Catheter ablation of intramural outflow tract premature ventricular complexes: a multicentre study.

AIMS: Ablation of outflow tract ventricular arrhythmias may be limited by a deep intramural location of the arrhythmogenic source. This study evaluates the acute and long-term outcomes of patients undergoing ablation of intramural outflow tract premature ventricular complexes (PVCs). METHODS AND RESULTS: This multicenter series included patients with structurally normal heart or nonischemic cardiomyopathy and intramural outflow tract PVCs defined by: (a) ≥ 2 of the following criteria: (1) earliest endocardial or epicardial activation < 20ms pre-QRS; (2) Similar activation in different chambers; (3) no/transient PVC suppression with ablation at earliest endocardial/epicardial site; or (b) earliest ventricular activation recorded in a septal coronary vein. Ninety-two patients were included, with a mean PVC burden of 21.5±10.9%. Twenty-six patients had had previous ablations. All PVCs had inferior axis, with LBBB pattern in 68%. In 29 patients (32%) direct mapping of the intramural septum was performed using an insulated wire or multielectrode catheter, and in 13 of these cases the earliest activation was recorded within a septal vein. Most patients required special ablation techniques (one or more), including sequential unipolar ablation in 73%, low-ionic irrigation in 26%, bipolar ablation in 15% and ethanol ablation in 1%. Acute PVC suppression was achieved in 75% of patients. Following the procedure, the PVC burden was reduced to 5.8±8.4%. The mean follow-up was 15±14 months and 16 patients underwent a repeat ablation. CONCLUSION: Ablation of intramural PVCs is challenging; acute arrhythmia elimination is achieved in 3/4 patients, and non-conventional approaches are often necessary for success.

Surgical timing prevails as the main factor over morphologic characteristics in the reduction by ligamentotaxis of thoracolumbar burst fractures.

BACKGROUND: thoracolumbar burst fractures are associated with spinal canal occupation. The indirect decompression of the spinal canal and reduction of the fragment can be achieved with the distraction of the middle column and ligamentotaxis. Nevertheless, the factors that influence the effectiveness of this procedure and its temporality are controversial. METHODS: The aim of this observational, cross-sectional study was to evaluate the effectiveness of indirect reduction by ligamentotaxis in thoracolumbar burst fractures according to the fracture's radiologic characteristics and the procedure's temporality. Patients diagnosed with a thoracolumbar burst fracture between 2010 and 2021 were submitted to indirect reduction by distraction and ligamentotaxis. A retrospective analysis of radiologic characteristics and temporality of the procedure was performed with an independent sample t-test or Pearson's correlation coefficient, as required. RESULTS: A total of 58 patients were included in the analysis. Postoperatively, ligamentotaxis significantly improved all radiologic parameters (canal occupation, endplates distance, and vertebra height). Still, none of the radiological characteristics of the fracture (width, height, position, sagittal angle) were associated with the postoperative change in canal occupation. The endplates distance and the temporality of ligamentotaxis significantly predicted the reduction of the fracture. CONCLUSION: Fragment reduction effectiveness is more significant when performed as early as possible and adequate distraction is achieved using the internal fixator system. The radiologic characteristics of the fractured fragment do not determine its reduction capacity.

In Silico Studies of Four Compounds of Cecropia obtusifolia against Malaria Parasite.

Malaria is a disease that affects many people in the world. In Mexico, malaria remains an active disease in certain regions, particularly in the states of Chiapas and Chihuahua. While antimalarial effects have been attributed to some species of Cecropia in various countries, no such studies have been conducted in Mexico. Therefore, the objective of this study was to evaluate the in silico antimalarial activity of some active compounds identified according to the literature in the species of Cecropia obtusifolia, belonging to the Cecropiaceae family, such as ursolic acid, α-amyrin, chrysin, and isoorientin. These compounds were evaluated with specific molecular docking and molecular dynamics (MD) studies using three different malarial targets with the PDB codes 1CET, 2BL9, and 4ZL4 as well as the prediction of their pharmacokinetic (Pk) properties. Docking analysis revealed the following best binding energies (kcal/mol): isoorientin-1CET (-9.1), isoorientin-2BL9 (-8.8), and chrysin-4ZL4 (-9.6). MD simulation validated the stability of the complexes. Pharmacokinetics analysis suggested that the compounds would generally perform well if administered. Therefore, these results suggest that these compounds may be used as potential drugs for the treatment of malaria.

