Advancing personalized medicine in brain cancer: exploring the role of mRNA vaccines

Advancing personalized medicine in brain cancer relies on innovative strategies, with mRNA vaccines emerging as a promising avenue. While the initial use of mRNA vaccines was in oncology, their stunning success in COVID-19 resulted in widespread attention, both positive and negative. Regardless of politically biased opinions, which relate more to the antigenic source than form of delivery, we feel it is important to objectively review this modality as relates to brain cancer. This class of vaccines trigger robust immune responses through MHC-I and MHC-II pathways, in both prophylactic and therapeutic settings. The mRNA platform offers advantages of rapid development, high potency, cost-effectiveness, and safety. This review provides an overview of mRNA vaccine delivery technologies, tumor antigen identification, combination therapies, and recent therapeutic outcomes, with a particular focus on brain cancer. Combinatorial approaches are vital to maximizing mRNA cancer vaccine efficacy, with ongoing clinical trials exploring combinations with adjuvants and checkpoint inhibitors and even adoptive cell therapy. Efficient delivery, neoantigen identification, preclinical studies, and clinical trial results are highlighted, underscoring mRNA vaccines' potential in advancing personalized medicine for brain cancer. Synergistic combinatorial therapies play a crucial role, emphasizing the need for continued research and collaboration in this area.

The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice

BACKGROUND

Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population.

METHODS AND RESULTS

Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8355 individuals, followed for a median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence [predicted-observed ratio 1.14 (95% CI 1.09-1.20)], which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk > 20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions.

INTERPRETATION

Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.

Alert-Grouping: Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Precision medicine in nasopharyngeal carcinoma: comprehensive review of past, present, and future prospect

Nasopharyngeal carcinoma (NPC) is an aggressive malignancy with high propensity for lymphatic spread and distant metastasis. It is prominent as an endemic malignancy in Southern China and Southeast Asia regions. Studies on NPC pathogenesis mechanism in the past decades such as through Epstein Barr Virus (EBV) infection and oncogenic molecular aberrations have explored several potential targets for therapy and diagnosis. The EBV infection introduces oncoviral proteins that consequently hyperactivate many promitotic pathways and block cell-death inducers. EBV infection is so prevalent in NPC patients such that EBV serological tests were used to diagnose and screen NPC patients. On the other hand, as the downstream effectors of oncogenic mechanisms, the promitotic pathways can potentially be exploited therapeutically. With the apparent heterogeneity and distinct molecular aberrations of NPC tumor, the focus has turned into a more personalized treatment in NPC. Herein in this comprehensive review, we depict the current status of screening, diagnosis, treatment, and prevention in NPC. Subsequently, based on the limitations on those aspects, we look at their potential improvements in moving towards the path of precision medicine. The importance of recent advances on the key molecular aberration involved in pathogenesis of NPC for precision medicine progression has also been reported in the present review. Besides, the challenge and future outlook of NPC management will also be highlighted.

The genes regulating sensitivity of tumor cells to T cell-mediated killing: could they be potential personalized immunotherapeutic targets in head and neck squamous cell carcinoma?

OBJECTIVE

To identify the pivotal genes, specifically the STTK genes, that govern the sensitivity of tumor cells to T cell-mediated killing in Head and Neck Squamous Cell Carcinoma (HNSC).

METHODS

The differentially expressed genes (DEGs) in HNSC and STTK genes were overlapped to obtain the DE-STTK genes. Univariate and LASSO regression analyses were conducted to identify the pivotal DE-STTK genes that serve as hubs in HNSC (i.e., hub DE-STTK genes). The risk model was established to divide HNSC tumor samples into high- and low-risk groups based on the hub DE-STTK genes. Further investigations were carried out by examing the expression level, prognostic values, diagnostic values, enriched signaling pathways, correlation with tumor mutation burden (TMB), and association with tumor immune infiltration cells (TIICs).

RESULTS

A total of 71 genes were found to be overlapped between DEGs in HNSC and STTK genes. Lasso regression analysis identified 9 hub genes which were MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1. The network analysis of hub DE-STTK genes-pathway reveals that these 9 hub genes exhibit enrichment in multiple signaling pathways, including toll-like receptor signaling, TNF signaling, NF-kappa B signaling, cytokine-cytokine receptor interaction, spliceosome, mRNA surveillance pathway, nucleocytoplasmic transport, GPI-anchor biosynthesis, as well as N-Glycan biosynthesis. The Pearson correlation analysis showed that the majority of correlations between 9 hub DE-STTK genes and immune cells were positive.

CONCLUSION

The 9 identified hub DE-STTK genes (MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1) are presumptively implicated in the modulation of tumor immunity in HNSC. These genes, along with their enriched pathways, hold promise as potential personalized immunotherapeutic targets for the treatment of HNSC, thereby offering novel avenues for therapeutic intervention in this malignancy.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners

Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.

A Machine Learning Approach to Predict Postoperative Pancreatic Fistula After Pancreaticoduodenectomy Using Only Preoperatively Known Data

BACKGROUND

Clinically-relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD) is a major postoperative complication and the primary determinant of surgical outcomes. However, the majority of current risk calculators utilize intraoperative and postoperative variables, limiting their utility in the preoperative setting. Therefore, we aimed to develop a user-friendly risk calculator to predict CR-POPF following PD using state-of-the-art machine learning (ML) algorithms and only preoperatively known variables.

METHODS

Adult patients undergoing elective PD for non-metastatic pancreatic cancer were identified from the ACS-NSQIP targeted pancreatectomy dataset (2014-2019). The primary endpoint was development of CR-POPF (grade B or C). Secondary endpoints included discharge to facility, 30-day mortality, and a composite of overall and significant complications. Four models (logistic regression, neural network, random forest, and XGBoost) were trained, validated and a user-friendly risk calculator was then developed.

RESULTS

Of the 8666 patients who underwent elective PD, 13% (n = 1160) developed CR-POPF. XGBoost was the best performing model (AUC = 0.72), and the top five preoperative variables associated with CR-POPF were non-adenocarcinoma histology, lack of neoadjuvant chemotherapy, pancreatic duct size less than 3 mm, higher BMI, and higher preoperative serum creatinine. Model performance for 30-day mortality, discharge to a facility, and overall and significant complications ranged from AUC 0.62-0.78.

CONCLUSIONS

In this study, we developed and validated an ML model using only preoperatively known variables to predict CR-POPF following PD. The risk calculator can be used in the preoperative setting to inform clinical decision-making and patient counseling.

