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Correction: The dynamic lives of osseous points from Late Palaeolithic/Early Mesolithic Doggerland: A detailed functional study of barbed and unbarbed points from the Dutch North Sea

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos

Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain’s functioning prominently. However, with the evolution of glioma, tumours form that invade healthy tissues in the brain, leading to neurological impairment, seizures, hormonal dysregulation, and venous thromboembolism. Medical tests, including medical resonance imaging (MRI), computed tomography (CT) scans, biopsy, and electroencephalograms are used for early detection of glioma. However, these tests are expensive and may cause irritation and allergic reactions due to ionizing radiation. The deep learning models are highly optimal for disease prediction, however, the challenge associated with it is the requirement for substantial memory and storage to amalgamate the patient’s information at a centralized location. Additionally, it also has patient data-privacy concerns leading to anonymous information generalization, regulatory compliance issues, and data leakage challenges. Therefore, in the proposed work, a distributed and privacy-preserved horizontal federated learning-based malignant glioma disease detection model has been developed by employing 5 and 10 different clients’ architectures in independent and identically distributed (IID) and non-IID distributions. Initially, for developing this model, the collection of the MRI scans of non-tumour and glioma tumours has been done, which are further pre-processed by performing data balancing and image resizing. The configuration and development of the pre-trained MobileNetV2 base model have been performed, which is then applied to the federated learning(FL) framework. The configurations of this model have been kept as 0.001, Adam, 32, 10, 10, FedAVG, and 10 for learning rate, optimizer, batch size, local epochs, global epochs, aggregation, and rounds, respectively. The proposed model has provided the most prominent accuracy with 5 clients’ architecture as 99.76% and 99.71% for IID and non-IID distributions, respectively. These outcomes demonstrate that the model is highly optimized and generalizes the improved outcomes when compared to the state-of-the-art models.

The Korean medicine for aging cohort (KoMAC) study: A protocol for a prospective, multicenter cohort study on healthy aging in the population entering old age in South Korea

Background South Korea is anticipated to enter a super-aged society by 2025, necessitating a focus on healthy aging. In Korean medicine (KM), aging and disease susceptibility are individual specific, emphasizing personalized treatments, and many Korean local governments have integrated KM services for elderly people into the public sector. However, there is a notable absence of research incorporating KM to treat older adults. Aim The proposed study aims to examine the comprehensive health profiles of individuals entering old age in rural and urban areas and explore the significant correlations between healthy aging and four key factors: biological, psychological, social, and KM-based phenotype factors. It will also establish a database and blood biobank, serving as a platform for future research to develop a traditional KM-based healthy aging model. Methods A multiple randomized controlled trial design will be adopted in this prospective, multicenter cohort study for the clinical investigation of the markers associated with KM-based healthy aging. The aim is to recruit 1,000 participants who are entering old age from both urban and rural settings for this study, and recruitment began in August 2023 with follow-up surveys planned at one-year intervals. Comprehensive health profiles, including biological, psychological, social, and KM-based phenotype factors, will be developed through the creation of a database, a blood biobank, and multi-omics data. Results In the baseline phase of this study, we will focus on identifying markers for KM-based phenotypes and examining how these phenotypes relate to aging and associated diseases. In the next phase, we will implement interventions tailored to KM-based phenotypes to verify the effects of KM on healthy aging. Ultimately, we intend to develop a KM-based integrated health management model, with further substudies aiming to explore factors related to healthy aging. This protocol was approved by the institutional review board of Wonkwang University Korean Medicine Hospital, Iksan, Republic of Korea (approval number: WKUIOMH-IRB-2023-05) on August 16, 2023 and Jangheung Integrative Medical Hospital (approval number: WKUJIM-202307-001) on August 21, 2023. Recruitment started on August 16, 2023. Conclusion The anticipated results of our study aim to establish personalized preventive and therapeutic interventions for individuals entering old age. Additionally, we seek to offer an KM-based integrated health management model that incorporates comprehensive diagnosis and an integrative medical treatment strategy for healthy aging. Trial Registration Clinical Research Information Service: KCT0008863 (registered on October 11, 2023, https://cris.nih.go.kr/cris/search/detailSearch.do/25718).

