Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

Objective:To analyze the factors influencing the prognoses of stroke survivors with pseudobulbar palsy (PBP) dysphagia and construct a prediction model.Methods:Data on two hundred and fifty-one stroke survivors with PBP dysphagia were collected and analyzed retrospectively. The subjects were divided into a good prognosis group ( n=164) and a poor prognosis group ( n=87) according to the results of Kubota water swallowing tests administered at discharge. Their clinical data were analyzed using univariate correlation analysis, and the statistically significant independent variables found were further analyzed using multivariate logistic regression to obtain the important predictors of prognosis. R software was then used to assemble the useful factors into a nomogram prediction model. The area under the receiver operating characteristics (ROC) curve was employed to verify the model′s predictive power. Results:According to the univariate correlation analysis, significant differences were found between the two groups′ average National Institutes of Health Stroke Scale (NIHSS) scores, mouth opening grades and ages. The multivariate logistic regression analysis showed that NIHSS score, mouth opening grade and age could be useful and independent predictors of poor prognosis. The ROC curve analysis showed that the area under the curve for the nomogram model was 0.727, with a sensitivity of 59.8% and a specificity of 81.6%, suggesting that the model had good predictive power and was properly calibrated.Conclusions:NIHSS score, mouth opening grade and age are independent predictors of prognosis for stroke patients with PBP dysphagia. The nomogram model constructed in this study is of great value in developing a prognosis for such patients, which is helpful in the prevention and treatment of post-stroke dysphagia.

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

Objective:To analyze the factors influencing the prognoses of stroke survivors with pseudobulbar palsy (PBP) dysphagia and construct a prediction model.Methods:Data on two hundred and fifty-one stroke survivors with PBP dysphagia were collected and analyzed retrospectively. The subjects were divided into a good prognosis group ( n=164) and a poor prognosis group ( n=87) according to the results of Kubota water swallowing tests administered at discharge. Their clinical data were analyzed using univariate correlation analysis, and the statistically significant independent variables found were further analyzed using multivariate logistic regression to obtain the important predictors of prognosis. R software was then used to assemble the useful factors into a nomogram prediction model. The area under the receiver operating characteristics (ROC) curve was employed to verify the model′s predictive power. Results:According to the univariate correlation analysis, significant differences were found between the two groups′ average National Institutes of Health Stroke Scale (NIHSS) scores, mouth opening grades and ages. The multivariate logistic regression analysis showed that NIHSS score, mouth opening grade and age could be useful and independent predictors of poor prognosis. The ROC curve analysis showed that the area under the curve for the nomogram model was 0.727, with a sensitivity of 59.8% and a specificity of 81.6%, suggesting that the model had good predictive power and was properly calibrated.Conclusions:NIHSS score, mouth opening grade and age are independent predictors of prognosis for stroke patients with PBP dysphagia. The nomogram model constructed in this study is of great value in developing a prognosis for such patients, which is helpful in the prevention and treatment of post-stroke dysphagia.

Objective:To construct an early warning model of postpartum urinary retention risk after painless delivery of primipara by using machine learning algorithm,and to find the best effective early warning model,so as to provide scientific basis for early and accurate identification of high-risk groups in clinical practice.Methods:This study was a single-center retrospective study,80 primipara who delivered painlessly in Tingzhou Hospital of Fujian Province from July 2021 to June 2024 were included,they were divided into urinary retention group(18 cases)and non urinary retention group(62 cases)according to whether there was urinary retention after delivery.General data between two groups were compared,Univariate and Multivariate logistic regression were used to screen for influencing factors,three machine learning algorithms:Random Forest,Support Vector Machine,and Logistic Regression were used to construct an early warning model,the area under the receiver operating characteristic curve(ROC-AUC),accuracy,sensitivity,and specificity were used as performance evaluation indicators to evaluate the predictive performance of the model.Results:Univariate analysis showed that age,body mass index(BMI),gestational week,length of the second stage of labor,anesthetic dose,lateral episiotomy were associated with postpartum urinary retention(P＜0.05);Multivariate logistic regression identified BMI ≥ 28 kg/m2(OR=3.210,95％CI:1.450-7.090),length of the second stage of labor ≥ 2 hours(OR=2.890,95％CI:1.230-6.810),anesthetic dose≥ 15 mL(OR=3.560,95％CI:1.670-7.620),and lateral episiotomy(OR=2.540,95％CI:1.120-5.780)as independent risk factors.After comprehensive evaluation of various indicators,the random forest model has the best predictive performance.Conclusion:The risk warning model constructed based on machine learning has good predictive performance,and the random forest model performs the best,which can provide effective support for early clinical intervention.

To meet the domestic clinical demand timely,the national health commission has released three batches of encourage generic drug catalogues,which plays a good guiding role in improving the supply level and accessibility of generic drugs.Based on literature investigation,the typical cases of novel pharmaceutical preparations were analyzed,and the pharmaceutical considerations were put forward in terms formulation,manufacturing process and quality control,aimed to provide scientific reference for research and development of such drugs.

To meet the domestic clinical demand timely,the national health commission has released three batches of encourage generic drug catalogues,which plays a good guiding role in improving the supply level and accessibility of generic drugs.Based on literature investigation,the typical cases of novel pharmaceutical preparations were analyzed,and the pharmaceutical considerations were put forward in terms formulation,manufacturing process and quality control,aimed to provide scientific reference for research and development of such drugs.

Objective:To construct an early warning model of postpartum urinary retention risk after painless delivery of primipara by using machine learning algorithm,and to find the best effective early warning model,so as to provide scientific basis for early and accurate identification of high-risk groups in clinical practice.Methods:This study was a single-center retrospective study,80 primipara who delivered painlessly in Tingzhou Hospital of Fujian Province from July 2021 to June 2024 were included,they were divided into urinary retention group(18 cases)and non urinary retention group(62 cases)according to whether there was urinary retention after delivery.General data between two groups were compared,Univariate and Multivariate logistic regression were used to screen for influencing factors,three machine learning algorithms:Random Forest,Support Vector Machine,and Logistic Regression were used to construct an early warning model,the area under the receiver operating characteristic curve(ROC-AUC),accuracy,sensitivity,and specificity were used as performance evaluation indicators to evaluate the predictive performance of the model.Results:Univariate analysis showed that age,body mass index(BMI),gestational week,length of the second stage of labor,anesthetic dose,lateral episiotomy were associated with postpartum urinary retention(P＜0.05);Multivariate logistic regression identified BMI ≥ 28 kg/m2(OR=3.210,95％CI:1.450-7.090),length of the second stage of labor ≥ 2 hours(OR=2.890,95％CI:1.230-6.810),anesthetic dose≥ 15 mL(OR=3.560,95％CI:1.670-7.620),and lateral episiotomy(OR=2.540,95％CI:1.120-5.780)as independent risk factors.After comprehensive evaluation of various indicators,the random forest model has the best predictive performance.Conclusion:The risk warning model constructed based on machine learning has good predictive performance,and the random forest model performs the best,which can provide effective support for early clinical intervention.

Metaflammation is a crucial mechanism in the onset and advancement of metabolic disorders, primarily defined by the activation of immune cells and increased concentrations of pro-inflammatory substances. The function of brain-derived neurotrophic factor (BDNF) in modulating immune and metabolic processes has garnered heightened interest, as BDNF suppresses glial cell activation and orchestrates inflammatory responses in the central nervous system via its receptor tyrosine kinase receptor B (TrkB), while also diminishing local inflammation in peripheral tissues by influencing macrophage polarization. Exercise, as a non-pharmacological intervention, is extensively employed to enhance metabolic disorders. A crucial mechanism underlying its efficacy is the significant induction of BDNF expression in central (hypothalamus, hippocampus, prefrontal cortex, and brainstem) and peripheral (liver, adipose tissue, intestines, and skeletal muscle) tissues and organs. This induction subsequently regulates inflammatory responses, ameliorates metabolic conditions, and decelerates disease progression. Consequently, BDNF is considered a pivotal molecule in the motor-metabolic regulation axis. Despite prior suggestions that BDNF may have a role in the regulation of exercise-induced inflammation, systematic data remains inadequate. Since that time, the field continues to lack structured descriptions and conversations pertinent to it. As exercise physiology research has advanced, the academic community has increasingly recognized that exercise is a multifaceted activity regulated by various systems, with its effects contingent upon the interplay of elements such as type, intensity, and frequency of exercise. Consequently, it is imperative to transcend the prior study paradigm that concentrated solely on localized effects and singular mechanisms and transition towards a comprehensive understanding of the systemic advantages of exercise. A multitude of investigations has validated that exercise confers health advantages for individuals with metabolic disorders, encompassing youngsters, adolescents, middle-aged individuals, and older persons, and typically enhances health via BDNF secretion. However, exercise is a double-edged sword; the relationship between exercise and health is not linearly positive. Insufficient exercise is ineffective, while excessive exercise can be detrimental to health. Consequently, it is crucial to scientifically develop exercise prescriptions, define appropriate exercise loads, and optimize health benefits to regulate bodily metabolism. BDNF mitigates metaflammation via many pathways during exercise. Initially, BDNF suppresses pro-inflammatory factors and facilitates the production of anti-inflammatory factors by modulating bidirectional transmission between neural and immune cells, therefore diminishing the inflammatory response. Secondly, exercise stimulates the PI3K/Akt, AMPK, and other signaling pathways via BDNF, enhancing insulin sensitivity, reducing lipotoxicity, and fostering mitochondrial production, so further optimizing the body’s metabolic condition. Moreover, exercise-induced BDNF contributes to the attenuation of systemic inflammation by collaborating with several organs, enhancing hepatic antioxidant capacity, regulating immunological response, and optimizing “gut-brain” axis functionality. These processes underscore the efficacy of exercise as a non-pharmacological intervention for enhancing anti-inflammatory and metabolic health. Despite substantial experimental evidence demonstrating the efficacy of exercise in mitigating inflammation and enhancing BDNF levels, numerous limitations persist in the existing studies. Primarily, the majority of studies have concentrated on molecular biology and lack causal experimental evidence that explicitly confirms BDNF as a crucial mediator in the exercise regulation of metaflammation. Furthermore, the outcomes of current molecular investigations are inadequately applicable to clinical practice, and a definitive pathway of “exercise-BDNF-metaflammation” remains unestablished. Moreover, the existing research methodology, reliant on animal models or limited human subject samples, constrains the broad dissemination of the findings. Future research should progressively transition from investigating isolated and localized pathways to a comprehensive multilevel and multidimensional framework that incorporates systems biology and exercise physiology. Practically, there is an immediate necessity to undertake extensive, double-blind, randomized controlled longitudinal human studies utilizing multi-omics technologies (e.g., transcriptomics, proteomics, and metabolomics) to investigate the principal signaling pathways of BDNF-mediated metaflammation and to elucidate the causal relationships and molecular mechanisms involved. Establishing a more comprehensive scientific evidence system aims to furnish a robust theoretical framework and practical guidance for the mechanistic interpretation, clinical application, and pharmaceutical development of exercise in the prevention and treatment of metabolic diseases.