Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device.

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects' awareness.

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy.

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

Assessing cardiovascular stress based on heart rate variability in female shift workers: a multiscale-multifractal analysis approach.

Introduction: Sleep-wake cycle disruption caused by shift work may lead to cardiovascular stress, which is observed as an alteration in the behavior of heart rate variability (HRV). In particular, HRV exhibits complex patterns over different time scales that help to understand the regulatory mechanisms of the autonomic nervous system, and changes in the fractality of HRV may be associated with pathological conditions, including cardiovascular disease, diabetes, or even psychological stress. The main purpose of this study is to evaluate the multifractal-multiscale structure of HRV during sleep in healthy shift and non-shift workers to identify conditions of cardiovascular stress that may be associated with shift work. Methods: The whole-sleep HRV signal was analyzed from female participants: eleven healthy shift workers and seven non-shift workers. The HRV signal was decomposed into intrinsic mode functions (IMFs) using the empirical mode decomposition method, and then the IMFs were analyzed using the multiscale-multifractal detrended fluctuation analysis (MMF-DFA) method. The MMF-DFA was applied to estimate the self-similarity coefficients, α(q, τ), considering moment orders (q) between -5 and +5 and scales (τ) between 8 and 2,048 s. Additionally, to describe the multifractality at each τ in a simple way, a multifractal index, MFI(τ), was computed. Results: Compared to non-shift workers, shift workers presented an increase in the scaling exponent, α(q, τ), at short scales (τ < 64 s) with q < 0 in the high-frequency component (IMF1, 0.15-0.4 Hz) and low-frequency components (IMF2-IMF3, 0.04-0.15 Hz), and with q> 0 in the very low frequencies (IMF4, < 0.04 Hz). In addition, at large scales (τ> 1,024 s), a decrease in α(q, τ) was observed in IMF3, suggesting an alteration in the multifractal dynamic. MFI(τ) showed an increase at small scales and a decrease at large scales in IMFs of shift workers. Conclusion: This study helps to recognize the multifractality of HRV during sleep, beyond simply looking at indices based on means and variances. This analysis helps to identify that shift workers show alterations in fractal properties, mainly on short scales. These findings suggest a disturbance in the autonomic nervous system induced by the cardiovascular stress of shift work.

Integrating multiple lines of evidence to better understand the evolutionary divergence of humpback dolphins along their entire distribution range: a new dolphin species in Australian waters?

The conservation of humpback dolphins, distributed in coastal waters of the Indo-West Pacific and eastern Atlantic Oceans, has been hindered by a lack of understanding about the number of species in the genus (Sousa) and their population structure. To address this issue, we present a combined analysis of genetic and morphologic data collected from beach-cast, remote-biopsied and museum specimens from throughout the known Sousa range. We extracted genetic sequence data from 235 samples from extant populations and explored the mitochondrial control region and four nuclear introns through phylogenetic, population-level and population aggregation frameworks. In addition, 180 cranial specimens from the same geographical regions allowed comparisons of 24 morphological characters through multivariate analyses. The genetic and morphological data showed significant and concordant patterns of geographical segregation, which are typical for the kind of demographic isolation displayed by species units, across the Sousa genus distribution range. Based on our combined genetic and morphological analyses, there is convincing evidence for at least four species within the genus (S. teuszii in the Atlantic off West Africa, S. plumbea in the central and western Indian Ocean, S. chinensis in the eastern Indian and West Pacific Oceans, and a new as-yet-unnamed species off northern Australia).

[Associated factors and types of eye injuries occurring in dentistry practice: systematic review]. / Factores asociados y tipos de lesiones oculares ocurridas en la práctica odontológica: revisión sistemática.

Introduction: The dentist and auxiliary personnel are in constant risk of occupational diseases such as: allergies, infections, skin or eye injuries; that could affect patients. A problem of social interest are the ocular injuries that occur to dental personnel. The literature reveals the prevalence of eye injuries in dental practice that are associated with solid particles, fluid splashes. Our objective was, through a systematic review, to review the types of ocular injuries and their associated factors that occurred in dental practice. Material and Methods: The relevant studies were searched in the databases: PubMed, Scopus, Ebsco, among others. The search included cross-sectional studies that evaluated the evidence on ocular injuries occurring in dental practice, excluding literature reviews, letters to the reader, and editorials. Results: Eight studies were included to develop the systematic review; determining that the associated factors of ocular injuries occurred in dental practice are: a) occasional use of ocular protection b) age c) sex d) years of practice and the types of ocular injuries are the entry of liquids and foreign bodies. Conclusions: Deficiency in compliance with eye or facial protection protocols in dental practice, cause eye injuries; Type of fluid entry, foreign bodies, splash of biological fluids or conjunctivitis. These eye injuries are generated from associated factors such as the occasional use of eye protection, age, sex, and others.

