虚拟现实情景互动训练在养老院阿尔茨海默病患者康复治疗中的应用

目的 探讨虚拟现实(virtual reality,VR)情景互动训练应用在养老院阿尔茨海默病(Alzheimer's disease,AD)患者康复治疗中的效果.方法 将56例养老院AD患者随机分为观察组和对照组,各28例.对照组给予养老院常规养老护理服务和健康宣教,观察组在对照组的基础上给予VR情景互动训练进行干预...     

鼓膜置管术治疗分泌性中耳炎

目的探讨分泌性中耳炎的治疗方法。方法应用鼻内窥镜镜对106例(116耳)患者采用鼓膜切开置入硅胶管方法治疗。结果106例患者,治愈78例,好转18例,总有效率达90.6%。结论鼓膜置管法治疗分泌性中耳炎效果良好。     

厚朴酚对大鼠坏死性胰腺炎肺损伤的保护作用

目的:观察厚朴酚(magnolol,Mag)对大鼠急性坏死性胰腺炎(ANP)肺组织中核因子-kB(NF-kB)、白介素-10(IL-10)和肿瘤坏死因子(TNF-a)表达的影响,以探讨厚朴酚对ANP急性肺损伤的保护作用.方法:以50g/L牛磺胆酸钠逆行胆胰管注射制备大鼠ANP模型,SD大鼠56只随机分为假手术(SO)组,ANP模型组,及Mag治疗组.以荧光定量PCR检测不同时间(6,12,24h)肺组织中NF-kB、TNF-a和IL-10的基因表达,同时检测各组血清淀粉酶(Amy)的水平,并观察胰腺和肺的组织病理学改变.结果:与SO组相比,ANP组肺组织中的NF-kB,TNF-a,IL-10mRNA表达量及血清Amy显著增加(P<0.01),其中NF-kB,TNF-amRNA及Amy以12h的升高最明显(P<0.01),IL-10mRNA随ANP时间的进展表达逐渐增加(6,12,24h分别为0.20±0.05,0.27±0.07,0.45±0.20).与ANP各组比较,Mag各组NF-kBmRNA,TNF-amRNA及Amy表达量下降,也以12h下降最显著(分别为1.30±0.14vs1.84±0.56,0.41±0.19vs0.72±0.36,P<0.05;104576±24886nkat/Lvs188621±23747nkat/L,P<0.01),IL-10mRNA的改变无统计学意义(P>0.05).Mag治疗组肺组织学改变较ANP组明显减轻,胰腺组织学损害无明显改善.结论:NF-kB和TNF-a是ANP发生急性肺损伤的重要炎症因子,Mag能抑制其在ANP大鼠肺组织中的表达,对ANP合并急性肺损伤有一定的治疗作用.     

Data-independent acquisition-based proteomics analysis correlating type 2 diabetes mellitus with osteoarthritis in total knee arthroplasty patients

Background:To explore the effects of type 2 diabetes mellitus (T2DM) on osteoarthritis (OA), 12 bone tissue samples were obtained surgically from the human total knee arthroplasty patients and analyzed by quantitative proteomics.Methods:Based on patient clinical histories, patient samples were assigned to diabetes mellitus osteoarthritis (DMOA) and OA groups. A data-independent acquisition method for data collection was used with proteomic data analysis to assess intergroup proteomic differences. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis were used to further find the correlation between T2DM and OA.Results:GO functional analysis found 153 differentially expressed proteins between DMOA and OA groups, of which 92 differentially expressed proteins were significantly up-regulated and 61 were significantly down-regulated. Kyoto Encyclopedia of Genes and Genome pathway analysis found 180 pathways, including 9 pathways significantly enriched. Further data analysis revealed that 6 signaling pathways were closely associated with T2DM and OA.Conclusion:OA and DMOA onset and progression were closely related to synthesis and metabolism of extracellular matrix components (e.g., fibronectin, decorin, etc.). The effects of T2DM on OA occur though 2 major ways of oxidative stress and low-grade chronic inflammation, involving in 2 inhibited signaling pathways and 4 activated signaling pathways.     

Emergency department presentation changes due to the coronavirus disease pandemic in Nova Scotia,Canada

<正>Multiple studies have reported decreased emergency department(ED) patient volumes during the coronavirus disease(COVID-19) pandemic,^[1-6] including areas most affected by the virus.^[7]Most existing studies have investigated general trends in ED presentations and have not examined the impact of COVID-19 on different types of EDs, specific ED patient groups, or illness presentations.     

低氧促进P19细胞向多巴胺能神经元分化

目的观察低氧对P19细胞神经分化的影响,针对其向多巴胺能神经元分化的现象及机制进行探讨。方法实验分为常氧组(20%O2)和低氧组(3%O2,每天低氧10 m in)。对诱导分化的神经元采用免疫细胞化学染色方法鉴定。用流式细胞术及W estern b lot检测多巴胺能神经元,高效液相色谱法测定分泌的多巴胺。用RT-PCR技术检测低氧诱导因子HIF-1αmRNA水平。结果①在分化的P19细胞中,低氧组神经元含量高于常氧组(P<0.05),低氧组多巴胺能神经元含量及所分泌的多巴胺显著高于常氧组(P<0.001);②低氧组诱导期HIF-1αmRNA表达水平明显高于常氧组。结论低氧可以促进P19细胞的神经分化,尤其促进P19细胞向多巴胺能神经元分化,HIF-1α可能在其中起了一定作用。     

LS‐106, a novel EGFR inhibitor targeting C797S,exhibits antitumor activities both in vitro and in vivo

With the wide clinical use of the third‐generation epidermal growth factor receptor (EGFR) inhibitor osimertinib for the treatment of EGFR‐mutated non–small cell lung cancer (NSCLC), acquired resistance caused by EGFR C797S tertiary mutation has become a concern. Therefore, fourth‐generation EGFR inhibitors that could overcome this mutation have gained increasing attention in recent years. Here, we identified LS‐106 as a novel EGFR inhibitor against C797S mutation and evaluated its antitumor activity both in vitro and in vivo. In cell‐free assay, LS‐106 potently inhibited the kinase activities of EGFR^{19del/T790M/C797S} and EGFR^{L858R/T790M/C797S} with IC₅₀ values of 2.4 nmol/L and 3.1 nmol/L, respectively, which was more potent than osimertinib. Meanwhile, LS‐106 exhibited comparable kinase inhibitory effect to osimertinib on EGFR^L858R/T790M and wild‐type EGFR. Results from cellular experiments demonstrated that LS‐106 potently blocked the phosphorylation of EGFR C797S triple mutations in the constructed BaF3 cells that highly expressed EGFR^{19del/T790M/C797S} or EGFR^{L858R/T790M/C797S}, and thus inhibited the proliferation of these cells. We also constructed tumor cells harboring EGFR^{19del/T790M/C797S} (named PC‐9‐OR cells) using the CRISPR/Cas9 system and found that LS‐106 markedly suppressed the activation of EGFR^{19del/T790M/C797S} and the proliferation of PC‐9‐OR cells. Moreover, cells harboring EGFR^{19del/T790M/C797S} underwent remarkable apoptosis upon LS‐106 treatment. In vivo experiments further demonstrated that oral administration of LS‐106 caused significant tumor regression in a PC‐9‐OR xenograft model, with a tumor growth inhibition rate (TGI) of 83.5% and 136.6% at doses of 30 and 60 mg/kg, respectively. Taken together, we identified LS‐106 as a novel fourth‐generation EGFR inhibitor against C797S mutation and confirmed its preclinical antitumor effects in C797S–triple‐mutant tumor models.     

大气细颗粒物诱导IL-17A表达增强香烟暴露小鼠气道炎症反应的机制

目的 探讨大气细颗粒物(PM)增强香烟暴露小鼠炎症反应的机制.方法 用野生型(WT)及IL-17A基因敲除(IL-17A-/-)小鼠,按随机数字表法随机分为对照组、熏烟组、PM组、熏烟+PM组,每组6～8只.采用香烟烟雾暴露装置烟熏,气道滴注方法吸入PM,观察气道炎症反应,连续干预3个月后取检.用HE检测肺组织炎症浸润...     

