A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies.

The key component of stroke diagnosis is the localization and delineation of brain lesions, especially from MRI studies. Nonetheless, this manual delineation is time-consuming and biased by expert opinion. The main purpose of this study is to introduce an autoencoder architecture that effectively integrates cross-attention mechanisms, together with hierarchical deep supervision to delineate lesions under scenarios of remarked unbalance tissue classes, challenging geometry of the shape, and a variable textural representation. This work introduces a cross-attention deep autoencoder that focuses on the lesion shape through a set of convolutional saliency maps, forcing skip connections to preserve the morphology of affected tissue. Moreover, a deep supervision training scheme was herein adapted to induce the learning of hierarchical lesion details. Besides, a special weighted loss function remarks lesion tissue, alleviating the negative impact of class imbalance. The proposed approach was validated on the public ISLES2017 dataset outperforming state-of-the-art results, achieving a dice score of 0.36 and a precision of 0.42. Deeply supervised cross-attention autoencoders, trained to pay more attention to lesion tissue, are better at estimating ischemic lesions in MRI studies. The best architectural configuration was achieved by integrating ADC, TTP and Tmax sequences. The contribution of deeply supervised cross-attention autoencoders allows better support the discrimination between healthy and lesion regions, which in consequence results in favorable prognosis and follow-up of patients.

Takayasu arteritis associated with focal and segmentary glomerulosclerosis

Takayasu arteritis (TA) is a large vessel vasculitis that affects young people, related to cardiovascular outcomes and chronic kidney disease. We present the case of a 20-year-old male with a diagnosis of TA, who developed chronic kidney disease, impaired renal blood flow was ruled out, renal biopsy was compatible with focal and segmental glomerulosclerosis of a collapsing variety, other possible aetiologies were excluded. The mechanisms that mediate this association have not been determined, immune-mediated mechanisms are proposed. According to our review, this is the second reported case of this association and the first with a collapsing variety.

La arteritis de Takayasu es una vasculitis de grandes vasos que afecta a personas jóvenes y se relaciona con desenlaces cardiovasculares y enfermedad renal crónica. Se presenta el caso de un paciente masculino de 20 arios, con diagnóstico de arteritis de Takayasu, que desarrolla enfermedad renal crónica. Se descartan alteraciones en el flujo sanguíneo renal, en tanto que la biopsia renal resulta compatible con glomeruloesclerosis focal y segmentaria de variedad colapsante. Se excluyeron otras posibles etiologías. No se han determinado los mecanismos que median en esta asociación; se proponen mecanismos inmunomediados. Según nuestra revisión, se trata del segundo caso reportado de esta asociación y el primero con variedad colapsante.

CT-based pathological lung opacities volume as a predictor of critical illness and inflammatory response severity in patients with COVID-19.

Objective: The aim of the study was to assess the impact of CT-based lung pathological opacities volume on critical illness and inflammatory response severity of patients with COVID-19. Methods: A retrospective, single center, single arm study was performed over a 30-day period. In total, 138 patients (85.2%) met inclusion criteria. All patients were evaluated with non-contrast enhanced chest CT scan at hospital admission. CT-based lung segmentation was performed to calculate pathological lung opacities volume (LOV). At baseline, complete blood count (CBC) and inflammation response biomarkers were obtained. The primary endpoint of the study was the occurrence of critical illness, as defined as, the need of mechanical ventilation and/or ICU admission. Mann-Whitney U test was performed for univariate analysis. Logistic regression analysis was performed to determine independent predictors of critical illness. Spearman analysis was performed to assess the correlation between inflammatory response biomarkers serum concentrations and LOV. Results: Median LOV was 28.64% (interquartile range [IQR], 6.33-47.22%). Correlation analysis demonstrated that LOV was correlated with higher levels of D-dimer (r = 0.51, p < 0.01), procalcitonin (r = 0.47, p < 0.01) and IL6 (r = 0.48, p < 0.01). Critical illness occurred in 51 patients (37%). Univariate analysis demonstrated that inflammatory response biomarkers and LOV were associated with critical illness (p < 0.05). However, multivariate analysis demonstrated that only D-dimer and LOV were independent predictors of critical illness. Furthermore, a ROC analysis demonstrated that a LOV equal or greater than 60% had a sensitivity of 82.1% and specificity of 70.2% to determine critical illness with an odds ratio of 19.4 (95% CI, 4.2-88.9). Conclusion: Critical illness may occur in up to 37% of the patients with COVID-19. Among patients with critical illness, higher levels of inflammatory response biomarkers with larger LOVs were observed. Furthermore, multivariate analysis demonstrated that pathological lung opacities volume was an independent predictor of critical illness. In fact, patients with a pathological lung opacities volume equal or greater than 60% had 19.4-fold increased risk of critical illness.

A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation.

Parkinson's Disease (PD), the second most common neurodegenerative disorder, is associated with voluntary movement disorders caused by progressive dopamine deficiency. Gait motor alterations constitute a main tool to diagnose, characterize and personalize treatments. Nonetheless, such evaluation is biased by expert observations, reporting a false positive diagnosis up to 24%. Learning computational tools are recently emerged as potential alternatives to support diagnosis and to quantify kinematic patterns during locomotion. Nonetheless, such learning schemes required a large amount of balanced and stratified data examples, which may result unrealistic in clinical scenarios. This work introduces a self-supervised generative representation to discover gait-motion related patterns, under the pretext of video reconstruction and an anomaly detection framework. From the learned scheme, it is recovered a hidden embedding gait descriptor that constitutes a digital biomarker, allowing to discover PD differences regarding a control population. The proposed approach was validated with 11 PD patients (H&Y scale between 2.5 and 3.0) and 11 control subjects, and trained with only control population, achieving an AUC of 99.4% in the classification task. Clinical Relevance- A digital biomarker that helps in the diagnosis of PD using videos of a patient's gait to capture important and relevant motion patterns to avoid subjectivity when an expert made a diagnosis.

Computed Tomography-based Lung Residual Volume and Mortality of Patients With Coronavirus Disease-19 (COVID-19).

RATIONALE AND OBJECTIVES: To assess the effect of computed tomography (CT)-based residual lung volume (RLV) on mortality of patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A single-center, retrospective study of a prospectively maintained database was performed. In total, 138 patients with COVID-19 were enrolled. Baseline chest CT scan was performed in all patients. CT-based automated and semi-automated lung segmentation was performed using the Alma Medical workstation to calculate normal lung volume, lung opacities volume, total lung volume, and RLV. The primary end point of the study was mortality. Univariate and multivariate analyses were performed to determine independent predictors of mortality. RESULTS: Overall, 84 men (61%) and 54 women (39%) with a mean age of 47.3 years (±14.3 y) were included in the study. Overall mortality rate was 21% (29 patients) at a median time of 7 days (interquartile range, 4 to 11 d). Univariate analysis demonstrated that age, hypertension, and diabetes were associated with death (P<0.01). Similarly, patients who died had lower normal lung volume and RLV than patients who survived (P<0.01). Multivariate analysis demonstrated that low RLV was the only independent predictor of death (odds ratio, 1.042; 95% confidence interval, 10.2-10.65). Furthermore, receiver operating characteristic curve analysis demonstrated that a RLV ≤64% significantly increased the risk of death (odds ratio, 4.8; 95% confidence interval, 1.9-11.7). CONCLUSION: Overall mortality of patients with COVID-19 may reach 21%. Univariate and multivariate analyses demonstrated that reduced RLV was the principal independent predictor of death. Furthermore, RLV ≤64% is associated with a 4-fold increase on the risk of death.

Towards understanding spatio-temporal parkinsonian patterns from salient regions of a 3D convolutional network.

Gait motion patterns such as step length, flexed posture, absent arm swing and bradykinesia, constitute the main source of information to describe and quantify Parkinson disease. Nevertheless, such quantification is commonly developed under marker based protocols, losing natural motion gestures, and only taking into account a limited description of the locomotion process. This work introduces a 3D convolutional gait representation, that uses markerless video sequences to automatically predict parkinsonian behaviours. A remarkable contribution herein presented is the quantification of spatio-temporal salient maps, that stand out body regions related with Parkinson disease, and result from activations that mainly contribute on the classification task. For doing so, a convolutional architecture is trained from a set of walking videos, recorded from parkinsonian and control subjects. Then, a prediction of disease is obtained according to motion patterns computed by convolutional learned scheme. Salience motion patterns are obtained by retro-propagating the output softmax network prediction over the video space. From a total of 22 patients, and a total of 176 video sequences, the proposed approach achieved an average accuracy score of 88%. Interestingly enough, the recovered salience maps focus the attention on relevant parkinsonian biomarkers such as the head motion and trunk posture, that namely is excluded on classical gait analysis.Clinical relevance- Salient spatio-temporal regions can potentially support and complement the diagnosis and the following of Parkinson's disease. Also, such complex relationships could potentially evolve in further understanding of this pathology.

Building Hierarchical Representations for Oracle Character and Sketch Recognition.

