Leptospirosis, also known as “rat-urine disease”, is a neglected zoonotic and waterborne disease that is caused by Leptospira spp. This disease is transmitted by direct and indirect exposure to the urine and stool of infected animals. The current estimate has highlighted that leptospirosis has caused at least one million cases and 60,000 deaths, with high endemicity in tropical regions. With climate change, urbanisation, and increasing human-animal interaction, the threat of leptospirosis and other zoonotic diseases will continue to emerge. Investing in multidisciplinary research, technology, and global collaboration is critical to anticipate, detect, and respond effectively to these evolving threats.

BACKGROUND:Hookworm is a widely known soil-transmitted helminth (STH) traditionally linked to iron-deficiency anemia caused by chronic blood loss in the small intestine. The classical human pathogens are Ancylostoma duodenale and Necator americanus. However, emerging evidence indicates that Ancylostoma ceylanicum, a hookworm of cats and dogs, is a significant cause of human infection, particularly in Southeast Asia and other tropical regions. Its detection in human populations signals an evolving epidemiological landscape, challenging the previous notion that zoonotic hookworms have a negligible impact on human health. CASE SERIES:This series presents three atypical cases that challenge classic paradigms of hookworm disease. In all cases, adult worms were retrieved from the colon by endoscopy and subsequently identified by molecular analysis. The first case is a young man with profound eosinophilia and watery diarrhea. Adult hookworms were found ectopically in the colon, an atypical site, demonstrating the worm's potential for aberrant migration and a severe systemic immune response. The second case is an elderly male with multiple chronic conditions whose refractory anemia was unexpectedly attributed to a colonic infection discovered during a routine exam, underscoring hookworm as a treatable cause of anemia in geriatric populations. The third case is a young female with colitis-like symptoms. Colonoscopy revealed widespread superficial ulceration and live worms, highlighting the mimicry of inflammatory bowel disease. CONCLUSION:Collectively, these cases emphasize the diagnostic value of endoscopy in identifying hookworm infection, its diverse clinical manifestations beyond chronic anemia, and the requirement for the consideration of hookworm infection in the differential diagnosis of eosinophilia, unexplained colitis, and refractory iron deficiency across a wide demographic spectrum.

Malaria remains a major public health concern, especially with zoonotic Plasmodium knowlesi presenting specific challenges in Southeast Asia. In Malaysia, Sarawak ranks second in P. knowlesi cases after Sabah. Rural populations in endemic areas experience increased exposure to forest and forest-edge zones where vector mosquitoes breed, thereby raising infection risk and complicating routine surveillance. These groups may carry asymptomatic or submicroscopic infections that act as hidden reservoirs, potentially hindering elimination efforts. This study aimed to assess the prevalence of Plasmodium infections, including submicroscopic cases, among rural communities in Kapit Division, a malaria-endemic area. A cross-sectional study was conducted from March 2024 to August 2025, including 376 participants aged 2 to 90 years. Venous blood samples were examined under a light microscope with Giemsa-stained thick and thin smears. Additionally, all samples underwent species-specific nested PCR to verify microscopy results and identify sub-microscopic infections. Hemoglobin levels were measured using a HemoCue® Hb 201 + analyzer. Face-to-face interviews were conducted using a standardized questionnaire. The data analysis was performed with IBM SPSS version 27.0. The overall malaria prevalence was 5.32

The human gut microbiota is essential for supporting the host’s health and immune system. Imbalances in this microbiota, known as dysbiosis, are associated with numerous inflammatory and autoimmune diseases. Despite extensive research on bacterial components, the significant modulatory role of eukaryotic gut inhabitants, particularly intestinal parasites, remains largely overlooked. While traditionally viewed as pathogens, emerging evidence highlights a profound duality in their role, suggesting they are integral and active modulators of the gut microbiota and challenging the conventional view of parasitism. For instance, helminths often induce microbial diversity and promote the expansion of anti-inflammatory microbes. Conversely, pathogenic protozoa are generally associated with reduced microbial diversity, fostering the growth of pathobionts and leading to significant gut dysbiosis. However, these interactions are highly context-dependent, and a comprehensive understanding is hindered by varied findings and limited data, particularly concerning protozoan infections. Furthermore, while mechanistic evidence is robust, human intervention studies remain limited. This narrative review synthesises the complex interrelationship between intestinal parasites and the human gut microbiota, distinguishing the distinct dynamics of helminthic and protozoan infections. Specifically, this review examines the inherent duality of intestinal parasites by investigating parasite-specific factors, including species, parasite burden, and coinfections, which can impact gut microbial composition and function. Ultimately, this review provides a comprehensive framework for understanding the profound influence of the gut ecosystem, shifting the paradigm beyond solely pathogenic views.

