Use of Self-Efficacy Scale in Mass Casualty Incidents During Drill Exercises.

INTRODUCTION: Medical First Responders (MFRs) in the emergency department SUMMA 112 are tasked with handling the initial management of Mass Casualty Incidents (MCI) and building response capabilities. Training plays a crucial role in preparing these responders for effective disaster management. Yet, evaluating the impact of such training poses challenges since true competency can only be proven amid a major event. As a substitute gauge for training effectiveness, self-efficacy has been suggested. OBJECTIVE: The purpose of this study is to employ a pre- and post-test assessment of changes in perceived self-efficacy among MFRs following an intervention focused on the initial management of MCI. It also aimed to evaluate a self-efficacy instrument for its validity and reliability in this type of training. METHOD: In this study, we used a pretest (time 1 = T1) - post-test (time 2 = T2) design to evaluate how self-efficacy changed after a training intervention with 201 MFRs in initial MCI management. ANOVA within-subjects and between subjects analyses were used. RESULTS: The findings reveal a noteworthy change in self-efficacy before and after training among the 201 participants. This suggests that the training intervention positively affected participants' perceived capabilities to handle complex situations like MCI. CONCLUSION: The results allow us to recommend a training program with theory components together with practical workshops and live, large-scale simulation exercises for the training of medical first responders in MCI, as it significantly increases their perception of the level of self-efficacy for developing competencies associated with disaster response.

Implementation of specialised attention mechanisms: ICD-10 classification of Gastrointestinal discharge summaries in English, Spanish and Swedish.

Multi-label classification according to the International Classification of Diseases (ICD) is an Extreme Multi-label Classification task aiming to categorise health records according to a set of relevant ICD codes. We implemented PlaBERT, a new multi-label text classification head with per-label attention, on top of a BERT model. The model assessment is conducted on Electronic Health Records, conveying Discharge Summaries in three languages - English, Spanish, and Swedish. The study focuses on 157 diagnostic codes from the ICD. We additionally measure the labelling noise to estimate the consistency of the gold standard. Our specialised attention mechanism computes attention weights for each input token and label pair, obtaining the specific relevance of every word concerning each ICD code. The PlaBERT model outputs the computed attention importance for each token and label, allowing for visualisation. Our best results are 40.65, 38.36, and 41.13 F1-Score points on the English, Spanish and Swedish datasets, respectively, for the 157 gastrointestinal codes. Besides, Precision is the metric that most significantly improves owing to the attention mechanism of PlaBERT, with an increase of 44.63, 40.93, and 12.92 points, respectively, for the Spanish, Swedish and English datasets.

Same sensitivity with shorter exposure: behavior as an appropriate parameter to assess metal toxicity.

The exposure of animals to toxicants may cause a depletion in the energy uptake, which compromises reproduction and growth. Although both parameters are ecologically relevant, they usually need long-term bioassays. This is a handicap for the availability of toxicological data for environmental risk assessment. Short-term bioassays conducted with environmental concentrations, and using relevant ecological parameters sensitive to short-term exposures, such as behavior, could be a good alternative. Therefore, to include this parameter in the risk assessment procedures, it is relevant the comparison of its sensitivity with that of growth and reproduction bioassays. The study aim was the assessment of differences between endpoints based on mortality, behaviour, reproduction, and growth for the toxicity of metals on aquatic animals. We used the ECOTOX database to gather data to construct chemical toxicity distribution (CTD) curves. The mean concentrations, the mean exposure time, and the ratio between the mean concentration and the exposure time were compared among endpoints. Our results showed that behavioral, growth, and reproduction bioassays presented similar sensitivity. The shortest exposure was found in behavioral and reproduction bioassays. In general, the amount of toxicant used per time was lower in growth and reproduction bioassays than in behavioral and mortality bioassays. We can conclude that, for metal toxicity, behavioral bioassays are less time-consuming than growth bioassays. As the sensitivity of behavior was similar to that of growth and reproduction, this endpoint could be a better alternative to longer bioassays.

