Evaluating the Distribution, Quality, and Educational Value of Videos Related to Knee Instability Exercises on the Social Media Platform TikTok.

Introduction TikTok, a globally popular short-form video platform, offers a unique space for healthcare professionals to share advice, particularly under common conditions such as knee pain or instability. Despite its popularity, doubts persist regarding the reliability of medical information disseminated on TikTok. This study aimed to evaluate the quality of TikTok videos as a source of patient information on knee instability, recognizing the need for a comprehensive assessment of potential misinformation on this influential social media platform. Methods A search for "knee stability exercises" on TikTok yielded 448 videos, of which 187 met the inclusion criteria. These videos were categorized by source and evaluated using the Knee Exercise Education Scoring Tool (KEEST) and an information analysis questionnaire, DISCERN. Results General user videos (69.84%) had notably lower DISCERN scores than healthcare professional videos (29.1%) across all categories (P < 0.001, P = 0.282, P = 0.131, and P = 0.010). The DISCERN scores were inversely linked to video metrics (views, likes, comments, favorites, and shares). General user videos were largely of poor quality (66.4%), whereas healthcare professional videos spanned poor (61.8%), fair (28.2%), good (9.1%), and excellent (1.8%) categories. Both general users (12.31/25) and healthcare professionals (12.18/25) exhibited average quality according to KEEST standards (P = 0.809), with an intriguing inverse correlation between video popularity and DISCERN score. Conclusion Healthcare professionals demonstrated superior evidence-based content (DISCERN), whereas both groups were comparatively educated on treatment plans and effects (KEEST). TikTok's prevalent knee instability videos lack quality, proper sourcing, treatment risk information, and explanation. Moreover, popularity is inversely correlated with quality, and healthcare professionals appear to offer better evidence-based content. TikTok's role in healthcare highlights the importance of ensuring accurate information and implementing content quality regulations.

Optimization of Polycaprolactone and Type I Collagen Scaffold for Tendon Tissue Regeneration.

Introduction Collagen synthesis is vital for restoring musculoskeletal tissues, particularly in tendon and ligamentous structures. Tissue engineering utilizes scaffolds for cell adhesion and differentiation. Although synthetic scaffolds offer initial strength, their long-term stability is surpassed by biological scaffolds. Combining polycaprolactone (PCL) toughness with collagen in scaffold design, this study refines fabrication via electrospinning, aiming to deliver enduring biomimetic matrices for widespread applications in musculoskeletal repair. Methods Electrospinning employed four solutions with varied collagen and PCL concentrations, dissolved in chloroform, methanol, and hexafluoro-2-propanol. Solutions were combined to yield 60 mg/mL concentrations with different collagen/PCL ratios. Electrospinning at 12-14kV voltage produced scaffolds, followed by vacuum-drying. Collagen coating was applied to PCL and 15% collagen/PCL scaffolds using a 0.1% collagen solution. SEM characterized fiber morphology, tensile testing was conducted to determine the mechanical properties of the scaffold, and Fourier-transform infrared (FTIR) spectroscopy analyzed scaffold composition. Atomic force microscopy (AFM) analyzed the stiffness properties of individual fibers, and a finite element model was developed to predict the mechanical properties. Cell culture involved seeding human bone marrow mesenchymal stem cells onto scaffolds, which were assessed through Alamar Blue assay and confocal imaging. Results Various scaffolds (100% PCL, PCL-15% collagen, PCL-25% collagen, PCL-35% collagen) were fabricated to emulate the extracellular matrix, revealing collagen's impact on fiber diameter reduction with increasing concentration. Tensile testing highlighted collagen's initial enhancement of mechanical strength, followed by a decline beyond PCL-15% collagen. FTIR spectroscopy detected potential hydrogen bonding between collagen and PCL. A finite element model predicted scaffold response to external forces which was validated by the tensile test data. Cell viability and proliferation assays demonstrated successful plating on all scaffolds, with optimal proliferation observed in PCL-25% collagen. Confocal imaging confirmed stem cell integration into the three-dimensional material. Collagen coating preserved nanofiber morphology, with no significant changes in diameter. Coating of collagen significantly altered the tensile strength of the scaffolds at the macro scale. AFM highlighted stiffness differences between PCL and collagen-coated PCL mats at the single fiber scale. The coating process did not significantly enhance initial cell attachment but promoted increased proliferation on collagen-coated PCL scaffolds. Conclusion The study reveals collagen-induced mechanical and morphological alterations, influencing fiber alignment, diameter, and chemical composition while emphasizing scaffolds' vital role in providing a controlled niche for stem cell proliferation and differentiation. The optimization of each of these scaffold characteristics and subsequent finite element modeling can lead to highly repeatable and ideal scaffold properties for stem cell integration and proliferation.

Artificial Intelligence and Machine Learning in Rotator Cuff Tears.

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.