Research advances and applications of biosensing technology for the diagnosis of pathogens in sustainable agriculture

An assessment of nanotechnology-based interventions for cleaning up toxic heavy metal/metalloid-contaminated agroecosystems: Potentials and issues

Heavy metals (HMs) are among the most dangerous environmental variables for a variety of life forms, including crops. Accumulation of HMs in consumables and their subsequent transmission to the food web are serious concerns for scientific communities and policy makers. The function of essential plant cellular macromolecules is substantially hampered by HMs, which eventually have a detrimental effect on agricultural yield. Among these HMs, three were considered, i.e., arsenic, cadmium, and chromium, in this review, from agro-ecosystem perspective. Compared with conventional plant growth regulators, the use of nanoparticles (NPs) is a relatively recent, successful, and promising method among the many methods employed to address or alleviate the toxicity of HMs. The ability of NPs to reduce HM mobility in soil, reduce HM availability, enhance the ability of the apoplastic barrier to prevent HM translocation inside the plant, strengthen the plant's antioxidant system by significantly enhancing the activities of many enzymatic and nonenzymatic antioxidants, and increase the generation of specialized metabolites together support the effectiveness of NPs as stress relievers. In this review article, to assess the efficacy of various NP types in ameliorating HM toxicity in plants, we adopted a 'fusion approach', in which a machine learning-based analysis was used to systematically highlight current research trends based on which an extensive literature survey is planned. A holistic assessment of HMs and NMs was subsequently carried out to highlight the future course of action(s).

The influence of γ-PGA on the quality of cooked frozen crayfish during temperature fluctuations

The purpose of this study was to compare the influences of gamma-poly glutamic acid (γ-PGA) (1, 2, 3, and 4 %) to see which could outperform conventional cryoprotectant mixture (4 % sorbitol + 4 % sucrose) on cooked crayfish properties, such as physicochemical, textural qualities, oxidation reaction, water distributions, and microstructure integrity, during different freeze-thaw cycles. Crayfish quality characteristics improved significantly as γ-PGA concentration increased compared to control samples.Adding γ-PGA 4 % reduced the carbonyl content from 4.20 to 3.00 nmol/ mg protein during fluctuation-1 (F1), and from 4.15 to 2.80 nmol/ mg protein during fluctuation-2 (F2) compared to control samples. Furthermore, it increased the total sulfhydryl content from 4.15 and 4.76 to 6.19 and 6.47 mol/105 g protein during F1 and F2 and after five freeze-thaw cycles (FTC). This suggests that this concentration was more effective at controlling protein changes than other concentrations. γ-PGA generally enhanced the water-holding capacity by preventing protein denaturation and limiting ice crystal recrystallization. As a result, microstructure stability was evident, texture degradation was avoided, and the crayfish's color was preserved.

Carbon Nanomaterial Fluorescent Probes and Their Biological Applications

Fluorescent carbon nanomaterials have broadly useful chemical and photophysical attributes that are conducive to applications in biology. In this review, we focus on materials whose photophysics allow for the use of these materials in biomedical and environmental applications, with emphasis on imaging, biosensing, and cargo delivery. The review focuses primarily on graphitic carbon nanomaterials including graphene and its derivatives, carbon nanotubes, as well as carbon dots and carbon nanohoops. Recent advances in and future prospects of these fields are discussed at depth, and where appropriate, references to reviews pertaining to older literature are provided.

A mechanical biosensor based on membrane-mediated magneto-stress-electric coupled sensitization for human serum albumin detection

Recently, mechanical biosensors have attracted more attention on single molecule detection due to its high accuracy, low cost, and convenience. However, the sensitivity of the mechanical biosensors restricted their clinical application. Herein, a mechanical biosensor based on membrane-mediated magneto-stress-electric coupled sensitization (MSEC-MMB) was developed to enhance performance. Through introducing Fe3O4 nanoparticles (MNPs) to traditional stress-electric biosensors and applying a magnetic field, a magneto-stress-electric coupled biosensing system was constructed. The sensitivity of the MSEC-MMB was improved via enhancing the deformation of the mechanical membrane, which was demonstrated by detecting HSA. The optimal limit of detection (LOD) was 24 pg mL-1 under a magnetic field of 50 mT. The LOD was significantly 1 order of magnitude lower than that without the magnetic field. Besides, the MSEC-MMB showed a high specificity, selectivity, and stability. The clinical proteinuria samples were accurately detected, suggesting a good practicability of the MSEC-MMB. All these results proved the high sensitivity and practicality of the MSEC-MMB and provide a platform for early nephropathy diagnosis.