Standardized spectral and radiometric calibration of consumer cameras.

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.

Absolute Radiometric Calibration of TESS-W and SQM Night Sky Brightness Sensors.

We develop a general optical model and describe the absolute radiometric calibration of the readings provided by two widely-used night sky brightness sensors based on irradiance-to-frequency conversion. The calibration involves the precise determination of the overall spectral sensitivity of the devices and also the constant G relating the output frequency of the light-to-frequency converter chip to the actual band-weighted and field-of-view averaged spectral radiance incident on the detector (brightness). From these parameters, we show how to define a rigorous astronomical absolute photometric system in which the sensor measurements can be reported in units of magnitudes per square arcsecond with precise physical meaning.

Pseudomonas syringae pv. tomato exploits light signals to optimize virulence and colonization of leaves.

Light is pervasive in the leaf environment, creating opportunities for both plants and pathogens to cue into light as a signal to regulate plant-microbe interactions. Light enhances plant defences and regulates opening of stomata, an entry point for foliar bacterial pathogens such as Pseudomonas syringae pv. tomato DC3000 (PsPto). The effect of light perception on gene expression and virulence was investigated in PsPto. Light induced genetic reprogramming in PsPto that entailed significant changes in stress tolerance and virulence. Blue light-mediated up-regulation of type three secretion system genes and red light-mediated down-regulation of coronatine biosynthesis genes. Cells exposed to white light, blue light or darkness before inoculation were more virulent when inoculated at dawn than dusk probably due to an enhanced entry through open stomata. Exposure to red light repressed coronatine biosynthesis genes which could lead to a reduced stomatal re-opening and PsPto entry. Photoreceptor were required for the greater virulence of light-treated and dark-treated PsPto inoculated at dawn as compared to dusk, indicating that these proteins sense the absence of light and contribute to priming of virulence in the dark. These results support a model in which PsPto exploits light changes to maximize survival, entry and virulence on plants.

Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity.

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

Public policy guidelines for a comprehensive, territorial and sustainable development to improve productivity and competitiveness. Case Tungurahua province-Ecuador.

In this work we elaborate a proposal for policy guidelines for the improvement of productivity and competitiveness of the province of Tungurahua -Ecuador-, such proposal is based on the theoretical foundations about the concept of a comprehensive, territorial and sustainable development applied to the territorial diagnosis. Herein three analysis techniques were used as a methodological strategy: The Rasmussen Method, which consists of a multisector model based on the Input-Output Tables-; the technique of focus groups for the analysis of perception on the prioritization of key sectors by the population and by the productive sectors; and the Shift-Share Analysis, to determine the level of growth of some sectors with respect to others. The results have allowed the identification of the strengths and weaknesses, as well as, the opportunities and threats associated with the levels of productivity and competitiveness of the province of Tungurahua. Therefore, strategies have been formulated aimed at the comprehensive, territorial and sustainable development of the province, which are built on the strengthening of endogenous capacities in science, technology and innovation, the encouragement of a coordination and articulation between the actors, the strengthening of the local business web and the internationalization of the territory.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science.

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications.

Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and feature extraction methods on the affective EEG-based DEAP dataset. Findings suggest that if Power Spectral Density or Wavelet Energy features are extracted from the artifact-free EEG signal, 1 and 3 s of data per subject is enough to achieve +95% and +99% accuracy, respectively. These findings contributes to the body of knowledge by paving a way for the application of EEG to real-world ecological biometric applications and by demonstrating methods to learn the minimal amount of data required for such applications.

Removal of mercury(II) from aqueous solution by partially reduced graphene oxide.

Mercury (Hg(II)) has been classified as a pollutant and its removal from aqueous sources is considered a priority for public health as well as ecosystem protection policies. Oxidized graphenes have attracted vast interest in water purification and wastewater treatment. In this report, a partially reduced graphene oxide is proposed as a pristine adsorbent material for Hg(II) removal. The proposed material exhibits a high saturation Hg(II) uptake capacity of 110.21 mg g-1, and can effectively reduce the Hg(II) concentration from 150 mg L-1 to concentrations smaller than 40 mg L-1, with an efficiency of about 75% within 20 min. The adsorption of Hg(II) on reduced graphene oxide shows a mixed physisorption-chemisorption process. Density functional theory calculations confirm that Hg atom adsorbs preferentially on clean zones rather than locations containing oxygen functional groups. The present work, therefore, presents new findings for Hg(II) adsorbent materials based on partially reduced graphene oxide, providing a new perspective for removing Hg(II).

