The gut microbiota is regarded as the "eighth organ" of the human body and plays a critical regulatory role in the occurrence and progression of various diseases. Ulcerative colitis （UC） is a chronic inflammatory bowel disease with a complex etiology and a tendency toward recurrent episodes. In recent years， studies have shown that gut microbiota dysbiosis plays a key role in its pathological processes. Meanwhile， an increasing number of studies have demonstrated that imbalances in the gut microbiota and abnormalities in its metabolites are closely associated with the development of atrial fibrillation （AF）. Although UC and AF belong to diseases of the digestive system and cardiovascular system， respectively， both exhibit systemic inflammatory characteristics and are often accompanied by gut microbiota dysregulation and abnormal metabolic products. However， systematic investigations into the mechanisms by which gut microbiota-derived metabolites act in these two diseases remain limited. Based on this， the present study adopts literature review and theoretical analysis methods， taking the "gut microbiota-gut-heart" axis as the entry point， to systematically summarize the signaling networks of three key classes of metabolites， i.e.， short-chain fatty acids （SCFAs）， bile acids （BAs）， and trimethylamine N-oxide （TMAO）， in the comorbidity mechanism of UC and AF. The findings indicate that these metabolites may activate key inflammatory pathways， such as NF-κB and NLRP3， thereby synergistically mediating intestinal barrier dysfunction and systemic inflammation and constructing a potential comorbidity network. On this basis， potential intervention strategies for the treatment of UC-AF comorbidity， including probiotic intervention and fecal microbiota transplantation， are further discussed. This study aims to provide new theoretical evidence and research perspectives for prevention and treatment strategies of cross-system diseases.

ObjectiveTo explore the training path of rehabilitation therapy professionals in local colleges and universities under the background of New Medicine. MethodsTaking Jining Medical University as an example, combining the interdisciplinary integration, intelligent driving and people-oriented New Medicine attributes of rehabilitation therapy, this study focused on the current practical problems in rehabilitation education, accurately aligned with the requirements of new medicine construction, reshaped the curriculum system with new concepts, empowered practical innovation with new technologies, and cultivated humanistic literacy with new connotations. By deeply integrating digital construction with educational practice, it carried out the construction and practice of the New Medicine talent training model in rehabilitation therapy, helping students form a stereoscopic knowledge structure, possess the ability to apply intelligent rehabilitation technology and solve complex rehabilitation problems, and become high-quality rehabilitation therapy talents with both morality and ability. ResultsThe overall construction of the rehabilitation therapy major established a model of Dual fusion foundation, Three element empowerment and Three dimensional soul casting, including reform and innovation in curriculum system, practical mode and moral education mechanism. It has formed a health oriented "vertical and horizontal integration-discipline fusion" dual-fusion stereoscopic curriculum system, an intelligent led "theory and practice-science and education-industry and education" three element integration practical mode, and a confucian medical culture "curriculum-practice-evaluation" three-dimensional integrated moral education mechanism. Through practical testing, this model achieved significant results and was widely recognized, including a significant improvement in the quality of talent cultivation, prominent achievements in resource construction, and prominent achievements in health services, providing strong support for the construction of regional rehabilitation medical systems. ConclusionThe reform of the rehabilitation treatment talent training model at Jining Medical University under the background of New Medicine provides a replicable and promotable practical paradigm for the training of rehabilitation talents in similar universities through reshaping the curriculum system, strengthening practical innovation, and cultivating humanistic literacy.

ObjectiveTo explore the training path of rehabilitation therapy professionals in local colleges and universities under the background of New Medicine. MethodsTaking Jining Medical University as an example, combining the interdisciplinary integration, intelligent driving and people-oriented New Medicine attributes of rehabilitation therapy, this study focused on the current practical problems in rehabilitation education, accurately aligned with the requirements of new medicine construction, reshaped the curriculum system with new concepts, empowered practical innovation with new technologies, and cultivated humanistic literacy with new connotations. By deeply integrating digital construction with educational practice, it carried out the construction and practice of the New Medicine talent training model in rehabilitation therapy, helping students form a stereoscopic knowledge structure, possess the ability to apply intelligent rehabilitation technology and solve complex rehabilitation problems, and become high-quality rehabilitation therapy talents with both morality and ability. ResultsThe overall construction of the rehabilitation therapy major established a model of Dual fusion foundation, Three element empowerment and Three dimensional soul casting, including reform and innovation in curriculum system, practical mode and moral education mechanism. It has formed a health oriented "vertical and horizontal integration-discipline fusion" dual-fusion stereoscopic curriculum system, an intelligent led "theory and practice-science and education-industry and education" three element integration practical mode, and a confucian medical culture "curriculum-practice-evaluation" three-dimensional integrated moral education mechanism. Through practical testing, this model achieved significant results and was widely recognized, including a significant improvement in the quality of talent cultivation, prominent achievements in resource construction, and prominent achievements in health services, providing strong support for the construction of regional rehabilitation medical systems. ConclusionThe reform of the rehabilitation treatment talent training model at Jining Medical University under the background of New Medicine provides a replicable and promotable practical paradigm for the training of rehabilitation talents in similar universities through reshaping the curriculum system, strengthening practical innovation, and cultivating humanistic literacy.

ObjectiveTo explore the training path of rehabilitation therapy professionals in local colleges and universities under the background of New Medicine. MethodsTaking Jining Medical University as an example, combining the interdisciplinary integration, intelligent driving and people-oriented New Medicine attributes of rehabilitation therapy, this study focused on the current practical problems in rehabilitation education, accurately aligned with the requirements of new medicine construction, reshaped the curriculum system with new concepts, empowered practical innovation with new technologies, and cultivated humanistic literacy with new connotations. By deeply integrating digital construction with educational practice, it carried out the construction and practice of the New Medicine talent training model in rehabilitation therapy, helping students form a stereoscopic knowledge structure, possess the ability to apply intelligent rehabilitation technology and solve complex rehabilitation problems, and become high-quality rehabilitation therapy talents with both morality and ability. ResultsThe overall construction of the rehabilitation therapy major established a model of Dual fusion foundation, Three element empowerment and Three dimensional soul casting, including reform and innovation in curriculum system, practical mode and moral education mechanism. It has formed a health oriented "vertical and horizontal integration-discipline fusion" dual-fusion stereoscopic curriculum system, an intelligent led "theory and practice-science and education-industry and education" three element integration practical mode, and a confucian medical culture "curriculum-practice-evaluation" three-dimensional integrated moral education mechanism. Through practical testing, this model achieved significant results and was widely recognized, including a significant improvement in the quality of talent cultivation, prominent achievements in resource construction, and prominent achievements in health services, providing strong support for the construction of regional rehabilitation medical systems. ConclusionThe reform of the rehabilitation treatment talent training model at Jining Medical University under the background of New Medicine provides a replicable and promotable practical paradigm for the training of rehabilitation talents in similar universities through reshaping the curriculum system, strengthening practical innovation, and cultivating humanistic literacy.

Despite that sleep disturbance and poor neurocognitive performance are common complaints among coronavirus disease 2019 (COVID-19) survivors, few studies have focused on the effect of post-COVID-19 sleep disturbance (PCSD) on cognitive function. This study aimed to identify the impact of PCSD on neurocognitive function and explore the associated risk factors for the worsening of this condition. This cross-sectional study was conducted via the web-based assessment in Chinese mainland. Neurocognitive function was evaluated by the modified online Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT). Propensity score matching (PSM) was utilized to match the confounding factors between individuals with and without PCSD. Univariate analyses were performed to evaluate the effect of PCSD on neurocognitive function. The risk factors associated with worsened neurocognitive performance in PCSD individuals were explored using binary logistic regression. A total of 8692 individuals with COVID-19 diagnosis were selected for this study. Nearly half (48.80%) of the COVID-19 survivors reported sleep disturbance. After matching by PSM, a total of 3977 pairs (7954 individuals in total) were obtained. Univariate analyses revealed that PCSD was related to worse ICA and NOT performance (P<0.05). Underlying disease, upper respiratory infection, loss of smell or taste, severe pneumonia, and self-reported cognitive complaints were associated with worsened neurocognitive performance among PCSD individuals (P<0.05). Furthermore, aging, ethnicity (minority), and lower education level were found to be independent risk factors for worsened neurocognitive performance in PCSD individuals (P<0.05). PCSD was related to impaired neurocognitive performance. Therefore, appropriate prevention and intervention measures should be taken to minimize or prevent PCSD and eliminate its potential adverse effect on neurocognitive function.
Humans ; COVID-19/epidemiology* ; Male ; Female ; Sleep Wake Disorders/epidemiology* ; Propensity Score ; Middle Aged ; Cross-Sectional Studies ; Adult ; SARS-CoV-2 ; Aged ; Risk Factors ; China/epidemiology* ; Cognition ; Cognitive Dysfunction/etiology* ; Neuropsychological Tests

