Experimental Biology and Medicine最新文献

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Mesenchymal stem cells (MSCs) have been widely used in the treatment of ischemic stroke. However, factors such as high glucose, oxidative stress, and aging can lead to the reduced function of donor MSCs. The p38 mitogen-activated protein kinase (MAPK) signaling pathway is associated with various functions, such as cell proliferation, apoptosis, senescence, differentiation, and paracrine secretion. This study examined the hypothesis that the downregulation of p38 MAPK expression in MSCs improves the prognosis of mice with ischemic stroke. Lentiviral vector-mediated short hairpin RNA (shRNA) was constructed to downregulate the expression level of p38 MAPK in mouse bone marrow-derived MSCs. The growth cycle, apoptosis, and senescence of MSCs after infection were examined. A mouse model of ischemic stroke was constructed. After MSC transplantation, the recovery of neurological function in the mice was evaluated. Lentivirus-mediated shRNA significantly downregulated the mRNA and protein expression levels of p38 MAPK. The senescence of MSCs in the p38 MAPK downregulation group was significantly reduced, but the growth cycle and apoptosis did not significantly change. Compared with the control group, the infarct volume was reduced, and the neurological function and the axonal remodeling were improved in mice with ischemic stroke after transplantation of MSCs with downregulated p38 MAPK. Immunohistochemistry confirmed that in the p38 MAPK downregulation group, apoptotic cells were reduced, and the number of neuronal precursors and the formation of white matter myelin were increased. In conclusion, downregulation of p38 MAPK expression in MSCs improves the therapeutic effect in mice with ischemic stroke, an effect that may be related to a reduction in MSC senescence. This method is expected to improve the efficacy of MSCs in patients, especially in patients with underlying diseases such as diabetes, thus providing a basis for clinical individualized treatment for cerebral infarction.

Curcumin, an antitumor agent, has been shown to inhibit cell growth and metastasis in osteosarcoma. However, there is no evidence of curcumin and its regulation of cell ferroptosis and nuclear factor E2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) signaling pathways in osteosarcoma. This study aimed to investigate the effects of curcumin on osteosarcoma both in vitro and in vivo. To explore the effects and mechanisms of curcumin on osteosarcoma, cells (MNNG/HOS and MG-63) and xenograft mice models were established. Cell viability, cell apoptosis rate, cycle distribution, cell migration, cell invasion, reactive oxygen species, malonaldehyde and glutathione abilities, and protein levels were detected by cell counting kit-8, flow cytometry, wound healing, transwell assay, respectively. Nrf2 and GPX4 expressions were detected using an immunofluorescence assay. Nrf2/GPX4-related protein levels were detected using western blotting. The results showed that curcumin effectively decreased cell viability and increased apoptosis rate. Meanwhile, curcumin inhibited tumor volume in the xenograft model, and Nrf2/GPX4-related protein levels were also altered. Interestingly, the effects of curcumin were reversed by liproxstatin-1 (an effective inhibitor of ferroptosis) and bardoxolone-methyl (an effective activator of Nrf2). Our results indicate that curcumin has therapeutic effects on osteosarcoma cells and a xenograft model by regulating the expression of the Nrf2/GPX4 signaling pathway.

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST). Steps 1-3 of our proposed pipeline improved our previous PSTP model in optimizing individual benefit-to-risk ratio; Step 4 used a decision tree model (DRST) to provide straightforward rules in the initial statin treatment plan; Step 5 aimed to evaluate the efficacy of these decision rules by conducting a clinical trial simulation. We included 107,739 de-identified patient data from Optum Labs Database Warehouse in this study. The final decision rules were compact and efficient, resulting from a decision tree with only a maximum depth of 3 and 11 nodes. The DRST identified three factors that are easily obtainable at the point of care: age, low-density lipoprotein cholesterol (LDL-C) level, and age-adjusted Charlson score. Moreover, it also identified six subpopulations that can benefit most from these decision rules. In our clinical trial simulations, DRST was found to improve statin benefit in LDL-C reduction by 4.15 percentage points (pp) and reduce risks of statin-associated symptoms (SAS) and statin discontinuation by 11.71 and 3.96 pp, respectively, when compared to the standard of care. Moreover, these DRST results were only less than 0.6 pp suboptimal to PSTP, demonstrating that building DRST that provide transparency with minimal compromise to the maximal benefit-to-risk ratio of statin treatments is feasible.

