BACKGROUND: This study aimed to analyze the rapidly evolving ecosystem of digital health applications (Digitale Gesundheitsanwendung; DiGAs) in Germany, spurred by the 2019 Digital Healthcare Act. With over 73 million people in Germany now having access to DiGAs, these prescribable digital health apps and web-based applications represent a substantial stride in health care modernization, supporting both patients and health care providers with digital solutions for disease management and care improvement.
OBJECTIVE: Through a data-driven approach, this research aimed to unpack the complexities of DiGA market dynamics, economic factors, and clinical evidence, offering insights into their impact over the past years.
METHODS: The analysis draws from a range of public data sources, including the DiGA directory, statutory health insurance reports, app store feedback, and clinical study results.
RESULTS: As of July 1, 2024, there are 56 DiGAs listed by the Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte), divided into 35 permanently and 21 preliminarily listed applications. Our findings reveal that a majority of DiGAs extend beyond the intended 1-year period to achieve permanent listing, reflecting the extensive effort required to demonstrate clinical efficacy. Economic analysis uncovered a dynamic pricing landscape, with initial prices ranging from approximately €200 to €700 (€1=US $1.07), averaging at a median of €514 for a 3-month DiGA prescription. Following negotiations or arbitration board decisions, prices typically see a 50% reduction, settling at a median of €221. Prescription data offer valuable insights into DiGA acceptance, with total prescriptions jumping from around 41,000 in the first period to 209,000 in the latest reporting period. The analysis of the top 15 DiGAs, representing 82% of the total prescriptions, shows that these best-performing apps receive from a minimum of 8 to a maximum of 77 daily prescriptions, with native apps and early market entrants achieving higher rates. Clinical evidence from all 35 permanently listed DiGAs indicates a uniform preference for randomized controlled trials to validate primary end points, with no noteworthy use of alternative study designs encouraged in the Digital Healthcare Act and related regulations. Moreover, all evaluated DiGAs focused on medical benefits, with health status improvement as a key end point, suggesting an underuse of patient-relevant structural and procedural improvement in demonstrating health care impact.
CONCLUSIONS: This study highlights the growth and challenges within the DiGA sector, suggesting areas for future research, such as the exploration of new study designs and the potential impact of patient-relevant structural and procedural improvements. For DiGA manufacturers, the strategic advantage of early market entry is emphasized. Overall, this paper underscores the evolving landscape of digital health, advocating for a nuanced understanding of digital health technology integration in Germany and beyond.

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Nonhuman primates (NHPs) are indispensable animal models by virtue of the continuity of behavioral repertoires across primates, including humans. However, behavioral assessment at the laboratory level has so far been limited. Employing the application of three-dimensional (3D) pose estimation and the optimal integration of subsequent analytic methodologies, we demonstrate that our artificial intelligence (AI)-based approach has successfully deciphered the ethological, cognitive, and pathological traits of common marmosets from their natural behaviors. By applying multiple deep neural networks trained with large-scale datasets, we established an evaluation system that could reconstruct and estimate the 3D poses of the marmosets, a small NHP that is suitable for analyzing complex natural behaviors in laboratory setups. We further developed downstream analytic methodologies to quantify a variety of behavioral parameters beyond motion kinematics. We revealed the distinct parental roles of male and female marmosets through automated detections of food-sharing behaviors using a spatial-temporal filter on 3D poses. Employing a recurrent neural network to analyze 3D pose time series data during social interactions, we additionally discovered that marmosets adjusted their behaviors based on others\' internal state, which is not directly observable but can be inferred from the sequence of others\' actions. Moreover, a fully unsupervised approach enabled us to detect progressively appearing symptomatic behaviors over a year in a Parkinson\'s disease model. The high-throughput and versatile nature of an AI-driven approach to analyze natural behaviors will open a new avenue for neuroscience research dealing with big-data analyses of social and pathophysiological behaviors in NHPs.

In the present study, I developed and tested an R module to explore the best models within the context of multilevel modeling in research in public health. The module that I developed, explore.models, compares all possible candidate models generated from a set of candidate predictors with information criteria, Akaike information criterion (AIC), and Bayesian information criterion (BIC), with multiprocessing. For testing, I ran explore.models with datasets analyzed in three previous studies in public health, which assumed candidate models with different degrees of model complexity. These three studies examined the predictors of psychological well-being, compliance with preventive measures, and vaccine intent during the COVID-19 pandemic. After conducting model exploration with explore.models, I cross-validated the nomination results with calculated model Bayes Factors to examine whether the model exploration was performed accurately. The results suggest that explore.models using AIC and BIC can nominate best candidate models and such nomination outcomes are supported by the calculated model Bayes Factors. In particular, all the identified models are superior to the full models in terms of model Bayes Factors. Also, by employing AIC and BIC with multiprocessing, explore.models requires a shorter processing time than model Bayes Factor calculations. These results indicate that explore.models is a reliable, valid, and feasible tool to conduct data-driven model exploration with datasets collected from multiple groups in research on health psychology and education.

