UNASSIGNED: 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.
UNASSIGNED: To establish a rapid and convenient detection method for PDCoV by combining RPA (Recombinase Polymerase Isothermal Amplification) with CRISPR/Cas13a.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

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.

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.

The presence of anaplastic lymphoma kinase (ALK) rearrangement defines a molecular subtype of non-small cell lung cancer (NSCLC). ALK inhibitors (ALKIs) confer significant clinical benefits in patients with ALK-positive advanced NSCLC; therefore, it is of great clinical significance to select accurate, rapid, and appropriate ALK testing methods to screen for patients who are suitable for anti-ALK treatment. In recent years, great progress has been made in the development and clinical application of ALKIs, as well as in our understanding of acquired drug resistance mechanisms. Meanwhile, new ALK companion diagnostic platforms have been developed and applied in clinical practice. Although many studies have shown that there is a high rate of concordance among these platforms, new problems continue to appear during testing. To maximize the benefit for patients, accurate testing results can be obtained by first selecting the appropriate testing method and then formulating, optimizing, and complying with the standardized testing process in accordance with the testing population and specimen types. With the ongoing accumulation of clinical practice data, experience from quality control of ALK testing, and results from multicenter research, an updated expert consensus is necessary. The experts who participated in the discussion and development of this guideline have a rich background in theoretical and clinical testing experience, which ensures the practical value of the information presented in this guideline.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Based on the results, none of the samples tested were found positive for P-35 S, T-nos, pat and cry1Ac.
UNASSIGNED: 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.

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.

Bladder cancer (BC) is the tenth most prevalent malignancy globally, presenting significant clinical and societal challenges because of its high incidence, rapid progression, and frequent recurrence. Presently, cystoscopy and urine cytology serve as the established diagnostic methods for BC. However, their efficacy is limited by their invasive nature and low sensitivity. Therefore, the development of highly specific biomarkers and effective non-invasive detection strategies is imperative for achieving a precise and timely diagnosis of BC, as well as for facilitating an optimal tumor treatment and an improved prognosis. microRNAs (miRNAs), short noncoding RNA molecules spanning around 20-25 nucleotides, are implicated in the regulation of diverse carcinogenic pathways. Substantially altered miRNAs form robust functional regulatory networks that exert a notable influence on the tumorigenesis and progression of BC. Investigations into aberrant miRNAs derived from blood, urine, or extracellular vesicles indicate their potential roles as diagnostic biomarkers and prognostic indicators in BC, enabling miRNAs to monitor the progression and predict the recurrence of the disease. Simultaneously, the investigation centered on miRNA as a potential therapeutic agent presents a novel approach for the treatment of BC. This review comprehensively analyzes biological roles of miRNAs in tumorigenesis and progression, and systematically summarizes their potential as diagnostic and prognostic biomarkers, as well as therapeutic targets for BC. Additionally, we evaluate the progress made in laboratory techniques within this field and discuss the prospects.