Objective: To provide evidence for improving emergency blood supply protocols by analyzing the clinical characteristics and disease distribution of emergency transfusion patients, especially those receiving≥10 units of red blood cells (RBCs). Methods: The data of 4 157 patients who urgently applied for large-volume blood transfusion in various hospitals in Shanghai from May 2024 to April 2025 were selected and analyzed statistically. Results: Tertiary gradeA hospitals accounted for the largest proportion of total transfusion volume (U) (48.79%, 8 420/17 256.5), with no statistically significant differences in RBC transfusion volumes among hospitals of different grades (P>0.05). All blood products are most widely used in tertiary hospitals. Obstetric blood transfusion (U)(19.07%, 3 277.5/17 190.5) was the most frequent. A-mong the hospitals of patients who received emergency blood transfusion with red blood cell suspension≥10 U, tertiary gradeA hospitals also had the largest transfusion volume (U)(47.19%, 1 107/2 346). In terms of disease types, the top three diseases in terms of blood transfusion volume (U) were obstetric transfusion (24.59%, 572/2 326), digestive diseases (14.53%, 338/2 326) and tumors (14.19%, 330/2 326). Conclusion: Tertiary grade A hospitals are the main demand units for emergency blood transfusion, with pregnant women and cancer patients being the core blood-using groups. It is suggested that the safety, timeliness and sufficiency of emergency blood transfusion be guaranteed by establishing a hierarchical blood supply mechanism, formulating single-disease blood transfusion plans and promoting precise blood transfusion guided by thromboelastography.

Objective To summarize the clinical efficacy of modified Morrow surgery in the treatment of hypertrophic obstructive cardiomyopathy. Methods A retrospective analysis was conducted on the clinical data of patients with hypertrophic obstructive cardiomyopathy treated with modified Morrow surgery at Zhongshan Hospital Affiliated to Fudan University from 2020 to 2023. Results A total of 318 patients were enrolled, including 156 males and 162 females, with an average age of (55.6±13.1) years. Preoperative echocardiography showed a mean interventricular septal thickness of (18.1±3.8) mm, peak left ventricular outflow tract pressure difference of (86.4±24.9) mm Hg. The surgery time was (162.3±51.0) min, extracorporeal circulation time was (80.9±31.0) min, and aortic occlusion time was (44.8±20.8) min. After the surgery, transesophageal echocardiography showed that the interventricular septal thickness was (11.0±1.8) mm and left ventricular outflow tract peak pressure difference was (9.4±5.1) mm Hg. The incidence rate of postoperative complete left bundle branch block was 45.3%, Ⅲ° atrioventricular block was 3.8%, and postoperative newly developed atrial fibrillation was 3.1%. The postoperative hospital stay was (6.6±4.9) days, and one perioperative death occurred, with a mortality rate of 0.3%. The follow-up time was (10.3±9.4) months, during which the transthoracic echocardiography revealed a ventricular septal thickness of (12.9±2.9) mm and a peak left ventricular outflow tract pressure difference of (13.9±10.0) mm Hg. Conclusion The modified Morrow procedure for the treatment of hypertrophic obstructive cardiomyopathy is safe and effective, with good results in the short and medium term.

