Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project.

BACKGROUND: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. METHODS: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. DISCUSSION: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

Climate Change, Migration, and Health Disparities at and Beyond the US-Mexico Border.

This JAMA Insights in the Climate Change and Health series discusses the increase in extreme weather events caused by climate change and how these events bring about increased migration due to effects on water availability, food access, and rates of endemic diseases.

Usage of model combination in computational toxicology.

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

J-filter: An experiment to simplify and isolate specific signals in 1 H NMR spectra of complex mixtures based on scalar coupling constants.

One-dimensional selective NMR experiments relying on a J-filter element are proposed to isolate specific signals in crowded 1 H spectral regions. The J-filter allows the edition or filtering of signals in a region of interest of the spectrum by exploiting the specific values of their 1 H-1 H coupling constants and certain parameters of protons coupled to them that appear in less congested parts of the spectrum (chemical shifts and coupling constants). The new experiments permitted the isolation of specific peaks of phytosterol components in a sample obtained from a liquid nutraceutical recommended for lowering blood cholesterol levels in regions with complete overlap in the 1 H spectrum.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Metabolomic Profile, Plasmatic Levels of Losartan and EXP3174, Blood Pressure Control in Hypertensive Patients and Their Correlation with COVID-19.

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and is related to serious health complications. It has been pointed out as a major risk factor for COVID-19. This study aimed to determine the impact of COVID-19 on the metabolomic profile, the correlation with the plasmatic levels of losartan and its active metabolite (EXP3174), biochemical markers, and blood pressure (BP) control in hypertensive patients. 1H NMR metabolomic profiles of hypertensive and normotensive patients with and without previous COVID-19 diagnosis were identified. Plasmatic levels of LOS and EXP3174 were correlated with BP, biochemical markers, and the metabolomic fingerprint of the groups. Biomarkers linked to important aspects of SAH and COVID-19 were identified, such as glucose, glutamine, arginine, creatinine, alanine, choline, erythritol, homogentisate, 0-tyrosine, and 2-hydroxybutyrate. Those metabolites are indicative of metabolic alterations, kidney damage, pulmonary dysfunction, and persistent inflammation, which can be found in both diseases. Some hypertensive patients did not reach the therapeutic levels of LOS and EXP3174, while the BP control was also limited among the normotensive patients with previous COVID-19 diagnoses. Metabolomics proved to be an important tool for assessing the effectiveness of losartan pharmacotherapy and the damage caused by SAH and COVID-19 in hypertensive patients.

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells.

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

Metabolomic Profile and Its Correlation with the Plasmatic Levels of Losartan, EXP3174 and Blood Pressure Control in Hypertensive and Chronic Kidney Disease Patients.

Systemic arterial hypertension (SAH) is one of the most prevalent chronic diseases worldwide and, when dysregulated, may cause serious complications. Losartan (LOS) blocks relevant physiological aspects of hypertension, acting mainly on the reduction of peripheral vascular resistance. Complications of hypertension include nephropathy, in which diagnosis is based on the observation of functional or structural renal dysfunction. Therefore, blood pressure control is essential to attenuate the progression of chronic kidney disease (CKD). In this study, 1H NMR metabolomics were used to differentiate hypertensive and chronic renal patients. Plasmatic levels of LOS and EXP3174, obtained by liquid chromatography coupled with mass-mass spectroscopy, were correlated with blood pressure control, biochemical markers and the metabolomic fingerprint of the groups. Some biomarkers have been correlated with key aspects of hypertension and CKD progression. For instance, higher levels of trigonelline, urea and fumaric acid were found as characteristic markers of kidney failure. In the hypertensive group, the urea levels found could indicate the onset of kidney damage when associated with uncontrolled blood pressure. In this sense, the results point to a new approach to identify CKD in early stages and may contribute to improving pharmacotherapy and reducing morbidity and mortality associated with hypertension and CKD.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

Zein Nanoparticles Containing Arginine-Based Surfactants: Physicochemical Characterization and Effect on the Biological Properties.

