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Background In diagnosing type 1 autoimmune pancreatitis (AIP), serum IgG4 (sIgG4) can be false-negative. EUS-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) pathology is key for diagnosis, but clinical features’ impact on pathologic confirmation is unclear. This study analyzed their link and factors improve diagnostic accuracy. Methods We analyzed data from a single-center retrospective study at Changhai Hospital (Jan 2009-Jan 2024). Type 1 AIP was diagnosed per International Consensus Diagnostic Criteria (ICDC). Patients with surgical diagnosis, no EUS, or incomplete biopsy data were excluded; eligible cases were grouped into “Confirmed”/“Unconfirmed” per ICDC. Baseline data, laboratory indicators, imaging, and EUS-FNA/FNB data were collected. Statistical analyses (ROC, χ² tests, multivariate logistic regression) were done with R 4.4.0. Results A total of 182 suspected type 1 AIP patients were enrolled; 84.07% were male, 88.46% middle-aged/elderly. Common symptoms: abdominal discomfort (65.93%), obstructive jaundice (43.41%). sIgG4 > 2×ULN (twice upper normal limit) occurred in 64.84%. Multivariate analysis: pathological confirmation rate 65.12% (EUS-FNB) vs. 18.75% (EUS-FNA) ( P  < .001, former higher). For IgG4-positive cells: 82.56% confirmation rate (> 10 cells/high-power field[HPF]) vs. 16.67% (< 10 cells/HPF) ( P  < .001). EUS-FNB (OR = 3.56, 95% CI: 1.55–8.18, P  = .003) and IgG4-positive cell count (> 10 cells/HPF) (OR = 15.71, 95% CI: 6.96–35.46, P  < .001) were independent confirmation predictors. Gender, age, sIgG4 had limited value; renal involvement, retroperitoneal fibrosis were auxiliary indicators. Conclusions Following systematic multi-dimensional factor screening, pathological confirmation of type 1 AIP relies on two key factors: EUS-FNB and histopathological detection of IgG4-positive cells (> 10 cells/HPF). Integrating these core diagnostic modalities with additional indicators—such as auxiliary markers of extrapancreatic involvement (e.g., renal involvement)—further enhances diagnostic precision, which facilitates the refinement of clinical diagnostic workflows for type 1 AIP.

Nanodrug delivery systems (NDDS) have demonstrated broad application prospects in disease treatment, prevention, and diagnosis due to several advantages, including functionalization capability, high drug‐loading capacity, drug stability protection, and the enhanced permeability and retention (EPR) effect. However, their clinical translation still faces multiple challenges, including rapid clearance by the reticuloendothelial system (RES), poor targeting specificity, and insufficient efficiency in crossing biological barriers. To address these limitations, researchers have developed the biological carrier‐mediated nanomedicine hitchhiking strategy (BCM‐NHS), which leverages circulating cells, proteins, or bacteria as natural “mobile carriers” to enhance drug delivery. This approach enables nanocarriers to inherit the intrinsic biological properties, endowing them with immune evasion, prolonged circulation, dynamic targeting, biocompatibility, biodegradability, and naturally optimized biological interfaces. Here, a systematic overview of the BCM‐NHS is provided. First, the review delves into the methods of nanoparticles (NPs) binding and immobilization, encompassing both the surface‐attachment‐mediated “backpack” strategy and the encapsulation‐based “Trojan horse” strategy. Second, the classification of biological carriers, including both cell‐based and non‐cell‐based carriers, is elucidated. Third, the physical properties and release mechanisms of these nanomaterials are thoroughly described. Finally, the latest applications of BCM‐NHS in therapeutic and diagnostic contexts across various disease models including tumor, ischemic stroke, and pneumonia are highlighted. Diagram illustrating the binding methods of NPs and biological barriers. The biological carrier‐mediated nanomedicine hitchhiking strategy (BCM‐NHS) utilizes two distinct techniques: the surface‐based “Backpack” method, which relies on ligand‐receptor binding, covalent conjugation, and non‐covalent binding, and the encapsulated “Trojan horse” approach, which employs neutrophils and monocytes/macrophages as natural delivery vehicles. Created with Biorender (Agreement number: GW2854RTK7).