Palmitoylation, which is mediated by protein acyltransferase (PAT) and performs important biological functions, is the only reversible lipid modification in organism. To study the effect of protein palmitoylation on hypopharyngeal squamous cell carcinoma (HPSCC), the expression levels of 23 PATs in tumor tissues of 8 HPSCC patients were determined, and high mRNA and protein levels of DHHC9 and DHHC15 were found. Subsequently, we investigated the effect of 2-bromopalmitate (2BP), a small-molecular inhibitor of protein palmitoylation, on the behavior of Fadu cells in vitro (50 μM) and in nude mouse xenograft models (50 μmol/kg), and found that 2BP suppressed the proliferation, invasion, and migration of Fadu cells without increasing cell apoptosis. Mechanistically, the effect of 2BP on the transduction of BMP, Wnt, Shh, and FGF signaling pathways was tested with qRT-PCR, and its drug target was explored with western blotting and acyl-biotinyl exchange assay. Our results showed that 2BP inhibited the transduction of the FGF/ERK signaling pathway. The palmitoylation level of Ras protein decreased after 2BP treatment, and its distribution in the cell membrane structure was reduced significantly. The findings of this work reveal that protein palmitoylation mediated by DHHC9 and DHHC15 may play important roles in the occurrence and development of HPSCC. 2BP is able to inhibit the malignant biological behaviors of HPSCC cells, possibly via hindering the palmitoylation and membrane location of Ras protein, which might, in turn, offer a low-toxicity anti-cancer drug for targeting the treatment of HPSCC.

This study aims to construct and evaluate a deep learning model, utilizing ultrasound images, to accurately differentiate benign and malignant thyroid nodules. The objective includes visualizing the model's process for interpretability and comparing its diagnostic precision with a cohort of 80 radiologists. We employed ResNet as the classification backbone for thyroid nodule prediction. The model was trained using 2096 ultrasound images of 655 distinct thyroid nodules. For performance evaluation, an independent test set comprising 100 cases of thyroid nodules was curated. In addition, to demonstrate the superiority of the artificial intelligence (AI) model over radiologists, a Turing test was conducted with 80 radiologists of varying clinical experience. This was meant to assess which group of radiologists' conclusions were in closer alignment with AI predictions. Furthermore, to highlight the interpretability of the AI model, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the model's areas of focus during its prediction process. In this cohort, AI diagnostics demonstrated a sensitivity of 81.67%, a specificity of 60%, and an overall diagnostic accuracy of 73%. In comparison, the panel of radiologists on average exhibited a diagnostic accuracy of 62.9%. The AI's diagnostic process was significantly faster than that of the radiologists. The generated heat-maps highlighted the model's focus on areas characterized by calcification, solid echo and higher echo intensity, suggesting these areas might be indicative of malignant thyroid nodules. Our study supports the notion that deep learning can be a valuable diagnostic tool with comparable accuracy to experienced senior radiologists in the diagnosis of malignant thyroid nodules. The interpretability of the AI model's process suggests that it could be clinically meaningful. Further studies are necessary to improve diagnostic accuracy and support auxiliary diagnoses in primary care settings.

