Socioeconomic indices guided linear mixed-effects and meta-regression modelling of the temporal, global and regional prevalence of Helicobacter pylori in environmental waters: A class I carcinogen

Environmental waters (EW) substantially lend to the transmission of Helicobacter pylori (Hp). But the increase in Hp infections and antimicrobial resistance is often attributed to socioeconomic status. The connection between socioeconomic status and Hp prevalence in EW is however yet to be investigated. This study aimed to assess the impacts of socioeconomic indices (SI: continent, world bank region (WBR), world bank income (WBI), WHO region, Socio-demographic Index (SDI quintile), Sustainable Development Index (SuDI), and Human Development Index (HDI)) on the prevalence of Hp in EW. Hp-EW data were fitted to a generalized linear mixed-effects model and SI-guided meta-regression models with a 1000-resampling test. The worldwide prevalence of Hp in EW was 21.76% [95% confidence interval [CI]: 10.29-40.29], which declined significantly from 59.52% [43.28-74.37] in 1990-99 to 19.36% [3.99-58.09] in 2010-19 and with increasing trend in 2020-22 (33.33%, 22.66-45.43). Hp prevalence in EW was highest in North America (45.12%, 17.07-76.66), then Europe (22.38%, 5.96-56.74), South America (22.09%, 13.76-33.49), Asia (2.98%, 0.02-85.17), and Africa (2.56%, 0.00-99.99). It was negligibly different among sampling settings, WBI, and WHO regions demonstrating highest prevalence in rural location [42.62%, 3.07-94.56], HIEs [32.82%, 13.19-61.10], and AMR [39.43%, 19.92-63.01], respectively. However, HDI, sample size, and microbiological method robustly predict Hp prevalence in EW justifying 26.08%, 21.15%, and 16.44% of the true difference, respectively. In conclusion, Hp is highly prevalence in EW across regional/socioeconomic strata and thus challenged the uses of socioeconomic status as surrogate for hygienic/sanitary practices in estimating Hp infection prevalence.

Global and regional prevalence of Helicobacter pylori in drinking waters: A sustainable, human development and socio-demographic indices based meta-regression-modelling

Helicobacter pylori (Hp) transmission dynamics via drinking water (DW) has a far much higher direct and indirect public health disease burden than previously thought. This study aimed to assess the global prevalence of Hp in DW, distributions across regions and socioeconomic indices (continent, world bank income, Human Development Index (HDI), Sustainable Development Index (SuDI), Socio-Demographic Index (SDI) quintile, and WHO regions). Hp-DW related data mined from five databases until 10/12/2022 according to PRISMA standard were quality-appraised and fitted to a generalized linear mixed-effects model. Sub-group analysis and meta-regression-modelling coupled with a 1000-permutation test (_⁎) were conducted. The global prevalence of Hp in DW was 15.7% (95% confidence interval [CI]: 7.98-27.5), which varied significantly by sampling methods (Moore swabbing (61.0% [0.00-100.0]) vs. grab sampling (13.68%[6.99-25.04])) and detection technique (non-culture (21.35%[9.13-42.31]) vs. cultured-based methods (Psubgroup < 0.01)). The period 1990-99 had the highest prevalence (41.24% [0.02-99.97]). Regarding regional designations, Hp prevalence in DW was significantly different being highest in North America (61.82% [41.03-79.02]) by continents, AMR (42.66% [20.81-67.82]) by WHO group, high HDI (24.64% [10.98-46.43]) by HDI group and North America (61.90% [2.79-98.93]) by world bank region (Psubgroup < 0.01). Generally, sample preparation, SuDI grouping, and detection/confirmation techniques, have significant effects on the detection/prevalence of Hp in DW (Psubgroup < 0.01). Hp prevalence in DW was not significantly different among rural and urban DW (Psubgroup = 0.90), world bank income groups (Psubgroup = 0.15), and SDI quintiles (Psubgroup = 0.07). Among the predictors examined, only sample size (p < 0.1, R_∗²(coefficient of determinant) = 15.29%), continent (p_∗val = 0.04), HDI (p_∗val = 0.02), HDI group (p_∗val = 0.05), and microbiological methods (p < 0.1; R_∗²=28.09 %) predicted Hp prevalence in DW robustly. In conclusion, Hp prevalence is still endemic in DW regardless of the regional designations/improve DW supplies.

Assessment of Bacterial Transfer From Patient to Chiropractor During Spinal Manipulation

Objective

This study aimed to measure the amount of bacteria that may be transferred from patient to chiropractor during spinal manipulation and the efficacy of decontaminants commonly used by chiropractic practitioners.

Methods

We chose Kwazulu-Natal, South Africa as the location, which is an area with an increased burden of immunocompromised patients resulting from HIV and tuberculosis. In this region, we obtained samples from 40 chiropractors' hands before and after spinal manipulation. Viable counts of colony-forming units were enumerated to verify the presence of bacteria. A modified Kirby-Bauer technique was used to ascertain the efficacy of the top 5 decontaminants used by chiropractors in this area against the bacteria isolated from their hands.