Behavioral Variables to Assess the Toxicity of Unionized Ammonia in Aquatic Snails: Integrating Movement and Feeding Parameters.

Behavioral endpoints are important parameters to assess the effects of toxicants on aquatic animals. These endpoints are useful in ecotoxicology because several toxicants modify the animal behavior, which may cause adverse effects at higher levels of ecological organization. However, for the development of new bioassays and for including the behavior in ecotoxicological risk assessment, the comparison of sensitivity between different behavioral endpoints is necessary. Additionally, some toxicants remain in aquatic environments for a few hours or days, which may lead to animal recovery after toxicant exposure. Our study aimed to assess the effect of unionized ammonia on the movement and feeding behaviors of the aquatic gastropod Potamopyrgus antipodarum (Tateidae, Mollusca) and its recovery after exposure. Four treatments were used: a control and three nominal concentrations of unionized ammonia (0.25, 0.5 and 1 mg N-NH3/L). Each treatment was replicated eight times, with six animals in each replicate. Animals were exposed to unionized ammonia for 48 h (exposure period) and, subsequently, to control water for 144 h (post-exposure period). Two movement variables were monitored without food and five feeding behavioral variables were monitored in the presence of food. Some of the feeding behavioral variables showed higher sensitivity (LOEC = 0.25-0.5 mg N-NH3/L) than the movement behavior variables monitored without food (LOEC = 1 mg N-NH3/L). After exposure to unionized ammonia, animals showed a recovery of most behavioral endpoints. The inclusion of post-exposure period and feeding behaviors in bioassays may make studies more realistic, which is crucial for a proper ecotoxicological risk assessment.

Molecular determinants of enhanced response to somatostatin receptor ligands after debulking in large GH-producing adenomas.

OBJECTIVE: Large somatotrophic adenomas depict poor response to somatostatin receptor ligands (SRLs). Debulking has shown to enhance SRLs effect in some but not all cases and tumour volume reduction has been proposed as the main predictor of response. No biological studies have been performed so far in this matter. We aimed to identify molecular markers of response to SRLs after surgical debulking in GH-secreting adenomas. DESIGN: We performed a multicenter retrospective study. PATIENTS: 24 patients bearing large GH-producing tumours. MEASUREMENTS: Clinical data and SRLs response both before and after surgical debulking were collected, and 21 molecular biomarkers of SRLs response were studied in tumour samples by gene expression. RESULTS: From the 21 molecular markers studied, only two of them predicted enhanced SRLs response after surgery. Tumours with improved response to SRLs after surgical debulking showed lower levels of Ki-67 (MKI67, FC = 0.17 and P = .008) and higher levels of RAR-related orphan receptor C (RORC) (FC = 3.1 and P Ë‚ .001). When a cut-off of no detectable expression was used for Ki-67, the model provided a sensitivity of 100% and a specificity of 52.6% with an area under the curve of 65.8%. Using a cut-off of 2 units of relative expression of RORC, the prediction model showed 100% of sensitivity and specificity. CONCLUSIONS: High levels of RORC and low levels of Ki-67 identify improved SRLs response after surgical debulking in large somatotropic adenomas. To determine their expression would facilitate medical treatment decision-making after surgery.

Y Stent-Assisted Coiling Technique for Bifurcation Aneurysms Using Double Neuroform® Stent: a Large Restrospective Series.

