Amyotrophic lateral sclerosis (ALS) is a universally fatal neurodegenerative disease with no cure. Human endogenous retroviruses (HERVs) have been implicated in its pathogenesis but their relevance to ALS is not fully understood. We examined bulk RNA-seq data from almost 2,000 ALS and unaffected control samples derived from the cortex and spinal cord. Using different methods of feature selection, including differential expression analysis and machine learning, we discovered that transcription of HERV-K loci 1q22 and 8p23.1 were significantly upregulated in the spinal cord of individuals with ALS. Additionally, we identified a subset of ALS patients with upregulated HERV-K expression in the cortex and spinal cord. We also found the expression of HERV-K loci 19q11 and 8p23.1 was correlated with protein coding genes previously implicated in ALS and dysregulated in ALS patients in this study. These results clarify the association of HERV-K and ALS and highlight specific genes in the pathobiology of late-stage ALS.

OBJECTIVE: The public medical universities in Austria (educating 11,000 students) developed a joint public distance learning series in which clinicians discussed current digital lighthouse projects in their specialty. This study aims to examine the changes in attitude and knowledge of the participants before and after the lecture series to gain insights for future curriculum developments.
METHODS: The lecture series was announced via various channels at the universities, in health newsletters and in social media. Attitudes toward digitalization in medicine were surveyed before and after the lecture series, together with demographic data. The data were analyzed statistically and descriptively for four groups of interest: female medical students, male medical students, faculty members and members from industry and public agencies.
RESULTS: Out of 351 subjects who attended at least 1 lecture, 117 took part in the survey before and 47 after the lectures. Most participants had a positive attitude towards digitalization (85.3%). They improved their self-assessment of their knowledge from 34.4% to 64.7% (p < 0.05). After the lecture series 55.8% of participants considered digital medical applications to be important or very important today and 68.6% in the future.
CONCLUSIONS: The study shows that the presentation and discussion of lighthouse projects improves understanding of digitalization in medicine but does not trigger a strong desire for additional further training.

OBJECTIVE: We assessed the feasibility of ChatGPT for patients with type 2 diabetes seeking information about exercise.
METHODS: In this pilot study, two physicians with expertise in diabetes care and rehabilitative treatment in Republic of Korea discussed and determined the 14 most asked questions on exercise for managing type 2 diabetes by patients in clinical practice. Each question was inputted into ChatGPT (V.4.0), and the answers from ChatGPT were assessed. The Likert scale was calculated for each category of validity (1-4), safety (1-4) and utility (1-4) based on position statements of the American Diabetes Association and American College of Sports Medicine.
RESULTS: Regarding validity, 4 of 14 ChatGPT (28.6%) responses were scored as 3, indicating accurate but incomplete information. The other 10 responses (71.4%) were scored as 4, indicating complete accuracy with complete information. Safety and utility scored 4 (no danger and completely useful) for all 14 ChatGPT responses.
CONCLUSIONS: ChatGPT can be used as supplementary educational material for diabetic exercise. However, users should be aware that ChatGPT may provide incomplete answers to some questions on exercise for type 2 diabetes.

BACKGROUND: Patients with language barriers encounter healthcare disparities, which may be alleviated by leveraging interpreter skills to reduce cultural, language, and literacy barriers through improved bidirectional communication. Evidence supports the use of in-person interpreters, especially for interactions involving patients with complex care needs. Unfortunately, due to interpreter shortages and clinician underuse of interpreters, patients with language barriers frequently do not get the language services they need or are entitled to. Health information technologies (HIT), including artificial intelligence (AI), have the potential to streamline processes, prompt clinicians to utilize in-person interpreters, and support prioritization.
METHODS: From May 1, 2023, to June 21, 2024, a single-center stepped wedge cluster randomized trial will be conducted within 35 units of Saint Marys Hospital & Methodist Hospital at Mayo Clinic in Rochester, Minnesota. The units include medical, surgical, trauma, and mixed ICUs and hospital floors that admit acute medical and surgical care patients as well as the emergency department (ED). The transitions between study phases will be initiated at 60-day intervals resulting in a 12-month study period. Units in the control group will receive standard care and rely on clinician initiative to request interpreter services. In the intervention group, the study team will generate a daily list of adult inpatients with language barriers, order the list based on their complexity scores (from highest to lowest), and share it with interpreter services, who will send a secure chat message to the bedside nurse. This engagement will be triggered by a predictive machine-learning algorithm based on a palliative care score, supplemented by other predictors of complexity including length of stay and level of care as well as procedures, events, and clinical notes.
CONCLUSIONS: This pragmatic clinical trial approach will integrate a predictive machine-learning algorithm into a workflow process and evaluate the effectiveness of the intervention. We will compare the use of in-person interpreters and time to first interpreter use between the control and intervention groups.
BACKGROUND: NCT05860777. May 16, 2023.

