Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding.

AIMS: We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS: A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS: Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION: This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.

Noninvasive Colorimetric Detection of Acetic Acid in Human Breath Based on an Alginate/Ni-Al-LDH/Dye Composite Film.

Alginate/Ni-Al-layered double hydroxide/dye (Alg/Ni-Al-LDH/dye) composite films were fabricated using the solution casting method. The dyes used included methyl red, phenol red, thymol blue, bromothymol blue, m-cresol purple, methyl orange, bromocresol purple (BP), and bromocresol green (BG) in the overall pH range of 3.8 to 9.6. The chemical composition and morphology of the Alg/Ni-Al-LDH/dye composite film structure were investigated by Fourier transform infrared spectroscopy, FESEM, atomic force microscopy, and X-ray diffraction. The Alg/Ni-Al-LDH/dye composite films were semitransparent and mechanically flexible. Acetic acid was investigated as a respiratory biomarker related to gastrointestinal diseases. The parameters studied included color volume, response time, Ni-Al-LDH nanosheet volume, reusability, and drawing of the calibration curve along with statistical features including standard deviation, relative standard deviation, limit of detection, and limit of quantitation. Colorimetric indicators BP and BG in the presence of acetic acid produce color changes that are almost visible to the naked eye. However, other used indicators have shown almost no change. Therefore, it can be reported that the sensors made in the presence of BP and BG act selectively in relation to acetic acid.

Scenic routing navigation using property valuation.

Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph in order to find the best route. Some users desire that the routing suggestion include consideration pertaining to the scenic-architectural quality of the path. For example, a user may seek a leisure walk via what they might deem as visually attractive architecture. Here, we are proposing a method to quantify such user preferences and scenic quality and to augment the standard routing methods by giving weight to the scenic quality. That is, instead of suggesting merely the time and cost-optimal route, we will find the best route that is tailored towards the user's scenic quality preferences as an additional criterion to the time and cost. The proposed method uniquely weighs the scenic interest or residential street segments based on the property valuation data.

Estimating the parameters of a dependent model and applying it to environmental data set.

In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.

Heart failure alters diagnostic yield for pulmonary embolism in patients undergoing computed tomography pulmonary angiogram.

INTRODUCTION: There is limited evidence regarding the effects of a pre-existing heart failure (HF) on the diagnostic yield of pulmonary embolism (PE) evaluation in the Emergency Department (ED). METHODS: Electronic medical record of consecutive adults who underwent a computed tomography pulmonary angiogram (CTPA) in the ED at Loma Linda University Medical Center between June 1, 2019 and March 25, 2022 were reviewed. Repeat studies for the same patient and patients with unspecified HF diagnoses or isolated right ventricular HF were excluded. Key demographics, lab values and vital signs, relevant medications were collected. Primary outcome was the incidence of PE on CTPA compared between patients with and without pre-existing HF. RESULTS: A total of 2846 patients were included in the study (602 patients with HF and 2244 without). In total cohort, 11.7% (n = 334) of patients had PE found on CTPA. The incidence of PE on CTPA was lower among patients with a history of HF than patients without a history of HF (12.5% vs 9%). A history of pre-existing HF was associated with a lower odds ratio for a positive PE study (OR 0.13, 95%CI: 0.03-0.57) in multivariable analyses. CONCLUSIONS: In this study, we observed that the incidence of PE among patients who undergo CTPA was lower among patients with pre-existing HF compared to those without. Further studies should determine if HF is an important mitigating factor when risk stratifying patients for PE.

Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions.

Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response-ie, timing of lockdown, timing of reopening, etc.-and disease outbreak indicators-ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics-eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.

Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic.

SARS-CoV-2 (COVID-19) is a new strain of coronavirus that is regarded as a respiratory disease and is transmittable among humans. At present, the disease has caused a pandemic, and COVID-19 cases are ballooning out of control. The impact of such turbulent situations can be controlled by tracking the patterns of infected and death cases through accurate prediction and by taking precautions accordingly. We collected worldwide COVID-19 case information and successfully predicted infected victims and possible death cases around the world and in the United States. In addition, we analyzed some leading stock market shares and successfully forecast their trends. We also scrutinized the share market price by proper reasoning and considered the state of affairs of COVID-19, including geographical dispersity. We publicly release our developed dashboard that presents statistical data of COVID-19 cases, shows predicted results, and reveals the impact of COVID-19 on leading companies and different countries' job markets.

Preclinical Western Blot in the Era of Digital Transformation and Reproducible Research, an Eastern Perspective.

