Impact of diabetes on COVID-19 and glucocorticoids on patients with COVID-19 and diabetes during the Omicron variant epidemic: a multicenter retrospective cohort study in South China.

BACKGROUND: To explore the impact of diabetes on the clinical features and prognosis of COVID-19 and assess the influence of glucocorticoid use on the prognosis of patients with COVID-19 and diabetes. METHODS: This retrospective multicenter cohort study included patients admitted between December 2022 and January 2023. The patients were grouped according to diabetes and glucocorticoid use. The primary outcome was in-hospital mortality. RESULTS: Among 400 patients with glucocorticoid data, 109 (27.3%) had diabetes. The inflammatory cytokines were higher in patients with diabetes, manifested by higher IL-6 (25.33 vs. 11.29 ng/L, p = 0.011), CRP (26.55 vs. 8.62 mg/L, p = 0.003), and PCT (0.07 vs. 0.04 ng/ml, p = 0.010), while CD4+ (319 vs. 506 /mL, p = 0.004) and CD8+ (141 vs. 261 /mL, p < 0.001) T lymphocytes were lower. The overall mortality rate of hospitalized COVID-19 patients with diabetes was 13.46%. The diabetic patients who received glucocorticoids vs. those who did not receive glucocorticoids had a similar mortality (15.00% vs. 11.39%, p = 0.591). CONCLUSIONS: Patients with COVID-19 and diabetes are more likely to experience hyperinflammatory response and T cell reduction, especially those with severe/critical disease. Glucocorticoid use was not associated with the prognosis of COVID-19 in patients with diabetes. Still, glucocorticoids should be used cautiously in diabetic patients with severe/critical COVID-19.

Hepatic IRE1α-XBP1 signaling promotes GDF15-mediated anorexia and body weight loss in chemotherapy.

Platinum-based chemotherapy drugs can lead to the development of anorexia, a detrimental effect on the overall health of cancer patients. However, managing chemotherapy-induced anorexia and subsequent weight loss remains challenging due to limited effective therapeutic strategies. Growth differentiation factor 15 (GDF15) has recently gained significant attention in the context of chemotherapy-induced anorexia. Here, we report that hepatic GDF15 plays a crucial role in regulating body weight in response to chemo drugs cisplatin and doxorubicin. Cisplatin and doxorubicin treatments induce hepatic Gdf15 expression and elevate circulating GDF15 levels, leading to hunger suppression and subsequent weight loss. Mechanistically, selective activation by chemotherapy of hepatic IRE1α-XBP1 pathway of the unfolded protein response (UPR) upregulates Gdf15 expression. Genetic and pharmacological inactivation of IRE1α is sufficient to ameliorate chemotherapy-induced anorexia and body weight loss. These results identify hepatic IRE1α as a molecular driver of GDF15-mediated anorexia and suggest that blocking IRE1α RNase activity offers a therapeutic strategy to alleviate the adverse anorexia effects in chemotherapy.

More attention should be paid to Omicron-associated sepsis: a multicenter retrospective study in south China.

Background: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible but causes less severe disease compared to other variants. However, its association with sepsis incidence and outcomes is unclear. This study aimed to investigate the incidence of Omicron-associated sepsis, as per the Sepsis 3.0 definition, in hospitalized patients, and to explore its relationship with clinical characteristics and prognosis. Methods: This multicenter retrospective study included adults hospitalized with confirmed SARS-CoV-2 infection across six tertiary hospitals in Guangzhou, China from November 2022 to January 2023. The Sequential Organ Failure Assessment (SOFA) score and its components were calculated at hospital admission to identify sepsis. Outcomes assessed were need for intensive care unit (ICU) transfer and mortality. Receiver operating characteristic curves evaluated the predictive value of sepsis versus other biomarkers for outcomes. Results: A total of 299 patients (mean age: 70.1±14.4 years, 42.14% female) with SOFA score were enrolled. Among them, 152 were categorized as non-serious cases while the others were assigned as the serious group. The proportion of male patients, unvaccinated patients, patients with comorbidity such as diabetes, chronic cardiovascular disease, and chronic lung disease was significantly higher in the serious than non-serious group. The median SOFA score of all enrolled patients was 1 (interquartile range, 0-18). In our study, 147 patients (64.19%) were identified as having sepsis upon hospital admission, with the majority of these septic patients (113, representing 76.87%) being in the serious group, the respiratory, coagulation, cardiovascular, central nervous, and renal organ SOFA scores were all significantly higher in the serious compared to the non-serious group. Among septic patients, 20 out of 49 (40.81%) had septic shock as indicated by lactate measurement within 24 hours of admission, and the majority of septic patients were in the serious group (17/20, 76.87%). Sepsis was present in 118 out of 269 (43.9%) patients in the general ward, and among those with sepsis, 34 out of 118 (28.8%) later required ICU care during hospitalization. By contrast, none of the patients without sepsis required ICU care. Moreover, the mortality rate was significantly higher in patients with than without sepsis. Conclusions: A considerable proportion of patients infected with Omicron present with sepsis upon hospital admission, which is associated with a poorer prognosis. Therefore, early recognition of viral sepsis by evaluation of the SOFA score in hospitalized coronavirus disease 2019 patients is crucial.

