Effects of preservation method on canine (Canis lupus familiaris) fecal microbiota.

Studies involving gut microbiome analysis play an increasing role in the evaluation of health and disease in humans and animals alike. Fecal sampling methods for DNA preservation in laboratory, clinical, and field settings can greatly influence inferences of microbial composition and diversity, but are often inconsistent and under-investigated between studies. Many laboratories have utilized either temperature control or preservation buffers for optimization of DNA preservation, but few studies have evaluated the effects of combining both methods to preserve fecal microbiota. To determine the optimal method for fecal DNA preservation, we collected fecal samples from one canine donor and stored aliquots in RNAlater, 70% ethanol, 50:50 glycerol:PBS, or without buffer at 25 °C, 4 °C, and -80 °C. Fecal DNA was extracted, quantified, and 16S rRNA gene analysis performed on Days 0, 7, 14, and 56 to evaluate changes in DNA concentration, purity, and bacterial diversity and composition over time. We detected overall effects on bacterial community of storage buffer (F-value = 6.87, DF = 3, P < 0.001), storage temperature (F-value=1.77, DF = 3, P = 0.037), and duration of sample storage (F-value = 3.68, DF = 3, P < 0.001). Changes in bacterial composition were observed in samples stored in -80 °C without buffer, a commonly used method for fecal DNA storage, suggesting that simply freezing samples may be suboptimal for bacterial analysis. Fecal preservation with 70% ethanol and RNAlater closely resembled that of fresh samples, though RNAlater yielded significantly lower DNA concentrations (DF = 8.57, P < 0.001). Although bacterial composition varied with temperature and buffer storage, 70% ethanol was the best method for preserving bacterial DNA in canine feces, yielding the highest DNA concentration and minimal changes in bacterial diversity and composition. The differences observed between samples highlight the need to consider optimized post-collection methods in microbiome research

ASLR: How Robust Is the Randomness?

This paper examines the security provided by different implementations of Address Space Layout Randomization (ASLR). ASLR is a security mechanism that increases control-flow integrity by making it more difficult for an attacker to properly execute a buffer-overflow attack, even in systems with vulnerable software. The strength of ASLR lies in the randomness of the offsets it produces in memory layouts. We compare multiple operating systems, each compiled for two different hardware architectures, and measure the amount of entropy provided to a vulnerable application. Our paper is the first publication that we are aware of that quantitatively compares the entropy of different ASLR implementations. In addition, we provide a method for remotely assessing the efficacy of a particular security feature on systems that are otherwise unavailable for analysis, and highlight the need for independent evaluation of security mechanisms

Depletion of B-cells with rituximab improves endothelial function and reduces inflammation among individuals with rheumatoid arthritis.

BackgroundIndividuals with rheumatoid arthritis (RA) are at increased risk for cardiovascular disease, partly due to systemic inflammation and endothelial dysfunction. B-cells play an important pathogenic role in the inflammatory process that drives RA disease activity. Rituximab, a chimeric murine/human monoclonal antibody that depletes B-cells, is an effective therapy for RA. The purpose of this study was to determine whether B-cell depletion with rituximab reduces systemic inflammation and improves macrovascular (brachial artery flow-mediated dilation, FMD) and microvascular (reactive hyperemia) endothelial function in RA patients.Methods and resultsRA patients received a single course of rituximab (1000 mg IV infusion at baseline and on day 15). FMD, reactive hyperemia, inflammatory markers, and clinical assessments were performed at baseline, week 12, and week 24. Twenty patients (95% female, median age 54 years) completed the study. Following treatment, FMD improved from a baseline of 4.5±0.4% to 6.4±0.6% at 12 weeks (mean±SE; P<0.0001), followed by a decline at week 24; a similar pattern was observed for hyperemic velocity. Significant decreases in RA disease scores, high-sensitivity C-reactive protein, erythrocyte sedimentation rate, and circulating CD19+ B-cells were sustained through week 24. Cholesterol and triglycerides became significantly although modestly elevated during the study.ConclusionsDepletion of B-cells with rituximab improved macrovascular and microvascular endothelial function and reduced systemic inflammation, despite modest elevation in lipids. Given these results, rituximab should be evaluated in the future for its possible role in reducing excess cardiovascular risk in RA.Clinical trial registrationURL http://ClinicalTrials.gov. Unique identifier: NCT00844714

Bacteria isolated from Bengal cat (Felis catus × Prionailurus bengalensis) anal sac secretions produce volatile compounds potentially associated with animal signaling.

