Search Results (5,415)

Background: The increasing use of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in patients with breast cancer has led to a growing number of incidentally detected thyroid lesions. Thyroid incidentaloma refers to a focal area of increased metabolic activity within the thyroid gland. Up to 35% of focal thyroid incidentalomas are malignant. Early detection of thyroid carcinoma may allow less invasive surgical management and reduce the need for more extensive treatment associated with advanced disease, including total thyroidectomy, neck dissection, and radioactive iodine therapy. The objective of this study was to estimate the prevalence of focal thyroid incidentaloma and incidental thyroid carcinoma detected on PET/CT scans performed in breast cancer patients at a single tertiary medical center serving a highly ethnically diverse population. Methods: Medical records and PET/CT scans were retrospectively reviewed at a single medical center. Data collected included the presence of thyroid incidentaloma, maximum standardized uptake value (SUVmax), thyroid cytology, primary malignancy type, breast cancer status, ethnicity, and age. A total of 1233 patients with cancer who underwent PET/CT imaging and received treatment at ZIV Medical Center between August 2018 and December 2024 were included. Forty-two patients with primary thyroid carcinoma or head and neck carcinoma were excluded. Patients were categorized into breast cancer and non-breast cancer groups and compared regarding the prevalence of thyroid incidentaloma, referral rates for further evaluation by head and neck specialists, and the rate of incidental thyroid carcinoma. Results: Among 330 patients with breast cancer, 16 (4.8%) had a focal incidental thyroid finding, compared with 24 (2.8%) of 861 patients with non-breast malignancies who underwent PET/CT imaging. Among the 16 breast cancer patients with thyroid incidentaloma, thyroid carcinoma was subsequently confirmed in 4 patients (25%) who underwent further evaluation. The proportion of patients with confirmed thyroid carcinoma among those with incidentalomas was 1.2% (4/330) in the breast cancer group compared with 0.46% (4/861) in the non-breast cancer group. Approximately half of the patients in both groups were referred for further thyroid evaluation. Among breast cancer patients, non-significant trends toward higher referral rates were observed in patients with non-advanced disease, higher SUVs, and Jewish ethnicity. Nearly all patients evaluated by head and neck specialists underwent fine-needle aspiration (FNA). The mean SUV among patients diagnosed with papillary thyroid carcinoma (PTC) on FNA (n = 8; breast and non-breast cancer combined) was 15.2 (IQR: 7.3–17.0), compared with 5.2 (IQR: 5.0–6.0) among patients with benign cytology (n = 5; p = 0.011). Conclusions: Thyroid incidentalomas identified on PET/CT scans in breast cancer patients were relatively common in this cohort, and a proportion of evaluated lesions were subsequently diagnosed as thyroid carcinoma. Referral and diagnostic follow-up rates were variable, with approximately half of patients referred for further evaluation and only one-third attending a head and neck clinic. Given the retrospective design, limited number of biopsy-confirmed cases, and potential verification bias, these findings should be interpreted with caution. Nevertheless, they highlight the importance of clinical awareness and support individualized assessment of PET/CT-detected thyroid incidentalomas rather than broad management recommendations. Full article

Cervical cancer remains a major global public health challenge, and the accurate classification of cervical transformation zones (TZs) constitutes a critical step in early detection and clinical decision-making. However, distinguishing between Type 2 and Type 3 transformation zones remains particularly challenging due to their high morphological similarity and the inherent interobserver variability associated with colposcopic assessment. In this study, we propose a novel Dual-Track Specialist Feature Fusion and Meta-Learning Stacking Ensemble architecture for the automated classification of cervical transformation zones using the Intel & MobileODT Cervical Cancer Screening dataset. The proposed framework integrates a global feature extractor based on ResNet50 (Gatekeeper) with a visual specialist based on InceptionResNetV2, trained exclusively on the most diagnostically ambiguous cases (Type 2 and Type 3). The extracted features are fused and processed through a multi-level stacking scheme composed of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting (GB), XGBoost, and LightGBM classifiers at the base level, followed by an XGBoost meta-learner and a clinically guided probability calibration strategy designed to maximize diagnostic sensitivity. Experimental results demonstrate a peak overall accuracy of 91.22%, substantially outperforming the baseline ResNet50 model (70%). Furthermore, the proposed system achieved Recall values of 0.90, 0.90, and 0.94 for Type 1, Type 2, and Type 3 transformation zones, respectively, highlighting its ability to accurately identify diagnostically challenging cases. Ablation studies, Grad-CAM visualizations, and external-image validation experiments confirm that the proposed architecture improves discrimination between ambiguous categories, learns clinically meaningful representations, and maintains strong generalization capability across heterogeneous scenarios. These findings demonstrate the potential of visual specialization and calibrated meta-learning strategies for the development of artificial intelligence-assisted colposcopic decision-support systems. Full article

