Personalized medicine最新文献

Background: Gastric cancer is an aggressive and heterogeneous disease, primarily sporadic, with only 1-3% of cases being hereditary. However, gastric cancer is a component of several hereditary cancer syndromes. The BRCA1 and BRCA2 genes encode key DNA repair proteins involved in homologous recombination. Studies suggest a significantly increased risk of gastric cancer in first-degree relatives of BRCA1/2 mutation carriers.

Methods: We systematically searched PubMed, Scopus, and Web of Science for relevant studies. Risk ratios (RRs) with 95% confidence intervals (CIs) were computed using DerSimonian and Laird random-effect models. Heterogeneity was assessed via I² statistics. Statistical analyses were performed using R (version 4.2.3).

Results: Fourteen studies with 160,551 patients were included, of whom 25,934 had BRCA1/2 mutations (BRCA1: 14322; BRCA2: 11612). BRCA1 and BRCA2 mutations were significantly associated with increased gastric cancer risk (RR 2.30; 95% CI: 1.33-3.97; p = 0.003; I² = 82% and RR 2.45; 95% CI: 1.82-3.28; p < 0.001; I² = 25%). Among the gastric cancer patients, BRCA1 and BRCA2 mutations were associated with RRs of 3.02 (p = 0.101; I² = 65%) and 4.86 (p < 0.001; I² = 0%), respectively.

Conclusions: This meta-analysis suggests that BRCA1/2 mutation carriers have a higher risk of developing gastric cancer.

Aims: This study explored employees' understanding of, and psychosocial responses to, workplace genetic testing (wGT) results.

Materials & methods: Employees of a US healthcare system who underwent wGT (hereditary cancer/heart disease risk, pharmacogenomics) and received results were surveyed. We ascertained pretest education engagement, test understanding, and psychosocial responses. Regression analyses identified predictors of scores on a modified Feelings About genomiC Test Results questionnaire (positive feelings, negative emotions, and uncertainty after wGT).

Results: N = 418 employees (mean age = 44 years; 88.3% female; 80.6% white) completed the survey. Mean scores (out of 12; higher scores indicate a greater extent of each feeling) were 5.2 (SD = 2.9) for positive feelings, 1.2 (SD = 2.2) for negative emotions, and 2.0 (SD = 2.5) for uncertainty. Identifying as non-Hispanic African American/Black and receiving increased risk (cancer/heart disease) wGT results were associated with lower positive feelings and higher negative emotions and uncertainty scores (all p < 0.05). Open-ended responses indicated difficulty interpreting, recalling, and utilizing results.

Conclusions: wGT was associated with low levels of measured psychosocial harm among participants. However, results suggested a greater likelihood of negative psychosocial responses among those with increased risk of cancer/heart disease and non-Hispanic African American/Black employees. Future studies should explore strategies to ensure all employees undergoing wGT have educational and psychosocial support.

Aims: While "tailored communication" and "precision medicine" have been well-defined in medical literature, the concept of "precision communication" in healthcare has yet to be clarified. We sought to review how "precision communication" has been used in the medical literature to date and propose a working definition for this term.

Materials & methods: We searched seven medical literature databases from inception until 22 May 2024, for articles using terms related to "precision communication." Multiple reviewers screened titles/abstracts and full-texts; an initial pool of full-text articles underwent thematic analysis to clarify relevant themes for inclusion. Data regarding the use of the term "precision communication" were manually charted and analyzed descriptively.

Results: Of the 7,648 articles identified, 21 full-text articles were included in the final descriptive analysis. These articles highlighted the personalization of tailored communication to patient characteristics, its impact on clinical outcomes, and the recipients of "precision communication." The latter may distinguish "precision communication" from similar terms: where "tailored communication" was mostly applied to undefined groups, we propose that "precision communication" is precise toward specific patient subpopulations, paralleling the use of genomics in precision medicine.

Conclusions: From this review, we defined precision communication as "the personalization of communication to subpopulations."

