Hasil untuk "q-bio"

Hongjian Wei, Wenzhi Liu, Xinyu Chen et al.

Abstract Due to excessive greenhouse gas emissions and high dependence on traditional petroleum jet fuel, the sustainable development of the aviation industry has drawn increasing attention worldwide. One of the most promising strategies is to develop and industrialize alternative aviation fuels produced from renewable resources, e.g. biomass. Renewable bio-jet fuel has the potential to reduce CO2 emissions over their life cycle, which make bio-jet fuels an attractive substitution for aviation fuels. This paper provided an overview on the conversion technologies, economic assessment, environmental influence and development status of bio-jet fuels. The results suggested that hydrogenated esters and fatty acids, and Fischer-Tropsch synthesis can be the most promising technologies for bio-jet fuels production in near term. Future works, such as searching for more suitable feedstock, improving competitiveness for alternative jet fuels, meeting emission reduction targets in large-scale production and making measures for the indirect impact are needed for further investigation. The large-scale deployment of bio-jet fuels could achieve significant potentials of both bio-jet fuels production and CO2 emissions reduction based on future available biomass feedstock.

Q. Gou, Haoran Luo, Qi Zhang et al.

Aqueous zinc-ion batteries hold attractive potential for large-scale energy storage devices owing to their prominent electrochemical performance and high security. Nevertheless, the applications of aqueous electrolytes have generated various challenges, including uncontrolled dendrite growth and parasitic reactions, thereby deteriorating the Zn anode's stability. Herein, inspired by the superior affinity between Zn2+ and amino acid chains in the zinc finger protein, a cost-effective and green glycine additive is incorporated into aqueous electrolytes to stabilize the Zn anode. As confirmed by experimental characterizations and theoretical calculations, the glycine additives can not only reorganize the solvation sheaths of hydrated Zn2+ via partial substitution of coordinated H2 O but also preferentially adsorb onto the Zn anode, thereby significantly restraining dendrite growth and interfacial side reactions. Accordingly, the Zn anode could realize a long lifespan of over 2000 h and enhanced reversibility (98.8%) in the glycine-containing electrolyte. Furthermore, the assembled Zn||α-MnO2 full cells with glycine-modified electrolyte also delivers substantial capacity retention (82.3% after 1000 cycles at 2 A g-1 ), showing promising application prospects. This innovative bio-inspired design concept would inject new vitality into the development of aqueous electrolytes.

C. Chavana-Bryant, C. Chavana-Bryant, P. Wilkes et al.

<p>The ForestScan project was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) aboveground biomass (AGB) cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates.</p> <p>We present data from the ForestScan project, a unique multiscale dataset of tropical forest three-dimensional (3D) structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle laser scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of EO estimates of forest biomass, as well as providing broader insights into tropical forest structure.</p> <p>Data are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, ALS and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits. All datasets described in this study are openly available. The TLS, UAV-LS and ALS datasets are provided through the ForestScan Project Data Collection in the CEDA archive (<a href="https://doi.org/10.5285/88a8620229014e0ebacf0606b302112d">https://doi.org/10.5285/88a8620229014e0ebacf0606b302112d</a>, Chavana-Bryant et al., 2025l). Tree census and plot description data for FBRMS-01 (Paracou, French Guiana) are hosted in the CIRAD Dataverse (<a href="https://doi.org/10.18167/DVN1/94XHID">https://doi.org/10.18167/DVN1/94XHID</a>, Derroire et al., 2025b). Tree census and ancillary data for FBRMS-02 (Lopé, Gabon) and FBRMS-03 (Kabili-Sepilok, Malaysia) are available via a ForestPlots.net data package (<a href="https://doi.org/10.5521/forestplots.net/2025_2">https://doi.org/10.5521/forestplots.net/2025_2</a>, Chavana-Bryant et al., 2025k). Together, these repositories provide access to the complete set of datasets released as part of the ForestScan project.</p>

Hongwei Zhang, Lijun Sun, Weihong Tan et al.

Substation robots face significant challenges in path planning due to the complex electromagnetic environment, dense equipment layout, and safety-critical operational requirements. This paper proposes a path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization, establishing a synergistic optimization framework that combines bio-inspired algorithms with deep learning. The proposed method addresses critical path planning issues in substation inspection and maintenance operations. The approach includes: 1) designing a pheromone-guided exploration strategy that transforms environmental prior knowledge into spatial bias to reduce ineffective exploration; 2) establishing a high-quality sample screening mechanism that enhances Q-network training through ant colony path experience to improve sample efficiency; 3) implementing dynamic decision weight adjustment that enables gradual transition from heuristic guidance to autonomous learning decisions. Experimental results in complex environments demonstrate the method’s superiority. Compared to state-of-the-art baselines including PPO, DDQN, and A*, the proposed method achieves 24% higher sample efficiency, 18% reduction in average path length, and superior dynamic obstacle avoidance. Field validation in a 2,500-square-meter substation confirms a 14.8% improvement in task completion rate compared to standard DRL approaches.

Hido Pinto, Eran Segal

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is often constrained by the lack of their available disease labels. In this research, we demonstrate how machine learning can be leveraged to enhance explainability and uncover biologically meaningful associations, even when predictive improvements in disease modeling are limited. We train LightGBM models from scratch on our dataset (10K) to impute metabolomics features and apply them to the UK Biobank (UKBB) for downstream analysis. The imputed metabolomics features are then used in survival analysis to assess their impact on disease-related risk factors. As a result, our approach successfully identified biologically relevant connections that were not previously known to the predictive models. Additionally, we applied a genome-wide association study (GWAS) on key metabolomics features, revealing a link between vascular dementia and smoking. Although being a well-established epidemiological relationship, this link was not embedded in the model's training data, which validated the method's ability to extract meaningful signals. Furthermore, by integrating survival models as inputs in the 10K data, we uncovered associations between metabolic substances and obesity, demonstrating the ability to infer disease risk for future patients without requiring direct outcome labels. These findings highlight the potential of leveraging external bio-banks to extract valuable biomedical insights, even in data-limited scenarios. Our results demonstrate that machine learning models trained on smaller datasets can still be used to uncover real biological associations when carefully integrated with survival analysis and genetic studies.

George Hong, Daniel Trejo Banos

We investigate the application of Small Language Models (<10 billion parameters) for genomics question answering via agentic framework to address hallucination issues and computational cost challenges. The Nano Bio-Agent (NBA) framework we implemented incorporates task decomposition, tool orchestration, and API access into well-established systems such as NCBI and AlphaGenome. Results show that SLMs combined with such agentic framework can achieve comparable and in many cases superior performance versus existing approaches utilising larger models, with our best model-agent combination achieving 98% accuracy on the GeneTuring benchmark. Notably, small 3-10B parameter models consistently achieve 85-97% accuracy while requiring much lower computational resources than conventional approaches. This demonstrates promising potential for efficiency gains, cost savings, and democratization of ML-powered genomics tools while retaining highly robust and accurate performance.

Gertrude Hattoh, Jeremiah Ayensu, Nyarko Prince Ofori et al.

Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This reinforces concerns about the potential misuse of LLMs in biodesign, posing a significant threat to research and development (R&D). The accessibility of such technology to individuals with limited technical expertise raises serious biosecurity risks. Our findings underscore the critical need for robust governance and technical safeguards to balance rapid biotechnological innovation with biosecurity imperatives.

Zang X, Zhang L, Ma J et al.

Xiaoyu Zang,1,&amp;ast; Lili Zhang,2,&amp;ast; Jing Ma,1,3,&amp;ast; Anzhu Wang,4,&amp;ast; Lu Ding,1,3,5 Yayun Wang,1 Jun Sun,1 Jing Li,1,5 Xing Hang,6 Xiangyan Li,1,5 Linhua Zhao2 1Changchun University of Chinese Medicine, Changchun, People’s Republic of China; 2Institute of Metabolic Diseases, Guang’ Anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 3The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, People’s Republic of China; 4National Center for Integrative Medicine, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 5Northeast Asia Research Institute of Traditional Chinese Medicine, Key Laboratory of Active Substances and Biological Mechanisms of Ginseng Efficacy, Ministry of Education, Jilin Provincial Key Laboratory of Bio-Macromolecules of Chinese Medicine, Changchun, People’s Republic of China; 6Beijing University of Chinese Medicine, Beijing, People’s Republic of China&amp;ast;These authors contributed equally to this workCorrespondence: Linhua Zhao, Email melonzhao@163.com Xiangyan Li, Email xiangyan_li1981@163.comPurpose: For early-stage Diabetic retinopathy (DR), various pharmacological agents and neuroprotective factors have been developed. However, these treatments often show limited efficacy, especially when initiated after retinal damage, and may cause adverse effects. Therefore, there is an urgent need to develop safer and more effective therapeutic strategies for early-stage DR. Shenzhuo Formula (SZF), a modified classical traditional Chinese medicine prescription, has shown promising clinical efficacy in early-stage DR treatment. This study aims to investigate the underlying mechanisms of SZF to expand treatment strategies for DR.Methods: SZF components were analyzed using Ultra Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry/Mass Spectrometry (UPLC-Q-TOF-MS/MS). Db/db mice received three different SZF doses for 12 weeks. Physiological parameters, including water and food consumption, body weight, and urine output, were monitored. Blood samples were analyzed for fasting blood glucose and other relevant parameters. Ocular changes were assessed using fundus photography (FP), fundus fluorescein angiography (FFA), optical coherence tomography (OCT) and hematoxylin and eosin (H&amp;E). Network pharmacology analysis (NP) identified potential SZF targets, while immunofluorescence staining evaluated SZF’s mechanism in delaying DR progression. The distribution of SZF pharmacological targets in critical DR target cells was analyzed using single-cell data from the GSE245561 dataset. Molecular docking predicted SZF-target interactions.Results: SZF improved diabetic symptoms, increased retinal thickness, and reducedvascular leakage and microcirculation issues. The HIF-1α-VEGFA axis was suggested as a potential core target. Single-cell analysis of clinical samples suggested macrophages as a common target cell for HIF-1α and VEGFA. Molecular docking identified effective SZF components.Conclusion: Results indicate that SZF may impede the progression of DR by inhibiting the HIF-1α-VEGFA signaling pathway in macrophages, with quercetin and apigenin identified as significant contributors, though further experimental validation is needed to confirm these mechanistic. Keywords: diabetic retinopathy, Shenzhuo formula, HIF-1α/VEGFA, macrophage, scRNA-seq

Mikayel Ginovyan, Silvard Tadevosyan, Anahit Shirvanyan et al.

Abstract The presented study aimed to assess the efficacy of crude leaf hydroethanolic extracts from blackcurrant, fig, and grape leaves in reversing antibiotic resistance and enhancing chemotherapeutic efficacy. The viability tests were employed to assess the resistance-modifying properties of the extracts both in antibiotic-resistant bacterial cells and cancer cell-lines. To elucidate the potential mechanisms of the antibiotic modulatory activity of test extracts, the changes in H+-fluxes across the cell membrane and their impact on the H+-translocating F0F1-ATPase activity in antibiotic-resistant Escherichia coli explored. Qualitative metabolomic characterization of the extracts was performed using LC-Q-Orbitrap HRMS, and quantitative analysis was carried out with UHPLC-PDA. Experiments on doxorubicin-resistant and susceptible HT-29 cells revealed that all three extracts reversed antibiotic resistance in HT-29R cells, making them susceptible to doxorubicin in a dose-dependent manner. Notably, blackcurrant, and fig significantly reduced the minimum inhibitory concentrations of ampicillin and kanamycin against resistant E. coli strains. Our results indicated that all plant extracts enhanced H+-fluxes in the investigated bacterial strain and promoted ATPase activity, suggesting a potential role in altering bacterial membrane integrity. LC-Q-Orbitrap HRMS analysis identified more than 100 major peaks, with flavonoids and phenolics being the dominant constituents. The study underscores the potential of the selected plant extracts in developing of new agents to overcome antibiotic resistance and enhance the efficacy of chemotherapeutic agents. Importantly, although these plant leaves are often considered as bio-waste, they can be used as valuable sources of bioactive compounds. This underlines the importance of re-evaluating agricultural by-products for their potential in pharmacological applications, fostering a sustainable approach in drug development. Graphical Abstract

Roozbeh Moazenzadeh, O. Katipoğlu, Ahmadreza Shateri et al.

