Unraveling the miRNA Puzzle in Atherosclerosis: Revolutionizing Diagnosis, Prognosis, and Therapeutic Approaches.

PURPOSE OF REVIEW: To eradicate atherosclerotic diseases, novel biomarkers, and future therapy targets must reveal the burden of early atherosclerosis (AS), which occurs before life-threatening unstable plaques form. The chemical and biological features of microRNAs (miRNAs) make them interesting biomarkers for numerous diseases. We summarized the latest research on miRNA regulatory mechanisms in AS progression studies, which may help us use miRNAs as biomarkers and treatments for difficult-to-treat diseases. RECENT FINDINGS: Recent research has demonstrated that miRNAs have a regulatory function in the observed changes in gene and protein expression during atherogenesis, the process that leads to atherosclerosis. Several miRNAs play a role in the development of atherosclerosis, and these miRNAs could potentially serve as non-invasive biomarkers for atherosclerosis in various regions of the body. These miRNAs have the potential to serve as biomarkers and targets for early treatment of atherosclerosis. The start and development of AS require different miRNAs. It reviews new research on miRNAs affecting endothelium, vascular smooth muscle, vascular inflammation, lipid retention, and cholesterol metabolism in AS. A miRNA gene expression profile circulates with AS everywhere. AS therapies include lipid metabolism, inflammation reduction, and oxidative stress inhibition. Clinical use of miRNAs requires tremendous progress. We think tiny miRNAs can enable personalized treatment.

Deciphering signaling pathway interplay via miRNAs in malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly invasive form of lung cancer that adversely affects the pleural and other linings of the lungs. MPM is a very aggressive tumor that often has an advanced stage at diagnosis and a bad prognosis (between 7 and 12 months). When people who have been exposed to asbestos experience pleural effusion and pain that is not explained, MPM should be suspected. After being diagnosed, most MPM patients have a one- to four-year life expectancy. The life expectancy is approximately six months without treatment. Despite the plethora of current molecular investigations, a definitive universal molecular signature has yet to be discovered as the causative factor for the pathogenesis of MPM. MicroRNAs (miRNAs) are known to play a crucial role in the regulation of gene expression at the posttranscriptional level. The association between the expression of these short, non-coding RNAs and several neoplasms, including MPM, has been observed. Although the incidence of MPM is very low, there has been a significant increase in research focused on miRNAs in the past few years. In addition, miRNAs have been found to have a role in various regulatory signaling pathways associated with MPM, such as the Notch signaling network, Wnt/ß-catenin, mutation of KRAS, JAK/STAT signaling circuit, protein kinase B (AKT), and Hedgehog signaling pathway. This study provides a comprehensive overview of the existing understanding of the roles of miRNAs in the underlying mechanisms of pathogenic symptoms in MPM, highlighting their potential as viable targets for therapeutic interventions.

Decoding the role of miRNAs in oral cancer pathogenesis: A focus on signaling pathways.

Oral cancer (OC) is the predominant type originating in the head and neck region. The incidence of OC is mostly associated with behavioral risk factors, including tobacco smoking and excessive alcohol intake. Additionally, there is a lower but still significant association with viral infections such as human papillomaviruses and Epstein-Barr viruses. Furthermore, it has been observed that heritable genetic variables are linked to the risk of OC, in addition to the previously mentioned acquired risk factors. The current absence of biomarkers for OC diagnosis contributes to the frequent occurrence of advanced-stage diagnoses among patients. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs, and circular RNAs, have been observed to exert a significant effect on the transcriptional control of target genes involved in cancer, either through direct or indirect mechanisms. miRNAs are a class of short ncRNAs that play a role in regulating gene expression by enabling mRNA degradation or translational repression at the post-transcriptional phase. miRNAs are known to play a fundamental role in the development of cancer and the regulation of oncogenic cell processes. Notch signaling, PTEN/Akt/mTOR axis, KRAS mutation, JAK/STAT signaling, P53, EGFR, and the VEGFs have all been linked to OC, and miRNAs have been shown to have a role in all of these. The dysregulation of miRNA has been identified in cases of OC and is linked with prognosis.

Feasibility and outcomes of living-donor liver transplantation utilizing the right hemi-liver graft with portal vein anatomical variations.

PURPOSE: Portal vein (PV) reconstruction is a key factor for successful living-donor liver transplantation (LDLT). Anatomical variations of right PV (RPV) are encountered among potential donors. METHODS: To evaluate a single center experience of reconstruction techniques for the right hemi-liver grafts with PV variations during the period between May 2004 and 2022. RESULTS: A total of 915 recipients underwent LDLT, among them 52 (5.8%) had RPV anatomical variations. Type II PV was found in 7 cases (13.5%), which were reconstructed by direct venoplasty. Type III PV was found in 27 cases (51.9%). They were reconstructed by direct venoplasty in 2 cases (3.8%), Y graft interposition in 2 cases (3.8%), and in situ double PV anastomoses in 23 cases (44.2%). Type IV PV was found in 18 cases (34.6%) and was reconstructed by Y graft interposition in 9 cases (17.3%), and in situ double PV anastomoses in 9 cases (17.3%). Early right posterior PV stenosis occurred in 2 recipients (3.8%). Early PV thrombosis occurred in 3 recipients (5.8%). The median follow-up duration was 54.5 months (4 - 185). The 1-, 3-, and 5-years survival rates were 91.9%, 86%, and 81.2%, respectively. Late PV stenosis occurred in 2 recipients (3.8%) and was managed conservatively. CONCLUSION: Utilization of potential living donors with RPV anatomic variations may help to expand the donor pool. We found that direct venoplasty and in situ dual PV anastomoses techniques were safe, feasible, and associated with successful outcomes.

Melodic maestros: Unraveling the role of miRNAs in the diagnosis, progression, and drug resistance of malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly lethal form of pleural cancer characterized by a scarcity of effective therapeutic interventions, resulting in unfavorable prognoses for afflicted individuals. Besides, many patients experience substantial consequences from being diagnosed in advanced stages. The available diagnostic, prognostic, and therapeutic options for MPM are restricted in scope. MicroRNAs (miRNAs) are a subset of small, noncoding RNA molecules that exert significant regulatory influence over several cellular processes within cell biology. A wide range of miRNAs have atypical expression patterns in cancer, serving specific functions as either tumor suppressors or oncomiRs. This review aims to collate, epitomize, and analyze the latest scholarly investigations on miRNAs that are believed to be implicated in the dysregulation leading to MPM. miRNAs are also discussed concerning their potential clinical usefulness as diagnostic and prognostic biomarkers for MPM. The future holds promising prospects for enhancing diagnostic, prognostic, and therapeutic modalities for MPM, with miRNAs emerging as a potential trigger for such advancements.

Artificial Intelligence and Deep Learning of Head and Neck Cancer.

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning.

PURPOSE: To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification. PATIENTS AND METHODS: In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests. RESULTS: The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % $92.9\%$ (confidence interval [CI]: 78.9 % -- 99.5 % $78.9\%\text{--}99.5\%$ ), 95.8 % $95.8\%$ (CI: 87.4 % -- 99.7 % $87.4\%\text{--}99.7\%$ ), 93 % $93\%$ (CI: 80.7 % -- 99.5 % $80.7\%\text{--}99.5\%$ ), 96 % $96\%$ (CI: 88.8 % -- 99.7 % $88.8\%\text{--}99.7\%$ ), 92.8 % $92.8\%$ (CI: 83.5 % -- 98.5 % $83.5\%\text{--}98.5\%$ ), and 95.5 % $95.5\%$ (CI: 88.8 % -- 99.2 % $88.8\%\text{--}99.2\%$ ), respectively, using the LOSO cross-validation approach. CONCLUSION: The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN.

Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.