Current Science, Engineering and Technology - Online First
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Growth, Distribution and Quality of Gastrodia Elata: Combined Effects of Environmental Factors, Soil Microorganisms, Symbiotic Fungi and Processing Methods
Authors: Xiaoyu Fu, Yurong Wang, Xingguo Zhang, Lijiao Yang and Hezhong JiangAvailable online: 13 November 2025More LessGastrodia elata Blume. (G. elata)—a heterotrophic orchid with high medicinal value—faces sustainability challenges due to its ecological specificity. This review highlights critical factors governing its cultivation: optimal growth occurs at 20–25°C, 60-80% humidity, and 1,400–1,800 m altitude, where temperature-humidity gradients shape biomass and bioactive compound synthesis. Genetic diversity is driven by environmental heterogeneity, with high-altitude populations adapting to climatic stress, while lowland cultivation risks genetic erosion. Soil microbiomes, particularly symbiotic Armillaria mellea (Armillaria), enable nutrient acquisition but degrade under monoculture, necessitating microbial diversity restoration via organic amendments. Post-harvest processing significantly impacts medicinal quality: steaming and Ultra-High-Pressure (UHP) methods preserve neuroprotective gastrodin and parishins, whereas sulfur fumigation introduces toxic residues. To ensure sustainability, we propose integrating climate-resilient breeding, microbiome engineering, and standardized processing. These strategies address environmental, genetic, and technological bottlenecks, positioning G. elata as a sustainable resource for global medicinal applications.
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Piezoelectric and Electrically Conducting Filler-based Elastomer Composite For Piezoelectric Nano Generators
Authors: Vineet Kumar, Md Najib Alam and Sang-Shin ParkAvailable online: 28 October 2025More LessIntroductionThere are many challenges in achieving high performance composites. These challenges are achieving uniform filler dispersion, strong interfacial interactions, and efficient load transfer. This study addresses some of these challenges and presents a strategy to balance properties with high-performance PENGs.
MethodsThese PENGs devices were fabricated using hybrid composites consisting of barium titanate (BaTiO3) and titanium carbide (TiC) as fillers, embedded in a silicone rubber matrix. The mechanical and electromechanical behavior were assessed using a universal testing machine and a digital multimeter.
ResultsThe results indicate that BaTiO3 and TiC were effective in achieving high modulus and robust power density. For example, the compressive modulus increased from 1.15 MPa (control) to 1.45 MPa at 10 phr BaTiO3 (BaTiO3-10), and further to 2.29 MPa at 40 phr (BaTiO3-40). Similarly, the generated power output increased from 0.79 pW/cm2 (BaTiO3-10) to a maximum of 2.29 pW/cm2 (BaTiO3-20), before decreasing to 0.39 pW/cm2 (BaTiO3-30).
DiscussionThese results show the effectiveness of BaTiO3 and TiC-filled silicone rubber composites in enhancing the performance of PENGs. The study covers a promising route for the development of self-powered devices.
ConclusionsThe study shows that the BaTiO3 and TiC hybrid used in silicone rubber provides a robust performance for portable electronics and biological applications. The results show that 20 phr of hybrid filler is sufficient to best performance, while the properties fall after this filler loading.
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A Comprehensive Review of Machine Learning-based Clustering Methods for Single-Cell RNA Sequencing Data: Advantages and Challenges
Authors: Teng Zhang, Yueying Yang, Lingyu Meng and Jianjun TanAvailable online: 23 October 2025More LessThe rapid development of single-cell RNA sequencing (scRNA-seq) technology has provided unprecedented opportunities to explore cell heterogeneity and function. However, the high dimensionality, sparsity, and noise inherent in scRNA-seq data present significant challenges for traditional clustering methods. This review aims to summarize machine learning-based clustering techniques for scRNA-seq data, including Traditional Methods, Graph-based Methods, Ensemble Methods, Deep Learning Methods, and Other Methods, with a focus on discussing the advantages, limitations, and challenges of these approaches. We first discuss key preprocessing steps such as normalization, dropout imputation, and dimensionality reduction, which are essential for addressing data sparsity and improving clustering performance. Furthermore, the review introduces commonly used clustering performance evaluation metrics, including Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), silhouette score, and marker gene validation. We also compare six distinct clustering methods across six datasets, evaluating the consistency in clustering accuracy with the selected methods. Our findings indicate that deep learning-based methods generally outperform other clustering methods in capturing complex relationships within the data, especially in high-dimensional and noisy datasets. However, challenges remain in areas such as computational efficiency, scalability for large-scale datasets, and handling batch effects. In this review, we systematically summarize the advantages and challenges of machine learning-based clustering algorithms. This work provides valuable insights and ideas for the development of new tools in the scRNA-seq clustering field and also helps address the numerous challenges faced in the downstream analysis of single-cell sequencing data.
