Life threatening hypercalcemia: An unusual cause

Hypercalcemia is commonly seen in patients with primary hyperparathyroidism and malignancy. Rarely, it can be seen in adrenal insufficiency. We report a case of a 42 year old female who presented with altered mental status and weakness. The patient had decreased appetite, nausea and significant weight loss of 60 pounds in the last few months. Laboratory evaluation was significant for hypercalcemia (15 mg/ dL) and acute kidney injury (1.5 mg/ dL). Work up for malignancy and hyperparathyroidism was negative. She was diagnosed with adrenal insufficiency based on cortisol levels prior to steroids of \u3c 0.5 mcg/ dL. She was treated with steroids and her hypercalcemia resolved within two days of steroids. This case shows that adrenal insufficiency may present as hypercalcemia and acute kidney injury. It should be considered as a potential cause while evaluating a patient for hypercalcemia.https://scholarlycommons.henryford.com/merf2020caserpt/1008/thumbnail.jp

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Large-scale database mining reveals hidden trends and future directions for cancer immunotherapy

LLC Cancer immunotherapy has fundamentally changed the landscape of oncology in recent years and significant resources are invested into immunotherapy research. It is in the interests of researchers and clinicians to identify promising and less promising trends in this field in order to rationally allocate resources. This requires a quantitative large-scale analysis of cancer immunotherapy related databases. We developed a novel tool for text mining, statistical analysis and data visualization of scientific literature data. We used this tool to analyze 72002 cancer immunotherapy publications and 1469 clinical trials from public databases. All source codes are available under an open access license. The contribution of specific topics within the cancer immunotherapy field has markedly shifted over the years. We show that the focus is moving from cell-based therapy and vaccination towards checkpoint inhibitors, with these trends reaching statistical significance. Rapidly growing subfields include the combination of chemotherapy with checkpoint blockade. Translational studies have shifted from hematological and skin neoplasms to gastrointestinal and lung cancer and from tumor antigens and angiogenesis to tumor stroma and apoptosis. This work highlights the importance of unbiased large-scale database mining to assess trends in cancer research and cancer immunotherapy in particular. Researchers, clinicians and funding agencies should be aware of quantitative trends in the immunotherapy field, allocate resources to the most promising areas and find new approaches for currently immature topics

The oxycoal process with cryogenic oxygen supply

Due to its large reserves, coal is expected to continue to play an important role in the future. However, specific and absolute CO2 emissions are among the highest when burning coal for power generation. Therefore, the capture of CO2 from power plants may contribute significantly in reducing global CO2 emissions. This review deals with the oxyfuel process, where pure oxygen is used for burning coal, resulting in a flue gas with high CO2 concentrations. After further conditioning, the highly concentrated CO2 is compressed and transported in the liquid state to, for example, geological storages. The enormous oxygen demand is generated in an air-separation unit by a cryogenic process, which is the only available state-of-the-art technology. The generation of oxygen and the purification and liquefaction of the CO2-enriched flue gas consumes significant auxiliary power. Therefore, the overall net efficiency is expected to be lowered by 8 to 12 percentage points, corresponding to a 21 to 36% increase in fuel consumption. Oxygen combustion is associated with higher temperatures compared with conventional air combustion. Both the fuel properties as well as limitations of steam and metal temperatures of the various heat exchanger sections of the steam generator require a moderation of the temperatures during combustion and in the subsequent heat-transfer sections. This is done by means of flue gas recirculation. The interdependencies among fuel properties, the amount and the temperature of the recycled flue gas, and the resulting oxygen concentration in the combustion atmosphere are investigated. Expected effects of the modified flue gas composition in comparison with the air-fired case are studied theoretically and experimentally. The different atmosphere resulting from oxygen-fired combustion gives rise to various questions related to firing, in particular, with regard to the combustion mechanism, pollutant reduction, the risk of corrosion, and the properties of the fly ash or the deposits that form. In particular, detailed nitrogen and sulphur chemistry was investigated by combustion tests in a laboratory-scale facility. Oxidant staging, in order to reduce NO formation, turned out to work with similar effectiveness as for conventional air combustion. With regard to sulphur, a considerable increase in the SO2 concentration was found, as expected. However, the H2S concentration in the combustion atmosphere increased as well. Further results were achieved with a pilot-scale test facility, where acid dew points were measured and deposition probes were exposed to the combustion environment. Besides CO2 and water vapour, the flue gas contains impurities like sulphur species, nitrogen oxides, argon, nitrogen, and oxygen. The CO2 liquefaction is strongly affected by these impurities in terms of the auxiliary power requirement and the CO2 capture rate. Furthermore, the impurity of the liquefied CO2 is affected as well. Since the requirements on the liquid CO2 with regard to geological storage or enhanced oil recovery are currently undefined, the effects of possible flue gas treatment and the design of the liquefaction plant are studied over a wide range

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Ensayo aleatorizado del cierre de orejuela izquierda vs varfarina para la prevención de accidentes cerebrovasculares tromboembólicos en pacientes con fibrilación auricular no relacionada con valvulopatía. Estudio PREVAIL

The successful application of poly­(<i>N</i>-vinylcaprolactam)-based microgels requires a profound understanding of their synthesis. For this purpose, a validated process model for the microgels synthesis by precipitation copolymerization with the cross-linker <i>N</i>,<i>N</i>′-methylenebis­(acrylamide) is formulated. Unknown reaction rate constants, reaction enthalpies, and partition coefficients are obtained by quantum mechanical calculations. The remaining parameter values are estimated from reaction calorimetry and Raman spectroscopy measurements of experiments with different monomer/cross-linker compositions. Because of high cross-propagation reaction rate constants, simulations predict a fast incorporation of the cross-linker. This agrees with reaction calorimetry measurements. Furthermore, the gel phase is predicted as the major reaction locus. The model is utilized for a prediction of the internal particle structure regarding its cross-link distribution. The highly cross-linked core reported in the literature corresponds to the predictions of the model

Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Background and Aims: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed. Methods: We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (n=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). Results: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59–0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC curve of 0.95 (range, 0.92–0.96) without image-preprocessing and an AUROC curve of 0.96 (range, 0.93–0.98) after color normalization. Conclusions: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using hematoxylin and eosin-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens

Are isomeric alkenes used in species recognition among neo-tropical stingless bees (Melipona spp)

The majority of our understanding of the role of cuticular hydrocarbons (CHC) in recognition is based largely on temperate ant species and honey bees. The stingless bees remain relatively poorly studied, despite being the largest group of eusocial bees, comprising more than 400 species in some 60 genera. The Meliponini and Apini diverged between 80-130 Myr B.P. so the evolutionary trajectories that shaped the chemical communication systems in ants, honeybees and stingless bees may be very different. Therefore, the main aim of this study was to study if a unique species CHC signal existed in Neotropical stingless bees, as shown for many temperate species, and if so what compounds are involved. This was achieved by collecting CHC data from 24 colonies belonging to six species of Melipona from North-eastern Brazil and comparing this new data with all previously published CHC studies on Melipona. We found that each of the eleven Melipona species studied so far each produced a unique species CHC signal based around their alkene isomer production. A remarkable number of alkene isomers, up to 25 in M. asilvai, indicated the diversification of alkene positional isomers among the stingless bees. The only other group to have really diversified in alkene isomer production are the primitively eusocial Bumblebees (Bombus spp), which are the sister group of the stingless bees. Furthermore, among the eleven Neotropical Melipona species we could detect no effect of the environment on the proportion of alkane production as has been suggested for some other species