Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

DPP4 is a serine exopeptidase that is immobilized on the cell membrane and plays a crucial regulatory role in various physiological and pathological activities within the human body.In addition to acting as a transcription factor to regulate the tran-scription and expression of downstream target genes,DPP4 also functions as a transcription-independent regulator through pro-tein-protein interactions.In recent studies,DPP4 has been strongly linked to various diseases,and several substances with the potential to target DPP4 have been identified.This paper mainly reviews the multifunctionality of DPP4 in regulating vari-ous aspects of energy metabolism,inflammation,tissue repair and carcinogenesis in the body.It also reviews the screening of in vitro inhibitors of DPP4 and its research progress in regulating chronic liver disease,based on the pathological development process of chronic liver disease.

Objective:To evaluate the difference in efficacy between liposuction combined with single-port endoscopic subcutaneous adenomectomy and traditional open surgery in treating adolescent male breast development disorder,and to compare their effects on patients'anxiety levels and quality of life.Methods:A total of sixty-four patients with adolescent male mammary gland dysplasia who underwent surgery at our hospital between March 2022 and June 2023 were included in this retrospective analysis,with 28 cases in the open surgery group and 36 cases in the single-hole endoscopic group.The study compared various parameters including surgery duration,intraoperative bleeding,incision length,hospitalization duration,treatment cost,and postoperative complications,alongside collecting GAD-7,SF-36,and SCAR questionnaire scores from the pa-tients.Results:The single-port endoscopy group,in comparison to the open surgery group,showed a slightly longer operation time and higher treatment cost but had a shorter hospitalization duration and smaller incision.There was no significant difference observed between the two groups concerning intraoperative bleeding.Notably,the single-port endoscopy group exhibited more significant improvements in SCAR,SF-36,and GAD-7 scores(P＜0.05).Conclusion:Com-pared with traditional open surgery,liposuction combined with single-port endoscopic subcutane-ous adenomectomy is more effective in treating adolescent gynecomastia.This approach not only offers better aesthetic outcomes but also proves effective in reducing patient anxiety and signifi-cantly enhancing their quality of life.

In recent years, it has been found that complex pathological manifestations of Alzheimer's disease (AD) cannot be fully explained by β-amyloid (Aβ) cascade hypothesis, and researchers question this hypothesis resulting from the poor effect of targeted Aβ drugs. Probabilistic apolipoprotein E (APOE) hypothesis affirms the Aβ pathogenesis but also emphasizes the important role of APOE gene in diagnosis, treatment and progression of AD. In AD diagnosis, subjects should be firstly grouped according to presence or absence of APOE ε4 allele, and then classified according to Aβ deposition/tau neurofibrillary tangles/neurodegeneration. This article reviews the recent advance in the pathogenesis, diagnosis and treatment of 3 types of AD variants (autosomal dominant AD, sporadic AD related to APOE ε4, and sporadic AD unrelated to APOE ε4), with a view to understand the importance of APOE ε4 allele in AD from a new perspective.

Objective:To evaluate the clinical application of urine sediment score (USS) in early diagnosis, etiological differentiation, staging and prognosis of acute kidney injury (AKI), and to investigate the diagnostic efficacy of independent USS and its combination with blood urea nitrogen(Bun) serum creatinine(sCr) and uric acid(UA) in AKI.Methods:From August 23 to September 28, 2023, 9 020 morning urine samples of hospitalized patients in the First Hospital of Jilin University were detected by Sysmex UF5000.A total of 3 226 ssamples with small and round cell (SRC) > 1/μl and/or CAST>1/μl were screened for microscopic examination, and 404 cases with positive renal tubular epithelial cells and/or cast were enrolled in this study. There were 218 males and 186 females, aged 59.5 (49.0, 71.0) years. The 404 cases were divided into the USS AKI group (345 cases) and the USS non-AKI group (59 cases) according to the USS results based on the microscopic findings. According to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, they were divided into KDIGO criteria AKI group (63 cases) and KDIGO criteria non-AKI group (341 cases), and the AKI group was divided into renal AKI group (33 cases) and non-renal AKI group (30 cases). According to the clinical diagnosis recorded in the medical records, they were divided into clinically diagnosed AKI group (29 cases) and clinically diagnosed non-AKI group (375 cases).The χ 2 test or Fisher exact test was used to compare USS in different AKI causes and stages. Logistic regression was used to calculate the odds ratio of renal AKI and stage 3 AKI. The area under the receiver operating characteristic curve was used to evaluate the sensitivity and specificity of USS, sCr, UA and Bun alone and in combination in the diagnosis of AKI, and the best cut-off value, sensitivity and specificity in the diagnosis of AKI were calculated. P < 0.05 was considered statistically significant. Results:The USS was used to identify the etiology of KDIGO standard AKI group,and there were significant differences in USS between renal AKI group and non-renal AKI group (χ 2=11.070, P<0.001). Compared to USS=1, the odds ratio of renal AKI was 8.125 when USS≥2 (95% CI 2.208—29.901). There was a statistically significant difference in the comparison of USS between groups in each stage of the AKI staging study based on USS (χ 2=15.724, P<0.05). Compared to USS=1, the odds ratio of stage 3 AKI was 9.714 when USS≥2 (95% CI 1.145-82.390). The AUC of independent USS in the diagnosis of AKI was 0.687 (95% CI 0.618-0.757, P<0.001), the specificity was 65.7% and the sensitivity was 61.9%. The AUC of USS combined with Bun, sCr, UA in the diagnosis of AKI was 0.794 (95% CI 0.608-0.980, P<0.05), the specificity was 82.4%, and the sensitivity was 88.9%. Conclusions:There wasan increased likelihood of renal AKI or stage 3 AKI while USS≥2,and whose combination with Bun, sCr and UA will improve the diagnostic efficiency of AKI.