Smartphone based non invasive real time white blood cell counter leveraging blue light and static magnetic field.

White blood cells (WBCs), also known as leukocytes, are one of the most significant parts of the immune system. They generate antibodies, protect the body from illnesses, and heal wounds. Accurate estimation of WBCs is key for diagnosing cancer, infections, leukemia, lymphoma, and other diseases.

Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP.

This study suggests a way to utilize the existing medical ontology and natural language processing techniques to extract major medical concepts from lay vocabularies of health consumers on social media and group them based on the defined semantic types in the ontology. Diabetes-related discussions on Tumblr was used to test the efficiency of SpaCy and the Markov-Viterbi algorithm to map lay medical terms to the defined medical concepts in the UMLS. The system discussed in this paper can better analyze free texts, take care of word ambiguity and extract the lifestyle indicators from the daily life discussions of diabetic people on Tumblr.

Equitable community-based participatory research engagement with communities of color drives All of Us Wisconsin genomic research priorities.

Objective: The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.

Materials And Methods: We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG).

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly.

A Scoping Review on Mobile Health Technology for Assessment and Intervention of Upper Limb Motor Function in Children with Motor Impairments.

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families.

Impact of veteran-led peer mentorship on posttraumatic stress disorder.

Peer mentorship shows promise as a strategy to support veteran mental health. A community-academic partnership involving a veteran-led nonprofit organization and institutions of higher education evaluated a collaboratively developed peer mentor intervention. We assessed posttraumatic stress disorder (PTSD), postdeployment experiences, social functioning, and psychological strengths at baseline, midpoint, and 12-week discharge using the PTSD Checklist for DSM-5 (PCL-5), Deployment Risk and Resilience Inventory-2, Social Adaptation Self-evaluation Scale, and Values in Action Survey.

Acceptance of bite presentations and feeding behaviors of 8 to 12-month-old infants: A reflection of typical feeding development.

Purpose: The lack of age-appropriate expectations for feeding acceptance patterns in early childhood is a barrier to early and accurate identification of pediatric feeding disorder (PFD). The objective of the study was to describe the process by which typically developing children 8-12 months of age accept or refuse bite presentations and their corresponding feeding behaviors, aiming to establish age-appropriate normative data for feeding acceptance.

Method: Using cross-sectional methodology, we studied the proportion of bite presentations accepted, the type of feeding behaviors-passive, disruptive, expulsion, feeding concerns- observed at presentation and acceptance or refusal, and the duration between presentation to acceptance or refusal in 63 healthy infants between 8 and 12 months of age.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed.

A Survey of Conversational Agents and Their Applications for Self-Management of Chronic Conditions.

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

A Consecutive Series Study of the Frequencies, Intensities, and Natural History of Symptoms Following COVID-19 Infection in Nepali Men.

Background: In Nepal, approximately one million individuals, two-thirds men, have tested positive for COVID-19. The recovery picture from this infection is undescribed.

Methods: At one major testing institution in Kathmandu, we attempted to contact men three-four months following documentation of a positive PCR Covid test.

An Artificial Intelligence-Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study.

Background: Opioid use disorder (OUD) is an addiction crisis in the United States. As recent as 2019, more than 10 million people have misused or abused prescription opioids, making OUD one of the leading causes of accidental death in the United States. Workforces that are physically demanding and laborious in the transportation, construction and extraction, and health care industries are prime targets for OUD due to high-risk occupational activities.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies.

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape.

Multimedia tablet or paper handout to supplement counseling during preterm birth hospitalization: a randomized controlled trial.

Background: Parents of premature infants engage in shared decision-making regarding the care of their infant. The process of prenatal counseling typically involves a verbal conversation with a neonatal provider during hospitalization. Support people may not be available, and the pregnant person's memory is impaired by medications, pain, and stress.

Towards Developing a Voice-activated Self-monitoring Application (VoiS) for Adults with Diabetes and Hypertension.

The integration of motivational strategies and self-management theory with mHealth tools is a promising approach to changing the behavior of patients with chronic disease. In this manuscript, we describe the development and current architecture of a prototype voice-activated self-monitoring application (VoiS) which is based on these theories. Unlike prior mHealth applications which require textual input, VoiS app relies on the more convenient and adaptable approach of asking users to verbally input markers of diabetes and hypertension control through a smart speaker.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate.

Can Technology Improve Participation From Underserved Children and Families in Rehabilitation Research?

The COVID-19 pandemic led to the suspension of research studies, exposing many of the limitations of in-person research conducted within a laboratory or clinical setting. However, these limitations existed long before the pandemic and have contributed to small and unrepresentative samples. The pandemic has provided an opportunity to re-evaluate the focus of pediatric rehabilitation research, utilizing existing technology for remote and offsite research that does not require in-person visits.

Data augmentation strategies for EEG-based motor imagery decoding.

The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

Background: Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer.

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks.

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands.

A Novel Framework for Mixed Reality-Based Control of Collaborative Robot: Development Study.

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.

Longitudinal Assessment of Acute Concussion Outcomes Through SMS Text (ConText Study).

Objective: Mild traumatic brain injury (mTBI), or concussion, is a common health problem that has seen a recent increase in US adolescents. This study uses SMS text messaging (a mobile health [mHealth] tool) to report patient symptoms. We aim to better characterize mTBI recovery and hypothesize that this mHealth tool will have high retention rates and correlate with a conventional means of assessing symptoms, the Post-Concussion Symptom Inventory (PCSI).

Eye-gaze control of a wheelchair mounted 6DOF assistive robot for activities of daily living.

Background: Building control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human-robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living.

Methods: In this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living.

An mHealth App-Based Self-management Intervention for Family Members of Pediatric Transplant Recipients (myFAMI): Framework Design and Development Study.

Background: Solid-organ transplantation is the treatment of choice for children with end-stage organ failure. Ongoing recovery and medical management at home after transplant are important for recovery and transition to daily life. Smartphones are widely used and hold the potential for aiding in the establishment of mobile health (mHealth) protocols.

An mCARE study on patterns of risk and resilience for children with ASD in Bangladesh.

Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh.