A novel quantitative real-time PCR with the GAPDH reference gene for peste des petits ruminants.

Peste des petits ruminants (PPR) is a serious acute, highly contagious disease caused by the peste des petits ruminants virus (PPRV). This study aims to establish a qRT-PCR assay with an internal amplification control for the rapid and accurate detection of PPRV. The primers and probes for PPRV N were based on the national standard of the diagnostic techniques for PPR of China, and a pair of primers and TaqMan probes for the internal reference gene of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was designed. Optimisation of the reaction conditions, specificity, sensitivity and reproducibility tests, and clinical sample detection were conducted. The results showed that the optimal primers and probe concentrations of PPRV were 0.4 µmol/l and 0.4 µmol/l, respectively, and were 0.4 µmol/l and 0.2 µmol/l for the reference gene GAPDH, respectively. The established method has no cross-reaction with other viruses. The minimum detection limit was 6.8 copies/µl for PPRV and 190 copies/µl for GAPDH. The coefficients of variation (CV%) of PPRV and GAPDH were both lower than 2%. The results suggest that the PPRV qRT-PCR method containing internal reference genes has strong specificity, high sensitivity, and good reproducibility. The addition of internal reference genes for the sample quality control improves the accuracy of the detection.

Comparative analysis of electrophoresis and interferometric optical detection method for molecular weight determination of proteins.

Analytical detection methods play a pivotal role in scientific research, enabling the identification and quantification of specific analytes in various disciplines. This scientific report aims to compare two very different methodologies for determining the Molecular Mass (MM, also known as Molecular Weight, MW) of proteins: electrophoresis gel and the Interferometric Optical Detection Method (IODM). For this purpose, several proteins with different MM were selected. The electrophoresis technique was employed to validate the structure and MM of different parts or fragments of the Matrix Metallopeptidase 9 antibody (anti-MMP9), antibody against S100 calcium binding protein A6 (anti-S100A6) and Cystatin S4 antibody (anti-CST4) by examining the presence of bands with expected sizes. The IODM was applied to study the above-mentioned proteins (part of the antibodies) together with the protein G, as a reference to correlate the MM and protein sizes with the measured signal. We report the evidence of IODM as a competitive analytical approach for the determination of the MM of proteins for the first time. This innovative method allows for accurate MM determination using minimal sample volumes and concentrations, employing a simple experimental procedure that eliminates the requirement for protein denaturation.

Application of photothermal effects of nanomaterials in food safety detection.

Numerous nanomaterials endowed with outstanding light harvesting and photothermal conversion abilities have been extensively applied in various fields, such as photothermal diagnosis and therapy, trace substance detection, and optical imaging. Although photothermal detection methods have been established utilizing the photothermal effect of nanomaterials in recent years, there is a scarcity of reviews regarding their application in food safety detection. Herein, the recent advancements in the photothermal conversion mechanism, photothermal conversion efficiency calculation, and preparation method of photothermal nanomaterials were reviewed. In particular, the application of photothermal nanomaterials in various food hazard analyses and the newly established photothermal detection methods were comprehensively discussed. Moreover, the development and promising future trends of photothermal nanomaterial-based detection methods were discussed, which provide a reference for researchers to propose more effective, sensitive, and accurate detection methods.

CRISPR/Cas13a-based rapid detection method for porcine deltacoronavirus.