Over the past decade, the occurrence of milk-borne infections caused by Shiga toxin-producing Escherichia coli (STEC) and Salmonella enterica serovar Typhimurium (S. Typhimurium) has adversely affected consumer health and the milk industry. We aimed to detect and genotype the strains of E. coli and S. Typhimurium isolated from cow and goat milks using two genotyping tools, BOX-PCR and ERIC-PCR. A total of 200 cow and goat milk samples were collected from the dairy farms in First, E. coli and Salmonella spp. detected in the samples were characterized using PCRs to identify pathogenic strains, STEC and S. Typhimurium. Next, the bacterial strains were genotyped using ERIC-PCR and BOX-PCR to determine their genetic relatedness. Out of 200 raw milk samples, 46.5% tested positive for non-STEC, 39.5% showed the presence of S. Typhimurium, and 11% were positive for STEC. The two genotyping tools showed different discrimination indexes, with BOX-PCR exhibiting a higher index mean (0.991) compared to ERIC-PCR (0.937). This suggested that BOX-PCR had better discriminatory power for genotyping the bacteria. Our study provides information on the safety of milk sourced from dairy farms, underscoring the importance of regular inspections and surveillance at the farm level to minimize the risk of E. coli and Salmonella outbreaks from milk consumption.

Malaysia's malaria rate has declined but remains a public health concern, with limited investigations into malaria and coinfections with soil-transmitted helminth (STH) infections. A cross-sectional study using convenience sampling in Orang Asli villages enrolled 437 villagers aged 1-83 years based on their willingness to participate. Blood samples were tested microscopically for malaria, followed by nested polymerase chain reaction (PCR), and stool samples were screened microscopically for STH eggs. Body temperature, demographic, and socioeconomic data were collected. Malaria parasite was detectable only via PCR, with a 15.3% prevalence, indicating submicroscopic malaria parasitemia; none of the positive cases presented fever. The identified species included Plasmodium vivax (8.7%), Plasmodium cynomolgi (5.5%), Plasmodium knowlesi (4.3%), Plasmodium falciparum (1.8%), Plasmodium inui (0.2%), and Plasmodium malariae (0.2%). Females had significantly higher rates of submicroscopic malaria parasitemia (19.6%) compared with males (9.3%, P = 0.003). STH infections were highly prevalent (71.4%), with Trichuris trichiura (65.2%), Ascaris lumbricoides (35.0%), and hookworm (14.6%). STH infection was associated with age (P <0.001), peaking in individuals aged 10-19 years (86.2%) and 1-9 years (83.0%), as well as with students (84.3% versus 60.8% in employed and 60.3% in unemployed; P <0.001) and low-income households (76.4% versus 61.7% in higher-income households; P = 0.002). Submicroscopic malaria parasitemia and STH coinfections were present in 8.9% of participants, with higher rates in low-income households (12.6% versus 5.2% in higher-income, P = 0.010). The Negrito tribe exhibited the highest prevalence of submicroscopic malaria parasitemia, STH, and coinfections (P <0.05). This study highlights the need for integrated malaria and STH control strategies, particularly for the Negrito tribe.

