It has been suggested that some individuals may present genetic susceptibility to SARS‐CoV‐2 infection, with particular research interest in variants of the ACE2 and TMPRSS2 genes, involved in viral penetration into cells, in different populations and geographic regions, although insufficient information is currently available. This study addresses the apparently reasonable hypothesis that variants of these genes may modulate viral infectivity, making some individuals more vulnerable than others. Through whole‐exome sequencing, the frequency of exonic variants of the ACE2, TMPRSS2 , and Furin genes was analyzed in relation to presence or absence of SARS‐CoV‐2 infection in a familial multiple sclerosis cohort including 120 individuals from Madrid. The ACE2 gene showed a low level of polymorphism, and none variant was significantly associated with SARS‐CoV‐2 infection. These variants have previously been detected in Italy. While TMPRSS2 is highly polymorphic, the variants found do not coincide with those described in other studies, with the exception of rs75603675, which may be associated with SARS‐CoV‐2 infection. The synonymous variants rs61735792 and rs61735794 showed a significant association with infection. Despite the limited number of patients with SARS‐CoV‐2 infection, some variants, especially in TMPRSS2 , may be associated with COVID‐19.

Bilateral testicular torsion is a very uncommon emergency, with a challenging differential diagnosis. We describe the case of a 15-year-old patient with a left testicular torsion of 48 hours of duration and a sudden onset of right scrotum pain during his stay at the emergency area. Bilateral testicular torsion was diagnosed after repeat physical examination and doppler ultrasound, which had been normal for right testis in a first evaluation. Surgical exploration was performed with orchiectomy in left testis and fixation in right testis. In previous literature, there are reported bilateral torsion only in four adolescents and five adults. With this case, we demonstrate that bilateral spermatic cord torsion may be easily overlooked in a patient with acute scrotum and we emphasize the importance of bilateral exploration in testicular torsion.

Background Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning‐based models for the diagnosis using cognitive tests. Methods Three hundred and twenty‐nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono‐objective and multi‐objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta‐model strategy. Results Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. Conclusions Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

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Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores.Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores.Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67–71%.Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.

Primary progressive aphasia (PPA) is mainly considered a sporadic disease and few studies have systematically analyzed its genetic basis. We here report the analyses of C9orf72 genotyping and whole-exome sequencing data in a consecutive and well-characterized cohort of 50 patients with PPA. We identified three pathogenic GRN variants, one of them unreported, and two cases with C9orf72 expansions. In addition, one likely pathogenic variant was found in the SQSTM1 gene. Overall, we found 12%of patients carrying pathogenic or likely pathogenic variants. These results support the genetic role in the pathophysiology of a proportion of patients with PPA.

Background: Primary progressive aphasia (PPA) is a neurodegenerative syndrome with three main clinical variants: non-fluent, semantic, and logopenic. Clinical diagnosis and accurate classification are challenging and often time-consuming. The Mini-Linguistic State Examination (MLSE) has been recently developed as a short language test to specifically assess language in neurodegenerative disorders. Objective: Our aim was to adapt and validate the Spanish version of MLSE for PPA diagnosis. Methods: Cross-sectional study involving 70 patients with PPA and 42 healthy controls evaluated with the MLSE. Patients were independently diagnosed and classified according to comprehensive cognitive evaluation and advanced neuroimaging. Results: Internal consistency was 0.758. The influence of age and education was very low. The area under the curve for discriminating PPA patients and healthy controls was 0.99. Effect sizes were moderate-large for the discrimination between PPA and healthy controls. Motor speech, phonology, and semantic subscores discriminated between the three clinical variants. A random forest classification model obtained an F1-score of 81%for the three PPA variants. Conclusion: Our study provides a brief and useful language test for PPA diagnosis, with excellent properties for both clinical routine assessment and research purposes.

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein–protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein–protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.