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.

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.

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.

There are many kinds of medical facial protective equipment with different functions. However, due to the lack of recognition of the hazards of facial occupational exposure, an incomplete understanding of the functions and effects of protective equipment and a lack of awareness, the proportion of staff that wear protective equipment in the Department of Stomatology is low. In this paper, the harmful and protective effects of face occupational exposure of dental staff were reviewed. A literature review showed that with the increasing prevalence of infection with AIDS, hepatitis B, hepatitis C and multidrug resistant bacteria in recent years, the occupational infection rate of medical staff in the Department of Stomatology has increased. The noise generated during oral treatment and the light from photosensitive curing lamps and treatment or surgical lasers can also cause occupational damage to hearing and vision. Face protection measures lack comprehensive functioning, and there is a lack of products that can be easily worn, indicating that the functions of face protection products need to be improved and strengthened. To minimize occupational infection and injury, we should not only improve the existing protective equipment but also improve personal protection awareness through advertising and education and comprehensively explore effective measures to improve occupational safety to ultimately achieve safe and high-quality medical services.

[ ABSTRACT] AIM: To explore the relationship between FRAS 1 protein and brain metastases of non-small cell lung cancer (NSCLC).METHODS:The mRNA expression of FRAS1 in the brain metastatic tumor tissues and primary tumor tissues of NSCLC was detected by qPCR .The protein expression of FRAS 1 in the tumor tissues and normal tissues adjacent to tumor tissues of NSCLC was measured by SP method of immunohistochemistry .The protein expression of FRAS 1 in NSCLC primary tumor tissues with or without brain metastases was also determined .RESULTS:The mRNA expression of FRAS1 in the brain metastatic zone was nearly 10 times higher than that in the primary tumor tissues , and there was sig-nificant difference between the 2 groups (P<0.05).FRAS1 protein was expressed in the NSCLC primary tumor tissues , but was not found in the normal tissues adjacent to primary tumor tissues .The protein expression of FRAS 1 in the NSCLC with brain metastases was significantly higher than that without brain metastases ( P<0.01 ) .CONCLUSION: FRAS1 protein may be associated with the occurrence of NSCLC .The over-expression of FRAS1 protein may be related to brain metastases with NSCLC .

Regorafenib,an oral multi-kinase inhibitor,can inhabit a class of receptor tyrosine kinase,such as angiogenic,stromal,oncogenic and so on.Studies in vitro and clinical trials indicate that Regorafenib has significant antitumor activity.The results of clinical trials are encouraging for the treatment of Refractory solid tumors,especially for colorectal carcinoma.