Intranasal administration is a promising route of delivery of stem cells to the central nervous system (CNS). Reports on this mode of stem cell delivery have not yet focused on the route across the cribriform plate by which cells move from the nasal cavity into the CNS. In the current experiments, human mesenchymal stem cells (MSCs) were isolated from Wharton’s jelly of umbilical cords and were labeled with extremely bright quantum dots (QDs) in order to track the cells efficiently. At 2 h after intranasal delivery in immunodeficient mice, the labeled cells were found under the olfactory epithelium, crossing the cribriform plate adjacent to the fila olfactoria, and associated with the meninges of the olfactory bulb. At all times, the cells were separate from actual nerve tracts; this location is consistent with them being in the subarachnoid space (SAS) and its extensions through the cribriform plate into the nasal mucosa. In their location under the olfactory epithelium, they appear to be within an expansion of a potential space adjacent to the turbinate bone periosteum. Therefore, intranasally administered stem cells appear to cross the olfactory epithelium, enter a space adjacent to the periosteum of the turbinate bones, and then enter the SAS via its extensions adjacent to the fila olfactoria as they cross the cribriform plate. These observations should enhance understanding of the mode by which stem cells can reach the CNS from the nasal cavity and may guide future experiments on making intranasal delivery of stem cells efficient and reproducible.

PurposeDetermination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies.ApproachA deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other diseases. It is composed of 1,322 CT studies from a diverse set of CT machines and institutions that make over 550,000 slices. Each CT study was labeled based on a clinical test, and no per-slice annotation took place. This enabled a classification into Normal vs. Abnormal patients, and for those that were considered abnormal, an extra classification step into Abnormal (other diseases) vs. COVID-19. Additionally, the pipeline features a methodology to segment and quantify lesions of COVID-19 patients on the complete CT study, enabling easier localization and progress tracking. Moreover, multiple ablation studies were performed to appropriately assess the elements composing the classification pipeline.ResultsThe best performing lung CT study classification models achieved 0.83 accuracy, 0.79 sensitivity, 0.87 specificity, 0.82 F1 score and 0.85 precision for the Normal vs. Abnormal task. For the Abnormal vs COVID-19 task, the model obtained 0.86 accuracy, 0.81 sensitivity, 0.91 specificity, 0.84 F1 score and 0.88 precision. The ablation studies showed that using the complete CT study in the pipeline resulted in greater classification performance, restating that relevant COVID-19 patterns cannot be ignored towards the top and bottom of the lung volume.DiscussionThe lung CT classification architecture introduced has shown that it can handle weakly-labeled, variable-sized and possibly sparse axial lung studies, reducing the need for expert annotations at a per-slice level.ConclusionsThis work presents a working methodology that can guide the development of decision support systems for clinical reasoning in future interventionist or prospective studies.

Introduction: Outcomes of human immunodeficiency virus (HIV) infected patients admitted to intensive care units (ICU) have improved with antiretroviral therapy (ART). However, whether the outcomes have improved in low- and middle-income countries, paralleling those of high-income countries is unknown. The objective of this study was to describe a cohort of HIV-infected patients admitted to ICU in a middle-income country and identify the risk factors associated with mortality. Methodology: A cohort study of HIV-infected patients admitted to five ICUs in Medellín, Colombia, between 2009 and 2014 was done. The association of demographic, clinical and laboratory variables with mortality was analyzed using a Poisson regression model with random effects. Results: During this time period, 472 admissions of 453 HIV-infected patients were included. Indications for ICU admission were: respiratory failure (57%), sepsis/septic shock (30%) and central nervous system (CNS) compromise (27%). Opportunistic infections (OI) explained 80% of ICU admissions. Mortality rate was 49%. Factors associated with mortality included hematological malignancies, CNS compromise, respiratory failure, and APACHE II score ≥ 20. Conclusions: Despite advances in HIV care in the ART era, half of HIV-infected patients admitted to the ICU died. This elevated mortality was associated to underlying disease severity (respiratory failure and APACHE II score ≥ 20), and host conditions (hematological malignancies, admission for CNS compromise). Despite the high prevalence of OIs in this cohort, mortality was not directly associated to OIs.

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