Cervical cancer is the fourth most frequent malignancy in women globally. Pap smear screening is important for early cancer detection, but manual smear analysis is time-consuming, labor-intensive and error-prone for diagnosis. Such issues in resource-limited areas have led to the introduction of deep learning (DL) methods for automated cervical cancer diagnosis. But the majority of current methodologies depend on models pretrained on natural image datasets like ImageNet, which may inadequately represent domain-specific pathological characteristics. To mitigate this constraint, this research employs a domain-specific transfer learning algorithm approach using the PathMNIST histopathological dataset to enhance cervical cell classification. An accuracy score of 96.77% is achieved for the proposed YOLO* model, using the SIPAKMED dataset. To the best of available knowledge, no previous study has reported the use of PathMNIST as a pretraining source for cytology image classification. As domain-specific medical pretraining is becoming more popular, our study shows the importance of cross-domain generalization. Full article

Background/Objectives: Gastric precancerous conditions and early gastric cancer represent a heterogeneous disease spectrum with variable malignant potential and complex management pathways. Despite well-established international guidelines, discrepancies remain between recommended strategies and routine clinical practice, particularly regarding endoscopic diagnosis, risk stratification, therapeutic selection, and follow-up. This review aims to synthesize current evidence and provide practice-oriented, question-based guidance for the management of gastric precancerous lesions and early gastric cancer. Methods: A comprehensive review of the literature was conducted using PubMed and Google Scholar, focusing on endoscopic diagnosis, histological risk assessment, therapeutic options, and surveillance strategies for gastric precancerous lesions and early gastric cancer. Key areas of clinical uncertainty and controversy were identified and translated into focused, practice-oriented clinical questions designed to reflect and possibly help to improve real-world gastroenterological practice. Results: Clinical questions were formulated to cover the entire management pathway, from endoscopic detection and characterization to therapeutic decision-making and post-treatment surveillance. Topics include high-quality endoscopic diagnosis, biopsy strategies, histological staging system, selection between endoscopic and surgical therapy, and follow-up according to individual risk profile. For each question, current evidence is summarized into concise, actionable recommendations. Conclusions: Management of gastric precancerous lesions and early gastric cancer requires a structured and individualized approach, integrating high-quality endoscopy, accurate histological risk stratification, and evidence-based therapeutic and surveillance strategies. Organizing available evidence into practice-oriented clinical questions may help harmonize clinical practice, reduce unwarranted variability, and support gastroenterologists in delivering optimal patient-centered care. Full article

Background: Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, necessitates improved non-invasive diagnostic and monitoring tools. Circulating tumor cells (CTCs), as intact cellular biomarkers in liquid biopsies, offer valuable morphological and genetic information and hold significant clinical potential for early detection, prognosis, therapy monitoring, and drug development. Bio-ferrography is a non-invasive immunomagnetic separation technique that isolates magnetically labeled entities from fluid samples onto a glass substrate via a focused external magnetic field. Methods: This study employs, for the first time, bio-ferrography for isolation, counting, and microscopic characterization of circulating tumor cells (CTCs) expressing the human epidermal growth factor receptor (EGFR) from blood biopsies taken from patients in the hospital. Magnetic beads conjugated with anti-EGFR antibodies were used to selectively capture CTCs from peripheral blood samples of patients with metastatic CRC and other epithelial malignancies. The method enabled both enumeration and microscopic characterization of isolated cells. Results: Preliminary clinical results demonstrate that bio-ferrography achieves a sensitivity of 90% in stage-IV patients and exhibits higher true positive detection rates compared to conventional tumor biomarkers, including carbohydrate antigen 19-9 (CA 19-9) and carcinoembryonic antigen (CEA). Conclusions: These findings highlight the potential of bio-ferrography as a robust platform for CTC isolation and analysis. Full article

