Prevalence and imaging characteristics of cerebral small vessel disease in a Colombian population aged 40 years and older.

Cerebral small vessel disease is a major contributor to both brain aging and cognitive decline. This study aimed to determine the prevalence of cerebral small vessel disease in a Colombian population over 40 years of age who attended a Radiology and Diagnostic Imaging service for brain MRI between October 2018 and March 2019. This was an observational, cross-sectional and analytical study of 710 adult patients over 40 years of age who attended the Radiology and Diagnostic Imaging service for a brain MRI. The analysed data were obtained from an anonymized database of the service. We studied 710 MRI scans of patients aged between 40 and 104 years. The most frequent risk factor was hypertension (36.2%). Brain abnormalities associated with cerebral small vessel disease, such as white matter hyperintensities, were seen in 56.20% of the population, and brain atrophy was observed in 12.96%. Brain disease prevalence increased with age (23.18% for those aged 55 years, 54.49% for those aged 55-64 years, 69.8% for those aged 65-74 years and 90.53% for those older than 75 years). The prevalence of cerebral small vessel disease in our population was similar to that reported in the world literature, as were the prevalence of the evaluated cardiovascular risk factors. Additionally, we identified an association between hypertension and advanced age with cerebral small vessel disease, with white matter hyperintensities being the most characteristic finding.

Optimizing Patient Care: A Multicentric Study on the Clinical Impact of Sim&Size™ Simulation Software in Intracranial Aneurysm Treatment With Pipeline Embolization Devices.

BACKGROUND: To determine the clinical effects (stent size, and number of stents used) of the Sim&Size™ simulation software on the endovascular treatment of unruptured saccular intracranial aneurysms with Pipeline Embolization Devices (PED). METHODS: This study is a retrospective analytical multicenter study of patients treated with PED (Flex and Flex with SHIELD) for intracranial aneurysm in FOSCAL clinic and CHU de Montpellier. RESULTS: The study included 253 patients, of which 75 were treated in Colombia and 178 were treated in France. The majority of patients were women (83.8%), with a median age of 57.48 years, and had large vessel location (88.1%), with most aneurysms located in the ICA paraclinoid segment (56.8%). Patients in the group with Sim&Size™ simulation had shorter stents than those without simulation (15.62 mm versus 17.36 mm, P-value = 0.001). Also, a lower proportion of these patients required more than one stent (1.4% versus 7.3%, P-value = 0.022). There were 7 complications reported in the group that used the Sim&Size™ simulation software, compared to 9 complications in the group that did not use the software. CONCLUSIONS: Using Sim&Size™ simulation software for endovascular treatment of patients with intracranial aneurysms using PED reduces the stent length and decreasing the number of devices needed per treatment.

Safety and Efficacy of Endovascular Coils and Non-Flow-Diverting Stents for Management of Unruptured Intracranial Aneurysms: A Location-Specific Outcomes Analysis.

INTRODUCTION: The relationship between the anatomical location of an unruptured saccular aneurysm, the efficacy, and the potential complications associated with coil and non-flow-diverting stents remains poorly documented. Therefore, the aim of this study is to evaluate the efficacy and safety of endovascular treatment based on the anatomical position of the unruptured intracranial aneurysm (UIA). METHODS: A retrospective cohort study was conducted using an anonymized database of patients who underwent endovascular therapy for UIAs between 2014 and 2021. RESULTS: A total of 138 patients with 147 UIAs were included. Immediate Raymond-Roy occlusion class I or II was achieved in 99.2% of patients in all anatomical locations, with a 96.2% occlusion rate at the 12-month follow-up. Complications occurred more frequently in the anterior cerebral artery (35%) and internal carotid artery in its ophthalmic segment (25%). However, the difference was not statistically significant. CONCLUSIONS: Our study shows that endovascular treatment with stents and coils is effective and safe for managing UIAs in various anatomical locations. The incidence of thromboembolic complications was significantly higher for UIAs located in the anterior cerebral artery.

