Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

Background and aim Millions suffer from anaemia worldwide, and systemic disorders like anaemia harm oral health. Anaemia is linked to periodontitis as certain inflammatory cytokines produced during periodontal inflammation can depress erythropoietin production leading to the development of anemia. Thus, detecting and treating it is crucial to tooth health. Hence, this study aimed to evaluate three different machine-learning approaches for the automated detection of anaemia using clinical intraoral pictures of a patient\'s gingiva. Methodology Orange was employed with squeeze net embedding models for machine learning. Using 300 intraoral clinical photographs of patients\' gingiva, logistic regression, neural network, and naive Bayes were trained and tested for prediction and detection. Accuracy was measured using a confusion matrix and receiver operating characteristic (ROC) curve. Results In the present study, three convolutional neural network (CNN)-embedded machine-learning algorithms detected and predicted anaemia. For anaemia identification, naive Bayes had an area under curve (AUC) of 0.77, random forest plot had an AUV of 0.78, and logistic regression had 0.85. Thus, the three machine learning methods detected anaemia with 77%, 78%, and 85% accuracy, respectively. Conclusion Using artificial intelligence (AI) with clinical intraoral gingiva images can accurately predict and detect anaemia. These findings need to be confirmed with larger samples and additional imaging modalities.

UNASSIGNED: To assess the utility of wearable cameras in medical examinations, we created a physician-view video-based examination question and explanation, and the survey results indicated that these cameras can enhance the evaluation and educational capabilities of medical examinations.

UNASSIGNED: During the coronavirus disease 2019 (COVID-19) pandemic, there was a reduction in access to prevention of mother-to-child transmission (PMTCT) of human Immunodeficiency virus (HIV) services globally, yet this programme is critical for reducing paediatric HIV incidence. To minimise the impact of COVID-19 and prevent disruptions to the PMTCT service provision, innovative strategies had to be developed and implemented.
UNASSIGNED: The study aimed to describe the approaches that were developed and utilised during the COVID-19 pandemic in enhancing PMTCT services in Tshwane primary healthcare facilities.
UNASSIGNED: Three primary healthcare facilities that were providing PMTCT services during the COVID-19 pandemic located in the Tshwane district, Gauteng province, South Africa.
UNASSIGNED: The study is part of a larger study that focused on the experiences of healthcare workers who were rendering PMTCT services during the COVID-19 pandemic. An interpretative phenomenological analysis (IPA) design was employed to gain insight into the experiences of 16 purposively sampled healthcare workers who were providing PMTCT services during the pandemic in Tshwane district facilities. In-depth individual audio-recorded interviews were conducted with study participants, following a semi-structured interview guide. Data analysis was performed using an IPA framework.
UNASSIGNED: Three superordinate themes emerged: strategies utilised for providing care, community-based initiatives, and support systems to enhance the PMTCT service access.
UNASSIGNED: Strengthening community-based initiatives and support systems is important for the enhancement of the PMTCT programme during and beyond the pandemic.
UNASSIGNED: Community-based initiatives are critical in continuity of PMTCT services, reducing HIV incidence, under-five child morbidity and mortality particularly during emergency situations.

Patients with schizophrenia often experience symptoms such as poor insight and disorganized thought, which limit their ability to seek and receive care consistently. In rural settings, systemic factors, including limited resources and transportation, further contribute to difficulties in health care access. Long-acting injectable antipsychotics (LAIs) can improve medication adherence and reduce hospitalizations from relapse. Opportunities exist for pharmacists to provide individualized care and improved health care access. The pilot service took place in ambulatory care clinics and home care settings. Pharmacists performed weekly reviews of patients with active orders for LAIs, coordinated care with nonadherent patients, and offered follow-up appointments in the Patient Centered Medical Home (PCMH). For patients unable to be reached, outreach pharmacists provided psychiatric assessment and LAI medication administration at home visits. There were 10 patients with LAI prescriptions in the past year selected for review. The period reviewed was 90 days before and after start of service. Pharmacist interventions resulted in 4 patients reestablished with care who were previously lost to follow-up. The percentage of days covered by LAI fills increased from an average 26% to 67% of days covered (P = .06). Total emergency room visits related to mental health episodes decreased from 11 to 2 visits (P = .03). Four patients who did not have metabolic lab monitoring in more than 1 year received lab monitoring as indicated. PCMH pharmacy services, including home visits by outreach pharmacists, may improve access and bridge care gaps for patients on LAIs by providing community-based services in addition to traditional clinic-based care.

Given that cancer is a challenging disease that plagues millions of individuals of all age groups and socioeconomic statuses globally, developmentally appropriate education is often lacking for young people, particularly adolescents. Increasing cancer awareness and prevention education among adolescents using innovative strategies, such as game-based learning, is critical in reducing the burden of this disease. Adolescents are understudied in the field of cancer prevention and control, yet vulnerable as they tackle creating life-long health behavior patterns. Targeting cancer prevention education for adolescents has the potential to support long-term healthy behavior and reduce their risk of cancer. This paper provides an overview of the Collaborative Research on MEdication use and family health (CRoME) Lab\'s novel game-based cancer prevention education tool. OutSMART Cancer is an innovative, novel educational intervention in the form of a serious game. Serious games are educational tools that seek to impart knowledge and improve behaviors in their players. This game covers information related to breast cancer, colon cancer, and lung cancer. This viewpoint is a summary of the developmental process for the OutSMART Cancer game. We describe in detail the work preceding initial game development, the current version of the game, future directions for the game, and its educational potential. The long-term goal of OutSMART Cancer is to improve cancer awareness and knowledge regarding prevention behaviors in adolescents and support a lifetime of health and wellness.

