Charge transport spectra in superconductor-InAs/GaSb-superconductor heterostructures.

Charge transport physics in InAs/GaSb bi-layer systems has recently attracted attention for the experimental search for two-dimensional topological superconducting states in solids. Here we report measurement of charge transport spectra of nano devices consisting of an InAs/GaSb quantum well sandwiched by tantalum superconductors. We explore the current-voltage relation as a function of the charge-carrier density in the quantum well controlled by a gate voltage and an external magnetic field. We observe three types of differential resistance peaks, all of which can be effectively tuned by the external magnetic field, and, however, two of which appear at electric currents independent of the gate voltage, indicating a dominant mechanism from the superconductor and the system geometry. By analyzing the spectroscopic features, we find that the three types of peaks identify Andreev reflections, quasi-particle interference, and superconducting transitions in the device, respectively. Our results provide a basis for further exploration of possible topological superconducting state in the InAs/GaSb system.

A CT-Derived Measurement of Sarcopenia Fails to Predict Falls.

Sarcopenia and frailty have both emerged as risk factors for elderly falls. We investigated whether radiologic sarcopenia or frailty are associated with falls in a high-risk geriatric outpatient population. We reviewed 114 patients followed at the Center for Healthy Senior Living who had undergone a computerized tomography (CT) of the abdomen and pelvis for any reason from 2013 to 2019. Sarcopenia was determined by psoas muscle cross-sectional area at L3 on CT scan. Their individual frailty score was calculated. The primary outcome was admission to hospital for falls. There were no statistical differences in frailty score or sarcopenia between the 2 groups (left/right psoas muscle: no hospital admission = 6.8 ± 2.4/6.4 ± 2.5 vs falls requiring hospital admission 6.5 ± 2.3/6.5 ± 2.3 cm2). We concluded that neither frailty score nor sarcopenia predicted the occurrence of falls in our high-risk geriatric outpatient population.

Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade Glioma.

We report domain knowledge-based rules for assigning voxels in brain multiparametric MRI (mpMRI) to distinct tissuetypes based on their appearance on Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced and contrast-enhanced, T2-weighted, and Fluid-Attenuated Inversion Recovery images. The development dataset comprised mpMRI of 18 participants with preoperative high-grade glioma (HGG), recurrent HGG (rHGG), and brain metastases. External validation was performed on mpMRI of 235 HGG participants in the BraTS 2020 training dataset. The treatment dataset comprised serial mpMRI of 32 participants (total 231 scan dates) in a clinical trial of immunoradiotherapy in rHGG (NCT02313272). Pixel intensity-based rules for segmenting contrast-enhancing tumor (CE), hemorrhage, Fluid, non-enhancing tumor (Edema1), and leukoaraiosis (Edema2) were identified on calibrated, co-registered mpMRI images in the development dataset. On validation, rule-based CE and High FLAIR (Edema1 + Edema2) volumes were significantly correlated with ground truth volumes of enhancing tumor (R = 0.85;p < 0.001) and peritumoral edema (R = 0.87;p < 0.001), respectively. In the treatment dataset, a model combining time-on-treatment and rule-based volumes of CE and intratumoral Fluid was 82.5% accurate for predicting progression within 30 days of the scan date. An explainable decision tree applied to brain mpMRI yields validated, consistent, intratumoral tissuetype volumes suitable for quantitative response assessment in clinical trials of rHGG.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI.

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

Salivary antioxidants in patients with type 1 or 2 diabetes and inflammatory periodontal disease: a case-control study.

BACKGROUND: The purpose of this study was to evaluate and compare salivary concentrations of reduced, oxidized glutathione, uric acid, ascorbic acid, and total antioxidant capacity in subjects with diabetes and systemically healthy subjects with inflammatory periodontal disease. METHODS: Sixteen patients with type 1 diabetes mellitus (DM), 25 patients with type 2 DM, and 24 systemically healthy patients, all with inflammatory periodontal disease, were recruited. Whole-saliva samples were obtained, and full-mouth clinical periodontal measurements, including plaque index, probing depth, gingival recession, clinical attachment level, and bleeding on probing, were recorded at six sites per tooth. Saliva flow rate and salivary levels of reduced and oxidized glutathione, vitamin C, uric acid, and total antioxidant capacity were determined. Data were analyzed statistically by non-parametric tests. RESULTS: The subjects with type 2 DM had fewer teeth and more sites with probing depths >4 mm than the patients with type 1 DM (both P <0.01). The mean salivary reduced-glutathione concentration was lower in patients with type 1 DM than in the other two groups (both P <0.05). No significant differences in the salivary concentrations of the other antioxidants measured were found among the groups (P >0.05). Oxidized glutathione levels in the patients with type 1 DM were significantly lower than in the systemically healthy group (P = 0.007). In both groups with diabetes, salivary reduced-glutathione levels correlated positively with probing depth, and total antioxidant capacity correlated with salivary flow rate (P <0.01). CONCLUSION: The decrease in salivary reduced-glutathione levels in patients with type 1 DM may have a role in periodontal tissue destruction by predisposing tissues to oxidative stress.

