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Human bocavirus (HBoV) is an emerging pathogen causing gastroenteritis/respiratory tract infection. Shellfish has been implicated in foodborne HBoV dissemination. The present investigation aimed at synthesising shellfish-associated HBoV data. Shellfish-HBoV data were mined from public repositories using topic-specific algorithm. A total of 30 data sources was identified of which 5 were synthesised. The average HBoV positivity and sample-size was 12 ± 9.2 and 134.2 ± 113.6, respectively. HBoV was studied in mollusc with 3.7-83.3% crude prevalence. The pooled HBoV prevalence in shellfish was 9.2% (7.2-11.8; 5 studies) and 12.9% (1.8-53.9; 5 studies) in common-effects and random-effects model respectively, with 0.12-94.89% prediction interval (PI). Sensitivity analysis yielded 8.7% (6.7-11.2; PI = 1.99-29.48%) prevalence. HBoV1 and HBoV2 pooled prevalence in shellfish was 7.91% (1.61-31.09; 3 studies) and 12.52% (0.01-99.60; 3 studies), respectively. HBoV3 prevalence was reported in one single study as 6.96% (4.41-10.35). In conclusion, the present study revealed high HBoV prevalence in shellfish, signifying the need to characterise HBoV and subtypes circulating in non-mollusc shellfish. Furthermore, there is an urgent need to mitigate the food safety risk that may result from HBoV contaminated shellfish since shellfish-borne HBoV is not routinely assessed and might be underestimated at present., (© 2024. The Author(s).)

Cronobacter sakazakii (Cz) infections linked with powdered milk/flour (PMF) are on the increase in recent times. The current study aimed at assessing worldwide and regional prevalence of Cz in PMF. Cz-PMF-directed data were conscientiously mined in four mega-databases via topic-field driven PRISMA protocol without any restriction. Bivariate analysis of datasets was conducted and then fitted to random-intercept logistic mixed-effects regressions with leave-one-study-out-cross-validation (LOSOCV). Small-study effects were assayed via Egger's regression tests. Contributing factors to Cz contamination/detection in PMF were determined using 1000-permutation-bootstrapped meta-regressions. A total of 3761 records were found out of which 68 studies were included. Sample-size showed considerable correlation with Cz positivity (r = 0.75, p = 2.5e-17), Milkprod2020 (r = 0.33, p = 1.820e-03), and SuDI (r = - 0.30, p = 4.11e-03). The global prevalence of Cz in PMF was 8.39% (95%CI 6.06-11.51, PI: 0.46-64.35) with LOSOCV value of 7.66% (6.39-9.15; PI: 3.10-17.70). Cz prevalence in PMF varies significantly (p < 0.05) with detection methods, DNA extraction method, across continents, WHO regions, and world bank regions. Nation, detection method, world bank region, WHO region, and sample size explained 53.88%, 19.62%, 19.03%, 15.63%, and 9.22% of the true differences in the Cz prevalence in PMF, respectively. In conclusion, the results indicated that national will power in the monitoring and surveillance of Cz in PMF matched with adequate sample size and appropriate detection methods will go a long way in preventing Cz contamination and infections., (© 2024. The Author(s).)

