Jaime Martín-Martín

Alcohol use disorder (AUD) is associated with gut dysbiosis through interactions with the immune system. The present study aimed to investigate whether endotoxin lipopolysaccharides (LPS) and high-mobility group box-1 protein (HMGB1), a key inflammatory mediator, as well as the metabolic fat mass hormone leptin, are reliable biomarkers for the estimation of alcohol dependence and abstinence. AUD outpatients (N = 122) and healthy volunteers (N = 63) were recruited and assessed by using the Psychiatric Research Interview for Substance and Mental Disorders according to DSM-IV-TR after blood extraction. The results indicated that AUD patients had higher plasma concentrations of LPS and HMGB1, and lower plasma concentrations of leptin and SDF-1α compared to healthy subjects. Two logistic models, including HMGB1, leptin and SDF-1α (model 1) or LPS (model 2), provided high discriminatory powers to identify AUD patients [prognostic probability: model 1 = 0.90 (0.78); model 2 = 0.86 (0.79); p < 0.001]. LPS and HMGB1 positively correlated with alcohol abstinence duration in male AUD patients only. Linear logistic regression included LPS, HMGB1, fractalkine, SDF-1α and/or leptin to accurately estimate the duration of problematic alcohol use and alcohol abstinence when sexes were analyzed separately. These results suggest that LPS and HMGB1 are relevant sex-specific actors for predicting alcohol abstinence and problematic alcohol use in AUD patients.

Age estimation is a major challenge in anthropology and forensic odontology laboratories, as well as in judicial settings, as one of the tools used in human identification. The aim of this study was to evaluate the usefulness of age estimation methods based on the accurate measurement of tooth color changes. A systematic review was carried out following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and in compliance with Cochrane criteria recommendations (PROSPERO registration number CRD 42022343371). An electronic search was performed in the following databases: Pubmed, Web of Science, Medline, Current Contents Connect, SciELO, KCI-Korean Journal Database, Derwent Innovations Index and Russian Citation Index. The search strategy yielded a total of 18 articles. A randomized meta-analysis model of the results for the CIE L*a*b* color variables stratified by age (less than 30 years, 30-60 years, 60 years and older) was performed with 9 of the 18 studies included in this systematic review. According to our results, sex and location of color measurement are the most influential factors in color estimation. All studies were carried out in healthy anterior teeth by spectrophotometry as the most commonly used method for color measurement, with CIE L*a*b* being the most commonly analyzed parameters. Studies based on age as a dependent variable showed R² values between 0.28 and 0.56, being higher in ex vivo teeth. Studies based on age as an independent variable showed R² values ranging from 0.10 to 0.48. The random model showed high heterogeneity for the L*, a* and b* parameters in all age groups, which is explained by discrepancies in age range and non-standardized conditions for color measurement. This systematic review highlights the need to protocolize age estimation studies that measure tooth color, in order to apply this method in different forensic settings.

Importance: Handwriting legibility and speed assessments have a critical role in identifying and evaluating handwriting problems, which are common among children. Objective: The objective was to evaluate the psychometric properties and clinical utility of handwriting assessments for children ages 3 to 16 yr. Data Sources: A systematic review was conducted in CINAHL, PubMed (MEDLINE), Scopus, and education databases, with no time limits. The search strategy included a combination of the following keywords: handwriting, write, children, assessment, and validity. The exclusion criteria were assessment tools that were electronic, that focused on cognitive components of handwriting, or that only evaluated alphabets other than Latin. Study Selection and Data Collection: The systematic review was carried out on the basis of the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. The protocol was registered in the Prospective Register of Systematic Reviews (PROSPERO). Findings: The 14 included instruments had a total sample of 4,987 children. Internal consistency ranged from moderate (.73; Writing Readiness Inventory Tool in Context) to high (.98; Letter Writing). The interexaminer reliability values of the 11 direct assessment instruments ranged from .77 (Systematic Screening for Handwriting Difficulties) to 1.00 (Handwriting Speed Test). Conclusions and Relevance: In this systematic review, existing tools were evaluated by clinical utility and the quality of psychometric properties. Direct assessments showed good psychometric properties. Indirect and self-assessment tools demonstrated poor psychometric properties. Further research on screening tools and tools in other languages is needed. What This Article Adds: Specific learning disorders (e.g., dysgraphia) negatively affect academic learning and, when prolonged in time, self-concept. However, handwriting legibility and speed assessments could be used to identify and evaluate these learning disorders if an early referral to occupational therapy is carried out.

