Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs.

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

New Primers for Detection and Differentiation between Leishmania viannia and L. leishmania Subgenera by Polymerase Chain Reaction.

Background: Leishmania is the parasitic protozoan responsible for leishmaniases, a disease that can cause a range of cutaneous, mucosal, and visceral infections. Two subgenera L. Viannia and L. Leishmania are known to infect humans in the tropics and subtropics of the Americas. The aim of the present study was to develop a new pair of primers for the two subgenera and test in clinical samples. Methods: We designed two new pairs of primers for a PCR method from two conserved genes, cysteine proteinase B (cpb) and N-acetylglucosamine-6-phosfate deacetylase-like protein (nagA), as specific markers for those two respective subgenera. Primers were tested with 16 microscopical positive clinical samples from the Amazon region of Ecuador obtained in 2010-2020 period. Results: The cpb presented a band of 172 bp and the nagA a band of 300 bp, thus clearly differentiating L. viannia from L. leishmania. Additionally, primers identified and differentiated the clinical samples in the two subgenera. Conclusion: The new primers targeting different two genes and standardized in a PCR assay could identified and differentiated Leishmania parasites at subgenus level. This protocol could be used for Leishmania genus identification and diagnosis at the subgenus level and for determining the parasite's geographical distribution where different Leishmania subgenera are found in the same area.

Clustering and graph mining techniques for classification of complex structural variations in cancer genomes.

For many years, a major question in cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, often composed of multiple DNA breaks, resulting in difficulties in classifying and interpreting them functionally. Despite recent efforts towards classifying structural variants (SVs), more robust statistical frames are needed to better classify these variants and isolate those that derive from specific molecular mechanisms. We present a new statistical approach to analyze SVs patterns from 2392 tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and identify significant recurrence, which can inform relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, randomization methods, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns across different cancer types but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified.

VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection.

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400-1000 nm] and near-infrared (NIR) [900-1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435-1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR-NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging.

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

Revalidation of the RACE scale after its regional implementation in Catalonia: a triage tool for large vessel occlusion.

BACKGROUND AND PURPOSE: Our aim was to revalidate the RACE scale, a prehospital tool that aims to identify patients with large vessel occlusion (LVO), after its region-wide implementation in Catalonia, and to analyze geographical differences in access to endovascular treatment (EVT). METHODS: We used data from the prospective CICAT registry (Stroke Code Catalan registry) that includes all stroke code activations. The RACE score evaluated by emergency medical services, time metrics, final diagnosis, presence of LVO, and type of revascularization treatment were registered. Sensitivity, specificity, and area under the curve (AUC) for the RACE cut-off value ≥5 for identification of both LVO and eligibility for EVT were calculated. We compared the rate of EVT and time to EVT of patients transferred from referral centers compared with those directly presenting to comprehensive stroke centers (CSC). RESULTS: The RACE scale was evaluated in the field in 1822 patients, showing a strong correlation with the subsequent in-hospital evaluation of the National Institute of Health Stroke Scale evaluated at hospital (r=0.74, P<0.001). A RACE score ≥5 detected LVO with a sensitivity 0.84 and specificity 0.60 (AUC 0.77). Patients with RACE ≥5 harbored a LVO and received EVT more frequently than RACE <5 patients (LVO 35% vs 6%; EVT 20% vs 6%; all P<0.001). Direct admission at a CSC was independently associated with higher odds of receiving EVT compared with admission at a referral center (OR 2.40; 95% CI 1.66 to 3.46), and symtoms onset to groin puncture was 133 min shorter. CONCLUSIONS: This large validation study confirms RACE accuracy to identify stroke patients eligible for EVT, and provides evidence of geographical imbalances in the access to EVT to the detriment of patients located in remote areas.

Total Cerebral Blood Flow in Patients with Cardioembolic Stroke: Is It Clinically Meaningful?

Chronic hypoperfusion may hinder the washout of emboli coming from the heart and facilitate the formation of intra-cavitary thrombi. We investigated whether a decreased total cerebral blood flow (tCBF) resulted in recurrence of stroke and other vascular events in consecutive patients with cardioembolic stroke. We excluded patients with extra-cranial carotid or vertebral stenosis. The recorded tCBF was the sum of blood flow in both the carotid and vertebral extra-cranial arteries as measured with ultrasonography. Patients were followed up to assess stroke recurrence, vascular events and mortality. We also recorded demographic data, vascular risk factors, treatment data, echocardiographic variables and the C congestive heart failure history H Hypertension history A Age D Diabetes S Sex S2 Stroke/TIA/Thromboembolism history Vasc Vascular Disease history (CHA2DS2-VASc) score. We studied 79 patients (age 77.9 ± 8.4 y). Mean tCBF was 65.5 ± 15.7 mL/100 g/min. Cox regression analysis found that CHA2 DS2-VASc score and ejection fraction were associated with tCBF. After a mean follow-up of 22 ± 8.5 mo, 7.6% of patients experienced a recurrent stroke, 12.7% experienced a vascular event and 21.5% of patients died. Clinical outcomes were not predicted by tCBF.