Browse Articles

Economics of vaccination against diarrhoea and respiratory diseases in French cow calves’ systems: A modelling approach

To assess the economic benefits of vaccination against diarrhoea and respiratory diseases in cow-calf systems, a stochastic mechanistic mathematical programming bio-economic model was developed for a beef Charolais breed farm. The model reproduced herd dynamics on a weekly basis over a 10-year period, including animal growth, reproduction and diseases risks and control (treatments and vaccination). Three baseline risk levels of diarrhoea and respiratory disease were considered to simulate low, average, and high infection risks on the farm. The results demonstrated the model’s ability to reproduce average farm dynamics, with diarrhoea prevalence ranging from 8% to 33% and respiratory diseases from 13% to 79%. On average, prevalences were decreased by 24% and 50%, respectively, due to vaccination, which also reduced the 2-day to 1-month mortality rates by 2–3% and 15–17%, respectively. The net value of vaccination was positive except in cases of low infection risk of diarrhoea and respiratory diseases: the return on investment (ROI) of vaccination was 0.4–0.5 (€1 invested yields €0.4–0.5) in the baseline scenario (middle infection risks), negative under low infection risks, and reached 1 (€1 invested yields €1) for high infection risks. The results also showed that farmers may earn €58–75 in additional revenue per extra hour of labour dedicated to vaccination in the medium-risk scenario. Despite the limited availability of data for cow-calf systems, this study demonstrated the economic benefit of vaccination in most combinations of scenarios and risks. The profitability of vaccination was challenged when low disease risks were considered, but in such cases, vaccination may serve as insurance against financial losses in case of health status deterioration.

Spinning Top-like Nanoprobes for Direct Visualization of Cooperative Ca²⁺-Binding-Induced Conformational Switching in Single Calmodulin Molecules

Explore the role of CBS in stomach adenocarcinoma based on the sulfur-containing amino acid metabolism network

Exploring the participant experience in controlled human infection model (CHIM) trials: A modified grounded theory study

Background Controlled human infection model (CHIM) trials are useful tools for accelerating vaccine development through the deliberate infection of healthy volunteers in a controlled environment to study the course of disease in humans. CHIM trials may pose physical, psychological, and social harms to participants. Participants are subjected to long inpatient stays and daily sample collection, which may be uncomfortable. Very little is known about the CHIM trial participant experience and decision-making process. Methods We conducted a modified grounded theory study, embedded within the Canadian Center for Vaccinology (CCfV)’s Bordetella pertussis CHIM trial. Twenty six participants engaged in semi-structured interviews at four time periods throughout their year-long involvement in the CHIM trial, using a constant comparative approach of simultaneous data collection and analysis. This process continued until saturation was reached and an emerging theoretical model was constructed depicting the CHIM trial participant experience over time. Results The emergent conceptual model centered around the core category “A Trusting Partnership”, depicting the evolution of the participant-researcher relationship throughout the experience. Establishing trust was critical during the decision-making phase, facilitated through transparent researcher-participant communication and consideration of past experiences and values. Supporting the trusting partnership during the inpatient isolation phase was facilitated through ongoing researcher-participant engagement and emotional support. During outpatient participation, maintaining the trusting partnership was achieved through effective communication, consistent follow-up care, and recognition of participants’ contributions. Quality improvement (QI) recommendations were identified across all phases of CHIM trial participation. Conclusions Researcher-participant trust is an integral component of the CHIM trial participant experience. QI recommendations should be taken into consideration when planning and coordinating future CHIM trials.

Clinical characteristics of thyroid eye disease and expression profile of peripheral blood immune cells

Strategies and responses to the effects of Climate Change on health systems in Sub-Saharan Africa: A scoping review protocol

Background Climate change is recognized as the greatest global health threat of the 21st century. Projections suggest that the Sub-Saharan African region will face more consequences of climate change than any other region globally. The health systems within the region have been affected by the negative effects of climate change. Mapping strategies and responses used in the region to address the effects of climate change on health systems in Sub-Saharan Africa could be a starting point for understanding evidence-based decision-making to inform best practices. Methods This scoping review will follow the methodological framework by Arksey & O’Malley. A wide range of databases will be searched to identify articles published on the strategies and responses to the effects of climate change on the health systems in Sub-Saharan Africa. Only peer-reviewed articles (original quantitative and qualitative studies, mixed methods, systematic reviews, editorials, and commentaries) published in English Language between 2010 and 2024 will be reviewed. All Book chapters and the grey literature (dissertations, conference proceedings, abstracts, reports) and publications primarily focusing on climate change strategies and responses without effects on health systems will be excluded. Covidence software will be used during study selection, data extraction, and summary. Deductive thematic analysis will be performed using predetermined themes from the objectives. Dissemination The results of this scoping review will be disseminated at local and international research conferences. Furthermore, the findings will be published in open-access journals targeting different audiences. The findings will also be shared with the Ministry of Health in Malawi for possible policy considerations.

Regression machine learning-based highly efficient dual band MIMO antenna design for mm-Wave 5G application and gain prediction

Exploring influences of health and wellbeing in Sydney’s apartment living: A qualitative study of residents’ perceptions

In Australia, and internationally, a shift is occurring towards high-density apartment living with initiatives and research showing an increased interest in the relations between health, wellbeing and apartment buildings. This study explores the complex associations between residents’ perceptions of their health and wellbeing and the apartment buildings where they live within the context of Sydney, Australia, as the case study. It challenges the fragmented approach previously used to study healthy apartment living and their underlying assumptions that do not account for a coupled human-environment systems view of health and wellbeing concerning apartment living. Qualitative research was used, which included in-depth, semi-structured interviews with 17 residents living in different apartment buildings, supplemented by fieldwork and narrated photographs. Using a structured iterative thematic analysis process, 20 areas of health and wellbeing influence (themes) were identified and further categorised using synthetic thinking into diverse, context-dependent, multilevel, and pervading influences. The findings from this exploratory study suggest a complex view of health and wellbeing by residents of apartment buildings and provide novel and important insights that have not been previously reported in such breadth.

Retrospective study of splenectomy specimens with clinical and pathological features in Southern Iran

Correction: Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data

3D spectral CT-based fusion model predicts prognosis and postoperative adjuvant chemotherapy benefit in locally advanced rectal cancer

Time series analysis of malaria in pregnancy, using wavelet and SARIMAX models

Malaria in pregnancy (MIP) remains a global health challenge, affecting approximately 40% of pregnant women. Despite malaria control efforts by the Nigerian Government and its partners, regional disparities in health outcomes and malaria incidence trends among pregnant women remain under-studied. This study objectives were to assess MIP variability compared to general malaria cases, and forecast short-term MIP incidence over two years. This was achieved by analyzing malaria in pregnancy (MIP) variability across Nigeria from January 2015 to January 2025, using wavelet coherence, patterns of transmission cycles and selecting best modelling approach by comparing ARIMA and SARIMAX models to assess temporal trends before the forecast of short-term MIP incidence. Findings showed significant regional variability, with Cross River peaking in 2017 and 2019, while Enugu recorded its lowest trough in 2017. Malaria peaks in southern states remained lower than troughs in northern regions. Strong cross-correlations between MIP and general malaria transmission cycles were observed in Kebbi, Niger, Yobe, and Ondo, indicating persistent trends, while South-South and South-East exhibited weaker correlations, likely due to intervention fluctuations. SARIMAX models captured MIP trends more effectively, except Kebbi, where ARIMA fit better, and Niger, where SARIMAX exaggerated forecasts due to sensitivity to exogenous variables. Thus, SARIMAX was adopted for Cross River, Enugu, Ondo, and Yobe; while ARIMA was used for Kebbi and Niger States. It was discovered that Cross River and Enugu exhibited intervention-driven malaria fluctuations, Ondo, Niger, and Yobe displayed unstable or cyclical trends, reinforcing the importance of climate-sensitive forecasting models and seasonal interventions for improving malaria prediction accuracy. South-South and South-East need improved healthcare access, North-Central and North-West require seasonality forecasting, while North-East demands urgent control measures. Targeted malaria interventions are crucial to support achievement of the Nigeria’s National Malaria Elimination Programme (NMEP) goals.

Mitochondrial subtypes in renal ischemia reperfusion injury guide delayed graft function and Long-Term graft prediction

Focused low-intensity hippocampal transcranial ultrasound stimulation (TUS) for sleep disturbances in patients with chronic tinnitus: A study protocol for a pilot randomized controlled trial

Background Sleep disturbances are very common in tinnitus sufferers with a high prevalence ranging from 50% to 77%. Untreated sleep disturbances and tinnitus can cause brain shrinkage and lead to cognitive impairments in late adulthood. Until now, non-pharmacological treatments are very few for older patients suffering from sleep disturbances and chronic tinnitus. Even though clinical trials of transcranial magnetic stimulation (TMS) have shown positive results in the treatment of either sleep disturbances or chronic tinnitus, the results are highly varied due to the superficial cortical target. Compared to TMS, focused low-intensity transcranial ultrasound stimulation (TUS) is a newly developed modality of non-invasive brain stimulation that offers promising therapeutic effects by transmitting acoustic energy into deep brain structures with a high spatial resolution (i.e., sub-millimeter), which sparks interest in managing the comorbidities in ageing populations. Methods and design Chinese individuals between the ages of 60 and 90 years, who are right-handed and have sleep disturbances and chronic tinnitus, will participate in this pilot randomized clinical trial (RCT). Eligible participants will be randomly assigned to two treatment groups (1:1 ratio): low-intensity TUS or sham TUS (i.e., placebo-controlled group). Each group will consist of 15 participants. Before the treatment, high-resolution T1-weighted magnetic resonance imaging (MRI) data will be used to create a computational head model for each participant. The head model will help identify the treatment target of the left hippocampus. The treatments schedule contains six sessions of low-intensity TUS, three times per week, lasting two weeks. Each session of treatment lasts for 80 seconds. Throughout the study, outcome measurements will be conducted at four time points, including baseline, 2nd week, 6th week, and 12th week. The primary outcomes include subjective sleep quality and severity of tinnitus. The secondary measurements include actigraphy, tinnitus handicap inventory and glymphatic function. Participants’ adherence to the program and any adverse event will be closely monitored throughout the duration of the clinical trial. Conclusions It is expected that a 2-week treatment of low-intensity TUS will show significant enhancement in sleep quality and the severity of tinnitus symptoms compared to sham TUS. This proposed clinical trial will provide high-level and valuable clinical evidence that could inform the effect size and personalized modeling of focused low-intensity TUS for different types of brain diseases. Trial registration ClinicalTrials.gov Identifier: NCT06776705.

Measurement analysis of strata deformation caused by construction of quasi-rectangular shield

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference

Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is needed to understand in more detail the mechanisms underlying biological processes in living organisms. Recently, studies have utilised mathematical ideas from natural language processing and self-supervised learning, to derive features based on protein sequence information. In the area of language modelling, it has been shown that learnt representations from self-supervised pre-training can capture the semantic information of words well for downstream applications. In this study, we tested the ability of sequence-based protein representations learnt using self-supervised pre-training on a large protein database, on multiple protein inference tasks. We show that simple baseline representations in the form of bag-of-words histograms perform better than those based on self-supervised learning, on sequence similarity and protein inference tasks. By feature selection we show that the top discriminant features help bag-of-words capture important information for data-driven function prediction. These findings could have important implications for self-supervised learning models on protein sequences, and might encourage the consideration of alternative pre-training schemes for learning representations that capture more meaningful biological information from the sequence alone.

Selective lowest and upper instrumented vertebra for the correction of Lenke type 6C adolescent idiopathic scoliosis

Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level object detection model. Both models demonstrated high overall accuracy, with the grid-based model achieving 90.8% and the image-level model 92.9%. Patch-wise accuracy of the image-level model was 74.2%, highlighting its ability to classify broader spatial context without requiring point-based labelling. Precision and recall values for both models often exceeded 70% for dominant habitat classes such as unconsolidated substrate, macroalgae, and seagrass. The development of these models supports the potential for cost-effective, robust, and scalable in-house habitat classification for fishery or ecoregion specific habitat data to support timely decision-making. Further, the grid-based model uniquely integrates spatial precision with compatibility to existing manual data workflows, enabling seamless adoption within many existing fisheries monitoring programs. Despite limitations, such as a class imbalanced dataset, both models present a scalable, data secure solution for fisheries management agencies. This study establishes a foundation for integrating artificial intelligence driven image analysis of proprietary fisheries data, to further support responsive, standardised and data-informed decision making.

Thermal behavior of the Klein Gordon oscillator in a dynamical noncommutative space

Sex differences in the association of BMI and weight perception with depression and suicidality among Korean adolescents

This study aimed to investigate sex-based differences in the effects of weight perception on depression and suicidality in Korean adolescents with and without obesity. A multiple logistic regression analysis stratified by sex was conducted using the 2023 Korea Youth Risk Behavior Survey data of 51,462 middle and high school students. BMI-based obese adolescents comprised a higher proportion of male participants (22.5%) than female participants (17.4%). The rate of underweight perception was higher among male participants (32.4%) than among female participants (23.3%); however, the rate of overweight perception was higher among female participants (37.3%) than among male participants (36.0%). The risk of depression or suicidality was higher among female participants (30.7% and 18.2%, respectively) than among male participants (21.3% and 10.7%, respectively). In female participants without obesity, the risk of depression and suicidality with overweight perception increased by 1.106 times (95% confidence interval [CI] 1.020–1.199) and 1.295 times (95% CI 1.176–1.427), respectively, compared to that with underweight perception as the reference group. However, this difference was not statistically significant among male participants without obesity. The findings of this study suggest that to improve the mental health of adolescents, proper weight recognition education along with obesity prevention should be implemented, and sex-specific interventions should be considered.