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.

A case of a 51-year-old male presented with leukocytosis, lymphocytosis, and splenomegaly. Comprehensive diagnostic evaluations, including cytomorphology, flow cytometric immunophenotyping, and splenic pathology, confirmed the diagnosis of splenic diffuse red pulp small B-cell lymphoma (SDRPL). The patient tested was positive for TP53 mutation and demonstrated poor response to various chemotherapy regimens. During follow-up, the patient′s condition deteriorated. PET-CT revealed multiple metabolically active lymph nodes throughout the body. Peripheral blood smear revealed abnormal lymphocytes, which suggests potential high-grade transformation. Pathological examination of axillary lymph nodes indicated lymphoma cell infiltration, with positive expression of BCL-2, BCL-6, and C-MYC. Subsequent treatment with Zanubrutinib+Obinutuzumab+Bendamustine yielded suboptimal results. Chimeric antigen receptor T-cell immunotherapy (CAR-T) was subsequently administered, and after 9 months, minimal residual disease (MRD) evaluation was negative. SDRPL is a rare indolent B-cell lymphoma, and its diagnosis relies on splenic pathology. The abnormal lymphocytes exhibit characteristic villous projections, and monitoring morphological changes during follow-up can indicate disease progression, providing a basis for selecting appropriate treatment strategies.

Babesia is a type of important blood parasitic pathogen that is transmitted between humans and animals. It has diverse transmission routes and poses a significant threat to human and animal health. The clinical manifestations of babesia infection are complex, and timely and accurate detection is of great significance for disease diagnosis, treatment, and prevention. Laboratory detection techniques for babesia include microscopic examination, artificial intelligence-based morphological detection methods, pathogen-based, immunological, and molecular diagnostic approaches. Although existing diagnostic techniques have their own characteristics, no single technique currently meets all clinical and public health requirements. Therefore, future detection technologies for babesia should focus on improving diagnostic accuracy, sensitivity, and specificity, exploring the feasibility of combined application of multiple detection techniques, and constructing a comprehensive diagnostic system to effectively address the challenges posed by babesiosis.

Objective:To analyze the main chemical components of Lanqin Granules based on UPLC-Q-TOF-MS/MS; To explore the potential targets, core components and related pathways of Lanqin Granules in the treatment of respiratory tract infection through network pharmacology.Methods:Using UPLC-Q-TOF-MS/MS secondary fragment cleavage information, combined with literature and database, the chemical components of Lanqin Granules were analyzed; the related action targets of Lanqin Granules were obtained by PharmMapper; The related targets of respiratory tract infection were obtained from GeneCards and OMIM databases, and the common targets were selected by intersection with the relevant action targets of Lanqin Granules. The common targets were imported into string database, and the protein interaction data were downloaded and input into Cytoscape 3.7.2 software to obtain hub gene; Go function and KEGG pathway enrichment analysis were carried out on DAVID platform, and chemical components closely related to hub gene were obtained by using the Network Anlyzer plug-in.Results:53 chemical constituents of Lanqin Granules were obtained, including 11 alkaloids, 20 flavonoids, 5 terpenoids, 7 organic esters, 2 amino acids and 8 other compounds. 28 hub genes and 38 related core components were obtained. The results of GO analysis showed that the treatment of respiratory tract infection with Lanqin Granules was related to neutrophil degranulation, negative regulation of apoptosis, protein hydrolysis and other biological processes; extracellular exosomes, cytoplasm, extracellular components and other cellular components; the same protein binding, RNA polymerase Ⅱtranscription factor activity, ligand activated sequence specific DNA binding, protein serine/ threonine/ tyrosine kinase activity and other molecular processes. KEGG analysis results mainly involved cancer pathway, lipid and atherosclerosis, metabolic pathway and other signaling pathways.Conclusion:Lanqin Granules can treat respiratory tract infection through multi-component, multi-target and multi-channel, and play the role of anti-inflammatory, antibacterial and anti-virus.

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.

A case of a 51-year-old male presented with leukocytosis, lymphocytosis, and splenomegaly. Comprehensive diagnostic evaluations, including cytomorphology, flow cytometric immunophenotyping, and splenic pathology, confirmed the diagnosis of splenic diffuse red pulp small B-cell lymphoma (SDRPL). The patient tested was positive for TP53 mutation and demonstrated poor response to various chemotherapy regimens. During follow-up, the patient′s condition deteriorated. PET-CT revealed multiple metabolically active lymph nodes throughout the body. Peripheral blood smear revealed abnormal lymphocytes, which suggests potential high-grade transformation. Pathological examination of axillary lymph nodes indicated lymphoma cell infiltration, with positive expression of BCL-2, BCL-6, and C-MYC. Subsequent treatment with Zanubrutinib+Obinutuzumab+Bendamustine yielded suboptimal results. Chimeric antigen receptor T-cell immunotherapy (CAR-T) was subsequently administered, and after 9 months, minimal residual disease (MRD) evaluation was negative. SDRPL is a rare indolent B-cell lymphoma, and its diagnosis relies on splenic pathology. The abnormal lymphocytes exhibit characteristic villous projections, and monitoring morphological changes during follow-up can indicate disease progression, providing a basis for selecting appropriate treatment strategies.

Babesia is a type of important blood parasitic pathogen that is transmitted between humans and animals. It has diverse transmission routes and poses a significant threat to human and animal health. The clinical manifestations of babesia infection are complex, and timely and accurate detection is of great significance for disease diagnosis, treatment, and prevention. Laboratory detection techniques for babesia include microscopic examination, artificial intelligence-based morphological detection methods, pathogen-based, immunological, and molecular diagnostic approaches. Although existing diagnostic techniques have their own characteristics, no single technique currently meets all clinical and public health requirements. Therefore, future detection technologies for babesia should focus on improving diagnostic accuracy, sensitivity, and specificity, exploring the feasibility of combined application of multiple detection techniques, and constructing a comprehensive diagnostic system to effectively address the challenges posed by babesiosis.