Background Family medicine plays a central role in strengthening primary healthcare systems and addressing evolving population health needs, particularly in regions undergoing rapid healthcare transformation. Over the past 15 years, the Emirates Health Services (EHS) Family Medicine Residency Program in the United Arab Emirates (UAE) has aimed to develop a competent family medicine workforce capable of delivering comprehensive, community-oriented, and patient-centered care. Methods This sequential mixed-methods study evaluated the evolution and perceived educational outcomes of the EHS Family Medicine Residency Program between 2009 and 2025. Quantitative data were collected using a structured survey administered to program alumni and current fourth-year residents (R4), while qualitative data were obtained through focus group discussions with residents, alumni, and faculty members. Quantitative analyses included descriptive statistics, non-parametric subgroup comparisons, and Spearman correlation analyses. Qualitative data were analyzed using inductive thematic analysis. Findings were integrated during interpretation. Results Of 71 eligible participants, 64 completed the survey (response rate: 90.1%), including 56 alumni and eight current R4 residents. Overall satisfaction with the residency program was high, with a median satisfaction score of 4.55/5.00. Faculty teaching, supervision, and academic day activities received the highest ratings, whereas hospital-based training and work-life balance demonstrated comparatively greater variability. Median perceived competency and preparedness scores were 4.36/5.00 and 4.37/5.00, respectively. Satisfaction demonstrated moderate positive correlations with perceived competency (Spearman ρ = 0.60, p < 0.0001) and preparedness for independent practice (ρ = 0.64, p < 0.0001). Outcome scores differed across post-residency duration groups, with alumni more than 10 years post-residency reporting the highest satisfaction, perceived competency, and preparedness scores. Qualitative findings suggested substantial program evolution toward a more structured, learner-centered, and competency-oriented training model, with strengths in academic-clinical integration, supervision, assessment systems, ambulatory learning, and resident professional development. Persistent challenges included workload pressures, administrative burden, and variability in hospital-based supervision. Conclusion Over the past 15 years, the EHS Family Medicine Residency Program has undergone substantial development toward a more structured and competency-oriented training model. Participants generally perceived the program positively in relation to supervision, educational experiences, preparedness for independent practice, and professional development. Ongoing operational and workload-related challenges warrant continued program refinement and longitudinal evaluation.
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly prescribed for weight management. Similar to the early implementation of metabolic and bariatric surgery (MBS), little is known about how GLP-1RAs affect individuals with eating disorders (EDs) or their potential to promote, mask, or exacerbate ED psychopathology. Lacking robust empirical data directly examining associations between GLP-1RAs and ED risk, we draw on lessons from the field of MBS to summarize current critical knowledge gaps and guide clinicians, educators, researchers, and advocates on interim decision-making. As providers working at the intersection of EDs and obesity treatment, we integrate published findings with our clinical and research expertise to identify potential ED risks associated with GLP-1RA use and identify opportunities to screen and intervene. Initially in MBS practice, ED symptoms were underrecognized due to lack of standardized screening and monitoring protocols. Early concerns arose from patient reports and provider observations, followed by empirical and systematic research investigation leading to the development of structured assessment tools and protocols. Similar concerns are arising with GLP-1RA treatment, where changes in appetite and weight may, without prospective or ongoing screening, worsen existing or contribute to de novo ED behaviors and cognitions. ED providers are uniquely positioned to identify and address disordered eating occurring with GLP-1RA treatment that may be overlooked by providers with less ED training and experience. We highlight opportunities for ED professionals to engage in advocacy, education, and interdisciplinary collaboration to reduce harm and promote patient wellbeing as GLP-1RA use increases.
The continued evolution of clinical ethics has prompted extensive discussion about the roles clinical ethicists ought to assume in practice, with the discussion focusing largely on what these professionals bring to healthcare. The emotional impact of assuming such roles is less commonly considered despite the fact that clinical ethicists frequently navigate situations of ethical complexity and conflict. We recently interviewed 34 U.S. clinical ethicists about their experiences and learned that the desire to be helpful is resulting in these professionals assuming a wide range of roles and responsibilities. Furthermore, we found that clinical ethicists are regularly emotionally impacted by their work and that they often carry an emotional burden as a result of the roles they assume in patient care. In this article, we explore this burden and discuss what it reveals about the field of clinical ethics at this pivotal point in its development.
