Biomolecules & biomedicine最新文献

Myasthenia gravis (MG) is an acquired autoimmune disease characterized by impaired transmission at the neuromuscular junction, primarily manifesting as fluctuating muscle weakness, fatigability, and partial paralysis. Due to its long disease course, treatment resistance, and frequent relapses, it places a significant burden on patients and their families. In recent years, advances in molecular biology have provided growing evidence that mitochondrial dysfunction impairs muscle function and affects immune cell proliferation and differentiation in patients. Mitochondria, as the cell's energy source, play a critical role in various pathological processes in MG, including oxidative stress, dynamic abnormalities, mitophagy, and mitochondrial metabolism. The role of mitochondrial dysfunction in the pathogenesis of MG has garnered increasing attention. This manuscript primarily explores mitochondrial function and abnormal morphological changes in MG, as well as mitochondrial quality control, metabolic reprogramming, and their potential mechanisms in the pathological changes of the disease. It also reviews the current status of drug therapies aimed at improving mitochondrial function. The goal is to provide novel perspectives and strategies for future mitochondrial-targeted therapies in MG.

The importance of evaluating the nutritional status and immune condition prior to surgery has gained significant attention in predicting the prognosis of cancer patients in recent years. The objective of this study is to establish a risk model for predicting the prognosis of gallbladder carcinoma (GBC) patients. Data from GBC patients who underwent radical resection at West China Hospital of Sichuan University (China) from 2014 to 2021 were retrospectively collected. A novel risk model was created by incorporating the prognostic nutritional index and glucose-to-lymphocyte ratio, and each patient was assigned a risk score. The patients were then divided into low- and high-risk cohorts, and comparisons were made between the two groups in terms of clinicopathological features and prognosis. Propensity score matching was conducted to reduce potential bias. A total of 300 GBC patients receiving radical surgery were identified and included in this study. Patients in the high-risk group were older, had higher levels of serum carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), and cancer antigen 19-9 (CA19-9), were more likely to experience postoperative complications, and had more aggressive tumor characteristics, such as poor differentiation, lymph node metastasis, and advanced tumor stage. They also had lower overall survival (OS) rates (5-year OS rate: 11.2% vs. 37.4%) and disease-free survival (DFS) rates (5-year DFS rate: 5.1% vs. 18.2%). After propensity score matching, the high-risk population still experienced poorer prognosis (5-year OS rate: 12.7% vs 20.5%; 5-year DFS rate: 3.2% vs 8.2%). The risk model combining prognostic nutritional index and glucose-to-lymphocyte ratio can serve as a standalone predictor for the prognosis and assist in optimizing the treatment approach for GBC patients.

Stress-induced hyperglycemia (SIH) is common in patients with traumatic brain injury (TBI) and has been suggested to influence mortality rates. This meta-analysis aims to evaluate the impact of SIH on mortality in TBI patients without preexisting diabetes mellitus (DM). A comprehensive search was performed in Medline, Web of Science, Embase, Wanfang, and China National Knowledge Infrastructure (CNKI) databases up to May 15, 2024, to retrieve relevant studies. Observational studies reporting the incidence of all-cause mortality among TBI patients without preexisting DM, comparing those with and without SIH, were included. The association between SIH and all-cause mortality was analyzed using risk ratios (RR) and 95% confidence intervals (CI) with a random-effects model. Twelve cohort studies comprising 15 datasets with 16,387 TBI patients were included. The pooled analysis showed that SIH was associated with a higher risk of all-cause mortality (RR: 2.00, 95% CI: 1.72-2.33, P < 0.001), with mild heterogeneity (I² = 25%). Sensitivity analysis confirmed the robustness of these findings. Subgroup analyses indicated no significant differences based on study design, patient age, gender proportion, SIH definition, or follow-up duration. However, the association was slightly weaker but still significant in studies using multivariate analyses (RR: 1.76) compared to univariate analyses (RR: 2.69). In conclusion, SIH was associated with a higher risk of all-cause mortality in TBI patients without preexisting DM. Further research should explore the underlying mechanisms and optimal management strategies for SIH in this population.

