Interferon stimulating factor STING, a transmembrane protein residing in the endoplasmic reticulum, is extensively involved in the sensing and transduction of intracellular signals and serves as a crucial component of the innate immune system. STING is capable of directly or indirectly responding to abnormal DNA originating from diverse sources within the cytoplasm, thereby fulfilling its classical antiviral and antitumor functions. Structurally, STING is composed of 4 transmembrane helices, a cytoplasmic ligand binding domain (LBD), and a C terminal tail structure (CTT). The transmembrane domain (TM), which is formed by the transmembrane helical structures, anchors STING to the endoplasmic reticulum, while the LBD is in charge of binding to cyclic dinucleotides (CDNs). The classical second messenger, cyclic guanosine monophosphate-adenosine monophosphate (cGAMP), represents a key upstream molecule for STING activation. Once cGAMP binds to LBD, STING experiences conformational alterations, which subsequently lead to the recruitment of Tank-binding kinase 1 (TBK1) via the CTT domain. This, in turn, mediates interferon secretion and promotes the activation and migration of dendritic cells, T cells, and natural killer cells. Additionally, STING is able to activate nuclear factor-κB (NF-κB), thereby initiating the synthesis and release of inflammatory factors and augmenting the body’s immune response. In recent years, an increasing number of studies have disclosed the non-classical functions of STING. It has been found that STING plays a significant role in organelle regulation. STING is not only implicated in the quality control systems of organelles such as mitochondria and endoplasmic reticulum but also modulates the functions of these organelles. For instance, STING can influence key aspects of organelle quality control, including mitochondrial fission and fusion, mitophagy, and endoplasmic reticulum stress. This regulatory effect is not unidirectional; rather, it is subject to organelle feedback regulation, thereby forming a complex interaction network. STING also exerts a monitoring function on the nucleus and ribosomes, which further enhances the role of the cGAS-STING pathway in infection-related immunity. The interaction mechanism between STING and organelles is highly intricate, which, within a certain range, enhances the cells’ capacity to respond to external stimuli and survival pressure. However, once the balance of this interaction is disrupted, it may result in the occurrence and development of inflammatory diseases, such as aseptic inflammation and autoimmune diseases. Excessive activation or malfunction of STING may trigger an over-exuberant inflammatory response, which subsequently leads to tissue damage and pathological states. This review recapitulates the recent interactions between STING and diverse organelles, encompassing its multifarious functions in antiviral, antitumor, organelle regulation, and immune regulation. These investigations not only deepen the comprehension of molecular mechanisms underlying STING but also offer novel concepts for the exploration of human disease pathogenesis and the development of potential treatment strategies. In the future, with further probing into STING function and its regulatory mechanisms, it is anticipated to pioneer new approaches for the treatment of complex diseases such as inflammatory diseases and tumors.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

BACKGROUND: T cell dysfunction, which includes exhaustion, anergy, and senescence, is a distinct T cell differentiation state that occurs after antigen exposure. Although T cell dysfunction has been a cornerstone of cancer immunotherapy, its potential in transplant research, while not yet as extensively explored, is attracting growing interest. Interferon regulatory factor 4 (IRF4) has been shown to play a pivotal role in inducing T cell dysfunction. METHODS: A novel ultra-low-dose combination of Trametinib and Rapamycin, targeting IRF4 inhibition, was employed to investigate T cell proliferation, apoptosis, cytokine secretion, expression of T-cell dysfunction-associated molecules, effects of mitogen-activated protein kinase (MAPK) and mammalian target of rapamycin (mTOR) signaling pathways, and allograft survival in both in