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Abstract Sepsis is a dysregulated host response to severe infections, and immune dysfunction plays a crucial role in its pathogenesis. Elderly patients, a special population influenced by immunosenescence, are more susceptible to sepsis and have a worse prognosis. However, the immunopathogenic mechanisms underlying sepsis in elderly patients remain unclear. Here, we performed single-cell RNA sequencing of peripheral blood samples from young and old subjects and patients with sepsis. By exploring the transcriptional profiles of immune cells, we analyzed immune cell compositions, phenotype shifts, expression heterogeneities, and intercellular communication. In elderly patients with sepsis, innate immune cells (e.g., monocytes and DCs) exhibit decreased antigen presentation, presenting an overactive inflammatory and senescent phenotype. However, the immunophenotype of T cells shifted to characterize effector, memory, and exhaustion. Moreover, we identified strong interferon-γ responses of T cells in both aging and sepsis groups and a deranged inflammaging status in elderly sepsis patients. Tregs in elderly patients with sepsis showed increased abundance and enhanced immunosuppressive effects. In addition, metabolism-associated pathways were upregulated in T cells in elderly patients with sepsis, and the lysine metabolism pathway was enriched in Tregs. Cell–cell interaction analysis showed that the expression profile of ligand-receptor pairs was probably associated with aggravated immune dysfunction in elderly patients with sepsis. A novel HLA-KIR interaction was observed between Tregs and CD8 + T cells. These findings illustrate the immunological hallmarks of sepsis in elderly patients, and highlight that immunosuppressive and metabolic regulatory pathways may undergo important alterations in elderly patients with sepsis.

Abstract Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets. However, most studies focus on artificially cultured bacteria and lack clinical data and algorithms. Therefore, we collected Gram-stained bacteria images from lower respiratory tract specimens of patients with lung infections in Chinese PLA General Hospital obtained by M-ROSE from 2018 to 2022 and desensitized images to produce 1705 images (4,912 × 3,684 pixels). A total of 4,833 cocci and 6,991 bacilli were manually labelled and differentiated into negative and positive. In addition, we applied the detection and segmentation networks for benchmark testing. Data and benchmark algorithms we provided that may benefit the study of automated bacterial identification in clinical specimens.

Abstract Background Many older adult patients receive low-dose teicoplanin with varied regimens, leading to a lack of clarity on its optimal regimens and toxicity profiles in China. This study aimed to clarify these aspects by analyzing teicoplanin treatment concentrations and toxicities. Methods We included older adult patients administered teicoplanin at four tertiary hospitals in Beijing from June 2021 to July 2023, targeting a trough concentration (Cmin) ≥ 10 mg/L. Teicoplanin concentrations and toxicities were monitored dynamically. Results From 204 patients, we obtained 632 teicoplanin concentrations. Most patients (83.3%) received low-dose regimens. Suboptimal concentrations were found in 66.4% of patients within 7 days of treatment and 17.0% after 15 days. Cmin gradually increased with treatment duration and was influenced initially by creatinine and by both body weight and creatinine from days 8 to 14. The target concentration was achieved in 53.1%, 33.9%, 15.6%, and 5.5% of patients at 3, ≤ 7, 8–14, and ≥ 15 days after withdrawal, respectively. Slow elimination was associated with average Cmin and eGFR. Nephrotoxicity, hepatotoxicity, and thrombocytopenia occurred in 12.5%, 4.1%, and 31.5% of patients, respectively, without significant differences between concentrations. Conclusions Most older adult patients were underdosed, indicating a need for dose adjustment. Given the varied risk factors for suboptimal concentrations in different treatment stages, a one-size-fits-all regimen was ineffective. We recommend an initial dose of 400 mg at 12-h intervals for the first three days, with subsequent doses from days 4 to 14 adjusted based on creatinine and body weight; after day 14, a maintenance dose of 200 mg daily is advised. Trial registration ChiCTR2100046811; 28/05/2021.