Moderators of functional improvement after integrative cognitive remediation in schizophrenia: Toward a personalized treatment approach

Cognitive remediation is an effective intervention for improving functional outcome in schizophrenia. However, the factors that moderate this improvement are still poorly understood. The study aimed to identify moderators of functional outcome improvement after integrative cognitive remediation (REHACOP) in schizophrenia. This was a secondary analysis of data from two randomized controlled trials, which included 182 patients (REHACOP group=94; active control group=88). Hierarchical regression analyses were conducted to identify moderators of functional outcome improvement. Two baseline level groups (low-level and high-level) were created to analyze the moderating role of this baseline level cluster using repeated measures ANCOVA. The REHACOP was effective regardless of participants' baseline level, but regression analyses indicated that the effectiveness on functional outcome was higher among those who were older, had fewer years in education, lower scores in baseline cognition and functional outcome, and more negative symptoms. Repeated measures ANOVA showed that the baseline level cluster influenced the improvement in functional outcome, with the low-level group showing greater improvements. The results reinforced the need to implement cognitive remediation programs more broadly as a treatment for schizophrenia in healthcare services. Furthermore, they provided evidence for the development of personalized cognitive remediation plans to improve benefits in different schizophrenia profiles.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes

AIMS

To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type.

METHODS

We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A1c (HbA1c) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory.

RESULTS

The study population was comprised of 119,952 adults with newly diagnosed diabetes, including 696 (0.58%) with type 1 diabetes. Among patients with type 1 diabetes, 52.6% were diagnosed at very high HbA1c, partially improved, but never achieved control; 32.5% were diagnosed at low HbA1c and deteriorated over time; and 14.9% had stable low HbA1c. Among patients with type 2 diabetes, 67.7% had stable low HbA1c, 14.4% were diagnosed at very high HbA1c, partially improved, but never achieved control; 10.0% were diagnosed at moderately high HbA1c and deteriorated over time; and 4.9% were diagnosed at moderately high HbA1c and improved over time.

CONCLUSIONS

Claims data identified distinct longitudinal trajectories of HbA1c after diabetes diagnosis, which can be used to anticipate challenges and individualize care plans to improve glycemic control.

Personalized lifetime prediction of survival and treatment benefit in patients with heart failure with reduced ejection fraction: The LIFE-HF model

AIMS

Although trials have proven the group-level effectiveness of various therapies for heart failure with reduced ejection fraction (HFrEF), important differences in absolute effectiveness exist between individuals. We developed and validated the LIFEtime-perspective for Heart Failure (LIFE-HF) model for the prediction of individual (lifetime) risk and treatment benefit in patients with HFrEF.

METHODS AND RESULTS

Cox proportional hazards functions with age as the time scale were developed in the PARADIGM-HF and ATMOSPHERE trials (n = 15 415). Outcomes were cardiovascular death, heart failure (HF) hospitalization or cardiovascular death, and non-cardiovascular mortality. Predictors were age, sex, New York Heart Association class, prior HF hospitalization, diabetes mellitus, extracardiac vascular disease, systolic blood pressure, left ventricular ejection fraction, N-terminal pro-B-type natriuretic peptide, and glomerular filtration rate. The functions were combined in life-tables to predict individual overall and HF hospitalization-free survival. External validation was performed in the SwedeHF registry, ASIAN-HF registry, and DAPA-HF trial (n = 51 286). Calibration of 2- to 10-year risk was adequate, and c-statistics were 0.65-0.74. An interactive tool was developed combining the model with hazard ratios from trials to allow estimation of an individual's (lifetime) risk and treatment benefit in clinical practice. Applying the tool to the development cohort, combined treatment with a mineralocorticoid receptor antagonist, sodium-glucose cotransporter 2 inhibitor, and angiotensin receptor-neprilysin inhibitor was estimated to afford a median of 2.5 (interquartile range [IQR] 1.7-3.7) and 3.7 (IQR 2.4-5.5) additional years of overall and HF hospitalization-free survival, respectively.

CONCLUSION

The LIFE-HF model enables estimation of lifelong overall and HF hospitalization-free survival, and (lifetime) treatment benefit for individual patients with HFrEF. It could serve as a tool to improve the management of HFrEF by facilitating personalized medicine and shared decision-making.

Circulating miRNAs drive personalized medicine based on subgroup classification in myasthenia gravis patients

Myasthenia gravis (MG) is a classic autoimmune neuromuscular disease with strong clinical heterogeneity. The concept of subgroup classification was proposed to guide the precise treatment of MG. Subgroups based on serum antibodies and clinical features include ocular MG, early-onset MG with AchR antibodies, late-onset MG with AchR antibodies, thymoma-associated MG, MuSK-associated MG, LRP4-associated MG, and seronegative MG. However, reliable objective biomarkers are still needed to reflect the individualized response to therapy. MicroRNAs (miRNAs) are small non-coding RNA molecules which can specifically bind to target genes and regulate gene expression at the post-transcriptional level, and then influence celluar biological processes. MiRNAs play an important role in the pathogenesis of autoimmune diseases, including MG. Several studies on circulating miRNAs in MG have been reported. However, there is rare systematic review to summarize the differences of these miRNAs in different subgroups of MG. Here, we summarize the potential role of circulating miRNAs in different subgroups of MG to promote personalized medicine.

Omics-CNN: A comprehensive pipeline for predictive analytics in quantitative omics using one-dimensional convolutional neural networks

Background and objective

The development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated tool, the Convolutional Neural Networks, making it applicable in multidimensional omics data classification problems and testing the newly introduced method on publicly available transcriptomics and proteomics data.

Methods

In this study, we introduce Omics-CNN, a Convolutional Neural Network-based pipeline, which couples Convolutional Neural Networks with dimensionality reduction, preprocessing, clustering, and explainability techniques to make them suitable to build highly accurate and interpretable classification models from high-throughput omics data. The developed tool has the potential to classify patients depending on the expression of genetic and clinical factors and identify features that can act as diagnostic biomarkers. Regarding dimensionality reduction, univariate and multivariate techniques were explored and compared. Gradient Weighted Class Activation Mapping analysis was performed to determine the most important features in the classification of the samples after training the model.

Results

The newly introduced pipeline was applied to one transcriptomics and one proteomics dataset for the identification of diagnostic models and biosignatures for Ischemic Stroke (IS) and COVID-19 infection, reporting highly accurate biosignatures with accuracies of 96 % and 95.41 %, respectively. Meanwhile, classification models based solely on a small part of attributes provided lower predictive accuracy, but identified compact transcript biosignature (KRT15, VPRBP, TNFRSF4, GORASP2) for Ischemic Stroke and protein biosignature (ADGRB3, VNN2, AGER, CIAPIN1) for Covid-19 infection diagnosis, respectively.

