SenNet recommendations for detecting senescent cells in different tissues.

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Artificial intelligence and machine learning in axial spondyloarthritis.

PURPOSE OF REVIEW: To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. RECENT FINDINGS: Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. SUMMARY: Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Comparison of diffusion-weighted MRI and [18F]FDG PET/MRI for treatment monitoring in pediatric Hodgkin and non-Hodgkin lymphoma.

OBJECTIVE: To compare tumor therapy response assessments with whole-body diffusion-weighted imaging (WB-DWI) and 18F-fluorodeoxyglucose ([18F]FDG) PET/MRI in pediatric patients with Hodgkin lymphoma and non-Hodgkin lymphoma. MATERIALS AND METHODS: In a retrospective, non-randomized single-center study, we reviewed serial simultaneous WB-DWI and [18F]FDG PET/MRI scans of 45 children and young adults (27 males; mean age, 13 years ± 5 [standard deviation]; age range, 1-21 years) with Hodgkin lymphoma (n = 20) and non-Hodgkin lymphoma (n = 25) between February 2018 and October 2022. We measured minimum tumor apparent diffusion coefficient (ADCmin) and maximum standardized uptake value (SUVmax) of up to six target lesions and assessed therapy response according to Lugano criteria and modified criteria for WB-DWI. We evaluated the agreement between WB-DWI- and [18F]FDG PET/MRI-based response classifications with Gwet's agreement coefficient (AC). RESULTS: After induction chemotherapy, 95% (19 of 20) of patients with Hodgkin lymphoma and 72% (18 of 25) of patients with non-Hodgkin lymphoma showed concordant response in tumor metabolism and proton diffusion. We found a high agreement between treatment response assessments on WB-DWI and [18F]FDG PET/MRI (Gwet's AC = 0.94; 95% confidence interval [CI]: 0.82, 1.00) in patients with Hodgkin lymphoma, and a lower agreement for patients with non-Hodgkin lymphoma (Gwet's AC = 0.66; 95% CI: 0.43, 0.90). After completion of therapy, there was an excellent agreement between WB-DWI and [18F]FDG PET/MRI response assessments (Gwet's AC = 0.97; 95% CI: 0.91, 1). CONCLUSION: Therapy response of Hodgkin lymphoma can be evaluated with either [18F]FDG PET or WB-DWI, whereas patients with non-Hodgkin lymphoma may benefit from a combined approach. CLINICAL RELEVANCE STATEMENT: Hodgkin lymphoma and non-Hodgkin lymphoma exhibit different patterns of tumor response to induction chemotherapy on diffusion-weighted MRI and PET/MRI. KEY POINTS: • Diffusion-weighted imaging has been proposed as an alternative imaging to assess tumor response without ionizing radiation. • After induction therapy, whole-body diffusion-weighted imaging and PET/MRI revealed a higher agreement in patients with Hodgkin lymphoma than in those with non-Hodgkin lymphoma. • At the end of therapy, whole-body diffusion-weighted imaging and PET/MRI revealed an excellent agreement for overall tumor therapy responses for all lymphoma types.

International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study.

AIMS: To explore international undergraduate pharmacy students' views on integrating artificial intelligence (AI) into pharmacy education and practice. METHODS: This cross-sectional institutional review board-approved multinational, multicentre study comprised an anonymous online survey of 14 multiple-choice items to assess pharmacy students' preferences for AI events in the pharmacy curriculum, the current state of AI education, and students' AI knowledge and attitudes towards using AI in the pharmacy profession, supplemented by 8 demographic queries. Subgroup analyses were performed considering sex, study year, tech-savviness, and prior AI knowledge and AI events in the curriculum using the Mann-Whitney U-test. Variances were reported for responses in Likert scale format. RESULTS: The survey gathered 387 pharmacy student opinions across 16 faculties and 12 countries. Students showed predominantly positive attitudes towards AI in medicine (58%, n = 225) and expressed a strong desire for more AI education (72%, n = 276). However, they reported limited general knowledge of AI (63%, n = 242) and felt inadequately prepared to use AI in their future careers (51%, n = 197). Male students showed more positive attitudes towards increasing efficiency through AI (P = .011), while tech-savvy and advanced-year students expressed heightened concerns about potential legal and ethical issues related to AI (P < .001/P = .025, respectively). Students who had AI courses as part of their studies reported better AI knowledge (P < .001) and felt more prepared to apply it professionally (P < .001). CONCLUSIONS: Our findings underline the generally positive attitude of international pharmacy students towards AI application in medicine and highlight the necessity for a greater emphasis on AI education within pharmacy curricula.

Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Infant feeding method and special educational need in 191,745 Scottish schoolchildren: A national, population cohort study.

BACKGROUND: While special educational needs (SEN) are increasingly recorded among schoolchildren, infant breastfeeding has been associated with reduced incidence of childhood physical and mental health problems. This study investigated relationships between infant feeding method and risk of all-cause and cause-specific SEN. METHODS AND FINDINGS: A population cohort of schoolchildren in Scotland was constructed by linking together health (maternity, birth, and health visitor records) and education (annual school pupil census) databases. Inclusion was restricted to singleton children, born in Scotland from 2004 onwards with available breastfeeding data and who attended local authority mainstream or special schools between 2009 and 2013. Generalised estimating equation models with a binomial distribution and logit link function investigated associations between infant feeding method at 6 to 8 weeks and all-cause and cause-specific SEN, adjusting for sociodemographic and maternity factors. Of 191,745 children meeting inclusion criteria, 126,907 (66.2%) were formula-fed, 48,473 (25.3%) exclusively breastfed, and 16,365 (8.5%) mixed-fed. Overall, 23,141 (12.1%) children required SEN. Compared with formula feeding, mixed feeding and exclusive breastfeeding, respectively, were associated with decreased all-cause SEN (OR 0.90, 95% CI [0.84,0.95], p < 0.001 and 0.78, [0.75,0.82], p < 0.001), and SEN attributed to learning disabilities (0.75, [0.65,0.87], p < 0.001 and 0.66, [0.59,0.74], p < 0.001), and learning difficulties (0.85, [0.77,0.94], p = 0.001 and 0.75, [0.70,0.81], p < 0.001). Compared with formula feeding, exclusively breastfed children had less communication problems (0.81, [0.74,0.88], p = 0.001), social-emotional-behavioural difficulties (0.77, [0.70,0.84], p = 0.001), sensory impairments (0.79, [0.65,0.95], p = 0.01), physical motor disabilities (0.78, [0.66,0.91], p = 0.002), and physical health conditions (0.74, [0.63,0.87], p = 0.01). There were no significant associations for mixed-fed children (communication problems (0.94, [0.83,1.06], p = 0.312), social-emotional-behavioural difficulties (0.96, [0.85,1.09], p = 0.541), sensory impairments (1.07, [0.84,1.37], p = 0.579), physical motor disabilities (0.97, [0.78,1.19], p = 0.754), and physical health conditions (0.93, [0.74,1.16], p = 0.504)). Feeding method was not significantly associated with mental health conditions (exclusive 0.58 [0.33,1.03], p = 0.061 and mixed 0.74 [0.36,1.53], p = 0.421) or autism (exclusive 0.88 [0.77,1.01], p = 0.074 and mixed 1.01 [0.84,1.22], p = 0.903). Our study was limited since only 6- to 8-week feeding method was available precluding differentiation between never-breastfed infants and those who stopped breastfeeding before 6 weeks. Additionally, we had no data on maternal and paternal factors such as education level, IQ, employment status, race/ethnicity, or mental and physical health. CONCLUSIONS: In this study, we observed that both breastfeeding and mixed feeding at 6 to 8 weeks were associated with lower risk of all-cause SEN, and SEN attributed to learning disabilities and learning difficulty. Many women struggle to exclusively breastfeed for the full 6 months recommended by WHO; however, this study provides evidence that a shorter duration of nonexclusive breastfeeding could nonetheless be beneficial with regard to the development of SEN. Our findings augment the existing evidence base concerning the advantages of breastfeeding and reinforce the importance of breastfeeding education and support.

Sex Differences in Renal Cell Carcinoma: The Importance of Body Composition.

PURPOSE: To examine sex-specific differences in renal cell carcinoma (RCC) in relation to abdominal fat accumulation, psoas muscle density, tumor size, pathology, and survival, and to evaluate possible associations with RCC characteristics and outcome. METHODS: A total of 470 patients with RCC who underwent nephrectomy between 2006 and 2019 were included in this retrospective study. Specific characteristics of RCC patients were collected, including sex, height, tumor size, grade, and data on patient survival, if available. Abdominal fat measurements and psoas muscle area were determined at the level of L3 (cm2). RESULTS: Women had a higher subcutaneous (p < 0.001) and men had a higher visceral fat area, relative proportion of visceral fat area (p < 0.001), and psoas muscle index (p < 0.001). Logistic regression analysis showed an association between higher psoas muscle index and lower grade tumors [women: odds ratio (OR) 0.94, 95% confidence interval (CI) 0.89-0.99, p = 0.011; men: OR 0.97 (95% CI, 0.95-0.99, p = 0.012]. Univariate regression analysis demonstrated an association between psoas muscle index and overall survival (women: OR 1.41, 95% CI 1.03-1.93, p = 0.033; men: OR 1.62 (95% CI, 1.33-1.97, p < 0.001). In contrast, there were no associations between abdominal fat measurements and tumor size, grade, or survival. Also, there were no sex-specific differences in tumor size or tumor grades. CONCLUSIONS: A higher preoperative psoas muscle index was independently associated with overall survival in RCC patients, with a stronger association in men compared with women. In addition, the psoas muscle index showed an inverse association with tumor grade, whereby this association was slightly more pronounced in women than in men.

Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose 18F-FDG PET data over the entire dose reduction spectrum. METHODS: In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg 18F-FDG dose. The comparisons were performed upon two independent cohorts - (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tübingen University - in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis. RESULTS: For low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887-0.910) for EDSR, 0.893 (0.881-0.905) for EDSR-ViT, 0.873 (0.859-0.887) for GAN, 0.885 (0.873-0.898) for U-Net, and 0.910 (0.900-0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%. CONCLUSION: Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques.

Tyrosine kinase inhibitor therapy in pediatric sarcoma: Prognostic implications of pulmonary metastatic cavitation.

PURPOSES: This study aims to ascertain the prevalence of cavitations in pulmonary metastases among pediatric and young adult patients with sarcoma undergoing tyrosine kinase inhibitor (TKI) therapy, and assess whether cavitation can predict clinical response and survival outcomes. METHODS: In a single-center retrospective analysis, we examined chest computed tomography (CT) scans of 17 patients (median age 16 years; age range: 4-25 years) with histopathologically confirmed bone (n = 10) or soft tissue (n = 7) sarcoma who underwent TKI treatment for lung metastases. The interval between TKI initiation and the onset of lung nodule cavitation and tumor regrowth were assessed. The combination of all imaging studies and clinical data served as the reference standard for clinical responses. Progression-free survival (PFS) was compared between patients with cavitating and solid nodules using Kaplan-Meier survival analysis and log-rank test. RESULTS: Five out of 17 patients (29%) exhibited cavitation of pulmonary nodules during TKI therapy. The median time from TKI initiation to the first observed cavitation was 79 days (range: 46-261 days). At the time of cavitation, all patients demonstrated stable disease. When the cavities began to fill with solid tumor, 60% (3/5) of patients exhibited progression in other pulmonary nodules. The median PFS for patients with cavitated pulmonary nodules after TKI treatment (6.7 months) was significantly longer compared to patients without cavitated nodules (3.8 months; log-rank p-value = .03). CONCLUSIONS: Cavitation of metastatic pulmonary nodules in sarcoma patients undergoing TKI treatment is indicative of non-progressive disease, and significantly correlates with PFS.

Ferumoxytol-Enhanced MRI in Children and Young Adults: State of the Art.

Ferumoxytol is an ultrasmall iron oxide nanoparticle that was originally approved by the FDA in 2009 for IV treatment of iron deficiency in adults with chronic kidney disease. Subsequently, its off-label use as an MRI contrast agent increased in clinical practice, particularly in pediatric patients in North America. Unlike conventional MRI contrast agents that are based on the rare earth metal gadolinium (gadolinium-based contrast agents), ferumoxytol is biodegradable and carries no potential risk of nephrogenic systemic fibrosis. At FDA-approved doses, ferumoxytol shows no long-term tissue retention in patients with intact iron metabolism. Ferumoxytol provides unique MRI properties, including long-lasting vascular retention (facilitating high-quality vascular imaging) and retention in reticuloendothelial system tissues, thereby supporting a variety of applications beyond those possible with gadolinium-based contrast agents (GBCAs). This Clinical Perspective describes clinical and early translational applications of ferumoxytol-enhanced MRI in children and young adults through off-label use in a variety of settings, including vascular, cardiac, and cancer imaging, drawing on the institutional experience of the authors. In addition, we describe current advances in pre-clinical and clinical research using ferumoxytol in cellular and molecular imaging as well as the use of ferumoxytol as a novel potential cancer therapeutic agent.

What Does DALL-E 2 Know About Radiology?

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection. Purpose To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA. Materials and Methods This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. P < .05 was considered indicative of statistically significant difference. Results Overall, 593 patients (mean age, 37 years ± 11 [SD]; 302 women) were studied. Inflammatory and structural changes were found in 197 of 477 patients (41%) and 244 of 477 (51%), respectively, in the training set and 25 of 116 patients (22%) and 26 of 116 (22%) in the test set. The AUCs were 0.94 (95% CI: 0.84, 0.97) for all inflammatory changes, 0.88 (95% CI: 0.80, 0.95) for inflammatory changes fulfilling the Assessment of SpondyloArthritis international Society definition, and 0.89 (95% CI: 0.81, 0.96) for structural changes indicative of axSpA. Sensitivity and specificity on the external test set were 22 of 25 patients (88%) and 65 of 91 patients (71%), respectively, for inflammatory changes and 22 of 26 patients (85%) and 70 of 90 patients (78%) for structural changes. Conclusion Deep neural networks can detect inflammatory or structural changes to the sacroiliac joint indicative of axial spondyloarthritis at MRI. © RSNA, 2022 Online supplemental material is available for this article.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

MOTIVATION: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. RESULTS: Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study.

BACKGROUND: Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time. PURPOSE: To investigate heat production as a function of femur temperature during and after battery-powered percutaneous bone drilling in a porcine in vivo model. METHODS: We performed 16 femur drillings in 13 domestic pigs with an average age of 22 weeks and an average body temperature of 39.7 °C, using a battery-powered drilling system and an intraosseous temperature monitoring device. The standardized duration of the drilling procedure was 20 s. The bone core specimens obtained were embedded in 4% formalin, stained with haematoxylin and eosin (H&E) and sent for pathological analysis of tissue quality and signs of thermal damage. RESULTS: No significant changes in the pigs' local temperature were observed after bone drilling with a battery-powered drill device. Across all measurements, the median change in temperature between the initial measurement and the temperature measured after drilling (at 20 s) was 0.1 °C. Histological examination of the bone core specimens revealed no signs of mechanical or thermal damage. CONCLUSION: Overall, this preliminary study shows that battery-powered, drill-assisted harvesting of bone core specimens does not appear to cause mechanical or thermal damage.

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND METHODS: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. RESULTS: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. CONCLUSION: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

Impact of SMS Text Messaging Reminders on Helmet Use Among Motorcycle Drivers in Dar es Salaam, Tanzania: Randomized Controlled Trial.

BACKGROUND: Road traffic injury is a pressing public health issue in Tanzania. Increasing helmet use among motorcycle drivers can help reduce the burden due to road traffic injuries in the country. Helmet adherence can be supported through mobile health interventions. OBJECTIVE: The aim of this study is to evaluate the comparative impact of two different types of SMS text messaging reminders on motorcycle helmet use. METHODS: Participants were 391 commercial motorcycle taxi drivers in Dar es Salaam, Tanzania. Participants were randomized into three groups, each receiving a different set of messages: (1) social norming messages aimed at emphasizing society's positive stance on helmet wearing, (2) fear appeal messages that emphasized the dangers of riding without a helmet, and (3) control group messages, which included basic road safety messages unrelated to helmet use. Every participant received the control messages. Adherence to helmet use was evaluated by self-report through surveys conducted at baseline, 3 weeks, and 6 weeks. RESULTS: At 6 weeks, the odds of self-reporting consistent helmet use were estimated to be 1.58 times higher in the social norming group than in the control group (P=.04), though this difference was not significant after accounting for multiple testing. There was little difference between fear appeal and control group recipients (odds ratio 1.03, P=.47). Subgroup analysis suggests that both fear appeal and social norming message types might have been associated with increased helmet use among participants who did not consistently wear helmets at baseline (odds ratio 1.66 and odds ratio 1.84, respectively), but this was not significant (P=.11 and P=.07, respectively). Among those who were consistent wearers at baseline, the social norming messages performed better than the fear appeal messages, and this difference reached traditional significance (P=.03), but was not significant after accounting for multiple testing. CONCLUSIONS: The use of SMS text messaging reminders may improve helmet use among motorcycle drivers when framed as social norming messages. Given that nearly half of the drivers in our sample did not consistently wear their helmets on every trip, strategies to increase consistent usage could greatly benefit public safety. TRIAL REGISTRATION: ClinicalTrials.gov NCT02120742; https://clinicaltrials.gov/ct2/show/NCT02120742.

Collagen-Specific Molecular Magnetic Resonance Imaging of Prostate Cancer.

