Prevalence and characteristics of cannabis-induced toxicoses in pets: Results from a survey of veterinarians in North America

Cannabis legalization in North America has coincided with an increase in reports of cannabis-induced toxicosis in pets, but the magnitude of this problem, as well as outcomes of these incidents remain unknown. Therefore, we examined the frequency, diagnostic criteria, clinical signs, and prognoses of cannabis toxicoses in pets in North America. We conducted an online survey between January, 2021 and April, 2021 targeting veterinarians practicing in Canada and the United States (US). Out of the 251 study participants, 191 practiced in Canada. Cannabis toxicosis was most commonly reported in dogs (n = 226 veterinarians), and the number of toxicosis cases increased significantly in Canada (p\u3c0.0001) and the US (p = 0.002) after October, 2018. Frequently reported clinical signs of cannabis toxicosis included: urinary incontinence (n = 195), disorientation (n = 182), ataxia (n = 178), lethargy (n = 150), hyperesthesia (n = 134), and bradycardia (n = 112). Edibles were most commonly suspected to be the cause of toxicosis (n = 116). The most common route of exposure was ingestion (n = 135), while the most cited reason was ingestion while unattended (n = 135). Cannabis toxicosis was mostly diagnosed using supportive clinical signs (n = 229), the most common treatment was outpatient monitoring (n = 182), and pets were most often treated as out-patients (n = 103). The legalization of cannabis use in Canada and the US is likely an important factor associated with the increased cannabis toxicosis cases in pets; however, the legal status may also increase reporting. The medicinal use of cannabis by pet-owners for pets may also contribute to a portion of the reported toxicoses. Most pets that experienced cannabis toxicosis recovered completely, suggesting that most cannabis toxicoses do not result in long-term ill effects. Even though some deaths (n = 16) were reported in association with cannabis toxicosis, the presence of confounders such as toxins, and underlying conditions cannot be ruled out, emphasizing the need for rigorous controlled laboratory studies to investigate this important issue

Extended Wiener-Khinchin theorem for quantum spectral analysis

The classical Wiener-Khinchin theorem (WKT), which can extract spectral information by classical interferometers through Fourier transform, is a fundamental theorem used in many disciplines. However, there is still need for a quantum version of WKT, which could connect correlated biphoton spectral information by quantum interferometers. Here, we extend the classical WKT to its quantum counterpart, i.e., extended WKT (e-WKT), which is based on two-photon quantum interferometry. According to the e-WKT, the difference-frequency distribution of the biphoton wavefunctions can be extracted by applying a Fourier transform on the time-domain Hong-Ou-Mandel interference (HOMI) patterns, while the sum-frequency distribution can be extracted by applying a Fourier transform on the time-domain NOON state interference (NOONI) patterns. We also experimentally verified the WKT and e-WKT in a Mach-Zehnder interference (MZI), a HOMI and a NOONI. This theorem can be directly applied to quantum spectroscopy, where the spectral correlation information of biphotons can be obtained from time-domain quantum interferences by Fourier transform. This may open a new pathway for the study of light-matter interaction at the single photon level.Comment: 13 pages, 5 figure

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

ObjectiveTelomerase reverse transcriptase (TERT) promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.MethodsThis was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3,948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 gray level run length matrix, 14 gray level dependence matrix, 16 gray level run length matrix, 16 gray level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.ResultsAmong the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.ConclusionThe LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models

Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma

ObjectiveTo investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).MethodsWe retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively.ResultsThe random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828–0.956) and 0.824 (95% CI: 0.677–0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group.ConclusionThe radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities

Index System Research on Environmental Impact Assessment of Ecological Project in Xishui River

The problems were analyzed about the environmental impact in the construction projects of water conservancy in China. Some relevant data and relevant guidelines were combined with the actual work which were referred to several environmental impact assessment reports. An index system was proposed about environmental impact assessment of ecological improvement project in Xishui River

Exploration and Innovation of Village Planning Compilation Based on the Background of Land Space Planning

Compilation work of land space planning at village level is important point in promoting the strategy of rural revitalization and the key for specific implementation of land space planning. Based on analyzing the existing problems in the planning at village level in China, overall guidelines and specific measures of land space planning compilation at village level are proposed, and some thoughts and suggestions on how to perfect its legal status, considering regional characteristics and valuing landscape design are put forward

Index System Research on Environmental Impact Assessment of Ecological Project in Xishui River

The problems were analyzed about the environmental impact in the construction projects of water conservancy in China. Some relevant data and relevant guidelines were combined with the actual work which were referred to several environmental impact assessment reports. An index system was proposed about environmental impact assessment of ecological improvement project in Xishui River

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Abstract Background and purpose The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients. Methods A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10‐fold cross validation was applied to the training dataset. Results A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations. Conclusions Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM

High-strength and high-toughness polyimide nanofibers: Synthesis and characterization

High-strength and high-toughness nanofibers were made from polyimide 6F-PI through electrospinning. The 6F-PI had a backbone made up with 3,3′,4, 4′-biphenyl-tetracarboxylic dianhydride and 2,2-bis[4-(4-aminophenoxy)phenyl]-hexafluoro-propane residues. Electrospun 6F-PI precursor nanofibers were collected in the form of aligned fiber sheet on the rim of a rotating disc. Heating process converted the precursor fiber sheets to 6F-PI nanofiber sheets. Gel permeation chromatography and Ostwald Viscometer were used to determine the molecular weight and the molecular weight distribution of the 6F-PI precursor, i.e., the 6F-polyamic acid. Scanning electron microscopy, infrared spectroscopy, X-ray scattering, tensile testing, dynamic mechanical analysis, thermogravimetric analysis, and differential scanning calorimetry were employed to characterize the surface morphology, thermal stability, and mechanical properties of the 6F-PI nanofiber sheets. Experimental results show that the nanofibers were well aligned in the sheets with fiber diameters ranging from 50 to 300 nm. The nanofiber sheets were stable to over 450°C, with a glass transition at 265.2°C. The uniaxial tension test showed that the 6F-PI nanofiber sheets had superior mechanical properties. The ultimate tensile strength, modulus, toughness, and elongation to break of the 6F-PI nanofiber sheets are respectively, 308 ± 14 MPa, 2.08 ± 0.25 GPa, 365 ± 20 MPa, and 202 ± 7%. It is expected that electrospun PI nanofibers with such high toughness and high ultimate tensile strength can find applications in high-performance textiles and composites, for example