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This study aims to assess the diagnostic value of targeted next-generation sequencing (tNGS) for pathogen identification from multiple sample types in patients with pulmonary infection, and to provide an alternative diagnostic method for clinical practice. Clinical data were collected from patients with suspected of pulmonary infection at the Thoracic Surgery Center of the Xinjiang Uygur Autonomous Region Sixth People's Hospital. Samples, including bronchial lavage fluid (BALF), fresh tissue, pleural effusion, and sputum, were collected by attending physicians based on the patients' clinical conditions. A total of 166 patients were enrolled, and their samples were subjected to pathogen detection using both tNGS and traditional pathogen detection methods (TPDs). The pathogen detection performance of tNGS was then compared with that of TPDs. The positive detection rate of tNGS was significantly higher than that of TPDs (81.33% vs. 32.53%, p < 0.001). Among the 166 samples, tNGS identified a total of 65 pathogens, whereas TPDs identified only 14 (11 bacterial species, 2 fungal species, and ). TPDs primarily identified bacteria (including ) and fungi, and were unable to detect viruses. In contrast, tNGS revealed a broader spectrum of pathogens, including 35 bacterial species, 10 fungal species, 18 viral species, as well as and . Notably, tNGS demonstrated greater efficiency in detecting mixed infections and further identified 16 antibiotic resistance genes (ARGs). tNGS exhibits higher sensitivity, a broader pathogen detection spectrum, and enhanced capability to identify mixed infections, along with the ability to detect ARGs. These advantages establish tNGS as a promising and reliable diagnostic modality for patients with pulmonary infections.

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Cronobacter spp. (formerly Enterobacter sakazakii) are special foodborne pathogens. Cronobacter infection can cause necrotizing enterocolitis, sepsis and meningitis in all age groups, especially neonates and infants, with a high fatality of up to 80%, although the infection is rare. Outbreaks of Cronobacter infection are epidemiologically proven to be associated with contaminated powdered infant formula (PIF). Cronobacter spp. can resist dry environments and survive for a long period in food with low water activity. Therefore, Cronobacter spp. have become serious pathogens of neonates and infants, as well as in the dairy industry. In this review, we present the taxonomy, pathogenesis, resistance, detection and control of Cronobacter spp.

Abstract Background Metagenomic next-generation sequencing (mNGS) of plasma cell-free DNA has emerged as a promising diagnostic technology for bloodstream infections. However, a major limitation of current mNGS assays is the high rate of false-positive results due to contamination. Methods We made novel use of 3 control groups—external negative controls under long-term surveillance, blood samples with a negative result in conventional tests, and a group of healthy people—that were combined and dedicated to distinguishing contaminants arising from specimen collection, sample processing, and human normal flora. We also proposed novel markers to filter out false-positive interspecies calls. This workflow was applied retrospectively to 209 clinical plasma samples from patients with suspected bloodstream infections. Every pathogen identified by the mNGS test was reviewed to assess the diagnostic performance of the workflow. Results Our mNGS workflow showed clinical sensitivity of 87.1%, clinical specificity of 80.2%, positive predictive value of 77.9%, and negative predictive value of 88.6% compared with the composite reference standard. Notably, mNGS showed great improvement in clinical specificity compared with the current test while keeping clinical sensitivity at a high level. Conclusion The mNGS workflow with multiple control groups dedicated to distinguishing nonpathogen microbes from real causal pathogens has reducing false-positive results. This contribution, with its optimization of workflow and careful use of controls, can help mNGS become a powerful tool for identifying the pathogens responsible for bloodstream infections.

False-positive identifications are a significant problem in metagenomics classification. We present KrakenUniq, a novel metagenomics classifier that combines the fast k -mer-based classification of Kraken with an efficient algorithm for assessing the coverage of unique k -mers found in each species in a dataset. On various test datasets, KrakenUniq gives better recall and precision than other methods and effectively classifies and distinguishes pathogens with low abundance from false positives in infectious disease samples. By using the probabilistic cardinality estimator HyperLogLog, KrakenUniq runs as fast as Kraken and requires little additional memory. KrakenUniq is freely available at https://github.com/fbreitwieser/krakenuniq .

Bacterial pathogens are one of the major threats to biosafety and environmental health, and advanced assessment is a prerequisite to combating bacterial pathogens. Currently, 16S rRNA gene sequencing is efficient in the open‐view detection of bacterial pathogens. However, the taxonomic resolution and applicability of this method are limited by the domain‐specific pathogen database, taxonomic profiling method, and sequencing target of 16S variable regions. Here, we present a pipeline of multiple bacterial pathogen detection (MBPD) to identify the animal, plant, and zoonotic pathogens. MBPD is based on a large, curated database of the full‐length 16S genes of 1986 reported bacterial pathogen species covering 72,685 sequences. In silico comparison allowed MBPD to provide the appropriate similarity threshold for both full‐length and variable‐region sequencing platforms, while the subregion of V3−V4 (mean: 88.37%, accuracy rate compared to V1−V9) outperformed other variable regions in pathogen identification compared to full‐length sequencing. Benchmarking on real data sets suggested the superiority of MBPD in a broader range of pathogen detections compared with other methods, including 16SPIP and MIP. Beyond detecting the known causal agent of animal, human, and plant diseases, MBPD is capable of identifying cocontaminating pathogens from biological and environmental samples. Overall, we provide a MBPD pipeline for agricultural, veterinary, medical, and environmental monitoring to achieve One Health. Multiple bacterial pathogen detection (MBPD) provides the accurate and comprehensive detection of bacterial pathogens from biological and environmental samples based on 16S rRNA gene sequencing. By constructing a relatively complete database of 72,685 sequences for wide identification across a broad range of bacterial pathogens causing animal, plant, and zoonotic diseases, MBPD can provide the appropriate threshold for common variable regions of 16S rRNA gene in taxonomy alignment based on the recommendation of in silico experiment. MBPD is freely available at https://github.com/LorMeBioAI/MBPDLorMeBioAI/MBPD. Highlights Multiple bacterial pathogen detection (MBPD) is a 16S‐based pipeline for detecting animal, plant, and zoonotic bacterial pathogens. The curated reference database of MBPD contains 72,685 sequences from 1986 known pathogen species. MBPD provides appropriate thresholds for taxonomic profiling of common 16S variable regions. MBPD outperforms other methods in detecting both causal and coinfecting pathogens.

Early and rapid diagnosis of pathogens is important for the prevention and control of epidemic disease. The polymerase chain reaction (PCR) technique requires expensive instrument control, a special test site, complex solution treatment steps and professional operation, which can limit its application in practice. The pathogen detection method based on the clustered regularly interspaced short palindromic repeats (CRISPRs) and CRISPR-associated protein (CRISPR/Cas) system is characterized by strong specificity, high sensitivity and convenience for detection, which is more suitable for practical applications. This article first reviews the CRISPR/Cas system, and then introduces the application of the two types of systems represented by Type II (cas9), Type V (cas12a, cas12b, cas14a) and Type VI (cas13a) in pathogen detection. Finally, challenges and prospects are proposed.