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Lysine acetylation is a reversible posttranslational modification of proteins and plays a key role in regulating gene expression. Technological limitations have so far prevented a global analysis of lysine acetylation's cellular roles. We used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 proteins and quantified acetylation changes in response to the deacetylase inhibitors suberoylanilide hydroxamic acid and MS-275. Lysine acetylation preferentially targets large macromolecular complexes involved in diverse cellular processes, such as chromatin remodeling, cell cycle, splicing, nuclear transport, and actin nucleation. Acetylation impaired phosphorylation-dependent interactions of 14-3-3 and regulated the yeast cyclin-dependent kinase Cdc28. Our data demonstrate that the regulatory scope of lysine acetylation is broad and comparable with that of other major posttranslational modifications.

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80–85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity.

The recent boom in the spread of false information on social media and web platforms has emerged as a worldwide threat to public opinion, social coherence, and democratic establishments. Traditional fact checking strategies are not sufficient to address the scale and speed of disinformation spreading. So, scalable, automatic, and intelligent fake news detection systems are now in high demand. In this paper, we present a new hybrid model named Graph-Augmented Transformer Ensemble (GETE) for efficient and scalable fake news detection. The primary objective of GETE is to leverage both linguistic and relational features of news spreading by integrating transformer-based language models with graph neural networks (GNNs) with a meta-learned ensemble strategy. The proposed architecture combines the semantic strength of transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly Optimized BERT Pretraining Approach) with the structure understanding provided by GNNs constructed from user-news interactions and source credibility graphs. The fusion module based on meta-learning is used to train the fusion of these heterogeneous modalities to allow dynamic weighting based on the characteristics of the input data. The combination of deep contextual language understanding and graph-based relational modeling produces synergistic advantages in detection accuracy and generalization. Experimental evaluations on benchmarking datasets FakeNewsNet and LIAR demonstrate GETE’s better performance than existing state-of-the-art methods. Specifically, GETE achieves 96.5% accuracy, 96.5% F1-score, and ROC-AUC of 97.3%, boosting F1-score by 4.2% and AUC by 5.6% over high-performing baseline methods. Additionally, proposed model demonstrates enhanced scalability, explainable predictions, and robustness across diversified domains and source distributions. The integration of the meta-ensemble module facilitates adaptive decision-making, hence enabling enhanced detection performance in real-world noisy situations. “With its high performance, explainability, and scalability, the GETE framework presents a solid foundation for the next generation of reliable and adaptive fake news detection systems.

Polymeric hydrogels have drawn considerable attention as a biomedical material for their unique mechanical and chemical properties, which are very similar to natural tissues. Among the conventional hydrogel materials, self-healing hydrogels (SHH) are showing their promise in biomedical applications in tissue engineering, wound healing, and drug delivery. Additionally, their responses can be controlled via external stimuli (e.g., pH, temperature, pressure, or radiation). Identifying a suitable combination of viscous and elastic materials, lipophilicity and biocompatibility are crucial challenges in the development of SHH. Furthermore, the trade-off relation between the healing performance and the mechanical toughness also limits their real-time applications. Additionally, short-term and long-term effects of many SHH in the in vivo model are yet to be reported. This review will discuss the mechanism of various SHH, their recent advancements, and their challenges in tissue engineering, wound healing, and drug delivery.

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.

Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80–85% of all renal tumors. Traditional diagnosis of kidney cancer requires manual examination and analysis of histopathology images, which is time-consuming, error-prone, and depends on the pathologist’s expertise. Recently, deep learning algorithms have gained significant attention in histopathology image analysis. In this study, we developed an efficient and robust deep learning architecture called RenalNet for the classification of subtypes of RCC from kidney histopathology images. The RenalNet is designed to capture cross-channel and inter-spatial features at three different scales simultaneously and combine them together. Cross-channel features refer to the relationships and dependencies between different data channels, while inter-spatial features refer to patterns within small spatial regions. The architecture contains a CNN module called multiple channel residual transformation (MCRT), to focus on the most relevant morphological features of RCC by fusing the information from multiple paths. Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. As a part of this study, we also introduced a novel benchmark dataset for the classification of subtypes of RCC from kidney histopathology images. We obtained digital hematoxylin and eosin (H&E) stained WSIs from The Cancer Genome Atlas (TCGA) and acquired region of interest (ROIs) under the supervision of experienced pathologists resulted in the creation of patches. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on three well-known datasets. Compared to the best-performing state-of-the-art model, RenalNet achieves accuracies of 91.67%, 97.14%, and 97.24% on three different datasets. Additionally, the proposed method significantly reduces the number of parameters and FLOPs, demonstrating computationally efficient with 2.71 × FLOPs & 0.2131 × parameters.