Background: Porcine deltacoronavirus (PDCoV) is a newly discovered porcine intestinal pathogenic coronavirus with a single-stranded positive-sense RNA genome and an envelope. PDCoV infects pigs of different ages and causes acute diarrhea and vomiting in newborn piglets. In severe cases, infection leads to dehydration, exhaustion, and death in sick piglets, entailing great economic losses on pig farms. The clinical symptoms of PDCoV infection are very similar to those of other porcine enteroviruses. Although it is difficult to distinguish these viral infections without testing, monitoring PDCoV is very important because it can spread in populations. The most commonly used methods for the detection of PDCoV is qPCR, which is time-consuming and require skilled personnel and equipment. Many farms cannot meet the conditions required for detection. Therefore, it is necessary to establish a faster and more convenient method for detecting PDCoV. Aims: To establish a rapid and convenient detection method for PDCoV by combining RPA (Recombinase Polymerase Isothermal Amplification) with CRISPR/Cas13a. Methods: Specific RPA primers and crRNA for PDCoV were designed, and the nucleic acids in the samples were amplified with RPA. Fluorescent CRISPR/Cas13a detection was performed. We evaluated the sensitivity and specificity of the RPA-CRISPR/Cas13a assay using qPCR as the control method. Results: CRISPR/Cas13a-assisted detection was completed within 90 min. The minimum detection limit of PDCoV was 5.7 × 101 copies/µL. A specificity analysis showed that the assay did not cross-react with three other porcine enteroviruses. Conclusion: The RPA-CRISPR/Cas13a method has the advantages of high sensitivity, strong specificity, fast response, and readily accessible results, and can be used for the detection of PDCoV.

Multisource information fusion method for vegetable disease detection.

Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the "Vegetable Town," to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model's resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

The Role of Changes in the Expression of Inflammation-Associated Genes in the Variants of Cerebral Small Vessel Disease.

Age-dependent cerebral small vessel disease (CSVD) is a common disease with a high social burden characterized by heterogeneity of forms and frequent comorbidity with Alzheimer's disease (AD). Previously, we identified two MRI types of CSVD with specific clinical presentation and, probably, different mechanisms. The present study included 34 patients with CSVD and white matter hyperintensity (WMH) of stage Fazekas (F) 3 (mean age 61.7 ± 8.9) and 11 volunteers (mean age 57.3 ± 9.7). Total RNA was isolated from peripheral blood leukocytes. The expression of 58 protein-coding genes associated with CSVD and/or AD and 4 reference genes were assessed as part of the original panel for the NanoString nCounter analyzer. Testing results were validated by real-time PCR. There was a significant decrease in the expression levels of the ACOX1, CD33, CD2AP, TNFR1, and VEGFC genes in MRI type 2 relative to the control group as well as a decrease in the expression level of the CD33 gene in MRI type 2 compared to MRI type 1. Processes associated with inflammatory pathways with decreased expression of the identified genes are important in the development of MRI type 2 of CSVD. Given the direct connection of the established genes with AD, the importance of this form of CSVD in comorbidity with AD has been assumed.

A Photonic Immunosensor Detection Method for Viable and Non-Viable E. coli in Water Samples.

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 106 CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample.

Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines.

Successful revascularization of coronary occlusion due to lotus root-like plaques by IVUS-based real-time three-dimensional wiring using the tip detection method in a patient with cardiac arrest.

A 32-year-old man, who was treated for T-cell lymphoma, presented in cardiac arrest. He had been treated for heart failure with reduced ejection fraction. Veno-arterial extracorporeal membrane oxygenation was initiated immediately. We diagnosed him as non-ST elevated myocardial infarction. Coronary angiography demonstrated the occlusion of the trifurcation in the proximal left anterior descending artery (LAD). We failed to advance the first guidewire into the distal LAD by angio-based conventional wiring. Intravascular ultrasonography (IVUS) of the proximal diagonal branch revealed two diaphragms separating the distal lumen without connection, which looks like lotus root-like appearance. We quickly penetrated the plaque using IVUS-based real-time 3D wiring using the tip detection method. The contrast injection via the microcatheter showed the distal diagonal branch (D2). After the balloon dilation in D2, IVUS image revealed a torn plaque between D2 and the distal LAD. Subsequently we advanced the guidewire to the distal LAD using IVUS-based real-time 3D wiring using the tip detection method through the tear of the plaque. Finally, we successfully performed the revascularization of LAD in a preferable procedure time. The patient recovered well and was discharged 39 days after cardiac arrest. This case highlights the efficacy of IVUS-based real-time 3D wiring using the tip detection method even in the emergent and challenging situation.

Recombinase polymerase amplification combined with lateral flow biosensor for rapid visual detection of Clostridium perfringens in chicken meat and milk.

