Findings of previous studies on associations between dairy consumption and metabolic health status were inconsistent. This study aimed to assess the link between consumption of dairy foods and metabolic health status, brain-derived neurotrophic factor (BDNF) and adropin levels in adults.

Cross-sectional.

An observational study in Isfahan, Iran.

Adults (n=527) selected by multistage cluster random sampling. Dietary intakes were assessed via a validated 168-item food frequency questionnaire.

Anthropometric indices, blood pressure and biochemical parameters were assessed. The criteria proposed by Wildman et al were used to categorise participants into metabolically healthy and metabolically unhealthy (MU).

Participants had a mean age of 42.66 years (45.7% women). Moderate consumption of total dairy was, respectively, linked to 58% lower odds of MU (OR _{T2 vs T1}=0.42; 95% CI 0.18 to 0.96), after taking all confounders into account. Participants in the middle versus low tertile of low-fat dairy intake showed 51% marginally lower odds of MU (OR _{T2 vs T1}=0.49; 95% CI 0.22 to 1.08; p=0.08). No significant association was discovered between high-fat dairy intake and MU chance. However, higher total dairy intake was associated with lower odds of hypertension (OR _{T3 vs T1}=0.36; 95% CI: 0.14 to 0.93). No significant associations were observed between dairy intake and BDNF or adropin levels.

Moderate consumption of total and low-fat dairy was associated with lower odds of being metabolically unhealthy in Iranian adults, but high-fat dairy intake was not. Hypertension was less common among individuals with higher dairy intake. No association was found between dairy intake and serum levels of BDNF or adropin.

With growing interest in applying artificial intelligence (AI) to population breast cancer screening, the evidence base has expanded rapidly. This systematic review aims to systematically review and summarise the published evidence on the use of AI in breast cancer screening.

We conducted a systematic review of primary studies assessing AI for screening mammography, extracting test-accuracy metrics (sensitivity, specificity, recall and cancer detection rates) and workflow outcomes.

We searched the Cochrane Breast Cancer Group Specialised Register, Cochrane CENTRAL, PubMed, Embase (Elsevier), Scopus, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform from January 2012 to June 2025; we also screened reference lists of included studies and relevant reviews. No language restrictions were applied.

Primary studies evaluating AI for screening mammography (digital mammography or digital breast tomosynthesis) in asymptomatic women, assessing AI as a standalone reader or AI-assisted radiologist workflows versus radiologists alone. Eligible designs included randomised trials, prospective paired reader studies, real-world implementation/registry cohorts, retrospective cohorts and multireader-multicase reader studies conducted in population-based or opportunistic screening settings. Key outcomes included diagnostic accuracy metrics (eg, sensitivity, specificity, Area Under the Curve (AUC) and/or programme metrics (cancer detection rate (CDR), recall/abnormal interpretation rate, positive predictive value, arbitration/workload). We excluded protocols, pilot/feasibility studies, case reports, editorials and studies without relevant accuracy or screening outcomes.

Two independent reviewers extracted data and assessed risk of bias. Study quality was appraised with Quality Assessment of Diagnostic Accuracy Studies-2 and an AI-specific critical appraisal tool, and findings were synthesised narratively with stratification by study design and AI integration role.

31 studies met the inclusion criteria, encompassing randomised controlled trials, prospective paired-reader studies, registry-based implementations and retrospective simulations, representing more than two million screening examinations across Europe, Asia, North America and Australia. When used as a second reader or within double-reading workflows, AI generally maintained or modestly increased sensitivity (up to +9 percentage points (PP)) while preserving or improving specificity. Triage and decision-referral configurations delivered the greatest operational benefit, reducing reading volumes by 40–90% while maintaining non-inferior cancer detection when thresholds were conservatively calibrated. Stand-alone AI achieved AUC values comparable to radiologists and similar cancer detection in real-world, non-enriched cohorts, although interval-cancer follow-up remains incomplete in several datasets. In prospective randomised evidence, including the Mammography Screening with Artificial Intelligence trial (MASAI) trial, AI-supported screening achieved higher CDRs (6.4 versus 5.0 per 1000; p=0.0021) with stable or reduced false-positive and recall rates. Across implementation and simulation settings, integration of AI reduced radiologist workload substantially, with triage and band-pass approaches reducing the number of reads by approximately 40–90%. Overall certainty is limited by heterogeneity across study designs, reliance on enriched datasets for some accuracy estimates and incomplete interval-cancer follow-up in several major studies.

Contemporary AI systems show diagnostic performance that is broadly comparable to radiologists and can substantially reduce reading workload, particularly when used as a second reader or triage tool. Emerging prospective evidence supports their safe integration in these roles, although transparent reporting, standardised evaluation and long-term population studies are still required before considering AI as a stand-alone reader. AI may improve workflow efficiency and possibly cancer detection, but definitive evidence on safety, especially interval cancer outcomes, remains essential.

Behavioural intention is a strong predictor of actual behaviour; however, many health interventions fail among individuals with a high intention to adopt healthy behaviours. This discrepancy, known as the intention-behaviour gap, remains a critical challenge in health promotion. The purpose of this study is to present a protocol designed to explore the predictability of perceptual factors that hinder the conversion of intention into weight loss behaviour among young adults.

This study employs a sequential explanatory mixed-methods design, comprising two distinct phases. In the first phase, a quantitative cross-sectional survey with a descriptive-analytical approach will be conducted to assess the frequency of behavioural intention to lose weight and weight loss behaviours. A multistage cluster sampling method will recruit young individuals aged 18–29 years with a body mass index of ≥25 kg/m² in Malekan County, Iran. Data will be collected using a structured questionnaire that includes sociodemographic information, physical activity measurements, a dietary adherence questionnaire and items assessing subjective norms, attitudes, perceived behavioural control and behavioural intention based on the Theory of Planned Behaviour. In the second phase, a qualitative study will be conducted using a purposive sampling strategy to select participants who can provide insights into the quantitative findings. Data collection in this phase will primarily involve in-depth individual interviews. A grounded theory approach will be used to develop a comprehensive understanding of the factors that impede the conversion of intention into behaviour among young individuals.

This protocol has been approved by the Ethics Committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1403.646). Written informed consent will be obtained from all participants. Findings will be disseminated through peer-reviewed publications, conference presentations and reports to relevant health authorities.

Although there are some drugs to control Parkinson’s disease (PD), they have several side effects and cannot control the inflammation and oxidative stress, the leading causes of PD progression. On the other hand, there is a growing interest in herbal medicine as a safe and cheap adjunctive therapy to prevent PD progression. In this regard, limited human studies investigated the effect of saffron, a rich source of antioxidants, on PD. Nevertheless, due to low sample size, saffron dosage and study duration, they could not completely affirm the efficacy of saffron on PD. Therefore, this triple-blind randomised controlled clinical trial aimed to investigate this subject in human patients with a proper sample size, saffron dosage and duration study.

In this parallel, randomised, triple-blind controlled clinical trial, 92 patients with PD will be randomised into two groups to receive either (1) a daily tablet containing 100 mg/d saffron for 12 weeks or (2) placebo tablets for the same duration. The following variables will be assessed before and after the intervention, as the outcomes of interest: serum values of C-reactive protein, total antioxidant capacity, malondialdehyde, glutathione, zonulin, the activity of catalase enzyme, PD stage, symptoms of PD (motor symptoms, non-motor symptoms), quality of life, mental health, sleep quality, cognitive status, anthropometric indices, blood pressure, gastrointestinal symptoms, appetite and fatigue. The intention-to-treat approach will be used for patients who were lost to follow-up. Additionally, to adjust for the potential confounders, a one-way analysis of covariance will be performed.

This study was confirmed by the Ethics Committee of Isfahan University of Medical Sciences with the code of IR.MUI.PHANUT.REC.1402.072. Written informed consent to participate will be obtained from all participants. Final findings will be presented in future publications and scientific congresses.

IRCT20121216011763N61; Pre-results.