Biomechanical Computed Tomography analysis (BCT) for clinical assessment of osteoporosis.

The surgeon general of the USA defines osteoporosis as "a skeletal disorder characterized by compromised bone strength, predisposing to an increased risk of fracture." Measuring bone strength, Biomechanical Computed Tomography analysis (BCT), namely, finite element analysis of a patient's clinical-resolution computed tomography (CT) scan, is now available in the USA as a Medicare screening benefit for osteoporosis diagnostic testing. Helping to address under-diagnosis of osteoporosis, BCT can be applied "opportunistically" to most existing CT scans that include the spine or hip regions and were previously obtained for an unrelated medical indication. For the BCT test, no modifications are required to standard clinical CT imaging protocols. The analysis provides measurements of bone strength as well as a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) T-score at the hip and a volumetric BMD of trabecular bone at the spine. Based on both the bone strength and BMD measurements, a physician can identify osteoporosis and assess fracture risk (high, increased, not increased), without needing confirmation by DXA. To help introduce BCT to clinicians and health care professionals, we describe in this review the currently available clinical implementation of the test (VirtuOst), its application for managing patients, and the underlying supporting evidence; we also discuss its main limitations and how its results can be interpreted clinically. Together, this body of evidence supports BCT as an accurate and convenient diagnostic test for osteoporosis in both sexes, particularly when used opportunistically for patients already with CT. Biomechanical Computed Tomography analysis (BCT) uses a patient's CT scan to measure both bone strength and bone mineral density at the hip or spine. Performing at least as well as DXA for both diagnosing osteoporosis and assessing fracture risk, BCT is particularly well-suited to "opportunistic" use for the patient without a recent DXA who is undergoing or has previously undergone CT testing (including hip or spine regions) for an unrelated medical condition

Sentiment Recognition in Egocentric Photostreams

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral)

The Hybrid Approach to Intervention of Chronic Total Occlusions

The "hybrid" approach to chronic total occlusion (CTO) percutaneous coronary intervention (PCI) was developed to provide guidance on optimal crossing strategy selection. Dual angiography remains the cornerstone of clinical decision making in CTO PCI. Four angiographic parameters are assessed: (a) morphology of the proximal cap (clear-cut or ambiguous); (b) occlusion length; (c) distal vessel size and presence of bifurcations beyond the distal cap; and (d) location and suitability of location and suitability of a retrograde conduit (collateral channels or bypass grafts) for retrograde access. Antegrade wire escalation is favored for short (<20 mm) occlusions, usually escalating rapidly from a soft tapered-tip polymer-jacketed guidewire to a stiff polymer-jacketed or tapered-tip guidewire. Antegrade dissection/re-entry is favored in long (≥20 mm long) occlusions, trying to minimize the dissection length by re-entering into the distal true lumen immediately after the occlusion. Primary retrograde approach is preferred for lesions with an ambiguous proximal cap, poor distal target, good interventional collaterals, and heavy calcification,as well as chronic kidney disease. The "hybrid" approach advocates early change between strategies to enable CTO crossing in the most efficacious, efficient, and safe way. Several early studies are demonstrating high success and low complication rates with use of the "hybrid" approach, supporting its expanding use in CTO PCI

Efusi Pleura Kanan Yang Disebabkan Oleh Carcinoma Mammae Dextra Metastase Ke Paru.

Latar Belakang. Efusi pleura merupakan keadaan di mana cairan menumpuk di dalam rongga pleura. Banyak penyakit yang mungkin mendasari terjadinya efusi pleura antara lain keganasan, tuberculosis, pneumonia, empiema toraks, gagal jantung kongestif, sirosis hepatis. Kasus. Ny S umur 48 tahun datang dengan keluhan sesak nafas sejak 1 minggu yang lalu. Pasien memiliki riwayat kanker payudara pada payudara kanannya 6 tahun yang lalu, pasien telah menjalani mastektomi unilateral. Pemeriksaan fisik didapatkan tekanan darah 110/70 mmHg, pernafasan 26 kali per menit. Status lokalis paru dipalpasi, vokal fremitus kanan lebih lemah dibandingkan kiri. Saat di perkusi, terdengar redup pada paru kanan dan sonor pada paru kiri. Pada auskultasi ditemukan penurunan suara napas vesikuler pada paru kanan. Foto toraks PA, didapatkan gambaran penumpulan sudut kostofrenikus. Diagnosis. efusi pleura kanan e.c keganasan paru. Terapi oksigenisasi 2-3 L/ menit, bed rest total, Racikan Salbutamol 0,5 mg/Metyl Prednisolon 1 mg/Cetirizine ½ tab/GG 1 tab 3 x 1 cap, Ceftriaxone 1 gr/ 12 jam, Pemasangan WSD dan dilakukan pleurodesis. Simpulan. Efusi pleura dapat disebabkan oleh keganasan paru akibat metastasis dari ca mamae. [Medula Unila.2014;2(1) : 22-29

Deconvolution for an atomic distribution: rates of convergence

Let $X_1,..., X_n$ be i.i.d.\ copies of a random variable $X=Y+Z,$ where $X_i=Y_i+Z_i,$ and $Y_i$ and $Z_i$ are independent and have the same distribution as $Y$ and $Z,$ respectively. Assume that the random variables $Y_i$'s are unobservable and that $Y=AV,$ where $A$ and $V$ are independent, $A$ has a Bernoulli distribution with probability of success equal to $1-p$ and $V$ has a distribution function $F$ with density $f.$ Let the random variable $Z$ have a known distribution with density $k.$ Based on a sample $X_1,...,X_n,$ we consider the problem of nonparametric estimation of the density $f$ and the probability $p.$ Our estimators of $f$ and $p$ are constructed via Fourier inversion and kernel smoothing. We derive their convergence rates over suitable functional classes. By establishing in a number of cases the lower bounds for estimation of $f$ and $p$ we show that our estimators are rate-optimal in these cases.Comment: 27 page

In-situ thermally-reduced graphene oxide/epoxy composites: thermal and mechanical properties

Graphene has excellent mechanical, thermal, optical and electrical properties and this has made it a prime target for use as a filler material in the development of multifunctional polymeric composites. However, several challenges need to be overcome in order to take full advantage of the aforementioned properties of graphene. These include achieving good dispersion and interfacial properties between the graphene filler and the polymeric matrix. In the present work we report the thermal and mechanical properties of reduced graphene oxide/epoxy composites prepared via a facile, scalable and commercially-viable method. Electron micrographs of the composites demonstrate that the reduced graphene oxide (rGO) is well-dispersed throughout the composite. Although no improvements in glass transition temperature, tensile strength, and thermal stability in air of the composites were observed, good improvements in thermal conductivity (about 36%), tensile and storage moduli (more than 13%) were recorded with the addition of 2 wt% of rGO

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. [Abstract copyright: AJTR Copyright © 2020.