Conclusions

Omics-CNN, overcame the inherent problems of applying Convolutional Neural Networks for the training diagnostic models with quantitative omics data, outperforming previous models of machine learning developed using the same datasets for Ischemic Stroke and Covid-19 infection diagnosis, determining the most contributing biomarkers for both diseases.

The perceived waning of biologics in severe asthma

BACKGROUND

Biologics are highly effective in severe asthma and used at fixed dosing intervals. However, in clinical practice, dosing intervals are sometimes shortened if patients perceive a decreased biologic effect before the next administration. The occurrence and clinical relevance of this perceived waning of biological effect is unknown.

OBJECTIVE

To explore (1) the frequency, severity and conditions, (2) associated symptoms and (3) relationship with clinical characteristics of the patient-perceived waning effect of biologics before the next administration.

METHODS

Severe asthma patients receiving biological treatment ≥4 months were included. Based on 17 semi-structured patient interviews, we developed a questionnaire focusing on the waning effect of biologics before the next administration, which was distributed among 129 patients. Clinical characteristics, including asthma control (ACQ) and quality of life (AQLQ) scores, were collected from patient files.

RESULTS

65/101 patients who completed the questionnaire reported a waning of biological effect, graded as severe (median (IQR) 6.5 (5-7.5) on a 0-10 BORG-scale). Waning manifested in a broad spectrum of symptoms. Patients reporting waning had higher ACQ and lower AQLQ scores versus those without (p < 0.05) and higher BORG-scores were associated with higher exacerbation rate (ρ = 0.309, p = 0.013). A third of all patients were in favor of extending or shortening their dosing interval.

CONCLUSION

Two-thirds of severe asthma patients report waning of biologic effect at the end of the dosing interval, which is associated with poorer asthma control and quality of life. The diversity in observed waning of effect opens the way for research into more individualized dosing of biologics.

Coronary atherosclerotic plaque phenotype and physiopathologic mechanisms: Is there an influence of sex? Insights from intracoronary imaging

Coronary artery disease (CAD) is the leading cause of disability and death in both women and men; considerable differences in clinical presentation, natural history and prognosis are reported between sexes. Different pathophysiological mechanisms play a major role, including sex-related and gender-related features or a combination of both. Reports from intracoronary imaging studies pointed towards morphological plaque features, which seemed to differ between men and women, albeit results reported so far were not conclusive. The purpose of this review is to shed light on differences in the pathophysiology underlying CAD in women vs men including the description of coronary plaque phenotype and mechanisms of plaque instability, as assessed by intracoronary imaging. We will also discuss potential clinical implications with the aim to move towards a sex and gender-based personalized approach in CAD patients.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

[Digital rheumatology]

Chronic inflammatory rheumatic diseases mostly run an undulating course and with unspecific symptoms. The initial clarification and timely initiation of treatment are challenging, which is additionally exacerbated by the lack of specialized physicians. Digital approaches, including artificial intelligence (AI), should be of assistance and enable an improved, personalized and needs-based treatment; however, the evidence is currently still very limited. This article provides a compact overview of the current state of digital rheumatology.

Classification of patients with metastatic colorectal cancer into consensus molecular subtypes into real-world: A pilot study

BACKGROUND

Colorectal cancer is a complex disease with high mortality rates. Over time, the treatment of metastatic colorectal cancer (mCRC) has gradually improved due to the development of modern chemotherapy and targeted therapy regimens. However, due to the inherent heterogeneity of this condition, identifying reliable predictive biomarkers for targeted therapies remains challenging. A recent promising classification system-the consensus molecular subtype (CMS) system-offers the potential to categorize mCRC patients based on their unique biological and molecular characteristics. Four distinct CMS categories have been defined: immune (CMS1), canonical (CMS2), metabolic (CMS3), and mesenchymal (CMS4). Nevertheless, there is currently no standardized protocol for accurately classifying patients into CMS categories. To address this challenge, reverse transcription polymerase chain reaction (RT-qPCR) and next-generation genomic sequencing (NGS) techniques may hold promise for precisely classifying mCRC patients into their CMSs.

AIM

To investigate if mCRC patients can be classified into CMS categories using a standardized molecular biology workflow.

METHODS

This observational study was conducted at the University of Chile Clinical Hospital and included patients with unresectable mCRC who were undergoing systemic treatment with chemotherapy and/or targeted therapy. Molecular biology techniques were employed to analyse primary tumour samples from these patients. RT-qPCR was utilized to assess the expression of genes associated with fibrosis (TGF-β and β-catenin) and cell growth pathways (c-MYC). NGS using a 25-gene panel (TumorSec) was performed to identify specific genomic mutations. The patients were then classified into one of the four CMS categories according to the clinical consensus of a Tumour Board. Informed consent was obtained from all the patients prior to their participation in this study. All techniques were conducted at University of Chile.

RESULTS

Twenty-six patients were studied with the techniques and then evaluated by the Tumour Board to determine the specific CMS. Among them, 23% (n = 6), 19% (n = 5), 31% (n = 8), and 19% (n = 5) were classified as CMS1, CMS2, CMS3, and CMS4, respectively. Additionally, 8% of patients (n = 2) could not be classified into any of the four CMS categories. The median overall survival of the total sample was 28 mo, and for CMS1, CMS2, CMS3 and CMS4 it was 11, 20, 30 and 45 mo respectively, with no statistically significant differences between groups.

CONCLUSION

A molecular biology workflow and clinical consensus analysis can be used to accurately classify mCRC patients. This classification process, which divides patients into the four CMS categories, holds significant potential for improving research strategies and targeted therapies tailored to the specific characteristics of mCRC.

Walking the path of treatable traits in interstitial lung diseases

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.

The New Zealand eating behavior questionnaire - Validation study for a novel assessment tool to describe actionable eating behavior traits

Individualised management of obesity remains challenging and, to date, most treatment is based on clinical judgement. This study aimed to develop and validate a novel questionnaire-based tool to identify three pre-defined eating behavior (EB) traits, emotional eating, reduced satiety (constant hunger) and reduced satiation (feasters) that may predict selective medication response given their targeted actions. We recruited 977 individuals from a tertiary academic diabetes clinic to participate in this two-phase validation study. Participants self-reported weight management activities and were asked to self-assess their EB characteristics. The initial questionnaire included 42 visual analogue scale questions. In Phase I, 729 participants completed the questionnaire, including Māori (11.8%) and Pacific peoples (19.3%). After random division of the study sample, Exploratory Factor Analysis (EFA) confirmed a three-factor model as the best fit. Stepwise removal of items with inadequate factor loading retained 27 of 42 items, which accounted for 96% of the variance. Confirmatory Factor Analysis (CFA), performed on the second half of the sample, demonstrated good model fit with the final 27-item questionnaire. Internal consistency was high for factor (α = 0.82-0.95) and demographic subgroups, and similar to those obtained in the EFA. Test-retest reliability in a subset of 399 participants who repeated the questionnaire after a four-week interval (Phase II) showed moderate to good reliability. Participants classified into one of three EB types based on the highest median score among the factors. Test-retest reliability was robust for emotional eaters (71.25%) and constant hunger (68.9%). The correlation between aggregate EB score (sum of three EB scores) and BMI was significant (Spearman rho = 0.314, P = .0005). The questionnaire reliably identified three distinct EB traits, which may be informative for precision medicine applications for obesity management.

