BACKGROUND Artificial intelligence (AI)-based algorithms are being implemented in breast screening to detect breast cancers on mammographic images. We aimed to apply an epidemiological approach to demonstrate how a cancer detection algorithm can be leveraged as an intermediate-term predictor of breast cancer (current and 4-year risk) to deliver greater risk-based personalisation in screening mammography. METHODS In this population cohort study, we used detection scores from an AI cancer detection algorithm (BRAIx AI Reader), which was calibrated using a training dataset of 397 648 women aged 40 years to 97 years from women who screened at BreastScreen Victoria, Australia between Jan 1, 2016, and Dec 31, 2017, to create a woman-specific mammography-based score for breast cancer risk, the BRAIx risk score. Subsequently, the BRAIx risk score was evaluated on an independent test dataset of women from BreastScreen Victoria, Australia, comprising a random population cohort of 96 348 women who screened from Jan 1, 2016, to Dec 31, 2017, aged 40 years to 74 years, and an independent, external dataset from woman screened at Karolinska University Hospital, Stockholm, Sweden. We applied logistic regression, using the BRAIx risk score to estimate risks of invasive breast cancers on the test dataset: (1) detected at cohort entry (n=525); and (2) for women given an all clear, diagnosed during the next 4 years either at future screens (n=790) or during intervals between screens (n=308). We also trained full multivariate risk models (logistic regression and elastic net) using the training dataset and evaluated their predictive performance on the test and external validation data, with assessment of familial aspects of the BRAIx risk score achieved with inference about causation from examining changes in regression coefficients in an innovative statistical analysis framework. FINDINGS In both Australian and Swedish test datasets, the BRAIx risk score predicted cancer detection at cohort entry and future cancer risk (all p<0·0001). The BRAIx risk score was the strongest tested explanatory factor for cancer detection at cohort entry (odds ratio 13·80 [95% CI 9·54-20·80] in Australian data; 8·89 [3·19-37·49] in Swedish data) and for intermediate-term cancer risk (2·29 [2·13-2.47] in Australian data; 2·15 [1·85-2·50] in Swedish data). We found that adding a thresholded binary version of the BRAIx risk score significantly improved model fit (p<2·2 × 10-16, Australian and Swedish data) and women with BRAIx risk scores of more than 2 were significantly at many-fold increased risk of intermediate-term cancer than women below that threshold (12·34 [7·33-20·91], Australia; 44·7 [11·9-184·9], Sweden; p<0·0001). For the top 2% of women given an all clear with the highest BRAIx risk score, the probability of a cancer diagnosis within 4 years was 9·7%. The BRAIx risk score explained 23% of why family history predicts 4-year risk (p<0·0001). After fitting the BRAIx risk score in a multivariate model, mammographic density was no longer significantly associated with breast cancer risk in the Australian test data (p>0·05) and became associated with lower risk for intermediate-term cancer in the external Swedish test dataset (0·83 [0·73-0·95]). INTERPRETATION The BRAIx risk score is a strong intermediate-term predictor of breast cancer (current to 4-year risk). Calibrating the score on a training dataset produces population-specific probabilities for calculating individual-specific risk scores for screening clients based on their mammogram images. These risk scores enable future development of personalised screening pathways to transform population breast cancer screening and save lives. Identification of women given an all clear but at very high risk, similar to those carrying BRCA1 and BRCA2 mutations, could reveal insights into both familial and non-familial causes of breast cancer. FUNDING Australian Government Medical Research Future Fund, the Ramaciotti Foundation, the National Breast Cancer Foundation, Cancer Australia, and the National Health and Medical Research Council.

Shigellosis remains a significant global cause of infectious colitis, increasingly complicated by multidrug-resistant strains and the microbiota-disrupting effects of broad-spectrum antibiotics. Although conventional antimicrobial therapy can reduce symptom duration and bacterial shedding, it also contributes to gut dysbiosis, loss of colonization resistance, and further selection for antimicrobial resistance. These challenges have renewed interest in precision antimicrobial strategies, particularly bacteriophage therapy, which provides strain-level specificity and preserves the gut microbiota. This narrative review evaluates the biological rationale, preclinical and early clinical evidence, safety considerations, and translational challenges associated with bacteriophage therapy targeting Shigella spp. The historical development and mechanistic basis of phage therapy are summarized, with emphasis on the advantages of obligately lytic phages, receptor-specific targeting, self-amplification at infection sites, and activity against both planktonic and biofilm-associated bacteria. Recent microbiota research indicates that shigellosis is closely associated with early and persistent disruption of gut ecology, including depletion of short-chain fatty acids-producing taxa and reduced microbial resilience. Phage-based approaches may reduce pathogen burden while preserving beneficial microbial communities. Evidence from in vitro systems, animal models, human intestinal organoids, and a Phase 1 clinical trial demonstrates targeted efficacy and favorable safety profiles for Shigella-specific phages and phage cocktails. Major barriers to clinical adoption include immune interactions, phage resistance dynamics, genomic safety screening, regulatory classification, and the need for standardized susceptibility testing. Future directions emphasize the development of personalized phage therapy platforms that integrate rapid diagnostics, phage libraries, metagenomics, and artificial intelligence-assisted matching to enable scalable, precision treatment.

Introduction: Stroke is a leading cause of long-term disability, and early functional prognostication is essential for individualized rehabilitation planning. Objective: The objective of the study is to examine the association between bedside motor and dexterity tests and standard outcome measures, and to evaluate their predictive value for functional independence in post-stroke patients. Materials and methods: This observational study was conducted at the Neurology Clinic, University Clinical Center Sarajevo, Sarajevo, Bosnia and Herzegovina, and included 61 patients with either ischemic or hemorrhagic stroke. Sociodemographic and clinical data were collected. Bedside functional assessments comprised the National Institutes of Health Stroke Scale (NIHSS), the Motor Assessment Scale (MAS), the Nine-Hole Peg Test (9-HPT) for upper limb dexterity, and the Berg Balance Scale (BBS). Functional outcomes were evaluated using the Barthel Index and the modified Rankin Scale (mRS) at admission and discharge. Associations between bedside assessments and functional outcomes were analyzed using correlation analyses, and independent predictors of functional independence were identified using multivariable linear regression models. Results: The mean age of participants was 74.8 ± 5.5 years, and 73.8% had ischemic stroke. Functional independence improved during hospitalization, with the Barthel Index increasing from 65.3 ± 22.9 at admission to 72.1 ± 26.5 at discharge. In multivariable regression analysis, the MAS emerged as the strongest independent predictor of functional independence measured by the Barthel Index (R² = 0.88, β = 1.49; p < 0.001). Performance on the 9-HPT using the non-dominant hand provided additional independent predictive value (β = -0.28; p = 0.048), while dominant-hand dexterity and balance performance were not significant predictors in the adjusted model. Conclusions: Simple bedside motor and dexterity assessments provide clinically relevant prognostic information in the early post-stroke phase. Their integration into routine clinical practice may enhance functional prognostication and support individualized rehabilitation strategies.

Objective This study aimed to examine the associations between objective functional performance tests, clinical disability, MRI lesion characteristics, and recent relapse activity with basic and instrumental activities of daily living (ADLs/IADLs) in people with multiple sclerosis (MS). Materials and methods In this cross-sectional study including 65 patients with MS, clinical disability was assessed using the Expanded Disability Status Scale (EDSS), while functional performance was evaluated with the Timed 25-Foot Walk test (T25FW) and the 9-Hole Peg Test (9-HPT). Functional independence was assessed using the Barthel Index for basic ADL and the Lawton IADL scale. MRI lesion characteristics and relapse activity during the preceding two years were recorded. Associations were analyzed using Spearman correlation and multivariable linear regression models. Results Higher EDSS scores and worse T25FW and 9-HPT performance were associated with lower Barthel and IADL scores. In adjusted models, T25FW remained independently associated with basic ADL, while non-dominant hand 9-HPT was the strongest independent predictor of IADL; EDSS showed a weaker independent association with IADL. MRI lesion variables and recent relapse activity were not independently associated with functional independence. Conclusions Simple performance-based measures of gait speed and upper limb dexterity are strongly associated with real-life functional independence in MS and may contribute to a more comprehensive functional assessment in routine clinical practice.

BACKGROUND Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. METHODS Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. FINDINGS In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (-11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by -29·9% (-33·6 to -25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. INTERPRETATION The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. FUNDING Gates Foundation, St Jude Children's Research Hospital.

Background Community pharmacies must balance public health obligations with economic sustainability. However, integrated methods that jointly manage medical and non-medical inventory in community pharmacies in LMICs are limited. Objective To develop and apply a dual-matrix model separating medical from non-medical products into operational control categories and introducing a High–Medium–Low profitability (HML-P) classification. Methods We conducted a retrospective, descriptive analysis of all items handled in six community pharmacies in the Republic of Srpska, Bosnia and Herzegovina, during the analyzed 2022 year (12-month period) (n = 10,541). Medical products were classified by Always Better Control (ABC) by purchase value and Fast-/Slow-/Non-moving (FSN) by dispensing frequency (predefined thresholds: >4/day = F, 1–4 = S, <1 = N) to form an ABC–FSN matrix. Non-medical products were classified by ABC and a new HML-P scheme (expert-defined Pareto cut-offs: 70%/20%/10% of cumulative gross profit) to form an ABC–HML-P matrix. Each matrix was consolidated into three control categories: I (strict), II (moderate) and III (minimal). Results Non-medical products constituted 76.4% of all items. The ABC–FSN matrix identified Im = 149 medical products for strict control, while the non-medical ABC–HML-P matrix identified Inm = 580 items for strict control and a large segment for minimal oversight (IIInm = 6218). A pronounced Pareto pattern was observed (≈10% of items accounted for 70% of spend and 70% of gross profit), alongside low daily movement (only 3.2% dispensed ≥1/day). Conclusions The proposed dual-matrix model provides a practical decision-support tool for community pharmacies. It helps prioritize availability of patient-critical medical products while supporting economic sustainability.

BACKGROUND Newborn screening (NBS) for severe combined immunodeficiency (SCID) using T cell receptor excision circles (TREC) in dried blood spots (DBS) has been implemented in the U.S. and many other regions and countries globally. The Clinical Immunology Society (CIS) and the Association of Public Health Laboratories (APHL) jointly formed the SCID Harmonization Initiative to facilitate comparison of NBS reporting practices to promote global consensus and collaboration. OBJECTIVE To assess current NBS SCID practices using a global survey and to report the findings from the Phase 1 component. METHODS An eighteen-question survey was distributed to all known SCID screening programs worldwide. Only one response per region was analyzed. Examples of international screening algorithms were also solicited and included. RESULTS A total of 200 responses were received, of which eighty responses were unique and used for further analysis. Of the 39 non-U.S. countries, 15 (38%) reported national universal screening, and 24 (62%) reported regional, pilot, or other screening. Additional questions pertained to methodology and reporting with particular emphasis on communication of the clinical urgency of an abnormal TREC result. CONCLUSIONS This global survey confirmed that the approach to NBS SCID varies widely, underscoring the need for harmonization at multiple steps, particularly for reporting and interpretation. This is the first study to capture global NBS SCID practices, and these findings provide the basis for creation of a Phase 2 consensus reporting framework, which will be developed by the same SCID Harmonization Committee that created the current study.

Background/Objectives: Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function with established prognostic value after acute coronary syndrome (ACS). The clinical relevance of temporal changes in short-term HRV remains insufficiently defined. This study evaluated short-term HRV dynamics and their association with mortality after ACS. Methods: This retrospective–prospective study included 230 patients with acute myocardial infarction. Five-minute resting ECG recordings were obtained on day 1 and day 21. Time- and frequency-domain HRV parameters were analyzed, and delta values were calculated. The primary endpoint was overall mortality. Survival was assessed using Kaplan–Meier analysis and Cox regression. Results: Patients who died during follow-up had lower HRV values on day 21 and more pronounced declines in selected parameters. In multivariable analysis, decreased ΔLF and shorter RR intervals independently predicted overall mortality. Conclusions: Short-term HRV provides a practical bedside assessment of autonomic function after ACS. Unfavorable temporal changes likely reflect persistent autonomic imbalance and may offer additional prognostic insight. Larger contemporary studies are needed to confirm these findings.

Background/Objectives: Aortic stenosis is associated with autonomic nervous system (ANS) imbalance, while diabetes mellitus is a major contributor to cardiac autonomic neuropathy. Their coexistence may result in more pronounced autonomic dysfunction not fully captured by conventional assessment. This study aimed to compare ANS function in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR), according to diabetes status. Methods: This cross-sectional study included 74 patients with severe aortic stenosis referred for TAVR, including 21 patients with diabetes mellitus. Autonomic function was evaluated using non-invasive ECG-based analysis, incorporating short-term and 24 h Holter-derived heart rate variability (HRV), nonlinear Poincaré plot indices, and deceleration and acceleration capacity. Ambulatory blood pressure monitoring and standard clinical and echocardiographic assessment were performed. Results: Patients with diabetes mellitus demonstrated significantly lower long-term HRV parameters and reduced nonlinear Poincaré plot indices compared with non-diabetic patients, indicating altered autonomic modulation. Short-term HRV showed similar trends without statistical significance. Echocardiographic severity of aortic stenosis and left ventricular systolic function were comparable between groups. Conclusions: Autonomic dysfunction appears to be more pronounced in patients with severe aortic stenosis and diabetes mellitus, predominantly affecting parasympathetic modulation. ECG-derived autonomic parameters may offer complementary insight into ANS involvement in this population and warrant further investigation.