This study aimed to develop an accurate and reliable model for predicting suspended sediment load (SL) in river systems, which is crucial for water resource management and environmental protection. While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). To this end, streamflow (Q) and sediment concentration (SC) values as well as their lag times (1 to 3 month lag times) were fed as input variables – under 9 scenarios – into ML models. A time series of datasets from March 1973 to December 2011 and January 2012 to March 2023 were used for training and testing of ML models, respectively. The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. According to the results, the simultaneous application of Q and SC lag time values as inputs has led to the best SL estimates by XGB-MPA, with XGB-MPA9 (RMSE = 103.7 ton/day; NSE = 0.96) exhibiting the lowest error rates. In addition, XGB-MPA has performed better than XGB in all scenarios, with the lowest and highest reduction in RMSE being 19.3% (scenario 5) and 97.4% (scenario 1), respectively. When comparing the performance of hybrid models, the proposed XGB-MPA model has performed best with MAE, RMSE and NSE of 40.94, 103.7 and 0.96, respectively, in comparison with 816.02, 1063.74 and −2.94 for XGB-PSO and 693.16, 981.68 and −2.37 for XGB-GWO. Further research can include the use of time series of efficient variables extracted from satellite images (e.g. land cover, river morphology, etc.) as model inputs. Highlights Improvement of SL estimation by coupling MPA with XGB model Using delayed combinations of streamflow and sediment concentration as model inputs Superiority of MPA compare to PSO and GWO in SL estimation Greater variations in SHAP caused by sediment concentration compared to streamflow Further studies required on the effects of hydrological and topographical features

Linda Shao, Zhihang Wang, Ning Mu et al.

High-quality factor Fano resonances offer exceptional potential for the creation of ultrasensitive refractive index sensors owing to their capacity to facilitate robust interactions between electromagnetic waves and analytes. In this article, we introduce a general approach for designing sensitive metasurface sensors leveraging high-Q Fano resonances. The metasurface, composed of metallic strips varying in length, produces the characteristic Fano line shape through the interference of bright and dark modes. Our findings reveal a remarkable sensitivity of up to 0.473 THz/RIU at 2.37 THz, with a maximum resonance Q value attainment of 38.12. The tunable properties of Fano resonances can be fine-tuned by adjusting geometric parameters. As a demonstration of the practical applicability of these high-Q resonances, we conducted experimental assessments of the metasurface sensor's performance in detecting the concentrations of bovine serum albumin and glucose. Notably, both the resonance frequency and amplitude undergo significant changes in response to increasing analyte concentrations. This allows for rapid and precise determination of both the concentration and molecule type based on observed frequency shifts. Our strategy paves the way for the design of ultrasensitive real-time sensors operating in the terahertz regime.

Zeng Qu, Jinfeng Kang, W. Li et al.

Suthep Mongkollertlop, Boonyaras Sookkheo

Kota Tejasree, Narute T. K., Navale A. M. et al.

The present investigations on the studies to see the effect of seed biopriming with bacterial consortia in green gram were conducted in a field experiment employing randomized block design with nine treatments and three replications during kharif season 2022-2023 at the Post Graduate Institute, research farm of the Department of Plant Pathology and Agricultural Microbiology, Mahatma Phule Krishi Vidyapeeth, Rahuri, Dist. Ahmednagar, Maharashtra. The result revealed that, among the treatment, T4 recorded the highest average plant height (65.66 cm) and average number of leaves (22.79 plant-1), average number of branches (5.23), average number of pods (30.93), average grain yield (9.18 q ha-1), average stalk yield (24.25 q ha-1) and 1000 seed weight i.e. test weight (33.78 g).

Abdelwahed Berrouane, M. Derradji, Karim Khiari et al.

Quercetin (Q), one of the most abundant molecules in nature, remains relatively unexplored in the realm of bio-based thermosets. In line with the pursuit of sustainability, we report the successful synthesis of a novel bio-based phthalonitrile (PN) monomer (Q-Ph) using Q. The synthesis involved a nitro displacement reaction with 4-nitrophthalonitrile (4-NPN). Confirmation of the monomer’s structure utilized hydrogen and carbon nuclear magnetic resonances (1H and 13C NMR), Fourier transform infrared spectra (FTIR), and elemental analysis. Curing characteristics were examined by differential scanning calorimetry (DSC), and polymerization was analyzed using FTIR. The resulting monomers showed a wide processing window and low melt viscosity via rheological analysis. Thermal and thermomechanical properties were assessed using dynamic mechanical analyzer (DMA) and thermogravimetric analysis (TGA), revealing lower curing and polymerization temperatures compared to petroleum-based counterparts. The synthesized resin achieved a high Tg exceeding 400°C, a char yield of 79% at 1000°C, and T5% and T10% values of 564 and 660°C, respectively. The Q-Ph polymer demonstrated superior performance, with evidence of an autocatalytic curing mechanism. These results highlight quercetin as a promising petrochemical replacement for the preparation of self-curable PN thermosets, especially for high-performance applications.

Tejal Vishandas Ahuja, Nidhi Bhatnagar, Mamta C. Shah et al.

Background and Objectives: Anemia is an extremely common condition in pregnancy worldwide, which confers several health risks to mother and child. Iron deficiency is the most widespread micronutritional deficiency in the world and disproportionately affects females because of increased iron requirements during menstruation, pregnancy, and lactation. Hemoglobinopathies are a group of inherited disorders because of abnormalities in hemoglobin (Hb) synthesis or structure. Thalassemia and sickle cell anemia are the most prevalent hemoglobinopathies and a national health burden in India so identifying these disorders during the antenatal period is necessary to take appropriate measures. This study aimed to ascertain the prevalence and spectrum of thalassemia/hemoglobinopathy amongst antenatal patients and also to analyze the ability of red cell indices to differentiate beta thalassemia trait from mild iron-deficiency anemia (IDA). Methods: A prospective study of screening for Hb variants in Antenatal Patients due to low Hb and evaluation of other causes was performed for 1 year with 570 samples. In low Hb, patients’ complete blood count, Reticulocyte staining, and sickling test were performed. Hb analysis was done by high-performance liquid chromatography Bio-Rad Variant II. In IDA Serum ferritin and Serum Iron level were done and in megaloblastic anemia (MA) Vitamin B12 levels were done. Results: The prevalence of anemia in antenatal patients was 90.25%; in this, IDA presented at 84.21%, MA at 4.73%, and dimorphic anemia at 1.27%. The prevalence of hemoglobinopathies in the current study was 9.75%; in this beta-thalassemia minor presented at 5.08%, sickle cell trait at 4.03%, Hb D Punjab at 0.52%, and Hb Q India at 0.17%. Conclusion: Antenatal screening for genetic disorders, such as beta-thalassemia and sickle cell anemia, aims to reduce the burden of these diseases by offering information to individuals with a high likelihood of giving birth to affected babies and giving parents more choices regarding their reproductive decisions. For this, premarital and antenatal screening should be mandatory to prevent the birth of affected offspring.

П.С. Орлов, О.П. Хрипко, Т.C. Кокорина et al.

В настоящее время необходима замена расходных материалов и реагентов от производителей из США и Евросоюза. В этой работе проводится сравнение наборов для измерения концентрации ДНК методом флюоресценции от производителей из Российской Федерации (Raissol Bio Spectra Q HS) и КНР (Vazyme Equalbit dsDNA HS) с набором производства США (Invitrogen™ Qubit™ dsDNA HS). С использованием данных наборов измерена концентрация ДНК 24 образцов плазмы периферической крови человека. Установлено, что абсолютные значения концентраций ДНК, определенных с помощью трех наборов, имели статистически значимые различия при парных сравнениях. При этом наибольшее медианное значение концентрации ДНК было зафиксировано с применением набора Raissol Bio Spectra Q HS (0.751 нг/мкл) по сравнению с наборами Vazyme Equalbit dsDNA HS (0.498 нг/мкл) и Invitrogen™ Qubit™ dsDNA HS (0.437 нг/мкл). Регрессионный анализ выявил значимые зависимости между концентрациями ДНК, измеренными с помощью различных наборов. Наибольший коэффициент детерминации (R2 = 0.93) был определен при сравнении значений концентраций ДНК, измеренных с использованием наборов Invitrogen : Vazyme. Для оставшихся двух пар, Invitrogen : Raissol и Vazyme : Raissol, коэффициенты детерминации не превышали значения в 0.521. Таким образом, измеренные концентрации ДНК с применением указанных наборов оказались сопоставимы между собой и могут быть пересчитаны относительно друг друга с помощью уравнений регрессии.

Yong-Qiang Tian, Jing Liu, Peng Cheng et al.

Cyclooxygenase 2 (COX-2) and 5-lipoxygenase (5-LOX) are overexpressed in gastric cancer cells, the dual inhibitors of which exhibit potential against metastasis and invasion with fewer side effects. To discover inhibitors targeting COX-2 and 5-LOX, we conducted ultrafiltration and enrichment calculation to screen candidates in quaternary alkaloids (QAs) from Zanthoxylum simulans through LC and LC-Q-TOF. For intensive peaks, peaks 19 (berberine) and 21 (chelerythrine) were observed as the most potent dual candidates and showed selective affinity to 5-LOX over COX-2. Peak 19 showed an enrichment at 4.36 for COX-2 and 22.81 for 5-LOX, while peak 21 showed an enrichment at 7.81 for COX-2 and 24.49 for 5-LOX. Molecular docking results revealed chelerythrine as a better dual inhibitor, showing time- and dose-dependent anti-proliferation against AGS cells. Bio-informatics strategies, such as Gene Expression Omnibus (GEO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), suggested that hormone pathways in gastric cancer cells might be mediated by chelerythrine. Further reviews and summaries helped outline the mechanisms by which COX-2/5-LOX inhibitors might promote apoptosis in gastric cancer cells via estrogen, thyroid, and oxytocin signaling pathways. Chelerythrine was also added to gastric cancer cells to verify the regulation of these three signaling pathways. As a result, significant calling back of thyroid-stimulating hormone receptor (TSHR), thyroid hormone α3 (TRα3), and thyroid hormone receptor β1 (TRβ1) and suppressing estrogen receptor α36 (ER-α36)–Src could benefit the anti-proliferation of chelerythrine. However, it was disappointing that regulation of estrogen receptor α66 (ER-α66), estrogen receptor β (ER-β), and oxytocin receptor (OTR) contributed inversely negative effects on anti-gastric cancer cells. At present, the integrative study not only revealed chelerythrine as the most potent dual COX-2/5-LOX inhibitor from QAs but also generally highlighted that comprehensive regulation of the estrogen, thyroid, and oxytocin pathway should be noted once gastric cancer cells were treated with inflammatory inhibitors.

Mubashir Qayyum, S. Afzal, S. T. Saeed et al.

This study is focused on modeling and simulations of hybrid nanofluid flow. Uranium dioxide UO2 nanoparticles are hybrid with copper Cu, copper oxide CuO and aluminum oxide Al2O3 while considering blood as a base fluid. The blood flow is initially modeled considering magnetic effect, non-linear thermal radiation and chemical reactions along with convective boundaries. Then for finding solution of the obtained highly nonlinear coupled system we propose a methodology in which q-homotopy analysis method is hybrid with Galerkin and least square Optimizers. Residual errors are also computed in this study to confirm the validity of results. Analysis reveals that rate of heat transfer in arteries increases up to 13.52 Percent with an increase in volume fraction of Cu while keeping volume fraction of UO2 fixed to 1% in a base fluid (blood). This observation is in excellent agreement with experimental result. Furthermore, comparative graphical study of Cu,CuO and Al2O3 for increasing volume fraction is also performed keeping UO2 volume fraction fixed. Investigation indicates that Cu has the highest rate of heat transfer in blood when compared with CuO and Al2O3. It is also observed that thermal radiation increases the heat transfer rate in the current study. Furthermore, chemical reaction decreases rate of mass transfer in hybrid blood nanoflow. This study will help medical practitioners to minimize the adverse effects of UO2 by introducing hybrid nano particles in blood based fluids.

Mohammad Jokar, A. Naraghi, M. Seifouri et al.