Biomolecules & biomedicine最新文献

HEK293T cells are extensively utilized for therapeutic protein production due to their human origin, which enables accurate post-translational modifications. This study aimed to enhance membrane protein production in HEK293T cells by knocking out the ATF4 gene using CRISPR-Cas9 technology. The ATF4 gene was edited by infecting HEK293T cells with a lentivirus carrying optimized single-guide RNA (ATF4-KO-3) and Cas9 genes. Comparative evaluations were conducted using all-in-one and two-vector systems. Genome sequencing and membrane protein productivity of ATF4-knockout (KO) cells were compared to wild-type (WT) cells using next-generation sequencing (NGS) and a membrane protein isolation kit, respectively. Single-cell analysis confirmed gene editing patterns, with NGS verifying the intended deletions. Membrane protein production was also assessed indirectly via flow cytometry, analyzing cells expressing Membrane-GFP. Compared to WT cells, ATF4-KO cells exhibited a significant increase in membrane protein production, with a 52.2 ± 19.0% improvement. Gene editing efficiency was compared between the two delivery systems, with the two-vector system demonstrating higher efficiency based on T7 endonuclease I assays. Western blot analysis confirmed ATF4 suppression and increased expression of membrane proteins, including E-cadherin and CD63. Quantitative analysis via PAGE revealed a 77.2 ± 30.6% increase in purified membrane protein yields, consistent with the observed enhancements. Flow cytometry using Membrane-GFP further demonstrated a 22.9 ± 9.7% increase in productivity. In summary, ATF4 knockout significantly enhances membrane protein production in HEK293T cells, offering potential improvements in biopharmaceutical manufacturing by enabling more efficient protein synthesis.

Diabetes mellitus (DM) has been suggested as a potential risk factor for tinnitus, but evidence remains inconclusive. This meta-analysis aimed to evaluate the association between DM and tinnitus by systematically reviewing and synthesizing data from observational studies. A comprehensive literature search was conducted in PubMed, Embase, and Web of Science up to August 16, 2024. Observational studies with a sample size of at least 100 participants that assessed the relationship between DM and tinnitus were included. Studies involving populations with specific diseases were excluded. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and sensitivity and subgroup analyses were performed. Publication bias was evaluated using funnel plots and Egger's regression test. Twelve studies comprising 2,277,719 participants were included. The pooled analysis revealed a significant association between DM and tinnitus (OR: 1.18, 95% CI: 1.06-1.31, P = 0.002), with moderate heterogeneity (I² = 51%). Sensitivity analyses confirmed the robustness of these findings. Subgroup analyses showed no significant differences by geographical region, mean age, sex distribution, tinnitus diagnosis method, or model used for association estimation. Publication bias was not detected (Egger's test P = 0.29). These findings suggest that DM is significantly associated with an increased risk of tinnitus. Further research is warranted to explore underlying mechanisms and causal relationships. Nonetheless, the results underscore the importance of monitoring tinnitus in patients with diabetes.

In recent years, the health challenges linked to frailty in the elderly, particularly those worsened by cigarette smoke, have become more pronounced. However, quantitative studies examining the impact of smoking dosage on frailty in this population remain limited. To address this gap, we developed a model using smoke-exposed elderly mice. Fifteen-month-old C57BL/6J mice were exposed to smoke from two burning cigarettes for 15 min in a whole-body chamber. This exposure occurred 4, 6, and 8 times daily for 30 days, representing low, medium, and high smoking dosages, respectively. Frailty levels were assessed through rotation and grip strength tests, alongside lung histopathology and inflammatory factor protein expression analyses across the three dosage groups. Additionally, we used the Gene Expression Omnibus (GEO) database to validate the correlation between frailty and inflammation in elderly smokers, facilitating cross-comparisons between animal model findings and human sample data. Our results show that mice exposed to high-dose smoking were significantly more prone to frailty, with notable reductions in maximal grip strength (P < 0.01) and drop time (P < 0.001). Among human samples, 69.2% of elderly smokers exhibited a frailty phenotype, compared to just 15.4% of nonsmokers. Both smoking-exposed mice and elderly smokers demonstrated upregulation of tumor necrosis factor-α (TNF-α) and interleukin-1 β (IL-1β) in lung tissue and serum. Mechanistically, this upregulation activates the NF-κB signaling pathway. Our findings quantitatively link smoking-induced frailty to increased levels of TNF-α and IL-1β, providing experimental evidence for the diagnosis and prevention of frailty in elderly populations.

Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. Unfortunately, labeled samples for pediatric supracondylar fractures are scarce and difficult to obtain. To address this issue, this paper introduces a deep learning-based multi-scale patch residual network (MPR) for the automatic detection and localization of subtle pediatric supracondylar fractures. The MPR framework combines a CNN for automatic feature extraction with a multi-scale generative adversarial network to model skeletal integrity using healthy samples. By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. On an independent test set, the proposed model achieves an accuracy of 90.5%, with 89% sensitivity, 92% specificity, and an F1 score of 0.906-outperforming the diagnostic accuracy of emergency medicine physicians and approaching that of pediatric radiologists. Furthermore, the model demonstrates a fast inference speed of 1.1 s per sheet, underscoring its substantial potential for clinical application.

Atherosclerosis (AS) is a chronic inflammatory disease associated with lipid deposition in the vascular intima. Copper is a vital trace element implicated in the onset and progression of AS. Excessive intracellular copper accumulation induces a unique form of cell death termed "cuproptosis." The emergence of the concept of cuproptosis has highlighted the potential role of copper in AS. This review explores the regulatory mechanisms of copper metabolism and cuproptosis, summarizes recent findings on the link between copper excess and AS, and examines how cuproptosis may influence AS progression. The goal is to propose novel diagnostic and therapeutic strategies for AS through the lens of cuproptosis.

Insulin resistance (IR) has been proposed as a contributing factor to major depressive disorder (MDD), with previous studies reporting a positive correlation between triglyceride-glucose (TyG) a proxy indicator of IR and MDD. However, limited information is available regarding their longitudinal association. This study aimed to clarify the connection between TyG levels and depression risk, as well as explore its predictive potential. A total of 3021 participants without a prior history of depression were recruited from the China Health and Retirement Longitudinal Study and followed for seven years. Participants were categorized into tertiles based on their TyG levels. The cumulative hazard of depression was analyzed using Kaplan-Meier curves, while cox regression analyses and multivariable-adjusted restricted cubic spline (RCS) curves were employed to assess the relationship between TyG levels and depression risk. Stratified analyses across various subgroups were also conducted to confirm the robustness of the conclusions. Over the follow-up period, 1782 participants (58.9%) developed depression, with incidence rates of 30.2%, 34.0%, and 35.8% in tertiles 1, 2, and 3, respectively. After adjusting for confounding factors, each 1-unit increase in TyG was associated with a significantly higher risk of depression. RCS curve analysis revealed a compelling dose-response relationship between TyG levels and depression susceptibility. These findings indicate that elevated TyG levels are strongly associated with an increased risk of depression and could serve as a reliable biomarker for assessing depression risk. These insights provide valuable guidance for developing more effective strategies for the prevention and treatment of depressive disorders.

Implant failure remains a significant challenge in oral implantology, necessitating a deeper understanding of its risk factors to improve treatment outcomes. This study aimed to enhance the clinical outcomes of oral implant restoration by investigating the factors contributing to implant failure in patients with partial dentition defects within two years of treatment. Additionally, the study sought to develop an early risk prediction model for implant failure. A retrospective analysis was conducted on 300 patients with partial dentition defects, dividing them into two groups: a failed implant group and a successful implant group, based on the occurrence of implant failure within two years. General clinical data and condition-specific clinical information were compared between the groups. Multivariate binary logistic regression analysis was used to identify influencing factors, while the predictive effectiveness of the model was assessed using a receiver operating characteristic (ROC) curve. The analysis revealed that factors, such as gender, post-implant smoking, oral hygiene status at the second-year follow-up, tooth position, number of implants, timing of loading, width of keratinized mucosa, and bone quantity significantly influenced the likelihood of implant failure (P < 0.05). Among these, post-implant smoking and tooth position were identified as independent risk factors. The area under the curve (AUC) for tooth position was 0.695, indicating low predictive performance. Although tooth position was determined to be an independent risk factor for implant failure within two years, its predictive performance was limited.