Measles has reemerged as a significant global public health threat, with increasing morbidity and mortality associated with declining vaccination rates. This review summarizes current global outbreaks, history of measles, vaccination and elimination status, vaccine hesitancy, and outbreak response and lessons learned highlighting different novel digital epidemiological tools. Measles continues to surge worldwide with an estimated 11 million infections in 2024, which is more than prepandemic levels. Developing and developed countries are both facing measles outbreaks, with the United States at risk of losing measles elimination status. Recent studies have showed that worldwide percentages of two-dose measles vaccination were lower than 95% that is required to interrupt measles transmission in all WHO regions. Novel epidemiological tools such as interactive simulators, real-time use of dynamic models, serosurveillance, and others are transforming measles outbreak response and enable earlier outbreak detection, tracking, and targeted public health interventions. Vaccine hesitancy is one of the top global health threats and developing a tailored evidence-based approach is necessary to establish and maintain measles elimination.
Though large-scale pre-trained models are vital for foundational cell modeling, most of them focus on human or mouse systems, with less emphasis on model organisms like yeast (Saccharomyces cerevisiae), and fail to use existing biological prior knowledge effectively. Here, we present scYeast, the first foundational cell model for yeast single-cell transcriptomics that effectively embeds biological priors. scYeast employs a novel asymmetric parallel architecture to infuse transcriptional regulatory information into the Transformer's attention mechanism, leveraging biological knowledge during training. Pre-trained on large-scale yeast single-cell transcriptomics data, scYeast demonstrates strong generalization and biological interpretability. It shows capability in zero-shot tasks, such as inferring regulatory relationships. After fine-tuning, scYeast performs well in diverse tasks, including cell state classification, growth doubling time prediction, and gene perturbation response prediction. Additionally, using transfer learning, scYeast can be adapted to other omics datasets, such as proteomics, thus broadening its utility. Overall, scYeast is a promising tool for yeast single-cell biology research and presents a new framework for integrating foundational models with biological priors, accelerating discovery in yeast synthetic and systems biology and providing a replicable framework for other organisms.
Our article is testimonios from the perspective of three Black, female MFTs. We explore various results of the intersecting pandemics that we have ourselves experienced and continue to navigate. A recurring narrative that has been amplified during this time is that therapy and therapists should not be political and human. Not only is there an expectation that clinicians should not express or maintain a political stance, but we are also expected to hold space for clients to express their discriminatory or marginalizing views. This notion reflects the repeated pattern of clinicians showing up for clients regardless of any personal turmoil. Clinicians are expected to intentionally consider the context of intersecting pandemics on the populations we serve and give grace to students we teach, yet the same context is disregarded when considering the clinician's wellbeing and expectations regarding productivity. We see this habit manifest in academia as well and it contributes to an internal disconnect and lack of work/life balance. Overall, we have found that clinicians impacted by the ongoing pandemics are unable to find time to reflect on and process what is happening. Our article features our experiences, the lessons we have learned as a result of continuing to survive, and how we intend to use our agency, autonomy, and voices to create change for ourselves and our communities. We hope that readers develop a better understanding of how to recognize and respect the humanity of mental health clinicians.
Understanding livestock-wildlife interactions, especially in forest ecosystems, is critical for biodiversity conservation and sustainable land management. However, the long-term and cascading impacts of livestock grazing on forest structure and community bioacoustics are important yet largely neglected areas of research. Here, we used acoustic indices and a sound event detection (SED) model to evaluate the effects of continuous cattle grazing on seasonal soundscapes in Northeast China. We collected and analyzed over 18,785 h of recordings from 10 cattle-grazed forest plots and 10 ungrazed forest plots in Northeast China. We identified sound events in each recording via deep learning and calculated six acoustic indices, as well as extracted vegetation characteristics using light detection and ranging point cloud data. Our results revealed that grazing activities significantly changed seasonal soundscape dynamics, with biophony being highest in grazed forests and lowest in ungrazed forests in winter. Livestock shifted the forest soundscape composition by increasing the audibility of birds and insects while decreasing the vocalizations of sika deer (Cervus nippon) and crows, resulting in reduced sound diversity and complexity in grazed forests. We also found that grazing can reduce the leaf area index, herbaceous plants, and canopy density, which can influence these effects indirectly. Interestingly, cowbells noticeably altered the dawn chorus of birds; during spring and summer grazing periods, the chorus was characterized by an increased bird calling rate and greater vocal complexity (elevated Acoustic Complexity Index), patterns consistent with a behavioral adjustment to acoustic masking. This study highlights how livestock modify forest acoustic communities. To preserve natural soundscapes, we suggest mitigating cowbell noise through silent trackers (e.g., GPS) or reduced bell density in priority zones. Sustainable practices, including rotational grazing and buffer zones, are also vital to maintain forest structure and acoustic diversity. We suggest that integrating SED models with acoustic indices provides a robust framework for monitoring such anthropogenic disturbances.