Abstract Introduction The mortality rate of hospitalized patients with severe hospital-acquired pneumonia (SHAP) remains high. Empirical broad-spectrum antibiotic coverage and the misuse of high-grade antibiotics could lead to the emergence of multi-drug and even pandrug-resistant bacteria. In addition to metagenomic next-generation sequencing (mNGS), microbiological rapid on-site evaluation (M-ROSE) might be a useful technique to identify the pathogens in the early stage; however, the effect of M-ROSE guiding anti-infection treatment on prognostic outcomes of SHAP patients is still unclear. Methods/design This is a multicenter, single-blind, prospective, randomized controlled trial to evaluate the effect of M-ROSE guiding anti-infection treatment in SHAP patients, which will provide new strategies for the prevention and control of clinical multi-drug resistance bacteria. A total of 166 patients with SHAP, aged 18 years and over, will be recruited from seven centers in Beijing and randomly assigned to the intervention group (M-ROSE combined with mNGS) or the control group (mNGS only) in a 1:1 ratio using the central randomization system. Patients in the intervention group will accept M-ROSE and mNGS analysis, and the control group will accept mNGS analysis. Individualized anti-infective treatment and routine treatment will be selected according to the analysis results. The primary outcome is the ICU outcome (mortality). The safety of the intervention measures will be evaluated during the entire trial period. This trial will be the first randomized controlled trial to evaluate the effect of M-ROSE guiding treatment on mortality in patients with SHAP and may change the prevalence of multi-drug resistant bacteria. Ethics and dissemination This trial adheres to the Declaration of Helsinki and guidelines of Good Clinical Practice. Signed informed consent will be obtained from all participants. The trial has been approved by the Chinese PLA General Hospital (Approval Number: 20220322001). Trial registration ClinicalTrials.gov NCT05300776. Registered on 25 March 2022.

ABSTRACTAcinetobacter baumannii is a major opportunistic pathogen causing hospital-acquired infections, and it is imperative to comprehend its evolutionary and epidemiological dynamics in hospitals to prevent and control nosocomial transmission. Here, we present a comprehensive genomic epidemiological study involving the genomic sequencing and antibiotic resistance profiling of 634 A. baumannii strains isolated from seven intensive care units (ICUs) of a Chinese general hospital over 2 consecutive years. Our study reveals that ST2 is highly dominant (90.54%) in the ICUs, with 98.90% of the ST2 exhibiting multidrug resistant or extensively drug resistant. Phylogenetic analyses of newly sequenced genomes and public data suggest that nosocomial isolates originated outside the hospital but evolved inside. The major lineages appear to be stable, with 9 of the 28 identified nosocomial epidemic clones infecting over 60% of the affected patients. However, outbreaks of two highly evolved clones have been observed in different hospitals, suggesting significant inter-hospital transmission chains. By coupling patient medical records and genomic divergence of the ST2, we found that cross-ward patient transfer played a crucial role in pathogen’s nosocomial transmission. Additionally, we identified 831 potential adaptive evolutionary loci and 44 associated genes by grouping and comparing the genomes of clones with different prevalence. Overall, our study provides a comprehensive and contemporary survey on the epidemiology and genomic evolution of A. baumannii in a large Chinese general hospital. These findings shed light on the nosocomial evolution and transmission of A. baumannii and offers valuable information for transmission prevention and antibiotic therapy.IMPORTANCEThis study delved into the genomic evolution and transmission of nosocomial Acinetobacter baumannii on a large scale, spanning both an extended time period and the largest sample size to date. Through molecular epidemiological investigations based on genomics, we can directly trace the origin of the pathogen, detecting and monitoring outbreaks of infectious diseases in a timely manner, and ensuring public health safety. In addition, this study also collects a large amount of genomic and antibiotic resistance detection data, which is helpful for phenotype prediction based on genomic sequencing. It enables patients to receive personalized antibiotic treatment quickly, helps doctors select antibiotics more accurately, and contributes to reducing the use of antibiotics and lowering the risk of antibiotic resistance development.

AbstractObjective To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU.Methods A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creatinine changed. We included 19 variables for AKI assessment using four machine learning models: support vector machines, logistic regression, and random forest. and XGBoost, using accuracy, specificity, precision, recall, F1 score, and AUROC (area under the ROC curve) to evaluate model performance. The four models predicted new-onset AKI 3–6–9–12 h ahead. The SHapley Additive exPlanation (SHAP) value is used to evaluate the feature importance of the model.Results We finally respectively extracted 1130 AKI patients and non-AKI patients from the MIMIC-III database. With the extension of the early warning time, the prediction performance of each model showed a downward trend, but the relative performance was consistent. The prediction performance comparison of the four models showed that the XGBoost model performed the best in all evaluation indicators in all the time point at new-onset AKI 3–6–9–12 h ahead (accuracy 0.809 vs 0.78 vs 0.744 vs 0.741, specificity 0.856 vs 0.826 vs 0.797 vs 0.787, precision 0.842 vs 0.81 vs 0.775 vs 0.766, recall 0.759 vs 0.734 vs 0.692 vs 0.694, Fl score 0.799 vs 0.769 vs 0.731 vs 0.729, AUROC 0.892 vs 0.857 vs 0.827 vs 0.818). In the prediction of AKI 6, 9 and 12 h ahead, the importance of creatinine, platelets, and height was the most important based on SHapley.Conclusions The machine learning model described in this study can predict AKI 3–6–9–12 h before the new-onset of AKI in ICU. In particular, platelet plays an important role.Key messageThe new-onset of AKI in ICU is a common and important problem, which early be identified the risk of AKI can improve patients’ outcomes.We explored MIMIC-III and determined the exact time point of occurrence of AKI as the basis for the new-onset of AKI in ICU.XGBoost model performed the best prediction in all the time point at new-onset AKI 3–6–9–12 h ahead.For patients with the new-onset of AKI in ICU, platelets become an important factor associated with AKI.

Abstract Background Identifying the causes of community-acquired pneumonia (CAP) is challenging due to the disease’s complex etiology and the limitations of traditional microbiological diagnostic methods. Recent advances in next generation sequencing (NGS)-based metagenomics allow pan-pathogen detection in a single assay, and may have significant advantages over culture-based techniques. Results We conducted a cohort study of 159 CAP patients to assess the diagnostic performance of a clinical metagenomics assay and its impact on clinical management and patient outcomes. When compared to other techniques, clinical metagenomics detected more pathogens in more CAP cases, and identified a substantial number of polymicrobial infections. Moreover, metagenomics results led to changes in or confirmation of clinical management in 35 of 59 cases; these 35 cases also had significantly improved patient outcomes. Conclusions Clinical metagenomics could be a valuable tool for the diagnosis and treatment of CAP. Trial registration Trial registration number with the Chinese Clinical Trial Registry: ChiCTR2100043628 .

ObjectiveIn order to facilitate education for clinical users, performance aspects of the high-flow nasal cannula (HFNC) devices were evaluated in the present study. A multidimensional HFNC clinical evaluation system was established accordingly.Materials and MethodsClinical staff from Chinese hospitals were invited to participate in an online questionnaire survey. The questionnaire was mainly about the accuracy of temperature, flow rate, and oxygen concentration of HFNC, as well as its humidification capacity. We also investigated how the clinical staff of different professions made decisions on HFNC evaluation indicators. Based on the results of the questionnaire survey of clinicians with rich experience in using HFNC, the relative weights of temperature accuracy, flow velocity accuracy, oxygen concentration accuracy, and humidification ability of HFNC equipment were calculated by the AHP to establish a clinical evaluation system. Four kinds of common HFNC devices were tested and evaluated, and the clinical performance of the four kinds of HFNC devices was evaluated by the new scoring system.ResultsA total of 356 clinicians participated in and completed the questionnaire survey. To ensure the reliability of the HFNC evaluation system, we only adopted the questionnaire results of clinicians with rich experience in using HFNCs. Data from 247 questionnaires (80 doctors, 105 nurses, and 62 respiratory therapists [RTs]) were analyzed. A total of 174 participants used HFNC more than once a week; 88.71% of RTs used HFNC ≥ 1 score daily, 62.86% of nurses used HFNC ≥ 1 score daily, and 66.25% of doctors used HFNC ≥ 1 daily. There was no significant difference in the frequency of use between doctors and nurses. Finally, the relative weights of temperature accuracy (0.088), humidification capacity (0.206), flow velocity accuracy (0.311), and oxygen concentration accuracy (0.395) in the HFNC clinical evaluation system were obtained. The relative weights of clinicians with different occupations and the frequency of HFNC use were obtained. After testing four kinds of HFNC devices through the evaluation system, it was found that the four kinds of HFNC devices have different advantages in different clinical performances, and AiRVO2 has excellent performance with regard to temperature accuracy and humidification ability. HF-75A and NeoHiF-i7 are good at ensuring the stability of oxygen concentration and the accuracy of the flow velocity of the transported gas, while OH-80S is relatively stable in all aspects.ConclusionThe clinical evaluation system of HFNC is based on the weight of the experience of clinical personnel with different medical backgrounds. Although the existing practitioners have different educational backgrounds (academic qualifications, majors), our evaluation system can enhance clinical staff’s awareness of HFNC and further optimize the clinical use of HFNC.