In social animals, scent secretions and marking behaviors play critical roles in communication, including intraspecific signals, such as identifying individuals and group membership, as well as interspecific signaling. Anal sacs are an important odor producing organ found across the carnivorans (species in the mammalian Order Carnivora). Secretions from the anal sac may be used as chemical signals by animals for behaviors ranging from defense to species recognition to signaling reproductive status. In addition, a recent study suggests that domestic cats utilize short-chain free fatty acids in anal sac secretions for individual recognition. The fermentation hypothesis is the idea that symbiotic microorganisms living in association with animals contribute to odor profiles used in chemical communication and that variation in these chemical signals reflects variation in the microbial community. Here we examine the fermentation hypothesis by characterizing volatile organic compounds (VOC) and bacteria isolated from anal sac secretions collected from a male Bengal cat (Felis catus × Prionailurus bengalensis), a cross between the domestic cat and the leopard cat. Both left and right anal sacs of a male Bengal cat were manually expressed (emptied) and collected. Half of the material was used to culture bacteria or to extract bacterial DNA and the other half was used for VOC analysis. DNA was extracted from the anal sac secretions and used for a 16S rRNA gene PCR amplification and sequencing based characterization of the microbial community. Additionally, some of the material was plated out in order to isolate bacterial colonies. Three taxa (Bacteroides fragilis, Tessaracoccus, and Finegoldia magna) were relatively abundant in the 16S rRNA gene sequence data and also isolated by culturing. Using Solid Phase Microextraction (SPME) gas chromatography-mass spectrometry (GC-MS), we tentatively identified 52 compounds from the Bengal cat anal sac secretions and 67 compounds from cultures of the three bacterial isolates chosen for further analysis. Among 67 compounds tentatively identified from bacterial isolates, 51 were also found in the anal sac secretion. We show that the bacterial community in the anal sac consists primarily of only a few abundant taxa and that isolates of these taxa produce numerous volatiles that are found in the combined anal sac volatile profile. Several of these volatiles are found in anal sac secretions from other carnivorans, and are also associated with known bacterial biosynthesis pathways. This is consistent with the fermentation hypothesis and the possibility that the anal sac is maintained at least in part to house bacteria that produce volatiles for the host

Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard

The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility

Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.Comment: 6 pages, 2 figures, 2 table

A comprehensive multi-domain dataset for mitotic figure detection

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species

Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors

The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.Comment: 6 page

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Long-Term Follow-Up of Cardiac Function and Quality of Life for Patients in NSABP Protocol B-31/NRG Oncology: A Randomized Trial Comparing the Safety and Efficacy of Doxorubicin and Cyclophosphamide (AC) Followed by Paclitaxel With AC Followed by Paclitaxel and Trastuzumab in Patients With Node-Positive Breast Cancer With Tumors Overexpressing Human Epidermal Growth Factor Receptor 2

Purpose Early cardiac toxicity is a risk associated with adjuvant chemotherapy plus trastuzumab. However, objective measures of cardiac function and health-related quality of life are lacking in long-term follow-up of patients who remain cancer free after completion of adjuvant treatment. Patients and Methods Patients in NSABP Protocol B-31 received anthracycline and taxane chemotherapy with or without trastuzumab for adjuvant treatment of node-positive, human epidermal growth factor receptor 2–positive early-stage breast cancer. A long-term follow-up assessment was undertaken for patients who were alive and disease free, which included measurement of left ventricular ejection fraction by multigated acquisition scan along with patient-reported outcomes using the Duke Activity Status Index (DASI), the Medical Outcomes Study questionnaire, and a review of current medications and comorbid conditions. Results At a median follow-up of 8.8 years among eligible participants, five (4.5%) of 110 in the control group and 10 (3.4%) of 297 in the trastuzumab group had a \u3e 10% decline in left ventricular ejection fraction from baseline to a value \u3c 50%. Lower DASI scores correlated with age and use of medications for hypertension, cardiac conditions, diabetes, and hyperlipidemia, but not with whether patients had received trastuzumab. Conclusion In patients without underlying cardiac disease at baseline, the addition of trastuzumab to adjuvant anthracycline and taxane-based chemotherapy does not result in long-term worsening of cardiac function, cardiac symptoms, or health-related quality of life. The DASI questionnaire may provide a simple and useful tool for monitoring patient-reported changes that reflect cardiac function