Systemic treatment for advanced pancreatic cancer

Pancreatic cancer is a deadly disease with an extremely poor 5-year survival rate due to treatment resistance and late-stage detection. Despite numerous years of research and pharmaceutical development, these figures have not changed. Treatment options for advanced pancreatic cancer are still limited. This illness is typically detected at a late stage, making curative surgical resection impossible. Chemotherapy is the most commonly utilized technique for treating advanced pancreatic cancer but has poor efficacy. Targeted therapy and immunotherapy have made significant progress in many other cancer types and have been proven to have extremely promising possibilities; these therapies also hold promise for pancreatic cancer. There is an urgent need for research into targeted treatment, immunotherapy, and cancer vaccines. In this review, we emphasize the foundational findings that have fueled the therapeutic strategy for advanced pancreatic cancer. We also address current advancements in targeted therapy, immunotherapy, and cancer vaccines, all of which continue to improve the clinical outcome of advanced pancreatic cancer. We believe that clinical translation of these novel treatments will improve the low survival rate of this deadly disease.

3D printed pH-responsive tablets containing N-acetylglucosamine-loaded methylcellulose hydrogel for colon drug delivery applications

The pH-responsive drug release approach in combination with three-dimensional (3D) printing for colon-specific oral drug administration can address the limitations of current treatments such as orally administered solid tablets. Such existing treatments fail to effectively deliver the right drug dosage to the colon. In order to achieve targeted drug release profiles, this work aimed at designing and producing 3D printed tablet shells using Eudragit® FS100 and polylactic acid (PLA) where the core was filled with 100 µl of N-acetylglucosamine (GlcNAc)-loaded methyl cellulose (MC) hydrogel. To meet the requirements of such tablets, the effects of polymer blending ratios and MC concentrations on physical, thermal, and material properties of various components of the tablets and most importantly in vitro drug release kinetics were investigated. The tablets with 80/20 wt% of Eudragit® FS100/PLA and the drug-loaded hydrogel with 30 mg/ml GlcNAc and 3% w/v MC showed the most promising results having the best printability, processability, and drug release kinetics besides being non-cytotoxic. Manufacturing of these tablets will be the first milestone in shifting from the conventional "one size fits all" approach to personalized medicine where different dosages and various combinations of drugs can be effectively delivered to the inflammation site.

An integrated personalized assistive devices approach to reduce the risk of foot ulcer recurrence in diabetes (DIASSIST): study protocol for a multicenter randomized controlled trial

BACKGROUND

Preventing foot ulcers in people with diabetes can increase quality of life and reduce costs. Despite the availability of various interventions to prevent foot ulcers, recurrence rates remain high. We hypothesize that a multimodal treatment approach incorporating various footwear, self-management, and education interventions that matches an individual person's needs can reduce the risk of ulcer recurrence with beneficial cost-utility. The aim of this study is to assess the effect on foot ulcer recurrence, footwear adherence, and cost-utility of an integrated personalized assistive devices approach in high-risk people with diabetes.

METHODS

In a parallel-group multicenter randomized controlled trial, 126 adult participants with diabetes mellitus type 1 or 2, loss of protective sensation based on the presence of peripheral neuropathy, a healed plantar foot ulcer in the preceding 4 years, and possession of any type of custom-made footwear will be included. Participants will be randomly assigned to either enhanced therapy or usual care. Enhanced therapy consists of usual care and additionally a personalized treatment approach including pressure-optimized custom-made footwear, pressure-optimized custom-made footwear for indoor use, at-home daily foot temperature monitoring, and structured education, which includes motivational interviewing and personalized feedback on adherence and self-care. Participants will be followed for 12 months. Assessments include barefoot and in-shoe plantar pressure measurements; questionnaires concerning quality of life, costs, disease, and self-care knowledge; physical activity and footwear use monitoring; and clinical monitoring for foot ulcer outcomes. The study is powered for 3 primary outcomes: foot ulcer recurrence, footwear adherence, and cost-utility, the primary clinical, patient-related, and health-economic outcome respectively.

DISCUSSION

This is the first study to integrate multiple interventions for ulcer prevention into a personalized state-of-the-art treatment approach and assess their combined efficacy in a randomized controlled trial in people with diabetes at high ulcer risk. Proven effectiveness, usability, and cost-utility will facilitate implementation in healthcare, improve the quality of life of high-risk people with diabetes, and reduce treatment costs.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05236660. Registered on 11 February 2022.

Acute myeloid leukemia: from NGS, through scRNA-seq, to CAR-T. dissect cancer heterogeneity and tailor the treatment

Acute myeloid leukemia (AML) is a malignant blood cancer with marked cellular heterogeneity due to altered maturation and differentiation of myeloid blasts, the possible causes of which are transcriptional or epigenetic alterations, impaired apoptosis, and excessive cell proliferation. This neoplasm has a high rate of resistance to anticancer therapies and thus a high risk of relapse and mortality because of both the biological diversity of the patient and intratumoral heterogeneity due to the acquisition of new somatic changes. For more than 40 years, the old gold standard "one size fits all" treatment approach included intensive chemotherapy treatment with anthracyclines and cytarabine.The manuscript first traces the evolution of the understanding of the pathology from the 1970s to the present. The enormous strides made in its categorization prove to be crucial for risk stratification, enabling an increasingly personalized diagnosis and treatment approach.Subsequently, we highlight how, over the past 15 years, technological advances enabling single cell RNA sequencing and T-cell modification based on the genomic tools are affecting the classification and treatment of AML. At the dawn of the new millennium, the advent of high-throughput next-generation sequencing technologies has enabled the profiling of patients evidencing different facets of the same disease, stratifying risk, and identifying new possible therapeutic targets that have subsequently been validated. Currently, the possibility of investigating tumor heterogeneity at the single cell level, profiling the tumor at the time of diagnosis or after treatments exist. This would allow the identification of underrepresented cellular subclones or clones resistant to therapeutic approaches and thus responsible for post-treatment relapse that would otherwise be difficult to detect with bulk investigations on the tumor biopsy. Single-cell investigation will then allow even greater personalization of therapy to the genetic and transcriptional profile of the tumor, saving valuable time and dangerous side effects. The era of personalized medicine will take a huge step forward through the disclosure of each individual piece of the complex puzzle that is cancer pathology, to implement a "tailored" therapeutic approach based also on engineered CAR-T cells.

