Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective:To investigate the specific mechanisms underlying the protective effect of interleukin (IL) -27 in the pathogenesis of psoriasis.Methods:Five skin tissue samples from healthy individuals and 6 lesional skin samples from psoriasis patients were collected, and IL-27 expression was determined by immunohistochemical staining. Il27ra gene knockout (KO) mice were constructed. Psoriasis-like mouse models were established with topical imiquimod in 5 wild-type (WT) mice and 6 KO mice. Mouse skin lesions were evaluated using the modified Psoriasis Area and Severity Index (mPASI), and lesional skin tissues were collected for hematoxylin and eosin (HE) staining to observe changes in epidermal thickness. Single-cell suspensions were prepared with skin lesions and skin-draining lymph nodes of 4 WT mice and 3 KO mice, and changes in immune cells (including T cells, γδ T cells, and neutrophils) were analyzed using flow cytometry. Additionally, skin-draining lymph node cells were isolated from 9 normal WT mice, and IL-17A expression was stimulated using a T-cell receptor agonist (CD3/28 activating antibodies, αCD3/28) or cytokines (IL-23 + IL-1β), followed by the addition of IL-27; peripheral blood mononuclear cells (PBMCs) were isolated from 6 psoriasis patients, and IL-17A expression was stimulated using the T-cell receptor agonist, followed by the addition of IL-27; the effect of IL-27 on IL-17A expression in T cells was analyzed using flow cytometry and enzyme-linked immunosorbent assay (ELISA). Measurement data were compared between two groups using the t test. Results:Immunohistochemical staining revealed a significant reduction in IL-27 expression in psoriatic lesions (mean fluorescence intensity: 9.85 ± 3.07) compared with the normal skin (19.45 ± 2.51, t = 5.60, P < 0.001). Animal experiments demonstrated that the KO mice exhibited significantly aggravated psoriasis-like skin inflammation (mPASI: 4.00 ± 0.89) and significantly increased epidermal thickness (115.50 ± 7.69 μm) compared with the WT mice (mPASI: 2.80 ± 0.84, t = 2.28, P = 0.049; epidermal thickness: 92.26 ± 8.76 μm, t = 4.70, P = 0.001) ; compared with the WT mice, the KO mice showed significantly increased proportions of T cells (11.22% ± 2.76% vs. 7.08% ± 0.85%) and dermal γδ T cells (4.78% ± 0.39% vs. 2.78% ± 0.49%) among live cells in the lesions ( t = 2.91, 2.75, respectively, both P < 0.05), as well as significantly increased proportions of Th17, IL-17 + γδ T, Th22, and IL-22 + γδ T cells in the skin-draining lymph nodes (all P < 0.05), but no significant difference in the proportion of neutrophils in the lesions (WT: 13.57% ± 8.36%, KO: 14.43% ± 9.13%; t = 0.13, P = 0.902). Experiments with different stimuli showed that IL-27 significantly suppressed T-cell receptor agonist-induced IL-17A expression in murine γδ T cells (αCD3/28 group: 1.00 ± 0.11, αCD3/28 + IL-27 group: 0.76 ± 0.13; t = 3.54, P = 0.004), while there was no significant difference in IL-17A expression between cells induced by IL-23 + IL-1β with the IL-27 co-culture and those without ( t = 1.34, P > 0.05). ELISA showed that IL-27 significantly reduced the IL-17A concentration in the culture supernatant of draining lymph node cells stimulated by the T-cell receptor agonist (αCD3/28 group: 1 535.00 ± 97.76 pg/ml, αCD3/28 + IL-27 group: 1 030.00 ± 287.90 pg/ml, t = 3.29, P = 0.031), but did not reduce the IL-17A concentration induced by IL-23 + IL-1β ( t = 0.09, P > 0.05). Flow cytometry indicated that IL-27 significantly inhibited the T-cell receptor agonist-induced IL-17A expression in T cells from psoriasis patients (αCD3/28 group: 4.28 ± 3.25, αCD3/28 + IL-27 group: 3.04 ± 2.65, t = 4.46, P = 0.007) . Conclusion:IL-27 appeared to play a protective role in psoriasis by suppressing IL-17A secretion from T cells.

Objective To analyze ancient prescriptions for knee osteoarthritis(KOA),and explore their application patterns,for providing clinical reference value.Methods Prescriptions were collected from ancient documents,and a database was created by using Excel 2016 for frequency analysis.Association rules and hidden structures were analyzed by using SPSS Modeler 18.0 and Lantern 5.0.Results A total of 373 prescriptions and 421 herbs were collected,with 3894 times of frequency of medication.Danggui was the most frequently used herb.Herbs primarily tonified deficiencies,relieved exterior symptoms,and expelled wind-dampness,with warm properties and pungent,sweet,or bitter tastes,mainly targeting the spleen and kidney meridians.Thirteen strong association rules were identified,including Chuanxiong+Niuxi-Danggui,and Bixie-Niuxi.Hidden structure analysis revealed 10 variables,leading to 4 clusters and 4 core prescriptions.Common syndromes included wind-damp,wind-cold-damp,wind-damp-heat,and qi-blood deficiency.Conclusion The treatment of KOA in ancient literature focuses on dispelling wind and removing dampness,taking into account both positive deficiency and evil excess,flexible use of tonifying spleen and kidney,clearing dampness-heat,tonifying Qi and blood and other drugs.

