Autosomic Dominant Tubulo Interstitial Kidney Disease: Case Report of a New Variant of the UMOD Gene.

Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a low-prevalence pathology mainly associated with pathogenic variants of the UMOD gene. It is characterized by the progressive deterioration of renal function, associated with hyperuricemia and accompanied by a family history of gout or hyperuricemia. Often, clinical variability and a lack of molecular testing results in diagnostic failure to determine the ADTKD-UMOD association. Case presentation: We describe the case of a 14-year-old male who presented to the nephrology service with hyperuricemia, renal ultrasonographic changes, and progression to chronic kidney disease in 4 years. He had a family history of hyperuricemia. A probable genetic disease with an autosomal dominant inheritance pattern was considered, confirmed by the presence of a probably pathogenic variant of the UMOD gene, not previously reported in the literature. Conclusion: The investigation of this case led to the identification of a new variant in the UMOD gene, broadening the spectrum of known variants for ADTKD-UMOD. In addition, in this case, a comprehensive anamnesis, that takes into account family history, was the key point to carry out genetic tests that confirmed the diagnosis suspicion. Directed Genetic tests are currently an essential diagnostic tool and should be performed as long as they are available and there is an indication to perform them.

Structural and functional connectome relationships in early childhood.

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.

Consenso de expertos sobre recomendaciones basadas en evidencia para el diagnóstico, tratamiento y seguimiento de enfermedad de Fabry en pediatría / Expert Consensus on Evidence-Based Recommendations for the Diagnosis, Treatment, and Follow-Up of Fabry Disease in Pediatric Patients / Consenso de especialistas colombianos sobre recomendações baseadas em evidências para o diagnóstico, tratamento e acompanhamento da doença de Fabry em pediatria

Antecedentes: la enfermedad de Fabry (Ef) es una enfermedad rara ligada a X secundaria al depósito lisosomal de glicoesfingolípidos, debido a la deficiencia de la enzima alfa galactosidasa A (α-Gal A). A pesar de su baja frecuencia, es una condición que afecta la calidad de vida de los pacientes y disminuye su esperanza de vida. Objetivo: generar recomendaciones informadas para el diagnóstico y tratamiento de pacientes pediátricos (menores de 18 años) con Ef. Material y Métodos: revisión de literatura en bases de datos y literatura gris a partir de 2010, incluyendo guías de práctica clínica, revisiones sistemáticas y estudios primarios. La calidad de evidencia se evaluó de acuerdo con el tipo. Las recomendaciones se sometieron a consenso de expertos a través de metodología Delphi modificada. El acuerdo se definió a partir del 80 %. Resultados: A partir del análisis de la evidencia recolectada se formularon un total de 45 recomendaciones para tamización, diagnóstico y tratamiento de paciente pediátrico con Ef. El panel revisor estuvo conformado por once expertos en el tema. Las recomendaciones fueron aprobadas con puntuaciones entre 82.3 % y 100 %. Conclusiones: las recomendaciones resultantes del consenso de expertos permitirán la toma de decisiones clínicas y estandarización de la práctica en la atención de pacientes pediátricos con Ef en el país y la región. El diagnóstico temprano y oportuno garantiza una disminución del impacto en la calidad de vida de los pacientes y sus familiares

Background: Fabry disease (fD) is a rare X-linked disease characterized by the accumulation of glyco- sphingolipids in lysosomes due to the deficiency in the production of alpha-galactosidase A (α-Gal A) enzyme. Despite its low frequency, this disease has a serious impact on the life expectancy and quality. Objective: To make evidence-based recommendations for the diagnosis and treatment of fD in pediatric patients (<18 years of age). Materials and Methods: A study of databases and gray literature was conducted in 2010, including clinical practice guidelines, systematic reviews, and primary research. The type of evidence was used to determine the quality of evidence. The recommendations were submitted to an expert consensus using the modified Delphi process. The agreement was set at 80%. Conclusions: The recommendations emerging from this expert consensus will enable the standardization of care provision for pediatric patients with fD in Colombia and Latin America and clinical decision-making for disease management. Notably, making an early diagnosis ensures a reduction in the impact of this disease on the quality of life of patients and their families