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Concise Brønsted Acid-catalyzed Synthesis of Multi-substituted γ-Benzo-(Thio)Pyrones
Authors: Zhong-Hao Li, Yun Li, Zong Li, Yu Zeng, Wan-Xin Ye, Xi-Ying Cao and Zhao-Yang WangAvailable online: 12 September 2025More LessIntroductionIn this study, we report the Brønsted acid-catalyzed synthesis of multi-substituted γ-benzo (thio)pyrones. Both multi-substituted γ-benzopyrones and 4H-1-benzothiopyran-4-ones can be concisely synthesized by directly using o-hydroxybenzoylacetones or substituted thiophenols as starting materials.
MethodsCompounds 2a-2p were synthesized by 1,3-aryldi-ketones mixed with MeOH/HCl at 65°C for 4 h and 4a-4e were synthesized by thiophenol and ethyl acetoacetate mixed with PPA at 160°C for 5 h. All these obtained compounds were characterized by NMR or HR-MS.
ResultsA series of γ-benzo(thio)pyrone compounds can be synthesized via a mild procedure under the catalyst of Brønsted acid (HCl or PPA).
DiscussionThis synthesis method based on Brønsted acid catalysis has good substrate scope. The reaction mechanism has been verified by density functional theory calculations.
ConclusionThis method aligns with the principles of green chemistry, offers a valuable reference for the synthesis of multi-substituted γ-benzo(thio)pyrones, and broadens the applicability of Brønsted acid-catalyzed green synthesis.
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Computational Screening of Natural Compounds Targeting VEGFR-2 for Anti-Angiogenic Therapy
Available online: 07 July 2025More LessBackgroundAngiogenesis is the growth of new capillaries from existing blood vessels that supply oxygen and nutrients and provide gateways for immune surveillance. Abnormal vessel growth in terms of excessive angiogenesis is a hallmark of cancer. VEGFR-2 (vascular endothelial growth factor receptor 2) dominating the process of angiogenesis has led to the approval of therapeutic inhibitors and is becoming a promising target for anti-angiogenic drugs. Notwithstanding these successes, the clinical use of current VEGFR-2 blockers is more challenging than anticipated.
ObjectiveIn this study, we employed computer-aided approaches to elucidate the potential inhibitors of VEGFR2 in order to tackle angiogenesis in tumour.
MethodsThe phytochemicals were retrieved from the Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target (NPACT) database and were virtually screened with the help of molecular docking. The study employed the prediction of the pIC50 value and cytotoxic property of the phytochemical against various cancer cell lines. The preclinical efficacy was predicted using the ADME/T profile of the compounds. PASS analysis was performed to predict the biological activity of the phytochemicals.
ResultsThe compounds, namely liquiritigenin, acacetin, and D-delta-tocotrienol stand out among all screened phytochemicals. Liquiritigenin showed -38.24 total binding free energy (PBTOT/GBTOT), while acacetin and D-delta-tocotrienol exhibited -43.01 and -53.91 PBTOT/GBTOT, respectively. Furthermore, these three compounds showed promising RMSD, Rg, and SASA trajectories, which signifies their stability with the VEGFR2. Their ADME/T profile exhibited their preclinical safety.
DiscussionLiquiritigenin, acacetin, and D-delta-tocotrienol demonstrated strong binding to VEGFR-2, with binding free energies of -38.24, -43.01, and -53.91 PBTOT/GBTOT, respectively, suggesting potential as anti-angiogenic agents. Stable RMSD, Rg, and SASA trajectories and favorable ADME/T profiles support their preclinical safety. However, experimental validation is needed to confirm efficacy and specificity, as the study relies solely on computational methods. Future in vitro and in vivo studies are essential for clinical translation.
ConclusionThe results from various computational analyses suggest that these three phytochemicals, namely, liquiritigenin, acacetin, and D-delta-tocotrienol, could possibly serve as potent VEGFR2 inhibitors to control the development of angiogenesis and tumorigenesis. However, experimental validation is required to test their efficacy as the study is solely based on a computational approach.
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