Chronic pancreatitis （CP） is a common disease in clinical practice characterized by progressive inflammatory fibrosis of the pancreas. Gut microbiota， known as the “second genome” of humans， bidirectionally modulates the progression of fibrosis in CP via the gut-pancreas axis. This article systematically elaborates on the characteristics of gut microbiota during the progression of CP and its molecular mechanism in mediating pancreatic fibrosis through bacterial translocation， metabolites， immune regulatory networks， and microbe-pancreatic stellate cell interactions， with a focus on the pivotal role of short-chain fatty acids and inflammatory cytokine networks in pancreatic stellate cell activation and extracellular matrix deposition. In addition， this article explores the potential value of gut microbiota-targeted interventions in the prevention and treatment of CP fibrosis， such as probiotics， prebiotics， and fecal microbiota transplantation， and discusses the translational potential of using multi-omics technologies to identify diagnostic biomarkers and novel therapeutic targets for CP， in order to provide new ideas for the precise diagnosis and treatment of CP.

Antibodies play a critical role in adaptive immune responses and serve as key components in disease diagnosis and treatment. These molecules exhibit dynamic post-translational modifications (PTMs), such as glycosylation and phosphorylation, which regulate their effector functions. To date, nearly all of our knowledge about antibody repertoires has come from B cell receptor (BCR) sequencing (BCR-seq), which facilitates the profiling of clonal composition and the tracing of maturation trajectories within B-cell repertoires. However, circulating antibodies found in bodily fluids—such as serum, saliva, milk, mucosal secretions, and cerebrospinal fluid—exhibit diversities and specificities beyond what BCR-seq alone can predict. Therefore, identifying and quantifying antibody clonotypes at the protein level could enhance diagnosis, prognosis, and treatment strategies in personalized medicine. The critical gap between genotype and phenotype necessitates complementary methodologies that enable the direct characterization of antibody proteins in their native functional states. Mass spectrometry (MS)-based antibody repertoire sequencing (Ab-seq) is currently the only feasible approach for this task and primarily includes database-dependent methods—such as bottom-up, middle-down, and top-down approaches—as well as database-independent de novo sequencing technology. These strategies enable multi-level, high-precision characterization ranging from peptides and domains to intact antibody molecules. Unlike the shotgun strategy commonly used in routine proteomics, obtaining full sequences of all antibodies presents unique challenges. It requires specialized methodological adaptations to address issues related to dynamic range, sequence variation, and sample complexity. This review introduces the technical principles, methodological workflows, and recent applications of various mass spectrometry-based antibody repertoire sequencing (Ab-seq) strategies, with a focus on approaches designed to improve sequence coverage and identification accuracy. These include multi-enzyme digestion, hybrid fragmentation methods, and artificial intelligence-assisted de novo sequencing. By systematically comparing database-dependent techniques—such as bottom-up, middle-down, and top-down approaches—with database-independent de novo sequencing, this review outlines their respective advantages and limitations in terms of sample throughput, sequence coverage, post-translational modification characterization, and data analysis complexity. In addition, this review discusses emerging technological trends, including the integration of ion mobility separation, native mass spectrometry, and artificial intelligence-driven data interpretation, which are expected to enhance the depth and accuracy of antibody characterization. Although current methods continue to face challenges related to sample complexity, dynamic range, and unambiguous sequence variant assignment, we emphasize the importance of integrating BCR-seq and Ab-seq data to construct gene-protein association maps. These maps help validate sequence accuracy and facilitate epitope discovery. This dual-platform strategy helps bridge the gap between genotype and phenotype, thereby enhancing both the resolution and scope of antibody repertoire studies. Such an integrative approach also offers a valuable tool for therapeutic antibody development, structure-function analysis, and precise evaluation of vaccine efficacy.

Accommodative dysfunction, particularly accommodative lag, acts as a core hub connecting near work activity to myopic axial elongation. This review thoroughly explores the multidimensional biological mechanisms by which accommodative function drives axial growth. In addition to the classic pathway where hyperopic defocus signals induce retinal-choroidal-scleral biochemical remodeling, two other mechanisms are highlighted: a biomechanical pathway involving direct mechanical traction on the equatorial sclera caused by sustained ciliary muscle contraction, and a neural pathway where abnormal accommodative micro fluctuations degrade retinal image quality, thereby triggering abnormal ocular growth. Based on these comprehensive mechanisms, this paper systematically analyzes the principles of pharmacological(atropine), optical(orthokeratology, defocus lenses), and vision therapy interventions. Myopia progression results from the integrated regulation of optical defocus, mechanical stress, and neural dynamics. Future myopia control should advance toward precise, personalized combination strategies tailored to individual accommodative and genetic profiles.