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

Hematoma hepático subcapsular roto y choque hipovolémico como una complicación inusual tras la realización de colangiopancreatografía retrógrada endoscópica: Reporte de un caso / Ruptured hepatic subcapsular hematoma and hypovolemic shock as an unusual complication after endoscopic retrograde cholangiopancreatography: Report of one case

Objetivo: Describir el caso de un hematoma subcapsular hepático roto con desarrollo de choque hipovolémico como una complicación inusual secundaria a la realización de colangiopancreatografía retrógrada endoscópica. Sede: Hospital de segundo nivel, Hospital General Xoco. Diseño: Reporte de un caso. Descripción del caso: Mujer de 66 años quien ingresa mediante el Servicio de Urgencias con diagnóstico de coledocolitiasis; decidiendo su manejo con colangiopancreatografía retrógrada endoscópica, la cual se realiza 24 horas después. En ésta se corrobora la presencia de litos en colédoco, observando la salida de material purulento de la vía biliar; durante el evento, ella presenta desaturación con la necesidad de manejo mecánico de la vía aérea y manejo por terapia intensiva con alteraciones primordialmente ventilatorias; veinticuatro horas después presenta estado de choque de inicio súbito con abdomen agudo, por lo que se decide manejo quirúrgico, en el cual se evidencia hematoma hepático subcapsular roto con 2,000 ml de hemoperitoneo. La paciente presenta complicaciones por estado de hipovolemia, alteraciones ventilatorias, coagulopatía y fondo infeccioso en vía biliar que conllevan al desenlace fatal. Conclusión: El hematoma subcapsular relacionado a la realización de la colangiopancreatografía retrógrada endoscópica es una entidad infrecuente; éste es el decimocuarto caso reportado y el primero con ruptura.

Objective: To describe a case of ruptured hepatic subcapsular hematoma accompanied by hypovolemic shock as an unusual complication secondary to an endoscopic retrograde cholangiopancreatography. Setting: Second level health care hospital. General Hospital Xoco, Mexico City. Design: Case report. Description of the case: A 66-year old woman, who was admitted through the emergency ward with a diagnosis of choledocholithiasis, and who was subjected to endoscopic retrograde cholangiopancreatography, performed 24 h after admission, confirming the presence of stones in the choledochus, observing discharge of purulent matter from the biliary tract; during the event, she presented desaturation, needing mechanical handling of the airways and intensive therapy management, with mainly ventilatory alterations. Twenty-four hours afterwards she presented a state of shock of sudden onset together with acute abdomen; surgical management was then decided, evidencing a ruptured hepatic subcapsular hematoma with 2000 ml of hemoperitoneum. Patient presenting complications due to hypovolemic state, ventilatory alterations, infectious focus in the biliary tract that lead to fatal outcome. Conclusion: A subcapsular hematoma related to endoscopic retrograde cholangiopancreatography is an infrequent entity; this is the 14th reported case and the first with rupture.

HDAC inhibitor valproic acid upregulates CAR in vitro and in vivo.

BACKGROUND: The presence of CAR in diverse tumor types is heterogeneous with implications in tumor transduction efficiency in the context of adenoviral mediated cancer gene therapy. Preliminary studies suggest that CAR transcriptional regulation is modulated through histone acetylation and not through promoter methylation. Furthermore, it has been documented that the pharmacological induction of CAR using histone deacetylase inhibitor (iHDAC) compounds is a viable strategy to enhance adenoviral mediated gene delivery to cancer cells in vitro. The incorporation of HDAC drugs into the overall scheme in adenoviral based cancer gene therapy clinical trials seems rational. However, reports using compounds with iHDAC properties utilized routinely in the clinic are pending. Valproic acid, a short chained fatty acid extensively used in the clinic for the treatment of epilepsy and bipolar disorder has been recently described as an effective HDAC inhibitor at therapeutic concentrations. METHODS: We studied the effect of valproic acid on histone H3 and H4 acetylation, CAR mRNA upregulation was studied using semiquantitative PCR and adenoviral transduction on HeLa cervical cancer cells, on MCF-7 breast cancer cells, on T24 transitional cell carcinoma of the bladder cells. CAR mRNA was studied using semiquantitative PCR on tumor tissue extracted from patients diagnosed with cervical cancer treated with valproic acid. RESULTS: CAR upregulation through HDAC inhibition was observed in the three cancer cell lines with enhancement of adenoviral transduction. CAR upregulation was also observed in tumor samples obtained from patients with cervical cancer treated with therapeutic doses of valproic acid. These results support the addition of the HDAC inhibitor valproic acid to adenoviral mediated cancer gene therapy clinical trials to enhance adenoviral mediated gene delivery to the tumor cells.