Microstructure-Property Correlation and Its Evolution during Aging in an Al4.4Co26Cr19Fe18Ni27Ti5.6 High-Entropy Alloy.

The efficient energy use in multiple sectors of modern industry is partly based on the efficient use of high-strength, high-performance alloys that retain remarkable mechanical properties at elevated and high temperatures. High-entropy alloys (HEAs) represent the most recent class of these materials with a high potential for high-temperature high-strength applications. Aside from their chemical composition and microstructure-property relationship, limited information on the effect of heat treatment as a decisive factor for alloy design is available in the literature. This work intends to contribute to this research topic by investigating the effect of heat treatment on the microstructure and mechanical performance of an Al4.4Co26Cr19Fe18Ni27Ti5.6 HEA. The solution annealed state is compared to aged states obtained at different heat treatment times at 750 °C. The temporal evolution of the matrix and the γ'-precipitates are analyzed in terms of chemical composition, crystallography, size, shape, and volume fraction by means of scanning electron microscopy, transmission electron microscopy, and atom probe tomography. The yield strength evolution and strength contributions are calculated by classical state-of-the-art models as well as by ab-initio-based calculations of the critical resolved shear stress. The findings indicate promising mechanical properties of the investigated alloy and provide insight not only into possible strengthening mechanisms but also into the evolution of main phases during the heat treatment.

[Validation of the instrument to measure the level of knowledge of postgraduate and university professors on the labor law of the dental surgeon]. / Validación de instrumento para medir el nivel de conocimiento de estudiantes de posgrado y docentes universitarios sobre la ley de trabajo del cirujano dentista.

Objective: To build and validate an instrument to measure the level of knowledge about the Law that regulates the work of the dental surgeon in the Peruvian territory (Law No. 27878) of postgraduate students and teachers of the Stomatology Career of the Scientific University of Peru. South. Material and methods: : The sample consisted of 38 dental surgeons, participants in the postgraduate program (20) and professors (18) of the Stomatology Career at the Universidad Científica del Sur. A structured questionnaire of 15 questions was developed based on the articles of Law No. 27878. Results: It was approved by Expert Judgment and submitted to Aiken's V with an average result of 0.95. A moderate level of correlation was obtained through the Pearson Correlation Coefficient and a Cronbach's Alpha of 0.8. Through the Exploratory Factor Analysis, 3 dimensions were formed. Conclusions: The instrument was correctly validated through 4 validity tests. The level of knowledge of postgraduate students and teachers was very good in 60.5%.

Body Composition Assessment in Mexican Children and Adolescents. Part 1: Comparisons between Skinfold-Thickness, Dual X-ray Absorptiometry, Air-Displacement Plethysmography, Deuterium Oxide Dilution, and Magnetic Resonance Imaging with the 4-C Model.

The evaluation of body composition (BC) is relevant in the evaluation of children's health-disease states. Different methods and devices are used to estimate BC. The availability of methods and the clinical condition of the patient usually defines the ideal approach to be used. In this cross-sectional study, we evaluate the accuracy of different methods to estimate BC in Mexican children and adolescents, using the 4-C model as the reference. In a sample of 288 Mexican children and adolescents, 4-C body composition assessment, skinfold-thickness (SF), dual-energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), and deuterium dilution (D2O) were performed, along with MRI in a subsample (52 participants). The analysis of validity was performed by correlation analysis, linear regression, and the Bland-Altman method. All methods analyzed showed strong correlations for FM with 4-C values and between each other; however, DXA and MRI overestimated FM, whereas skinfolds and ADP under-estimated FM. Conclusion: The clinical assessment of BC by means of SF, ADP, DXA, MRI and D2O correlated well with the 4-C model and between them, providing evidence of their clinical validity and utility. The results from different methods are not interchangeable. Preference between methods may depend on their availability and the specific clinical setting.

3D nanoscale elemental mapping of precipitates in steel: Evaluation of analytical electron tomography and comparison to atom probe tomography.

To identify different types of precipitates and their composition, analytical electron tomography analysis on a needle-shaped sample was performed. Three-dimensional chemical maps from each element in a high alloyed steel are acquired and the resulting elemental maps are jointly reconstructed. Since analytical electron tomography data suffers from noise, total generalized variation regularization is used to improve the reconstruction quality compared to conventional reconstruction techniques. We analyse the influence of regularization parameters on these reconstructions in terms of elemental quantification, and compare these results to atom probe tomography measurements done on another sample of the same material.