Listeria outbreaks and food recalls is on the raise globally. Milk particularly is highly susceptible to Listeria as its production and storage adequately support Listeria growth. The extent of milk contamination with Listeria monocytogenes (Lm) and preventative actions to halt milk associated outbreaks in Africa are unknown. Hence, this study aimed at assessing the national and subregional prevalence of Lm in milk in Africa and identify impacting factors via generalized logistic mixed-effects (GLMEs) and meta-regression modelling. Lm-milk-specific data acquired from primary studies according to standard protocol were fitted using a GLMEs. The GLMEs was subjected to leave-one-study-out-cross-validation (LOSOCV). Factors impacting Lm prevalence in milk were assayed via a 1000-permutation-assisted meta-regression-modelling. The pooled prevalence of Lm in milk in Africa was 4.35% [2.73-6.86] with a prediction interval (PI) of 0.14-59.86% and LOSOCV value of 2.43% [1.62-3.62; PI: 0.32-16.11%]. Western Africa had the highest prevalence [20.13%, 4.13-59.59], then Southern Africa [5.85%, 0.12-75.72], Northern Africa [4.67%, 2.82-7.64], Eastern Africa [1.91%, 0.64-5.55], and there was no record from Central Africa. In term of country, Lm prevalence in milk significantly (p < 0.01) varied from 0.00 to 90.00%. Whereas the Lm prevalence was negligibly different (p = 0.77) by milk type, raw-milk had the highest prevalence [5.26%], followed by fermented-milk [4.76%], boiled-milk [2.90%], pasteurized-milk [1.64%], and powdered-milk [1.58%]. DNA extraction approach did not significantly (p = 0.07) affect Lm prevalence (Boiling [7.82%] versus Kit [7.24%]) as well as Lm detection method (p = 0.10; (ACP [3.64%] vs. CP [8.92%] vs. CS [2.27%] vs. CSP [6.82%]). Though a bivariate/multivariate combination of all tested variables in meta-regression explained 19.68-68.75% (R 2 ) variance in Lm prevalence in milk, N, nation, and subregion singly/robustly accounted for 17.61% (F 1;65  = 7.5994; p = 0.005), 63.89% (F 14;52  = 4.2028; p = 0.001), and 16.54% (F 3;63  = 3.4743; p = 0.026), respectively. In conclusion, it is recommended that adequate sample size should be prioritized in monitoring Lm in milk to prevent spuriously high or low prevalence to ensure robust, plausible, and credible estimate. Also, national efforts/interests and commitments to Lm monitoring should be awaken., (© 2023. Springer Nature Limited.)

A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models' performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040-4.5828)] and Cubist [3.1736 (1.1012-4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R 2  = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R 2  = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00-83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes., (© 2023. The Author(s).)

Global increase in diabetes (DM) prevalence necessitated the need to establish the association between DM and environmental triggers including MAP (Mycobacterium avium subsp. paratuberculosis) that have been postulated to play a role in DM etiopathology for effective management. The present investigation aimed to assess the odds ratio (OR) presenting the association between MAP and DM. MAP-related DM studies were systematically retrieved from 6 databases until 31 September 2021 according to PRISMA principles for data abstraction. The abstracted dataset was fitted to the fixed-effects (FE) and random-effects (RE) models using the Mantel-Haenszel approach. Sixteen studies involving 2072 participants (1152 DM patients (957 type 1 diabetes mellitus (T1DM) & 195 type 2 diabetes mellitus (T2DM)) and 920 healthy controls) met the inclusion criteria. Results revealed a significant association between anti-MAP antibodies (abs) seroprevalence and T1DM (FE: OR 7.47, 95% CI 5.50-10.14, p value < 0.0001; RE: OR 7.92, 95% CI 4.39-14.31, p < 0.0001) and MAP DNA with T1DM (FE: OR 4.70 (95% CI 3.10-7.13, p value < 0.0001), RE: OR 3.90 (95% CI 0.93-16.38, p value = 0.06)). Both anti-MAP abs and MAP DNA based meta-analyses had medium heterogeneity (I 2  = 47.2-61.0%). Meanwhile, no significant association between MAP and T2DM (FE: OR 1.13, 95% CI 0.54-2.37, p value = 0.74; RE: OR 1.19; 95% CI 0.34-4.12, p value = 0.69), its OR magnitude exceeded 1 and prediction interval (0.09-15.29) suggest possibility of association between the duo in the future. The leave-one-out sensitivity analysis depicts a robust meta-analysis in all cases. In conclusion, the study manifests a positive association between MAP and T1DM, highlighting that MAP prevention and environmental control would indubitably revolutionize T1DM management. Also, its projects possible link between MAP and T2DM as more data becomes available. However, it remains elusive whether MAP triggers T1/T2DM or a mere comorbidity in T1/T2DM. Epidemiological activities to fill the global/regional data gaps on MAP-related T1DM and T2DM are advocated in order to assess the burden of MAP-related DM and improve their clinical management., (© 2022. The Author(s).)