ObjectiveTo investigate the mechanism of action of Kaixinsan in the treatment of Alzheimer's disease （AD） based on network pharmacology， molecular docking， and animal experimental validation. MethodsThe Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform（TCMSP） and the Encyclopedia of Traditional Chinese Medicine（ETCM） databases were used to obtain the active ingredients and targets of Kaixinsan. GeneCards， Online Mendelian Inheritance in Man（OMIM）， TTD， PharmGKB， and DrugBank databases were used to obtain the relevant targets of AD. The intersection （common targets） of the active ingredient targets of Kaixinsan and the relevant targets of AD was taken， and the network interaction analysis of the common targets was carried out in the STRING database to construct a protein-protein interaction（PPI） network. The CytoNCA plugin within Cytoscape was used to screen out the core targets， and the Metascape platform was used to perform gene ontology（GO） functional enrichment analysis and Kyoto encyclopedia of genes and genomes（KEGG） pathway enrichment analysis. The “drug-active ingredient-target” interaction network was constructed with the help of Cytoscape 3.8.2， and AutoDock Vina was used for molecular docking. Scopolamine （SCOP） was utilized for modeling and injected intraperitoneally once daily. Thirty-two male C57/BL6 mice were randomly divided into blank control （CON） group （0.9% NaCl， n=8）， model （SCOP） group （3 mg·kg^-1·d^-1， n=8）， positive control group （3 mg·kg^-1·d^-1 of SCOP+3 mg·kg^-1·d^-1 of Donepezil， n=8）， and Kaixinsan group （3 mg·kg^-1·d^-1 of SCOP+6.5 g·kg^-1·d^-1 of Kaixinsan， n=8）. Mice in each group were administered with 0.9% NaCl， Kaixinsan， or Donepezil by gavage twice a day for 14 days. Morris water maze experiment was used to observe the learning memory ability of mice. Hematoxylin-eosin （HE） staining method was used to observe the pathological changes in the CA1 area of the mouse hippocampus. Enzyme linked immunosorbent assay（ELISA） was used to determine the serum acetylcholine （ACh） and acetylcholinesterase （AChE） contents of mice. Western blot method was used to detect the protein expression levels of signal transducer and activator of transcription 3（STAT3） and nuclear transcription factor（NF）-κB p65 in the hippocampus of mice. ResultsA total of 73 active ingredients of Kaixinsan were obtained， and 578 potential targets （common targets） of Kaixinsan for the treatment of AD were screened out. Key active ingredients included kaempferol， gijugliflozin， etc.. Potential core targets were STAT3， NF-κB p65， et al. GO functional enrichment analysis obtained 3 124 biological functions， 254 cellular building blocks， and 461 molecular functions. KEGG pathway enrichment obtained 248 pathways， mainly involving cancer-related pathways， TRP pathway， cyclic adenosine monophosphate（cAMP） pathway， and NF-κB pathway. Molecular docking showed that the binding of the key active ingredients to the target targets was more stable. Morris water maze experiment indicated that Kaixinsan could improve the learning memory ability of SCOP-induced mice. HE staining and ELISA results showed that Kaixinsan had an ameliorating effect on central nerve injury in mice. Western blot test indicated that Kaixinsan had a down-regulating effect on the levels of NF-κB p65 phosphorylation and STAT3 phosphorylation in the hippocampal tissue of mice in the SCOP model. ConclusionKaixinsan can improve the cognitive impairment function in SCOP model mice and may reduce hippocampal neuronal damage and thus play a therapeutic role in the treatment of AD by regulating NF-κB p65， STAT3， and other targets involved in the NF-κB signaling pathway.

