Recurrent exercise-induced acute kidney injury by idiopathic renal hypouricemia with a novel mutation in the SLC2A9 gene and literature review

OBJETIVO: Comparar a sensibilidade do método de difusão em ágar e do método de extração utilizando as linhagens celulares RC-IAL (células fibroblásticas de rim de coelho) e HeLa (células epiteliais de carcinoma do colo do útero humano), na avaliação da citotoxicidade "in vitro" de materiais de uso médico-hospitalar. MATERIAL E MÉTODO: Foram testadas 50 amostras escolhidas por sorteio, entre as já conhecidamente positivas e negativas e identificadas como: algodão, espuma, borracha, látex, celulose e acrílico. Além, das amostras citadas foram testadas experimentalmente várias concentrações de SDS (duodecil sulfato de sódio) nas culturas celulares RC-IAL e HeLa. RESULTADOS: Das 50 amostras testadas , 44 (88%) foram positivas para os dois métodos. Mas quando comparado o SDS nos dois métodos foram observados resultados positivos nas concentrações de 0,5 a 0,05 µg/ml no método de difusão em ágar e no método de extração somente foi observado efeito citotóxico até a concentração de 0,25 µg/ml. CONCLUSÃO: Os resultados encontrados são similares aos observados por outros autores que testaram materiais como, por exemplo, ligas metálicas. Quando foi usado o SDS observou-se, nas duas linhagens celulares, diferenças favoráveis ao método de difusão em ágar em duas concentrações, isto é, a sensibilidade deste método foi significantemente maior, por inspecção, em relação ao método de extração, além de se constituir em método mais simples de ser realizado

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids

Identification and characterization of novel amphioxus microRNAs by Solexa sequencing

An analysis of amphioxus miRNAs suggests an expansion of miRNAs played a key role in the evolution of chordates to vertebrate

Modeling and Experimental Testing of an Unmanned Surface Vehicle with Rudderless Double Thrusters.

Motion control of unmanned surface vehicles (USVs) is a crucial issue in sailing performance and navigation costs. The actuators of USVs currently available are mostly a combination of thrusters and rudders. The modeling for USVs with rudderless double thrusters is rarely studied. In this paper, the three degrees of freedom (DOFs) dynamic model and propeller thrust model of this kind of USV were derived and combined. The unknown parameters of the propeller thrust model were reduced from six to two. In the three-DOF model, the propulsion of the USV was completely provided by the resultant force generated by double thrusters and the rotational moment was related to the differential thrust. It combined the propeller thrust model to represent the thrust in more detail. We performed a series of tests for a 1.5 m long, 50 kg USV, in order to obtain the model parameters through system identification. Then, the accuracy of the modeling and identification results was verified by experimental testing. Finally, based on the established model and the proportional derivative+line of sight (PD+LOS) control algorithm, the path-following control of the USV was achieved through simulations and experiments. All these demonstrated the validity and practical value of the established model

Prognostic value of N-terminal Pro–B-Type natriuretic peptide in patients with intermediate coronary lesions

BackgroundThe optimal treatment strategy for patients with coronary intermediate lesions, defined as diameter stenosis of 50–70%, remains a great challenge for cardiologists. Identification of potential biomarkers predictive of major adverse cardiovascular events (MACEs) risk may assist in risk stratification and clinical decision.MethodsA total of 1,187 patients with intermediate coronary lesions and available N-terminal pro-brain natriuretic peptide (NT-proBNP) levels were enrolled in the current study. A baseline NT-proBNP level was obtained. The primary endpoint was defined as MACEs, the composite endpoint of all-cause death and non-fatal myocardial infarction. A multivariate Cox regression model was used to explore the association between NT-proBNP level and MACE risk.ResultsThe mean age of the study cohort was 59.2 years. A total of 68 patients experienced MACE during a median follow-up of 6.1 years. Restricted cubic spline analysis delineated a linear relationship between the baseline NT-proBNP level and MACE risk. Both univariate and multivariate analyses demonstrated that an increased NT-proBNP level was associated with an increased risk of MACE [adjusted hazard ratio (HR) per doubling: 1.412, 95% confidence interval (CI): 1.022–1.952, p = 0.0365]. This association remains consistent in clinical meaningful subgroups according to age, sex, body mass index (BMI), and diabetes.ConclusionAn increased NT-proBNP level is associated with an increased risk of MACE in patients with intermediate coronary lesions and may serve as the potential biomarker for risk stratification and treatment decision guidance

