Intestinal fibrosis is a major complication of inflammatory bowel disease (IBD), leading to a high incidence of surgical interventions and significant disability. Despite its clinical relevance, no targeted pharmacological therapies are currently available. This review aims to explore the underlying mechanisms driving intestinal fibrosis and address unresolved scientific questions, offering insights into potential future therapeutic strategies. We conducted a literature review using data from PubMed up to October 2024, focusing on studies related to IBD and fibrosis. Intestinal fibrosis results from a complex network involving stromal cells, immune cells, epithelial cells, and the gut microbiota. Chronic inflammation, driven by factors such as dysbiosis, epithelial injury, and immune activation, leads to the production of cytokines like interleukin (IL)-1β, IL-17, and transforming growth factor (TGF)-β. These mediators activate various stromal cell populations, including fibroblasts, pericytes, and smooth muscle cells. The activated stromal cells secrete excessive extracellular matrix components, thereby promoting fibrosis. Additionally, stromal cells influence the immune microenvironment through cytokine production. Future research would focus on elucidating the temporal and spatial relationships between immune cell-driven inflammation and stromal cell-mediated fibrosis. Additionally, investigations are needed to clarify the differentiation origins of excessive extracellular matrix-producing cells, particularly fibroblast activation protein (FAP) + fibroblasts, in the context of intestinal fibrosis. In conclusion, aberrant stromal cell activation, triggered by upstream immune signals, is a key mechanism underlying intestinal fibrosis. Further investigations into immune-stromal cell interactions and stromal cell activation are essential for the development of therapeutic strategies to prevent, alleviate, and potentially reverse fibrosis.
Humans ; Fibrosis/metabolism* ; Inflammatory Bowel Diseases/pathology* ; Animals ; Transforming Growth Factor beta/metabolism* ; Intestines/pathology*

Objective:To explore a method that can improve the detecting accuracy for faults of magnetic resonance(MR)through diagnostic technique based on firefly algorithm for fault of MR coil of magnetic resonance imaging(MRI)equipment with superconductivity,so as to enhance the performance and reliability of MRI equipment.Methods:A diagnosis technique for fault of MR coils in MRI equipment with superconductivity was proposed.Wavelet basis function was used to decompose initial vibration signals,and they were separated into resonance components with high-frequency and that with low-frequency,so as to enhance signal purity.The signal entropy value of deconvolution became the minimum value through iteratively adjusted the filter's coefficients,and identified the optimal system function of inverse filter,thus,to effectively recover the resonance characteristic of initially high and low frequency.A multi-objective function,which integrated entropy value of low-resonance component,smoothness and the differentiation of that with high-resonance components,was constructed.An improved firefly algorithm was introduced to combine linearly decreasing inertia weight factor,and simulate the process of swarm intelligence optimization,so as to realize global search and precise identification for faults'characteristics.Finally,the optimally diagnostic result was outputted.Results:The diagnostic technique based on firefly algorithm for fault of MR coil of MRI equipment with superconductivity can effectively decompose and enhance the original signal,and significantly suppress the interference of background noise.The algorithm can approach the true solution of the fault,which only need 298 iterations through global search for the combination of faults'features and diagnostic parameters.It can realize accurate identification for the faults of MR coil.Conclusion:The diagnostic technique,which based on firefly algorithm for fault of MR coil of MRI equipment with superconductivity,appears obvious advantages on diagnostic accuracy and convergence speed.It can provide a feasible approach for rapid diagnosis for fault of MRI equipment with superconductivity.

This article reviews the benifits and challenges of nano-microspheres (NPs) in the treatment of osteoarthritis (OA). OA is a degenerative disease associated with aging, trauma, and excessive loading, with treatment strategies including basic therapy, drug therapy, reparative therapy, and reconstructive surgery. As emerging nanomaterials, NPs offer unique advantages in promoting cartilage repair due to their high surface area, excellent drug-loading capacity, and good biocompatibility. These advantages include facilitating chondrocyte generation through magnetic-mechanical control of mesenchymal stem cell microspheres and enhancing antioxidant levels using biomimetic liposomal NPs combined with glucosamine. Additionally, NPs can effectively modulate inflammatory responses, such as by inhibiting the formation of M1 macrophages and promoting their polarization to the M2 type to alleviate inflammation. Some NPs also enhance joint lubrication and relieve pain, such as hyaluronic acid-based NPs modified with choline phosphate groups. However, the application of NPs faces challenges such as high production costs, poor biocompatibility for certain types, and unknown long-term safety. Despite these challenges, with advancements in nanotechnology and a deeper understanding of the pathological mechanisms of OA, NPs are expected to provide new therapeutic approaches and more comprehensive and effective treatment options for OA patients in the future.