Fundamento: a doença de Fabry (Df) é uma rara doença ligada ao cromossomo X secundária à deposi- ção lisossômica de glicoesfingolipídeos devido à deficiência da enzima alfa galactosidase A (α-Gal A). Apesar de sua baixa frequência, é uma condição que afeta a qualidade de vida dos pacientes e diminui sua expectativa de vida. Objetivo: gerar recomendações baseadas em evidências para o diagnóstico e tratamento de pacientes pediátricos (com menos de 8 anos de idade) com Df. Materais e Métodos: foi realizada uma revisão da literatura em bases de dados e literatura cinza a partir de 2010, incluindo diretrizes de prática clínica, revisões sistemáticas e estudos primários. A qualidade da evidência foi avaliada de acordo com o tipo de evidência. As recomendações foram submetidas ao consenso de especialistas usando a metodologia Delphi modificada. A concordância foi definida a partir de 80%. Resultados: com base na análise das evidências coletadas, foram formuladas um total de 45 recomendações para triagem, diagnóstico e tratamento de pacientes pediátricos com doença de Fabry. O painel de revisão foi composto por onze especialistas no assunto. As recomendações foram aprovadas com pontuações entre 82,3% e 100%. Conclusões: as recomendações resultantes do consenso de especialistas permitirão a tomada de decisão clínica e a padronização da prática no cuidado de pacientes pediátricos com Df em nível nacional e regional; o diagnóstico precoce e oportuno garante a redução do impacto na qualidade de vida dos pacientes e seus familiares.

Dmriprep: open-source diffusion MRI quality control framework with graphical user interface.

In the last decade, investigating white matter microstructure and connectivity via diffusion MRI (dmri) has become a crucial cornerstone in neuroimaging studies. However, even modern dmri sequences have inherently a low signal-to-noise ratio and long acquisition times, depending on the spatial resolution. Furthermore, many types of artifacts complicate the appropriate analysis of dmri, necessitating appropriate quality control (QC) procedures, including exclusion and/or correction of inappropriate/erroneous dmri data. Our group has been developing and promoting QC procedures and tools to the community to enable appropriate dmri analyses. Since its development in 2011, our DTIPrep QC tool has become a major tool due its ease of use and dmri QC performance. Over the years, novel developments in acquisition and artifact correction methods have led to a need to modernize DTIPrep. Here, we present a novel diffusion MRI analysis environment called dtiplayground with a fully redesigned and significantly enhanced QC module dmriprep, and its graphical user interface dmriprep-ui, building on in-house developed code, FSL and dipy. The user interface is designed to be a unified, user friendly tool for thorough QC of dMRI data.Artifacts addressed by dmriprep include eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, slice-wise and gradient-wise intensity inconsistencies, and susceptibility artifacts. It further provides an user interface for visual QC of gradients and automated tractography. In summary, our work presents a novel open-source framework for modern comprehensive dmri QC.

Automatic landmark identification in cone-beam computed tomography.

OBJECTIVE: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. MATERIALS AND METHODS: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. RESULTS: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. CONCLUSION: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.

Scrotal flap phalloplasty as temporary neophallus in infants and children with penile agenesis: Multi-institutional experience and long-term follow-up.

