ObjectiveTo observe the effects of Yiqi Huoxue Jiedu formula（YHJF） on immune cell exhaustion in the spleen of septic mice and to explore and validate its potential intervention targets. MethodsMice were randomly divided into the sham-operated， model， low-dose YHJF（4.1 g·kg^-1）， and high-dose YHJF（8.2 g·kg^-1） groups. Except for the sham-operated group， a cecal ligation and puncture（CLP） procedure was performed to establish a mouse sepsis model. The treatment groups received oral administration of the corresponding doses， while the sham-operated and model groups received an equal volume of physiological saline. After the intervention， the 7-day survival rate of each group was recorded， and spleen samples were collected 72 h post-intervention， and the spleen index was calculated. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate（dUTP） nick end labeling（TUNEL） staining was used to detect apoptosis in spleen cells. Enzyme-linked immunosorbent assay（ELISA） was performed to measure the levels of interleukin（IL）-4 and IL-10 in the serum. Transcriptomics and metabolomics were used to screen for differentially expressed genes（DEGs） and differential metabolites in the spleen， followed by bioinformatics analysis to identify key targets. Real-time quantitative polymerase chain reaction（Real-time PCR）， flow cytometry， and multiplex immunofluorescence were used to verify the expressions of key genes and proteins. ResultsThe high-dose YHJF group significantly improved the 7-day survival rate of septic mice（P0.05）. Compared with the sham-operated group， the model group showed a significant increase in apoptosis of spleen cells and a decrease in the spleen index at 72 h post-modeling， with markedly elevated peripheral serum IL-4 and IL-10 levels（P0.01）. Compared with the model group， the high-dose YHJF group showed a reduction in apoptosis of spleen cells， an increase in the spleen index， and a significant decrease in peripheral serum IL-4 and IL-10 levels（P0.05）. Spleen transcriptomics identified 255 DEGs between groups， potentially serving as intervention targets for YHJF. Gene Ontology（GO） enrichment analysis revealed that DEGs were mainly involved in biological processes such as natural killer（NK） cell-mediated positive immune regulation， cell killing， cytokine production， positive regulation of innate immune cells， and interferon production. Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis showed that DEGs were mainly involved in cytokine-cytokine receptor interactions， viral protein interactions with cytokines and cytokine receptors， chemokine signaling pathway， and nuclear transcription factor-κB（NF-κB） signaling pathway. Protein-protein interaction（PPI） network analysis identified CD160， granzyme B（GZMB）， and chemokine ligand 4（CCL4） as key targets for YHJF in treating sepsis. Metabolomics identified 46 differential metabolites that were significantly reversed by YHJF intervention， and combined transcriptomics and metabolomics analysis identified 17 differential metabolites closely related to CD160. Pathway enrichment revealed that these metabolites were mainly involved in glycerophospholipid metabolism， arachidonic acid metabolism， glycosylphosphatidylinositol（GPI） anchor biosynthesis， linoleic acid metabolism， and α-linolenic acid metabolism pathways. Verification results showed that， compared with the sham-operated group， the model group exhibited significantly elevated CD160 mRNA expression level in the spleen， along with markedly decreased CCL4 and GZMB mRNA expression， and had a significant increase in CD160 expression on the surface of natural killer T（NKT） cells in the spleen（P0.01）. Compared with the model group， the high-dose YHJF group had a significant decrease in CD160 mRNA expression in the spleen， a significant increase in CCL4 and GZMB mRNA expressions. Further flow cytometry and immunofluorescence revealed that compared with the sham-operated group， CD160 expression on the surface of splenic NKT cells in the model group was significantly increased（P0.01）， while high-dose YHJF intervention significantly reduced CD160 expression（P0.01）. ConclusionYHJF may alleviate NKT cell exhaustion in sepsis by downregulating the expression of the negative co-stimulatory molecule CD160， and this regulatory effect is closely related to fatty acid metabolism pathways. This study provides new insights and targets for further exploration of strengthening vital Qi and detoxifying strategy to improve immune cell exhaustion in acute deficiency syndrome of sepsis.

ObjectiveTo observe the effects of Yiqi Huoxue Jiedu formula（YHJF） on immune cell exhaustion in the spleen of septic mice and to explore and validate its potential intervention targets. MethodsMice were randomly divided into the sham-operated， model， low-dose YHJF（4.1 g·kg^-1）， and high-dose YHJF（8.2 g·kg^-1） groups. Except for the sham-operated group， a cecal ligation and puncture（CLP） procedure was performed to establish a mouse sepsis model. The treatment groups received oral administration of the corresponding doses， while the sham-operated and model groups received an equal volume of physiological saline. After the intervention， the 7-day survival rate of each group was recorded， and spleen samples were collected 72 h post-intervention， and the spleen index was calculated. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate（dUTP） nick end labeling（TUNEL） staining was used to detect apoptosis in spleen cells. Enzyme-linked immunosorbent assay（ELISA） was performed to measure the levels of interleukin（IL）-4 and IL-10 in the serum. Transcriptomics and metabolomics were used to screen for differentially expressed genes（DEGs） and differential metabolites in the spleen， followed by bioinformatics analysis to identify key targets. Real-time quantitative polymerase chain reaction（Real-time PCR）， flow cytometry， and multiplex immunofluorescence were used to verify the expressions of key genes and proteins. ResultsThe high-dose YHJF group significantly improved the 7-day survival rate of septic mice（P0.05）. Compared with the sham-operated group， the model group showed a significant increase in apoptosis of spleen cells and a decrease in the spleen index at 72 h post-modeling， with markedly elevated peripheral serum IL-4 and IL-10 levels（P0.01）. Compared with the model group， the high-dose YHJF group showed a reduction in apoptosis of spleen cells， an increase in the spleen index， and a significant decrease in peripheral serum IL-4 and IL-10 levels（P0.05）. Spleen transcriptomics identified 255 DEGs between groups， potentially serving as intervention targets for YHJF. Gene Ontology（GO） enrichment analysis revealed that DEGs were mainly involved in biological processes such as natural killer（NK） cell-mediated positive immune regulation， cell killing， cytokine production， positive regulation of innate immune cells， and interferon production. Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis showed that DEGs were mainly involved in cytokine-cytokine receptor interactions， viral protein interactions with cytokines and cytokine receptors， chemokine signaling pathway， and nuclear transcription factor-κB（NF-κB） signaling pathway. Protein-protein interaction（PPI） network analysis identified CD160， granzyme B（GZMB）， and chemokine ligand 4（CCL4） as key targets for YHJF in treating sepsis. Metabolomics identified 46 differential metabolites that were significantly reversed by YHJF intervention， and combined transcriptomics and metabolomics analysis identified 17 differential metabolites closely related to CD160. Pathway enrichment revealed that these metabolites were mainly involved in glycerophospholipid metabolism， arachidonic acid metabolism， glycosylphosphatidylinositol（GPI） anchor biosynthesis， linoleic acid metabolism， and α-linolenic acid metabolism pathways. Verification results showed that， compared with the sham-operated group， the model group exhibited significantly elevated CD160 mRNA expression level in the spleen， along with markedly decreased CCL4 and GZMB mRNA expression， and had a significant increase in CD160 expression on the surface of natural killer T（NKT） cells in the spleen（P0.01）. Compared with the model group， the high-dose YHJF group had a significant decrease in CD160 mRNA expression in the spleen， a significant increase in CCL4 and GZMB mRNA expressions. Further flow cytometry and immunofluorescence revealed that compared with the sham-operated group， CD160 expression on the surface of splenic NKT cells in the model group was significantly increased（P0.01）， while high-dose YHJF intervention significantly reduced CD160 expression（P0.01）. ConclusionYHJF may alleviate NKT cell exhaustion in sepsis by downregulating the expression of the negative co-stimulatory molecule CD160， and this regulatory effect is closely related to fatty acid metabolism pathways. This study provides new insights and targets for further exploration of strengthening vital Qi and detoxifying strategy to improve immune cell exhaustion in acute deficiency syndrome of sepsis.

BackgroundMonitoring the blood concentrations of antipsychotic drugs and their metabolites can guide the adjustment of clinical treatment plans， improving therapeutic efficacy while reducing adverse effects. However， there is currently a lack of a method that can accurately and efficiently quantitatively detect multiple antipsychotic drugs and their metabolites. ObjectiveTo establish a ultra-high performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） method for the simultaneous identification and quantitation of 12 antipsychotic drugs and their main metabolites in human serum. MethodsUsing UPLC-MS/MS technology， protein precipitation method was employed for sample pretreatment. An Agela Technologies Durashell C₈ chromatographic column （50 mm×3.0 mm， 5 μm） was selected for chromatographic separation with gradient elution. The flow rate was 0.4 mL/min， and the total analysis time was 5 minutes. The column temperature was 40℃. The mass spectrometry detection was carried out in the multiple reaction monitoring （MRM） mode， and the isotope internal standard method was used for quantification. ResultsThe relative standard deviation （RSD） of the internal standard normalization matrix effect factor for 12 antipsychotic drugs and their main metabolites at low and high quality concentrations was all less than 15%. The extraction recovery rate was 85% to 115%. They showed good linear relationships within their respective standard curve ranges （r>0.995）. At low， medium， and high quality concentrations， the accuracy was 85.24% to 114.71%， and the RSD of intra-batch and inter-batch precision was all ≤14.15%， with good stability. ConclusionAll the analytical performance indicators of this method meet the verification requirements， providing an analytical means for the quantitative detection of antipsychotic drugs and their main metabolites in human serum. ［Funded by The Third Batch of Science and Technology Projects in Chaozhou City in 2023 （number， 202303GY02）］

The integration of large language model （LLM） and traditional Chinese medicine （TCM） promotes the informatization of TCM，and also provides a new direction for the inheritance and innovation of TCM in the new era. Based on the research background of LLM，the development process of LLM based on the Transformer architecture is summarized，and the research progress of LLM in TCM is reviewed. The main process of constructing TCM LLMs and the key techniques used by researchers during model development are summarized. Based on the related literature， the main application scenarios of TCM LLMs and the research explorations conducted by TCM researchers using LLMs are outlined. Meanwhile，the current challenges faced in the development of TCM LLMs are analyzed. Further improvements are urgently needed in the construction of high-quality data，the evaluation methodology of TCM LLMs，the interpretability of the model， multimodal fusion of TCM LLMs， and the development of TCM prescription recommendation models. Looking forward to the future development of LLM in TCM，it is expected to provide a reference for the deeper integration of LLMs and TCM，and facilitate the modernization of TCM.

Allergen-specific immunotherapy（AIT） remains the only therapeutic approach with the potential to modify the natural course of allergic rhinitis（AR）. Nevertheless, considerable inter-individual variability exists in patients'responses to AIT. To facilitate more reliable assessment of treatment efficacy, the China Rhinopathy Research Cooperation Group（CRRCG） convened young and middle-aged nasal experts in China to formulate the present consensus. The recommended subjective outcome measures for AIT comprise symptom scores, medication scores, combined symptom and medication scores, quality-of-life assessments, evaluation of disease control, and assessment of comorbidities. Objective indicators may supplement these measures. Currently available objective approaches include skin prick testing, nasal provocation testing, and allergen exposure chambers. However, these methods remain constrained by practical limitations and are not yet appropriate for routine implementation in clinical efficacy evaluation. In addition, several biomarkers, including sIgE and the sIgE/tIgE ratio, sIgG4, serum IgE-blocking activity, IgA, cytokines and chemokines, as well as immune cell surface molecules and their functional activity, have been shown to have associations with AIT outcomes. While these biomarkers may complement subjective assessments, they are subject to significant limitations. Consequently, large-scale multicenter trials and real-world evidence are required to strengthen the evidence base. The present consensus underscores the necessity of integrating patients'subjective experiences with objective testing throughout the treatment process, thereby providing a more comprehensive and accurate framework for efficacy evaluation. Looking forward, future investigations should prioritize the incorporation of multi-omics data and artificial intelligence methodologies, which hold promise for overcoming current limitations in assessment strategies and for advancing both the standardization and personalization of AIT.
Humans ; Allergens/immunology* ; China ; Consensus ; Desensitization, Immunologic ; Immunoglobulin E ; Quality of Life ; Rhinitis, Allergic/therapy* ; Treatment Outcome ; East Asian People

