Biomarkers of axonal damage to favor early diagnosis in variant transthyretin amyloidosis (A-ATTRv).

Early identification of ATTRv amyloidosis disease onset is still often delayed due to the lack of validated biomarkers of this disease. Light chain neurofilament (NfL) have shown promising results in early diagnosis in this disease, but data is still needed, including with alternative measuring methods. Our aim was to study the levels of NfL measured by ELISA. Furthermore, interstitial matrix metalloproteinase type 1 (MMP-1) serum levels were measured as a potential new biomarker in ATTRv. Serum NfL and MMP-1 were measured using ELISA assays in 90 participants (29 ATTR-V30M patients, 31 asymptomatic V30M-TTR variant carriers and 30 healthy controls). Median NfL levels among ATTRv amyloidosis patients were significantly higher (116 pg/mL vs 0 pg/mL in both comparison groups). The AUC comparing ATTRv amyloidosis patients and asymptomatic carriers was 0.90 and the NfL concentration of 93.55 pg/mL yielded a sensitivity of 79% and a specificity of 87%. NfL levels had a significant positive correlation with NIS values among patients. We found a negative significant correlation between eGFR and NfL levels. Finally, MMP1 levels were not different between groups. Evidence of NfL use for early diagnosis of ATTR-PN amyloidosis is growing. ELISA seems a reliable and available technique for it quantification. Decreased GFR could influence NfL plasma levels.

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models.

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models.

Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation.

BACKGROUND: The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain's health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. OBJECTIVE: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. METHODS: After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. RESULTS: After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. CONCLUSIONS: Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.

Hallazgo casual mediante array de polimorfismo de nucleótido simple. Síndrome de Warkany / Casual finding using single nucleotide polymorphisms arrays: warkany syndrome

Introducción: La trisomía del cromosoma 8, conocida como síndrome de Warkany, es una rara enfermedad genética que cursa con un fenotipo muy variable. Su principal característica clínica es la discapacidad intelectual, facies dismórficas y pliegues plantares profundos. Presentamos el caso de un paciente de 10 años de edad, con facies gargoloides, retraso mental y rigidez en las articulaciones. El estudio inicial del cariotipo, en el que se analizaron 20 metafases, fue normal. Se solicitó array de polimorfismos de nucleótido único (SNPs) al laboratorio. Resultados: Se detectó una ganancia completa del cromosoma 8, que se interpretó como una trisomía 8 en mosaico de aproximadamente un 20%, y que era compatible con la clínica que presentaba el paciente. Discusión: Este caso muestra las limitaciones que tiene el análisis de solo 20 metafases en el cariotipo en pacientes con aneuploidías en mosaico. En estos casos estaría recomendado ampliar el estudio a al menos 30 metafases de cara a detectar mosaicismos en baja proporción

Introduction: Chromosome 8 trisomy, known as Warkany syndrome, is a rare genetic disease that has a very variable phenotype. Its main clinical characteristic is intellectual disability, dysmorphic facies, and deep plantar folds. The case is presented of a 10-year-old patient with gargoyle-like facies, mental retardation, and joint stiffness. The initial study of the karyotype, in which 20 metaphases were analysed, was normal. A single nucleotide polymorphisms (SNPs) array was requested from the laboratory. Results: A complete gain of chromosome 8 was detected, which was interpreted as a mosaic trisomy 8 of approximately 20%, and which was compatible with the clinical presentation of the patient. Discussion: This case shows the limitations of the analysis of only 20 metaphases in the karyotype in patients with mosaic aneuploidies. In these cases it would be recommended to extend the study to at least 30 metaphases in order to detect mosaicisms in low proportion

Utilidad del estudio molecular de la hipercalcemia hipocalciúrica familiar; detección de una nueva mutación genética / Usefulness of molecular study of familial hypocalciuric hypercalcemia; detection of a new genetic mutation

La hipercalcemia hipocalciúrica familiar representa una causa benigna de hipercalcemia de herencia autosómica dominante que no precisa normalmente tratamiento. Presentamos el caso de un varón de 21 años con perfil bioquímico típico de hipercalcemia hipocalciúrica familiar: hipercalcemia leve, niveles normales de PTH e hipocalciuria. Se solicitó el estudio genético del gen CASR, que objetivó en heterocigosis una variante no descrita previamente en la literatura. El padre, con hipercalcemia leve, también era portador de dicha variante. Aunque se trata de una enfermedad sin repercusiones clínicas importantes, es de utilidad realizar la confirmación genética de la hipercalcemia hipocalciúrica familiar para diferenciarla del hiperparatiroidismo primario y para ofrecer un asesoramiento genético adecuado a los pacientes (AU)

Familial hypocalciuric hypercalcemia is a benign cause of hypercalcemia of autosomal dominant inheritance that does not normally require treatment. The case is presented on a 21 year-old male with a typical familial hypocalciuric hypercalcemia biochemical profile: mild hypercalcaemia, normal PTH levels, and hypocalciuria. A genetic study was requested on the CASR gene, which showed a heterozygous variant not previously described in the literature. The father, with mild hypercalcaemia, was also a carrier of this variant. Although it is a disease with no significant clinical repercussions, it is useful to perform genetic confirmation of familial hypocalciuric hypercalcemia to differentiate it from primary hyperparathyroidism and to provide adequate genetic counselling to patients (AU)