The incidence and mortality rates of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are gradually increasing worldwide. Numerous studies have demonstrated that N-methyl-D-aspartic acid receptor (NMDAR)-mediated excitotoxicity contributes to neurodegenerative diseases. Ifenprodil, a subtype-selective NMDAR antagonist, showed strong therapeutic potential. However, it suffers from low oral bioavailability and off-target side effects. In this study, natural compounds were identified for selective inhibition of GluN1/GluN2B NMDAR of human. We obtained a set of natural compounds (n = 81,366) from COCONUT, TIPdb, PAMDB, CMNPD, YMDB, and NPAtlas databases, and then virtually screened by an ifenprodil-structure-based pharmacophore model and molecular docking. The top 100 compounds were selected for binding affinity prediction via batch drug-target affinity (BatchDTA). Then, the top 50 compounds were analyzed by absorption, distribution, metabolism, excretion, toxicity (ADMET). Molecular dynamics involving molecular mechanics/position-Boltzmann surface area (MM-PBSA) calculation were performed to further screening. The top 15 compounds with strong binding affinity and ifenprodil, a proven GluN2B-selective NMDAR antagonist, were subjected to molecular dynamic simulations (100 ns), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), H-bonds, solvent accessible surface area (SASA), principal component analysis (PCA), and binding free energy calculations. Based on these analyses, one possible lead compound carrying positive charges (CNP0099440) was identified, with great binding affinity and less off-target activity by contrast to ifenprodil. CNP0099440 has great potential to be a GluN1/GluN2B NMDAR antagonist candidate and can be further detected via in vitro and in vivo experiments.

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly growing field with the possibility to be utilized in practice. Deep learning has received much attention in detecting AD from structural magnetic resonance imaging (sMRI). However, training a convolutional neural network from scratch is problematic because it requires a lot of annotated data and additional computational time. Transfer learning can offer a promising and practical solution by transferring information learned from other image recognition tasks to medical image classification. Another issue is the dataset distribution's irregularities. A common classification issue in datasets is a class imbalance, where the distribution of samples among the classes is biased. For example, a dataset may contain more instances of some classes than others. Class imbalance is challenging because most machine learning algorithms assume that each class should have an equal number of samples. Models consequently perform poorly in prediction. Class decomposition can address this problem by making learning a dataset's class boundaries easier. Motivated by these approaches, we propose a class decomposition transfer learning (CDTL) approach that employs VGG19, AlexNet, and an entropy-based technique to detect AD from sMRI. This study aims to assess the robustness of the CDTL approach in detecting the cognitive decline of AD using data from various ADNI cohorts to determine whether comparable classification accuracy for the two or more cohorts would be obtained. Furthermore, the proposed model achieved state-of-the-art performance in predicting mild cognitive impairment (MCI)-to-AD conversion with an accuracy of 91.45%.

The mammalian target of rapamycin (mTOR) inhibitors, everolimus (but not dactolisib), is frequently associated with lung injury in clinical therapies. However, the underlying mechanisms remain unclear. Endothelial cell barrier dysfunction plays a major role in the pathogenesis of the lung injury. This study hypothesizes that everolimus increases pulmonary endothelial permeability, which leads to lung injury. We tested the effects of everolimus on human pulmonary microvascular endothelial cell (HPMEC) permeability and a mouse model of intraperitoneal injection of everolimus was established to investigate the effect of everolimus on pulmonary vascular permeability. Our data showed that everolimus increased human pulmonary microvascular endothelial cell (HPMEC) permeability which was associated with MLC phosphorylation and F-actin stress fiber formation. Furthermore, everolimus induced an increasing concentration of intracellular calcium Ca²⁺ leakage in HPMECs and this was normalized with ryanodine pretreatment. In addition, ryanodine decreased everolimus-induced phosphorylation of PKCα and MLC, and barrier disruption in HPMECs. Consistent with in vitro data, everolimus treatment caused a visible lung-vascular barrier dysfunction, including an increase in protein in BALF and lung capillary-endothelial permeability, which was significantly attenuated by pretreatment with an inhibitor of PKCα, MLCK, and ryanodine. This study shows that everolimus induced pulmonary endothelial hyper-permeability, at least partly, in an MLC phosphorylation-mediated EC contraction which is influenced in a Ca²⁺-dependent manner and can lead to lung injury through mTOR-independent mechanisms.