Risk Stratification for Sudden Cardiac Death In Patients With Non-ischemic Dilated Cardiomyopathy

Non ischemic dilated cardiomyopathy (NIDCM) is a disorder of myocardium. It has varying etiologies. Albeit the varying etiologies of this heart muscle disorder, it presents with symptoms of heart failure, and rarely as sudden cardiac death (SCD). Manifestations of this disorder are in many ways similar to its counterpart, ischemic dilated cardiomyopathy (IDCM). A proportion of patients with NIDCM carries a grave prognosis and is prone to sudden cardiac death from sustained ventricular arrhythmias. Identification of this subgroup of patients who carry the risk of sudden cardiac death despite adequate medical management is a challenge .Yet another method is a blanket treatment of patients with this disorder with anti arrhythmic medications or anti tachyarrhythmia devices like implantable cardioverter defibrillators (ICD). However this modality of treatment could be a costly exercise even for affluent economies. In this review we try to analyze the existing data of risk stratification of NIDCM and its clinical implications in practice

Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges

Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply commonsense. A relevant application is to use them for creating high quality synthetic datasets for downstream tasks. In this work, we probe whether GPT-4 can be used to augment existing extractive reading comprehension datasets. Automating data annotation processes has the potential to save large amounts of time, money and effort that goes into manually labelling datasets. In this paper, we evaluate the performance of GPT-4 as a replacement for human annotators for low resource reading comprehension tasks, by comparing performance after fine tuning, and the cost associated with annotation. This work serves to be the first analysis of LLMs as synthetic data augmenters for QA systems, highlighting the unique opportunities and challenges. Additionally, we release augmented versions of low resource datasets, that will allow the research community to create further benchmarks for evaluation of generated datasets.Comment: 5 pages, 1 figure, 3 table

Diabetic Mastopathy as a Radiographically Occult Palpable Breast Mass

Diabetic mastopathy is an uncommon, benign disease of the breast that can occur in women with diabetes and clinically mimic breast cancer. We describe a patient with long-standing type 1 diabetes who presented with a palpable breast mass with negative imaging findings on mammography, ultrasonography, and breast MRI. Surgical biopsy and histopathology confirmed diabetic mastopathy. We use this case to highlight the recognition, radiographic features, pathology, and management of this benign breast condition and emphasize that, in diabetic patients, the differential diagnosis of a new breast mass should include diabetic mastopathy

Canonical forms of two-qubit states under local operations

Canonical forms of two-qubits under the action of stochastic local operations and classical communications (SLOCC) offer great insight for understanding non-locality and entanglement shared by them. They also enable geometric picture of two-qubit states within the Bloch ball. It has been shown (Verstraete et.al. {Phys. Rev. A, 64, 010101(R) (2001)) that an arbitrary two-qubit state gets transformed under SLOCC into one of the {\em two} different canonical forms. One of these happens to be the Bell diagonal form of two-qubit states and the other non-diagonal canonical form is obtained for a family of rank deficient two-qubit states. The method employed by Verstraete et.al. required highly non-trivial results on matrix decompositions in $n$ dimensional spaces with indefinite metric. Here we employ an entirely different approach -- inspired by the methods developed by Rao et. al., (J. Mod. Opt. 45, 955 (1998)) in classical polarization optics -- which leads naturally towards the identification of two inequivalent SLOCC invariant canonical forms for two-qubit states. In addition, our approach results in a simple geometric visualization of two-qubit states in terms of their SLOCC canonical forms.Comment: 14 pages, two figures; proof of the theorem in page 4 is outlined in the Appendix A of this revised version; accepted for publication in Phys. Rev.

Frequency format diagram and probability chart for breast cancer risk communication: a prospective, randomized trial

<p>Abstract</p> <p>Background</p> <p>Breast cancer risk education enables women make informed decisions regarding their options for screening and risk reduction. We aimed to determine whether patient education regarding breast cancer risk using a bar graph, with or without a frequency format diagram, improved the accuracy of risk perception.</p> <p>Methods</p> <p>We conducted a prospective, randomized trial among women at increased risk for breast cancer. The main outcome measurement was patients' estimation of their breast cancer risk before and after education with a bar graph (BG group) or bar graph plus a frequency format diagram (BG+FF group), which was assessed by previsit and postvisit questionnaires.</p> <p>Results</p> <p>Of 150 women in the study, 74 were assigned to the BG group and 76 to the BG+FF group. Overall, 72% of women overestimated their risk of breast cancer. The improvement in accuracy of risk perception from the previsit to the postvisit questionnaire (BG group, 19% to 61%; BG+FF group, 13% to 67%) was not significantly different between the 2 groups (<it>P </it>= .10). Among women who inaccurately perceived very high risk (≥ 50% risk), inaccurate risk perception decreased significantly in the BG+FF group (22% to 3%) compared with the BG group (28% to 19%) (<it>P </it>= .004).</p> <p>Conclusion</p> <p>Breast cancer risk communication using a bar graph plus a frequency format diagram can improve the short-term accuracy of risk perception among women perceiving inaccurately high risk.</p

Breast manifestations of systemic diseases

Although much emphasis has been placed on the primary presentations of breast cancer, little focus has been placed on how systemic illnesses may affect the breast. In this article, we discuss systemic illnesses that can manifest in the breast. We summarize the clinical features, imaging, histopathology, and treatment recommendations for endocrine, vascular, systemic inflammatory, infectious, and hematologic diseases, as well as for the extramammary malignancies that can present in the breast. Despite the rarity of these manifestations of systemic disease, knowledge of these conditions is critical to the appropriate evaluation and treatment of patients presenting with breast symptoms

Unfolding grain size effects in barium titanate ferroelectric ceramics

Grain size effects on the physical properties of polycrystalline ferroelectrics have been extensively studied for decades; however there are still major controversies regarding the dependence of the piezoelectric and ferroelectric properties on the grain size. Dense BaTiO3 ceramics with different grain sizes were fabricated by either conventional sintering or spark plasma sintering using micro- and nano-sized powders. The results show that the grain size effect on the dielectric permittivity is nearly independent of the sintering method and starting powder used. A peak in the permittivity is observed in all the ceramics with a grain size near 1μm and can be attributed to a maximum domain wall density and mobility. The piezoelectric coefficient d33 and remnant polarization Pr show diverse grain size effects depending on the particle size of the starting powder and sintering temperature. This suggests that besides domain wall density, other factors such as back fields and point defects, which influence the domain wall mobility, could be responsible for the different grain size dependence observed in the dielectric and piezoelectric/ferroelectric properties. In cases where point defects are not the dominant contributor, the piezoelectric constant d33 and the remnant polarization Pr increase with increasing grain size

Internet of Things for Sustainable Mining

The sustainable mining Internet of Things deals with the applications of IoT technology to the coupled needs of sustainable recovery of metals and a healthy environment for a thriving planet. In this chapter, the IoT architecture and technology is presented to support development of a digital mining platform emphasizing the exploration of rock–fluid–environment interactions to develop extraction methods with maximum economic benefit, while maintaining and preserving both water quantity and quality, soil, and, ultimately, human health. New perspectives are provided for IoT applications in developing new mineral resources, improved management of tailings, monitoring and mitigating contamination from mining. Moreover, tools to assess the environmental and social impacts of mining including the demands on dwindling freshwater resources. The cutting-edge technologies that could be leveraged to develop the state-of-the-art sustainable mining IoT paradigm are also discussed