BACKGROUND: Stent assisted coiling technique have shown to be an effective and safe endovascular strategy for wide neck bifurcation aneurysms in achieving greater packing, allowing the closure of the aneurysm and preserving the parent arteries, compared to simple coiling. MATERIAL AND METHODS: We conducted a retrospective analysis of 79 patients with cerebral aneurysms treated using 'Y'-configuration double Neuroform® stent-assisted coiling at our center from July 2009 to July 2022. RESULTS: Of the 79 patients, 76% (60/79) were incidental unruptured cerebral aneurysm and 24% were patients treated for aneurysm recanalization of a previous ruptured aneurysm (19/79). The most frequent locations were anterior communicating artery (AComA) 44.3% (35/79) and middle cerebral artery (MCA) 32.9% (26/79). We found a complete and almost complete aneurysm occlusion (Raymond-Roy occlusion classification (RROC) 1 and 2): in 100% (79/79) in the angiography after procedure, in 97.6% (42/43) at the first follow-up at 6-8 months and 100% (57/57) at the first 1-2 years of follow-up. No mortality related to treatment was detected. We registered 2.5% (2/79) major ipsilateral strokes, one due to acute in stent thrombosis (patient had a mRS: 0 in follow up at 90 days) and a spinal anterior artery occlusion (patient had a mRS: 3 in follow up at 90 days). CONCLUSION: The 'Y' stent-assisted coiling technique with double Neuroform® is a safe and effective technique for the treatment of wide-neck bifurcation aneurysms, with high rates of complete occlusion, preserving the permeability of the afferent and efferent arteries and low rate of complications.

Extended reality training for mass casualty incidents: a systematic review on effectiveness and experience of medical first responders.

INTRODUCTION: Mass casualty incidents (MCI) are unforeseeable and complex events that occur worldwide, therefore enhancing the training that medical first responders (MFRs) receive is fundamental to strengthening disaster preparedness and response. In recent years, extended reality (XR) technology has been introduced as a new approach and promising teaching technique for disaster medicine education. OBJECTIVE: To assess the effectiveness of XR simulation as a tool to train MFRs in MCIs, and to explore the perception and experience of participants to these new forms of training. DESIGN: Systematic review. METHODS: This systematic review was conducted in accordance with the "Preferred reporting items for systematic reviews and meta-analyses" (PRISMA) statement. Four databases were searched (MEDLINE, EMBASE, CINAHL and LILACs) using a comprehensive search strategy to identify relevant articles, and MetaQAT was used as a study quality assessment tool. Data from included studies was not pooled for meta-analysis due to heterogeneity. Extracted data was synthesised in a narrative, semi-quantitative manner. RESULTS: A total of 18 studies were included from 8 different countries. Studies encompassed a variety of participants (e.g., nurses, paramedics, physicians), interventions (virtual, mixed and augmented reality), comparators (comparison between two groups and single groups with pre-post evaluation), and outcomes (effectiveness and MFR perception). The synthesis of data indicated that XR was an effective tool for prehospital MCI training by means of improved triage accuracy, triage time, treatment accuracy, performance correctness and/or knowledge acquired. These XR systems were well perceived by MFRs, who expressed their interest and satisfaction towards this learning experience and emphasized its usefulness and relevance. CONCLUSION: This research supports the usefulness and significance of XR technology that allows users to enhance their skills and confidence when facing forthcoming disasters. The findings summarize recommendations and suggestions for the implementation, upgrade and/or assessment of this novel and valuable teaching method.

Tomographic and Tension Analysis of Polypropylene Reinforced with Carbon Fiber Fabric by Injection Molding.

The use of thermoplastic materials has had significant growth in recent years. However, with great mechanical requirements, thermoplastics have limitations to their use. To improve these restrictions, these materials are reinforced to obtain better properties. Polypropylene is one of the most versatile polymers and is used in almost all modern industries. Thus, the aim of this study is to create composite materials that offer performance for various industrial fields using carbon fiber fabric reinforcement, which is an inexpensive material widely used by the aerospace, automotive, and marine industries. The samples are produced by the over-injection molding of polypropylene. The investigation is focused on the impact of two critical control parameters in the injection molding process: temperature and pressure. Twelve experiments have therefore been considered, taking into account the combination of three factors: the presence or absence of carbon fiber fabric reinforcement, three levels of temperature (200 °C, 220 °C, and 240 °C), and two injection pressures (5000 kPa and 10,000 kPa). To evaluate the influence of these factors, three analyses were carried out: first, on the samples' shrinkage using a portable metrology-grade 3D laser scanner; second, on the internal defects using computed tomography (CT); and third, on the mechanical properties with tensile tests. From the results obtained, it is observed that the mold shrinkage fell slightly when PP samples were reinforced with carbon fiber, with both materials (PP and carbon-fiber-reinforced PP) having linear behavior with temperature. It is also noticed that polypropylene behaves as a crystalline material when processed at higher temperatures and pressures. From tests on the mechanical properties, it is concluded that the mean yield strength of PP-CF for injection temperatures of 220 °C and 240 °C represents an increase of 43% compared to the non-reinforced material.