The triglyceride-glucose index (TyGI) is a novel indicator of insulin resistance, which has been associated with an increased risk of cardiovascular diseases. The aim of this meta-analysis was to determine the association between TyGI and the prognosis of patients with heart failure (HF). Cohort studies relevant to the aim of the meta-analysis were retrieved by searching electronic databases, including PubMed, Web of Science, and Embase. A random-effects model was used to combine the data, incorporating the influence of between-study heterogeneity. Twelve studies involving 20,639 patients with HF were included. Pooled results showed that compared to patients with the lowest category of TyGI at baseline, those with the highest TyGI index were associated with a higher risk of all-cause mortality during follow-up (relative risk [RR] 1.71, 95% confidence interval [CI] 1.46 - 2.00; P < 0.001; I² = 55%). Sensitivity analyses limited to studies after adjustment for confounding factors showed similar results (RR 1.89, 95% CI 1.67 - 2.21; P < 0.001; I² = 13%). Subsequent meta-analyses also showed that a high TyGI at baseline was related to the incidence of cardiovascular death (RR 1.87, 95% CI 1.42 - 2.47; P < 0.001; I² = 57%), HF rehospitalization (RR 1.33, 95% CI 1.04 - 1.69; P < 0.02; I² = 46%), and major adverse cardiovascular events (RR 1.69, 95% CI 1.39 - 2.06; P < 0.001; I² = 17%) during follow-up. In conclusion, a high TyGI may be associated with a poor clinical prognosis for patients with HF.

Neuromodulation is being utilized across a variety of medical subspecialties to treat both painful and non-painful medical conditions. However, publications on neuromodulation topics infrequently occur in journals targeting generalists and medical specialties outside of pain medicine and neurosurgery. This study reviewed implantable neuromodulation devices, their respective Food and Drug Administration-approved indications for use, as well as off-label usage, and the associated potential risks and benefits for each device. PubMed and Medline databases were queried for systematic reviews with or without meta-analyses and randomized controlled trials of implantable neuromodulation devices. The literature review resulted in 106 studies eligible for inclusion, and 67 were included in the final review. In conclusion, as the clinical volume of neuromodulation continues to grow, supporting and educating medical professionals who care for patients that receive implanted neuromodulation devices is paramount. It is likely the use of neuromodulation will continue to expand across all medical subspecialties, and as such, every clinician should have a baseline understanding of this treatment.

Cardiovascular diseases (CVDs) are a major challenge in global health. Despite significant advances in treatment and management, the incidence and mortality rates of CVDs have been rising in recent years, particularly in the United States. With continuous advancements in medical technology, perioperative transesophageal echocardiography (TEE) has become a key technology in cardiac surgery, enhancing surgical success rates and patient safety. The application of TEE spans preoperative planning, intraoperative monitoring, and postoperative evaluation, especially in complex procedures such as mitral valve repair and aortic valve replacement, where it plays an indispensable role. Simultaneously, the introduction of artificial intelligence (AI) brings new prospects for TEE image analysis and diagnostic support, significantly improving diagnostic accuracy and real-time decision-making capabilities. However, the application of TEE technology faces challenges such as high costs, uneven technological diffusion, and the high skill requirements for medical personnel. Therefore, establishing standardized training protocols and strengthening multidisciplinary collaboration are crucial. This paper reviews the application of TEE in cardiac surgery and its path toward educational and practical standardization from a global perspective, emphasizing its importance in improving the postoperative quality of life for patients and exploring future directions in technological innovation and educational optimization.