vitro and BALB/c to C57BL/6 mouse cardiac transplantation models. RESULTS: In vitro , blockade of IRF4 in T cells effectively inhibited T cell proliferation, increased apoptosis, and significantly upregulated the expression of programmed cell death protein 1 (PD-1), Helios, CD160, and cytotoxic T lymphocyte-associated antigen (CTLA-4), markers of T cell dysfunction. Furthermore, it suppressed the secretion of pro-inflammatory cytokines interferon (IFN)-γ and interleukin (IL)-17. Combining ultra-low-dose Trametinib (0.1 mg·kg -1 ·day -1 ) and Rapamycin (0.1 mg·kg -1 ·day -1 ) demonstrably extended graft survival, with 4 out of 5 mice exceeding 100 days post-transplantation. Moreover, analysis of grafts at day 7 confirmed sustained IFN regulatory factor 4 (IRF4) inhibition, enhanced PD-1 expression, and suppressed IFN-γ secretion, reinforcing the in vivo efficacy of this IRF4-targeting approach. The combination of Trametinib and Rapamycin synergistically inhibited the MAPK and mTOR signaling network, leading to a more pronounced suppression of IRF4 expression. CONCLUSIONS Targeting IRF4, a key regulator of T cell dysfunction, presents a promising avenue for inducing transplant immune tolerance. In this study, we demonstrate that a novel ultra-low-dose combination of Trametinib and Rapamycin synergistically suppresses the MAPK and mTOR signaling network, leading to profound IRF4 inhibition, promoting allograft acceptance, and offering a potential new therapeutic strategy for improved transplant outcomes. However, further research is necessary to elucidate the underlying pharmacological mechanisms and facilitate translation to clinical practice.
Animals ; Mice ; Mice, Inbred BALB C ; Mice, Inbred C57BL ; Interferon Regulatory Factors/metabolism* ; Heart Transplantation/methods* ; T-Lymphocytes/immunology* ; Sirolimus/therapeutic use* ; Pyridones/therapeutic use* ; Graft Survival/drug effects* ; Pyrimidinones/therapeutic use* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Male ; Signal Transduction/drug effects*

The aim of this study was to investigate the contribution of lung zinc ions to pathogenesis of lung ischemia/reperfusion (I/R) injury in rats. Male Sprague Dawley (SD) rats were randomly divided into control group, lung I/R group (I/R group), lung I/R + low-dose zinc chloride group (LZnCl₂+I/R group), lung I/R + high-dose ZnCl₂ group (HZnCl₂+I/R group), lung I/R + medium-dose ZnCl₂ group (MZnCl₂+I/R group) and TPEN+MZnCl₂+I/R group (n = 8 in each group). Inductively coupled plasma mass spectrometry (ICP-MS) was used to measure the concentration of zinc ions in lung tissue. The degree of lung tissue injury was analyzed by observing HE staining, alveolar damage index, lung wet/dry weight ratio and lung tissue gross changes. TUNEL staining was used to detect cellular apoptosis in lung tissue. Western blot and RT-qPCR were used to determine the protein expression levels of caspase-3 and ZIP8, as well as the mRNA expression levels of zinc transporters (ZIP, ZNT) in lung tissue. The mitochondrial membrane potential (MMP) of lung tissue was detected by JC-1 MMP detection kit. The results showed that, compared with the control group, the lung tissue damage, lung wet/dry weight ratio and alveolar damage index were significantly increased in the I/R group. And in the lung tissue, the concentration of Zn²⁺ was markedly decreased, while the cleaved caspase-3/caspase-3 ratio and apoptotic levels were significantly increased. The expression levels of ZIP8 mRNA and protein were down-regulated significantly, while the mRNA expression of other zinc transporters remained unchanged. There was also a significant decrease in MMP. Compared with the I/R group, both MZnCl₂+I/R group and HZnCl₂+I/R group exhibited significantly reduced lung tissue injury, lung wet/dry weight ratio and alveolar damage index, increased Zn²⁺ concentration, decreased ratio of cleaved caspase-3/caspase-3 and apoptosis, and up-regulated expression levels of ZIP8 mRNA and protein. In addition, the MMP was significantly increased in the lung tissue. Zn²⁺ chelating agent TPEN reversed the above-mentioned protective effects of medium-dose ZnCl₂ on the lung tissue in the I/R group. The aforementioned results suggest that exogenous administration of ZnCl₂ can improve lung I/R injury in rats.