Carbon monoxide (CO) catalytic oxidation, as one of the most extensively studied model reactions, serves as a critical platform for understanding how electronic structures determine catalytic performance. Although descriptors such as CO adsorption energy are widely used to correlate catalytic activity, they are often closely tied to specific surface structures or material types, making it difficult to establish a universal framework. In contrast, electronic structure descriptors, derived directly from the intrinsic electronic properties of atoms, offer the potential for constructing a unified understanding. This review systematically categorizes four key electronic structure descriptor systems: energy-level structure, d-band structure, charge and valence state distribution, and spin structure - progressively revealing their inherent hierarchical relationships across energy-level distribution, orbital hybridization, charge population, and electron spin, as well as their decisive roles in determining the reaction pathways of CO oxidation. In addition, this paper expands the scope of descriptors and explores the construction pathways for dynamic electronic structure descriptors as well as multiscale descriptors. By employing physics-based descriptors and interpretable machine learning models, it is possible to overcome the limitations of traditional performance prediction and lay the foundation for establishing causal models that encompass dynamic evolution and cross-scale coupling processes.
Milk is important constituent of daily food worldwide. Quality of milk is compromised because of multiple issues like sub-standard practices in dairy industry, frequent adulterations, microbial contaminations, storage and climatic conditions. The problem gets multifold grave because of lack of portable solutions of milk quality assessment and testing. Still the milk quality testing remains centralized and lab oriented. So, the need of the hour is to study and figure out solutions which can be portable, accurate and provide results in timely fashion. The current study tries to bridge this gap by analyzing recent development in the field of milk testing especially sensor-based techniques coupled with the power of Machine Learning based classification strategies. The search is for a milk quality testing solution which is reliable, all-in-one testing solution, low cost, portable, anywhere accessible, friendly user interface and operates in real time. It should employ state of the art technology like connectivity and novel Artificial Intelligence and Machine learning methods. The study concludes that sensor infused machine learning solutions provides an upper edge with respect to traditional lab based slow and costly solutions. Internet of Things and Sensor-based technology is opening doors for real time portable milk testing kit which is showing promising result with backend prowess of Machine learning methods. Our study is driven of societal impact especially in the area of food safety and rural empowerment. The online version contains supplementary material available at 10.1007/s13197-026-06746-0.
In high-risk occupational groups, current mental health status may be underreported because of stigma or concerns about disadvantage. Wearable data can capture short-term behavioral and physiological changes, but may be limited in capturing relatively stable biological characteristics. Structural brain magnetic resonance imaging (MRI) may reflect such individual differences and provide complementary information when combined with dynamic wearable signals. We evaluated whether a multimodal deep learning approach combining wearable-derived time-series data with structural brain MRI radiomics could help identify current depressive symptom status in firefighters and prosecution investigators. A total of 291 participants (184 firefighters and 107 prosecution investigators) were included, with a mean monitoring duration of 4.8 weeks. Elevated current depressive symptoms were defined as a Patient Health Questionnaire-9 (PHQ-9) score of 10 or higher at the final observation. Temporal deep learning models were evaluated under wearable-only and multimodal configurations. Multimodal models consistently outperformed wearable-only models, and the final multimodal LSTM model showed the best discriminative performance (AUROC = 0.867; 95% CI 0.814-0.914). In subgroup robustness analysis, performance was generally maintained across broader monitoring-duration categories and occupational groups. SHAP analysis identified the left paracentral lobule, right lateral ventricle, left amygdala, fragmented activity patterns, and minimum oxygen saturation during sleep as major contributors. These findings suggest that a multimodal approach integrating wearable-derived dynamic behavioral characteristics with relatively stable neuroanatomical information from structural brain MRI may serve as a supportive tool for more objectively identifying current depressive symptom status in high-risk occupational populations.