Glucarpidase (carboxypeptidase G2): Biotechnological production, clinical application as a methotrexate antidote, and placement in targeted cancer therapy

Patients receiving high-dose methotrexate (HDMTX) for malignancies are exposed to diverse complications, including nephrotoxicity, hepatotoxicity, mucositis, myelotoxicity, neurological symptoms, and death. Glucarpidase is a recombinant carboxypeptidase G2 (CPG2) that converts MTX into nontoxic metabolites. In this study, the role of vector type, gene optimization, orientation, and host on the expression of CPG2 is investigated. The effectiveness of various therapeutic regimens containing glucarpidase is classified and perspectives on the dose adjustment based on precision medicine are provided. Conjugation with cell-penetrating peptides, human serum albumin, and polymers such as PEG and dextran for delivery, higher stability, and production of the biobetter variants of CPG2 is highlighted. Conjugation of CPG2 to F(ab՜)2 or scFv antibody fragments against tumor-specific antigens and the corresponding prodrugs for tumor-targeted drug delivery using the antibody-directed enzyme prodrug therapy (ADEPT) is communicated. Trials to reduce the off-target effects and the possibility of repeated ADEPT cycles by adding pro-domains sensitive to tumor-overexpressed proteases, antiCPG2 antibodies, CPG2 mutants with immune-system-unrecognizable epitopes, and protective polymers are reported. Intracellular cpg2 gene expression by gene-directed enzyme prodrug therapy (GDEPT) and the concerns regarding the safety and transfection efficacy of the GDEPT vectors are described. A novel bifunctional platform using engineered CAR-T cell micropharmacies, known as Synthetic Enzyme-Armed KillER (SEAKER) cells, expressing CPG2 to activate prodrugs at the tumor niche is introduced. Taken together, integrated data in this review and recruiting combinatorial strategies in novel drug delivery systems define the future directions of ADEPT, GDEPT, and SEAKER cell therapy and the placement of CPG2 therein.

Assessing frailty at the centers for dementia and cognitive decline in Italy: potential implications for improving care of older people living with dementia

INTRODUCTION

Frailty is strongly associated with the clinical course of cognitive impairment and dementia, thus arguing for the need of its assessment in individuals affected by cognitive deficits. This study aimed to retrospectively evaluate frailty in patients aged 65 years and older referred to two Centers for Cognitive Decline and Dementia (CCDDs).

METHODS

A total of 1256 patients consecutively referred for a first visit to two CCDDs in Lombardy (Italy) between January 2021 to July 2022 were included. All patients were evaluated by an expert physician in diagnosis and care of dementia according to a standardized clinical protocol. Frailty was assessed using a 24-items Frailty Index (FI) based on routinely collected health records, excluding cognitive decline or dementia, and categorized as mild, moderate, and severe.

RESULTS

Overall, 40% of patients were affected by mild frailty and 25% of the sample has moderate to severe frailty. The prevalence and severity of frailty increased with decreasing Mini Mental State Examination (MMSE) score and advancing age. Frailty was also detected in 60% of patients with mild cognitive impairment.

CONCLUSION

Frailty is common in patients referring to CCDDs for cognitive deficits. Its systematic assessment using a FI generated with readily available medical information could help develop appropriate models of assistance and guide personalization of care.

Interleukin-1 Receptor Antagonist as Therapy for Traumatic Brain Injury

Traumatic brain injury is a common type of acquired brain injury of varying severity carrying potentially deleterious consequences for the afflicted individuals, families, and society. Following the initial, traumatically induced insult, cellular injury processes ensue. These are believed to be amenable to treatment. Among such injuries, neuroinflammation has gained interest and has become a specific focus for both experimental and clinical researchers. Neuroinflammation is elicited almost immediately following trauma, and extend for a long time, possibly for years, after the primary injury. In the acute phase, the inflammatory response is characterized by innate mechanisms such as the activation of microglia which among else mediates cytokine production. Among the earliest cytokines to emerge are the interleukin- (IL-) 1 family members, comprising, for example, the agonist IL-1β and its competitive antagonist, IL-1 receptor antagonist (IL-1ra). Because of its early emergence following trauma and its increased concentrations also after human TBI, IL-1 has been hypothesized to be a tractable treatment target following TBI. Ample experimental data supports this, and demonstrates restored neurological behavior, diminished lesion zones, and an attenuated inflammatory response following IL-1 modulation either through IL-1 knock-out experiments, IL-1β inhibition, or IL-1ra treatment. Of these, IL-1ra treatment is likely the most physiological. In addition, recombinant human IL-1ra (anakinra) is already approved for utilization across a few rheumatologic disorders. As of today, one randomized clinical controlled trial has utilized IL-1ra inhibition as an intervention and demonstrated its safety. Further clinical trials powered for patient outcome are needed in order to demonstrate efficacy. In this review, we summarize IL-1 biology in relation to acute neuroinflammatory processes following TBI with a particular focus on current evidence for IL-1ra treatment both in the experimental and clinical context.

[Treatment-Quo vadis neurodegeneration?]

BACKGROUND

Hallmarks of neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease are pathological protein aggregation, neuroinflammation, neurodegeneration and progressive symptoms. Due to the limited causal treatment options they represent a big challenge.

OBJECTIVE

Overview of disease-modifying strategies in neurodegenerative diseases and outlook regarding future treatment development.

MATERIAL AND METHODS

Literature search regarding treatment development in neurodegenerative diseases and integration of the results. Additionally, consideration of expert opinions.

RESULTS

The development of biomarkers and genetic parameters for the detection of causal pathologies of neurodegenerative diseases as an indispensable basis for the development of disease-modifying treatment is rapidly advancing. Targets for causal interventions are all steps in the pathophysiological cascade of neurodegenerative diseases. Therapeutic antibodies are most advanced in the development and are able to remove protein deposits from the brain and to reduce the clinical progression in Alzheimer's disease. A combination of biomarkers, genetic characteristics and clinical parameters could enable an individualized treatment.