Animals ; Reperfusion Injury/pathology* ; Male ; Rats, Sprague-Dawley ; Rats ; Chlorides/administration & dosage* ; Lung/pathology* ; Zinc Compounds/administration & dosage* ; Apoptosis/drug effects* ; Caspase 3/metabolism* ; Cation Transport Proteins/metabolism*

The chemical constituents of the petroleum ether extract derived from the 90% ethanol extract of Elephantopus scaber were investigated. By silica gel column chromatography, C_(18), MCI column chromatography and semi-preparative high performance liquid chromatography, ten compounds were isolated. Their structures were identified as 3β-hydroxy-6β,7β-epoxytaraxeran-14-ene(1), 3β-hydroxyolean-12-en-28-oic acid(2), D-friedoolean-14-ene-3β,7α-diol(3), 3β-hydroxy-11α-methoxyolean-12-ene(4), 3β-hydroxyolean-11,13(18)-diene(5), 11α-hydroxy-β-amyrin(6), betulinic acid(7), 3β-hydroxy-30-norlupan-20-one(8), 6-acetonylchelerythrine(9), and 4',5'-dehydrodiodictyonema A(10) by analysis of the 1D NMR, 2D NMR, MS, and IR spectral data. Among them, compound 1 was a new triterpene and other compounds except compounds 2 and 7 were isolated from this plant for the first time.
Triterpenes/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Asteraceae/chemistry* ; Chromatography, High Pressure Liquid ; Magnetic Resonance Spectroscopy

This study aims to explore the mechanism of Jiaotai Pills in treating depression based on metabolomics and network pharmacology. The chemical constituents of Jiaotai Pills were identified by UHPLC-Orbitrap Exploris 480, and the targets of Jiaotai Pills and depression were retrieved from online databases. STRING and Cytoscape 3.7.2 were used to construct the protein-protein interaction network of core targets of Jiaotai Pills in treating depression and the "compound-target-pathway" network. DAVID was used for Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses of the core targets. The mouse model of depression was established with chronic unpredictable mild stress(CUMS) and treated with different doses of Jiaotai Pills. The behavioral changes and pathological changes in the hippocampus were observed. UHPLC-Orbitrap Exploris 120 was used for metabolic profiling of the serum, from which the differential metabolites and related metabolic pathways were screened. A "metabolite-reaction-enzyme-gene" network was constructed for the integrated analysis of metabolomics and network pharmacology. A total of 34 chemical components of Jiaotai Pills were identified, and 143 core targets of Jiaotai Pills in treating depression were predicted, which were mainly involved in the arginine and proline, sphingolipid, and neurotrophin metabolism signaling pathways. The results of animal experiments showed that Jiaotai Pills alleviated the depression behaviors and pathological changes in the hippocampus of the mouse model of CUMS-induced depression. In addition, Jiaotai Pills reversed the levels of 32 metabolites involved in various pathways such as arginine and proline metabolism, sphingolipid metabolism, and porphyrin metabolism in the serum of model mice. The integrated analysis showed that arginine and proline metabolism, cysteine and methionine metabolism, and porphyrin metabolism might be the key pathways in the treatment of depression with Jiaotai Pills. In conclusion, metabolomics combined with network pharmacology clarifies the antidepressant mechanism of Jiaotai Pills, which may provide a basis for the clinical application of Jiaotai Pills in treating depression.
