Experimental Biology and Medicine最新文献

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.

In this study, we aimed to investigate the effect of paraoxonase 1 (PON1) rs662 polymorphism, arylesterase (ARE) activity, and the serum lipid profile in patients with coronavirus disease 2019 (COVID-19) in different stages of the disease considering post-COVID outcomes. A total of 470 COVID-19 patients (235 female and 235 male patients) were recruited into the study, and based on the World Health Organization (WHO) criteria, the patients were divided into three groups: moderate, severe, and critical. PON1 rs662 polymorphism was determined by the Alw 1 enzyme followed by agarose gel electrophoresis. Moreover, serum levels of triglycerides (TG), cholesterol (Chol), high-density lipoprotein-cholesterol (HDL-c), and low-density lipoprotein-cholesterol (LDL-c), as well as the level of the ARE activity of PON1 in the sera of patients were measured at the time of infection and one and three months after hospitalization. There was a significant relationship between the G allele and the severity of the disease. In addition, the probability of death in homozygous individuals (GG) was higher than in heterozygous patients (GA), and it was higher in heterozygous patients than in wild-type individuals (AA). There was also a significant relationship between the decrease in serum lipids and the intensity of COVID-19. On the contrary, at the onset of the disease, the HDL-c level and serum ARE activity were reduced compared to one and three months after COVID-19 infection. The findings of this study indicated the significant impact of PON1 rs662 polymorphism on ARE activity, lipid profiles, disease severity, and mortality in COVID-19 patients.

Renal ischemia-reperfusion injury (IRI) is a common clinical complication of multiple severe diseases. Owing to its high mortality and the lack of effective treatment, renal IRI is still an intractable problem for clinicians. Itaconate, which is a metabolite of cis-aconitate, can exert anti-inflammatory and antioxidant roles in many diseases. As a derivative of itaconate with high cell membrane permeability, 4-octyl itaconate (4-OI) could provide a protective effect for various diseases. However, the role of 4-OI in renal IRI is still unclear. Herein, we examined whether 4-OI afforded kidney protection through attenuating endoplasmic reticulum stress (ERS) via nuclear factor erythroid-2-related factor 2 (Nrf2) pathway. To observe the effects of 4-OI on alleviating renal pathologic injury, improving renal dysfunction, decreasing inflammatory cytokines, and reducing oxidative stress, we utilized C57BL/6J mice with bilateral renal pedicle clamped and HK-2 cells with hypoxia/reoxygenation (H/R) exposure in our study. In addition, through western blot assay, we found 4-OI ameliorated renal IRI-induced ERS, and activated Nrf2 pathway. Moreover, Nrf2-knockout (KO) mice and Nrf2 knockdown HK-2 cells were used to validate the role of Nrf2 signaling pathway in 4-OI-mediated alleviation of ERS caused by renal IRI. We demonstrated that 4-OI relieved renal injury and suppressed ERS in wild-type mice, while the therapeutic role was not shown in Nrf2-KO mice. Similarly, 4-OI could exert cytoprotective effect and inhibit ERS in HK-2 cells after H/R, but not in Nrf2 knockdown cells. Our in vivo and in vitro studies revealed that 4-OI protected renal IRI through attenuating ERS via Nrf2 pathway.

Ventilator-associated pneumonia (VAP) is a serious complication in neonates requiring mechanical ventilation. This study aimed to determine the risk factors associated with the development of VAP in neonates admitted to the neonatal intensive care unit (NICU) of the Affiliated Hospital of Southwest Medical University. In a retrospective observational study, neonates admitted to the NICU from 1 January 2019, to 31 December 2021, requiring ventilation for more than 48 h were included. Neonates who died within 48 h of NICU admission, those without obtainable consent, or identified with a genetic syndrome were excluded. Various neonatal and clinical variables were evaluated. Univariate and multivariate analyses were performed to determine risk factors associated with VAP. Of the total neonates included, several risk factors were identified for VAP, such as being a premature infant and use of dexamethasone and sedatives. Moreover, reintubation was found to decrease the risk of VAP. Some factors like gestational age, birth weight, Apgar scores at 5 min, and other parameters were found not significantly associated with the development of VAP. The study identified several risk factors associated with the development of VAP in neonates. Recognizing these risk factors could help in the prevention and early management of VAP, thus improving the prognosis for these patients. Further studies are needed to validate these findings and explore the mechanistic links between these factors and VAP.

N6-methyladenosine (m6A) RNA methylation plays a pivotal role in immune responses and the onset and advancement of cancer. Nonetheless, the precise impact of m6A modification in lung adenocarcinoma (LUAD) and its associated tumor microenvironment (TME) remains to be fully elucidated. Here, we distinguished distinct m6A modification patterns within two separate LUAD cohorts using a set of 21 m6A regulators. The TME characteristics associated with these two patterns align with the immune-inflamed and immune-excluded phenotypes, respectively. We identified 2064 m6A-related genes, which were used as a basis to divide all LUAD samples into three distinct m6A gene clusters. We applied a scoring system to evaluate the m6A gene signature of the m6A modification pattern in individual patients. To authenticate the categorization significance of m6A modification patterns, we established a correlation between m6A score and TME infiltration profiling, tumor somatic mutations, and responses to immunotherapy. A high level of m6A modification may be associated with the aggressiveness and poor prognosis of LUAD. Further studies should investigate the mechanism of action of m6A regulators and m6A-related genes to improve the diagnosis and treatment of patients with LUAD.