Aims: Clostridium perfringens is one of the major anaerobic pathogen causing food poisoning and animal enteritis. With the rise of antibiotic resistance and the restrictions of the use of antibiotic growth promoting agents (AGPs) in farming, Clostridium enteritis and food contamination have become more common. It is time-consuming and labor-intensive to confirm the detection by standard culture methods, and it is necessary to develop on-site rapid detection tools. In this study, a combination of recombinase polymerase amplification (RPA) and lateral flow biosensor (LFB) was used to visually detect C. perfringens in chicken meat and milk. Methods and results: Two sets of primers were designed for the plc gene of C. perfringens, and the amplification efficiency and specificity of the primers. Selection of primers produces an amplified fragment on which the probe is designed. The probe was combined with the lateral flow biosensor (LFB). The reaction time and temperature of RPA-LFB assay were optimized, and the sensitivity of the assay was assessed. Several common foodborne pathogens were selected to test the specificity of the established method. Chicken and milk samples were artificially inoculated with different concentrations (1 × 102 CFU/mL to 1 × 106 CFU/mL) of C. perfringens, and the detection efficiency of RPA-LFB method and PCR method was compared. RPA-LFB can be completed in 20 min and the results can be read visually by the LFB test strips. The RPA-LFB has acceptable specificity and the lowest detection limit of 100 pg./µL for nucleic acid samples. It was able to stably detect C. perfringens contamination in chicken and milk at the lowest concentration of 1 × 104 CFU/mL and 1 × 103 CFU/mL, respectively. Conclusion: In conclusion, RPA-LFB is specific and sensitive. It is a rapid, simple and easy-to-visualize method for the detection of C. perfringens in food and is suitable for use in field testing work.

Development of a rapid and reliable method to simultaneously detect seven food allergens in processed foods using LC-MS/MS.

Rapid analysis of multiple food allergens is required to confirm the appropriateness of food allergen labelling in processed foods. This study aimed to develop a rapid and reliable method to simultaneously detect trace amounts of seven food allergenic proteins (wheat, buckwheat, milk, egg, crustacean, peanut, and walnut) in processed foods using LC-MS/MS. Suspension-trapping (S-Trap) columns and on-line automated solid-phase extraction were used to improve the complex and time-consuming pretreatment process previously required for allergen analysis using LC-MS/MS. The developed method enabled the simultaneous detection of selected marker peptides for specific proteins derived from seven food ingredients in five types of incurred samples amended with trace amounts of allergenic proteins. The limit of detection values of the method for each protein were estimated to be <1 mg/kg. The developed analytical approach is considered an effective screening method for confirming food allergen labelling on a wide range of processed foods.

Analysis of Negative-Strand RNA Viruses by RT-qPCR: Rift Valley Fever Virus and Toscana Virus.

RT-qPCR allows the detection of viruses and the monitoring of viral replication. This technique was extensively employed during the SARS-CoV-2 pandemic, where it demonstrated its efficiency and robustness. Here we describe the analysis of Rift Valley fever and Toscana virus infections over time, achieved through the RT-qPCR quantification of the viral genome. We further elaborate on the method to discriminate between genomic and antigenomic viral RNAs by using primers specific for each strand during the reverse transcription step.

[Verification Study of the Detection Method for Unauthorized Genetically Modified Papaya by Combining DNA Polymerases and Real-time PCR Instruments].

