Valence band and core-level analysis of highly luminescent ZnO nanocrystals for designing ultrafast optical sensors

Highly luminescent ZnO:Na nanocrystals of size ~2 nm were synthesized using a improved sol-lyophilization process. The surface analysis such as survey scan, core-level and valence band spectra of ZnO:Na nanocrystals were studied using x-ray photoelectron spectroscopy (XPS) to establish the presence of Na+ ions. The observed increase in band gap from 3.30 (bulk) to 4.16 eV (nano), is attributed to the quantum confinement of the motion of electron and holes in all three directions. The photoluminescence and decay measurements have complemented and supported our study to design an efficient and ultrafast responsive optical sensing device.Comment: 10 Pages, 5 Figure

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

Abstract: Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions

Commercial Immunoglobulin Products Contain Neutralizing Antibodies Against Severe Acute Respiratory Syndrome Coronavirus 2 Spike Protein

BACKGROUND: Patients with antibody deficiency respond poorly to COVID-19 vaccination and are at risk of severe or prolonged infection. They are given long-term immunoglobulin replacement therapy (IRT) prepared from healthy donor plasma to confer passive immunity against infection. Following widespread COVID-19 vaccination alongside natural exposure, we hypothesised that immunoglobulin preparations will now contain neutralising SARS-CoV-2 spike antibodies which confer protection against COVID-19 disease and may help to treat chronic infection. METHODS: We evaluated anti-SARS-CoV-2 spike antibody in a cohort of patients before and after immunoglobulin infusion. Neutralising capacity of patient samples and immunoglobulin products was assessed using in vitro pseudo-virus and live-virus neutralisation assays, the latter investigating multiple batches against current circulating omicron variants. We describe the clinical course of nine patients started on IRT during treatment of COVID-19. RESULTS: In 35 individuals with antibody deficiency established on IRT, median anti-spike antibody titre increased from 2123 to 10600 U/ml post-infusion, with corresponding increase in pseudo-virus neutralisation titres to levels comparable to healthy donors. Testing immunoglobulin products directly in the live-virus assay confirmed neutralisation, including of BQ1.1 and XBB variants, but with variation between immunoglobulin products and batches.Initiation of IRT alongside Remdesivir in patients with antibody deficiency and prolonged COVID-19 infection (median 189 days, maximum over 900 days with an ancestral viral strain) resulted in clearance of SARS-CoV-2 virus at a median of 20 days. CONCLUSIONS: Immunoglobulin preparations now contain neutralising anti-SARS-CoV-2 antibodies which are transmitted to patients and help to treat COVID-19 in individuals with failure of humoral immunity

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.</jats:p

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Purpose: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pre-treatment DOS functional maps for predicting LABC response to NAC. Methods: LABC patients (n = 37) underwent DOS-breast imaging before starting neoadjuvant chemotherapy. Breast-tissue parametric maps were constructed and texture analyses were performed based on grey level co-occurrence matrices (GLCM) for feature extraction. Ground-truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS-textural features computed on volumetric tumour data before the start of treatment (i.e. “pre-treatment”) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naïve Bayes, and k-nearest neighbour (k-NN) classifiers. Results: Data indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5 and 89.0%, respectively and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn/%Sp = 78.0/81.0% and an accuracy of 79.5%. Conclusions: This study demonstrated that pre-treatment tumour DOS-texture features can predict breast cancer response to NAC and potentially guide treatments

A Systematic Review and Appraisal of International Early Breast Cancer Guidelines for Systemic Therapy, and a Global Physician Survey Examining Practice Patterns by Resource Setting: Potential Implications for International Health Policy

Breast cancer is a growing international health epidemic, and patients in low and middle income countries (LMCs) have worse outcomes than those in high income countries. High quality, well-implemented guidelines help improve patient outcomes, but are often not resource-sensitive, and support therapies that may not be feasible in LMCs. A systematic review to address the content, quality, and resource-sensitivity of international breast cancer guidelines was completed. Also, a survey of global physicians evaluated the impact of resource setting on practice patterns and guideline use. Guideline use did not appear to be directed by quality (which was variable across guidelines) or resource-sensitivity (found in few guidelines). However, practice patterns were found to vary by resource setting and by continent, often due to the cost of certain therapies. In order for guidelines to better impact global breast cancer outcomes, they need to be of higher quality, more resource-sensitive, and better implemented.MAS

Rare cause of gastric outlet obstruction

Bouveret’s syndrome is a rare cause of gastric outlet obstruction. The stones enter the small bowel via cholecysto-enteric fistula. The most common presenting symptoms are abdominal pain, nausea and vomiting. The gold standard diagnostic test isesophagogastroduodenoscopy (EGD). Rigler’s triad on abdominal x-ray is classic. CT scan findings are pneumobilia, cholecystoduodenal fistula and a gallstone in the duodenum. We present a case of a 75-year-old female who presents with 3 week history of nausea, vomiting, and diffuse abdominal pain. Initial presentation, imaging and EGD was concerning for malignancy. She was later diagnosed to have Bouveret’s syndrome and underwent laparoscopic small bowel enterotomy with removal of gallstone

Rare cause of gastric outlet obstruction

Bouveret’s syndrome is a rare cause of gastric outlet obstruction. The stones enter the small bowel via cholecysto-enteric fistula. The most common presenting symptoms are abdominal pain, nausea and vomiting. The gold standard diagnostic test isesophagogastroduodenoscopy (EGD). Rigler’s triad on abdominal x-ray is classic. CT scan findings are pneumobilia, cholecystoduodenal fistula and a gallstone in the duodenum. We present a case of a 75-year-old female who presents with 3 week history of nausea, vomiting, and diffuse abdominal pain. Initial presentation, imaging and EGD was concerning for malignancy. She was later diagnosed to have Bouveret’s syndrome and underwent laparoscopic small bowel enterotomy with removal of gallstone