Molecular Approaches for Detection of Trichoderma Green Mold Disease in Edible Mushroom Production

Due to the evident aggressive nature of green mold and the consequently huge economic damage it causes for producers of edible mushrooms, there is an urgent need for prevention and infection control measures, which should be based on the early detection of various Trichoderma spp. as green mold causative agents. The most promising current diagnostic tools are based on molecular methods, although additional optimization for real-time, in-field detection is still required. In the first part of this review, we briefly discuss cultivation-based methods and continue with the secondary metabolite-based methods. Furthermore, we present an overview of the commonly used molecular methods for Trichoderma species/strain detection. Additionally, we also comment on the potential of genomic approaches for green mold detection. In the last part, we discuss fast screening molecular methods for the early detection of Trichoderma infestation with the potential for in-field, point-of-need (PON) application, focusing on isothermal amplification methods. Finally, current challenges and future perspectives in Trichoderma diagnostics are summarized in the conclusions.

Potential of nanobiosensor in sustainable agriculture: the state-of-art

A rapid surge in world population leads to an increase in worldwide demand for agricultural products. Nanotechnology and its applications in agriculture have appeared as a boon to civilization with enormous potential in transforming conventional farming practices into redefined farming activities. Low-cost portable nanobiosensors are the most effective diagnostic tool for the rapid on-site assessment of plant and soil health including plant biotic and abiotic stress level, nutritional status, presence of hazardous chemicals in soil, etc. to maintain proper farming and crop productivity. Nanobiosensors detect physiological signals and convert them into standardized detectable signals. In order to achieve a reliable sensing analysis, nanoparticles can aid in signal amplification and sensor sensitivity by lowering the detection limit. The high selectivity and sensitivity of nanobiosensors enable early detection and management of targeted abnormalities. This study identifies the types of nanobiosensors according to the target application in agriculture sector.

Enhancement of nitrogen use efficiency through agronomic and molecular based approaches in cotton

Cotton is a major fiber crop grown worldwide. Nitrogen (N) is an essential nutrient for cotton production and supports efficient crop production. It is a crucial nutrient that is required more than any other. Nitrogen management is a daunting task for plants; thus, various strategies, individually and collectively, have been adopted to improve its efficacy. The negative environmental impacts of excessive N application on cotton production have become harmful to consumers and growers. The 4R's of nutrient stewardship (right product, right rate, right time, and right place) is a newly developed agronomic practice that provides a solid foundation for achieving nitrogen use efficiency (NUE) in cotton production. Cropping systems are equally crucial for increasing production, profitability, environmental growth protection, and sustainability. This concept incorporates the right fertilizer source at the right rate, time, and place. In addition to agronomic practices, molecular approaches are equally important for improving cotton NUE. This could be achieved by increasing the efficacy of metabolic pathways at the cellular, organ, and structural levels and NUE-regulating enzymes and genes. This is a potential method to improve the role of N transporters in plants, resulting in better utilization and remobilization of N in cotton plants. Therefore, we suggest effective methods for accelerating NUE in cotton. This review aims to provide a detailed overview of agronomic and molecular approaches for improving NUE in cotton production, which benefits both the environment and growers.

