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.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

PURPOSE: To investigate the regulatory role of nerve injury-induced protein 1 (NINJ1) in the anti-inflammatory function of human umbilical cord mesenchymal stem cells (hUC-MSCs) co-stimulated by interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). METHODS: hUC-MSCs were expanded in vitro using standard protocols, with stem cell characteristics confirmed by flow cytometry and multilineage differentiation assays. The immunomodulatory properties and cellular activity of cytokine-co-pretreated hUC-MSCs were systematically evaluated via quantitative reverse transcription RT-qPCR, lymphocyte proliferation suppression assays, and Cell Counting Kit-8 viability tests. Transcriptome sequencing, Western blotting and small interfering RNA interference were integrated to analyze the regulatory mechanisms of NINJ1 expression. Functional roles of NINJ1 in pretreated hUC-MSCs were elucidated through gene silencing combined with lactate dehydrogenase release assays, Annexin V/Propidium Iodide apoptosis analysis, macrophage co-culture models, and cytokine Enzyme-Linked Immunosorbent Assay. Therapeutic efficacy was validated in a cecal ligation and puncture-induced septic mouse model: 80 mice were randomly allocated into 4 experimental groups (n=20/group): sham group (laparotomy without cecal ligation); phosphate-buffered saline-treated group (cecal ligation and puncture (CLP) + 0.1 mL phosphate-buffered saline); hUC-MSCs (small interfering RNA (siRNA)-interferon-gamma and tumor necrosis factor-alpha co-stimulation (IT))-treated group (CLP + hUC-MSCs transfected with scrambled siRNA); and hUC-MSCs (siNINJ1-IT)-treated group (CLP + hUC-MSCs with NINJ1-targeting siRNA). RESULTS: hUC-MSCs demonstrated compliance with International Society for Cellular Therapy criteria, confirming their stem cell identity. IFN-γ/TNF-α co-pretreatment enhanced the immunosuppressive capacity of hUC-MSCs, accompanied by the reduction of cellular viability, while concurrently upregulating pro-inflammatory cytokines such as interleukin-6 and interleukin-1β. This co-stimulation significantly elevated NINJ1 expression in hUC-MSCs, whereas genetic silencing of NINJ1 effectively suppressed pro-inflammatory cytokine production and attenuated damage-associated molecular patterns release through inhibition of programmed plasma membrane rupture. Furthermore, the NINJ1 interference potentiated the ability of cytokine-pretreated hUC-MSCs to suppress LPS-induced pro-inflammatory responses in RAW264.7 macrophages. In cecal ligation and puncture-induced sepsis model, NINJ1-silenced hUC-MSCs exhibited enhanced therapeutic efficacy, manifested by reduced systemic inflammation and multi-organ damage. CONCLUSION Our findings shed new light on the immunomodulatory functions of cytokine-primed MSCs, offering groundbreaking insights for developing MSC-based therapies against inflammatory diseases via interfering the expression of NINJ1.
Mesenchymal Stem Cells/drug effects* ; Animals ; Interferon-gamma/pharmacology* ; Tumor Necrosis Factor-alpha/pharmacology* ; Humans ; Mice ; Umbilical Cord/cytology* ; Cells, Cultured ; Apoptosis ; Male

Iron overload is strongly associated with heart disease. Ferroptosis is a new form of regulated cell death indicated in cardiac ischemia-reperfusion (I/R) injury. However, the specific molecular mechanism of myocardial injury caused by iron overload in the heart is still unclear, and the involvement of ferroptosis in iron overload-induced myocardial injury is not fully understood. In this study, we observed that ferroptosis participated in developing of iron overload and I/R-induced cardiomyopathy. Mechanistically, we discovered that Parkin inhibited iron overload-induced ferroptosis in cardiomyocytes by promoting the ubiquitination of long-chain acyl-CoA synthetase 4 (ACSL4), a crucial protein involved in ferroptosis-related lipid metabolism pathways. Additionally, we identified p53 as a transcription factor that transcriptionally suppressed Parkin expression in iron-overloaded cardiomyocytes, thereby regulating iron overload-induced ferroptosis. In animal studies, cardiac-specific Parkin knockout mice (Myh6-CreER ^T2 /Parkin ^fl/fl ) fed a high-iron diet presented more severe myocardial damage, and the high iron levels exacerbated myocardial I/R injury. However, the ferroptosis inhibitor Fer-1 significantly suppressed iron overload-induced ferroptosis and myocardial I/R injury. Moreover, Parkin effectively protected against impaired mitochondrial function and prevented iron overload-induced mitochondrial lipid peroxidation. These findings unveil a novel regulatory pathway involving p53-Parkin-ACSL4 in heart disease by inhibiting of ferroptosis.

