Network target for screening synergistic drug combinations with application to traditional Chinese medicine

<p>Abstract</p> <p>Background</p> <p>Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.</p> <p>Results</p> <p>In this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.</p> <p>Conclusions</p> <p>NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.</p

Construction of a Fish-like Robot Based on High Performance Graphene/PVDF Bimorph Actuation Materials.

Smart actuators have many potential applications in various areas, so the development of novel actuation materials, with facile fabricating methods and excellent performances, are still urgent needs. In this work, a novel electromechanical bimorph actuator constituted by a graphene layer and a PVDF layer, is fabricated through a simple yet versatile solution approach. The bimorph actuator can deflect toward the graphene side under electrical stimulus, due to the differences in coefficient of thermal expansion between the two layers and the converse piezoelectric effect and electrostrictive property of the PVDF layer. Under low voltage stimulus, the actuator (length: 20 mm, width: 3 mm) can generate large actuation motion with a maximum deflection of about 14.0 mm within 0.262 s and produce high actuation stress (more than 312.7 MPa/g). The bimorph actuator also can display reversible swing behavior with long cycle life under high frequencies. on this basis, a fish-like robot that can swim at the speed of 5.02 mm/s is designed and demonstrated. The designed graphene-PVDF bimorph actuator exhibits the overall novel performance compared with many other electromechanical avtuators, and may contribute to the practical actuation applications of graphene-based materials at a macro scale

Updated Smoothed Particle Hydrodynamics for Simulating Bending and Compression Failure Progress of Ice

In this paper, an updated Smoothed Particle Hydrodynamics (SPH) method based on the Simplified Finite Difference Interpolation scheme (SPH_SFDI) is presented to simulate the failure process of ice. The Drucker–Prager model is embedded into the SPH code to simulate the four point bending and uniaxial compression failure of ice. The cohesion softening elastic–plastic model is also used in the SPH_SFDI framework. To validate the proposed modeling approach, the numerical results of SPH_SFDI are compared with the standard SPH and the experimental data. The good agreement demonstrated that the proposed SPH_SFDI method including the elastic–plastic cohesion softening Drucker–Prager failure model can provide a useful numerical tool for simulating failure progress of the ice in practical field. It is also shown that the SPH_SFDI can significantly improve the capability and accuracy for simulating ice bending and compression failures as compared with the original SPH scheme

Research on CVDs Prediction and Early Warning Techniques in Healthcare Monitoring System

Abstract-Chronic diseases are gradually becoming the principal factors of harm to people&apos;s health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients

Machine learning facilitated the modeling of plastics hydrothermal pretreatment towards constructing an on-ship marine litter-to-methanol plant

An onboard facility shows promise in efficiently converting floating plastics into valuable products, such as methanol, negating the need for regional transport and land-based treatment. Gasification presents an effective means of processing plastics, requiring their transformation into gasification-compatible feedstock, such as hydrochar. This study explores hydrochar composition modeling, utilizing advanced algorithms and rigorous analyses to unravel the intricacies of elemental composition ratios, identify influential factors, and optimize hydrochar production processes. The investigation begins with decision tree modeling, which successfully captures relationships but encounters overfitting challenges. Nevertheless, the decision tree vote analysis, particularly for the H/C ratio, yielding an impressive R2 of 0.9376. Moreover, the research delves into the economic feasibility of the marine plastics-to-methanol process. Varying payback periods, driven by fluctuating methanol prices observed over a decade (ranging from 3.3 to 7 yr for hydrochar production plants), are revealed. Onboard factories emerge as resilient solutions, capitalizing on marine natural gas resources while striving for near-net-zero emissions. This comprehensive study advances our understanding of hydrochar composition and offers insights into the economic potential of environmentally sustainable marine plastics-to-methanol processes

The diagnostic value of metagenomics next-generation sequencing in HIV-infected patients with suspected pulmonary infections

BackgroundTraditional microbiological detection methods used to detect pulmonary infections in people living with HIV (PLHIV) are usually time-consuming and have low sensitivity, leading to delayed treatment. We aimed to evaluate the diagnostic value of metagenomics next-generation sequencing (mNGS) for microbial diagnosis of suspected pulmonary infections in PLHIV.MethodsWe retrospectively analyzed PLHIV who were hospitalized due to suspected pulmonary infections at the sixth people hospital of Zhengzhou from November 1, 2021 to June 30, 2022. Bronchoalveolar lavage fluid (BALF) samples of PLHIV were collected and subjected to routine microbiological examination and mNGS detection. The diagnostic performance of the two methods was compared to evaluate the diagnostic value of mNGS for unknown pathogens.ResultsThis study included a total of 36 PLHIV with suspected pulmonary infections, of which 31 were male. The reporting period of mNGS is significantly shorter than that of CMTs. The mNGS positive rate of BALF samples in PLHIV was 83.33%, which was significantly higher than that of smear and culture (44.4%, P&lt;0.001). In addition, 11 patients showed consistent results between the two methods. Futhermore, mNGS showed excellent performance in identifying multi-infections in PLHIV, and 27 pathogens were detected in the BALF of 30 PLHIV by mNGS, among which 15 PLHIV were found to have multiple microbial infections (at least 3 pathogens). Pneumocystis jirovecii, human herpesvirus type 5, and human herpesvirus type 4 were the most common pathogen types.ConclusionsFor PLHIV with suspected pulmonary infections, mNGS is capable of rapidly and accurately identifying the pathogen causing the pulmonary infection, which contributes to implement timely and accurate anti-infective treatment