Animals ; Drugs, Chinese Herbal/chemistry* ; Depression/genetics* ; Mice ; Network Pharmacology ; Metabolomics ; Male ; Disease Models, Animal ; Humans ; Protein Interaction Maps/drug effects* ; Antidepressive Agents

The chemical constituents of the chloroform extract of the 90% methanol extract obtained from the dried branches and leaves of Croton lauioides were investigated. By using silica gel column chromatography, C_(18 )column chromatography, MCI column chromatography, and semi-preparative high-performance liquid chromatography(HPLC), six compounds were isolated. Their structures were identified as lauioidine(1), 2α-methoxy-8α-hydroxy-6-oxogermacra-1(10),7(11)-dien-8,12-olide(2), myrrhanolide B(3), gossweilone(4), 6β,7β-epox-4α-hydroxyguaian-10-ene(5), and 4(15)-eudesmane-1β,5α-diol(6) by analyzing the HR-ESI-MS, IR, ECD, 1D NMR and 2D NMR data, as well as their physicochemical properties. All compounds were isolated from C. lauioides for the first time, among which compound 1 is a new nor-clerodane diterpenoid.
Croton/chemistry* ; Diterpenes, Clerodane/isolation & purification* ; Molecular Structure ; Drugs, Chinese Herbal/isolation & purification* ; Magnetic Resonance Spectroscopy ; Chromatography, High Pressure Liquid

Analyzing the spatial distribution pattern and formation factors of medicinal plant resources can provide a scientific basis for the protection and development of traditional Chinese medicine(TCM) resources. This study is based on the survey data of medicinal plant resources in 104 county-level administrative regions of Anhui province in the Fourth National Survey of TCM Resources. The global spatial autocorrelation analysis, trend surface analysis, local spatial autocorrelation analysis, hotspot analysis, and a geodetector were employed to analyze the spatial distribution pattern of medicinal plant richness, and its relationship with natural factors was explored. The results can provide a basis for the formulation of development strategies such as the protection and utilization of TCM resources, as well as offer a scientific foundation for the establishment of regional planning schemes for TCM resources in Anhui province. The results indicated that the richness of medicinal plant resources in Anhui province had significant spatial heterogeneity, exhibiting highly clustered distribution characteristics. Cold spots and hot spots presented clustered distribution patterns, with cold spots mostly located north of the Huaihe River and hot spots south of the Yangtze River. Overall, the distribution of medicinal plant resources in Anhui province showed an overall trend of high in the south and low in the north, which was consistent with the overall geomorphic trend of this province. In addition, natural factors such as altitude, precipitation, and vegetation type played an important role in the diversity and spatial distribution pattern formation of medicinal plant resources. The extraction and analysis of the spatial distribution characteristics of natural factors in cold and hot spot regions discovered that the heterogeneity of eco-environments constituted a fundamental condition for the formation of species diversity.
Plants, Medicinal/classification* ; China ; Spatial Analysis ; Conservation of Natural Resources ; Biodiversity

To improve the quality evaluation system of Hibisci Mutabilis Folium, this study established high performance liquid chromatography(HPLC) fingerprints of Hibisci Mutabilis Folium and evaluated the quality differences of medicinal materials from different places of production by chemometrics. Furthermore, a content measurement method of differential components was established based on quantitative analysis of multi-components by single-marker(QAMS). The fingerprints of 17 batches of Hibisci Mutabilis Folium from different places of production were constructed, with a total of 19 common peaks marked and seven components confirmed. The similarity between the sample fingerprints and the reference fingerprints ranged from 0.890 to 0.974. By utilizing principal component analysis(PCA), hierarchical cluster analysis(HCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA), the chemical patterns of fingerprints were identified. Five components that could be used to evaluate the quality differences of Hibisci Mutabilis Folium were screened, namely peak 6(quercetin 3-O-β-robinobioside), peak 7(rutin), peak 9(kaempferol-3-O-β-robinobioside), peak 10(kaempferol-3-O-rutinoside), and peak 14(tiliroside). The relative correction factors of isoquercitrin, kaempferol-3-O-β-robinobioside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside, and tiliroside were measured with rutin as the internal reference. The QAMS method was established for the content measurement of six flavonoids, and the results showed there was no significant difference compared to the results obtained by an external standard method. In summary, the HPLC fingerprints and QAMS method established in the study, demonstrating stability and accuracy, can provide a reference for the overall quality evaluation of Hibisci Mutabilis Folium.