Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data.

Clinically diagnosed pulmonary tuberculosis (PTB) patients lack microbiological evidence of Mycobacterium tuberculosis , and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of long noncoding RNAs (lncRNAs) and corresponding predictive models to diagnose these patients. We enrolled 1,764 subjects, including clinically diagnosed PTB patients, microbiologically confirmed PTB cases, non-TB disease controls, and healthy controls, in three cohorts (screening, selection, and validation).

The capacity to identify small amounts of pathogens in real samples is extremely useful. Herein, we proposed a sensitive platform for detecting pathogens using cyclic DNA nanostructure@AuNP tags (CDNA) and a cascade primer exchange reaction (cPER). This platform employs wheat germ agglutinin-modified Fe₃O₄@Au magnetic nanoparticles (WMRs) to bind the E. coli O157:H7, and then triggers the cPER to generate branched DNA products for CDNA tag hybridization with high stability and amplified SERS signals. It can identify target pathogens as low as 1.91 CFU/mL and discriminate E. coli O157:H7 in complex samples such as water, milk, and serum, demonstrating comparable or greater sensitivity and accuracy than traditional qPCR. Moreover, the developed platform can detect low levels of E. coli O157:H7 in mouse serum, allowing the discrimination of mice with early-stage infection. Thus, this platform holds promise for food analysis and early infection diagnosis.

(1) Background: Culture-negative endocarditis is challenging to diagnose. Here, we retrospectively identified 23 cases of Coxiella burnetii and Bartonella endocarditis by metagenomic next-generation sequencing. (2) Methods: Twenty-three patients with culture-negative endocarditis were retrospectively enrolled from Guangdong Provincial People’s Hospital (n = 23) between April 2019 and December 2021. Metagenomic next-generation sequencing was performed on blood (n = 22) and excised cardiac valvular tissue samples (n = 22) for etiological identification, and Sanger sequencing was performed for pathogenic diagnostic verification. The demographic and clinical data of the 23 patients were obtained from hospital electronic health records. (3) Results: A total of 23 male patients (median age, 56 years (interquartile range, 16)) with culture-negative endocarditis were diagnosed with Coxiella burnetii (n = 21) or Bartonella (n = 2) species infection by metagenomic next-generation sequencing. All patients underwent cardiac surgery. The resected tissue exhibited both a significantly higher number of unique suspected pathogen read-pairs and more unique pathogen read-pairs than the blood specimens. The results of Sanger sequencing tests on all remaining tissue and blood specimens were positive. Oral doxycycline was added to the antibiotic regimen for at least 1.5 years according to etiology. A total of 21 patients (91%) were discharged, and 20 patients were healthy at the 21-month (interquartile range, 15) follow-up visit. One patient exhibited endocarditis relapse with the same pathogen from inadequate antibiotic administration. The last 2 patients (9%) developed septic shock and multiple organ dysfunction syndrome postoperatively and died shortly after discharge. (4) Conclusions: CNE caused by C. burnetii and Bartonella species is challenging to diagnose and exhibits poor outcome due to delayed treatment. In response, mNGS, characterized by high sensitivity and rapid results, is an effective alternative for the etiological identification of C. burnetii and Bartonella endocarditis.

Background: Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model.Methods: A total of 2,759 subjects were included in this study, including 12 in the primary screening cohort [7 TB patients and 5 healthy controls (HCs)] and 2,747 in the selection cohort (798 TB patients, 299 patients with non-TB lung disease, and 1,650 HCs). An Affymetrix HTA2.0 array and qRT-PCR were applied to screen new specific lncRNA markers for TB in individual nucleated cells from host peripheral blood. A ML algorithm was established to combine the patients’ EHR information and lncRNA data via logistic regression models and nomogram visualization to differentiate PTB from suspected patients of the selection cohort.Results: Two differentially expressed lncRNAs (TCONS_00001838 and n406498) were identified (p < 0.001) in the selection cohort. The optimal model was the “LncRNA + EHR” model, which included the above two lncRNAs and eight EHR parameters (age, hemoglobin, lymphocyte count, gamma interferon release test, weight loss, night sweats, polymorphic changes, and calcified foci on imaging). The best model was visualized by a nomogram and validated, and the accuracy of the “LncRNA + EHR” model was 0.79 (0.75–0.82), with a sensitivity of 0.81 (0.78–0.86), a specificity of 0.73 (0.64–0.79), and an area under the ROC curve (AUC) of 0.86. Furthermore, the nomogram showed good compliance in predicting the risk of TB and a higher net benefit than the “EHR” model for threshold probabilities of 0.2–1.Conclusion: LncRNAs TCONS_00001838 and n406498 have the potential to become new molecular markers for PTB, and the nomogram of “LncRNA + EHR” model is expected to be effective for the early clinical diagnosis of TB.

Reliable diagnostics are necessary to identify influenza infections, and coronavirus disease 2019 (COVID-19) highlights the need to develop highly specific and sensitive viral detection methods to distinguish severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory pathogens to prevent their further spread. In this prospective study, 1070 clinical respiratory samples were collected from patients with acute respiratory infections from January 2019 to February 2021 to evaluate the diagnostic performance of a multiplex probe amplification (MPA) assay, designed to screen 18 pathogens, mainly those causing acute respiratory infections. Ninety-six positive samples and twenty negative samples for the 18 respiratory pathogens defined by the MPA assay and reverse transcription polymerase chain reaction (RT–PCR) were further confirmed by reference next-generation sequencing (NGS). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the MPA assay were 95.00%, 93.75%, 98.96% and 75.00%, respectively. Additionally, the co-infection rate for these positive samples were 25% (24/95). The MPA assay demonstrated a highly concordant diagnostic performance with NGS in the diagnosis of 18 respiratory pathogens and might play an important role in clinical respiratory pathogen diagnosis.