bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reduced restenosis and enhanced re-endothelialization of functional biodegradable vascular scaffolds by everolimus and magnesium hydroxide
Authors: Baek Seung-Woon , Kim Da-Seul , Song Duck Hyun , Kim Han Byul , Lee Semi , Kim Jun Hyuk , Lee Jun-Kyu , Hong Young Joon , Park Chun Gwon , Han Dong Keun 
Published: December 2022

Abstract

Background
Coronary artery disease is a cardiovascular disease with a high mortality and mortality rate in modern society. Vascular stent insertion to restore blood flow is essential to treat this disease. A fully biodegradable vascular scaffold (BVS) is a vascular poly (L-lactic acid) (PLLA) stent that is receiving growing interest as this is biodegradable in the body and does not require secondary removal surgery. However, acidic byproducts composed of PLLA produced during the biodegradation of the BVS can induce an inflammatory response. Magnesium hydroxide, a basic inorganic particle, neutralizes the acidic byproducts of PLLA.

Methods
In this study, we investigated using a BVS coated with everolimus and surface-modified magnesium hydroxide that suppresses smooth muscle cell proliferation and protects endothelial cells, respectively. The various characteristics of the functional stent were evaluated using in vitro and in vivo analyses.

Results
The BVS was successfully prepared with evenly coated everolimus and surface-modified magnesium hydroxide. A neutral pH value was maintained by magnesium hydroxide during degradation, and everolimus was released for one month. The coated BVS effectively inhibited protein adsorption and platelet adhesion, demonstrating excellent blood compatibility. In vitro analysis showed that BVS protects endothelial cells with magnesium hydroxide and selectively inhibits smooth muscle cell proliferation via everolimus treatment. The functional BVS was inserted into porcine coronary arteries for 28 days, and the results demonstrated that the restenosis and inflammation greatly decreased and re-endothelialization was enhanced as compared to others.

Conclusions
This study provides new insights into the design of drug-incorporated BVS stent for coronary artery disease.

Graphical Abstract
Formulation of a kit under Good Manufacturing Practices (GMP) for preparing [111In]In-BnDTPA-trastuzumab-NLS injection: a theranostic agent for imaging and Meitner-Auger Electron (MAE) radioimmunotherapy of HER2-positive breast cancer
Authors: Chan Conrad , Prozzo Vanessa , Aghevlian Sadaf , Reilly Raymond M. 
Published: December 2022

Abstract
Background
111In[In]-BnDTPA-trastuzumab-NLS is a radiopharmaceutical with theranostic applications for imaging and Meitner-Auger electron (MAE) radioimmunotherapy (RIT) of HER2-positive breast cancer (BC). Nuclear localization sequence (NLS) peptides route the radiopharmaceutical to the nucleus of HER2-positive BC cells following receptor-mediated internalization for RIT with subcellular range MAEs. The γ-photons emitted by 111In permit tumour imaging by SPECT. Our aim was to formulate a kit under Good Manufacturing Practices conditions to prepare 111In[In]-BnDTPA-trastuzumab-NLS injection for a first-in-human clinical trial.

Results
Trastuzumab was derivatized with p-SCN-BnDTPA to introduce Bn-DTPA for complexing 111In, then modified with maleimide groups for conjugation to the thiol on cysteine in NLS peptides [CGYGPKKKRKVGG]. BnDTPA-trastuzumab-NLS (5 mg in 1.0 mL of 0.05 M ammonium acetate buffer, pH 5.5) was dispensed into unit dose sterile glass vials to produce kits for labeling with 100–165 MBq of 111In[In]Cl3. The kits met specifications for protein concentration (4.5–5.5 mg/mL), volume (0.95–1.05 mL), pH (5.5–6.0), appearance (clear, pale-yellow, particulate-free), BnDTPA substitution level (2.0–7.0 BnDTPA/trastuzumab), purity and homogeneity (SDS-PAGE and SE-HPLC), 111In labeling efficiency (> 90%), binding to HER2-positive SK-BR-3 human breast cancer cells (Ka = 1–8 × 108 L/mmol; Bmax = 0.5–2 × 106 sites/cell), NLS peptide conjugation (upward band shift on SDS-PAGE), sterility (USP Sterility Test) and endotoxins (USP Bacterial Endotoxins Test). 111In-BnDTPA-trastuzumab-NLS injection met specifications for pH (5.5–6.5), radiochemical purity (≥ 90%), radionuclide purity (≥ 99%), appearance (clear, colourless, particle-free) and sterility (retrospective USP Sterility Test). Kits were stable stored at 2–8 °C for up to 661 days (d) meeting all key specifications. Protein concentration remained within or just slightly greater than the specification for up to 139 d. 111In[In]-BnDTPA-trastuzumab-NLS injection was stable for up to 24 h. An expiry of 180 d was assigned for the kits and 8 h for the final radiopharmaceutical.