Gastric cancer is a globally prevalent malignancy, with early detection being pivotal for improving patient survival. While endoscopy remains the diagnostic gold standard, it frequently faces challenges such as missed lesions and operator dependency. Artificial intelligence (AI) has emerged as a powerful tool to address these limitations. This narrative review synthesizes recent evidence from PubMed and Web of Science, focusing on four core functional domains of AI-assisted gastric endoscopy: lesion detection and characterization, margin delineation, invasion depth prediction, and blind-spot monitoring. Furthermore, we summarize current limitations, including single-center data biases and algorithmic “black-box” issues, and discuss future directions such as multimodal data integration and real-time video analysis systems. Ultimately, carefully validated AI represents a vital clinical adjunct that holds great potential to significantly enhance diagnostic accuracy and patient outcomes. Full article

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we report a cyclic olefin copolymer (COC) chip-integrated ZFP array-based molecular sensor that bypasses the need for bisulfite conversion and PCR amplification to recognize the specific site of DNA methylation in the RASSF1A promoter. Building upon the SEER-LAC (SEquence-Enabled Reassembly of β-Lactamase) framework, we engineered a dual-recognition split-enzyme system in which a COC chip-immobilized ZFP array confers sequence specificity while a co-recruited methyl-binding domain (MBD) enforces methylation-dependent gating, together driving the proximity-induced reconstitution of functional β-lactamase at methylated target loci. Accordingly, this sensor specifically reassembles and restores enzymatic activity only in the presence of specific methylated DNA in the RASSF1A promoter region. We demonstrate that this dual-component array effectively differentiates methylation status with high specificity. Given its rapid turnaround and non-PCR-based mechanism, this system can be well-suited for developing diagnostic assays for bladder cancer, offering a potential alternative to conventional epigenetic screening methods. Full article

Background/Objectives: The N-NOSE test is a non-invasive, urine-based multi-cancer screening assay that uses Caenorhabditis elegans chemotaxis toward cancer-associated volatile organic compounds in human urine. Scaling the test from a manual research-grade workflow to a high-throughput clinical service has required automation, and the central question this raises, centering around whether mechanization alters the analytical performance of the test, must be answered with operational, not bench-top, data. Methods: Here, we present a nine-month (January–September 2023) real-world, side-by-side comparison of the two workflows operating under their actual routine clinical laboratory conditions: the manual chemotaxis assay performed by trained technicians at the Fukuoka Research and Development Center (R&D) and the fully automated Chemotaxis Scoring Apparatus (CSA) running continuously at the Tokyo Testing Center. Results: The manual workflow generated 551 paired chemotaxis index (CI) measurements from positive-control (PC) and negative-control (NC) synthetic urine/volatile organic compound (VOC)-mimic reference materials at each of two standard urine dilutions (10⁻¹ and 10⁻²); over the same period, the CSA processed 2448 quality control samples (612 per control type) with both biobank-derived urine-based comparison materials and synthetic volatile organic compound reference standards. Both workflows produced large, highly significant, and quantitatively comparable PC-versus-NC separation under genuine operating conditions (manual: Δ_CI = 0.096 and 0.103; Welch’s t = 19.83 and 21.95; p < 0.0001; Cohen’s d = 1.19 and 1.32; CSA risk scale Δ_P–N = 14.47 with biobank-derived urine-based materials and 10.17 with synthetic VOC standards). The CSA risk score is a linear, monotonic transformation of the CI. Standardized separation is directly comparable across workflows and is concordant (Cohen’s d: manual 1.19–1.32; CSA 0.80–1.44, all large); the manual and automated processes therefore show no meaningful difference in discriminative performance. Because the CSA mechanizes only the handling of worms, samples, and machine-vision counting around an unchanged biological transducer, the live nematode, analytical equivalence is the predicted outcome, and these data confirm it at scale in a real clinical laboratory setting. Conclusions: Automation of the N-NOSE process does not compromise its ability to discriminate cancer from non-cancer urine. These results provide real-world evidence supporting the validity, reproducibility, and reliability of the N-NOSE testing process and large-scale validation studies. Full article