The Woven EndoBridge device, an effective and safe alternative endovascular treatment of intracranial aneurysm-systematic review.

PURPOSE: This study is a systematic review about the WEB device and addresses the efficacy and safety of this device for the endovascular treatment of ruptured and unruptured intracranial aneurysms. MATERIAL AND METHODS: This systematic literature review followed PRISMA-P guidelines and included studies published until 2010. PubMed and ScienceDirect databases were searched, resulting in 22 articles meeting the inclusion criteria. RESULTS: The studies involved 1705 patients and 1224 aneurysms, predominantly wide-neck aneurysms in the middle cerebral artery, internal carotid artery, and basilar artery. The treatment success rate was 28.1%, with the WEB-SL and WEB-SLS devices being commonly used. The immediate post-treatment adequate occlusion rate was 33.3%, increasing to 49.7% at follow-up. Thromboembolic complications occurred in 6.5% of cases, while other complications were observed in 3.1% of cases. The mortality rate associated with the WEB device was low, approximately 1%. CONCLUSION: The WEB device demonstrates favorable outcomes in treating patients with intracranial aneurysms, with adequate occlusion rates improving over time. Thromboembolic complications are the primary concern, but overall complication and mortality rates remain low. Further research is needed to optimize device selection, standardize classification systems, and enhance long-term evaluation and training protocols.

Hematopoietic stem and progenitor cells confer cross-protective trained immunity in mouse models.

Recent studies suggest that infection reprograms hematopoietic stem and progenitor cells (HSPCs) to enhance innate immune responses upon secondary infectious challenge, a process called "trained immunity." However, the specificity and cell types responsible for this response remain poorly defined. We established a model of trained immunity in mice in response to Mycobacterium avium infection. scRNA-seq analysis revealed that HSPCs activate interferon gamma-response genes heterogeneously upon primary challenge, while rare cell populations expand. Macrophages derived from trained HSPCs demonstrated enhanced bacterial killing and metabolism, and a single dose of recombinant interferon gamma exposure was sufficient to induce similar training. Mice transplanted with influenza-trained HSPCs displayed enhanced immunity against M. avium challenge and vice versa, demonstrating cross protection against antigenically distinct pathogens. Together, these results indicate that heterogeneous responses to infection by HSPCs can lead to long-term production of bone marrow derived macrophages with enhanced function and confer cross-protection against alternative pathogens.

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.

Clinical perspective in relation to age in patients treated with thrombectomy for anterior circulation stroke in a stroke center in Colombia.

BACKGROUND AND AIMS: Endovascular treatment for cerebrovascular disease is accepted as a first-line option with level I evidence in patients with an early and late time of window of onset symptoms, and an additional option in patients who do not respond or with contraindications to systemic thrombolysis; nevertheless the efficacy and outcomes of some groups were not clear, one of them are patients aged 80 years and older, because they were excluded of the trials, so the evidence is controversial with significant heterogeneity, for that reason in our study, we decided to analyze the age in the patients treated in our stroke center, as a predictor of prognosis, and to provide a baseline for the establishment of personalized treatment plans. METHODS: Observational, retrospective study of patients that received endovascular treatment for cerebrovascular disease in a Colombian stroke center between 2016 and 2020, continuous and categorical variables were compared using the Student's t test and Chi-Square. To determine cut-off points in the variable against death and Rankin score variable on 90th day. RESULTS: In total, 108 patients were recruited, 35 of them were of 80 or more years, and the mean age was 72.7 years, we found age as a significant variable to predict the risk in the population over 80 years of age [RR 3.37 CI (95% 1.14-103) p = 0.029]. CONCLUSIONS: Age younger than 80 is a significant predictor for results and long-term outcomes in patients suffering from stroke, and in patients older than 80 years old a Thrombolysis in Cerebral Infarction score 2b-3 is a predictor of good outcomes. Further studies are needed to evaluate the relationship between intrahospital complications and long-term outcomes.

Heterogeneous model to evaluate CFD in intracranial bifurcation aneurysms treated with the WEB device to predict angiographic outcome.

INTRODUCTION: The Woven EndoBridge device (WEB) was developed as an alternative to treat Wide-Necked bifurcation aneurysms. It has proven to be effective and safe, however, cases of recanalization have been reported. The purpose of this study was to evaluate and quantify hemodynamic parameters and indexes with CFD of the intracranial aneurysms before and after WEB simulation and to establish their relationship to complete occlusion. MATERIALS AND METHODS: Using the heterogeneous model based on the marching cubes algorithm, we created 3D representations of 27 bifurcated intracranial aneurysms treated with the single-layer WEB device to evaluate hemodynamics parameters with CFD, calculated with and without the WEB. RESULTS: We observed a lower treatment entry concentration indices (ICI) (2.12 ± 1.31 versus 3.14 ± 0.93, p-value: 0.029) previous to placement of WEB and higher pre-treatment FN (7.56 ± 5.92 versus 3.35 ± 1.51, p-value: 0.018) and post-treatment FN (5.34 ± 5.89 versus 1.99 ± 0.83, p-value: 0.021) for cases with successful occlusions. Lower post-treatment SRa (197.81 ± 221.29 versus 80.02 ± 45.25, p-value: 0.044) and higher pre (0.11 ± 0.07 versus 0.25 ± 0.19, p-value: 0.011) and post-treatment MATT (0.69 ± 1.23 versus 1.02 ± 0.46, p-value: 0.006) were observed in non-occluded cases. CONCLUSIONS: In our CFD analysis of the hemodynamic parameters of IA, we found lower ICI before the placement of the WEB device and higher FN pre- and post-treatment for cases with successful occlusions. Non-occluded cases had lower post-treatment SRa and higher pre-treatment and post-treatment MATT.

Flow Diverter Treatment for Non-Ruptured Carotid Aneurysms: Efficacy and Safety.

PURPOSE: Internal carotid artery (ICA) aneurysm treatment with a flow diverter (FD) has shown an adequate efficacy and safety profile, presenting high complete occlusion or near occlusion rates with low complications during follow-up. The purpose of this study was to evaluate the efficacy and safety of FD treatment in non-ruptured internal carotid aneurysms. MATERIALS AND METHODS: This is a retrospective, single-center, observational study evaluating patients diagnosed with unruptured ICA aneurysms treated with an FD between January 1, 2014, and January 1, 2020. We analyzed an anonymized database. The primary effectiveness endpoint was complete occlusion (O'Kelly-Marotta D, OKM-D) of the target aneurysm through 1-year follow-up. The safety endpoint was the evaluation of modified Rankin Scale (mRS) 90 days after treatment, considering a favorable outcome an mRS 0-2. RESULTS: A total of 106 patients were treated with an FD, 91.5% were women; the mean follow- up was 427.2±144.8 days. Technical success was achieved in 105 cases (99.1%). All patients included had 1-year follow-up digital subtraction angiography control; 78 patients (73.6%) completed the primary efficacy endpoint by achieving total occlusion (OKM-D). Giant aneurysms had a higher risk of not achieving complete occlusion (risk ratio, 3.07; 95% confidence interval, 1.70 - 5.54]). The safety endpoint of mRS 0-2 at 90 days was accomplished in 103 patients (97.2%). CONCLUSION: Treatment of unruptured ICA aneurysms with an FD showed high 1-year total occlusion results, with very low morbidity and mortality complications.

Geometry of intracranial aneurysms and of intrasaccular devices may influence aneurysmal occlusion rates after endovascular treatment.

