Olfactory receptors (ORs) form the largest superfamily of G protein-coupled receptors (GPCRs). Traditionally recognized for their role in the nasal olfactory epithelium, where they mediate the sense of smell, accumulating evidence has firmly established their ectopic expression in non-olfactory tissues, including the intestine, lungs, and kidneys. The intestine, as the primary site for nutrient digestion and absorption, harbors a highly complex chemical environment. To adapt to this environment, the gut employs a sophisticated network of “chemosensors” to monitor luminal contents and maintain homeostasis. Among these sensors, intestinal ORs have emerged as crucial functional components, serving as a molecular bridge that connects environmental chemical signals—such as food-derived odorants—to specific physiological responses. This discovery has significantly deepened our understanding of how dietary flavors and compounds influence intestinal physiology at the molecular level. This review systematically summarizes the expression profiles, ligand classification, and biological functions of ORs within the gastrointestinal tract. Studies indicate that intestinal ORs exhibit distinct spatial distribution patterns across different gut segments and display cell-type specificity, particularly within enterocytes and enteroendocrine cells. These receptors function as versatile sensors capable of recognizing a wide variety of ligands, including exogenous dietary components, gut microbiota metabolites such as short-chain fatty acids, and endogenous small molecules like azelaic acid. Upon activation by specific ligands, intestinal ORs trigger intracellular signaling cascades, primarily involving the AC-cAMP-PKA pathway or calcium influx channels. A major focus of this review is to elucidate the molecular mechanisms by which these receptors regulate the secretion of gut hormones. Activation of specific ORs in enteroendocrine cells has been shown to stimulate the release of hormones such as glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and serotonin (5-HT), thereby modulating systemic energy metabolism, glucose homeostasis, and gastrointestinal motility. Furthermore, the review addresses the critical roles of ORs in immune regulation and pathology. Evidence suggests that specific ORs contribute to the maintenance of intestinal immune homeostasis and may offer protection against inflammation. Beyond their involvement in inflammatory responses, ORs such as Olfr78 have been shown to regulate the differentiation and function of intestinal endocrine cells. Similarly, Olfr544 has been demonstrated to alleviate intestinal inflammation by remodeling the gut microbiome and metabolome. These findings collectively suggest that specific ORs hold promise as therapeutic targets for mitigating intestinal inflammation and maintaining gut homeostasis. Additionally, the review explores the emerging role of ORs in cancer. Although OR expression is often downregulated in tumor tissues compared to normal mucosa, activation of specific ORs by certain ligands can inhibit tumor cell proliferation and migration and induce apoptosis via pathways such as MEK/ERK and p38 MAPK. Conversely, other receptors, such as OR7C1, may serve as biomarkers for cancer-initiating cells. In conclusion, intestinal ORs represent a vital component of the gut’s sensory network. The review also discusses the translational potential of these findings. By elucidating the precise pairing relationships between dietary components and specific ORs, novel therapeutic strategies could be developed. Intestinal ORs may thus emerge as promising targets for nutritional and pharmacological interventions in metabolic diseases, inflammatory bowel diseases, and malignancies.

To investigate the risk factors for acute kidney injury (AKI) following the use of amphotericin B deoxycholate and to develop a predictive model to guide clinical monitoring and intervention.

