Ventilator-associated pneumonia (VAP) is the most frequent intensive care unit (ICU)-acquired infection that is independently associated with mortality. Accurate diagnosis and timely treatment have been shown to improve the prognosis of VAP. Chest X-ray or computed tomography imaging are used for conventional assessment of VAP, but these methods are impractical for real-time measurement in critical patients. Therefore, lung ultrasound (LUS) has been increasingly used for the assessment of VAP in the ICU. Traditionally, LUS has seemed unsuitable for the detection of lung parenchyma owing to the high acoustic impedance of air; however, the fact that the reflection and reverberation in the detection region of the ultrasound reflect the underlying pathology of lung diseases has led to the increased use of ultrasound imaging as a standard of care supported by evidence-based and expert consensus in the ICU. Considering that any type of pneumonia causes air volume changes in the lungs, accumulating evidence has shown that LUS effectively measures the presence of VAP as well as dynamic changes in VAP. This review offers evidence for ultrasound as a noninvasive, easily repeatable, and bedside means to assess VAP; in addition, it establishes a protocol for qualitative and quantitative monitoring of VAP.

Background: The 2019 novel coronavirus (COVID-19) has continuous outbreaks around the world. Lung is the main organ that be involved. There is a lack of clinical data on the respiratory sounds of COVID-19 infected pneumonia, which includes invaluable information concerning physiology and pathology. The medical resources are insufficient, which are now mainly supplied for the severe patients. The development of a convenient and effective screening method for mild or asymptomatic suspicious patients is highly demanded. Methods: This is a retrospective case series study. 10 patients with positive results of nucleic acid were enrolled in this study. Lung auscultation was performed by the same physician on admission using a hand-held portable electronic stethoscope delivered in real time via Bluetooth. The recorded audio was exported, and was analyzed by six physicians. Each physician individually described the abnormal breathing sounds that he heard. The results were analyzed in combination with clinical data. Signal analysis was used to quantitatively describe the most common abnormal respiratory sounds. Results: All patients were found abnormal breath sounds at least by 3 physicians, and one patient by all physicians. Cackles, asymmetrical vocal resonance and indistinguishable murmurs are the most common abnormal breath sounds. One asymptomatic patient was found vocal resonance, and the result was correspondence with radiographic computed tomography. Signal analysis verified the credibility of the above abnormal breath sounds. Conclusions: This study describes respiratory sounds of patients with COVID-19, which fills up for the lack of clinical data and provides a simple screening method for suspected patients.

Asthma, a chronic inflammatory disease in which type 2 T helper cells (Th2) play a causative role in the development of T2 asthma. N6-methyladenosine (m6A) modification, an mRNA modification, and methyltransferase-like 3 (METTL3) is involved in the development of T2 asthma by inhibiting Th2 cell differentiation. Sex determining region Y-box protein 5 (SOX5) is involved in regulating T cell differentiation, but its role in T2 asthma was unclear. The objective of this study was to explore the role of METTL3 and SOX5 in T2 asthma and whether there is an interaction between the two. Adults diagnosed with T2 asthma (n = 14) underwent clinical information collection and pulmonary function tests. In vivo and in vitro T2 asthma models were established using female C57BL/6 mice and human bronchial epithelial cells (HBE). The expressions of METTL3 and SOX5 were detected by Western blot and qRT-PCR and Western blot. Th2 cell differentiation was determined by flow cytometry and IL-4 level was detected by ELISA. m6A methylation level was determined by m6A quantitative assay. The relationship between METTL3 expression and clinical parameters was determined by Spearman rank correlation analysis. The function of METTL3 and SOX5 genes in asthma was investigated in vitro and in vivo. The RNA immunoprecipitation assay detected the specific interaction between METTL3 and SOX5. Patients with T2 asthma displayed lower METTL3 levels compared to healthy controls. Within this group, a negative correlation was observed between METTL3 and Th2 cells, while a positive correlation was noted between METTL3 and clinical parameters as well as Th1 cells. In both in vitro and in vivo models representing T2 asthma, METTL3 levels decreased significantly, while SOX5 levels showed the opposite trend. Overexpression of METTL3 gene in HBE cells significantly inhibited Th2 cell differentiation and increased m6A methylation activity. From a mechanism perspective, low METTL3 negatively regulates SOX5 expression through m6A modification dependence, while high SOX5 expression is positively associated with T2 asthma severity. Cell transfection experiments confirmed that METTL3 regulates Th2 cell differentiation and IL-4 release through SOX5. Overall, our results indicate that METTL3 alleviates Th2 cell differentiation in T2 asthma by modulating the m6A methylation activity of SOX5 in bronchial epithelial cells. This mechanism could potentially serve as a target for the prevention and management of T2 asthma.

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients.