Sepsis is a lethal condition resulting from the host's dysregulated response, involving complex pathophysiological mechanisms, including the host's biphasic immune response and metabolic disturbances. Diagnosing and treating sepsis remain formidable challenges, with the absence of definitive biomarkers and effective therapeutic interventions to date. Animal models of sepsis are pivotal in unraveling the disease's pathogenesis and identifying potential treatments, playing a crucial role in enhancing our comprehension of its intrinsic nature. However, there is no animal model that can comprehensively and accurately simulate the complex pathophysiological process of human sepsis. This review discusses the widely used sepsis animal models, exploring their advantages and limitations in terms of pathogenesis, inflammatory response, pathophysiological changes, and organ dysfunction. It summarizes the application scenarios and latest research advancements of these models and provides an outlook on potential future improvements.
Sepsis/physiopathology* ; Animals ; Disease Models, Animal ; Humans

Objective:To assess the fall risk of hospitalized patients with cardiovascular diseases, analyze the related influencing factors, and construct a prediction model based on machine learning technology, so as to provide a basis for the fall management of hospitalized patients with cardiovascular diseases.Methods:This study was a retrospective cohort study. A total of 450 patients admitted to the Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University from June 2017 to June 2024 were selected as the research objects by convenience sampling method. By reviewing electronic medical records, trained nurses extracted the patients' general information and Activities of Daily Living Scale (ADL) scores during hospitalization. Lasso regression was used to screen risk factors, and machine learning libraries were used to construct support vector machine (SVM), decision tree, XGBoost, and neural network models. Bootstrap resampling method and area under the curve (AUC) were used to verify the model performance.Results:Among the 450 patients, there were 261 males and 189 females, with a mean age of (66.0 ± 8.4) years. Among them, 90 patients fell during hospitalization and 360 patients did not fall. The results of Lasso regression showed that ADL score ≤60 points, use of hypnotics, hypokalemia, nighttime toilet visits≥2 times, use of antihypertensive drugs, no caregiver, and history of atrial fibrillation were all risk factors for falls in hospitalized patients with cardiovascular diseases (regression coefficients ranging from 0.61 to 1.20, all P<0.01). Among the machine learning models, XGBoost had the best comprehensive performance (AUC=0.98), which was better than decision tree (AUC=0.66), SVM (AUC=0.95), and neural network (AUC=0.87). Conclusions:The fall risk of hospitalized patients with cardiovascular diseases is jointly affected by physiological, medication and behavioral factors, and the XGBoost model can effectively identify high-risk groups. In actual clinical work, nursing strategies can be optimized in combination with risk factors, and the application of intelligent fall prediction and assessment tools can be promoted.

Objective:To assess the fall risk of hospitalized patients with cardiovascular diseases, analyze the related influencing factors, and construct a prediction model based on machine learning technology, so as to provide a basis for the fall management of hospitalized patients with cardiovascular diseases.Methods:This study was a retrospective cohort study. A total of 450 patients admitted to the Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University from June 2017 to June 2024 were selected as the research objects by convenience sampling method. By reviewing electronic medical records, trained nurses extracted the patients' general information and Activities of Daily Living Scale (ADL) scores during hospitalization. Lasso regression was used to screen risk factors, and machine learning libraries were used to construct support vector machine (SVM), decision tree, XGBoost, and neural network models. Bootstrap resampling method and area under the curve (AUC) were used to verify the model performance.Results:Among the 450 patients, there were 261 males and 189 females, with a mean age of (66.0 ± 8.4) years. Among them, 90 patients fell during hospitalization and 360 patients did not fall. The results of Lasso regression showed that ADL score ≤60 points, use of hypnotics, hypokalemia, nighttime toilet visits≥2 times, use of antihypertensive drugs, no caregiver, and history of atrial fibrillation were all risk factors for falls in hospitalized patients with cardiovascular diseases (regression coefficients ranging from 0.61 to 1.20, all P<0.01). Among the machine learning models, XGBoost had the best comprehensive performance (AUC=0.98), which was better than decision tree (AUC=0.66), SVM (AUC=0.95), and neural network (AUC=0.87). Conclusions:The fall risk of hospitalized patients with cardiovascular diseases is jointly affected by physiological, medication and behavioral factors, and the XGBoost model can effectively identify high-risk groups. In actual clinical work, nursing strategies can be optimized in combination with risk factors, and the application of intelligent fall prediction and assessment tools can be promoted.

Objective To investigate the dosimetric comparison of target volumes and organs at risk (OAR) between volumetric-modulated arc therapy (VMAT) and intensity-modulated radiotherapy (IMRT) for esophageal cancer by a meta-analysis.Methods A literature search was performed to collect the clinical studies on dosimetric comparison between VMAT and IMRT.The primary endpoints of interest were dosimetric parameters of target volumes and OAR, number of monitor units (MUs), and treatment time (TT).Results A total of 17 studies involving 323 patients were included in this meta-analysis.When the total dose was>50.4 Gy, VMAT showed significantly lower mean dose (Dmean) of gross tumor volume (GTV) and maximum dose (Dmax) of planning target volume (PTV) than IMRT (P=0.009;P=0.039).There were no significant differences in Dmean, V30, and V40 of the heart, Dmax of the spinal cord, and V5, V10, and Dmean of the lung between VMAT and IMRT (P>0.05).VMAT showed significantly lower V15, V20, and V30 of the lung than IMRT (P=0.001;P=0.000;P=0.023).When the single dose was 1.8 Gy and 2.0 Gy, VMAT showed significantly lower TT (reduced by 323.5 s and 193.7 s) and number of MUs (reduced by 275.4 MU and 134.2 MU) than IMRT (P=0.000 and 0.009;P=0.000 and 0.022).Conclusions VMAT can significantly reduce TT, MUs, irradiation dose to the lung, and the risk of radiation pneumonitis, and improve the utilization rate of equipment.Compared with IMRT, VMAT has no significant advantages in protection of the spinal cord and the heart and dosimetric parameters of target volumes except Dmean of PTV and Dmean and Dmax of GTV when the total dose was ≤50.4 Gy.

Objective To explore the experience in diagnosis and treatment ot primary transitional cell carcinoma of prostate for the early and accurate to diagnosis and treatment.Methods The clinical data and features of 3 cases were retrospectively reviewed.Results All patients were diagnosed as primary transitional cell carcinoma of prostate.Two cases were advanced tumor.The preoperative examinations (ultrasound and serum,PSA) have failed to accurately indicate the diagnosis.All of them were confirmed by pathological examination.1 case lost follow-up,1 case performed TURP + chemotherapy through intravesical administration have survived 17 months respectively till today.The other case has already survived 2 months postoperatively but with lumbar spine bone metastasis.Conclusions Early diagnosis of primary transitional cell carcinoma of prostate is difficult.The diagnosis of the disease depends on the transrectal needle biopsy of the prostate or the specimens of the prostate after the Urethroscopy.Because of the prognosis is bad,and prone to pathological missed diagnosis or misdiagnosis.Need to exclude multicentric lesions and mixed tumor and choice of treatment method.

Objective To report the first case of cutaneous type adult T-cell leukemia/lymphoma(cATLL) in China. Methods The skin lesion was examined by histopathology and direct immunofluorescence (DIF), and the immunophenotype was also studied. ELISA and Western blot were used to test the serum antibodies to HTLV-Ⅰ, and HTLV-Ⅰproviral DNA of lymphoid cells was detected by PCR. Results The patient had polymorphic skin lesions including papules, plaques, and bullae with tense or flaccid walls. Histopathogical examination showed subepidermal bullae, and there were small-to-medium-sized atypical lymphoid cell infiltrations in the dermal papilla in the bottom, and border of the bulla. CD45+ and CD45RO+ staining, and negative DIF were observed in the atypical lymphoid cells. The serum antibodies to HTLV-Ⅰ, and proviral DNA of HTLV-Ⅰin the blood lymphoid cells were detected. The patient died with a disease course of one year and ten months. Conclusion ATLL is not extremely rare in China, and cATLL may also exist.

Objective To report a case of lymphomatoid papulosis(LyP)followed by mycosis fun-goides(MF)during his27years course and determine whether these diseases are clonally related.Methods Characteristics of clinicopathology and immunophenotype of skin tissues of different phases of the patients were compared.Simultaneously,clonal T-cell receptor gene rearrangement of skin lesion tissues and blood specimens were detected by means of polymerase chain reaction and Southern blot analysis.Results A dis-tinct clinical and pathological feature was shown in this case of LyP followed by MF.The proliferating lym-phocytes in LyP and MF were mature T helper lymphocytes,and with same T-cell clone.Conclusion LyP followed by MF displays a same T-cell clone.The clonal T-cell is irrelevant to the biological behavior of these diseases.