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Articles 100741 - 100743 of 100743
Full-Text Articles in Entire DC Network
A Global Survey Of Self-Reported Cancer Screening Practices By Health Professionals For Kidney Transplant Candidates And Recipients, Nida Saleem, Naoka Murakami, Et Al.
A Global Survey Of Self-Reported Cancer Screening Practices By Health Professionals For Kidney Transplant Candidates And Recipients, Nida Saleem, Naoka Murakami, Et Al.
2020-Current year OA Pubs
Cancer is a major cause of morbidity and mortality in kidney transplant recipients. Health professionals have a critical role in promoting cancer screening participation. From March 2023 to February 2024, an online survey was distributed to kidney transplant health professionals globally to assess their screening practices. We compared their reported screening practices to recommended guidelines and analyzed factors associated with these practices. We received 97 responses, and most were nephrologists (70%), and around 80% recommended breast, colorectal, and cervical cancer screening for kidney transplant candidates and recipients. About 85% recommended lung cancer screening for higher-risk individuals. Skin cancer screening recommendations …
The Epidemiology Of Pediatric Oncology And Hematopoietic Cell Transplant Admissions To U.S. Intensive Care Units From 2001-2019, Kyle B Lenz, R Scott Watson, Jennifer J Wilkes, Matthew R Keller, Mary E Hartman, Elizabeth Y Killien
The Epidemiology Of Pediatric Oncology And Hematopoietic Cell Transplant Admissions To U.S. Intensive Care Units From 2001-2019, Kyle B Lenz, R Scott Watson, Jennifer J Wilkes, Matthew R Keller, Mary E Hartman, Elizabeth Y Killien
2020-Current year OA Pubs
Children with cancer or hematopoietic cell transplant (HCT) frequently require ICU care. We conducted a retrospective cohort study using Healthcare Cost and Utilization Project's State Inpatient Databases from 21 U.S. states from 2001-2019. We included children < 18 years with oncologic or HCT diagnosis and used ICD-9-CM and ICD-10-CM codes to identify diagnoses, comorbidities, and organ failures. We used generalized linear Poisson regression and Cuzick's test of trend to evaluate changes from 2001-2019. Among 2,157,991 total pediatric inpatient admissions, 3.9% (n=82,988) were among oncology patients and 0.3% (n=7,381) were among HCT patients. ICU admission prevalence rose from 13.6% in 2001 to 14.4% in 2019 for oncology admissions and declined from 23.9% to 19.5%, for HCT admissions. Between 2001-2019, the prevalence of chronic non-oncologic comorbidities among ICU patients rose from 44.3% to 69.1% for oncology patients (RR 1.60 [95% CI 1.46-1.66]) and from 41.4% to 81.5% (RR 1.94 [95% CI 1.61-2.34]) for HCT patients. The risk of Multiple Organ Dysfunction Syndrome more than tripled for oncology (9.5% to 33.3%; RR 3.52 [95% CI 2.97-4.18]) and HCT (12.4% to 39.7%; RR 3.20 [95% CI 2.09-4.89]) patients. Mortality decreased most for ICU patients with acute myeloid leukemia (AML) (14.6% to 8.5%) and oncology-related HCTs (15.5% to 9.2%). Critically ill pediatric oncology and HCT patients are increasingly medically complex with greater prevalence of chronic comorbidities and organ failure, but mortality did not increase. Pediatric ICUs may require increased financial and staffing support to care for these patients in the future.
Can Micro-Expressions Be Used As A Biomarker For Autism Spectrum Disorder, Mindi Ruan, Na Zhang, Xiangxu Yu, Wenqi Li, Chuanbo Hu, Paula J Webster, Lynn K Paul, Shuo Wang, Xin Li
Can Micro-Expressions Be Used As A Biomarker For Autism Spectrum Disorder, Mindi Ruan, Na Zhang, Xiangxu Yu, Wenqi Li, Chuanbo Hu, Paula J Webster, Lynn K Paul, Shuo Wang, Xin Li
2020-Current year OA Pubs
INTRODUCTION: Early and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it remains a significant challenge due to its complexity and variability. Micro-expressions are rapid, involuntary facial movements indicative of underlying emotional states. It is unknown whether micro-expression can serve as a valid bio-marker for ASD diagnosis.
METHODS: This study introduces a novel machine-learning (ML) framework that advances ASD diagnostics by focusing on facial micro-expressions. We applied cutting-edge algorithms to detect and analyze these micro-expressions from video data, aiming to identify distinctive patterns that could differentiate individuals with ASD from typically developing peers. Our computational …