The benefits of breast feeding may be associated with better formation of eating habits beyond childhood. This study was designed to verify the association between breast feeding and food consumption according to the degree of processing in four Brazilian birth cohorts.

The duration of exclusive, predominant and total breast feeding was evaluated. The analysis of the energy contribution of fresh or minimally processed foods (FMPF) and ultra-processed foods (UPF) in the diet was evaluated during childhood (13–36 months), adolescence (11–18 years) and adulthood (22, 23 and 30 years).

Those who were predominantly breastfed for less than 4 months had a higher UPF consumption (β 3.14, 95% CI 0.82 to 5.47) and a lower FMPF consumption (β –3.47, 95% CI –5.91 to –1.02) at age 22 years in the 1993 cohort. Exclusive breast feeding (EBF) for less than 6 months was associated with increased UPF consumption (β 1.75, 95% CI 0.25 to 3.24) and reduced FMPF consumption (β –1.49, 95% CI –2.93 to –0.04) at age 11 years in the 2004 cohort. In this same cohort, total breast feeding for less than 12 months was associated with increased UPF consumption (β 1.12, 95% CI 0.24 to 2.19) and decreased FMPF consumption (β –1.13, 95% CI –2 .07 to –0.19). Children who did not receive EBF for 6 months showed an increase in the energy contribution of UPF (β 2.36, 95% CI 0.53 to 4.18) and a decrease in FMPF (β –2.33, 95% CI –4 .19 to –0.48) in the diet at 13–36 months in the 2010 cohort. In this cohort, children who were breastfed for less than 12 months in total had higher UPF consumption (β 2.16, 95% CI 0.81 to 3.51) and lower FMPF consumption (β –1.79, 95% CI –3.09 to –0.48).

Exposure to breast feeding is associated with lower UPF consumption and higher FMPF consumption in childhood, adolescence and adulthood.

Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen.

This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria.

This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital on 28 November 2022 (Yan-2022–442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants.

ChiCTR2200067044.

Many studies have explored the food environment to characterise it and understand its role in food practices. Assessment of the organisational food environment can contribute to the development of more effective interventions to promote adequate and healthy eating. However, few instruments and indicators have been developed and validated for assessing this type of setting. The systematisation of those can be useful to support the planning of future assessments and the development of wide-ranging instruments. This study aims to conduct a scoping review to systematise evidence on instruments and indicators for assessing organisational food environments.

This scoping review was planned according to the methodological framework for scoping reviews proposed by Arksey and O’Malley and subsequently enhanced by Levac et al. For the report of the review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Extension for Scoping Reviews (PRISMA-ScR) checklist and guidelines will be used. The search will be conducted using PubMed, Embase, Web of Science, PsycINFO, Scopus and Google Scholar databases. The studies to be included were required to have been published in peer-reviewed journals since January 2005. No geographical, population or language restrictions will be applied given the desired breadth of the review. Two researchers will select the articles and extract the data independently. The conceptual model proposed by Castro and Canella will guide the data extraction and analysis. The results will be presented with narrative synthesis for the extracted data accompanying the tabulated and charted results.

This study is based on the analysis of published scientific literature and did not involve patients, medical research, or any type of personal information; therefore, no ethical approval was obtained for this study. The results of this scoping review will be submitted for publication in an international peer-reviewed journal, preferably open access.

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

A systematic review.

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

CRD42022322166.

Invasive non-typhoidal Salmonella (iNTS) serovars are a major cause of community-acquired bloodstream infections in sub-Saharan Africa (SSA). In this setting, Salmonella enterica serovar Typhimurium accounts for two-thirds of infections and is associated with an estimated case fatality rate of 15%–20%. Several iNTS vaccine candidates are in early-stage assessment which—if found effective—would provide a valuable public health tool to reduce iNTS disease burden. The CHANTS study aims to develop a first-in-human Salmonella Typhimurium controlled human infection model, which can act as a platform for future vaccine evaluation, in addition to providing novel insights into iNTS disease pathogenesis.

This double-blind, safety and dose-escalation study will randomise 40–80 healthy UK participants aged 18–50 to receive oral challenge with one of two strains of S. Typhimurium belonging to the ST19 (strain 4/74) or ST313 (strain D23580) lineages. 4/74 is a global strain often associated with diarrhoeal illness predominantly in high-income settings, while D23580 is an archetypal strain representing invasive disease-causing isolates found in SSA. The primary objective is to determine the minimum infectious dose (colony-forming unit) required for 60%–75% of participants to develop clinical or microbiological features of systemic salmonellosis. Secondary endpoints are to describe and compare the clinical, microbiological and immunological responses following challenge. Dose escalation or de-escalation will be undertaken by continual-reassessment methodology and limited within prespecified safety thresholds. Exploratory objectives are to describe mechanisms of iNTS virulence, identify putative immune correlates of protection and describe host–pathogen interactions in response to infection.

Ethical approval has been obtained from the NHS Health Research Authority (London—Fulham Research Ethics Committee 21/PR/0051; IRAS Project ID 301659). The study findings will be disseminated in international peer-reviewed journals and presented at national/international stakeholder meetings. Study outcome summaries will be provided to both funders and participants.

NCT05870150

We aimed to develop and externally validate a generalisable risk prediction model for 30-day stroke mortality suitable for supporting quality improvement analytics in stroke care using large nationwide stroke registers in the UK and Sweden.

Registry-based cohort study.

Stroke registries including the Sentinel Stroke National Audit Programme (SSNAP) in England, Wales and Northern Ireland (2013–2019) and the national Swedish stroke register (Riksstroke 2015–2020).

Data from SSNAP were used for developing and temporally validating the model, and data from Riksstroke were used for external validation. Models were developed with the variables available in both registries using logistic regression (LR), LR with elastic net and interaction terms and eXtreme Gradient Boosting (XGBoost). Performances were evaluated with discrimination, calibration and decision curves.

The primary outcome was all-cause 30-day in-hospital mortality after stroke.

In total, 488 497 patients who had a stroke with 12.4% 30-day in-hospital mortality were used for developing and temporally validating the model in the UK. A total of 128 360 patients who had a stroke with 10.8% 30-day in-hospital mortality and 13.1% all mortality were used for external validation in Sweden. In the SSNAP temporal validation set, the final XGBoost model achieved the highest area under the receiver operating characteristic curve (AUC) (0.852 (95% CI 0.848 to 0.855)) and was well calibrated. The performances on the external validation in Riksstroke were as good and achieved AUC at 0.861 (95% CI 0.858 to 0.865) for in-hospital mortality. For Riksstroke, the models slightly overestimated the risk for in-hospital mortality, while they were better calibrated at the risk for all mortality.

The risk prediction model was accurate and externally validated using high quality registry data. This is potentially suitable to be deployed as part of quality improvement analytics in stroke care to enable the fair comparison of stroke mortality outcomes across hospitals and health systems across countries