Hasil untuk "q-bio"

William W. M. Verbiest, Pauline Hicter, Hans Beeckman et al.

Abstract Wood density is a key plant property, indispensable for estimating forest biomass. Yet, despite tropical regions’ substantial contributions to global tree diversity and carbon cycling, they remain underrepresented in wood density datasets such as the CIRAD and Global Wood Density Database (GWDD). To address this gap, we present the ‘Tervuren xylarium Wood Density Database’ (TWDD), containing 13,332 samples from 2,994 species, 1,022 genera, and 156 plant families across six continents (72% from Africa). TWDD offers direct measurements of oven-dry (oven-dry mass/oven-dry volume, all samples), air-dry (air-dry mass/air-dry volume, 6,408 samples), green (green mass/green volume, 1,657 samples), and basic wood density (oven-dry mass/green volume, 1,686 samples). Basic density was estimated for the remaining 11,646 samples via conversion from oven-dry density. TWDD closes a substantial wood density data gap, especially in Africa, adding 1,164 new species, 160 new genera, and 8 new plant families not included in GWDD or CIRAD datasets. The TWDD provides a critical resource for advancing research on forest community dynamics, ecosystem functioning, carbon cycling, and trait-based ecology worldwide.

Shiwei Lin, Jianguo Wang, Bomin Huang et al.

Abstract Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randomly generated angles, which enhances the algorithm’s searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning.

Esmat Rafiee

Abstract In this work, a two-dimensional ring-shaped photonic crystal structure is proposed. The proposed structure is consisted of 28*25 silicon rods situated in air medium. Fundamental properties of the proposed structure (acting as a biosensor) are investigated through photonic bandgap (PBG) and field distribution diagrams. The proposed structure is considered for diagnosis of cholesterol and creatinine levels in blood samples. Proposed structure is designed to operate as a 2:1 multiplexer (also functioning as a biosensor). Thus, by inserting incident signals with specific wavelengths (placed in TE PBG region) to input0, input1 and select ports, light wave can be transmitted to output port. Cholesterol concentrations in blood sample can be detected by considering input0 (I0 = 1), input1 (I1 = 0) and select (S = 0). For cholesterol following bio sensing factors will be calculated. Q: (45.4–52.88), S: 2673.4 nm/RIU, DL: (0.00125–0.00143) RIU, FOM: (80.91–82.06) RIU−1. Creatinine levels in blood samples can also be diagnosed by considering input0 (I0 = 0), input1 (I1 = 1) and select (S = 1). For creatinine, Q: (101.1–109.4), S: 3582.7 nm/RIU, DL: (4.98e−4–5.26e−4) RIU and FOM: (199.01–201.3) RIU−1 will be calculated. Finally, proposed system can effectively help physician in early and precise prognosis of hypercholesterolemia and acute kidney injuries.

Eugenio Simao

Motivation: Biological Petri Nets (Bio-PNs) model biochemical pathways where multiple reactions simultaneously affect shared metabolites through convergent production or regulatory coupling. However, classical Petri net independence theory requires transitions to share no places -- a constraint that fails to capture biological reality. This mismatch prevents parallel simulation and incorrectly flags biologically valid models as structurally problematic. Results: To resolve this fundamental limitation, we introduce weak independence -- a novel formalization distinguishing resource conflicts from biological coupling. Building on this theory, we extend the Bio-PN definition from a classical 5-tuple to a 12-tuple by adding regulatory structure, environmental exchange classification, dependency taxonomy, heterogeneous transition types, and biochemical formula tracking. This extended formalism enables systematic classification of three place-sharing modes: competitive (conflict), convergent (superposition), and regulatory (read-only). Validating our approach on 100 diverse BioModels (1,775 species, 2,234 reactions across metabolism, signaling, and gene regulation), we find that 96.93% of transition pairs exhibit weak independence -- confirming that biological networks inherently favor cooperation over competition. Our SHYpn implementation demonstrates the practical impact, achieving up to 2.6x speedup on 30% of evaluated models. Availability and Implementation: Open-source at https://github.com/simao-eugenio/shypn (MIT License).