Background: Prostate cancer (CaP) is the most commonly diagnosed malignant tumor and the leading cause of cancer-related deaths among men. Due to their potential functional significance, microRNA genes are considered promising candidates for identifying cancer-related genetic biomarkers. This study investigates the association between microRNA-196a2 (rs11614913), microRNA-146a (rs2910164), and microRNA-149 (rs2292832) and the risk of prostate cancer among males in the Jammu region of Jammu and Kashmir (J&K).

Material and methods: A total of 320 male participants were recruited from various areas of the Jammu region, including 120 confirmed cases and 200 unrelated healthy controls. Genotyping was performed using PCR-RFLP, and the results were validated through Sanger sequencing. Additionally, a meta-analysis was also conducted to validate the results.

Results: Our study found a significant association between microRNA-196a2 and the risk of developing prostate cancer (CaP) in our population, with an odds ratio (OR) of 1.62 and a p-value of 0.05. In contrast, we observed no significant associations for microRNA-146 (rs2910164) and microRNA-149 (rs2292832). Additionally, a meta-analysis of microRNA-146a also confirmed its lack of association with prostate cancer.

Conclusion: MicroRNA-196a2 gene polymorphism is linked to a higher risk of prostate cancer (CaP), while microRNA-146 and microRNA-149 did not show an association with CaP.

Parkinson's disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction models. A Parkinson's dataset with demographic, lifestyle, medical, clinical, and cognitive features was analyzed using three feature selection techniques: Whale Optimization Algorithm, Artificial Bee Colony Optimization, and Backward Elimination (BE). Random Forest (RF) models were optimized using Artificial Ant Colony Optimization for hyperparameter tuning. The optimized RF model with BE achieved 93% accuracy and 97% AUC, outperforming K-Nearest Neighbors, Support Vector Machines, Logistic Regression, XGBoost, and Stacked Ensemble models. Optimization reduced tuning time from 133 to 18 minutes. A comparison with traditional approaches and negative controls validated the results, though clinical validation remains essential before deployment. Meta-heuristic optimization significantly improves Parkinson's prediction performance and efficiency.

Objective: The study aims to develop an Evidence Gap Map (EGM) to summarize the current evidence cited in personalized medicine (PM) in bladder cancer, focusing on systematic reviews and high-level evidence syntheses.

Methods: The review involved a comprehensive search in databases up to June 2024, and involved a two-phase analysis, using data from PubMed for scientometric analysis and R Studio with the Biblioshiny tool for co-occurring word network analysis.

Results: After filtering out irrelevant articles, the selection was narrowed to 3,705 items. The most frequently occurring words were aged, middle-aged, animals, cell lines, tumor, prognosis, gene expression, and mice. The study identified gaps and under-researched categories in PM and bladder cancer research, with Immunotherapy, Neoadjuvant/Adjuvant Therapy, and Gene Therapy being the most researched areas. The evidence map revealed a predominance of low or moderate quality evidence in most domains of PM in bladder cancer, particularly within clinical trials for immunotherapy and biomarker.

Conclusions: The field of PM in bladder cancer requires robust research methodologies and interdisciplinary collaboration to overcome challenges. By improving study design and data quality, the field can translate scientific discoveries into clinical innovations, utilizing molecular profiling and targeted therapies to enhance treatment strategies and improve outcomes.

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages, resulting in poor survival rates. Early detection significantly improves outcomes, yet current screening methods, such as low-dose computed tomography (LDCT), are limited by high false-positive rates, radiation exposure, and restricted eligibility criteria. This review highlights the transformative potential of genomic and molecular technologies in advancing the early detection of LC. Key innovations include liquid biopsy tools, such as circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) analysis, which offer minimally invasive approaches to detect tumor-specific genetic and epigenetic alterations. Emerging biomarkers, including methylation signatures, cfDNA fragmentomics, and multi-omics profiles, demonstrate improved sensitivity and specificity in identifying early-stage tumors. Advanced platforms like next-generation sequencing (NGS) and machine-learning algorithms further enhance diagnostic accuracy. Integrated approaches that combine genomic data with LDCT imaging and artificial intelligence (AI) show promise in addressing current limitations by improving risk stratification and nodule characterization. The review also explores multi-cancer early detection assays and precision diagnostic strategies tailored for diverse at-risk populations. By leveraging these advancements, clinicians can achieve earlier diagnoses, reduce unnecessary procedures, and ultimately decrease LC mortality.