Hookworms are blood-sucking intestinal parasites that can cause anaemia and protein loss in humans. Ancylostoma ceylanicum, a zoonotic hookworm species of dogs, is the second most common cause of human hookworm infections. With the increasing anthelmintic resistance risks and the uncontrolled stray dog population in Sarawak Borneo, East Malaysia, understanding the genetic structure of A. ceylanicum is crucial for tracking mutation patterns and assessing zoonotic transmission risks. This study determined the prevalence and genetic diversity of dog hookworm species using microscopy, PCR and sequencing, revealing A. ceylanicum (43.6%; 89/204), followed by mixed infections of A. ceylanicum and A. braziliense (9.3%; 19/204), single infections of A. caninum (6.3%; 13/204), and A. braziliense (1.4%; 3/204) in stray dogs in East Malaysia (Sarawak Borneo). Phylogenetic analysis of the cytochrome oxidase subunit 1 (COX1) gene showed that A. ceylanicum from Sarawak Borneo clustered across all major clades, indicating high genetic divergence and admixture. Haplotype analysis revealed that the Malaysian A. ceylanicum population highly mirrors those in Cambodia and Thailand, suggesting significant gene flow across Southeast Asia, while regional disparities exist compared to other countries. These findings provide critical epidemiological insights for hookworm control strategies, including stray dog management and potential adjustments to mass drug administration programs. The high genetic connectivity of A. ceylanicum population across borders underscores the need for enhanced surveillance, One Health approaches, and monitoring anthelminthic resistance to mitigate the risk of zoonotic transmission.

Plasmodium knowlesi has emerged as a significant zoonotic malaria threat, particularly in Southeast Asia, where its incidence continues to rise. Timely and accurate diagnosis of its blood stages is critical for effective diagnosis and treatment, as disease severity and transmission dynamics vary across different stages. Microscopic examination is the gold standard for malaria diagnosis; however, it is labour-intensive and requires professional interpretation. This makes it prone to variability and possible misclassification, especially among morphologically identical Plasmodium species. Recent advancements in artificial intelligence (AI)-driven approaches, particularly deep learning, offer significant potential to assist microscopists in automating blood-stage identification, reducing diagnostic variability, and improving efficiency without replacing expert validation. However, the research on AI-based classification of P. knowlesi blood stages remains limited. This systematic review critically evaluates the datasets, preprocessing methods, and deep learning techniques used for Plasmodium blood-stage classification with a specific focus on P. knowlesi. Unlike previous reviews that primarily address species classification, this study provides an in-depth comparative analysis of AI-driven blood-stage identification, emphasizing the effectiveness of convolutional neural networks (CNNs), transfer learning, ensemble learning, and object detection models such as YOLO and Faster R-CNN. Additionally, this review highlights key challenges, including limited annotated datasets, class imbalance, and interpretability concerns that persist. Addressing these gaps through enhanced dataset curation, domain adaptation strategies, and explainable AI approaches will be crucial in advancing AI-driven P. knowlesi diagnostics.

Recent studies suggested a potential connection between gut microbiota changes and cancer onset. However, conflicting results make it challenging to understand the role of gut microbiota dysbiosis in cancer, particularly in underrepresented populations like those in Southeast Asia. To address this gap, we analysed the diversity and composition of gut microbiota in 65 faecal samples, which included 48 from cancer patients with various malignancies and 17 from healthy controls. Patients were categorised into four groups: symptomatic patients undergoing cancer treatment, asymptomatic pre-treatment and during cancer treatment, and healthy controls. Genomic DNA was extracted, and the V3-V4 region of the 16 S rRNA gene was sequenced. Our findings revealed significant differences in the alpha diversity (p ≤ 0.05) between cancer patients and controls. Asymptomatic patients under treatment showed slightly lower alpha diversity than pre-treatment patients, but this difference was not statistically significant (p = 0.06). We identified 13 genera with over 20% difference in abundance between patient groups and controls. Asymptomatic patients receiving treatment and pre-treatment patients exhibited enrichment in Enterococcus, whereas Prevotella, Faecalibacterium, Brevundimonas, and Pseudomonas were significantly reduced compared to controls. Symptomatic patients had higher levels of Enterococcus and Staphylococcus, while Ruminococcus was enriched in asymptomatic patients. These underscore the distinct differences in gut microbiota composition between cancer patients and healthy individuals, particularly in symptomatic cases with potential biomarkers such as Enterococcus, Prevotella, and Faecalibacterium. Our study suggests that cancer treatment may not significantly alter the gut profile of cancer patients. Further research is needed to comprehend the implications of these findings fully.