Electrical impedance tomography-guided positive end-expiratory pressure titration in ARDS: a systematic review and meta-analysis.

PURPOSE: Assessing efficacy of electrical impedance tomography (EIT) in optimizing positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients to enhance respiratory system mechanics and prevent ventilator-induced lung injury (VILI), compared to traditional methods. METHODS: We carried out a systematic review and meta-analysis, spanning literature from January 2012 to May 2023, sourced from Scopus, PubMed, MEDLINE (Ovid), Cochrane, and LILACS, evaluated EIT-guided PEEP strategies in ARDS versus conventional methods. Thirteen studies (3 randomized, 10 non-randomized) involving 623 ARDS patients were analyzed using random-effects models for primary outcomes (respiratory mechanics and mechanical power) and secondary outcomes (PaO2/FiO2 ratio, mortality, stays in intensive care unit (ICU), ventilator-free days). RESULTS: EIT-guided PEEP significantly improved lung compliance (n = 941 cases, mean difference (MD) = 4.33, 95% confidence interval (CI) [2.94, 5.71]), reduced mechanical power (n = 148, MD = - 1.99, 95% CI [- 3.51, - 0.47]), and lowered driving pressure (n = 903, MD = - 1.20, 95% CI [- 2.33, - 0.07]) compared to traditional methods. Sensitivity analysis showed consistent positive effect of EIT-guided PEEP on lung compliance in randomized clinical trials vs. non-randomized studies pooled (MD) = 2.43 (95% CI - 0.39 to 5.26), indicating a trend towards improvement. A reduction in mortality rate (259 patients, relative risk (RR) = 0.64, 95% CI [0.45, 0.91]) was associated with modest improvements in compliance and driving pressure in three studies. CONCLUSIONS: EIT facilitates real-time, individualized PEEP adjustments, improving respiratory system mechanics. Integration of EIT as a guiding tool in mechanical ventilation holds potential benefits in preventing ventilator-induced lung injury. Larger-scale studies are essential to validate and optimize EIT's clinical utility in ARDS management.

Effects of freezing on the gelation behaviors of liquid egg yolks affected by saccharides: thermal behaviors and rheological and structural changes.

Monitoring and controlling the freezing process and thermal properties of foods is an important means to understand and maintain product quality. Saccharides were used in this study to regulate the gelation of liquid egg yolks induced by freeze‒thawing; the selected saccharides included sucrose, L-arabinose, xylitol, trehalose, D-cellobiose, and xylooligosaccharides. The regulatory effects of saccharides on frozen egg yolks were investigated by characterizing their thermal and rheological properties and structural changes. The results showed that L-arabinose and xylitol were effective gelation regulators. After freeze‒thawing, the sugared egg yolks exhibited a lower consistency index and fewer rheological units than those without saccharides, indicating controlled gelation. Weaker aggregation of egg yolk proteins was confirmed by smaller aggregates observed by confocal laser scanning microscopy and smaller particle sizes. Saccharides alleviated the freeze-induced conversion of α-helices to ß-sheets in egg yolk proteins, exposing fewer Trp residues. Overall, L-arabinose showed the greatest improvement in regulating the gelation of egg yolks, followed by xylitol, which is correlated with its low molecular weight.

Nail psoriasis in China: A prospective multicentre study.

