Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

Cryptosporidiosis is one of the leading causes of diarrhoeal illness and mortality induced by protozoan pathogens worldwide. As a largely waterborne disease, emphasis has been given to the study of Cryptosporidium spp. in surface waters, readily susceptible to pathogenic contamination. Conversely, the status of Cryptosporidium in potable groundwater sources, generally regarded as a pristine and "safe" drinking-water supply owing to (sub)-soil protection, remains largely unknown. As such, this investigation presents the first literature review aimed to ascertain the global prevalence of Cryptosporidium in groundwater supply sources intended for human consumption. Thirty-seven peer-reviewed studies were identified and included in the review. Groundwater sample and supply detection rates (estimated 10-20%) indicate Cryptosporidium is frequently present in domestic groundwater sources, representing a latent health concern for groundwater consumers. Specifically, sample (10.4%) and source (19.1%) detection rates deriving from comprehensive "temporal" investigations are put forward as representative of a contamination 'baseline' for Cryptosporidium in 'domestic' groundwater supplies. Proposed 'baseline' prevalence figures are largely applicable in preventive risk-based catchment and groundwater quality management including the formulation of Quantitative Microbial Risk Assessment (QMRA). Notwithstanding, a large geographical disparity in available investigations and lack of standardized reporting restrict the transferability of research findings. Overall, the mechanisms responsible for Cryptosporidium transport and ingress into groundwater supplies remain ambiguous, representing a critical knowledge gap, and denoting a distinctive lack of integration between groundwater and public-health sub-disciplines among investigations. Key recommendations and guidelines are provided for prospective studies directed at more integrative and multi-disciplinary research.

SUMMARY: Ireland reports the highest incidence of verotoxigenicEscherichia coli(VTEC) infection in Europe. This study investigated potential risk factors for confirmed sporadic and outbreak primary VTEC infections during 2008–2013. Overall, 989 VTEC infections including 521 serogroup O157 and 233 serogroup O26 were geo-referenced to 931 of 18 488 census enumeration areas. The geographical distribution of human population, livestock, unregulated groundwater sources, domestic wastewater treatment systems (DWWTS) and a deprivation index were examined relative to notification of VTEC events in 524 of 6242 rural areas. Multivariate modelling identified three spatially derived variables associated with VTEC notification: private well usage [odds ratio (OR) 6·896,P< 0·001], cattle density (OR 1·002,P< 0·001) and DWWTS density (OR 0·978,P= 0·002). Private well usage (OR 18·727,P< 0·001) and cattle density (OR 1·001,P= 0·007) were both associated with VTEC O157 infection, while DWWTS density (OR 0·987,P= 0·028) was significant within the VTEC O26 model. Findings indicate that VTEC infection in the Republic of Ireland is particularly associated with rural areas, which are associated with a ubiquity of pathogen sources (cattle) and pathways (unregulated groundwater supplies).