Conclusion
A kit was formulated under GMP conditions for preparing 111In[In]-BnDTPA-trastuzumab-NLS injection. This radiopharmaceutical was safely administered to 4 patients with HER2-positive BC to trace the uptake of trastuzumab into brain metastases before and after MRI-guided focused ultrasound (MRIg-FUS) by SPECT imaging.
Seroprevalence of hepatitis B and C virus among highly active antiretroviral therapy experienced children in Gondar, Ethiopia
Authors: Kasew Desie , Wondmagegn Mitikie , Bayleyegn Biruk 
Published: December 2022

Abstract
Background
Globally, chronic viral hepatitis is the cause of mortality alongside human immunodeficiency virus/acquired immunodeficiency syndrome and tuberculosis. Published reports on the seroprevalence of hepatitis B and C viruses among HIV-infected children are lacking in sub-Saharan Africa. Hence, this study aimed to determine the seroprevalence of hepatitis B and C viruses among highly active antiretroviral therapy (HAART)-experienced children at the University of Gondar Comprehensive Specialized Hospital.

Methods
We conducted a hospital-based cross-sectional study to determine the seroprevalence of hepatitis B and C viruses among HAART-experienced children from January to May 2020. We collected the socio-demographic characteristics of study participants with pretested questioners and clinical data from medical records. We performed enzyme-linked immunosorbent assay-based laboratory test for serum hepatitis B surface antigens and anti-hepatitis C virus antibodies. Finally, we analyzed the frequency of all variables, determined the association of independent variables with hepatitis B and C viruses by using univariable and then multivariable logistic regression.

Results
A total of 241 HAART-experienced children were enrolled, 49.8% of whom were girls. The median age of participants was 13 years (interquartile range 11–14). The seroprevalence of hepatitis B and C virus infection among HAART-experienced children were 9.5% and 2.9%, respectively. Being underweight was significantly associated with both hepatitis B virus (AOR = 3.87: 95% CI; 1.04–14.46, P = 0.044) and hepatitis C virus infections (AOR = 4.54: 95% CI; 1.21–17.04, P = 0.025).

Conclusions
This study showed that the magnitude of hepatitis B and C viruses was high among HIV-infected children who were under HAART and did not know their hepatitis B and C infection status before. Being underweight was associated with both hepatitis viruses. Therefore, screening for hepatitis B and C viruses should be a routine measure for all HIV-infected children.
Association between Rheumatic Disease Comorbidity Index and factors of poor prognosis in a cohort of 280 patients with rheumatoid arthritis
Authors: Ben Tekaya Aicha , Hannech Emna , Saidane Olfa , Rouached Leila , Bouden Selma , Tekaya Rawdha , Mahmoud Ines , Abdelmoula Leila 
Published: December 2022

Abstract
Background
Rheumatoid arthritis (RA) is commonly associated with higher rates of comorbidities. Recent recommendations highlight screening comorbidities during the disease course because of their impact on patients’ ability to function, on disease outcome, but also on treatment choices. Hence the interest of our study that aimed to quantify the impact of comorbidities among RA patients using a validated tool the Rheumatic Disease Comorbidity Index (RDCI) and to explore the association between comorbidities and disease characteristics.

Methods
We conducted a cross-sectional study over 12 months period, including patients followed for an established RA according to the ACR/EULAR 2010 criteria and hospitalized in our rheumatology department. Patients’ characteristics and disease features were collected for each patient. Comorbidities were quantified using the RDCI. We looked for the association between RDCI and patients characteristics and RA parameters. Univariable and multivariable analysis were made.

Results
They were 280 patients: 233 female (83.2%) and 47 male (16.8%) with a mean age of 58.07 (SD 11.12) years. The mean follow-up period was 14.74 (SD 1.63) years. Comorbidities were noted in 133 patients (47.5%). The mean comorbidity score measured by the RDCI was 1.05 (SD 1.23). RDCI was positively correlated with age (p < 0.001, r = 0.359). RA patients whose age of disease onset exceeds 40 years have significantly higher RDCI (1.8 (SD 1.3) [CI 95%: 1.36–1.88] vs. 1.5 (SD 1.2), p = 0.007). Moreover, RDCI was significantly associated with the presence pulmonary involvement (p < 0.001) and ocular involvement (p = 0.002). RDCI was also associated with erosive RA (p = 0.006), the presence of atlanto-axial dislocation (p = 0.014), and coxitis (p = 0.029). Regarding therapy regimen, RDCI was statistically increased in patients receiving bDMARDs compared to patients under csDMARDs (2.8 (SD 1.6) vs. 1.0 (SD 1.0), p = 0.021).

Conclusion
In this study, comorbidity index was associated with signs of poor prognosis such as erosions, coxitis, and atlanto-axial dislocation. This confirmed the hypothesis that comorbidity can be a threat to the improvement in the long-term prognosis in RA patients.
Can we benefit from game engines to develop digital twins for planning the deployment of photovoltaics?
Authors: Clausen Christian Skafte Beck , Ma Zheng Grace , Jørgensen Bo Nørregaard 
Published: December 2022

AbstractDigital Twins (DTs) have attracted great attention in the energy sector. Game engines have been suggested to model DTs of their physical counterparts because they provide realistic graphics, lighting-, fluid- and physics engines that simulate the real world. However, the application of game engines to develop DTs for photovoltaics (PVs) has not yet been discussed in the literature. Therefore, this paper assesses the built-in game engine features' ability to support the DT development of PVs with Unreal Engine 5. This paper mainly focuses on visual representation because the surrounding environment significantly impacts PV deployment, and the existing software tools do not allow the study of the environmental factors at the early planning phase of a project’s lifecycle. Furthermore, this paper investigates the position of the sun, shadows and reflections from nearby objects that influence the PVs' power output, and if the built-in light engine can be used for planning the deployment of PVs. The result shows that in-game objects in the environment can be used to affect the simulated PV output estimate over a year. It also indicates that applying Unreal Engine 5 to model PV systems that rely on mirroring real-world behaviour is promising if accurate data is used in the modelling. Real data and mathematical PV models are necessary since Unreal Engine 5’s Lumen subsystem cannot provide realistic solar radiance on PVs for a given location on earth.
Probabilistic Flexoffers in residential heat pumps considering uncertain weather forecast
Authors: Albano Michele , Cibin Nicola , Golmohamadi Hessam , Skou Arne 
Published: December 2022

AbstractThe penetration of renewable energies is increasing in energy systems worldwide. Consequently, the intermittency of the energy sources raises technical challenges for sustainable energy supply. Demand-side flexibility is an effective solution to counterbalance renewable power fluctuations. In the residential sector, electrical heat pumps exhibit great flexibility potential. In this paper, a novel approach is proposed to generate FlexOffers for individual heat pumps considering the uncertain nature of weather conditions. To achieve the aim, firstly, the thermal dynamic model of residential buildings is presented mathematically. The model addresses different temperature zones. The constant coefficients of the thermal dynamics are estimated using Continuous-Time Stochastic Model (CTSM) in R software. Afterward, the building model is integrated with a success function to generate FlexOffers. The success function is comprised of two objective functions including minimization and maximization of the energy consumption of heat pumps. The FlexOffers are generated considering the existing gap between the minimum and maximum energy consumption. The patterns of energy consumption are defined based on occupants’ thermal comfort temperature setpoint. The FlexOffers are programmed in UPPAAL-STRATEGO software. Finally, a high-fidelity building model with four rooms is used to examine the proficiency of the suggested approaches. The simulation results confirm that the proposed method generates flexibility potentials for the upstream network in both optimistic and pessimistic states of energy consumption patterns.
Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine
Authors: Sørensen Jonas Vedsted , Ma Zheng , Jørgensen Bo Nørregaard 
Published: December 2022

AbstractDigital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.
Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning
Authors: Pereira Helder , Gomes Luis , Vale Zita 
Published: December 2022

AbstractIn the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets based on the double-auction trading mechanism. This novel methodology is based on two reinforcement learning algorithms, used separately, to optimize the amount of energy to be transacted and the price to pay/charge for the purchase/sale of energy. The proposed methodology uses a competitive approach, and that is why all agents seek the best result for themselves, which in this case means reducing as much as possible the costs related to the purchase of energy, or if we are talking about sellers, maximizing profits. The proposed methodology was integrated into an agent-based ecosystem where there is a direct connection with agents, thus allowing application to real contexts in a more efficient way. To test the methodology, a case study was carried out in an energy community of 50 players, where each of the proposed models were used in 20 different players, and 10 were left without training. The players with training managed, over the course of a week, to save 44.65 EUR when compared to a week of peer-to-peer without training, a positive result, while the players who were left without training increased costs by 17.07 EUR.
Framework for dimensioning battery energy storage systems with applied multi-tasking strategies in microgrids
Authors: Nielsen Troels S. , Thomsen Jens S. P. , Santos Athila Q. , Kaad Jutte 
Published: December 2022

AbstractThe shifting from the traditional centralized electric sector to a distributed and renewable system presents some challenges. Battery energy storage technologies have proven effective in relieving some aspects of this transition by facilitating load control and providing flexibility to non-dispatchable renewable production. Therefore, this paper investigates how to dimension battery energy storage systems with applied multi-tasking strategies in microgrids. To this end, it proposes a framework to accurately depict how BESS can be financially and technically feasible by deploying multi-tasking strategies that fit the system characteristics of a microgrid while providing arguments for the financial incentive. The framework development is based on the principles of the analytical approach and is conceptualized in a three-part funnel structure. This framework has been tested using the case study of Aeroe microgrid and resulted in a proposed battery energy storage configuration. Based on the findings, the BESS implementation contributes to improve load behavior and to increase internal production utilization. A sensitivity analysis was performed, to investigate the robustness of the configurations. Collectively, the framework has proven to provide feasible results within a wide range of parameters. This framework could help the preliminary investigation phase when analyzing future battery energy storage system investments.
Identification of natural disaster impacted electricity load profiles with k means clustering algorithm
Authors: Jessen Simon Hedegård , Ma Zheng Grace , Wijaya Francisco Danang , Vasquez Juan C. , Guerrero Josep , Jørgensen Bo Nørregaard 
Published: December 2022

AbstractNatural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system’s recovering process and progress after natural disasters’ hit which strongly influence the system operators’ planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and ½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes’ impact on the electricity system and conduct better long-term energy management strategies.
Progress and perspective on cotton breeding in Pakistan
Authors: Shahzad Kashif , Mubeen Iqra , Zhang Meng , Zhang Xuexian , Wu Jianyong , Xing Chaozhu 
Published: December 2022

AbstractCotton is the prime natural fiber with economic significance globally. Cotton farming and breeding have a long history in Pakistan. The development of high yielding upland cotton (Gossypium hirsutum) varieties gradually replaced the cultivation of diploid Gossypium species. Climate change along with emergence of new epidemic diseases caused yield loss in recent years. The biotic stress considerably reduced the performance and yield potential of cotton. Suitable breeding strategies are essential to generate useful genetic variations and to identify desired traits. Conventional breeding has remarkably increased cotton yield and fiber quality, which has cultivated the NIAB-78, S-12, MNH‐786, and FH‐Lalazar like cultivars. However, this phenotypic selection based breeding method has low efficiency to produce stress resilient cotton. The efficiency of traditional breeding has significantly improved by the marker assisted selection technology. Breakthroughs in molecular genetics, bioinformatics analysis, genetic engineering, and genome sequencing have opened new technique routes for cotton breeding. In addition, genetic improvement through quantitative trait loci, transcriptome, and CRISPR/Cas9 mediated genomic editing can provide suitable platform to improve the resistance to stresses induced by bollworms, cotton leaf curl virus, heat, drought, and salt. The approval of transgenic lines harboring triple gene Cry1Ac + Cry2A + GTG are critical for cotton crop. This review has critically discussed the progress and limitations of cotton breeding in Pakistan, and reviewed the utilization of novel genetic variations and selection tools for sustainable cotton production.