Jingyi Dai

Purpose: Severe community-acquired pneumonia (SCAP) causes high morbidity and mortality. Metagenomic next-generation sequencing (mNGS) data comparing pathogen profiles in SCAP between people living with human immunodeficiency virus (HIV) (PLWH) and HIV-uninfected individuals remain scarce. Patients and Methods: We retrospectively enrolled 72 SCAP patients at Kunming Third People’s Hospital. We compared alpha diversity of respiratory microbiota, pathogen spectrum and healthcare resource utilization (HRU) between the two groups. We also assessed whether HIV infection was an independent risk factor for 30-day mortality. Results: mNGS detected pathogens in 70 of 72 patients (97.2%). PLWH showed higher detection rates of Pneumocystis jirovecii (p < 0.001), Human gammaherpesvirus 4 (EBV) (p = 0.013), and Human betaherpesvirus 5 (CMV) (p < 0.001). Among the 54 SCAP patients who survived 30 days, HRU metrics did not differ between groups. Elevated D-dimer level was an independent risk factor for 30-day mortality in SCAP patients (hazard ratio [HR]: 1.02, 95% confidence interval [CI]: 1.004– 1.030; p = 0.0127). Conclusion: HIV co‑infection in SCAP patients is associated with a distinct pathogen spectrum but does not affect HRU or 30‑day mortality. Elevated D‑dimer level is an independent risk factor for 30‑day mortality in SCAP patients.

Tuberculosis (TB), including drug-resistant forms, is a significant global health issue requiring accurate and rapid diagnostic tools. Traditional diagnostic methods suffer from low sensitivity and slow results, while nucleic acid amplification tests (NAATs) like Xpert MTB/RIF provide faster but incomplete solutions. Targeted next-generation sequencing (tNGS) has the potential to simultaneously detect Mycobacterium tuberculosis , drug resistance mutations, and co-infecting pathogens. This study evaluates the effectiveness of tNGS for TB detection across various pulmonary and extrapulmonary TB samples. We retrospectively enrolled 159 patients with suspected TB at Kunming Third People’s Hospital. Specimens included 126 pulmonary and 33 extrapulmonary samples. All samples underwent tNGS, and results were compared against conventional microbiological tests and NAATs. tNGS demonstrated high diagnostic sensitivity for both pulmonary TB and extrapulmonary TB, achieving 83.9% sensitivity in bronchoalveolar lavage fluid, 89.5% in sputum, and 100% in extrapulmonary samples. tNGS showed an 83.3% agreement with Xpert in detecting rifampicin resistance. Additionally, tNGS also identified 22 drug resistance mutations, which are critical for predicting multidrug-resistant TB and pre-extensively drug-resistant TB. Additionally, tNGS effectively detected co-infecting respiratory pathogens, enhancing the understanding of complex TB cases. tNGS offers a highly sensitive and comprehensive approach for detecting TB and drug resistance, outperforming traditional methods in both pulmonary and extrapulmonary samples. It effectively identifies co-infections, providing a holistic view that enhances patient management, particularly in cases involving multidrug-resistant strains. These findings underscore tNGS’s potential to improve TB diagnostics and patient management through faster and more precise detection methods. IMPORTANCE: Tuberculosis (TB) continues to be a leading cause of morbidity and mortality worldwide, particularly among marginalized populations. Timely and accurate diagnosis of TB, particularly extrapulmonary TB, remains challenging in low-resource settings, primarily due to non-specific clinical presentations that hinder early suspicion, along with limitations in both diagnosis and drug resistance detection. Additionally, targeted next-generation sequencing can identify co-infections with other clinically relevant pathogens, providing a more comprehensive understanding of each patient’s infectious profile. By leveraging advanced sequencing technologies, our findings highlight a powerful diagnostic approach that can improve TB diagnosis, support appropriate treatment strategies, and may increasingly benefit patients in underserved settings as sequencing platforms become more accessible and costs continue to decrease.

Tuberculosis remains a major cause of mortality in people living with HIV (PLWH), yet early diagnosis remains challenging. This study aimed to identify novel biomarker combinations and develop machine learning models, and to predict active TB in PLWH in a random and a chronological subset. We enrolled 760 PLWH with pulmonary symptoms. Demographic and clinical data and cytokine profiles were analyzed. Participants were first randomly split into training and validation sets. Subsequently, the whole dataset was re-analysed using the first 609 records as the training set and subsequent 151 records as the test set. Four models were developed with 10-fold cross-validation, incorporating feature selection and hyperparameter optimization. Model performance was assessed through ROC-AUC, sensitivity, specificity, and variable importance analysis. For the randomly split datasets, with active TB patients showed significantly elevated IFN-γ (median 5.7 vs 3.9 pg/mL, P<0.001) and IL-6 levels (25.3 vs 13.2 pg/mL, P<0.001) compared to without active TB cases. These two biomarkers were strong predictors based on the gradient boosting machine (GBM) model. AUCs (95% CI) on the randomly selected training dataset, was 0.96 (0.95, 0.97). That on the randomly selected test dataset was 0.73 (95% CI: 0.65–0.81). However, on chronological order, GBM model trained from the first 609 records AUC of 0.92 (0.91, 0.94) poorly predicted the 151 final records with the AUC of 0.66 (0.58, 0.75). TB might have activated the two inflammatory biomarkers among the PLWH. The best predictive machine learning method still have limitation in generalizability to predict the outcome on other data sets.

