Undergraduate Research in Natural and Clinical Science and Technology Journal

The Role of Truth in Double-Blind Clinical Trials

2024-10-28T12:48:42+00:00

Double-blind clinical trials are crucial in the fields of medicine and clinical research for producing reliable and unbiased scientific evidence. Historically, double-blind trials have been instrumental in establishing a rigorous standard for evaluating treatment efficacy by eliminating researcher and participant biases. By concealing treatment allocation, these trials aim to produce objective and replicable data that drive contemporary medical practice.

However, the ethical dimensions of double-blind trials raise substantial concerns. Key issues include the potential harm from administering placebos instead of effective treatments and the ethical dilemma of withholding care. This paper seeks to address and highlight these concerns by discussing the relationship between double-blind clinical trials and the scientific community as well as the advantages and disadvantages of double-blind clinical trials within the scientific community and society at large.

The analysis emphasizes the necessity of adapting double-blind trials to incorporate ethical considerations without compromising the integrity of the research. By refining trial designs to include usual-care controls, the medical community can uphold both ethical standards and methodological rigor. This balanced approach ensures that double-blind trials continue to contribute valuable insights into treatment efficacy while safeguarding participant welfare.

Enhancing Genomic Medicine with AI-Integrated CRISPR-Cas9 Technologies

2024-10-24T23:02:24+00:00

Introduction: CRISPR-Cas9, a ground breaking gene editing tool, has transformed genetic engineering. With the advent of Artificial intelligence (AI) in recent years, this tool has been applied to the development of new CRISPR-Cas9 therapies. This integration with artificial intelligence (AI) “Helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases”

Methods: A comprehensive literature review was conducted, with the assistance of AI to compile and assess the current state and future prospects of integrating AI with CRISPR-Cas9 technology in genomic medicine. Relevant articles were identified through database searches in PubMed, Web of Science, and Google Scholar, using keywords such as "CRISPR-Cas9," "artificial intelligence," "machine learning," "gene editing," and "genomic medicine." Studies published up to June 2024 were considered.

Current Research and Findings: Current research revolves around the development of CRISPR systems with improved efficiency and specificity, facilitated by advanced machine learning models. These focus specifically on sgRNA development and its consequences on genetic research.

New Research and Implications on Future Directions: New research avenues suggest exploring CRISPR’s use in complex genetic disorders involving multiple genes. AI's predictive capabilities are vital in designing multi-target strategies for such complex conditions. These include the diagnosis and treatment of cancer, the early identification of rare diseases, and the faster design of vaccines.

Summary: The convergence of these advanced technologies offers a pathway to more precise and personalized therapeutic interventions. By leveraging AI's capabilities in data analysis and pattern recognition, researchers can enhance the accuracy and efficiency of CRISPR-Cas9 gene editing. AI aids in predicting the most effective guide RNA (gRNA) designs, reducing off-target effects, and improving the specificity of gene edits. This synergy not only accelerates the gene editing process but also expands its applications across various medical fields. The ongoing refinement of AI algorithms and their integration with CRISPR technology could unlock new possibilities in medical science, potentially revolutionizing the diagnosis and treatment of genetic and multifactorial diseases.

Evaluating Accufix Head and Neck Shoulder Immobilization for Head and Neck Radiation Therapy

2024-11-29T20:37:34+00:00

The field of radiation therapy has seen significant advancements in treatment precision, such as proton therapy and intensity-modulated radiotherapy, often involving various immobilization devices for patient positioning and motion monitoring. However, the effectiveness of shoulder immobilization systems, particularly in the neck and shoulder regions, requires further investigation due to conflicting results and limited studies. This study aims to evaluate the accuracy and effectiveness of the Accufix™ Head and Neck Device shoulder cantilever depression system in reducing interfractional and intrafractional movement in head and neck cancer (HNC) patients. This device positions the head, neck, and shoulders, lowering the shoulders to precisely target head and neck tumors with radiation beams. Patient data was collected for 3 larynx cancer patients and 1 tongue cancer patient undergoing volumetric-modulated arc therapy (VMAT) radiation therapy at the Juravinski Cancer Centre. Patients were immobilized using a head and neck thermoplastic mask and an Accufix™ Head and Neck device. AlignRT, an optical surface monitoring system (OSMS), was utilized to track real-time body surface movements during treatment in 3 translation directions (AP: anterior-posterior, SI: superior-inferior, and LR: left-right) and 3 rotations (pitch, roll, and yaw). Interfractional shoulder positioning discrepancies were evaluated by conducting cone-beam computed tomography (CBCT) scan and planning computed tomography (CT) scan image registration in two anatomical locations: the target volume (T-IM) and the mid-clavicles (C-IM). Intrafractional motion across all patients remained low for both translational and rotational shifts, with 6.42% exceeding a 5mm margin and 0.02% exceeding a 3º margin, respectively. Differences between target TV-IM and C-IM remained within +/- 3mm of shifts for the majority of fractions. Little consistency was found between AlignRT data and C-IM data, with shifts ranging from 10mm to -5mm, attributed to the surface geometry and shape of the region of interest (ROI) we tracked. While shoulder immobilization using the Accufix™ system was found to be sufficient, AlignRT's accuracy in reproducing patient shoulder positioning was limited in our study. Factors influencing surface-guided systems, such as ROI size and location, need careful evaluation. Further studies on SGRT use compared to immobilization devices are needed to validate these findings and explore potential improvements.

OSTEO-AI: A Systematic Review and Meta-Analysis of Artificial Intelligence Models for Osteoarthritis and Osteoporosis Detection and Prognosis

2025-01-07T02:50:16+00:00

Introduction: Osteoarthritis (OA) and osteoporosis are leading degenerative bone diseases that diminish quality of life and impose significant socioeconomic costs. Traditional diagnostic approaches, including imaging and bone density assessments, often fail to detect disease in its early stages, delaying critical interventions. Emerging artificial intelligence (AI) techniques, particularly those employing machine learning (ML) and deep learning (DL), offer promising avenues for early detection and more accurate prognostication.

Methods: We conducted a systematic review of AI models developed between 2018 and 2024, assessing their performance in diagnosing and predicting the progression of OA and osteoporosis. Studies utilizing supervised or unsupervised methods applied to imaging modalities (e.g., X-ray, MRI, DXA) or clinical data were included. We evaluated model accuracy, reliability, clinical applicability, and generalizability. Quality and risk of bias were assessed using a modified CLAIM framework, ensuring alignment with transparency, validity, and clinical integration standards.

Results: Of 2,300 identified articles, 33 studies met the inclusion criteria. Top-performing models for OA reached up to 97% accuracy, with one study achieving an AUC of 0.93 for MRI-based progression prediction. For osteoporosis, the strongest models attained a C-index of 0.90 using DXA imaging, indicating robust fracture risk prediction. Nevertheless, many studies relied on geographically or demographically homogeneous datasets, limiting broader applicability. Only 15% included external validation, and a substantial proportion lacked interpretability features essential for clinical adoption.

Discussion: AI-driven models outperformed conventional diagnostic tools in accuracy and early disease detection. However, the limited dataset diversity, infrequent external validation, and insufficient model interpretability pose barriers to clinical integration. The reliance on male-dominant datasets for osteoporosis and geographically narrow cohorts for OA underscores the need for broader data representation. Standardizing evaluation metrics and improving explainability will enhance cross-study comparisons and support adoption in practice.

Conclusion: AI holds transformative potential for improving OA and osteoporosis diagnostics, facilitating earlier interventions, and informing personalized patient management. Future work should prioritize diverse, well-validated datasets; transparent, clinician-friendly interfaces; and standardized performance metrics. Addressing these challenges will enable AI to evolve from a promising innovation into a cornerstone of global musculoskeletal healthcare.