Notes

Utility involving Urgent situation Healthcare Send (EMD) Mobile phone Screening in Determining COVID-19 Good Patients.
Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.
Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.
Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. JNJ-64619178 solubility dmso As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods.

Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients.

In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%.

It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.
It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.
MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of popular benchmarking datasets. The medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion dedicated to complex, diverse, and fine-grained medical data.

MedMeshCNN follows the functionality of MeshCNN with a significantly increased memory efficiency that allows retaining patient-specific properties during processing. Furthermore, it enables the segmentation of pathological structures that often come with highly imbalanced class distributions.

MedMeshCNN achieved an Intersection over Union of 63.24% on a highly complex part segmentation task of intracranial aneurysms and their surrounding vessel structures. Pathological aneurysms were segmented with an Intersection over Union of 71.4%.

MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. It considers imbalanced class distributions derived from pathological findings and retains patient-specific properties during processing.
MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. It considers imbalanced class distributions derived from pathological findings and retains patient-specific properties during processing.
Bioimpedance analysis-derived phase angle (PhA), as marker of body cell mass and cell integrity, might be altered in obesity, a condition which is characterized by alterations in muscle structure and function. The aim of this systematic review was to evaluate whether and to which extent PhA varies in individuals/patients with excess body weight focusing on a) changes in PhA due to obesity; b) changes in PhA after bariatric interventions or training programs.

According to PRISMA criteria, a systematic literature search until February 2021 using PubMed, Embase, Scopus, and Web of Science was performed. Selection criteria included studies on patients with obesity without comorbidities other than metabolic diseases.

A total of 278 articles were first identified. After removing duplicates and excluding studies that did not fulfil the inclusion criteria, the full text of the remaining 80 potentially relevant studies was examined to finally retrieve 11 cross-sectional and 10 longitudinal studies. Few studies hs with obesity with sometimes contradictory and preliminary results. PhA might be useful to evaluate muscle quality in individuals/patients with obesity but further studies are needed to more accurately associate this variable with changes in muscle structure and strength, as well as in metabolic functions.
Some cognitive profiles might facilitate successful weight loss and its maintenance. Also, weight reductions may result in cognitive benefits. However, little work to date has examined the interactions between cognition and weight changes in the context of interventions with the Mediterranean diet (MedDiet). We studied the within-subject longitudinal relationships between cognition, body mass index (BMI), physical activity (PA), and quality of life (QoL), in older adults following a MedDiet.

The PREDIMED-Plus is a primary prevention trial testing the effect of a lifestyle intervention program with an energy-restricted MedDiet (er-MedDiet), weight-loss goals and PA promotion on cardiovascular disease. The PREDIMED-Plus-Cognition sub-study included 487 participants (50% women, mean age 65.2 ± 4.7 years), with overweight/obesity, metabolic syndrome and normal cognitive performance at baseline. A comprehensive neurocognitive test battery was administered at baseline and after 1 and 3 years.

Baseline higher loss, adding further evidence to the cognitive benefits associated with better adherence to a MedDiet. Our results also suggest that weight loss interventions tailored to the cognitive profile and gender of participants are promising avenues for future studies.
This study refines the understanding of the determinants and mutual interrelationships between longitudinally-assessed cognitive performance and weight loss, adding further evidence to the cognitive benefits associated with better adherence to a MedDiet. Our results also suggest that weight loss interventions tailored to the cognitive profile and gender of participants are promising avenues for future studies.A densitometry method based on steady-state and time-resolved fluorescence assessments for thioridazine and its photoproducts applied on HPTLC plates has been developed. The excitation source was a picosecond diode laser emitting at 375 nm. This method was used for the analysis of the photoproducts resulted from thioridazine irradiation with 266 nm nanosecond-pulsed laser. The validation of the developed method was performed for thioridazine in terms of linearity, precision, limits of detection and quantification. Furthermore, analysis of the photoproducts of irradiated thioridazine was performed by steady-state and time-resolved fluorescence. The fluorescence spectra and fluorescence lifetime of each photoproduct were obtained and the horizontal chromatograms of fluorescence maxima were generated.In this study, we describe the development of an analytical method to profile naphthenic acids (NAs) from produced water (PW). The NAs were isolated by hollow fiber liquid-phase microextraction (HF-LPME). A microwave-assisted methylation method was used to convert the free acids into its corresponding naphthenic methyl esters (NAMEs). The best reaction conditions were ascertained using central composite design. The optimized sample preparation method exhibited an improved analytical eco-scale value (80 vs. 61) compared to conventional liquid-liquid extraction. Although the primary goal was qualitative analysis of NAMEs (e.g., group-type separation) in produced water, the quantitative performance was also evaluated for future investigations. The instrumental detection and quantification limits were 0.10 ng mL-1 and 0.16 ng mL-1, respectively, using full spectrum data acquisition. The accuracy and precision of the proposed method ranged from 90.4 to 96.6 % and 3.3 to 13.1 %, respectively, using matrix-matched working solutions (0.1, 0.5, and 1.0 µg mL-1). The monoisotopic masses of the adduct ions ([M+H]+) and its corresponding fine isotopic patterns were used to determine the elemental composition of the NAMEs in the PW samples. Qualitative analysis indicated the O2 class as the predominant class in all samples with carbon numbers ranging from C5 to C19 and double bond equivalent (DBE) values of 1 to 8. Additional classes of polar compounds, i.e., O3, O4 and nitrogen-containing classes, are reported for the first time by gas chromatography coupled to Fourier transform Orbitrap mass spectrometry and chemical ionization.In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed individuals, (b) identifying individuals who are asymptomatic, and (c) prescription of optimal testing when testing capacity is limited. First, we present a dynamic-graph based SEIR epidemiological model in order to describe the dynamics of the disease propagation. Our model considers a dynamic graph/network that accounts for the interactions between individuals over time, such as the ones obtained by manual or automated contact tracing, and uses a diffusion-reaction mechanism to describe the state dynamics. This dynamic graph model helps identify likely exposed/infected individuals to whom we can provide early warnings, even before they display any symptoms and/or are asymptomatic. Moreover, when the testing capacity is limited compared to the population size, reliable estimation of individual's health state and disease transmissibility using epidemiological models is extremely challenging. Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription. Therefore, we propose the use of arbitrary Polynomial Chaos Expansion, a popular technique used for uncertainty quantification, to represent the states, and quantify the uncertainties in the dynamic model. This design enables us to assign uncertainty of the state of each individual, and consequently optimize the testing as to reduce the overall uncertainty given a constrained testing budget. These tools can also be used to optimize vaccine distribution to curb the disease spread when limited vaccines are available. We present a few simulation results that illustrate the performance of the proposed framework, and estimate the impact of incomplete contact tracing data.
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