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this \'phenomics\' type data. Here, we present \'Cell PLasticity Analysis Tool\' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a \'behavioural ID\' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

Bacterial P450 cytochromes (BacCYPs) are versatile heme-containing proteins responsible for oxidation reactions on a wide range of substrates, contributing to the production of valuable natural products with limitless biotechnological potential. While the sequencing of microbial genomes has provided a wealth of BacCYP sequences, functional characterization lags behind, hindering our understanding of their roles. This study employs a comprehensive approach to predict BacCYP substrate specificity, bridging the gap between sequence and function. We employed an integrated approach combining sequence and functional data analysis, genomic context exploration, 3D structural modeling with molecular docking, and phylogenetic clustering. The research begins with an in-depth analysis of BacCYP sequence diversity and structural characteristics, revealing conserved motifs and recurrent residues in the active site. Phylogenetic analysis identifies distinct groups within the BacCYP family based on sequence similarity. However, our study reveals that sequence alone does not consistently predict substrate specificity, necessitating additional perspectives. The study delves into the genetic context of BacCYPs, utilizing neighboring gene information to infer potential substrates, a method proven very effective in many cases. Molecular docking is employed to assess BacCYP-substrate interactions, confirming potential substrates and providing insights into selectivity. Finally, a comprehensive strategy is proposed for predicting BacCYP substrates, involving all the evaluated approaches. The effectiveness of this strategy is demonstrated with two case studies, highlighting its potential for substrate discovery.

Waterborne viral epidemics are a major threat to public health. Increasing interest in wastewater reclamation highlights the importance of understanding the health risks associated with potential microbial hazards, particularly for reused water in direct contact with humans. This study focused on identifying viral epidemic patterns in municipal wastewater reused for recreational applications based on long-term, spatially explicit global literature data during 2000-2021, and modelled human health risks from multiple exposure pathways using a well-established quantitative microbial risk assessment methodology. Global median viral loads in municipal wastewater ranged from 7.92 × 104 to 1.4 × 106 GC L-1 in the following ascending order: human adenovirus (HAdV), norovirus (NoV) GII, enterovirus (EV), NoV GI, rotavirus (RV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Following secondary or tertiary wastewater treatment, NoV GI, NoV GII, EV, and RV showed a relatively higher and more stable log reduction value with medians all above 0.8 (84%), whereas SARS-CoV-2 and HAdV showed a relatively lower reduction, with medians ranging from 0.33 (53%) to 0.55 (72%). A subsequent disinfection process effectively enhanced viral removal to over 0.89-log (87%). The predicted event probability of virus-related gastrointestinal illness and acute febrile respiratory illnesses in reclaimed recreational water exceeded the World Health Organization recommended recreational risk benchmark (5% and 1.9%, respectively). Overall, our results provided insights on health risks associated with reusing wastewater for recreational purposes and highlighted the need for establishing a regulatory framework ensuring the safety management of reclaimed waters.

Advances in imaging, cell segmentation, and cell tracking now routinely produce microscopy datasets of a size and complexity comparable to transcriptomics or proteomics. New tools are required to process this \'phenomics\' type data. Cell PLasticity Analysis TOol (cellPLATO) is a Python-based analysis software designed for measurement and classification of diverse cell behaviours based on clustering of parameters of cell morphology and motility. cellPLATO is used after segmentation and tracking of cells from live cell microscopy data. The tool extracts morphological and motility metrics from each cell per timepoint, before being using them to segregate cells into behavioural subtypes with dimensionality reduction. Resultant cell tracks have a \'behavioural ID\' for each cell per timepoint corresponding to their changing behaviour over time in a sequence. Similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Trajectories and underlying behaviours generate a phenotypic fingerprint for each experimental condition, and representative cells are mathematically identified and graphically displayed for human understanding of each subtype. Here, we use cellPLATO to investigate the role of IL-15 in modulating NK cell migration on ICAM-1 or VCAM-1. We find 8 behavioural subsets of NK cells based on their shape and migration dynamics, and 4 trajectories of behaviour. Therefore, using cellPLATO we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Intermittent bolus feeding for E. coli cultivations in minibioreactor systems (MBRs) profoundly affects the cell metabolism. Bolus feeding leads to temporal substrate surplus and transient oxygen limitation, which triggers the formation of inhibitory byproducts. Due to the high oxygen demand right after the injection of the substrate, the dissolved oxygen tension (DOT) signal exhibits a negative pulse. This contribution describes and analyzes this DOT response in E. coli minibioreactor cultivations. In addition to gaining information on culture conditions, a unique response behavior in the DOT signal was observed in the analysis. This response appeared only at a dilution ratio per biomass unit higher than a certain threshold. The analysis highlights a plausible relationship between a metabolic adaptation behavior and the newly observed DOT signal segment not reported in the literature. A hypothesis that links particular DOT segments to specific metabolic states is proposed. The quantitative analysis and mechanistic model simulations support this hypothesis and show the possibility of obtaining cell physiological and growth parameters from the DOT signal.