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Sharing health data for research purposes across international jurisdictions has been a challenge due to privacy concerns. Two privacy enhancing technologies that can enable such sharing are synthetic data generation (SDG) and federated analysis, but their relative strengths and weaknesses have not been evaluated thus far. In this study we compared SDG with federated analysis to enable such international comparative studies. The objective of the analysis was to assess country-level differences in the role of sex on cardiovascular health (CVH) using a pooled dataset of Canadian and Austrian individuals. The Canadian data was synthesized and sent to the Austrian team for analysis. The utility of the pooled (synthetic Canadian + real Austrian) dataset was evaluated by comparing the regression results from the two approaches. The privacy of the Canadian synthetic data was assessed using a membership disclosure test which showed an F1 score of 0.001, indicating low privacy risk. The outcome variable of interest was CVH, calculated through a modified CANHEART index. The main and interaction effect parameter estimates of the federated and pooled analyses were consistent and directionally the same. It took approximately one month to set up the synthetic data generation platform and generate the synthetic data, whereas it took over 1.5 years to set up the federated analysis system. Synthetic data generation can be an efficient and effective tool for enabling multi-jurisdictional studies while addressing privacy concerns., (© 2023. The Author(s).)

Salbutamol is a drug used to treat the pulmonary diseases by ameliorate the medium and large airways in the lungs. Partitioning of salbutamol drug on the aqueous two-phase systems (ATPSs) of PEG 1000,1500,2000,4000,6000  + trilithium citrate + water was determined at T = 298.15 K. The effect of molecular mass of polymer (MMP) on the binodal and tie-line compositions were studied. Results showed that the biphasic area was extended as the MMP was increased. The salting-out ability were quantified using the Setschenow model, and the binodal curves were modeled by a nonlinear 3-parameter equation. Furthermore, electrolyte Wilson along with the osmotic virial models have adequately been implemented to fit the tie-line compositions. Also, the studied ATPSs were implemented to study the partitioning of salbutamol drug on the salt-affluent and polymer-affluent phases. It is observed that, ATPSs of PEG 1000 is premium to extract the salbutamol to the polymer-affluent phase, where, the ATPSs of PEG 6000 is more favorable to extract the drug to the salt-affluent phase., (© 2023. The Author(s).)

The uncontrolled discharge of industrial wastes causes the accumulation of high heavy metal concentrations in soil and water, leading to many health issues. In the present study, a Gram-negative Aeromonas sobria was isolated from heavily contaminated soil in the Tanjaro area, southwest of Sulaymaniyah city in the Kurdistan Region of Iraq; then, we assessed its ability to uptake heavy metals. A. sobria was molecularly identified based on the partial amplification of 16S rRNA using novel primers. The sequence was aligned with 33 strains to analyze phylogenetic relationships by maximum likelihood. Based on maximum tolerance concentration (MTC), A. sobria could withstand Zn, Cu, and Ni at concentrations of 5, 6, and 8 mM, respectively. ICP-OES data confirmed that A. sobria reduced 54.89% (0.549 mM) of the Cu, 62.33% (0.623 mM) of the Ni, and 36.41% (0.364 mM) of the Zn after 72 h in the culture medium. Transmission electron microscopy (TEM) showed that A. sobria accumulated both Cu and Ni, whereas biosorption was suggested for the Zn. These findings suggest that metal-resistant A. sobria could be a promising candidate for heavy metal bioremediation in polluted areas. However, more broadly, research is required to assess the feasibility of exploiting A. sobria in situ., (© 2022. The Author(s).)

Human papillomavirus infections are associated with most cervical cancers, which are the fourth most common cancer in women. HPV-E6 protein binds to protein p53 and inhibits its function, leading to the switching of normal cells toward cancer cells. Here, we disrupted the HPV-E6 gene and investigated its effects on the proliferation and apoptosis of HeLa cells. The HPV18-E6 gene was targeted with two designed sgRNAs cloned into an AAV-CRISPR-based plasmid. The AAV-E6-CRISPR/Cas9 virions were prepared and titrated in HEK293t cells. The cleavage created in the HPV-E6 gene was detected using the T7E1 assay. Cell cycle profiling, MTT assay, and annexin V/PI staining were performed. Also, the p53 protein level was measured by Western blotting. Our data showed that disruption of the HPV-E6 gene led to increased cell apoptosis and decreased cell proliferation. A significant accumulation of infected cells in sub-G1 phase was observed in the cell profiling assay. Also, HPV-E6 gene disruption resulted in a significant increase in the level of P53 protein. Our findings indicated that AAV-mediated delivery of CRISPR/Cas9 can effectively target the HPV-E6 gene in HeLa cells, and its antiproliferative effects may provide therapeutic benefits of local administration of this gene-editing system for HPV-related cervical cancers., (© 2022. The Author(s).)

Considering the large number of natural disasters on the planet, many areas in the world are at risk of these hazards; therefore, providing an integrated map as a guide map for multiple natural hazards can be applied to save human lives and reduce financial losses. This study designed a multi-hazard map for three important hazards (earthquakes, floods, and landslides) to identify endangered areas in Kermanshah province located in western Iran using ensemble SWARA-ANFIS-PSO and SWARA-ANFIS-GWO models. In the first step, flood and landslide inventory maps were generated to identify at-risk areas. Then, the occurrence places for each hazard were divided into two groups for training susceptibility models (70%) and testing the models applied (30%). Factors affecting these hazards, including altitude, slope aspect, slope degree, plan curvature, distance to rivers, distance to roads, distance to the faults, rainfall, lithology, and land use, were used to generate susceptibility maps. The SWARA method was used to weigh the subclasses of the influencing factors in floods and landslides. In addition, a peak ground acceleration (PGA) map was generated to investigate earthquakes in the study area. In the next step, the ANFIS machine learning algorithm was used in combination with PSO and GWO meta-heuristic algorithms to train the data, and SWARA-ANFIS-PSO and SWARA-ANFIS-GWO susceptibility maps were separately generated for flood and landslide hazards. The predictive ability of the implemented models was validated using the receiver operating characteristics (ROC), root mean square error (RMSE), and mean square error (MSE) methods. The results showed that the SWARA-ANFIS-PSO ensemble model had the best performance in generating flood susceptibility maps with ROC = 0.936, RMS = 0.346, and MSE = 0.120. Furthermore, this model showed excellent results (ROC = 0.894, RMS = 0.410, and MSE = 0.168) for generating a landslide map. Finally, the best maps and PGA map were combined, and a multi-hazard map (MHM) was obtained for Kermanshah Province. This map can be used by managers and planners as a practical guide for sustainable development., (© 2022. The Author(s).)