Epma Journal最新文献

Background/aims: Timely detection and treatment of retinal detachment (RD) could effectively save vision and reduce the risk of progressing visual field defects. High myopia (HM) is known to be associated with an increased risk of RD. Evidently, it should be clearly discriminated the individuals with high or low risk of RD in patients with HM. By using multi-parametric analysis, risk assessment, and other techniques, it is crucial to create cutting-edge screening programs that may be utilized to improve population eye health and develop person-specific, cost-effective preventative, and targeted therapeutic measures. Therefore, we propose a novel, routine blood parameters-based prediction model as a screening program to help distinguish who should offer detailed ophthalmic examinations for RD diagnosis, prevent visual field defect progression, and provide personalized, serial monitoring in the context of predictive, preventive, and personalized medicine (PPPM/3 PM).

Methods: This population-based study included 20,870 subjects (HM = 19,284, HMRD = 1586) who underwent detailed routine blood tests and ophthalmic evaluations. HMRD cases and HM controls were matched using a nested case-control design. Then, the HMRD cases and HM controls were randomly assigned to the discovery cohort, validation cohort 1, and validation cohort 2 maintaining a 6:2:2 ratio, and other subjects were assigned to the HM validation cohort. Receiver operating characteristic curve analysis was performed to select feature indexes. Feature indexes were integrated into seven algorithm models, and an optimal model was selected based on the highest area under the curve (AUC) and accuracy.

Results: Six feature indexes were selected: lymphocyte, basophil, mean platelet volume, platelet distribution width, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Among the algorithm models, the algorithm of conditional probability (ACP) showed the best performance achieving an AUC of 0.79, a diagnostic accuracy of 0.72, a sensitivity of 0.71, and a specificity of 0.74 in the discovery cohort. A good performance of the ACP model was also observed in the validation cohort 1 (AUC = 0.81, accuracy = 0.72, sensitivity = 0.71, specificity = 0.73) and validation cohort 2 (AUC = 0.77, accuracy = 0.71, sensitivity = 0.70, specificity = 0.72). In addition, ACP model calibration was found to be good across three cohorts. In the HM validation cohort, the ACP model achieved a diagnostic accuracy of 0.81 for negative classification.

Conclusion: We have developed a routine blood parameters-based model with an ACP algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring and predicting the occurrence of RD in HM and can facilitate the prevention of HM progression to RD. According to the current study, routine blood measures are essential in patient risk classific

Delayed graft function (DGF) is one of the key post-operative challenges for a subset of kidney transplantation (KTx) patients. Graft survival is significantly lower in recipients who have experienced DGF than in those who have not. Assessing the risk of chronic graft injury, predicting graft rejection, providing personalized treatment, and improving graft survival are major strategies for predictive, preventive, and personalized medicine (PPPM/3PM) to promote the development of transplant medicine. However, since PPPM aims to accurately identify disease by integrating multiple omics, current methods to predict DGF and graft survival can still be improved. Renal ischemia/reperfusion injury (IRI) is a pathological process experienced by all KTx recipients that can result in varying occurrences of DGF, chronic rejection, and allograft failure depending on its severity. During this process, a necroinflammation-mediated necroptosis-dependent secondary wave of cell death significantly contributes to post-IRI tubular cell loss. In this article, we obtained the expression matrices and corresponding clinical data from the GEO database. Subsequently, nine differentially expressed necroinflammation-associated necroptosis-related genes (NiNRGs) were identified by correlation and differential expression analysis. The subtyping of post-KTx IRI samples relied on consensus clustering; the grouping of prognostic risks and the construction of predictive models for DGF (the area under the receiver operating characteristic curve (AUC) of the internal validation set and the external validation set were 0.730 and 0.773, respectively) and expected graft survival after a biopsy (the internal validation set's 1-year AUC: 0.770; 2-year AUC: 0.702; and 3-year AUC: 0.735) were based on the least absolute shrinkage and selection operator regression algorithms. The results of the immune infiltration analysis showed a higher infiltration abundance of myeloid immune cells, especially neutrophils, macrophages, and dendritic cells, in the cluster A subtype and prognostic high-risk groups. Therefore, in the framework of PPPM, this work provides a comprehensive exploration of the early expression landscape, related pathways, immune features, and prognostic impact of NiNRGs in post-KTx patients and assesses their capabilities as.predictors of post-KTx DGF and graft loss,targets of the vicious loop between regulated tubular cell necrosis and necroinflammation for targeted secondary and tertiary prevention, andreferences for personalized immunotherapy.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00320-w.

