Living Biobanks of Organoids: Valuable Resource for Translational Research.

The emergence of organoids is considered a revolutionary model, changing the landscape of traditional translational research. These three-dimensional miniatures of human organs or tissues, cultivated from stem cells or biospecimens obtained from patients, faithfully replicate the structural and functional characteristics of specific target organs or tissues. In this extensive review, we explore the profound impact of organoids and assess the current state of living organoid biobanks, which are essential repositories for cryopreserving organoids derived from a variety of diseases. These resources hold significant value for translational research. We delve into the diverse origins of organoids, the underlying technologies, and their roles in recapitulating human development, disease modeling, as well as their potential applications in the pharmaceutical field. With a particular emphasis on biobanking organoids for prospective applications, we discuss how these advancements expedite the transition from bench to bedside translational research, thereby fostering personalized medicine and enriching our comprehension of human health.

European survey: citizens' attitudes on personalized medicine, genetic testing and health data sharing - design and delivery.

In the transformative landscape of healthcare, personalized medicine emerges as a pivotal shift, harnessing genetic, environmental and lifestyle data to tailor medical treatments for enhanced outcomes and cost efficiency. Central to its success is public engagement and consent to share health data amidst rising data privacy concerns. To investigate European public opinion on this paradigm, we executed a comprehensive cross-sectional survey to capture the general public's views on personalized medicine and data-sharing modalities, including digital tools and electronic records. The survey was distributed in eight major European Union countries and the results aim at guiding future policymaking and trust-building measures for secure health data exchange. This article delineates our methodological approach, whereby survey findings will be expounded in subsequent publications.

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The Treatment Selection for a 36-Year-Old Woman With Stress Urinary Incontinence Using a Discrete Mathematical Approach: A Case Report.

This case report describes the treatment selection process for a 36-year-old woman with stress urinary incontinence (SUI) and an overactive bladder (OAB) who desired pregnancy. The patient had comorbidities of hypertension and type 2 diabetes, which required consideration to improve her quality of life and reproductive health. A recently developed decision support tool using a discrete mathematical approach was used to select a treatment method tailored to the patient's individual situation. The analysis determined that vaginal erbium laser (VEL) treatment (Renovalase SP Dynamis Fotona d.o.o, Ljubljana, Slovenia) was the most suitable for this patient. VEL treatment significantly improved both SUI and OAB and changing antihypertensive medication eliminated nocturia. This case suggests the potential application of graph theory in treatment selection for SUI patients.

Frequency of and reasons behind non-listing in adult patients referred for liver transplantation: Results from a retrospective study.

BACKGROUND: Few studies have evaluated the frequency of and the reasons behind the refusal of listing liver transplantation candidates. AIM: To assess the ineligibility rate for liver transplantation and its motivations. METHODS: A single-center retrospective study was conducted on adult patients which entailed a formal multidisciplinary assessment for liver transplantation eligibility. The predictors for listing were evaluated using multivariable logistic regression. RESULTS: In our center, 314 patients underwent multidisciplinary work-up before liver transplantation enlisting over a three-year period. The most frequent reasons for transplant evaluation were decompensated cirrhosis (51.6%) and hepatocellular carcinoma (35.7%). The non-listing rate was 53.8% and the transplant rate was 34.4% for the whole cohort. Two hundred and five motivations for ineligibility were collected. The most common contraindications were psychological (9.3%), cardiovascular (6.8%), and surgical (5.9%). Inappropriate or premature referral accounted for 76 (37.1%) cases. On multivariable analysis, a referral from another hospital (OR: 2.113; 95%CI: 1.259-3.548) served as an independent predictor of non-listing. CONCLUSION: A non-listing decision occurred in half of our cohort and was based on an inappropriate or premature referral in one case out of three. The referral from another hospital was taken as a strong predictor of non-listing.

Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.

