Mcconnelldudley4351

To propose a new method for glenoid bone loss measurement, the constellation technique (CST); determine its reliability and accuracy; and compare the validity of CST with that of the conventional technique (CVT) and standard measurements for ratio calculation.

Sixty shoulders with intact glenoids and no glenohumeral instability and arthritis underwent CT scans. Simulated osteotomies were conducted on the 3D models of glenoids at two cutting locations, expressed as clock face times (230-420; 130-500). Two experienced surgeons compared three methods for glenoid bone loss measurement; CVT (best-fit circle), CST ('5S' steps), and standard measurement. Eight undergraduates remeasured five randomly chosen shoulders with moderate to severe bone loss. Intraclass correlation coefficients (ICCs) were calculated for raters.

With a defect range between 230 and 420, all 60 glenoids demonstrated minimal bone loss (< 15%); while between 130 and 500, 42 shoulders were with moderate bone loss (15-20%), and 18 shoulders with severe bone loss (≥ 20%). For experienced raters, no significant differences were noted between protocos for all categories of bone loss (

 ≥ 0.051), with good inter- and intraobserver reliability indicated by ICC. For novice raters, post hoc Tukey analysis found that CST was more accurate in one patient with a standard mean bone loss of 23.2% ± 1.9% compared with CVT.

The CST turned the key step of glenoid defect evaluation from deciding an

view to determining the glenoid inferior rim. The protocol is simple, accurate, and reproducible, especially for novice raters.

The CST turned the key step of glenoid defect evaluation from deciding an en face view to determining the glenoid inferior rim. The protocol is simple, accurate, and reproducible, especially for novice raters.Coronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to its ability to define features such as perspective, orientation, and size. Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure that new samples were generated close to the sample center, avoiding the production of outliers or changes in data distribution. As the results may change by changing capsule network parameters (Capsule dimensionality and routing number), the Gaussian optimization method has been used to optimize these parameters. Four experiments have been done, (1) CapsNet with the unbalanced data sets, (2) CapsNet with balanced data sets based on class weight, (3) CapsNet with balanced data sets based on SMOTE, and (4) CapsNet hyperparameters optimization with balanced data sets based on SMOTE. selleck chemicals llc The performance has improved and achieved an accuracy rate of 96.58% and an F1- score of 97.08%, a competitive optimized model compared to other related models.Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR)mplements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8-10% of better system performance than other existing techniques.Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.Reversible electrochemical magnesium plating/stripping processes are important for the development of high-energy-density Mg batteries based on Mg anodes. Ether glyme solutions such as monoglyme (G1), diglyme (G2), and triglyme (G3) with the MgTFSI2 salt are one of the conventional and commonly used electrolytes that can obtain the reversible behavior of Mg electrodes. However, the electrolyte cathodic efficiency is argued to be limited due to the enormous parasitic reductive decomposition and passivation, which is governed by impurities. In this work, a systematic identification of the impurities in these systems and their effect on the Mg deposition-dissolution processes is reported. The mitigation methods generally used for eliminating impurities are evaluated, and their beneficial effects on the improved reactivity are also discussed. By comparing the performances, we proposed a necessary conditioning protocol that can be easy to handle and much safer toward the practical application of MgTFSI2/glyme electrolytes containing impurities.Herein, we reported that KOH impregnation can generate a large number of porous structures with fruitful nitrogen self-doped groups during the carbonized process for poly (p-phenylene terephthalamide) fiber and poly (p-phenylene benzobisoxazole) fiber (denoted as PPTA and PBO, respectively). The intrinsical insulation, volume change, and shuttle effect of polysulfides then can be more significantly improved for the PBO-coated separator than the PPTA case. The discharge capacity primary achieves 1,322 mA h/g, which retains 827 mA h/g even after 200 cycles at 0.2 C for the cell with PBO-coated separator. The reversible specific discharge capacity maintains 841 mA h/g with a Coulomb efficiency of 99.7% at 5 C. The nitrogen self-doped nanocarbon particles are etched by KOH with the simple one-step preparation, which has promising application as Li-S battery cathode.Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.Perovskite La2/3xLi3xTiO3 (LLTO) materials are promising solid-state electrolytes for lithium metal batteries (LMBs) due to their intrinsic fire-resistance, high bulk ionic conductivity, and wide electrochemical window. However, their commercialization is hampered by high interfacial resistance, dendrite formation, and instability against Li metal. To address these challenges, we first prepared highly dense LLTO pellets with enhanced microstructure and high bulk ionic conductivity of 2.1 × 10 - 4 S cm-1 at room temperature. Then, the LLTO pellets were coated with three polymer-based interfacial layers, including pure (polyethylene oxide) (PEO), dry polymer electrolyte of PEO-LITFSI (lithium bis (trifluoromethanesulfonyl) imide) (PL), and gel PEO-LiTFSI-SN (succinonitrile) (PLS). It is found that each layer has impacted the interface differently; the soft PLS gel layer significantly reduced the total resistance of LLTO to a low value of 84.88 Ω cm-2. Interestingly, PLS layer has shown excellent ionic conductivity but performs inferior in symmetric Li cells. On the other hand, the PL layer significantly reduces lithium nucleation overpotential and shows a stable voltage profile after 20 cycles without any sign of Li dendrite formation. This work demonstrates that LLTO electrolytes with denser microstructure could reduce the interfacial resistance and when combined with polymeric interfaces show improved chemical stability against Li metal.Ignition of magnesium alloys during casting processes limits their processability and applications. For identifying the ignition mechanism of magnesium alloys during solidification, a Mg-Al-Zn alloy was solidified with different cooling rates and pouring temperatures. The oxide scale morphologies and thicknesses were identified by SEM and energy dispersive spectrometer. Based on the experimental results, the oxidation kinetics and heat released were calculated and the relationship between oxidation and ignition was discussed in detail. The calculation results indicate that oxide rupture directly induces combustion of the melt. The rupture route of the oxide scale was determined to be buckling cracks according to the experimental and calculation results. Based on the buckling mechanism of the oxide scale, the ignition criterion during solidification was correlated to the pouring temperature, cooling rate and casting modulus. This work reveals the underlying relationship between ignition and casting process parameters, and it helps to develop new technology for inhibiting ignition of molten magnesium alloys.