Decoding the information structure underlying the neural representation of concepts

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

The capacity for conceptual knowledge is arguably one of the most defining properties of human cognition, and yet it is still unclear how concepts are represented in the brain. Recent developments in functional neuroimaging and computational linguistics have sparked renewed interest in elucidating the information structures and neural circuits underlying concept representation (1–5). Attempts to characterize the representational code for concepts typically involve information structures based on three qualitatively distinct types of information, namely, taxonomic, experiential, and distributional information. As the term implies, a taxonomic information system relies on category membership and intercategory relations. Our tendency to organize objects, events, and experiences into discrete categories has led most authors—dating back at least to Plato (6)—to take taxonomic structure as the central property of conceptual knowledge (7). The taxonomy for concepts is traditionally seen as a hierarchically structured network, with basic-level categories (e.g., “apple,” “orange”) grouped into superordinate categories (e.g., “fruit,” “food”) and subdivided into subordinate categories (e.g., “Gala apple,” “tangerine”) (8). A prominent account in cognitive science maintains that such categories are represented in the mind/brain as purely symbolic entities, whose semantic content and usefulness derive primarily from how they relate to each other (9, 10). Such representations are seen as qualitatively distinct from the sensory-motor processes through which we interact with the world, much like the distinction between software and hardware in digital computers.An experiential representational system, on the other hand, encodes information about the experiences that led to the formation of particular concepts. It is motivated by a view, often referred to as embodied, grounded, or situated semantics, in which concepts arise primarily from generalization over particular experiences, as information originating from the various modality-specific systems (e.g., visual, auditory, tactile, motor, affective) is combined and re-encoded into progressively more schematic representations that are stored in memory. Since, in this view, there is a degree of continuity between conceptual and modality-specific systems, concept representations are thought to reflect the structure of the perceptual, affective, and motor processes involved in those experiences (11–14).Finally, distributional information pertains to statistical patterns of co-occurrence between lexical concepts (i.e., concepts that are widely shared within a population and denoted by a single word) in natural language usage. As is now widely appreciated, these co-occurrence patterns encode a substantial amount of information about word meaning (15–17). Although word co-occurrence patterns primarily encode contextual associations, such as those connecting the words “cow,” “barn,” and “farmer,” semantic similarity information is indirectly encoded since words with similar meanings tend to appear in similar contexts (e.g., “cow” and “horse,” “pencil” and “pen”). This has led some authors to propose that concepts may be represented in the brain, at least in part, in terms of distributional information (15, 18).Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessments of patients with semantic deficits, with results typically interpreted in terms of taxonomic (19–24), experiential (13, 25–34), or distributional (2, 3, 5, 35, 36) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial (23, 37, 38), in part, because their representations of common lexical concepts are strongly intercorrelated. Patterns of word co-occurrence in natural language are driven in part by taxonomic and experiential similarities between the concepts to which they refer, and the taxonomy of natural categories is systematically related to the experiential attributes of the exemplars (39–41). Consequently, the empirical evidence currently available is unable to discriminate between these representational systems.Several computational models of concept representation have been proposed based on these structures. While earlier models relied heavily on hierarchical taxonomic structure (42, 43), more recent proposals have emphasized the role of experiential and/or distributional information (34, 44–46). The model by Chen and colleagues (45), for example, showed that graded taxonomic structure can emerge from the statistical coherent covariation found across experiences and exemplars without explicitly coding such taxonomic information per se. Other models propose that concepts may be formed through the combination of experiential and distributional information (44, 46), suggesting a dual representational code akin to Paivio’s dual coding theory (47).We investigated the relative contribution of each representational system by deriving quantitative predictions from each system for the similarity structure of a large set of concepts and then using representational similarity analysis (RSA) with high-resolution functional MRI (fMRI) to evaluate those predictions. Unlike the more typical cognitive subtraction technique, RSA focuses on the information structure of the pattern of neural responses to a set of stimuli (48). For a given stimulus set (e.g., words), RSA assesses how well the representational similarity structure predicted by a model matches the neural similarity structure observed from fMRI activation patterns (Fig. 1). This allowed us to directly compare, in quantitative terms, predictions derived from the three representational systems.Open in a separate windowFig. 1.Representational similarity analysis. (A) An fMRI activation map was generated for each concept presented in the study, and the activation across voxels was reshaped as a vector. (B) The neural RDM for the stimulus set was generated by computing the dissimilarity between these vectors (1 − correlation) for every pair of concepts. (C) A model-based RDM was computed from each model, and the similarity between each model’s RDM and the neural RDM was evaluated via Spearman correlation. (D) Anatomically defined ROIs. The dashed line indicates the boundary where temporal lobe ROIs were split into anterior and posterior portions (see main text for acronyms). (E) Cortical areas included in the functionally defined semantic network ROI (49).