Most patients with hepatocellular carcinoma (HCC) lose the chance of survival due to lack of timely diagnosis and effective treatment. The specific gut microbiota (GM) spectrum may become the target of early diagnosis and treatment of HCC. However, the metabolic mechanisms that affect the occurrence of liver cancer are still unclear. In this study, we called the dataset of HCC, and 1091 serum metabolites 309 metabolite ratios as well as 211 GM taxa through genome-wide association studies instrumental variables for Mendelian randomization causal association analysis and metabolome intermediary effects exploration. Through functional enrichment of intermediate metabolites, the effects of metabolic pathways involved in GM on HCC were analyzed. Inverse variance weighting was the main model for establishing causal associations. Additionally horizontal pleiotropy test, linkage-disequilibrium test and the sensitivity analysis were employed to test the explanatory power of instrumental variables (single nucleotide polymorphisms). Our study found Coriobacteriia class, Coriobacteriales order, Coriobacteriaceae family, and 4 specific genera were strongly related to HCC ( P <.05). Meanwhile, through 2 samples-MR Analysis, 49 metabolites levels/ratios were shown to be closely related to the development of HCC. A total of 10 related metabolic intermediary factors have been selected, and 4 metabolic pathways of pyrimidine ( P = .0031), caffeine ( P = .0072), urea cycle ( P = .0105) and glutamate ( P = .0298) were significantly enriched in this GM related HCC process. Coriobacteriia class and its lower taxa were associated with the risk factors of developing HCC through the regulation of pyrimidine, caffeine, urea cycle and glutamate metabolic pathways. These GM taxa and metabolitesmay serve as potential targets for the diagnosis of HCC.

Introduction: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, posing a significant public health burden. Early diagnosis and effective treatment options are crucial for improving the curative effect and quality of life of patients. However, the detection of early-stage HCC remains challenging due to its strong compensatory function. HCC surveillance is an evidence-based practice with sufficient evidence. Numerous studies have demonstrated that HCC surveillance effectively increases early diagnosis rates and improves survival rates among liver cancer patients. Yet, its utilisation in clinical practice remains limited, at approximately 24%. Given the pivotal role clinicians play in HCC surveillance, their implementation barriers are more likely to lead to underutilisation of surveillance. This study aims to identify the barriers to implementing HCC surveillance guidelines among clinicians, subsequently developing and optimising efficient implementation strategies to enhance adherence to these guidelines. Methods and analysis: This study will be carried out at hospitals within the Yunnan Infectious Diseases Specialist Alliance. Focusing on promoting clinicians’ implementation of HCC surveillance, the study pursues two objectives: (1) Under the guidance of the Consolidated Framework for Implementation Research, semistructured interviews and a literature review will be conducted to analyse the barriers clinicians face in implementing HCC surveillance guidelines; (2) Employing the preparation and optimisation phase of the Multiphase Optimisation Strategy framework, effective implementation strategies will be developed and optimised to enhance clinicians’ implementation of HCC surveillance. The primary outcome measure is fidelity, assessed through rigorous patient-reported data collection methods. Statistical analyses will include independent samples t-tests, χ ² tests, Wilcoxon rank sum tests and multiple logistic regression analyses, performed using R V.4.4.1. The statistical significance level will be set at 0.05. Ethics and dissemination: This study has been approved by the Ethics Committee of the Kunming Third People’s Hospital (KSLL20230711005). Participants will provide informed consent before taking part in the study. Results will be published in peer-reviewed journals in open-access formats, and anonymised data will be made available. Trial registration number: ChiCTR2300071570.

Purpose: To evaluate the survival outcomes of patients with intermediate to advanced hepatocellular carcinoma (HCC), patients who underwent transarterial chemoembolization (TACE) alone were compared with those who underwent a combination of TACE and ablation therapy. Patients and Methods: This study retrospectively evaluated 536 HCC patients in our hospital from July 2016 to November 2022. All patients underwent TACE, with a subset also receiving ablation therapy. To ensure comparability, propensity score matching (PSM) was performed. We then compared overall survival (OS) and progression-free survival (PFS) between these two groups. Survival outcomes were analyzed utilizing Kaplan-Meier curves and compared via the Cox regression. Results: 200 among these 536 HCC patients received TACE combined with ablation whereas the remaining 336 received TACE alone. With PFS analysis, the numbers were reduced to 176 in combination therapy group and 250 in TACE alone group. With and without PSM, the OS and PFS were consistently and significantly better in the former than the latter group. In patients with Barcelona Clinic Liver Cancer (BCLC) stage B or C, those who received combination therapy demonstrated significantly higher OS compared to those treated with TACE alone. For stage B patients, PFS was also significantly longer in the combination therapy group, before and after PSM [hazard ratio (HR), 0.563; 95% CI: 0.360– 0.879; P = 0.012, HR, 0.613; 95% CI: 0.382– 0.985; P = 0.043]. However, after PSM, no statistical difference in survival outcomes was observed between the two groups for stage C patients (HR, 0.673; 95% CI: 0.395– 1.146; P = 0.145). Conclusion: Our data suggested that for OS, the combination therapy has sustained benefits for both patients with stage B and C, But for PFS, the benefits of the combination therapy among the stage C patients, could not be persistently demonstrated by the current datasets.

Corrigendum: Distribution characteristics of human herpes viruses in the lower respiratory tract and their impact on 30-day mortality in Community-Acquired Pneumonia patientsIncorrect Funding In the published article, there was an error in the Funding statement. [This research was supported by a postdoctoral fellowship from Prince of Songkla University, Hat Yai, Thailand, and supported by Kunming Science and Technology Bureau (2023-1-NS-007)]. The correct Funding statement appears below.FUNDING [This research was financially supported by Prince of Songkla University and Ministry of Higher Education, Science, Research and Innovation under the Reinventing University Project (Grant Number REV65022), and Kunming Science and Technology Bureau (2023-1-NS-007).]The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Human herpes viruses (HHVs) are commonly detected in community-acquired pneumonia (CAP) patients, particularly those with complex complications, attracting increased attention from clinical practitioners. However, the significance of detecting HHVs in bronchoalveolar lavage fluid (BALF) with CAP patients is still unclear. This study retrospectively analyzed BALF samples from 64 CAP patients at the Kunming Third People’s Hospital between August 2021 and December 2023. Metagenomic next generation sequencing (mNGS) was conducted on BALF samples during CAP onset. Multivariate Cox regression models were used to identify independent risk factors for 30-day all-cause mortality in CAP. HHVs were found in 84.4% of CAP patients, which were the most common pathogens (45.1%), followed by bacteria (30.2%) and fungi (11.5%). Bacterial-viral co-infections were most common, occurring in 39 patients. Notably, there was no significant difference in HHV presence between severe and non-severe CAP patients (EBV: P = 0.431, CMV: P = 0.825), except for HHV-7 (P = 0.025). In addition, there was no significant difference in the 30-day mortality between HHV positive and HHV negative groups (P = 0.470), as well as between the HHV-7 positive and HHV-7 negative groups (P = 0.910). However, neither HHVs nor HHV-7 was independent risk factors for 30-day mortality in CAP patients (HHVs: HR 1.171, P = 0.888; HHV-7: HR 1.947, P = 0.382). In summary, among the prevalent presence of multiple HHVs, EBV and CMV were the most prevalent in CAP patients. Patients with sCAP were more susceptible to HHV-7 than those with non-sCAP. These results provide valuable insights for clinicians in guiding appropriate interventions for CAP treatment.

This case reports a middle-aged male patient who was HIV-negative and initially misdiagnosed as pulmonary tuberculosis but was eventually diagnosed with disseminated Talaromyces marneffei (T. marneffei) infection by next-generation sequencing. The patient presented with respiratory symptoms, recurrent bone pain, and subcutaneous masses as the main symptoms. After one year of antifungal treatment, the symptoms improved obviously, but the symptoms recurred after two weeks of drug withdrawal, and the symptoms were relieved after re-administration of antifungal drugs again. This report highlights the need for the rapid evaluation of fungal infections with metagenomic next-generation sequencing (mNGS) in patients with an inadequate diagnostic basis for tuberculosis infection or a poor response to antituberculosis drugs. In addition, long-term follow-up is needed to observe disease recurrence in patients with disseminated T. marneffei infection.

Background: Factors associated with the incubation period of COVID-19 are not fully known. The aim of this study was to estimate the incubation period of COVID-19 using epidemiological contact tracing data, and to explore whether there were different incubation periods among different age gr1oups. Methods: We collected contact tracing data in a municipality in Hubei province during the full outbreak period of COVID-19. The exposure periods were inferred from the history of travel in Wuhan and/or history of exposure to confirmed cases. The incubation periods were estimated using parametric accelerated failure time models accounting for interval censoring of exposures. Results: The incubation period of COVID-19 follows a Weibull distribution and has a median of 5.8 days with a bootstrap 95% CI: 5.4– 6.7 days. Of the symptomatic cases, 95% showed symptoms by 14.3 days (95% CI: 13.0– 15.7), and 99% showed symptoms by 18.7 days (95% CI: 16.7– 20.9). The incubation periods were not found significantly different between male and female. Elderly cases had significant longer incubation periods than young age cases (HR 1.49 with 95% CI: 1.09– 2.05). The median incubation period was estimated at 4.0 days (95% CI: 3.5– 4.4) for cases aged under 30, 5.8 days (95% CI: 5.6– 6.0) for cases aged between 30 and 59, and 7.7 days (95% CI: 6.9– 8.4) for cases aged greater than or equal to 60. Conclusion: The current practice of a 14-day quarantine period in many regions is reasonable for any age. Older people infected with SARS-CoV2 have longer incubation period than that of younger people. Thus, more attention should be paid to asymptomatic elderly people who had a history of exposure.