La importancia del sueño en los adolescentes: impacto de variables sociodemográficas y de factores personales / The Importance of Sleep in Adolescents: Impact of Sociodemographic Variables and Personal Factors

Resumen Objetivo: Ante la escasez de investigaciones que traten de manera conjunta el conocimiento, la calidad y la higiene del sueño en el adolescente, el objetivo del presente estudio es analizar las diferencias y las relaciones que existen entre estas variables en función de características sociodemográficas y personales. Método: A través de los instrumentos Sleep Beliefs Scale (SBS), Spanish Adolescents and Young Adults Pittsburgh Sleep Quality Index (AYA-PSQI-S) y Adolescents Sleep Hygiene Scale Revied (ASHSr), se midieron las características del sueño de 140 estudiantes (M = 16,75; DE = 0,75). Resultados: El 89,3% de los adolescentes presentaban problemas de sueño, subyacentes de la mejorable higiene del sueño por parte del 62,2% y de un escaso conocimiento general del sueño. El sexo femenino y el alumnado en cursos superiores presentaron mayores problemas para conciliar el sueño, somnolencia y una baja gestión cognitiva-emocional (p < 0,05). La higiene del sueño (β = - 0,344), la edad (β = 0,154) y el autoconcepto (β = -0,349) son los factores que predijeron significativamente (p < 0,05) la calidad del sueño del adolescente. Conclusiones: La adquisición de una adecuada higiene del sueño se vuelve fundamental para mejorar la calidad del descanso y la funcionalidad diurna en adolescentes, destacando su importancia, sobre todo, en niveles académicos avanzados y en el caso específico de las mujeres.

Abstract Objective: Given the scarcity of research addressing the intersection of knowledge, sleep quality, and hygiene among adolescents, the primary objective of this study is to analyse the variations and correlations among these variables based on socio-demographic and personal characteristics. Method: The Sleep Beliefs Scale (SBS), the Spanish Adolescents and Young Adults Pittsburgh Sleep Quality Index (AYA-PSQI-S), the Adolescents Sleep Hygiene Scale Revied (ASHSr) were used to measure the sleep characteristics of the 140 Spanish students (M = 16,75; SD = 0,75). Results: Sleep problems were found in 89,3% of the adolescents, underlying poor sleep hygiene in 62,2% and poor general sleep knowledge (SBS = 12,04). Females and students in higher grades presented greater problems in falling asleep, sleepiness, and poor cognitive-emotional management (p < 0,05). Sleep hygiene (β = - 0,344), age (β = 0,154), and self-concept (β = -0,349) were the factors that significantly (p < 0,05) predicted adolescent sleep quality. Conclusions: The acquisition of proper sleep hygiene becomes crucial to enhance the quality of rest and daytime functionality in adolescents, emphasizing its significance, especially in advanced academic levels and specifically in the case of females.

Loneliness and pain among community-dwelling middle-aged and older Black, Latino, and White adults in the United States.

Background: Prior research has demonstrated a strong and independent association between loneliness and pain, but few studies to date have explored this relationship in racially and ethnically diverse groups of midlife and older adults. We drew on the diathesis stress model of chronic pain and cumulative inequality theory to examine the relationship of loneliness and the presence and intensity of pain in a nationally representative sample of Black, Latino, and White adults aged 50 or older in the United States. Methods: Data were from Wave 3 of the National Social Life, Health, and Aging Project (n = 2,706). We used weighted logistic and ordinary least squares regression analyses to explore main and interactive effects of loneliness and race and ethnicity while adjusting for well-documented risk and protective factors (e.g., educational attainment, perceived relative income, inadequate health insurance, perceived discrimination) and salient social and health factors. Results: Almost half (46%) of the participants reported feeling lonely and 70% reported the presence of pain. Among those who reported pain (n = 1,910), the mean intensity score was 2.89 (range = 1-6) and 22% reported severe or stronger pain. Greater loneliness was associated with increased odds of pain presence (AOR = 1.154, 95% CI [1.072, 1.242]) and higher pain intensity (ß = 0.039, p < 0.01). We found no significant interaction effects involving Black participants. However, Latino participants who reported greater loneliness had significantly higher levels of pain (ß = 0.187, p < 0.001) than their White counterparts with similar levels of loneliness. Discussion: Loneliness is an important correlate of pain presence and intensity and may have a stronger effect on pain intensity among Latino adults aged 50 or older. We discuss clinical and research implications of these findings, including the need for more fine-grained analyses of different types of loneliness (e.g., social, emotional, existential) and their impact on these and other pain-related outcomes (e.g., interference). Our findings suggest a need for interventions to prevent and manage pain by targeting loneliness among middle-aged and older adults, particularly Latino persons.