Aim: Vancomycin dosing in neonates is challenging due to developmental pharmacokinetic variability. The study was to characterize vancomycin pharmacokinetics in a large cohort of preterm and term neonates and develop individualized dosing regimens.

Materials & methods: A 5-year retrospective study of a cohort of 255 neonates was included.

Results: An allometric one-compartment model with first-order elimination best described the vancomycin concentrations. The population pharmacokinetic estimates (between subject variability) of clearance (CL) and volume of distribution (V) were 2.58 L·h^-1·70 kg^-1 (9.00 %) and 52.09 L·70 kg^-1 (29.00%), respectively. CL and V were significantly influenced by body weight and postmenstrual age. Vancomycin CL reached 50% of adult values at 43.6 weeks PMA (a sigmoid Emax model). Renal maturation, estimated by creatinine production rate, was a significant covariate. Bayesian-guided individualized dosage regimens were developed and evaluated.

Conclusions: Vancomycin overdosage should be avoided in very young premature babies (PMA = 25 weeks). Optimization of efficacy while minimizing toxicity of vancomycin in preterm and term neonates is needed, especially guided by personalized body weight, postmenstrual age, and renal function.

This review comprehensively evaluates personalized public health strategies using artificial intelligence (AI) in disease prediction/management and genetic data analysis. In the field of healthcare, AI has achieved significant advancements in the analysis of public health and genetic data. Its applications in public health include predicting the spread of infectious diseases, evaluating individual risk factors, and optimizing resource management. In the realm of genetic data, AI offers groundbreaking contributions such as identifying disease risk factors, analyzing genetic mutations, and developing personalized treatment plans. In this review, we evaluated the importance of AI in preventive medicine in a structured way and by including concrete application examples. Ethical and legal responsibilities must be considered due to the significant implications of AI-generated decisions. By integrating AI into public health and genetics, we are poised to unlock unprecedented opportunities for advancing human health. This approach not only enhances our ability to understand and address complex health challenges but also paves the way for equitable, effective, and individualized care solutions on a global scale. In this review, we addressed to the interactions between particular subdomains of personalized public health strategies and AI with most recent literature and legal/ethical perspective.

Introduction: Advanced Therapy Medicinal Products (ATMPs), which include gene therapies, somatic cell therapies, and tissue-engineered products, are a new paradigm for treating previously intractable diseases. Their regenerative and personalized approach makes them, unlike conventional treatments, require changing regulatory systems to adjust to their intricacies.

Areas covered: This review gives a comprehensive critique of international regulatory programs that include the FDA's RMAT designation, EMA's PRIME program, and Japan's Sakigake program intended to bring ATMPs to patients faster while ensuring patient safety. It also considers innovation-led strategies like adaptive licensing, rolling reviews, and real-world evidence (RWE) led decision-making for pre-market authorization and post-market monitoring. In addition, it discusses problems like regulatory divergence, intricate manufacturing standards, price constraints, and the transformative role of digital technologies such as artificial intelligence and blockchain in traceability and regulatory compliance. Patient-centric models and early access schemes are also extensively debated as part and parcel of the future of regulatory science.

Expert opinion/commentary: To achieve the maximum potential of ATMPs across the world, regulatory systems need to be harmonized and responsive, involving real-time data analysis, flexible approval processes, and improved stakeholder cooperation. New technologies, coupled with more patient engagement and global convergence efforts, are crucial for providing equal access to effective and safe advanced therapies.