The current research is an interdisciplinary endeavor to develop a necessary tool in preclinical protein studies of diseases or disorders through western blotting. In the era of digital transformation and open access principles, an interactive cloud-based database called East-West Blot ( https://rancs-lab.shinyapps.io/WesternBlots ) is designed and developed. The online interactive subject-specific database built on the R shiny platform facilitates a systematic literature search on the specific subject matter, here set to western blot studies of protein regulation in the preclinical model of TBI. The tool summarizes the existing publicly available knowledge through a data visualization technique and easy access to the critical data elements and links to the study itself. The application compiled a relational database of PubMed-indexed western blot studies labeled under HHS public access, reporting downstream protein regulations presented by fluid percussion injury model of traumatic brain injury. The promises of the developed tool include progressing toward implementing the principles of 3Rs (replacement, reduction, and refinement) for humane experiments, cultivating the prerequisites of reproducible research in terms of reporting characteristics, paving the ways for a more collaborative experimental design in basic science, and rendering an up-to-date and summarized perspective of current publicly available knowledge.

DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images.

The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic that has infected millions of people causing millions of deaths around the world. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the standard screening method for COVID-19 detection but it requires specific molecular-biology training. Moreover, the general workflow is difficult e.g. sample collection, processing time, and analysis expertise, etc. Chest radiographic image analysis can be a good alternative screening method that is faster, more efficient, and requires minimal clinical or molecular biology trained laboratory personnel. Early studies have shown that abnormalities on the chest radiographic images are likely to be the consequence of COVID-19 infection. In this study, we propose DeepCOVIDNet, a deep learning based COVID-19 detection model. Our proposed deep-learning model is a multiclass classifier that can distinguish COVID-19, viral pneumonia, bacterial pneumonia, and healthy chest X-ray images. Our proposed model classifies radiographic images into four distinct classes and achieves the accuracy of 89.47% along with a high degree of precision, recall and F1 score. On a different dataset setting (COVID-19, bacterial pneumonia, viral pneumonia) our model achieves the maximum accuracy of 98.25%. We demonstrate generalizability of our proposed method using 5-fold cross validation for COVID-19 vs pneumonia and COVID-19 vs healthy classification that also manifests promising results.

Interdependent Networks: A Data Science Perspective.

Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.

Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks.

A major issue in translation of the artificial intelligence platforms for automatic segmentation of echocardiograms to clinics is their generalizability. The present study introduces and verifies a novel generalizable and efficient fully automatic multi-label segmentation method for four-chamber view echocardiograms based on deep fully convolutional networks (FCNs) and adversarial training. For the first time, we used generative adversarial networks for pixel classification training, a novel method in machine learning not currently used for cardiac imaging, to overcome the generalization problem. The method's performance was validated against manual segmentations as the ground-truth. Furthermore, to verify our method's generalizability in comparison with other existing techniques, we compared our method's performance with a state-of-the-art method on our dataset in addition to an independent dataset of 450 patients from the CAMUS (cardiac acquisitions for multi-structure ultrasound segmentation) challenge. On our test dataset, automatic segmentation of all four chambers achieved a dice metric of 92.1%, 86.3%, 89.6% and 91.4% for LV, RV, LA and RA, respectively. LV volumes' correlation between automatic and manual segmentation were 0.94 and 0.93 for end-diastolic volume and end-systolic volume, respectively. Excellent agreement with chambers' reference contours and significant improvement over previous FCN-based methods suggest that generative adversarial networks for pixel classification training can effectively design generalizable fully automatic FCN-based networks for four-chamber segmentation of echocardiograms even with limited number of training data.

Comparison home care service versus hospital-based care in patients with diabetic foot ulcer: an economic evaluation study.

INTRODUCTION: Providing health care to patients at home could be causing the mortality and readmission rates reduction in addition to satisfaction of both patients and health care providers increase. The aim of this study was to assess the cost-effectiveness of home care service compared to hospital based care in patients with diabetic foot ulcer. METHODS: An economic evaluation study and a trial study were simultaneously conducted in Iran. In trial phase, patients with diabetic foot ulcer were randomly assigned to the home care or hospital care. The Cost and Quality of life data were determined as measures of the study. Incremental cost-effectiveness ratio was calculated for comparative purposes. The model consisted of five stages of the disease. The Tree Age Pro 2009 and R software's were used for data analysis. RESULTS: 120 patients were enrolled in our trial; among which 30 patients were in home care service group and 90 patients in hospital based care group. The rate of ulcer size reduction in hospital based care was significant (P value = 0.003) in comparison with home care service. The total cost of the home care and hospital strategies were 1720.4 US$, 3940.3 US$ and the total effectiveness were 0.31 and 0.29, respectively. The incremental cost-effectiveness ratio (ICER) was 117,300 US$ per quality-adjusted life year for home care intervention compared to hospital based care. Based on ICER plane home care treatment will be placed on the southeastern quadrant of the Cost-Effectiveness Plane, and is suggested as a more dominant treatment alternative. CONCLUSIONS: Regarding current evidence, home care strategy for patients suffering diabetic foot ulcer enjoys more cost effectiveness compared to hospital care. It is suggested that healthcare policy makers determine the tariff for health care services for disease groups according to the activity based costing approach.

