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Background: In Uganda maternal and neonatal mortality remains high due to a number of factors, including poor quality of care at health facilities. Objective: This paper describes the experience of building capacity for maternal and newborn care at a district hospital and lower-level health facilities in eastern Uganda within the existing system parameters and a robust community outreach programme. Design: This health system strengthening study, part of the Uganda Newborn Study (UNEST), aimed to increase frontline health worker capacity through district-led training, support supervision, and mentoring at one district hospital and 19 lower-level facilities. A once-off supply of essential medicines and equipment was provided to address immediate critical gaps. Health workers were empowered to requisition subsequent supplies through use of district resources. Minimal infrastructure adjustments were provided. Quantitative data collection was done within routine process monitoring and qualitative data were collected during support supervision visits. We use the World Health Organization Health System Building Blocks to describe the process of district-led health facility strengthening. Results: Seventy two per cent of eligible health workers were trained. The mean post-training knowledge score was 68% compared to 32% in the pre-training test, and 80% 1 year later. Health worker skills and competencies in care of high-risk babies improved following support supervision and mentoring. Health facility deliveries increased from 3,151 to 4,115 (a 30% increase) in 2 years. Of 547 preterm babies admitted to the newly introduced kangaroo mother care (KMC) unit, 85% were discharged alive to continue KMC at home. There was a non-significant declining trend for in-hospital neonatal deaths across the 2-year study period. While equipment levels remained high after initial improvement efforts, maintaining supply of even the most basic medications was a challenge, with less than 40% of health facilities reporting no stock-outs. Conclusion: Health system strengthening for care at birth and the newborn period is possible even in low-resource settings and can be associated with improved utilisation and outcomes. Through a participatory process with wide engagement, training, and improvements to support supervision and logistics, health workers were able to change behaviours and practices for maternal and newborn care. Local solutions are needed to ensure sustainability of medical commodities.

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of soil samples collected. Unfortunately, direct measurement of the soil hydraulic functions is tedious, expensive and time consuming. Here we present a simple and cost-effective hybrid scaling approach that combines the use of ancillary information (e.g., particle-size distribution and soil bulk density) with direct measurements of saturated soil water content and saturated hydraulic conductivity. Our results demonstrate that the presented approach requires far fewer laboratory measurements than conventional scaling methods to adequately capture the spatial variability of soil hydraulic properties. © 2013. American Geophysical Union. All Rights Reserved.

Introduction: Chimeric Antigen Receptor T-cell therapy (CAR-T) represents the most recent immunotherapy’s innovation to cure some refractory and/or relapsing haematological tumours. However, because of the life-threatening toxicities it might cause such as Cytokine Release Syndrome and Immune Cell Associated Neurotoxicity Syndrome, patients are closely monitored by nurses for the early identification of toxicities during the post-infusion phase of CAR-T cell therapy. Exploring the nurses’ experience with respect to any difficulties related to the monitoring is important since these issues can be perceived by patients and affect the nurse-patient’s caring relationship, considered as a shared lived experience between the patient and the nurse. Aim: This study aims to investigate haematology nurses’ lived experience with monitoring CAR-T’s induced toxicities. Materials and methods: A qualitative study following Cohen's phenomenological methodology will be conducted through semi-structured interviews in a sample of Italian nurses working in haematology units, who have had previous experience in the management of patients undergoing CAR-T therapy for at least two months and who have performed the monitoring for the same months of experience; the interviews will be audio-recorded and then transcribed verbatim. Two researchers will carry out the manual analysis and interpretation of the collected data independently, identifying themes and sub-themes. Conclusion: To explore the nurses’ experiences in this field could facilitate the identification of the educational needs, at individual and group level. Despite it is important to consider contextual variables, the findings of this study could contribute to develop evidence supporting advanced and specialized nursing care in the haematological setting.

