Notes

[Medication blunders in pediatrics].
In this respect, we pay particular attention to interactions and reciprocal interplay between cell cycle regulators and androgen receptor. Somatic alterations that impact the cell cycle-associated and -regulated genes encoding p53, PTEN and MYC during progression from treatment-naïve, to castration-recurrent, and in some cases, neuroendocrine CaP are discussed. We considered also non-genomic events that impact cell cycle determinants, including transcriptional, epigenetic and micro-environmental switches that occur during CaP progression. Finally, we evaluate the therapeutic potential of cell cycle regulators and address challenges and limitations in the approaches modulating their action for CaP treatment.Chronic kidney disease is a common complication and concomitant condition of diabetes mellitus. The treatment of patients with diabetes and chronic kidney disease, including intensive control of blood sugar and blood pressure, has been very similar for type 1 and type 2 diabetes patients. New therapeutic targets have shown promising results and may lead to more specific treatment options for patients with type 1 and type 2 diabetes.
Big data technology provides unlimited potential for efficient storage, processing, querying, and analysis of medical data. Technologies such as deep learning and machine learning simulate human thinking, assist physicians in diagnosis and treatment, provide personalized health care services, and promote the use of intelligent processes in health care applications.

The aim of this paper was to analyze health care data and develop an intelligent application to predict the number of hospital outpatient visits for mass health impact and analyze the characteristics of health care big data. Designing a corresponding data feature learning model will help patients receive more effective treatment and will enable rational use of medical resources.

A cascaded depth model was successfully implemented by constructing a cascaded depth learning framework and by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. To develop a medical data feattween outpatient volume data and obtain the precise predictive value of the outpatient volume, which is very helpful for the rational allocation of medical resources and the promotion of intelligent medical treatment.
Several data feature learning models are proposed to extract the relationships between outpatient volume data and obtain the precise predictive value of the outpatient volume, which is very helpful for the rational allocation of medical resources and the promotion of intelligent medical treatment.
With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data.

The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident.

At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data-neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)-from older adults. Approximle impairments in cognitive function.
Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.
There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device.

In this study, we analyzed existing invasive, minimally invasive, andure a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal's features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.
Alcohol misuse is common among Operation Enduring Freedom and Operation Iraqi Freedom veterans, yet barriers limit treatment participation. Mobile apps hold promise as means to deliver alcohol interventions to veterans who prefer to remain anonymous, have little time for conventional treatments, or live too far away to attend treatment in person.

This pilot study evaluated the usability and acceptability of Step Away, a mobile app designed to reduce alcohol-related risks, and explored pre-post changes on alcohol use, psychological distress, and quality of life.

This single-arm pilot study recruited Operation Enduring Freedom and Operation Iraqi Freedom veterans aged 18 to 55 years who exceeded National Institute on Alcohol Abuse and Alcoholism drinking guidelines and owned an iPhone. Enrolled veterans (N=55) completed baseline and 1-, 3-, and 6-month assessments. The System Usability Scale (scaled 1-100, ≥70 indicating acceptable usability) assessed the effectiveness, efficiency, and satisfaction dimens seen in heavy drinking days, alcohol-related problems, and Kessler-10 scores. A larger randomized trial is warranted to confirm our findings.
Chronic health conditions are affecting an increasing number of individuals, who experience various symptoms that decrease their quality of life. Digital communication interventions that enable patients to report their symptoms have been shown to positively impact chronic disease management by improving access to care, patient-provider communication, clinical outcomes, and health-related quality of life. These interventions have the potential to prepare patients and health care providers (HCPs) before visits and improve patient-provider communication. Despite the recent rapid development and increasing number of digital communication interventions that have shown positive research results, barriers to realizing the benefits offered through these types of interventions still exist.

The aim of this study is to prepare for the implementation of a digital patient-provider communication intervention in the daily workflow at 2 outpatient clinics by identifying potential determinants of implementation using the plementation planning.
MySurgery is a smartphone app designed to empower patients and their caregivers to contribute toward safer surgical care by following practical advice to help reduce susceptibility to errors and complications.

The aim of this study is to evaluate service users' perceptions of MySurgery, including its perceived acceptability, the potential barriers and facilitators to accessing and using its content, and ideas about how to facilitate its effective implementation. The secondary aim is to analyze how the intended use of the app might differ for diverse patients, including seldom-heard groups.

We implemented a diversity approach to recruit participants from a range of backgrounds with previous experience of surgery. We aimed to achieve representation from seldom-heard groups, including those from a Black, Asian, and minority ethnic (BAME) background; those with a disability; and those from the lesbian, gay, bisexual, transgender, queer (LGBT+) community. see more A total of 3 focus groups were conducted across a 2-mnts and their caregivers in maintaining patient safety. The diversity approach allowed for a better understanding of the needs of different population groups and highlighted opportunities for increasing accessibility and involvement in the app.
Despite the rapid adoption of electronic health records (EHRs) resulting from the reimbursement program of the US government, EHR adoption in behavioral hospitals is still slow, and there remains a lack of evidence regarding barriers and facilitators to the implementation of mental health care EHRs.

The aim of this study is to analyze the experience of mental health professionals to explore the perceived barriers, facilitators, and critical ideas influencing the implementation and usability of a mental health care EHR.

In this phenomenological qualitative study, we interviewed physicians, nurses, pharmacists, mental health clinicians, and administrative professionals separately at 4 behavioral hospitals in the United States. We conducted semistructured interviews (N=43) from behavioral hospitals involved in the adoption of the mental health care EHR. Purposeful sampling was used to maximize the diversity. Transcripts were coded and analyzed for emergent domains. An exploratory data analysis was conductetions and adopting a seamless workflow of mental health care into the system.
Health care professionals expected to enhance their work productivity and interprofessional collaboration by introducing the mental health care EHR. Routine education for end users is an essential starting point for the successful implementation of mental health care EHR electronic decision support. When adopting the mental health care EHR, managers need to focus on common practices in behavioral hospitals, such as documenting structured data in their organizations and adopting a seamless workflow of mental health care into the system.
Tinnitus Talk is a nonprofit online self-help forum. Asking inactive users about their reasons for discontinued usage of health-related online platforms such as Tinnitus Talk is important for quality assurance.

The aim of this study was to explore reasons for discontinued use of Tinnitus Talk, and their associations to the perceptions of Tinnitus Talk and the age of users who ceased logging on to the platform.

Initially, 13,745 users that did not use Tinnitus Talk within the previous 2 months were contacted and the response rate was 20.47% (n=2814). After dataset filtering, a total of 2172 past members of Tinnitus Talk were included in the analyses. Nine predefined reasons for discontinued usage of Tinnitus Talk were included in the survey as well as one open question. Moreover, there were 14 predefined questions focusing on perception of Tinnitus Talk (usefulness, content, community, and quality of members' posts). Mixed methods analyses were performed. Frequencies and correlation coefficients were calculated for quantitative data, and grounded theory methodology was utilized for exploration of the qualitative data.
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