Immunohistochemical Expression of Anaplastic Lymphoma Kinase in Invasive Breast Carcinomas- A Retrospective Study

Introduction: Breast carcinoma remains one of the most common causes of mortality among female cancer patients inspite of improvements in treatment modalities. Increased survival rate can be achieved by identification of new targeted therapies. Anaplastic Lymphoma Kinase (ALK) alterations are present in many solid and haematological tumours indicating its role in pathogenesis and treatment. There are studies regarding the expression of ALK in few breast cancers but its importance was not clearly mentioned. Hence, identification of ALK overexpression in breast cancers, particularly in Triple Negative Breast Cancers (TNBC) might play a role in their chemotherapy with the help of ALK inhibitors. Aim: To study the ALK expression in different subtypes of invasive carcinomas of breast. Materials and Methods: This was a retrospective cross-sectional study conducted in the Department of Pathology at Great Eastern Medical School (GEMS) and Hospital, Srikakulam, Andhra Pradesh, India, from January 2022 to July 2022. The data of 60 patients, from January 2021 to December 2021 was retrieved using Hospital Information Management System (HIMS) and the Haematoxylin and Eosin (H&E)-stained slides and formalin fixed paraffin embedded tissue blocks of breast tumour were retrieved and reviewed. Estrogen receptor (ER), Progesterone receptor (PR) and Human Epidermal growth factor Receptor 2 (HER2) immunostains were performed and categorised based on molecular classification as Luminal, Her2 and Triple negative. ALK Staining was performed on all cases and its expression was studied. Statistical Package for Social Sciences (SPSS) software version 2.0 was used for analysis of data. Mean with standard deviation is used for quantitative variables and prevalence, ratio is used for quantitative variables. Chi-square test and Fischer exact test were used for detecting significance. The p-value <0.05 was considered as significant. Results: Out of 60 cases, majority (N=22; 36.66%) of patients were in the age group of 51-60 years. The mean tumour size was 3.2±0.5 cm. The most common histological type was invasive breast carcinoma, No Special Type (N=44; 73.34%). Majority of the tumours showed grade 1 and 2 with 24 (40%) and 25 (41.67%) cases, respectively. A total of 30 cases (50%) of tumours belonged to stage T2. Luminal molecular subtype was the most common 31 (51.67%) cases followed by TNBC, 16 (26.67%) cases and Her2neu 13 (21.67%) cases. Among all the cases, ALK overexpression was seen in 17 (28.33%) cases and among different molecular subtypes, its expression was seen in 5 (8.33%) cases of Luminal type, 3 (5.0%) cases of HER2 type and in 9 (15.0%) cases of TNBC cases. Conclusion: Immunohistochemical analysis showed ALK over expression in a substantial proportion of cases and possibly plays a significant role in aggressive behaviour of breast cancer. ALK inhibitors offer an opportunity to treat aggressive subtypes of breast cancer

Respostas ecofisiológicas e morfológicas do pau-rosa (Aniba rosaeodora Ducke) aos diferentes níveis de sombreamento, em condição de viveiro.

Publicado também em: Boletim da Faculdade de Ciências Agrárias do Pará, Belém, PA, n. 30, p. 119-132, jul./dez. 1998

Generalized coupled common fixed point results in partially ordered A-metric spaces

Sedghi et al. (Mat. Vesn. 64(3):258-266, 2012) introduced the notion of a S-metric as a generalized metric in 3-tuples S : X3→[0,∞), where X is a nonempty set. The aim of this paper is to introduce the concept of an n-tuple metric A : Xn→[0,∞) and to study its basic topological properties. We also prove some generalized coupled common fixed point theorems for mixed weakly monotone maps in partially ordered A-metric spaces. Some examples are presented to support the results proved herein. Our results generalize and extend various results in the existing literature.http://link.springer.com/journal/11784am201

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Bark anatomy, chemical composition and ethanol-water extract composition of Anadenanthera peregrina and Anadenanthera colubrina

The bark of Anadenanthera peregrina (L.) Speg and Anadenanthera colubrina (Vell.) Brenan were characterized in relation to anatomical and chemical features. The barks were similar and included a thin conducting phloem, a largely dilated and sclerified non-conducting phloem, and a rhyridome with periderms with thin phellem interspersed by cortical tissues. Only small differences between species were observed that cannot be used alone for taxonomic purposes. The summative chemical composition of A. peregrina and A. colubrina was respectively: 8.2% and 7.7% ash; 28.8% and 29.3% extractives; 2.4% and 2.6% suberin; and 18.9% lignin. The monosaccharide composition showed the predominance of glucose (on average 82% of total neutral sugars) and of xylose (9%). The ethanol-water extracts of A. peregrina and A. colubrina barks included a high content of phenolics, respectively: total phenolics 583 and 682 mg GAE/g extract; 148 and 445 mg CE/g extract; tannins 587 and 98 mg CE/g extract. The antioxidant activity was 238 and 269 mg Trolox/g extract. The barks of the Anadenanthera species are a potential source of polar extractives that will represent an important valorization and therefore contribute to improve the overall economic potential and sustainability of A. peregrina and A. colubrinainfo:eu-repo/semantics/publishedVersio