Clinical utility of lung ultrasound for the detection of interstitial lung disease in patients with rheumatoid arthritis.

OBJECTIVE: To establish the diagnostic value of lung ultrasound (LUS) in patients with rheumatoid arthritis (RA) for the detection of interstitial lung disease (ILD). METHODS: A cross-sectional study was performed. Consecutive patients with RA (American College of Rheumatology/European League Against Rheumatism 2010 criteria) who had a chest high-resolution computed tomography (HRCT) performed within 12 months before inclusion, regardless of symptomatology, were included. Demographic, clinical, laboratory, and pharmacological data were recorded. Each patient underwent a LUS with assessment of B-lines (BL) and pleural irregularities (PI). HRCT was considered the gold standard for the confirmatory diagnosis of ILD. Receiver operating characteristic (ROC) curves were calculated to test the ability of LUS findings (BL and PI) in discriminating patients with ILD. RESULTS: A total of 104 RA patients were included, of which 21.8% had ILD. Patients with ILD had more BL (median 26 versus 1, p<0.001) and PI (median 16 versus 5, p<0.001) than patients without ILD. The diagnostic accuracy in ROC curves was: area under the curve (AUC) 0.88 and 95% confidence interval (CI) 0.78-0.93 for BL and AUC 0.82 and 95% CI 0.74-0.89 for PI. The best cut-off points for (ILD detection) discriminating the presence of significant interstitial lung abnormalities were 8 BL and 7 PI. CONCLUSIONS: The presence of 8 BL and/or 7 PI in the LUS showed an adequate cut-off value for discriminating the presence of significant interstitial lung abnormalities, evocative of ILD.

Encapsulation of a 5FU-curcumin hybrid on bacterial nanocellulose for colorectal cancer treatment.

The traditional treatment of colorectal cancer (CRC) involves a combination of chemotherapy and synthetic and natural drugs. In this study, a hybrid compound of 5-fluorouracil-curcumin encapsulated in bacterial nanocellulose (BNC) was evaluated for CRC treatment. Bacterial nanocellulose was produced using K. medellinensis and spray-dried. The encapsulation technique involved solvent evaporation. The interactions between cellulose and the hybrid were evaluated using adsorption isotherms and kinetics, and the system was morphologically and physiochemically characterized. The capsules were tested in vitro using Dukes' C and B CRC cells. The results indicated heterogeneous and incomplete adsorption of the hybrid onto the active sites of cellulose. Capsules with a BNC:hybrid mass ratio of 1:1 maintained the encapsulant properties while maximizing the drug load according to desorption in simulated stomach and colon fluids, where desorption in the colon was 1.79 times greater than that in the stomach. Finally, the cancer cell inhibition results indicated that the encapsulated hybrid performed better on Dukes' C-stage cells than on Duke's B-stage cells. In this study, a new system based on a hybrid cellulose compound was proposed for CRC treatment, specifically for metastatic CRC.

Novel application for graphene oxide-based ionanofluids in flat plate solar thermal collectors.

This study presents new ionanofluids (INF) composed of 1-ethyl-3-methylimidazolium acetate ionic liquid (IL) and graphene oxide (GO) nanoparticles which have been assessed for the first time in an experimental flat plate solar thermal collector (FPSC). For this purpose, four types of INFs were synthesized, maintaining a constant concentration of GO nanoparticles dispersed in different base fluids: ionic liquid (IL/GO), a mixture of ionic liquid and water in varying concentrations (IL-water (75-25)%/GO and IL-water (50-50)%/GO), and water (Water/GO). These four INFs were characterized and their thermophysical and physicochemical properties were determined. The results indicated a 37.4% improvement in efficiency and up to a 2.5-fold increase in temperature within the collector when the IL was applied exclusively as the base fluid, compared to water. Furthermore, IL/GO demonstrated excellent stability, showing no signs of deterioration or nanoparticle precipitation two years after preparation and testing. These findings suggest that INFs based on IL and GO nanoparticles significantly enhance the efficiency of FPSC, presenting a promising option for solar energy applications and opening a new research avenue for INFs in the production of domestic hot water.