OBJECTIVE To investigate the application status of prescription pre-review systems in healthcare institutions of Yunnan province， evaluate their system functions and management capabilities， and provide a practical basis for promoting rational drug use. METHODS A questionnaire survey was conducted among public healthcare institutions at or above the secondary level in Yunnan province to investigate the deployment status of the systems. A capability maturity assessment framework was constructed， encompassing 6 dimensions and 39 indicators， including real-time prescription review， prescription correlation review， rule setting， evidence-based information support， prescription authority management， and system operation management. This framework was then used to evaluate the institutions that had implemented the pre-review systems. RESULTS A total of 100 valid questionnaires were collected， with 37 institutions having adopted prescription pre-review systems， mainly tertiary hospitals. The system predominantly adopted a modular architecture and was embedded into the hospital information system through application programming interfaces and middleware， providing certain capabilities for real-time prescription risk identification. Evaluation results indicated that basic functions such as reviewing indications， contraindications， and drug compatibility performed well， while deficiencies remained in functions related to parenteral nutrition prescription， review of drug dosage for specific diseases， individual patient characteristic recognition， and rule setting. Moreover， the construction of review centers and establishment of management systems were also not well-developed. CONCLUSIONS The overall application rate of prescription pre-review systems in Yunnan province remains low. System functions and management mechanisms require further improvement. It is recommended to enhance information infrastructure in lower-level institutions and explore regionally unified review models to promote standardized and intelligent development of prescription review practices.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To analyze the burden of disease of malignant tumors in Kunshan City from 2006 to 2021. Methods The global burden of disease research methodology was applied. The indicators of cancer incidence, mortality, and disability-adjusted life years (DALYs) in Kunshan were calculated using the data from the Tumor Registry System and Death Registry System in Kunshan. The changes in cancer were compared. Results In 2021, the number of incidences and deaths and the DALYs of cancer were 4724 cases, 1618 cases, and 20887.90 person-years, respectively. The incidence, mortality, and DALYs rates were 427.42/100000, 146.39/100000, and 18.90‰. Compared with those in 2006, the incidence rate increased by 53.40%, and the mortality and DALYs rates decreased by 24.23% and 25.73%, respectively. The DALYs rate of cancer was higher for males than for females at 22.12‰ and 15.91‰ in 2021, respectively. The DALYs rate increased with age, and it started to increase sharply after 45 years of age. The top 10 cancers in 2021 in terms of DALYs were lung cancer, stomach cancer, colorectal cancer, liver cancer, pancreatic cancer, breast cancer, leukemia, esophageal cancer, brain and nervous system cancer, and gallbladder and biliary tract cancer, accounting for 83.83% of all cancers. The proportion of years lived with disability (YLD) in DALYs increased continuously (27% in 2021), with females increasing faster than males. Conclusion The disease burden of cancer in Kunshan City remains high, and the incidence of cancer must be reduced by developing comprehensive measures and continuously strengthening the capacity of early screening and treatment of cancer.

ObjectiveTo investigate the chemical constituents of Chuanxiong Rhizoma-Paeoniae Radix Rubra（CRPRR） that cross the blood-brain barrier in rats with ischemic stroke， their brain-protective effects， and their impact on inflammatory factors including tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-18 （IL-18） based on ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF-MS） and pharmacodynamic experiments. MethodsA focal cerebral ischemia-reperfusion injury model was established in rats via the middle cerebral artery occlusion/reperfusion （MCAO/R） method using intraluminal suture. Neurological function was evaluated using behavioral scoring. UPLC-Q-TOF-MS was employed to identify the chemical constituents of CRPRR that crossed the blood-brain barrier and entered the cerebrospinal fluid in MCAO/R model rats. Male Sprague-Dawley rats were randomly divided into six groups： sham operation group， model group， low-， medium-， and high-dose CRPRR groups （1.35， 2.7， 5.4 g·kg^-1， respectively）， and an edaravone group （5 mg·kg^-1）， with 12 rats in each group. The sham and model groups received normal saline， while the treatment groups received the respective doses of CRPRR once daily by gavage for three consecutive weeks. The brain-protective effects of CRPRR were assessed using the Longa five-point scoring method， open field test， Morris water maze， 2，3，5-triphenyltetrazolium chloride （TTC） staining， hematoxylin and eosin （HE） staining， and transmission electron microscopy. ResultsNine chemical constituents were identified in the cerebrospinal fluid containing CRPRR， namely paeoniflorin， senkyunolide F， senkyunolide G， paeonimetabolin Ⅰ， paeoniflorin derivative， senkyunolide H， benzoylpaeoniflorin， senkyunolide A， and ligustilide. Animal experiment results showed that compared with the sham operation group， the model group exhibited disordered neuronal arrangement， severe vacuolation， nuclear pyknosis， and evident mitochondrial swelling. Chromatin aggregation and peripheralization were also observed. Neurological scores and the number of crossings in the central region were significantly increased （P<0.01）， while platform crossings were significantly decreased （P<0.01）， and clear infarct areas were present （P<0.01）. Serum levels and protein expression of TNF-α， IL-1β， and IL-18 were significantly elevated （P<0.01）. Compared with the model group， all dose groups of CRPRR showed marked improvement in neuronal morphology which was close to the normal level， with mitochondrial swelling alleviated and chromatin distribution more uniform. The medium- and high-dose groups significantly reduced neurological scores （P<0.01）， while the low-， medium-， and high-dose groups significantly reduced the number of central crossings （P<0.01） and infarct volume （P<0.01）， and decreased TNF-α， IL-1β， and IL-18 levels （P<0.05， P<0.01） compared with the model group. Furthermore， the medium- and high-dose groups significantly reduced TNF-α protein expression （P<0.05，P<0.01）， and the high-dose group significantly reduced IL-1β and IL-18 protein expression （P<0.01）. ConclusionThis study confirmed that CRPRR improves neurological function and alleviates brain tissue damage in MCAO/R rats. Its mechanism may be associated with the downregulation of inflammatory factors TNF-α， IL-1β， and IL-18， as well as the presence of nine active chemical constituents in cerebrospinal fluid， namely paeoniflorin， senkyunolide F， senkyunolide G， paeonimetabolin Ⅰ， paeoniflorin derivative， senkyunolide H， benzoylpaeoniflorin， senkyunolide A， and ligustilide， which are closely related to their brain-protective effects.