To date, cardiotocography (CTG) is the only non-invasive and cost-effective tool available for continuous monitoring of the fetal health. In spite of a marked growth in the automation of the CTG analysis, it still remains a challenging signal processing task. Complex and dynamic patterns of fetal heart are poorly interpreted. Particularly, the precise interpretation of the suspected cases is fairly low by both visual and automated methods. Also, the first and second stage of labor produce very different fetal heart rate (FHR) dynamics. Thus, a robust classification model takes both stages into consideration separately. In this work, the authors propose a machine-learning-based model, which was applied separately to both the stages of labor, using standard classifiers such as SVM, random forest (RF), multi-layer perceptron (MLP), and bagging to classify the CTG. The outcome was validated using the model performance measure, combined performance measure, and the ROC-AUC. Though AUC-ROC was sufficiently high for all the classifiers, the other parameters established a better performance by SVM and RF. For suspicious cases the accuracies of SVM and RF were 97.4% and 98%, respectively, whereas sensitivity was 96.4% and specificity was 98% approximately. In the second stage of labor the accuracies were 90.6% and 89.3% for SVM and RF, respectively. Limits of agreement for 95% between the manual annotation and the outcome of SVM and RF were (−0.05 to 0.01) and (−0.03 to 0.02). Henceforth, the proposed classification model is efficient and can be integrated into the automated decision support system.

New or mild heart failure (HF) is mainly caused by left ventricular dysfunction. We hypothesised that gene expression differ between the left (LV) and right ventricle (RV) and secondly by type of LV dysfunction. We compared gene expression through myocardial biopsies from LV and RV of patients undergoing elective coronary bypass surgery (CABG). Patients were categorised based on LV ejection fraction (EF), diastolic function and NT-proBNP into pEF (preserved; LVEF ≥ 45%), rEF (reduced; LVEF < 45%) or normal LV function. Principal component analysis of gene expression displayed two clusters corresponding to LV and RV. Up-regulated genes in LV included natriuretic peptides NPPA and NPPB, transcription factors/coactivators STAT4 and VGLL2, ion channel related HCN2 and LRRC38 associated with cardiac muscle contraction, cytoskeleton, and cellular component movement. Patients with pEF phenotype versus normal differed in gene expression predominantly in LV, supporting that diastolic dysfunction and structural changes reflect early LV disease in pEF. DKK2 was overexpressed in LV of HFpEF phenotype, potentially leading to lower expression levels of β-catenin, α-SMA (smooth muscle actin), and enhanced apoptosis, and could be a possible factor in the development of HFpEF. CXCL14 was down-regulated in both pEF and rEF, and may play a role to promote development of HF.

Technological developments make mass spectrometry (MS)-based proteomics a central pillar of biochemical research. MS has been very successful in cell culture systems, where sample amounts are not limiting. To extend its capabilities to extremely small, physiologically distinct cell types isolated from tissue, we developed a high sensitivity chromatographic system that measures nanogram protein mixtures for 8 h with very high resolution. This technology is based on splitting gradient effluents into a capture capillary and provides an inherent technical replicate. In a single analysis, this allowed us to characterize kidney glomeruli isolated by laser capture microdissection to a depth of more than 2,400 proteins. From pooled pancreatic islets of Langerhans, another type of \"miniorgan,\" we obtained an in-depth proteome of 6,873 proteins, many of them involved in diabetes. We quantitatively compared the proteome of single islets, containing 2,000-4,000 cells, treated with high or low glucose levels, and covered most of the characteristic functions of beta cells. Our ultrasensitive analysis recapitulated known hyperglycemic changes but we also find components up-regulated such as the mitochondrial stress regulator Park7. Direct proteomic analysis of functionally distinct cellular structures opens up perspectives in physiology and pathology.

Cannabidiol (CBD), derived from Cannabis sativa, has gained remarkable attention for its potential therapeutic applications. This thorough analysis explores the increasing significance of CBD in treating neurological conditions including epilepsy, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease, which present major healthcare concerns on a worldwide scale. Despite the lack of available therapies, CBD has been shown to possess a variety of pharmacological effects in preclinical and clinical studies, making it an intriguing competitor. This review brings together the most recent findings on the endocannabinoid and neurotransmitter systems, as well as anti-inflammatory pathways, that underlie CBD’s modes of action. Synthesized efficacy and safety assessments for a range of neurological illnesses are included, covering human trials, in vitro studies, and animal models. The investigation includes how CBD could protect neurons, control neuroinflammation, fend off oxidative stress, and manage neuronal excitability. This study emphasizes existing clinical studies and future possibilities in CBD research, addressing research issues such as regulatory complications and contradicting results, and advocates for further investigation of therapeutic efficacy and ideal dose methodologies. By emphasizing CBD’s potential to improve patient well-being, this investigation presents a revised viewpoint on its suitability as a therapeutic intervention for neurological illnesses.