Artificial Intelligence in Biological Sciences

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

Enhancing the Stability of Bacteriophages Using Physical, Chemical, and Nano-Based Approaches: A Review

Phages are efficient in diagnosing, treating, and preventing various diseases, and as sensing elements in biosensors. Phage display alone has gained attention over the past decade, especially in pharmaceuticals. Bacteriophages have also found importance in research aiming to fight viruses and in the consequent formulation of antiviral agents and vaccines. All these applications require control over the stability of virions. Phages are considered resistant to various harsh conditions. However, stability-determining parameters are usually the only additional factors in phage-related applications. Phages face instability and activity loss when preserved for extended periods. Sudden environmental changes, including exposure to UV light, temperature, pH, and salt concentration, also lead to a phage titer fall. This review describes various formulations that impart stability to phage stocks, mainly focusing on polymer-based stabilization, encapsulation, lyophilization, and nano-assisted solutions.

Research progress on detection techniques for point-of-care testing of foodborne pathogens

The global burden of foodborne disease is enormous and foodborne pathogens are the leading cause of human illnesses. The detection of foodborne pathogenic bacteria has become a research hotspot in recent years. Rapid detection methods based on immunoassay, molecular biology, microfluidic chip, metabolism, biosensor, and mass spectrometry have developed rapidly and become the main methods for the detection of foodborne pathogens. This study reviewed a variety of rapid detection methods in recent years. The research advances are introduced based on the above technical methods for the rapid detection of foodborne pathogenic bacteria. The study also discusses the limitations of existing methods and their advantages and future development direction, to form an overall understanding of the detection methods, and for point-of-care testing (POCT) applications to accurately and rapidly diagnose and control diseases.

Toxicity of Bacillus thuringiensis Strains Derived from the Novel Crystal Protein Cry31Aa with High Nematicidal Activity against Rice Parasitic Nematode Aphelenchoides besseyi

The plant parasitic nematode, Aphelenchoides besseyi, is a serious pest causing severe damage to various crop plants and vegetables. The Bacillus thuringiensis (Bt) strains, GBAC46 and NMTD81, and the biological strain, FZB42, showed higher nematicidal activity against A. besseyi, by up to 88.80, 82.65, and 75.87%, respectively, in a 96-well plate experiment. We screened the whole genomes of the selected strains by protein-nucleic acid alignment. It was found that the Bt strain GBAC46 showed three novel crystal proteins, namely, Cry31Aa, Cry73Aa, and Cry40ORF, which likely provide for the safe control of nematodes. The Cry31Aa protein was composed of 802 amino acids with a molecular weight of 90.257 kDa and contained a conserved delta-endotoxin insecticidal domain. The Cry31Aa exhibited significant nematicidal activity against A. besseyi with a lethal concentration (LC₅₀) value of 131.80 μg/mL. Furthermore, the results of in vitro experiments (i.e., rhodamine and propidium iodide (PI) experiments) revealed that the Cry31Aa protein was taken up by A. besseyi, which caused damage to the nematode's intestinal cell membrane, indicating that the Cry31Aa produced a pore-formation toxin. In pot experiments, the selected strains GBAC46, NMTD81, and FZB42 significantly reduced the lesions on leaves by up to 33.56%, 45.66, and 30.34% and also enhanced physiological growth parameters such as root length (65.10, 50.65, and 55.60%), shoot length (68.10, 55.60, and 59.45%), and plant fresh weight (60.71, 56.45, and 55.65%), respectively. The number of nematodes obtained from the plants treated with the selected strains (i.e., GBAC46, NMTD81, and FZB42) and A. besseyi was significantly reduced, with 0.56, 0.83., 1.11, and 5.04 seedling mL^-1 nematodes were achieved, respectively. Moreover, the qRT-PCR analysis showed that the defense-related genes were upregulated, and the activity of hydrogen peroxide (H₂O₂) increased while malondialdehyde (MDA) decreased in rice leaves compared to the control. Therefore, it was concluded that the Bt strains GBAC46 and NMTD81 can promote rice growth, induce high expression of rice defense-related genes, and activate systemic resistance in rice. More importantly, the application of the novel Cry31Aa protein has high potential for the efficient and safe prevention and green control of plant parasitic nematodes.