INTRODUCTION: Aphallia is a rare congenital disorder pertaining to genotypic males. Early surgical creation of a neophallus is recommended to reinforce the child's male gender-identity, favoring proper psychosexual development. Modern microsurgical techniques used to create a neophallus in adults are not recommended in children due to the invasiveness and complexity of the procedures, along with high complication rates. Scrotal flap phalloplasty is a simple and reproducible technique to create a temporary neophallus in prepubertal boys with aphallia. OBJECTIVE: We present a multi-institutional experience, ten years after the initial description of the scrotal flap phalloplasty (SFP) technique, in which a flap from the well-developed scrotum is used to build a temporary neophallus, without obvious scars in patients with aphallia. STUDY DESIGN: The records of surgical neophalloplasty for aphallia patients from 4 centers between 2011 and 2021 were reviewed. All patients had at least one year follow-up to assess for short and long-term complications. Age at initial operation, associated anomalies, and other related surgical procedures were analyzed. RESULTS: The post-operative aesthetic result in all patients was satisfactory and has been maintained in the long-term follow-up, with all patients presenting a cylindrical structure resembling an uncircumcised penis, without evidence of significant contraction or loss of length. (Summary Figure) DISCUSSION: Non-microsurgical neophalloplasty techniques in patients with penile agenesis are temporary procedures that help to establish the body image and preserve the psychosexual development of the patient with aphallia. These techniques do not involve tissue transplant from a distant region, and are simpler to perform, with less scarring at the donor sites. Due to significant donor scars and considerable morbidity and complexity associated with the definitive phalloplasty techniques, we created a simple, reproducible and straightforward procedure to serve as a temporary neophallus in young boys with aphallia. As affected patients usually have a well-formed scrotum with normal and orthotopic testicles, it is the ideal donor site for a temporary neo-phallus in childhood. Furthermore, other donor sites are preserved for a definitive phalloplasty. There are limitations to this study, as quality of life could not be assessed and psychological or gender-identity investigations have not been carried out. None of these children have reached puberty, and hence decision and outcomes of definitive neophallus reconstruction has not been considered to date. CONCLUSION: Scrotal flap phalloplasty is a minimally invasive, simple and reproducible technique used to create a temporary neophallus in boys with aphallia, while waiting for definitive reconstructive surgery after puberty.

Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years.

BACKGROUND: The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood. METHODS: The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age. RESULTS: The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years. CONCLUSIONS: Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.

SlicerSALT: From Medical Images to Quantitative Insights of Anatomy.

Three-dimensional (3D) shape lies at the core of understanding the physical objects that surround us. In the biomedical field, shape analysis has been shown to be powerful in quantifying how anatomy changes with time and disease. The Shape AnaLysis Toolbox (SALT) was created as a vehicle for disseminating advanced shape methodology as an open source, free, and comprehensive software tool. We present new developments in our shape analysis software package, including easy-to-interpret statistical methods to better leverage the quantitative information contained in SALT's shape representations. We also show SlicerPipelines, a module to improve the usability of SALT by facilitating the analysis of large-scale data sets, automating workflows for non-expert users, and allowing the distribution of reproducible workflows.

DENTALMODELSEG: FULLY AUTOMATED SEGMENTATION OF UPPER AND LOWER 3D INTRA-ORAL SURFACES.

In this paper, we present a deep learning-based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as a UNET. We test our method in a dental application for the segmentation of dental crowns. The neural network is trained for multi-class segmentation, using image labels as ground truth. A 5-fold cross-validation was performed, and the segmentation task achieved an average Dice of 0.97, sensitivity of 0.98 and precision of 0.98. Our method and algorithms are available as a 3DSlicer extension.

IcoConv : Explainable brain cortical surface analysis for ASD classification.

In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms. We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method. One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.

Presurgical orthodontic decompensation with clear aligners.

INTRODUCTION: Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques. METHODS: The study sample consisted of 20 patients who have completed presurgical orthodontics using Invisalign clear aligners. Initial (pretreatment) digital dental models, presurgical digital dental models, and ClinCheck prediction models were obtained. Presurgical models were superimposed onto initial ones using stable anatomic landmarks; ClinCheck models were superimposed onto presurgical models using surface best-fit superimposition. Five hundred forty-five teeth were measured for 3 angular movements (buccolingual torque, mesiodistal tip, and rotation) and 4 linear movements (buccolingual, mesiodistal, vertical, and total scalar displacement). The predicted tooth movement was compared with the achieved amount for each movement and tooth, using both percentage accuracy and numerical difference. RESULTS: Average percentage accuracy (63.4% ± 11.5%) was higher than in previously reported literature. The most accurate tooth movements were buccal torque and mesial displacement compared with lingual torque and distal displacement, particularly for mandibular posterior teeth. Clinically significant inaccuracies were found for the buccal displacement of maxillary second molars, lingual displacement of all molars, intrusion of mandibular second molars, the distal tip of molars, second premolars, and mandibular first premolars, buccal torque of maxillary central and lateral incisors, and lingual torque of premolars and molars. CONCLUSIONS: Superimposition techniques used in this study lay the groundwork for future studies to analyze advanced clear aligner patients. Invisalign is a treatment modality that can be considered for presurgical orthodontics-tooth movements involved in arch leveling and decompensation are highly accurate when comparing the simulated and the clinically achieved movements.

