MRI signal intensity differentiation of brainstem encephalitis induced by Enterovirus 71: a classification approach for acute and convalescence stages

Additional file 3. After tick the segment region, Medflovan show the segmentation region of lesion

New Findings, Classification and Long-Term Follow-Up Study Based on MRI Characterization of Brainstem Encephalitis Induced by Enterovirus 71

Background To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease. Methods Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients’ conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study. Results 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration. Conclusions This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level

Physical Model Test on the Deformation Mechanism of Reservoir Bank Slopes With Sand Layers Under Coupled Hydro-Mechanical Conditions

A reservoir area is mostly located in the canyon area, and the geological structure is complex. There are a large number of unstable slopes on the bank of the reservoir. The stability of bank slope is greatly affected by water storage and reservoir water regulation. In addition, sudden rainstorm and other external factors can reduce slope stability. In this article, the physical model test is used to study the seepage field and deformation characteristics of typical reservoir bank slopes with sand layers under different rainfall intensities, different water level fluctuation rates, and their coupling effects. The model has a length of 4.0 m, a width of 1.0 m, and a height of 0.9 m, and the piezometers and white balls are used to monitor the pore water pressures and displacements inside the slope model individually. The results show that the responsiveness of pore water pressure inside the slope lags behind both water level fluctuation and rainfall. The lag time is inversely proportional to the water level fluctuation rates under the single water level fluctuation condition, while it is proportional to water level fluctuation rates in the water level decline stage under the coupling effect condition. The rapid impoundment of the reservoir area has a strengthening effect on the stability of the reservoir bank slope. However, accelerated deformation of the slope occurs in the stage of water level decline, and the deformation rate is proportional to the water level fluctuation rates

YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model’s ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a [email protected] of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the [email protected] of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production

The association between ambient temperature and antimicrobial resistance of Klebsiella pneumoniae in China: a difference-in-differences analysis

IntroductionAntimicrobial resistance (AMR) of Klebsiella pneumoniae (K. pneumoniae) poses a significant global public health threat and is responsible for a high prevalence of infections and mortality. However, knowledge about how ambient temperature influences the AMR of K. pneumoniae is limited in the context of global warming.MethodsAMR data of 31 Chinese provinces was collected from the China Antimicrobial Resistance Surveillance System (CARSS) between 2014 and 2020. Socioeconomic and meteorological data were collected from the China Statistical Yearbook during the same period. A modified difference-in-differences (DID) approach was applied to estimate the association between ambient temperature and third-generation cephalosporin-resistant K. pneumoniae (3GCRKP) and carbapenem-resistant K. pneumoniae (CRKP). Furthermore, moderating effects of socioeconomic factors were also evaluated.ResultsEvery 1°C increase in annual average temperature was associated with a 4.7% (relative risk (RR):1.047, 95% confidence intervals (CI): 1.031–1.082) increase in the detection rate of 3GCRKP, and a 10.7% (RR:1.107, 95% CI: 1.011–1.211) increase in the detection rate of CRKP. The relationships between ambient temperature and 3GCRKP and CRKP were found to be moderated by socioeconomic status (GDP per capita, income per capita, and consumption per capita; the interaction p-values <0.05), where higher economic status was found to strengthen the effects of temperature on the detection rate of 3GCRKP and weaken the effects on the detection rate of CRKP.DiscussionAmbient temperature was found to be positively associated with AMR of K. pneumoniae, and this association was moderated by socioeconomic status. Policymakers should consider the impact of global warming and high temperatures on the spread of 3GCRKP and CRKP when developing strategies for the containment of AMR

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

IntroductionPine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.MethodsTo address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.ResultsThe ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.DiscussionComparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease

GraphScope Flex: LEGO-like Graph Computing Stack

Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced GraphScope as a system addressing a wide array of graph computing needs, including graph traversal, analytics, and learning in one system. Since its inception, GraphScope has achieved significant technological advancements and gained widespread adoption across various industries. However, one key lesson from this journey has been understanding the limitations of a "one-size-fits-all" approach, especially when dealing with the diversity of programming interfaces, applications, and data storage formats in graph computing. In response to these challenges, we present GraphScope Flex, the next iteration of GraphScope. GraphScope Flex is designed to be both resource-efficient and cost-effective, while also providing flexibility and user-friendliness through its LEGO-like modularity. This paper explores the architectural innovations and fundamental design principles of GraphScope Flex, all of which are direct outcomes of the lessons learned during our ongoing development process. We validate the adaptability and efficiency of GraphScope Flex with extensive evaluations on synthetic and real-world datasets. The results show that GraphScope Flex achieves 2.4X throughput and up to 55.7X speedup over other systems on the LDBC Social Network and Graphalytics benchmarks, respectively. Furthermore, GraphScope Flex accomplishes up to a 2,400X performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness

Association between admission-blood-glucose-to-albumin ratio and clinical outcomes in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention

IntroductionIt is unclear whether admission-blood-glucose-to-albumin ratio (AAR) predicts adverse clinical outcomes in patients with ST-segment elevation myocardial infarction (STEMI) who are treated with percutaneous coronary intervention (PCI). Here, we performed a observational study to explore the predictive value of AAR on clinical outcomes.MethodsPatients diagnosed with STEMI who underwent PCI between January 2010 and February 2020 were enrolled in the study. The patients were classified into three groups according to AAR tertile. The primary outcome was in-hospital all-cause mortality, and the secondary outcomes were in-hospital major adverse cardiac events (MACEs), as well as all-cause mortality and MACEs during follow-up. Logistic regression, Kaplan–Meier analysis, and Cox proportional hazard regression were the primary analyses used to estimate outcomes.ResultsAmong the 3,224 enrolled patients, there were 130 cases of in-hospital all-cause mortality (3.9%) and 181 patients (5.4%) experienced MACEs. After adjustment for covariates, multivariate analysis demonstrated that an increase in AAR was associated with an increased risk of in-hospital all-cause mortality [adjusted odds ratio (OR): 2.72, 95% CI: 1.47–5.03, P = 0.001] and MACEs (adjusted OR: 1.91, 95% CI: 1.18–3.10, P = 0.009), as well as long-term all-cause mortality [adjusted hazard ratio (HR): 1.64, 95% CI: 1.19–2.28, P = 0.003] and MACEs (adjusted HR: 1.58, 95% CI: 1.16–2.14, P = 0.003). Receiver operating characteristic (ROC) curve analysis indicated that AAR was an accurate predictor of in-hospital all-cause mortality (AUC = 0.718, 95% CI: 0.675–0.761) and MACEs (AUC = 0.672, 95% CI: 0.631–0.712).DiscussionAAR is a novel and convenient independent predictor of all-cause mortality and MACEs, both in-hospital and long-term, for STEMI patients receiving PCI

LDC7559 inhibits microglial activation and GSDMD-dependent pyroptosis after subarachnoid hemorrhage

Mounting evidence indicates that inhibition of microglial activation and neuronal pyroptosis plays important roles in brain function recovery after subarachnoid hemorrhage (SAH). LDC7559 is a newly discovered gasdermin D (GSDMD) inhibitor. Previous studies have demonstrated that LDC7559 could inhibit microglial proliferation and pyroptosis. However, the beneficial effects of LDC7559 on SAH remain obscure. Based on this background, we investigated the potential role and the mechanism of LDC7559 on SAH-induced brain damage both in vivo and in vitro. The findings revealed that microglial activation and neuronal pyroptosis were evidently increased after SAH, which could be markedly suppressed by LDC7559 both in vivo and in vitro. Meanwhile, LDC7559 treatment reduced neuronal apoptosis and improved behavior function. Mechanistically, LDC7559 decreased the levels of GSDMD and cleaved GSDMD after SAH. In contrast, nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation by nigericin increased GSDMD-mediated pyroptosis and abated the beneficial effects of LDC7559 on SAH-induced brain damage. However, LDC7559 treatment did not significantly affect the expression of NLRP3 after SAH. Taken together, LDC7559 might suppress neuronal pyroptosis and microglial activation after SAH by inhibiting GSDMD, thereby promoting brain functional recovery