Over the last few decades, survival among patients with lung cancer (LC) has progressively improved due to major advances in treatment strategies, particularly the introduction of immunotherapy and targeted therapies, as well as the increased detection of early-stage disease resulting from the widespread use of chest computed tomography (CT). Although the reduction in mortality, frequently achieved through effective control of the primary disease, represents a major therapeutic success, it also raises new clinical challenges, including the long-term management of cancer remission or disease stability and the competing risk of adverse outcomes related to comorbidities and treatment-related toxicities. Among these, cardiovascular (CV) complications have emerged as particularly relevant because of their frequency and prognostic impact. Within the framework of a holistic long-term management approach, increasing attention should be directed toward non-pharmacological interventions targeting lifestyle factors, particularly nutrition and physical exercise, whose role remains underestimated. These interventions may modulate chronic inflammation and immune responses, which are key drivers influencing both the effectiveness of novel anticancer therapies and the progression of cardiovascular complications. Patients with LC frequently present malnutrition and unfavorable lifestyle patterns associated with substantial physical and psychological stress, factors that may negatively affect treatment outcomes and overall prognosis. This narrative review examines the emerging role of targeted nutritional strategies and structured physical exercise as integral components of supportive care in LC, with a specific focus on their impact on cardiac metabolism, CV risk, and response to anticancer therapies, including immunotherapy. Full article

Pancreatic cancer ranks among the most lethal malignancies, characterized by a five-year survival rate of approximately 10%. This dismal prognosis is largely attributable to diagnoses occurring at advanced stages and the inherent limitations of conventional imaging modalities in detecting early lesions, identifying metastases, and assessing tumor heterogeneity. Consequently, there is a critical need for non-invasive imaging techniques capable of visualizing pancreatic cancer lesions to enable accurate diagnosis, risk assessment, and the development of personalized treatment strategies. Molecular imaging, which combines highly specific targeted probes with advanced imaging technologies, offers the potential to elucidate disease-associated pathways. This review provides a comprehensive overview of recent advancements in molecular imaging platforms for pancreatic cancer, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), optical molecular imaging, photoacoustic imaging, and molecular MRI. We begin by elucidating the biological rationale for targeting key molecules, including fibroblast activation protein (FAP), integrins, and programmed death ligand 1 (PD-L1). Moreover, we critically evaluate the development and clinical translation of these probes, highlighting their ability to enhance lesion detectability, characterize intratumoral heterogeneity, and guide both targeted therapy and surgical resection. Compared with existing reviews, this work uniquely integrates a comprehensive cross-modality analysis of the latest molecular imaging strategies for pancreatic cancer. Furthermore, we examine prevailing challenges and emerging frontiers in this domain, specifically focusing on multimodal hybrid imaging, artificial intelligence-driven analytics, and integrated theranostic platforms as pivotal strategies to advance precision oncology. Full article

Early detection of lung cancer remains challenging due to the extremely low concentrations of disease-specific biomarkers, which limit the development of highly sensitive and reliable point-of-care (PoC) diagnostic devices. Electrochemical biosensors integrating graphene oxide (GO) and gold nanoparticles (AuNPs) have emerged as promising platforms for the rapid, sensitive, and selective detection of lung cancer biomarkers, enabling more timely diagnosis. Biomarkers such as carcinoembryonic antigen (CEA), cytokeratin-19 fragments (CYFRA 21-1), neuron-specific enolase (NSE), and circulating tumour DNA are increasingly investigated for PoC applications since they can be detected in various biological fluids associated with lung cancer. Nanocomposite materials, particularly GO/AuNP hybrids, provide synergistic advantages by combining the large surface area and abundant functional groups of GO for stable immobilization of biorecognition elements with the excellent conductivity and bioconjugation capability of AuNPs that enhance signal transduction. This review critically discusses key biomarker targets for lung cancer, the properties of GO and Au in biosensing, and the role of AuNP/GO nanocomposites in improving biosensor performance. It further examines the application of electrochemical biosensors for lung cancer biomarker detection, highlighting recent developments. Additionally, the review outlines current challenges limiting clinical translation and PoC implementation, provides recommendations to address these barriers, and discusses future perspectives for improving the detection of low-abundance biomarkers for early lung cancer diagnosis. Ultimately, these technologies seem promising for the development of rapid diagnostic tools equivalent to established platforms such as lateral-flow immunoassays. Full article

