OBJECTIVE To construct and evaluate nomogram prediction model for refractory chemotherapy-induced nausea and vomiting （CINV）. METHODS The data of malignant tumor patients who received chemotherapy at the Third People’s Hospital of Zhengzhou from January 2017 to December 2023 were collected. These patients were categorized into the occurrence group and the non-occurrence group according to the occurrence of refractory CINV. Multivariate Logistic regression analysis was employed to screen predictive factors for refractory CINV and constructing a nomogram prediction model. Model performance was assessed via receiver operating characteristic curve analysis. Model calibration was evaluated using Bootstrap resampling. Decision curve analysis （DCA） was used to determine the clinical net benefit of three strategies under different risk thresholds. Clinical impact curves were utilized to assess the clinical value of the model at different risk thresholds. Shapley additive explanations （SHAP） analysis was performed to evaluate individual factor contributions to the predictive model. RESULTS A total of 388 patients were included， with 219 experiencing refractory CINV. Multivariate Logistic regression identified 11 predictive factors for refractory CINV， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， etc. The model’s area under the curve was 0.80 [95% confidence interval （0.76， 0.84）]， with a mean error of 0.036. DCA demonstrated the prediction model had higher clinical net benefit when the risk threshold was between 0.05 and 0.85. SHAP analysis revealed the top three predictive factors as gastrointestinal disease history （0.924）， chemotherapy- induced emetic risk classification （0.866）， and electrolyte levels （0.581）. CONCLUSIONS Eleven factors， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， are identified as predictors of refractory CINV. The model based on these factors has good predictive ability， which can be used to predict the risk of refractory CINV.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Exercise fatigue is a complex physiological and psychological phenomenon that includes peripheral fatigue in the muscles and central fatigue in the brain. Peripheral fatigue refers to the loss of force caused at the distal end of the neuromuscular junction, whereas central fatigue involves decreased motor output from the primary motor cortex, which is associated with modulations at anatomical sites proximal to nerves that innervate skeletal muscle. The central regulatory failure reflects a progressive decline in the central nervous system’s capacity to recruit motor units during sustained physical activity. Emerging evidence highlights the critical involvement of central neurochemical regulation in fatigue development, particularly through neurotransmitter-mediated modulation. Alterations in neurotransmitter release and receptor activity could influence excitatory and inhibitory signal pathways, thus modulating the perception of fatigue and exercise performance. Increased serotonin (5-HT) could increase perception of effort and lethargy, reduce motor drive to continue exercising, and contribute to exercise fatigue. Decreased dopamine (DA) and noradrenaline (NE) neurotransmission can negatively impact arousal, mood, motivation, and reward mechanisms and impair exercise performance. Furthermore, the serotonergic and dopaminergic systems interact with each other; a low 5-HT/DA ratio enhances motor motivation and improves performance, and a high 5-HT/DA ratio heightens fatigue perception and leads to decreased performance. The expression and activity of neurotransmitter receptors would be changed during prolonged exercise to fatigue, affecting the transmission of nerve signals. Prolonged high-intensity exercise causes excess 5-HT to overflow from the synaptic cleft to the axonal initial segment and activates the 5-HT1A receptor, thereby inhibiting the action potential of motor neurons and affecting the recruitment of motor units. During exercise to fatigue, the DA secretion is decreased, which blocks the binding of DA to D1 receptor in the caudate putamen and inhibits the activation of the direct pathway of the basal ganglia to suppress movement, meanwhile the binding of DA to D2 receptor is restrained in the caudate putamen, which activates the indirect pathway of the basal ganglia to influence motivation. Furthermore, other neurotransmitters and their receptors, such as adenosine (ADO), glutamic acid (Glu), and γ‑aminobutyric acid (GABA) also play important roles in regulating neurotransmitter balance and fatigue. The occurrence of central fatigue is not the result of the action of a single neurotransmitter system, but a comprehensive manifestation of the interaction between multiple neurotransmitters. This review explores the important role of neurotransmitters and their receptors in central motor fatigue, reveals the dynamic changes of different neurotransmitters such as 5-HT, DA, NE, and ADO during exercise, and summarizes the mechanisms by which these neurotransmitters and their receptors regulate fatigue perception and exercise performance through complex interactions. Besides, this study presents pharmacological evidence that drugs such as agonists, antagonists, and reuptake inhibitors could affect exercise performance by regulating the metabolic changes of neurotransmitters. Recently, emerging interventions such as dietary bioactive components intake and transcranial electrical stimulation may provide new ideas and strategies for the prevention and alleviation of exercise fatigue by regulating neurotransmitter levels and receptor activity. Overall, this work offers new theoretical insights into the understanding of exercise central fatigue, and future research should further investigate the relationship between neurotransmitters and their receptors and exercise fatigue.