CONCLUSION

The future of the treatment of neurodegenerative diseases focuses on disease modification using molecular-based approaches. Targeted interventions against protein aggregation, inflammation and genetic factors as well as a personalized stratification of treatment hold promise for more effective forms of treatment. Although challenges still remain, current research and clinical studies give optimism for the development of disease-modifying treatment for neurodegenerative diseases.

AMBAR - Interactive Alteration annotations for molecular tumor boards

MOTIVATION

Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required.

RESULTS

To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations.

AVAILABILITY

AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de.

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

BACKGROUND

The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome.

AIM OF REVIEW

From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.

Is there a role for microbiome-based approach in common variable immunodeficiency?

Common variable immunodeficiency (CVID) is a primary immunodeficiency characterized by low levels of serum immunoglobulins and increased susceptibility to infections, autoimmune disorders and cancer. CVID embraces a plethora of heterogeneous manifestations linked to complex immune dysregulation. While CVID is thought to be due to genetic defects, the exact cause of this immune disorder is unknown in the large majority of cases. Compelling evidences support a linkage between the gut microbiome and the CVID pathogenesis, therefore a potential for microbiome-based treatments to be a therapeutic pathway for this disorder. Here we discuss the potential of treating CVID patients by developing a gut microbiome-based personalized approach, including diet, prebiotics, probiotics, postbiotics and fecal microbiota transplantation. We also highlight the need for a better understanding of microbiota-host interactions in CVID patients to prime the development of improved preventive strategies and specific therapeutic targets.

[From conventional to cutting edge imaging in rheumatology]

Imaging instruments, such as conventional X‑ray, ultrasound and magnetic resonance imaging (MRI) are now fully established and highly valued in the care of rheumatology patients. However, the information provided by these imaging modalities in their current form is of limited utility for the prognostic prediction of individual patient outcomes. This article illuminates an important part of the development of imaging and shows that the vision of personalized medicine is becoming increasingly more tangible due to the further development of high-resolution imaging techniques, molecular imaging and artificial intelligence.

Experiences of adults living with refractory epilepsy and their views and expectations on receiving results from whole genome sequencing

INTRODUCTION

Researchers, clinicians and patients are turning to new innovations in research and clinical practice to further their knowledge in the genetic domain and improve diagnostics or treatment. However, with increased knowledge in genetics, societal issues may arise. Being conscious of these issues is crucial in order to implement standardized and efficient testing on a wider scale that is accessible to a greater number of individuals while simultaneously returning test results, including incidental findings, in a timely manner.

METHODS

Within the framework of a genomics research project, we invited 20 participants who suffer from refractory epilepsy to provide insight on their personal experiences with epilepsy, as well as their thoughts on receiving Whole Genome Sequencing (WGS) results and with whom they would feel comfortable sharing these results with.

RESULTS

All participants had their own unique experience with epilepsy, such as how they handled their diagnosis, their struggles following the diagnosis, the healthcare services they received, how they shared their diagnosis with others, and how they managed stigmatization from others. Most participants would be eager to know their WGS results, whether the results be related to epilepsy (n = 19), response to pharmaceutical drugs including AEDs (n = 16), comorbidities (n = 19) and incidental findings (n = 15).

CONCLUSION

Our findings reinforce the need to improve access to genetic testing for epilepsy patients in clinical settings. Furthermore, while acquiring more genetic knowledge (i.e. WGS) about epilepsy can provide answers for the affected population, it also requires the simultaneous involvement of several medical disciplines, with greater emphasis on genetic and psychological counseling.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.

Histological response to radiotherapy is an early event in myxoid liposarcoma

Compared to other sarcomas, myxoid liposarcoma (MLS) is exceptionally sensitive to radiation therapy, but the underlying mechanism remains unknown. The objective was to assess the tissue-based changes in MLS during and after neoadjuvant radiotherapy in 26 patients of the DOREMY trial. Morphological assessment was performed on biopsies pre-treatment, after 8 fractions, 16 factions, and after surgical resection and included percentage of viable tumor cells, hyalinization, necrosis, and fatty maturation. Furthermore, immunohistochemistry was performed for apoptosis (cleaved caspase-3), anti-apoptosis (Bcl-2), activity of mTOR signaling (phospho-S6), hypoxia (CAIX), proliferation (Ki67), inflammation (CD45 and CD68), and microvessel density (CD34 Chalkley count). A pronounced reduction in vital tumor cells was observed early with a drop to 32.5% (median) tumor cells (IQR 10-93.8%) after 8 fractions. This decreased further to 10% (IQR 5-30%) after 16 fractions and 7.5% (IQR 5-15%) in the surgical specimen. All but one patient had an excellent response with < 50% remaining tumor cells. Inversely, treatment response was mainly observed as hyalinization and less often as fatty maturation. Additionally, a decrease of inflammatory cells was noticed especially during the first eight fractions. Microvessel density remained stable over time. Immunohistochemical markers for apoptosis, anti-apoptosis, activity of mTOR signaling, proliferation, and hypoxia did not show any marked changes within the remaining tumor cells during and after radiotherapy. As a modest dose of neoadjuvant radiotherapy induces profound tissue changes in MLS, mainly during the first 8 fractions, current findings might suggest that in a carefully selected patient population further deintensification of radiotherapy might be explored.

The role of DNA methylation in personalized medicine for immune-related diseases

Epigenetics functions as a bridge between host genetic & environmental factors, aiding in human health and diseases. Many immune-related diseases, including infectious and allergic diseases, have been linked to epigenetic mechanisms, particularly DNA methylation. In this review, we summarized an updated overview of DNA methylation and its importance in personalized medicine, and demonstrated that DNA methylation has excellent potential for disease prevention, diagnosis, and treatment in a personalized manner. The future implications and limitations of the DNA methylation study have also been well-discussed.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study

Aims

Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.

Methods and results

We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.

Conclusion

Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

Machine learning in precision diabetes care and cardiovascular risk prediction

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

Mediator complex in neurological disease

Neurological diseases, including traumatic brain injuries, stroke (haemorrhagic and ischemic), and inherent neurodegenerative diseases cause acquired disability in humans, representing a leading cause of death worldwide. The Mediator complex (MED) is a large, evolutionarily conserved multiprotein that facilities the interaction between transcription factors and RNA Polymerase II in eukaryotes. Some MED subunits have been found altered in the brain, although their specific functions in neurodegenerative diseases are not fully understood. Mutations in MED subunits were associated with a wide range of genetic diseases for MED12, MED13, MED13L, MED20, MED23, MED25, and CDK8 genes. In addition, MED12 and MED23 were deregulated in the Alzheimer's Disease. Interestingly, most of the genomic mutations have been found in the subunits of the kinase module. To date, there is only one evidence on MED1 involvement in post-stroke cognitive deficits. Although the underlying neurodegenerative disorders may be different, we are confident that the signal cascades of the biological-cognitive mechanisms of brain adaptation, which begin after brain deterioration, may also differ. Here, we analysed relevant studies in English published up to June 2023. They were identified through a search of electronic databases including PubMed, Medline, EMBASE and Scopus, including search terms such as "Mediator complex", "neurological disease", "brains". Thematic content analysis was conducted to collect and summarize all studies demonstrating MED alteration to understand the role of this central transcriptional regulatory complex in the brain. Improved and deeper knowledge of the regulatory mechanisms in neurological diseases can increase the ability of physicians to predict onset and progression, thereby improving diagnostic care and providing appropriate treatment decisions.