Cationic surfactants carry antimicrobial activity, based on their interaction and disruption of cell membranes. Nonetheless, their intrinsic toxicity limits their applicability. To overcome this issue, a feasible strategy consists of using solid nanoparticles to improve their delivery. The zein nanoparticles were loaded with four cationic arginine-based surfactants: one single chain Nα-lauroyl-arginine (LAM) and three Gemini surfactants Nα Nω-Bis (Nα-lauroyl-arginine) α, ω-diamide) (C3(LA)2, C6(LA)2 and C9(LA)2). Blank and loaded zein nanoparticles were characterized in terms of size, polydispersity and zeta potential. Furthermore, the antimicrobial activity against bacteria and yeasts and the hemolytic activity were investigated and compared to the surfactants in a solution. Nanoparticles were found to be monodisperse, presenting a size of between 180-341 nm, a pdI of <0.2 and a positive zeta potential of between +13 and +53 mV, remaining stable over 365 days. The nanoencapsulation maintained the antimicrobial activity as unaltered, while the extensive hemolytic activity found for the surfactants in a solution was reduced drastically. Nuclear Magnetic Ressonance (NMR), molecular docking and monolayer findings indicated that zein entraps the surfactants, interfering in the surfactant-membrane interactions. Accordingly, the nanoepcasulation of arginine surfactants improved their selectivity, while the cationic charges were free to attack and destroy bacteria and fungi; the aliphatic chains were not available to disrupt the cellular membranes.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry.

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.

BACKGROUND AND OBJECTIVE: In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of precomputed simulation results, allowing instantaneous computation of biomarkers such as action potential duration at 90% of the repolarisation (APD90). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions. In this work, we aim to optimise the generation of these matrices so that they can be obtained with less effort and for a broader range of input values. METHODS: Machine learning methods were applied, building models trained with only a small fraction of the originally simulated results. The predictive performances of the models were assessed by comparing their predicted values with the actual simulation results, using percentual mean absolute error and mean relative error, as well as the percentage of data with a relative error below 5%. RESULTS: Our method obtained highly accurate estimations of the original values, leading to a nearly one hundred-fold decrease in computation time. This method also allows precomputing more complex matrices, describing the effect of more ion channels on the APD90. The best results were obtained by applying Support Vector Machine models, which yielded errors below 1% in most cases. This approach was further validated by predicting the APD90 of a set of 12 CiPA compounds and exporting the optimal settings for predicting APD90 using a different set of ion channels, always with satisfactory results. CONCLUSIONS: The proposed method effectively reduces the computational effort required to generate matrices of precomputed electrophysiological simulation values. The same approach can be applied in other fields where computationally costly simulations are applied repeatedly using slightly different input values.

Zein Nanoparticles Containing Arginine-Phenylalanine-Based Surfactants: Stability, Antimicrobial and Hemolytic Activity.

Although cationic surfactants have a remarkable antimicrobial activity, they present an intrinsic toxicity that discourages their usage. In this work novel zein nanoparticles loaded with arginine-phenylalanine-based surfactants are presented. The nanoparticles were loaded with two single polar head (LAM and PNHC12) and two with double amino acid polar head surfactants, arginine-phenylalanine (C12PAM, PANHC12). The formulations were characterized and their stability checked up to 365 days. Furthermore, the antimicrobial and hemolytic activities were investigated. Finally, NMR and molecular docking studies were carried out to elucidate the possible interaction mechanisms of surfactant-zein. The nanoparticles were obtained with satisfactory size, zeta potential and dispersibility. The surfactants containing arginine-phenylalanine residues were found to be more stable. The nanoencapsulation maintained the antimicrobial activities unaltered in comparison to the surfactants' solutions. These results are in agreement with the NMR and docking findings, suggesting that zein interacts with the surfactants by the aromatic rings of phenylalanine. As a result, the cationic charges and part of the aliphatic chains are freely available to attack the bacteria and fungi, while not available to disrupt the cellular membranes. This approach opens new possibilities for using cationic surfactants and benefits from their extraordinary antimicrobial responses for several applications.

Explicit meshfree u - p w solution of the dynamic Biot formulation at large strain.

