Intrafraction Imaging Can Replace the Midtreatment Cone Beam Tomography for Lung Stereotactic Ablative Radiation Therapy Patients for Increased Treatment Efficiency.

Purpose: To determine the feasibility of replacing the mid treatment cone beam computed tomography (MT CBCT) image with Intrafraction Imaging (IFI) acquired concurrently during dose delivery in lung Stereotactic Ablative Body Radiation therapy (SABR) patients, and thus improve treatment efficiency. Methods and Materials: A review of departmental imaging data was performed on ten lung SABR patients treated with dual arc volumetric modulated arc therapy (VMAT) on an Elekta Versa HD linear accelerator with XVI imaging software.IFI data was extracted and a database of the translational (TX, TY, TZ) and the rotational (RX, RY, RZ) position errors was created for retrospective comparison, with the values of the MT CBCT for the same patients, treated between March 2021 and March 2022 at our center. The data was evaluated for correlation between the values in all 6° of freedom. Results: The inter-class correlation (ICC) coefficient for Tx was 0.89 (95% CI, 0.80-0.94), Ty was 0.69 (95% CI, 0.49-0.82), Tz was 0.89 (95% CI, 0.82-0.95) in the translational planes, and Rx was 0.79 (95% CI, 0.65-0.88), Ry was 0.79 (95% CI, 0.65-0.88), and Rz was 0.91 (95% CI, 0.84-0.95) in rotational planes.The Bland-Altman (BA) statistics for Tx had a bias of -1.22 × 10-3, with an upper limit of agreement (UOA) of 0.07, and a lower limit of agreement (LOA) of -0.07, for Ty the bias was 0.01 (UOA: 0.18; LOA: -0.16), Tz bias was 2.6 × 10-3(UOA: 0.10; LOA: -0.09), Rx bias was 0.09 (UOA: 0.82; LOA: -0.64), Ry bias was -0.04 (UOA: 1.08; LOA: -1.16) and Rz was -0.03 (UOA: 0.44; LOA: -0.51). Conclusions: The ICC was excellent for Tx, Tz, Rx, y, z, and good for Ty. The data demonstrated promising correlation between IFI and MT CBCT values, and therefore supports the use of IFI for clinical decision making and improving treatment efficiency.

The unknown unknown: A framework for assessing environmental DNA assay specificity against unsampled taxa.

Taxon-specific quantitative PCR (qPCR) assays are commonly used for environmental DNA sampling-based inference of animal presence. These assays require thorough validation to ensure that amplification truly indicates detection of the target taxon, but a thorough validation is difficult when there are potentially many non-target taxa, some of which may have incomplete taxonomies. Here, we use a previously published, quantitative model of cross-amplification risk to describe a framework for assessing qPCR assay specificity when there is missing information and it is not possible to assess assay specificity for each individual non-target confamilial. In this framework, we predict assay specificity against unsampled taxa (non-target taxa without sequence data available) using the sequence information that is available for other confamilials. We demonstrate this framework using four case study assays for: (1) An endemic, freshwater arthropod (meltwater stonefly; Lednia tumana), (2) a globally distributed, marine ascidian (Didemnum perlucidum), (3) a continentally distributed freshwater crustacean (virile crayfish; Faxonius virilis, deanae and nais species complex) and (4) a globally distributed freshwater teleost (common carp; Cyprinus carpio and its close relative C. rubrofuscus). We tested the robustness of our approach to missing information by simulating application of our framework for all possible subsamples of 20-all non-target taxa. Our results suggest that the modelling framework results in estimates which are largely concordant with observed levels of cross-amplification risk using all available sequence data, even when there are high levels of data missingness. We explore potential limitations and extensions of this approach for assessing assay specificity and provide users with an R Markdown template for generating reproducible reports to support their own assay validation efforts.

A novel combination treatment for fragile X syndrome predicted using computational methods.

Fragile X syndrome is a neurodevelopmental disorder caused by silencing of the fragile X messenger ribonucleotide gene. Patients display a wide spectrum of symptoms ranging from intellectual and learning disabilities to behavioural challenges including autism spectrum disorder. In addition to this, patients also display a diversity of symptoms due to mosaicism. These factors make fragile X syndrome a difficult syndrome to manage and suggest that a single targeted therapeutic approach cannot address all the symptoms. To this end, we utilized Healx's data-driven drug discovery platform to identify a treatment strategy to address the wide range of diverse symptoms among patients. Computational methods identified the combination of ibudilast and gaboxadol as a treatment for several pathophysiological targets that could potentially reverse multiple symptoms associated with fragile X syndrome. Ibudilast is an approved broad-spectrum phosphodiesterase inhibitor, selective against both phosphodiesterase 4 and phosphodiesterase 10, and has demonstrated to have several beneficial effects in the brain. Gaboxadol is a GABAA receptor agonist, selective against the delta subunit, which has previously displayed encouraging results in a fragile X syndrome clinical trial. Alterations in GABA and cyclic adenosine monophosphate metabolism have long since been associated with the pathophysiology of fragile X syndrome; however, targeting both pathways simultaneously has never been investigated. Both drugs have a good safety and tolerability profile in the clinic making them attractive candidates for repurposing. We set out to explore whether the combination of ibudilast and gaboxadol could demonstrate therapeutic efficacy in a fragile X syndrome mouse model. We found that daily treatment with ibudilast significantly enhanced the ability of fragile X syndrome mice to perform a number of different cognitive assays while gaboxadol treatment improved behaviours such as hyperactivity, aggression, stereotypy and anxiety. Importantly, when ibudilast and gaboxadol were co-administered, the cognitive deficits as well as the aforementioned behaviours were rescued. Moreover, this combination treatment showed no evidence of tolerance, and no adverse effects were reported following chronic dosing. This work demonstrates for the first time that by targeting multiple pathways, with a combination treatment, we were able to rescue more phenotypes in a fragile X syndrome mouse model than either ibudilast or gaboxadol could achieve as monotherapies. This combination treatment approach holds promise for addressing the wide spectrum of diverse symptoms in this heterogeneous patient population and may have therapeutic potential for idiopathic autism.