Technological innovation and management transformation not only introduce negentropy into public hospital organizations but may also exacerbate entropy accumulation.Different combinations of diagnostic and treatment technologies and management transformations have different impacts on the total entropy.While improved diagnostic-therapeutic technologies and management transformations enhance technological negentropy and managerial negentropy,thereby increasing operational efficiency,it may simultaneously increases organizational complexity,causing inefficient information flow,delayed decision-making,resource wastage,and elevated technological entropy and managerial entropy.Specialty-driven deployment of hospital management strategies enables a dual-driven mechanism to introduce negentropy.By constructing dissipative structures that enhance"synergistic negentropy"while reducing"internal friction entropy",hospitals can decrease organizational disorder and increase diagnostic-therapeutic coordination to achieve high-quality development.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

At present,the development of clinical specialties in public hospitals is faced with internal and external superposition problems such as high internal and external operating pressure,lagging connotation construction,insufficient driving force for sustainable development,and inadequate implementation of the autonomy of specialty development.In this regard,from the perspective of building an internal and external linkage mechanism,external governance policies and internal management measures of public hospitals should be effectively linked and coordinated to promote the transformation of clinical specialties from high-speed development to high-quality development.Among them,improving the technical ability of diagnosis and treatment of special diseases and ensuring the health of regional people is the fundamental goal,the dual-wheel drive of talent and technology is the key,the personnel and salary management oriented by business development and value output is the driving force,the reform of external governance mechanism and the effective allocation of resources is the guarantee,the performance evaluation and supervision and management is the means,and the continuous medical service system is the support.To provide a fundamental guarantee for the high-quality development of clinical specialties in public hospitals.

At present,the development of clinical specialties in public hospitals is faced with internal and external superposition problems such as high internal and external operating pressure,lagging connotation construction,insufficient driving force for sustainable development,and inadequate implementation of the autonomy of specialty development.In this regard,from the perspective of building an internal and external linkage mechanism,external governance policies and internal management measures of public hospitals should be effectively linked and coordinated to promote the transformation of clinical specialties from high-speed development to high-quality development.Among them,improving the technical ability of diagnosis and treatment of special diseases and ensuring the health of regional people is the fundamental goal,the dual-wheel drive of talent and technology is the key,the personnel and salary management oriented by business development and value output is the driving force,the reform of external governance mechanism and the effective allocation of resources is the guarantee,the performance evaluation and supervision and management is the means,and the continuous medical service system is the support.To provide a fundamental guarantee for the high-quality development of clinical specialties in public hospitals.

Technological innovation and management transformation not only introduce negentropy into public hospital organizations but may also exacerbate entropy accumulation.Different combinations of diagnostic and treatment technologies and management transformations have different impacts on the total entropy.While improved diagnostic-therapeutic technologies and management transformations enhance technological negentropy and managerial negentropy,thereby increasing operational efficiency,it may simultaneously increases organizational complexity,causing inefficient information flow,delayed decision-making,resource wastage,and elevated technological entropy and managerial entropy.Specialty-driven deployment of hospital management strategies enables a dual-driven mechanism to introduce negentropy.By constructing dissipative structures that enhance"synergistic negentropy"while reducing"internal friction entropy",hospitals can decrease organizational disorder and increase diagnostic-therapeutic coordination to achieve high-quality development.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective:To investigate the action targets and mechanism of celastrol in alleviating liver inflammatory injuries using network pharmacology; To verify the key targets through liver ischemia-reperfusion injury inducing inflammation mouse model.Methods:The targets of celastrol were integrated by searching SymMap, BATMAN-TCM, TCMSP, HIT 2.0, LigTMap, SEA, SwissTarget, Super-PRED, STITCH databases. The molecular targets of hepatic inflammatory injuries were investigated by GeneCards and DisGeNET databases. The intersection of drug targets and disease targets was obtained by Venn diagram to obtain the possible targets of celastrol in alleviating hepatic inflammatory diseases. Cytoscape 3.9.1 software was used to analyze the key targets of protein interaction (PPI) networks based on STRING database, and enrichment analysis was conducted through DAVID database. Based on the key targets, the ceRNA network was mapped by retrieving the starBase database. Molecular docking was used to evaluated the binding of celastrol with the key target proteins. The mice were divided into sham-operation solvent group, sham- operation medication group, model group, and Tripterygium wilfordii extract low- (0.1 mg/kg), medium- (0.3 mg/kg), and high- (1 mg/kg) dosage groups according to body weight, with 3-4 mice in each group. After 7 days of corresponding drug intervention, except for the sham-operation solvent group and sham-operation medication group, other groups were prepared with ischemia-reperfusion induced liver inflammation mouse models. The serum transaminase levels in mice were detected; the pathological morphology of mouse liver tissue was observed using HE staining; the expressions of IL-6 and TNF-α were detected in liver tissue using immunohistochemistry staining.Results:The key targets of celastrol in alleviating liver inflammation were inflammatory cytokines such as IL6 and TNF. The analysis of functional enrichment results showed that the key signaling pathways of Tripterygium wilfordii extract in reducing liver inflammatory injury included inflammatory response, cell apoptosis and proliferation, HIF1, and other pathways. Triptolide pretreatment could reduce serum aminotransferase level ( P<0.01) and liver inflammatory factors expression such as IL-6 and TNF-α ( P<0.05, P<0.01) after hepatic ischemia-reperfusion. Conclusion:Celastrol can alleviate hepatic ischemia-reperfusion injury, and its mechanism is closely related to the reduction of inflammatory factors such as IL-6 and TNF-α and the alleviation of hepatic inflammatory injury.