Activated specific T cells possess the capability to directly recognize and eradicate tumor cells, a process pivotal in the anti-tumor immune response. In recent years, the field of tumor immunotherapy, particularly T cell-based strategies, has seen rapid advancements, resulting in several drugs receiving clinical approval and the initiation of numerous basic research endeavors and clinical trials. Among these strategies, adoptive T cell transfer therapy emerges as a principal focus, encompassing approaches such as chimeric antigen receptor T-cell (CAR-T), T-cell receptor-engineered T cell (TCR-T), tumor-infiltrating lymphocytes (TILs), and cytotoxic T lymphocytes (CTLs) therapies. This review endeavors to encapsulate the global research and development strides made in four categories of adoptive T cell therapies, while also dissecting their individual merits and limitations. The objective is to furnish insights that may bolster the development of tumor immunotherapy pharmacopeia and their efficacious clinical application in cancer treatment.

Lung cancer is one of the most common cancers worldwide and is the leading cause of cancer deaths. Lung adenocarcinoma is the most common type of lung cancer. Due to the lack of effective biomarkers and therapeutic targets in the proliferation and metastasis of lung adenocarcinoma, the overall treatment of lung adenocarcinoma is not optimistic. Therefore, there is a need to find new ideas and methods for lung adenocarcinoma treatment. The 26S proteasome is a multiprotein complex responsible for degrading misfolded proteins and maintaining intracellular protein homeostasis. During the development of non-small cell lung cancer (NSCLC), the regulatory granule subunit of the 26S proteasome promotes the malignant progression of tumours by regulating tumour-associated proteins, immune cells, and related signalling pathways. The proteasome core particle is a key subunit for degrading proteins, and its inhibitors have shown promising anti-tumour effects when combined with conventional chemotherapeutic agents. However, limited by toxic side effects and tumour heterogeneity, targeted inhibitors against the 26S proteasome are still not widely used in NSCLC treatment. This article reviews the mechanism of action and related therapeutic research of 26S proteasome regulatory particle subunits and core particle subunits in NSCLC, and explores the potential of these inhibitors in clinical application.
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Humans ; Proteasome Endopeptidase Complex/chemistry* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Lung Neoplasms/genetics* ; Animals ; Proteasome Inhibitors/therapeutic use* ; Antineoplastic Agents/therapeutic use*

Current experimental and computational methods have limitations in accurately and efficiently classi-fying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set con-taining approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC's generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.

Intestinal fibrosis is a significant clinical challenge in inflammatory bowel diseases, but no effective anti-fibrotic therapy is currently available. Glucagon receptor (GCGR) and glucagon-like peptide 1 receptor (GLP1R) are both peptide hormone receptors involved in energy metabolism of epithelial cells. However, their role in intestinal fibrosis and the underlying mechanisms remain largely unexplored. Herein GCGR and GLP1R were found to be reduced in the stenotic ileum of patients with Crohn's disease as well as in the fibrotic colon of mice with chronic colitis. The downregulation of GCGR and GLP1R led to the accumulation of the metabolic byproduct lactate, resulting in histone H3K9 lactylation and exacerbated intestinal fibrosis through epithelial-to-mesenchymal transition (EMT). Dual activating GCGR and GLP1R by peptide 1907B reduced the H3K9 lactylation in epithelial cells and ameliorated intestinal fibrosis in vivo. We uncovered the role of GCGR/GLP1R in regulating EMT involved in intestinal fibrosis via histone lactylation. Simultaneously activating GCGR/GLP1R with the novel dual agonist peptide 1907B holds promise as a treatment strategy for alleviating intestinal fibrosis.

Notum, a negative feedback regulator of the Wnt signaling, has emerged as a promising target for treating glucocorticoid-induced osteoporosis (GIOP). This study showcases an efficient strategy for discovering the anti-Notum constituents from herbal medicines (HMs) as novel anti-GIOP agents. Firstly, a rapid-responding near-infrared fluorogenic substrate for Notum was rationally engineered for high-throughput identifying the anti-Notum HMs. The results showed that Bu-Gu-Zhi (BGZ), a known anti-osteoporosis herb, potently inhibited Notum in a competitive-inhibition manner. To uncover the key anti-Notum constituents in BGZ, an efficient strategy was adapted via integrating biochemical, phytochemical, computational, and pharmacological assays. Among all identified BGZ constituents, three furanocoumarins were validated as strong Notum inhibitors, while 5-methoxypsoralen (5-MP) showed the most potent anti-Notum activity and favorable safety profiles. Mechanistically, 5-MP acted as a competitive inhibitor of Notum via creating strong hydrophobic interactions with Trp128 and Phe268 in the catalytic cavity of Notum. Cellular assays showed that 5-MP remarkably promoted osteoblast differentiation and activated Wnt signaling in dexamethasone (DXMS)-challenged MC3T3-E1 osteoblasts. In dexamethasone-induced osteoporotic mice, 5-MP strongly elevated bone mineral density (BMD) and improved cancellous and cortical bone thickness. Collectively, this study constructs a high-efficient platform for discovering key anti-Notum constituents from HMs, while 5-MP emerges as a promising anti-GIOP agent.

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces. Here we have developed a deep learning algorithm, GPT2 Ion Channel Classifier (GPT2-ICC), which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins. GPT2-ICC integrates representation learning with a large language model (LLM)-based classifier, enabling highly accurate identification of potential ion channels. Several potential ion channels were predicated from the unannotated human proteome, further demonstrating GPT2-ICC's generalization ability. This study marks a significant advancement in artificial-intelligence-driven ion channel research, highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data. Moreover, it provides a valuable computational tool for uncovering previously uncharacterized ion channels.