Monitoring the quality of frozen-thawed venous segments using bioimpedance spectroscopy.

OBJECTIVE: Storage at temperatures as low as -80 °C and below (cryopreservation) is considered a method for long-term preservation of cells and tissues, and especially blood vessel segments, which are to be used for clinical operations such as transplantation. However, the freezing and thawing processes themselves can induce injuries to the cells and tissue by damaging the structure and consequently functionality of the cryopreserved tissue. In addition, the level of damage is dependent on the rate of cooling and warming used during the freezing-thawing process. Current methods for monitoring the viability and integrity of cells and tissues after going through the freezing-thawing cycle are usually invasive and destructive to the cells and tissues. Therefore, employing monitoring methods which are not destructive to the cryopreserved tissues, such as bioimpedance measurement techniques, is necessary. In this study we aimed to design a bioimpedance measurement setup to detect changes in venous segments after freezing-thawing cycles in a noninvasive manner. APPROACH: A bioimpedance spectroscopy measurement technique with a two-electrode setup was employed to monitor ovine jugular vein segments after each cycle during a process of seven freezing-thawing cycles. MAIN RESULTS: The results demonstrated changes in the impedance spectra of the measured venous segments after each freezing-thawing cycle. SIGNIFICANCE: This indicates that bioimpedance spectroscopy has the potential to be developed into a novel method for non-invasive and non-destructive monitoring of the viability of complex tissue after cryopreservation.

Finite Element Simulation of the Impedance Response of a Vascular Segment as a Function of Changes in Electrode Configuration.

Monitoring a biological tissue as a three dimensional (3D) model is of high importance. Both the measurement technique and the measuring electrode play substantial roles in providing accurate 3D measurements. Bioimpedance spectroscopy has proven to be a noninvasive method providing the possibility of monitoring a 3D construct in a real time manner. On the other hand, advances in electrode fabrication has made it possible to use flexible electrodes with different configurations, which makes 3D measurements possible. However, designing an experimental measurement set-up for monitoring a 3D construct can be costly and time consuming and would require many tissue models. Finite element modeling methods provide a simple alternative for studying the performance of the electrode and the measurement set-up before starting with the experimental measurements. Therefore, in this study we employed the COMSOL Multiphysics finite element modeling method for simulating the effects of changing the electrode configuration on the impedance spectroscopy measurements of a venous segment. For this purpose, the simulations were performed for models with different electrode configurations. The simulation results provided us with the possibility of finding the optimal electrode configuration including the geometry, number and dimensions of the electrodes, which can be later employed in the experimental measurement set-up.

Quantum transport through the edge states of zigzag phosphorene nanoribbons in presence of a single point defect: analytic Green's function method.

Zigzag phosphorene nanoribbons have quasi-flat band edge modes entirely detached from bulk states. We analytically study the electronic transport through such edge states in the presence of a localized defect for semi-infinite and finite ribbon widths. Using the tight-binding model, we derive analytical expressions for the Green's function and transmission amplitude of both pristine and defective nanoribbons. We find that the transmission of ribbons with both semi-infinite and finite width is sensitive to the location of a single impurity defect with respect to the edge. By the presence of an impurity on the outermost edge site of the ribbon, the transmission through the edge channel, similar to a one-dimensional chain, strongly suppresses for the entire energy spectrum of the quasi-flat band. In contrast, the transmission of low-energy [Formula: see text] states, is robust as the impurity is moved one position far away from the edge on the same sub-lattice. The analytical calculations are also complemented by exact numerical transport computations using the Landauer approach.

Applications of Bioimpedance Measurement Techniques in Tissue Engineering.

Rapid development in the field of tissue engineering necessitates implementation of monitoring methods for evaluation of the viability and characteristics of the cell cultures in a real-time, non-invasive and non-destructive manner. Current monitoring techniques are mainly histological and require labeling and involve destructive tests to characterize cell cultures. Bioimpedance measurement technique which benefits from measurement of electrical properties of the biological tissues, offers a non-invasive, label-free and real-time solution for monitoring tissue engineered constructs. This review outlines the fundamentals of bioimpedance, as well as electrical properties of the biological tissues, different types of cell culture constructs and possible electrode configuration set ups for performing bioimpedance measurements on these cell cultures. In addition, various bioimpedance measurement techniques and their applications in the field of tissue engineering are discussed.