Abstract Intestinal parasitic infections (IPIs) can lead to significant morbidity and mortality in cancer patients. While they are unlikely to cause severe disease and are self-limiting in healthy individuals, cancer patients are especially susceptible to opportunistic parasitic infections. The gut microbiota plays a crucial role in various aspects of health, including immune regulation and metabolic processes. Parasites occupy the same environment as bacteria in the gut. Recent research suggests intestinal parasites can disrupt the normal balance of the gut microbiota. However, there is limited understanding of this co-infection dynamic among cancer patients in Malaysia. A study was conducted to determine the prevalence and relationship between intestinal parasites and gut microbiota composition in cancer patients. Stool samples from 134 cancer patients undergoing active treatment or newly diagnosed were collected and examined for the presence of intestinal parasites and gut microbiota composition. The study also involved 17 healthy individuals for comparison and control. Sequencing with 16S RNA at the V3–V4 region was used to determine the gut microbial composition between infected and non-infected cancer patients and healthy control subjects. The overall prevalence of IPIs among cancer patients was found to be 32.8%. Microsporidia spp. Accounted for the highest percentage at 20.1%, followed by Entamoeba spp. (3.7%), Cryptosporidium spp. (3.0%), Cyclospora spp. (2.2%), and Ascaris lumbricoides (0.8%). None of the health control subjects tested positive for intestinal parasites. The sequencing data analysis revealed that the gut microbiota diversity and composition were significantly different in cancer patients than in healthy controls (p

Abstract Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.

The gold standard for diagnosing malaria remains microscopic examination; however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ $0.5=96$ %) than YOLOv4 (mAP@ $0.5=89$ %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ $0.5=90$ %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4’s localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.

Abstract Background Malaria parasites such as Plasmodium knowlesi, P. inui, and P. cynomolgi are spread from macaques to humans through the Leucosphyrus Group of Anopheles mosquitoes. It is crucial to know the distribution of these vectors to implement effective control measures for malaria elimination. Plasmodium knowlesi is the most predominant zoonotic malaria parasite infecting humans in Malaysia. Methods Vector data from various sources were used to create distribution maps from 1957 to 2021. A predictive statistical model utilizing logistic regression was developed using significant environmental factors. Interpolation maps were created using the inverse distance weighted (IDW) method and overlaid with the corresponding environmental variables. Results Based on the IDW analysis, high vector abundances were found in the southwestern part of Sarawak, the northern region of Pahang and the northwestern part of Sabah. However, most parts of Johor, Sabah, Perlis, Penang, Kelantan and Terengganu had low vector abundance. The accuracy test indicated that the model predicted sampling and non-sampling areas with 75.3% overall accuracy. The selected environmental variables were entered into the regression model based on their significant values. In addition to the presence of water bodies, elevation, temperature, forest loss and forest cover were included in the final model since these were significantly correlated. Anopheles mosquitoes were mainly distributed in Peninsular Malaysia (Titiwangsa range, central and northern parts), Sabah (Kudat, West Coast, Interior and Tawau division) and Sarawak (Kapit, Miri, and Limbang). The predicted Anopheles mosquito density was lower in the southern part of Peninsular Malaysia, the Sandakan Division of Sabah and the western region of Sarawak. Conclusion The study offers insight into the distribution of the Leucosphyrus Group of Anopheles mosquitoes in Malaysia. Additionally, the accompanying predictive vector map correlates well with cases of P. knowlesi malaria. This research is crucial in informing and supporting future efforts by healthcare professionals to develop effective malaria control interventions. Graphical Abstract

Background: It is unknown how the novel Coronavirus SARS‐CoV‐2, the cause of the current acute respiratory illness COVID‐19 pandemic that has infected millions of people, affects people with intellectual and developmental disability (IDD). The aim of this study is to describe how individuals with IDD have been affected in the first 100 days of the COVID‐19 pandemic. Methods: Shortly after the first COVID‐19 case was reported in the USA, our organisation, which provides continuous support for over 11 000 individuals with IDD, assembled an outbreak committee composed of senior leaders from across the health care organisation. The committee led the development and deployment of a comprehensive COVID‐19 prevention and suppression strategy, utilising current evidence‐based practice, while surveilling the global and local situation daily. We implemented enhanced infection control procedures across 2400 homes, which were communicated to our employees using multi‐faceted channels including an electronic resource library, mobile and web applications, paper postings in locations, live webinars and direct mail. Using custom‐built software applications enabling us to track patient, client and employee cases and exposures, we leveraged current public health recommendations to identify cases and to suppress transmission, which included the use of personal protective equipment. A COVID‐19 case was defined as a positive nucleic acid test for SARS‐CoV‐2 RNA. Results: In the 100‐day period between 20 January 2020 and 30 April 2020, we provided continuous support for 11 540 individuals with IDD. Sixty‐four per cent of the individuals were in residential, community settings, and 36% were in intermediate care facilities. The average age of the cohort was 46 ± 12 years, and 60% were male. One hundred twenty‐two individuals with IDD were placed in quarantine for exhibiting symptoms and signs of acute infection such as fever or cough. Sixty‐six individuals tested positive for SARS‐CoV‐2, and their average age was 50. The positive individuals were located in 30 different homes (1.3% of total) across 14 states. Fifteen homes have had single cases, and 15 have had more than one case. Fifteen COVID‐19‐positive individuals were hospitalised. As of 30 April, seven of the individuals hospitalised have been discharged back to home and are recovering. Five remain hospitalised, with three improving and two remaining in intensive care and on mechanical ventilation. There have been three deaths. We found that among COVID‐19‐positive individuals with IDD, a higher number of chronic medical conditions and male sex were characteristics associated with a greater likelihood of hospitalisation. Conclusions: In the first 100 days of the COVID‐19 outbreak in the USA, we observed that people with IDD living in congregate care settings can benefit from a coordinated approach to infection control, case identification and cohorting, as evidenced by the low relative case rate reported. Male individuals with higher numbers of chronic medical conditions were more likely to be hospitalised, while most younger, less chronically ill individuals recovered spontaneously at home. [ABSTRACT FROM AUTHOR]

Abstract Background While microbiomes in industrialized societies are well characterized, indigenous populations with traditional lifestyles have microbiomes that are more akin to those of ancient humans. However, metagenomic data in these populations remains scarce, and the association with soil-transmitted helminth infection status is unclear. Here, we sequenced 650 metagenomes of indigenous Malaysians from five villages with different prevalence of helminth infections. Results Individuals from villages with higher prevalences of helminth infections have more unmapped reads and greater microbial diversity. Microbial community diversity and composition were most strongly associated with different villages and the effects of helminth infection status on the microbiome varies by village. Longitudinal changes in the microbiome in response to albendazole anthelmintic treatment were observed in both helminth infected and uninfected individuals. Inference of bacterial population replication rates from origin of replication analysis identified specific replicating taxa associated with helminth infection. Conclusions Our results indicate that helminth effects on the microbiota were highly dependent on context, and effects of albendazole on the microbiota can be confounding for the interpretation of deworming studies. Furthermore, a substantial quantity of the microbiome remains unannotated, and this large dataset from an indigenous population associated with helminth infections is a valuable resource for future studies. Video Abstract

Objective(s): The occurrence of asymptomatic verocytotoxin (VT)-producing Escherichia coli (VTEC) infections among humans in recent years is posing a high risk to public health. Thus, the role of asymptomatic human carriers as a source of dissemination should not be underestimated. This study aimed to elucidate the phenotypic and genotypic characteristics of E. coli in the stool samples collected from indigenous individuals in Malaysia. Materials and Methods: E. coli strains (n=108) were isolated from stool samples obtained from 41 indigenous individuals. All strains were subjected to Repetitive Extragenic Palindromic-Polymerase Chain Reaction (REP-PCR) typing and confirmation of VTEC variants. Non-duplicate strains were selected based on REP-PCR profiles and further subjected to antimicrobial susceptibility test (AST). The genotypic and phenotypic characteristics of the strains were then correlated with the demographic data of the subjects. Results: A total of 66 REP-PCR profiles grouped in 53 clusters (F=85%) were obtained. Four genetically distinct strains were confirmed as VTEC (eaeA-positive). The predominant resistance was against ampicillin (34.2%), followed by trimethoprim-sulfamethoxazole (32.9%), ampicillin-sulbactam (5.5%), and ciprofloxacin (1.4%). All isolates were sensitive to amoxicillin-clavulanate, cefuroxime, ceftriaxone, imipenem, and meropenem. Conclusion: Genetically diverse E. coli and VTEC strains were found to colonize the intestines of the indigenous populations. This study is important for the prospective surveillance of E. coli among the indigenous individuals in Malaysia, especially in asymptomatic VTEC infection and antimicrobial resistance phenomenon.

Abstract Dengue remains a major public threat and existing dengue control/surveillance programs lack sensitivity and proactivity. More efficient methods are needed. A cluster randomized controlled trial was conducted for 18 months to determine the efficacy of using a combination of gravid oviposition sticky (GOS) traps and dengue non-structural 1 (NS1) antigen for early surveillance of dengue among Aedes mosquito. Eight residential apartments were randomly assigned into intervention and control groups. GOS traps were placed at the intervention apartments weekly to trap Aedes mosquitoes and these tested for dengue NS1 antigen. When dengue-positive pool was detected, the community were notified and advised to execute protective measures. Fewer dengue cases were recorded in the intervention group than the control. Detection of NS1-positive mosquitoes was significantly associated with GOS Aedes index (rs = 0.68, P

Abstract Background Plasmodium knowlesi, a simian malaria parasite infection, increases as Plasmodium falciparum and Plasmodium vivax infections decrease in Johor, Malaysia. Therefore, this study aimed to identify the distribution of vectors involved in knowlesi malaria transmission in Johor. This finding is vital in estimating hotspot areas for targeted control strategies. Methods Anopheles mosquitoes were collected from the location where P. knowlesi cases were reported. Cases of knowlesi malaria from 2011 to 2019 in Johor were analyzed. Internal transcribed spacers 2 (ITS2) and cytochrome c oxidase subunit I (COI) genes were used to identify the Leucosphyrus Group of Anopheles mosquitoes. In addition, spatial analysis was carried out on the knowlesi cases and vectors in Johor. Results One hundred and eighty-nine cases of P. knowlesi were reported in Johor over 10 years. Young adults between the ages of 20–39 years comprised 65% of the cases. Most infected individuals were involved in agriculture and army-related occupations (22% and 32%, respectively). Four hundred and eighteen Leucosphyrus Group Anopheles mosquitoes were captured during the study. Anopheles introlatus was the predominant species, followed by Anopheles latens. Spatial analysis by Kriging interpolation found that hotspot regions of P. knowlesi overlapped or were close to the areas where An. introlatus and An. latens were found. A significantly high number of vectors and P. knowlesi cases were found near the road within 0–5 km. Conclusions This study describes the distribution of P. knowlesi cases and Anopheles species in malaria-endemic transmission areas in Johor. Geospatial analysis is a valuable tool for studying the relationship between vectors and P. knowlesi cases. This study further supports that the Leucosphyrus Group of mosquitoes might be involved in transmitting knowlesi malaria cases in Johor. These findings may provide initial evidence to prioritize diseases and vector surveillance.

Cover crops can provide substantial quantities of N for subsequent crops, but estimating the amount of N that will be mineralized from residues is challenging. Complex interactions of residue chemistry with soil temperature and soil water content affect N mineralization during residue decomposition. A simulation model can describe these interactions and provide estimates of N mineralized if specific soil water and temperature data are available. Our objectives are (i) to describe a web-based N mineralization model and its operation, (ii) to calibrate the model with results from published N mineralization studies, and (iii) to validate it using field studies investigating decomposition of surface-applied or incorporated crimson clover (Trifolium incarnatum L.) or rye (Secale cereale L.) residues over 3 yr. Inputs required by the model include residue N, nonstructural carbohydrates, cellulose + hemi-cellulose, and lignin contents, as well as 5-yr average values of daily soil temperature and soil water content from a user-selected weather station. The model was successfully calibrated with published data from eight laboratory and field studies and was validated with data from field studies that used soil cores with cover crop residues. Simulated values of N mineralized were acceptable for incorporated residues but tended to overpredict N mineralized from surface residues because soil temperature and water content are not good drivers to simulate N mineralization from residues on the soil surface. Additional research is needed to develop algorithms to estimate temperature and water content/water potential of surface residues so they can be used as driver variables for the model. [ABSTRACT FROM AUTHOR]

Summary: The reproductive biology of cobia, Rachycentron canadum, from the coastal waters of Dungun, Malaysia was studied from June 2014 to May 2015. From commercial trawls, a total of 201 samples (combined sexes) were collected (fork lengths [FL] 37.5–124.0 cm; body weights 0.5–20.4 g). The overall sex ratio of females to males was 1:0.9, which was not significantly different (χ2 = 2.12, df = 1; p < .05). Estimations of length at 50% maturity (L50) showed that both sexes matured at approximately 75 cm FL; estimated spawning frequency was 6 days. Mean batch fecundity (BF) ranged from 0.55 to 4.32 million eggs. The average number of eggs per gram of ovary was from 2,100 to 5,400 eggs, and relative fecundity 147 eggs/g. There was a weak positive correlation (r2 = .48) between BF and female FL as well as BF with an ovary‐free body weight (r2 = .56), possibly due to females being in a continuous spawning condition and some possibly half‐spent, based on the histological examination of the female gonads. Despite cobia being asynchronous spawners, the gonadosomatic index in both males and females showed peaks in June, November, and particularly March. Based on histological examination, spawning‐capable males were encountered throughout the study period, whereas spawning‐capable females in the late developing subphase were found mostly in March and April. This is the first study on the reproductive aspects of cobia in Malaysian waters. [ABSTRACT FROM AUTHOR]