Structured Excitation Energy Transfer: Tracking Exciton Diffusion below Sunlight Intensity.

With the increasing demand for new materials for light-harvesting applications, spatiotemporal microscopy techniques are receiving increasing attention as they allow direct observation of the nanoscale diffusion of excitons. However, the use of pulsed and tightly focused laser beams generates light intensities far above those expected under sunlight illumination, leading to photodamage and nonlinear effects that seriously limit the accuracy and applicability of these techniques, especially in biological or atomically thin materials. In this work, we present a novel spatiotemporal microscopy technique that exploits structured excitation in order to dramatically decrease the excitation intensity, up to 10,000-fold when compared with previously reported spatiotemporal photoluminescence microscopy experiments. We tested our method in two different systems, reporting the first exciton diffusion measurement at illumination conditions below sunlight, both considering average power and peak exciton densities in an organic photovoltaic sample (Y6), where we tracked the excitons for up to five recombination lifetimes. Next, nanometer-scale energy transport was directly observed for the first time in both space and time in a printed monolayer of the light-harvesting complex 2 from purple bacteria.

Molecular diagnosis of Chagas disease: a systematic review and meta-analysis.

BACKGROUND: The complexity of the Chagas disease and its phases is impossible to have a unique test for both phases and a lot of different epidemiological scenarios. Currently, serology is the reference standard technique; occasionally, results are inconclusive, and a different diagnostic technique is needed. Some guidelines recommend molecular testing. A systematic review and meta-analysis of available molecular tools/techniques for the diagnosis of Chagas disease was performed to measure their heterogeneity and efficacy in detecting Trypanosoma cruzi infection in blood samples. METHODS: A systematic review was conducted up to July 27, 2022, including studies published in international databases. Inclusion and exclusion criteria were defined to select eligible studies. Data were extracted and presented according to PRISMA 2020 guidelines. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I2 and Tau2 statistics and P values. Funnel plots and Deek's test were used to assess publication bias. A quantitative meta-analysis of the different outcomes in the two different clinical phases was performed. RESULTS: We identified 858 records and selected 32 papers. Studies pertained to endemic countries and nonendemic areas with adult and paediatric populations. The sample sizes ranged from 17 to 708 patients. There were no concerns regarding the risk of bias and applicability of all included studies. A positive and nonsignificant correlation coefficient (S = 0.020; P = 0.992) was obtained in the set of studies that evaluated diagnostic tests in the acute phase population (ACD). A positive and significant correlation coefficient (S = 0.597; P < 0.000) was obtained in the case of studies performed in the chronic phase population (CCD). This resulted in high heterogeneity between studies, with the master mix origin and guanidine addition representing significant sources. INTERPRETATION/CONCLUSIONS AND RELEVANCE: The results described in this meta-analysis (qualitative and quantitative analyses) do not allow the selection of the optimal protocol of molecular method for the study of Trypanosoma cruzi infection in any of its phases, among other reasons due to the complexity of this infection. Continuous analysis and optimization of the different molecular techniques is crucial to implement this efficient diagnosis in endemic areas.

Multi-omics Characterization of Response to PD-1 Inhibitors in Advanced Melanoma.

Immunotherapy improves the survival of patients with advanced melanoma, 40% of whom become long-term responders. However, not all patients respond to immunotherapy. Further knowledge of the processes involved in the response and resistance to immunotherapy is still needed. In this study, clinical paraffin samples from fifty-two advanced melanoma patients treated with anti-PD-1 inhibitors were assessed via high-throughput proteomics and RNA-seq. The obtained proteomics and transcriptomics data were analyzed using multi-omics network analyses based on probabilistic graphical models to identify those biological processes involved in the response to immunotherapy. Additionally, proteins related to overall survival were studied. The activity of the node formed by the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation machinery was higher in responders compared to non-responders; the activity of the immune and inflammatory response node was also higher in those with complete or partial responses. A predictor for overall survival based on two proteins (AMBP and PDSM5) was defined. In summary, the response to anti-PD-1 therapy in advanced melanoma is related to protein processing in the endoplasmic reticulum, and also to genes involved in the immune and inflammatory responses. Finally, a two-protein predictor can define survival in advanced disease. The molecular characterization of the mechanisms involved in the response and resistance to immunotherapy in melanoma leads the way to establishing therapeutic alternatives for patients who will not respond to this treatment.

A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities.

Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means-consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means-consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease.

Sorting Transcriptomics Immune Information from Tumor Molecular Features Allows Prediction of Response to Anti-PD1 Therapy in Patients with Advanced Melanoma.

Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy.

Combining deep learning with SUPPOSe and compressed sensing for SNR-enhanced localization of overlapping emitters.

We present gSUPPOSe, a novel, to the best of our knowledge, gradient-based implementation of the SUPPOSe algorithm that we have developed for the localization of single emitters. We study the performance of gSUPPOSe and compressed sensing STORM (CS-STORM) on simulations of single-molecule localization microscopy (SMLM) images at different fluorophore densities and in a wide range of signal-to-noise ratio conditions. We also study the combination of these methods with prior image denoising by means of a deep convolutional network. Our results show that gSUPPOSe can address the localization of multiple overlapping emitters even at a low number of acquired photons, outperforming CS-STORM in our quantitative analysis and having better computational times. We also demonstrate that image denoising greatly improves CS-STORM, showing the potential of deep learning enhanced localization on existing SMLM algorithms. The software developed in this work is available as open source Python libraries.

Chest CT Characteristics are Strongly Predictive of Mortality in Patients with COVID-19 Pneumonia: A Multicentric Cohort Study.

RATIONALE AND OBJECTIVES: The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19. MATERIALS AND METHODS: A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality. RESULTS: A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan. CONCLUSION: Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification.

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

The Other Side of SARS-CoV-2 Infection: Neurological Sequelae in Patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the globe causing coronavirus disease 2019 (COVID-19). Because it affects the respiratory system, common symptoms are cough and breathing difficulties with fever and fatigue. Also, some cases progress to acute respiratory distress syndrome (ARDS). The acute phase of COVID-19 has been also related to nervous system symptoms, including loss of taste and smell as well as encephalitis and cerebrovascular disorders. However, it remains unclear if neurological complications are due to the direct viral infection of the nervous system, or they appear as a consequence of the immune reaction against the virus in patients who presented pre-existing deficits or had a certain detrimental immune response. Importantly, the medium and long-term consequences of the infection by SARS-CoV-2 in the nervous system remain at present unknown. This review article aims to give an overview of the current neurological symptoms associated with COVID-19, as well as attempting to provide an insight beyond the acute affectation.

Loss of Hepatic Carcinoembryonic Antigen-Related Cell Adhesion Molecule 1 Links Nonalcoholic Steatohepatitis to Atherosclerosis.