Effect of FeCl3 concentration in chemically enhanced primary treatment on the performance of a conventional wastewater treatment plant. A case study.

The effect of coagulant dosage in a chemically enhanced primary treatment (CEPT) on the performance of a conventional wastewater treatment plant (WWTP) has been investigated. Lab-scale experiments simulations were carried out in order to evaluate the effect of coagulant addition on the primary settling performance. In these experiments, FeCl3 was used as coagulant. Later, the WWTP was theoretically simulated using a commercial software (WEST®) to evaluate the effect of coagulation/flocculation on the global system, based on the results obtained at lab-scale. According to these results, the CEPT modifies the organic matter balance in the WWTP, decreasing the contribution of readily (SS) and slowly (XS) biodegradable fractions of COD to the aerobic biological process up to 27.3% and 80.8%, respectively, for a dosage of FeCl3 of 24 mg L-1. Consequently, total suspended solids in the aerobic reactor and the secondary purged sludge decreased up to 33% and 13%, respectively. However, the influence on effluent quality was negligible. On the contrary, suspended solids concentration in the sludge to be treated by anaerobic digestion increased, mainly regarding the Ss and Xs fractions, which caused an 8.1% increase in biogas production potential, with approximately 60% of CH4 concentration.

Giant orbital leiomyoma in a pediatric patient: diagnostic and therapeutic challenge.

This manuscript describes an exceptional case of a long-standing orbital leiomyoma in a 14-year-old male. The tumor was unusually large, causing severe proptosis and significant involvement of the ocular muscles. The patient presented with amaurosis, complete ophthalmoplegia, spontaneous eye pain, and the inability to close the eyelids, leading to psychological distress. Due to the tumor's size and progression, a right orbital exenteration was performed to remove all orbital contents, including the tumor and the eyeball. The surgical procedure aimed to prevent tumor recurrence and improve the patient's quality of life. The histopathological analysis confirmed the diagnosis of orbital leiomyoma. This case presents a particular interest due to the degree of evolution it has reached. Complete tumor excision and long-term follow-up are necessary to prevent recurrence and ensure optimal patient outcomes. This report underscores global healthcare disparities and the complexity of managing rare orbital neoplasms in diverse country settings.

Mediterranean springs: Keystone ecosystems and biodiversity refugia threatened by global change.

Mediterranean spring ecosystems are unique habitats at the interface between surface water and groundwater. These ecosystems support a remarkable array of biodiversity and provide important ecological functions and ecosystem services. Spring ecosystems are influenced by abiotic, biotic, and anthropogenic factors such as the lithology of their draining aquifers, their climate, and the land use of their recharge area, all of which affect the water chemistry of the aquifer and the spring discharges. One of the most relevant characteristics of spring ecosystems is the temporal stability of environmental conditions, including physicochemical features of the spring water, across seasons and years. This stability allows a wide range of species to benefit from these ecosystems (particularly during dry periods), fostering an unusually high number of endemic species. However, global change poses important threats to these freshwater ecosystems. Changes in temperature, evapotranspiration, and precipitation patterns can alter the water balance and chemistry of spring water. Eutrophication due to agricultural practices and emergent pollutants, such as pharmaceuticals, personal care products, and pesticides, is also a growing concern for the preservation of spring biodiversity. Here, we provide a synthesis of the main characteristics and functioning of Mediterranean spring ecosystems. We then describe their ecological value and biodiversity patterns and highlight the main risks these ecosystems face. Moreover, we identify existing knowledge gaps to guide future research in order to fully uncover the hidden biodiversity within these habitats and understand the main drivers that govern them. Finally, we provide a brief summary of recommended actions that should be taken to effectively manage and preserve Mediterranean spring ecosystems for future generations. Even though studies on Mediterranean spring ecosystems are still scarce, our review shows there are sufficient data to conclude that their future viability as functional ecosystems is under severe threat.

[Autism spectrum disorder: the impact of an online training strategy on the knowledge of the healthcare staff of a tertiary care hospital]. / Trastorno del espectro autista: impacto de una estrategia de formación en línea en los conocimientos del personal sanitario de un hospital de tercer nivel.

INTRODUCTION: Autism spectrum disorder (ASD) often presents related medical disorders that require specialised healthcare. Professionals in the health sector therefore face difficulties that require specific training in the healthcare needs of this population. AIM: The aim of this study is to quantify paediatric healthcare professionals' knowledge about ASD and to assess the impact of online training. SUBJECTS AND METHODS: It is a quasi-experimental, longitudinal, prospective before-and-after study; study subjects: health professionals; independent variable: online training in ASD; dependent variable: knowledge about ASD. An online training course was held for paediatric professionals to address the core characteristics of diagnosis, as well as the needs they present in the hospital context and the adaptations it is recommended that should be carried out. Fifty-eight healthcare professionals took part. RESULTS: An increase in knowledge about ASD was observed at the end of the intervention (from 73.9% to 85% according to the ASD background knowledge questionnaire), which showed that more than 90% of the participants had the highest level of knowledge about ASD. CONCLUSIONS: Online training courses are a useful and effective way to increase knowledge about ASD and the adaptations that are recommended in the hospital setting. More training in ASD should be made available in these settings.

TITLE: Trastorno del espectro autista: impacto de una estrategia de formación en línea en los conocimientos del personal sanitario de un hospital de tercer nivel.Introducción. El trastorno del espectro autista (TEA) frecuentemente presenta trastornos médicos relacionados que requieren una atención sanitaria especializada. En este sentido, los profesionales del ámbito sanitario se enfrentan a dificultades que precisan una formación específica en las necesidades sanitarias que presenta esta población. Objetivo. Cuantificar los conocimientos sobre el TEA de los profesionales sanitarios del área pediátrica y valorar el impacto de una formación en línea. Sujetos y métodos. Estudio cuasi experimental del antes y después, longitudinal y prospectivo; sujetos a estudio: profesionales sanitarios; variable independiente: formación en línea en TEA; variable dependiente: conocimiento sobre el TEA. Se llevó a cabo una formación en línea para profesionales del área de pediatría en la que se abordaron las características nucleares del diagnóstico, así como las necesidades que presentan en el contexto hospitalario y las adaptaciones que se recomiendan llevar a cabo. Participaron 58 profesionales sanitarios. Resultados. Se observó un aumento en el conocimiento sobre el TEA al finalizar la intervención (del 73,9 al 85% según el cuestionario de conocimientos previos del TEA), que mostró que más del 90% de los participantes tenía el grado máximo de conocimiento sobre el TEA. Conclusiones. Las formaciones en línea son un método para ampliar conocimiento útil y eficaz para aumentar el conocimiento sobre el TEA y las adaptaciones que se recomiendan en el ámbito hospitalario. Se recomienda aumentar la disponibilidad de formación sobre TEA en estos entornos.

Myotonic dystrophy type 1: 13 years of experience at a tertiary hospital. Clinical and epidemiological study and genotype-phenotype correlation.