Background Since 2017, the state of Sarawak in Malaysia has been in a relentless battle to contain the rabies outbreak, with 79 recorded cases of human rabies and 72 fatalities. This study, therefore, is not just an investigation but a call to action to understand the factors influencing rabies protective practices among dog owners in the southern zone of Sarawak, Malaysia. Methodology This comprehensive study, conducted in the southern zone of Sarawak from January to August 2024, involved 295 dog owners aged 18 and above residing in urban and rural areas with diverse socioeconomic backgrounds and ethnic groups. Researchers used a range of statistical methods, including descriptive statistics, Pearson's chi-square, Fisher's exact test, and binary logistic regression to analyse data and identify predictors influencing rabies protective practices, with a significance level of p<0.05 for all analyses. The thoroughness of the study ensures the reliability of the findings. Results . Among the 295 respondents, the average age was 40.5 ± 13.5 years. The majority of respondents, 76 (25.8%), were aged 30–39 years. Of the participants, 164 (55.6%) were female, 109 (36.9%) were of Iban ethnicity, 130 (44.1%) had tertiary education, and 169 (57.3%) resided in rural areas. Regarding rabies awareness, 149 (50.5%) strongly agreed that dogs can spread rabies, while 115 (30.0%) agreed that the symptoms of rabies in dogs are easily recognisable. Additionally, 147 (40.8%) strongly supported the idea that vaccinating all stray dogs is the most effective way to control the spread of rabies. Good rabies protective practices were significantly associated with the level of knowledge (p < 0.001) and attitude level (p < 0.002). Specifically, those with good knowledge and a positive attitude were likelier to exhibit good rabies protective practices. Conclusion Strong knowledge, positive attitudes, and effective practices related to rabies prevention among dog owners in Southern Sarawak provide an opportunity for local government and non-governmental organisations to improve rabies control through community education, regular vaccination campaigns, and professional training. These efforts can help put an end to the rabies outbreak in Sarawak.

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 < 0.001). A significant dissimilarity was observed in the bacterial composition between parasite-infected and non-infected patients based on Bray–Curtis (p = 0.041) and Jaccard (p = 0.021) measurements. Bacteria from the genus Enterococcus were enriched in the parasite-infected groups, while Faecalibacterium prausnitzii reduced compared to non-infected and control groups. Further analysis between different IPIs and non-infected individuals demonstrated a noteworthy variation in Entamoeba-infected (unweighted UniFrac: p = 0.008), Cryptosporidium-infected (Bray–Curtis: p = 0.034) and microsporidia-infected (unweighted: p = 0.026; weighted: p = 0.019; Jaccard: p = 0.031) samples. No significant dissimilarity was observed between Cyclospora-infected groups and non-infected groups. Specifically, patients infected with Cryptosporidium and Entamoeba showed increased obligate anaerobic bacteria. Clostridiales were enriched with Entamoeba infections, whereas those from Coriobacteriales decreased. Bacteroidales and Clostridium were found in higher abundance in the gut microbiota with Cryptosporidium infection, while Bacillales decreased. Additionally, bacteria from the genus Enterococcus were enriched in microsporidia-infected patients. In contrast, bacteria from the Clostridiales order, Faecalibacterium, Parabacteroides, Collinsella, Ruminococcus, and Sporosarcina decreased compared to the non-infected groups. These findings underscore the importance of understanding and managing the interactions between intestinal parasites and gut microbiota for improved outcomes in cancer patients.

The challenge of dengue control due to the unavailability of a specific medication stresses the importance of releasing Wolbachia-carrying mosquitoes through vector control programs. This study investigated the sustainability and frequency of Wolbachia in Wolbachia-dengue-carrying mosquitoes in two dengue hotspot localities in Selangor. A modified sticky ovitrap was used to collect adult mosquitoes in two Wolbachia-releasing areas in Selangor, Kelana Puteri and Kelana D'Putera condominiums. All mosquito samples were subjected to PCR using wsp-specific primers for Wolbachia detection. Dengue virus was detected using RT-PCR, followed by multiplex-PCR. Out of the 80 Aedes spp. collected, Ae. aegypti was the most predominant species. More than one-third of Ae. aegypti were positive for Wolbachia, with 22.9

