Metabolic dysfunction-associated steatotic liver disease (MASLD) has rapidly become the leading cause of chronic liver disease and cirrhosis worldwide, driven by the global surge in metabolic disorders such as obesity, diabetes, hypertension, and dyslipidemia. In parallel, heart failure with preserved ejection fraction (HFpEF) has surpassed heart failure with reduced ejection fraction (HFrEF) as the predominant form of heart failure, particularly in individuals with metabolic comorbidities. Mounting evidence points to a significant overlap in the pathophysiological underpinnings of MASLD and HFpEF, with metabolic dysfunction serving as a common foundation. This review synthesizes current knowledge on the mechanistic links between MASLD and HFpEF, examining metabolic, inflammatory, and fibrotic pathways. We also explore the clinical implications of this association, including diagnostic considerations and therapeutic targets. Shared risk factors and inflammatory pathways have highlighted a strong bidirectional association between MASLD and cardiovascular diseases, particularly HFpEF. Significantly, the degree of hepatic fibrosis in MASLD correlates with HFpEF prognosis and severity, emphasizing the systemic nature of these conditions. Emerging pharmacological and lifestyle-based interventions aimed at managing both conditions underscore the importance of integrated, multidisciplinary care in improving long-term outcomes.
BACKGROUND:Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI-assisted CE in the diagnosis of small bowel lesions in comparison to CE. METHODS:Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta-analyses, and case series reporting outcomes on AI-assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta-analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta-Analysis v4.0 (Biostat Inc.) was used for the analysis of the data. RESULTS:A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925-0.988), whereas for AI-assisted CE, it was 0.9185 (95% CI: 0.9138-0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786-0.934) compared with AI-assisted CE at 0.9239 (95% CI: 0.8648-0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976-0.987), whereas AI-assisted CE had a PPV of 0.8928 (95% CI: 0.7554-0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996-0.999) compared with 0.5367 (95% CI: 0.5244-0.5492) for AI-assisted CE. Negative predictive values were higher in AI-assisted CE at 0.9425 (95% CI: 0.9389-0.9462) versus 0.760 (95% CI: 0.577-0.943) for conventional CE. CONCLUSION:AI-assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.
Lipotoxicity is one of the causes for the progression of fatty liver in chronic hepatitis (CH) towards end-stage liver diseases. The role of miRNAs in the signalling pathways of lipid metabolism has been studied, but their direct targets in this pathway have not been identified yet. Here, we have characterized a downregulated miRNA in CH namely miR-451a, which has a direct impact on the lipid metabolism pathway. Liver tissue samples and blood were collected from CHC/CHB patients and normal individuals. Huh7 and SNU449 cell lines were used for in vitro assays. Expressions of miRNA/mRNAs and proteins were confirmed by qRT-PCR and immuno-blot analysis. Oil Red O staining, Colorimetric, and Fluorometric assay kit were used to quantify triglyceride (TG) and cholesterol from tissue and serum, respectively. Target prediction and pathway analysis were performed using Targetscan, miRWalk, and DAVID respectively. 3’UTR-Luciferase assay and Co-immuno-precipitation were conducted to determine direct interaction between miRNA-mRNA and protein-protein, respectively. Unpaired two-tailed Student’s t-test and Mann-Whitney test were employed as required using GraphPad prism. P < 0.05 was considered as significant. The miRNA, miR-451a was selected as one of the downregulated miRNAs in progressive liver disease stages of CHC and CHB. Target identification and pathway analysis of this miRNA revealed that lipid metabolism pathway gene, glycerol kinase (GK), could be the target of this miRNA. Subsequent 3’UTR Luciferase assay and immuno-blot analysis confirmed the binding of miR-451a to GK. Though both hepatitis viruses, HCV and HBV, could alter the lipid metabolism pathways, intracellular TG and cholesterol content were observed to be significantly higher upon HCV infection only. It also suppressed the expression of miR-451a, resulting in overshooting of GK expression. GK interacted positively with the transcription factor SREBP1, which led to overexpression of Fatty acid synthase, Acetyl- CoA Carboxylase, and Stearoyl-CoA desaturase. As a result, intracellular fatty acids, TG, and cholesterol synthesis and accumulation heightened but trafficking dropped, resulting in hypo-cholesterolemia in blood. While, restoration of miR-451a impeded lipid accumulation, reduced steatohepatitis and suppressed HCV replication as well. These findings suggest that the alteration in the hepatic lipid profile upon HCV/HBV infection is attributed to the downregulation of miR-451a, which has the potential to restrict the expression of GK and SREBP1 in the TG biosynthesis pathway, implying that supplementation of miR-451a may be a potential therapeutic strategy for impeding CHC.
