Intratumoural microbiota: a new frontier in cancer development and therapy.

Human microorganisms, including bacteria, fungi, and viruses, play key roles in several physiological and pathological processes. Some studies discovered that tumour tissues once considered sterile actually host a variety of microorganisms, which have been confirmed to be closely related to oncogenesis. The concept of intratumoural microbiota was subsequently proposed. Microbiota could colonise tumour tissues through mucosal destruction, adjacent tissue migration, and hematogenic invasion and affect the biological behaviour of tumours as an important part of the tumour microenvironment. Mechanistic studies have demonstrated that intratumoural microbiota potentially promote the initiation and progression of tumours by inducing genomic instability and mutations, affecting epigenetic modifications, promoting inflammation response, avoiding immune destruction, regulating metabolism, and activating invasion and metastasis. Since more comprehensive and profound insights about intratumoral microbiota are continuously emerging, new methods for the early diagnosis and prognostic assessment of cancer patients have been under examination. In addition, interventions based on intratumoural microbiota show great potential to open a new chapter in antitumour therapy, especially immunotherapy, although there are some inevitable challenges. Here, we aim to provide an extensive review of the concept, development history, potential sources, heterogeneity, and carcinogenic mechanisms of intratumoural microorganisms, explore the potential role of microorganisms in tumour prognosis, and discuss current antitumour treatment regimens that target intratumoural microorganisms and the research prospects and limitations in this field.

Lipid metabolism of plasma-derived small extracellular vesicles in COVID-19 convalescent patients.

The coronavirus disease 2019 (COVID-19), which affects multiple organs, is causing an unprecedented global public health crisis. Most COVID-19 patients recover gradually upon appropriate interventions. Viruses were reported to utilize the small extracellular vesicles (sEVs), containing a cell-specific cargo of proteins, lipids, and nucleic acids, to escape the attack from the host's immune system. This study aimed to examine the sEVs lipid profile of plasma of recovered COVID-19 patients (RCs). Plasma sEVs were separated from 83 RCs 3 months after discharge without underlying diseases, including 18 recovered asymptomatic patients (RAs), 32 recovered moderate patients (RMs), and 33 recovered severe and critical patients (RSs), and 19 healthy controls (HCs) by Total Exosome Isolation Kit. Lipids were extracted from sEVs and then subjected to targeted liquid chromatography-mass spectrometry. The size, concentration, and distribution of sEVs did not differ in RCs and HCs as validated by transmission electron microscopy, nanoparticle tracking analysis, and immunoblot analysis. Fifteen subclasses of 508 lipids were detected in plasma sEVs from HCs, RAs, RMs, and RSs, such as phosphatidylcholines (PCs) and diacylglycerols (DAGs), etc. Total lipid intensity displayed downregulation in RCs compared with HCs. The relative abundance of DAGs gradually dropped, whereas PCs, lysophosphatidylcholines, and sphingomyelins were higher in RCs relative to HCs, especially in RSs. 88 lipids out of 241 in sEVs of RCs were significantly different and a conspicuous increase was revealed with disease status. The sEVs lipids alternations were found to be significantly correlated with the clinical indices in RCs and HCs, suggesting that the impact of COVID-19 on lipid metabolism lingered for a long time. The lipid abnormalities bore an intimate link with glycerophospholipid metabolism and glycosylphosphatidylinositol anchor biosynthesis. Furthermore, the lipidomic analysis showed that RCs were at higher risk of developing diabetes and sustaining hepatic impairment. The abnormality of immunomodulation in RCs might still exist. The study may offer new insights into the mechanism of organ dysfunction and help identify novel therapeutic targets in the RCs.

The Application of Enhanced Recovery After Surgery for Gastrectomy and Colorectal Resection: A Systematic Review and Meta-Analysis.