Autoimmune pancreatitis (AIP) is identified as a severe chronic immune‐related disorder in pancreas, including two subtypes. In this study, pancreatic lesions in patients diagnosed as either type 1 AIP or type 2 AIP are examined, and these patients’ peripheral blood at single‐cell level. Furthermore, flow cytometry, immunofluorescence, and functional assays are performed to verify the identified cell subtypes. In type 1 AIP, there is a notable increase in the amount of B cells and plasma cells, and IgG4+ plasma cells are key pathogenic cells of AIP. The differentiation path of naïve‐stage B cells into IgG4+ produced plasma cells is observed, and an increased amount of T helper cells and T follicular helper (Tfh) cells. This study also reveals that HIF‐1α, an activated transcriptional factor, can directly bind to promoter site of NAMPT, promoting higher levels of visfatin production in HIF1A+ classical monocytes. Pancreatic stellate cells can be activated by extracellular visfatin and promote the development of fibrotic response in pancreatic lesions across both AIP subtypes. The current findings shed light on the exploration of dynamic alterations in peripheral blood cells and cell subgroups in pancreatic lesions of AIP, while elucidating a pathogenic cell subset and potential fibrosis mechanism of AIP. Single‐cell RNA sequencing reveals that type 1 AIP exhibits increased amount and subtypes of B and plasma cells, particularly IgG4+ plasma cells, along with an expansion of Tfh and T helper CD4+ cells. Both AIP types share upregulation of HIF1A+ classical monocytes, which promote pancreatic fibrosis through increased visfatin production.

Alpinetin(APT) is a natural product with anti-inflammatory and antioxidant effects. Iron overload has been recognized in recent years as a new way to exacerbate osteoarthritis. This study evaluated the effects of ATP on iron overload related osteoarthritis. C57BL/6J mice were randomly allocated to five groups as follows (n = 10 mice each): (1) sham; (2) destabilized medial meniscus(DMM); (3) DMM + ID; (4) DMM + ID + APT-L (50 mg/kg APT gavage daily); (5) DMM + ID + APT-H (100 mg/kg APT gavage daily). The chondrocytes treated by FAC (100μM) were used as an in vitro model of iron overload and the effect of APT was observed. Flow cytometry, fluorescence microscopy, Western blot, qRT-PCR and micro-CT were used to detect the mechanism of action of the APT. Our studies showed that APT improved the viability of chondrocytes induced by iron overload. APT can reduce apoptosis of chondrocytes (19.41 ± 2.12% vs. 9.82 ± 1.74%). Furthermore, APT was found significantly attenuated ROS accumulation (2.04 ± 0.31 vs. 1.44 ± 0.15-fold) of chondrocytes through upregulating antioxidant genes NRF2 (1.18 ± 0.13 vs. 1.55 ± 0.17-fold) and HO-1 (1.27 ± 0.15 vs. 1.77 ± 0.20-fold). In vivo experiments revealed that APT attenuated cartilage damage (OARSI score 5.75 ± 1.32 vs. 3.75 ± 0.96) and subchondral bone proliferation in iron overload osteoarthritis mice. Our results show that APT can attenuate iron overload-induced cartilage damage in vivo and in vitro via the NRF2/HO-1 pathway. We demonstrated for the first time that APT has promising applications in iron overload diseases.

IntroductionOptical coherence tomography (OCT) is a non-invasive approach for detecting changes in the retinal layers, which may also reflect changes in brain structure and function. As one of the leading causes of disability worldwide, depression has been associated with alteration of brain neuroplasticity. However, the role of OCT measurements in detecting depression remains unknown. This study aims to employ a systematic review and meta-analysis approach to explore ocular biomarkers measured by OCT for detecting depression.Methods and analysisWe will search studies describing the relationship between OCT and depression across seven electronic databases, and retrieve articles published from database inception to date. We will also manually search grey literature and reference lists included in the retrieved studies. Two independent reviewers will screen studies, extract data and assess risk of bias. Target outcomes will include peripapillary retinal nerve fibre layer thickness, macular ganglion cell complex thickness and macular volume, as well as other related indicators. Next, we will conduct subgroup analysis and meta-regression to explore study heterogeneity, then perform sensitivity analysis to investigate the robustness of the synthesised results. Meta-analysis will be performed using Review Manager (V.5.4.1) and STATA (V.12.0), and the certainty of evidence will be graded according to the Grading of Recommendations Assessment, Development and Evaluation system.Ethics and disseminationEthics approval is not necessary because the data used in this systematic review and meta-analysis will be extracted from published studies. Study results will be disseminated by publishing our findings in a peer-reviewed journal.