Objective:To evaluate surgical strategies and clinical outcomes in supra-labyrinthine cholesteatoma management, providing evidence-based guidance for therapeutic decision-making. Methods:Seven patients with supra-labyrinthine cholesteatoma in our hospital from 2021 to 2023 were enrolled in this study. The clinical manifestations, imaging findings, and surgical outcomes of patients were retrospectively analyzed. A systematic literature review focused on surgical anatomy correlations and imaging-based approach selection. Results:All seven cases of supra-labyrinthine cholesteatoma were unilateral. Preoperative otoendoscopy, CT, and intraoperative findings confirmed that they were classified as supral-abyrinthine cholesteatoma according to Sanna's classification. Two cases were operated entirely with otoendoscopy, three cases used a postauricular approach with microscopic assistance, and two cases involved a combined approach with endoscopy and microscopy. Hearing reconstruction with ossicular prosthesis was performed in five cases, while two cases did not undergo hearing reconstruction due to preoperative anacusis confirmed by both subjective and objective hearing tests. In all seven cases, various segments of the facial nerve were exposed during surgery, but postoperative facial nerve function remained intact, hearing was preserved, no cerebrospinal fluid leakage occurred, and no recurrences have been observed to date（as of June 2024）. Conclusion:With the advancement of imaging techniques and microsurgical technology, early diagnosis and surgical methods for supral-abyrinthine cholesteatoma have significantly improved. Compared to traditional approaches, the newer methods reduce unnecessary complications and offer advantages such as minimal surgical trauma, superior hearing preservation rates, and shorter recovery times with better postoperative neural function. This study reviews recent literature on petroclival cholesteatomas, combined with our own cases, to analyze the classification of supral-abyrinthine cholesteatoma and surgical approach selection. The findings aim to optimize treatment strategies and guide appropriate surgical methods, ultimately improving patient prognosis and quality of life.
Humans ; Cholesteatoma/surgery* ; Ear, Inner/surgery* ; Retrospective Studies ; Treatment Outcome

Objective:To construct an prediction model based on magnetic resonance imaging (MRI) machine learning algorithm and radiomics and investigate its application value in predicting lymphovascular invasion (LVI) status of rectal cancer without lymph node metastasis.Methods:The retrospective cohort study was conducted. The clinicopathological data of 204 rectal cancer patients without lymph node metastasis who were admitted to Gansu Provincial Hospital from February 2016 to January 2024 were collected. There were 123 males and 81 females, aged (61±7)years. All 204 patients were randomly divided into the training dataset of 163 cases and the testing dataset of 41 cases by a ratio of 8∶2 using the electronic computer randomization method. The training dataset was used to construct the prediction model, and the testing dataset was used to validate the prediction model. The clinical prediction model, radiomics model and joint prediction model were constructed based on the selected clinical and/or imaging features. Measurement data with normal distribution were represented as Mean± SD. Count data were described as absolute numbers, and the chi-square test or Fisher exact probability were used for comparison between the groups. Comparison of ordinal data was conducted using the nonparameter rank sum test. The inter-class correlation coefficient (ICC) was used to evaluate the consistency of the radiomics features of the two doctors, and ICC >0.80 was good consistency. Univariate analysis was conducted by corres-ponding statistic methods. Multivariate analysis was conducted by Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC), Delong test, decision curve and clinical impact curve were used to evaluate the diagnostic efficiency and clinical utility of the model. Result:(1) Analysis of factors affecting LVI status of patients. Of the 204 rectal cancer patients without lymph node metastasis, there were 71 cases with positive of LVI and 133 cases with negative of LVI. Results of multivariate analysis showed that gender, platelet (PLT) count and carcinoembryonic antigen (CEA) were independent factors affecting LVI status of rectal cancer without lymph node metastasis in training dataset [ odds ratio=2.405, 25.062, 2.528, 95% confidence interval ( CI) as 1.093-5.291, 2.748-228.604, 1.181-5.410, P<0.05]. (2) Construction of clinical prediction model. The clinical prediction model was conducted based on the results of multivariate analysis including gender, PLT count and CEA. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of clinical prediction model were 0.721 (95% CI as 0.637-0.805), 0.675, 0.632 and 0.698 for the training dataset, and 0.795 (95% CI as 0.644-0.946), 0.805, 1.000 and 0.429 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of clinical prediction model between the training dataset and the testing dataset ( Z=-0.836, P>0.05). (3) Construction of radiomics model. A total of 851 radiomics features were extracted from 204 patients, and seven machine learning algorithms, including logistic regression, support vector machine, Gaussian process, logistic regression-lasso algorithm, linear discriminant analysis, naive Bayes and automatic encoder, were used to construct the prediction model. Eight radiomics features were finally selected from the optimal Gaussian process learning algorithm to construct a radiomics prediction model. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.857 (95% CI as 0.800-0.914), 0.748, 0.947 and 0.642 for the training dataset, and 0.725 (95% CI as 0.571-0.878), 0.634, 1.000 and 0.444 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of radiomics prediction model between the training dataset and the testing dataset ( Z=1.578, P>0.05). (4) Construction of joint prediction model. The joint prediction model was constructed based on the results of multivariate analysis and the radiomics features. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.885 (95% CI as 0.832-0.938), 0.791, 0.912 and 0.726 for the training dataset, and 0.857 (95% CI as 0.731-0.984), 0.854, 0.714 and 0.926 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of joint prediction model between the training dataset and the testing dataset ( Z=0.395, P>0.05). (5) Performance comparison of three prediction models. Results of the Hosmer-Lemeshow goodness-of-fit test showed that all of the clinical prediction model, radiomics prodiction model and joint prediction model having good fitting degree ( χ2=1.464, 12.763, 10.828, P>0.05). Results of Delong test showed that there was no signifi-cant difference in the AUC between the clinical prediction model and the joint prediction model or the radiomics model ( Z=1.146, 0.658, P>0.05), and there was a significant difference in the AUC between the joint prediction model and the radiomics model ( Z=2.001, P<0.05). Results of calibra-tion curve showed a good performance in the joint prediction model. Results of decision curve and clinical impact curve showed that the performance of joint prediction model in predicting LVI status of rectal cancer without lymph node metastasis was superior to the clinical prediction model and the radiomics model. Conclusions:The clinical prediction model is constructed based on gender, PLT count and CEA. The radiomics predictive model is constructed based on 8 selected radiomics features. The joint prediction model is constructed based on the clinical prediction model and the radiomics predictive model. All of the three models can predict the LVI status of rectal cancer with-out lymph node metastasis, and the joint prediction model has a superior predictive performance.

Objective:To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making.Methods:A retrospective study was conducted. The clinical information of stroke patients ( n = 53?793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients ( n = 1?028) were extracted from Dalian Central Hospital for external verification of the model. Results:A total of 14?015 stroke patients with complete information were selected, including 11?212 in the training set and 2?803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133?922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models were between 0.771-0.780, the sensitivity were between 0.978-0.987, the F-index were between 0.690-0.707, and the AUC were between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model.Conclusion:The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.

With the wide application of artemisinin and its derivatives,its safety has become particularly important.Previous studies have shown that artemisinin and its derivatives have adverse reactions such as nausea,vomiting and diarrhea in clinical use,but they are all within the controllable range.In animal experiments,it has toxic effects on kidney,liver,heart,nerve,blood,embryo and DNA at a high dose.Toxicity and adverse reactions can be alleviated or eliminated by combining medication or changing the drug administration mode,solvent and preparation type.This article mainly discusses the drug safety,toxicity mechanism and attenuation countermeasures of artemisinin and its derivatives,in order to improve the understanding of the potential toxicity of artemisinin and its derivatives and provide reference for the safe use of the drug in clinic.

Objective To summarize the preliminary application results of ultrasound-guided biliary drainage tube replacement, present the corresponding technical points, and discuss the operation strategy and clinical application value. Methods The clinical data of 60 patients who underwent ultrasound-guided biliary drainage tube replacement in Qilu Hospital of Shandong University between August 2014 and August 2020 were retrospectively analyzed. The operation procedure, clinical applications, and postoperative complications were summarized and analyzed. Results Fifty-eight of the 60 patients (96.67%) were successfully replaced with drainage tubes along the original sinus. Among them, dilated sinus tracts of 47 patients were placed with coarse-grade drainage tubes, and dilated sinus tracts of the remaining 11 patients were placed with the original type of drainage tubes, with the mean operation time of 15.8(12.0-19.0) min under local anesthesia. In total, bile was drained from 28 patients receiving PTCD drainage, 23 patients receiving gallbladder drainage, and 9 patients receiving T-tube drainage. The post-operation evaluation revealed that the drainage situation has improved, with a 100% effective rate. No obvious abnormality was found in the postoperative follow-up visit. Conclusion The replacement of drainage tube under ultrasound guidance is simple, safe and feasible, and it provides further promotion in clinical practice with sufficient data support.