The Impact of Early Antibiotic Use on Clinical Outcomes of Patients Hospitalized with COVID-19: A Propensity Score-Matched Analysis.

Purpose: Early empiric antibiotics were prescribed to numerous patients during the Coronavirus disease 2019 (COVID-19) pandemic. However, the potential impact of empiric antibiotic therapy on the clinical outcomes of patients hospitalized with COVID-19 is yet unknown.

Methods: In this retrospective cohort study, early antibiotics use cohort was defined as control group, which was compared with no antibiotic use and delayed antibiotic use cohorts for all-cause mortality during hospitalization.

The first reported pulmonary nocardiosis caused by Nocardia gipuzkoensis resisted to trimethoprim/sulfamethoxazol (TMP-SMZ) in an immunocompetent patient.

Objectives: Nocardia gipuzkoensis was first described as a novel and distinct species in 2020 by Imen Nouioui and pulmonary nocardiosis associated with N. gipuzkoensis was once reported in two bronchiectasis patients. Noteworthy, both reported N.

A training program for improving the capacity of infection high-throughput sequencing and diagnosis in China.

Background: Infectious diseases are a serious threat to human especially since the COVID-19 outbreak has proved the importance and urgency of their diagnosis and treatment again. Metagenomic next-generation sequencing (mNGS) has been widely used and recognized in clinical and carried out localized testing in hospitals. Increasing the training of mNGS detection technicians can enhance their professional quality and more effectively realize the application value of the hospital platform.

Risk factors, outcomes, and epidemiological and etiological study of hospitalized COVID-19 patients with bacterial co-infection and secondary infections.

Background: As a common complication of viral respiratory tract infection, bacterial infection was associated with higher mortality and morbidity. Determining the prevalence, culprit pathogens, outcomes, and risk factors of co-infection and secondary infection occurring in hospitalized patients with coronavirus disease 2019 (COVID-19) will be beneficial for better antibiotic management.

Methods: In this retrospective cohort research, we assessed clinical characteristics, laboratory parameters, microbiologic results, and outcomes of laboratory-confirmed COVID-19 patients with bacterial co-infection and secondary infection in West China Hospital from 2022 December 2nd to 2023 March 15th.

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.

Objectives: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

Materials And Methods: Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes.

Metatranscriptomic analysis revealed as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection.

Background And Objectives: Disease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.

Background: This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules.

Methods: We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window.

Metatranscriptomic Analysis Reveals Disordered Alterations in Oropharyngeal Microbiome during the Infection and Clearance Processes of SARS-CoV-2: A Warning for Secondary Infections.

This study was conducted to investigate oropharyngeal microbiota alterations during the progression of coronavirus disease 2019 (COVID-19) by analyzing these alterations during the infection and clearance processes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The diagnosis of COVID-19 was confirmed by using positive SARS-CoV-2 quantitative reverse transcription polymerase chain reaction (RT-qPCR). The alterations in abundance, diversity, and potential function of the oropharyngeal microbiome were identified using metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 47 patients with COVID-19 (within a week after diagnosis and within two months after recovery from COVID-19) and 40 healthy individuals.

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

Background: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.

Methods: Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data.

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice.

Methods: This study recruited participants prospectively in two rural sites of western China.

Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result.

Background: Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting.

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline.

Clinical Question: The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty.

Current Practice: There have been several guidelines for SPN assessment.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.

Background: Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment.

Clinical features and prognostic factors of combined small cell lung cancer: development and validation of a nomogram based on the SEER database.

Background: Combined small-cell lung cancer (CSCLC) refers to the simultaneous presence of small cell lung cancer (SCLC) and any subtype of the non-small cell lung cancer (NSCLC). This study aimed to explore the prognosis of CSCLC, NSCLC, and pure SCLC patients, and to develop a nomogram to estimate the overall survival (OS) for CSCLC patients.

Methods: Patients diagnosed with NSCLC, CSCLC, and pure SCLC between 2004 and 2015 were identified from the Surveillance Epidemiology and End Results (SEER) database.

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images.

Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions.

Metastatic patterns and prognosis of young lung cancer patients: a population-based study by age.

Background: We aimed to examine the different metastatic patterns and corresponding survival outcomes between all ages of young (aged <60 years) and elderly lung cancer patients.

Methods: Lung cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were divided into a young and elderly group. The young group was subdivided into four consecutive subgroups.

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

Background: Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

Methods: This study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically.

Increased microRNA-30a levels in bronchoalveolar lavage fluid as a diagnostic biomarker for lung cancer.

Background: MicroRNA-30a (miRNA-30a) levels have been shown to increase in the plasma of lung cancer patients. Herein, we evaluated the miRNA-30a levels in the bronchoalveolar lavage fluid (BALF) of lung cancer patients as a potential biomarker for lung cancer diagnosis.

Methods: BALF miRNA-30a expression of 174 subjects was quantified using quantitative real-time reverse transcription-polymerase chain reaction and compared between lung cancer patients and control patients with benign lung diseases.

Immune Checkpoint Blockade Therapy May Be a Feasible Option for Primary Pulmonary Lymphoepithelioma-like Carcinoma.

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of non-small cell lung cancer (NSCLC) for which there is currently no recognized treatment. Recently, favorable immune checkpoint blockade responses have been observed in PPLELC. This study aimed to review the effects of this regimen in patients with advanced PPLELC.

Molecular characteristics of primary pulmonary lymphoepithelioma-like carcinoma based on integrated genomic analyses.

Primary pulmonary lymphoepithelioma-like carcinoma (pLELC) is a rare non-small cell lung cancer (NSCLC) subtype. Clinical features have been described in our previous report, but molecular characteristics remain unclear. Herein, pLELC genomic features were explored.

Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis.

Background: Early and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients.

Methods: PubMed, Scopus, Web of Science, and Embase were systematically searched for studies considering the relationship between COVID-19 and mortality up to 4 June 2020.