Kupffer cells (KCs), as residents and sentinels of the liver, are involved in the formation of hepatic fibrosis (HF). However, the biological functions of circular RNAs (circRNAs) in KCs to HF have not been determined. In this study, the expression levels of circRNAs, microRNAs, and messenger RNAs (mRNAs) in KCs from a mouse model of HF mice were investigated using microarray and circRNA-Seq analyses. circDcbld2 was identified as a candidate circRNA in HF, as evidenced by its up-regulation in KCs. Silver staining and mass spectrometry showed that Wtap and Igf2bp2 bind to cirDcbld2. The suppression of circDcbld2 expression decreased the KC inflammatory response and oxidative stress and inhibited hepatic stellate cell (HSCs) activation, attenuating mouse liver fibrogenesis. Mechanistically, Wtap mediated the N ⁶-methyladenosine (m6A) methylation of circDcbld2, and Igf2bp2 recognized m6A-modified circDcbld2 and increased its stability. circDcbld2 contributes to the occurrence of HF by binding miR-144-3p/Et-1 to regulate the inflammatory response and oxidative stress. These findings indicate that circDcbld2 functions via the m6A/circDcbld2/miR-144-3p/Et-1 axis and may act as a potential biomarker for HF treatment.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To investigate the influencing factors of auditory hypersensitivity in normal hearing population and build a risk nomogram model according to the results,so as to provide scientific basis for early identi-fication of high risk population and formulation of prevention strategy.Methods A total of 410 volunteers with nor-mal pure tone hearing were recruited from March to July 2024.The hyperacusis questionnaire(HQ)was used to as-sess the audiroty hypersensitivity of the subjects.The participants were divided into a training set(n=287)and a validation set(n=123)according to a ratio of 7∶3.Binary Logistic model was used to construct risk model and no-mogram.Receiver operating characteristic(ROC)curve,Hosmer-Lemeshow calibration curve,clinical decision curve(DCA)and clinical impact curve were used to verify the differentiation,accuracy and clinical applicability of the model,respectively.Results Among 410 participants,54(13.17％)had hyperacusis including 38(13.24％)in the training set amd 16(13.01)in the validation set.LASSO regression and Logistic regression analysis showed that tinnitus(OR=3.784,95％CI=1.627-8.804),HADS-A(OR=3.860,95％CI=1.503-9.913),HADS-D(OR=3.118,95％CI=1.249-7.785),migraine(OR=2.821,95％CI=1.147-6.937)and noise exposure histo-ry(OR=3.799,95％CI=1.715-8.416)were the influential factors for hyperacusis in participants with normal hearing.Conclusion The incidence of hyperacusis in normal hearing population is 13.17％.Tinnitus,HADS-A,HADS-D,migraine and noise exposure history are related to the occurrence of hyperacusis in normal hearing popula-tion.The risk prediction nomogram model based on the above factors has good differentiation and calibration degree.It can effectively predict the risk of hyperacusis in normal hearing people,and has certain clinical practicability.

Objective To investigate the clinical value of baseline 18F-fluorodeoxyglucose(18F-FDG)PET/CT metabolic parame-ters and certain clinical indicators in predicting the prognosis of patients with diffuse large B-cell lymphoma(DLBCL).Methods A retrospec-tive analysis was conducted on the baseline 18 F-FDG PET/CT data of 73 DLBCL patients who received R-CHOP treatment.Patients were divided into progression group(24 cases)and non-progression group(49 cases)based on disease progression within 2 years post-treatment.The lesion maximum standardized uptake value(SUVmax),tumour-to-liver blood pool SUVmax ratio(LLR)and tumour-to-mediastinal blood pool SUVmax ratio(L-BPR)were analyzed using receiver operating characteristic(ROC)curves.Kaplan-Meier(K-M)survival curves analysis were performed based on the optimal thresholds of SUVmax,LLR and L-BPR.x2 tests were used to analyze and compare the relationship between each parameter and disease progression.Indicators that were significant in the x2 tests were included in the multivariate Cox regression analysis.Results The area under the curve(AUC)for LLR,L-BPR,and SUVmax were 0.920,0.914,and 0.848,respectively,with optimal thresholds of 7.41,10.67,and 14.70.Based on these thresholds,K-M survival curves analysis showed that the 2-year progression-free survival(PFS)rates for DLBCL patients were 79.2％vs 30.8％(P＜0.001),74.3％vs 22.0％(P=0.009),and 79.5％vs 51.6％(P=0.002),respectively.Significant differences were observed between the progression and non-progression groups in terms of LLR,L-BPR,SUVmax,extranodal involvement,international prognostic index(IPI)score,lactate dehydrogenase(LDH)level,Eastern Cooperative Oncology Group(ECOG)score,and β2-microglobulin(β2-MG)levels(P＜0.05).Multivariate Cox regression analysis revealed that the IPI score and LLR were independent predictors affecting the 2-year PFS of DLBCL patients(P＜0.05).Conclusion Baseline 18F-FDG PET/CT metabolic parameter LLR and IPI score are inde-pendent factors for predicting the prognosis of DLBCL patients.