In the Japanese official detection method for unauthorized genetically modified (GM) papayas, one of two types of real-time PCR reagents with DNA polymerase (TaqMan Gene Master Mix [TaqMan Gene] or FastGene QPCR Probe Mastermix w/ROX [FastGene]) is primarily used for measurement. In 2022, we conducted a laboratory performance study on the unauthorized GM papaya line PRSV-YK, and the results revealed that high threshold cycle (Cq) values for the PRSV-YK detection test were obtained using TaqMan Gene with the 7500 Fast & 7500 Real-Time PCR System (ABI7500) and QuantStudio 12K Flex (QS12K), indicating the possibility of false negatives. The possibility of similar problems with all unauthorized GM papaya lines detection tests needs to be evaluated. In this study, we performed detection tests on unauthorized GM papaya lines (PRSV-YK, PRSV-SC, and PRSV-HN), the cauliflower mosaic virus 35S promotor (CaM), and a papaya positive control (Chy), and examined how the limits of detection (LOD) for each test are affected by two types of DNA polymerases (TaqMan Gene and FastGene) and three types of real-time PCR instruments (ABI7500, QS12K, and LightCycler 480 Instrument II [LC480]). In the PRSV-YK and PRSV-SC detection tests using ABI7500 and QS12K, measurement with TaqMan Gene showed a higher LOD than FastGene. In this case, an exponential amplification curve was confirmed on the amplification plot; however, the amplification curve did not cross the ΔRn threshold line and the correct Cq value was not obtained with a threshold line=0.2. The other tests (PRSV-HN, CaM, and Chy with ABI7500 and QS12K, and all detection tests with LC480) showed no important differences in the LOD for each test using either DNA polymerase. Therefore, when performing PRSV-YK and PRSV-SC detection tests with the ABI7500 or QS12K, FastGene should be used to avoid false negatives for foods containing GM papaya lines PRSV-YK and PRSV-SC at low mixing levels.

MICA Legionella, an innovative and rapid method for enumeration of Legionella pneumophila in French sanitary water.

A new innovative method, MICA Legionella, allows for the automatic enumeration of Legionella pneumophila in domestic water samples in 2 days, with a detection limit of 2 CFU per test portion. Here we show that it gives equivalent results to those obtained by the French standard method NF T90-431 in 7 to 15 days.

Development, Optimization, and Clinical Relevance of Lactoferrin Delivery Systems: A Focus on Ocular Delivery.

Lactoferrin (Lf), a multifunctional protein found abundantly in secretions, including tears, plays a crucial role in ocular health through its antimicrobial, immunoregulatory, anti-inflammatory, and antioxidant activities. Advanced delivery systems are desirable to fully leverage its therapeutic potential in treating ocular diseases. The process of Lf quantification for diagnostic purposes underscores the importance of developing reliable, cost-effective detection methods, ranging from conventional techniques to advanced nano-based sensors. Despite the ease and non-invasiveness of topical administration for ocular surface diseases, challenges such as rapid drug elimination necessitate innovations, such as Lf-loaded contact lenses and biodegradable polymeric nanocapsules, to enhance drug stability and bioavailability. Furthermore, overcoming ocular barriers for the treatment of posterior segment disease calls for nano-formulations. The scope of this review is to underline the advancements in nanotechnology-based Lf delivery methods, emphasizing the pivotal role of multidisciplinary approaches and cross-field strategies in improving ocular drug delivery and achieving better therapeutic outcomes for a wide spectrum of eye conditions.

A simplified molecular tool for detecting the Chagas etiological agent using a vector feces sample in field conditions.

Triatomine bugs are vectors of Trypanosoma cruzi, the etiologic agent of Chagas disease in the American continent. Here, we have tested a loop-mediated isothermal amplification (LAMP) test for a direct detection of T. cruzi in feces of Triatoma infestans, the main vector of this parasite in the Southern Cone of America. The analytical evaluation showed positive results with samples of triatomine feces artificially inoculated with DNA from strains of T. cruzi corresponding to each Discrete Typing Units (I-VI), with a sensitivity of up to one parasite per reaction. Conversely, the reaction yielded negative results when tested with DNA from Trypanosoma rangeli and other phylogenetically related and unrelated organisms. In triatomines captured under real field conditions (from urban households), and defined as positive or negative for T. cruzi using the reference microscopy technique, the LAMP test achieved a concordance of 100 %. Our results demonstrate that this LAMP reaction exhibits excellent analytical specificity and sensitivity without interference from the fecal matrix, since all the reactions were conducted without purification steps. This simple molecular diagnostic technique can be easily used by vector control agencies under field conditions.

One-step triplex TaqMan quantitative reverse transcription polymerase chain reaction for the detection of feline coronavirus, feline panleukopenia virus, and feline leukemia virus.

Background and Aim: Feline coronavirus (FCoV), feline panleukopenia virus (FPV), and feline leukemia virus (FeLV) are prevalent throughout China and significantly threaten cat health. These viruses cause similar manifestations and pathological damage. Rapid and accurate diagnosis depends on detection in the laboratory. This study aimed to establish a reliable and rapid method for accurate detection of FCoV, FPV, and FeLV so that a definite diagnosis can be made and effective measures can be taken to prevent and control viral infection. Materials and Methods: We designed three pairs of specific primers and probes for the detection of FCoV 5' untranslated region, FPV viral protein 2, and FeLV pol genes. Recombinant plasmid constructs were generated for use as standard plasmid constructs. Optimal reaction conditions, including primer and probe concentrations, reaction cycles, and annealing temperatures, were obtained on the basis of optimization tests. One-step triplex real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was successfully established to simultaneously detect FCoV, FPV, and FeLV. The specificity, sensitivity, and repeatability of the assay were analyzed, and its applicability was validated by testing 1175 clinical samples. Results: One-step triplex RT-qPCR had a high degree of specificity only for the detection of FCoV, FPV, and FeLV; it had high sensitivity with limits of detection of 139.904, 143.099, and 152.079 copies/reaction for p-FCoV, p-FPV, and p-FeLV standard plasmid constructs, respectively, and it had reliable repeatability with 0.06%-0.87% intra-assay coefficients of variations. A total of 1175 clinical samples were examined for FCoV, FPV, and FeLV using triplex RT-qPCR, and the FCoV, FPV, and FeLV positivity rates were 18.47%, 19.91%, and 47.57%, respectively. The clinical sensitivity and specificity of one-step triplex RT-qPCR were 93.07% and 97.99%, respectively. Conclusion: We developed a rapid and reliable one-step triplex RT-qPCR method for the detection of FCoV, FPV, and FeLV, which could be used as a diagnostic tool for clinical monitoring and diagnosis.

Tracking genetically modified (GM) rice ingredients in samples of packed rice and food products from the marketplace in India: a pilot study for regulatory compliance.

Purpose: More than 20 genetically modified (GM) food crops including rice have been approved in many countries. GM rice and derived products have not yet been approved in India so they are considered as unauthorized genetically modified organisms (GMOs) in the country. Therefore it is important to track whether the rice containing food items, available in the marketplace are GMO-free. Methods: A pilot study was conducted to check the GM status of 30 samples of packed rice grains and processed food products with rice as an ingredient, using polymerase chain reaction (PCR) assays targeting Cauliflower Mosaic Virus 35 S promoter (P-35 S), nopaline synthase terminator (T-nos), phosphinothricin-N-acetyltransferase (pat) and cry1Ac gene, which could cover screening for all the globally approved GM rice events. Results: Based on the results, none of the samples tested were found positive for P-35 S, T-nos, pat and cry1Ac. Conclusion: The unauthorized presence of GM rice ingredients was not detected in the samples tested. Such studies may further be conducted for the testing of GM ingredients derived from cereals other than rice in the food products imported from the country where GM events of respective cereal crop are approved, as a part of regulatory requirement.

A method to detect uniformity of road base course based on impact imaging technology.

The damage of road base course has the characteristics of strong concealment and difficulty in detecting. For this reason, the impact imaging method has been used for detection of road base course. This paper discussed systematically collection points setting, excitation mode and data processing method. Through the application in testing for highway pavement base before and after grouting maintenance, the results show that the method is simple and accurate. The detection results can be displayed in a two-dimensional image form and it is easy to be used in road maintenance. This method can be used to identify and locate the damages of the pavement base, to judge the uniformity of the pavement base structure. It can also be used to evaluate the effectiveness of internal damage after grouting repairing.

Expanding the horizons of levan: from microbial biosynthesis to applications and advanced detection methods.

Levan, a ß-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries.