570 Biowissenschaften; Biologie
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Simplified Sample Preparation and Lateral Flow Immunoassay for the Detection of Plant Viruses
(2026)
Lateral flow immunoassays (LFAs) are widely used for on-site testing; however, their use for the rapid detection of plant viruses in the field is often limited by inconvenient sample preparation. Here, we present a new sampling method and a simplified dipstick LFA format for the detection and monitoring of cowpea chlorotic mottle virus (CCMV) as a model plant pathogen. The assay employs a monoclonal mouse antibody for capture and a poly-clonal rabbit antibody conjugated to 80 nm gold nanoparticles for detection. Conventional sample and conjugate pads are omitted, allowing the test strips to be dipped directly into wells containing plant extract and antibody–gold conjugate. No plastic casing was required, which could lead to a reduction in waste. It was shown that CCMV concentrations as low as 3.5 μg/L or 350 pg per sample could be reliably detected in 15 min. Specificity tests confirmed that other plant viruses, cowpea mosaic virus (CPMV) and tobacco mosaic virus (TMV), did not produce false-positive results. In addition, we describe a new method for on-site sampling using a manual punch and a syringe equipped with a frit. This step combines grinding the sample, extraction, filtration, and reconstitution and mixing of the antibody-gold conjugate, enabling the analysis of punched leaf disks without laboratory equipment. When applied to CCMV-infected cowpea plants, the assay revealed systemic infection before visual symptoms became apparent. This work demonstrates that simplified LFAs combined with innovative sampling techniques can provide sensitive, specific, and rapid diagnostics for crop monitoring and support early intervention strategies in agriculture.
This study presents a new mathematical model for determining the specific growth rate of biomass in biotechnological production processes, which aims to optimize the production of biotechnological products such as the advanced material polyhydroxyalkanoates. The specific growth rate is classified by the FDA as a critical process parameter that affects product quality and quantity, but is difficult for laboratory personnel to determine. Therefore, a simple and robust method for real-time monitoring and control is crucial. According to the current state of the art, the established Luedeking-Piret model for determining the specific growth rate requires the determination of the biomass as an absolute value to initialize the model and to determine two further model parameters. However, determining the biomass is time-consuming and error-prone. The new relative model replaces this value with the relative change in biomass, which can be easily recorded using standard laboratory methods such as optical density measurement. This eliminates the need for time-consuming and resource-intensive preliminary work. Despite this simplification, simulation tests have shown that the new model delivers identical results to the established model. It represents an independent, precise alternative and offers advantages in terms of handling. The results underline the model’s potential to make bioprocesses more sustainable and efficient. Especially in research, material consumption, laboratory time and costs can be reduced compared to the established model. Future experiments will further investigate the performance of the new approach compared to the established model.
Modern, high-throughput methods for the analysis of genetic information, gene and metabolic products and their interactions offer new opportunities to gain comprehensive information on life processes. The data and knowledge generated open diverse application possibilities with enormous innovation potential. To unlock that potential skills in generating but also properly annotating the data for further data integration and analysis are needed. The data need to be made computer readable and interoperable to allow integration with existing knowledge leading to actionable biological insights. To achieve this, we need common standards and standard operating procedures as well as workflows that allow the combination of data across standards. Currently, there is a lack of experts who understand the principles and possess knowledge of the principles and relevant tools. This is a major barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles and the actual reusability of data. This is mainly due to insufficient and unequal education of the scientists and other stakeholders involved in producing and handling big data in life science that is inherently varied and complex in nature, and large in volume. Due to the interdisciplinary nature of life science research, education within this field faces numerous hurdles including institutional barriers, lack of local availability of all required expertise, as well as lack of appropriate teaching material and appropriate adaptation of curricula.
(1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models.
The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull.
Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology.
The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses.
Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared.
In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal.
Solutions to the 3D Model Problem of Pressure Measurement in the Area of Maxillary Sinus Anastomosis
(2020)
The ostiomeatal complex (OMC) is a key area that determines the occurrence of inflammatory processes in the paranasal sinuses (PNSs). The aim of our work was to develop a procedure for studying the OMC components in the preoperative period that allows for identification the impact of anatomical peculiarities on change of physiological pressure in the maxillary sinus. Materials and methods: The study was carried out on the basis of the otorhinolaryngological department of Kharkiv Regional Clinical Hospital in 2019-2020. It involved 100 patients of both sexes aged 20-59 years with chronic non-polyposis maxillary sinusitis. Results: The sizes of the uncinate process, the middle turbinate and the natural anastomosis were determined using the calculation of uncertainty. Basing on the data obtained, all the patients were divided into three groups. Conclusions: Changes in the size of the natural anastomosis (both an increase and its narrowing) lead to changes in pressure in the area of the anastomosis, and a decrease in ventilation in the paranasal sinuses. SCT study with subsequent 3D modeling is an informative, accurate and effective method for assessment of OMC and PNSs condition. It allows surgeons to presume the method and volume of surgery as early as at the preoperative stage, without resorting to invasive research methods.
Processing biological materials with 3-dimensional (3D) printers has attracted increasing interest in several research areas. At the same time, off-the-shelf solutions are increasingly available in a wider variety to meet the current market demand. However, most of the available bioprinters are closed source, thus, modifications are quite challenging or require an avoidable consultation process with the manufacturer. Furthermore, the entry prices for basic machines amount to several thousands of euros. Whereas, high-end 3D bio printers with a vast array of features are available for several hundred thousand euros. Due to the immense potential of this tool in the field of biotechnology it is important to extend the availability of this technology for research purposes in terms of adaptability and price. This ongoing work focuses on open-source 3D printer heads with the ability to extrude biological materials. The print heads include several techniques to process low as well as high viscose biomaterials such as agar and gelatin. Additionally, obstacles such as continuous substrate tempering or integration into existing 3D printers are addressed. The work presented is open source and thus freely adaptable to any user’s specific needs. Our goal is to process a diverse range of biomaterials with different print techniques.
The sample preparation for biological and chemical probes involves following a strict workflow to eliminate any contamination to the sample beforehand. Furthermore, it is time consuming and must be carried out by trained personnel such as a nurse or other supervisors, making it therefore expensive. The development of novel sample preparation techniques paired with modern sample analysis systems is focused on improving the operability while keeping a constant quality of results. This is important to analyse samples, which cannot be determined with current screening conditions. The analysis of analytes is required to receive a more detailed picture of the patient and to fully understand its complexity. Possible samples for in-depth analysis of chemical origin can be cholesterol or glucose. More complex samples, such as blood or saliva, require a sophisticated system, which analyses the samples for their individual compounds.
The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications.