Short-chain fatty acids （SCFA） are the main metabolic products generated by the fermentation of dietary fiber by gut microbiota. Studies have shown that SCFA not only play a role in energy metabolism， but also act as important signaling molecules， exhibiting a significant potential in alleviating liver and pancreatic fibrosis. The core mechanism of SCFA mainly involves the regulation of various key signaling pathways by activating G protein-coupled receptors and inhibiting the activity of histone deacetylase， thereby suppressing the activation and proliferation of hepatic stellate cell （HSC） and pancreatic stellate cell （PSC）， which is a key link in fibrosis formation. In addition， SCFA can effectively alleviate tissue inflammation response， improve intestinal barrier function， and regulate gut microbiota balance， thus indirectly preventing the process of fibrosis mediated by the “gut-liver/pancreas axis”. Compared with the research on SCFA in liver fibrosis， studies on their role in pancreatic fibrosis are limited. Given that HSC and PSC are highly homologous， the transcription factors and proteins that have been confirmed in liver fibrosis-related studies are also similarly expressed in PSC， suggesting that they may also influence the activation of PSC. This article systematically summarizes the recent advances in the research on SCFA in alleviating liver and pancreatic fibrosis， in order to provide new perspectives for exploring the mechanism of pancreatic fibrosis and developing related interventional strategies.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

ObjectiveTo explore the mechanisms of Salviae Miltiorrhizae Radix et Rhizoma in treating metabolic hypertension induced by a high-sugar and high-fat diet in rats based on network pharmacology， proteomics， and animal experiments. MethodsThirty male SD rats were randomized into the control， model， positive drug （tetrandrine， Tet， 50 mg·kg^-1·d^-1）， low-dose Salviae Miltiorrhizae Radix et Rhizoma （DS-L， 45×10⁴ mg·kg^-1·d^-1）， and high-dose Salviae Miltiorrhizae Radix et Rhizoma （DS-H， 90×10⁴ mg·kg^-1·d^-1） groups. Except for the control group， each group was fed a high-fat and high-sugar diet for 8 weeks for the modeling of metabolic hypertension. Following successful modeling， drug interventions were conducted through gavage for 4 weeks. Blood pressure and lipid indicators were monitored， and cardiac function was assessed via echocardiography. Samples from the thoracic aorta and cardiac tissue were collected for histopathological examination. Network pharmacology analysis identified key active components， potential targets， and mechanisms of Salviae Miltiorrhizae Radix et Rhizoma in treating hypertension. Proteomics technology was employed to analyze the differential proteins and major pathway targets to synergistically elucidate the antihypertensive mechanism of Salviae Miltiorrhizae Radix et Rhizoma. Ca²⁺ concentrations in the thoracic aorta and cardiac tissue were measured. The protein levels of transient receptor potential cation channel （TRPC）3， TRPC6， NADPH oxidase （NOX）2， and NOX4 were quantified via Western blotting. The levels of oxidative stress markers and inflammatory factors， including superoxide dismutase （SOD）， malondialdehyde （MDA）， tumor necrosis factor-α （TNF-α）， and interleukin-1β （IL-1β）， were determined by enzyme-linked immunosorbent assay （ELISA）. Molecular docking analysis was performed for the main components of Salviae Miltiorrhizae Radix et Rhizoma with TRPC3， TRPC6， NOX2， and NOX4. ResultsThe experimental results indicated that compared with the control group， the model group exhibited abnormally elevated blood pressure and increased lipid levels （P<0.01）. Compared with the model group， the DS-L group exhibited reduced systolic blood pressure （SBP）， diastolic blood pressure （DBP）， and mean arterial pressure （MAP） （P<0.05）， decreased serum levels of triglycerides （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） （P<0.05）， and increased level of high-density lipoprotein cholesterol （HDL-C） （P<0.01）. The DS-H and Tet groups showed more significant effects （P<0.01）. Moreover， the interventions attenuated vascular wall thickening， myocardial cell injury， and collagen and lipid deposition. Network pharmacology and proteomics prediction results indicated that the core targets of Salviae Miltiorrhizae Radix et Rhizoma in treating hypertension were TRPC3， TRPC6， NOX2， and NOX4， and the core pathways included calcium signaling， cyclic guanosine monophosphate （cGMP）/cGMP-dependent protein kinase （PKG） signaling， and atherosclerosis-related pathways. The molecular mechanism experiment results indicated that compared with the control group， the model group exhibited significantly elevated tissue Ca²⁺ concentrations， exacerbated oxidative stress and inflammatory responses， and upregulated protein levels of TRPC3， TRPC6， NOX2， and NOX4 in the thoracic aorta and myocardial tissue （P<0.01）. Compared with the model group， DS-L reduced the free Ca²⁺ concentration， lowered the levels of IL-1β， TNF-α， SOD， and MDA （P<0.05）， and downregulated the protein levels of TRPC3， TRPC6， NOX2， and NOX4 in the thoracic aorta and myocardial tissue （P<0.05）. DS-H and Tet exhibited more significant effects （P<0.01）. Meanwhile， the main components of Salviae Miltiorrhizae Radix et Rhizoma had strong binding affinity with the core targets of hypertension. ConclusionSalviae Miltiorrhizae Radix et Rhizoma may ameliorate oxidative stress and mitigate inflammatory responses by regulating the TRPC3/6/NOX2/4 signaling pathway， thereby alleviating abnormal blood pressure abnormality and myocardial injury in hypertensive rats.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

Sepsis is a systemic inflammatory response syndrome caused by the invasion of pathogenic microorganisms such as bacteria. In addition to the manifestations of systemic inflammatory response syndrome and primary infection lesions， critical cases often have manifestations of organ hypoperfusion. The morbidity and mortality of sepsis have remained high in recent years， which seriously affect the quality of life of the patients. The pathogenesis of sepsis is complicated， in which uncontrollable inflammation is a key mechanism. The phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） signaling pathway plays a key role in mediating inflammation in sepsis. The available therapies of sepsis mainly include resuscitation， anti-infection， vasoactive drugs， intensive insulin therapy， and organ support， which show limited effects of reducing the mortality. Therefore， finding new therapeutic drugs is a key problem to be solved in the clinical treatment of sepsis. In recent years， studies have shown that traditional Chinese medicine （TCM） can regulate the PI3K/Akt pathway via multiple pathways， multiple effects， and multiple targets to inhibit inflammation and curb the occurrence and development of sepsis， which has gradually become a hot spot in the prevention and treatment of sepsis. Moreover， studies have suggested that TCM has unique advantages in the treatment of sepsis. TCM can regulate the PI3K/Akt signaling pathway to inhibit inflammation， reduce oxidative stress， and control apoptosis in the prevention and treatment of sepsis. Despite the research progress， a systematic review remains to be performed regarding the TCM treatment of sepsis by regulating the PI3K/Akt signaling pathway. After reviewing relevant papers published in recent years， this study systematically summarizes the relationship between PI3K/Akt pathway and sepsis and the role of TCM in the treatment of sepsis， aiming to provide new ideas for the potential treatment of sepsis and the development of new drugs.

Objective To analyze the influencing factors for postoperative anastomotic leak (AL) in carcinoma of the esophagus and gastroesophageal junction and construct a nomogram predictive model. Methods The patients who underwent radical esophagectomy at Jinling Hospital Affiliated to Nanjing University School of Medicine from January 2018 to June 2020 were included in this study. Relevant variables were screened using univariate and multivariate logistic regression analyses. A nomogram was then developed to predict the risk factors associated with postoperative AL. The predictive performance of the nomogram was validated using the receiver operating characteristic (ROC) curve. Results A total of 468 patients with carcinoma of the esophagus and gastroesophageal junction were included in the study, comprising 354 males and 114 females, with a mean age of (62.8±7.2) years. The tumors were predominantly located in the middle or lower esophagus, and 51 (10.90%) patients experienced postoperative AL. Univariate logistic regression analysis indicated that age, body mass index (BMI), tumor location, preoperative albumin levels, diabetes mellitus, anastomosis technique, anastomosis site, and C-reactive protein (CRP) levels were potentially associated with AL (P<0.05). Multivariate logistic regression analysis identified age, BMI, tumor location, diabetes mellitus, anastomosis technique, and CRP levels as independent risk factors for AL (P<0.05). A nomogram was developed based on the findings from the multivariate logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was 0.803, indicating a strong concordance between the actual observations and the predicted outcomes. Furthermore, decision curve analysis demonstrated that the newly established nomogram holds significant value for clinical decision-making. Conclusion The predictive model for postoperative AL in patients with carcinoma of the esophagus and gastroesophageal junction demonstrates strong predictive validity and is essential for guiding clinical monitoring, early detection, and preventive strategies.