during early pregnancy may be crucial for the rational regulation of gestational weight gain.

ObjectiveTo analyze the infection characteristics of human respiratory syncytial virus (HRSV) among children hospitalized with acute lower respiratory tract infection (ALRTI) in a specialized pediatric hospital in Shanghai, so as to provide evidence-based support for optimizing the prevention and control strategies and clinical diagnosis and treatment of respiratory tract infections in children in this region. MethodsA retrospective analysis was performed to the clinical and etiological data of 29 260 children hospitalized for ALRTI in Shanghai Children’s Hospital from January 2021 to December 2024. HRSV and 12 other common respiratory pathogens were detected with multiplex polymerase chain reaction (PCR) and capillary electrophoresis. Demographic and clinical data were collected for statistical analyses. A total of2 412 cases with positive HRSV were divided into the severe group and the non-severe group. Clinical characteristics between the two groups were compared using the Mann-Whitney U test and the chi- square (χ²) test. Additionally, the related influencing factors of severe HRSV infection were explored. ResultsThe overall positivity rate of HRSV from 2021 to 2024 was 8.24% (2 412/29 260), with statistically significant differences observed across the four years (χ²=389.42, P<0.001). The highest positivity rate was in 2021 (14.76%), with a high prevalence throughout the year. In 2022, when non-pharmaceutical interventions (NPIs) were implemented, the HRSV positivity rate was the lowest (4.93%), with a winter-dominant epidemic pattern. In 2023, after the NPIs were lifted, the HRSV positivity rate showed a slight rebound (8.14%), presenting a double-peak pattern. In 2024, the HRSV positivity rate slightly decreased compared to that in 2023 (6.29%), exhibiting a winter and spring-dominant epidemic pattern. Among the hospitalized children with ALRTI, the HRSV positivity rate in males (8.85%) was higher than that in females (7.51%), and the difference was statistically significant (χ²=17.33, P<0.001). Age distribution showed that 82.26% (1 984/2 412) of HRSV infections occurred in children aged 3 years old and below. Besides, as age increased, the infection rate of HRSV showed a gradually decreasing trend (P<0.001). Among the 2 412 children with HRSV infection, the proportion of severe cases was 22.31% (538/2 412), while the non-severe cases accounted for 77.69% (1 874/2 412). Compared with non-severe cases, severe cases were more frequently presented with high fever, longer duration of wheezing, as well as higher rates of underlying diseases or co-infection with Mycoplasma pneumoniae (P<0.001). ConclusionThe prevalence intensity of HRSV varied yearly from 2021 to 2024. After the removal of NPIs in 2023, a slight rebound with a double-peak epidemic pattern was observed. HRSV remained a common pathogen in children hospitalized for ARLTI, and children aged 3 years old and below constituted the highest proportion for infection. Compared with non-severe cases, those with severe HRSV infections were more prone to presenting with high fever and a longer duration of wheezing. Children with positive HRSV who had underlying diseases or co-infection with Mycoplasma pneumonia were more likely to develop severe conditions.

Objective To preliminarily investigate the safety and efficacy of donor pancreas needle biopsy in simultaneous pancreas-kidney transplantation. Methods Clinical data of 7 cases undergoing donor pancreas biopsy were collected retrospectively. All cases underwent donor pancreas biopsy before or during simultaneous pancreas-kidney transplantation. Frozen section or paraffin sectioning techniques were used for tissue preparation, and hematoxylin-eosin and Masson staining were performed to histologically evaluate the donor pancreas. The quality of donor pancreas was comprehensively assessed by combining histological findings with the donor's clinical data. Postoperative follow-up data of 5 simultaneous pancreas-kidney transplant recipients were collected to summarize the safety of donor pancreas biopsy and the prognosis of transplant recipients. Results The 7 pancreas donors were aged 28 to 62 years, with a body mass index ranging from 20.76 to 27.68 kg/m². Liver ultrasound indicated fatty liver in 3 cases, while pancreatic ultrasound did not reveal any significant abnormalities. Among them, biopsy was performed on 2 donors after completion of pancreatic procurement and processing, and the frozen section histology showed moderate acute pancreatitis changes (edema of acinar cells, necrosis and inflammatory cell infiltration). Combined with a serum amylase level elevated more than 3 times the upper limit of normal value, these two donor pancreases were finally discarded. The remaining 5 cases underwent biopsy immediately after pancreatic vascular anastomosis during simultaneous pancreas-kidney transplantation, and histological evaluation was performed on paraffin-embedded sections. No biopsy-related complications (such as bleeding, pancreatic fistula, etc.) occurred after transplantation. One recipient died of severe infection 2 months after transplantation, while the other 4 recipients were followed up for more than 5 years, with well-functioning transplant kidneys and pancreases. Conclusions Donor pancreas biopsy is relatively safe, and the risk of biopsy-related complications after transplantation is controllable. Comprehensive assessment of donor pancreas quality by combining histological evaluation with the donor's clinical indicators is conducive to improving the accuracy of donor pancreas selection and organ utilization.