Background: Glioblastoma (GBM) remains associated with a dismal prognoses despite standard therapies. While population-level survival statistics are established, generating individualized prognosis remains challenging. We aim to develop machine learning (ML) models that generate personalized survival predictions for GBM patients to enhance prognostication. Methods: Adult patients with histologically confirmed IDH-wildtype GBM from the National Cancer Database (NCDB) were analyzed. ML models were developed with TabPFN, TabNet, XGBoost, LightGBM, and Random Forest algorithms to predict mortality at 6, 12, 18, and 24 months postdiagnosis. SHapley Additive exPlanations (SHAP) were employed to enhance the interpretability of the models. Models were primarily evaluated using the area under the receiver operating characteristic (AUROC) values, and the top-performing models indicated by the highest AUROCs for each outcome were deployed in a web application that was created for individualized predictions. Results: A total of 7537 patients were retrieved from the NCDB. Performance evaluation revealed the top-performing models for each outcome were built using the TabPFN algorithm. The TabPFN models yielded mean AUROCs of 0.836, 0.78, 0.732, and 0.724 in predicting 6, 12, 18, and 24 month mortality, respectively. Conclusions: This study establishes ML models tailored to individual patients to enhance GBM prognostication. Future work should focus on external validation and dynamic updating as new data emerge.

Comparison of primary and passaged tumor cell cultures and their application in personalized medicine.

Passaged cell lines represent currently an integral component in various studies of malignant neoplasms. These cell lines are utilized for drug screening both in monolayer cultures or as part of three-dimensional (3D) tumor models. They can also be used to model the tumor microenvironment in vitro and in vivo through xenotransplantation into immunocompromised animals. However, immortalized cell lines have some limitations of their own. The homogeneity of cell line populations and the extensive passaging in monolayer systems make these models distant from the original disease. Recently, there has been a growing interest among scientists in the use of primary cell lines, as these are passaged directly from human tumor tissues. In this case, cells retain the morphological and functional characteristics of the tissue from which they were derived, an advantage often not observed in passaged cultures. This review highlights the advantages and limitations of passaged and primary cell cultures, their similarities and differences, as well as existing test systems that are based on primary and passaged cell cultures for drug screening purposes.

Exploring heterogeneity of treatment effect in patients with sepsis: Protocol for a scoping review.

BACKGROUND: The average treatment effect (ATE) reported by most randomised clinical trials provides estimates of treatment effects for the theoretical, non-existent average patient. However, ATE may not accurately reflect the outcomes for all subsets of the trial population; some individuals may benefit from the intervention, while others experience worse outcomes or no effect at all. Heterogeneity of treatment effect (HTE) is the non-random and explainable variation in the magnitude or direction of a treatment effect among individuals within a population. Predictive approaches to HTE seek to provide estimates of which treatment of choice is better suited for the individual patient, using regression and/or machine learning techniques. This scoping review aims to investigate the extent to which such predictive approaches to HTE are applied to data from trials on sepsis or septic shock as well as the results of these analyses. METHODS: The planned review will be conducted in accordance with the PRISMA extension for scoping reviews. We will search Medline, EMBASE, Central, Cinahl and Google Scholar for studies on sepsis or septic shock in which HTE was analysed using predictive approaches. We plan to chart data regarding trial characteristics, patient demographics, disease severity, interventions, outcomes of interest and ATEs, type of predictive approach for the HTE analysis, results from HTE analysis and whether HTE analysis would change an ATE-based trial conclusion. RESULTS: Studies included in the scoping review will be presented as narrative summaries, supplemented with descriptive statistics of quantitative data. CONCLUSION: The planned scoping review will systematically investigate, summarise and delineate the existing evidence of analysis of HTE in trials on sepsis or septic shock patients as well as their findings, when performed using predictive approaches.

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

Individualized computational models of epilepsy surgery capture some of the key aspects of seizure propagation and the resective surgery. It is to be established whether this information can be integrated during the presurgical evaluation of the patient to improve surgical planning and the chances of a good surgical outcome. Here we address this question with a pseudo-prospective study that applies a computational framework of seizure propagation and epilepsy surgery­the ESSES framework­in a pseudo-prospective study mimicking the presurgical conditions. We found that within this pseudo-prospective setting, ESSES could correctly predict 75% of NSF and 80.8% of SF cases. This finding suggests the potential of individualised computational models to inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection.

A real-life ImmunoCAT study: impact of molecular diagnosis through ImmunoCAPTM ISAC 112 on immunotherapy prescription in pollen-polysensitized patients in Catalonia, Spain.

BACKGROUND: Molecular diagnosis in allergology helps to identify multiple allergenic molecules simultaneously. The use of purified and/or recombinant allergens increases the accuracy of individual sensitization profiles in allergic patients. OBJECTIVE: To assess the impact of molecular diagnosis through the ImmunoCAPTM ISAC 112 microarray on etiological diagnosis and specific immunotherapy (SIT) prescription. This was compared to the use of conventional diagnoses in pediatric, adolescent, and young adult patients with rhinitis or rhinoconjunctivitis and/or allergic asthma, sensitized to three or more pollen allergens of different botanical species. METHODS: A multicenter, prospective, observational study was conducted in patients aged 3-25 years who received care at the Allergology service of 14 hospitals in Catalonia from 2017 to 2020. Allergology diagnosis was established based on the patient's clinical assessment and the results of the skin prick test and specific immunoglobulin E assays. Subsequently, molecular diagnosis was conducted using ImmunoCAPTM ISAC® 112 to recombinant and/or purified allergen components. RESULTS: A total of 109 patients were included; 35 (32.1%) were pediatric patients and 74 (67.9%) were adolescents or young adults (mean age: 18 years), with 58.0% being females. A change of 51.0% was observed in SIT prescription following molecular etiological diagnosis by means of a multi-parameter microarray. CONCLUSIONS: Molecular diagnosis by means of multi-parameter tests increases the accuracy of etiological diagnosis and helps to define an accurate composition of SIT.

Potential clinical applications of advanced genomic analysis in cerebral palsy.

Cerebral palsy (CP) has historically been attributed to acquired insults, but emerging research suggests that genetic variations are also important causes of CP. While microarray and whole-exome sequencing based studies have been the primary methods for establishing new CP-gene relationships and providing a genetic etiology for individual patients, the cause of their condition remains unknown for many patients with CP. Recent advancements in genomic technologies offer additional opportunities to uncover variations in human genomes, transcriptomes, and epigenomes that have previously escaped detection. In this review, we outline the use of these state-of-the-art technologies to address the molecular diagnostic challenges experienced by individuals with CP. We also explore the importance of identifying a molecular etiology whenever possible, given the potential for genomic medicine to provide opportunities to treat patients with CP in new and more precise ways.

The global patient-reported outcomes for multiple sclerosis initiative: bridging the gap between clinical research and care - updates at the 2023 plenary event.

Significant advancements have been achieved in delineating the progress of the Global PROMS (PROMS) Initiative. The PROMS Initiative, a collaborative endeavor by the European Charcot Foundation and the Multiple Sclerosis International Federation, strives to amplify the influence of patient input on MS care and establish a cohesive perspective on Patient-Reported Outcomes (PROs) for diverse stakeholders. This initiative has established an expansive, participatory governance framework launching four dedicated working groups that have made substantive contributions to research, clinical management, eHealth, and healthcare system reform. The initiative prioritizes the global integration of patient (For the purposes of the Global PROMS Initiative, the term "patient" refers to the people with the disease (aka People with Multiple Sclerosis - pwMS): any individual with lived experience of the disease. People affected by the disease/Multiple Sclerosis: any individual or group that is affected by the disease: E.g., family members, caregivers will be also engaged as the other stakeholders in the initiative). insights into the management of MS care. It merges subjective PROs with objective clinical metrics, thereby addressing the complex variability of disease presentation and progression. Following the completion of its second phase, the initiative aims to help increasing the uptake of eHealth tools and passive PROs within research and clinical settings, affirming its unwavering dedication to the progressive refinement of MS care. Looking forward, the initiative is poised to continue enhancing global surveys, rethinking to the relevant statistical approaches in clinical trials, and cultivating a unified stance among 'industry', regulatory bodies and health policy making regarding the application of PROs in MS healthcare strategies.

Neuroendocrine neoplasms of the breast: a review of literature.

Primary neuroendocrine neoplasms (NENs) of the breast are characterized by neuroendocrine architectural and cytological features, which must be supported by immunohistochemical positivity for neuroendocrine markers (such as Chromogranin and Synaptophysin). According to the literature, making a diagnosis of primary neuroendocrine breast cancer always needs to rule out a possible primary neuroendocrine neoplasm from another site. Currently, the latest 2022 version of the WHO of endocrine and neuroendocrine neoplasms has classified breast NENs as well-differentiated neuroendocrine tumours (NETs) and aggressive neuroendocrine carcinomas (NECs), differentiating them from invasive breast cancers of no special type (IBCs-NST). with neuroendocrine features. The current review article describes six cases from our series and a comprehensive review of the literature in the field of NENs of the breast.

Personalized Medicine in Pancreatic Cancer: The Promise of Biomarkers and Molecular Targeting with Dr. Michael J. Pishvaian.

Pancreatic cancer, with its alarming rising incidence, is predicted to become the second deadliest type of solid tumor by 2040, highlighting the urgent need for improved diagnostic and treatment strategies. Despite medical advancements, the five-year survival rate for pancreatic cancer remains about 14%, dropping further when metastasized. This review explores the promise of biomarkers for early detection, personalized treatment, and disease monitoring. Molecular classification of pancreatic cancer into subtypes based on genetic mutations, gene expression, and protein markers guides treatment decisions, potentially improving outcomes. A plethora of clinical trials investigating different strategies are currently ongoing. Targeted therapies, among which those against CLAUDIN 18.2 and inhibitors of Claudin 18.1, have shown promise. Next-generation sequencing (NGS) has emerged as a powerful tool for the comprehensive genomic analysis of pancreatic tumors, revealing unique genetic alterations that drive cancer progression. This allows oncologists to tailor therapies to target specific molecular abnormalities. However, challenges remain, including limited awareness and uptake of biomarker-guided therapies. Continued research into the molecular mechanisms of pancreatic cancer is essential for developing more effective treatments and improving patient survival rates.

A network analysis of catatonia symptoms across diagnoses.

BACKGROUND: Catatonia, as a transdiagnostic construct, manifests across various psychiatric and non-psychiatric conditions. Understanding how symptom variations impact the catatonia construct and differ across primary diagnoses (schizophrenia, bipolar disorder, unipolar depression, and neurological/metabolic/immunological condition) is essential to refine diagnostic and therapeutic approaches. This study aims to compare the symptom networks and centrality measures of these diagnoses. METHODS: We conducted a network analysis using Bush-Francis Catatonia Rating Scale (BFCRS) data from 118 patients, examining centrality measures and network comparisons across the four primary diagnostic groups. RESULTS: In the general catatonia network, the three most central symptoms identified were Excitement (1.462), Perseveration (1.456), and Impulsivity (1.332). While the overall structure of the catatonia networks did not show significant differences between diagnoses in terms of symptom connections and centrality, variations in centrality measures were observed among the different networks. CONCLUSIONS: The study reinforces the notion of catatonia as an independent syndrome relatively to psychiatric or non-psychiatric diagnoses. However, the variation in centrality of symptoms across different primary diagnoses provides critical insights that could aid clinicians in tailoring diagnostic and therapeutic strategies. Future research should further explore these relationships and develop more refined approaches to managing catatonia.

Biomarkers: how to consolidate them in clinical practice.

An inadequate biomarker validation can affect many patients' diagnosis, treatment, and follow-up. Therefore, special interest should be placed on performing these analyses correctly so that biomarkers can be applicable to patients and evidence of their clinical usefulness can be generated. A methodological work on the concept of biomarkers is presented, as well as the difficulties associated with the methodological approach to their development, validation, and implementation in clinical practice.

The role of coronary CT angiography in athletes.

The sudden death of a young or high-level athlete or adolescent during recreational sports is one of the events with the greatest impact on public opinion in modern society. Sudden cardiac death (SCD) is the principal medical cause of death in athletes and can be the first and last clinical presentation of underlying disease. To prevent such episodes, pre-participation screening has been introduced in many countries to guarantee cardiovascular safety during sports and has become a common target among medical sports/governing organizations. Different cardiac conditions may cause SCD, with incidence depending on definition, evaluation methods, and studied populations, and a prevalence and etiology changing according to the age of athletes, with CAD most frequent in master athletes, while coronary anomalies and non-ischemic causes prevalent in young. To detect silent underlying causes early would be of considerable clinical value. This review summarizes the pre-participation screening in athletes, the specialist agonistic suitability visit performed in Italy, the anatomical characteristics of malignant coronary anomalies, and finally, the role of coronary CT angiography in such arena. In particular, the anatomical conditions suggesting potential disqualification from sport, the post-treatment follow-up to reintegrate young athletes, the diagnostic workflow to rule-out CAD in master athletes, and their clinical management are analyzed.