ObjectiveTo investigate the effect of Yunkang oral liquid on postpartum kidney deficiency in mice. MethodPostpartum mice were randomized into model and low-dose （6 mL·kg^-1）， medium-dose （9 mL·kg^-1）， and high-dose （12 mL·kg^-1） Yunkang oral liquid groups. The mouse model of postpartum kidney deficiency was established by sleep deprivation combined with forced swimming. Another 9 female ICR mice were selected as the normal control group. The mice were administrated with Yunkang oral liquid during the period of modeling. The levels of estradiol （E₂）， progesterone （P）， follicle-stimulating hormone （FSH）， and luteinizing hormone （LH） in the serum were measured by the enzyme-linked immunosorbent assay. The morphological changes of ovaries and uterus were observed by hematoxylin-eosin （HE） staining， and the expression of transforming growth factor （TGF）-β and Smad2/3 was determined by immunohistochemistry and Western blotting. ResultThe mice in the model group showed prolonged estrous cycle， reduced voluntary activity， dorsal temperature， grip strength， and bone strength， and whitening tongue coating. Compared with the model group， Yunkang oral liquid shortened the estrous cycle， increased the voluntary activity， dorsal temperature， grip strength， and bone strength， and alleviated the whitening of tongue coating. Moreover， it elevated the E₂ and P levels and lowered the FSH and LH levels in the serum， decreased ovarian follicular atresia rate， promoted uterine repair， and down-regulated the expression of TGF-β and Smad2/3 in the ovarian and uterine tissues. ConclusionYunkang oral liquid can ameliorate postpartum kidney deficiency in mice by regulating the TGF-β/Smads signaling pathway.

Hospitals without disciplines have no performance performance,the focus of performance reform lies in mechanism transformation.Hospital performance management and discipline construction are interdependent relationships.Performance management provides guidance and motivation for the process of discipline construction,the construction of disciplines provides a value basis for the business connotation of performance management.Guiding discipline construction by clarifying the disease efficacy system.Clarify discipline construction by refining the technical system.Quantify discipline construction by organizing departmental projects.Transform financial and personnel performance assessment into technical value management.The conjugate mechanism can trigger and drive the deployment,support,and fulfillment of business.Performance management plans are only effective when they are publicly available.Only solutions that comply with business logic can be made public.By mining valuable data,we tap into potential business value.

Objective:To explore eye movement characteristics in newly diagnosed, drug-naive Parkinson′s disease (PD) patients and their correlation with motor and non-motor symptoms.Methods:Seventy-five newly diagnosed, drug-naive PD patients and 46 healthy controls (HCs) were included in this cross-sectional study. Patients were recruited from the Department of Neurology, Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine from November 2017 to December 2021, while HCs were recruited from the local community during the same period. For PD patients, motor severity was measured with the modified Hoehn and Yahr stage, Movement Disorder Society Unified Parkinson′s Disease Rating Scale part Ⅲ and the Freezing of Gait questionnaire. Non-motor symptoms were evaluated by serial scales such as Non-Motor Symptoms Questionnaire, 16-item odor identification test from Sniffin Sticks, 17-item Hamilton Rating Scale for Depression, Chinese version of Mini-Mental State Examination, Montreal Cognitive Assessment Basic and REM Behavior Disorder Screening Questionnaire. All subjects underwent oculomotor test including pro-saccade task and smooth pursuit eye movement (SPEM) task in the horizontal direction via videonystagmography. Visually guided saccade latency, saccadic accuracy and gain in SPEM at three frequencies (0.1, 0.2, 0.4 Hz) of the horizontal axis were compared between the 2 groups. The association between key oculomotor parameters and clinical phenotypes was explored in PD patients. The receiver operating characteristic (ROC) analyses of eye movement parameters as independent factors were also performed for detecting PD from HCs, then combining the saccadic latency, saccadic accuracy and the most significant SPEM gain (0.4 Hz) as the model to distinguish PD from HCs.Results:Relative to HCs, newly diagnosed, drug-naive PD patients showed prolonged saccadic latency [(210.4±41.3) ms vs (191.3±18.9) ms, t=-3.445, P=0.001] and decreased saccadic accuracy (88.4%±6.8% vs 92.2%±6.1%, t=3.064, P=0.003). SPEM gain in PD was uniformly reduced at each frequency(0.1 Hz: 0.68±0.15 vs 0.74±0.14, t=2.261, P=0.026; 0.2 Hz: 0.72±0.16 vs 0.79±0.16, t=2.704, P=0.008; 0.4 Hz: 0.67±0.19 vs 0.78±0.19, t=2.937, P=0.004). The ROC analyses of saccade latency, saccadic accuracy and gain in SPEM at 0.1, 0.2, 0.4 Hz as independent factors for detecting PD from HCs showed that the area under the curve (AUC) of each parameter was lower than 0.7: the AUC of saccade latency was 0.641 ( P=0.010), the AUC of saccadic accuracy was 0.681 ( P=0.001), the AUC of gain in SPEM at 0.1 Hz was 0.616 ( P=0.032), at 0.2 Hz was 0.652 ( P=0.005), at 0.4 Hz was 0.660 ( P=0.003). Combining the saccadic latency, saccadic accuracy and the most significant SPEM gain (0.4 Hz) revealed that the model could significantly distinguish PD from HCs with an 80.4% sensitivity and a 73.3% specificity (AUC=0.780, P<0.001). Prolonged saccadic latency was correlated with long disease duration ( β=0.334, 95% CI 0.014-0.654, P=0.041), whereas decreased SPEM gain was associated with severe motor symptoms in newly diagnosed drug-naive PD patients (0.1 Hz: β=-0.004, 95% CI -0.008--0.001, P=0.036; 0.4 Hz: β=-0.006, 95% CI -0.011--0.001, P=0.012). Conclusions:Ocular movements are impaired in newly diagnosed, drug-naive PD patients. These changes could be indicators for disease progression in PD.