Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes.

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

Quantitative Structure-Permittivity Relationship Study of a Series of Polymers.

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems.

The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.

Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission.

Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.

Identification of serologically active Helicobacter pylori antigens related to alterations in serum pepsinogen levels / Identificación de antígenos de Helicobacter pylori serológicamente activos relacionados con alteraciones en los niveles de pepsinógenos en suero

Resumen El adenocarcinoma gástrico se asocia con la infección por Helicobacter pylori. La transición a un proceso de carcinogénesis está precedida por atrofia glandular, y los niveles séricos de pepsinógeno I y II (PGI y PGII) se correlacionan con este tipo de lesiones gástricas. El objetivo del trabajo fue estudiar posibles asociaciones de los niveles de pepsinógenos (PG) en suero en relación con la frecuencia de actividad serológica hacia antígenos de H. pylori. Se utilizaron muestras de suero de pacientes con patología gástrica asociada a H. pylori (n = 26) y de individuos asintomáticos como controles (n = 37). Los antígenos seroactivos se identificaron mediante inmunoblot utilizando un extracto proteico de H. pylori. Los títulos de anticuerpos anti-H. pylori y la concentración de PG en suero se determinaron por ELISA. De los 31 antígenos seroactivos identificados, 9 presentaron una frecuencia diferencial entre ambos grupos (116,7; 68,8; 61,9; 54,9; 45,6; 38,3; 36,5; 33,8 y 30,1 kDa) y solo 3 se relacionaron con niveles alterados de PG en suero. En el grupo control, la seropositividad del antígeno de 33,8 kDa se relacionó con un aumento de PGII, mientras que el antígeno de 68,8kDa se relacionó con valores normales de PG (PGII disminuido y PGI/PGII elevado), sugiriendo que la seropositividad a este antígeno podría ser un factor protector frente a patologías gástricas. La seropositividad del antígeno de 54,9 kDa se relacionó con valores alterados de PG indicadores de inflamación y atrofia gástrica (aumento de PGII y disminución de PGI/PGII). La identificación de alteraciones séricas en los niveles de pepsinógeno relacionadas con la seropositividad a los antígenos de 33,8; 54,9 y 68,8 kDa de H. pylori sienta un precedente para futuros estudios como posibles biomarcadores serológicos pronósticos.

Buccopharyngeal route administered high polyphenolic olive oil and COVID-19: A pilot clinical trial.

INTRODUCTION: Waning immunity after vaccination justifies the need for additional effective COVID-19 treatments. Immunomodulation of local immune response at the oropharyngeal mucosa could hypothetically activate mucosal immunity, which can prevent SARS-CoV-2 main immune evasion mechanisms in early stages of the disease and send an effective warning to other components of immune system. Olive polyphenols are biologically active compounds with immunomodulatory activity. There are previous studies based on immunomodulation with olive polyphenols and respiratory infections using an enteral route, which point to potential effects on time to resolution of symptoms. The investigators sought to determine whether participants following immunomodulation with tiny quantities of high polyphenolic olive oil administered through an oromucosal route could have a better outcome in COVID-19. SUMMARY: This pilot clinical trial investigated the effect of buccopharyngeal administered high polyphenolic olive oil on COVID-19 incidence, duration, and severity. IMPORTANCE: Waning immunity after vaccination justifies the need of further research for additional effective treatments for COVID-19. OBJECTIVE: Immunomodulation of local immune response at the buccopharyngeal mucosa could hypothetically activate mucosal immunity, which would in turn difficult SARS-CoV-2 immune evasion mechanisms in early stages of the disease and send an effective warning to other components of immune system. Olive polyphenols are biologically active compounds with immunomodulatory activity. There are previous studies based on immunomodulation with olive polyphenols and respiratory infections, using an enteral route, which suggest potential shortening of time to resolution of symptoms. The investigators sought to determine whether participants following immunomodulation with tiny quantities of high polyphenolic olive oil administered through an oromucosal route could have a better outcome in COVID-19. DESIGN, SETTING, AND PARTICIPANTS: Double blind, randomized pilot clinical trial conducted at a single site, Talavera de la Reina, Spain. Potential study participants were identified by simple random sampling from the epidemiological database of contact patients recently diagnosed of COVID-19 during the study period. A total of 88 adult participants were enrolled and 84 completed the 3-month study, conducted between July 1, 2021 and August 31, 2022. INTERVENTION: Participants were randomized to receive oromucosal administered high polyphenolic olive oil, 2 mL twice a day for 3 months or no treatment. MAIN OUTCOME AND MEASURES: Primary outcomes were incidence, duration, and severity of COVID-19 after intervention. RESULTS: There were no differences in incidence between both groups but there were significant differences in duration, the median time to resolution of symptoms was 3 days in the high polyphenolic olive oil group compared with 7 days in the no-treatment group. Although time to resolution is directly related to severity, this study did not find any differences in severity. CONCLUSION AND RELEVANCE: Among full-vaccinated adults recent infected with COVID-19, a daily intake of tiny quantities of oromucosal administered high polyphenolic olive oil before infection significantly improved the time to symptom resolution. This finding strongly support the appropriateness of further deep research on the use of oromucosal administered high polyphenolic olive oil as an effective immune strategy against COVID-19.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands.

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logßML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logßML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Identification of serologically active Helicobacter pylori antigens related to alterations in serum pepsinogen levels.

Gastric adenocarcinoma is associated with Helicobacter pylori infection. The transition to a carcinogenic process is preceded by glandular atrophy and serum levels of pepsinogen I and II (PGI and PGII) correlate with this type of gastric lesions. Possible associations of serum PG levels in relation to the frequency of serological activity against H. pylori antigens were studied. Serum samples from patients with gastric pathology associated with H. pylori (n=26) and asymptomatic individuals as controls (n=37) were used. Seroactive antigens were identified by immunoblot using a protein extract of H. pylori. The antibody titers anti-H. pylori and the concentration of PGs in serum was determined by ELISA. Thirty-one seroactive antigens were identified, nine of which exhibited a differential frequency between both groups (116.7, 68.8, 61.9, 54.9, 45.6, 38.3, 36.5, 33.8 and 30.1kDa) and only 3 were related to altered levels of PGs in serum. In the control group, the seropositivity of the 33.8kDa antigen was related to an increase in PGII, while the 68.8kDa antigen was related to normal PG values (decreased PGII and elevated PGI/PGII levels) indicating that seropositivity to this antigen could be a protective factor to gastric pathology. The seropositivity of the 54.9kDa antigen was related to altered values of PGs indicative of inflammation and gastric atrophy (increased in PGII and decreased in PGI/PGII). The identification of serum alterations in pepsinogen levels related to seropositivity to H. pylori 33.8, 54.9 and 68.8kDa antigens sets a precedent for further study as possible prognostic serological biomarkers.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and ß-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.

Climate change maladaptation for health: Agricultural practice against shifting seasonal rainfall affects snakebite risk for farmers in the tropics.

Snakebite affects more than 1.8 million people annually. Factors explaining snakebite variability include farmers' behaviors, snake ecology and climate. One unstudied issue is how farmers' adaptation to novel climates affect their health. Here we examined potential impacts of adaptation on snakebite using individual-based simulations, focusing on strategies meant to counteract major crop yield decline because of changing rainfall in Sri Lanka. For rubber cropping, adaptation led to a 33% increase in snakebite incidence per farmer work hour because of work during risky months, but a 17% decrease in total annual snakebites because of decreased labor in plantations overall. Rice farming adaptation decreased snakebites by 16%, because of shifting labor towards safer months, whereas tea adaptation led to a general increase. These results indicate that adaptation could have both a positive and negative effect, potentially intensified by ENSO. Our research highlights the need for assessing adaptation strategies for potential health maladaptations.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach.

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R2 = 0.88), validation set (R2 = 0.95), and true external test set (R2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data.

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R2 values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes.

A Fuzzy System Classification Approach for QSAR Modeling of α- Amylase and α-Glucosidase Inhibitors.

INTRODUCTION: This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of druglike compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase. METHODS: The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Fuzzyrules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with those previously reported, was also included. RESULTS: The Holm's test comparison showed significant differences (p-value<0.05) between FURIA-C, Linear Discriminating Analysis (LDA), and Bayesian Networks, the former beating the two latter according to the relative ranking score of the Holm's test. CONCLUSION: From these results, the FURIA-C algorithm could be used as a cutting-edge technique to predict (classify or screen) the α-amylase and α-glucosidase inhibitory activity of new compounds and hence speed up the discovery of new potent multi-target antidiabetic agents.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

A mechanistic model of snakebite as a zoonosis: Envenoming incidence is driven by snake ecology, socioeconomics and its impacts on snakes.

Snakebite is the only WHO-listed, not infectious neglected tropical disease (NTD), although its eco-epidemiology is similar to that of zoonotic infections: envenoming occurs after a vertebrate host contacts a human. Accordingly, snakebite risk represents the interaction between snake and human factors, but their quantification has been limited by data availability. Models of infectious disease transmission are instrumental for the mitigation of NTDs and zoonoses. Here, we represented snake-human interactions with disease transmission models to approximate geospatial estimates of snakebite incidence in Sri Lanka, a global hotspot. Snakebites and envenomings are described by the product of snake and human abundance, mirroring directly transmitted zoonoses. We found that human-snake contact rates vary according to land cover (surrogate of occupation and socioeconomic status), the impacts of humans and climate on snake abundance, and by snake species. Our findings show that modelling snakebite as zoonosis provides a mechanistic eco-epidemiological basis to understand snakebites, and the possible implications of global environmental and demographic change for the burden of snakebite.

Propuesta de intervención enfermera multicomponente para la prevención de caídas en personas mayores / Proposal for a multicomponent nursing intervention for preventing falls in elderly persons

Objetivo: desarrollar y evaluar una propuesta de intervención enfermera multicomponente en Atención Primaria para la prevención de caídas en personas de 65 años o más.Metodología: estudio cuasi-experimental antes-después de grupo único. Se reclutaron 30 pacientes con alto riesgo de caídas por conveniencia. La intervención consistió en una revisión individualizada del tratamiento farmacológico y en dos sesiones grupales: una sobre identificación de riesgos y prevención de caídas, y otra sobre ejercicio físico y fortalecimiento muscular. Para la evaluación se administró un cuestionario ad hoc antes de la intervención, inmediatamente después y transcurridos cuatro meses post-intervención. Para el análisis estadístico se emplearon pruebas de contraste de hipótesis no paramétricas para muestras apareadas con un nivel de significación p< 0,05.Resultados: participaron 27 personas (90%). La mayoría era mujer (77,8%), viudo/a (51,9%), sin estudios (48,1%) y vivía en pareja (40,7%). Un 74% era polimedicado y solo en 2/4 casos se pudieron retirar benzodiacepinas. Se observó una mejora significativa en los conocimientos sobre prevención de caídas y, aunque a los cuatro meses eran inferiores que en el análisis postintervención, fueron mayores que los preintervención. También se halló un aumento significativo en el número de horas practicando ejercicio físico (p= 0,008) y una disminución destacada en el número de caídas, de 15 en el año preintervención a seis en el año post-intervención (p= 0,004).Conclusiones: esta intervención enfermera multicomponente puede contribuir a disminuir el riesgo de caídas en personas mayores a corto y medio plazo y/o complementar otras intervenciones encaminadas para dicho fin.(AU)

Objective: to develop and evaluate a proposal for a multicomponent nursing intervention in Primary Care for the prevention of falls in persons who are over 65 years old.Methodology: a quasi-experimental before-and-after single-arm study, where 30 patients with high risk of falls were recruited by convenience. The intervention consisted in an individualized review of their pharmacological treatment, and of two group sessions: one on risk detection and prevention of falls, and another one on physical exercise and muscular strengthening. An ad-hoc questionnaire was administered before the intervention, immediately after, and four months after the intervention. Non-parametric hypothesis contrast tests were used for paired samples with a p< 0.05 level of significance.Results: twenty-seven (27) persons (90%) were included; the majority were female (77.8%), widowed (51.9%), without formal education (48.1%) and living with a partner (40.7%). Of these, 74% were on multiple medication, and benzodiazepines could only be discontinued in 2/4 cases. A significant improvement was observed regarding knowledge for preventing falls, and even though this was lower at four months than in the post-intervention analysis, it was higher than pre-intervention. There was also a significant increase in the number of hours doing physical exercise (p= 0.008) and a significant reduction in the number of falls, from 15 in the pre-intervention year to six in the post-intervention year (p= 0.004).Conclusions: this multicomponent nursing intervention can contribute to reduce the risk of falls in elderly persons at short and medium term and/or complement other interventions targeted to the same aim.(AU)