Chemotherapeutic effect of a novel temozolomide analog on nasopharyngeal carcinoma in vitro and in vivo.

BackgroundMany patients with nasopharyngeal carcinoma (NPC) face poor prognosis. Due to its hidden anatomical location, the tumor is usually diagnosed quite late, and despite initially successful treatment with radiation and cisplatin, many patients will relapse and succumb to the disease. New treatment options are urgently needed. We have performed preclinical studies to evaluate the potential NPC therapeutic activity of a newly developed analog of temozolomide (TMZ), an alkylating agent that is the current chemotherapeutic standard of care for patients with malignant glioma.ResultsTMZ was covalently conjugated to the natural monoterpene perillyl alcohol (POH), creating the novel fusion compound NEO212. Its impact on two NPC cell lines was studied through colony formation assays, cell death ELISA, immunoblots, and in vivo testing in tumor-bearing mice. In vitro, NEO212 effectively triggered tumor cell death, and its potency was significantly greater than that of its individual components, TMZ or POH alone. Intriguingly, merely mixing TMZ with POH also was unable to achieve the superior potency of the conjugated compound NEO212. Treatment of NPC cells with NEO212 inactivated the chemoprotective DNA repair protein MGMT (O6-methylguanine methyltransferase), resulting in significant chemosensitization of cells to a second round of drug treatment. When tested in vivo, NEO212 reduced tumor growth in treated animals.ConclusionOur results demonstrate anticancer activity of NEO212 in preclinical NPC models, suggesting that this novel compound should be evaluated further for the treatment of patients with NPC

Repositioning of Verrucosidin, a purported inhibitor of chaperone protein GRP78, as an inhibitor of mitochondrial electron transport chain complex I.

Verrucosidin (VCD) belongs to a group of fungal metabolites that were identified in screening programs to detect molecules that preferentially kill cancer cells under glucose-deprived conditions. Its mode of action was proposed to involve inhibition of increased GRP78 (glucose regulated protein 78) expression during hypoglycemia. Because GRP78 plays an important role in tumorigenesis, inhibitors such as VCD might harbor cancer therapeutic potential. We therefore sought to characterize VCD's anticancer activity in vitro. Triple-negative breast cancer cell lines MDA-MB-231 and MDA-MB-468 were treated with VCD under different conditions known to trigger increased expression of GRP78, and a variety of cellular processes were analyzed. We show that VCD was highly cytotoxic only under hypoglycemic conditions, but not in the presence of normal glucose levels, and VCD blocked GRP78 expression only when glycolysis was impaired (due to hypoglycemia or the presence of the glycolysis inhibitor 2-deoxyglucose), but not when GRP78 was induced by other means (hypoxia, thapsigargin, tunicamycin). However, VCD's strictly hypoglycemia-specific toxicity was not due to the inhibition of GRP78. Rather, VCD blocked mitochondrial energy production via inhibition of complex I of the electron transport chain. As a result, cellular ATP levels were quickly depleted under hypoglycemic conditions, and common cellular functions, including general protein synthesis, deteriorated and resulted in cell death. Altogether, our study identifies mitochondria as the primary target of VCD. The possibility that other purported GRP78 inhibitors (arctigenin, biguanides, deoxyverrucosidin, efrapeptin, JBIR, piericidin, prunustatin, pyrvinium, rottlerin, valinomycin, versipelostatin) might act in a similar GRP78-independent fashion will be discussed

Systematic Review of Adaptive Learning Research Designs, Context, Strategies, and Technologies From 2009 to 2018

This systematic review of research on adaptive learning used a strategic search process to synthesize research on adaptive learning based on publication trends, instructional context, research methodology components, research focus, adaptive strategies, and technologies. A total of 61 articles on adaptive learning were analyzed to describe the current state of research and identify gaps in the literature. Descriptive characteristics were recorded, including publication patterns, instructional context, and research methodology components. The count of adaptive learning articles published fluctuated across the decade and peaked in 2015. During this time, the largest concentration of adaptive learning articles appeared in Computers and Education. The majority of the studies occurred in higher education in Taiwan and the United States, with the highest concentration in the computer science discipline. The research focus, adaptive strategies, and adaptive technologies used in these studies were also reviewed. The research was aligned with various instructional design phases, with more studies examining design and development, and implementation and evaluation. For examining adaptive strategies, the authors examined both adaptive sources based on learner model and adaptive targets based on content and instructional model. Learning style was the most observed learner characteristic, while adaptive feedback and adaptive navigation were the most investigated adaptive targets. This study has implications for adaptive learning designers and future researchers regarding the gaps in adaptive learning research. Future studies might focus on the increasing availability and capacities of adaptive learning as a learning technology to assist individual learning and personalized growth

Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence.

BackgroundPatients with symptomatic intracranial atherosclerosis (ICAS) of ≥ 70% luminal stenosis are at high risk of stroke recurrence. We aimed to evaluate the relationships between hemodynamics of ICAS revealed by computational fluid dynamics (CFD) models and risk of stroke recurrence in this patient subset.MethodsPatients with a symptomatic ICAS lesion of 70-99% luminal stenosis were screened and enrolled in this study. CFD models were reconstructed based on baseline computed tomographic angiography (CTA) source images, to reveal hemodynamics of the qualifying symptomatic ICAS lesions. Change of pressures across a lesion was represented by the ratio of post- and pre-stenotic pressures. Change of shear strain rates (SSR) across a lesion was represented by the ratio of SSRs at the stenotic throat and proximal normal vessel segment, similar for the change of flow velocities. Patients were followed up for 1 year.ResultsOverall, 32 patients (median age 65; 59.4% males) were recruited. The median pressure, SSR and velocity ratios for the ICAS lesions were 0.40 (-2.46-0.79), 4.5 (2.2-20.6), and 7.4 (5.2-12.5), respectively. SSR ratio (hazard ratio [HR] 1.027; 95% confidence interval [CI], 1.004-1.051; P = 0.023) and velocity ratio (HR 1.029; 95% CI, 1.002-1.056; P = 0.035) were significantly related to recurrent territorial ischemic stroke within 1 year by univariate Cox regression, respectively with the c-statistics of 0.776 (95% CI, 0.594-0.903; P = 0.014) and 0.776 (95% CI, 0.594-0.903; P = 0.002) in receiver operating characteristic analysis.ConclusionsHemodynamics of ICAS on CFD models reconstructed from routinely obtained CTA images may predict subsequent stroke recurrence in patients with a symptomatic ICAS lesion of 70-99% luminal stenosis

iNeRF: Inverting Neural Radiance Fields for Pose Estimation

We present iNeRF, a framework that performs mesh-free pose estimation by "inverting" a Neural RadianceField (NeRF). NeRFs have been shown to be remarkably effective for the task of view synthesis - synthesizing photorealistic novel views of real-world scenes or objects. In this work, we investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free, RGB-only 6DoF pose estimation - given an image, find the translation and rotation of a camera relative to a 3D object or scene. Our method assumes that no object mesh models are available during either training or test time. Starting from an initial pose estimate, we use gradient descent to minimize the residual between pixels rendered from a NeRF and pixels in an observed image. In our experiments, we first study 1) how to sample rays during pose refinement for iNeRF to collect informative gradients and 2) how different batch sizes of rays affect iNeRF on a synthetic dataset. We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF. Finally, we show iNeRF can perform category-level object pose estimation, including object instances not seen during training, with RGB images by inverting a NeRF model inferred from a single view.Comment: Website: http://yenchenlin.me/inerf