BackgroundTo evaluate the value of metagenomic next-generation sequencing (mNGS) for the early diagnosis of psittacosis, and to investigate its epidemiology by whole-genome capture.MethodsTwenty-one bronchoalveolar lavage fluid (BALF) and blood samples of 16 psittacosis patients from multiple centers during August 2019 to September 2021 were analyzed retrospectively. mNGS with normal datasets (10 M 75-bp single-end reads after sequencing) and larger datasets (30 M 150-bp paired-end reads after sequencing) as well as quantitative real-time polymerase chain reaction (qPCR) were used to detect the pathogen. Also, whole-genome capture of Chlamydophila psittaci was applied to draw the phylogenetic tree.ResultsmNGS successfully detected the pathogen in all 16 cases (100%), while qPCR was positive only in 5 out of 10 cases (50%), indicating a significantly higher sensitivity of mNGS than qPCR (p < 0.01). BALF-mNGS performed better than blood-mNGS (16/16 versus 3/5, p < 0.05). In addition, larger datasets (the read counts have tripled, and the base number was 12-fold larger compared to clinical mNGS with a normal dataset) of mNGS showed significantly increased contents of human DNA (p < 0.05) and decreased reads per million of the pathogen, suggesting no improvement. Whole-genome capture results of five samples (>60% coverage and >1 depth) were used to construct the phylogenetic tree.ConclusionSignificant advantages of mNGS with normal datasets were demonstrated in early diagnosing psittacosis. It is the first study to use whole-genome capture to analyze C. psittaci epidemiological information.

ObjectiveCritical illnesses in the intensive care unit (ICU) have been a global burden. We aimed to determine the correlation between the lung and gut in critically ill patients to find novel evidence of the lung-gut axis, which may be a new treatment for patients with critical illness in the ICU.MethodsWe collected bronchoalveolar lavage specimens and fecal samples of 31 patients with critical illness within 24 h after admission. Metagenomics was used to detect lung and intestinal samples. Immune cells were detected by flow cytometry.ResultsThere are 86 common species in both lung and gut. The abundance of Enterococcus faecium is high in both the lung and gut of patients with critical illness in the respiratory intensive care unit (RICU). Corynebacterium striatum in the lung and gut is correlated with different immune cells. In addition, C. striatum in the lung and gut might share the same source, supporting the concept of a gut-lung axis in humans.ConclusionsThe microbiome in the lung and gut showed a correlation to some extent, and C. striatum in the lung and gut might share the same source. In addition, the microbiome showed a correlation with immunity, indicating a potential therapeutic target in patients with critical illness. The lung-gut axis might play an important role in patients with critical illness in the RICU.

Abstract Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture‐based diagnosis in clinics is increasingly supplemented by metagenomic next‐generation‐sequencing (mNGS). Here, RNA/cDNA‐targeted sequencing (meta‐transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta‐transcriptomics using third‐generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple‐strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct‐RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA‐targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.

Background: Some patients discharged automatically are classified as terminal discharge, while their clinical outcome is survival, disrupting the results of clinical research. Methods: The data of this study were taken from inpatients admitted to the ICU of the First Medical Center of the People's Liberation Army General Hospital, Beijing, China from 2008-2017. We collected the data regarding medications used over the three days before discharge from the group of patients who survived and the group of patients who died, and the outcomes of all patients were recalculated by three classification algorithms (AdaBoosting, Pearson correlation coefficient, observed to expected ratio-weighted cosine similarity). Our basic assumption is that if the classification result is death but the actual in-hospital outcome is survival, the associated patient was likely terminally discharged. Results: The coincidence rate of the outcomes calculated by the AdaBoosting algorithm was 98.1%, the coincidence rate calculated by the Pearson correlation coefficient was 61.1%, and the coincidence rate calculated by the observed to expected ratio-weighted cosine similarity was 93.4%. When the three classification methods were combined, the accuracy reached 98.56%. Conclusion: The combination of clinical rules and classification methods has a synergistic effect on judgments of patients’ discharge outcomes, greatly saving time on manual retrieval and reducing the negative influence of statistics or rules.