Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission

What Causes Cancer Gallbladder?: A Review

Gallbladder cancer is a common malignancy of the biliary tract. It is the fifth common malignancy of the gastrointestinal tract in United States [1] and third in Northern India [2]. Despite such high prevalence, there is scanty published literature about this disease in indexed journals. Therefore, this article is intended to provide a brief overview of gallbladder cancer risk factors, based mainly on published evidence from analytical epidemiology and recent research findings of biologists and practising oncologists. Furthermore, an attempt has been made to establish an association between different causative factors and the occurrence of the disease

Flexible web-based integration of distributed large-scale human protein interaction maps

Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data

On the Eccentricities and Merger Rates of Double Neutron Star Binaries and the Creation of "Double Supernovae"

We demonstrate that a natural consequence of an asymmetric kick imparted to neutron stars at birth is that the majority of double neutron star binaries should possess highly eccentric orbits. This leads to greatly accelerated orbital decay, due to the enormous increase in the emission of gravitational radiation at periastron as originally demonstrated by Peters (1964). A uniform distribution of kick velocities constrained to the orbital plane would result in ~24% of surviving binaries coalescing at least 10,000 times faster than an unperturbed circular system. Even if the planar kick constraint is lifted, ~6% of bound systems still coalesce this rapidly. In a non-negligible fraction of cases it may even be possible that the system could coalesce within 10 years of the final supernova, resulting in what we might term a "double supernova''. For systems resembling the progenitor of PSR J0737-3039A, this number is as high as \~9% (in the planar kick model). Whether the kick velocity distribution extends to the range required to achieve this is still unclear. We do know that the observed population of binary pulsars has a deficit of highly eccentric systems at small orbital periods. In contrast, the long-period systems favour large eccentricities, as expected. We argue that this is because the short-period highly eccentric systems have already coalesced and are thus selected against by pulsar surveys. This effect needs to be taken into account when using the scale-factor method to estimate the coalescence rate of double neutron star binaries. We therefore assert that the coalesence rate of such binaries is underestimated by a factor of several.Comment: 7 pages, 6 figures, submitted to Ap

Back and Forth: Reverse Phase Transitions in Numerical Relativity Simulations

Multi-messenger observations of binary neutron star mergers provide a uniqueopportunity to constrain the dense-matter equation of state. Although it isknown from quantum chromodynamics that hadronic matter will undergo a phasetransition to exotic forms of matter, e.g., quark matter, the onset density ofsuch a phase transition cannot be computed from first principles. Hence, itremains an open question if such phase transitions occur inside isolatedneutron stars or during binary neutron star mergers, or if they appear at evenhigher densities that are not realized in the Cosmos. In this article, weperform numerical-relativity simulations of neutron-star mergers andinvestigate scenarios in which the onset density of such a phase transition isexceeded in at least one inspiralling binary component. Our simulations revealthat shortly before the merger it is possible that such stars undergo a"reverse phase transition", i.e., densities decrease and the quark core insidethe star disappears leaving a purely hadronic star at merger. After the merger,when densities increase once more, the phase transition occurs again and leads,in the cases considered in this work, to a rapid formation of a black hole. Wecompute the gravitational-wave signal and the mass ejection for our simulationsof such scenarios and find clear signatures that are related to the postmergerphase transition, e.g., smaller ejecta masses due to the softening of theequation of state through the quark core formation. Unfortunately, we do notfind measurable imprints of the reverse phase transition.<br

Artificial Intelligence for Caries Detection: Value of Data and Information.

If increasing practitioners' diagnostic accuracy, medical artificial intelligence (AI) may lead to better treatment decisions at lower costs, while uncertainty remains around the resulting cost-effectiveness. In the present study, we assessed how enlarging the data set used for training an AI for caries detection on bitewings affects cost-effectiveness and also determined the value of information by reducing the uncertainty around other input parameters (namely, the costs of AI and the population's caries risk profile). We employed a convolutional neural network and trained it on 10%, 25%, 50%, or 100% of a labeled data set containing 29,011 teeth without and 19,760 teeth with caries lesions stemming from bitewing radiographs. We employed an established health economic modeling and analytical framework to quantify cost-effectiveness and value of information. We adopted a mixed public-private payer perspective in German health care; the health outcome was tooth retention years. A Markov model, allowing to follow posterior teeth over the lifetime of an initially 12-y-old individual, and Monte Carlo microsimulations were employed. With an increasing amount of data used to train the AI sensitivity and specificity increased nonlinearly, increasing the data set from 10% to 25% had the largest impact on accuracy and, consequently, cost-effectiveness. In the base-case scenario, AI was more effective (tooth retention for a mean [2.5%-97.5%] 62.8 [59.2-65.5] y) and less costly (378 [284-499] euros) than dentists without AI (60.4 [55.8-64.4] y; 419 [270-593] euros), with considerable uncertainty. The economic value of reducing the uncertainty around AI's accuracy or costs was limited, while information on the population's risk profile was more relevant. When developing dental AI, informed choices about the data set size may be recommended, and research toward individualized application of AI for caries detection seems warranted to optimize cost-effectiveness

Multienzymatic immobilization of laccases on polymeric microspheres:A strategy to expand the maximum catalytic efficiency

Laccase enzymes of were covalently coimmobilized on poly(glycidyl methacrylate) microspheres. The objective of this work was to create a biocatalyst that works efficiently in a wide range of pH. The coimmobilization was performed using two different strategies to compare the most efficient. The results showed that by correctly selecting the enzymes and concentrations involved in the commobilization, it is possible to obtain a biocatalyst that works efficiently at a wide pH range (2.0-7.0). The maximum activity values reached per gram of support for the obtained biocatalyst were 41.90 U (pH 3.0), 40.89 U (pH 4.0), and 39.54 U (pH 6.0). Moreover, the thermal, storage, and mechanical stabilities were improved compared to the free and single-immobilized laccases. It was concluded that enzymatic coimmobilization is an excellent alternative to obtain a robust biocatalyst that works in a wide pH range, with potential environmental and industrial applications