OBJECTIVE: To investigate the effects of the combined Yiqi Huoxue Jiedu formula (YHJF) on intestinal microbiota in elderly patients with pulmonary-derived sepsis and identify potential microbial targets. METHODS: A prospective randomized double-blind controlled trial was conducted. Elderly patients with pulmonary infection-induced sepsis admitted to the emergency department of Guangdong Provincial Hospital of Traditional Chinese Medicine (TCM), intensive care unit (ICU) of Fangcun Hospital, and ICU of Daxuecheng Hospital, from November 2020 to October 2021 were enrolled and randomized into two groups. Both groups received conventional Western medicine treatment. The observation group additionally received YHJF (composed of 15 g of Panax ginseng, 9 g of Panax notoginseng, and 3 g of Rheum palmatum, dissolved in 50 mL warm water) orally or via nasogastric tube twice daily for 7 days; while the control group received a placebo. Clinical data and fresh fecal samples were collected before treatment and on days 5-7 of treatment. Intestinal microbiota diversity and structure were analyzed via 16S rDNA sequencing and bioinformatics [α diversity, β diversity, and linear discriminant analysis effect size (LEfSe)]. RESULTS: Fifty-five patients were included (29 in the control group, 26 in the observation group). There were no significantly differences in gender, age, comorbidities, and baseline sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), acute gastrointestinal injury (AGI) classification score, and gastrointestinal failure (GIF) score between the two groups. Compared to the control group, the observation group showed significantly lower serum procalcitonin, APACHE II score, and greater reduction in GIF score by day 7. Thirty fecal samples were collected pre-treatment (baseline group), 29 post-treatment from the control group, and 26 from the observation group. Gut microbiota α diversity analysis revealed that Simpson index in the observation group and control group were significantly decreased compared to the baseline group [0.75 (0.53, 0.91), 0.81 (0.32, 0.91) vs. 0.88 (0.87, 0.89), both P < 0.05], but there was no significantly difference between the observation group and the control group. There were no significantly differences in Chao1, Ace, and Shannon indices among three groups. β diversity analysis indicated that distinct microbiota structures among three groups (R² = 0.096, P = 0.026). Species difference analysis showed that, at the phylum level, Firmicutes (53.69%), Actinobacteria (16.23%), Proteobacteria (15.39%), and Bacteroidetes (9.57%) dominated, with no significant intergroup differences. At the genus level, 38 taxa showed significant differences. Compared to the control group, the observation group exhibited increased Erysipelatoclostridium (P = 0.014) and Faecalibacterium (P = 0.013), and decreased Bacteroides (P = 0.009), Bilophila (P = 0.005), Eggerthella (P = 0.002), and Collinsella (P = 0.043). LEfSe analysis highlighted Lactobacillus salivarius, Erysipelatoclostridium, Collinsella, Cloacibacillus, and Bacteroides as key discriminators. CONCLUSION YHJF combined with conventional therapy alters intestinal microbiota structure in patients with elderly pulmonary-derived sepsis, with Bacteroides, Erysipelatoclostridium, and Collinsella identified as potential microbial targets.
Humans ; Gastrointestinal Microbiome/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Double-Blind Method ; Sepsis/drug therapy* ; Aged ; Prospective Studies ; RNA, Ribosomal, 16S/genetics* ; Male ; Female ; Panax notoginseng ; Rheum

Andrographolide sulfonate (AS) is a sulfonated derivative of andrographolide extracted from Andrographis paniculata (Burm.f.) Nees, and has been approved for several decades in China. The present study aimed to investigate the novel therapeutic application and possible mechanisms of AS in the treatment of rheumatoid arthritis. Results indicated that administration of AS by injection or gavage significantly reduced the paw swelling, improved body weights, and attenuated pathological changes in joints of rats with adjuvant-induced arthritis. Additionally, the levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and IL-1β in the serum and ankle joints were reduced. Bioinformatics analysis, along with the spleen index and measurements of IL-17 and IL-10 levels, suggested a potential relationship between AS and Th17 cells under arthritic conditions. In vitro, AS was shown to block Th17 cell differentiation, as evidenced by the reduced percentages of CD4⁺ IL-17A⁺ T cells and decreased expression levels of RORγt, IL-17A, IL-17F, IL-21, and IL-22, without affecting the cell viability and apoptosis. This effect was attributed to the limited glycolysis, as indicated by metabolomics analysis, reduced glucose uptake, and pH measurements. Further investigation revealed that AS might bind to hexokinase2 (HK2) to down-regulate the protein levels of HK2 but not glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or pyruvate kinase M2 (PKM2), and overexpression of HK2 reversed the inhibition of AS on Th17 cell differentiation. Furthermore, AS impaired the activation of phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signals in vivo and in vitro, which was abolished by the addition of lactate. In conclusion, AS significantly improved adjuvant-induced arthritis (AIA) in rats by inhibiting glycolysis-mediated activation of PI3K/AKT to restrain Th17 cell differentiation.
Animals ; Th17 Cells/immunology* ; Diterpenes/pharmacology* ; Arthritis, Rheumatoid/metabolism* ; Proto-Oncogene Proteins c-akt/immunology* ; Glycolysis/drug effects* ; Cell Differentiation/drug effects* ; Phosphatidylinositol 3-Kinases/genetics* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Andrographis paniculata/chemistry* ; Arthritis, Experimental/drug therapy* ; Interleukin-17/immunology* ; Signal Transduction/drug effects*

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Under the trend of increasing aging， integrated elderly care and medical services is an important measure to optimize the supply of elderly care services and promote the good death of the elderly. Using the cooperative production theory and the classical grounded theory， a qualitative analysis was conducted on 38 cases of elderly palliative care and 25 cases of hospital-based palliative care under the integrated elderly care and medical services model from a hospital in Nanning City using Nvivo 20.0 software. This paper found that the integrated elderly care and medical services mode emphasized the deep integration of medical and elderly care services by integrating resources and improving service efficiency， to achieve the basic experience of comprehensive health care for the elderly. The promotion of these experiences has a positive significance for building a multi-agent cooperative production system， strengthening personnel training， perfecting the performance distribution mechanism， and further promoting the development of the national palliative care pilot.

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.