OBJECTIVE To explore a model for constructing a platform for medical institution formulation and provide insights for promoting their development. METHODS By systematically reviewing the development status and challenges of medical institution preparations in China， and based on the theory of industry-university-research collaborative innovation， the organizational structure， collaborative processes， and safeguard mechanisms of the platform were designed. RESULTS & CONCLUSIONS Medical institution formulations in China mainly faced challenges such as weak research and development （R&D） capacity， uneven quality standards， and blocked transformation pathways. This study established a full-chain， whole- industry collaborative innovation network covering the government， medical institutions， universities/research institutes， pharmaceutical enterprises， and the market， forming a new “government-industry-university-research-application” five-in-one platform model for medical institution formulations. By establishing mechanisms such as multi-entity collaborative cooperation， full- chain intellectual property management， contribution-based benefit distribution， staged risk-sharing， and third-party evaluation， the model clarified the responsibilities and collaborative pathways of all parties. The new model highlights the whole-process transformation of clinical experience-based prescriptions， enabling precise alignment between clinical needs and technological R&D， as well as between preparation achievements and industrial transformation. While breaking down the barriers of traditional platform construction， it effectively achieves optimal resource allocation and complementary advantages， addresses problems emerging in the development of medical institution preparations， and provides reference value for the formulation of relevant systems.

Objective To investigate the relationship of skin failure (SF) with nutritional status in elderly critically ill patients and analyze the predictive efficiency of nutritional status on SF. Methods A total of 340 elderly critically ill patients admitted to the hospital from January 2020 to January 2025 were selected as research subjects. According to whether skin failure occurred, the above patients were classified into skin failure group and non-skin failure group. The nutritional status indicators [serum albumin (ALB), prealbumin (PA), total protein (TP), hemoglobin (Hb), body mass index (BMI), nutritional risk score (NRS 2002)] were compared between both groups. Multivariate analysis was performed on statistically significant indicators, and ROC curve was applied to analyze the predictive value. Results Among the 340 elderly critically ill patients, 142 cases (41.76%) developed skin failure. The ALB, PA, TP, Hb and BMI in the skin failure group were lower than those in the non-skin failure group (P<0.05) while the NRS2002 score was higher (P<0.05). After logistic multivariate analysis, ALB, PA and Hb were independent influencing factors of skin failure (P<0.05). ROC curve analysis revealed that the predictive value of ALB (AUC=0.850, 95%CI: 0.808-0.888, Z=-3.707, P<0.001) was better than that of PA (AUC=0.770, 95%CI: 0.717-0.816, Z=-3.100, P=0.002) or Hb (AUC=0.773, 95%CI: 0.722-0.819, Z=-2.556, P=0.011). Conclusion The occurrence of skin failure in elderly critically ill patients is closely related to nutritional status. ALB, PA and Hb are independent risk factors of SF, and ALB has the best predictive efficiency on SF.

Background: and Purpose In patients with large vessel occlusion (LVO), intravenous thrombolysis (IVT) frequently alters thrombus location; however, the clinical impact of this phenomenon remains unclear. We aimed to compare post-IVT thrombus dynamics between tenecteplase and alteplase and to evaluate the association between thrombus dynamics and 3-month outcomes. Methods: This retrospective study analyzed prospectively collected, multicenter data from consecutive patients with LVO who underwent bridging therapy between January 2022 and December 2024. Thrombus dynamics were classified as resolution, migration, or stability. Analyses incorporated propensity score matching with weighting to balance baseline characteristics. Results: Of the 806 initially included patients, 746 were included after matching (373 treated with tenecteplase and 373 treated with alteplase). The incidence of thrombus migration was significantly higher in the tenecteplase group than in the alteplase group (19.3% vs. 11.3%; odds ratio [OR]: 1.92; 95% confidence interval [CI] 1.27–2.91). The advantage of tenecteplase over alteplase was restricted to patients with an IVT-to-puncture time of <60 minutes (18.6% vs. 6.2%; p=0.001) and was no longer significant when the interval ≥60 minutes (19.7% vs. 15.0%; p=0.204; pinteraction=0.043). Additionally, thrombus migration was associated with a better functional outcome (OR: 1.62; 95% CI 1.04–2.53). Finally, tenecteplase was associated with improved functional independence compared with alteplase (OR: 1.43; 95% CI 1.04–1.95). Conclusions Tenecteplase demonstrated superior efficacy in inducing thrombus migration compared with alteplase, particularly within 60 minutes of IVT administration. Thrombus migration independently predicted improved functional independence. These findings support the preferential use of tenecteplase for bridging therapy in patients with LVO.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.