Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents.

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; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 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.

Systematic and meta-analysis of Mycobacterium avium subsp. paratuberculosis related type 1 and type 2 diabetes mellitus.

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 (I2 = 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.

Systematic review and meta-analysis of Mycobacterium avium subsp. paratuberculosis as environmental trigger of multiple sclerosis.

Mycobacterium avium subsp. paratuberculosis (MAP) has been identified as one of the environmental agents that causes multiple sclerosis (MS). The global prevalence of MS has been upsurging over the years; however, efforts to divulge the role of MAP in MS have been limited. As a result, the present study aimed at assessing the odd ratios (ORs) associated MAP with the risk of MS. MAP-related MS data were obtained from 6 databases using the terms 'multiple sclerosis' or 'MS' and 'paratuberculosis' without regard for time or language restrictions following PRISMA standards. A total of 2,538 participants' data from 12 studies presenting anti-MAP antibodies and MAP DNA from 4 studies were fitted in random-effects (RE) and fixed-effects (FE) meta-analytic models. Furthermore, the between-study heterogeneity was measured using I2-values with a significant limit set at an I² > 75%. Analytical rigor and publication bias was determined using leave-one-out-analytics, Egger's tests, and p-curve analysis. In the FE and RE models, anti-MAP antibodies data significantly associated MS risk with MAP as 10.71 OR (95%-CI [7.78; 14.74], p-value < 0.0001) and 12.76 OR (95%-CI [8.13; 20.02], p-value < 0.0001) respectively, with an I2 value of 34.9% (95%-CI [0.0%; 67.2%]; p-value = 0.11). Similarly, the MAP DNA dataset in FE significantly present MS risk due to MAP as 5.53 OR (95%-CI [3.54; 8.66], p-value< 0.0001) while, RE showed 5.27 OR (95%-CI [3.22; 8.60], p = 0.0017), with an I2-value = 0.0% (95%-CI [0.0%; 84.7%]; p-value = 0.71). Eggers' test, on the other hand, found publication bias in anti-MAP antibodies data (intercept = 1.61, 95% CI: 0.45 - 2.77, t = 2.72, p = 0.021), but not in MAP DNA dataset (intercept = -5.57, 95% CI: -20.44 - 9.29, t = -0.74, p = 0.54). The robustness of the meta-analyses was demonstrated by all sensitivity analyses. In addition, there is no evidence of p-hacking observed (right-skewness test (PFull < 0.001, PHalf <0.001; statistical power ≥ 94% (95%-CI: 72.5%-99%)). In conclusion, the synthesis revealed a strong association between MAP and MS, indicating that MAP is a significant environmental agent that may trigger MS. Thus, early screening of MAP in MS cases may assist in the therapeutic approach to its management/treatment. Therefore, future studies should be tailored towards the role of MAP in the severity of MS phenotypes, as well as address global data gaps and low disease surveillance.