Corrigendum: Loneliness and global cognitive functioning in racially and ethnically diverse US midlife and older adults.

[This corrects the article DOI: 10.3389/fpsyg.2024.1344044.].

Loneliness and global cognitive functioning in racially and ethnically diverse US midlife and older adults.

Introduction: Few studies have examined the association of loneliness and cognitive functioning in the US. We used two common measures of loneliness and examined their association in a large sample of US Black, Latino, and White adults (ages ≥ 50). Methods: We analyzed Wave 3 of the National Social Life, Health, and Aging Project (N = 2,757). We examined loneliness using one item from the CES-D and the Felt Loneliness Measure (NFLM); cognitive functioning was assessed using the Montreal Cognitive Assessment (MoCA) tool, where higher scores indicated better functioning. We used weighted ordinary least squares regressions to examine the effects of loneliness (CES-D loneliness and NFLM in separate models) on MoCA scores. In exploratory analyses, we examined if these relationships varied by race and ethnicity. We adjusted all models for sociodemographic and other salient factors (e.g., chronic disease, depressive symptoms, living alone). Results: Mean age was 63.49 years, 52% were female, and 9% were Black and 6% Latino persons. Approximately 54% endorsed feeling lonely on at least one measure; 31% (CES-D) and 46% (NFLM). The relationship between loneliness measures was positive and significant, X 2 (1, N = 2,757) = 435.493 p < 0.001. However, only 40% of lonely individuals were identified as lonely on both assessments. CES-D loneliness was inversely (߈ = -0.274, p = 0.032) associated with MoCA scores and this association did not vary by race and ethnicity. Greater NFLM loneliness was positively associated (߈ = 0.445, p < 0.001) with higher MoCA scores for Latino participants only. Discussion: Loneliness appears to be an important predictor of cognitive functioning. However, the association of loneliness and cognitive functioning varied when using the CES-D loneliness item or the NFLM. Future work is needed to understand how loneliness and its clinically relevant dimensions (social, emotional, existential, chronicity) relate to global and individual cognitive domains. Research is needed with racially and ethnically diverse midlife and older adults, particularly to understand our counterintuitive finding for Latino participants. Finally, findings also support the need for research on interventions to prevent cognitive decline targeting loneliness.

Changes In Serum CXCL13 Levels Are Associated With Outcomes of Colorectal Cancer Patients Undergoing First-Line Oxaliplatin-Based Treatment.