INTRODUCTION: The incidence of myotonic dystrophy type 1 (DM1), a disease with great phenotypic variety, in our region is unknown. This study aims to estimate the incidence of DM1 at our hospital (a reference centre in Aragon, Spain) and to identify the characteristics of our population (genotype-phenotype correlation). METHODS: Retrospective, descriptive study of 459 patients classified according to the number of CTG repeats, as follows: normal (5-35), premutation (36-50), protomutation (51-80), small expansions (81-150), intermediate expansions (151-1000), and large expansions (> 1000). Furthermore, according to clinical phenotype, patients were categorised as unaffected (5-50 CTG repeats), mild form or asymptomatic (51-150), classical form (151-1000), and severe form (> 1000). RESULTS: The incidence of DM1 was 20.61 cases per million person-years (95% CI, 19.59-21.63). An inverse correlation was observed between the number of CTG repeats and the age at genetic diagnosis (ρ = -0.547; 95% CI, -0.610 to -0.375; P < .001). CTG5 was the most frequent polymorphic allele in healthy individuals. Of all patients with DM1, 28.3% presented the mild or asymptomatic form, 59.1% the classical form, and 12.6% the severe form. Inheritance was maternal in 35.1% of cases, paternal in 59.4%, and uncertain in 5.5%. In mild forms, frontal balding in men was the most prevalent phenotypic trait, as well as myotonia and cataracts, while in the classical form, ptosis, facial weakness, voice and pronunciation alterations, myotonia, and fatigue/sleepiness were most frequent. CONCLUSIONS: The incidence of DM1 in Aragon is significant. Multidisciplinary study of the phenotype of patients with DM1 is key to early diagnosis and personalised management.

Using deep LSD to build operators in GANs latent space with meaning in real space.

Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.

Novel paradigm enables accurate monthly gestational screening to prevent congenital toxoplasmosis and more.

Background: Congenital toxoplasmosis is a treatable, preventable disease, but untreated causes death, prematurity, loss of sight, cognition and motor function, and substantial costs worldwide. Methods/Findings: In our ongoing USA feasibility/efficacy clinical trial, data collated with other ongoing and earlier published results proved high performance of an Immunochromatographic-test(ICT) that enables accurate, rapid diagnosis/treatment, establishing new paradigms for care. Overall results from patient blood and/or serum samples tested with ICT compared with gold-standard-predicate-test results found ICT performance for 4606 sera/1876 blood, 99.3%/97.5% sensitive and 98.9%/99.7% specific. However, in the clinical trial the FDA-cleared-predicate test initially caused practical, costly problems due to false-positive-IgM results. For 58 persons, 3/43 seronegative and 2/15 chronically infected persons had false positive IgM predicate tests. This caused substantial anxiety, concerns, and required costly, delayed confirmation in reference centers. Absence of false positive ICT results contributes to solutions: Lyon and Paris France and USA Reference laboratories frequently receive sera with erroneously positive local laboratory IgM results impeding patient care. Therefore, thirty-two such sera referred to Lyon's Reference laboratory were ICT-tested. We collated these with other earlier/ongoing results: 132 of 137 USA or French persons had false positive local laboratory IgM results identified correctly as negative by ICT. Five false positive ICT results in Tunisia and Marseille, France, emphasize need to confirm positive ICT results with Sabin-Feldman-Dye-test or western blot. Separate studies demonstrated high performance in detecting acute infections, meeting FDA, CLIA, WHO ASSURED, CEMark criteria and patient and physician satisfaction with monthly-gestational-ICT-screening. Conclusions/Significance: This novel paradigm using ICT identifies likely false positives or raises suspicion that a result is truly positive, rapidly needing prompt follow up and treatment. Thus, ICT enables well-accepted gestational screening programs that facilitate rapid treatment saving lives, sight, cognition and motor function. This reduces anxiety, delays, work, and cost at point-of-care and clinical laboratories. Author's Summary: Toxoplasmosis is a major health burden for developed and developing countries, causing damage to eyes and brain, loss of life and substantial societal costs. Prompt diagnosis in gestational screening programs enables treatment, thereby relieving suffering, and leading to > 14-fold cost savings for care. Herein, we demonstrate that using an ICT that meets WHO ASSURED-criteria identifying persons with/without antibody to Toxoplasma gondii in sera and whole blood with high sensitivity and specificity, is feasible to use in USA clinical practice. We find this new approach can help to obviate the problem of detection of false positive anti- T.gondii IgM results for those without IgG antibodies to T.gondii when this occurs in present, standard of care, predicate USA FDA cleared available assays. Thus, this accurate test facilitates gestational screening programs and a global initiative to diagnose and thereby prevent and treat T.gondii infection. This minimizes likelihood of false positives (IgG and/or IgM) while maintaining maximum sensitivity. When isolated IgM antibodies are detected, it is necessary to confirm and when indicated continue follow up testing in ∼2 weeks to establish seroconversion. Presence of a positive ICT makes it likely that IgM is truly positive and a negative ICT makes it likely that IgM will be a false positive without infection. These results create a new, enthusiastically-accepted, precise paradigm for rapid diagnosis and validation of results with a second-line test. This helps eliminate alarm and anxiety about false-positive results, while expediting needed treatment for true positive results and providing back up distinguishing false positive tests.

