Hasil untuk "Biotechnology"

V. Ghormade, M. Deshpande, K. Paknikar

E. Hansen, M. Miró

Ncbi

Y. Bào, P. Bolotov, D. Dernovoy et al.

A. Arora, A. Gambardella

D. Audretsch, Paula E. Stephan

W. Powell

L. Zucker, L. Zucker, Michael R. Darby et al.

J. Liebeskind, Amalya L. Oliver, L. Zucker et al.

Martin Dragosits, D. Mattanovich

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

Pablo Dorta-González, María Isabel Dorta-González

This study investigates how YouTube content creators utilize scientific evidence in videos. Log-linear regression examines the influence of alternative communication channels on video creators in Biotechnology, using data from 81,302 papers (2018-2023). This reveals a positive association with news articles and Wikipedia pages, but a negative association with scientific papers, policy documents, and patents. Despite the potential for enriching discussions, science video creators seem to favor materials with wider public attention over influential science, technology, and policy papers. These findings suggest a need for improved dissemination strategies for scientific research. Authors, universities, and journals should consider how their work can be made more accessible and engaging for science communicators on video.

Debora Sabotinova, Petya Boycheva, Nadezhda Ivanova et al.

<b>Background</b>: <i>Geraniaceae</i> species are widely used in traditional medicine. <i>Pelargonium radula</i> and <i>Geranium macrorrhizum</i> are aromatic medicinal plants traditionally used in Bulgaria for their antimicrobial, anti-inflammatory, and wound-healing properties. Comparative phytochemical data on <i>Pelargonium radula</i> and <i>Geranium macrorrhizum</i> cultivated in Bulgaria, however, remain limited. The present work aimed to characterize and compare the chemical composition of essential oils and main phenols, in support of future pharmacological evaluation. <b>Methods</b>: Essential oils from aerial parts of both species were obtained by hydrodistillation and analyzed by GC-MS. Through HPLC-UV, ethanol extracts were evaluated to quantify the major phenolic acids and flavonoids. <b>Results</b>: The yield of essential oils was 0.10% for <i>P. radula</i> and 0.03% for <i>G. macrorrhizum</i>, dominated by oxidized monoterpenes, mainly citronellol and geraniol-type compounds. HPLC analysis revealed marked differences in their phenolic profiles. <i>P. radula</i> showed a composition with six phenolic acids—primary protocatechuic and ferulic acids, and very low levels of flavonoids, with rutin being the only quantifiable glycoside. In contrast, <i>G. macrorrhizum</i> contained nine phenolic acids and four flavonoids, with remarkably high levels of salicylic, rosmarinic, and <i>p</i>-coumaric acids, as well as catechins, absent in <i>P. radula</i>. <b>Conclusions</b>: The two species showed different phytochemical characteristics in both their volatile and non-volatile fractions. <i>P. radula</i> is characterized by a citronellol/geraniol-rich essential oil and a moderate phenolic profile, while <i>G. macrorrhizum</i> exhibits significantly higher phenolic diversity and abundance. These findings expand the current phytochemical knowledge of both taxa and provide a solid basis for future chemotaxonomic and pharmacological studies. The obtained results suggest that <i>Geranium macrorrhizum</i> may be more promising for antioxidant and anti-inflammatory applications, while <i>Pelargonium radula</i> may be preferentially explored for ant-microbial purposes.

Y. Nancharaiah, Piet N.L. Lens

Aram Ansary Ogholbake, Qiang Cheng

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Several diagnostic methods, such as imaging modalities and Serum Alpha-Fetoprotein (AFP) testing, have been used for HCC detection; however, their effectiveness is limited to later stages of the disease. In contrast, transcriptomic analysis of biposy samples has shown promise for early detection. While machine learning techniques have been applied to transcriptomic data for cancer detection, their clinical adoption remains limited due to challenges such as poor generalizability across different datasets, lack of interpretability, and high computational complexity. To address these limitations, we developed a novel predictive formula for HCC detection using the Kolmogorov-Arnold Network (KAN). This formula is based on the expression levels of five genes: VIPR1, CYP1A2, FCN3, ECM1, and LIFR. Derived from the GSE25097 dataset, the formula offers a simple, interpretable, efficient, and accessible approach for HCC identification. It achieves 99% accuracy on the GSE25097 test set and demonstrates robust performance on six additional independent datasets, achieving accuracies of above 90% in all cases. These findings highlight the critical role of these five genes as biomarkers for HCC detection, offering a foundation for future research and clinical applications to improve HCC diagnostic approaches.