Results

Both normal microflora and potentially pathogenic bacteria were found. These included Staphylococcus spp., Micrococcus spp., Bacillus spp., Pseudomonas spp., Escherichia coli, Streptococcus spp., and Staphylococcus aureus. Higher bacterial counts were found on chiropractors' hands after patient contact for spinal manipulation, which are similar to findings from studies of other healthcare personnel. The 5 commonly used disinfectants tested in a lab setting were not as effective against bacterial samples when compared with an antimicrobial pharmaceutical medication.

Conclusions

The net transfer of bacteria to chiropractors' hands from the patient to the practitioner during spinal manipulation included primary and opportunistic pathogens. The detergents commonly used by chiropractors in this region showed varying efficacies against microbial growth when tested in vitro.

Microbiota of a Full-scale UASB Reactor Treating Brewery Wastewater Using Illumina MiSeq Sequencing

Background:

The efficiency of biological wastewater treatment plant is determined by bacterial metabolism. There are data on the effect of operational parameters on microbial consortia present in laboratory scale reactor. However, knowledge on the full-scale reactor is still limited at present, hence the need to define the relations between the microbial structure and the performance of full-scale reactor.

Objective:

In this study, the microbial community structure in a full-scale UASB reactor treating brewery wastewater was assessed using metagenomics Next-Generation Sequencing technique.

Method:

Granular sludge samples were collected from the UASB reactor treating brewery wastewater and extracted genomic DNA was amplified using barcoded bacterial primer sets targeting V3-V4 region of the 16S rRNA genes on sequencing Illumina MiSeq platform.

Results:

The taxonomic analysis revealed the abundance of bacteria (~95%) with considerable Archaea community (~2%) in the granular sludge. After trimming, 18 bacterial phyla, 29 orders, 36 families and 44 genera were recovered from the 48,488 sequences reads of the 16S rRNA genes analysed, where the most abundant community belongs to Firmicutes, Bacteroidetes, Synergistetes and Proteobacteria phyla.

Conclusion:

For a sustainable bioenergy generation, understanding the mechanisms of anaerobic system in relation to microbial community is an important factor to increase the production of biogas production during wastewater treatment. To the best of our knowledge, this report is one of the studies that explored and described bacterial diversity and community structure of a full-scale UASB reactor treating brewery wastewater using high-throughput sequencing. This study provides insight into the dominant microbial community and their phylogenetic diversity in biogas producing reactor.

Phenotypic and genotypic characterisation of an unique indigenous hypersaline unicellular cyanobacterium, Euhalothece sp.nov

A novel halotolerant species of cyanobacterium of the order Chroococcales was isolated from hypersaline estuary in Kwa-Zulu Natal, South Africa. A comprehensive polyphasic approach viz., cell morphology, pigment composition and complete genome sequence analysis was conducted to elucidate the taxonomic position of the isolated strain. The blue-green oval to rod-shaped cells were 14-18 μm in size, and contained a high amount of phycocyanin pigments. The strain was moderate thermotolerant/alkalitolerant halophile with the optimum conditions for growth at 35 °C, pH 8.5 and 120 g/l of NaCl. Based on 16S rRNA gene sequence phylogeny, the strain was related to members of the 'Euhalothece' subcluster (99%). The whole genome sequence was determined, and the annotated genes showed a 90% sequence similarity to the gas-vacuolate, spindle-shaped Dactylococcopsis salina PCC 8305. The size of the genome was determined to be 5,113,178 bp and contained 4332 protein-coding genes and 69 RNA genes with a G + C content of 46.7%. Genes encoding osmoregulation, oxidative stress, heat shock, persister cells, and UV-absorbing secondary metabolites, among others, were identified. Based on the phylogenetic analysis of the 16S rRNA gene sequences, physiological data, pigment compositions and genomic data, the strain is considered to represent a novel species of Euhalothece.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

Kinetic modelling and characterization of microbial community present in a full-scale UASB reactor treating brewery effluent

The performance of a full-scale upflow anaerobic sludge blanket (UASB) reactor treating brewery wastewater was investigated by microbial analysis and kinetic modelling. The microbial community present in the granular sludge was detected using fluorescent in situ hybridization (FISH) and further confirmed using polymerase chain reaction. A group of 16S rRNA based fluorescent probes and primers targeting Archaea and Eubacteria were selected for microbial analysis. FISH results indicated the presence and dominance of a significant amount of Eubacteria and diverse group of methanogenic Archaea belonging to the order Methanococcales, Methanobacteriales, and Methanomicrobiales within in the UASB reactor. The influent brewery wastewater had a relatively high amount of volatile fatty acids chemical oxygen demand (COD), 2005 mg/l and the final COD concentration of the reactor was 457 mg/l. The biogas analysis showed 60-69% of methane, confirming the presence and activities of methanogens within the reactor. Biokinetics of the degradable organic substrate present in the brewery wastewater was further explored using Stover and Kincannon kinetic model, with the aim of predicting the final effluent quality. The maximum utilization rate constant U max and the saturation constant (K(B)) in the model were estimated as 18.51 and 13.64 g/l/day, respectively. The model showed an excellent fit between the predicted and the observed effluent COD concentrations. Applicability of this model to predict the effluent quality of the UASB reactor treating brewery wastewater was evident from the regression analysis (R(2) = 0.957) which could be used for optimizing the reactor performance.