Eugenio Simao

Biological Petri Nets (Bio-PNs) require extensions beyond classical formalism to capture biochemical reality: multiple reactions simultaneously affect shared metabolites through convergent production or regulatory coupling, while signal places carry hierarchical control information distinct from material flow. We present a unified 13-tuple Extended Bio-PN formalism integrating two complementary theories: Weak Independence Theory (enabling coupled parallelism despite place-sharing) and Signal Hierarchy Theory (separating information flow from mass transfer). The extended definition adds signal partition (Psi subset P), arc type classification (A), regulatory structure (Sigma), environmental exchange (Theta), dependency taxonomy (Delta), heterogeneous transition types (tau), and biochemical formula tracking (rho). We formalize signal token consumption semantics through two-phase execution (enabling vs. consumption) and prove weak independence correctness for continuous dynamics. Application to Vibrio fischeri quorum sensing demonstrates how energy metabolism (ENERGY signals) orchestrates binary ON/OFF decisions through hierarchical constraint propagation to regulatory signals (LuxR-AHL complex), with 133-fold difference separating states. Analysis reveals signal saturation timing as the orchestrator forcing threshold-crossing, analogous to bacteriophage lambda lysogeny-lysis decisions. This work establishes formal foundations for modeling biological information flow in Petri nets, with implications for systems biology, synthetic circuit design, and parallel biochemical simulation.

Rodrigo Olivares, Camilo Ravelo, Ricardo Soto et al.

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

Sarah Iaquinta, Shahram Khazaie, Frédéric Jacquemin et al.

In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion.

Praveen Sahu, Ignacio G. Camarillo, Pragatheiswar Giri et al.

In this research, we investigated the efficacy of Metformin, the most commonly administered type-2 diabetes drug for triple negative breast cancer (TNBC) treatment, due to its various anticancer properties. It is a plant-based bio-compound, synthesized as a novel biguanide, called dimethyl biguanide or metformin. One of the ways it operates is by hindering electron transport chain-complex I, in mitochondria, which causes a drop-in energy (ATP) generation. This eventually builds energetic stress and a decline in energy. Therefore, the natural cellular processes and proliferating tumor cells are obstructed. Here, we used electroporation, where, the MDA-MB-231, human TNBC cells were subjected to high intensity, short-duration electrical pulses (EP) in the presence of Metformin. The cell viability results indicate lower cell viability of 43.45% as compared to 85.20% with drug alone at 5mM concentration. This indicates that Metformin, the most common diabetes drug could also be explored for cancer treatment.

Lucian Smith, Herbert M Sauro

Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe biochemical networks and systems using a simple and intuitive syntax. It allows users to easily create, modify, and distribute reproducible computational models. By allowing the concise representation of complex biological processes, Antimony enhances collaborative efforts, improves reproducibility, and accelerates the iterative development of models in systems biology. This paper provides an update to the Antimony language since it was introduced in 2009. In particular, we highlight new annotation features, support for flux balance analysis, a new rateOf method, support for probability distributions and uncertainty, named stochiometries, and algebraic rules. Antimony is also now distributed as a C/C++ library, together with python and Julia bindings, as well as a JavaScript version for use within a web browser. Availability: https://github.com/sys-bio/antimony.

Herbert M Sauro

In this paper, a set of Python methods is described that can be used to compute the frequency response of an arbitrary biochemical network given any input and output. Models can be provided in standard SBML or Antimony format. The code takes into account any conserved moieties so that this software can be used to also study signaling networks where moiety cycles are common. A utility method is also provided to make it easy to plot standard Bode plots from the generated results. The code also takes into account the possibility that the phase shift could exceed 180 degrees which can result in ugly discontinuities in the Bode plot. In the paper, some of the theory behind the method is provided as well as some commentary on the code and several illustrative examples to show the code in operation. Illustrative examples include linear reaction chains of varying lengths and the effect of negative feedback on the frequency response. Software License: MIT Open Source Availability: The code is available from https://github.com/sys-bio/frequencyResponse.