In this paper, an efficient and robust methodology to simulate saturated soils subjected to low-medium frequency dynamic loadings under large deformation regime is presented. The coupling between solid and fluid phases is solved through the dynamic reduced formulation u - p w (solid displacement - pore water pressure) of the Biot's equations. The additional novelty lies in the employment of an explicit two-steps Newmark predictor-corrector time integration scheme that enables accurate solutions of related geomechanical problems at large strain without the usually high computational cost associated with the implicit counterparts. Shape functions based on the elegant Local Maximum Entropy approach, through the Optimal Transportation Meshfree framework, are considered to solve numerically different dynamic problems in fluid saturated porous media.

Silk fibroin/cholinium gallate-based architectures as therapeutic tools.

The combination of natural resources with biologically active biocompatible ionic liquids (Bio-IL) is presented as a combinatorial approach for developing tools to manage inflammatory diseases. Innovative biomedical solutions were constructed combining silk fibroin (SF) and Ch[Gallate], a Bio-IL with antioxidant and anti-inflammatory features, as freeze-dried 3D-based sponges. An evaluation of the effect of the Ch[Gallate] concentration (≤3% w/v) on the SF/Ch[Gallate] sponges was studied. Structural changes observed on the sponges revealed that the Ch[Gallate] presence positively affected the ß-sheet formation while not influencing the silk native structure, which was suggested by the FTIR and solid-state NMR results, respectively. Also, it was possible to modulate their mechanical properties, antioxidant activity and stability/degradation in an aqueous environment, by changing the Ch[Gallate] concentration. The architectures showed high water uptake ability and a weight loss that follows the controlled Ch[Gallate] release rate studied for 7 days. Furthermore, the sponges supported human adipose stem cells growth and proliferation, up to 7 days. TNF-α, IL-6 (pro-inflammatory) and IL-10 (anti-inflammatory) release quantification from a human monocyte cell line revealed a decrease in the pro-inflammatory cytokines concentrations in samples containing Ch[Gallate]. These outcomes encourage the use of the developed architectures as tissue engineering solutions, potentially targeting inflammation processes. STATEMENT OF SIGNIFICANCE: Combining natural resources with active biocompatible ionic liquids (Bio-IL) is herein presented as a combinatorial approach for the development of tools to manage inflammatory diseases. We propose using silk fibroin (SF), a natural protein, with cholinium gallate, a Bio-IL, with antioxidant and anti-inflammatory properties, to construct 3D-porous sponges through a sustainable methodology. The morphological features, swelling, and stability of the architectures were controlled by Bio-IL content in the matrices. The sponges were able to support human adipose stem cells growth and proliferation, and their therapeutic effect was proved by the blockage of TNF-α from activated and differentiated THP-1 monocytes. We believe that these bio-friendly and bioactive SF/Bio-IL-based sponges are effective for targeting pathologies with associated inflammatory processes.

Design, optimization, and in vitro characterization of idebenone-loaded PLGA microspheres for LHON treatment.

Biodegradable poly(lactic-co-glycolic acid) microspheres (PLGA MSs) are attractive delivery systems for site-specific maintained release of therapeutic active substances into the intravitreal chamber. The design, development, and characterization of idebenone-loaded PLGA microspheres by means of an oil-in-water emulsion/solvent evaporation method enabled the obtention of appropriate production yield, encapsulation efficiency and loading values. MSs revealed spherical shape, with a size range of 10-25 µm and a smooth and non-porous surface. Fourier-transform infrared spectroscopy (FTIR) spectra demonstrated no chemical interactions between idebenone and polymers. Solid-state nuclear magnetic resonance (NMR), X-ray diffractometry, differential scanning calorimetry (DSC) and thermogravimetry (TGA) analyses indicated that microencapsulation led to drug amorphization. In vitro release profiles were fitted to a biexponential kinetic profile. Idebenone-loaded PLGA MSs showed no cytotoxic effects in an organotypic tissue model. Results suggest that PLGA MSs could be an alternative intraocular system for long-term idebenone administration, showing potential therapeutic advantages as a new therapeutic approach to the Leber's Hereditary Optic Neuropathy (LHON) treatment by intravitreal administration.

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry.

The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.