Patients with nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) commonly develop atherosclerosis through a mechanism that is not well delineated. These diseases are associated with steatosis, inflammation, oxidative stress, and fibrosis. The role of insulin resistance in their pathogenesis remains controversial. Albumin (Alb)Cre+ Cc1flox ( fl ) /fl mice with the liver-specific null deletion of the carcinoembryonic antigen-related cell adhesion molecule 1 (Ceacam1; alias Cc1) gene display hyperinsulinemia resulting from impaired insulin clearance followed by hepatic insulin resistance, elevated de novo lipogenesis, and ultimately visceral obesity and systemic insulin resistance. We therefore tested whether this mutation causes NAFLD/NASH and atherosclerosis. To this end, mice were propagated on a low-density lipoprotein receptor (Ldlr) -/- background and at 4 months of age were fed a high-cholesterol diet for 2 months. We then assessed the biochemical and histopathologic changes in liver and aortae. Ldlr-/-AlbCre+Cc1fl/fl mice developed chronic hyperinsulinemia with proatherogenic hypercholesterolemia, a robust proinflammatory state associated with visceral obesity, elevated oxidative stress (reduced NO production), and an increase in plasma and tissue endothelin-1 levels. In parallel, they developed NASH (steatohepatitis, apoptosis, and fibrosis) and atherosclerotic plaque lesions. Mechanistically, hyperinsulinemia caused down-regulation of the insulin receptor followed by inactivation of the insulin receptor substrate 1-protein kinase B-endothelial NO synthase pathway in aortae, lowering the NO level. This also limited CEACAM1 phosphorylation and its sequestration of Shc-transforming protein (Shc), activating the Shc-mitogen-activated protein kinase-nuclear factor kappa B pathway and stimulating endothelin-1 production. Thus, in the presence of proatherogenic dyslipidemia, hyperinsulinemia and hepatic insulin resistance driven by liver-specific deletion of Ceacam1 caused metabolic and vascular alterations reminiscent of NASH and atherosclerosis. Conclusion: Altered CEACAM1-dependent hepatic insulin clearance pathways constitute a molecular link between NASH and atherosclerosis.

Super-resolved edge detection in optical microscopy images by superposition of virtual point sources.

A new approach to the edge detection problem is presented which is specially designed to achieve high accuracy detection, below instrumental resolution (super resolution) in microscopy images. The method is based in a modified version of a recently published algorithm known as SUPPOSe, which performs a numerical reconstruction of an image as a superposition of virtual point sources. The method was tested in simulated and experimental optical microscopy images and compared to the standard Laplacian of Gaussian algorithm, showing huge differences when the size of the object is smaller than the lateral resolution provided by the instrument.

Public Health Academic Alliance for COVID-19 Response: The Role of a National Medical Task Force in Puerto Rico.

Alliances between the government and academic communities can be a key component of the public health response to an emergency such as the coronavirus disease 2019 (COVID-19) pandemic. The Governor of Puerto Rico designated the Puerto Rico Medical Task Force (MTF) COVID-19 to provide direct guidance and evaluation of the government response to the epidemic in Puerto Rico. Several work groups were formed within the MTF to create protocols and provide evidence-based recommendations on different public health aspects. The collaboration between the academia and the government enhanced the Puerto Rican public health response and contributed to the reduction seen in the contagion curve. Healthcare services and hospitals have not reached their maximum patient care capacity and the death toll has been controlled. Incorporating a national MTF with members of the academia into the government structure was beneficial during the COVID-19 response in Puerto Rico. A similar strategy could serve as a model for other states or territories and countries in similar scenarios.

Bayesian networks established functional differences between breast cancer subtypes.

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.

Comprehensive Characterization of the Mutational Landscape in Localized Anal Squamous Cell Carcinoma.

Anal squamous cell carcinoma (ASCC) is a rare neoplasm. Chemoradiotherapy is the standard of care, with no therapeutic advances achieved over the past three decades. Thus, a deeper molecular characterization of this disease is still necessary. We analyzed 46 paraffin-embedded tumor samples from patients diagnosed with primary ASCC by exome sequencing. A bioinformatics approach focused in the identification of high-impact genetic variants, which may act as drivers of oncogenesis, was performed. The relation between genetics variants and prognosis was also studied. The list of high-impact genetic variants was unique for each patient. However, the pathways in which these genes are involved are well-known hallmarks of cancer, such as angiogenesis or immune pathways. Additionally, we determined that genetic variants in BRCA2, ZNF750, FAM208B, ZNF599, and ZC3H13 genes are related with poor disease-free survival in ASCC. This may help to stratify the patient's prognosis and open new avenues for potential therapeutic intervention. In conclusion, sequencing of ASCC clinical samples appears an encouraging tool for the molecular portrait of this disease.

Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer.

BACKGROUND: Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances. METHODS: In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. RESULTS: On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient's clinical outcome. CONCLUSIONS: Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.

miRNA profiling in renal carcinoma suggest the existence of a group of pro-angionenic tumors in localized clear cell renal carcinoma.

Renal cell carcinoma comprises a variety of entities, the most common being the clear-cell, papillary and chromophobe subtypes. These subtypes are related to different clinical evolution; however, most therapies have been developed for clear-cell carcinoma and there is not a specific treatment based on different subtypes. In this study, one hundred and sixty-four paraffin samples from primary nephrectomies for localized tumors were analyzed. MiRNAs were isolated and measured by microRNA arrays. Significance Analysis of Microarrays and Consensus Cluster algorithm were used to characterize different renal subtypes. The analyses showed that chromophobe renal tumors are a homogeneous group characterized by an overexpression of miR 1229, miR 10a, miR 182, miR 1208, miR 222, miR 221, miR 891b, miR 629-5p and miR 221-5p. On the other hand, clear cell renal carcinomas presented two different groups inside this histological subtype, with differences in miRNAs that regulate focal adhesion, transcription, apoptosis and angiogenesis processes. Specifically, one of the defined groups had an overexpression of proangiogenic microRNAs miR185, miR126 and miR130a. In conclusion, differences in miRNA expression profiles between histological renal subtypes were established. In addition, clear cell renal carcinomas had different expression of proangiogenic miRNAs. With the emergence of antiangiogenic drugs, these differences could be used as therapeutic targets in the future or as a selection method for tailoring personalized treatments.

Genetic Profile and Functional Proteomics of Anal Squamous Cell Carcinoma: Proposal for a Molecular Classification.

Anal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed. High-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groups de novo Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level. A molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The functional analysis by the probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such as ATM, SLFN11 and DST Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.

An Archetype Query Language interpreter into MongoDB: Managing NoSQL standardized Electronic Health Record extracts systems.

The fast development of today's healthcare and the need to extract new medical knowledge from exponentially-growing volumes of standardized Electronic Health Records data, as required by studies in Precision Medicine, brings up a challenge that may probably only be addressed using NoSQL DBMSs, due to the non-optimal performance of traditional relational DBMSs on standardized data; and these database systems operated by semantic archetype-based query languages, because of the expected generalized extension of standardized EHR systems. An AQL into MongoDB interpreter has been developed to its first version. It translates system-independent AQL queries posed on ISO/EN 13606 standardized EHR extracts into the NoSQL MongoDB query language. The new interpreter has the advantages of both the archetype-based system-independent AQL queries and the dual-model-based standardized EHR extracts stored on document-centric NoSQL DBMSs, such as MongoDB. AQL queries are independent of applications, programming languages and system environments due to the use of the dual model, but EHR extracts featuring this model are best persisted on document-based NoSQL databases. Consequently, the interpreter allows us to query standardized EHR extracts semantically, and also affording optimal performance.

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients.

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and ß-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

Three-dimensional confocal Raman temperature characterization of electrokinetically pumped microchannels.

A novel method for noninvasive, three-dimensional temperature characterization in microfluidic devices is presented. A specially designed confocal microscope was built and used to measure water temperature by sensing the Raman spectrum variations of the liquid. This is achieved by splitting the spectrum in the isosbestic point and detecting it with two photon counters. The difference between the signals of each detector divided by their sum shows a linear dependence with temperature. A fiber-coupled laser beam is used to pump the sample with 25 mW of optical power at 405 nm. This allows a 0.8 K temperature precision and a 9 µm axial resolution using a 1 s integration time. These features make temperature profiling in all dimensions possible, in contrast with previous methods, where the information present in the height of the channel is lost and the whole spectrum needs to be recovered before computing the sample temperature. Using this technique, different geometries of polydimethylsiloxane microchannels sealed with a 150 µm thick glass coverslip were studied, showing that heat flow through the glass is the dominating dissipation mechanism and defines the maximum temperature in the channel. The results show good agreement with previous work found in the literature.