A novel explanation for the increased conductivity in annealed Al-doped ZnO: an insight into migration of aluminum and displacement of zinc.

A combined experimental and first-principles study is performed to study the origin of conductivity in ZnO:Al nanoparticles synthesized under controlled conditions via a reflux route using benzylamine as a solvent. The experimental characterization of the samples by Raman, nuclear magnetic resonance (NMR) and conductivity measurements indicates that upon annealing in nitrogen, the Al atoms at interstitial positions migrate to the substitutional positions, creating at the same time Zn interstitials. We provide evidence for the fact that the formed complex of AlZn and Zni corresponds to the origin of the Knight shifted peak (KS) we observe in 27Al NMR. As far as we know, the role of this complex has not been discussed in the literature to date. However, our first-principles calculations show that such a complex is indeed energetically favoured over the isolated Al interstitial positions. In our calculations we also address the charge state of the Al interstitials. Further, Zn interstitials can migrate from AlZn and possibly also form Zn clusters, leading to the observed increased conductivity.

Functionalization of Graphene Oxide with 9-aminoanthracene for the Adsorptive Removal of Persistent Aromatic Pollutants from Aqueous Solution.

A novel modified graphene oxide nanocomposite was fabricated via a facial procedure, aiming to removal of the aromatic pollutants from aqueous solution. The graphene oxide (GO) was functionalized with 9-aminoanthracene and produced graphene oxide-9-aminoanthracene (GO-9-AA). FTIR, XRD, TGA, TEM and Raman spectroscopy techniques were used for characterization of the adsorbents. Adsorption of naphthalene (NAP), acenaphthylene (ACN), and phenanthrene (PHN) as a model of polycyclic aromatic hydrocarbons (PAHs) was investigated by GO-9-AA. The adsorbent showed excellent removal efficiency towards PAHs from aqueous solution. Equilibrium data of the adsorption process were successfully fitted with Freundlich model from single solute system, and the maximum adsorption capacities followed the order of NAP > ACN >PHN. The kinetic analysis revealed that the overall adsorption process was fast and successfully fitted with the pseudo-second-order kinetic model. The anthracene ring makes GO-9-AA π-electron rich, thus facilitating π-π EDA interaction between NAP, ACN and PHN with GO-9-AA.

In vivo precision of three HR-pQCT-derived finite element models of the distal radius and tibia in postmenopausal women.

BACKGROUND: The distal radius is the most common osteoporotic fracture site occurring in postmenopausal women. Finite element (FE) modeling is a non-invasive mathematical technique that can estimate bone strength using inputted geometry/micro-architecture and tissue material properties from computed tomographic images. Our first objective was to define and compare in vivo precision errors for three high-resolution peripheral quantitative computed tomography (HR-pQCT, XtremeCT; Scanco) based FE models of the distal radius and tibia in postmenopausal women. Our second objective was to assess the role of scan interval, scan quality, and common region on precision errors of outcomes for each FE model. METHODS: Models included: single-tissue model (STM), cortical-trabecular dual-tissue model (DTM), and one scaled model using imaged bone mineral density (E-BMD). Using HR-pQCT, we scanned the distal radius and tibia of 34 postmenopausal women (74 ± 7 years), at two time points. Primary outcomes included: tissue stiffness, apparent modulus, average von Mises stress, and failure load. Precision errors (root-mean-squared coefficient of variation, CV%RMS) were calculated. Multivariate ANOVA was used to compare the mean of individual CV% among the 3 HR-pQCT-based FE models. Spearman correlations were used to characterize the associations between precision errors of all FE model outcomes and scan/time interval, scan quality, and common region. Significance was accepted at P < 0.05. RESULTS: At the distal radius, CV%RMS precision errors were <9 % (Range STM: 2.8-5.3 %; DTM: 2.9-5.4 %; E-BMD: 4.4-8.7 %). At the distal tibia, CV%RMS precision errors were <6 % (Range STM: 2.7-4.8 %; DTM: 2.9-3.8 %; E-BMD: 1.8-2.5 %). At the radius, Spearman correlations indicated associations between the common region and associated precision errors of the E-BMD-derived apparent modulus (ρ = -0.392; P < 0.001) and von Mises stress (ρ = -0.297; P = 0.007). CONCLUSION: Results suggest that the STM and DTM are more precise for modeling apparent modulus, average von Mises stress, and failure load at the distal radius. Precision errors were comparable for all three models at the distal tibia. Results indicate that the noted differences in precision error at the distal radius were associated with the common scan region, illustrating the importance of participant repositioning within the cast and reference line placement in the scout view during the scanning process.