AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

PURPOSE: The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising. METHODS: A novel DL-based filter using adaptive residual (AdaRes) learning was proposed. Five image quality metrics (IQMs) and 27 radiomics features were used to evaluate denoising results. The effect of our proposed filter, AdaRes, on four pre-trained convolutional neural network (CNN) classification models and three radiologists was assessed. RESULTS: AdaRes filter was tested on both natural and ultrasound image databases. IQMs results indicate that AdaRes could remove noises in three different noise levels with the highest performances. In addition, a radiomics study proved that AdaRes did not distort tissue textures and it could preserve most radiomics features. AdaRes could also improve the performance classification using CNNs in different settings. Finally, AdaRes also improved the mean overall performance (AUC) of three radiologists from 0.494 to 0.702 in the classification of benign and malignant lesions. CONCLUSIONS: AdaRes filtered out noises on ultrasound images more effectively and can be used as an auxiliary preprocessing step in computer-aided diagnosis systems. Radiologists may use it to remove unwanted noises and improve the ultrasound image quality before the interpretation.

Quantification of Artifacts and Image Distortions in 1.5 Tesla Magnetic Resonance Images of a Commercial Multi-Channel Vaginal Cylinder Brachytherapy Applicator Set.

Background: The BEBIG Portio multi-channel applicator provides better target dose coverage and sparing organs-at-risk compared to a single-channel cylinder. However, artifacts and distortions of Portio in magnetic resonance images (MRI) have not yet been reported. Objective: We aimed to quantify the artifacts and distortions in its 1.5-Tesla MR images before clinical use. Material and Methods: In this experimental study, we employed a gelatin-filled phantom to conduct our measurements. T2-weighted (T2W) images were examined for artifacts and distortions. Computed tomography (CT) images were used as a reference to assess image distortions. Artifact severity was measured by recording the full-width-at-half-maximum (FWHM) image pixel values at various positions along the length of the applicator/channels. CT and MRI-based applicator reconstruction accuracy were then compared, and signal-to-noise ratio (SNR) and contrast were also determined for the applicator images. Results: The applicator distortion level for the Portio applicator was less than the image spatial resolution (0.5±0.5 pixels). The average FWHM for the tandem applicator images was 5.23±0.39 mm, while it was 3.21±0.37 mm for all channels (compared to their actual diameters of 5.0 mm and 3.0 mm, respectively). The average applicator reconstruction difference between CT and MR images was 0.75±0.30 mm overall source dwell positions. The image SNR and contrast were both acceptable. Conclusion: These findings indicate that the Portio applicator has a satisfactory low level of artifacts and image distortions in 1.5-Tesla, T2W images. It may, therefore, be a promising option for MRI-guided multi-channel vaginal brachytherapy.

In Vitro Assessment of SWEEPS and Antimicrobial Photodynamic Therapy Alone or in Combination for Eradicating Enterococcus faecalis Biofilm in Root Canals.

OBJECTIVES: This study investigates the efficacy of antimicrobial photodynamic therapy (aPDT) using riboflavin and a blue diode laser (BDL), combined with shock wave-enhanced emission photoacoustic streaming (SWEEPS), against Enterococcus faecalis. MATERIALS AND METHODS: A total of 48 extracted single-rooted human teeth were used. The root canals were instrumented, sealed at their apices, had the smear layer removed, and then underwent autoclave sterilization. Subsequently, each canal was inoculated with E. faecalis bacterial suspension and allowed to incubate for ten days. After confirming the presence of biofilms through scanning electron microscopy (SEM) in three teeth, the remaining teeth were randomly allocated into nine groups, each containing five teeth: control, 5.25% sodium hypochlorite (NaOCl), BDL, SWEEPS + normal saline, SWEEPS + NaOCl, riboflavin, riboflavin + SWEEPS, riboflavin + BDL, and riboflavin + BDL + SWEEPS. After the treatment, the numbers of colony-forming units (CFUs)/mL were calculated. The data were analysed using one-way ANOVA followed by Tukey's test for comparisons. RESULTS: All groups, with the exception of the BDL group, exhibited a significant reduction in E. faecalis CFU/mL when compared to the control group (p < 0.001). The difference in CFU/mL value between riboflavin + BDL + SWEEPS and riboflavin + SWEEPS was significant (p = 0.029), whereas there was no significant difference between riboflavin + BDL + SWEEPS and riboflavin + BDL (p = 0.397). Moreover, there was no statistically significant difference between the riboflavin + SWEEPS group and the riboflavin + BDL group (p = 0.893). CONCLUSIONS: The results demonstrated that combining the SWEEPS technique with riboflavin as a photosensitizer activated by BDL in aPDT effectively reduced the presence of E. faecalis in root canals.

Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi-Center Study.

OBJECTIVES: Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator-dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep-radiomics features. METHODS: We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep-radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep-radiomics features were fed to 9 common machine-learning algorithms to choose the best-performing classifier. The 2 best-performing AI models were then externally validated. RESULTS: Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively. CONCLUSION: Our proposed AI models fed with deep-radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.

OBJECTIVES: Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS: Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models. RESULTS: Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%. DISCUSSION: The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

An open-access breast lesion ultrasound image database : Applicable in artificial intelligence studies.

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS: An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts.

PURPOSE: To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images. METHOD: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized. RESULTS: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). CONCLUSIONS: These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. METHODS: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. RESULTS: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. CONCLUSIONS: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies.

Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images.

Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics.

Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker.

Cardiac pacemakers are used in the treatment of patients with symptomatic bradycardia. The pacemaker paces the heart at the predetermined rate to maintain uninterrupted cardiac activity. Usually, pacemaker lead will be connected to the right atrium (RA) and right ventricle (RV) in dual-chamber pacemaker implantation and RV alone in single-chamber pacemaker implantation. This alters the route of proper conduction across the myocardial cells. The cell-to-cell conduction transmission in pacing delays the activation of selected intraventricular myocardial activation. Pacing-induced cardiomyopathy (PICM) is most commonly defined as a drop in left ventricle ejection fraction (LVEF) in the setting of chronic, high-burden right ventricle (RV) pacing. Currently, very few effective treatments are standard for PICM which rely on the detection of the RV pacing. Such treatments have primarily focused on upgrading to cardiac resynchronization therapy (CRT) when LVEF has dropped. However, the early and accurate detection of these stress factors is challenging. Cardiac desynchrony and interventricular desynchrony can be determined by various echocardiographic techniques, including M-mode, Doppler method, tissue Doppler method, and speckle tracking echocardiography which is subjective measures and shows a significant difference between RV and LV preejection period where the activation of LV is delayed considerably. Computer-aided diagnosis (CAD) is a noninvasive technique that can classify the ultrasound images of the heart in pacemaker-implanted patients and healthy patients with normal left ventricular systolic function and further detect the variations in pacemaker functions in its early stage using heart ultrasound images. Developing such a system requires a vast and diverse database to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost. The models have been used to extract radiomics features in terms of textures and then screened by their Relief-F scores for selection and ranking to be classified into nine groups consisting of up to 250 radiomics features. Ten best features were fed to the machine learning models. The R-wave dataset achieved a maximum test performance accuracy of 97.73% with four features in the random forest model. The T-wave dataset achieved a maximum test performance accuracy of 96.59% with three features in the SVM model. Our experimental results demonstrate the system's robustness, which can be developed as an early and accurate detection system for pacing-induced cardiomyopathy.

Toward a Quantitative Colorimeter for Point-of-Care Nitrite Detection.

This paper reports on a low-cost, quantitative, point-of-care solution for the early detection of nitrite, a common biomarker for urinary tract infections (UTIs). In a healthy individual, nitrite is not found in the urine. However, a subject with a suspected UTI will produce nitrite in their urine since the bacteria present will convert nitrate into nitrite. Traditionally, nitrite is monitored by urinary dipsticks that are either read by eye or using a reflectance spectrophotometer. Both methods provide a semiquantitative positive or negative result at best. In this paper, we described a novel, affordable, portable transmission-based colorimeter for the quantitative measurement of nitrite. A unique permutation of the Griess reaction was optimized for the clinical detection of nitrite in urine and is reported. By using nitrite spiked in a salt buffer, artificial, and human urine samples, the performance of the colorimeter was evaluated against dipsticks read using two commercial dipstick analyzers, Urisys 1100 (Roche Diagnostics) and Clinitek Status+ (Siemens Medical Solutions). The colorimeter was able to detect the clinically relevant range of nitrite from 0.78 to 200 µM in a salt buffer. The detection limit in artificial urine was determined as 1.6 µM, which is ∼16× more sensitive than commercial dipstick reflectance analyzers, enabling the possibility for earlier detection of urinary infections. The colorimeter is assembled using off-the-shelf components (<$80) and controlled by a smartphone application via low-energy bluetooth. It has a built-in color correction algorithm and is designed to enable for a turbidity correction in samples containing bacteria or other cellular debris as well. The mobile application can display the nitrite concentration for a single sample or display the results over a period of time. Tracking urinalysis results longitudinally can help identify trends such as increases in nitrite concentrations over an individual's baseline and identify possible infections earlier. While the detection of nitrite was showcased here, this portable analyzer can be expanded to other colorimetric-based chemistries to detect a panel of biomarkers, which can improve the overall sensitivity and specificity of the desired assay.