Persistent high-risk human papillomavirus (HPV) infection drives cervical cancer, a leading cause of cancer-related mortality among women in low- and middle-income countries; its clinical course is shaped by the cervicovaginal microbiome, in which Lactobacillus-dominated communities are associated with enhanced viral clearance. Despite this, vaginal probiotic interventions often demonstrate limited colonization efficiency, and autochthonous strain libraries from Central Asia remain absent. We applied a tiered functional screening workflow to a collection of 235 vaginal lactic acid bacterial isolates recovered from 400 women undergoing routine gynecological examination in Astana, Kazakhstan. The workflow sequentially filtered isolates on (i) antimicrobial activity against seven urogenital indicator pathogens using the deferred antagonism assay, (ii) surface adhesion by the Brilis erythrocyte assay, and (iii) biofilm-forming capacity by crystal violet retention and laser-capture-microdissection (LCM) microscopy. Species-level identification of the selected candidate was performed by whole-genome shotgun sequencing followed by Kraken2 taxonomic classification. From 235 isolates, three rounds of phenotypic filtering identified four broad-spectrum antimicrobial candidates (127-3, 127-4, 107-2, 107-4) with non-overlapping inhibitory profiles against seven urogenital indicator strains. Adhesion phenotyping segregated candidates into low- and moderate-adhesion groups, with none reaching the high-adhesion threshold. Among all four candidates, only strain 127-4 produced a reproducible biofilm-associated signal (crystal violet retention OD490 = 0.09 ± 0.07 at 24 h; 0.08 ± 0.03 at 48 h), consistent with early surface attachment under static conditions. Whole-genome shotgun sequencing assigned 97.81% of classified reads to Lactiplantibacillus plantarum, supporting preliminary identification of the selected isolate as L. plantarum strain 127-4. Composite ranking confirmed 127-4 as the only isolate combining broad antimicrobial activity (5/7 indicators), moderate adhesion (specific adhesion index, SPA = 2.95), and a detectable biofilm-associated phenotype. We report the first systematic functional screening of autochthonous cervicovaginal lactic acid bacteria from a Central Asian population and identify L. plantarum 127-4 as a probiotic candidate with an integrated trait profile rarely identified through single-criterion screening approaches. Beyond candidate identification, this work establishes a transferable workflow for assembling functionally annotated vaginal Lactobacillus collections from underrepresented populations, providing a foundation for future population-specific probiotic interventions targeting cervicovaginal health. Full article

Background/Objectives: The global health crisis of the COVID-19 pandemic in 2020 severely impacted healthcare systems, particularly affecting cancer patients, including those with lung cancer. Understanding the pandemic’s effects on lung cancer diagnosis is important for providing guidelines for future similar crises. Methods: A comprehensive search was conducted using the SCOPUS and PubMed databases, including keywords such as ‘COVID-19 pandemic,’ ‘lung cancer,’ ‘diagnosis,’ and ‘screening.’ In the initial screening phase, studies were selected based on their title and abstract, followed by the removal of duplicates. A second round of full-text screening was then performed using predefined inclusion and exclusion criteria. Specific checklists were applied to ensure methodological quality. Results: Out of 564 articles, 78 met the inclusion criteria and were grouped according to changes in lung cancer incidence, screening and diagnostic procedures, and staging during the COVID-19 pandemic compared to pre-pandemic. Multiple studies reported an average decline of 20% in newly diagnosed lung cancer cases, alongside significant decreases in screenings, follow-ups, and diagnostic procedures. In contrast, only two studies reported an increase in lung cancer incidence, and three studies reported increases in screening or diagnostic activity. Eight studies reported a shift toward more advanced-stage diagnoses and fewer early-stage detections, while four studies observed a decrease in advanced-stage lung cancer during the pandemic compared to pre-pandemic years. Conclusions: Studies show that overall, the COVID-19 pandemic disrupted lung cancer diagnosis in many countries, highlighting the need for stronger healthcare systems that can support crisis response and equitable care. Full article

Background: Esophageal cancer is a highly lethal malignancy, the prognosis of which depends largely on early diagnosis. Artificial intelligence (AI) has emerged as a promising tool to enhance endoscopic detection and characterization of early esophageal cancer. This scoping review aims to map and synthesize the available evidence regarding the diagnostic performance and clinical utility of artificial intelligence systems applied to upper gastrointestinal endoscopy for the detection and characterization of premalignant squamous lesions and early-stage esophageal squamous cell carcinoma (ESCC). Methods: A scoping review was conducted according to Arksey and O’Malley, Levac, Joanna Briggs Institute, and PRISMA-ScR recommendations. The review question focused on patients with premalignant lesions or early ESCC, artificial intelligence-based diagnostic systems, and upper gastrointestinal endoscopy. Searches were performed in PubMed, Scopus, and Embase. Original studies reporting sensitivity, specificity, accuracy, AUC, or F1-score were included. Results: A total of 30 publications were included, consisting mainly of retrospective observational and diagnostic test studies (26/30; 86.7%), followed by randomized clinical trials (3/30; 10.0%) and a multicenter validation study (1/30; 3.3%). The studies were predominantly from China (18/30; 60%), followed by Japan (8/30; 26.7%), Taiwan (3/30; 10%), and the United Kingdom + Taiwan (1/30; 3%). Automatic lesion detection was predominant (21/30; 70.0%), followed by diagnostic classification (11/30; 36.7%), while segmentation (3/30; 10.0%), histological prediction (2/30; 6.7%), estimation of invasion depth (3/30; 10.0%), and lesion delineation (1/30; 3.3%) were evaluated less frequently, and in some cases combined within the same model. The most used endoscopic imaging modalities were narrow-band imaging (23/30; 76.7%) and white light endoscopy (20/30; 66.7%), followed by magnifying endoscopy with narrow-band imaging (5/30; 16.7%), blue light imaging (2/30; 6.7%), and hyperspectral imaging (1/30; 3.3%). Conclusions: Available studies suggest that AI has the potential to achieve high diagnostic performance under controlled conditions. However, the current evidence is derived predominantly from single-center retrospective studies using selected high-quality static images, with limited external, prospective, and real-world validation. Full article

Background: The D-Health Trial, by far the largest trial investigating the impact of vitamin D supplementation on infection-related hospitalizations, did not find any protective effects. However, this trial was conducted in a mostly vitamin D-replete population of older adults from Australia. Objectives: We aimed to derive trial results that would have been expected if the trial had been conducted in a vitamin D-insufficient or -deficient population. Methods: Our analyses are based on data from the UK Biobank cohort. Participants meeting the D-Health Trial eligibility criteria (n = 185,809) were either weighted to match the trial’s baseline 25-hydroxy-vitamin D (25(OH)D) distribution (mean 77 nmol/L [30.8 ng/mL]) or restricted to those with insufficiency or deficiency (25(OH)D < 50 nmol/L [<20 ng/mL] or <30 nmol/L [<12 ng/mL], respectively). A 38 nmol/L (15.2 ng/mL) increase in 25(OH)D was assumed, as observed in the trial. Infection-related hospitalizations were identified via International Classification of Diseases, 10th Revision (ICD-10) codes. Incidence rate ratios (IRRs), adjusted for a comprehensive list of potential confounders, were estimated using negative binomial models over a follow-up period of 5.7 years, corresponding to the median follow-up time of the D-Health Trial. Results: In analyses reflecting the vitamin D-replete trial population, no protective association was observed (IRR 1.08, 95% CI 0.99–1.17), consistent with the reported trial results. In contrast, among participants with baseline 25(OH)D < 50 nmol/L, a 38-nmol/L increase in 25(OH)D was associated with lower risks of hospitalization for any infection (IRR 0.85, 95% CI 0.80–0.90), with even stronger associations for participants with baseline 25(OH)D < 30 nmol/L (IRR 0.79, 95% CI 0.73–0.86). Findings were consistent across infection types, sex, and body mass index (BMI). Conclusions: Null findings in the vitamin D-replete D-Health trial population were to be expected. Substantial protective effects might have been expected in vitamin D-insufficient or -deficient populations. Full article