Complicaciones cardiovasculares en pacientes COVID-19 hospitalizados en Unidades de Pacientes Críticos en Chile. Registro COVICAR / Cardiovascular complications in COVID-19 patients admitted to intensive care units in Chilean hospitals

BACKGROUND: Patients with a cardiovascular (CV) history may be at greater risk of becoming ill and die due to SARS-CoV-2. AIM: To assess the incidence of CV complications in COVID-19 patients, the type of complication, and their association with CV history. MATERIAL AND METHODS: The clinical course of 1,314 patients with COVID-19 admitted consecutively to critical care units of 10 Chilean hospitals was registered between April and August of 2020. RESULTS: The median age of patients was 59 years and 66% were men. One hundred-four (8%) had a CV history, namely heart failure (HF) in 53 (4.1%), coronary heart disease in 50 (3.8 %), and atrial fibrillation in 36 (2.7 %). There were CV complications in 359 patients (27.3%). The most common were venous thrombosis in 10.7% and arrhythmias in 10.5%, HF in 7.2%, type 2 acute myocardial infarction in 4.2%, arterial thrombosis in 2.0% and acute coronary syndrome (ACS) in 1.6%. When adjusted by age, sex and risk factors, only HF (Odds ratio (OR) = 7.16; 95% confidence intervals (CI), 3.96-12.92) and ACS (OR = 5.44; 95% CI, 1.50-19.82) were significantly associated with CV history. There was no association with arrhythmias, type 2 acute myocardial infarction, arterial or venous thrombosis. CONCLUSIONS: Patients with a history of CV disease are at greater risk of suffering HF and ACS when hospitalized due to COVID-19. Arrhythmias, type 2 AMI, and arterial or venous thrombosis occur with the same frequency in patients with or without CV history, suggesting that these complications depend on inflammatory phenomena related to the infection.

Risperidone in analgesia induced by paracetamol and meloxicam in experimental pain.

Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the best therapeutic options to treat pain. Their use in combination with other drugs may broaden their applicability in analgesia if their ceiling and adverse effects are reduced. The aim of this study was to evaluate the pharmacological interaction of two NSAIDs, paracetamol and meloxicam, with the antipsychotic drug risperidone in mice, in several experimental tests of nociceptive and inflammatory pain. Antinociception was assessed by dose-response curves to paracetamol and meloxicam before and after the i.p. administration of 0.5 mg/kg of risperidone. Results are presented as means ± SEM and differences were calculated by one-way ANOVA followed by Tukey's post-test. Paracetamol and meloxicam produced a dose-related antinociceptive effect with diverse potencies. Risperidone increased the analgesia mediated by paracetamol and meloxicam only in the tonic tests that detected inflammatory pain. This suggests that COX inhibition is only a partial explanation of the increased analgesic potency of paracetamol and meloxicam since the effects of NSAIDs in the CNS are mediated by multiple mechanisms. These results indicate that the combination of risperidone with paracetamol or meloxicam could be a new and effective alternative for the management of inflammatory pain.

Study of coliforms and Clostridium bacteria inactivation in wastewaters by a pilot photolysis process and by the maturation lagoons of a low-cost nature-based WWTP.

The inactivation processes of coliform bacteria (total and fecal) and sulphito-reducing Clostridium bacteria (vegetative species and spores) in water maturation lagoon of a low-cost nature-based wastewater treatment plant using constructed wetlands and through processes of photolysis in a pilot photoreactor have been comparatively studied. The different inactivation mechanisms by photolysis of these bacteria have been studied following the criteria of different statistical and kinetic models. Clostridium disinfection treatments fit models in which two types of bacteria populations coexist, one sensitive (vegetative species) and the other (spores) resistant to the treatment, the sensitive one (94%) with an inactivation rate of k = 0.24 ± 0.07 min-1 and the resistant one (6%) with k = 0.11 ± 0.05 min-1. Total coliform photolytic disinfection also shows two populations with different physiological state. The time required to reduce the first logarithmic decimal cycle of the different types of bacteria (physiological states) are δ1 = 4.2 ± 0.9 and δ2 = 8.3 ± 1.1 min, respectively. For fecal coliform photolytic disinfection, only bacteria population, with k = 1.15 ± 0.19 min-1, is found. The results obtained confirm the photolytic disinfection processes and maturation lagoon are effective systems for Clostridia bacteria removal after water treatment by nature-based systems. Total removal of coliform bacteria is not achieved by maturation lagoons, but their reduction is significant using low doses of cumulative radiation.