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition.

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

Raman Spectroscopy Study of Skin Biopsies from Patients with Parkinson's Disease: Trends in Alpha-Synuclein Aggregation from the Amide I Region.

Parkinson's disease (PD) is one of the most common neurological pathologies with a high prevalence worldwide. PD is characterized by Lewy bodies, whose major component is the aggregates of α-synuclein (αSyn) protein. Interestingly, recent works have demonstrated that skin biopsy studies are a promising diagnostic tool for evaluating α-synucleinopathies. In this sense, this work focuses on the detection of αSyn in skin biopsies employing Raman spectroscopy, using three different approaches: (i) the in vitro Raman spectrum of α-synuclein, (ii) the ex vivo Raman spectra of human skin biopsies from healthy and Parkinson's disease patients, and (iii) theoretical calculations of the Raman spectra obtained from different model αSyn fragments using density functional theory (DFT). Significant differences in the intensity and location of Raman active frequencies in the amide I region were found when comparing healthy and PD subjects related to α-synuclein conformational changes and variations in their aggregation behavior. In samples from healthy patients, we identified well-known Raman peaks at 1655, 1664, and 1680 cm-1 associated with the normal state of the protein. In PD subjects, shifted Raman bands and intensity variations were found at 1650, 1670, and 1687 cm-1 associated with aggregated forms of the protein. DFT calculations reveal that the shape of the amide I Raman peak in model αSyn fragments strongly depends on the degree of aggregation. Sizable frequency shifts and intensity variations are found within the highly relevant 1600-1700 cm-1 domain, revealing the sensitivity of the amide I Raman band to the changes in the local atomic environment. Interestingly, we obtain that the presence of surrounding waters also affects the structure of the amide I band, leading to the appearance of new peaks on the low-frequency side and a notable broadening of the Raman spectra. These results strongly suggest that, through Raman spectroscopy, it is possible to infer the presence of aggregated forms of αSyn in skin biopsies, a result that could have important implications for understanding α-synuclein related diseases.

Isolation by environmental distance in mobile marine species: molecular ecology of franciscana dolphins at their southern range.

The assessment of population structure is a valuable tool for studying the ecology of endangered species and drafting conservation strategies. As we enhance our understanding about the structuring of natural populations, it becomes important that we also understand the processes behind these patterns. However, there are few rigorous assessments of the influence of environmental factors on genetic patterns in mobile marine species. Given their dispersal capabilities and localized habitat preferences, coastal cetaceans are adequate study species for evaluating environmental effects on marine population structure. The franciscana dolphin, a rare coastal cetacean endemic to the Western South Atlantic, was studied to examine these issues. We analysed genetic data from the mitochondrial DNA and 12 microsatellite markers for 275 franciscana samples utilizing frequency-based, maximum-likelihood and Bayesian algorithms to assess population structure and migration patterns. This information was combined with 10 years of remote sensing environmental data (chlorophyll concentration, water turbidity and surface temperature). Our analyses show the occurrence of genetically isolated populations within Argentina, in areas that are environmentally distinct. Combined evidence of genetic and environmental structure suggests that isolation by distance and a process here termed isolation by environmental distance can explain the observed correlations. Our approach elucidated important ecological and conservation aspects of franciscana dolphins, and has the potential to increase our understanding of ecological processes influencing genetic patterns in other marine species.

A novel automatic method for monitoring Tourette motor tics through a wearable device.

The aim of this study was to propose a novel automatic method for quantifying motor-tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three-axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video-based ones provided by expert clinicians, which were taken as the gold-standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland-Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video-based systems. Furthermore, there was substantial concurrency with the gold-standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor-tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long-term monitoring out of clinical environments.

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier.

Heat Treatments and Critical Quenching Rates in Additively Manufactured Al-Si-Mg Alloys.

Laser powder-bed fusion (LPBF) has significantly gained in importance and has become one of the major fabrication techniques within metal additive manufacturing. The fast cooling rates achieved in LPBF due to a relatively small melt pool on a much larger component or substrate, acting as heat sink, result in fine-grained microstructures and high oversaturation of alloying elements in the α-aluminum. Al-Si-Mg alloys thus can be effectively precipitation hardened. Moreover, the solidified material undergoes an intrinsic heat treatment, whilst the layers above are irradiated and the elevated temperature in the built chamber starts the clustering process of alloying elements directly after a scan track is fabricated. These silicon-magnesium clusters were observed with atom probe tomography in as-built samples. Similar beneficial clustering behavior at higher temperatures is known from the direct-aging approach in cast samples, whereby the artificial aging is performed immediately after solution annealing and quenching. Transferring this approach to LPBF samples as a possible post-heat treatment revealed that even after direct aging, the outstanding hardness of the as-built condition could, at best, be met, but for most instances it was significantly lower. Our investigations showed that LPBF Al-Si-Mg exhibited a high dependency on the quenching rate, which is significantly more pronounced than in cast reference samples, requiring two to three times higher quenching rate after solution annealing to yield similar hardness results. This suggests that due to the finer microstructure and the shorter diffusion path in Al-Si-Mg fabricated by LPBF, it is more challenging to achieve a metastable oversaturation necessary for precipitation hardening. This may be especially problematic in larger components.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA (ρ>0.91) outperforms VRA (ρ>0.86) for moderate to high noise levels whereas mVRA outperformed EMD (ρ>0.90) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (ρ>0.94). For Raman spectra of synthetic material, good performance of the three methods (ρ=0.99 for VRA, ρ=0.93 for EMD, and ρ=0.99 for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results (ρ=0.96 for VRA, ρ=0.85 for EMD, and ρ=0.91 for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation.

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract.

Accuracy of National Early Warning Score 2 (NEWS2) in Prehospital Triage on In-Hospital Early Mortality: A Multi-Center Observational Prospective Cohort Study.

INTRODUCTION: In cases of mass-casualty incidents (MCIs), triage represents a fundamental tool for the management of and assistance to the wounded, which helps discriminate not only the priority of attention, but also the priority of referral to the most suitable center. HYPOTHESIS/PROBLEM: The objective of this study was to evaluate the capacity of different prehospital triage systems based on physiological parameters (Shock Index [SI], Glasgow-Age-Pressure Score [GAP], Revised Trauma Score [RTS], and National Early Warning Score 2 [NEWS2]) to predict early mortality (within 48 hours) from the index event for use in MCIs. METHODS: This was a longitudinal prospective observational multi-center study on patients who were attended by Advanced Life Support (ALS) units and transferred to the emergency department (ED) of their reference hospital. Collected were: demographic, physiological, and clinical variables; main diagnosis; and data on early mortality. The main outcome variable was mortality from any cause within 48 hours. RESULTS: From April 1, 2018 through February 28, 2019, a total of 1,288 patients were included in this study. Of these, 262 (20.3%) participants required assistance for trauma and injuries by external agents. Early mortality within the first 48 hours due to any cause affected 69 patients (5.4%). The system with the best predictive capacity was the NEWS2 with an area under the curve (AUC) of 0.891 (95% CI, 0.84-0.94); a sensitivity of 79.7% (95% CI, 68.8-87.5); and a specificity of 84.5% (95% CI, 82.4-86.4) for a cut-off point of nine points, with a positive likelihood ratio of 5.14 (95% CI, 4.31-6.14) and a negative predictive value of 98.7% (95% CI, 97.8-99.2). CONCLUSION: Prehospital scores of the NEWS2 are easy to obtain and represent a reliable test, which make it an ideal system to help in the initial assessment of high-risk patients, and to determine their level of triage effectively and efficiently. The Prehospital Emergency Medical System (PhEMS) should evaluate the inclusion of the NEWS2 as a triage system, which is especially useful for the second triage (evacuation priority).

Predictors of suicidal ideation and depressive symptoms among adolescents in Chiapas, Mexico. / Variables predictoras de la ideación suicida y sintomatología depresiva en adolescentes de Chiapas, México.

The aim of this study was to assess the presence of suicidal ideation and depressive symptoms among adolescents in Chiapas, Mexico based on predictor variables. A cross-sectional study adopting an ex post facto design was conducted with a non-probability sample of 4,759 students of both sexes with an average age of 18.4 years and using the following tools: the Center for Epidemiologic Studies Depression Scale, Roberts' Suicidal Ideation Scale, the Impulsiveness Scale (IS), and the Rosenberg Self-Esteem Scale. The mean score obtained by the suicidal ideation scale was 0.2, which is lower than that reported by other studies, while the mean score for depressive symptoms was similar to those in the literature. The prevalence of suicidal ideation was 7.8%, which is lower than reported in national and international studies. Multivariable logistic regression showed that impulsiveness (OR = 1.907) and depressive symptoms (OR = 9.006) lead to a twofold and ninefold increase in the likelihood of suicidal ideation, respectively. The findings also showed a strong association between suicidal ideation and depressive symptoms, showing that the latter is a predictor of suicidal ideation among adolescents.