Natural History of Patients Undergoing Therapeutic Endoscopies for Acute Gastrointestinal Bleeding.

When endoscopy is performed for acute GI bleeding, therapeutic endoscopic procedures are infrequently required (only 6% of cases). We sought to determine the natural history of GI hemorrhage in patients who have undergone therapeutic endoscopy. We queried our hospital database for inpatients with acute GI bleeding who underwent therapeutic endoscopy between 2015 and 2017. The primary endpoints were recurrence of bleeding and the subsequent need for repeated endoscopic interventions, angioembolization, or surgery. Demographic information was collected. We reviewed 205 hospitalized patients: mean age was 70 years, 58 per cent were male, and mean hemoglobin was 9 g/dL. Patients had medical conditions predisposing them to bleeding in 59 per cent and history of previous GI bleeding in 37 per cent of cases. Sixty per cent were on antiplatelet/anticoagulation medications, and 10 per cent were receiving nonsteroidal anti-inflammatory medications. Blood transfusions were given to 78 per cent of patients, with an average of 2.3 units of packed red blood cells transfused per patient before intervention. Recurrence of hemorrhage after therapeutic endoscopy was seen in 9 per cent of patients. Only 2 per cent underwent a second therapeutic endoscopic procedure, and 5 per cent had surgery or angioembolization (half of these patients then had a further recurrence of bleeding). In total, seven patients died (3%). Recurrence of GI bleeding after therapeutic endoscopies is uncommon (9%). Surgery and angioembolization are not commonly necessary, but when used are only successful in 50 per cent of cases.

How low can you go: Achieving postoperative outpatient pain control without opioids.

BACKGROUND: Postoperative outpatient narcotic overprescription plays a significant role in the opioid epidemic. Outpatient opioid prescription ranges from 150 to 350 oral morphine equivalent (OME) for a laparoscopic cholecystectomy or appendectomy, with 75 OME (10 pills of 5 mg of oxycodone) being the lowest recommendation (National Institute on Drug Abuse, 2018). We hypothesized that the addition of nonopioid medications to the outpatient pain control regimen would decrease the need for narcotics. METHODS: In this prospective, observational pilot study, we prescribed a 3-day regimen of ibuprofen and acetaminophen to patients after uncomplicated laparoscopic cholecystectomies and appendectomies. An additional opioid prescription for 5 pills of 5 mg of oxycodone (37.5 OME) was written for breakthrough pain. During their postoperative visit, we evaluated patients' adherence to the pain control regime, their postdischarge opioid use, and the adequacy of their pain control. RESULTS: Sixty-five patients were included in the study (52% male). The majority (80%) of surgeries were performed urgently or emergently. The visual analog scale pain score at home was significantly better than upon discharge (3.7 vs. 5.5, p = 0.001). The average number of oxycodone pills taken postdischarge was 1.8 pills. Half (51%) of the patients did not take any opioids. All but four patients reported that their pain was adequately controlled. No patient required additional opioid prescriptions or visited the emergency department. CONCLUSION: This study demonstrated that opioids can be eliminated in at least half of the patients and that five pills of 5 mg of oxycodone (37.5 OME) is sufficient for outpatient pain control when a 3-day course of ibuprofen and acetaminophen is prescribed. LEVEL OF EVIDENCE: Therapeutic study, level V.

Measurement of carrier lifetime in micron-scaled materials using resonant microwave circuits.

The measurement of minority carrier lifetimes is vital to determining the material quality and operational bandwidth of a broad range of optoelectronic devices. Typically, these measurements are made by recording the temporal decay of a carrier-concentration-dependent material property following pulsed optical excitation. Such approaches require some combination of efficient emission from the material under test, specialized collection optics, large sample areas, spatially uniform excitation, and/or the fabrication of ohmic contacts, depending on the technique used. In contrast, here we introduce a technique that provides electrical readout of minority carrier lifetimes using a passive microwave resonator circuit. We demonstrate >105 improvement in sensitivity, compared with traditional photoemission decay experiments and the ability to measure carrier dynamics in micron-scale volumes, much smaller than is possible with other techniques. The approach presented is applicable to a wide range of 2D, micro-, or nano-scaled materials, as well as weak emitters or non-radiative materials.