Purpose: Enhanced recovery after surgery (ERAS) protocols not only positively affect gastrointestinal surgery outcomes but may also increase the risk of some complications. This meta-analysis was conducted to assess the impact of ERAS on the recovery and complications following gastrointestinal surgery. Materials and Methods: Studies published before December 2022 were retrieved from the following databases, EMBASE, PubMed, Cochrane Library, and Web of Science, without limitations of language or race. The endpoints included lung infection, surgical site infection, postoperative ileus, length of hospitalization, urinary tract infection, readmission, anastomotic leakage, and C-reactive protein serum levels. Results: A total of 23 studies were included. The results of the meta-analysis revealed that there was a decrease in incidence of the lung infection (risk ratio = 0.46, 95% confidence interval 0.27-0.74, P = .002) and postoperative length of hospitalization (P < .00001). However, ERAS protocol groups had higher readmission rates, nausea, and vomiting. There was no significant difference in the incidence of anastomotic leakage, ileus, surgical site infection, and urinary tract infection between the experimental and control groups. Conclusions: ERAS protocols can reduce the risk of postoperative lung infections, shorten hospital stays, and expedite patient recovery. Furthermore, ERAS protocols are not associated with serious complications following gastrointestinal surgeries.

Prognosis prediction of icotinib as targeted therapy for advanced EGFR-positive non-small cell lung cancer patients.

Clinical trials on icotinib, a first-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), have shown promising results as targeted therapy for non-small cell lung cancer (NSCLC). This study aimed to establish an effective scoring system to predict the one-year progression-free survival (PFS) of advanced NSCLC patients with EGFR mutations treated with icotinib as targeted therapy. A total of 208 consecutive patients with advanced EGFR-positive NSCLC treated with icotinib were enrolled in this study. Baseline characteristics were collected within 30 days before icotinib treatment. PFS was taken as the primary endpoint and the response rate as the secondary endpoint. Least absolute shrinkage and selection operator (LASSO) regression analysis and Cox proportional hazards regression analysis were used to select the optimal predictors. We evaluated the scoring system using a five-fold cross-validation. PFS events occurred in 175 patients, with a median PFS of 9.9 months (interquartile range, 6.8-14.5). The objective response rate (ORR) was 36.1%, and the disease control rate (DCR) was 67.3%. The final ABC-Score consisted of three predictors: age, bone metastases and carbohydrate antigen 19-9 (CA19-9). Upon comparison of all three factors, the combined ABC-score (area under the curve (AUC)= 0.660) showed a better predictive accuracy than age (AUC = 0.573), bone metastases (AUC = 0.615), and CA19-9 (AUC = 0.608) individually. A five-fold cross-validation showed good discrimination with AUC = 0.623. The ABC-score developed in this study was significantly effective as a prognostic tool for icotinib in advanced NSCLC patients with EGFR mutations.

Association of general and abdominal obesity with lung function, FeNO, and blood eosinophils in adult asthmatics: Findings from NHANES 2007-2012.

Purpose: Obesity is considered a risk factor for asthma exacerbation. However, limited studies have focused on the association of different levels of weight clusters with asthma. As such, we study the associations between different weight clusters with FeNO, blood eosinophils, and lung function among adult asthmatics. Methods: Data from 789 participants aged 20 years or older in the National Health and Nutrition Examination Survey 2007-2012 were analyzed. Body mass index (BMI) and waist circumference (WC) were used to determine the weight status. The study population was divided into five groups, including normal weight and low WC (153), normal weight and high WC (43), overweight and high WC (67), overweight and abdominal obesity (128), and general and abdominal obesity (398). A Multivariate linear regression model was used to evaluate the abovementioned associations after adjusting for potential confounding factors. Results: The adjusted models showed that general and abdominal obesity cluster (adjusted ß = -0.63, 95% confidence interval (CI): -1.08, -0.17 p < 0.01), and the normal weight with high WC cluster (adjusted ß = -0.96, 95% CI: -1.74, -0.19 p < 0.05) were associated with lower levels of blood eosinophils percentage than normal weight and low WC cluster. A similar tendency was shown in the levels of FeNO, but the differences were not significant (p > 0.05). Furthermore, abdominal obesity clusters were significantly associated with lower FVC, FVC% predicted, and FEV1 measures than normal weight and low WC cluster, especially those individuals with general and abdominal obesity cluster. No association was found between different weight clusters and FEV1/FVCF ratio. The two other weight clusters did not show the association with any of the lung function measures. Conclusion: General and abdominal obesity were associated with lung function impairment and a significant reduction of FeNO and blood eosinophil percentage. This study emphasized the importance of concurrent determination of BMI and WC in asthma clinical practice.

