Publications by authors named "G Aleman"

Abortion clients who experience economic hardship face barriers paying for abortion care. Between September 2020 and June 2021, we conducted a facility-based survey with 211 abortion clients who obtained care in Mississippi, and 25 respondents completed in-depth interviews. We computed the frequency with which survey respondents used social network-based, agency-based, and individual strategies to pay for care and we employed thematic analysis to explore in-depth interviewees' decision-making and experiences with these strategies.

View Article and Find Full Text PDF

The global circulation of SARS-CoV-2 has been extensively documented, yet the dynamics within Central America, particularly Nicaragua, remain underexplored. This study characterizes the genomic diversity of SARS-CoV-2 in Nicaragua from March 2020 through December 2022, utilizing 1064 genomes obtained via next-generation sequencing. These sequences were selected nationwide and analyzed for variant classification, lineage predominance, and phylogenetic diversity.

View Article and Find Full Text PDF

The bacterial spirochete Borrelia burgdorferi, the causative agent of Lyme Disease, can disseminate and colonize various tissues and organs, orchestrating severe clinical symptoms including arthritis, carditis, and neuroborreliosis. Previous research has demonstrated that breast cancer tissues could provide an ideal habitat for diverse populations of bacteria, including B. burgdorferi, which is associated with a poor prognosis.

View Article and Find Full Text PDF

Pecans () are considered a functional food due to the high content of polyunsaturated fatty acids, dietary fiber and polyphenols. To determine the effect of whole pecans (WP) or a pecan polyphenol (PP) extract on the development of metabolic abnormalities in mice fed a high-fat (HF) diet, we fed C57BL/6 mice with a Control diet (7% fat), HF diet (23% fat), HF containing 30% WP or an HF diet supplemented with 3.6 or 6 mg/g of PP for 18 weeks.

View Article and Find Full Text PDF

Objectives: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs.

Methods: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs.

View Article and Find Full Text PDF