Since 2009, the European Association for Predictive, Preventive and Personalised Medicine (EPMA, Brussels) promotes the paradigm change from reactive approach to predictive, preventive, and personalized medicine (PPPM/3PM) to protect individuals in sub-optimal health conditions from the health-to-disease transition, to increase life-quality of the affected patient cohorts improving, therefore, ethical standards and cost-efficacy of healthcare to great benefits of the society at large. The gene-editing technology utilizing CRISPR/Cas gene-editing approach has demonstrated its enormous value as a powerful tool in a broad spectrum of bio/medical research areas. Further, CRISPR/Cas gene-editing system is considered applicable to primary and secondary healthcare, in order to prevent disease spread and to treat clinically manifested disorders, involving diagnostics of SARS-Cov-2 infection and experimental treatment of COVID-19. Although the principle of the proposed gene editing is simple and elegant, there are a lot of technological challenges and ethical considerations to be solved prior to its broadly scaled clinical implementation. This article highlights technological innovation beyond the state of the art, exemplifies current achievements, discusses unsolved technological and ethical problems, and provides clinically relevant outlook in the framework of 3PM.

Background: Allergic conjunctivitis is an ocular immune disease which affects the conjunctiva, eyelids, and cornea. Growing evidence implicates the gut microbiota in balancing and modulating immunity response, and in the pathogenesis of allergic disease. As a result, gut microbial imbalance could be a useful indicator for allergic conjunctivitis. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of gut microbial imbalance in the development of allergic conjunctivitis could provide a window of opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

Working hypothesis and methodology: In our study, we hypothesized that individuals with microbial dysbiosis may be more susceptible to allergic conjunctivitis due to an increased inflammatory response. To verify the working hypothesis, our analysis selected genetic variants linked with gut microbiota features (N = 18,340) and allergic conjunctivitis (4513 cases, 649,376 controls) from genome-wide association studies. The inverse-variance weighted (IVW) estimate, Mendelian randomization (MR)-Egger, weighted median estimator, maximum likelihood estimator (MLE), and MR robust adjusted profile score (MR.RAPS) were employed to analyze the impact of gut microbiota on the risk of allergic conjunctivitis and identify allergic conjunctivitis-related gut microbes. Ultimately, these findings may enable the identification of individuals at risk of allergic conjunctivitis through screening of gut microbial imbalances, and allow for new targeted prevention and personalized treatment strategies.

Results: Genetic liability to Ruminococcaceae_UCG_002 (OR, 0.83; 95% CI, 0.70-0.99; P = 4.04×10^-2), Holdemanella (OR, 0.78; 95% CI, 0.64-0.96; P = 2.04×10^-2), Catenibacterium (OR, 0.69; 95% CI, 0.56-0.86; P = 1.09×10^-3), Senegalimassilia (OR, 0.71; 95% CI, 0.55-0.93; P = 1.23×10^-2) genus were associated with a low risk of allergic conjunctivitis with IVW. Besides, we found suggestive associations of a genetic-driven increase in the Oscillospira (OR, 1.41; 95% CI, 1.00-2.00; P = 4.63×10^-2) genus with a higher risk of allergic conjunctivitis. Moreover, MLE and MR.RAPS show consistent results with IVW after further validation and strengthened confidence in the true causal associations. No heterogeneity and pleiotropy was detected.

Conclusions: Our study suggests that gut microbiota may play a causal role in the development of allergic conjunctivitis and provides new insights into the microbiota-mediated mechanism of the disease. Gut microbiota may serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in allergic conjunctivitis, facilitating the transition from reactive med

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods: Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results: We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion: Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.