ObjectiveType I interferon (IFN-I) signaling is essential for antiviral innate immunity, yet its sustained or excessive activation contributes to the pathogenesis of several autoimmune diseases and interferonopathies, such as systemic lupus erythematosus and Aicardi-Goutières syndrome. Current strategies targeting this pathway, exemplified by JAK inhibitors, act mainly on downstream signal transduction and provide limited direct control over upstream IFN-I production, while also carrying the risk of broad immunosuppression. Phyllanthus emblica L. has long been used in traditional medicine for inflammatory disorders, but the bioactive constituent responsible for its regulation of IFN-I signaling and the underlying molecular mechanism have not been clearly defined. This study aimed to identify the active anti-inflammatory component of P. emblica and to characterize its mechanism of action on the IFN-I pathway in macrophages. MethodsActive components ofP. emblica and their candidate targets were screened by network pharmacology using the TCMSP and DrugBank databases (oral bioavailability≥30%, drug-likeness≥0.18) and intersected with inflammation-related genes retrieved from public databases. The predicted interaction between EGCG and IFN-I pathway proteins (TBK1, IRF3, STAT1) was evaluated by molecular docking, with BX795 and GSK8612 used as reference TBK1 inhibitors. Mechanistic experiments were performed in THP-1-derived macrophages and primary bone marrow-derived macrophages (BMDM). Upstream signaling was activated by transfection of the nucleic acid analogs poly(I∶C) and poly(dA∶dT) or by lipopolysaccharide (LPS) stimulation, whereas downstream signaling was activated by exogenous IFN-β. An siRNA-mediated TREX1 knockdown model was used to mimic endogenous nucleic acid-driven interferonopathy. Expression of IFN-β1 and interferon-stimulated genes (ISGs) was measured by RT-qPCR, protein phosphorylation by Western blot, and IFN-β secretion by ELISA. Cellular thermal shift assay (CETSA) and drug affinity responsive target stability (DARTS) were used to probe the interactionbetween EGCG and IRF3. ResultsNetwork pharmacology identified (-)-epigallocatechin-3-gallate (EGCG) as a candidate IFN-I-suppressive constituent of P. emblica, with predicted binding to TBK1, IRF3, and STAT1. Molecular docking yielded binding energies of -9.2, -7.2, and -8.2 kcal/mol for TBK1, IRF3, and STAT1, respectively, indicating an affinity for TBK1 comparable to that of the reference inhibitors BX795 (-5.7 kcal/mol) and GSK8612 (-6.4 kcal/mol). EGCG suppressed IFN-β1 and ISG mRNA expression under poly (I∶C), poly (dA∶dT), and LPS stimulation in both THP-1 macrophages and BMDM. At the protein level, EGCG reduced the phosphorylation of TBK1 and IRF3 without affecting the levels of the upstream sensors cGAS and RIG-I, and lowered IFN-β secretion in a concentration-dependent manner. CETSA and DARTS showed that EGCG did not enhance the thermal stability or protease resistance of IRF3, indicating that its effect on IRF3 is indirect. Following IFN-β stimulation, prolonged EGCG treatment reduced STAT1 phosphorylation in a time-dependent manner without an apparent change in IRF9, and partially attenuated ISG transcription; this effect was not monotonicly concentration-dependent, and CXCL10 showed the most consistent suppression. In TREX1-knockdown cells, the elevated mRNA levels of ISG15, ISG56, and CXCL10 were reduced by EGCG. ConclusionEGCG suppresses IFN-I responses by concurrently inhibiting TBK1-IRF3-dependent IFN‑β production and JAK-STAT1-mediated downstream transcription. These in vitro findings provide a mechanistic basis for the anti-inflammatory use of P. emblica in traditional medicine and identify EGCG as a candidate for further evaluation in interferon-driven autoimmune disease models.

BACKGROUND:Parkinson's disease has the main pathological changes in the midbrain,especially in the dense substantia nigra,leading to impaired motor and non-motor function in patients.At present,research is limited by cellular heterogeneity,and its pathogenesis still needs to be further elucidated.In recent years,single-cell RNA sequencing(scRNA-seq)has gradually been applied in neurodegenerative diseases,which is of great significance for understanding intercellular heterogeneity,disease development mechanisms,and treatment strategies. OBJECTIVE:To review the research progress of scRNA-seq technology applied to Parkinson's disease in recent years,providing a theoretical basis for the application of scRNA-seq in the treatment and diagnosis of Parkinson's disease. METHODS:The first author used a computer system to search for relevant literature in the CNKI,WanFang,PubMed,and Web of Science databases,with the Chinese search terms"single-cell RNA sequencing,Parkinson's disease,cell heterogeneity,cell subtypes,dopaminergic neurons,glial cells"and English search terms"single-cell RNA seq,Parkinson disease,heterogenicity,subtypes,dopaminergic neurons,glial cells."71 articles were ultimately included for review and analysis. RESULTS AND CONCLUSION:(1)scRNA-seq is a high-throughput experimental technique that utilizes RNA sequencing at the single-cell level to quantify gene expression profiles in specific cell populations,revealing cellular mysteries at the molecular level.Compared with traditional sequencing techniques,scRNA-seq technology is used to reveal the diversity of cell types and changes in specific gene expression in complex tissues under various physiological and pathological conditions through automatic clustering analysis of cell transcriptome.(2)By using scRNA-seq,the development process of dopaminergic neurons and the unique functional characteristics of various cell subtypes are elucidated,in order to better understand potential therapeutic molecular targets.(3)The use of scRNA-seq analysis has improved our understanding of the response of Parkinson's disease glial cells,enabling us to comprehensively map and characterize different cell type populations,identify specific glial cell subpopulations related to neurodegeneration,and draw valuable single cell maps as reference data for future research.(4)The application of scRNA-seq to detect embryonic mice and stem cells will help improve the in vitro differentiation protocol and quality control of cell therapy,as well as evaluate the overall cell quality and developmental stage of dopaminergic neurons derived from stem cells.