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

Advances, limitations, and prospects of biosensing technology for detecting phytopathogenic bacteria

Phytopathogenic bacteria cause severe economic losses in agricultural production worldwide. The spread rates, severity, and emerging plant bacterial diseases have become serious threat to the sustainability of food sources and the fruit industry. Detection and diagnosis of plant diseases are imperative in order to manage plant diseases in field conditions, greenhouses, and food storage conditions as well as to maximize agricultural productivity and sustainability. To date, various techniques including, serological, observation-based, and molecular methods have been employed for plant disease detection. These methods are sensitive and specific for genetic identification of bacteria. However, these methods are specific for genetic identification of bacteria. Currently, the innovative biosensor-based disease detection technique is an attractive and promising alternative. A biosensor system involves biological recognition and transducer active receptors based on sensors used in plant-bacteria diagnosis. This system has been broadly used for the rapid diagnosis of plant bacterial pathogens. In the present review, we have discussed the conventional methods of bacterial-disease detection, however, the present review mainly focuses on the applications of different biosensor-based techniques along with point-of-care (POC), robotics, and cell phone-based systems. In addition, we have also discussed the challenges and limitations of these techniques.

A Deep Learning-Based Model for Date Fruit Classification

A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.

Genome Engineering Technology for Durable Disease Resistance: Recent Progress and Future Outlooks for Sustainable Agriculture

Crop production worldwide is under pressure from multiple factors, including reductions in available arable land and sources of water, along with the emergence of new pathogens and development of resistance in pre-existing pathogens. In addition, the ever-growing world population has increased the demand for food, which is predicted to increase by more than 100% by 2050. To meet these needs, different techniques have been deployed to produce new cultivars with novel heritable mutations. Although traditional breeding continues to play a vital role in crop improvement, it typically involves long and laborious artificial planting over multiple generations. Recently, the application of innovative genome engineering techniques, particularly CRISPR-Cas9-based systems, has opened up new avenues that offer the prospects of sustainable farming in the modern agricultural industry. In addition, the emergence of novel editing systems has enabled the development of transgene-free non-genetically modified plants, which represent a suitable option for improving desired traits in a range of crop plants. To date, a number of disease-resistant crops have been produced using gene-editing tools, which can make a significant contribution to overcoming disease-related problems. Not only does this directly minimize yield losses but also reduces the reliance on pesticide application, thereby enhancing crop productivity that can meet the globally increasing demand for food. In this review, we describe recent progress in genome engineering techniques, particularly CRISPR-Cas9 systems, in development of disease-resistant crop plants. In addition, we describe the role of CRISPR-Cas9-mediated genome editing in sustainable agriculture.

Enantioselective evaluation of the chiral fungicide mandipropamid: Dissipation, distribution and potential dietary intake risk in tomato, cucumber, Chinese cabbage and cowpea

A chiral analytical method was developed and validated for the determination of mandipropamid enantiomers in tomato, cucumber, Chinese cabbage and cowpea. The linearity (R² > 0.99), accuracy (recovery: 73.8-106%) and precision (relative standard deviation: < 11%) were adequate for the detection of mandipropamid enantiomers in four vegetables. Field trials were further conducted to investigate the dissipation and residue distribution of mandipropamid and the possible enantioselectivity in different vegetables. Due to the shorter half-lives, mandipropamid dissipated more rapidly in Chinese cabbage (1.8-2.0 d) and cowpea (1.6-2.4 d) than in tomato (5.0-8.4 d) and cucumber (2.4-5.5 d). The residues of mandipropamid were 45-179 µg/kg in tomato 14 d at low dose, 48-98 µg/kg in cucumber 7 d after spraying twice at low dose, and < 2.5-1942 µg/kg in Chinese cabbage and cowpea in all treatments, which were below the maximum residue limits of mandipropamid set by the European Union and Codex Alimentarius Commission. Enantioselectivity was observed during the mandipropamid dissipation process in four vegetables. The S-(+)-enantiomer dissipated more rapidly than the R-(-)-enantiomer in tomato and Chinese cabbage (enantiomeric fractions > 0.5). For cucumber and cowpea, the dissipation of the R-enantiomer was preferential (enantiomeric fractions < 0.5). According to the risk quotient data (<< 100%), the residues of mandipropamid in four vegetables were safe for Chinese consumers. This study could provide useful information for the dissipation fate and residue distribution of mandipropamid in vegetables at the enantiomer level and offer some guidance for the dietary intake risk evaluation of mandipropamid in vegetables.