L1077P CFTR pathogenic variant function rescue by Elexacaftor-Tezacaftor-Ivacaftor in cystic fibrosis patient-derived air-liquid interface (ALI) cultures and organoids: in vitro guided personalized therapy of non-F508del patients

Cystic fibrosis (CF) is caused by defects of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CFTR-modulating drugs may overcome specific defects, such as the case of Trikafta, which is a clinically approved triple combination of Elexacaftor, Tezacaftor and Ivacaftor (ETI) that exhibited a strong ability to rescue the function of the most frequent F508del pathogenic variant even in genotypes with the mutated allele in single copy. Nevertheless, most rare genotypes lacking the F508del allele are still not eligible for targeted therapies. Via the innovative approach of using nasal conditionally reprogrammed cell (CRC) cell-based models that mimic patient disease in vitro, which are obtainable from each patient due to the 100% efficiency of the cell culture establishment, we theratyped orphan CFTR mutation L1077P. Protein studies, Forskolin-induced organoid swelling, and Ussing chamber assays congruently proved the L1077P variant function rescue by ETI. Notably, this rescue takes place even in the context of a single-copy L1077P allele, which appears to enhance its expression. Thus, the possibility of single-allele treatment also arises for rare genotypes, with an allele-specific modulation as part of the mechanism. Of note, besides providing indication of drug efficacy with respect to specific CFTR pathogenic variants or genotypes, this approach allows the evaluation of the response of single-patient cells within their genetic background. In this view, our studies support in vitro guided personalized CF therapies also for rare patients who are nearly excluded from clinical trials.

Pathogenic variants of human GABRA1 gene associated with epilepsy: A computational approach

Critical for brain development, neurodevelopmental and network disorders, the GABRA1 gene encodes for the α1 subunit, an abundantly and developmentally expressed subunit of heteropentameric gamma-aminobutyric acid A receptors (GABAARs) mediating primary inhibition in the brain. Mutations of the GABAAR subunit genes including GABRA1 gene are associated with epilepsy, a group of syndromes, characterized by unprovoked seizures and diagnosed by integrative approach, that involves genetic testing. Despite the diagnostic use of genetic testing, a large fraction of the GABAAR subunit gene variants including the variants of GABRA1 gene is not known in terms of their molecular consequence, a challenge for precision and personalized medicine. Addressing this, one hundred thirty-seven GABRA1 gene variants of unknown clinical significance have been extracted from the ClinVar database and computationally analyzed for pathogenicity. Eight variants (L49H, P59L, W97R, D99G, G152S, V270G, T294R, P305L) are predicted as pathogenic and mapped to the α1 subunit's extracellular domain (ECD), transmembrane domains (TMDs) and extracellular linker. This is followed by the integration with relevant data for cellular pathology and severity of the epilepsy syndromes retrieved from the literature. Our results suggest that the pathogenic variants in the ECD of GABRA1 (L49H, P59L, W97R, D99G, G152S) will probably manifest decreased surface expression and reduced current with mild epilepsy phenotypes while V270G, T294R in the TMDs and P305L in the linker between the second and the third TMDs will likely cause reduced cell current with severe epilepsy phenotypes. The results presented in this study provides insights for clinical genetics and wet lab experimentation.

Numerical simulations to determine the stimulation of the crista ampullaris during the Head Impulse Test

The Head Impulse Test, the most widely accept test to assess the vestibular function, comprises rotations of the head based on idealized orientations of the semicircular canals, instead of their individual arrangement specific for each patient. In this study, we show how computational modelling can help personalize the diagnosis of vestibular diseases. Based on a micro-computed tomography reconstruction of the human membranous labyrinth and their simulation using Computational Fluid Dynamics and Fluid-Solid Interaction techniques, we evaluated the stimulus experienced by the six cristae ampullaris under different rotational conditions mimicking the Head Impulse Test. The results show that the maximum stimulation of the crista ampullaris occurs for directions of rotation that are more aligned with the orientation of the cupulae (average deviation from alignment of 4.7°, 9.8°, and 19.4° for the horizontal, posterior, and superior maxima, respectively) than with the planes of the semicircular canals (average deviation from alignment of 32.4°, 70.5°, and 67.8° for the horizontal, posterior, and superior maxima, respectively). A plausible explanation is that when rotations are applied with respect to the center of the head, the inertial forces acting directly over the cupula become dominant over the endolymphatic fluid forces generated in the semicircular canals. Our results indicate that it is necessary to consider cupulae orientation to ensure optimal conditions for testing the vestibular function.

Within subject, double blind, examination of opioid sensitivity in participant-reported, observed, physiologic, and analgesic outcomes

Background

Inter-individual differences in opioid sensitivity may underlie different opioid risk profiles but have often been researched in persons who have current or past opioid use disorder or physical dependence. This study examined how opioid sensitivity manifests across various assessments of opioid effects in a primarily opioid-naïve population.

Procedures

Data were harmonized from two within-subject, double-blind trials wherein healthy participants (N = 123) received placebo and 4 mg oral hydromorphone. Demographics, self-report ratings, observer ratings, physiological, and cold pressor measures were collected. Participants were categorized as being responsive or nonresponsive to the opioid dose tested and compared using mixed-models, Pearson product correlations, and paired t-tests.

Findings

Participants were 49.6% female, mean 33.0 (SD=9.3) years old, and 44.7% Black/African American and 41.5% White, with 89.4% reporting no prior exposure to opioids. Within-subject sensitivity to opioids varied depending on the measure. One in five participants did not respond subjectively to the 4 mg hydromorphone dose based on their "Drug Effects" rating. Persons who were responsive showed more evidence of drug-dependent effects than did persons who were not responsive on ratings of Bad Effects (p= .03), feeling High (p= .01), Nausea (p= .03), pupil diameter (p< 0.01), and on the circular lights task (p< 0.001).

