Assessing the impact of arsenic on symbiotic and free-living PGPB: plant growth promoting traits, bacterial compatibility and adhesion on soybean seed.

Arsenic (As) contamination in agricultural groundwater and soil is a significant economic and health problem worldwide. It inhibits soybean (Glycine max (L.) Merr.

Characterization and identification of Pseudomonas sp. AW4, an arsenic-resistant and plant growth-promoting bacteria isolated from the soybean (Glycine max L.) rhizosphere.

Pseudomonas sp. AW4 is a highly arsenic (As) resistant bacterium with plant growth promoting properties, originally isolated from the soybean (Glycine max L.) rhizosphere.

Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91).

Application of biohybrid membranes for arsenic and chromium removal and their impact on pollutant accumulation in soybean (Glycine max L.) seedlings.

Chromium and arsenic are among the priority pollutants to be controlled by regulatory and health agencies due to their ability to accumulate in food chains and the harmful effects on health resulting from the ingestion of food contaminated with metals and metalloids. In the present work, four biohybrid membrane systems were developed as alternatives for the removal of these pollutants, three based on polyvinyl alcohol polymeric mesh (PVA, PVA-magnetite, PVA L-cysteine) and one based on polybutylene adipate terephthalate (PBAT), all associated with bioremediation agents. The efficiency of the bioassociation process was assessed through count methods and microscopy.

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction.