Medicinal, Psychological, and Health Research Journal (MPHRJ)

Abstract Background and Objective: Pneumonia is an inflammatory respiratory disease that can affect the patient's adaptive patterns. Nurses play a key role in facilitating the adaptation process of the patient. Therefore, this study aimed to explore the use of Roy’s Adaptation Model in the care of a patient with pneumonia.

Materials and Methods: This case report study was conducted in May 2025 at a hospital in Lahore, focusing on a patient with pneumonia. Nursing care based on the Roy’s Adaptation Model was implemented in six stages according to the nursing process.

Results: The results showed a significant reduction in the patient's maladaptive behaviors across four modes: physiological, self-concept, role function, and interdependence and independence.

Conclusion: Nursing care based on the Roy's Adaptation Model effectively reduced maladaptive behaviors in patients with pneumonia. Therefore, the implementation of organized nursing care based on appropriate nursing models is essential to improve patient health.

Abstract The aesthetic rejuvenation of the lower third of the face, particularly the jawline, has become a pivotal focus in modern cosmetic dermatology. This study introduces “Contouring Plus,” a novel, minimally invasive approach that combines the biostimulatory properties of Calcium Hydroxylapatite (CaHA) with the versatile volumizing and contouring capabilities of Hyaluronic Acid (HA) fillers. The protocol involves a strategic layering technique where CaHA is injected supraperiosteally along the mandibular border to provide structural support and stimulate neocollagenesis, while HA fillers are administered in the subcutaneous plane to refine contours and enhance soft tissue projection. The article presents a case series involving four patients aged 29 to 54, treated in a single session and evaluated over a 90-day period using clinical photography and three-dimensional imaging. Results demonstrated significant improvement in jawline definition and patient satisfaction, with minimal adverse events such as mild bruising and swelling. The safety profile is supported by adherence to precise anatomical landmarks and injection planes, minimizing risks of vascular complications. The combined filler technique capitalizes on the long-term collagen-stimulating effects of CaHA alongside the immediate, moldable benefits of HA, resulting in durable and natural-looking facial rejuvenation. This hybrid approach provides clinicians with an evidence-based, adaptable protocol for addressing age-related volume loss and contour irregularities in the lower face. Future studies are warranted to assess long-term outcomes and optimize filler combinations.

Abstract Artificial intelligence (AI) is rapidly transforming dermatology, particularly in the realm of aesthetic procedures such as injectable filler administration. The integration of AI technologies into filler treatments offers promising advancements in precision, safety, and personalization, addressing longstanding challenges associated with manual injection techniques. AI-driven imaging and diagnostic tools enable detailed analysis of facial anatomy, volume loss patterns, and skin characteristics, allowing clinicians to develop highly individualized treatment plans tailored to each patient’s unique facial structure and aesthetic goals. Machine learning algorithms can predict patient outcomes by analyzing vast datasets of previous treatments, helping practitioners optimize filler type, volume, and injection sites to maximize efficacy and minimize adverse effects. Moreover, AI-powered real-time guidance systems, including augmented reality, provide dynamic visualization of critical anatomical landmarks and vascular structures during injection procedures, reducing the risk of complications such as vascular occlusion and nerve injury. These technologies also support less experienced clinicians by enhancing accuracy and confidence in filler placement. Furthermore, AI facilitates post-procedure monitoring through automated assessment of treatment results and early detection of potential complications, enabling timely interventions. The adoption of AI in filler administration not only improves clinical outcomes but also elevates patient satisfaction by enabling more predictable and natural-looking rejuvenation. Despite its potential, challenges remain in integrating AI into routine dermatological practice, including data privacy concerns, algorithm transparency, and the need for comprehensive clinical validation. Nonetheless, ongoing research and technological development position AI as a critical tool to revolutionize facial aesthetic treatments, making filler administration safer, more effective, and personalized.

Abstract Hospital laboratories play a fundamental role in ensuring timely and accurate diagnostic information that supports clinical decision-making and patient care. However, laboratory processes—such as specimen collection, handling, analysis, and reporting—are inherently complex and susceptible to various errors that may compromise patient safety and treatment outcomes. Failure Mode and Effects Analysis (FMEA) is a structured, proactive risk assessment tool that allows healthcare institutions to systematically identify, evaluate, and mitigate potential failures within critical workflows. This paper examines the application of FMEA in the context of hospital laboratory risk management. Through process mapping and collaboration among multidisciplinary teams—including laboratory staff, clinicians, and quality managers—potential failure modes were identified across different laboratory phases. Each failure mode was analyzed using a standardized scoring system for severity (S), occurrence (O), and detectability (D), leading to the calculation of a Risk Priority Number (RPN). High-priority risks such as sample mislabeling, equipment malfunction, and reagent expiration were addressed with targeted interventions, including barcode implementation, enhanced equipment maintenance protocols, and improved inventory systems. The findings indicate that FMEA significantly improves error detection and prevention in laboratory operations, promotes a culture of safety, and enhances compliance with international quality standards such as ISO 15189. Moreover, the participatory nature of FMEA fosters organizational learning and continuous quality improvement. Despite requiring time and training investment, FMEA offers a cost-effective approach to reducing diagnostic errors and improving patient outcomes. The study recommends routine application of FMEA and its integration with digital health technologies for real-time risk monitoring in clinical laboratories.