Autoimmune pancreatitis is a special type of chronic pancreatitis that can lead to abnormal pancreatic exocrine function in patients. Autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency has a complex pathogenesis， and there is limited research on this topic， leading to the lack of understanding of such patients in clinical practice. This article introduces the epidemiology of autoimmune pancreatitis， briefly describes the pathogenesis of pancreatic exocrine insufficiency caused by autoimmune pancreatitis， and summarizes the various detection methods for pancreatic exocrine function， nutritional assessments， lifestyle management， and drug therapy， in order to strengthen the understanding of autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency and improve the clinical diagnosis and treatment of pancreatic exocrine insufficiency.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo establish a rat model of osteoarthritis and study the anti-inflammatory effects and mechanisms of fraxetin. MethodsEighteen 8-week-old male SPF-grade SD rats were randomly divided into three groups: Rats in the blank group received a right articular cavity injection of 50 μL of normal saline for 1 week; the model and intervention groups were injected with monosodium iodoacetate (MIA) into the right joint cavity to induce osteoarthritis, while the intervention group subsequently received fraxetin (5 mg·kg^-1·d^-1) for 1 week. Four weeks after drug intervention, abdominal aortic blood was collected. The animals were then euthanized, and knee joint cartilage were collected. The cartilage samples were stained with hematoxylin-eosin, safranin O-fast green, and toluidine blue for histopathological examination and scoring using the Mankin and OARSI scoring systems. The trabecular bone volume/total volume (Tb.BV/TV), trabecular bone surface density/total volume (Tb.BS/TV), and trabecular number (Tb.N) of each group were compared and analyzed using a micro-CT scanning system. The expression levels of various inflammatory factors [tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)], and cartilage oligomeric matrix protein (COMP) were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of mitogen-activated protein kinase p38 (p38 MAPK), phosphorylation-p38 MAPK (p-p38 MAPK), c-Jun N-terminal kinase (JNK), and phosphorylation-JNK (p-JNK) were measured by western blotting. ResultsThe staining of cartilage sections of rat knee joints showed that the articular surface defects in the model group were severe, while the cartilage destruction in the intervention group was relatively reduced. Micro-CT results showed that Tb.BV/TV, Tb.BS/TV and Tb.N in the intervention group were significantly higher than those in the model group (P < 0.05); the Mankin score in the model group was significantly higher than that in the blank group (P < 0.05), the Mankin score in the intervention group was significantly lower than that in the model group (P < 0.05); while the OARSI score in the intervention group was significantly lower than that in the model group (P < 0.05). The results of the enzyme-linked immunosorbent assay showed that the serum levels of TNF-α, IL-1β, IL-6, and COMP in the model group were significantly higher than those in the blank group (all P < 0.05), while those in the intervention group were significantly lower than in the model group (P < 0.05). Western blot results showed that the expression levels of p-p38 MAPK and p-JNK in the knee cartilage tissue were significantly lower in the intervention group than in the model group (both P < 0.05), and significantly higher in the model group than in the blank group (both P < 0.05). ConclusionFraxetin may play a therapeutic role in a monosodium iodoacetate-induced rat model of osteoarthritis through the p38 MAPK pathway.