Rojarej Nunta, Charin Techapun, Sumeth Sommanee et al.

Abstract Open burning of agricultural residues causes numerous complications including particulate matter pollution in the air, soil degradation, global warming and many more. Since they possess bio-conversion potential, agro-industrial residues including sugarcane bagasse (SCB), rice straw (RS), corncob (CC) and sweet sorghum bagasse (SSB) were chosen for the study. Yeast strains, Candida tropicalis, C. shehatae, Saccharomyces cerevisiae, and Kluyveromyces marxianus var. marxianus were compared for their production potential of bioethanol and phenylacetylcarbinol (PAC), an intermediate in the manufacture of crucial pharmaceuticals, namely, ephedrine, and pseudoephedrine. Among the substrates and yeasts evaluated, RS cultivated with C. tropicalis produced significantly (p ≤ 0.05) higher ethanol concentration at 15.3 g L−1 after 24 h cultivation. The product per substrate yield (Y eth/s) was 0.38 g g-1 with the volumetric productivity (Q p) of 0.64 g L−1 h−1 and fermentation efficiency of 73.6% based on a theoretical yield of 0.51 g ethanol/g glucose. C. tropicalis grown in RS medium produced 0.303 U mL−1 pyruvate decarboxylase (PDC), a key enzyme that catalyzes the production of PAC, with a specific activity of 0.400 U mg−1 protein after 24 h cultivation. This present study also compared the whole cells biomass of C. tropicalis with its partially purified PDC preparation for PAC biotransformation. The whole cells C. tropicalis PDC at 1.29 U mL−1 produced an overall concentration of 62.3 mM PAC, which was 68.4% higher when compared to partially purified enzyme preparation. The results suggest that the valorization of lignocellulosic residues into bioethanol and PAC will not only aid in mitigating the environmental challenge posed by their surroundings but also has the potential to improve the bioeconomy.

Renbing Pan, Mingjia Xiao, Zhigang Wu et al.

BackgroundTelomere length (TL) has been regarded as a biomarker of aging, and TL shortening is associated with numerous chronic illnesses. The mounting evidence has shown that inflammatory cytokines are involved in maintaining or shortening TL, the causality of cytokines with TL remains unknown. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to estimate the underlying correlations of circulating inflammatory cytokines with TL.MethodsGenetic instrumental variables for inflammatory cytokines were identified through a genome-wide association study (GWAS) involving 8,293 European individuals. Summary statistics of TL were derived from a UK Bio-bank cohort comprising 472,174 samples of individuals with European descent. We employed the inverse-variance weighted (IVW) approach as our main analysis, and to ensure the reliability of our findings, we also conducted additional analyses including the weighted median, MR-Egger, MR pleiotropy residual sum and outlier test, and weighted model. Lastly, the reverse MR analyses were performed to estimate the likelihood of inverse causality between TL and the cytokines identified in the forward MR analysis. Cochran’s Q test were employed to quantify the degree of heterogeneity.ResultsAfter applying Bonferroni correction, a higher circulating level of Interleukin-7 (IL-7) was suggestively associated with TL maintaining (OR:1.01, 95%CI:1.00-1.02, P=0.032 by IVW method). The study also revealed suggestive evidence indicating the involvement of Interleukin-2 receptor, alpha subunit (IL-2Rα) level was negatively associated with TL maintaining (OR:0.98, 95%CI:0.96-1.00, P=0.045 by IVW method), and the weighted median approach was consistent (OR:0.99, 95%CI:0.97-1.00, P=0.035). According to the findings of reverse MR analysis, no significant causal relationship between TL and cytokines was explored. Our analysis did not reveal any substantial heterogeneity in the Single nucleotide polymorphisms or horizontal pleiotropy.ConclusionsOur MR analysis yielded suggestive evidence supporting the causality between circulating IL-7 and IL-2Rα and telomere length, necessitating further investigations to elucidate the mechanisms by which these inflammatory cytokines may impact the progression of telomeres.

Tarek A. Yousef, Haitham Alrabiah, Mohamed H. Al-Agamy et al.

The (R)-(6-Methoxyquinolin-4-yl)[(1S,2S,4S,5R)-5-vinylquinuclidin-2-yl]methanol (quinine)-tetraphenylborate complex was synthesized by reacting sodium tetraphenyl borate with quinine in deionized water at room temperature through an ion-pair reaction (green chemistry) at room temperature. The solid complex was characterized by several physicochemical methods. The formation of ion-pair complex between bio-active molecules and/or organic molecules is crucial to comprehending the relationships between bioactive molecules and receptor interactions. The complex under study was examined for antimicrobial activity. All theoretical calculations were carried out in vacuum and water using the B3LYP level 6–311G(d,p) levels of theory. The theoretical computation allowed for the prediction and visualization of ionic interactions, which explained the complex’s stability. The results of energy optimization showed that the Q-TPB complex is stable with a negative complexation energy. The obtained geometries showed that the boron (B⁻) and nitrogen (N⁺) in piperidine of the two molecules tetraphenylborate and quinine are close to each other, which makes it possible for ions to interact. The modest energy gap between HOMO and LUMO showed that the compound was stable. The computation of the electron transitions of the two models by density functional theory (TD-DFT) in the solvent at the theoretical level B3LYP/6–311G(d,p) allowed for the detection of three UV/visible absorption bands for both models and the discovery of a charge transfer between the host and the guest. The UV absorption, infrared, and H NMR are comparable with the experimental part.

Idemauro Antonio Rodrigues de Lara, Gabriel Rodrigues Palma, Victor José Bon et al.

Competition between parasitoids can reduce the success of pest control in biological programs using two species as bio-control agents or when multiple species exploit the same host crop. Parasitoid foraging behavior and the ability to identify already parasitized hosts affect the efficacy of parasitoid species as bio-agents to regulate pest insects. We evaluated the behavioural changes of parasitoids according to the quality of hosts ({\it i.e.}, previously parasitised or not), and the characterisation of these transitions over time via multi-state models. We evaluated the effects of previous parasitism of the brown stinkbug {\it Euschistus heros} eggs on the parasitism rate of the species {\it Trissolcus basalis} and {\it Telenomus podisi}. We successively modelled the choice of eggs (with three possibilities: non parasitised eggs, eggs previously parasitised by {\it T. podisi}, and eggs previously parasitised by {\it T. basalis}) and the conditional behaviour given the choice (walking, drumming, ovipositing or marking the chosen egg). We consider multi-state models in two successive stages to calculate double transition probabilities, and the statistical methodology is based on the maximum likelihood procedure. Using the Cox model and assuming a stationary process, we verified that the treatment effect was significant for the choice, indicating that the two parasitoid species have different choice patterns. For the second stage, i.e. behaviour given the choice, the results also showed the influence of the species on the conditional behaviour, especially for previously parasitised eggs. Specifically, {\it T.podisi} avoids intraspecific competition and makes decisions faster than {\it T. basalis}. In this work, we emphasise the methodological contribution with multi-state models, especially in the context of double transitions.

Muzammil Kuddushi, Aatif Ali Shah, Cagri Ayranci et al.

Optically transparent wound dressings offer a range of potential applications in the biomedical field, as they allow for the monitoring of wound healing progress without having to replace the dressing. These dressings must be impermeable to water and bacteria, yet permeable to moisture vapor and atmospheric gases in order to maintain a moist environment at the wound site. This review article provides a comprehensive overview of the types of wound dressings, novel wound dressing materials, advanced fabrication techniques for transparent wound dressing materials, and the key features and applications of transparent dressings for the healing process, as well as how it can improve healing outcomes. This review mainly focuses on representing specifications of transparent polymeric wound dressing materials, such as transparent electrospun nanofibers, transparent crosslinked hydrogels, and transparent composite films/membranes. Due to the advance properties of electrospun nanofiber such as large surface area, enable efficient incorporation of antibacterial molecules, a structure similar to the extracellular matrix, and high mechanical stability, is often used in wound dressing applications. We also highlight the hydrogels or films for wound healing applications, it's promote the healing process, provide a moisture environment, and offer pain relief with their cool, high-water content, excellent biocompatibility, and bio-biodegradability.

Xue Lin, Yousheng Shi, Pan Wen et al.

Free, conjugated, and bound phenolic fractions of peel and pulp in four wampee varieties from South China were analyzed for their contents, composition, antioxidant capacities, and inhibitory activities against <i>α</i>-glucosidase. We found that there were significant differences in phenolic/flavonoid contents among diverse varieties and different parts (peel and pulp), and the contents were highest in the peel’s bound form. The results of UHPL-Q-Exactive HF-X and HPLC showed that chlorogenic acid, gentisic acid, and rutin were abundantly distributed over the three phenolic fractions in peel and pulp of all wampee samples, while isoquercitrin was the most abundant in the conjugated form of peel/pulp and myricetin had the richest content in the free form of peel/pulp. Wampee peel had stronger antioxidant capacities of ABTS+, DPPH, ·OH, and FRAP than the pulp, and the bound phenolic fraction of the peel/pulp had much higher antioxidant activities than FP and CP fractions. It is interesting that the same phenolic fraction of the wampee peel displayed roughly close IC₅₀ values of <i>α</i>-glucosidase inhibition to those from the pulp samples. The relationship between individual phenolic and TPC/TFC/the bio-activities and the similarity among the free, conjugated, and bound phenolic fractions in peel and pulp samples were explored by using Pearson correlation analysis, principal component analysis, and hierarchical cluster analysis. This work provides a systematic and comprehensive comparison of the three phenolic fractions of diverse wampee varieties and different parts, and a rationale for applying phenolics from wampee fruits.

Laura Adarve-Cardona, David Ezenarro-Salcedo, Mario A. Macías et al.

Dioxime oxalates, a type of carbonyl oximes, are well-known as clean sources of iminyl radicals that undergo key organic chemistry transformations. A series of dioxime oxalates is reported in this manuscript, obtained by the reaction of the corresponding oximes with oxalyl chloride and Et₃N at room temperature. This one-pot method afforded three novel dioxime oxalates and the crystal structure of cyclopentanone dioxime oxalate analysis is also presented.

Sijia Xiao, Xike Xu, Xintong Wei et al.

Euphorbiasteroid, a lathyrane-type diterpene from <i>Euphorbiae semen</i> (the seeds of <i>Euphorbia lathyris</i> L.), has been shown to have a variety of pharmacological effects such as anti-tumor and anti-obesity. This study aims to investigate the metabolic profiles of euphorbiasteroid in rats and rat liver microsomes (RLMs) and <i>Cunninghamella elegans</i> bio-110930 by integrating ultra-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS), UNIFI software, and NMR techniques. A total of 31 metabolites were identified in rats. Twelve metabolites (<b>M1</b>–<b>M5</b>, <b>M8</b>, <b>M12</b>–<b>M13</b>, <b>M16</b>, <b>M24</b>–<b>M25</b>, and <b>M29</b>) were matched to the metabolites obtained by RLMs incubation and the microbial transformation of <i>C. elegans</i> bio-110930 and their structures were exactly determined through analysis of NMR spectroscopic data. In addition, the metabolic pathways of euphorbiasteroid were then clarified, mainly including hydroxylation, hydrolysis, oxygenation, sulfonation, and glycosylation. Finally, three metabolites, <b>M3</b> (20-hydroxyl euphorbiasteroid), <b>M24</b> (epoxylathyrol) and <b>M25</b> (15-deacetyl euphorbiasteroid), showed significant cytotoxicity against four human cell lines with IC₅₀ values from 3.60 μM to 40.74 μM. This is the first systematic investigation into the in vivo metabolic pathways of euphorbiasteroid and the cytotoxicity of its metabolites, which will be beneficial for better predicting the metabolism profile of euphorbiasteroid in humans and understanding its possible toxic material basis.