Interpretation of radiomics features-A pictorial review.

Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.

Nonlinear optical response of cancer cells following conventional and nano-technology based treatment strategies: Results of chemo-, thermo- and radiation therapies.

BACKGROUND: Although traditional treatments are able to increase cancer survival rate, undesirable impact on off-target tissues are considered a limitation of these approaches. Nanotechnology-based treatments have been proposed as a possible option to enhance targeting., Further,current methods for evaluating cellular damage, are time consuming, highly dependent on the operator skills, and expensive. The aim of this study was to evaluate the capability of nonlinear optical response of cells to determine cellular damages during conventional and nano-technology based treatments. METHODS: Three different cancer cell lines, CT26, KB, and MCF-7 were used in this study. The alginate hydrogel co-loaded with cisplatin and Au nanoparticle (ACA) nanocomplex and gold-coated iron oxide nanoparticle (Au@IONP) were considered for chemo- and chemo-photothermal therapies, and thermo-radiation therapy, respectively. The sign and value of nonlinear optical absorption coefficient and imaginary part of the third-order nonlinear susceptibility of cells were computed. MTT assay was utilized as a reference method. RESULTS: The value of nonlinear optical indices increased with increasing cellular damage and cell death. The linear regression analysis indicated high correlation between nonlinear optical indices and MTT results, in all treatments. CONCLUSION: The nonlinear optical indices are robust from confounding factors, namely treatment approach (traditional and nano-technology based), treatment modality (chemotherapy, thermotherapy, photothermal therapy, and radiation therapy), and cell types. Nonlinear optical properties of cells can be used as a rapid estimation method for cell damage, at the nanoscale level.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Cortical Bone Mechanical Assessment via Free Water Relaxometry at 3 T.

BACKGROUND: Investigation of cortical bone using magnetic resonance imaging is a developing field, which uses short/ultrashort echo time (TE) pulse sequences to quantify bone water content and to obtain indirect information about bone microstructure. PURPOSE: To improve the accuracy of the previously proposed technique of free water T1 quantification and to seek the relationship between cortical bone free water T1 and its mechanical competence. STUDY TYPE: Prospective. SUBJECTS: Twenty samples of bovine tibia bone. FIELD STRENGTH/SEQUENCES: 3.0 T; ultra-fast two-dimensional gradient echo, Radio frequency-spoiled three-dimensional gradient echo. ASSESSMENT: Cortical bone free water T1 was quantified via three different methods: inversion recovery (IR), variable flip angle (VFA), and variable repetition time (VTR). Signal-to-noise ratio was measured by dividing the signal of each segmented sample to background noise. Segmentation was done manually. The effect of noise on T1 quantification was evaluated. Then, the samples were subjected to mechanical compression test to measure the toughness, yield stress, ultimate stress, and Young modulus. STATISTICAL TESTS: All the statistical analysis (Shapiro-Wilk, way analysis of variance, paired t test, Pearson correlation, and Bland-Altman plot) were done using SPSS. RESULTS: Significant difference was found between T1 quantification groups (P < 0.05). Average T1 of each quantification method differed significantly after adding noise (P < 0.05). VFA-T1 values significantly correlated with toughness (r = -0.68, P < 0.05), ultimate stress (r = -0.71, P < 0.05), and yield stress (r = -0.62, P < 0.05). No significant correlation was found between VTR-T1 values and toughness (P = 0.07), ultimate stress (P = 0.47), yield stress (P = 0.30), and Young modulus (P = 0.39). DATA CONCLUSION: Pore water T1 value is associated with bone mechanical competence, and VFA method employing short-TE pulse sequence seems a superior technique to VTR method for this quantification. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: 1.

Target Product Profile Analysis of COVID-19 Vaccines in Phase III Clinical Trials and Beyond: An Early 2021 Perspective.