Conclusions

This study provides initial evidence that the experience of opioids may be domain specific. Data suggest potentially clinically meaningful differences exist regarding opioid response patterns, evident following one dose among opioid inexperienced individuals.

The gut microbiota composition has no predictive value for the endotoxin-induced immune response or development of endotoxin tolerance in humans invivo

BACKGROUND

It is largely unknown whether the gut microbiome regulates immune responses in humans. We determined relationships between the microbiota composition and immunological phenotypes in 108 healthy volunteers, using 16S sequencing, an ex vivo monocyte challenge model, and an in vivo challenge model of systemic inflammation induced by lipopolysaccharide (LPS).

RESULTS

Significant associations were observed between the microbiota composition and ex vivo monocytic cytokine responses induced by several stimuli, most notably IL-10 production induced by Pam3Cys, Pseudomonas aeruginosa and Candida albicans, although the explained variance was rather low (0.3-4.8%). Furthermore, a number of pairwise correlations between Blautia, Bacteroides and Prevotella genera and cytokine production induced by these stimuli were identified. LPS administration induced a profound transient in vivo inflammatory response. A second LPS challenge one week after the first resulted in a severely blunted response, reflecting endotoxin tolerance. However, no significant relationships between microbiota composition and in vivo parameters of inflammation or tolerance were found (explained variance ranging from 0.4 to 1.5%, ns).

CONCLUSIONS

The gut microbiota composition explains a limited degree of variance in ex vivo monocytic cytokine responses to several pathogenic stimuli, but no relationships with the LPS-induced in vivo immune response or tolerance was observed.

Development and validation of AI-based triage support algorithms for prevention of intradialytic hypotension

BACKGROUND

Intradialytic hypotension remains one of the most recurrent complications of dialysis sessions. Inadequate management can lead to adverse outcomes, highlighting the need to develop personalized approaches for the prevention of intradialytic hypotension. Here, we sought to develop and validate two AI-based risk models predicting the occurrence of symptomatic intradialytic hypotension at different time points.

METHODS

The models were built using the XGBoost algorithm and they predict the occurrence of intradialytic hypotension in the next dialysis session and in the next month. The initial dataset, obtained from routinely collected data in the EuCliD® Database, was split to perform model derivation, training and validation. Model performance was evaluated by concordance statistic and calibration charts; the importance of features was assessed with the Shapley Additive Explanation (SHAP) methodology.

RESULTS

The final dataset included 1,249,813 dialysis sessions, and the incidence rate of intradialytic hypotension was 10.07% (95% CI 10.02-10.13). Our models retained good discrimination (AUC around 0.8) and a suitable calibration yielding to the selection of three classification thresholds identifying four distinct risk groups. Variables providing the most significant impact on risk estimates were blood pressure dynamics and other metrics mirroring hemodynamic instability over time.

CONCLUSIONS

Recurrent symptomatic intradialytic hypotension could be reliably and accurately predicted using routinely collected data during dialysis treatment and standard clinical care. Clinical application of these prediction models would allow for personalized risk-based interventions for preventing and managing intradialytic hypotension.

Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses

Purpose of Review

Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology.

Recent Findings

Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology.

Summary

Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.

Clinical Relevance of Patient-Derived Organoid of Surgically Resected Lung Cancer as an In Vitro Model for Biomarker and Drug Testing

Introduction

Lung tumor organoids (LTOs) have attracted attention as in vitro preclinical models; however, their clinical and experimental applications have not been fully established.

Methods

We attempted to establish LTOs from resected specimens of patients with lung cancer who underwent lung resection. Clinicopathologic characteristics related to the establishment of LTOs were evaluated. Histologic assessment and genetic analysis were conducted for both LTOs and their parental tumors. Organoid-derived xenografts were generated in immunocompetent mice. Drug sensitivity was assessed using cell proliferation assays.

Results

We established 53 LTOs from 79 lung cancer samples, including 10 long-term culture models. The establishment rate was significantly lower in squamous cell carcinomas than in other histologic types (48% versus 75%, p = 0.034). Histologic similarities were confirmed among LTOs, the parental tumors, and organoid-derived xenografts. Seven mutations, including two EGFR L858R and one EGFR exon 20 H773delinsYNPY mutations, were detected in both LTO and parental tumors; the other four mutations were detected in either LTO or parental tumors. The extensive culture ability of LTO (passaged >10 times) correlated with poor patient prognosis. LTO9 cells harboring EGFR H773delinsYNPY were sensitive to osimertinib. The parental patient, who had new metastatic lesions, was treated with osimertinib and exhibited a remarkable response.

Conclusions

The establishment and growth rates of LTOs were associated with the histologic subtype and tumor size. LTOs derived from resected specimens have become preclinical models that can be used to predict drug responses and accelerate the development of treatment strategies for patients with rare mutations.

Development of machine learning models to predict lymph node metastases in major salivary gland cancers

INTRODUCTION

Indications for elective treatment of the neck in patients with major salivary gland cancers are still debated. Our purpose was to develop a machine learning (ML) model able to generate a predictive algorithm to identify lymph node metastases (LNM) in patients with major salivary gland cancer (SGC).

METHODS

A Retrospective study was performed with data obtained from the Surveillance, Epidemiology, and End Results (SEER) program. Patients diagnosed with a major SGC between 1988 and 2019 were included. Two 2-class supervised ML decision models (random forest, RF; extreme gradient boosting, XGB) were used to predict the presence of LNM, implementing thirteen demographics and clinical variables collected from the SEER database. A permutation feature importance (PFI) score was computed using the testing dataset to identify the most important variables used in model prediction.

RESULTS

A total of 10 350 patients (males: 52%; mean age: 59.9 ± 17.2 years) were included in the study. The RF and the XGB prediction models showed an overall accuracy of 0.68. Both models showed a high specificity (RF: 0.90; XGB: 0.83) and low sensitivity (RF: 0.27; XGB: 0.38) in identifying LNM. According, a high negative predictive value (RF: 0.70; XGB: 0.72) and a low positive predictive value (RF: 0.58; XGB: 0.56) were measured. T classification and tumor size were the most important features in the construction of the prediction algorithms.

CONCLUSIONS

Classification performance of the ML algorithms showed high specificity and negative predictive value that allow to preoperatively identify patients with a lower risk of LNM.

LAY SUMMARY

Based on data from the Surveillance, Epidemiology, and End Results (SEER) program, our study showed that machine learning algorithms owns a high specificity and negative predictive value, allowing to preoperatively identify patients with a lower risk of lymph node metastasis.

LEVEL OF EVIDENCE: 3

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models

PURPOSE

The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas.

METHODS

In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application.

RESULTS

From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively.

CONCLUSION

With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.