Supplementary Material for: Relationship Between Short-Term Blood Pressure Variability and Incidence of Acute Kidney Injury in Critically Ill Patients

<b><i>Background/Aims:</i></b> Blood pressure (BP) variability is associated with cardiovascular events, and cerebral and renal damage. The aim of this study was to investigate any potential relationship between short-term BP variability and incidence of acute onset conditions, such as acute kidney injury (AKI), in critically ill patients. <b><i>Methods:</i></b> BP was monitored to analyze its variability in critically ill patients in present study. Short-term BP variability was assessed as average real variability (ARV), standard deviation (SD) and coefficient of variation (CV) of 24-hour BP. <b><i>Results:</i></b> A total of 565 patients were included, 41.2% (n=233) of which presented with AKI after admission (AKI stage I, n = 94; stage II, n = 37; stage III, n = 102). The mean APACHE II score was 21.5 for all patients. ARV of 24 h systolic BP was significantly higher in patients with AKI (p<0.001). This association remained (p=0.006) after adjustment for potential confounders. The incidence of AKI increased with the ARV from 14.0% (ARV ≤6 mmHg) to 73.9% (ARV >14 mmHg). A weak association was also found between BP variability and hospital mortality in critically ill patients. <b><i>Conclusion:</i></b> BP variability is correlated with the incidence of AKI in critically ill patients

Supplementary Material for: Identification and Characterization of Long Non-Coding RNAs in Osteogenic Differentiation of Human Adipose-Derived Stem Cells

<strong><em>Background/Aims:</em></strong> Long noncoding RNAs (lncRNAs) play important roles in stem cell differentiation. However, their role in osteogenesis of human adipose-derived stem cells (ASCs), a promising cell source for bone regeneration, remains unknown. Here, we investigated the expression profile and potential roles of lncRNAs in osteogenic differentiation of human ASCs. <b><i>Methods:</i></b> Human ASCs were induced to differentiate into osteoblasts <i>in vitro</i>, <i>and</i> the expression profiles of lncRNAs and mRNAs in undifferentiated and osteogenic differentiated ASCs were obtained by microarray. Bioinformatics analyses including subgroup analysis, gene ontology analysis, pathway analysis and co-expression network analysis were performed. The function of lncRNA <i>H19</i> was determined by <i>in vitro</i> knockdown and overexpression. Quantitative reverse transcription polymerase chain reaction was utilized to examine the expression of selected genes. <b><i>Results:</i></b> We identified 1,460 upregulated and 1,112 downregulated lncRNAs in osteogenic differentiated human ASCs as compared with those of undifferentiated cells (Fold change ≥ 2.0, <i>P</i> < 0.05). Among these, 94 antisense lncRNAs, 85 enhancer-like lncRNAs and 160 lincRNAs were further recognized. We used 12 lncRNAs and 157 mRNAs to comprise a coding-non-coding gene expression network. Additionally, silencing of <i>H19</i> caused a significantly increase in expression of osteogenesis-related genes, including <i>ALPL</i> and <i>RUNX2</i>, while a decrease was observed after <i>H19</i> overexpression. <b><i>Conclusion:</i></b> This study revealed for the first time the global expression profile of lncRNAs involved in osteogenic differentiation of human ASCs and provided a foundation for future investigations of lncRNA regulation of human ASC osteogenesis

Supplementary Material for: A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study

Introduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor; ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients. Methods: Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN). Results: An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients. Discussion: ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery

Supplementary Material for: A Methodological Perspective on the Longitudinal Cognitive Change after Stroke

<b><i>Background/Aims:</i></b> Most studies of poststroke cognitive impairment (PSCI) have analyzed cognitive levels at specific time points rather than their changes over time. Furthermore, they seldom consider correlations between cognitive domains. We aimed to investigate the effects of these methodological considerations on determining significant PSCI predictors in a longitudinal stroke cohort. <b><i>Methods:</i></b> In patients who underwent neuropsychological tests at least twice after stroke, we adopted a multilevel hierarchical mixed-effects model with domain-specific cognitive changes and a multivariate model for multiple outcomes to reflect their correlations. <b><i>Results:</i></b> We enrolled 375 patients (median follow-up of 34.1 months). Known predictors of PSCI were generally associated with cognitive levels; however, most of the statistical significances disappeared when cognitive changes were set as outcomes, except age for memory, prior stroke and baseline cognition for executive/attention domain, and baseline cognition for visuospatial function. The multivariate analysis which considered multiple outcomes simultaneously further altered these associations. <b><i>Conclusions:</i></b> This study shows that defining outcomes as changes over time and reflecting correlations between outcomes may affect the identification of predictors of PSCI