Objectives: The aim of this study was to present the baseline results of a pilot project conducted to evaluate the effectiveness of lung cancer screening using low-dose chest computed tomography (CT) in regions with excessive radon levels in the Republic of Kazakhstan.Methods: In total, 3671 participants were screened by low-dose chest CT. Current, former, and never-smokers who resided in regions with elevated levels of radon in drinking water sources and indoor air, aged between 40 and 75 with no history of any cancer, and weighing less than 140 kg were included in the study. All lung nodules were categorized according to the American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS 1.0).Results: Overall, 614 (16.7%) participants had positive baseline CT findings (Lung-RADS categories 3 and 4). Seventy-four cancers were detected, yielding an overall cancer detection rate of 2.0%, with 10.8% (8/74) stage I and a predominance of stage III (59.4%; 44/74). Women never-smokers and men current smokers had the highest cancer detection rates, at 2.9% (12/412) and 6.1% (12/196), respectively. Compared to never-smokers, higher odds ratios (ORs) of lung cancer detection were found in smokers (OR,2.48; 95% confidence interval [CI], 1.52 to 4.05, p<0.001) and former smokers (OR, 2.32; 95% CI, 1.06 to 5.06, p=0.003). The most common histologic type of cancer was adenocarcinoma (58.1%).Conclusions: Implementation of low-dose CT screening for lung cancer in regions with elevated radon levels is an effective method for both smokers and never-smokers.

Introduction: Increased natural killer cells activity (NKCA) is linked to the reduced risk of colorectal cancer (CRC). Several prior studies investigated the association of NKCA and the incidence of CRC in high-risk subjects. The aim of our study was to investigate NKCA sensitivity in...

Early detection of breast cancer is critical for improving patient outcomes, and deep learning (DL) has shown promise in mammographic analysis. However, many existing systems tackle a single task (e.g., malignancy classification) and provide limited interpretability, which hinders clinical adoption. We present a multi-task, attention-guided framework that jointly predicts malignancy (benign vs. malignant) and BI-RADS breast density (A–D) from a single mammogram while producing interpretable attention maps. The architecture couples a pretrained convolutional backbone with lightweight channel–spatial attention and two task-specific heads. We evaluate six backbones (ResNet-50, DenseNet-121, EfficientNet-B0/B3/B7, MobileNetV3-Large) under a rigorous evaluation protocol with stratified hold-out split (64%/16%/20%) and 5-fold cross-validation. To support reliable deployment, we report comprehensive metrics including discrimination measures (ROC-AUC, PR-AUC, F1-score, sensitivity, specificity), calibration metrics (Brier score, Expected Calibration Error), and ablation studies contrasting single-task vs. multi-task learning and models with vs. without attention mechanisms. Across all evaluation protocols, multi-task learning consistently outperforms single-task baselines by 2.8% in pathology accuracy and 3.5% in density estimation, indicating that density cues provide complementary information for malignancy assessment. Among backbones, EfficientNet-B3 attains the strongest discrimination (AUC=0.962, accuracy=93.6%) on hold-out test with excellent calibration (Brier=0.010), while MobileNetV3-Large achieves competitive performance (AUC=0.940, accuracy=91.2%) with only 4.26M parameters—an optimal accuracy–efficiency trade-off for resource-constrained deployment. Cross-validation results demonstrate robust generalization (AUC=0.954 ± 0.003, accuracy=93.0 ± 0.3%). Attention maps highlight diagnostically relevant regions including architectural distortions and suspicious calcifications, offering interpretable visual rationale aligned with radiological assessment. We discuss limitations including dataset size (410 images), class imbalance in rare density categories, and calibration challenges (ECE=0.365–0.419), and outline directions for external validation on multi-institutional cohorts and integration of uncertainty quantification methods.

Introduction To assess magnetic resonance imaging (MRI) features of rectal carcinoma in correlation with pathology, and to evaluate the diagnostic accuracy of 3-Tesla MRI. Material and methods A total of 86 patients were included. 3T MR imaging was conducted pre-operatively, with imaging...

Background/Aims: The aim of our study was to determine the efficacy of computed tomography colonography (CTC) in screening for colorectal cancer (CRC).Methods: A total of 612 females and 588 males aged 45 to 75 years were enrolled in CTC screening. CTC was performed following standard bowel preparation and colonic insufflation with carbon dioxide. The main outcomes were the detection rate of CRC and advanced adenoma (AA), prevalence of colorectal lesions in relation to socio-demographic and health factors, and overall diagnostic performance of CTC.Results: Overall, 56.5% of the 1,200 invited subjects underwent CTC screening. The sensitivity for CRC and AA was 0.89 and 0.97, respectively, while the specificity was 0.71 and 0.99, respectively. The prevalence of CRC and AA was 3.0% (18/593) and 7.1% (42/593), respectively, with the highest CRC prevalence in the 66-75 age group (≥12 times; odds ratio [OR], 12.11; 95% confidence interval [CI], 4.45-32.92). CRC and AA prevalence were inversely correlated with Asian descent, physical activity, and negative fecal immunochemical test results (OR=0.43; 95% CI, 0.22-0.83; OR=0.16; 95% CI, 0.04-0.68; OR=0.5; 95% CI, 0.07-3.85, respectively).Conclusions: Our study revealed high accuracy of CTC in diagnosing colonic neoplasms, good compliance with CTC screening, and high detection rate of CRC.

The persistent evolution of SARS-CoV-2 necessitates novel antiviral strategies. This study evaluated the anti-HIV prodrugs tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF) for repurposing against SARS-CoV-2, assessing key pharmacological indices (CC₅₀, EC₅₀, cytostatic effect, and therapeutic window). In vitro screening in Vero E6 cells measured cytotoxicity (via CCK-8/MTT assays) and antiviral activity against Kazakh B.1 and Wuhan strains. TDF (50 µg/mL) reduced high viral loads (MOI 2) by ~2 log₁₀ (100% inhibition), with minimal cytotoxicity (≥75% viability). TAF achieved near-complete suppression (100% inhibition) at 50 µg/mL, exhibiting dose-dependent inhibition (68–100%) at lower viral loads (MOI 0.01). Both prodrugs showed enhanced antiviral activity with prolonged exposure (96 h). Synergy assessments demonstrated favourable combination indices (CI < 1). Electron microscopy confirmed virion integrity post-treatment. These findings highlight TDF and TAF as promising candidates against SARS-CoV-2, with particular potential for targeting lymphoid reservoirs—sites implicated in persistent viral reservoirs that may contribute to long COVID pathogenesis. Further clinical validation is warranted.