Static internal representation of dynamic situations reveals time compaction in human cognition.

INTRODUCTION: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. OBJECTIVES: The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. METHODS: We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. RESULTS: The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. CONCLUSIONS: The static internal representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.

Universal principles justify the existence of concept cells.

The widespread consensus argues that the emergence of abstract concepts in the human brain, such as a "table", requires complex, perfectly orchestrated interaction of myriads of neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells (CCs), may be responsible for complex tasks performed by humans. This finding, with deep implications for neuroscience and theory of neural networks, has no solid theoretical grounds so far. Our recent advances in stochastic separability of highdimensional data have provided the basis to validate the existence of CCs. Here, starting from a few first principles, we layout biophysical foundations showing that CCs are not only possible but highly likely in brain structures such as the hippocampus. Three fundamental conditions, fulfilled by the human brain, ensure high cognitive functionality of single cells: a hierarchical feedforward organization of large laminar neuronal strata, a suprathreshold number of synaptic entries to principal neurons in the strata, and a magnitude of synaptic plasticity adequate for each neuronal stratum. We illustrate the approach on a simple example of acquiring "musical memory" and show how the concept of musical notes can emerge.

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

Evaluating Human Photoreceptoral Inputs from Night-Time Lights Using RGB Imaging Photometry.

Night-time lights interact with human physiology through different pathways starting at the retinal layers of the eye; from the signals provided by the rods; the S-, L- and M-cones; and the intrinsically photosensitive retinal ganglion cells (ipRGC). These individual photic channels combine in complex ways to modulate important physiological processes, among them the daily entrainment of the neural master oscillator that regulates circadian rhythms. Evaluating the relative excitation of each type of photoreceptor generally requires full knowledge of the spectral power distribution of the incoming light, information that is not easily available in many practical applications. One such instance is wide area sensing of public outdoor lighting; present-day radiometers onboard Earth-orbiting platforms with sufficient nighttime sensitivity are generally panchromatic and lack the required spectral discrimination capacity. In this paper, we show that RGB imagery acquired with off-the-shelf digital single-lens reflex cameras (DSLR) can be a useful tool to evaluate, with reasonable accuracy and high angular resolution, the photoreceptoral inputs associated with a wide range of lamp technologies. The method is based on linear regressions of these inputs against optimum combinations of the associated R, G, and B signals, built for a large set of artificial light sources by means of synthetic photometry. Given the widespread use of RGB imaging devices, this approach is expected to facilitate the monitoring of the physiological effects of light pollution, from ground and space alike, using standard imaging technology.

Fast social-like learning of complex behaviors based on motor motifs.

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n-1)! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n-1) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Plantas medicinales utilizadas en la región de Teziutlán, Puebla, México / Medicinal plants used in the region of Teziutlán, Puebla, México

The objective of this work was to collect information on the curative use of plants in the municipality of Teziutlán, Puebla through semi-structured interviews. Thus, 78 plants used for medicinal purposes were identified, of which 40 are native to Mexico and 38 introduced; The value of use (UV) of each one and the Informant's Consensus Factor (FCI) of 10 categories of diseases were calculated. The five most frequently used plants are Ruta chalepensis L., Rosmarinus officinalis, Arnica montana, Loeselia mexicana (Lam.) Brandegee and Sambucus cerulea var., Neomexicana, which underwent a chemical and pharmacological review. On the other hand, the most frequent preparations are infusion and decoction, using mainly leaves (49.34%) and flowers (19.51%). It is concluded that the inhabitants of Teziutlán have a fairly homogeneous ethnomedical knowledge, setting the standard for research on its pharmacological properties.

El objetivo del presente trabajo fue recopilar información sobre el uso curativo de las plantas del municipio de Teziutlán, Puebla por medio de entrevistas semiestructuradas. Así, se identificaron 78 plantas utilizadas con fines medicinales, de las cuales 40 son propias de México y 38 introducidas; se calculó el valor de uso (UV) de cada una y el Factor de Consenso del Informante (FCI) de 10 categorías de padecimientos. Las cinco plantas utilizadas con mayor frecuencia son Ruta chalepensis L., Rosmarinus officinalis, Arnica montana, Loeselia mexicana (Lam.) Brandegee y Sambucus cerulea var., neomexicana, a las que se les hizo una revisión química y farmacológica. Por otro lado, las preparaciones más frecuentes son infusión y decocción, utilizando principalmente hojas (49.34 %) y flores (19.51 %). Se concluye que los habitantes de Teziutlán cuentan con un conocimiento etnomédico bastante homogéneo, dando la pauta para investigaciones sobre sus propiedades farmacológicas.

Renal replacement therapy in Latin American end-stage renal disease.

The Latin American Dialysis and Renal Transplant Registry (RLADTR) was founded in 1991; it collects data from 20 countries which are members of Sociedad Latinoamericana de Nefrología e Hipertension. This paper presents the results corresponding to the year 2010. This study is an annual survey requesting data on incident and prevalent patients undergoing renal replacement treatment (RRT) in all modalities: hemodialysis (HD), peritoneal dialysis (PD) and living with a functioning graft (LFG), etc. Prevalence and incidence were compared with previous years. The type of renal replacement therapy was analyzed, with special emphasis on PD and transplant (Tx). These variables were correlated with the gross national income (GNI) and the life expectancy at birth. Twenty countries participed in the surveys, covering 99% of the Latin American. The prevalence of end stage renal disease (ESRD) under RRT in Latin America (LA) increased from 119 patients per million population (pmp) in 1991 to 660 pmp in 2010 (HD 413 pmp, PD 135 pmp and LFG 111 pmp). HD proportionally increased more than PD, and Tx HD continues to be the treatment of choice in the region (75%). The kidney Tx rate increased from 3.7 pmp in 1987 to 6.9 pmp in 1991 and to 19.1 in 2010. The total number of Tx's in 2010 was 10 397, with 58% deceased donors. The total RRT prevalence correlated positively with GNI (r2 0.86; P < 0.05) and life expectancy at birth (r2 0.58; P < 0.05). The HD prevalence and the kidney Tx rate correlated significantly with the same indexes, whereas the PD rate showed no correlation with these variables. A tendency to rate stabilization/little growth was reported in the most regional countries. As in previous reports, the global incidence rate correlated significantly only with GNI (r2 0.63; P < 0.05). Diabetes remained the leading cause of ESRD. The most frequent causes of death were cardiovascular (45%) and infections (22%). Neoplasms accounted for 10% of the causes of death. The prevalence of RRT continues to increase, particularly in countries with 100% public health or insurance coverage for RRT, where it approaches rates comparable to those displayed by developed countries with a better GNI. The incidence also continues to increase in both countries that have not yet extended its coverage to 100% of the population as well as in those that have an adequate program for timely detection and treatment of chronic kidney disease (CKD) and its associated risk factors. PD is still an underutilized strategy for RRT in the region. Even though renal Tx is feasible, its growth rate is still not as fast as it should be in order to compensate for the increased prevalence of patients on waiting lists. Diagnostic and prevention programs for hypertension and diabetes, appropriate policies promoting the expansion of PD and organ procurement as well as transplantation as cost-effective forms of RRT are needed in the region. Regional cooperation among Latin American countries, allowing the more developed to guide and train others in starting registries and CKD programs, may be one of the key initiatives to address this deficit.

Estridor y dificultad respiratoria: aproximación neurológica: caso clínico / Stridor and dyspnea caused by a conversion disorder: a case report

Paradoxical vocal cord motion or vocal cord dysfunction is a descriptive term for an inappropriate adduction of the vocal cords during respiration, which can cause respiratory obstruction and stridor. It is associated with psychiatric conditions in the great majority of cases. We report a 23 year-old high performance female athlete, referred for a recurrent bilateral paralysis of the vocal cords, with a history of four intensive care unit admissions for severe dyspnea and stridor, which were treated several times with intubation and with tracheostomy on two occasions. Myasthenia gravis was suspected and she was treated with pyridostigmine and prednisone. She was discharged but despite the treatment, she presented new episodes of stridor and was readmitted six months later. This time the pharmacological treatment was suspended. The neurological study disclosed a normal brain magnetic resonance, normal cerebrospinal fluid analysis and a normal electromyography. A conversion disorder was suspected and the patient was successfully treated with psychotherapy.