ObjectiveTo investigate the association of particulate matter and ozone with the prevalence of non-alcoholic fatty liver disease (NAFLD) in a district of Shanghai, and to provide epidemiological evidence for the further identification of early health hazards of air pollution and for the prevention and control of NAFLD. MethodsBased on Songjiang Sub-cohort of Shanghai Natural Population Cohort, a cross-sectional survey design was used to recruit participants from 2016 to 2017. Annual average exposure levels to air pollution from 2009 to 2017 were matched to the participant’s residential address using a high-resolution and high-quality ambient air pollutants dataset in China. NAFLD was diagnosed according to the “Guidelines for the prevention and treatment of metabolism⁃associated (non⁃alcoholic) fatty liver disease” by the Chinese Medical Association. Multivariate logistic regression models were employed to analyze the association between air pollution and the prevalence of NAFLD, and stratified analyses were used to compare differences by age, gender, obesity, and lifestyle habits within subgroups. ResultsA total of 32 791 individuals were included in the study. The prevalence of NAFLD among community residents in suburban Shanghai was 38.88%. For every 1 μg·m^-3 increase in PM₁, PM_2.5, PM₁₀, or O₃, the risk of NAFLD increased correspongdinglt, with the odds ratios (95%CI) of 1.071 (1.043‒1.099), 1.065 (1.042‒1.089), 1.041 (1.027‒1.055), or 1.061 (1.032‒1.091), respectively. There were differences in effects across different gender, age, and obesity status subgroups. ConclusionPM₁, PM_2.5, PM₁₀, and O₃ are positively associated with an increased risk of NAFLD. Stratified analyses reveal that individuals aged 65 years old or above exhibited greater susceptibility to PM₁, PM_2.5, and O₃, whereas those aged less than 65 years old are more vulnerable to PM₁₀. Males are more sensitive to PM₁ and O₃, and females are more susceptible to PM_2.5 and PM₁₀. The association between air pollutant exposure and NAFLD risk is more pronounced among obese participants compared to that in non-obese counterparts.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) has overcome the reliance on surgical specimens in traditional pancreatic ductal adenocarcinoma (PDAC) research, enabling minimally invasive acquisition and evaluation of in situ tumor tissues from a large number of patients previously ineligible for surgery. This article systematically discusses the current applications of EUS-FNA/B combined with multi-omics technologies in the precision diagnosis and treatment of pancreatic cancer. We explores how this approach leverages its advantages of minimally invasive sampling and dynamic monitoring to integrate multi-omics analyses, thereby deciphering the dynamic evolution of the tumor microenvironment and drug resistance mechanisms to guide clinical decision-making. Finally, the review highlights future translational research directions aimed at optimizing therapeutic strategies and improving patient outcomes.

This article reviews the benifits and challenges of nano-microspheres (NPs) in the treatment of osteoarthritis (OA). OA is a degenerative disease associated with aging, trauma, and excessive loading, with treatment strategies including basic therapy, drug therapy, reparative therapy, and reconstructive surgery. As emerging nanomaterials, NPs offer unique advantages in promoting cartilage repair due to their high surface area, excellent drug-loading capacity, and good biocompatibility. These advantages include facilitating chondrocyte generation through magnetic-mechanical control of mesenchymal stem cell microspheres and enhancing antioxidant levels using biomimetic liposomal NPs combined with glucosamine. Additionally, NPs can effectively modulate inflammatory responses, such as by inhibiting the formation of M1 macrophages and promoting their polarization to the M2 type to alleviate inflammation. Some NPs also enhance joint lubrication and relieve pain, such as hyaluronic acid-based NPs modified with choline phosphate groups. However, the application of NPs faces challenges such as high production costs, poor biocompatibility for certain types, and unknown long-term safety. Despite these challenges, with advancements in nanotechnology and a deeper understanding of the pathological mechanisms of OA, NPs are expected to provide new therapeutic approaches and more comprehensive and effective treatment options for OA patients in the future.

Objective: To explore the correlation and lag effects of environmental factors on pediatric respiratory disease visits at hospital, so as to provide scientific basis for disease prediction and optimizing clinical diagnosis and treatment. Methods: Data from 503 889 pediatric respiratory disease outpatient and emergency visits a central hospital in Minhang District of Shanghai between 2017 and 2019, along with concurrent meteorological data were collected. A distributed lag non-linear models (DLNM) was constructed to explore the specific relationship between pediatric respiratory disease consultations and various environmental factors and to quantify the cumulative lag effects of environmental factors on respiratory disease consultations. Results: Among the environmental factors, temperature, fine particulate matter (PM 2.5 ), inhalable particulate matter (PM 10 ), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) were associated with pediatric respiratory disease visits. After adjusting for temperature, PM 2.5 and PM 10 concentrations did not show significant immediate or lag effects. The relative risk (RR) of pediatric respiratory disease visits increased with rising NO 2 concentrations. When NO 2 concentration ≥55 μg/m 3, significant immediate and lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.05, 1.13, 1.17, and 1.21( P <0.05). The RR values showed an inverted “U” shaped relationship with SO 2 concentrations. When SO 2 concentration ≥5 μg/m 3, significant lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.03 , 1.03, and 1.04 ( P <0.05). Conclusion High concentrations of NO 2 and SO 2 increase the risk of pediatric respiratory disease visits, with observable lag effects.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) has overcome the reliance on surgical specimens in traditional pancreatic ductal adenocarcinoma (PDAC) research, enabling minimally invasive acquisition and evaluation of in situ tumor tissues from a large number of patients previously ineligible for surgery. This article systematically discusses the current applications of EUS-FNA/B combined with multi-omics technologies in the precision diagnosis and treatment of pancreatic cancer. We explores how this approach leverages its advantages of minimally invasive sampling and dynamic monitoring to integrate multi-omics analyses, thereby deciphering the dynamic evolution of the tumor microenvironment and drug resistance mechanisms to guide clinical decision-making. Finally, the review highlights future translational research directions aimed at optimizing therapeutic strategies and improving patient outcomes.