BACKGROUND:Studies have shown that lipid metabolism and related diseases can affect the development of osteoporosis.OBJECTIVE:Using bibliometric visualization analysis software to analyze and summarize the frontier content and research hotspots in the field of lipid metabolism affecting osteoporosis.METHODS:Using the Web of Science core collection database as the retrieval platform,relevant literature regarding the effect of lipid metabolism on osteoporosis from 2004 to 2024 was retrieved.VOSviewer and CiteSpace were used for bibliometric and visual analyses.RESULTS AND CONCLUSION:A total of 1 277 articles were included,and the number of articles on the effect of lipid metabolism on osteoporosis at home and abroad was increasing year by year.The number of articles published in China was 417,ranking first,and the United States was 243,ranking second.Shanghai Jiao Tong University ranked first with 30 articles.Professor Rosen Clifford J from Tufts University School of Medicine and Professor Recker Robert R from Clayton University were the most cited authors.The number of documents published in BONE in the Netherlands ranked first,and the JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM in England was the most cited journal.Bone mineral density,bone metabolism,menopause,and obesity were the core keywords,and they were also research hotspots in this field.The above results show that in the past 20 years,research in the field of lipid metabolism affecting osteoporosis has focused on the role of abnormal lipid metabolism in bone mineral density and bone metabolism,thereby regulating osteoporosis and post-menopause osteoporosis.Clarifying the pathway of this mechanism and"bone-lipid balance"is the future research idea and direction.

BACKGROUND:Studies have shown that lipid metabolism and related diseases can affect the development of osteoporosis.OBJECTIVE:Using bibliometric visualization analysis software to analyze and summarize the frontier content and research hotspots in the field of lipid metabolism affecting osteoporosis.METHODS:Using the Web of Science core collection database as the retrieval platform,relevant literature regarding the effect of lipid metabolism on osteoporosis from 2004 to 2024 was retrieved.VOSviewer and CiteSpace were used for bibliometric and visual analyses.RESULTS AND CONCLUSION:A total of 1 277 articles were included,and the number of articles on the effect of lipid metabolism on osteoporosis at home and abroad was increasing year by year.The number of articles published in China was 417,ranking first,and the United States was 243,ranking second.Shanghai Jiao Tong University ranked first with 30 articles.Professor Rosen Clifford J from Tufts University School of Medicine and Professor Recker Robert R from Clayton University were the most cited authors.The number of documents published in BONE in the Netherlands ranked first,and the JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM in England was the most cited journal.Bone mineral density,bone metabolism,menopause,and obesity were the core keywords,and they were also research hotspots in this field.The above results show that in the past 20 years,research in the field of lipid metabolism affecting osteoporosis has focused on the role of abnormal lipid metabolism in bone mineral density and bone metabolism,thereby regulating osteoporosis and post-menopause osteoporosis.Clarifying the pathway of this mechanism and"bone-lipid balance"is the future research idea and direction.

Objective To explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). Methods The Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. Gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients’ 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. Results Differentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. Conclusion The prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.

This article reports a rare case of autoinflammatory disease presenting initially with skin induration and swelling after trauma as the initial manifestation, followed by progressive limb weakness. The patient was a middle-aged female who developed skin induration and swelling after trauma, which gradually progressed to limb weakness, dysarthria and bilateral facial paralysis, accompanied by livedo reticularis of the lower extremities, diffuse skin induration of the limbs, and beaded subcutaneous nodules in the right upper limb. The patient had a susceptibility to infection since childhood and a history of chronic livedo reticularis. Skin pathological examination revealed panniculitis. A comprehensive etiological screening for special infections and autoimmune diseases was completed with an unremarkable results, and whole-exome sequencing showed no abnormal findings. Following a multidisciplinary discussion combined with RNA sequencing results, the patient was diagnosed with an autoinflammatory disease, with a suspected type Ⅰ interferonopathy. Treatment with tofacitinib resulted in gradual improvement of clinical symptoms. This case highlights the importance of detailed medical history collection, systematic physical examination and multidisciplinary collaborative diagnosis and treatment, and underscores the pivotal role of molecular diagnosis in the confirmation of rare diseases. It can provide a reference for the clinical diagnosis and management of similar rare cases.

Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.

