Cabozantinib for relapsed neuroblastoma: Single institution case series

Relapsed high‐risk neuroblastoma has few effective therapies currently available or in development. Cabozantinib is an Food and Drug Administration approved multitargeted tyrosine kinase inhibitor for select adult malignancies with preclinical data suggesting efficacy against neuroblastoma. A safe and tolerable dose has been identified for children, but its efficacy remains unknown. We describe four children with relapsed metastatic neuroblastoma treated with cabozantinib. All four patients had extended disease control (two complete responsesfor >12 months, 2 stable disease >6 months) with manageable predictable toxicities requiring dose reduction in two patients. We discuss the potential for the use of cabozantinib in neuroblastoma.     

壮药材滇桂艾纳香及其易混品种东风草和高艾纳香比较研究

目的： 建立壮药材滇桂艾纳香专属性检验方法。方法： 采用基原鉴别、性状鉴别、显微鉴别、薄层鉴别、分子生物学及特征图谱等方法对滇桂艾纳香及其易混品种东风草和高艾纳香进行对比研究。结果： 三者的根、茎、叶性状基本相同，区别点在于滇桂艾纳香头状花序小（直径0.5~0.8 cm），东风草头状花序大（直径1.5~2 cm），高艾纳香头状花序具密集的长绒毛；三者的根、茎横切面，粉末显微特征基本相同，高艾纳香具基部膨大的非腺毛；薄层鉴别（1）中高艾纳香比滇桂艾纳香和东风草少1个斑点；薄层鉴别（2）中滇桂艾纳香比东风草和高艾纳香少1个特征斑点；ITS2序列N-J树聚类分析显示三者具有良好的单系性，三者种间最小遗传距离均大于种内最大遗传距离；与滇桂艾纳香对照图谱相比，东风草平均相似度为0.962，高艾纳香平均相似度仅为0.789。结论： 高艾纳香与滇桂艾纳香区别较大，不宜混用，在日常使用中应注意甄别；东风草与滇桂艾纳香在性状、显微、薄层、分子生物学、特征图谱等方面相似度较高，在对二者进行充分的药理活性以及临床试验等基础上，可考虑作为滇桂艾纳香的替代品使用，以缓解药用资源不足的问题。     

Integrating Incomplete Information With Imperfect Advice

When our own knowledge is limited, we often turn to others for information. However, social learning does not guarantee accurate learning or better decisions: Other people's knowledge can be as limited as our own, and their advice is not always helpful. This study examines how human learners put two “imperfect” heads together to make utility‐maximizing decisions. Participants played a card game where they chose to “stay” with a card of known value or “switch” to an unknown card, given an advisor's advice to stay or switch. Participants used advice strategically based on which cards the advisor could see (Experiment 1), how helpful the advisor was (Experiment 2), and what strategy the advisor used to select advice (Experiment 3). Overall, participants benefited even from imperfect advice based on incomplete information. Participants’ responses were consistent with a Bayesian model that jointly infers how the advisor selects advice and the value of the advisor's card, compared to an alternative model that weights advice based on the advisor's accuracy. By reasoning about others’ minds, human learners can make the best of even noisy, impoverished social information.     

A Simple Computational Theory of General Collective Intelligence

Researchers have recently demonstrated that group performance across tasks tends to be correlated, motivating the use of a single metric for the general collective intelligence of groups akin to general intelligence metrics for individuals. High general collective intelligence is achieved when a group performs well across a wide variety of tasks. A number of factors have been shown to be predictive of general collective intelligence, but there is sparse formal theory explaining the presence of correlations across tasks, betraying a fundamental gap in our understanding of what general collective intelligence is measuring. Here, we formally argue that general collective intelligence arises from groups achieving commitment to group goals, accurate shared beliefs, and coordinated actions. We then argue for the existence of generic mechanisms that help groups achieve these cognitive alignment conditions. The presence or absence of such mechanisms can potentially explain observed correlations in group performance across tasks. Under our view, general collective intelligence can be conceived as measuring group performance on classes of tasks that have particular combinations of cognitive alignment requirements.     

How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications

BackgroundAbnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI.MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10–10), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity.ResultsCompared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43 mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = ?0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between stimulation sites.ConclusionThis study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.     

Sodium Channel Inhibitors Reduce DMPK mRNA and Protein

Myotonic dystrophy type 1 (DM1) is caused by an expanded trinucleotide (CTG)n tract in the 3′ untranslated region (UTR) of the dystrophia myotonica protein kinase (DMPK) gene. This results in the aggregation of an expanded mRNA forming toxic intranuclear foci which sequester splicing factors. We believe down‐regulation of DMPK mRNA represents a potential, and as yet unexplored, DM1 therapeutic avenue. Consequently, a computational screen for agents which down‐regulate DMPK mRNA was undertaken, unexpectedly identifying the sodium channel blockers mexiletine, prilocaine, procainamide, and sparteine as effective suppressors of DMPK mRNA. Analysis of DMPK mRNA in C2C12 myoblasts following treatment with these agents revealed a reduction in the mRNA levels. In vivo analysis of CD1 mice also showed DMPK mRNA and protein down‐regulation. The role of DMPK mRNA suppression in the documented efficacy of this class of compounds in DM1 is worthy of further investigation.     

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.     

Transforming vaccine development

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines that are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, the smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.     

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation

The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer‐reviewed publications. However, no human‐ or machine‐readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state‐of‐the‐art information technology, we have developed a cloud‐based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org , an open‐access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org . Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large‐scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open‐source at Hippocampome.org in order to further research across disciplines.