A Chemiluminescent Method for the Detection of H�O� and Glucose Based on Intrinsic Peroxidase-Like Activity of WS� Quantum Dots

Currently, researchers are looking for nanomaterials with peroxidase-like activity to replace natural peroxidase enzymes. For this purpose, WS� quantum dots (WS� QDs) were synthesized via a solvothermal method, which improved the mimetic behavior. The resulting WS� QDs with a size of 1�1.5 nm had a high fluorescence emission, dependent on the excitation wavelength. WS� QDs with uniform morphology showed a high catalytic effect in destroying H�O�. The peroxidase-like activity of synthesized nanostructures was studied in H�O� chemical and electrochemical reduction systems. The mimetic effect of WS� QDs was also shown in an H�O��rhodamine B (RB) chemiluminescence system. For this aim, a stopped-flow chemiluminescence (CL) detection system was applied. Also, in order to confirm the peroxidase-like effect of quantum dots, colorimetry and electrochemical techniques were used. In the enzymatic reaction of glucose, H�O� is one of the products which can be determined. Under optimum conditions, H�O� can be detected in the concentration range of 0�1000 nmol·L-1, with a detection limit of 2.4 nmol·L-1. Using this CL assay, a linear relationship was obtained between the intensity of the CL emission and glucose concentration in the range of 0.01�30 nmol·L-1, with a limit of detection (3S) of 4.2 nmol·L-1

Pantothenate kinase-associated neurodegeneration: Clinical aspects, diangnosis and treatments

Pantothenate Kinase-Associated, Neurodegeneration (PKAN) is an autosomal, recessive disorder characterized by a, mutation in the PANK2 gene. The clinical, presentation may range from only speech, disorder to severe generalized dystonia, spasticity, Visual loss, dysphagia and, dementia. The hallmark of this disease is, eyes of the tiger signs in the medial aspect, of bilateral globus pallidus on T2-weighted, MRI that is a hyperintense lesion surrounded, by hypointensity. Common treatments, for PKAN disease include anticholinergics, botulinum toxin, Oral and Intrathecal, baclofen, Iron chelation drugs and surgical, procedures such as ablative pallidotomy or, thalamotomy, Deep brain stimulation., There are many controversies about the, pathogenesis and treatment of this disease, and in recent years interesting studies have, been done on PKAN disease and other similar, diseases. This review summarizes the, clinical presentation, etiology, imaging, modalities and treatment. © S. Razmeh et al

Pantothenate kinase-associated neurodegeneration: Clinical aspects, diangnosis and treatments

Pantothenate Kinase-Associated, Neurodegeneration (PKAN) is an autosomal, recessive disorder characterized by a, mutation in the PANK2 gene. The clinical, presentation may range from only speech, disorder to severe generalized dystonia, spasticity, Visual loss, dysphagia and, dementia. The hallmark of this disease is, eyes of the tiger signs in the medial aspect, of bilateral globus pallidus on T2-weighted, MRI that is a hyperintense lesion surrounded, by hypointensity. Common treatments, for PKAN disease include anticholinergics, botulinum toxin, Oral and Intrathecal, baclofen, Iron chelation drugs and surgical, procedures such as ablative pallidotomy or, thalamotomy, Deep brain stimulation., There are many controversies about the, pathogenesis and treatment of this disease, and in recent years interesting studies have, been done on PKAN disease and other similar, diseases. This review summarizes the, clinical presentation, etiology, imaging, modalities and treatment. © S. Razmeh et al

A method for body fat composition analysis in abdominal magnetic resonance images via self-organizing map neural network

Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VAT was accomplished using a new level set method called distance regularized level set evolution (DRLSE). To evaluate the suggested method, the whole-body abdominal MRI was performed on 23 subjects, and three slices were selected for each case. Results: The results of the automatic segmentation were compared with those of the manual segmentation and previous artificial intelligent methods. According to the results, there was a significant correlation between the automatic and manual segmentation results of VAT and SAT. Conclusion: As the findings indicated, the suggested method improved detection of body fat. In this study, a fully automated abdominal adipose tissue segmentation algorithm was suggested, which used the SOM neural network and DRLSE level set algorithm. The proposed methodology was concluded to be accurate and robust with a significant advantage over the manual and previous segmentation methods in terms of speed and accuracy. © 2018, Mashhad University of Medical Sciences

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing pot

A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment

The implementation of immunotherapy has radically changed the treatment of oncological patients. Currently, immunotherapy is indicated in the treatment of patients with head and neck tumors, melanoma, lung cancer, bladder tumors, colon cancer, cervical cancer, breast cancer, Merkel cell carcinoma, liver cancer, leukemia and lymphomas. However, its efficacy is restricted to a limited number of cases. The challenge is, therefore, to identify which subset of patients would benefit from immunotherapy. To this end, the establishment of immunotherapy response criteria and predictive and prognostic biomarkers is of paramount interest. In this report, a group of experts of the Spanish Society of Medical Oncology (SEOM), the Spanish Society of Medical Radiology (SERAM), and Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) provide an up-to-date review and a consensus guide on these issues

Comparing of data mining techniques for predicting in-hospital mortality among patients with covid-19

Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31 and a sensitivity of 64.2 has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID-19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS). © 2021 Tehran University of Medical Sciences

Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

Objective: The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results: We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features. © 2021, The Author(s)