Predicting malignant nodules by fusing deep features with classical radiomics features.

Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict cancer incidence and prognosis. With the advancement of deep learning and convolutional neural networks (CNNs), deep features can be identified to analyze lung CTs for prognosis prediction and diagnosis. Due to a limited number of available images in the medical field, the transfer learning concept can be helpful. Using subsets of participants from the National Lung Screening Trial (NLST), we utilized a transfer learning approach to differentiate lung cancer nodules versus positive controls. We experimented with three different pretrained CNNs for extracting deep features and used five different classifiers. Experiments were also conducted with deep features from different color channels of a pretrained CNN. Selected deep features were combined with radiomics features. A CNN was designed and trained. Combinations of features from pretrained, CNNs trained on NLST data, and classical radiomics were used to build classifiers. The best accuracy (76.79%) was obtained using feature combinations. An area under the receiver operating characteristic curve of 0.87 was obtained using a CNN trained on an augmented NLST data cohort.

Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.

BACKGROUND: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow-up screens and performed size-specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6-16 mm [intermediate], and ≥16 mm [large]). METHODS: We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow-up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow-up screen (T2) and for 307 nodule-positive controls that had three consecutive positive screens not diagnosed as lung cancer. The cases and controls were split into training and test cohorts; classifier models were used to identify the most predictive features. RESULTS: The final models revealed modest improvements for baseline and delta features when compared to only baseline features. The AUROCs for small- and intermediate-sized nodules were 0.83 (95% CI 0.76-0.90) and 0.76 (95% CI 0.71-0.81) for baseline-only radiomic features, respectively, and 0.84 (95% CI 0.77-0.90) and 0.84 (95% CI 0.80-0.88) for baseline and delta features, respectively. When intermediate and large nodules were combined, the AUROC for baseline-only features was 0.80 (95% CI 0.76-0.84) compared with 0.86 (95% CI 0.83-0.89) for baseline and delta features. CONCLUSIONS: We found modest improvements in predicting lung cancer incidence by combining baseline and delta radiomics. Radiomics could be used to improve current size-based screening guidelines.

Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed 18Fluorine-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-regional recurrent disease (LRR). We aimed to fit the highest predictive features in multiple logistic regression models (MLRs). To generate such models, we applied backward feature selection method as part of Leave-One-Out Cross Validation (LOOCV) within a training set consisting of 70% of the original patient cohort. The trained MLRs were tested on an independent set consisted of 30% of the original cohort. We evaluated the performance of the final models using the Area under the Receiver Operator Characteristic Curve (AUC). Accordingly, six models demonstrated superior predictive performance for both outcomes (four for DM and two for LRR) when compared to both univariate-radiomic feature models and Standard Uptake Value (SUV) measurements. This demonstrated approach suggests that the ability of the pre-radiochemotherapy PET radiomics to stratify patient risk for DM and LRR could potentially guide management decisions such as adjuvant systemic therapy or radiation dose escalation.

Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.

PURPOSE: The purpose of this study was to investigate the potential of computed tomography (CT) based radiomic features of primary tumors to predict pathological nodal involvement in clinically node-negative (N0) peripheral lung adenocarcinomas. METHODS: A total of 187 patients with clinical N0 peripheral lung adenocarcinomas who underwent preoperative CT scan and subsequently received systematic lymph node dissection were retrospectively reviewed. 219 quantitative 3D radiomic features of primary lung tumor were extracted; meanwhile, nine radiological semantic features were evaluated. Univariate and multivariate logistic regression analysis were used to explore the role of these features in predicting pathological nodal involvement. The areas under the ROC curves (AUCs) were compared between multivariate logistic regression models. RESULTS: A total of 153 patients had pathological N0 status and 34 had pathological lymph node metastasis. On univariate analysis, fissure attachment and 17 radiomic features were significantly associated with pathological nodal involvement. Multivariate analysis revealed that semantic features of pleural retraction (P = 0.048) and fissure attachment (P = 0.023) were significant predictors of pathological nodal involvement (AUC = 0.659); and the radiomic feature F185 (Histogram SD Layer 1) (P = 0.0001) was an independent prognostic factor of pathological nodal involvement (AUC = 0.73). A logistic regression model produced from combining radiomic feature and semantic feature showed the highest AUC of 0.758 (95% CI: 0.685-0.831), and the AUC value computed by fivefold cross-validation method was 0.737 (95% CI: 0.73-0.744). CONCLUSIONS: Features derived on primary lung tumor described by semantic and radiomic could provide information of pathological nodal involvement in clinical N0 peripheral lung adenocarcinomas.