Metastatic colorectal cancer (mCRC) currently lacks reliable biomarkers for precision medicine, particularly for chemotherapy-based treatments. This study examines the behavior of 11 CXC chemokines in the blood of 104 mCRC patients undergoing first-line oxaliplatin-based treatment to pinpoint predictive and prognostic markers. Serum samples were collected before treatment, at response evaluation (EVAR), and at disease progression or last follow-up. Chemokines were assessed in all samples using a Luminex® custom panel. CXCL13 levels increased at EVAR in responders, while in non-responders it decreased. Increasing levels of CXCL13 at EVAR, independently correlated with improved progression-free survival (PFS) and overall survival (OS). Nanostring® analysis in primary tumor samples showed CXCL13 gene expression's positive correlation not only with gene profiles related to an immunogenic tumor microenvironment, increased B cells and T cells (mainly CD8+) but also with extended OS. In silico analysis using RNAseq data from liver metastases treated or not with neoadjuvant oxaliplatin-based combinations, and deconvolution analysis using the MCP-counter algorithm, confirmed CXCL13 gene expression's association with increased immune infiltration, improved OS, and Tertiary Lymphoid Structures (TLSs) gene signatures, especially in neoadjuvant-treated patients. CXCL13 analysis in serum from 36 oxaliplatin-treated patients from the METIMMOX study control arm, reported similar findings. In conclusion, the increase of CXCL13 levels in peripheral blood and its association with the formation of TLSs within the metastatic lesions, emerges as a potential biomarker indicative of the therapeutic efficacy in mCRC patients undergoing oxaliplatin-based treatment.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning.

INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. OBJECTIVE: This study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. METHOD: To achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. RESULTS: Simulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. CONCLUSION: In light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

AAV2 vector optimization for retinal ganglion cell-targeted delivery of therapeutic genes.

Recombinant adeno-associated virus (AAV)-2 has significant potential as a delivery vehicle of therapeutic genes to retinal ganglion cells (RGCs), which are key interventional targets in optic neuropathies. Here we show that when injected intravitreally, AAV2 engineered with a reporter gene driven by cytomegalovirus (CMV) enhancer and chicken ß-actin (CBA) promoters, displays ubiquitous and high RGC expression, similar to its synthetic derivative AAV8BP2. A novel AAV2 vector combining the promoter of the human RGC-selective γ-synuclein (hSNCG) gene and woodchuck hepatitis post-transcriptional regulatory element (WPRE) inserted upstream and downstream of a reporter gene, respectively, induces widespread transduction and strong transgene expression in RGCs. High transduction efficiency and selectivity to RGCs is further achieved by incorporating in the vector backbone a leading CMV enhancer and an SV40 intron at the 5' and 3' ends, respectively, of the reporter gene. As a delivery vehicle of hSIRT1, a 2.2-kb therapeutic gene with anti-apoptotic, anti-inflammatory and anti-oxidative stress properties, this recombinant vector displayed improved transduction efficiency, a strong, widespread and selective RGC expression of hSIRT1, and increased RGC survival following optic nerve crush. Thus, AAV2 vector carrying hSNCG promoter with additional regulatory sequences may offer strong potential for enhanced effects of candidate gene therapies targeting RGCs.

"These Places are easy to get into but Impossible to get out of": Women's Pathways to Psychiatric Institutions and Barriers to Community Reentry in India.

In India, where institutional-based mental health care is common, gender and other intersecting marginalized identities along with absent familial support contribute to women's admission and prolonged confinement to psychiatric institutions. However, an intersectional analysis of factors that prevent women with limited familial support from returning to their communities is lacking. This article is based on narratives of eleven women residing at a halfway home in an urban city in India, awaiting return to their communities. We include descriptions and an intersectional analysis of women's pathways to psychiatric institutions, their experiences receiving institutional-based mental health care, and the challenges they face as they contemplate returning to their communities. This study adds to the minimal research examining women's gendered pathways to psychiatric institutions in India. Women's narratives highlight that gender and illness-related disadvantages coupled with economic adversity that led to the initial admission also serve as deterrents to reentering the community.

Analysis of the contribution of sport events to sustainable development: Impacts, support and resident's perception.

The current study intends to evaluate the link between the affects observed by citizens on athletic events, the perception of their contribution to municipalities' sustainable development, and support for their celebration. A total of 2049 inhabitants from the Valencian Community's three provincial capitals (Spain) took part in this study, which used a stratified random sample with proportionate allocation. The causal association model fit well, with RMSEA = 0.044, NNFI = 0.94, CFI = 0.95, and IFI = 0.95. The study's findings demonstrate that residents' perceptions of the positive and negative impacts caused by sporting events in their communities are an antecedent that explains how these events contribute to sustainable urban development. A positive relationship between perceived sustainable development and support for the hosting of sport events in these localities was also found. These results highlight the need to promote sustainable management development of any sporting event to increase local support from the residents.

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. METHODS: For the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. RESULTS: The CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2-99.9) and 77.7 (95% CrI, 63.3-98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). CONCLUSIONS: Through the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.

Emerging Gene Therapy Technologies for Retinal Ganglion Cell Neuroprotection.

ABSTRACT: Optic neuropathies encompass a breadth of diseases that ultimately result in dysfunction and/or loss of retinal ganglion cells (RGCs). Although visual impairment from optic neuropathies is common, there is a lack of effective clinical treatments. Addressing a critical need for novel interventions, preclinical studies have been generating a growing body of evidence that identify promising new drug-based and cell-based therapies. Gene therapy is another emerging therapeutic field that offers the potential of specifically and robustly increasing long-term RGC survival in optic neuropathies. Gene therapy offers additional benefits of driving improvements following a single treatment administration, and it can be designed to target a variety of pathways that may be involved in individual optic neuropathies or across multiple etiologies. This review explores the history of gene therapy, the fundamentals of its application, and the emerging development of gene therapy technology as it relates to treatment of optic neuropathies.

Retrospective validation of parkland grading scale in a Latin-American high-volume center.

BACKGROUND: Increased complication rates following laparoscopic cholecystectomies have been described, likely related to surgical difficulty, anatomical variations, and gallbladder inflammation severity. Parkland Grading Scale (PGS) stratifies the severity of intraoperative findings to predict operative difficulty and complications. This study aims to validate PGS as a postoperative-outcome predictive tool, comparing its performance with Tokyo Guidelines Grading System (TGGS). METHODS: This is a single-center retrospective cohort study where PGS and TGGS performances were evaluated regarding intraoperative and postoperative outcomes. Both univariate and bivariate analyses were performed on each severity grading scale using STATA-SE 16.0 software. Additionally, we proposed a Logistic Regression Model for each scale. Their association with outcomes was compared between both scales by their Receiver Operating Characteristic Curve. RESULTS: 400 Patients were included. Grade 1 predominance was observed for both PGS and TGGS (47.36% and 25.3%, respectively). A positive association was observed between higher PGS grades and inpatient postoperative care, length of stay, ICU care, and antibiotic requirement. Based on the area under the ROC curve, better performance was observed for PGS over TGGS in the evaluated outcomes. CONCLUSION: PGS performed better than TGGS as a predictive tool for inpatient postoperative care, length of stay, ICU, and antibiotic requirement, especially in severe cases.

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter.

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an increment of the different tensions underlying politics, as has been reported after many other political and economical crises during the twentieth century. This article proposes the study of the political discourse, and its underlying tension, during Madrid's elections (Spain) in May 2021 by using a mixed approach. To demonstrate if an aggressive tone is used during the campaign, a mixed methodology approach is applied: quantitative computational techniques, related to natural language processing, are used to conduct a first general analysis of the information screened; then, these methods are used for detecting specific trends that can be later filtered and analyzed using a qualitative approach (content analysis), which is also conducted to extract insights about the information found. The main outcomes of this study show that the electoral campaign is not as negative as perceived by the citizens and that there was no relationship between the tone of the discourse and its dissemination. The analysis confirms that the most ideologically extreme parties tend to have a more aggressive language than the moderate ones. The content analysis carried out using our methodology showed that Twitter is used as a sentiment thermometer more than as a way of communicating concrete politics.

Exploring Dimensionality Reduction Techniques in Multilingual Transformers.

In scientific literature and industry, semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks are unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. An increase in size has accompanied the growing need to provide more complex models implementing all these features without being conservative in the number of dimensions required. This paper aims to provide a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual siamese transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the embeddings from the pre-trained version of five models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction of 91.58 % ± 2.59 % in the number of dimensions of embeddings from pre-trained models requiring a fitting time 96.68 % ± 0.68 % faster than the fine-tuning process. Besides, we achieve 54.65 % ± 32.20 % dimensionality reduction in embeddings from fine-tuned models. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for other highly demanding NLP tasks.

Employment of trauma informed principles in the Palabras Fuertes project: Implications for narrative research with older Latinx communities.

In the US, there is a growing number of older Latinx communities. Qualitative approaches such as narrative inquiry may be fruitful endeavors to elucidate their lived experiences. However, older Latinx communities, including sexual minorities, are disproportionately exposed to social, health, and historical challenges that may result in exposure to potentially traumatic events (e.g. discrimination, illness, grief, etc.). The recognition of high rates of exposure to potentially traumatic events among participants has led to the recommended adoption of Trauma Informed (TI) principles for use in non-trauma specific research. At present, there are limited examples and discussions about the implementation of TI principles in qualitative research and our literature review yielded no discussion of the use of TI principles in narrative inquiry or with older Latinx communities. In this manuscript, we advocate for the adoption of TI principles when engaging in narrative inquiry with older Latinx adults. Second, we discuss examples of TI guided practices we employed while conducting the Palabras Fuertes study of life history narratives with older Latino immigrant gay men living in New York City. Finally, based on these experiences, we provide recommendations for incorporating TI into future narrative research with older Latinx communities.

Older immigrant Latino gay men and childhood sexual abuse: Findings from the Palabras Fuertes project.

Childhood Sexual Abuse (CSA) and maltreatment have long-term negative impacts on survivors, including older adults. Yet, limited qualitative examinations of how these experiences impact the lives of older adults exists and even fewer among older Latino gay men. We drew data from life-history narratives the first author conducted with five Spanish speaking older Latino gay men in New York City. Our analyses were guided by an Ecological Model, a Suffering lens, and our clinical social work experience with older adults, sexual minorities and people of color. All participants reported sexual experiences prior to the age of 15 and possible emotional and physical maltreatment. Yet, not all participants perceived these experiences as abuse. Our findings indicate how cultural, linguistic and contextual factors may affect disclosure and coping. Despite the fact that CSA and maltreatment occurred decades ago, these early experiences affected long-term psychosocial functioning. Our findings support a need for future research and clinical practice that considers the subjective perceptions of childhood sexual experiences and maltreatment and how these relate to psychosocial functioning in Latino gay men during older adulthood.

Recent advances on effective and efficient deep learning-based solutions.

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges.

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

The Permeating Effects of Violence on Health Services and Health in Mexico.

Mexico's violence related to organized crime activity has grown to epidemic levels in the last 12 years. We interviewed 22 Mexican health care providers from five states to examine how violence impacts health care services and health. We transcribed and analyzed semi-structured interviews using framework analysis. Our findings describe the ways in which community violence in Mexico permeates health care services, impacting health care providers, and the health of patients. We developed a model to reflect our main themes that illustrate how violence permeates health care services over geographic space and time. We identified three thematic categories: (a) the impact of violence on health care facilities and service provision, (b) the impact of violence on providers, and (c) the impact of violence on the health of the community. Our model articulates a dynamic process of the spread and permeation of violence. Prior literature focuses on the impact of violence as an occupational hazard and the effect of war or civil conflict on health care services. We extend this literature by documenting the impacts of widespread violence on Mexican health care services and providers. We discuss how violence impacts services, providers, and health in a country that is not officially at war. We compare our findings to previous literature on occupational violence in health professions and the impacts on health services in official war zones. Finally, we highlight the implications for health care practice and policy. We suggest that violence should be considered throughout the care continuum in Mexico and make the case for violence as a structural contributor to health and health disparities in Mexico. We suggest additional research on this under-investigated topic.

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions.

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.