Searching for predictors of the variability of impacts caused by non-native trees on regulating ecosystem services worldwide.

Humans have introduced non-native trees (NNT) all over the world to take advantage of the plethora of benefits they provide. However, depending on the context, NNT may present a diverse range of effects on ecosystem services (ES), from benefits to drawbacks, which may hinder the development of policies for these species. Unfortunately, the attempts so far to understand the impacts of NNT on ES only explained a low proportion of their variation. Here we analyze the variation in impacts of NNT on regulating ecosystem services (RES) by using a global database, which covers the effect size of multiple NNT species on six RES (climate regulation, soil erosion regulation, soil fertility, soil formation, hydrological cycle regulation, and fire protection). We used a wide range of predictors to account for the context-dependency of impacts distributed in five groups: the RES type, functional traits of both the NNT and the dominant NT of the recipient ecosystem, phylogenetic and functional distances between NNT and NT, climatic context, and human population characteristics. Using boosted regression trees and regression trees, we found that the most influential predictors of NNT impacts on RES were annual mean temperatures and precipitation seasonality, followed by the type of RES, human population density, and NNT height. In regions with warm temperatures and low seasonality, NNT tended to increase RES. NNT impacts were greater in densely populated regions. Smaller NNT exerted greater positive impacts on climate regulation and soil erosion regulation in tropical regions than in other climates. We highlight that benign climates and high population density exacerbate the effects of NNT on RES, and that soil fertility is the most consistently affected RES. Knowledge of the factors that modulate NNT impacts can help to predict their potential effects on RES in different parts of the world and at various environmental settings.

Extended reality as a training method for medical first responders in mass casualty incidents: A protocol for a systematic review.

INTRODUCTION/BACKGROUND: Mass-casualty incidents (MCIs) and disasters require an organised and effective response from medical first responders (MFRs). As such, novel training methods have emerged to prepare and adequately train MFRs for these challenging situations. Particular focus should be placed on extended reality (XR), which encompasses virtual, augmented and mixed reality (VR, AR, and MR, respectively), and allows participants to develop high-quality skills in realistic and immersive environments. Given the rapid evolution of high-fidelity simulation technology and its advantages, XR simulation has become a promising tool for emergency medicine. Accordingly, this systematic review aims to: 1) evaluate the effectiveness of XR training methods and 2) explore the experience of MFRs undergoing such training. METHODS: A comprehensive search strategy will encompass four distinct themes: MFRs, disasters/MCIs, education and simulation, and XR. Four databases (MEDLINE, EMBASE, CINAHL and LILACs) will be searched along with an in-depth examination of the grey literature and reference lists of relevant articles. MetaQAT will be used as a study quality assessment tool and integrated into Covidence as part of the data extraction form. Given the predicted high heterogeneity between studies, it may not be possible to standardise data for quantitative comparison and meta-analysis. Thus, data will be synthesised in a narrative, semi-quantitative manner. DISCUSSION: This review will examine the existing literature on the effectiveness of XR simulation as a tool to train MFRs for MCIs, which could ultimately improve preparedness and response to disasters. TRIAL REGISTRATION: Protocol registration: PROSPERO CRD42021275692.

Molecular characterization of epithelial-mesenchymal transition and medical treatment related-genes in non-functioning pituitary neuroendocrine tumors.

Introduction: Different medical therapies have been developed for pituitary adenomas. However, Non-Functioning Pituitary Neuroendocrine Tumors (NF-PitNET) have shown little response to them. Furthermore, epithelial-mesenchymal transition (EMT) has been linked to resistance to medical treatment in a significant number of tumors, including pituitary adenomas. Methods: We aimed to evaluate the expression of EMT-related markers in 72 NF-PitNET and 16 non-tumoral pituitaries. To further explore the potential usefulness of medical treatment for NF-PitNET we assessed the expression of somatostatin receptors and dopamine-associated genes. Results: We found that SNAI1, SNAI2, Vimentin, KLK10, PEBP1, Ki-67 and SSTR2 were associated with invasive NF-PitNET. Furthermore, we found that the EMT phenomenon was more common in NF-PitNET than in GH-secreting pituitary tumors. Interestingly, PEBP1 was overexpressed in recurrent NF-PitNET, and could predict growth recurrence with 100% sensitivity but only 43% specificity. In parallel with previously reported studies, SSTR3 is highly expressed in our NF-PitNET cohort. However, SSTR3 expression is highly heterogeneous among the different histological variants of NF-PitNET with very low levels in silent corticotroph adenomas. Conclusion: NF-PitNET showed an enhanced EMT phenomenon. SSTR3 targeting could be a good therapeutic candidate in NF-PitNET except for silent corticotroph adenomas, which express very low levels of this receptor. In addition, PEBP1 could be an informative biomarker of tumor regrowth, useful for predictive medicine in NF-PitNET.

Exploiting ICD Hierarchy for Classification of EHRs in Spanish Through Multi-Task Transformers.

Electronic Health Records (EHRs) convey valuable information. Experts in clinical documentation read the report, understand the prior work, procedures, tests carried out, and encode the EHRs according to the International Classification of Diseases (ICD). Assigning these codes to the EHRs helps to share information, and extract statistics. In this paper, we explore computer-aided multi-label classification approaches. While Natural Language Understanding has evolved for clinical text mining, there is still a gap for languages other than English. Language-modeling aware Transformers has demonstrated state of the art approaches through exploiting contextual dependencies. Here we focus on EHRs written in Spanish, and try to benefit from the Language Model itself, with unannotated corpus with less data but in-house, in-domain and closely-related EHRs to that of the downstream task. The International Classification of Diseases coding scheme is hierarchical, but its synergies among hierarchical levels are rarely exploited. In this work, we implement and release a hierarchical head for multi-label classification, which benefits from the hierarchy of the ICD via multi-task classification.

Laboratory versus wild populations: the importance of population origin in aquatic ecotoxicology.

The origin of the populations used in ecotoxicological bioassays (from nature (wild populations) or from cultures (laboratory populations)) could have a key influence on the sensitivity of the tested species to different toxicants. However, the available information on this subject is scarce. To assess the likely influence of the population origin (wild vs. laboratory) of species-genus on the toxicant tolerance, we performed a quantitative review of the ECOTOX database, from which we collected the effective concentrations for a wide range of compounds (metals and organics), endpoints, and exposure times. We found a general trend of lower sensitivity of wild populations to toxicants than laboratory populations, although sensitivity was dependent on species and toxicant groups. This suggests that the results of bioassays with laboratory populations may overestimate the toxicity of most of the compounds. Our study highlights the relevance of the origin of the populations in the determination of the sensitivity of species to toxicants. This study also warns about the biases in the species and toxicants used in ecotoxicology, which may lead to an underrepresentation of the biodiversity and the toxicological context of aquatic ecosystems.

Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention.