Revealing plant growth-promoting mechanisms of Bacillus strains in elevating rice growth and its interaction with salt stress

Soil salinity is a major environmental stress that has been negatively affecting the growth and productivity of rice. However, various salt-resistant plant growth-promoting rhizobacteria (PGPR) have been known to promote plant growth and alleviate the damaging effects of salt stress via mitigating physio-biochemical and molecular characteristics. This study was conducted to examine the salt stress potential of Bacillus strains identified from harsh environments of the Qinghai-Tibetan plateau region of China. The Bacillus strains NMTD17, GBSW22, and FZB42 were screened for their response under different salt stress conditions (1, 4, 7, 9, 11, 13, and 16%). The screening analysis revealed strains NMTD17, GBSW22, and FZB42 to be high-salt tolerant, moderate-salt tolerant, and salt-sensitive, respectively. The NMTD17 strain produced a strong biofilm, followed by GBSW22 and FZB42. The expression of salt stress-related genes in selected strains was also analyzed through qPCR in various salt concentrations. Further, the Bacillus strains were used in pot experiments to study their growth-promoting ability and antioxidant activities at various concentrations (0, 100, 150, and 200 mmol). The analysis of growth-promoting traits in rice exhibited that NMTD17 had a highly significant effect and GSBW22 had a moderately significant effect in comparison with FZB42. The highly resistant strain NMTD17 that stably promoted rice plant growth was further examined for its function in the composition of rhizobacterial communities. The inoculation of NMTD17 increased the relative abundance and richness of rhizobacterial species. These outcomes propose that NMTD17 possesses the potential of PGPR traits, antioxidants enzyme activities, and reshaping the rhizobacterial community that together mitigate the harmful effects of salinity in rice plants.

Ovalbumin and Kappa-Carrageenan Mixture Suppresses the Oxidative and Structural Changes in the Myofibrillar Proteins of Grass Carp (Ctenopharyngodon idella) during Frozen Storage

This study was done to analyze the cryoprotective influence of ovalbumin (OVA) with kappa-carrageenan (KC) in grass carp myofibrillar proteins during frozen storage. Ca2+-ATPase activity of MP was significantly reduced due to protein denaturation and showed a direct association with decreased sulphydryl (SH) contents and tertiary structural properties. Besides that, an increase in carbonyl, surface hydrophobicity, and dityrosine contents was observed. The addition of OVA-KC significantly restricted the decline in Ca2+-ATPase and SH groups, which were further confirmed by the retarded increase in carbonyls. Furthermore, the addition of OVA-KC increased the stability of α-helix contents. Moreover, MP treated with 6% OVA-KC also improved intermolecular interaction forces linked with gelling and water holding properties of MP. Therefore, it can be concluded that OVA-KC could be used as an effective cryoprotectant in fish and related products for preservation and commercialization.

Ecotypic Morphological and Physio-Biochemical Responses of Two Differentially Adapted Forage Grasses, Cenchrus ciliaris L. and Cyperus arenarius Retz. to Drought Stress

Crop performance and yield are the results of genotypic expression as modulated by continuous interaction with the environment. Among the environmental aspects, drought and salinity are the most important factors, which limit the forages, including grasses, on a global basis. Grass species have the ability to grow under low water conditions and can produce high dry yield, proteins, and energy in areas exposed to drought stress. For this purpose, we conducted the present study to understand the response of forage grasses under drought stress from two different regions (Salt Range and Faisalabad) of Punjab, Pakistan. Two ecotypes of each grass species (Cenchrus ciliaris L. and Cyperus arenarius Retz.) were grown in pots at the botanical research area, Government College University Faisalabad, Pakistan. A group of plants were subjected to drought stress (60% field capacity) and controlled (100% field capacity) after three weeks of seed germination. The results from the present study depicted that the fresh and dry weights of root and shoot were decreased significantly under drought conditions. Moreover, C. ciliaris of the Salt Range area showed more resistance and higher growth production under drought stress. The chlorophyll (a and b) contents were also decreased significantly, while MDA, total soluble sugars, and proline levels were increased significantly under water-limited environments in the C. arenarius of Salt Range area. Enzymatic antioxidants (superoxidase dismutase (SOD) and peroxidase (POD)) and leaf Na+ were significantly raised in C. arenarius under drought stress collected from the Faisalabad region. Cenchrus ciliaris showed higher level of H2O2, total soluble proteins, glycinebetaine, catalase (CAT) and POD compared to C. arenarius. It also retained more leaf and root Ca2+, and root K+ under drought stress. It was concluded from the study that C. ciliaris is more resistant to drought in biomass production collected from the Salt Range area. The results suggested that C. ciliaris can be more widely used as a forage grass under water-scarce conditions as compared to C. arenarius.

Substrate Materials for Biomolecular Immobilization within Electrochemical Biosensors

Electrochemical biosensors have potential applications for agriculture, food safety, environmental monitoring, sports medicine, biomedicine, and other fields. One of the primary challenges in this field is the immobilization of biomolecular probes atop a solid substrate material with adequate stability, storage lifetime, and reproducibility. This review summarizes the current state of the art for covalent bonding of biomolecules onto solid substrate materials. Early research focused on the use of Au electrodes, with immobilization of biomolecules through ω-functionalized Au-thiol self-assembled monolayers (SAMs), but stability is usually inadequate due to the weak Au-S bond strength. Other noble substrates such as C, Pt, and Si have also been studied. While their nobility has the advantage of ensuring biocompatibility, it also has the disadvantage of making them relatively unreactive towards covalent bond formation. With the exception of Sn-doped In2O3 (indium tin oxide, ITO), most metal oxides are not electrically conductive enough for use within electrochemical biosensors. Recent research has focused on transition metal dichalcogenides (TMDs) such as MoS2 and on electrically conductive polymers such as polyaniline, polypyrrole, and polythiophene. In addition, the deposition of functionalized thin films from aryldiazonium cations has attracted significant attention as a substrate-independent method for biofunctionalization.

Foliar application of ascorbic acid enhances salinity stress tolerance in barley (Hordeum vulgare L.) through modulation of morpho-physio-biochemical attributes, ions uptake, osmo-protectants and stress response genes expression

Barley (Hordeum vulgare L.) is a major cereal grain and is known as a halophyte (a halophyte is a salt-tolerant plant that grows in soil or waters of high salinity). We therefore conducted a pot experiment to explore plant growth and biomass, photosynthetic pigments, gas exchange attributes, stomatal properties, oxidative stress and antioxidant response and their associated gene expression and absorption of ions in H. Vulgare. The soil used for this analysis was artificially spiked at different salinity concentrations (0, 50, 100 and 150 mM) and different levels of ascorbic acid (AsA) were supplied to plants (0, 30 and 60 mM) shortly after germination of the seed. The results of the present study showed that plant growth and biomass, photosynthetic pigments, gas exchange parameters, stomatal properties and ion uptake were significantly (p < 0.05) reduced by salinity stress, whereas oxidative stress was induced in plants by generating the concentration of reactive oxygen species (ROS) in plant cells/tissues compared to plants grown in the control treatment. Initially, the activity of antioxidant enzymes and relative gene expression increased to a saline level of 100 mM, and then decreased significantly (P < 0.05) by increasing the saline level (150 mM) in the soil compared to plants grown at 0 mM of salinity. We also elucidated that negative impact of salt stress in H. vulgare plants can overcome by the exogenous application of AsA, which not only increased morpho-physiological traits but decreased oxidative stress in the plants by increasing activities of enzymatic antioxidants. We have also explained the negative effect of salt stress on H. vulgare can decrease by exogenous application of AsA, which not only improved morpho-physiological characteristics, ions accumulation in the roots and shoots of the plants, but decreased oxidative stress in plants by increasing antioxidant compounds (enzymatic and non-enzymatic). Taken together, recognizing AsA's role in nutrient uptake introduces new possibilities for agricultural use of this compound and provides a valuable basis for improving plant tolerance and adaptability to potential salinity stress adjustment.