Search

Background: 18F-fluciclovine is a synthetic amino acid positron emission tomography (PET) radiotracer that is approved for use in prostate cancer. In this clinical study, we characterised the kinetic model best describing the uptake of 18F-fluciclovine in breast cancer and assessed differences in tracer kinetics and static parameters for different breast cancer receptor subtypes and tumour grades.Methods: Thirty-nine patients with pathologically proven breast cancer underwent 20-min dynamic PET/computed tomography imaging following the administration of 18F-fluciclovine. Uptake into primary breast tumours was evaluated using one- and two-tissue reversible compartmental kinetic models and static parameters.Results: A reversible one-tissue compartment model was shown to best describe tracer uptake in breast cancer. No significant differences were seen in kinetic or static parameters for different tumour receptor subtypes or grades. Kinetic and static parameters showed a good correlation.Conclusions: 18F-fluciclovine has potential in the imaging of primary breast cancer, but kinetic analysis may not have additional value over static measures of tracer uptake.Clinical Trial Registration: NCT03036943. [ABSTRACT FROM AUTHOR]

In this letter, we attempt to quantify the significance of increasing the number of neurons in the hidden layer of a feedforward neural network architecture using the singular value decomposition (SVD). Through this, we extend some well-known properties of the SVD in evaluating the generalizability of single hidden layer feedforward networks (SLFNs) with respect to the number of hidden layer neurons. The generalization capability of the SLFN is measured by the degree of linear independency of the patterns in hidden layer space, which can be indirectly quantified from the singular values obtained from the SVD, in a postlearning step. A pruning/growing technique based on these singular values is then used to estimate the necessary number of neurons in the hidden layer. More importantly, we describe in detail properties of the SVD in determining the structure of a neural network particularly with respect to the robustness of the selected model. [ABSTRACT FROM AUTHOR]

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors. [ABSTRACT FROM AUTHOR]

Purpose: Tumor hypoxia fuels an aggressive tumor phenotype and confers resistance to anticancer treatments. We conducted a clinical trial to determine whether the antimalarial drug atovaquone, a known mitochondrial inhibitor, reduces hypoxia in non-small cell lung cancer (NSCLC)., Patients and Methods: Patients with NSCLC scheduled for surgery were recruited sequentially into two cohorts: cohort 1 received oral atovaquone at the standard clinical dose of 750 mg twice daily, while cohort 2 did not. Primary imaging endpoint was change in tumor hypoxic volume (HV) measured by hypoxia PET-CT. Intercohort comparison of hypoxia gene expression signatures using RNA sequencing from resected tumors was performed., Results: Thirty patients were evaluable for hypoxia PET-CT analysis, 15 per cohort. Median treatment duration was 12 days. Eleven (73.3%) atovaquone-treated patients had meaningful HV reduction, with median change -28% [95% confidence interval (CI), -58.2 to -4.4]. In contrast, median change in untreated patients was +15.5% (95% CI, -6.5 to 35.5). Linear regression estimated the expected mean HV was 55% (95% CI, 24%-74%) lower in cohort 1 compared with cohort 2 ( P = 0.004), adjusting for cohort, tumor volume, and baseline HV. A key pharmacodynamics endpoint was reduction in hypoxia-regulated genes, which were significantly downregulated in atovaquone-treated tumors. Data from multiple additional measures of tumor hypoxia and perfusion are presented. No atovaquone-related adverse events were reported., Conclusions: This is the first clinical evidence that targeting tumor mitochondrial metabolism can reduce hypoxia and produce relevant antitumor effects at the mRNA level. Repurposing atovaquone for this purpose may improve treatment outcomes for NSCLC., (©2021 American Association for Cancer Research.)

Background: Mobile health applications (mHealth apps) are increasingly being used to perform tasks that are conventionally performed by general practitioners (GPs), such as those involved in promoting health, preventing disease, diagnosis, treatment, monitoring, and support for health services. This raises an important question: can mobile apps replace GPs? This study aimed to systematically search for and identify mobile apps that can perform GP tasks., Methods: A scoping review was carried out. The Google Play Store and Apple App Store were searched for mobile apps, using search terms derived from the UK Royal College of General Practitioners (RCGP) guideline on GPs' core capabilities and competencies. A manual search was also performed to identify additional apps., Results: The final analysis included 17 apps from the Google Play Store and Apple App Store, and 21 apps identified by the manual search. mHealth apps were found to have the potential to replace GPs for tasks such as recording medical history and making diagnoses; performing some physical examinations; supporting clinical decision making and management; assisting in urgent, long-term, and disease-specific care; and health promotion. In contrast, mHealth apps were unable to perform medical procedures, appropriately utilise other professionals, and coordinate a team-based approach., Conclusions: This scoping review highlights the functions of mHealth apps that can potentially replace GP tasks. Future research should focus on assessing the performance and quality of mHealth apps in comparison with that of real doctors.

Tapentadol is a centrally acting synthetic analgesic which is prescribed for the treatment of a range of chronic pain conditions. Its use in treating various pain conditions is increasing and, as with other opioids, it has the potential to be abused. We describe a three-stage incorporation of tapentadol into validated screening and quantitative methods through: (i) addition of retention time/mass spectral data to a database, (ii) qualitative validation and (iii) quantitative validation. This represents an efficient and flexible approach to the incorporation of new compounds of interest to existing screening methods. Tapentadol was analyzed in blood and serum samples using alkaline liquid-liquid extraction with identification and quantitation by liquid chromatography/time-of-flight mass spectrometry. In a series of six post-mortem cases where tapentadol was detected but was not a primary causative factor in death, blood concentrations ranged from 0.01 to 1.0 mg/L. In two cases where tapentadol was a significant contributor to death, post-mortem blood concentrations were 1.7 and 3.9 mg/L. In one of these fatal cases, ante-mortem blood and serum were also analyzed. The tapentadol concentration in the post-mortem blood was 30% higher than in the ante-mortem blood after a post-mortem interval of 13 days, indicating some potential for post-mortem redistribution. The measured ante-mortem blood:serum ratio was 1.7, and is the first such ratio to be reported. Other drugs were detected in almost all cases, with the majority being prescription analgesics, sedatives and antidepressants. The number of cases in which tapentadol has been detected has increased in recent years, highlighting the importance of screening for this drug in forensic toxicological laboratories.

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors.

In this paper, we propose an energy formulation for homomorphic graph matching by the Hopfield network and a Lyapunov indirect method-based learning approach to adaptively learn the constraint parameter in the energy function. The adaptation scheme eliminates the need to specify the constraint parameter empirically and generates valid and better quality mappings than the analog Hopfield network with a fixed constraint parameter. The proposed Hopfield network with constraint parameter adaptation is applied to match silhouette images of keys and results are presented.