Purpose: The significance of detecting human herpesvirus 7 (HHV-7) in the lower respiratory tract of patients with severe pneumonia is unclear. This study aims to evaluate the clinical characteristics and prognosis of detecting HHV-7 in the lower respiratory tract of patients with severe pneumonia. Methods: Patients with severe pneumonia requiring invasive mechanical ventilation and underwent commercial metagenomic next-generation sequencing (mNGS) testing of bronchoalveolar lavage fluid from January 2019 to March 2023 were enrolled in 12 medical centers. Clinical data of patients were collected retrospectively, and propensity score matching was used for subgroup analysis and mortality assessment. Results: In a total number of 721 patients, 45 cases (6.24%) were identified with HHV-7 positive in lower respiratory tract. HHV-7 positive patients were younger (59.2 vs 64.4, p = 0.032) and had a higher rate of co-detection with Cytomegalovirus (42.2% vs 20.7%, p = 0.001) and Epstein–Barr virus (35.6% vs 18.2%, p = 0.008). After propensity score matching for gender, age, SOFA score at ICU admission, and days from ICU admission to mNGS assay, there was no statistically significant difference in the 28-day mortality rate between HHV-7 positive and negative patients (46.2% vs 36.0%, p = 0.395). Multivariate Cox regression analysis adjusting for gender, age, and SOFA score showed that HHV-7 positive was not an independent risk factor for 28-day mortality (HR 1.783, 95%CI 0.936–3.400, p = 0.079). Conclusion: HHV-7 was detected in the lungs of 6.24% of patients with severe pneumonia. The presence of HHV-7 in patients with severe pneumonia requiring invasive mechanical ventilation is associated with a younger age and co-detected of Cytomegalovirus and Epstein–Barr virus. While HHV-7 positivity was not found to be an independent risk factor for mortality in this cohort, this result may have been influenced by the relatively small sample size of the study.

Sepsis-associated acute kidney injury (SA-AKI) is one of the most frequent complications of sepsis and one of the leading causes of acute kidney injury (AKI), posing a significant threat to patient survival. The pathogenesis of SA-AKI has been linked to mitochondrial dysfunction, according to emerging evidence. Mitochondria serve as the primary energy-producing organelles in cells, and mitochondrial dysfunction can result in insufficient renal energy supply, oxidative stress, and inflammatory responses, all of which can both initiate and exacerbate SA-AKI. Thus, elucidating the role of mitochondrial dysfunction in SA-AKI is of critical importance. Starting from the fundamental mechanisms of mitochondrial dysfunction, this review draws upon more than 130 relevant publications through August 2025, comprehensively summarizes the role of mitochondrial dysfunction in the pathophysiological processes of SA-AKI and the drugs for treating SA-AKI through the improvement of mitochondrial function, while also discussing in detail the potential therapeutic applications of several well-characterized therapeutic targets in SA-AKI, providing important insights into the diagnosis and treatment of this disease.