Nanopore membrane chip-based isolation method for metabolomic analysis of plasma small extracellular vesicles from COVID-19 survivors.

Multiple studies showed that metabolic disorders play a critical role in respiratory infectious diseases, including COVID-19. Metabolites contained in small extracellular vesicles (sEVs) are different from those in plasma at the acute stage, while the metabolic features of plasma sEVs of COVID-19 survivors remain unknown. Here, we used a nanopore membrane-based microfluidic chip for plasma sEVs separation, termed ExoSEC, and compared the sEVs obtained by UC, REG, and ExoSEC in terms the time, cost, purity, and metabolic features. The results indicated the ExoSEC was much less costly, provided higher purity by particles/proteins ratio, and achieved 205-fold and 2-fold higher sEVs yield, than UC and REG, respectively. Moreover, more metabolites were identified and several signaling pathways were significantly enriched in ExoSEC-sEVs compared to UC-sEVs and REG-sEVs. Furthermore, we detected 306 metabolites in plasma sEVs using ExoSEC from recovered asymptomatic (RA), moderate (RM), and severe/critical COVID-19 (RS) patients without underlying diseases 3 months after discharge. Our study demonstrated that COVID-19 survivors, especially RS, experienced significant metabolic alteration and the dysregulated pathways mainly involved fatty acid biosynthesis, phenylalanine metabolism, etc. Metabolites of the fatty acid biosynthesis pathway bore a significantly negative association with red blood cell counts and hemoglobin, which might be ascribed to hypoxia or respiratory failure in RM and RS but not in RA at the acute stage. Our study confirmed that ExoSEC could provide a practical and economical alternative for high throughput sEVs metabolomic study.

Differentiation of malignant from benign pleural effusions based on artificial intelligence.

INTRODUCTION: This study aimed to construct artificial intelligence models based on thoracic CT images to perform segmentation and classification of benign pleural effusion (BPE) and malignant pleural effusion (MPE). METHODS: A total of 918 patients with pleural effusion were initially included, with 607 randomly selected cases used as the training cohort and the other 311 as the internal testing cohort; another independent external testing cohort with 362 cases was used. We developed a pleural effusion segmentation model (M1) by combining 3D spatially weighted U-Net with 2D classical U-Net. Then, a classification model (M2) was built to identify BPE and MPE using a CT volume and its 3D pleural effusion mask as inputs. RESULTS: The average Dice similarity coefficient, Jaccard coefficient, precision, sensitivity, Hausdorff distance 95% (HD95) and average surface distance indicators in M1 were 87.6±5.0%, 82.2±6.2%, 99.0±1.0%, 83.0±6.6%, 6.9±3.8 and 1.6±1.1, respectively, which were better than those of the 3D U-Net and 3D spatially weighted U-Net. Regarding M2, the area under the receiver operating characteristic curve, sensitivity and specificity obtained with volume concat masks as input were 0.842 (95% CI 0.801 to 0.878), 89.4% (95% CI 84.4% to 93.2%) and 65.1% (95% CI 57.3% to 72.3%) in the external testing cohort. These performance metrics were significantly improved compared with those for the other input patterns. CONCLUSIONS: We applied a deep learning model to the segmentation of pleural effusions, and the model showed encouraging performance in the differential diagnosis of BPE and MPE.

Magnetic-Transition-Metal Oxides Modified Pollen-Derived Porous Carbon for Enhanced Absorption Performance.

In our work, the transition-metal-oxide precursor (TMO@BC, M = Fe, Co, Ni) has been loaded on the pollen carbon by the hydrothermal method and annealed at different temperatures to generate a composite material of metal oxide and pollen carbon in this study, which can effectively prevent agglomeration caused by a small size and magnetism. The XRD patterns of the samples showed that the as-synthesized metal oxides were γ-Fe2O3, CoO, and NiO. In the 20 mg/L methyl orange adsorption experiment, the adsorption amount of CoO@C at 500 ℃ reached 19.32 mg/g and the removal rate was 96.61%. Therefore, CoO@C was selected for the adsorption correlation-model-fitting analysis, which was in line with the secondary reaction. The pseudo-second-order kinetic model (R2: 0.9683-0.9964), the intraparticle diffusion model, and the Freundlich adsorption isotherm model indicated that the adsorption process was the result of both physical and chemical adsorptions, and the judgment was based on the electrostatic action. The adsorption and removal efficiency of ciprofloxacin (CIP) by changing the pH of the reaction was about 80%, so the electrostatic attraction worked, but not the main factor. Recovered by an external magnetic field, the three-time recycling efficiency was still maintained at more than 80%. This novel biomass-derived magnetic porous carbon material embedded with transition-metal-oxide nanoparticles is highly promising for many applications, especially in the field of environmental remediation.

Fasudil Increased the Sensitivity to Gefitinib in NSCLC by Decreasing Intracellular Lipid Accumulation.

Tyrosine kinase inhibitors (TKIs) resistance is a challenge in patients with epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC). Here, we examined the effect of Fasudil in reversing TKIs resistance. The results of CCK8 assay, clone formation assay, cell cycle arrest analysis, and apoptosis analysis show that Fasudil treatment effectively suppressed the growth and induced apoptosis of the EGFR-mutant NSCLC cells. Furthermore, Fasudil in combination with gefitinib showed a synergistic anti-tumor effect in gefitinib-resistant NSCLC cells. RNA-seq analysis and immunoblotting indicated that Fasudil treatment significantly inhibited intracellular lipid accumulation and EGFR/PI3K/AKT pathway activation. Mechanistic investigations showed that Fasudil regulated lipogenic gene expressions via AMPK signal pathway. In vivo, Fasudil and gefitinib co-administration significantly attenuated the growth of H1975 nude mouse xenograft models, suggesting that Fasudil treatment combined with gefitinib can be applied as a therapy for gefitinib-resistant NSCLC cells.

The potential applications of microparticles in the diagnosis, treatment, and prognosis of lung cancer.

Microparticles (MPs) are 100-1000 nm heterogeneous submicron membranous vesicles derived from various cell types that express surface proteins and antigenic profiles suggestive of their cellular origin. MPs contain a diverse array of bioactive chemicals and surface receptors, including lipids, nucleic acids, and proteins, which are essential for cell-to-cell communication. The tumour microenvironment (TME) is enriched with MPs that can directly affect tumour progression through their interactions with receptors. Liquid biopsy, a minimally invasive test, is a promising alternative to tissue biopsy for the early screening of lung cancer (LC). The diverse biomolecular information from MPs provides a number of potential biomarkers for LC risk assessment, early detection, diagnosis, prognosis, and surveillance. Remodelling the TME, which profoundly influences immunotherapy and clinical outcomes, is an emerging strategy to improve immunotherapy. Tumour-derived MPs can reverse drug resistance and are ideal candidates for the creation of innovative and effective cancer vaccines. This review described the biogenesis and components of MPs and further summarised their main isolation and quantification methods. More importantly, the review presented the clinical application of MPs as predictive biomarkers in cancer diagnosis and prognosis, their role as therapeutic drug carriers, particularly in anti-tumour drug resistance, and their utility as cancer vaccines. Finally, we discussed current challenges that could impede the clinical use of MPs and determined that further studies on the functional roles of MPs in LC are required.

Metagenomic next-generation sequencing for accurate diagnosis and management of lower respiratory tract infections.

OBJECTIVES: This study aimed to evaluate the clinical value of metagenomic next-generation sequencing (mNGS) in patients with suspected lower respiratory tract infections. METHODS: This retrospective study reviewed patients with suspected lower respiratory tract infections at the Wuhan Union Hospital. Data including demographic, laboratory, and radiological profiles; treatment; and outcomes were recorded and analyzed. RESULTS: mNGS identified pathogenic microbes in 100/140 (71.4%) patients, although 135 (96.4%) had received empiric antibiotic treatment before the mNGS tests. Single bacterial infection (35/100, 35%) was the most common type of infection in patients with positive mNGS results, followed by single fungal infection (14/100, 14%), bacterial-viral coinfection (14/100, 14%), single viral infection (12/100, 12%), bacterial-fungal coinfection (9/100, 9%), fungal-viral coinfection (9/100, 9%), and bacterial-fungal-viral coinfection (7/100, 7%). Moreover, compared with culture test, mNGS showed higher sensitivity (63/85, 74.1% vs 22/85, 25.9% P = 0.001) and lower processing time (24 hours vs 48 hours). Antibiotic treatment was adjusted or confirmed based on the mNGS results in 123 (87.9%) patients, including five (3.6%), 33 (23.6%), and 85 (60.7%) patients, in whom treatment was downgraded, upgraded, and unchanged, respectively. Almost all patients, regardless of escalation, de-escalation, or no change in treatment, showed significant improvement in clinical symptoms and inflammatory indicators. In addition, 17 (12.1%) patients were referred to Wuhan Pulmonary Hospital for further treatment because of confirmed or suspected tuberculosis. CONCLUSION: mNGS could be a promising technique for microbiological diagnosis and antibiotic management, potentially improving outcomes for patients.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study.

BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). METHODS: In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. RESULTS: The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. CONCLUSIONS: In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

POU6F1 cooperates with RORA to suppress the proliferation of lung adenocarcinoma by downregulation HIF1A signaling pathway.

Lung adenocarcinoma (LUAD) represents the most frequently diagnosed histological subtype of non-small cell lung cancer with the highest mortality worldwide. Transcriptional dysregulation is a hallmark of nearly all kinds of cancers. In the study, we identified that the POU domain, class 6, transcription factor 1 (POU6F1), a member of the POU family of transcription factors, was closely associated with tumor stage and death in LUAD. We revealed that POU6F1 was downregulated in LUAD tissues and downregulated POU6F1 was predictive of an unfavorable prognosis in LUAD patients. In vitro assays, including CCK8, soft agar, transwell, clone formation, wound-healing assay, and nude mouse xenograft model all revealed that POU6F1 inhibited the growth and invasion of LUAD cells. Mechanistically, POU6F1 bound and stabilized retinoid-related orphan receptor alpha (RORA) to exert the transcriptional inhibition of hypoxia-inducible factor 1-alpha (HIF1A) and alter the expression of HIF1A signaling pathway-associated genes, including ENO1, PDK1, and PRKCB, thereby leading to the suppression of LUAD cells. Collectively, these results demonstrated the suppressive role of POU6F1/RORA in the progression of LUAD and may potentially be used as a target for the treatment of LUAD.

Changes in glomerular filtration rate and metabolomic differences in severely ill coronavirus disease survivors 3 months after discharge.

To explore the recovery of renal function in severely ill coronavirus disease (COVID-19) survivors and determine the plasma metabolomic profile of patients with different renal outcomes 3 months after discharge, we included 89 severe COVID-19 survivors who had been discharged from Wuhan Union Hospital for 3 months. All patients had no underlying kidney disease before admission. At patient recruitment, renal function assessment, laboratory examination, chest computed tomography (CT) were performed. Liquid chromatography-mass spectrometry was used to detect metabolites in the plasma. We analyzed the longitudinally change in the estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin-c levels using the CKD-EPI equation and explored the metabolomic differences in patients with different eGFR change patterns from hospitalization to 3 months after discharge. Lung CT showed good recovery; however, the median eGFR significantly decreased at the 3-month follow-up. Among the 89 severely ill COVID-19 patients, 69 (77.5%) showed abnormal eGFR (<90 mL/min per 1.73 m2) at 3 months after discharge. Age (odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.08-1.47, p = 0.003), body mass index (OR = 1.97, 95% CI = 1.20-3.22, p = 0.007), and cystatin-c level (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003) at discharge were independent risk factors for post-discharge abnormal eGFR. Plasma metabolomics at the 3-months follow-up revealed that ß-pseudouridine, uridine, and 2-(dimethylamino) guanosine levels gradually increased with an abnormal degree of eGFR. Moreover, the kynurenine pathway in tryptophan metabolism, vitamin B6 metabolism, cysteine and methionine metabolism, and arginine biosynthesis were also perturbed in survivors with abnormal eGFR.

Long-Term Effects of COVID-19 on Health Care Workers 1-Year Post-Discharge in Wuhan.

INTRODUCTION: To assess the long-term consequences of coronavirus disease (COVID-19) among health care workers (HCWs) in China (hereafter surviving HCWs). METHODS: A total of 303 surviving HCWs were included. Lung (pulmonary function test, 6-min walk test [6MWT], chest CT), physical (St. George's Respiratory Questionnaire [SGRQ], Modified Medical Research Council dyspnea scale [mMRC], and Borg scale), and psychiatric functions (Essen Trauma Inventory) were evaluated during the 1-year follow-up. RESULTS: Surviving HCWs had an abnormal diffusion capacity 1 year post-discharge. Participants with a reduced carbon monoxide diffusing capacity (DLCO) comprised 43.48%. The proportion of HCWs with a median 6MWT distance below the lower limit of the normal was 19.4%. An abnormal CT pattern was observed in 37.5% of the HCWs. The SGRQ, mMRC, and Borg scores of surviving HCWs, especially those with critical/severe disease, were significantly higher than those in the normal population. Probable post-traumatic stress disorder (PTSD) was reported in 21.9% of the surviving HCWs. Diffusion capacity impairment was associated with women. Critical/severe illness and nurses were associated with impaired physical function. CONCLUSIONS: Most surviving HCWs, especially female HCWs, still had an abnormal diffusion capacity at 1 year. The physical and psychiatric functions of surviving HCWs were significantly worse than those of the healthy population. Long-term follow-up of pulmonary, physical, and psychiatric functions for surviving HCWs is required.

Clinical features and outcomes of male patients with lymphangioleiomyomatosis: A review.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare disease involving multiple systems, which is divided into sporadic LAM (S-LAM) and tuberous sclerosis complex-LAM, mostly affecting women who are in childbearing age stage. Data on male patients are limited and scattered. Therefore, it is necessary to conduct a systematic review to investigate the clinical features, diagnosis, treatment, and outcomes of LAM in male. METHODS: We performed a literature review by searching for all the published reported cases of LAM in male during the past 35 years (April 1986-October 2021). RESULTS: 36 male patients described in 26 references were included in this article. The median age of onset was 34 years (interquartile range: 1-79). The most common initial manifestations were cough, dyspnea, respite, and hemoptysis, with pulmonary complications such as pneumothorax and chylothorax. Five patients (13.9%) were asymptomatic at admission. Nearly half of the 36 male patients had thin-walled air-filled cysts that were visible throughout both lungs. Considering the abovementioned atypical clinical features, misdiagnosis was committed in 8 patients (22.2%). In addition, patients with tuberous sclerosis complex lymphangioleiomyomatosis often have no pulmonary manifestations at onset but present multiple extrapulmonary manifestations and have higher rates of renal angiomyolipomas than patients with S-LAM (P < 0.01). Eventually, 4 patients with S-LAM eventually died. CONCLUSION: Physicians should increase the awareness of LAM in male. Early monitoring of various systems should be recommended to ensure early management and active follow-up. Tuberous sclerosis complex patients should immediately be tracked for the onset of LAM disease to improve prognosis.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge.

BACKGROUND: It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. METHODS: COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. RESULTS: Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. CONCLUSION: We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Comparison of Residual Pulmonary Abnormalities 3 Months After Discharge in Patients Who Recovered From COVID-19 of Different Severity.

Background and Objectives: To investigate whether coronavirus disease 2019 (COVID-19) survivors who had different disease severities have different levels of pulmonary sequelae at 3 months post-discharge. Methods: COVID-19 patients discharged from four hospitals 3 months previously, recovered asymptomatic patients from an isolation hotel, and uninfected healthy controls (HCs) from the community were prospectively recruited. Participants were recruited at Wuhan Union Hospital and underwent examinations, including quality-of-life evaluation (St. George Respiratory Questionnaire [SGRQ]), laboratory examination, chest computed tomography (CT) imaging, and pulmonary function tests. Results: A total of 216 participants were recruited, including 95 patients who had recovered from severe/critical COVID-19 (SPs), 51 who had recovered from mild/moderate disease (MPs), 28 who had recovered from asymptomatic disease (APs), and 42 HCs. In total, 154 out of 174 (88.5%) recovered COVID-19 patients tested positive for serum SARS-COV-2 IgG, but only 19 (10.9%) were still positive for IgM. The SGRQ scores were highest in the SPs, while APs had slightly higher SGRQ scores than those of HCs; 85.1% of SPs and 68.0% of MPs still had residual CT abnormalities, mainly ground-glass opacity (GGO) followed by strip-like fibrosis at 3 months after discharge, but the pneumonic lesions were largely absorbed in the recovered SPs or MPs relative to findings in the acute phase. Pulmonary function showed that the frequency of lung diffusion capacity for carbon monoxide abnormalities were comparable in SPs and MPs (47.1 vs. 41.7%), while abnormal total lung capacity (TLC) and residual volume (RV) were more frequent in SPs than in MPs (TLC, 18.8 vs. 8.3%; RV, 11.8 vs. 0%). Conclusions: Pulmonary abnormalities remained after recovery from COVID-19 and were more frequent and conspicuous in SPs at 3 months after discharge.

Proteomics of extracellular vesicles in plasma reveals the characteristics and residual traces of COVID-19 patients without underlying diseases after 3 months of recovery.

More and more patients suffered from Coronavirus disease 2019 (COVID-19) have got recovery gradually due to suitable intervention. Increasing data mainly studies the clinical characteristics of recovered COVID-19 patients, and their molecular changes especially proteome changes also play the same important role in understanding of biological characteristics of recovered COVID-19 patients as clinical characteristics do. In our study, we reported the whole lung-ground glass-CT value-average of mild/severe recovered patients 3 months after discharge without underlying diseases was significantly lower than that of healthy subjects. Then we isolated the extracellular vesicles (EVs) of plasma from 19 healthy subjects and 67 recovered COVID-19 patients. Mass Spectrometry was used to catalogue the proteins of these EVs compared to a defined group of controls. Identified 174 proteins were differentially expressed in the EVs of COVID-19 patients compared with healthy subjects, which involved in lipid metabolic process, response to cellular, and response to stress oxygen-containing compound. Besides, we identified several protein of plasma EVs in recovered patients associated with coagulation activity, inflammatory reaction, immune response, and low organ function. In addition, proteins correlating with clinical index such as alkaline phosphatase (ALP) and alanine aminotransferase (ALT) were also detected. Moreover, we also identified many unique or characteristic associations found in the recovered COVID-19 patients, which especially involved the kidney, serum electrolyte levels, and inflammation functions. This finding suggests that monitoring the situation of recovered patients might be useful, especially the indexes of coagulation, inflammation, immunity, and organ function, which can prevent bleeding, reinfection and organ dysfunction.