BACKGROUND: Data on nail psoriasis (PsO) in China are scarce. OBJECTIVES: To provide nail PsO-related data regarding epidemiologic characteristics, manifestations, fungal infections, arthritic complaints and treatments that may facilitate improved patient management globally. METHODS: From August 2021 to August 2022, patients with nail PsO were enrolled in a prospective multicentre observational study at 25 hospitals in China. We collected and analysed data concerning nail PsO demography, clinical signs, fungal detection, arthritic symptoms and treatment. RESULTS: A total of 817 patients with nail PsO were involved, with a mean body mass index of 24.13 ± 2.93. In addition, 71.41% of the patients were male. The Nail PsO Severity Index score was weakly positively correlated with body surface area. The percentage of nail involvement was 95.29% for fingernails and 57.18% for toenails, with pitting (67.11%) and subungual hyperkeratosis (60.40%) being the most prevalent manifestations, respectively. Toenails showed a significantly higher frequency of nailfold scales, subungual hyperkeratosis and nail plate crumbling and a lower frequency of splinter haemorrhages, pitting and erythema of the lunula. A total of 13.26% of the PsO patients had onychomycosis, and 77.08% were observed in the toenails. Articular symptoms were reported by 12.17% of the patients, with the peripheral type being predominant. Significant associations between articular symptoms and nailfold swelling, subungual hyperkeratosis, nailfold scales, onycholysis and longitudinal ridges were found. Only 2.30% (20 out of 871) of patients with nail PsO received treatment. The most frequently employed therapy for cutaneous PsO with nail involvement was biologic therapy (n = 366). CONCLUSIONS: PsO showed distinct manifestations in the toenails and fingernails. Additionally, toenail PsO combined with onychomycosis requires special attention. Articular symptoms in psoriatic patients are associated with specific nail changes. It is important to research and advocate for more potent treatments for nail PsO.

A protein-based prognostic model for pancreatic ductal adenocarcinoma: Construction and validation.

BACKGROUND: Probing relevant proteomic biomarkers may facilitate effective pancreatic adenocarcinoma (PDAC) diagnosis, treatment and prevention. Here, we developed a protein-based prognostic model for PDAC by using relevant proteomic biomarkers data from The Cancer Genome Atlas (TCGA). METHODS: We obtained PDAC's proteomic and clinical data from TCGA and used various analytical tools to identify differentially expressed proteins between normal and cancer tissues. We constructed our protein-based prognostic model and confirmed its accuracy using receiver operating characteristic curve and Kaplan-Meier survival analyses. We elucidated clinical factor-signature protein correlations by clinical correlation assessments and protein coexpression networks. We also used immunohistochemistry (protein expression assessment), Gene Set Enrichment Analysis (protein role identification) and CIBERSORT (infiltrating immune cell distribution assessment). RESULTS: CIITA, BRAF_pS445, AR, YTHDF2, IGFBP2 and CDK1_pT14 were identified as PDAC-associated prognostic proteins. All risk scores calculated using our model provided 1-, 3-, 5-year survival probability at 70 % accuracy. The reliability of our model was validated by the GEO as well. In high- and low-risk groups, age, sex, T- and N- stage disparities were significant, and prognostic and coexpressed proteins correlated. PDAC tissues demonstrated significant CDK1_pT14 overexpression but significant BRAF_pS445, YTHDF2, and IGFBP2 underexpression. Downstream proteins of BRAF were validated by IHC. Low-risk tissues demonstrated more naïve B cells, eosinophils, activated NK cells and regulatory T cells, whereas high-risk tissues demonstrated more activated memory T cells, monocytes, neutrophils, dendritic cells and resting NK cells. CONCLUSIONS: Our protein-based prognostic model for PDAC, along with six signature proteins, might aid in predicting PDAC prognosis and therapeutic targets.

Venovenous extracorporeal membrane oxygenation for COVID-19 and influenza H1N1 associated acute respiratory distress syndrome: A comparative cohort study in China.

Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) has been demonstrated to be effective in treating patients with virus-induced acute respiratory distress syndrome (ARDS). However, whether the management of ECMO is different in treating H1N1 influenza and coronavirus disease 2019 (COVID-19)-associated ARDS patients remains unknown. Methods: This is a retrospective cohort study. We included 12 VV-ECMO-supported COVID-19 patients admitted to The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Eighth People's Hospital, and Wuhan Union Hospital West Campus between January 23 and March 31, 2020. We retrospectively included VV-ECMO-supported patients with COVID-19 and H1N1 influenza-associated ARDS. Clinical characteristics, respiratory mechanics including plateau pressure, driving pressure, mechanical power, ventilatory ratio (VR) and lung compliance, and outcomes were compared. Results: Data from 25 patients with COVID-19 (n=12) and H1N1 (n=13) associated ARDS who had received ECMO support were analyzed. COVID-19 patients were older than H1N1 influenza patients (P=0.004). The partial pressure of arterial carbon dioxide (PaCO2) and VR before ECMO initiation were significantly higher in COVID-19 patients than in H1N1 influenza patients (P <0.001 and P=0.004, respectively). COVID-19 patients showed increased plateau and driving pressure compared with H1N1 subjects (P=0.013 and P=0.018, respectively). Patients with COVID-19 remained longer on ECMO support than did H1N1 influenza patients (P=0.015). COVID-19 patients who required ECMO support also had fewer intensive care unit and ventilator-free days than H1N1. Conclusions: Compared with H1N1 influenza patients, COVID-19 patients were older and presented with increased PaCO2 and VR values before ECMO initiation. The differences between ARDS patients with COVID-19 and influenza on VV-ECMO detailed herein could be helpful for obtaining a better understanding of COVID-19 and for better clinical management.

