Gender-based differences in the high-risk sexual behaviours of young people aged 15-29 in Melilla (Spain): a cross-sectional study

Background: Research confirms the existence of gender-based differences regarding the high-risk sexual behaviour (non-use of condoms and casual partners) of young men and women. The objectives were to provide evidence for this association; to analyse the reasons why both sexes have sexual relations with casual partners and to ascertain the motives for condom use or non-use during casual sex. Methods: A cross-sectional study was performed on a sample of 900 participants, 524 males and 376 females. All participants were 15-29 (20.93 ± 4.071) years of age and came from four different centres (a university, two secondary schools, and a military base) in Melilla (Spain). The participants were given a socio-demographic survey as well as a psychometric text on high-risk sexual behaviour. Results: The results found gender-based significant differences for sexual relations with penetration (p = 0.001), number of sexual partners (p = 0.001), and sexual relations with casual partners (p = 0.001). In all of these variables, male participants had higher percentages than female participants. Reasons for having casual sexual relations were also different for men and women, differences were found for the items, opportunity (p = 0.001), interest in knowing the other person (p = 0.015), physical excitement (p = 0.056) and drug consumption (p = 0.059). Regarding the reasons for consistent condom use with casual partners, there were differences for the item, my demand of a condom (p = 0.002). For the non-use of condoms with casual partners, differences were found for the items, I do not like to use condoms (p = 0.001) and condoms lessen sensitivity and reduce pleasure (p = 0.009). Conclusions: Men and women were found to have different high-risk sexual behaviours and practices. Of the motives for having sexual relations with casual partners, male participants considered opportunity and interest in knowing the other person to be more important than the female participants. Regarding condom use, the female participants’ demand to use a condom was a significant gender-based difference. In contrast to the young women, the male participants mostly justified not using a condom because it lessened sensitivity and reduced pleasure

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.Ministerio de Ciencia e Innovacion (Espana)/FEDER RTI2018-098913B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC-080-UGR18 P20-00525National Health and Medical Research Council (NHMRC) of Australia 18/0490

Perinatal and maternal outcomes according to the accurate term antepartum ultrasound estimation of extreme fetal weights

Background: The accuracy of ultrasound estimation of fetal weight (EFW) at term may be useful in addressing obstetric complications since birth weight (BW) is a parameter that represents an important prognostic factor for perinatal and maternal morbidity. (2) Methods: In a retrospective cohort study of 2156 women with a singleton pregnancy, it is verified whether or not perinatal and maternal morbidity differs between extreme BWs estimated at term by ultrasound within the seven days prior to birth with Accurate EFW (difference < 10% between EFW and BW) and those with Non-Accurate EFW (difference ≥ 10% between EFW and BW). (3) Results: Significantly worse perinatal outcomes (according to different variables such as higher rate of arterial pH at birth < 7.20, higher rate of 1-min Apgar < 7, higher rate of 5-min Apgar < 7, higher grade of neonatal resuscitation and need for admission to the neonatal care unit) were found for extreme BW estimated by antepartum ultrasounds with Non-Accurate EFW compared with those with Accurate EFW. This was the case when extreme BWs were compared according to percentile distribution by sex and gestational age following the national reference growth charts (small for gestational age and large for gestational age), and when they were compared according to weight range (low birth weight and high birth weight). (4) Conclusions: Clinicians should make a greater effort when performing EFW by ultrasound at term in cases of suspected extreme fetal weights, and need to take an increasingly prudent approach to its management.Partial funding for open access charge: Universidad de Málag

Optimized One vs One approach in multiclass classification for early Alzheimer’s Disease and Mild Cognitive Impairment diagnosis

The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs development. Motivated by this fact, the neuroimaging community has extensively applied machine learning techniques to the early diagnosis problem with promising results. The organization of challenges has helped the community to address different raised problems and to standardize the approaches to the problem. In this work we use the data from international challenge for automated prediction of MCI from MRI data to address the multiclass classification problem. We propose a novel multiclass classification approach that addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification, outperforming all the proposals of the competition.Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS RTI2018-098913-B100Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia A-TIC-080-UGR18 TIC FRONTERAGerman Research Foundation (DFG) FPU 18/04902United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Neurological Disorders & Stroke (NINDS) U01 AG024904DOD ADNI Department of Defense W81XWH-12-2-001

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

Test-retest reliability of the isometric contraction test (IC test) of the masticatory muscles in subjects with and without temporomandibular muscle disorders

Recently, the DC/TMD has become an essential tool for the diagnosis of temporomandibular disorders (TMD). However, as they fail to include functional activities, new assessment proposals have emerged, such as the isometric contraction test (IC test) of the masticatory muscles, which uses muscle contractions to identify muscular TMD. Objective: This study aimed to determine the test-retest reliability of the IC test. Methods: A total of 64 participants (40 women and 24 men) completed the IC test administered by two different physical therapists on two non-consecutive days. Cohen’s kappa (k), PABAK, and percent agreement (PA) between days were estimated. Results: The IC test showed good to excellent test-retest reliability values (k&gt;0.77; PABAK&gt;0.90), both globally and individually for the muscles evaluated, and PA&gt;90%, therefore above the thresholds for clinical applicability. However, the global assessment of myofascial pain and the evaluation of the medial pterygoid muscle showed slightly lower reliability values. Conclusion: The IC test is reliable for the assessment of subjects with muscular TMD, both in terms of the global assessment and the evaluation of each muscle, which supports its clinical applicability. Care should be taken when assessing myofascial pain globally and when evaluating the medial pterygoid in all types of pain