The coronavirus SARS-CoV-2, which causes Coronavirus disease 2019 (COVID-19), has infected more than 100 million people globally and caused over 2.5 million deaths in just over one year since its discovery in Wuhan, China in December 2019. The pandemic has evoked widespread collateral damage to societies and economies, and has destabilized mental health and well-being. Early in 2020, unprecedented efforts went into the development of vaccines that generate effective antibodies to the SARS-CoV-2 virus. Teams developing twelve candidate vaccines, based on four platforms (messenger RNA, non-replicating viral vector, protein/virus-like particle, and inactivated virus) had initiated or announced the Phase III clinical trial stage by early November 2020, with several having received emergency use authorization in less than a year. Vaccine rollout has proceeded around the globe. Previously, we and others had proposed a target product profile (TPP) for ideal/optimal and acceptable/minimal COVID-19 vaccines. How well do these candidate vaccines stack up to a harmonized TPP? Here, we perform a comparative analysis in several categories of these candidate vaccines based on the latest available trial data and highlight the early successes as well as the hurdles and barriers yet to be overcome for ending the global COVID-19 pandemic.

Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists' assessment.

PURPOSE: Ultrasonography is the most common imaging modality used to diagnose carpal tunnel syndrome (CTS). Recently artificial intelligence algorithms have been used to diagnose musculoskeletal diseases accurately without human errors using medical images. In this work, a computer-aided diagnosis (CAD) system is developed using radiomics features extracted from median nerves (MN) to diagnose CTS accurately. METHOD: This study is performed on 228 wrists from 65 patients and 57 controls, with an equal number of control and CTS wrists. Nerve conduction study (NCS) is considered as the gold standard in this study. Two radiologists used two guides to evaluate and categorize the pattern and echogenicity of MNs. Radiomics features are extracted from B-mode ultrasound images (Ultrasomics), and the robust features are fed into support vector machine classifier for automated classification. The diagnostic performances of two radiologists and the CAD system are evaluated using ROC analysis. RESULTS: The agreement of two radiologists was excellent for both guide 1 and 2. The honey-comb pattern clearly appeared in control wrists (based on guide 1). In addition, CTS wrists indicated significantly lower number of fascicles in MNs (based on guide 2). The area under ROC curve (AUC) of the radiologist 1 and 2 are 0.658 and 0.667 based on guide 1 and 0.736 and 0.721 based on guide 2, respectively. The CAD system indicated higher performance than two radiologists with AUC of 0.926. CONCLUSION: The proposed CAD system shows the benefit of using ultrasomics features and can assist radiologists to diagnose CTS accurately.

COVID-19 Point-of-Care Diagnostics That Satisfy Global Target Product Profiles.

COVID-19 pandemic will continue to pose a major public health threat until vaccination-mediated herd immunity is achieved. Most projections predict vaccines will reach a large subset of the population late in 2021 or early 2022. In the meantime, countries are exploring options to remove strict lockdown measures and allow society and the economy to return to normal function. One possibility is to expand on existing COVID-19 testing strategies by including large-scale rapid point-of-care diagnostic tests (POCTs). Currently, there is significant variability in performance and features of available POCTs, making selection and procurement of an appropriate test for specific use case difficult. In this review, we have used the World Health Organization's (WHO) recently published target product profiles (TPPs) for specific use cases of COVID-19 diagnostic tests to screen for top-performing POCTs on the market. Several POCTs, based on clinical sensitivity/specificity, the limit of detection, and time to results, which meet WHO TPP criteria for direct detection of SARS-CoV-2 (acute infection) or indirect diagnosis of past infection (host antibodies), are highlighted here.

Z-scan method to measure the nonlinear optical behavior of cells for evaluating the cytotoxic effects of chemotherapy and hyperthermia treatments.

The effects of new treatments must be investigated in vitro before using clinically or in vivo. The aim of this study was to introduce the Z-scan technique as a fast, accurate, inexpensive, and safe in vitro method to distinguish the cytotoxic effects of various treatments. C6 and OLN-93 cell lines were prepared and treated with Temozolomide (TMZ), radiofrequency hyperthermia (HT), and chemo-hyperthermia (HT+TMZ). The cytotoxic effects of different treatments on both cell lines were evaluated using colony formation assay and Z-scan method. The results of colony assay showed that the surviving fraction (SF) of C6 cells treated with TMZ, HT, and HT + TMZ were significantly decreased compared to the control group. Whereas, hyperthermia treatment had no significant effect on the SF of OLN-93 cells. The results of Z-scan technique indicated that the control group of C6 cells had the negative nonlinear refractive index (n2). Whereas, the C6 cells treated with HT, TMZ, and HT + TMZ had the positive n2 index. The sign of n2 index in the control and HT groups of OLN-93 cells was positive but treatment of cells with TMZ and HT + TMZ changed the sign of it. Moreover, with increasing the cytotoxic effects of different treatments, the SF value of both cell lines decreased and the magnitude of n2 index increased. The results of Z-scan technique were completely in line with the results of colony assay. Therefore, Z-scan method could distinguish the cytotoxic effects of various treatments by examining the nonlinear optical properties of the samples.