Antibody-negative autoimmune encephalitis （AE） in children poses certain challenges for clinical diagnosis and treatment due to the lack of characteristic autoantibodies. This study conducts a retrospective analysis of the clinical data of a boy， aged 7 years， who had the main manifestations of “irritability， vomiting， pyrexia， and lethargy”. Video electroencephalography showed diffuse slow waves， with the presence of type 2 cerebrospinal fluid oligoclonal bands， and the boy was tested negative for related autoantibodies in blood and cerebrospinal fluid. Finally the boy was diagnosed with antibody-negative AE. After admission， the boy received first-line immunotherapy with methylprednisolone and intravenous immunoglobulin， combined with tocilizumab for intensive treatment， and the symptoms were rapidly relieved. At 1 week after discharge， the boy experienced memory loss， and head MRI suggested cerebral atrophy. After 1 month of adjuvant hyperbaric oxygen therapy， the boy’s memory recovered to the level before disease onset， and reexaminations of MRI and video electroencephalography obtained normal results.No recurrence was observed during follow-up for 6 months， and the boy achieved satisfactory academic performance.This case suggests that for children with antibody-negative AE， first-line immunotherapy combined with tocilizumab can rapidly control inflammation， and the addition of hyperbaric oxygen therapy can effectively improve subsequent cerebral structural and cognitive abnormalities， with few adverse reactions， which provides a new clinical approach for the treatment of this type of disease.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

ObjectiveTo investigate the therapeutic effect of sequential administration of Dihuang Baoyuan granules （DHBY， the prescription for consolidating body resistance） and Fuling Yunhua granules （FLYH， the prescription for treating symptoms） on spontaneous type 2 diabetes mellitus （T2DM） in mice. MethodsAccording to the fasting blood glucose （FBG） level， 12-week-old db/db mice were randomized into six groups： model， DHBY （18.02 g·kg^-1）， FLYH （14.80 g·kg^-1）， sequential administration 1 （SEQ-1， DHBY 18.02 g·kg^-1+FLYH 14.80 g·kg^-1）， sequential administration 2 （SEQ-2， FLYH 14.80 g·kg^-1+DHBY 18.02 g·kg^-1）， and dapagliflozin （Dapa， 1.3 mg·kg^-1）. The m/m mice in the same litter were selected as the normal group. The mice were administrated with corresponding drugs by gavage for 8 consecutive weeks. During the 8 weeks of drug administration and 2 weeks after withdrawal， the retinal thickness， FBG， hemoglobin A1c （HbA1c）， and insulin were determined， and histopathological changes of the pancreas， liver， kidney， and retina were observed by hematoxylin-eosin （HE） staining. ResultsCompared with the model group， SEQ-1 for 4 weeks lowered the FBG level （P<0.05）， raised the insulin level， decreased the triglyceride （TG） level （P<0.05）， increased the number of optic ganglion cells and diminished vacuolar degeneration of pancreatic islet and liver. SEQ-2 lowered FBG and HbA1c levels （P<0.05）， rose the insulin level， increased the retinal thickness and the number of optic ganglion cells （P<0.05）， and alleviated vacuolar degeneration of pancreatic islet and liver. Two weeks after drug withdrawal， Dapa tended to increase FBG and HbA1c compared with those at the time of drug withdrawal. However， the levels of FBG and HbA1c in the SEQ-2 group remained decreasing （P<0.05）. ConclusionSEQ-1 and SEQ-2 can lower the blood glucose level and ameliorate diabetic retinopathy， and SEQ-2 outperformed DHBY and FLYH in lowering the blood glucose level. Moreover， SEQ-2 can maintain the blood glucose-lowering effect after drug withdrawal.

ObjectiveTo investigate the therapeutic effect of sequential administration of Dihuang Baoyuan granules （DHBY， the prescription for consolidating body resistance） and Fuling Yunhua granules （FLYH， the prescription for treating symptoms） on spontaneous type 2 diabetes mellitus （T2DM） in mice. MethodsAccording to the fasting blood glucose （FBG） level， 12-week-old db/db mice were randomized into six groups： model， DHBY （18.02 g·kg^-1）， FLYH （14.80 g·kg^-1）， sequential administration 1 （SEQ-1， DHBY 18.02 g·kg^-1+FLYH 14.80 g·kg^-1）， sequential administration 2 （SEQ-2， FLYH 14.80 g·kg^-1+DHBY 18.02 g·kg^-1）， and dapagliflozin （Dapa， 1.3 mg·kg^-1）. The m/m mice in the same litter were selected as the normal group. The mice were administrated with corresponding drugs by gavage for 8 consecutive weeks. During the 8 weeks of drug administration and 2 weeks after withdrawal， the retinal thickness， FBG， hemoglobin A1c （HbA1c）， and insulin were determined， and histopathological changes of the pancreas， liver， kidney， and retina were observed by hematoxylin-eosin （HE） staining. ResultsCompared with the model group， SEQ-1 for 4 weeks lowered the FBG level （P<0.05）， raised the insulin level， decreased the triglyceride （TG） level （P<0.05）， increased the number of optic ganglion cells and diminished vacuolar degeneration of pancreatic islet and liver. SEQ-2 lowered FBG and HbA1c levels （P<0.05）， rose the insulin level， increased the retinal thickness and the number of optic ganglion cells （P<0.05）， and alleviated vacuolar degeneration of pancreatic islet and liver. Two weeks after drug withdrawal， Dapa tended to increase FBG and HbA1c compared with those at the time of drug withdrawal. However， the levels of FBG and HbA1c in the SEQ-2 group remained decreasing （P<0.05）. ConclusionSEQ-1 and SEQ-2 can lower the blood glucose level and ameliorate diabetic retinopathy， and SEQ-2 outperformed DHBY and FLYH in lowering the blood glucose level. Moreover， SEQ-2 can maintain the blood glucose-lowering effect after drug withdrawal.