Objective: It is well recognized that high heterogeneity represents a key driver of the elevated mortality in severe community-acquired pneumonia (sCAP). Precise subtype classification is therefore critical for both treatment strategy formulation and prognostic evaluation in this patient population. This study aimed to develop a predictive model for novel clinical subtypes of sCAP, leveraging microbiome profiles identified via metagenomic next-generation sequencing (mNGS). Methods: This retrospective multicenter cohort study enrolled adult patients with sCAP who underwent clinical mNGS testing of bronchoalveolar lavage fluid in intensive care units (ICUs) across 17 medical centers in China. Based on mNGS-identified microbiome characteristics, unsupervised machine learning (UML) was employed for clustering analysis of sCAP patients. LASSO regression and random forest (RF) algorithms were applied to screen and identify predictors of novel sCAP subtypes. A predictive model for the new clinical subtypes was constructed according to the screening results, with a nomogram generated. The discriminative ability, calibration, and clinical utility of the model were evaluated using ROC curves, calibration curves, and decision curve analysis, respectively. Results: A total of 1,051 sCAP patients were included in the final analysis. The 28-day all-cause mortality rate was 45% (473/1,051). UML clustering identified two distinct sCAP subtypes: the 28-day mortality rate was 42.19% (343/813) in subtype 1 and 54.62% (130/238) in subtype 2. Incorporating clinical and microbial features, a predictive model for the novel sCAP subtypes was developed using the following predictors: immunosuppression (OR = 37,411.46, P < 0.001), connective tissue disease (CTD) (OR = 12,144.60, P = 0.004), hematological malignancy (HM) (OR = 107,768.13, P < 0.001), chronic kidney disease (CKD) (OR = 49.71, P < 0.001), cytomegalovirus (CMV) (OR = 0.00, P < 0.001), Epstein-Barr virus (EBV) (OR = 131.97, P < 0.001), Pneumocystis (OR = 47,949.56, P < 0.001), and Klebsiella (OR = 0.02, P = 0.003). The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.992. Calibration curves showed good agreement between predicted and observed outcomes. Decision curve analysis confirmed high clinical utility for predicting novel sCAP subtypes. Conclusion: This study identified novel clinical subtypes of sCAP based on mNGS-derived microbiome characteristics. This approach exhibits superior performance in identifying high-risk sCAP patients, facilitating precise subtyping.

Extracorporeal membrane oxygenation (ECMO) is a critical life-support intervention for patients with severe cardiopulmonary failure. However, its use is associated with a substantially increased risk of nosocomial infections, with reported incidence rates ranging from 8.8% to 64.0%. These infections—particularly ventilator-associated pneumonia and bloodstream infections—are linked to heightened morbidity, prolonged intensive care and hospital stays, and elevated mortality. This review aims to systematically compile Chinese and English literature published between 2018 and 2025, clarify the unique pathophysiological mechanisms of ECMO-related infections, analyze the limitations and breakthroughs of existing prediction models, and explore the potential role of machine learning in developing personalized early warning systems. Additionally, it seeks to establish a clinical decision-making framework for precise prevention and control. We conclude that improving ECMO infection control requires establishing standardized, clinically applicable diagnostic criteria, conducting a multicenter prospective validation study, and developing transparent, AI-enhanced predictive tools to enable real-time infection monitoring and improved patient prognosis during ECMO support.

Background: Acute kidney injury (AKI) is a common complication in patients admitted to intensive care unit (ICU) and mortality rates for this condition are high. To reduce the high incidence of short-term mortality, reliable prognostic indicators are required to facilitate early diagnosis and treatment of AKI. We assessed the ability of plasma proenkephalin (p‑PENK) and plasma neutrophil gelatinase-associated lipocalin (p‑NGAL) to predict 28-day mortality in AKI patients in intensive care. Methods: This prospective study, carried out between January 2019 and December 2019, comprised 150 patients (100 male) diagnosed with AKI after excluding 20 patients discharged within 24 h and those with missing hospitalization data. Blood samples were collected to determine admission p-PENK and p-NGAL levels. The study outcome was 28‑day mortality. Results: The mean patient age was 68 years (female, 33%). The average P‑PENK and p‑NGAL levels were 0.24 ng/µL and 223.70 ng/mL, respectively. P‑PENK levels >0.36 ng/µL and p‑NGAL levels >230.30 ng/mL were used as critical values to reliably indicate 28‑day mortality for patients with AKI (adjusted hazard ratios 0.785 [95% confidence interval 0.706–0.865, P<0.001] and 0.700 [95% confidence interval 0.611–0.789, P<0.001], respectively). This association was significant for mortality in patients in intensive care with AKI. Baseline p-PENK (0.36 ng/µL) and p-NGAL (230.30 ng/mL) levels and their respective cut-off values showed clinical value in predicting 28-day mortality. Conclusion: Serum PENK and NGAL levels, when used in conjunction, improved the accuracy of predicting 28-day mortality in patients with AKI while retaining sensitivity and specificity.