ObjectiveTo compare the clinical features of patients with hepatitis B virus （HBV）-related early-onset liver cancer and those with late-onset liver cancer， to assess the severity of the disease， and to provide a theoretical basis for the early diagnosis and treatment of liver cancer. MethodsA retrospective analysis was performed for 695 patients who were diagnosed with HBV-related liver cancer for the first time in Guangzhou Eighth People’s Hospital， Guangzhou Medical University， from January 2019 to August 2023， among whom 93 had early-onset liver cancer （defined as an age of50 years for female patients and40 years for male patients） and 602 had late-onset liver cancer （defined as an age of ≥50 years for female patients and ≥40 years for male patients）. Related clinical data were collected， including demographic data， clinical symptoms at initial diagnosis， comorbidities， smoking history， drinking history， family history， routine blood test results， biochemical parameters of liver function， serum alpha-fetoprotein（AFP）， virological indicators， coagulation function， and imaging findings. The pan-inflammatory indices neutrophil-to-lymphocyte ratio （NLR）， platelet-to-lymphocyte ratio （PLR）， and lymphocyte-to-monocyte ratio （LMR） were calculated， as well as FIB-4 index， aspartate aminotransferase-to-platelet ratio index （APRI）， S index， Model for End-Stage Liver Disease （MELD） score， Child-Turcotte-Pugh （CTP） score， albumin-bilirubin （AIBL） grade， and Barcelona Clinic Liver Cancer （BCLC） stage. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Wilcoxon rank-sum test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or Fisher’s exact test were used for comparison of categorical data between two groups. ResultsThere were significant differences between the two groups in the proportion of male patients and the incidence rates of diabetes， hypertension， and fatty liver disease （χ²=6.357， 15.230， 11.467， and 14.204， all P0.05）， and compared with the late-onset liver cancer group， the early-onset liver cancer group had a significantly higher proportion of patients progressing to liver cancer without underlying cirrhosis （χ²=24.657， P0.001） and a significantly higher proportion of patients with advanced BCLC stage （χ²=6.172， P=0.046）. For the overall population， the most common clinical symptoms included abdominal distension， abdominal pain， poor appetite， weakness， a reduction in body weight， edema of both lower limbs， jaundice， yellow urine， and nausea， and 55 patients （7.9%） had no obvious symptoms at the time of diagnosis and were found to have liver cancer by routine reexamination， physical examination suggesting an increase in AFP， or radiological examination indicating hepatic space-occupying lesion； compared with the late-onset liver cancer group， the patients in the early-onset liver cancer group were more likely to have the symptoms of abdominal distension， abdominal pain， and jaundice （all P0.05）. Compared with the late-onset liver cancer group， the early-onset liver cancer group had a significantly larger tumor diameter （Z=2.845， P=0.034）， with higher prevalence rates of multiple tumors and intrahepatic， perihepatic， or distant metastasis （χ²=5.889 and 4.079， both P0.05）， and there were significant differences between the two groups in tumor location and size （χ²=3.948 and 11.317， both P0.05）. Compared with the late-onset liver cancer group， the early-onset liver cancer group had significantly lower FIB-4 index， proportion of patients with HBsAg ≤1 500 IU/mL， and levels of LMR and Cr （all P0.05）， as well as significantly higher positive rate of HBeAg and levels of log₁₀ HBV DNA， AFP， WBC， Hb， PLT， NLR， PLR， TBil， ALT， Alb， and TC （all P0.05）. ConclusionCompared with late-onset liver cancer， patients with early-onset liver cancer tend to develop liver cancer without liver cirrhosis and have multiple tumors， obvious clinical symptoms， and advanced BCLC stage， which indicates a poor prognosis.

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.

The disposable sterile urethral catheter was described in terms of the current status of the standards of foreign countries and China and its regulation and registration.The national supervision and sampling inspection and exploratory research of the disposable sterile urethral catheter in 2018,2019 and 2021 were introduced,and the problems found and the causes were analyzed and then the countermeasures were proposed accordingly.References were provided for guiding and standardizing the development of catheter products industry.[Chinese Medical Equipment Journal,2024,45(1):84-88]