BACKGROUND:Previous studies by our research group discovered that Jiawei Xiaoyao San has a significant anti-liver cancer effect,but the specific mechanism of action was unclear. OBJECTIVE:To investigate the regulatory effects of the traditional Chinese medicine formula Jiawei Xiaoyao San on the levels of miRNAs in plasma exosomes of rats with diethylnitrosamine chronically induced primary liver cancer,based on high-throughput sequencing combined with bioinformatics. METHODS:SD rats were randomly divided into a blank control group,a liver cancer model group,and a Jiawei Xiaoyao San treatment group.Liver cancer models were induced by continuous administration of diethylnitrosamine for 12 weeks.Starting from the 17th week,rats in the Jiawei Xiaoyao San treatment group were administered Jiawei Xiaoyao San once daily until the end of the 20th week,while rats in the blank control and liver cancer model groups were given an equivalent volume of saline.Anti-hepatocellular carcinoma effects were validated by assessing the morphological structure of rat liver tissues,along with the expression of the hepatocellular carcinoma markers,Glypican-3 protein and serum alpha-fetoprotein.Plasma exosomes from each group of rats were isolated using ultracentrifugation.High-throughput sequencing technology was used to screen for differentially expressed miRNAs in rat plasma exosomes.Bioinformatics was used to predict the potential biomarkers through which Jiawei Xiaoyao San exerts its anti-liver cancer effects via liver cancer-derived exosomal miRNAs,followed by functional analysis. RESULTS AND CONCLUSION:(1)Jiawei Xiaoyao San significantly improved the morphological structure of liver tissues in a rat model of liver cancer.Compared with the liver cancer model group,the expression of liver cancer markers Glypican-3 protein and serum alpha-fetoprotein was significantly reduced in the Jiawei Xiaoyao San treatment group.(2)Bioinformatics analysis showed that in the Jiawei Xiaoyao San group,upregulated miR-223-3p in the liver cancer model group had target binding sites with genes E2F1 and NCOA1,which were closely related to liver cancer survival and prognosis.Therefore,Jiawei Xiaoyao San has a therapeutic effect on liver cancer,possibly by targeting negative regulation of NCOA1/E2F1 through liver cancer plasma-derived exosomal miR-223-3p,thereby playing anti-liver cancer effect.

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This paper reports the clinical data and the diagnosis and treatment of a patient with exogenous insulin autoimmune syndrome(EIAS)induced by long-term use of exogenous insulin(Eucrin 50).For diabetes patients with hyperinsulin-induced hypoglycemia,detection of IAA is helpful for diagnosis EIAS.Due to different test methods affect IAA results,if negative,EIAS cannot be completely excluded.The polyethylene glycol precipitation method can assist in early diagnosis.

This paper reports the clinical data and the diagnosis and treatment of a patient with exogenous insulin autoimmune syndrome(EIAS)induced by long-term use of exogenous insulin(Eucrin 50).For diabetes patients with hyperinsulin-induced hypoglycemia,detection of IAA is helpful for diagnosis EIAS.Due to different test methods affect IAA results,if negative,EIAS cannot be completely excluded.The polyethylene glycol precipitation method can assist in early diagnosis.