BACKGROUND: Social media is essential in the lives of adolescents, with 97% of US teenagers engaging daily. While it facilitates communication, learning, and identity development, it also poses risks like harmful content exposure and psychological distress, particularly for adolescents in their critical developmental stage. Teaching digital life skills innovatively counters these risks, adapting traditional competencies such as decision-making, problem-solving, creative and critical thinking, communication, interpersonal skills, self-awareness, empathy, and emotional and stress management to digital challenges.
OBJECTIVE: This study evaluates the accessibility of the \"leduin\" program, a novel intervention designed to impart digital life skills through Instagram. The program aims to leverage social media\'s educational potential, focusing on effective strategies to engage adolescents. Emphasizing accessibility is crucial, as it determines the program\'s overall impact.
METHODS: The leduin program, developed through intervention mapping, applies behavior change techniques via social media for 9th and 10th graders. It is a 14-week spaced learning curriculum with daily sessions <5 minutes. Emphasizing the \"reach\" aspect of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) model, the recruitment targets diverse educational settings across 6 German states, aiming for inclusivity. Recruitment will involve schools, youth centers, and therapeutic facilities. The study seeks at least 128 participants, a calculated minimum to detect medium-sized effects in the quasi-experimental design and explore varying engagement levels and program responses. Data collection includes preintervention, postintervention, and 6-month follow-up surveys, using multilevel regression, latent growth models, and qualitative analysis to extensively assess reach and gain first insights on effectiveness, acceptance, implementation, and maintenance. The study aims to reveal key factors influencing program participation and interaction; a detailed analysis of engagement patterns will reveal the effectiveness of the recruitment strategies and barriers to participation. Additionally, initial indications of the program\'s impact on life skills, social media-related skills, health status, risk behaviors, and academic performance will be analyzed.
RESULTS: Recruitment was planned from May 2023 until the beginning of the leduin program in October 2023. As of March 2024, we have recruited 283 participants.
CONCLUSIONS: The leduin program stands as an innovative and essential initiative in adolescent health promotion, harnessing the power of social media to teach important digital life skills. This study highlights the critical role of accessibility in the success of social media interventions. Effective adolescent engagement strategies are imperative, as they dictate the overall impact of such interventions. The insights gained from this study will be instrumental in shaping future programs, laying groundwork for a subsequent, more comprehensive cluster-randomized controlled trial. The study\'s design acknowledges the limitations of the current quasi-experimental approach, including the anticipated sample size and the absence of a control group, and aims to provide a foundational understanding for future research in this field.
BACKGROUND: Deutsches Register Klinischer Studien DRKS00032308; https://drks.de/search/de/trial/DRKS00032308.
UNASSIGNED: PRR1-10.2196/51085.

Cystic lesions in the preauricular may have various histological origins, ranging from the skin to the acinar and non-acinar lesions from the parotid. Though advanced radiological investigations provide a good insight into the diagnosis of these lesions, diagnostic dilemmas may still prevail and warrant good clinical and surgical acumen to provide optimal treatment. The aim of the current report is to describe a case of an epidermoid cyst that mimicked a parotid cyst and discuss in detail the probable differential diagnosis and their management strategies.

Many behavior change theories have evolved over time. Originally, the first conceptions prioritized interventions based on information, such as raising awareness and transferring knowledge. Second-generation theories prioritize the development of skills and the promotion of awareness. The emergence of evidence-based techniques in the 1990s gave rise to third-generation theories such as the Theory of Planned Behavior and Social Cognitive Theory. Presently, fourth-generation trends amalgamate various components from multiple theories to implement accurate treatments, employing technology and emphasizing targeted behavior change. This paper aims to do a concise evaluation of the multi-theory model (MTM) of health behavior change interventions in the context of planning substance use prevention and treatment. The current area of intervention programs aimed at preventing and treating substance use may benefit from MTM, an innovative fourth-generation behavior change model. Tobacco, alcohol, and other drugs have all been the subjects of experimental, cross-sectional, and qualitative research. We have presented that additional research is required to compare MTM with knowledge-based therapies or interventions grounded in other theories. A gold standard would be the randomized controlled trials and behavioral change interventions particularly useful for this purpose. In addition, research evaluating the interventions\' efficacy must be carefully planned and executed.

UNASSIGNED: Implicit bias is as prevalent among health care professionals as among the wider population and is significantly associated with lower health care quality.
UNASSIGNED: The study goal was to develop and evaluate the preliminary efficacy of an innovative mobile app, VARIAT (Virtual and Augmented Reality Implicit Association Training), to reduce implicit biases among Medicaid providers.
UNASSIGNED: An interdisciplinary team developed 2 interactive case-based training modules for Medicaid providers focused on implicit bias related to race and socioeconomic status (SES) and sexual orientation and gender identity (SOGI), respectively. The simulations combine experiential learning, facilitated debriefing, and game-based educational strategies. Medicaid providers (n=18) participated in this pilot study. Outcomes were measured on 3 domains: training reactions, affective knowledge, and skill-based knowledge related to implicit biases in race/SES or SOGI.
UNASSIGNED: Participants reported high relevance of training to their job for both the race/SES module (mean score 4.75, SD 0.45) and SOGI module (mean score 4.67, SD 0.50). Significant improvement in skill-based knowledge for minimizing health disparities for lesbian, gay, bisexual, transgender, and queer patients was found after training (Cohen d=0.72; 95% CI -1.38 to -0.04).
UNASSIGNED: This study developed an innovative smartphone-based implicit bias training program for Medicaid providers and conducted a pilot evaluation on the user experience and preliminary efficacy. Preliminary evidence showed positive satisfaction and preliminary efficacy of the intervention.