Bundles reduce anastomosis leak in patients undergoing elective colorectal surgery. A propensity score-matched study.

Background: anastomosis leak still being a handicap in colorectal surgery. Bowel mechanical preparation and oral antibiotics are not a practice recommended in many clinical practice guides. The aim is to analyse the decrease in frequency and severity of postoperative complications, mainly related to anastomotic leak, after the establishment of a bundle. Methods: Single-center, before-after study. A bundle was implemented to reduce anastomotic leaks and their consequences. The Bundle group were matched to Pre-bundle group by propensity score matching. Mechanical bowel preparation, oral and intravenous antibiotics, inflammatory markers measure and early diagnosis algorithm were included at the bundle. Results: The bundle group shown fewer complications, especially in Clavien Dindós Grade IV complications (2.3% vs. 6.2% p < 0.01), as well as a lower rate of anastomotic leakage (15.5% vs. 2.2% p < 0.01). A significant decrease in reinterventions, less intensive unit care admissions, a shorter hospital stay and fewer readmissions were also observed. In multivariate analysis, the application of a bundle was an anastomotic leakage protective factor (OR 0.121, p > 0.05). Conclusions: The implementation of our bundle in colorectal surgery which include oral antibiotics, mechanical bowel preparation and inflammatory markers, significantly reduces morbidity adjusted to severity of complications, the anastomotic leakage rate, hospital stay and readmissions. Register study: The study has been registered at clinicaltrials.gov Code: nct04632446.

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks.

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets.

Ceria-based coatings on magnesium alloys for biomedical applications: a literature review.

Magnesium alloys are being studied for use in temporary orthopedic implants because of their mechanical properties, which are similar to those of human bone, and their good biocompatibility. However, their application is limited due to their rapid degradation, and early loss of their mechanical properties, decreasing the stability of the implant and its proper synchronization with tissue regeneration. In this regard, various surface coatings have been used to improve their biological, physico-chemical and biodegradation properties. Currently, one of the most explored strategies is using smart coatings because of their self-healing properties that can slow down the corrosion process of Mg and its alloys. Ceria-based materials show promise as coatings for these alloys. Their unique redox capacity not only provides Mg alloys with good self-healing properties but also interesting biological properties, which are described in this paper. Despite this, some problems and challenges related to the biocompatibility and application of these materials in coatings remain unsolved. In this article, a critical review is presented summarizing the most representative literature on ceria-based coatings on Mg alloys for their potential use as biomaterials. The results show that ceria is a versatile material that may be used in industrial and biomedical applications.

Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model.