BACKGROUND: This work deals with Natural Language Processing applied to Electronic Health Records (EHRs). EHRs are coded following the International Classification of Diseases (ICD) leading to a multi-label classification problem. Previously proposed approaches act as black-boxes without giving further insights. Explainable Artificial Intelligence (XAI) helps to clarify what brought the model to make the predictions. GOAL: This work aims to obtain explainable predictions of the diseases and procedures contained in EHRs. As an application, we show visualizations of the attention stored and propose a prototype of a Decision Support System (DSS) that highlights the text that motivated the choice of each of the proposed ICD codes. METHODS: Convolutional Neural Networks (CNNs) with attention mechanisms were used. Attention mechanisms allow to detect which part of the input (EHRs) motivate the output (medical codes), producing explainable predictions. RESULTS: We successfully applied methods in a Spanish corpus getting challenging results. Finally, we presented the idea of extracting the chronological order of the ICDs in a given EHR by anchoring the codes to different stages of the clinical admission. CONCLUSIONS: We found that explainable deep learning models applied to predict medical codes store helpful information that could be used to assist medical experts while reaching a solid performance. In particular, we show that the information stored in the attention mechanisms enables DSS and a shallow chronology of diagnoses.

Data mining analyses for precision medicine in acromegaly: a proof of concept.

Predicting which acromegaly patients could benefit from somatostatin receptor ligands (SRL) is a must for personalized medicine. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to assign this pharmacologic treatment according to biomarker levels. Our aim is to provide better predictive tools for an accurate acromegaly patient stratification regarding the ability to respond to SRL. We took advantage of a multicenter study of 71 acromegaly patients and we used advanced mathematical modelling to predict SRL response combining molecular and clinical information. Different models of patient stratification were obtained, with a much higher accuracy when the studied cohort is fragmented according to relevant clinical characteristics. Considering all the models, a patient stratification based on the extrasellar growth of the tumor, sex, age and the expression of E-cadherin, GHRL, IN1-GHRL, DRD2, SSTR5 and PEBP1 is proposed, with accuracies that stand between 71 to 95%. In conclusion, the use of data mining could be very useful for implementation of personalized medicine in acromegaly through an interdisciplinary work between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise and personalized medicine for acromegaly patients.

Extracting Cause of Death From Verbal Autopsy With Deep Learning Interpretable Methods.

The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provided by a family member or friend together with a questionnaire is designed by the World Health Organization as the main information source. Until now technology allowed us to automatically analyze the responses of the VA questionnaire with the narrative captured by the interviewer excluded. Our work addresses this gap by developing a set of models for automatic Cause of Death (CoD) ascertainment in VAs with a focus on the textual information. Empirical results show that the open response conveys valuable information towards the ascertainment of the Cause of Death, and the combination of the closed-ended questions and the open response lead to the best results. Model interpretation capabilities position the Deep Learning models as the most encouraging choice.

[Clinical and epidemiological characteristics of rotavirus infection in children of Cumaná, Venezuela]. / Características clínicas y epidemiológicas de la infección por rotavirus en niños de Cumaná, Venezuela.

The detection rate of group A human rotavirus (HRV-A), as well as its association with clinical and epidemiological parameters, was studied in children younger than 5 years old with acute diarrhea attending to the University Hospital "Antonio Patricio de Alcalá" of Cumaná, between march 2006 and september 2007. Of 241 fecal samples collected in this study, 47 (19.5%) were positive to HRV-A by immunoassay. Rotavirus were present throughout the study and the major detection rates were on march, april and may of 2006 (rates were 30,0%, 28,6% y 43,8%, respectively) and september of 2007 (37,5%). Thirty four percent of cases with HRV-A occurred in children of 7 - 12 months and males were the most affected (55.3%), as well as the worker and marginal socioeconomic classes (72,4%). Children that not received maternal feeding were the group mainly infected by HRV-A (61.7%). Most of the children (72.4%) had one to four evacuations/day, with few vomits (38.2%) and fever (10.6%). Almost all the feces (83.0%) had a liquid or semi-liquid aspect. When these results were compared with previous data of the same geographic area, we observed a two-fold decrease of the detection rate of HRV-A and the clinical symptoms were the same as reported by other authors. Of 32 children vaccinated against rotavirus, 30 (93.8%) did not have HVR-A in their feces and there was a significant association between the vaccinated children and protection.

