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Introduction: Cerebral cavernous malformations (CCMs) are pathologic lesions comprised of clusters of thin-walled capillaries characterized by abnormal proliferation, angiogenesis, and bleeding secondary to somatic or germline mutations in endothelial cells. CCMs can cause headaches, seizures and/or neurological defects. There is a clinical need to develop better tools to detect CCMs and follow their progression in conjunction with the current use of neuroimaging techniques. Here we present data supporting the utility of LOX-1 (lectin-type oxidized LDL receptor 1), a 50 kDa transmembrane protein implicated in endothelial cell dysfunction and ischemia, as a putative biomarker for CCM. Methods: CCM urine samples (n = 23) were collected from pediatric CCM patients. Matched healthy controls (n = 24) were collected from pediatric patients with either Chiari I malformation or fatty filum terminale, and otherwise normal findings. All samples were collected with patient/family consent and institutional review board approval. Samples were analyzed with Olink Proteomic Proximity Extension Assay (PEA). Differences in expression for 2,925 unique proteins were quantified between healthy control urine samples and CCM urine samples. The results were normalized, validated, and analyzed for demographic bias. In addition to urine samples, CCM tissue from patients was harvested and used to create primary cell lines for in vitro analysis of LOX-1 expression, in addition to immunofluorescence of lesional tissue excised at surgery. Results: ANOVA analysis of the CCM urine samples showed a statistically significant increase in LOX-1 compared to the control samples, with CCM patients exhibiting a > 5-fold increase in urinary expression. Corroborating these elevated levels of circulating marker, analysis of source tissue from surgically resected CCMs revealed that LOX-1 is increased in both CCM patient cavernoma primary cell lines and operative specimens. Conclusion: LOX-1 is involved with pathways implicated in CCM pathogenesis and our data here reveals that LOX-1 expression is significantly elevated in CCM patients as compared to matched healthy control individuals, including both source tissue from surgically excised CCMs and in analysis of samples collected from outside of the central nervous system, particularly urine. This proof-ofprinciple data suggests that LOX-1 may have potential utility as a target for CCM treatment and supports further investigation related to its potential mechanistic impact on CCM pathogenesis. [ABSTRACT FROM AUTHOR]

Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3's better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier's efficiency. [ABSTRACT FROM AUTHOR]

Background: Anatomy of circle of Willis (CW) shows wide variation in different individuals, population groups, and has vital clinical significance in causation and presentation of clinical disease. This study evaluates the anatomical variations, incidence of various common anomalies of CW in south Indian tertiary hospital set up, using three-dimensional time-of-flight (3D-TOF) magnetic resonance angiography (MRA). Materials and Methods: A total of 300 patients referred for neuroimaging study over a period of 2-year were included in the analysis. In this prospective and retrospective study, 198 men and 102 women; mean age, 55 years) underwent 3D-TOF MR angiograms of the CW using a 1.5-tesla MR scanner. Images were reviewed for anatomical configuration of the CW using maximum intensity projection (MIP) and 3D volume rendered images. Results: On analysis, a complete CW was seen in 50 (16.6%) of 300 subjects. An incomplete anterior and posterior CW was found in 66 (22%) The remaining 184 (61.3%) subjects had partially complete CW configuration. The most common type of CW in a single subject was anterior variant type A and posterior type variant E. Conclusion: We observed wide variation in CW configuration in our patients. The prevalence of complete configuration of the circle is 16.6%; slightly higher in females and younger subjects. Complete anterior circle was present in 77.3%. Most common anterior variant is type A (normal anterior configuration) with a prevalence of 66%. The most common posterior circle variant is type E (hypoplasia or absence of both PcomA) with 32.6%. Overall, CW variants are slightly more common among the women in comparison to men. Incidence of associated anomalies like aneurysm or arteriovenous malformation (AVM) was comparable to that described in literature.