Despite the persistency of World Health Organization to eliminate malaria since 1987, malaria disease continues to pose a significant threat to global health. As the severity of malaria persists over the years, there is a critical need for an automated diagnosis system for more efficient diagnosis and effective treatment administration. To mitigate the increase in mosquito-borne diseases, there has been a heightened interest in the application of Artificial intelligence (AI), specifically deep learning. With the assistance of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework, an extensive review of current state of automated malaria diagnosis systems utilizing machine learning and deep learning approaches was performed across eight scientific databases, with 50 articles shortlisted from the years 2015-2023. Besides, identifying the research gaps, we synthesise the existing literature, analyse the outcomes, and explore the critical parameters that influence model performance. From the review, the prevailing models primarily focus on binary classification while disregarding cross-dataset validations and multi-stage classification. This gap challenges the delivering effective treatments, especially considering potential drug resistance. Established protocols and classification models are needed to anticipate the specific malaria species. The keywords in automated malaria diagnosis that we identified include machine learning, deep learning, transfer learning, and convolutional neural networks. Through examinations of the constraints in current methodologies, we provide valuable suggestions that could propel the field of automated malaria diagnosis. This systematic review provides a comprehensive overview, critical insights, and a roadmap for future research endeavours in this vital domain of healthcare.

Leptospirosis is a severe and potentially fatal re-emerging zoonotic and waterborne disease caused by pathogenic and intermediate species of Leptospira. Given the high global rates of morbidity and mortality associated with this disease, there is an urgent need to explore alternative therapeutic agents to enhance treatment options. This study investigates the anti-leptospiral efficacy of several common antibiotics-penicillin G, doxycycline, ampicillin, amoxicillin, cefotaxime, chloramphenicol, and erythromycin, as well as extracts from local herbs, Hydnophytum formicarum Jack and Boesenbergia stenophylla, against pathogenic and intermediate Leptospira strains. A broth microdilution method determined the minimum inhibitory concentration (MIC) for the antibiotics and herb extracts. Both herbs were extracted using four different solvents: ethyl acetate, methanol, hexane, and chloroform. The extracts were then analysed using gas chromatography-mass spectrometry (GC-MS) to identify their phytochemical compounds. The results demonstrated that cefotaxime and erythromycin exhibited the highest anti-leptospiral activity, with MIC values of 0.2 µg/mL. This was followed by amoxicillin and ampicillin (0.2-0.39 µg/mL), penicillin G (0.39-3.13 µg/mL), chloramphenicol (0.78-3.13 µg/mL), and doxycycline (0.78-12.5 µg/mL). H. formicarum Jack and B. stenophylla extract extractions displayed the lowest MICs (62.5 µg/mL) for the ethyl acetate, methanol, and hexane extracts. They contained various phytochemical constituents, including some with anti-leptospiral properties. These findings indicate that different strains of Leptospira respond with varying levels of inhibition to the antibiotics and herb extracts studied. The extracts from H. formicarum Jack and B. stenophylla may have potential as anti-leptospiral drugs. However, further in-vivo studies are needed to better understand their efficacy against Leptospira.

Soil-transmitted helminths (STHs) are known as one of the neglected parasitic diseases, leading to significant health issues and associated complications. This study aims to assess the current prevalence of STH infections and the associated risk factors among rural primary schoolchildren in Malaysia. A cross-sectional study was conducted among 638 schoolchildren (7-11 years old) from 10 rural primary schools in five regions of Malaysia. The overall prevalence of STH infections among schoolchildren was 54.5%, with T. trichiura being the predominant STH species (50.9%), followed by A. lumbricoides (19.6%) and hookworms (7.4%). The highest prevalence of STH infections was recorded in the schools in Perak (96.6%), followed by Pahang (85.4%), Johor (42.1%) and Sabah (6.2%). At the same time, none of the schoolchildren in Sarawak were infected with STHs. The findings also highlighted that the older age group (10-11 years old) exhibited a higher prevalence of STH infection and T. trichiura compared to those aged 7-9 years old (P = 0.01) among the schools with a high prevalence of STH infections (>= 70%). Several variables, such as being female (1.9 [1.2, 3.0]) (Adjusted odd ratio [95% confidence interval]), low household income (30.9 [7.0, 136.5]), using untreated water supply (1.9 [1.1, 3.2]), indiscriminate defaecation (1.9 [1.1, 3.1]), indiscriminate garbage disposal (2.8 [1.3, 6.0]), eating with hands (5.9 [3.4, 10.4]) and experiencing pallor signs (2.3 [1.1, 5.0]), emerged as significant predictors of STH infections in this study population. The present study underscores that in specific rural community areas of Malaysia, STH infections continue to pose health concerns among primary schoolchildren. Hence, to ensure the sustained effectiveness of the measures taken to control STH infections, a collaborative and ongoing effort between various stakeholders is imperative to provide targeted support to rural communities, especially those in areas lacking essential amenities and healthcare services.