TEJPAL BAJAYA, R P GHASOLIA, MAMTA BAJYA et al.

Collar rot disease caused by Aspergillus niger van Teighem is an important seed and soil borne disease of groundnut (Arachis hypogaea L.) which deteriorates kernel quality and reduces yield. The experiments were conducted during kharif 2019 and 2020 at SKN College of Agriculture, Jobner, Jaipur, Rajasthan. To see the interactive effect of seed treatment and drenching, fungicides and bio-agents were applied through seeds [carbendazim (0.1%), carboxin+ thiram (0.25%), hexaconazole (0.2%), carbendazim + mancozeb (0.25%), Trichoderma harzianum (0.6%) and Trichoderma viride (0.6%)] and through drenching [carbendazim (0.1%), carboxin + thiram (0.25%) and carbendazim + mancozeb (0.25%)] at 20 days after sowing (DAS). The effect of six mineral nutrients i.e. Cu (5 kg/ha), K (30 kg/ha), S (25 kg/ha), B (10 kg/ha), Fe (5 kg/ha) and Zn (5 kg/ha) was also evaluated through soil application against the disease. The lowest disease incidence (4.62%) and highest pod yield (25.86 q/ha) were recorded by treating the seeds with hexaconazole (@0.2%) and drenching with carbendazim + mancozeb (@0.25%) at 20 DAS and next best was seed treatment with hexaconazole (0.2%) and drenching with carbendazim (0.1%). Among six mineral nutrients, copper (@5 kg/ha) was most significant in reducing the disease incidence (46.05%) and in increasing pod yield (24.81%) followed by potassium. Conclusively, seed treatment with hexaconazole (@0.2%) and drenching with carbendazim + mancozeb (@0.25%) at 20 days after sowing results in significantly higher disease control with increased pod yield.

Byungjun Park, Bonhan Koo, Jisub Kim et al.

Given the fatal health conditions caused by emerging infectious pathogens, such as severe acute respiratory syndrome coronavirus 2, their rapid diagnosis is required for preventing secondary infections and guiding correct treatments. Although various molecular diagnostic methods based on nucleic acid amplification have been suggested as gold standards for identifying different species, these methods are not suitable for the rapid diagnosis of pathogens owing to their long result acquisition times and complexity. In this study, we developed a rapid bio-optical sensor that uses a ball-lensed optical fiber (BLOF) probe and an automatic analysis platform to precisely diagnose infectious pathogens. The BLOF probe is easy to align and has a high optical sensing sensitivity (1.5-fold) and a large detection range (1.2-fold) for an automatic optical sensing system. Automatic signal processing of up to 250 copies/reaction of DNA of Q-fever-causing <i>Coxiella burnetii</i> was achieved within 8 min. The clinical utility of this system was demonstrated with 18 clinical specimens (9 Q-fever and 9 other febrile disease samples) by measuring the resonant wavelength shift of positive or negative samples for <i>Coxiella burnetii</i> DNA. The results from the system revealed the stable and automatic optical signal measurement of DNA with 100% accuracy. We envision that this BLOF probe-based sensor would be a practical tool for the rapid, simple, and sensitive diagnosis of emerging infectious pathogens.