Background Prader-Willi syndrome (PWS) is a complex disorder caused by impaired paternally expressed genes on chromosome 15q11-q13. Variable findings have been reported about the phenotypic differences among PWS genetic subtypes. Methods A total of 110 PWS patients were diagnosed from 8,572 pediatric patients included from July 2013 to December 2021 by MLPA and MS-MLPA assays. Atypical deletions were defined by genomic CNV-sequencing. Maternal uniparental disomy (UPD) was subgrouped by microsatellite genotyping. Clinical data were collected for phenotype-genotype associations. Twenty-one patients received growth hormone (GH) treatment, and the anthropometric and laboratory parameters were evaluated and compared. Results Genetically, the 110 patients with PWS included 29 type I deletion, 56 type II deletion, 6 atypical deletion, 11 heterodisomy UPD, and 8 isodisomy UPD. The UPD group had significantly higher maternal age (31.4 ± 3.4 vs 27.8 ± 3.8 years), more anxiety (64.29% vs 26.09%) and autistic traits (57.14% vs 26.09%), and less hypopigmentation (42.11% vs 68.24%) and skin picking (42.86% vs 71.01%) than the deletion group. The type I deletion group was diagnosed at earlier age (3.7 ± 3.3 vs 6.2 ± 3.2 years) and more common in speech delay (95.45% vs 63.83%) than the type II. The isodisomy UPD group showed a higher tendency of anxiety (83.33% vs 50%) than the heterodisomy. GH treatment for 1 year significantly improved the SDS of height (− 0.43 ± 0.68 vs − 1.32 ± 1.19) and IGF-I (− 0.45 ± 0.48 vs − 1.97 ± 1.12). No significant changes were found in thyroid function or glucose/lipid metabolism. Conclusion We explored the physical, psychological and behavioral phenotype-genotype associations as well as the GH treatment effect on PWS from a large cohort of Chinese pediatric patients. Our data might promote pediatricians' recognition and early diagnosis of PWS.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

Purpose This paper focuses on developing and testing three versions of interactive bike (iBikE) interfaces for remote monitoring and control of cycling exercise sessions to promote upper and lower limb rehabilitation. Methods Two versions of the system, which consisted of a portable bike and a tablet PC, were designed to communicate through either Bluetooth low energy (BLE) or Wi-Fi interfaces for real-time monitoring of exercise progress by both the users and their clinical team. The third version of the iBikE system consisted of a motorized bike and a tablet PC. It utilized conventional Bluetooth to implement remote control of the motorized bike’s speed during an exercise session as well as to provide real-time visualization of the exercise progress. We developed three customized tablet PC apps with similar user interfaces but different communication protocols for all the platforms to provide a graphical representation of exercise progress. The same microcontroller unit (MCU), ESP-32, was used in all the systems. Results Each system was tested in 1-minute exercise sessions at various speeds. To evaluate the accuracy of the measured data, in addition to reading speed values from the iBikE app, the cycling speed of the bikes was measured continuously using a tachometer. The mean differences of averaged RPMs for both data sets were calculated. The calculated values were 0.38 ± 0.03, 0.25 ± 0.27, and 6.7 ± 3.3 for the BLE system, the Wi-Fi system, and the conventional Bluetooth system, respectively. Conclusion All interfaces provided sufficient accuracy for use in telerehabilitation.

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The aim of this pilot study was to identify social determinants of health (SDH) that affect disparities in cancer survival. A limited dataset was generated by querying electronic medical records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics that affected survival in 22,096 cancer patients were analyzed using descriptive statistics and logistic regression analyses. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. Logistic regression identified SDH factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of a broader spectrum of SDH factors is warranted.