Pregnancies complicated by maternal osteogenesis imperfecta type III: a case report and review of literature.

The restrictive lung disease can be exacerbated by growing fundus in women with osteogenesis imperfecta type III. Regional anesthesia can be performed in these women. Mode of delivery for women with osteogenesis imperfecta type III is generally cesarean delivery. Neonatal outcomes are complicated due to indicated preterm deliveries

The Band Gap in Silicon Nanocrystallites

The gap in semiconductor nanocrystallites has been extensively studied both theoretically and experimentally over the last two decades. We have compared a recent ``state-of-the-art'' theoretical calculation with a recent ``state-of-the-art'' experimental observation of the gap in Si nanocrystallite. We find that the two are in substantial disagreement, with the disagreement being more pronounced at smaller sizes. Theoretical calculations appear to over-estimate the gap. Recognizing that the experimental observations are for a distribution of crystallite sizes, we proffer a phenomenological model to reconcile the theory with the experiment. We suggest that similar considerations must dictate comparisons between the theory and experiment vis-a-vis other properties such as radiative rate, decay constant, absorption coefficient, etc.Comment: 5 pages, latex, 2 figures. (Submitted Physical Review B

An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy