Military hospital preparation rooms are an important part of military medical institutions and have played an important role in military pharmacy support in history. However, with the development of science and technology, the improvement of domestic pharmaceutical production and innovation capabilities, and the adjustment of the military establishment and system, the establishment structure, functional tasks, and business forms of military medical institutions have undergone significant changes. The historical evolution of military preparation rooms were reviewed, the current situation were analyzed and the development challenges faced were identified. It was also explored how military hospital preparation rooms, as an important link in military pharmaceutical support, can face new situations and adapt to new forms of warfare. By enhancing the military efficiency of preparation rooms, it could play a greater role in improving medical support capabilities and enhancing the combat effectiveness of troops.

Objective: To investigate the urban and rural differences in adverse outcomes of pulmonary tuberculosis (PTB) in patients with pulmonary tuberculosis-diabetes mellitus comorbidity (PTB-DM), so as to provide insights into improving the prevention and treatment measures for PTB-DM. Methods: Patients with PTB-DM who were admitted and discharged from 14 designated tuberculosis hospitals in Hangzhou City from 2018 to 2022 were selected. Basic information, and history of diagnosis and treatment were collected through hospital information systems. The adverse outcomes of PTB were defined as endpoints, and the proportions of adverse outcomes of PTB in urban and rural patients with PTB-DM were analyzed. Factors affecting the adverse outcomes of PTB were identified using a multivariable Cox proportional hazards regression model. Results: A total of 823 patients with PTB-DM were enrolled, including 354 (43.01%) urban and 469 (56.99%) rural patients. There were 112 (13.61%) patients with adverse outcomes of PTB. The proportions of adverse outcomes of PTB in urban and rural patients were 14.41% and 13.01%, respectively, with no statistically significant difference (P>0.05). Multivariable Cox proportional hazards regression analysis identified first diagnosed in county-level hospitals or above (HR=2.107, 95%CI: 1.181-3.758) and drug resistance (HR=3.303, 95%CI: 1.653-6.600) as the risk factors for adverse outcomes of PTB in urban patients with PTB-DM, while the treatment/observed management throughout the process (HR=0.470, 95%CI: 0.274-0.803) and fixed-dose combinations throughout the process (HR=0.331, 95%CI: 0.151-0.729) as the protective factors for adverse outcomes in rural patients with PTB-DM. Conclusions There are differences in influencing factors for adverse outcomes of PTB in urban and rural patients with PTB-DM. The adverse outcomes of PTB are associated with first diagnosed hospitals and drug resistance in urban patients, and are associated with the treatment/observed management and fixed-dose combinations throughout the process in rural patients.

ObjectiveTo investigate the association and translational mechanism between the hepatotoxicity of Xianling Gubao （XLGB） and its treatment of osteoporosis based on a zebrafish model. MethodsZebrafish were randomly selected four days after fertilization （4 dpf） and exposed to different concentrations of XLGB （0.7，0.35 mg·L^-1） for 96 h. At the endpoint of the exposure， the mortality rates of zebrafish in the treatment groups of different concentrations were counted， and the "dose-toxicity" curves were plotted. The 10% sublethal concentration （LC₁₀） was calculated. The liver area， acridine orange staining， and pathological tissue sections of transgenic zebrafish ［CZ16 （gz15Tg.Tg （fabp 10a： ds Red； ela31： EGFP）］ were used as indicators to confirm the hepatic damage caused by the sublethal concentration of XLGB. By using the prednisolone （PNSL）-induced osteoporosis model of zebrafish， the anti-osteoporosis activity of XLGB was evaluated by using the area of skull stained by alizarin red and the cumulative optical density value as indicators. Then， the toxicity difference of XLGB on the liver of zebrafish in healthy and osteoporotic states was compared， and the mechanism of the translational action of the toxicity of XLGB was predicted based on network pharmacology and real-time polymerase chain reaction（Real-time PCR）. ResultsThe LC₁₀ of XLGB on zebrafish （8 dpf） was 0.7 mg·L^-1. Compared with the blank group， the sublethal concentration （LC₁₀=0.7 mg·L^-1， 1/2 LC₁₀=0.35 mg·L^-1） of XLGB induced an increase in the number of apoptosis of hepatocytes in a dose-dependent manner， and the tissue arrangement of the liver was disordered and loose. The vacuoles were obvious， and the fluorescence area of the liver was significantly reduced （P<0.01）. Compared with the blank group， the mineralized area and cumulative optical density value of zebrafish skull in the PNSL model group were significantly reduced （P<0.01）， and those in the 0.7，0.35 mg·L^-1 XLGB treatment group were significantly increased compared with the model group （P<0.01）. Most importantly， 0.7 mg·L^-1 XLGB had no significant effect on the liver of zebrafish in the osteoporosis disease model compared with the blank group. The results of network pharmacology and real-time PCR experiments showed that the toxic transformation of XLGB might be related to the differences in the expression levels of key targets， such as tumor protein 53 （TP53）， cysteine aspartic acid specific protease-3（Caspase-3）， interleukin（IL）-6， and alkaline phosphatase（ALP） in different organismal states. ConclusionUnder certain conditions， XLGB has hepatotoxicity in normal zebrafish， but under osteoporotic conditions， XLGB not only exerts significant anti-osteoporosis activity but also alleviates hepatotoxicity significantly， which provides a reference for the safe clinical use of XLGB and real evidence for the theories of traditional Chinese medicine of attacking poison with poison and of treating disease with corresponding drugs without damage to the body.

Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.