Lupus nephritis (LN) is an autoimmune disease that rapidly progresses as a secondary consequence of systemic lupus erythematosus (SLE) and has a very poor prognosis. Therefore, this study aimed to identify characteristics of immune cell infiltration and investigate potential therapeutic targets using bioinformatics methods and the Murphy Roths Large/lymphoproliferation (MRL/lpr) mouse model. In this study, a total of 2,810 differentially expressed genes (DEGs) were identified, which were primarily enriched in inflammatory and immune regulation pathways. From these DEGs, 226 immune-related genes (IRGs) were also identified. The single-sample gene set enrichment analysis (ssGSEA) revealed that patients with LN had increased infiltration of effector memory CD4+ T cells, effector memory CD8+ T cells, gamma delta T cells, myeloid-derived suppressor cells (MDSC), follicular helper T cells, Th1 cells, and Th2 cells, and this was closely correlated with the DEG-IRGs. Furthermore, the potential therapeutic biomarkers, CD244, S100 calcium binding protein P (S100P), and vascular endothelial growth factor C (VEGFC), were identified by Random Forest Approach (RFA), which were validated in LN mice. These findings provide new evidence and insights for further research on diagnosis and treatment of LN by identifying critical genes and their associations with immune infiltration.

Postoperative pneumonia (PP) is one of the most serious complications following coronary artery bypass graft (CABG) surgery. The recently developed admission blood glucose (ABG)/estimated average glucose (eAG) ratio has been identified as a prognostic marker in cardiovascular diseases. This study aimed to investigate the predictive role of the modified ABG/eAG (mABG/eAG) ratio in the development of pneumonia during the early postoperative period in diabetic patients undergoing CABG surgery. In this single-center study, diabetic patients who underwent isolated coronary bypass surgery at the Training and Research Hospital between 1 January 2018 and 1 January 2023 were included. Patients who did not develop PP were assigned to the control group, while those who developed PP were assigned to the PP group. A total of 549 patients were included in the study, 478 patients in the control group (median age = 58 years [range 35-81]) and 71 patients in the PP group (median age = 63 years [37-86]). In the multivariate analysis, the use of packed blood products (odds ratio [OR] = 1.685, 95% confidence interval [CI]: 1.453 - 1.892; P = 0.027), mABG/eAG ratio (OR = 1.659, 95% CI: 1.190 - 2.397; P = 0.019), and re-intubation (OR = 1.829, 95% CI: 1.656 - 1.945; P = 0.034) were identified as independent predictors for the development of PP. Our findings demonstrate that the mABG/eAG ratio is an independent predictor of PP in diabetic patients undergoing CABG surgery. Based on our results, high-risk patients can be identified by calculating the mABG/eAG ratio.

Chronic pain is increasing in prevalence, with new cases now outnumbering those of diabetes, depression, or hypertension. Advanced practice providers have reported that their training in pain management inadequately prepared them to care for patients suffering from painful conditions. In response, the authors of this work developed a basic pain management conceptual framework to provide physician assistant (PA) students with the foundational knowledge necessary to manage and treat patients suffering from a wide variety of painful conditions. The devised framework activity includes categories of pain management such as conservative therapies, medications, injections, minimally invasive procedures, and moderately to highly invasive procedures. This framework can be incorporated into the existing PA educational curriculum and presented alongside a realistic pain patient case study. Furthermore, other health science educational programs, such as nurse practitioner or pharmacy programs, could adopt this framework to increase student knowledge in pain management.