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Principal Component Analysis

OBJECTIVE: To explore clinical characteristics, lesion sites and correlation differences of different types of diabetic foot gangrene, and to provide evidence-based basis for clinical classification of diabetic foot gangrene. METHODS: A retrospective analysis was conducted on 266 patients with newly diagnosed diabetic foot gangrene who were admitted from January 2018 to December 2018, including 183 males and 83 females, aged from 35 to 92 years old with an average of (69.55±10.84) years old, and they were divided into wet gangrene group and dry gangrene group according to the different natures of gangrene. There were 139 patients in wet gangrene group, including 98 males and 41 females, aged from 35 to 90 years old with an average of (68.95±10.93) years old. There were 127 patients in dry gangrene group, including 85 males and 42 females, aged from 38 to 92 years old with an average of (70.21±10.75) years old. Body mass index (BMI), waist-to-hip ratio (WHR), body temperature, skin temperature difference between the affected and healthy sides of the lower extremities, and Wagner grade between two groups were recorded to evaluate symptoms and signs. The white blood cell count (WBC), neutrophil percentage (NEUT%), and C-reactive protein (C-reactive protein), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), and interleukin-6 (IL-6) in peripheral blood between two groups were detected and compared to evaluate the infection status;the severity of diabetic peripheral neuropathy (DPN) was evaluated by using Toronto Clinical Scoring System (TCSS);the degree of pain in patients with diabetic foot gangrene was evaluated by numerical rating scale (NRS); ankle-brachial index (ABI) and popliteal artery blood flow velocity were used to evaluate the degree of arterial lesions. Spearman correlation analysis was used to analyze the correlations between gangrene TCSS, ABI and age, BMI, WHR, body temperature, calf skin temperature difference, WBC, NEUT%, CRP, ESR, PCT, IL-6, NRS, and Wagner classification indicators. RESULTS: The body temperature, skin temperature difference between the affected and healthy sides of the lower extremities, Wagner grade, WBC, NEUT%, CRP, ESR, PCT, IL-6, TCSS score, ABI, and popliteal artery blood flow velocity in wet gangrene group were higher than those in dry gangrene group (P<0.01), and BMI, WHR, and NRS score in dry gangrene group were higher than those in wet gangrene group;the differences were all statistically significant (P<0.01). The results of Spearman correlation analysis showed TCSS score of gangrene patients was correlated with body temperature (r=0.214), calf skin temperature difference (r=0.364), WBC (r=0.240), NEUT% (r=0.291), CRP (r=0.347), ESR (r=0.167), PCT (r=0.241), IL-6 (r=0.316), and popliteal fossa arterial blood flow velocity (r=0.261) and Wagner grade (r=0.273) were positively correlated, and the differences were statistically significant (P<0.01). ABI was negatively correlated with age (r=-0.183), BMI (r=-0.252), WHR (r=-0.288), and NRS score (r=-0.354), and the differences were statistically significant (P<0.01). CONCLUSION Diabetic foot gangrene is an extremely difficult and critical disease. Wet gangrene has a significant synergic effect with infection and neuropathy, while dry gangrene is closely related to vascular occlusion. The main contradiction of gangrene could be revealed through blood vessels, nerves and infection, providing evidence-based basis for the selection of debridement timing, anti-infection strategies and revascularization, with the aim of reducing the risk of amputation.
Humans ; Male ; Female ; Aged ; Middle Aged ; Diabetic Foot/diagnosis* ; Aged, 80 and over ; Adult ; Retrospective Studies ; Gangrene/physiopathology* ; C-Reactive Protein