Architecture Support for Customizable Domain-Specific Computing

This dissertation investigates the power-efficient high-performance architecture support for customizable domain-specific computing at both memory and communication levels in a customizable heterogeneous platform (CHP).In domain-specific computing, the memory access pattern can be obtained through offline analysis. With this knowledge, the cores and the accelerators in the CHP can use on-chip scratchpad memory (SPM) and buffers to directly manage the data replacement in order to save off-chip memory bandwidth. We propose efficient schemes to hybrid the SPM and primary caches, and to also hybrid buffers and the shared last-level cache (LLC). In the hybrid primary cache, due to its low associativity, the problem of balancing the cache set utilization when the SPM is allocated in the cache is critical. We propose an adaptive hybrid cache (AH-Cache) to dynamically remap SPM blocks from high-demand cache sets to low-demand cache sets. In the hybrid LLC (typically designed as a nonuniform cache architecture, NUCA), the problem of resource contention and fragmentation becomes crucial. We propose a buffer-in-NUCA (BiN) scheme to assign shared buffer spaces to accelerators that can best utilize the additional buffer space, and use flexible paged buffer allocation to limit the impact of buffer fragmentation.In domain-specific computing, the communication pattern can be also obtained through offline analysis. With this knowledge, the topology and routing scheme in the CHP communication subsystem can be customized to dynamically adapt to the known communication pattern. For the topology customization, we propose application-specific shortcuts and multicast realized by radio frequency interconnects (RF-I) overlaid network-on-chip (NoC). At runtime, we can flexibly allocate RF-I bandwidth to adapt the NoC topology to the known communication requirement of an application. For the routing customization, we propose an power-efficient application-specific cycle elimination and splitting (ACES) routing scheme to avoid restricting the critical routes of an application while achieving deadlock-free for irregular NoCs.To further demonstrate the feasibility and effectiveness of these techniques, we develop a FPGA prototype of the proposed CHP with shared accelerators and buffers. The buffer sharing is achieved through a cost-efficient partial-crossbar to reduce the sharing overhead on timing and area

Architecture Support for Customizable Domain-Specific Computing

This dissertation investigates the power-efficient high-performance architecture support for customizable domain-specific computing at both memory and communication levels in a customizable heterogeneous platform (CHP).In domain-specific computing, the memory access pattern can be obtained through offline analysis. With this knowledge, the cores and the accelerators in the CHP can use on-chip scratchpad memory (SPM) and buffers to directly manage the data replacement in order to save off-chip memory bandwidth. We propose efficient schemes to hybrid the SPM and primary caches, and to also hybrid buffers and the shared last-level cache (LLC). In the hybrid primary cache, due to its low associativity, the problem of balancing the cache set utilization when the SPM is allocated in the cache is critical. We propose an adaptive hybrid cache (AH-Cache) to dynamically remap SPM blocks from high-demand cache sets to low-demand cache sets. In the hybrid LLC (typically designed as a nonuniform cache architecture, NUCA), the problem of resource contention and fragmentation becomes crucial. We propose a buffer-in-NUCA (BiN) scheme to assign shared buffer spaces to accelerators that can best utilize the additional buffer space, and use flexible paged buffer allocation to limit the impact of buffer fragmentation.In domain-specific computing, the communication pattern can be also obtained through offline analysis. With this knowledge, the topology and routing scheme in the CHP communication subsystem can be customized to dynamically adapt to the known communication pattern. For the topology customization, we propose application-specific shortcuts and multicast realized by radio frequency interconnects (RF-I) overlaid network-on-chip (NoC). At runtime, we can flexibly allocate RF-I bandwidth to adapt the NoC topology to the known communication requirement of an application. For the routing customization, we propose an power-efficient application-specific cycle elimination and splitting (ACES) routing scheme to avoid restricting the critical routes of an application while achieving deadlock-free for irregular NoCs.To further demonstrate the feasibility and effectiveness of these techniques, we develop a FPGA prototype of the proposed CHP with shared accelerators and buffers. The buffer sharing is achieved through a cost-efficient partial-crossbar to reduce the sharing overhead on timing and area

An Evacuation Route Model of Crowd Based on Emotion and Geodesic

Making unconventional emergent plan for dense crowd is one of the critical issues of evacuation simulations. In order to make the behavior of crowd more believable, we present a real-time evacuation route approach based on emotion and geodesic under the influence of individual emotion and multi-hazard circumstances. The proposed emotion model can reflect the dynamic process of individual in group on three factors: individual emotion, perilous field, and crowd emotion. Specifically, we first convert the evacuation scene to Delaunay triangulation representations. Then, we use the optimization-driven geodesic approach to calculate the best evacuation path with user-specified geometric constraints, such as crowd density, obstacle information, and perilous field. Finally, the Smooth Particle Hydrodynamics method is used for local avoidance of collisions with nearby agents in real-time simulation. Extensive experimental results show that our algorithm is efficient and well suited for real-time simulations of crowd evacuation