[Objective] To retrospectively analyze the detection results of alanine aminotransferase (ALT) unqualified repeat blood donors in Guangzhou, so as to provide evidence for further expanding the repeat blood donor pool, reducing the rate of blood discarding and improving the qualified rate of blood test. [Methods] Blood donors with unqualified ALT in Guangzhou Blood Center from January 2018 to April 2024 were selected as the research objects. The past blood donation and population characteristics were analyzed according to the number of blood donations and ALT unqualified times. [Results] Among repeat blood donors with previous ALT disqualification, 99.5% to 99.7% did not have reactive markers for transfusion-transmitted diseases (TTD), which was higher than the rate among first-time blood donors with unqualified ALT (95.8%) (P<0.05). The rate of single-item ALT disqualification in repeat blood donors was higher in males than in females (P<0.05); it also varied by age (18-25 years > 26-35 years > 36-45 years > over 45 years) (P<0.05); and by quarter (third and fourth quarters > first and second quarters) (P<0.05). The ALT unqualified rate was significantly higher whole blood donors than that of platelet donors and returning blood donors (P<0.05). The overall ALT level (51.0 U/L), individual ALT level (56.0 U/L) and individual ALT unqualified rate (66.7%) of repeat blood donors with multiple ALT disqualifications were higher than those of repeat blood donors with single-item ALT disqualifications (26.0 U/L, 38.5 U/L, and 33.3%, respectively) (P<0.05). Moreover, as the number of ALT disqualifications increased, the overall level of ALT in repeat blood donors also increased (P<0.05), and the average level of individual ALT and individual ALT unqualified ratio tended to increase. Repeat blood donors with frequent ALT disqualifications had higher ALT levels (69.0 U/L). [Conclusion] The ALT unqualified rates of repeat blood donors were mostly non-specific elevation without TTD. Repeat blood donors with multiple ALT disqualifications tend to have continuous high ALT. Moreover, and with the increase of ALT disqualifications times, the overall ALT levels the average individual ALT levels and individual ALT unqualified rates showed an increasing trend.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Inflammatory bowel disease (IBD), including ulcerative colitis and Crohn's disease, is a chronic inflammation of the gastrointestinal tract with unknown etiology. The cause of IBD is widely considered multifactorial, with prevailing hypotheses suggesting that the microbiome and various environmental factors contribute to inappropriate activation of the mucosal immune system in genetically susceptible individuals. Although the incidence of IBD has stabilized in Western countries, it is rapidly increasing in newly industrialized countries, particularly China, making IBD a global disease. Significant changes in multiple biomarkers before IBD diagnosis during the preclinical phase provide opportunities for earlier diagnosis and intervention. Advances in technology have driven the development of telemonitoring tools, such as home-testing kits for fecal calprotectin, serum cytokines, and therapeutic drug concentrations, as well as wearable devices for testing sweat cytokines and heart rate variability. These tools enable real-time disease activity assessment and timely treatment strategy adjustments. A wide range of novel drugs for IBD, including interleukin-23 inhibitors (mirikizumab, risankizumab, and guselkumab) and small-molecule drugs (etrasimod and upadacitinib), have been introduced in the past few years. Despite these advancements, approximately one-third of patients remain primary non-responders to the initial treatment, and half eventually lose response over time. Precision medicine integrating multi-omics data, advanced combination therapy, and complementary approaches, including stem cell transplantation, psychological therapies, neuromodulation, and gut microbiome modulation therapy, may offer solutions to break through the therapeutic ceiling.
Humans ; Inflammatory Bowel Diseases/therapy*