Constant interactions between tumor cells and the extracellular matrix (ECM) influence the progression of prostate cancer (PCa). One of the key components of the ECM are collagen fibers, since they are responsible for the tissue stiffness, growth, adhesion, proliferation, migration, invasion/metastasis, cell signaling, and immune recruitment of tumor cells. To explore this molecular marker in the content of PCa, we investigated two different tumor volumes (500 mm3 and 1000 mm3) of a xenograft mouse model of PCa with molecular magnetic resonance imaging (MRI) using a collagen-specific probe. For in vivo MRI evaluation, T1-weighted sequences before and after probe administration were analyzed. No significant signal difference between the two tumor volumes could be found. However, we detected a significant difference between the signal intensity of the peripheral tumor area and the central area of the tumor, at both 500 mm3 (p < 0.01, n = 16) and at 1000 mm3 (p < 0.01, n = 16). The results of our histologic analyses confirmed the in vivo studies: There was no significant difference in the amount of collagen between the two tumor volumes (p > 0.05), but within the tumor, higher collagen expression was observed in the peripheral area compared with the central area of the tumor. Laser ablation with inductively coupled plasma mass spectrometry further confirmed these results. The 1000 mm3 tumors contained 2.8 ± 1.0% collagen and the 500 mm3 tumors contained 3.2 ± 1.2% (n = 16). There was a strong correlation between the in vivo MRI data and the ex vivo histological data (y = −0.068x + 1.1; R2 = 0.74) (n = 16). The results of elemental analysis by inductively coupled plasma mass spectrometry supported the MRI data (y = 3.82x + 0.56; R2 = 0.79; n = 7). MRI with the collagen-specific probe in PCa enables differentiation between different tumor areas. This may help to differentiate tumor from healthy tissue, potentially identifying tumor areas with a specific tumor biology.

Telehealth's New Horizon: Providing Smart Hospital-Level Care in the Home.

During the COVID-19 pandemic, medical providers have expanded telehealth into daily practice, with many medical and behavioral health care visits provided remotely over video or through phone. The telehealth market was already facilitating home health care with increasing levels of sophistication before COVID-19. Among the emerging telehealth practices, telephysical therapy; teleneurology; telemental health; chronic care management of congestive heart failure, chronic obstructive pulmonary disease, diabetes; home hospice; home mechanical ventilation; and home dialysis are some of the most prominent. Home telehealth helps streamline hospital/clinic operations and ensure the safety of health care workers and patients. The authors recommend that we expand home telehealth to a comprehensive delivery of medical care across a distributed network of hospitals and homes, linking patients to health care workers through the Internet of Medical Things using in-home equipment, including smart medical monitoring devices to create a "medical smart home." This expanded telehealth capability will help doctors care for patients flexibly, remotely, and safely as a part of standard operations and during emergencies such as a pandemic. This model of "telehomecare" is already being implemented, as shown herein with examples. The authors envision a future in which providers and hospitals transition medical care delivery to the home just as, during the COVID-19 pandemic, students adapted to distance learning and adults transitioned to remote work from home. Many of our homes in the future may have a "smart medical suite" as well as a "smart home office."

Targeting the Extracellular Matrix in Abdominal Aortic Aneurysms Using Molecular Imaging Insights.

This review outlines recent preclinical and clinical advances in molecular imaging of abdominal aortic aneurysms (AAA) with a focus on molecular magnetic resonance imaging (MRI) of the extracellular matrix (ECM). In addition, developments in pharmacologic treatment of AAA targeting the ECM will be discussed and results from animal studies will be contrasted with clinical trials. Abdominal aortic aneurysm (AAA) is an often fatal disease without non-invasive pharmacologic treatment options. The ECM, with collagen type I and elastin as major components, is the key structural component of the aortic wall and is recognized as a target tissue for both initiation and the progression of AAA. Molecular imaging allows in vivo measurement and characterization of biological processes at the cellular and molecular level and sets forth to visualize molecular abnormalities at an early stage of disease, facilitating novel diagnostic and therapeutic pathways. By providing surrogate criteria for the in vivo evaluation of the effects of pharmacological therapies, molecular imaging techniques targeting the ECM can facilitate pharmacological drug development. In addition, molecular targets can also be used in theranostic approaches that have the potential for timely diagnosis and concurrent medical therapy. Recent successes in preclinical studies suggest future opportunities for clinical translation. However, further clinical studies are needed to validate the most promising molecular targets for human application.

Workplace harassment: The leadership factor.

Healthcare professionals are in the business of providing care and compassion. It is ironic that healthcare has the highest rate of workplace harassment. Although harassment comes from people of various positions, its existence in healthcare fractures the intention and values that guide its mission. When mental health is compromised, mistakes happen. Healthcare professionals take the health/life of others into their care; distractions and fear from being bullied compromise that care. Health leaders are in a strategic position to address workplace harassment and bullying. Failing to do so results in complacency, jeopardizes our patients, tarnishes the organization, and damages society as a whole. It is a cost that all will pay dearly to remedy, unless it is addressed, enforced with standards, and taken to task for the better health of all.