The crisis caused by the COVID-19 outbreak around the globe raised an increasing concern about the ongoing emergence of variants of the virus that may evade the immune response provided by vaccines. New variants appear due to mutation, and as the cases accumulate, the probability of the emergence of a variant of concern increases. In this article, we propose a modified susceptible, infected, and recovered (SIR) model with waning immunity that captures the competition of two strain classes of an infectious disease under the effect of vaccination with a highly contagious and deadlier strain class emerging from a prior strain due to mutation. When these strains compete for a limited supply of susceptible individuals, changes in the efficiency of vaccines may affect the behaviour of the disease in a non-trivial way, resulting in complex outcomes. We characterise the parameter space including intrinsic parameters of the disease, and using the vaccine efficiencies as control variables. We find different types of transcritical bifurcations between endemic fixed points and a disease-free equilibrium and identify a region of strain competition where the two strain classes coexist during a transient period. We show that a strain can be extinguished either due to strain competition or vaccination, and we obtain the critical values of the efficiency of vaccines to eradicate the disease. Numerical studies using parameters estimated from publicly reported data agree with our theoretical results. Our mathematical model could be a tool to assess quantitatively the vaccination policies of competing and emerging strains using the dynamics in epidemics of infectious diseases.

Effect of positive pressure ventilation and bariatric surgery on extracellular vesicle microRNAs in patients with severe obesity and obstructive sleep apnea.

INTRODUCTION: Obstructive sleep apnea (OSA) and severe obesity share a common pathophysiological phenomenon, systemic and tissue hypoxia. Hypoxaemia modifies microRNA expression, particularly, extracellular vesicles microRNAs which are involved in the progression of cardiovascular diseases, metabolic syndrome and cancer. We aim to evaluate extracellular vesicle miRNAs among patients with severe obesity with and without OSA and the effect of OSA and severe obesity treatment: continuous positive airway pressure (CPAP) and bariatric surgery. METHODS: Patients were selected from the Epigenetics Modification in Morbid Obesity and Obstructive Sleep Apnea (EPIMOOSA) study (NCT03995836), a prospective observational study of patients undergoing bariatric surgery. Patients were divided into OSA (Apnea-hyponea index (AHI) > 10) and non-OSA (AHI < 10). Patients with OSA were treated with CPAP for 6 months. Then, all patients had bariatric surgery and re-evaluated 12 months later. At each visit, blood samples were obtained for biobanking. Subsequently, extracellular vesicles were extracted, and then, miRNA expression was analysed. RESULTS: 15 patients with OSA and 9 without OSA completed the protocol. At baseline, patients with OSA showed higher miR16, miR126 and miR320 (p < 0.05) and lower miR223 expression (p < 0.05) than those without OSA. In patients with severe obesity and OSA, after 6 months with CPAP, we observed a significant decrease in miR21 (p < 0.01), miR126 (p < 0.001) and miR320 (p < 0.001), with no changes in any miRNA in patients without OSA. No changes were detected in any miRNA after 6 months of bariatric surgery in patients with or without OSA. CONCLUSION: Co-existance of OSA and severe obesity alters the profile of extracellular vesicle miRNAs. Bariatric surgery and weight loss did not reverse this effect meanwhile the treatment with CPAP in patients with severe obesity and OSA showed a recovery outcome in those extracellular vesicle miRNAs. Those facts remark the need for OSA screening in patients with severe obesity. CLINICAL TRIAL REGISTRATION: The study has also been registered at ClinicalTrials.gov identifier: NCT03995836.

A randomised controlled trial in patients undergoing arthroscopic shoulder surgery comparing interscalene block with either 10 ml or 20 ml levobupivacaine 0.25.

