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Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Abstract Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods: In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). Results: Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions: The clear need for such a platform will accelerate the DBDP’s adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

Fetal Alcohol Spectrum Disorder (FASD) is considered a lifelong disability that has been framed with neurobiological descriptions focused on the brain. These are important features but fail to tell the story of living with FASD. By surveying those with FASD, this work expanded upon prior survey work which illustrated a multitude of early-onset physiological issues occurring at rates much higher than is typical of the general population. The current project, again using an anonymous survey methodology, sought to open up other direct experiences to better understand the complexity of living with FASD. An anonymous online survey was used to gather data on adversity in childhood, schooling, employment, housing and finances, and involvement with the criminal justice system, as well as relationships and parenting. Results indicate high levels of adversity throughout the life span; vulnerability to manipulation, which is connected to involvement in the criminal justice system; struggles with housing; economic instability; and struggles maintaining employment, as well as difficulties with social and familial relationships. Systemic stigma was also identified. Suggestions are offered to inform others on how support can be enhanced and targeted with a goal of improving quality of life, as well as dealing with self-imposed stigma. The survey was developed by adults living with FASD who have served as a long-standing advocacy and educational group influencing policy and practice in the field.

Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.

We present the case of a 99-year-old Caucasian female who was referred for treatment of a painless, 8.0 cm × 7.8 cm exophytic, pedunculated, ulcerated tumor of the left medial canthus. Pathology showed spindled, oval, and polygonal cells with pleomorphic nuclei. Many multinuclear giant cells and mitotic figures were also noted. The tumor was highlighted with CD10, showed focal positivity with actin, desmin, and CD68, and had increased Ki67 immunohistochemical staining. The tumor was negative for pancytokeratin, CK5/6, p63, MART-1/MelanA, S100, Sox10, p40, CD34, and CD23. Based on clinicopathologic correlation, the diagnosis of pleomorphic dermal sarcoma (PDS) was made. Pleomorphic dermal sarcoma (PDS) refers to a deep, histologically high-grade tumor that often resembles other tumors clinically and histologically. As PDS is frequently aggressive and related to adverse outcomes, it is important to recognize its distinguishing features in comparison to other similar entities, including atypical fibroxanthoma (AFX) and pleomorphic leiomyosarcoma (PLMS). To our knowledge, there is only one other reported case in the literature of PDS occurring on the eye. By reviewing and understanding characteristic etiologies, locations of presentation, histopathological features, and management techniques, pathologists can make a more accurate diagnosis and dermatologists can provide more effective patient care in a timely manner.

Fetal alcohol spectrum disorder (FASD) encompasses a range of complex neurodevelopmental challenges that arise because of maternal alcohol use during pregnancy. Contrary to previous beliefs, FASD is a wide-ranging condition that is mostly invisible, affecting cognitive, social, and daily living skills. Furthermore, living with FASD may present other challenges, such as mental health issues, substance abuse, and engagement in criminal behavior. FASD is a long-term disability that requires support across the lifespan. The main objective of this research was to determine what, if anything, has changed since a prior review in 2020 of parents with FASD appearing in child intervention courts in Canada. We found eleven relevant reported cases throughout Canada from 2020 to 2024 where parents had a confirmed diagnosis or a potential FASD to be eligible for our study. Within these cases, only one parent was able to reunite with their children because of the strong and supportive system they had. There were four cases where ongoing contact between parent and child(ren) was permitted, Additionally, we found that professionals lack education regarding the potential skills that people with FASD may demonstrate. A person with FASD is seen through the lens of their diagnosis rather than a strengths-based approach, creating stigma, fear, and power imbalance. Stigma has an impact not just on how professionals see people and make decisions but also on policymakers, funding, and support from governments or other social groups. Individuals or parents with FASD are often reluctant to report their diagnosis for fear of being judged or having their children taken from their care. Thus, parents are unable to obtain access to services, and even when they do have access, they must navigate the system on their own. Parents with FASD who are involved in child intervention may be required to participate in many programs simultaneously, potentially resulting in an overwhelming experience.