Enhancement of pulmonary tumour seeding by human coagulation factors II, IX, X--an investigation into the possible mechanisms involved.

Warfarin inhibits metastasis in the animal model and injection of the Warfarin-dependent coagulation factor complex II, IX, X enhances pulmonary metastasis in the same model. We have studied two possible mechanisms responsible for the observed effect. Mtln3, rat mammary carcinoma cells, radiolabelled with 5-(125) Iodo-2'-deoxyuridine (IUDR) were injected intravenously in female Fisher 344 rats either alone or in combination with factor complex II, IX, X or bovine serum albumin. Following sacrifice at various intervals, measured lung radioactivity was significantly higher (20%) in animals administered cells with the factor complex than in the other two groups (P less than 0.001, ANOVA and Student's t-test). These results indicate increased entrapment of tumour cells in the pulmonary microcirculation. In a second experiment, rat factor complex II, IX, X was prepared, and Mtln3 cells were then injected in female Fisher 344 rats alone or in combination with either human factor complex or rat factor complex. Following sacrifice, the number of pulmonary nodules in animals receiving cells with rat factor complex was similar to that in animals receiving human factor complex, and significantly higher than that in the control (P less than 0.001, ANOVA and Mann-Whitney), indicating that the observed enhancement of pulmonary seeding is unrelated to the xenogeneic properties of the human factor complex

Psoriasis Skin Disease Classification based on Clinical Images

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%)

Microscopic changes in the spinal extensor musculature in people with chronic spinal pain: a systematic review.

Chronic spinal pain is one the most common musculoskeletal disorders. Previous studies have observed microscopic structural changes in the spinal extensor muscles in people with chronic spinal pain. This systematic review synthesizes and analyses all the existing evidence of muscle microscopic changes in people with chronic spinal pain. To assess the microscopy of spinal extensor muscles including the fiber type composition, the area occupied by fiber types, fiber size/cross sectional area (CSA) and narrow diameter (ND) in people with and without chronic spinal pain. Further, to compare these outcome measures across different regions of the spine in people with chronic neck, thoracic and low back pain. Systematic review with meta-analysis METHODS: MEDLINE (Ovid Interface), Embase, PubMed, CINAHL Plus and Web of Science were searched from inception to October 2020. Key journals, conference proceedings, grey literature and hand searching of reference lists from eligible studies were also searched. Two independent reviewers were involved in the selection process. Only studies examining the muscle microscopy of the spinal extensor muscles (erector spinae (ES) and/or multifidus (MF)) between people with and without chronic spinal pain were selected. The risk of bias from the studies was assessed using modified Newcastle Ottawa Scale and the level of evidence was established using the GRADE approach. Data were synthesized based on homogeneity on the methodology and outcome measures of the studies for ES and MF muscles and only four studies were eligible for analysis. All the five studies included were related to chronic low back pain (CLBP). Meta-analysis (inverse variance method for random effect to calculate mean difference and 95% CI) was performed for the ES fiber type composition by numbers for both type I and type II fibers (I =43% and 0% respectively indicating homogeneity of studies) and showed no difference between the people with and without CLBP with an overall effect estimate Z= 1.49 (p=0.14) and Z=1.06 (p=0.29) respectively. Meta-analysis was performed for ES fiber CSA for both type I and type II fibers (I =0 for both) and showed no difference between people with and without CLBP with an overall effect estimate Z= 0.08 (p=0.43) and Z=0.75 (p=0.45) respectively. Analysis was not performed for ES area occupied by fiber types and ND due to heterogeneity of studies and lack of evidence respectively. Similarly, meta-analysis was not performed for MF fiber type composition by numbers due to heterogeneity of studies. MF analysis for area occupied by fiber type, fiber CSA and ND did not yield sufficient evidence. For the ES muscle, there was no difference in fiber type composition and fiber CSA between people with and without CLBP and no conclusions could be drawn for ND for the ES. For the MF, no conclusions could be drawn for any of the muscle microscopy outcome measures. Overall, the quality of evidence is very low and there is very low evidence that there are no differences in microscopic muscle features between people with and without CLBP. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.

Putative markers for the detection of breast carcinoma cells in blood.

The aim of this study was to investigate certain genes for their suitability as molecular markers for detection of breast carcinoma cells using the reverse transcriptase-polymerase chain reaction (RT-PCR). RNA was prepared from MCF-7 breast carcinoma cells and peripheral blood leucocytes of healthy female volunteers. This RNA was screened for mRNA of MUC1, cytokeratin 19 (CK19) and CD44 (exons 8-11) by RT-PCR and the results validated by Southern blots. Variable degrees of expression of MUC1 and CD44 (exons 8-11) were detected in normal peripheral blood, rendering these genes non-specific for epithelial cells and therefore unsuitable for use as markers to detect breast carcinoma cells. Although CK19 mRNA was apparently specific, it was deemed unsuitable for use as a marker of breast cancer cells in light of its limited sensitivity. Furthermore, an attempt at using nested primers to increase sensitivity resulted in CK19 mRNA being detected after two amplification rounds in blood from healthy volunteers

A metadata approach for clinical data management in translational genomics studies in breast cancer.

BACKGROUND: In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete. METHODS: In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it. RESULTS: We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium). CONCLUSION: We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are