INTRODUCTION: The Woven EndoBridge device (WEB) is used to treat wide-neck bifurcation aneurysms. These devices are deployed inside the sac. Therefore, the mesh structure provides apposition with the aneurysm wall and induces aneurysmal thrombosis. The objective of our study was to evaluate the anatomic and device-related parameters and indexes with Computational Fluid Dynamics (CFD) of the intracranial aneurysms before and after WEB simulation and find their relationship to complete occlusion. MATERIALS AND METHODS: Using the heterogeneous model based on the marching cubes algorithm, we created 3D representations of 27 bifurcated intracranial aneurysms treated with the single-layer WEB device to evaluate anatomic and device-related parameters with CFD. RESULTS: In our CFD analysis, we observed higher large volumes (Va) (0.25 ± 0.18 versus 0.39 ± 0.09, p-value= 0.025) and higher volume to neck surface ratio (Ra) (1.32 ± 0.17 versus 1.54 ± 0.14, p-value= 0.021) in cases with occlusion failure. CONCLUSIONS: Large aneurysm volumes (Va) and higher volume to neck surface ratio (Ra) could be associated with occlusion failure in aneurysms treated with the WEB device.

Clinical impact of Sim & Size® simulation software in the treatment of patients with cerebral aneurysms with flow-diverter Pipeline stents.

OBJECTIVES: This study evaluated the clinical impact of the Sim&Size® simulation software on the endovascular treatment with flow-diverter stents of patients with unruptured saccular intracranial aneurysms. METHODS: This monocentric retrospective study evaluated a cohort of patients treated with flow-divert stents between June 1, 2014, and December 31, 2019, for cerebral aneurysms. Patients belonged to two groups, patients treated with and without the Sim&Size® simulation software. Univariate, bivariate, and multivariate analyses were used to evaluate the clinical impact of simulation software. RESULTS: Out of the 73 interventions involving 68 patients analyzed by the study, 76.7% were simulated using the Sim&Size® simulation software, and 23.3% were not. Patients treated with the simulation software had shorter stent lengths (16.00 mm vs. 20.00 mm p-value = 0.001) and surgical time (100.00 min vs. 118.00 min p-value = 0.496). Also, fewer of them required more than one stent (3.6% vs. 17.6% p-value = 0.079). Three patients belonging to the non-stimulated group presented hemorrhagic complications. CONCLUSIONS: Using the Sim&Size® simulation software for the endovascular treatment of intracranial aneurysms with pipeline flow-diverter stents reduces the stent length.

Validation of three-dimensional printed models of intracranial aneurysms.

INTRODUCTION: Three-dimensional (3D) printing has evolved for medical applications as it can produce customized 3D models of devices and implants that can improve patient care. In this study, we aimed to validate the geometrical accuracy of the 3D models of intracranial aneurysms printed using Stereolithography 3D printing technology. MATERIALS AND METHODS: To compare the unruptured intracranial aneurysm mesh between the five patients and 3D printed models, we opened the DICOM files in the Sim&Size® simulation software, selected the region of interest, and performed the threshold check. We juxtaposed the 3D reconstructions and manually rotated the images to get the same orientation when needed and measured deviations at different nodes of the patient and 3D printed model meshes. RESULTS: In the first patient, 80% of the nodes were separated by <0.56 mm and 0.17 mm. In the second patient, the deviations were below 0.17 mm for 80% of the meshes' nodes. In the next three patients, the deviations were below 0.21, 0.23, and 0.11 mm for 80% of the meshes' nodes. Finally, the overall deviation was below 0.21 mm for 80% of the mesh nodes of the five aneurysms. CONCLUSIONS: 3D printed models of intracranial aneurysms are accurate, having surfaces that resemble that of patients' angiographies with an 80% cumulative deviation below 0.21 mm.

A deep CT to MRI unpaired translation that preserve ischemic stroke lesions.

Stroke is the second-leading cause of death world around. The immediate attention is key to patient prognosis. Ischemic stroke diagnosis typically involves neuroimaging studies (MRI and CT scans) and clinical protocols to characterize lesions and support decisions about treatment to be administered to the patient. Nowadays, multiparametric MRI images are the standard tool to visualize core and penumbra of ischemic stroke, supporting diagnosis and lesion prognosis. Specially, DWI modality (Diffusion Weighted Imaging) allows to quantify the cellular density of the tissue, and therefore allowing to quantify the lesion aggressiveness, and the recognition of micro-circulation properties. Nevertheless, MRI availability at hospitals is not widespread, and acquisition require special conditions requiring considerable time. Contrary, CT scans commonly have major availability but brain structures are poorly delineated, and even worse, ischemic lesions are only visible at advanced stages of the disease. This work introduces a deep generative strategy that allows ischemic stroke lesion translation over synthetic DWI-MRI images. This encoder-decoder architecture, include U-net modules, hierarchically organized, with inter-level connections that preserve brain structures, while codifying an embedding representation. Then a cyclic loss was here implemented to receive CT inputs and decode DWI-MRI images. To avoid mode collapse, this learning is inversely propagated, i.e., from synthetic DWI-MRI images to original CT-scans. Finally, an embedding projection is recovered to show a proper lesion-slice discrimination, regarding control studies. Clinical relevance- To recover synthetic DWI-MRI that preserved ischemic lesion using CT scans as an input and following an unpaired image translation setup.

FGD5 marks a subpopulation of hematopoietic stem and progenitor cells that resist interferon-γ-mediated differentiation.

Hematopoietic stem and progenitor cells (HSPCs) are responsible for the production of all immune and blood cells in both steady state and emergency settings. The rates at which HSPCs divide and differentiate vary widely in accordance with both cell intrinsic and cell extrinsic factors. However, the kinetics of these events remain poorly understood. In prior work, we determined that the inflammatory cytokine interferon-γ (IFN-γ) induces HSPC division and differentiation. Here, we report that a subset of hematopoietic stem cells (HSCs) that express Fgd5 do not divide or differentiate in response to IFN-γ. This suggests that FGD5 marks a subset of HSCs that remains unperturbed during emergency hematopoiesis and is potentially a mechanism of preservation of the HSC compartment.

Deep learning representations to support COVID-19 diagnosis on CT slices. / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

INTRODUCTION: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. OBJECTIVE: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. MATERIALS AND METHODS: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. RESULTS: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. CONCLUSION: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Deep learning representations to support COVID-19 diagnosis on CT slices / Representaciones basadas en aprendizaje profundo como apoyo del diagnóstico de la COVID-19 en cortes de tomografía computarizada

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist's expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic's dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

Introducción. La enfermedad por coronavirus (COVID-19) es actualmente el principal problema de salud pública en el mundo. En este contexto, el análisis automático de tomografías computarizadas (TC) surge como una herramienta diagnóstica complementaria que permite caracterizar hallazgos radiológicos, y categorizar y hacer el seguimiento de pacientes con COVID-19. Sin embargo, este análisis depende de la experiencia de los radiólogos, por lo que las valoraciones pueden ser subjetivas. Objetivo. Explorar representaciones de aprendizaje profundo entrenadas con cortes de TC torácica para diferenciar automáticamente entre los casos de COVID-19 y personas no infectadas. Materiales y métodos. Se usaron dos conjuntos de datos de TC: de SARS-CoV-2 CT (conjunto 1) y de la clínica FOSCAL (conjunto 2). Los modelos de aprendizaje supervisados y previamente entrenados en imágenes naturales, se ajustaron usando aprendizaje por transferencia. La clasificación se llevó a cabo mediante aprendizaje de extremo a extremo y clasificadores tales como los árboles de decisiones y las máquinas de soporte vectorial, alimentados por la representación profunda previamente aprendida. Resultados. El enfoque de extremo a extremo alcanzó una exactitud promedio de 92,33 % (89,70 % de precisión) para el conjunto 1 y de 96,99 % (96,62 % de precisión) para el conjunto-2. La máquina de soporte vectorial alcanzó una exactitud promedio de 91,40 % (precisión del 95,77 %) para el conjunto-1 y del 96,00 % (precisión del 94,74 %) para el conjunto 2. Conclusión. Las representaciones profundas lograron resultados sobresalientes al caracterizar patrones radiológicos usados en la detección de casos de COVID-19 a partir de estudios de TC y demostraron ser una potencial herramienta de apoyo del diagnóstico.

