COVID-19 prevention measures reduce dengue spread in Yunnan Province, China, but do not reduce established outbreak.

ABSTRACTThe COVID-19 pandemic and measures against it provided a unique opportunity to understand the transmission of other infectious diseases and to evaluate the efficacy of COVID-19 prevention measures on them. Here we show a dengue epidemic in Yunnan, China, during the pandemic of COVID-19 was dramatically reduced compared to non-pandemic years and, importantly, spread was confined to only one city, Ruili. Three key features characterized this dengue outbreak: (i) the urban-to-suburban spread was efficiently blocked; (ii) the scale of epidemic in urban region was less affected; (iii) co-circulation of multiple strains was attenuated. These results suggested that countermeasures taken during COVID-19 pandemic are efficient to prevent dengue transmission between cities and from urban to suburban, as well to reduce the co-circulation of multiple serotypes or genotypes. Nevertheless, as revealed by the spatial analysis, once the dengue outbreak was established, its distribution was very stable and resistant to measures against COVID-19, implying the possibility to develop a precise prediction method.

[Advances in researches of serotype 2 novel oral polio vaccine].

In April 2016, the Global Polio Eradication Initiative (GPEI) adjusted its polio vaccination strategy, converting trivalent oral polio vaccine (tOPV) into bivalent oral polio vaccine (bOPV), and withdrawing type 2 oral polio vaccine (OPV2) globally. However, after the withdrawal of OPV2, there were many outbreaks of type-2 circulating vaccine-derived poliovirus (cVDPV2) in Asia and Africa. In order to eradicate poliovirus completely, GPEI launched the research and development of the novel serotype 2 oral polio vaccine (nOPV2) in 2010 and considering whether it is necessary to reuse OPV. This paper summarizes the epidemiological situation of cVDPV2 before and after OPV2's withdrawal, the related factors affecting the reuse of OPV and the related research progress of nOPV2.

[Risk factors analysis of acute kidney injury in patients with exertional heatstroke].

Objective: To explore the risk factors of acute renal injury (AKI) in exertional heat radiation disease (EHS) . Methods: In november 2019, the clinical data of 69 EHS patients admitted from July 2015 to September 2019 were reviewed. The general data, laboratory indexes, Glasgow score (GCS) at admission, 24-hour acute physiology and chronic health score Ⅱ (APACHE Ⅱ) , exposure time rate and physical labor intensity were collected. According to the occurrence of AKI, the patients were divided into AKI group and non-AKI group, 31 and 38 in each group. The differences of general data and laboratory indexes between the two groups were compared, and the t and Mann-Whitney U test were used to compare the two groups. The enumeration data are expressed by examples and constituent ratio (%) . Independent sample χ(2) test is used for inter-group comparison, and multiple test is used for multi-sample comparison. The correlation was analyzed by linear regression. Risk factors were analyzed by Logistic regression analysis. Results: At discharge, 31 of 69 EHS patients developed AKI. Compared with the non-AKI group, the heart rate, white blood cell count, lactic acid, D-dimer and myoglobin were higher; MAP, platelet count and PH were lower in the AKI group. The difference was statistically significant (P<0.05) . APACHE Ⅱ score, core temperature, time to drop to 38.5 ℃, contact time rate, platelet count, pH, lactic acid, D-dimer and myoglobin were all correlated with creatinine (r=0.57, 0.42, 0.80, 0.78, 0.57, 0.43, 0.51, 0.55, 0.79) . APACHE Ⅱ score, time to drop to 38.5C, Lac and MYO are the risk factors of AKI in EHS patients. Multivariate Logistic regression analysis showed that the time required to drop to 38.5C was an independent risk factor for the occurrence of AKI. Conclusion: AKI is a serious complication of EHS. EHS complicated with AKI, should be identified early and effective intervention measures should be taken.

[Comparison of different critical care scoring systems in prognosis evaluation of heat stroke].

Objective: To evaluate the prognostic value of different critical care scoring systems in 28-day survival rate of patients with heat stroke. Methods: A retrospective analysis was conducted on the clinical data of 71 patients with heat stroke admitted to the department of emergency medicine of Beijing Luhe Hospital. Capital Medical University from July 2015 to September 2018. The general information and the worst values of vital signs and related pathophysiological indicators within 24 hours were collected and the sequential organ failure assessment (SOFA) , multiple organ dysfunction (MODS) , simplified acute physiological scoreⅡ (SAPS Ⅱ) and acute physiology and chronic health evaluationⅡ (APACHE Ⅱ) were calculated. The patients were divided into the survival group (n=45) and the non-survival group (n=26) according to 28-day prognosis, and the clinical data and scores of the two groups were compared.The ROC curve was drawn to analyze the evaluation value of each scoring system on the survival rate of patients at 28-day. Kaplan-Meier method was used to plot the survival curve of patients. Results: There were no significant differences in age, sex, vital signs and laboratory parameters between two groups (P>0.05) . In non-survival patients, SOFA, SAPS Ⅱ, APACHE Ⅱ scores were significantly elevated in the survival group (P<0.05) . ROC curve analysis showed that the area under ROC curve (AUC) of SOFA score for predicting 28-day survival rate was the highest, which was significantly higher than the APACHE Ⅱ, SAPS Ⅱ, MODS score. When the best cut-off value of SOFA score was 9.0, the sensitivity was 84.6%, and the specificity was 71.1%. Kaplan-Meier survival analysis showed that 28-day survival rate after hospital discharge in patients with SOFA score<9 (n=27) was significantly higher than that in patients with SOFA score ≥9.0 (χ(2)=1.0, P<0.01) . Conclusion: SOFA, APACHE Ⅱ, SAPS Ⅱ on admission have been proved to have good prognostic ability to predict 28-day prognosis in heat stroke patients. Among them, SOFA score system has more accurate prediction value.

[Prognostic significance of combined TERT and IDH gene mutation analysis in diffusely infiltrating gliomas].

Objective: To investigate the status and prognostic significance of TERT and IDH1/2 genes mutations in diffusely infiltrating gliomas. Methods: Hot spot mutations of TERT and IDH1/2 genes were detected by DNA sequencing in 236 cases of gliomas at West China Hospital from 2012 to 2016, including pilocytic astrocytoma (WHO grade Ⅰ, 16 cases), diffuse astrocytoma and oligodendroglioma (WHO grade Ⅱ, 89 cases), anaplastic astrocytoma and oligodendroglioma (WHO grade Ⅲ, 72 cases) and glioblastoma (WHO grade Ⅳ, 59 cases). The prognostic significance of TERT and IDH1/2 hot spot mutations was evaluated. Results: No IDH or TERT mutations were detected in pilocytic gliomas. TERT promoter mutation frequency was higher in patients aged ≥40 years(60.8%, 93/153) than in patients aged <40 years (32.8%, 22/67; P<0.01). TERT promoter mutation rate was also significantly higher in oligodendroglioma (87.5% , 56/64) than that in astrocytoma(37.8%, 59/156; P<0.01). Young age (<40 years), oligodendroglioma and IDH1 mutation were favorable prognostic factors for diffusely infiltrating astrocytic and oligodendroglial tumors. TERT mutation alone was not of prognostic significance. Diffusely infiltrating astrocytic and oligodendroglial tumors were divided into four molecular subtypes according to TERT and IDH1 mutation status: IDH(+ )/TERT(+ ), IDH(+ )/TERT(-), IDH(-)/TERT(-) and IDH(-)/TERT(+ ). There was significant prognostic difference among the 4 subtypes. Conclusions: Combined IDH and TERT gene mutation analysis may be useful for prognostic subgrouping. Notably, IDH1 wild-type cases can be further subdivided into TERT(+ ) or (-) subgroups with significant prognostic difference.

Correction: Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs.

[This corrects the article DOI: 10.1371/journal.pone.0172111.].

Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs.

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.

Preparation of vesicular stomatitis virus pseudotype with Chikungunya virus envelope protein.

Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus that causes Chikungunya fever (CHIKF) in millions of people mainly in developing countries. CHIKF is characterized by high fever, fatigue, headache, nausea, vomiting, rash, myalgia and severe arthralgia. To date, there is no specific treatment and no licensed vaccine against CHIKV infection. In this study, we developed a safe, efficient and easy neutralization assay of CHIKV based on vesicular stomatitis virus (VSV) pseudotype with CHIKV envelope protein and the green fluorescent protein (GFP) or luciferase as reporter gene, which could be used under a reduced safety level. The VSV pseudotype can be applied to the epidemic survey by measuring the expression of GFP or luciferase activity in infected cells. This system can also be used to study the mechanisms of virus entry.

Complex extreme learning machine applications in terahertz pulsed signals feature sets.

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Implementation and community involvement in DOTS strategy: a systematic review of studies in China.

OBJECTIVE: To summarise data on the implementation of the DOTS strategy in China in terms of actual observation and treatment adherence, and to review the effectiveness of quality improvement interventions for tuberculosis (TB) control in China. DESIGN: We included survey studies that reported data on the implementation of DOTS in China and controlled studies that evaluated TB care in specified communities. We excluded studies outside mainland China, pharmacological intervention trials and reviews. RESULTS: We included 12 survey studies that reported on the performance of TB control services in China. The pooled analysis showed that more than half of TB patients were treated by self-administration (52%) and that only 20% actually had their treatment observed by health workers. We include 85 intervention studies that evaluated the effect of quality improvement interventions. Treatment observers were family members in 37 studies, and health workers in 20 studies. The pooled odds ratio (OR) for cure was 2.48 (95%CI 1.97-3.11, I(2) = 70.9%, P < 0.001); the pooled OR for treatment completion was 2.87 (95%CI 2.23-3.69, I(2) = 66.3%, P < 0.001). Sensitivity analysis found that the estimated treatment effects in meta-analyses using reported and imputed data were much reduced, but still statistically significant. CONCLUSION: The proportion of TB patients whose treatment was strictly observed was much lower than reported by official statistics in China. The treatment completion rate was not optimal, which may be an important reason for the reported increases in drug resistance. Community health personnel have become the main work force for TB control in China.

Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.

It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a model-based method for the restoration of MRIs. The reduced-order model, in which a full-system-response is projected onto a subspace of lower dimensionality, has been used to accelerate image reconstruction by reducing the size of the involved linear system. In this paper, the singular value threshold (SVT) technique is applied as a denoising scheme to reduce and select the model order of the inverse Fourier transform image, and to restore multi-slice breast MRIs that have been compressively sampled in k-space. The restored MRIs with SVT for denoising show reduced sampling errors compared to the direct MRI restoration methods via spatial FD, or DCT. Compressive sampling is a technique for finding sparse solutions to underdetermined linear systems. The sparsity that is implicit in MRIs is to explore the solution to MRI reconstruction after transformation from significantly undersampled k-space. The challenge, however, is that, since some incoherent artifacts result from the random undersampling, noise-like interference is added to the image with sparse representation. These recovery algorithms in the literature are not capable of fully removing the artifacts. It is necessary to introduce a denoising procedure to improve the quality of image recovery. This paper applies a singular value threshold algorithm to reduce the model order of image basis functions, which allows further improvement of the quality of image reconstruction with removal of noise artifacts. The principle of the denoising scheme is to reconstruct the sparse MRI matrices optimally with a lower rank via selecting smaller number of dominant singular values. The singular value threshold algorithm is performed by minimizing the nuclear norm of difference between the sampled image and the recovered image. It has been illustrated that this algorithm improves the ability of previous image reconstruction algorithms to remove noise artifacts while significantly improving the quality of MRI recovery.

Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging.

In this article, we present a novel approach to localize anatomical features-breast costal cartilage-in dynamic contrast-enhanced MRI using level sets. Current breast MRI diagnosis involves magnetic-resonance compatible needles for localization. However, if the breast costal cartilage structure can be used as an alternative to the MR needle, this will not only assist in avoiding invasive procedures, but will also facilitate monitoring of the movement of breasts caused by cardiac and respiratory motion. This article represents a novel algorithm for achieving reliable detection and extraction of costal cartilage structures, which can be used for the analysis of motion artifacts, with possible shape variations of the structure caused by uptake of contrast agent, as well as a potential for the registration of breast. The algorithm represented in this article is to extract volume features from post-contrast MR images at three different time slices for the analysis of motion artifacts, and we validate the current algorithm according to the anatomic structure. This utilizes the level-set method for the size selection of the region of interest. The variable shape of contours acquired from a level-set-based segment image actually determines the feature region of interest, which is used as a guide to achieve initial masks for feature extraction. Following this, the algorithm uses a K-means method for classification of the feature regions from other types of tissue and morphological operations with a choice of an appropriate structuring element to achieve reliable masks and extraction of features. The segments of features can be therefore obtained with the application of extracted masks for subsequent motion analysis of breast and for potential registration purposes.

Tensor based sparse decomposition of 3D shape for visual detection of mirror symmetry.