[Cardiovascular complications in COVID-19 patients admitted to intensive care units in Chilean hospitals]. / Complicaciones cardiovasculares en pacientes COVID-19 hospitalizados en Unidades de Pacientes Críticos en Chile. Registro COVICAR.

BACKGROUND: Patients with a cardiovascular (CV) history may be at greater risk of becoming ill and die due to SARS-CoV-2. AIM: To assess the incidence of CV complications in COVID-19 patients, the type of complication, and their association with CV history. MATERIAL AND METHODS: The clinical course of 1,314 patients with COVID-19 admitted consecutively to critical care units of 10 Chilean hospitals was registered between April and August of 2020. RESULTS: The median age of patients was 59 years and 66% were men. One hundred-four (8%) had a CV history, namely heart failure (HF) in 53 (4.1%), coronary heart disease in 50 (3.8 %), and atrial fibrillation in 36 (2.7 %). There were CV complications in 359 patients (27.3%). The most common were venous thrombosis in 10.7% and arrhythmias in 10.5%, HF in 7.2%, type 2 acute myocardial infarction in 4.2%, arterial thrombosis in 2.0% and acute coronary syndrome (ACS) in 1.6%. When adjusted by age, sex and risk factors, only HF (Odds ratio (OR) = 7.16; 95% confidence intervals (CI), 3.96-12.92) and ACS (OR = 5.44; 95% CI, 1.50-19.82) were significantly associated with CV history. There was no association with arrhythmias, type 2 acute myocardial infarction, arterial or venous thrombosis. CONCLUSIONS: Patients with a history of CV disease are at greater risk of suffering HF and ACS when hospitalized due to COVID-19. Arrhythmias, type 2 AMI, and arterial or venous thrombosis occur with the same frequency in patients with or without CV history, suggesting that these complications depend on inflammatory phenomena related to the infection.

Joint Cardioprotective Effect of Vitamin C and Other Antioxidants against Reperfusion Injury in Patients with Acute Myocardial Infarction Undergoing Percutaneous Coronary Intervention.

Percutaneous coronary intervention (PCI) has long remained the gold standard therapy to restore coronary blood flow after acute myocardial infarction (AMI). However, this procedure leads to the development of increased production of reactive oxygen species (ROS) that can exacerbate the damage caused by AMI, particularly during the reperfusion phase. Numerous attempts based on antioxidant treatments, aimed to reduce the oxidative injury of cardiac tissue, have failed in achieving an effective therapy for these patients. Among these studies, results derived from the use of vitamin C (Vit C) have been inconclusive so far, likely due to suboptimal study designs, misinterpretations, and the erroneous conclusions of clinical trials. Nevertheless, recent clinical trials have shown that the intravenous infusion of Vit C prior to PCI-reduced cardiac injury biomarkers, as well as inflammatory biomarkers and ROS production. In addition, improvements of functional parameters, such as left ventricular ejection fraction (LVEF) and telediastolic left ventricular volume, showed a trend but had an inconclusive association with Vit C. Therefore, it seems reasonable that these beneficial effects could be further enhanced by the association with other antioxidant agents. Indeed, the complexity and the multifactorial nature of the mechanism of injury occurring in AMI demands multitarget agents to reach an enhancement of the expected cardioprotection, a paradigm needing to be demonstrated. The present review provides data supporting the view that an intravenous infusion containing combined safe antioxidants could be a suitable strategy to reduce cardiac injury, thus improving the clinical outcome, life quality, and life expectancy of patients subjected to PCI following AMI.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

Clinical decision support systems in orthodontics: A narrative review of data science approaches.

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Web Infrastructure for Data Management, Storage and Computation.

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).