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Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach—leveraging a combination of these methods—will yield the most accurate diagnosis of BRD.

Nucleic-acid biosensors have emerged as useful tools for on-farm detection of foodborne pathogens on fresh produce. Such tools are specifically designed to be user-friendly so that a producer can operate them with minimal training and in a few simple steps. However, one challenge in the deployment of these biosensors is delivering precise sample volumes to the biosensor’s reaction sites. To address this challenge, we developed an innovative drop dispenser using advanced 3D printing technology, combined with a hydrophilic surface chemistry treatment. This dispenser enables the generation of precise sample drops, containing DNA or bacterial samples, in volumes as small as a few micro-liters (∼20 to ∼33 μL). The drop generator was tested over an extended period to assess its durability and usability over time. The results indicated that the drop dispensers have a shelf life of approximately one month. In addition, the device was rigorously validated for nucleic acid testing, specifically by using loop-mediated isothermal amplification (LAMP) for the detection of Escherichia coli O157, a prevalent foodborne pathogen. To simulate real-world conditions, we tested the drop dispensers by integrating them into an on-farm sample collection system, ensuring they deliver samples accurately and consistently for nucleic acid testing in the field. Our results demonstrated similar performance to commercial pipettors in LAMP assays, with a limit of detection of 7.8×10 6 cells/mL for whole-cell detection. This combination of precision, ease of use, and durability make our drop dispenser a promising tool for enhancing the effectiveness of nucleic acid biosensors in the field.

Process analytical technology (PAT) for the manufacture of monoclonal antibodies (mAbs) is defined by an integrated set of advanced and automated methods that analyze the compositions and biophysical properties of cell culture fluids, cell-free product streams, and biotherapeutic molecules that are ultimately formulated into concentrated products. In-line or near-line probes and systems are remarkably well developed, although challenges remain in the determination of the absence of viral loads, detecting microbial or mycoplasma contamination, and applying data-driven deep learning to process monitoring and soft sensors. In this review, we address the current status of PAT for both batch and continuous processing steps and discuss its potential impact on facilitating the continuous manufacture of biotherapeutics. Process analytical technology (PAT) has evolved from hardware-based analyses for defined biological, biomolecular, and biochemical analytes to a toolbox that encompasses data analytics and soft sensors to monitor and control monoclonal antibody (mAb) manufacture.Engineered cell lines used in batch processes and continuous manufacturing have helped improve qualities and production rates for mAbs.Data analytics has become increasingly important as sensors become smaller, more robust, and increasingly ubiquitous, with soft sensors enabling determination of a rolling baseline of process conditions and consequences during the production of biologics.In-line sensors utilized for downstream processes provide a template for how such sensors might be used as part of PAT in the real-time monitoring of the manufacture of biotherapeutic proteins in both upstream and downstream unit operations.

Avian influenza outbreaks have had significant economic and public health consequences worldwide. Therefore, prompt, reliable, and cost-effective diagnostic devices are crucial for scrutinizing and confining highly pathogenic avian influenza viruses (HPAIVs). Our study introduced and evaluated a novel paper-based loop-mediated isothermal amplification (LAMP) test for diagnosing the H5 subtype of the avian influenza virus (AIV). We meticulously designed and screened LAMP primers targeting the H5-haemagglutinin (H5-HA) gene of AIV and fine-tuned the paper-based detection assay for best performance. The paper-based LAMP assay demonstrated a detection limit of 500 copies per reaction (25 copies/µl). Additionally, the assay exhibited no cross-reactivity with common bovine and avian pathogens, confirming its specificity. Spiking experiments revealed that the assay could accurately detect 1000 copies of synthetic HPAIV RNA (per reaction) when spiked into oropharyngeal swab samples, achieving 100% analytical sensitivity, specificity, and accuracy. This inexpensive, user-friendly point-of-need diagnostic tool holds great promise, especially in resource-limited settings. It only requires a water bath for incubation and enables visual detection of results without special equipment. Overall, the paper-based LAMP assay provides a promising method for rapidly and reliably detecting the H5 subtype of AIV, contributing to improved surveillance and early intervention strategies.

Deep learning has the potential to enhance the output of in‐line, on‐line, and at‐line instrumentation used for process analytical technology in the pharmaceutical industry. Here, we used Raman spectroscopy‐based deep learning strategies to develop a tool for detecting microbial contamination. We built a Raman dataset for microorganisms that are common contaminants in the pharmaceutical industry for Chinese Hamster Ovary (CHO) cells, which are often used in the production of biologics. Using a convolution neural network (CNN), we classified the different samples comprising individual microbes and microbes mixed with CHO cells with an accuracy of 95%–100%. The set of 12 microbes spans across Gram‐positive and Gram‐negative bacteria as well as fungi. We also created an attention map for different microbes and CHO cells to highlight which segments of the Raman spectra contribute the most to help discriminate between different species. This dataset and algorithm provide a route for implementing Raman spectroscopy for detecting microbial contamination in the pharmaceutical industry. We use Raman spectroscopy to identify microbial contaminants that are common in the pharmaceutical industry. These contaminants span across Gram‐negative bacteria, Gram‐positive bacteria, and fungi. The use of a convolution neural network achieves identification accuracy in the range of 95%–100%.

