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Abstract Background Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas. Methods We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters. Results We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients. Conclusions Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

In this work, a formulation of alternating direction method of multipliers (ADMM) for addressing power amplifiers (PA) modeling and linearization problems is presented. The proposal consists on leveraging the implicit redundancy of the equations in order to achieve a distributed architecture. A detailed theoretical formulation of the method is provided in order to get a better comprehension of its advantages and the approach to integrating the technique in the standard direct learning architecture scheme for digital predistortion. In addition, a discussion on how the implicit regularization helps to deal with numerical problems is presented. It is proven that the implicit regression for the modeling and linearization of PAs can be carried out in a distributed fashion with similar accuracy, enabling the use of resource-constrained devices for digital predistortion. Furthermore, a computational assessment is presented for measuring in terms of arithmetic operations how simpler the devices that implement the proposed ADMM can be, compared with the classical least squares (LS) data-centralized approach. A proof of concept with three scenarios is included: the first two scenarios feature a commercial PA driven by 5G-NR signals with a bandwidth of 30 MHz, and the third scenario involves a commercial Doherty PA with a 100-MHz signal. In a first scenario, experimental results show that an ADMM implementation of a digital predistorter can achieve the same performance as the classical LS solution. The benefits of the proposed regularization are examined by assessing ADMM in a second scenario characterized by significant numerical instabilities. Finally, the robustness of the technique is illustrated through the linearization of the Doherty PA with a 100-MHz OFDM signal.

In this paper, the quadratic buck converter (QBC) is proposed as competitive alternative to implement a battery charger. Since QBC is a high order system, the required control is designed to follow the conventional constant-current constant-voltage protocol by means of three loops. Namely, 1) an inner-loop operating in sliding mode to control the current of the closest inductor to the input port providing the proper stability of the system, 2) a first outer loop designed to regulate the battery voltage providing the reference of the inner loop, and finally 3) a second outer loop to regulate the battery current modifying the reference of the voltage loop. Proportional Integral (PI) controllers are used in both outer loops, one of them synthesized by means of the robust loop shaping M-constrained integral gain optimization (RLS-MIGO) method, and the other designed using classical considerations for cascaded controllers. Both simulation and experimental results are presented validating the theoretical study and confirming the feasibility of the proposed control by means of analogue electronics.

In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat classification. However, accurately discerning ECG beat types faces challenges due to noise interference and inherent imbalances among different classes. Moreover, understanding mathematical models enclosed by black-box learning systems is an issue today. Our study employed a manifold learning algorithm capable of mapping high-dimensional data into a latent space to conduct a comprehensive analysis within a neural network learning framework. This approach involved the following studies: 1) examining the intermediate high-dimensional latent space in simple architectures by studying its projection into a visualizable latent space; 2) exploring the influence of class imbalance on the configuration of the latent space; 3) evaluating and analysing the compensatory effects of employing diverse DL architectures, such as modified autoencoders and Generative Adversarial Networks (GANs), specifically in generating data augmentation through synthetic beats. The experimental results demonstrated the effectiveness of our methodology in mitigating noise and addressing inter-class imbalance, notably enhancing the diagnostic Area Under the ROC Curve (AUC) in ECG signal analysis. Implementation of GAN data augmentation techniques resulted in a 2% improvement, elevating the AUC from 0,9332 to 0,9520 in the biclass dataset. Similarly, the AUC values increased by 2%, from 0,9020 to 0,9223, for the multiclass dataset. These findings highlight the impact of appropriate data augmentation techniques on AUC improvement. Furthermore, visualizing latent spaces during beat classifier design significantly contributes to developing solid and principled multiclass beat-discriminating systems.

The research aimed to analyze the contribution of Corporate Social Responsibility (CSR) to the competitiveness of supermarkets in Florencia, Caquetá in southern Colombia. In a sample of six supermarkets, a self-assessment survey of CSR and competitiveness variables was carried out for 2021 to 2022, consisting of six dimensions: values and ethical principles, responsible marketing, economy and finance, social commitment, quality of working life, and environment. The tool makes it possible to measure the commitment of the organization's CSR activities to competitiveness. The environment is the dimension with the lowest score and contribution to competitiveness in the dimensions evaluated in the supermarkets (2.0 on a scale of 0.0 - 3.0). According to the correlation analysis, there is a relationship between CSR and competitive position. Awareness-raising and training actions aimed at stakeholders are proposed.

Abstract Recently, the use of machine‐learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures which integrate mechanistic knowledge. In this work, we use as the test case a one‐compartment PK model using a scientific machine learning (SciML) framework and consider learning an unknown absorption using neural networks, while simultaneously estimating other parameters of drug distribution and elimination. We generate simulated data with different sampling strategies to show that our model can accurately predict concentrations in extrapolation tasks, including new dosing regimens with different sparsity levels, and produce reliable forecasts even for new patients. By using a scenario of fitting PK data with complex absorption, we demonstrate that including known physiological structure into an SciML model allows us to obtain highly accurate predictions while preserving the interpretability of classical compartmental models.

Esta investigación, analiza los contextos de educación no formal como potenciales entornos para la enseñanza de la física. La investigación se estructuró en dos fases. En la primera fase, se realizó un análisis bibliométrico utilizando el paquete Bibliometrix de Rstudio y los datos obtenidos de la base de datos Web of Science de publicaciones que relacionan dichos escenarios y la enseñanza de la física, permitiendo identificar el estado actual de la enseñanza de la física en contextos no formales. En la segunda fase, se llevó a cabo una búsqueda exhaustiva y seguimiento de la información sobre los escenarios de educación no formal en Colombia, como museos, centros de ciencia, planetarios y observatorios astronómicos. Se examinaron las actividades realizadas en estos lugares y se analizaron sus potencialidades en la enseñanza de la física. Este estudio contribuirá a una comprensión más completa de la enseñanza de la física en entornos no formales y permitirá identificar oportunidades de mejora en este ámbito.

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

The deployment of electric, connected, and autonomous vehicles on public roads presents a significant challenge that can be addressed through previously established frameworks developed globally for implementing these technologies as part of an urban living lab (ULL). This systematic review, based on records from four distinct databases, focuses on projects that have conducted deployments of self-driving technologies in streets within urban environments. The review describes relevant information about various initiatives, including a classification of the stages of development reached according to the urban area covered, safety considerations, and lessons learned for optimal deployment. On-board sensing technology, digital infrastructure, and energy and communication systems emerge as the essential components of a ULL with autonomous vehicles (AVs). A crucial goal for smart cities is ensuring the scalability of large-scale deployments of such ULLs for safe, clean, and future mobility experimentation. This can only be achieved through effective coordination among academia, government, industry, and society to guarantee the successful integration of multiple projects in a unique environment.

Malesherbia auristipulata is an endemic plant species of the Atacama Desert, with unique morphological and physiological adaptations. This research was conducted at Cuesta El Águila, Arica and Parinacota Region, Chile. Adult and juvenile plants were monitored, recording their growth, flowering, and fruiting phases. Additionally, plant community species were identified. For the study of endophytic mycoflora, samples of seeds, roots, stems, and leaves were collected, disinfected, and cultivated in specific media. The isolated fungi were analyzed morphologically and molecularly, determining their distribution in different plant organs. The diversity of endophytic fungi associated with M. auristipulata and the associated fungal community was determined. The presence of endophytic fungi varied depending on the organ studied, suggesting dynamic interactions in the structure of its fungal community. Among the identified endophytic fungi, Alternaria sorghi, A. alstroemeriae, and Fusarium nurragi stand out for their presence in the root and stem of the plant. Of particular interest is the presence of F. circinatum in the leaves. This study provides valuable information for the conservation of M. auristipulata and other organisms in the Atacama Desert, highlighting the importance of ecological interactions in the resilience of plants to extreme environmental conditions.

Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force (Vo_null) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor’s performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to Vo_null readings; nonetheless, a relationship between Vo_null and hysteresis was not found. However, a classification rule base on k-means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on Vo_null readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques.

Hip and knee osteoarthritis (OA) is common among Chilean adults over 60, impacting their functionality. This study aimed to determine how health factors such as disease severity, type of OA, medication use, and physical activity levels relate to functionality in individuals with OA. The sample size included 67 women and 13 men aged 38 to 92 (66.6 ± 11.2) with diagnosed knee or hip OA. The International Physical Activity Questionnaire-Short Form (IPAQ-SF) was used to estimate physical activity, the Lequesne Index for functional impairment, and the VAS (Visual Analog Scale) for pain. Data were collected from December 2022 to March 2023. We performed bivariate analyses, which indicated that greater OA severity and bilateral OA are associated with increased functional impairment and pain. Medication use was correlated with higher pain levels, and physical activity was correlated with functionality; there was no correlation with age. Here, 65.4% of the variance in functionality problems was explained by OA severity, bilateral involvement, and pain level, highlighting their impact on functionality. Functionality problems were higher in individuals who suffered higher levels of severity when they performed lower levels of physical activity. These findings underscore the importance of promoting physical activity to improve the quality of life for individuals with OA.

Organic compounds with antibacterial and antiparasitic properties are gaining significance for biomedical applications. This study focuses on the solvent-free synthesis (green synthesis) of 1,4-naphthoquinone or 2,3-dichloro-1,4-naphthoquinone with different phenylamines using silica gel as an acid solid support. The study also includes in silico PASS predictions and the discovery of antibacterial and antiparasitic properties of phenylaminonaphthoquinone derivatives 1–12, which can be further applied in drug discovery and development. These activities were discussed in terms of molecular descriptors such as hydrophobicity, molar refractivity, and half-wave potentials. The in vitro antimicrobial potential of the synthesized compounds 1–12 was evaluated against a panel of six bacterial strains (three Gram-positive: Staphylococcus aureus, Proteus mirabilis, and Enterococcus faecalis; and three Gram-negative bacteria: Escherichia coli, Salmonella typhimurium, and Klebsiella pneumoniae). Six compounds (1, 3, 5, 7, 10, and 11) showed better activity toward S. aureus with MIC values between 3.2 and 5.7 μg/mL compared to cefazolin (MIC = 4.2 μg/mL) and cefotaxime (MIC = 8.9 μg/mL), two cephalosporin antibiotics. Regarding in vitro antiplasmodial activity, compounds 1 and 3 were the most active against the Plasmodium falciparum strain 3D7 (chloroquine-sensitive), displaying IC50 values of 0.16 and 0.0049 μg/mL, respectively, compared to chloroquine (0.33 μg/mL). In strain FCR-3 (chloroquine-resistant), most of the compounds showed good activity, with compounds 3 (0.12 μg/mL) and 11 (0.55 μg/mL) being particularly noteworthy. Additionally, docking studies were used to better rationalize the action and prediction of the binding modes of these compounds. Finally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions were performed.

Background/Objectives: An extract of calyces from Physalis peruviana with hypoglycemic activity is being considered as a potential herbal medicine. Preclinical pharmacokinetics (PK) studies of the extract in rats, focusing on plasma concentrations of its main compound, rutin, and its metabolites, revealed PK interactions in the extract matrix that improved the absorption of rutin metabolites compared to the pure compound, among other PK effects. This research aimed to study the PK of rutin alone and in the extract and assess potential PK interactions in the extract matrix on the flavonoid and its metabolites in rabbits, a nonrodent species; Methods: Animals received pure rutin or extract orally and intravenously. The PK analysis used noncompartmental and population pharmacokinetics (popPK) methods, and simple allometry was applied to predict human PK parameters; Results: The rutin concentration–time profile fit a two-compartment model with first-order elimination, while its metabolites fit a double first-order absorption model. The extract matrix led to increased absorption, distribution, and elimination of rutin as well as increased bioavailability of its metabolites in rabbits; Conclusions: The popPK model defined the equations for PK parameters describing these findings, and the increased volume of distribution and clearance of rutin was maintained in human predictions. These results will support the development of a new herbal medicine.

Resistance exercise and protein supplementation are recognized as effective treatment strategies for age-related sarcopenia; however, there are limited data on their feasibility, tolerability, and safety. The primary outcome of this study was feasibility, evaluated through the 15-item TELOS (Technological, Economics, Legal, Operational, and Scheduling) feasibility components and by recruitment, retention, and consent rates. Tolerability was measured by examining permanent treatment discontinuation, treatment interruption, exercise dose modification, early termination, rescheduling of missed sessions, losses to follow-up, attendance, and nutritional compliance. Safety was evaluated using the parameters provided by the European Medicines Agency, adapted for exercise interventions. Thirty-two subjects were recruited (average age 81.6 [SD 9.3] years). The TELOS components were assessed before the intervention; out of 15 questions relevant for successful implementation, 4 operational needs answers required specific actions to prevent potential barriers. The recruitment rate was 74%. Eleven patients (34.4%) had permanent treatment interruption (retention rate = 65.6%). Patients attended a mean of 23 (SD 12.0) exercise sessions, with a mean of 56 (SD 32.6) nutritional compliances. A total of 21 patients (65.6%) experienced adverse events unrelated to the intervention, while 7 patients (21.9%) presented adverse reactions to strength exercise. The main barriers to feasibility were operational components and recruitment challenges. Although the intervention was generally safe, the high rate of probable adverse effects, unrelated to the intervention but associated with the individual’s baseline health condition, may affect adherence to treatment programs of this kind.

Widely available and reliable testing for SARS-CoV-2 is essential for the public health response to the COVID-19 pandemic. We estimated the diagnostic performance of reverse transcription PCR (RT-PCR) performed on saliva and the SD Biosensor STANDARD Q antigen test performed on nasopharyngeal swab compared to the reference standard, nasopharyngeal swab (NP) RT-PCR. We enrolled participants living and/or seeking care in health facilities in North Lima, Peru from November 2020 to January 2021. Consenting participants underwent same-day RT-PCR on both saliva and nasopharyngeal swab specimens, antigen testing on a nasopharyngeal swab specimen, pulse oximetry, and standardized symptom assessment. We calculated sensitivity, specificity, and predictive values for the nasopharyngeal antigen and saliva RT-PCR compared to nasopharyngeal RT-PCR. Of 896 participants analyzed, 567 (63.3%) had acute signs/symptoms of COVID-19. The overall sensitivity and specificity of saliva RT-PCR were 85.8% and 98.1%, respectively. Among participants with and without acute signs/symptoms of COVID-19, saliva sensitivity was 87.3% and 37.5%, respectively. Saliva sensitivity was 97.4% and 56.0% among participants with cycle threshold (CT) values of ≤30 and >30 on nasopharyngeal RT-PCR, respectively. The overall sensitivity and specificity of nasopharyngeal antigen were 73.2% and 99.4%, respectively. The sensitivity of the nasopharyngeal antigen test was 75.1% and 12.5% among participants with and without acute signs/symptoms of COVID-19, and 91.2% and 26.7% among participants with CT values of ≤30 and >30 on nasopharyngeal RT-PCR, respectively. Saliva RT-PCR achieved the WHO-recommended threshold of >80% for sensitivity for the detection of SARS-CoV-2, while the SD Biosensor nasopharyngeal antigen test did not. IMPORTANCE In this diagnostic validation study of 896 participants in Peru, saliva reverse transcription PCR (RT-PCR) had >80% sensitivity for the detection of SARS-CoV-2 among all-comers and symptomatic individuals, while the SD Biosensor STANDARD Q antigen test performed on nasopharyngeal swab had 80% sensitivity.

BackgroundRisk of anal squamous cell carcinoma (anal cancer) is greater among men who have sex with men (MSM) living with human immunodeficiency virus (HIV). We describe the frequency of and factors associated with abnormal anal cytology results in Colombian MSM living with HIV.MethodsThis retrospective observational cohort study included MSM ≥18 years old living with HIV screened with anal cytology at Hospital Universitario San Ignacio in Bogotá, Colombia between January 2019 and February 2020. A multivariable log-binomial regression model estimated associations with abnormal anal cytology.ResultsA total of 211 patients were included. Mean age was 35.6 years. Sixty-eight (32.3%) had an abnormal anal cytology result: ASC-US 33.8% (n = 23); LSIL 60.3% (n = 41); and HSIL 5.9% (n = 4). MSM with an STI diagnosis in the previous 12 months (RR 1.48, [95% CI 1.03-2.12], p = 0.032) or with a CD4+ T cell count

A process for adapting a case study as a high impact practice for undergraduate microbiology classes is described and its use in assessing global awareness as an institutional outcome at the course level. Documenting the student learning of content objectives and retention is addressed. Four student artifacts are assessed with an adaption of American Association of Colleges & Universities (AAC&U) rubrics at various points in the unit for formative and summative assessment. Student attrition, critical thinking, global awareness, and engagement in biology are addressed.