Diisononyl cyclohexane-1,2-dicarboxylate (DINCH), a widely used plasticizer substitute, has been widely detected in human populations and has emerged as a new environmental pollutant of growing concern. Despite the established association between exposure to traditional plasticizer and an increased risk of osteoarthritis (OA), it remains unclear whether DINCH, as a major substitute, contributes to OA pathogenesis through direct chondrotoxicity. This study integrated network toxicology, single-cell transcriptomics, machine learning, and experimental validation to systematically investigate the role of DINCH exposure in the onset and progression of OA and its potential molecule mechanisms. Through network toxicology analysis, we identified 14 common targets shared by DINCH and OA, which were significantly enriched in pathways related to cellular senescence, mitochondrial, and stress responses. In vitro experiments confirmed that DINCH inhibited chondrocyte viability in a concentration- and time-dependent manner and mediated mitochondrial stress and chondrocyte senescence. Random Forest algorithm, a nomogram model based on four high-confidence predictive biomarker-TSPO, RAF1, STAT6, and CAPN2-was constructed to predict the progression of DINCH-exposed related OA. Molecular docking and molecular dynamics simulations suggested that DINCH may stably interact with the aforementioned targets in silico, with TSPO emerging as the candidate core target through multi-algorithm integration. Single-cell transcriptomic analysis revealed that TSPO is highly expressed in OA chondrocytes and specifically enriched in the hypertrophic chondrocyte subpopulation. Virtual gene knockout suggested that TSPO functional disruption was associated with extracellular matrix(ECM) homeostasis remodeling. Moreover, TRO-40303, as a TSPO-specific inhibitor, can effectively reverse TSPO upregulation-induced mitochondrial stress and chondrocyte senescence, thereby restoring the ECM metabolic balance in cartilage. Overall, this study elucidated the molecular mechanism by which DINCH exposure induced chondrotoxicity through TSPO targeting to drive OA development, providing new targets and theoretical foundations for the early diagnosis and intervention of environment-related OA.
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More than six decades ago, the first successful repair of a congenital heart defect opened a new frontier in pediatric medicine. Driven by advances in fetal imaging, computational fluid dynamics, machine learning, and surgical perfusion, our ability to diagnose and treat these conditions has grown remarkably. Many of today's greatest challenges in congenital cardiology - early and accurate diagnosis, precise risk stratification, and lifelong management - are increasingly being addressed through methods borrowed from engineering and data science. The Congenital heart defects: Diagnosis and treatment Collection at Scientific Reports is dedicated to this research.
Aquablation is a semi-automated, ejaculation-sparing surgical option for treatment of benign prostatic hyperplasia (BPH). While previous studies have demonstrated that approximately 50 cases are required to achieve surgical competence in prostate laser enucleation, objective evaluations of Aquablation learning curve remain limited. The aim of this study was to assess Aquablation learning curve using cumulative sum (CUSUM) method, based on operative parameters and postoperative outcomes. We retrospectively analyzed data from 80 patients who underwent Aquablation by a single surgeon with prior experience in prostate resection and enucleation (01/2024 - 07/2025). Patient demographics, including prostate volume (PV) and comorbidities, were recorded. Operative parameters were collected. Patient evaluations were conducted at baseline, 3, 6 months postoperatively, including uroflowmetry and assessment using the International Prostate Symptom Score (IPSS). Ejaculation preservation was assessed. Trifecta was defined as preserved ejaculation, IPSS < 8, and maximum urinary flow rate (Qmax) > 15 mL/s at 6 months. Descriptive statistics, linear regression analyses, and CUSUM method were applied. Median (interquartile range [IQR]) prostate volume and baseline IPSS-total score were 60 (45-78) mL and 21 (14-27). Median total operative time was 39 (31-47) minutes. Postoperative complications occurred in 18 patients (22.5%). Median IPSS-total score and Qmax improved significantly at 3- and 6-month follow-up visits (p < 0.01). Antegrade ejaculation was preserved in 81.2% of patients at 6-month follow-up. Trifecta at 6 months was achieved in 49 patients (61.2%). CUSUM analysis demonstrated that learning curve for setup and ablation tissue time reached plateau after 14 cases, while coagulation time stabilized after 21 procedures. CUSUM charts for trifecta achievement revealed an initial learning phase requiring 18 cases to achieve stable performance, subsequently maintained. Aquablation is a reproducible, ejaculation-sparing surgical option for BPH, with procedural efficiency and stable functional outcomes achieved after approximately 12-21 cases. Aquablation offers a faster acquisition of technical proficiency due to its high degree of automation and standardized planning, leading to a rapid integration into clinical practice.