Early detection of dental problems, such as Furcation Radiolucency (FR), plays a vital role in ensuring effective treatment and maintaining oral health, particularly in pediatric dentistry. FR, often associated with deep dental decay, can be identified across various dental radiographic modalities, typically appearing as a dark, radiolucent area between the tooth roots. However, accurate interpretation of extraoral and intraoral dental radiographs can be challenging due to the subtle nature of early lesions and variability in image quality. This review explores the transformative potential of Artificial Intelligence (AI)- driven Computer-Aided Diagnosis (CAD) systems, which enhance the detection and analysis of FR, offering clear advantages over traditional methods. AI technologies, particularly Machine Learning (ML) and Deep Learning (DL), enhance critical stages of dental radiographic analysis, including image preprocessing, segmentation, and classification. These advancements enable early identification of subtle radiographic changes, reducing the need for invasive treatments and fostering more proactive treatment planning. The paper provides a comprehensive review of both traditional diagnostic techniques and recent AI-driven innovations, highlighting their impact on improving dental image quality, segmentation precision and classification accuracy. Focusing on powerful AI models such as U-Net, Mask Region-based Convolutional Neural Network (R-CNN), and Vision Transformers (ViTs), along with lightweight deep Convolutional Networks (ConvNets) like MobileNetV2 or EfficientNetV2, the review highlights the potential of these systems to identify dental problems more effectively and facilitate efficient clinical decision-making. Additionally, the paper addresses ongoing challenges, including the need for large-scale validation and multi-modal data integration, and offers actionable insights for researchers and dental practitioners to further leverage AI in pediatric dental care. This review bridges the gap between traditional diagnostic practices and AI-enhanced methods, underscoring the future potential of AI to revolutionize dental diagnostics and treatment planning.
BACKGROUND AND AIMS:To prospectively evaluate the diagnostic performance of a rapid abbreviated noncontrast MRI (AMRI) protocol compared to ultrasound (US) for HCC surveillance in a high-risk population with cirrhosis. APPROACH AND RESULTS:This prospective, single-center, diagnostic accuracy study (ClinicalTrials.gov: NCT05716620) enrolled patients with cirrhosis and annual HCC risk >5%. Participants underwent paired screening with US and noncontrast AMRI across 2 rounds, 6 months apart. Patients with positive findings on either imaging modality or clinical suspicion of HCC underwent multiphasic contrast-enhanced MRI (CE-MRI) as the reference standard. The primary outcome was the HCC detection rate (per-patient sensitivity) comparing AMRI and US. In 614 paired screening examinations across 404 patients, 97 underwent CE-MRI (based on positive screening), identifying 39 HCCs in 37 patients. AMRI demonstrated significantly superior sensitivity (94.6% [95% CI: 83.3-98.9] vs. 51.4% [95% CI: 34.7-67.8]; p <0.001) and specificity (96.6% [95% CI: 88.9-99.5] vs. 69.5% [95% CI: 55.8-80.8]; p <0.001). AUROC was 0.956 [95% CI: 0.913-0.997] vs. 0.604 [95% CI: 0.469-0.738] ( p <0.001). In the per-lesion analysis, AMRI detected 37 of 39 lesions (94.9%) versus US 20 of 39 (51.3%). Of HCCs detected by AMRI, 97.3% were early-stage Barcelona Clinic Liver Cancer Staging System 0 or A]. Interobserver agreement was "almost perfect" for AMRI (κ=0.929) versus "moderate" for US (κ=0.631). CONCLUSIONS:A rapid, noncontrast AMRI protocol shows superior per-patient sensitivity compared to US for HCC detection in patients with cirrhosis under surveillance. While these diagnostic findings are encouraging, prospective trials evaluating patient-level outcomes are essential before definitive guideline recommendations can be made.