Hiroyuki Aoyanagi, Yasuhiro Magi, Shoichi Toyabe

We experimentally demonstrate that information replication by templated ligation of DNA strands inherits a kinetic proofreading mechanism and achieves significant error suppression through cascade replication. A simple simulation model derived from the experimental results shows that templated ligation has a significant advantage over replication by polymerization for error suppression of long strands. Specifically, longer chains show lower error rates, significantly distinct from the chain-growth polymerization where errors typically accumulate with chain length. This mechanism provides a plausible route for high-fidelity replication in prebiotic chemistry and illustrates how physical principles such as nonequilibrium kinetics and network architecture can drive reliable molecular information replication. The approach also offers new strategies for error suppression in biotechnology.

Jon Crowcroft, Anil Madhavapeddy, Chris Hicks et al.

What if you could really revoke your actual biometric identity, and install a new one, by live rewriting your biological self? We propose some novel mechanisms for hot swapping identity based in novel biotechnology. We discuss the potential positive use cases, and negative consequences if such technology was to become available and affordable. Biometrics are selected on the basis that they are supposed to be unfakeable, or at least not at reasonable cost. If they become easier to fake, it may be much cheaper to fake someone else's biometrics than it is for you to change your own biometrics if someone does copy yours. This potentially makes biometrics a bad trade-off for the user. At the time of writing, this threat is highly speculative, but we believe it is worth raising and considering the potential consequences.

Diego Riofrío-Luzcando, Jaime Ramírez, Marta Berrocal-Lobo

Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are firstly grouped into clusters. Then an extended automaton is created for each cluster based on the sequences of events found in the cluster logs. The main objective of this model is to predict the actions of new students for improving the tutoring feedback provided by an intelligent tutoring system. The proposed model has been validated using student logs collected in a 3D virtual laboratory for teaching biotechnology. As a result of this validation, we concluded that the model can provide reasonably good predictions and can support tutoring feedback that is better adapted to each student type.

Ian Jhemes Oliveira Sousa, Ian Jhemes Oliveira Sousa, Kerolayne de Melo Nogueira et al.

Ameni Ben Abdennebi, Iness Bettaieb Rebey, Rym Essid et al.

Pomegranate (<i>Punica granatum</i> L.), is renowned for its bioactive compounds, offering significant potential in cosmetic applications due to its antioxidant, anti-inflammatory, and antimicrobial properties. This study presents a sustainably sourced cosmetic ingredient developed by enriching pomegranate seed oil with peel powder using optimized ultrasonication, followed by encapsulation in alginate nanobeads and integration into a minimalist hydrogel formulation. A Box–Behnken experimental design was employed to optimize ultrasonication parameters (15 min, 90% power, 202 mg/mL powder-to-oil ratio), yielding an enriched PSO with significantly enhanced total phenolic content (TPC: 69.23 ± 1.66 mg GAE/g), total flavonoid content (TFC: 61.09 ± 1.66 mg QE/g), and robust DPPH antioxidant activity (78.63 ± 3.81%). The enriched oil exhibited enhanced oxidative stability (peroxide value: 5.75 ± 0.30 meq O₂/kg vs. 50.95 ± 0.07 meq O₂/kg for neutral oil), improved fatty acid profile, and significant anti-inflammatory (IC₅₀ = 897.25 µg/mL for NO inhibition) and antibacterial activities. Alginate nanobeads (432.46 ± 12.59 nm, zeta potential: −30.74 ± 3.20 mV) ensured bioactivity preservation, while the hydrogel maintained physicochemical and microbial stability over 60 days under accelerated conditions (40 ± 2 °C, 75 ± 5% RH). This multifunctional formulation, integrating sustainable extraction, advanced encapsulation, and a minimalist delivery system, represents a highly promising natural ingredient for anti-aging and antioxidant cosmetic applications.

Worrawoot Jumlongnark

This review article explores the challenges and opportunities faced by the Bank for Agriculture and Agricultural Cooperatives (BAAC) in Thailand from a microfinance perspective. It examines the role of BAAC as a specialized financial institution in assisting underprivileged households and small businesses in accessing financial services. The study emphasizes the challenges and opportunities faced by BAAC in promoting sustainable development. It also explores BAAC's role in advancing the BCG Model policy, which fosters sustainability in the agricultural sector through Bio Economy Credit, Circular Economy Credit, and Green Credit. These initiatives support investments in biotechnology, waste reduction (Zero Waste), organic farming, and safe food production, all aimed at enhancing farmers' quality of life, stimulating growth in agriculture, and preserving the environment. Moreover, BAAC remains committed to upholding transparency, fairness, and operational standards.