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

BACKGROUND AND OBJECTIVE: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseases, which is the foundation for the identification of international health statistics, and the standard for reporting diseases and health conditions. Within the framework of data mining, the goal is the multi-label classification, as each health record has assigned multiple International Classification of Diseases codes. We investigate five Deep Learning architectures with a dataset obtained from the Basque Country Health System, and six different perspectives derived from shifts in the input and the output. METHODS: We evaluate a Feed Forward Neural Network as the baseline and several Recurrent models based on the Bidirectional GRU architecture, putting our research focus on the text representation layer and testing three variants, from standard word embeddings to meta word embeddings techniques and contextual embeddings. RESULTS: The results showed that the recurrent models overcome the non-recurrent model. The meta word embeddings techniques are capable of beating the standard word embeddings, but the contextual embeddings exhibit as the most robust for the downstream task overall. Additionally, the label-granularity alone has an impact on the classification performance. CONCLUSIONS: The contributions of this work are a) a comparison among five classification approaches based on Deep Learning on a Spanish dataset to cope with the multi-label health text classification problem; b) the study of the impact of document length and label-set size and granularity in the multi-label context; and c) the study of measures to mitigate multi-label text classification problems related to label-set size and sparseness.

Data of the life cycle impact assessment and cost analysis of prospective direct recycling of end-of-life reverse osmosis membrane at full scale.

This data includes the geographical data, the Life Cycle Inventory data and Life Cycle Assessment data of the implementation of end-of-life (EoL) reverse osmosis (RO) direct recycling implementation at full scale in a Spanish region. Besides, the data allows the comparison of the environmental profile between recycled membrane products with the commercial counterparts. The EoL-RO stock potential was analysed constrained to the Segura´s watershed. However, the distribution of recycled membranes was considered within the European Union´s borders. The International Life Cycle Data system (ILCD) midpoint impact categories and the indicator Service Life Ratio (SLR) are presented. This data could be used for deepening analyses as the externalities monetarisation or business model identification or policymakers This data article is related to J. Senán-Salinas, A. Blanco, R. García-Pacheco, J. Landaburu-Aguirre, E- García-Calvo. J Prospective Life Cycle Assessment and economic analysis of direct recycling of end-of-life reverse osmosis membranes based on Geographic Information Systems. J. Clean. Prod. In Press.

Green synthesis of silver nanoparticles using Lysiloma acapulcensis exhibit high-antimicrobial activity.

The scientific community is exploiting the use of silver nanoparticles (AgNPs) in nanomedicine and other AgNPs combination like with biomaterials to reduce microbial contamination. In the field of nanomedicine and biomaterials, AgNPs are used as an antimicrobial agent. One of the most effective approaches for the production of AgNPs is green synthesis. Lysiloma acapulcensis (L. acapulcensis) is a perennial tree used in traditional medicine in Mexico. This tree contains abundant antimicrobial compounds. In the context of antimicrobial activity, the use of L. acapulcensis extracts can reduce silver to AgNPs and enhance its antimicrobial activity. In this work, we demonstrate such antimicrobial activity effect employing green synthesized AgNPs with L. acapulcensis. The FTIR and LC-MS results showed the presence of chemical groups that could act as either (i) reducing agents stabilizing the AgNPs or (ii) antimicrobial capping agents enhancing antimicrobial properties of AgNPs. The synthesized AgNPs with L. acapulcensis were crystalline with a spherical and quasi-spherical shape with diameters from 1.2 to 62 nm with an average size diameter of 5 nm. The disk diffusion method shows the magnitude of the susceptibility over four pathogenic microorganisms of clinical interest. The antimicrobial potency obtained was as follows: E. coli ≥ S. aureus ≥ P. aeruginosa > C. albicans. The results showed that green synthesized (biogenic) AgNPs possess higher antimicrobial potency than chemically produced AgNPs. The obtained results confirm a more significant antimicrobial effect of the biogenic AgNPs maintaining low-cytotoxicity than the AgNPs produced chemically.