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

BACKGROUND: The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods.
METHODS: A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS).
RESULTS: The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI -0.098-0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI -0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods.
CONCLUSIONS: The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

BACKGROUND: Governments and public health agencies worldwide experienced difficulties with social media-mediated infodemics on the internet during the COVID-19 pandemic. Existing public health crisis communication strategies need to be updated. However, crisis communication experiences of governments and public health agencies worldwide during the COVID-19 pandemic have not been systematically compiled, necessitating updated crisis communication strategies.
OBJECTIVE: This systematic review aims to collect and organize the crisis communication experiences of senders (ie, governments and public health agencies) during the COVID-19 pandemic. Our focus is on exploring the difficulties that governments and public health agencies experienced, best practices in crisis communication by governments and public health agencies during the COVID-19 pandemic in times of infodemic, and challenges that should be overcome in future public health crises.
METHODS: We plan to begin the literature search on May 1, 2024. We will search PubMed, MEDLINE, CINAHL, PsycINFO, PsycARTICLES, Communication Abstracts, and Web of Science. We will filter our database searches to search from the year 2020 and beyond. We will use a combination of keywords by referring to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research type) tool to search the abstracts in databases. We intend to include qualitative studies on crisis communication by governments and public health agencies (eg, officials, staff, health professionals, and researchers) to the public. Quantitative data-based studies will be excluded. Only papers written in English will be included. Data on study characteristics, study aim, participant characteristics, methodology, theoretical framework, object of crisis communication, and key results will be extracted. The methodological quality of eligible studies will be assessed using the Joanna Briggs Institute critical appraisal checklist for qualitative research. A total of 2 independent reviewers will share responsibility for screening publications, data extraction, and quality assessment. Disagreement will be resolved through discussion, and the third reviewer will be consulted, if necessary. The findings will be summarized in a table and a conceptual diagram and synthesized in a descriptive and narrative review.
RESULTS: The results will be systematically integrated and presented in a way that corresponds to our research objectives and interests. We expect the results of this review to be submitted for publication by the end of 2024.
CONCLUSIONS: To our knowledge, this will be the first systematic review of the experiences of governments and public health agencies regarding their crisis communication to the public during the COVID-19 pandemic. This review will contribute to the future improvement of the guidelines for crisis communication by governments and public health agencies to the public.
BACKGROUND: PROSPERO CRD42024528975; https://tinyurl.com/4fjmd8te.
UNASSIGNED: PRR1-10.2196/58040.

Background: Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep learning models and physicians. We aim to determine the extent of restoration in diagnostic accuracy after blurry images are deblurred by deep learning models. Methods: We used 19,191 skin images from a public skin image dataset that includes 23 skin disease categories, 54 skin images from a public dataset of blurry skin images, and 53 blurry dermatology consultation photos in a medical center to compare the diagnosis accuracy of trained diagnostic deep learning models and subjective sharpness between blurry and deblurred images. We evaluated five different deblurring models, including models for motion blur, Gaussian blur, Bokeh blur, mixed slight blur, and mixed strong blur. Main Outcomes and Measures: Diagnostic accuracy was measured as sensitivity and precision of correct model prediction of the skin disease category. Sharpness rating was performed by board-certified dermatologists on a 4-point scale, with 4 being the highest image clarity. Results: The sensitivity of diagnostic models dropped 0.15 and 0.22 on slightly and strongly blurred images, respectively, and deblurring models restored 0.14 and 0.17 for each group. The sharpness ratings perceived by dermatologists improved from 1.87 to 2.51 after deblurring. Activation maps showed the focus of diagnostic models was compromised by the blurriness but was restored after deblurring. Conclusions: Deep learning models can restore the diagnostic accuracy of diagnostic models for blurry images and increase image sharpness perceived by dermatologists. The model can be incorporated into teledermatology to help the diagnosis of blurry images.

UNASSIGNED: This cross-sectional descriptive study evaluates the experiences and perceptions of healthcare providers (HCPs) regarding the 937 medical call center in Saudi Arabia, a key telemedicine initiative.
UNASSIGNED:  To assess HCP satisfaction, identify challenges, and provide recommendations for improvement.
UNASSIGNED: Conducted from November 20th to December 15th, 2022, the study surveyed 454 HCPs, achieving a 90.5% response rate.
UNASSIGNED: A majority (86.8%) of respondents were satisfied with the call center, valuing its ease of use and effectiveness in healthcare delivery. However, challenges such as the accuracy of remote medical assessments, the need for clearer telehealth regulations, and concerns over management support and consultation overlaps were identified. The study also highlights the importance of ongoing support and updates, comprehensive telehealth regulations, integration of more medical specialties, and improvements in system integration and data confidentiality.
UNASSIGNED: The study underscores the need for strategic enhancements to the 937 call center to further improve healthcare accessibility and efficiency in Saudi Arabia. These enhancements are vital for aligning telehealth services with Saudi Arabia\'s healthcare objectives under Saudi Vision 2030.