Coarctation of the aorta (CoA) ranks among the most prevalent congenital heart defects and poses a life-threatening risk if left undiagnosed. Herein, we utilized fetal heart quantification (HQ) technology to improve the prenatal prediction of CoA. A retrospective analysis was conducted on 64 fetal cases with suspected aortic arch constriction, identified through prenatal ultrasound findings between November 2020 and March 2022 at the Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University. According to the follow-up results, these cases were divided into two groups: 35 cases confirmed as CoA by postpartum surgery or induction, and 29 cases initially suspected of CoA prenatally but subsequently ruled out postnatally. Additionally, 88 cases of normal fetuses were randomly selected as the control group. Both conventional M-mode ultrasound techniques and Fetal HQ software were utilized for fetal analysis across all groups. Parameters related to the heart were measured, including fetal 4-CV length, width, Global Spherical Index (GSI), Mitral Annular Plane Systolic Excursion (MAPSE), areas and ratios of the left and right ventricles, as well as lengths and ratios of the left and right ventricles. Functional measurements of the left and right ventricles included ejection fraction (EF), fractional area change (FAC), global longitudinal strain (GLS), fractional shortening (FS), end-diastolic diameter (ED), and sphericity index (SI). Left ventricular (LV)-GLS, LV-FAC, LV-EF, and LV-EF Z-score could potentially differentiate between true CoA and false CoA or normal groups and serve as potential indicators for the clinical diagnosis of CoA. The receiver operating characteristic (ROC) curves indicated that LV-GLS and LV-EF Z-score have the greatest predictive power for CoA diagnosis. The segments 6-12 of FS in the confirmed CoA group were significantly lower than those in the false CoA and normal groups. Fetal HQ technology, by assessing changes in the size and shape of the heart, can provide relatively reliable parameter support for the prenatal diagnosis of fetal aortic coarctation.

Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications in biochemistry are poised to expand, revolutionizing both theoretical and applied research. This review explores current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering...

The wider availability of continuous respiratory monitors and advanced data abstraction techniques has led to a substantial increase in understanding of postoperative opioid-induced respiratory depression (OIRD), particularly regarding its incidence, presentation, temporal distribution, and risk factors. Self-limited episodes of OIRD are relatively common, typically presenting as repetitive apneas beginning in the postoperative period and continuing through the first night after surgery. In contrast, life-threatening episodes of OIRD are rare and usually occur on the day of surgery. Traditional monitoring of patient vital signs may be insensitive in detecting OIRD, while healthcare staff may be more adept at recognizing the concurrent development of somnolence. Although obstructive sleep apnea (OSA) is a known risk factor for OIRD, a more comprehensive phenotype is emerging-elderly patients with debility and substantial comorbidity. These advances have significant implications for managing postoperative OIRD. This review will focus on how these new insights into OIRD have highlighted knowledge gaps and created opportunities for future research and practice initiatives.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteoporotic fractures and leading to considerable health deterioration and increased mortality. In addressing the critical need for early identification of osteoporosis through routine screening of femoral neck bone mineral density (FNBMD), this study developed a user-friendly prediction model aimed at men aged 50 years and older, a demographic often overlooked in osteoporosis screening. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), the study involved outlier detection and handling, missing value imputation via the K nearest neighbor (KNN) algorithm, and data normalization and encoding. The dataset was split into training and test sets with a 7:3 ratio, followed by feature screening through the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Eight different machine learning algorithms were then employed to construct predictive models, with their performance evaluated through a comprehensive metric suite. The random forest regressor (RFR) emerged as the most effective model, characterized by key predictors such as age, body mass index (BMI), poverty income ratio (PIR), serum calcium, and race, achieving a coefficient of determination (R²) of 0.218 and maintaining robustness in sensitivity analyses. Notably, excluding race from the model resulted in sustained high performance, underscoring the model's adaptability. Interpretations using Shapley additive explanations (SHAP) highlighted the influence of each feature on FNBMD. These findings indicate that our predictive model effectively aids in the early detection of osteoporosis, potentially reducing the incidence of OFNFs in this high-risk population.