The interscalene brachial plexus block is recommended for analgesia after shoulder surgery but it may cause hemidiaphragmatic dysfunction. We tested whether ipsilateral hemidiaphragmatic contraction was better after a smaller dose of local anaesthetic without impairing analgesic effect. We randomly allocated 48 adults to 10 ml or 20 ml levobupivacaine 0.25% before arthroscopic shoulder surgery. The primary outcome was hemidiaphragmatic paralysis, defined as inspiratory thickness < 1.2 times expiratory thickness, measured by ultrasound 4 h after block. Hemidiaphragmatic paralysis was recorded for 6/24 vs. 23/24 supine participants after 10 ml vs. 20 ml levobupivacaine 0.25%, respectively, and for 4/24 vs. 23/24 sitting participants, respectively, p < 0.001 for both. Pain scores after 10 ml injectate were not worse than after 20 ml injectate. Median (IQR [range]) morphine doses in the first 24 postoperative hours after 10 ml and 20 ml levobupivacaine 0.25% were 2 (0-6 [0-23]) mg vs. 1 (0-2 [0-11]) mg, respectively, p = 0.12. No participant had a complication after 10 ml interscalene levobupivacaine, whereas seven had complications after 20 ml levobupivacaine, p = 0.009. Hemidiaphragmatic function was better after 10 ml vs. 20 ml interscalene levobupivacaine 0.25% without impairing analgesia for 24 postoperative hours.

Vaginal birth after caesarean section before and during COVID-19 pandemic. Factors associated with successful vaginal birth.

BACKGROUND: The ratio of caesarean has been increasing considerably in many countries. Planning a vaginal birth after a previous caesarean is considered an important option for women in a subsequent pregnancy. AIMS: To analyse obstetric and neonatal outcomes in women in labour after caesarean section before and during the COVID-19 pandemic, and to determine factors associated with successful vaginal birth after caesarean (VBAC). METHODS: Observational cohort study of women in labour with history of caesarean section who gave birth between March 2019 and December 2020 in a tertiary hospital in southern Spain. Consecutive sampling was performed using the maternal birth database and a descriptive and inferential analysis of the study variables was carried out. Socio-demographic, obstetric and neonatal variables were compared between the pre-pandemic and pandemic periods. Multiple logistic regression analysis was performed to determine variables associated with VBAC success. FINDINGS: The VBAC success rate was 67.4%. The caesarean section rate was significantly higher during the COVID-19 pandemic period. Factors associated with VBAC success were: birth before the pandemic (OR 0.32) and at night (OR 0.45), use of epidural analgesia (OR 2.14), and having had a previous vaginal birth (OR 1.98). CONCLUSIONS: The success rate of VBAC was lower during the pandemic. Knowledge of the factors related to VBAC success is critical for practitioners when supporting women in decision-making about mode of birth after a previous caesarean section.

Removal efficiency for emerging contaminants in a WWTP from Madrid (Spain) after secondary and tertiary treatment and environmental impact on the Manzanares River.

The effluents from wastewater treatment plants (WWTPs) can be an important contamination source for receiving waters. In this work, a comprehensive study on the impact of a WWTP from Madrid on the aquatic environment has been performed, including a wide number of pharmaceuticals and pesticides, among them those included in the European Watch List. 24-h composite samples of influent (IWW) and effluent wastewater after secondary (EWW2) and after secondary + tertiary treatment (EWW3) were monitored along two campaigns. Average weekly concentrations in IWW and EWW2 and EWW3 allowed estimating the removal efficiency of the WWTP for pharmaceutical active substances (PhACs). In addition, the impact of EWW3 on the water quality of the Manzanares River was assessed, in terms of PhAC and pesticide concentrations, through analysis of the river water collected upstream and downstream of the discharge point. After a preliminary risk assessment, a detailed evaluation of the impact on the aquatic environment, including a toxicological study and screening of pharmaceutical metabolites, was made for the seven most relevant PhACs: sulfamethoxazole, azithromycin and clarithromycin (antibiotics), metoprolol (antihypertensive), diclofenac (anti-inflammatory/analgesic), irbesartan (antihypertensive), and the antidepressant venlafaxine. Among selected PhACs, irbesartan, clarithromycin and venlafaxine presented moderate or high risk in the river water downstream of the discharge. Albeit no acute toxicity was detected, more detailed studies should be carried out for these substances, including additional toxicological studies, to set up potential sublethal and chronic effects on aquatic organisms.

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations.

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.