ObjectiveTo investigate the chemical constituents of Chuanxiong Rhizoma-Paeoniae Radix Rubra（CRPRR） that cross the blood-brain barrier in rats with ischemic stroke， their brain-protective effects， and their impact on inflammatory factors including tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-18 （IL-18） based on ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF-MS） and pharmacodynamic experiments. MethodsA focal cerebral ischemia-reperfusion injury model was established in rats via the middle cerebral artery occlusion/reperfusion （MCAO/R） method using intraluminal suture. Neurological function was evaluated using behavioral scoring. UPLC-Q-TOF-MS was employed to identify the chemical constituents of CRPRR that crossed the blood-brain barrier and entered the cerebrospinal fluid in MCAO/R model rats. Male Sprague-Dawley rats were randomly divided into six groups： sham operation group， model group， low-， medium-， and high-dose CRPRR groups （1.35， 2.7， 5.4 g·kg^-1， respectively）， and an edaravone group （5 mg·kg^-1）， with 12 rats in each group. The sham and model groups received normal saline， while the treatment groups received the respective doses of CRPRR once daily by gavage for three consecutive weeks. The brain-protective effects of CRPRR were assessed using the Longa five-point scoring method， open field test， Morris water maze， 2，3，5-triphenyltetrazolium chloride （TTC） staining， hematoxylin and eosin （HE） staining， and transmission electron microscopy. ResultsNine chemical constituents were identified in the cerebrospinal fluid containing CRPRR， namely paeoniflorin， senkyunolide F， senkyunolide G， paeonimetabolin Ⅰ， paeoniflorin derivative， senkyunolide H， benzoylpaeoniflorin， senkyunolide A， and ligustilide. Animal experiment results showed that compared with the sham operation group， the model group exhibited disordered neuronal arrangement， severe vacuolation， nuclear pyknosis， and evident mitochondrial swelling. Chromatin aggregation and peripheralization were also observed. Neurological scores and the number of crossings in the central region were significantly increased （P<0.01）， while platform crossings were significantly decreased （P<0.01）， and clear infarct areas were present （P<0.01）. Serum levels and protein expression of TNF-α， IL-1β， and IL-18 were significantly elevated （P<0.01）. Compared with the model group， all dose groups of CRPRR showed marked improvement in neuronal morphology which was close to the normal level， with mitochondrial swelling alleviated and chromatin distribution more uniform. The medium- and high-dose groups significantly reduced neurological scores （P<0.01）， while the low-， medium-， and high-dose groups significantly reduced the number of central crossings （P<0.01） and infarct volume （P<0.01）， and decreased TNF-α， IL-1β， and IL-18 levels （P<0.05， P<0.01） compared with the model group. Furthermore， the medium- and high-dose groups significantly reduced TNF-α protein expression （P<0.05，P<0.01）， and the high-dose group significantly reduced IL-1β and IL-18 protein expression （P<0.01）. ConclusionThis study confirmed that CRPRR improves neurological function and alleviates brain tissue damage in MCAO/R rats. Its mechanism may be associated with the downregulation of inflammatory factors TNF-α， IL-1β， and IL-18， as well as the presence of nine active chemical constituents in cerebrospinal fluid， namely paeoniflorin， senkyunolide F， senkyunolide G， paeonimetabolin Ⅰ， paeoniflorin derivative， senkyunolide H， benzoylpaeoniflorin， senkyunolide A， and ligustilide， which are closely related to their brain-protective effects.

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.