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Twelve pre-processing protocols for formalin-fixed paraffin-embedded(FFPE)tissue samples were developed by orthogonal experimental design,incorporating different dewaxing buffers(Triton X-100 and xylene),lysis buffers(TFE and RapiGest),and enzyme digestion methods(iST,SP3,and FASP)to explore the optimal experimental conditions.These protocols were assessed based on protein and peptide identification depth,identification stability,and quantitative levels of protein abundance.The results indicated that Triton X-100 and xylene minimally impacted proteomics identification,whereas the TFE lysis buffer and iST digestion method significantly enhanced the proteomics analysis of FFPE samples.Considering the potential toxicity of xylene,the TTI protocol based on Triton X-100,TFE,and iST was determined to be the optimal choice.This protocol exhibited the best repeatability and stability,and a higher number of proteins associated with significant biological functions were identified.In conclusion,the established TTI protocol offered an efficient and comprehensive approach for proteomic analysis of FFPE samples,significantly enhancing the repeatability and stability of protein identification.

Physiologically based pharmacokinetic (PBPK) models have been widely used to predict various stages of drug absorption, distribution, metabolism and excretion. Models based on machine learning (ML) and artificial intelligence (AI) can provide better ideas for the construction of PBPK models, which can accelerate the prediction speed and improve the prediction quality of PBPK. ML and AL can complement the advantages of PBPK model to accelerate the progress of drug research and development. This review introduces the application of machine learning and artificial intelligence in pharmacokinetics, summarizes the research progress of physiological pharmacokinetic models based on machine learning and artificial intelligence, and analyzes the limitations of machine learning and artificial intelligence applications and their application prospects and prospects.

Programmed cell death 1 ligand 1 (PD-L1) is an important immunosuppressive molecule, which inhibits the function of T cells and other immune cells by binding to the receptor programmed cell death-1. The PD-L1 expression disorder plays an important role in the occurrence, development, and treatment of sepsis or other inflammatory diseases, and has become an important target for the treatment of these diseases. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with multiple differentiation potential. In recent years, MSCs have been found to have a strong immunosuppressive ability and are used to treat various inflammatory insults caused by hyperimmune diseases. Moreover, PD-L1 is deeply involved in the immunosuppressive events of MSCs and plays an important role in the treatment of various diseases. In this review, we will summarize the main regulatory mechanism of PD-L1 expression, and discuss various biological functions of PD-L1 in the immune regulation of MSCs.

Highly sensitive multiple detection and accurate identification of gases are of great importance in the fields of public safety,environmental protection,health diagnosis and industrial production.However,the traditional means of gas detection have many shortcomings such as low sensitivity,long time-consuming,bulky equipment,cumbersome processes and expensive costs.In recent years,Raman spectroscopy has become a hotspot in the field of gas detection because of its fast,sensitive and non-destructive characteristics,and has been more and more closely combined with artificial intelligence.This paper reviews the progress of Raman spectroscopy in gas detection in recent years,including conventional Raman spectroscopy and enhanced Raman spectroscopy,and also introduces the integration of artificial intelligence algorithms in gas Raman detection technology,and discusses the future development of gas Raman detection.

Professor ZHOU Peng has deeply discussed the pathological characteristics of psoriasis vulgaris,emphasizing that the disease is usually manifested deficiency interweaved with excess,leading to frequent recurrence and persistent refractory,which may lead to psychological and emotional problems of patients.This paper further expounds the effect of blood stasis on the pathogenesis,progression and prognosis of psoriasis,and puts forward a new method of combining Lingnan fire needling and filiform needling acupuncture technique to treat psoriasis vulgaris with blood stasis syndrome.Professor ZHOU Peng believes that the treatment principle of this disease is"regulating the mind first,rectifying blood as a base,syndrome differentiating and eliminating pathogenic factors",aiming at comprehensively considering the etiology and symptoms,in order to achieve more effective treatment results.Combined with the analysis of dermoscopic signs,it provides a possible improvement direction for the treatment of psoriasis vulgaris from a new perspective.