Background: Timely diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a prerequisite for treatment and prevention. The serology characteristics and complement diagnosis value of the antibody test to RNA test need to be demonstrated.Method: Serial sera of 80 patients with PCR-confirmed coronavirus disease 2019 (COVID-19) were collected at the First Affiliated Hospital of Zhejiang University, Hangzhou, China. Total antibody (Ab), IgM and IgG antibodies against SARS-CoV-2 were detected, and the antibody dynamics during the infection were described.Results: The seroconversion rates for Ab, IgM and IgG were 98.8%, 93.8% and 93.8%, respectively. The first detectible serology marker was Ab, followed by IgM and IgG, with a median seroconversion time of 15, 18 and 20 days post exposure (d.p.e.) or 9, 10 and 12 days post onset (d.p.o.), respectively. The antibody levels increased rapidly beginning at 6 d.p.o. and were accompanied by a decline in viral load. For patients in the early stage of illness (0–7 d.p.o), Ab showed the highest sensitivity (64.1%) compared with IgM and IgG (33.3% for both; p<0.001). The sensitivities of Ab, IgM and IgG increased to 100%, 96.7% and 93.3%, respectively, 2 weeks later. When the same antibody type was detected, no significant difference was observed between enzyme-linked immunosorbent assays and other forms of immunoassays.Conclusions: A typical acute antibody response is induced during SARS-CoV-2 infection. Serology testing provides an important complement to RNA testing in the later stages of illness for pathogenic-specific diagnosis and helpful information to evaluate the adapted immunity status of patients.

In this study, we show that although plasma mNGS testing significantly improved the detection rate of tested samples, nearly one in four (24.5%, 48/196) mNGS tests reported organisms were not clinically relevant, emphasizing the importance of cautious interpretation and infectious disease consultation. Moreover, based on clinical adjudication, plasma mNGS testing resulted in no or a negative impact in nearly half (43.5%, 64/147) of patients in the current study, indicating that how best to integrate this advanced method into current infectious disease diagnostic frameworks to maximize its clinical utility in real-world practice is an important question.

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs). We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023. The positivity rate for NTS (69.5%, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7%, 289/387; p=0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9%, 131/387; p<0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n=54), Pseudomonas aeruginosa (n=36), Escherichia coli (n=36), Enterococcus faecium(n=30), Acinetobacter baumannii(n=26), Staphylococcus aureus(n=23), and Human cytomegalovirus (n=37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0% (95% CI 79.5%-87.7%) and 90.1% (95% CI 81.7%-88.5%), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2% (359/387), whereas the consistency between NTS and BC was only 60.2% (233/387). In 80.6% (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95% of samples can be tested and analyzed in approximately 7hours, allowing for early patient diagnosis. NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.

Background: The emergence and rapid spread of the deadly novel coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a swiftly evolving public health crisis worldwide. SARS‐CoV‐2 infection is characterized by the development and progression of inflammatory responses. Hematological parameters, such as white blood cells (WBCs) and their subpopulations, red cell distribution width, platelet count, mean platelet volume, plateletcrit, and derived markers such as neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and lymphocyte‐to‐monocyte ratio, are established biomarkers of inflammatory responses. We aimed to investigate associations between hematological parameters and disease severity in patients with SARS‐CoV‐2 infection.Methods: We retrospectively analyzed data from 68 patients with confirmed SARS‐CoV‐2 infection. Twenty‐two patients had mild illness, and 46 had moderate or severe illness at the time of admission. Univariate and multivariate regression analyses were used to identify correlates of disease severity. The areas under receiver operating characteristic curves were calculated to estimate and compare the predictive values of different diagnostic markers.Results: Mean lymphocyte and monocyte counts were lower while WBC counts, neutrophil counts, NLR, and PLR were higher in patients with severe disease compared with those with mild disease (all P < .01). Univariate analysis revealed that older age, high WBC counts, high neutrophil counts, high NLR, high PLR, low monocyte counts, and low lymphocyte counts were independent correlates of severe illness. Multivariate analysis identified high NLR as the only independent correlate of severe illness. Receiver operating characteristic curve analysis showed that NLR had the highest area under curve of all hematological parameters.Conclusion: Among hematological parameters, the NLR showed superior prediction of disease severity in patients with SARS‐CoV‐2 infection. Thus, the NLR could be a valuable parameter to complement conventional measures for identification of patients at high risk for severe disease.

Metagenomic next-generation sequencing (mNGS) provides innovative solutions for predicting complex infections. A comprehensive understanding of its strengths and limitations in real-world clinical settings is necessary to ensure that it is not overused or misinterpreted. Two hundred nine cases with suspected pneumonia were recruited to compare the capabilities of 2 available mNGS assays (bronchoalveolar lavage fluid [BALF] mNGS and plasma mNGS) to identify pneumonia-associated DNA/RNA pathogens and predict antibiotic resistance. Compared to clinical diagnosis, BALF mNGS demonstrated a high positive percent agreement (95.3%) but a low negative percent agreement (63.1%). Plasma mNGS revealed a low proportion of true negatives (30%) in predicting pulmonary infection. BALF mNGS independently diagnosed 65.6% (61/93) of coinfections and had a remarkable advantage in detecting caustic, rare, or atypical pathogens. Pathogens susceptible to invasive infection or bloodstream transmission, such as Aspergillus spp, Rhizopus spp, Chlamydia psittaci, and human herpesviruses, are prone to be detected by plasma mNGS. BALF mNGS tests provided a positive impact on the diagnosis and treatment of 128 (61.2%) patients. Plasma mNGS, on the other hand, turned out to be more suitable for diagnosing patients who received mechanical ventilation, developed severe pneumonia, or developed sepsis (all P < .01). BALF mNGS was able to identify resistance genes that matched the phenotypic resistance of 69.4% (25/36) of multidrug-resistant pathogens. Our data reveal new insights into the advantages and disadvantages of 2 different sequencing modalities in pathogen identification and antibiotic resistance prediction for patients with suspected pneumonia.

The role of respiratory tract microbes and the relationship between respiratory tract and gut microbiomes in coronavirus disease 2019 (COVID‐19) remain uncertain. Here, the metagenomes of sputum and fecal samples from 66 patients with COVID‐19 at three stages of disease progression are sequenced. Respiratory tract, gut microbiome, and peripheral blood mononuclear cell (PBMC) samples are analyzed to compare the gut and respiratory tract microbiota of intensive care unit (ICU) and non‐ICU (nICU) patients and determine relationships between respiratory tract microbiome and immune response. In the respiratory tract, significantly fewer Streptococcus, Actinomyces, Atopobium, and Bacteroides are found in ICU than in nICU patients, while Enterococcus and Candida increase. In the gut, significantly fewer Bacteroides are found in ICU patients, while Enterococcus increases. Significant positive correlations exist between relative microbiota abundances in the respiratory tract and gut. Defensin‐related pathways in PBMCs are enhanced, and respiratory tract Streptococcus is reduced in patients with COVID‐19. A respiratory tract–gut microbiota model identifies respiratory tract Streptococcus and Atopobium as the most prominent biomarkers distinguishing between ICU and nICU patients. The findings provide insight into the respiratory tract and gut microbial dynamics during COVID‐19 progression, considering disease severity, potentially contributing to diagnosis, and treatment strategies.