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Prosthesis-induced pathological calcification is a significant challenge in biomaterial applications and is often associated with various reconstructive medical procedures. It is uncertain whether the fibrous extracellular matrix (ECM) adjacent to biomaterials directly triggers osteogenic trans-differentiation in nearby cells. To investigate this possibility, we engineered a heterogeneous polystyrene fibrous matrix (PSF) designed to mimic the ECM. Our findings revealed that the myoblasts grown on this PSF acquired osteogenic properties, resulting in mineralization both in vitro and in vivo. Transcriptomic analyses indicated a notable upregulation in the expression of the long noncoding RNA metastsis-associated lung adenocarcinoma transcript 1 (Malat1) in the C2C12 myoblasts cultured on PSF. Intriguingly, silencing Malat1 curtailed the PSF-induced mineralization and downregulated the expression of bone morphogenetic proteins (Bmps) and osteogenic markers. Further, we found that PSF prompted the activation of Yap1 signaling and epigenetic modifications in the Malat1 promoter, crucial for the expression of Malat1. These results indicate that the fibrous matrix adjacent to biomaterials can instigate Malat1 upregulation, subsequently driving osteogenic trans-differentiation in myoblasts and ectopic calcification through its transcriptional regulation of osteogenic genes, including Bmps. Our findings point to a novel therapeutic avenue for mitigating prosthesis-induced pathological calcification, heralding new possibilities in the field of biomaterial-based therapies.

Abstract This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer’s features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p

This study identifies senescent osteocytes in the femur and tibia of young rodents and explores their role in bone remodeling. The proximity of osteoclasts to senescent osteocytes was observed, which is a new finding. Cultured osteocytes, sorted using a podoplanin antibody in FACS, exhibited osteocytic characteristics and increased senescence-related genes. Senescent osteocytes secreted cytokines associated with senescence, remodeling, and inflammation. Notably, IGF1 and MMP2 were elevated in podoplanin-positive (pdpn+) osteocytes. Migration assays demonstrated significant osteoclast precursor migration towards senescent osteocytes, further confirmed by co-culture experiments leading to osteoclast differentiation. These findings suggest that senescent osteocytes have a pivotal role in initiating bone resorption, with recruitment of osteoclast precursors during early bone remodeling stages. In conclusion, our research enhances our understanding of complicated bone remodeling mechanisms and bone homeostasis.

Abstract Background To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. Results We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. Conclusions Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.

General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.

Abstract The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

Abstract Background Accumulating evidence suggests that tobacco smoking affects the susceptibility to and severity of chronic periodontitis. Epigenetics may explain the role of smoking in the development and progress of periodontal disease. In this study, we performed transcriptomic and methylomic analyses of non-periodontitis and periodontitis-affected gingival tissues according to smoking status. Methods Human gingival tissues were obtained from 20 patients, including non-smokers with and without periodontitis (n = 5 per group) and smokers with and without periodontitis (n = 5 per group). Total RNA and genomic DNA were isolated, and their quality was validated according to strict standards. The Illumina NextSeq500 sequencing system was used to generate transcriptome and methylome datasets. Results Comprehensive analysis, including between-group correlation, differential gene expression, DNA methylation, gene set enrichment, and protein-protein interaction, indicated that smoking may change the transcription and methylation states of extracellular matrix (ECM) organization-related genes, which exacerbated the periodontal condition. Conclusions Our results suggest that smoking-related changes in DNA methylation patterns and subsequent alterations in the expression of genes coding for ECM components may be causally related to the increased susceptibility to periodontitis in smokers as they could influence ECM organization, which in turn may have an effect on disease characteristics.

Objectives The aim of this paper was evaluating the ratios of electrical impedance measurements reported in previous studies through a correlation analysis in order to explicit it as the contributing factor to the accuracy of electronic apex locator (EAL). Materials and Methods The literature regarding electrical property measurements of EALs was screened using Medline and Embase. All data acquired were plotted to identify correlations between impedance and log-scaled frequency. The accuracy of the impedance ratio method used to detect the apical constriction (APC) in most EALs was evaluated using linear ramp function fitting. Changes of impedance ratios for various frequencies were evaluated for a variety of file positions. Results Among the ten papers selected in the search process, the first-order equations between log-scaled frequency and impedance were in the negative direction. When the model for the ratios was assumed to be a linear ramp function, the ratio values decreased if the file went deeper and the average ratio values of the left and right horizontal zones were significantly different in 8 out of 9 studies. The APC was located within the interval of linear relation between the left and right horizontal zones of the linear ramp model. Conclusions Using the ratio method, the APC was located within a linear interval. Therefore, using the impedance ratio between electrical impedance measurements at different frequencies was a robust method for detection of the APC.