High fludarabine exposure and relationship with treatment-related mortality after nonmyeloablative hematopoietic cell transplantation.

Despite its common use in nonmyeloablative preparative regimens, the pharmacokinetics of fludarabine are poorly characterized in hematopoietic cell transplantation (HCT) recipients and exposure-response relationships remain undefined. The objective of this study was to evaluate the association between plasma F-ara-A exposure, the systemically circulating moiety of fludarabine, and engraftment, acute GVHD, TRM and OS after HCT. The preparative regimen consisted of CY 50 mg/kg/day i.v. day -6; plus fludarabine 30-40 mg/m²/day i.v. on days -6 to -2 and TBI 200 cGy on day -1. F-ara-A pharmacokinetics were carried out with the first dose of fludarabine in 87 adult patients. Median (range) F-ara-A area-under-the-curve (AUC((0-∞))) was 5.0 μg h/mL (2.0-11.0), clearance 15.3 L/h (6.2-36.6), C(min) 55 ng/mL (17-166) and concentration on day(zero) 16.0 ng/mL (0.1-144.1). Despite dose reductions, patients with renal insufficiency had higher F-ara-A exposures. There was strong association between high plasma concentrations of F-ara-A and increased risk of TRM and reduced OS. Patients with an AUC((0-∞)) greater than 6.5 μg h/mL had 4.56 greater risk of TRM and significantly lower OS. These data suggest that clinical strategies are needed to optimize dosing of fludarabine to prevent overexposure and toxicity in HCT

Overexpression Of Muts Alpha Complex Proteins Predicts Poor Prognosis In Oral Squamous Cell Carcinoma

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The DNA mismatch repair (MMR) system is responsible for the detection and correction of errors created during DNA replication, thereby avoiding the incorporation of mutations in dividing cells. The prognostic value of alterations in MMR system has not previously been analyzed in oral squamous cell carcinoma (OSCC). The study comprised 115 cases of OSCC diagnosed between 1996 and 2010. The specimens collected were constructed into tissue microarray blocks. Immunohistochemical staining for MutS alpha complex proteins hMSH2 and hMSH6 was performed. The slides were subsequently scanned into high-resolution images, and nuclear staining of hMSH2 and hMSH6 was analyzed using the Nuclear V9 algorithm. Univariable and multivariable Cox proportional hazard regression models were performed to evaluate the prognostic value of hMSH2 and hMSH6 in OSCC. All cases in the present cohort were positive for hMSH2 and hMSH6 and a direct correlation was found between the expression of the proteins (P<0.05). The mean number of positive cells for hMSH2 and hMSH6 was 64.44 +/- 15.21 and 31.46 +/- 22.38, respectively. These values were used as cutoff points to determine high protein expression. Cases with high expression of both proteins simultaneously were classified as having high MutS alpha complex expression. In the multivariable analysis, high expression of the MutS alpha complex was an independent prognostic factor for poor overall survival (hazard ratio: 2.75, P = 0.02). This study provides a first insight of the prognostic value of alterations in MMR system in OSCC. We found that MutS alpha complex may constitute a molecular marker for the poor prognosis of OSCC.95e3725Brazilian National Council for Scientific and Technological Development (CNPq)Postgraduate Research Group of the Porto Alegre University Hospital [GPPG/FIPE: 15-0210]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS: Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY: Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly

Effects of Laser Source Parameters on the Generation of Narrow Band and Directed Laser Ultrasound

The successful application of laser techniques for ultrasonic testing depends on the efficient coupling of optical energy into elastic energy so that laser probe detection sensitivity may be maximized. Through optimization of the laser source which is used to generate ultrasonic waves, the overall performance of laser ultrasonic systems may be enhanced by improving the efficiency with which optical energy is converted to elastic energy. This optimization depends primarily on the source laser wavelength which governs the physical interaction of the optical energy with the material of interest. For a given laser source wavelength, several techniques have been demonstrated which modify the laser source to enhance the detectability of laser ultrasonic waves and include the repetitively pulsed laser source [1,2], or temporal array, and the phased array laser source [3],or phased array. These techniques directly address the wave detectability issue by controlling the amplitude and/or the frequency content of the laser ultrasonic wave. Even though the overall conversion efficiency of optical energy to elastic energy is not improved primarily by repetitive pulsing or phasing laser arrays, the detectability of a given laser ultrasonic wave may be enhanced beyond that obtained using a single laser source

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning

PURPOSE: To evaluate the predictive utility of quantitative imaging biomarkers, acquired automatically from optical coherence tomography (OCT) scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. DESIGN: Retrospective cohort study. PARTICIPANTS: Treatment-naïve, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, UK single-centre) undergoing anti-VEGF therapy METHODS: Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137,379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria 926 eyes of 926 patients were taken forward for analysis. MAIN OUTCOME MEASURES: Correlation coefficients (R2) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual-function. The predictive value of these parameters for short-term visual change i.e. incremental visual acuity [VA] resulting from an individual injection, as well as, VA at distant timepoints (up to 12 months post-baseline). RESULTS: VA at distant timepoints could be predicted: R2 0.80 (MAE 5.0 ETDRS letters) and R2 0.7 (MAE 7.2) post-injection 3 and at 12 months post-baseline (both p < 0.001), respectively. Best performing models included both baseline qOCT parameters and treatment-response. Furthermore, we present proof-of-principle evidence that the incremental change in VA from an injection can be predicted: R2 0.14 (MAE 5.6) for injection 2 and R2 0.11 (MAE 5.0) for injection 3 (both p < 0.001). CONCLUSIONS: Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine

Differential impact of two risk communications on antipsychotic prescribing to people with dementia in Scotland: segmented regression time series analysis 2001-2011

The two risk communications were associated with reductions in antipsychotic use, in ways which were compatible with marked differences in their content and dissemination. Further research is needed to ensure that the content and dissemination of regulatory risk communications is optimal, and to track their impact on intended and unintended outcomes. Although rates are falling, antipsychotic prescribing in dementia in Scotland remains unacceptably hig

A Human Islet Cell-Culture System for High-Throuput screening.

A small-molecule inducer of beta-cell proliferation in human islets represents a potential regeneration strategy for treating type 1 diabetes. However, the lack of suitable human beta cell lines makes such a discovery a challenge. Here, we adapted an islet cell culture system to high-throughput screening to identify such small molecules. We prepared microtiter plates containing extracellular matrix from a human bladder carcinoma cell line. Dissociated human islets were seeded onto these plates, cultured for up to 7 days, and assessed for proliferation by simultaneous Ki67 and C-peptide immunofluorescence. Importantly, this environment preserved beta-cell physiological function, as measured by glucose-stimulated insulin secretion. Adenoviral overexpression of cdk-6 and cyclin D(1), known inducers of human beta cell proliferation, was used as a positive control in our assay. This induction was inhibited by cotreatment with rapamycin, an immunosuppressant often used in islet transplantation. We then performed a pilot screen of 1280 compounds, observing some phenotypic effects on cells. This high-throughput human islet cell culture method can be used to assess various aspects of beta-cell biology on a relatively large number of compounds

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines