Publications by authors named "M D Fiuza Perez"

Background: Postharvest lemons are affected by several fungal infections, and as alternatives to chemical fungicides for combating these infections, different microbial biocontrol agents have been studied, with the Clavispora lusitaniae 146 strain standing out. Although strain 146 has proven to be an effective agent, the influence of a microbial biological control agent on the postharvest lemon microbiome has not been studied until now. Thus, this study aimed to evaluate how the epiphytic microbiome of postharvest lemons is affected by the application of the biocontrol yeast C.

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Introduction: Postoperative ileus is a known complication of gastrointestinal (GI) surgery. In adult populations, ileus is associated with higher amounts of intraoperative intravenous (IV) fluids. This study examines the relationship between intraoperative IV fluids and postoperative ileus in pediatric patients undergoing GI surgery.

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Mitochondrial diseases, caused by mutations in either nuclear or mitochondrial DNA (mtDNA), currently have limited treatment options. For mtDNA mutations, reducing mutant-to-wild-type mtDNA ratio (heteroplasmy shift) is a promising therapeutic option, though current approaches face significant challenges. Previous research has shown that severe mitochondrial dysfunction triggers an adaptive nuclear epigenetic response, characterized by changes in DNA methylation, which does not occur or is less important when mitochondrial impairment is subtle.

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Introduction Despite limited knowledge of its potential health effects, electronic cigarette (e-cigarette) use has become increasingly popular in the United States (US). Cigarette smoking is linked to a higher risk of asthma, and e-cigarettes may have similar effects. This study's aim was to examine the association between e-cigarette use and asthma exacerbations in US adults with known asthma.

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Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.

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