Experimental Biology and Medicine最新文献

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528-0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models' applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

This research study was directed towards to assessing whether coenzyme Q10 (CoQ10) is linked to neuroprotection and induces anti-inflammatory and anti-neuronal death responses in an Intracerebral hemorrhage (ICH) mouse model via right caudate nucleus injection with collagenase VII. Autologous blood was injected into mice to induce ICH. We found that FoxM1 was upregulated in the ICH-injured animals. Moreover, CoQ10 treatment effectively ameliorated neurological deficits, mitigated cerebral edema, and minimized hematoma in model mice, demonstrating dose-dependent efficacy and promoting the functional recovery of the animals. ELISA and real-time PCR assays of pro-inflammatory cytokines indicated that CoQ10 was capable of alleviating neuroinflammation in ICH. In line with the part of CoQ10 in attenuating the inflammatory response, CoQ10 also suppressed cell apoptosis in the ICH-injured brain, which partly accounts for its neuroprotective effect. Furthermore, our analysis of different inflammatory pathways indicated that CoQ10 targeted the nuclear factor-kappa B signaling axis. Our findings suggest that CoQ10 protects against ICH by mitigating neuroinflammatory responses and preventing neuronal apoptosis, with the underlying mechanism possibly being connected with nuclear factor-kappa B pathway regulation. Therefore, CoQ10 holds significant potential as a therapeutic strategy for treating ICH.

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: "opioid," "cardiovascular," and "women." Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

More complex surgeries are being performed in increasingly sicker patients, resulting in a greater burden of postoperative morbidity. Delineating the metabolic and bioenergetic changes that occur in response to surgical stress may further our understanding about how humans respond to injury and aid the identification of resilient and frail phenotypes. Skeletal muscle biopsies were taken from patients undergoing hepato-pancreatico-biliary surgery at the beginning and end of the procedure to measure mitochondrial respiration and thiol status. Blood samples were taken at the same timepoints to measure markers of inflammation and systemic redox state. A sub-group of patients underwent cardiopulmonary exercise testing prior to surgery, and were assigned to two groups according to their oxygen consumption at anaerobic threshold (≤10 and >10 mL/kg/min) to determine whether redox phenotype was related to cardiorespiratory fitness. No change in mitochondrial oxidative phosphorylation capacity was detected. However, a 26.7% increase in LEAK (uncoupled) respiration was seen after surgery (P = 0.03). Free skeletal muscle cysteine also increased 27.0% (P = 0.003), while S-glutathionylation and other sulfur and nitrogen-based metabolite concentrations remained unchanged. The increase in LEAK was 200% greater in fit patients (P = 0.004). Baseline plasma inflammatory markers, including TNF-⍺ and IL-6 were greater in unfit patients, 96.6% (P = 0.04) and 111.0% (P = 0.02) respectively, with a 58.7% lower skeletal muscle nitrite compared to fit patients. These data suggest that oxidative phosphorylation is preserved during the acute intraoperative period. Increase in free cysteine may demonstrate the muscle's response to surgical stress to maintain redox balance. The differences in tissue metabolism between fitness groups suggests underlying metabolic phenotypes of frail and resilient patients. For example, increased LEAK in fitter patients may indicate mitochondrial adaptation to stress. Higher baseline measurements of inflammation and lower tissue nitrite in unfit patients, may reflect a state of frailty and susceptibility to postoperative demise.

Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavily on manually selected image features, which can limit diagnostic accuracy and lead to variability in results. In contrast, deep learning models, particularly convolutional neural networks (CNNs), are capable of automatically analyzing medical images, identifying complex patterns, and enhancing diagnostic precision. This review evaluates recent studies that apply machine learning and deep learning approaches to classify uveal melanoma using imaging modalities such as fundus photography, optical coherence tomography (OCT), and ultrasound. The review critically examines each study's research design, methodology, and reported performance metrics, discussing strengths as well as limitations. While fundus photography is the predominant imaging modality being used in current research, integrating multiple imaging techniques, such as OCT and ultrasound, may enhance diagnostic accuracy by combining surface and structural information about the tumor. Key limitations across studies include small dataset sizes, limited external validation, and a reliance on single imaging modalities, all of which restrict model generalizability in clinical settings. Metrics such as accuracy, sensitivity, and area under the curve (AUC) indicate that deep learning models have the potential to outperform traditional methods, supporting their further development for integration into clinical workflows. Future research should aim to address current limitations by developing multimodal models that leverage larger, diverse datasets and rigorous validation, thereby paving the way for more comprehensive, reliable diagnostic tools in ocular oncology.

This study aimed to explore the correlation between the deletion of the CDKN2A/B gene and the prognosis of pediatric acute lymphoblastic leukemia (ALL) patients. A total of 310 pediatric patients who were diagnosed with acute lymphoblastic leukemia at our hospital from January 2020 to September 2023 were included in this study. Among them, 78 patients with CDKN2A/B deletion were included in the final analysis. Additionally, 78 ALL patients without CDKN2A/B deletion, who were diagnosed during the same period, were randomly selected for comparison. A statistical analysis was conducted to compare the clinical characteristics and prognosis between the CDKN2A/B deletion group and the non-deletion group in ALL patients. The results showed that pediatric ALL patients with CDKN2A/B deletion had higher white blood cell counts and a greater proportion of immature cells in peripheral blood at diagnosis. The age at diagnosis was older in the deletion group, with a greater proportion in the >10-year-old group. CDKN2A/B deletion occurred more frequently in pediatric patients with T-ALL than in pediatric patients with B-ALL. Patients with CDKN2A/B deletion were more likely to have positive BCR-ABL1 expression combined with IKZF1 deletion. The overall survival (OS) rate was 89.7%, and the event-free survival (EFS) rate was 83.3% in the CDKN2A/B deletion group, which was lower than the OS rate of 97.4% and EFS rate of 93.6% in the non-deletion group. These results suggest that CDKN2A/B deletion may be one of the factors affecting poor prognosis. It provides a new perspective for clinical treatment, risk stratification, and prognostic assessment in pediatric ALL patients.

The sex difference in depression has long been an unsolved issue. Women are twice as likely to suffer from depression as men. However, there were significant differences in the composition of gut microbiota between women and men. There is a lack of studies linking sex differences in depression to microbiota, and the specific mechanisms of this process have not been explained in detail. The main purpose of this study was to explore the gender differences in the intestinal tract of male and female depressed mice. In this study, chronic restraint stress (CRS) mouse models were used to simulate chronic stress, and behavioral tests were conducted, including the open field test (OFT), tail suspension test (TST) and forced swimming test (FST). Microbial diversity analysis and metabolomics were performed on collected mouse feces. The results showed that female mice were highly active and prone to anxious behavior before stress, and the levels of f-Rikenellaceae, f-Ruminococcaceae and 16α-hydroxyestrone were significantly different from those in male mice. After 21 days (Days) of stress, female mice showed depression-like behavior, and the levels of f-Erysipelotrichaceae, 5α-pregnane-3,20-dione, and 2-hydroxyestradiol were significantly different from those in male mice. After 14 days of stress withdrawal, the depression-like behavior continued to worsen in female mice, and the levels of 5α-pregnane-3,20-dione, estrone glucuronide and f-Erysipelotrichaceae were significantly different from those in male mice. In summary, female mice have stronger stress sensitivity and weaker resilience than male mice, which may be related to differences in bacterial diversity and estrogen metabolism disorders.

In recent years, the relationship between the immunosuppressive niche of the bone marrow and therapy resistance in acute myeloid leukemia (AML) has become a research focus. The abnormal number and function of immunosuppressive cells, including regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), along with the dysfunction and exhaustion of immunological effector cells, including cytotoxic T lymphocytes (CTLs), dendritic cells (DCs) and natural killer cells (NKs), can induce immune escape of leukemia cells and are closely linked to therapy resistance in leukemia. This article reviews the research progress on the relationship between immune cells in the marrow microenvironment and chemoresistance in AML, aiming to provide new ideas for the immunotherapy of AML.

The current meta-analysis was performed to analyze the efficacy and safety of platelet-rich plasma (PRP) as an epidural injectate, in comparison with steroids in the management of radiculopathy due to lumbar disc disease (LDD). We conducted independent and duplicate searches of the electronic databases (PubMed, Embase and Cochrane Library) in March 2024 to identify randomized controlled trials (RCTs) analyzing the efficacy of epidural PRP for pain relief in the management of LDD. Animal or in vitro studies, clinical studies without a comparator group, and retrospective or non-randomised clinical studies were excluded. Diverse post-intervention pain scores [visual analog score (VAS)] and functional scores [Oswestry Disability Index (ODI), SF-36], as reported in the reviewed studies, were evaluated. Statistical analysis was performed using STATA 17 software. 5 RCTs including 310 patients (PRP/Steroids = 153/157) were included in the analysis. The included studies compared the efficacy and safety of epidural PRP and steroids at various time-points including 1, 3, 6, 12, 24, and 48 weeks. Epidural PRP injection was found to offer comparable pain relief (VAS; WMD = -0.09, 95% CI [-0.66, 0.47], p = 0.641; I² = 96.72%, p < 0.001), functional improvement (ODI; WMD = 0.72, 95% CI [-6.81, 8.25], p = 0.524; I² = 98.73%, p < 0.001), and overall health improvement (SF-36; WMD = 1.01, 95% CI [-1.14, 3.17], p = 0.224; I² = 0.0%, p = 0.36) as epidural steroid injection (ESI) at all the observed time points in the included studies without any increase in adverse events or complications. Epidural administration of PRP offers comparable benefit as epidural steroid injection (ESI) in the management of radiculopathy due to LDD. The safety profile of the epidural PRP is also similar to ESI.

Cancer progression is orchestrated by the accrual of mutations in driver genes, which endow malignant cells with a selective proliferative advantage. Identifying cancer driver genes is crucial for elucidating the molecular mechanisms of cancer, advancing targeted therapies, and uncovering novel biomarkers. Based on integrated analysis of Multi-Omics data and Network models, we present MONet, a novel cancer driver gene identification algorithm. Our method utilizes two graph neural network algorithms on protein-protein interaction (PPI) networks to extract feature vector representations for each gene. These feature vectors are subsequently concatenated and fed into a multi-layer perceptron model (MLP) to perform semi-supervised identification of cancer driver genes. For each mutated gene, MONet assigns the probability of being potential driver, with genes identified in at least two PPI networks selected as candidate driver genes. When applied to pan-cancer datasets, MONet demonstrated robustness across various PPI networks, outperforming baseline models in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. Notably, MONet identified 37 novel driver genes that were missed by other methods, including 29 genes such as APOBEC2, GDNF, and PRELP, which are corroborated by existing literature, underscoring their critical roles in cancer development and progression. Through the MONet framework, we successfully identified known and novel candidate cancer driver genes, providing biologically meaningful insights into cancer mechanisms.