“Chinese Pharmacopoeia” is the legal basis for drug development, production, operation, use and management in China, and the Chinese Pharmacopoeia 2025 Edition is going to be issued and implemented. This article introduces the revision and amendment situations, analyzes the characteristics of the new edition of the Pharmacopoeia and the future development direction of national standards for better understanding and implementation of the latest edition of pharmacopoeia.

Objective To investigate the effect of nutritional deficiency on autophagic flux in human hepatoblastoma HepG2 cells and the autophagy-dependent molecular pathway mediated by ATG5 against nutritional stress.Methods Lentiviral siRNA knockdown of ATG5 was performed to knock down the expression in HepG2 cells,and the transfection was verified by Western blotting and qRT-PCR.HBSS was used to treat HepG2 cells for 0,1,2,3,4,5,6 and 7 h for starvation-induced autophagy.Monodansylcadaverine(MDC)fluorescence staining was employed to detect the formation of intracellular autophagic vesicles.Western blotting was performed to measure the expression changes in microtubule-associated protein 1A/1B-light chain 3(LC3),autophagy receptor protein(sequestosome 1,P62),autophagy-related gene 5(ATG5)and nutrient stress pathway(AMPK/mTOR pathway)related proteins.CCK-8 assay was utilized to test cell proliferation.Results Compared with the HepG2 cells under conventional culture medium,starvation induction resulted in more autophagic vacuoles(accumulation of autofluorescent marker,MDC),which peaked at 6 h of starvation(P<0.000 1),up-regulated protein levels of LC3B-Ⅱ and ATG5(P<0.05),decreased protein expression of P62(P<0.05),gradual activation of the AMPK/mTOR pathway with elapse of starvation time,and increase in the intensity of autophagic flux.However,in the HepG2 cells with ATG5 knockdown,starvation treatment led to decreased ATG5 expression,no significant difference in LC3B-II and P62 changes,inhibited autophagic flux,and suppressed cell proliferation(P<0.000 1).Conclusion Human hepatoblastoma HepG2 cells can regulate autophagic flux to against nutritional stress by inducing ATG5 expression.

"Chinese Pharmacopoeia"is the legal basis for drug development,production,operation,use and man-agement in China,and the Chinese Pharmacopoeia 2025 Edition is going to be issued and implemented.This article introduces the revision and amendment situations,analyzes the characteristics of the new edition of the Pharmaco-poeia and the future development direction of national standards for better understanding and implementation of the latest edition of pharmacopoeia.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To investigate the impact of the interval period between biopsy and MR examination on tumor detection and extraprostatic extension (EPE) assessment for prostate cancer (PCa) using multi-parametric MRI (mpMRI).Methods:The study was cross-sectional and retrospectively included 130 patients with PCa who underwent RP and preoperative systematic biopsies followed by mpMRI between January 2021 and December 2022 in the First Medical Center of Chinese PLA General Hospital. Patients were divided into 3 groups according to interval following biopsy (group A,<3 weeks, 31 cases; group B, 3-6 weeks, 67 cases; group C,>6 weeks, 32 cases). The percentages of hemorrhage volume in the total prostate were drawn on T 1WI and calculated. The junior, senior and expert radiologists independently localized the index lesions and calculated the accuracy for tumor detection, in addition to assessing the probabilities of EPE according to EPE grade. The correlation between the hemorrhage extent and interval was analyzed using the Spearman correlation coefficient. The accuracy for tumor detection was compared using χ2 test among groups. The diagnostic performance of the radiologists for EPE prediction was assessed using the receiver operating characteristic curve, and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. Results:The percentage of hemorrhage was correlated with the interval between biopsy and MR examination ( r=-0.325, P<0.001). The detection accuracy of junior radiologist was 83.9% (26/31), 76.1% (51/67), and 78.1% (25/32) in group A, B and C, respectively; no differences were observed in the detection accuracy among three groups ( χ2=0.76, P=0.685). The detection accuracy of senior radiologist was 83.9% (26/31), 80.6% (54/67), and 71.9% (23/32) in 3 groups with no differences ( χ2=1.53, P=0.464). The detection accuracy of expert radiologist was 80.6% (25/31), 77.6% (52/67), and 93.8% (30/32) with no differences ( χ2=3.95, P=0.139). The AUC (95% CI) for predicting EPE were 0.830 (0.652-0.940), 0.704 (0.580-0.809), 0.800 (0.621-0.920) in the group A, B and C for junior radiologist; 0.876 (0.708-0.966), 0.768 (0.659-0.863), 0.896 (0.736-0.975) for senior radiologist; and 0.866 (0.695-0.961), 0.813 (0.699-0.895), 0.852 (0.682-0.952) for expert radiologist, respectively. No differences were observed among the subgroups in each radiologist ( P>0.05). Conclusion:The interval period does not significantly affect the detection accuracy and EPE assessment of PCa using mpMRI. There is probably no necessity for prolonged intervals following systematic biopsy to preserve the clarity of MRI interpretation for PCa.