ObjectiveTo investigate the effects of modified Buyang Huanwu Tang on cerebral ischemia-reperfusion injury （CI/RI） in mice via the PTEN-induced putative kinase 1/E3 ubiquitin ligase （PINK1/Parkin） signaling pathway-mediated mitophagy， and to explore the underlying mechanism by which modified Buyang Huanwu Tang improves CI/RI. MethodsSeventy-two male C57BL/6J mice were randomly divided into six groups （n = 12 per group）： Sham-operated group， middle cerebral artery occlusion/reperfusion （MCAO/R） model group， low-， medium-， and high-dose modified Buyang Huanwu Tang groups （8.84， 17.68， 35.36 g·kg^-1·d^-1）， and an aspirin group （13.00 mg·kg^-1·d^-1）. Neurological deficit scores were assessed using the Zea-Longa method. Cerebral infarct volume ratio was measured by 2，3，5-triphenyltetrazolium chloride （TTC） staining. Histopathological changes and neuronal injury in brain tissues were observed using hematoxylin-eosin （HE） staining and Nissl staining. Apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL） assay. Mitochondrial ultrastructure in brain tissue was observed by transmission electron microscopy （TEM）. Serum levels of superoxide dismutase （SOD） and malondialdehyde （MDA） were determined by enzyme-linked immunosorbent assay （ELISA）. The mRNA and protein expression levels of PINK1， Parkin， microtubule-associated protein 1 light chain 3B （LC3B， LC3Ⅱ/Ⅰ）， and p62 in brain tissues were detected by real-time quantitative reverse transcription PCR （Real-time PCR） and Western blot， respectively. ResultsCompared with the sham-operated group， the MCAO/R model group showed significantly increased neurological deficit scores and cerebral infarct volume ratios （P<0.01）. Severe cortical injury on the infarct side was observed， characterized by decreased neuronal density， cytoplasmic vacuolation， nuclear pyknosis， a marked reduction in Nissl bodies， dissolution of Nissl bodies in the cytoplasm of some pyramidal neurons， and blurred cellular boundaries. The number of TUNEL-positive cells increased significantly （P<0.01）. Mitochondria exhibited cristae membrane rupture and matrix vacuolation， with rupture of the outer mitochondrial membrane and formation of autophagosomes， the number of which increased significantly. Serum SOD activity decreased significantly （P<0.01）， while MDA content increased significantly （P<0.01）. In infarcted brain tissues of model mice， the relative mRNA expression and protein levels of PINK1， Parkin and LC3B were significantly increased （P<0.05， P<0.01）， whereas p62 mRNA and protein expression were significantly decreased （P<0.05， P<0.01）， showing statistical significance. Compared with the model group， all treatment groups showed significantly decreased neurological deficit scores and cerebral infarct volume ratios （P<0.01）. Neuronal density increased significantly， cytoplasmic vacuolation was alleviated， nuclear morphology tended to be more regular and clearer， Nissl body density increased significantly with reduced dissolution and improved contour clarity. The mitochondrial cristae structure was partially restored， with some mitochondria showing autophagosome encapsulation， and the degree of mitochondrial damage was alleviated. Serum SOD activity increased significantly （P<0.01）， while MDA content decreased significantly. The mRNA and protein expression levels of PINK1， Parkin， and LC3Ⅱ/Ⅰ were significantly increased （P<0.05， P<0.01）， while p62 mRNA and protein expression in the low- and medium-dose modified Buyang Huanwu Tang groups were significantly decreased （P<0.05， P<0.01）， showing statistical significance. ConclusionModified Buyang Huanwu Tang can upregulate the protein expression levels of PINK1， Parkin， and LC3Ⅱ/Ⅰ and downregulate p62 protein expression， suggesting that it may improve CI/RI by regulating the expression of proteins related to the PINK1/Parkin signaling pathway. Regulation of the mitophagy pathway may be one of the mechanisms by which modified Buyang Huanwu Tang alleviates CI/RI in mice.

ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

[Objective] To compare the diagnostic accuracy of three imaging modalities, inlducing CT urography (CTU), contrast-enhanced MRI (CE-MRI), and contrast-enhanced ultrasound (CEUS) in the qualitative diagnosis of renal space-occupying lesions. [Methods] A retrospective analysis was performed on 542 patients with renal lesions confirmed by surgical pathology in our hospital during Jan.2019 and May 2024.The diagnostic results of CTU, CE-MRI and CEUS were compared and analyzed based on the patients' clinical and pathological data. [Results] The diagnostic accuracy rate of CTU, CE-MRI and CEUS were 84.50%, 83.14% and 86.14%, respectively.For the 161 patients who underwent all three examinations, CEUS was significantly more accurate than CTU (84.16% vs. 77.02%, P=0.018), while there was no significant difference between CTU or CEUS and CE-MRI (79.81%) (P>0.05). Further analysis found that for lesions ≤4 cm, the accuracy of the three examinations was as follows: CEUS=CTU 79.55%, CE-MRI 76.14%, with no significant difference (P>0.05). However, for lesions >4 cm, CEUS ranked the first, followed by CE-MRI and CTU (89.73% vs. 84.25% vs. 73.97%), and CEUS and CE-MRI were better than CTU (P<0.05). Additionally, for the diagnosis of clear cell renal carcinoma and benign renal space-occupying lesions, there was no statistically significant difference among the three imaging modalities (P>0.05), while for the qualitative diagnosis of non-clear cell renal carcinoma, CEUS ranked the first, followed by CE-MRI and CTU (83.87% vs. 74.19% vs. 56.45%), and CE-MRI and CEUS were better than CTU (P<0.05). [Conclusion] All of them have important diagnostic value, and the appropriate selection should be based on patients' specifc conditions.CEUS and CE-MRI are more accurate in the qualitative diagnosis of renal space-occupying lesions than CTU, especially for large lesions and non-clear cell carcinoma.

Guided by the theory of "mutual dependence of ascending and descending"， recurrent pediatric cough and asthma are considered to result from the imbalance of the ascending and descending of zang-fu organ qi， which is closely associated with deficiency， phlegm， and blood stasis. In clinical practice， the disease is treated based on differentiation during the initial stage， progression stage， and stable stage. In the initial stage， the pathogenesis is attributed to lung deficiency complicated by external pathogens and liver qi rising to attack the lung； the treatment should focus on restraining the lung and calming the liver， using the self-formulated Longchai Yuchuan Fomulation （龙柴愈喘方）. During the progression stage， the disorder is due to the spleen failing to ascend clear qi and the kidney failing to grasp qi， leading to phlegm formation from deficiency； treatment should focus on tonifying spleen yang， supplementing kidney qi， and resolving phlegm and fluid retention， using a modified combination of Linggui Zhugan Decoction （苓桂术甘汤） and Jinkui Shenqi Pill （金匮肾气丸）. In the stable stage， qi deficiency leads to poor consolidation， with phlegm turbidity and blood stasis； the treatment should aim at tonifying qi， transforming phlegm， and dispelling blood stasis， using a modified version of Guizhi Fuling Pill （桂枝茯苓丸）.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.