Objective To systematically evaluate the efficacy and safety of immune checkpoint inhibitors (ICIs) as first-line treatment for advanced non-small cell lung cancer (NSCLC). Methods PubMed, The Cochrane Library, and EMbase databases were searched for clinical randomized controlled trials (RCTs) of ICIs as first-line treatment for NSCLC patients. The search period was from database inception to January 2023. Quality evaluation was conducted using the improved Jadad scale, and meta-analysis was performed using RevMan 5.4 software. Results Twelve RCTs were included, all of which were assessed as high-quality literature, involving a total of 7 121 patients. Meta-analysis results showed that, compared with chemotherapy, ICIs as first-line treatment for NSCLC patients significantly improved median overall survival (OS) [HR=0.72, 95%CI (0.64, 0.80), P<0.001] and median progression-free survival (PFS) [HR=0.65, 95%CI (0.53, 0.78), P<0.001], and improved objective response rate (ORR) [RR=1.52, 95%CI (1.28, 1.79), P<0.001]. Subgroup analysis showed that, compared with the ICIs monotherapy group, the ICIs combination therapy group significantly improved OS, PFS, and ORR in NSCLC patients. In terms of safety, the risk of any grade treatment-related adverse events (TRAEs) and grade 3-5 TRAEs in the ICIs group was lower than that in the chemotherapy group. The incidence of TRAEs leading to treatment discontinuation was higher in the ICIs group than in the chemotherapy group. Subgroup analysis showed that the incidence of any grade, grade 3-5, and TRAEs leading to treatment discontinuation was higher in the immune combination therapy group than in the immune monotherapy group. Conclusion ICIs as first-line treatment for NSCLC patients can significantly improve OS, PFS, and ORR compared with chemotherapy. Compared to immune monotherapy, immune combination therapy can significantly improve the efficacy in NSCLC patients, but patients have a higher risk of TRAEs.

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*

This study aimed to investigate the correlation between changes in intestinal toxicity and compositional alterations of Euphorbiae Ebracteolatae Radix(commonly known as Langdu) before and after milk processing, and to explore the detoxification mechanism of milk processing. Mice were intragastrically administered the 95% ethanol extract of raw Euphorbiae Ebracteolatae Radix, milk-decocted(milk-processed), and water-decocted(water-processed) Euphorbiae Ebracteolatae Radix. Fecal morphology, fecal water content, and the release levels of inflammatory cytokines tumor necrosis factor-α(TNF-α) and interleukin-1β(IL-1β) in different intestinal segments were used as indicators to evaluate the effects of different processing methods on the cathartic effect and intestinal inflammatory toxicity of Euphorbiae Ebracteolatae Radix. LC-MS/MS was employed to analyze the small-molecule components in the raw product, the 95% ethanol extract of the milk-processed product, and the milky waste(precipitate) formed during milk processing, to assess the impact of milk processing on the chemical composition of Euphorbiae Ebracteolatae Radix. The results showed that compared with the blank group, both the raw and water-processed Euphorbiae Ebracteolatae Radix significantly increased the fecal morphology score, fecal water content, and the release levels of TNF-α and IL-1β in various intestinal segments(P<0.05). Compared with the raw group, all indicators in the milk-processed group significantly decreased(P<0.05), while no significant differences were observed in the water-processed group, indicating that milk, as an adjuvant in processing, plays a key role in reducing the intestinal toxicity of Euphorbiae Ebracteolatae Radix. Mass spectrometry results revealed that 29 components were identified in the raw product, including 28 terpenoids and 1 acetophenone. The content of these components decreased to varying extents after milk processing. A total of 28 components derived from Euphorbiae Ebracteolatae Radix were identified in the milky precipitate, of which 27 were terpenoids, suggesting that milk processing promotes the transfer of toxic components from Euphorbiae Ebracteolatae Radix into milk. To further investigate the effect of milk adjuvant processing on the toxic terpenoid components of Euphorbiae Ebracteolatae Radix, transmission electron microscopy(TEM) was used to observe the morphology of self-assembled casein micelles(the main protein in milk) in the milky precipitate. The micelles formed in casein-terpenoid solutions were characterized using particle size analysis, fluorescence spectroscopy, ultraviolet spectroscopy, and Fourier-transform infrared(FTIR) spectroscopy. TEM observations confirmed the presence of casein micelles in the milky precipitate. Characterization results showed that with increasing concentrations of toxic terpenoids, the average particle size of casein micelles increased, fluorescence intensity of the solution decreased, the maximum absorption wavelength in the UV spectrum shifted, and significant changes occurred in the infrared spectrum, indicating that interactions occurred between casein micelles and toxic terpenoid components. These findings indicate that the cathartic effect of Euphorbiae Ebracteolatae Radix becomes milder and its intestinal inflammatory toxicity is reduced after milk processing. The detoxification mechanism is that terpenoid components in Euphorbiae Ebracteolatae Radix reassemble with casein in milk to form micelles, promoting the transfer of some terpenoids into the milky precipitate.
Animals ; Mice ; Milk/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Male ; Tumor Necrosis Factor-alpha/immunology* ; Intestines/drug effects* ; Interleukin-1beta/immunology* ; Tandem Mass Spectrometry ; Female

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness