Postpartum thyroiditis (PPT) is a common autoimmune sequela of pregnancy, affecting 5-10% of women, a considerable percentage of whom ultimately develop lifelong hypothyroidism. The disorder's characteristic symptoms, such as debilitating tiredness and cognitive impairment, are frequently misattributed to the routine challenges of new parenthood, leading to delayed diagnosis and adverse effects on maternal quality of life and newborn development. Evidence suggests that the currently utilized reactive, clinic-based model of care may be insufficient to address this significant and often overlooked public health concern.The fundamental paradigm change toward a proactive, community-based treatment strategy to secure long-term maternal thyroid health. We synthesize available evidence on PPT pathophysiology, including the post-delivery immunological rebound, and examine the potential role of diet and micronutrient supervision in both primary and secondary prevention. We detail the function of essential micronutrients, including the delicate balance of iodine, the antioxidant protection provided by selenium, and the necessity of iron for thyroid hormone production. Available data suggest that while high iodine consumption is a modifiable risk factor, dietary shortages in these and other key nutrients may significantly exacerbate thyroid dysfunction.This review outlines a comprehensive Community-Based Care (CBC) framework that transcends conventional clinical settings, advocating for the integration of telehealth with trained community health professionals to provide systematic screening, education, and professional dietary oversight. By prioritizing the identification of at-risk women and implementing consistent, personalized nutritional management, this methodology offers a robust approach to proactive prevention, thereby reducing the significant long-term burden of PPT and improving maternal health outcomes.

COVID-19 continues to be associated with substantial morbidity and mortality, and understanding vaccine-induced immune regulation beyond antibody responses remains important. This study characterized cytokine profiles in peripheral blood mononuclear cells (PBMC) and neutrophils following SARS-CoV-2 vaccination with Pfizer-BioNTech (mRNA-based) and AstraZeneca (adenoviral vector-based) vaccines in Kuwait. The study included 200 adult participants: 100 unvaccinated healthy controls; 18 recipients of two Pfizer-BioNTech doses; 32 recipients of two AstraZeneca doses; and 50 recipients of a third Pfizer-BioNTech dose. Blood samples were collected at 0-2 months and 3-6 months after vaccination, and cytokines were measured in mitogen-stimulated PBMC and neutrophil culture supernatants. Both vaccine platforms induced measurable cytokine responses, but the cytokine patterns differed according to vaccine type, cell population, and time after vaccination. Pfizer-BioNTech vaccination was associated with an early Th2-skewed profile in PBMC, while AstraZeneca vaccination showed broader activation of Th1/Th2/Th17-associated cytokines. IL-6 and IL-10 decreased over time in several comparisons; however, these findings should be interpreted with caution because cytokine concentrations are dynamic and may vary with sampling time, cell type, and stimulation conditions. Overall, vaccine-induced cytokine profiles waned by 3-6 months after primary vaccination, whereas the third Pfizer-BioNTech dose enhanced several PBMC cytokine responses. These findings support the immunomodulatory effects of SARS-CoV-2 vaccines and highlight the value of booster vaccination for sustaining cellular immune responsiveness.

Radiomics makes it possible to extract quantitative data from medical images, revealing details about tissue heterogeneity that are not apparent from visual evaluation alone. Conventional evaluation of temporomandibular joint (TMJ) imaging still primarily relies on qualitative morphological criteria that primarily detect late-stage structural alterations. This narrative review summarizes current evidence on quantitative imaging of the mandibular condyle and subchondral bone within the TMJ, with emphasis on radiomics applications using computed tomography (CT), cone-beam computed tomography (CBCT), and magnetic resonance imaging (MRI). Studies have shown that texture and morphometric features derived from CT and CBCT can reflect trabecular organization and subchondral remodelling, with some parameters validated against micro-computed tomography in experimental settings. MRI-based approaches are particularly sensitive to changes in subchondral bone marrow, especially in inflammatory and degenerative conditions, even when morphological alterations are subtle or absent. Radiomic features can detect patterns associated with joint degeneration and adaptation across imaging modalities. However, variability in acquisition protocols, segmentation methods, and limited longitudinal data still restrict their clinical application. At present, quantitative imaging should be considered a research tool that may support the development of imaging biomarkers and contribute to a better understanding of TMJ disease.

Different degrees of chronic metabolic inflammation and adiponectin resistance significantly contribute to the development of cardiovascular diseases and metabolic syndrome. Adiponectin is a unique pleiotropic adipokine with anti-inflammatory, anti-atherogenic, and anti-diabetic effects. In early metabolic dysfunction, adiponectin levels increase as a compensatory response, whereas in advanced coronary disease, hypoadiponectinemia develops due to adiponectin resistance. The aim of this review is to highlight how contemporary cardiovascular therapies alter adiponectin levels and why this is clinically important for reducing low-grade systemic and vascular inflammation in chronic coronary syndromes. Evidence suggests that drug classes such as PPAR agonists, SGLT2 inhibitors, GLP-1 receptor agonists, RAAS blockers, metformin, certain statins, and fibrates may increase adiponectin levels, although effects vary by drug class and disease stage. Although adiponectin represents a promising therapeutic target for the treatment of coronary syndromes, further experimental and pharmacological research is necessary to develop drugs based on its mechanism of action.

Aberrant DNA methylation of tumor suppressor genes (TSGs) plays a critical role in chronic myeloid leukemia (CML) pathogenesis. SOCS-3, a TSG, regulates various proliferative pathways, including MAPK signaling. Enhancing expression of TSGs through demethylation provides a potential target for cancer treatment. One of Nigella Sativa's main ingredients, thymoquinone (TQ), has proven anticancer properties. However, the mechanisms driving TQ's antileukemic influences were not entirely clarified. This research aimed to assess TQ's abilities to demethylate and re-express SOCS-3 in CML mice model. A nude mouse model of CML was established, and the apoptotic influence of TQ was identified. TQ's impact on the target gene expressions was examined using RT-qPCR, and SOCS-3 methylation was assessed by pyrosequencing. TQ's effects on ERK1/2 phosphorylation were assessed using Jess Simple Western analysis. Molecular docking of TQ's interaction with the anti-apoptotic proteins. TQ exerted a marked hypomethylating influence on SOCS-3 and reduced ERK1/2 phosphorylation level in K562-xenograft tumours. TQ also significantly upregulated Caspase-3, Caspase-9, Bim, Bax, Bak, PUMA, and Bmf and downregulated BCL-2, BCL-w, BCL-xL, MCL-1, BFL-1, Survivin, and XIAP in K562 cells. TQ also exhibited marked inhibitory properties on BFL-1, BCL-xL, MCL-1, and BCL-2. This led to growth inhibition and apoptosis of K562-tumor xenograft. These results indicate that TQ enhances CML cells' apoptosis by hypomethylating and re-expressing SOCS-3, inhibiting ERK1/2 and modulating the apoptosis-regulator gene expression and by showing consistent interaction with most of anti-apoptotic proteins, enhancing its potential as a scaffold for designing novel inhibitors to combat CML cells.

Artificial intelligence is increasingly embedded within radiology workflows. In radiology, large language models may support reporting and communication tasks, while machine learning and deep learning models are increasingly used for image classification, segmentation, and quantitative analysis. This article is an educational review and practical guide rather than an experimental study. It summarizes how convolutional neural networks work, outlines the key stages of model development and validation, and discusses how modern language models may assist with code prototyping and troubleshooting. Although these tools may reduce technical barriers, clinically useful models still require appropriate datasets, external validation, transparent reporting, governance, and human oversight. This review aims to help radiologists understand the principles, potential, and limitations of convolutional neural networks in contemporary practice.