Improving malignancy prediction through feature selection informed by nodule size ranges in NLST.

Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected by feature selectors, for making a decision. In this paper, we investigate automated selection of different image features informed by different nodule size ranges to increase the overall accuracy of the classification. The NLST dataset is one of the largest available datasets on CT screening for NSCLC. We used 261 cases as a training dataset and 237 cases as a test dataset. The nodule size, which may indicate biological variability, can vary substantially. For example, in the training set, there are nodules with a diameter of a couple millimeters up to a couple dozen millimeters. The premise is that benign and malignant nodules have different radiomic quantitative descriptors related to size. After splitting training and testing datasets into three subsets based on the longest nodule diameter (LD) parameter accuracy was improved from 74.68% to 81.01% and the AUC improved from 0.69 to 0.79. We show that if AUC is the main factor in choosing parameters then accuracy improved from 72.57% to 77.5% and AUC improved from 0.78 to 0.82. Additionally, we show the impact of an oversampling technique for the minority cancer class. In some particular cases from 0.82 to 0.87.

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.

Predicting Malignant Nodules from Screening CT Scans.

OBJECTIVES: The aim of this study was to determine whether quantitative analyses ("radiomics") of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer. METHODS: Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer. RESULTS: The best models used 23 stable features in a random forests classifier and could predict nodules that would become cancerous 1 and 2 years hence with accuracies of 80% (area under the curve 0.83) and 79% (area under the curve 0.75), respectively. Radiomics outperformed the Lung Imaging Reporting and Data System and volume-only approaches. The performance of the McWilliams risk assessment model was commensurate. CONCLUSIONS: The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.

The GCR1, GPA1, PRN1, NF-Y signal chain mediates both blue light and abscisic acid responses in Arabidopsis.

Different classes of biotic (e.g. plant hormones) and abiotic (e.g. different wavelengths of light) signals act through specific signal transduction mechanisms to coordinate higher plant development. While a great deal of progress has been made, full signal transduction chains have not yet been described for most blue light- or abscisic acid-mediated events. Based on data derived from T-DNA insertion mutants and yeast (Saccharomyces cerevisiae) two-hybrid and coprecipitation assays, we report a signal transduction chain shared by blue light and abscisic acid leading to light-harvesting chlorophyll a/b-binding protein expression in etiolated Arabidopsis (Arabidopsis thaliana) seedlings. The chain consists of GCR1 (the sole Arabidopsis protein coding for a potential G-protein-coupled receptor), GPA1 (the sole Arabidopsis Galpha-subunit), Pirin1 (PRN1; one of four members of an iron-containing subgroup of the cupin superfamily), and a nuclear factor Y heterotrimer comprised of A5, B9, and possibly C9. We also demonstrate that this mechanism is present in imbibed seeds wherein it affects germination rate.

Effects of smoking and gingival inflammation on salivary antioxidant capacity.

AIM: This study evaluated possible effects of smoking and gingival inflammation on salivary antioxidants in gingivitis patients. METHODS: Twenty otherwise healthy gingivitis patients (10 self-reported smokers) and 20 periodontally and systemically healthy volunteer subjects were enrolled in the study. Whole saliva samples and full-mouth clinical periodontal recordings were obtained at baseline and one month following initial phase of treatment in gingivitis patients. Salivary cotinine, glutathione and ascorbic acid concentrations, and total antioxidant capacity were determined, and the data generated were tested by non-parametric tests. RESULTS: Salivary cotinine measurements resulted in re-classification of three self-reported non-smokers as smokers. Smoker patients revealed significantly higher probing depths but lower bleeding values than non-smoker patients (p=0.044 and 0.001, respectively). Significant reductions in clinical recordings were obtained in non-smoker (all p<0.05) and smoker (all p<0.01) patients following periodontal treatment. Salivary total glutathione concentrations were reduced following therapy in gingivitis patients who smoke (p<0.01). Otherwise, no statistically significant differences were found between the groups in biochemical parameters at baseline or following treatment (p>0.05). CONCLUSIONS: Within the limits of this study, neither smoking nor gingival inflammation compromised the antioxidant capacity of saliva in systemically healthy gingivitis patients.