This study explores an approach for analysing the mirror (reflective) symmetry of 3D shapes with tensor based sparse decomposition. The approach combines non-negative tensor decomposition and directional texture synthesis, with symmetry information about 3D shapes that is represented by 2D textures synthesised from sparse, decomposed images. This technique requires the center of mass of 3D objects to be at the origin of the coordinate system. The decomposition of 3D shapes and analysis of their symmetry are useful for image compression, pattern recognition, as well as there being an emerging interest in the medical community due to its potential to find morphological changes between healthy and pathological structures. This paper postulates that sparse texture synthesis can be used to describe the decomposed basis images acting as symmetry descriptors for a 3D shape. We apply the theory of non-negative tensor decomposition and sparse texture synthesis, deduce the new representation, and show some application examples.

Enhanced T-ray signal classification using wavelet preprocessing.

This study demonstrates the application of one-dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. Fast Fourier transforms (FFTs) are used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. Soft threshold wavelet shrinkage de-noising is used and plays an important role in de-noising and reconstruction of T-ray pulsed signals. An iterative algorithm is applied to obtain three optimal frequency components and to achieve preferred classification performance.

[Pharmacokinetic-pharmacodynamic modeling of metoprolol enantiomers in the dog].

The PK-PD model of (+)-Met and (-)-Met was studied in anesthetized dogs. The plasma drug concentration-time profiles were most adequately described by two compartment model. Significant differences of Vd/F, CLs/F and AUC between (+)-Met and (-)-Met were observed. The peak times of plasma (+)-Met and (-)-Met concentration in dogs were 24 +/- 5 and 30 +/- 5 min, respectively. But the peak times of drug inhibitory effects on Vmax, dp/dtmax, LVSP, SBP and HR were about 90-120 min, showing the hysteresis loops. When using the effect compartment model, the counterclockwise hysteresis collapsed and the relationships between the effects and concentration in effect compartment were fit by using Sigmoid-Emax model. The Ce50 values of inhibitory effects on Vmax, dp/dtmax and HR of (+)-Met were 250 +/- 80, 450 +/- 210, 520 +/- 210 micrograms.L-1 and those of (-)-Met were 70 +/- 30, 70 +/- 40 and 82 +/- 27 micrograms.L-1, respectively. Significant differences of Ce50 of (+)-Met and (-)-Met were found. The values of Ce50+/Ce50- were 3.7, 6.8 and 6.3, indicating that the inhibitory effects on Vmax, dp/dtmax and HR of (-)-Met were stronger than those of (+)-Met in dogs.

Pharmacokinetic-pharmacodynamic modeling of metoprolol stereoisomers in spontaneously hypertensive rat.

AIM: To study the combined pharmacokinetic-pharmacodynamic (PK-PD) model of metoprolol stereoisomers, and compare their inhibitory effects on cardiovascular system in the spontaneously hypertensive rats (SHR). METHODS: The drug concentration in plasma was measured by the reversed phase HPLC and the drug effects were recorded by polygraph. The pharmacokinetic parameters and the PK-PD model parameters were calculated. RESULTS: The plasma concentration-time profiles were adequately described by two-compartment model. Differences of Vd between (+)-Met and (-)-Met were found. The relationships between effects and concentration of effect compartment were represented by the sigmoid-Emax model. The Css50 of Vmax, dp/dtmax, and HR inhibitory effects of (+)-Met were larger than those of (-)-Met. CONCLUSION: Stereo-selective drug distribution and different potencies of the inhibitory effects of (+)-Met and (-)-Met existed in SHR.

Effects of anticholinergic drugs on rabbit efferent phrenic discharges.

In conscious, vagotomized, curarized, and artificially-ventilated rabbits, the efferent phrenic discharges were recorded. When scopolamine, atropine, pirenzepine or AF-DX 116 (11-2[[2-[(diethylamino)methyl]-1-piperidinyl]acetyl]-5,11-dihydro- 6H[2,3-6] [1,4]benzodiazepine-6-one) was injected into the cerebello-medullary cistern, the frequency and voltage of phrenic discharges were decreased (P less than 0.05) by scopolamine (0.5 mg.kg-1) and pirenzepine (0.5 mg.kg-1), but were increased (P less than 0.01) by atropine (0.05 mg.kg-1) and AF-DX 116 (0.1 mg.kg-1). It is probable that scopolamine inhibits the respiratory center by blocking the M1 cholinergic receptors while atropine excites the respiratory center blocking the M2 cholinergic receptors.