Efficacy of AI-Guided (GenAISTM) Dietary Supplement Prescriptions versus Traditional Methods for Lowering LDL Cholesterol: A Randomized Parallel-Group Pilot Study.

Emerging evidence suggests that personalized dietary supplement regimens can significantly influence lipid metabolism and cardiovascular risk. The efficacy of AI-guided dietary supplement prescriptions, compared with standard physician-guided prescriptions, remains underexplored. In a randomized, parallel-group pilot study, 70 patients aged 40-75 years with LDL-C levels between 70 and 190 mg/dL were enrolled. Participants were randomized to receive either AI-guided dietary supplement prescriptions or standard physician-guided prescriptions for 90 days. The primary endpoint was the percent change in LDL-C levels. Secondary endpoints included changes in total cholesterol, HDL-C, triglycerides, and hsCRP. Supplement adherence and side effects were monitored. Sixty-seven participants completed the study. The AI-guided group experienced a 25.3% reduction in LDL-C levels (95% CI: -28.7% to -21.9%), significantly greater than the 15.2% reduction in the physician-guided group (95% CI: -18.5% to -11.9%; p < 0.01). Total cholesterol decreased by 15.4% (95% CI: -19.1% to -11.7%) in the AI-guided group compared with 8.1% (95% CI: -11.5% to -4.7%) in the physician-guided group (p < 0.05). Triglycerides were reduced by 22.1% (95% CI: -27.2% to -17.0%) in the AI-guided group versus 12.3% (95% CI: -16.7% to -7.9%) in the physician-guided group (p < 0.01). HDL-C and hsCRP changes were not significantly different between groups. The AI-guided group received a broader variety of supplements, including plant sterols, omega-3 fatty acids, red yeast rice, coenzyme Q10, niacin, and fiber supplements. Side effects were minimal and comparable between groups. AI-guided dietary supplement prescriptions significantly reduce LDL-C and triglycerides more effectively than standard physician-guided prescriptions, highlighting the potential for AI-driven personalization in managing hypercholesterolemia.

The Significance of Genetically Determined Methylation and Folate Metabolism Disorders in the Pathogenesis of Coronary Artery Disease: A Target for New Therapies?

Methylation is a biochemical process involving the addition of a methyl group (-CH3) to various chemical compounds. It plays a crucial role in maintaining the homeostasis of the endothelium, which lines the interior surface of blood vessels, and has been linked, among other conditions, to coronary artery disease (CAD). Despite significant progress in CAD diagnosis and treatment, intensive research continues into genotypic and phenotypic CAD biomarkers. This review explores the significance of the methylation pathway and folate metabolism in CAD pathogenesis, with a focus on endothelial dysfunction resulting from deficiency in the active form of folate (5-MTHF). We discuss emerging areas of research into CAD biomarkers and factors influencing the methylation process. By highlighting genetically determined methylation disorders, particularly the MTHFR polymorphism, we propose the potential use of the active form of folate (5-MTHF) as a novel CAD biomarker and personalized pharmaceutical for selected patient groups. Our aim is to improve the identification of individuals at high risk of CAD and enhance their prognosis.

Development of an Ex Vivo Functional Assay for Prediction of Irradiation Related Toxicity in Healthy Oral Mucosa Tissue.

Radiotherapy in the head-and-neck area is one of the main curative treatment options. However, this comes at the cost of varying levels of normal tissue toxicity, affecting up to 80% of patients. Mucositis can cause pain, weight loss and treatment delays, leading to worse outcomes and a decreased quality of life. Therefore, there is an urgent need for an approach to predicting normal mucosal responses in patients prior to treatment. We here describe an assay to detect irradiation responses in healthy oral mucosa tissue. Mucosa specimens from the oral cavity were obtained after surgical resection, cut into thin slices, irradiated and cultured for three days. Seven samples were irradiated with X-ray, and three additional samples were irradiated with both X-ray and protons. Healthy oral mucosa tissue slices maintained normal morphology and viability for three days. We measured a dose-dependent response to X-ray irradiation and compared X-ray and proton irradiation in the same mucosa sample using standardized automated image analysis. Furthermore, increased levels of inflammation-inducing factors-major drivers of mucositis development-could be detected after irradiation. This model can be utilized for investigating mechanistic aspects of mucositis development and can be developed into an assay to predict radiation-induced toxicity in normal mucosa.