Biotechnology in modern medicine and pathology encompasses molecular diagnostics, multi-omics analysis, nanotechnology-enabled platforms, digital pathology, bioinformatics, and computational approaches that support disease detection, therapeutic development, and clinical decision-making. This review examines how these technologies have contributed to diagnostic workflows, therapeutic development, disease classification, and clinical decision-making in contemporary healthcare. The integration of molecular biology, nanotechnology, and computational sciences has expanded the evaluation of disease-related processes such as genetic variation, biomarker expression, tumor heterogeneity, immune regulation, and molecular pathway alterations in conditions including cancer, inherited disorders, infectious diseases, cardiovascular disease, and autoimmune disease. Therapeutic innovations such as gene editing, nanomedicine, immunotherapy, biologics, and targeted drug delivery systems have further supported mechanism-based treatment strategies by improving tissue targeting, reducing off-target toxicity, and enabling more individualized therapeutic planning. Artificial intelligence (AI) and bioinformatics approaches, including machine learning, deep learning, computational pathology, digital whole-slide image analysis, and omics-data integration, have supported biomarker discovery, disease classification, diagnostic image analysis, risk stratification, and pathology-based clinical decision support. Additionally, molecular and digital pathology have improved disease subclassification and prognostic assessment by integrating histomorphologic findings with molecular and computational data. Despite these advances, high costs, technical complexity, data standardization challenges, infrastructure limitations, and ethical concerns continue to restrict widespread clinical adoption. Future work should prioritize low-cost point-of-care molecular and biosensor platforms for resource-limited settings, interoperable standards for omics and digital pathology data, multicenter prospective validation of AI-assisted diagnostic tools and nanomedicine-based therapies, transparent reporting of algorithm provenance, and regulatory pathways addressing data privacy, clinical accountability, and equitable access. Overall, biotechnology represents an important component of precision healthcare, with the potential to strengthen diagnostic accuracy, targeted therapy, disease monitoring, and patient-centered clinical outcomes.
The rapid growth of bacterial gene expression databases has enabled computational inference of transcriptional regulatory networks (TRNs), yet it remains unclear why mathematically simple models often capture their apparent complexity. Using a 1035-sample E. coli expression database, we identify two transcriptome principles that support successful TRN inference. First, regulons defined from measured binding sites show limited overlap in gene membership, consistent with statistical independence exhibited by many successful inference methods. Second, 21% of genes, or 877 genes, exhibit regulator "dominance," in which expression strongly correlates with a single regulator activity and receives minimal contributions from other regulators under most conditions. We formalize these properties with quantitative metrics and provide a reference catalog of dominantly regulated E. coli genes. Regulator dominance explains differences between expression-inferred and binding site-defined regulons, and removing dominated genes sharply reduces inference performance, suggesting that simply regulated promoter subsets are central to effective TRN inference.
The neurotoxic organophosphorus metabolite paraoxon poses severe food safety and environmental risks, underscoring the need for decentralized, field-deployable diagnostics. This study reports a novel portable dual-mode biosensor integrating rapid colorimetric screening with machine learning (ML)-assisted electrochemical analysis for the highly sensitive detection of paraoxon. Central to the platform's architecture is the strategic utilization of cellulose nanofibers (CNFs), which serve a multifunctional macromolecular matrix. The CNF network provides an expansive, highly porous surface area for the stable, biocompatible immobilization of acetylcholinesterase (AChE) while simultaneously driving passive sample capillary absorption. For the electrochemical channel, a conductive graphene/CNF (G/CNF) nanocomposite interface was engineered to accelerate interfacial electron transfer while preserving native enzymatic conformation. Transduction is based on paraoxon-mediated AChE inhibition, which proportionally attenuates both a voltammetric signal and the chromogenic generation of yellow 5-thio-2-nitrobenzoate (TNB2-). To ensure high-fidelity analytical reliability, an ML computational framework was incorporated to classify and deconvolve the electrochemical response profiles. The integrated biosensor features a quantitative linear dynamic range spanning from 0 to 1.0 ppm and incorporates a visual colorimetric screening threshold at 0.5 ppm. Validated in complex agri-food and water matrices, this macromolecular hybrid platform provides a robust, dual-verified approach for point-of-need pesticide monitoring.
To describe and evaluate a community- and theory-driven multilevel opioid overdose fatality prevention intervention for Black Indianapolis communities for 2022-2025. A community-driven opioid overdose fatality prevention project was designed based on Community Coalition Action Theory, the Citizen Health Care Model, and Legal Epidemiology. The intervention was implemented in inner-city Indianapolis (zip code areas 46202, 46205, 46208, 46218) with cluster-matched comparison areas in Indiana (46408, 46410, 46628, 46806). Data were collected in Indiana, U.S.A., in 2022-2025. Evaluation included mortality analysis and community probability surveys in Year 1 and 3. There was a 45% decrease in Black overdose deaths in the intervention communities versus a 22% decrease in the comparison communities from 2022 to 2024. Probability community surveys showed improvement in overdose knowledge [difference-in-differences (DiD) estimator = 0.68, p = 0.03], perceived competency to manage an opioid overdose (DiD = 0.54, p < 0.01), and accessibility of naloxone (DiD = 1.06, p < 0.01) in the intervention communities compared to the comparison communities between Year 1 (N = 772) and 3 (N = 764). Similar community- and theory-driven multi-level interventions may be effective in reducing overdose mortalities in the Black communities.
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YouTube is widely used by patients seeking information about robot-assisted radical prostatectomy (RARP), but the reliability, transparency, and counseling coverage of RARP-related videos remain variable, particularly in a fragmented and search-driven digital environment. To evaluate the quality, reliability, transparency, and patient-oriented counseling coverage of YouTube videos on RARP and identify underrepresented topics relevant to shared decision-making. Cross-sectional observational study. YouTube was searched on February 16, 2026, using "robotic radical prostatectomy," "RARP prostate," and "da Vinci prostatectomy" in incognito mode. After duplicate removal and eligibility assessment, 174 videos were included. Video characteristics, uploader type, target audience, and format were recorded. Educational quality, reliability, and transparency were assessed using the Global Quality Scale (GQS), modified DISCERN, and Journal of the American Medical Association (JAMA) benchmark criteria. Counseling-related coverage was assessed using a 15-item RARP-specific checklist designed to evaluate key patient-centered domains rather than stand-alone adequacy. Non-parametric tests and Spearman correlations were used. Among 174 videos, 93.7% targeted patients. Median duration was 3.09 min (interquartile range (IQR), 1.92-5.91), and median view count was 1658 (IQR, 371-9747). Educational quality and reliability were low to moderate (median GQS 2 (IQR, 2-3); modified DISCERN 2 (IQR, 2-3)). Transparency was limited (median JAMA 2 (IQR, 1-2)), with only 10.9% achieving JAMA ⩾3. Clinically important counseling topics were frequently underrepresented, including postoperative prostate-specific antigen follow-up (84.5%), complications/complication rates (55.2%), erectile dysfunction (40.8%), urinary continence outcomes (39.7%), and recovery timeline (31.6%). Transparency differed by uploader type (p < 0.001), whereas quality and counseling coverage did not differ by uploader source. Popularity metrics showed no meaningful correlation with GQS. YouTube provides accessible and highly searchable information on RARP; however, the reliability, transparency, and coverage of key counseling-related topics vary considerably across videos. While short-form or highly specialized content may help address specific patient questions, important topics related to complications, functional outcomes, and postoperative follow-up are frequently underrepresented. Development of patient-centered and platform-adapted educational frameworks may improve the balance, transparency, and practical value of YouTube-based patient education in RARP. Not applicable. How helpful are YouTube videos about robotic prostate cancer surgery for patients? Many people search YouTube before meeting a doctor or deciding on treatment. This is also true for men considering robotic surgery to remove the prostate for prostate cancer. These videos are easy to find and can help patients learn about specific aspects of the procedure. However, easy access does not always mean that the information is reliable, transparent, or covers the topics that matter most for treatment decisions. In this study, we reviewed 174 English-language YouTube videos about robot-assisted radical prostatectomy, also called robotic prostate cancer surgery. We looked at who uploaded the videos, what counseling-related topics they covered, and how reliable and transparent they were for patients. Most videos were aimed at patients. Overall, reliability was limited, and many videos did not clearly state who created the content, what sources were used, or whether the information was up to date. Important topics were frequently missing across the videos we reviewed. These included PSA follow-up after surgery, possible complications, erectile dysfunction, and urinary control outcomes. While some videos focused on specific questions a patient might have, key counseling topics were often absent from the broader set of available videos. We also looked at whether longer or more transparent videos performed differently. Longer videos and more transparent videos tended to score higher for quality, reliability, and counseling coverage. They also tended to receive more daily views and a higher proportion of likes, suggesting that more informative and transparent content does not necessarily perform worse on the platform. Our findings suggest that YouTube can be a useful starting point for patients, but it should not replace a conversation with a doctor before surgery. Better patient-friendly videos, built around the topics patients most need to understand, may improve preparation and decision-making before robotic prostate cancer surgery.