BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data.
METHODS: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.
RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation.
CONCLUSIONS: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .

BACKGROUND: Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians\' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography.
METHODS: A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review.
RESULTS: The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20).
CONCLUSIONS: This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.

In recent decades, aqueous two phase systems have gained a lot of attention for extraction of different materials. In this work, an aqueous two phase system was made by polyethylene glycol 600 and potassium hydroxide and phase diagram were determined for this system. The experimental binodal data were described using two empirical nonlinear three parameter expressions developed by Merchuk and Zafarani-Moattar. The consistency of the experimental tie-line data was determined by utilizing the Othmer-Tobias, Bancraft, and Setschenow correlations. Also, the extraction of two analgesic drugs, namely ibuprofen and acetaminophen were investigated by the mentioned ATPS. For this purpose, partition coefficients and extraction efficiencies of each drug were calculated. The trend of extraction efficiencies indicated that the responsibility of extraction of the mentioned drugs into the polymer-rich top phase is related to their hydrophobicity. The Diamond-Hsu equation and its modified version were used to correlate the drugs experimental partition coefficients. Furthermore, the interaction of mentioned drugs with polyethylene glycol was investigated employing quantum computing techniques based upon density functional theory (DFT). These results were in good agreement with the trend of extraction efficiencies of studied drugs.

BACKGROUND: Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments\' results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case.
RESULTS: We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS\' focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases.
CONCLUSIONS: CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.

OBJECTIVE: Electronic health records (EHR) are receiving growing attention from regulators, biopharmaceuticals and payors as a potential source of real-world evidence. However, their suitability for the study of diseases with complex activity measures is unclear. We sought to evaluate the use of EHR data for estimating treatment effectiveness in inflammatory bowel disease (IBD), using tofacitinib as a use case.
METHODS: Records from the University of California, San Francisco (6/2012 to 4/2019) were queried to identify tofacitinib-treated IBD patients. Disease activity variables at baseline and follow-up were manually abstracted according to a preregistered protocol. The proportion of patients meeting the endpoints of recent randomised trials in ulcerative colitis (UC) and Crohn\'s disease (CD) was assessed.
RESULTS: 86 patients initiated tofacitinib. Baseline characteristics of the real-world and trial cohorts were similar, except for universal failure of tumour necrosis factor inhibitors in the former. 54% (UC) and 62% (CD) of patients had complete capture of disease activity at baseline (month -6 to 0), while only 32% (UC) and 69% (CD) of patients had complete follow-up data (month 2 to 8). Using data imputation, we estimated the proportion achieving the trial primary endpoints as being similar to the published estimates for both UC (16%, p value=0.5) and CD (38%, p-value=0.8).
CONCLUSIONS: This pilot study reproduced trial-based estimates of tofacitinib efficacy despite its use in a different cohort but revealed substantial missingness in routinely collected data. Future work is needed to strengthen EHR data and enable real-world evidence in complex diseases like IBD.

UNASSIGNED: Researchers divided the pancreas distal to the neck into 2 equal parts as the body and tail region by an arbitrary line. Surgeons considered the part of the pancreas, left to the aorta as the tail region. We performed this study to identify the transition zone of low-density to high-density islet cells for redefining the tail region.We quantified islets area proportion, beta-cell area proportion, and inter-islet distance in 9 Indian-adult-human non-diabetic pancreases from autopsy by using anti-synaptophysin and anti-insulin antibodies. Data were categorized under 3 regions like the proximal body, distal body, and distal part of the pancreas.Islet and beta-cell area proportion are progressively increased from head to tail region of the pancreas with a significant reduction in inter-islet distance and beta-cell percentage distal to the aorta. There is no significant difference in inter-islet distance and beta-cell percentage of the distal part of the body and tail region.Crowding of islets with intermingled microarchitecture begins in the pancreas distal to the aorta, which may be the beginning of the actual tail region. This study will provide insight into the preservation of islets-rich part of the pancreas during pancreatectomy and future prediction of new-onset diabetes.

Recent advancements in active assisted living (AAL) technologies allow older adults to age well in place. However, sensing technologies increase the complexity of data collection points, making it difficult for users to consent to data collection. One possible solution for improving transparency in the consent management process is the use of blockchain, an immutable and timestamped ledger.
This study aims to provide a conceptual framework based on technology aimed at mitigating trust issues in the consent management process.
The consent management process was modeled using established methodologies to obtain a mapping of trust issues. This mapping was then used to develop a conceptual framework based on previous monitoring and surveillance architectures for connected devices.
In this paper, we present a model that maps trust issues in the informed consent process; a conceptual framework capable of providing all the necessary underlining technologies, components, and functionalities required to develop applications capable of managing the process of informed consent for AAL, powered by blockchain technology to ensure transparency; and a diagram showing an instantiation of the framework with entities comprising the participants in the blockchain network, suggesting possible technologies that can be used.
Our conceptual framework provides all the components and technologies that are required to enhance the informed consent process. Blockchain technology can help overcome several privacy challenges and mitigate trust issues that are currently present in the consent management process of data collection involving AAL technologies.

The disease roots of Alzheimer\'s disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

The goal of this study is to get a better understanding of the fundamentals of perceptual timing deficits, that is, difficulties with estimating durations of explicitly attended temporal intervals, in children with attention-deficit/hyperactivity disorder (ADHD). Whereas these deficits were repeatedly demonstrated in laboratory studies using computer-based timing tasks, we will additionally implement a more practical task reflecting real-life activity. In doing so, the research questions of the planned study follow a hierarchically structured path \'from lab to life\': Are the timing abilities of children with ADHD really disturbed both in the range of milliseconds and in the range of seconds? What causes these deficits? Do children with ADHD rather display a global perceptual timing deficit, or do different \'timing types\' exist? Are timing deficits present during real-life activities as well, and are they based on the same mechanisms as in computerised tasks?
A quasi-experimental study with two groups of male children aged 8-12 years (ADHD; controls) and with a cross-sectional design will be used to address our research questions. Statistical analyses of the dependent variables will comprise (repeated) measures analyses of variance, stepwise multiple regression analyses and latent class models. With an estimated dropout rate of 25%, power analysis indicated a sample size of 140 subjects (70 ADHD, 70 controls) to detect medium effect sizes.
Ethics approval was obtained from the ethics committee of the Faculty of Medicine, University of Rostock. Results will be disseminated to researcher, clinician and patient communities in peer-reviewed journals and at scientific conferences, at a meeting of the local ADHD competence network and on our web page which will summarise the study results in an easily comprehensible manner.
DRKS00015760.

BACKGROUND: There is evidence that consistent sitting for prolonged periods is associated with upper quadrant musculoskeletal pain (UQMP). It is unclear whether postural alignment is a significant risk factor.
OBJECTIVE: The aim of the prospective study (2010-2011) was to ascertain if three-dimensional sitting postural angles, measured in a real-life school computer classroom setting, predict seated-related UQMP.
METHODS: Asymptomatic Grade 10 high-school students, aged 15-17 years, undertaking Computer Application Technology, were eligible to participate. Using the 3D Posture Analysis Tool, sitting posture was measured while students used desk-top computers. Posture was reported as five upper quadrant angles (Head flexion, Neck flexion; Craniocervical angle, Trunk flexion and Head lateral bending). The Computer Usage Questionnaire measured seated-related UQMP and hours of computer use. The Beck Depression Inventory and the Multidimensional Anxiety Scale for Children assessed psychosocial factors. Sitting posture, computer use and psychosocial factors were measured at baseline. UQMP was measured at six months and one-year follow-up.
RESULTS: 211, 190 and 153 students participated at baseline, six months and one-year follow-up respectively. 34.2% students complained of seated-related UQMP during the follow-up period. Increased head flexion (HF) predicted seated-related UQMP developing over time for a small group of students with pain scores greater than the 90th pain percentile, adjusted for age, gender, BMI, computer use and psychosocial factors (p = 0.003). The pain score increased 0.22 points per 1° increase in HF.
CONCLUSIONS: Classroom ergonomics and postural hygiene should therefore focus on reducing large HF angles among computing adolescents.