Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

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.

Objective To reform the teaching content of acupuncture and moxibustion for undergraduate of Nursing College and to evaluate the effectiveness of the teaching reform,so to provide constructive information for optimizing the curriculum.Methods To revise the teaching content of Traditional Chinese Medicine(TCM)acu-puncture course based on the results of previous questionnaires,including reducing boring theoretical knowledge,increasing commonly used acupuncture techniques and clinical case analysis,organizing all undergraduates atten-ding the TCM teaching in the Nursing College of Peking Union Medical College to fill out the"Acupuncture Course Content Questionnaire"in 2023.The content covers four aspects:students'basic cognition of acupuncture and moxibustion before the course,students' learning feedback,students'learning effectiveness after the course and students' suggestions for further optimization of acupuncture and moxibustion course.Results Totally 149 students participated in the questionnaire survey and 73.15%of them were interested in the acupuncture course.There were 62.42%of the students thought that the most difficult part of the course was the"Acupuncture Points",and 71.81%of them complained the most constraining factor to the learning effectiveness was the diffi-culty of memorizing the meridians and acupoints.The most interesting part of the course was"Overview of Acu-puncture Treatment and Acupuncture Treatment of Common Diseases",which accounted for 44.3%of the students.Through the study,88.59%of the students were willing to recommend acupuncture treatment to pa-tients with indications.Students' suggestions for improvement of the acupuncture course was increase of classroom practice or learning by observation of operation videos.Conclusions The reformed acupuncture course highlights the practicability,improves the learning interest and subjective initiative,but the training of practice skill still needs to be strengthened.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Objective:To explore the clinical features and genetic etiology of a child with Baraitser-Winter syndrome (BWS).Methods:A BWS child who had sought medical attention at the Linyi People′s Hospital on April 8, 2022 was selected as the study subject. Clinical data of the child was collected, and peripheral blood samples were obtained from the child and his parents. Whole exome sequencing (WES) was carried out, and candidate variant was verified by Sanger sequencing and bioinformatic analysis.Results:The child, a 5-year-and-6-month-old male, had typical clinical features of BWS including congenital non-myogenic ptosis, arched eyebrows, wide philtrum, and pointed chin. Neurological symptoms included microcephaly, developmental delay, epilepsy, and deafness. Cranial MRI revealed enlarged frontal lobes, decreased white matter, and hydrocephalus. WES has identified a heterozygous c. 430G>A (p.Asn144Tyr) missense variant in the ACTG1 gene. Sanger sequencing confirmed that neither of his parents has carried the same variant. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PS2+ PM2_Supporting+ PP3_Moderate+ PP4). Conclusion:The heterozygous c. 430G>A (p.Asn144Tyr) missense variant of the ACTG1 gene probably underlay the pathogenesis of BWS in this child. Above finding has enriched the mutation spectrum of BWS-related genes and provided a basis for clinical diagnosis and genetic counseling.

Objective To understand the respiratory protection competency of staff in hospitals.Methods Staff from six hospitals of different levels and characteristics in Beijing were selected,including doctors,nurses,medical technicians,and servicers,to conduct knowledge assessment on respiratory protection competency.According to exposure risks of respiratory infectious diseases,based on actual cases and daily work scenarios,content of respira-tory protection competency assessment was designed from three aspects:identification of respiratory infectious di-seases,transmission routes and corresponding protection requirements,as well as correct selection and use of masks.The assessment included 6,6,and 8 knowledge points respectively,with 20 knowledge points in total,all of which were choice questions.For multiple-choice questions,full marks,partial marks,and no mark were given respective-ly if all options were correct,partial options were correct and without incorrect options,and partial options were correct but with incorrect options.Difficulty and discrimination analyses on question of each knowledge point was conducted based on classical test theory.Results The respiratory protection competency knowledge assessment for 326 staff members at different risk levels in 6 hospitals showed that concerning the 20 knowledge points,more than 60％participants got full marks for 6 points,while the proportion of full marks for other questions was relatively low.Less than 10％participants got full marks for the following 5 knowledge points:types of airborne diseases,types of droplet-borne diseases,conventional measures for the prevention and control of healthcare-associated infec-tion with respiratory infectious diseases,indications for wearing respirators,and indications for wearing medical protective masks.Among the 20 knowledge questions,5,1,and 14 questions were relatively easy,medium,and difficult,respectively;6,1,4,and 9 questions were with discrimination levels of ≥0.4,0.30-0.39,0.20-0.29,and ≤0.19,respectively.Conclusion There is still much room for hospital staff to improve their respiratory protection competency,especially in the recognition of diseases with different transmission routes and the indications for wearing different types of masks.

Objective This study aims to assess the role of DNA methylation changes in tongue cancer through a comprehensive analysis of global DNA methylation alterations during experimental lingual carcinogenesis.Methods C57BL/6J mice were subjected to 16-week oral administration of 4-nitroquinoline-1-oxide(4NQO,50 mg/L).Lingual mucosa samples,being representative of normal tissue(week 0)and early(week 12)and advanced(week 28)tumorigen-esis,were harvested for microarray and methylated DNA immunoprecipitation sequencing(MeDIP-Seq).The mRNA and promoter methylation of transforming growth factor-beta-signaling protein 1(SMAD1)were evaluated with real-time quantitative reverse transcription polymerase chain reaction and Massarray in human lingual mucosa and tongue cancer cell lines.Results The cytosine guanine island(CGI)methylation level observed at 28 weeks surpassed that of both 12 weeks and 0 weeks.The promoter methylation level at 12 weeks exceeded that at 0 weeks.Notably,208 differentially expressed genes were negatively correlated to differential methylation in promoters among 0,12,and 28 weeks.The mRNA of SMAD1 was upregulated,con-current with a decrease in promoter methylation levels in cell lines compared to normal mucosa.Conclusion DNA methylation changed during lingual carcinogenesis.Overexpression of SMAD1 was correlated to promoter hypomethyl-ation in tongue cancer cell lines.

Purpose/Significance A prediction model is constructed based on real-world data to achieve prediction and early screening of type 2 diabetic microvascular complications.Method/Process Based on the real world data of Nanjing Drum Tower Hospital in the past 10 years,a particle swarm optimization based deep belief network(PSO-DBN)prediction model for microvascular complica-tions in type 2 diabetes mellitus is constructed by taking test results and medical record documents into consideration.Result/Conclusion The PSO-DBN model can predict diabetic microvascular complications,and the performance is better than that of random forest and sup-port vector machine(SVM)benchmark models,it provides references for the research of disease prediction model of real-world data.