ObjectiveTo establish a rat model of osteoarthritis and study the anti-inflammatory effects and mechanisms of fraxetin. MethodsEighteen 8-week-old male SPF-grade SD rats were randomly divided into three groups: Rats in the blank group received a right articular cavity injection of 50 μL of normal saline for 1 week; the model and intervention groups were injected with monosodium iodoacetate (MIA) into the right joint cavity to induce osteoarthritis, while the intervention group subsequently received fraxetin (5 mg·kg^-1·d^-1) for 1 week. Four weeks after drug intervention, abdominal aortic blood was collected. The animals were then euthanized, and knee joint cartilage were collected. The cartilage samples were stained with hematoxylin-eosin, safranin O-fast green, and toluidine blue for histopathological examination and scoring using the Mankin and OARSI scoring systems. The trabecular bone volume/total volume (Tb.BV/TV), trabecular bone surface density/total volume (Tb.BS/TV), and trabecular number (Tb.N) of each group were compared and analyzed using a micro-CT scanning system. The expression levels of various inflammatory factors [tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)], and cartilage oligomeric matrix protein (COMP) were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of mitogen-activated protein kinase p38 (p38 MAPK), phosphorylation-p38 MAPK (p-p38 MAPK), c-Jun N-terminal kinase (JNK), and phosphorylation-JNK (p-JNK) were measured by western blotting. ResultsThe staining of cartilage sections of rat knee joints showed that the articular surface defects in the model group were severe, while the cartilage destruction in the intervention group was relatively reduced. Micro-CT results showed that Tb.BV/TV, Tb.BS/TV and Tb.N in the intervention group were significantly higher than those in the model group (P < 0.05); the Mankin score in the model group was significantly higher than that in the blank group (P < 0.05), the Mankin score in the intervention group was significantly lower than that in the model group (P < 0.05); while the OARSI score in the intervention group was significantly lower than that in the model group (P < 0.05). The results of the enzyme-linked immunosorbent assay showed that the serum levels of TNF-α, IL-1β, IL-6, and COMP in the model group were significantly higher than those in the blank group (all P < 0.05), while those in the intervention group were significantly lower than in the model group (P < 0.05). Western blot results showed that the expression levels of p-p38 MAPK and p-JNK in the knee cartilage tissue were significantly lower in the intervention group than in the model group (both P < 0.05), and significantly higher in the model group than in the blank group (both P < 0.05). ConclusionFraxetin may play a therapeutic role in a monosodium iodoacetate-induced rat model of osteoarthritis through the p38 MAPK pathway.

Objective To observe the effect of comorbidity for patients with non-small cell lung cancer (NSCLC) on exercise tolerance and cardiopulmonary function. Methods NSCLC patients who underwent cardiopulmonary exercise testing (CPET) before surgery were retrospectively included. According to the Charlson comorbidity index (CCI) score, patients were divided into two groups: a CCI≥3 group and a CCI＜3 group. The patients were matched with a ratio of 1 : 1 by propensity score matching according to the age, body mass index, sex, smoking history, exercise habits, pathological stage and type of surgery. After matching, CPET indexes were compared between the two groups to explore the differences in exercise tolerance and cardiopulmonary function. Results A total of 276 patients were included before matching. After matching, 56 patients were enrolled with 28 patients in each group, including 38 (67.9%) males and 18 (32.1%) females with an average age of (70.7±6.8) years. Compared with the CCI＜3 group, work rate at peak (WR peak), WR peak/predicted value (WR peak%), kilogram oxygen uptake at anaerobic threshold (VO2/kg AT), VO2/kg peak, VO2/kg peak%, peak carbon dioxide output, the minute ventilation to carbon dioxide production slope, O2 pulse peak and O2 pulse peak% of CCI≥3 group were statistically different (P＜0.05). Among them, the rate of postoperative pulmonary complication in the CCI≥3 group was higher than that in the CCI＜3 group (60.7% vs. 32.1%, P=0.032). Conclusion In the NSCLC patients, exercise tolerance and cardiopulmonary function decreased in patients with CCI≥3 compared with those with CCI<3. CPET can provide an objective basis for risk assessment in patients with comorbidity scored by CCI for pulmonary resection.

Objective To investigate and analyze epidemiological characteristics of patients with mycoplasma pneumoniae pneumonia (MPP) from 2020 to 2023, and the risk factors for plastic bronchitis (PB), To provide data support for developing preventive measures. Methods The medical records of 2 257 patients with respiratory tract infection treated at Huai'an Hospital Affiliated to Xuzhou Medical University from 2020 to 2023 were collected. Count the number of MPP patients and analyze the MP detection rate. Multivariate logistic regression analysis and ROC curve was used to screen the risk factors for PB. Results A total of 858 cases were positive for MP antibodies, and the detection rate was 38.02%. There are statistically significant differences in MP detection rates among different genders, age groups, and years (P<0.05). Among the 286 patients diagnosed with MPP and undergoing bronchoscopy, 68 (23.78%) patients had PB. According to univariate and multivariate logistic regression analysis, small age, higher N%, D-D, LDH and AST levels were independent risk factors for PB (P<0.05). ROC curve analysis shows that age and combined detection are the most effective indicators for PB prediction, with areas under the curve of 0.998 and 0.961, respectively. Conclusion MP is the main pathogen of respiratory tract infections in the area from 2020 to 2023. Women and children are more susceptible to MP infection. Small age, high N%, DD, LDH and AST levels are independent risk factors for PB in patients with MPP. Targeted preventive measures should be taken for MP susceptible population, and close attention should be paid to PB related risk factors to prevent disease progression and the occurrence of PB.