Paper-based biosensors are microfluidic analytical devices used for the detection of biochemical substances. The unique properties of paper-based biosensors, including low cost, portability, disposability, and ease of use, make them an excellent tool for point-of-care testing. Among all analyte detection methods, nucleic acid-based pathogen detection offers versatility due to the ease of nucleic acid synthesis. In a point-of-care testing context, the combination of nucleic acid detection and a paper-based platform allows for accurate detection. This review offers an overview of contemporary paper-based biosensors for detecting nucleic acids from pathogens. The methods and limitations of implementing an integrated portable paper-based platform are discussed. The review concludes with potential directions for future research in the development of paper-based biosensors.

This work modifies a loop-mediated isothermal amplification (LAMP) assay to detect the bovine respiratory disease (BRD) bacterial pathogens Pasteurella multocida , Mannheimia haemolytica , and Histophilus somni in a colorimetric format on a farm. BRD causes a significant health and economic burden worldwide that partially stems from the challenges involved in determining the pathogens causing the disease. Methods such as polymerase chain reaction (PCR) have the potential to identify the causative pathogens but require lab equipment and extensive sample processing making the process lengthy and expensive. To combat this limitation, LAMP allows accurate pathogen detection in unprocessed samples by the naked eye allowing for potentially faster and more precise diagnostics on the farm. The assay developed here offers 66.7–100% analytical sensitivity, and 100% analytical specificity (using contrived samples) while providing 60–100% concordance with PCR results when tested on five steers in a feedlot. The use of a consumer-grade water bath enabled on-farm execution by collecting a nasal swab from cattle and provided a colorimetric result within 60 min. Such an assay holds the potential to provide rapid pen-side diagnostics to cattle producers and veterinarians.

Machine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks (CNNs) and random forests (RFs) to predict the concentration of the samples containing measles, mumps, rubella, and varicella‐zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra data sets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R2 values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman‐absorption with principal component analysis. Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles. We applied machine learning techniques to Raman and absorption spectra to determine the concentration of samples containing viral particles (measles, mumps, rubella, and varicella‐zoster viruses). We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Ultimately we were able to make predictions with accuracies as high as 95%.

Herein, we present novel quantitative loop-mediated isothermal amplification (qLAMP) and reverse-transcription qLAMP (RT-qLAMP) assays for the detection of five viruses implicated with the onset and progression of bovine respiratory disease (BRD): Bovine Alphaherpesvirus Type 1 (BHV-1), Bovine Adenovirus Type 3 (BAV-3), Bovine Respiratory Syncytial Virus (BRSV), Bovine Viral Diarrhea Virus Type 1 (BVDV-1), and Bovine Parainfluenza Virus Type 3 (BPIV-3). Using contrived samples spiked with whole viruses, our extraction-free assays have limits of detection between 30 and 1,057 copies per reaction (1.8% final sample concentration) with minimal sample processing. Using dual-tipped swabs and 1.4 mL resuspension volumes, limits of detection are on the order of 2 × 10 copies per swab for BAV-3 and BHV-1 and between 6.31 × 10 to 8.22 × 10 copies per swab for BPIV-3, BRSV, and BVDV-1. Analytical sensitivities ranged from 73 - 100% and analytical specificities ranged from 90 - 100%. Additionally, we introduced a streamlined pipeline to minimize the experimental workload to design, screen, select, and characterize LAMP performance for developing assays. Our assays support the development of colorimetric LAMP assays that enable the sensitive and specific detection of these viruses' chute side to aid in diagnosing and treating BRD. The associated pipeline enables more rapid development of LAMP-based diagnostic tools targeting emerging pathogens.

Background Bovine respiratory disease (BRD) is an ongoing health and economic challenge in the dairy and beef cattle industries. Multiple risk factors make an animal susceptible to BRD. The presence of Mannheimia haemolytica , Pasteurella multocida , Histophilus somni , and Mycoplasma bovis in lung tissues have been associated with BRD mortalities, but they are also commonly present in the upper respiratory tract of healthy animals. This study aims to compare the cattle nasal microbiome (diversity, composition and community interaction) and the abundance of BRD pathogens (by qPCR) in the nasal microbiome of Holstein steers that are apparently healthy (Healthy group, n = 75) or with BRD clinical signs (BRD group, n = 58). We then used random forest models based on nasal microbial community and qPCR results to classify healthy and BRD-affected animals and determined the agreement with the visual clinical signs. Additionally, co-occurring species pairs were identified in visually BRD or healthy animal groups. Results Cattle in the BRD group had lower alpha diversity than pen-mates in the healthy group. Amplicon sequence variants (ASVs) from Trueperella pyogenes , Bibersteinia and Mycoplasma spp. were increased in relative abundance in the BRD group, while ASVs from Mycoplasma bovirhinis and Clostridium sensu stricto were increased in the healthy group. Prevalence of H. somni (98%) and P. multocida (97%) was high regardless of BRD clinical signs whereas M. haemolytica (81 and 61%, respectively) and M. bovis (74 and 51%, respectively) were more prevalent in the BRD group than the healthy group. In the BRD group, the abundance of M. haemolytica and M. bovis was increased, while H. somni abundance was decreased. Visual observation of clinical signs agreed with classification by the nasal microbial community (misclassification rate of 32%) and qPCR results (misclassification rate 34%). Co-occurrence analysis demonstrated that the nasal microbiome of BRD-affected cattle presented fewer bacterial associations than healthy cattle. Conclusions This study offers insight into the prevalence and abundance of BRD pathogens and the differences in the nasal microbiome between healthy and BRD animals. This suggests that nasal bacterial communities provide a potential platform for future studies and potential pen-side diagnostic testing.