Ecological momentary assessment of physical and eating behaviours: The WEALTH feasibility and optimisation study with recommendations for large-scale data collection

Ecological Momentary Assessment (EMA) enables the real-time capture of health-related behaviours, their situational contexts, and associated subjective experiences. This study aimed to evaluate the feasibility of an EMA targeting physical and eating behaviours, optimise its protocol, and provide recommendations for future large-scale EMA data collections. The study involved 52 participants (age 31±9 years, 56% females) from Czechia, France, Germany, and Ireland completing a 9-day free-living EMA protocol using the HealthReact platform connected to a Fitbit tracker. The EMA protocol included time-based (7/day), event-based (up to 10/day), and self-initiated surveys, each containing 8 to 17 items assessing physical and eating behaviours and related contextual factors such as affective states, location, and company. Qualitative insights were gathered from post-EMA feedback interviews. Compliance was low (median 49%), particularly for event-based surveys (median 34%), and declined over time. Many participants were unable or unwilling to complete surveys in certain contexts (e.g., when with family), faced interference with their daily schedules, and encountered occasional technical issues, suggesting the need for thorough initial training, an individualised protocol, and systematic compliance monitoring. The number of event-based surveys was less than desired for the study, with a median of 2.4/day for sedentary events, when 4 were targeted, and 0.9/day for walking events, when 3 were targeted. Conducting simulations using participants’ Fitbit data allowed for optimising the triggering rules, achieving the desired median number of sedentary and walking surveys (3.9/day for both) in similar populations. Self-initiated reports of meals and drinks yielded more reports than those prompted in time-based and event-based EMA surveys, suggesting that self-initiated surveys might better reflect actual eating behaviours. This study highlights the importance of assessing feasibility and optimising EMA protocols to enhance subsequent compliance and data quality. Conducting pre-tests to refine protocols and procedures, including simulations using participants’ activity data for optimal event-based triggering rules, is crucial for successful large-scale data collection in EMA studies of physical and eating behaviours.

Application of 3D point cloud and visual-inertial data fusion in Robot dog autonomous navigation

The study proposes a multi-sensor localization and real-timeble mapping method based on the fusion of 3D LiDAR point clouds and visual-inertial data, which addresses the issue of decreased localization accuracy and mapping in complex environments that affect the autonomous navigation of robot dogs. Through the experiments conducted, the proposed method improved the overall localization accuracy by 42.85% compared to the tightly coupled LiDAR-inertial odometry method using smoothing and mapping. In addition, the method achieved lower mean absolute trajectory errors and root mean square errors compared to other algorithms evaluated on the urban navigation dataset. The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. When applied to a real robot dog, the rotational error was reduced to 1.86°, and the localization error in GPS environments was 0.89m. Furthermore, the proposed approach closely followed the theoretical path, with the smallest average error not exceeding 0.12 m. Overall, the proposed technique effectively improves both autonomous navigation and mapping for robot dogs, significantly increasing their stability.

Comparative analysis of VMAT plans on Halcyon and infinity for lung cancer radiotherapy

Objective The dosimetric characteristics and treatment efficiency of VMAT plans using two linear accelerator platforms, Halcyon and Infinity, in conventional radiotherapy for non-small cell lung cancer (NSCLC) are compared to provide data for selecting clinical equipment. The study also explores potential confounding factors that may influence treatment outcomes. Methods This retrospective cohort study aims to compare the dosimetric characteristics and treatment efficiency of VMAT plans delivered using Halcyon and Infinity linear accelerator platforms in patients with NSCLC. A retrospective analysis was performed on 60 NSCLC patients receiving conventional fractionated radiotherapy with VMAT plans developed for both Halcyon and Infinity. These plans were optimized with RayStation 9A with identical dose constraints and optimization parameters. The groups were compared in terms of target dose coverage, normal tissue sparing, plan complexity, and treatment efficiency. The dosimetric parameters included D98%, D2%, and Dmean for both the CTV and PTV and dose distributions for organs at risk (OARs), including the heart, lungs, and spinal cord. Logistic regression was performed to account for potential confounding factors, such as PTV volume, tumor stage, and tumor location. Results The VMAT plans of both platforms met the clinical dosimetric requirements. Halcyon showed superior protection of normal tissues in low-dose areas (e.g., Lungs V5Gy and Heart V30Gy), whereas Infinity excelled in controlling hot spots and achieving rapid dose fall-off at the target margins. Furthermore, Halcyon has fewer plan monitoring units and lower complexity than Infinity and reduced treatment time by 24.0%. Logistic regression analysis revealed that PTV volume was a significant predictor for dose metric differences, while tumor stage and tumor location had variable effects depending on the dose metric, highlighting the need to account for these factors in clinical comparisons. Overall, there was no significant difference in target dose coverage or uniformity between the platforms; each demonstrated specific strengths in protecting different OARs and in treatment execution efficiency. Conclusion Halcyon and Infinity offer distinct advantages in radiotherapy for NSCLC. Halcyon provides better protection of normal tissues and performance in low-dose regions, whereas Infinity offers greater treatment efficiency and superior control in high-dose regions. The study also highlights that PTV volume is an important factor influencing dosimetric outcomes. In choosing optimal radiotherapy equipment in clinical practice, the study results suggest that treatment planning should leverage the unique technical features of different accelerators to achieve the best individualized outcomes. Future studies should increase the sample size and employ prospective research designs to confirm the clinical relevance of these findings.

Optimization study of mine fire sensor based on grey correlation analysis

In order to address the issues of fire alarm delay, omission, and false alarms caused by the current setup of mine fire sensors, which collect single disaster information, have fixed distribution, and relatively independent data collection, this study utilized FDS numerical simulation software and fire similarity experiments. The aim was to investigate the characteristics of fire gases, temperature, and wind speed. To optimize the number and location of fire sensors in mines, the mathematical method of grey correlation analysis was proposed. Additionally, the critical time for fire hazards to spread to other tunnels was determined by detecting the CO content of ventilation nodes in tunnels with different wind speeds. Grey correlation analysis was employed to determine the critical time for fire hazards to spread to other tunnels. The CO content of the ventilation node is measured to determine the time at which the fire hazard may spread to other tunnels. Grey correlation analysis is then used to compare and correlate the CO content of the ventilation node and the wind speed with the fire characteristic gases, wind speed, and temperature of the tunnel under different wind speeds. Additionally, taking into account the safe escape time for personnel suggested by Marchant, an optimization scheme for the sensors is proposed.

Predicting Type 2 diabetes onset age using machine learning: A case study in KSA

The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital, aims to predict the age at onset of T2D using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR). It also seeks to identify key predictors influencing the age at onset of T2D in Saudi Arabia, which ranks 7th globally in prevalence. Medical records from 1,000 diabetic patients from 2018 to 2022 that contain demographic, lifestyle, and lipid profile data are used to develop the models. The average onset age was 65 years, with the most common onset range between 40 and 90 years. The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. Key factors influencing the age at onset included triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), ferritin, body mass index (BMI), systolic blood pressure (SBP), white blood cell count (WBC), diet, and vitamin D levels. This study is the first in Saudi Arabia to employ MLR, ANN, RF, SVR, and DTR models to predict T2D onset age, providing valuable tools for healthcare practitioners to monitor and design intervention strategies aimed at reducing the impact of T2D in the region.

Highly deformable flapping membrane wings suppress the leading edge vortex in hover to perform better

Airborne insects generate a leading edge vortex when they flap their wings. This coherent vortex is a low-pressure region that enhances the lift of flapping wings compared to fixed wings. Insect wings are thin membranes strengthened by a system of veins that does not allow large wing deformations. Bat wings are thin compliant skin membranes stretched between their limbs, hand, and body that show larger deformations during flapping wing flight. This study examines the role of the leading edge vortex on highly deformable membrane wings that passively change shape under fluid dynamic loading maintaining a positive camber throughout the hover cycle. Our experiments reveal that unsteady wing deformations suppress the formation of a coherent leading edge vortex as flexibility increases. At lift and energy optimal aeroelastic conditions, there is no more leading edge vortex. Instead, vorticity accumulates in a bound shear layer covering the wing’s upper surface from the leading to the trailing edge. Despite the absence of a leading edge vortex, the optimal deformable membrane wings demonstrate enhanced lift and energy efficiency compared to their rigid counterparts. It is possible that small bats rely on this mechanism for efficient hovering. We relate the force production on the wings with their deformation through scaling analyses. Additionally, we identify the geometric angles at the leading and trailing edges as observable indicators of the flow state and use them to map out the transitions of the flow topology and their aerodynamic performance for a wide range of aeroelastic conditions.

Impact of manufacturers’ eco-design decisions on the closed-loop supply chain under recycling rate regulations

To address increasingly severe environmental issues, various countries have introduced relevant environmental protection regulations. This paper proposes a new government regulation measure to encourage manufacturers to improve recycling rates. Governments set recycling rate targets and reward-penalty mechanisms. This paper constructs a game model involving a manufacturer and a remanufacturer within a closed-loop supply chain system. It studies the equilibrium decisions in three scenarios: no government intervention, manufacturers not taking improvement measures despite government-set recycling rate targets, and manufacturers adopting ecological design after such targets are established. Results indicate that after governments establish recycling rate target: (1) After manufacturers adopt ecological design, the prices of new and remanufactured products decrease, sales volume increases, and the profits of both manufacturers and remanufacturers rise. Therefore, manufacturers would be well-advised to adopt eco-design strategies to enhance the level of recycling. (2) As the recycling rate target increase, the level of ecological design decreases, and the prices of new and remanufactured products rise. It is recommended that governments initially set lower recycling rate targets and then gradually increase them. (3) With the increase in the reward-penalty coefficient, the level of ecological design rises, and the price of new products first increases and then decreases. When remanufacturing is unrestricted, the prices of remanufactured products decrease; however, when remanufacturing is restricted, the prices of remanufactured products first increase and then decrease. Therefore, governments would be well-advised to establish a relatively high reward-penalty coefficient.

Environmental impact and phenotypic stability in potato clones resistant to late blight Phytophthora infestans (Mont) de Bary, resilient to climate change in Peru

Potato is one of the three most important foods in the world’s diet and is staple in the Peruvian highlands. This crop is affected by late blight, a disease that if not controlled in time can decimate production. The oomycete (Phytophthora infestans) causing this disease is controlled using fungicides, which affect the environment and human health, another form of control is the use of resistant cultivars. 30 potato clones from the LBHTC2 population were evaluated, with the objective of selecting clones with high levels of resistance to this disease, stable for tuber yield, low environmental impact and high economic profitability. The clones were planted in three field experiments in the 2021–2022 growing season. Two experiments with and without late blight chemical control in Oxapampa and Huánuco and one experiment under normal conditions of a potato crop in El Mantaro, Junin, using randomized complete blocks with three replications. The cultivars Yungay, Amarilis and Kory were used as controls for late blight resistance and tuber yield. Late blight resistance and environmental impact were determined based on experiments with and without control in Huánuco and Oxapampa. Yield stability and economic profitability were evaluated based on information from the three experiments. Clones CIP316375.102, CIP316361.187, CIP316367.117, CIP316356.149, CIP316367.147 were the ones that presented the highest yields, high Late blight resistance, phenotypically stable for tuber yield, with low environmental impact and high economic profitability, superior to control cultivars. These clones have high potential for sustainable production systems that allow reducing environmental impact, increasing economic profitability and improving producers’ living standards.

The temporal variation in pesticide concentrations within matured French wines

Numerous organizations worldwide are diligently working to regulate the composition of food products, with a particular focus on pesticide content. Each year, several substances are classified as hazardous to human health and subsequently banned from agricultural use. In this study, we address the age-old question: "Does wine improve with time?" from the context of pesticide composition. We gathered wine samples from renowned French winemaking regions, covering the years 1935 to 2000, to assess pesticide levels and identify specific substances. Our objective was to determine if any currently banned pesticides were present in these aged wines and whether the detected levels pose health risks under typical daily consumption patterns. Our findings revealed the presence of trace amounts of 21 different pesticides proceeding from Plant Protection Products (PPPs), in most of the wine samples, albeit at levels considered non-threatening to human health. Notably, one sample exhibited an alarmingly high concentration of carbaryl, surpassing toxic consumption thresholds. This study prompts discussions regarding the prioritization of pesticide testing in various products and whether stringent regulations should be upheld in the wine selling collectors sector.

Macrophage-P2X4 receptors pathway is essential to persistent inflammatory muscle hyperalgesia onset, and is prevented by physical exercise

Peripheral inflammation may lead to severe inflammatory painful conditions. Macrophages are critical for inflammation; modulating related pathways could be an essential therapeutic strategy for chronic pain diseases. Here we hypothesized that 1) Macrophage-P2X4 receptors are involved in the transition from acute to persistent inflammatory muscle hyperalgesia and that 2) P2X4 activation triggers a pro-inflammatory phenotype leading to Interleukin-1β (IL-1β) increase. Once physical exercise prevents exacerbated inflammatory processes related to chronic diseases including chronic muscle pain, we also hypothesized that 3) physical exercise, through PPARγ receptors, prevents P2X4 receptors activation. With pharmacological behaviour, biomolecular analysis and swimming physical exercise in a mouse model of persistent inflammatory muscle hyperalgesia we demonstrated that P2X4 receptors are essential for transitioning from acute to persistent inflammatory muscle hyperalgesia; Phosphorylation of p38MAPK indicated P2X4 signalling activation associated with inflammatory macrophage and an increase of IL-1β expression in skeletal muscle; Exercise-PPARγ receptors prevented phosphorylation of p38MAPK in muscle tissue. Our findings suggest that exercise-PPARγ modulates the acute inflammatory phase of developing persistent muscle hyperalgesia by controlling p38MAPK-related P2X4 signalling. These highlight the great potential of modulating macrophage phenotypes and P2X4 receptors to prevent pain conditions and the ability of physical exercise to prevent inflammatory processes related to chronic muscle pain.

Home-based nurturing care practices for children under five with low socioeconomic position in Santo Domingo, Dominican Republic

Background In the Dominican Republic about 14.5% of children do not reach their full potential by age five, with children of low socioeconomic position most affected. The Nurturing Care Framework is an evidence-informed actionable framework to help children thrive, but we must first understand cultural contexts and childrearing practices that contribute to delay. This study applies the Nurturing Care Framework to explore the context of home-based care among young children in the Dominican Republic. Methods We conducted a sociodemographic survey and semi-structured qualitative interview with 25 mothers ages 19–42 (7 under the age of 18 at first birth) with low socioeconomic position and children under five that live in the capital city Santo Domingo. We asked in-depth questions about the Nurturing Care Framework’s domains of responsive caregiving and opportunities for early learning. We used consensual coding and deductive thematic analysis to analyze transcriptions, examined convergence and divergence in themes between adolescent and adult mothers, and organized themes using concept mapping. Results A few mothers provide responsive caregiving to their child, but they are unaware of its benefit to their child’s development. Adolescent mothers expressed lower confidence in their mothering skills. Across age groups, mothers did not see themselves as agents of change in their child’s early learning process and allow several hours of videos each day. Mothers provide children opportunities for learning through social interaction, a possible strength among this population. With regards to security and safety, about half of mothers use corporal punishment, all but one of these is an adolescent mother. Conclusion Findings highlight the need for parenting programs that build on strengths such as child-to-child social interaction and provide parents with opportunities to develop knowledge and skills to provide early learning opportunities. Interventions should target families with low socioeconomic position and adolescent mothers.

Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks

In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the temperatures in a city can vary from year to year due to various unclear reasons. In this paper, we utilized three distinct statistical techniques to analyze annual data drifting at various stations. These techniques calculate the P values for each station by comparing data from five years (2014-2018) to identify data drifting phenomena. To find out the data drifting scenario those statistical techniques and calculate the P value from those techniques to measure the data drifting in specific locations. From those statistical techniques, the highest drifting stations can be identified from the previous year’s datasets To identify data drifting and highlight areas with significant drift, we utilized meteorological air quality and weather data in this study. We proposed two models that consider the characteristics of data drifting for PM2.5 prediction and compared them with various deep learning models, such as Long Short-Term Memory (LSTM) and its variants, for predictions from the next hour to the 64th hour. Our proposed models significantly outperform traditional neural networks. Additionally, we introduced a wrapped loss function incorporated into a model, resulting in more accurate results compared to those using the original loss function alone and prediction has been evaluated by RMSE, MAE and MAPE metrics. The proposed Front-loaded connection model(FLC) and Back-loaded connection model (BLC) solve the data drifting issue and the wrap loss function also help alleviate the data drifting problem with model training and works for the neural network models to achieve more accurate results. Eventually, the experimental results have shown that the proposed model performance enhanced from 24.1% -16%, 12%-8.3% respectively at 1h-24h, 32h-64h with compared to baselines BILSTM model, by 24.6% -11.8%, 10%-10.2% respectively at 1h-24h, 32h-64h compared to CNN model in hourly PM2.5 predictions.

Analogical reasoning in first and second languages

This study investigated how linguistic predictors such as word frequencies, the difficulty and creativity of problems, and the category of problems contribute to analogical reasoning in L1 and L2. This study also investigated how different types of similarities (i.e., perceptual and relational similarities) are processed in analogical reasoning. In Experiment 1, Japanese participants were asked to solve 100 multiple-choice A:B::C:D analogy problems (e.g., skeleton: bone:: tornado: wind) in their first language, Japanese (L1). In this experiment, participants also rated the difficulty and creativity of problems. In Experiment 2, Japanese participants completed the same tasks, but the problems were shown in their second language, English (L2). The results showed that problems presented in L1 elicited higher accuracies and faster response times than in L2. A significant interaction was found between languages (L1/L2) and the category of problems which indicates that finding a perceptual similarity (e.g., the shape image of word concepts) with verbal stimuli in L2 is more challenging than in L1. Moreover, our results on response times indicated that processing relations between words would be carried out in L1 without any specific instruction while it would not be completed in L2 possibly due to the cognitive demand related to lexical processing. Considering these results, it is advisable in an educational setting to provide L2 learners with enough time and explicit instruction on understanding word relationships when forming analogies.

Risk assessment of antibiotic residues and resistance profile of E. coli in typical rivers of Sichuan, China

The presence and distribution of antibiotics and antibiotic resistance genes (ARGs) in rivers have attracted significant global concern. However, research on the contamination of typical rivers in Sichuan Province, China, remains limited. This study aimed to assess the residual levels of antibiotics across 42 national and provincial monitoring sites in nine rivers within Sichuan using UPLC-MS/MS. Ecological risk levels were evaluated through established risk assessment methods, and antibiotic resistance in Escherichia coli(E.coli) isolated from these waters was determined using the Kirby-Bauer disk diffusion method. Additionally, redundancy analysis (RDA) was conducted to explore the impact of residual antibiotics on the microbial community structure in the Minjiang River basin. Antibiotics were detected in all nine rivers studied, with the Minjiang, Tuojiang, and Jialingjiang rivers exhibiting particularly severe contamination, with concentrations ranging from 0.29 to 2233.71 ng/L. The level of antibiotic pollution in the Sichuan Basin was significantly higher than in other regions of Sichuan, likely due to the area’s high population density. Furthermore, 9.77% of E. coli isolates from the nine rivers exhibited antibiotic resistance, with over 5.8% demonstrating multidrugs resistance. Norfloxacin, amoxicillin, ampicillin, and tetracycline were identified as the primary contributors to the high ecological risk at 26 of the 42 monitoring sites. A strong correlation was observed between residual antibiotics and changes in microbial community structure. These findings provide critical insights into the distribution of antibiotics and ARGs in the rivers of Sichuan Province and highlight the urgent need for targeted strategies to mitigate antibiotic pollution. Addressing this issue is essential to protect both ecological integrity and public health.

Spatio-temporal distribution of foot and mouth disease outbreaks in Western Amhara region from January 2018 to June 2023

Foot and mouth disease (FMD) is a contagious and economically important viral disease of cloven hoofed animals caused by FMD virus. This retrospective study was conducted to determine the spatio-temporal distribution, estimate the morbidity and case fatality of FMD outbreaks in Western Amhara region of Ethiopia from January 2018 to June 2023. The FMD outbreaks reported to Bahir Dar regional laboratory and confirmed by Sandwich ELISA were used for this study. A total of 164 FMD outbreaks were reported in Western Amhara region of Ethiopia between 2018 and 2023. The highest and lowest number of FMD outbreaks were reported in 2022 (n = 42 outbreaks) and 2018 (n = 9), respectively; however, there was no statistically significant difference in the occurrence of FMD outbreaks between years (p = 0.224). There was no significant difference in case fatality and morbidity rates between years (p> 0.05). Based on months, high number of outbreaks were reported during January (n = 32) across all years and the lowest during April, June and September (n = 3 for each district). There was statistically significant difference in the occurrence of FMD outbreaks between months (p < 0.001). On average, 13.66 FMD outbreaks were reported in each month. All administrative zones in Western Amhara region reported more than one FMD outbreaks during January 2018 –June 2023. The highest and lowest outbreaks were reported from East Gojjam Zone (62 outbreaks/6 district years), and Bahirdar Special Zone and Gondar Town (2 outbreaks for each district). There was no statistically significant difference in the occurrence of FMD outbreaks between the administrative zones (p > 0.05). District wise, the highest and lowest number of FMD outbreaks were reported from Goncha Siso district (n = 14) and Banja, Simada, Zigem, Machakel, Debre Elias and Debark (with one outbreak in each district). This study indicated that FMD outbreaks regular occurs in Western Amhara region. To reduce the occurrence and spreading, animal movement and transport should be controlled, and vaccination should be implemented regularly.

Correction: Integration of metabolomics and transcriptomics provides insights into the molecular mechanism of temporomandibular joint osteoarthritis

Effects of trauma-related amputations in children on caregivers: An exploratory descriptive study in a developing country

Amputation in children is rare. However, in recent times, amputation in children has increased and trauma is the leading cause in Ghana. Few studies on the effects of amputation on caregivers particularly of children are available. This study aimed to explore the effects of trauma-related amputations in children on caregivers using qualitative descriptive phenomenological approach. In-depth interviews were conducted with semi-structured interview guide. Ten (10) informal caregivers were purposively selected from the trauma registry of a tertiary facility in Ghana. Data were analyzed manually using the thematic approach described by Collaizi. The findings revealed that trauma-related amputations in children affect the work-role, social life, finances and mental health of the caregivers. Provision of counselling services to address the mental health needs of caregivers and decentralization of orthopaedic and rehabilitation services would lessen the burden of caregiving.