Pathologic myopia (PM), characterized by serious myopic macular degeneration (MMD), is a detrimental subtype of high myopia (HM) and has become one of the leading causes of blindness worldwide. In this concern, precise and high‐throughput molecular diagnosis and further pathologic insights are urgently needed. Here, through the combined strategy of nanoparticle‐enhanced laser desorption/ionization mass spectrometry‐based rapid metabolic analysis (<30 s) and machine learning, a precise molecular diagnostic approach of PM (HM with MMD grade ≥ 2) is proposed, which achieves areas under the curve of 0.874 and 0.889 for diagnosing PM and early‐stage PM, respectively. Further, the biomarkers indicate the PM‐associated systemic metabolic reprogramming of amino acid and lipid metabolism, which may mediate dysfunctional oxidative stress, inflammation, hormone/neurotransmitter systems, and energy metabolism. Notably, MMD grade 4, featuring characteristic macula atrophy, exhibits specificity in this metabolic reprogramming. Of these biomarkers, azelaic acid shows a significant protective effect in the ARPE‐19 cells under abnormal oxidative stress, which may be involved in PM development as a key antioxidative active metabolite. This work will contribute to PM molecular diagnosis and pathology exploration. Plasma metabolic detection combined with machine learning offers a precise (area under the curve of 0.874) and high‐throughput (<30 s) diagnosis for pathologic myopia (serious myopic macular degeneration). The biomarkers indicate the pathologic myopia‐associated systemic metabolic reprogramming, which exhibits specificity for myopic macular degeneration grade 4. Of these, azelaic acid may be involved in pathologic myopia development as a key antioxidative active metabolite.

The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization, hardness and undissolved phase distribution of the same sample in the range of 60 to 150 square millimeters were obtained by high-throughput testing instrument. The distribution characteristics and rules of a single data set were analyzed. In addition, each data set was divided into micro-areas according to the corresponding relationship of location, and the mapping between multi-source heterogeneous micro-area data sets was established to analyze and quantify the correlation between material composition, structure and hardness. The conclusions are as follows: (1) the average size of the insoluble phase in the middle of the two materials is larger than that of the surface, but due to the existence of central segregation, the average area of the T4 insoluble phase showed an abnormal decrease; (2) there was positive micro-segregation of Al, Cr, Ti, and Zr elements, and negative micro-segregation of Zn, Cu, and Fe elements in the recrystallized grains of the T5 middle segregation zone; (3) the growth process of the insoluble phase was synchronous with the recrystallization proportion and the size of the recrystallized grains; (4) the composition segregation and recrystallized coarse grains were the main reasons for the formation of low hardness zone in T4 and T5 materials, respectively.

Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing λ¯ (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization.

Microscopic content segregation is among the important reasons for the anisotropy of mechanical properties in the cast-rolled sheets of the 7B05 aluminum alloy. It is of great significance to study the uniformity of aluminum alloys in terms of the microscopic composition and structure. In this study, a precise method for composition quantification based on micro-beam X-ray fluorescence spectroscopy is established by parameter optimization and a calibration coefficient. Furthermore, this method was applied for exploring and quantifying the relationship between recrystallization and deformation microstructures. The results show that the comprehensive measurement effects of all elements are the best when the X-ray tube voltage is 50 kV, the current is 150 μA, and the single-pixel scanning time is 100 ms. After verification, the sum of differences between the measured values and the standard values for all elements using the calibration coefficient is only 0.107%, which confirms the accuracy of the optimized quantitative method. Three types of segregation indexes in national standards were used to capture small differences, and finally ensure that the segregation degrees of elements are Ti > Fe > Cr > Cu > Mn > Zr > Zn > Al. The quantitative segregation results obtained by the spatial-mapping method show that the difference in the content of Al and Zn is approximately 0.2% between the recrystallization region and the deformation region, the difference in the content of Fe and Ti is 0.018% and 0.013%, the difference in the content of Cr, Cu and Zr is approximately 0.01%, and the difference in the content of Mn is not obvious, only 0.004%.