Measuring Resilience in Women with Endometriosis

Endometriosis is a multifactorial disease with pathophysiological factors not yet well known; it also presents a wide symptomatic range that makes us think about the need for multidisciplinary management. It is a chronic disease in which there is no definitive treatment, and is associated in a large majority of cases with psychological pathology. Connecting comorbidities and multimorbidities on a neurobiological, neuropsychological, and pathophysiological level could significantly contribute to their more successful prevention and treatment. In our study, resilience is analyzed as an adjunctive measure in the management of endometriosis. Methods: A multi-centre, cross-sectional study was performed to analyse resilience levels in a sample of Spanish women suffering from endometriosis. CDRIS-25, CDRIS-10, BDI, the STAI, and the SF-36 Health Questionnaire were used for assessments. A representative group of 202 women with endometriosis was recruited by consecutive sampling. Exploratory and confirmatory factor analyses were performed for both resilience scales. Results: Mean CDRIS-25 and CDRIS-10 scores were 69.58 (SD 15.1) and 29.37 (SD 7.2), respectively. Women with adenomyosis and without signs of deep endometriosis showed the lowest scores. The best predictive model included women's age, years of endometriosis evolution, number of pregnancies, and history of fertility problems as the best predictive factors. Conclusions: Women build resilience as the number of years of evolution of the disease increases. Symptoms such as dyspareunia and continued abdominal pain were more prevalent among less resilient women

Fournier’s Gangrene under Sodium–Glucose Cotransporter-2 Inhibitors Therapy in Gynecological Patients

La gangrena de Fournier (GF) es una patología grave de los tejidos blandos y fascia del perineo y región genital con una alta morbimortalidad. En los últimos años, el antidiabético oral inhibidor de SGLT-2 se ha relacionado con esta entidad. Según las nuevas advertencias de las principales agencias de drogas, se ha iniciado una recopilación de casos para establecer o negar una posible relación causal. La mayoría de estos casos han sido reportados en hombres. Sin embargo, es importante no subestimar esta entidad en el campo ginecológico, ya que es de extrema gravedad y requiere un tratamiento agresivo intenso y rápido basado en cirugía y antibioterapia empírica. Posteriormente son necesarios algunos cuidados que implican la reconstrucción quirúrgica de los defectos introducidos por el desbridamiento. Como resultado de la baja incidencia de FG, los datos de los ensayos clínicos pueden ser insuficientes para evaluar de manera sólida este problema debido al número limitado de participantes. La evidencia del mundo real puede ayudar a aclarar la asociación entre SGLT2i y FG. El objetivo de esta revisión es describir y comparar los casos informados de FG en mujeres diabéticas que recibieron inhibidores de SGLT2 como agentes antiglucemiantesThis research received no external funding. Partial funding for open access charge: Universidad de Málag

Los sistemas de frecuencia modulada en alumnos con implante coclear

Due to the limitations of the Cochlear Implant (CI),&nbsp; the use of Frequency Modulated (FM) systems is advised to improve the signal-to-noise ratio in auditory-verbal learning environments in adverse acoustic conditions. Previous studies have shown that the acoustic conditions of most of the classrooms are acceptable for normal hearing students, however, these conditions are not appropriate for students with CI or other types of assistive listening devices. In this study, a simulation of the speech identification skills of a group of 1st year secondary school students is carried out based on the acoustic characteristics of the classroom. The Speech Audibility Index (AI) obtained for the analyzed classroom was 52%. From this AI, an estimate of speech perception is carried out for normal hearing students, with IC and IC + FM for the same position of the student in the classroom. Speech recognition is studied in three conditions of difficulty: (i) isolated high and low familiarity words (ii) phonemes in syllables and (iii) high and low familiarity words in simple and complex sentences. The results show that the linguistic competences of identification of the spoken message obtained by normal hearing students are higher than 92%. The estimate for students with CI showed significantly lower scores, being 34% in the recognition of novel words in complex sentences. The results obtained with IC + FM significantly improve linguistic performance, reaching 80% in the recognition of novel words in complex sentences. This study shows the acoustic barriers of the analyzed classroom for students with HF and the advantages of using FM systems.Las limitaciones del Implante Coclear (IC) en condiciones acústicas adversas aconsejan el uso de sistemas de Frecuencia Modulada (FM) para mejorar la relación señal - ruido en entornos de aprendizaje auditivo - verbal. Estudios previos han mostrado como las condiciones acústicas de la mayoría de las aulas resultan aceptables para los alumnos normoyentes, sin embargo, estas condiciones son poco apropiadas para los alumnos con IC u otros tipos de Ayudas Técnicas Auditivas. En este estudio se lleva acabo una simulación de las competencias de identificación del habla de un grupo de alumnos de 1º de la ESO en función de las características acústicas del aula. El Índice de Audibilidad del Habla (Speech Audibility Index; SAI) obtenido para el aula analizada fue de un 52%. A partir de este índice SAI se lleva acabo una estimación de la percepción del habla para los alumnos normoyentes, con IC e IC+FM para la misma posición del alumno en el aula. Se estudia el reconocimiento del habla en tres condiciones de dificultad: (i) palabras de alta y baja familiaridad aisladas (ii) fonemas en sílabas y (iii) palabras de alta y baja familiaridad en frases sencillas y complejas. Los resultados muestran que las competencias lingüísticas de identificación del mensaje hablado obtenidos por los alumnos normoyentes son superiores al 92%. La estimación para los alumnos con IC mostró unas puntuaciones significativamente inferiores siendo del 34% en el reconocimiento de palabras noveles en frases complejas. Los resultados obtenidos con IC+FM mejoran de forma significativa el rendimiento lingüístico llegando al 80% en el reconocimiento de palabras noveles en frases complejas. Este estudio evidencia las barreras acústicas del aula analizada para los alumnos con IC y las ventajas de uso de sistemas de FM