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

Vitamin A deficiency (VAD) remains a significant contributor to childhood morbidity and mortality in developing countries; therefore, the implementation of sustainable and cost-effective approaches to control VAD is of utmost pertinence. This study aims to investigate the efficacy of red palm olein (RPO)-enriched biscuit supplementation in improving vitamin A, haematological, iron, and inflammatory status among vitamin A-deficient schoolchildren. We conducted a double-blinded, randomised controlled trial involving 651 rural primary schoolchildren (8–12 years) with VAD in Malaysia. The schoolchildren were randomised to receive either RPO-enriched biscuits (experimental group, n = 334) or palm olein-enriched biscuits (control group, n = 317) for 6-month duration. Significant improvements in retinol and retinol-binding protein 4 levels were observed in both groups after supplementation (P < 0.001). The improvement in retinol levels were similar across groups among subjects with confirmed VAD (P = 0.40). Among those with marginal VAD, greater improvement in retinol levels was recorded in the control group (P < 0.001) but lacked clinical significance. The levels of α- and β-carotenes, haematological parameters (haemoglobin, packed cell volume, mean corpuscular volume and mean corpuscular haemoglobin) and iron enhanced more significantly in the experimental group (P < 0.05). The significant reduction in the prevalence of microcytic anaemia (− 21.8

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.

Additional file 1: Table S1. MaAsLin2 results of the bacterial taxa differentially abundant between OA and KL subjects independent of village, age, and sex as covariates. Table S2. Relative impact of village, helminth infection and Trichuris infection on gut microbiome dissimilarity across samples (ADONIS, ANOSIM, and Betadisper; permutation = 999) in the cross-sectional analysis. Table S3. MaAsLin2 results of the bacterial taxa that are independent and associated with the interaction between helminth and village. Table S4. Relative impact of deworming on gut microbiome dissimilarity of different group of Orang Asli samples based on their microbiome data in pre and post anthelmintic treatment (ADONIS, ANOSIM, and Betadisper; permutation = 999). Table S5. MaAsLin2 results of the bacterial taxa that are altered by treatment response, controlling for infection status and village as fixed effects. Table S6. MaAsLin2 results of the bacterial taxa that are associated with treatment response, associated with helminth status or not, correcting for village as a covariate. Table S7. Relative impact of the deworming on gut microbiome dissimilarity of different group of Orang Asli samples based on their gut microbiome data in pre, 21-day, and 42-day post- anthelmintic treatment (ADONIS, ANOSIM, and Betadisper; permutation = 999). Table S8. Spearman correlation analysis on the growth rate (GRiD score) of the bacterial species with Trichuris burden among the pre-treatment samples. Table S9. Spearman correlation analysis on the growth rate (GRiD score) of the bacterial species with Trichuris burden among the Responders. Table S10. MaAsLin2 results of the bacterial replication (based on GRiD score) that are associated with Trichuris infection while controlling for treatment group and villages.

Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effectiveness depends on the trained microscopist's skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to determine the most suitable deep-learning object detection architecture and their applicability to detect and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and from four stages of infection with 80/20 train and test data partition. The performance of object detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The best-performing model was also assessed with an independent dataset to verify the models' ability to generalize in different domains. The results show that upon training, the Yolov4 model achieves a precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin blood smear images. Object detectors can complement a deep learning classification model in detecting infected cells since they eliminate the need to train on single-cell images and have been demonstrated to be more feasible for a different target domain.