Hematopoietic stem and progenitor cells improve survival from sepsis by boosting immunomodulatory cells.

New therapeutic strategies to reduce sepsis-related mortality are urgently needed, as sepsis accounts for one in five deaths worldwide. Since hematopoietic stem and progenitor cells (HSPCs) are responsible for producing blood and immune cells, including in response to immunological stress, we explored their potential for treating sepsis. In a mouse model of Group A Streptococcus (GAS)-induced sepsis, severe immunological stress was associated with significant depletion of bone marrow HSPCs and mortality within approximately 5-7 days. We hypothesized that the inflammatory environment of GAS infection drives rapid HSPC differentiation and depletion that can be rescued by infusion of donor HSPCs. Indeed, infusion of 10,000 naïve HSPCs into GAS-infected mice resulted in rapid myelopoiesis and a 50-60% increase in overall survival. Surprisingly, mice receiving donor HSPCs displayed a similar pathogen load compared to untreated mice. Flow cytometric analysis revealed a significantly increased number of myeloid-derived suppressor cells in HSPC-infused mice, which correlated with reduced inflammatory cytokine levels and restored HSPC levels. These findings suggest that HSPCs play an essential immunomodulatory role that may translate into new therapeutic strategies for sepsis.

Cigarette Smoke Exposure in Mice using a Whole-Body Inhalation System.

Close to 14% of adults in the United States were reported to smoke cigarettes in 2018. The effects of cigarette smoke (CS) on lungs and cardiovascular diseases have been widely studied, however, the impact of CS in other tissues and organs such as blood and bone marrow remain incompletely defined. Finding the appropriate system to study the effects of CS in rodents can be prohibitively expensive and require the purchase of commercially available systems. Thus, we set out to build an affordable, reliable, and versatile system to study the pathologic effects of CS in mice. This whole-body inhalation exposure system (WBIS) set-up mimics the breathing and puffing of cigarettes by alternating exposure to CS and clean air. Here we show that this do-it-yourself (DIY) system induces airway inflammation and lung emphysema in mice after 4-months of cigarette smoke exposure. The effects of whole-body inhalation (WBI) of CS on hematopoietic stem and progenitor cells (HSPCs) in the bone marrow using this apparatus are also shown.

Virtual simulation with Sim&Size software for Pipeline Flex Embolization: evaluation of the technical and clinical impact.

INTRODUCTION: During flow diversion, the choice of the length, diameter, and location of the deployed stent are critical for the success of the procedure. Sim&Size software, based on the three-dimensional rotational angiography (3D-RA) acquisition, simulates the release of the stent, suggesting optimal sizing, and displaying the degree of the wall apposition. OBJECTIVE: To demonstrate technical and clinical impacts of the Sim&Size simulation during treatment with the Pipeline Flex Embolization Device. METHODS: Consecutive patients who underwent aneurysm embolization with Pipeline at our department were retrospectively enrolled (January 2015-December 2017) and divided into two groups: treated with and without simulation. Through univariate and multivariate models, we evaluated: (1) rate of corrective intervention for non-optimal stent placement, (2) duration of intervention, (3) radiation dose, and (4) stent length. RESULTS: 189 patients, 95 (50.2%) without and 94 (49.7%) with software assistance were analyzed. Age, sex, comorbidities, aneurysm characteristics, and operator's experience were comparable among the two groups. Procedures performed with the software had a lower rate of corrective intervention (9% vs 20%, p=0.036), a shorter intervention duration (46 min vs 52 min, p=0.002), a lower median radiation dose (1150 mGy vs 1558 mGy, p<0.001), and a shorter stent length (14 mm vs 16 mm, p<0.001). CONCLUSIONS: In our experience, the use of the virtual simulation during Pipeline treatment significantly reduced the need for corrective intervention, the procedural time, the radiation dose, and the length of the stent.