An Algorithmic Approach to Understanding Osteoarthritic Knee Pain.

Background: Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a single radiograph view. Methods: We used data from the Osteoarthritis Initiative, a 10-year observational study of patients with knee osteoarthritis. We paired >50,000 weight-bearing, posteroanterior knee radiographs with corresponding Knee Injury and Osteoarthritis Outcome Score (KOOS) pain, symptoms, and activities of daily living subscores and used them to train a series of deep learning models to predict those scores solely from raw radiographic input. We created regression models for specific score predictions and classification models to predict whether the modeled KOOS subscore exceeded a range of thresholds. Results: The root-mean-square errors were 15.7 for KOOS pain, 13.1 for KOOS symptoms, and 14.2 for KOOS activities of daily living. Modeling was performed to predict whether pain was above or below given pain thresholds, and was able to predict extreme pain (KOOS pain < 40) with an area under the curve (AUC) of 0.78. Notably, the system was also able to correctly predict numerous cases where the Kellgren-Lawrence (KL) grade assigned by the radiologist was 0 but patient pain was high, and cases where the KL grade was 4 but patient pain was low. Conclusions: A deep neural network can be trained to predict the osteoarthritic knee pain that a patient experienced and other symptoms with reasonable accuracy from a single posteroanterior view of the knee, even using low-resolution images. The system can predict pain and dysfunction that the traditional KL grade does not capture. Deep learning applied to raw imaging inputs holds promise for disentangling sources of pain within the knee from aggravating factors external to the knee. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Big and Mini: A Promising Intergenerational Program for Social Connections.

INTRODUCTION: To help older adults cope with loneliness during COVID-19, a weekly, telephone-based intergenerational program called "Big and Mini" was created in April 2020 to link young and older adults together. As part of an evaluation of Big and Mini, a survey with both close and open-ended questions was sent to participants. METHODS: A total of 63 Bigs and 53 Minis completed the survey. Their stress compared to before COVID-19, loneliness, life satisfaction, intergenerational closeness, and satisfaction with the program were measured for participants. Descriptive, bivariate correlation and conventional content analyses were conducted. RESULTS: On average, Bigs and Minis had participated in the program for 3.73 and 3.49 months, respectively. Approximately half of the Bigs (47.6%) and Minis (52.8%) felt the same stress level compared to before COVID-19. A few participants felt "less stressed" compared to before COVID -9 (14.3 and 7.5%, respectively, for Bigs and Minis). All participants reported medium levels of loneliness, high levels of satisfaction with life, satisfaction with the program, and intergenerational closeness. Content analysis suggested that the reasons to join or expectations of the program were friendship, mutually beneficial intergenerational connections, and coping with loneliness. CONCLUSIONS: The Big and Mini program offers a promising approach with mutual benefits for participants. Strategies to improve the program and implications for intergenerational programs are presented.

An anti-apoptotic peptide improves survival in lethal total body irradiation.

Cell penetrating peptides (CPPs) have been used to deliver the anti-apoptotic Bcl-xL-derived BH4 peptide to prevent injury-induced apoptosis both in vitro and in vivo. Here we demonstrate that the nuclear localization sequence (NLS) from the SV40 large T antigen has favorable properties for BH4 domain delivery to lymphocytes compared to sequences based on the HIV-1 TAT sequence. While both TAT-BH4 and NLS-BH4 protected primary human mononuclear cells from radiation-induced apoptotic cell death, TAT-BH4 caused persistent membrane damage and even cell death at the highest concentrations tested (5-10 microM) and correlated with in vivo toxicity as intravenous administration of TAT-BH4 caused rapid death. The NLS-BH4 peptide has significantly attenuated toxicity compared to TAT-BH4 and we established a dosing regimen of NLS-BH4 that conferred a significant survival advantage in a post-exposure treatment model of LD90 total body irradiation.