El presente manual titulado Bioética y Legislación Aplicada en Ciencias de la Salud ha sido realizado en base al material docente que se imparte al alumnado de la Facultad de Ciencias de la Salud (Grados de Enfermería, Podología y Terapia Ocupacional) de la Universidad de Málaga por parte del Area de Medicina Legal y Forense perteneciente al Departamento de Anatomía Humana, Medicina Legal e Historia de la Ciencia. Así mismo algunos de los temas incluidos en el presente manual tambien pueden ser utilizados en los Grados de Criminología y Medicina. Este compendio docente, esta concebido como material de ayuda al estudio de las diferentes materias que se imparten desde el área. A lo largo del documento se realiza una síntesis de los diferentes aspectos éticos y legales que mantienen una relación mas o menos directa con los diferentes profesionales de la salud. En este sentido, se recogen no solo aspectos legislativos, haciendo referencia al Código Penal o los Boletines Oficiales del Estado (entre otros documentos legales); sino tambien a su relación directa con la aplicación práctica. Cada una de las lecciones contenidas en el manual, contiene la bibliografía de referencia para que alumno o el profesional que lo necesite pueda consulta la fuente origianl para así profundizar en su estudio. El manual se ha concebido como material de estudio que permita al alumno ampliar los contenidos teóricos que han sido impartidos en el aula en cada una de las asignaturas. El formato digital de libre distribución supone una reducción en el coste de publicación, asi como para el propio alumno facilitando de este modo el acceso a la información. El alumno actual, se encuentra más familiarizado con los soportes digitales que con los manuales impresos, por este motivo se opta por este diseño el cual permite la toma rápida de anotaciones como comentarios en el texto o el subrayado de palabras clave al igual que lo haría en un soporte físico.

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy.

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

Breast cancer survivors (BCS) may face functional alterations after surgical intervention. Upper Limb Disorders (ULDs) are highly prevalent even years after a diagnosis. Clinicians may assess the upper limbs after breast cancer. The Upper Limb Functional Index (ULFI) has been validated across different populations and languages. This study aimed to assess the psychometric properties of the Upper Limb Functional Index Spanish version (ULFI-Sp) in the BCS. Methods: A psychometric validation study of the ULFI-Sp was conducted on 216 voluntary breast cancer survivors. The psychometric properties were as follows: analysis of the factor structure by maximum likelihood extraction (MLE), internal consistency, and construct validity by confirmatory factor analysis (CFA). Results: The factor structure was one-dimensional. ULFI-Sp showed a high internal consistency for the total score (α = 0.916) and the regression score obtained from MLE (α = 0.996). CFA revealed a poor fit, and a new 14-item model (short version) was further tested. The developed short version of the ULFI-SP is preferable to assess upper limb function in Spanish BCS. Conclusions: Given the high prevalence of ULD in this population and the broader versions of ULFI across different languages, this study’s results may be transferred to clinical practice and integrated as part of upper limb assessment after breast cancer.

Background: The lateral sprain of the ankle is a very frequent injury in the population in general, appearing in the emergency services frequently. The general objective was to review the current clinical practice guidelines (CPGs) on management and treatment of ankle sprains, assess their quality, analyze the levels of evidence and summarize the grades of recommendation. Methods: A systematic search of the literature in relevant databases with the search terms “ankle,” “sprain,” “practice guideline,” and “guideline” was carried out. There were included those guidelines that had the system of grades of recommendation and level of evidence concerning to management and treatment of ankle sprain. The quality of the guides was assessed using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool. Results: Seven clinical practice guides were included in this review. The AGREE II scores ranged from 42% to 100%, with only six CPGs explicitly declaring the use of a systematic methodology. Seventeen recommendations were extracted and summarized. Discussion: Six of the recommendations analyzed present enough evidence to be applied in clinical practice and are highly recommended for ankle sprain management: Ottawa rules, manual therapy, cryotherapy, functional supports, early ambulation, short term NSAIDs and rehabilitation.

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65.