Biomax Informatics

Background:Hematopoietic stem cell transplantation is a potentially curative therapy for various hematologic conditions. Though physical exercise has been shown to positively affect physical and psychosocial function in patients undergoing hematopoietic stem cell transplantation, they often face multiple barriers to accessing comprehensive exercise-based rehabilitation programs. Telemedicine has emerged as a promising strategy to overcome these challenges by facilitating remote exercise interventions and potentially improving patient outcomes. Objective:This scoping review aimed to map the current landscape of telemedicine-supported exercise interventions in hematopoietic stem cell transplantation, assessing their clinical and technical characteristics, feasibility, effectiveness, and gaps in the literature. Methods:Following the Arksey and O'Malley framework and subsequent enhancements, we systematically searched MEDLINE, SCOPUS, Web of Science, and Embase from inception to July 31, 2024. We included experimental studies examining telemedicine-supported exercise interventions for hematopoietic stem cell transplantation recipients and extracted data on study design, participant characteristics, technology used, exercise modalities, and outcomes measured. Findings were synthesized narratively. Results:Our search resulted in 1116 papers, with 10 included in the final review. Most studies (90%) focused on feasibility and employed prospective designs, including quasi-experimental (60%) and randomized controlled trials (40%). Sample sizes were generally small, with 80% of studies enrolling fewer than 50 participants. Aerobic exercise was the most frequently implemented modality (80%), with reported improvements in VO2peak, 6-minute walk test performance, and quality of life. Functional outcomes were assessed in 90% of studies, while 50% evaluated quality-of-life metrics. Interventions were largely feasible, safe, and acceptable, overcoming barriers to care for hematopoietic stem cell transplantation patients. Conclusion:Telemedicine-supported exercise interventions are feasible and show promise in improving physical function and quality of life for hematopoietic stem cell transplantation patients.

Background: Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions. Methods: We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis. Results: Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians. Limitations: Missing data limited the power to identify biological predictors of treatment outcome from certain modalities. Conclusions: So far, the inclusion of available biological measures in addition to psychometric and clinical in-formation did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.

Extensive investigation and characterisation of nanoparticle-protein conjugates are imperative to assess potential nanoparticle-induced hazards for humans and the environment, predict adverse biological effects, and identify suitable nanoparticles for medical applications. Investigating the formation of the nanoparticle protein corona solely based on experimental analysis is currently very time-consuming and cost-intensive. Therefore, development of prediction tools based on in silico modelling is much-needed in order to provide viable alternative approaches and accelerate nanomaterial risk assessment at the early development stage. This work aimed to validate currently emerging in silico protein corona modelling tools with experimental results and to reveal the models’ potentials and limitations thereby contributing to the improvement of their predictive power. Comprehensive data and metadata sets of the obtained in vitro and in silico results were collected and annotated in the NanoCommons Knowledge Base to facilitate data Findability, Accessibility, Interoperability, and Reusability (FAIRness) in nanosafety assessment. In silico protein corona predictions (in silico modelling with UnitedAtom) and in vitro investigation of corona formation (binding and selectivity studies with eight different proteins, mixtures thereof, and an allergenic effector cell degranulation assay) on differently coated SiO2 nanoparticles were aligned and the results, in the first run, revealed substantial deviations. Therefore, we attempted to identify the potential and limitations in the modelling and provided recommendations to improve the model. Similar iteractive approaches, as described here, based on the verification versus rebuttal of data from in silico procedures by in vitro analyses, complemented by comprehensive data and metadata collection according to the FAIR principles, are expected to help optimise future prediction certainties and improve in silico modelling.

Background The standard treatment for patients with advanced HER2-positive gastric cancer is a combination of the antibody trastuzumab and platin-fluoropyrimidine chemotherapy. As some patients do not respond to trastuzumab therapy or develop resistance during treatment, the search for alternative treatment options and biomarkers to predict therapy response is the focus of research. We compared the efficacy of trastuzumab and other HER-targeting drugs such as cetuximab and afatinib. We also hypothesized that treatment-dependent regulation of a gene indicates its importance in response and that it can therefore be used as a biomarker for patient stratification. Methods A selection of gastric cancer cell lines (Hs746T, MKN1, MKN7 and NCI-N87) was treated with EGF, cetuximab, trastuzumab or afatinib for a period of 4 or 24 h. The effects of treatment on gene expression were measured by RNA sequencing and the resulting biomarker candidates were tested in an available cohort of gastric cancer patients from the VARIANZ trial or functionally analyzed in vitro. Results After treatment of the cell lines with afatinib, the highest number of regulated genes was observed, followed by cetuximab and trastuzumab. Although trastuzumab showed only relatively small effects on gene expression, BMF , HAS2 and SHB could be identified as candidate biomarkers for response to trastuzumab. Subsequent studies confirmed HAS2 and SHB as potential predictive markers for response to trastuzumab therapy in clinical samples from the VARIANZ trial. AREG , EREG and HBEGF were identified as candidate biomarkers for treatment with afatinib and cetuximab. Functional analysis confirmed that HBEGF is a resistance factor for cetuximab. Conclusion By confirming HAS2 , SHB and HBEGF as biomarkers for anti-HER therapies, we provide evidence that the regulation of gene expression after treatment can be used for biomarker discovery. Trial registration. Clinical specimens of the VARIANZ study (NCT02305043) were used to test biomarker candidates.

The prospective multicenter VARIANZ study aimed to identify resistance biomarkers for HER2-targeted treatment in advanced gastric and esophago-gastric junction cancer (GC, EGJC). HER2 test deviations were found in 90 (22.3