Txt2Img-MHN: Remote Sensing Image Generation From Text Using Modern Hopfield Networks.

The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we propose a novel text-to-image modern Hopfield network (Txt2Img-MHN). The main idea of Txt2Img-MHN is to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers. Instead of directly learning concrete but highly diverse text-image joint feature representations for different semantics, Txt2Img-MHN aims to learn the most representative prototypes from text-image embeddings, achieving a coarse-to-fine learning strategy. These learned prototypes can then be utilized to represent more complex semantics in the text-to-image generation task. To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images. Despite its simplicity, we find that the overall accuracy in the zero-shot classification may serve as a good metric to evaluate the ability to generate an image from text. Extensive experiments on the benchmark remote sensing text-image dataset demonstrate that the proposed Txt2Img-MHN can generate more realistic remote sensing images than existing methods. Code and pre-trained models are available online (https://github.com/YonghaoXu/Txt2Img-MHN).

High driving pressure ventilation induces pulmonary hypertension in a rabbit model of acute lung injury.

BACKGROUND: Mechanical ventilation may cause pulmonary hypertension in patients with acute lung injury (ALI), but the underlying mechanism remains elucidated. METHODS: ALI was induced in rabbits by a two-hit injury, i.e., hydrochloric acid aspiration followed by mechanical ventilation for 1 h. Rabbits were then ventilated with driving pressure of 10, 15, 20, or 25 cmH2O for 7 h. Clinicopathological parameters were measured at baseline and different timepoints of ventilation. RNA sequencing was conducted to identify the differentially expressed genes in high driving pressure ventilated lung tissue. RESULTS: The two-hit injury induced ALI in rabbits was evidenced by dramatically decreased PaO2/FiO2 in the ALI group compared with that in the control group (144.5 ± 23.8 mmHg vs. 391.6 ± 26.6 mmHg, P < 0.001). High driving pressure ventilation (20 and 25 cmH2O) significantly elevated the parameters of acute pulmonary hypertension at different timepoints compared with low driving pressure (10 and 15 cmH2O), along with significant increases in lung wet/dry ratios, total protein contents in bronchoalveolar lavage fluid, and lung injury scores. The high driving pressure groups showed more pronounced histopathological abnormalities in the lung compared with the low driving pressure groups, accompanied by significant increases in the cross-sectional areas of myocytes, right ventricular weight/body weight value, and Fulton's index. Furthermore, the expression of the genes related to ferroptosis induction was generally upregulated in high driving pressure groups compared with those in low driving pressure groups. CONCLUSIONS: A rabbit model of ventilation-induced pulmonary hypertension in ALI was successfully established. Our results open a new research direction investigating the exact role of ferroptosis in ventilation-induced pulmonary hypertension in ALI.

A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications.

BACKGROUND: A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers. METHODS: The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. The other datasets were considered as the validation sets. To further evaluate the performance and predictive value of diagnostic model, nomogram, Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were constructed. Finally, the potential small molecular compounds interacting with selected features were explored from the CTD database. RESULTS: The results of GSEA showed that immune response and metabolism might play an important role in the pathogenesis of sepsis-induced ALI. Then, 52 genes were identified as putative biomarkers by consensus feature selection from all four methods. Among them, 5 genes (ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3) were selected by all methods and used to predict ALI diagnosis with high accuracy. The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively. In addition, the nomogram, DCA and CIC showed that the diagnostic model had great performance and predictive value. Finally, the small molecular compounds (Curcumin, Tretinoin, Acetaminophen, Estradiol and Dexamethasone) were screened as the potential therapeutic agents for sepsis-induced ALI. CONCLUSION: This consensus of multiple machine learning algorithms identified 5 genes that were able to distinguish ALI from septic patients. The diagnostic model could identify septic patients at high risk of ALI, and provide potential therapeutic targets for sepsis-induced ALI.

Effect of the Timing of Amubarvimab/Romlusevimab (BRII-196/198) Administration on Progression to Severe Disease in Elderly Patients with COVID-19 Infection: A Retrospective Cohort Study.

Objective: Early intervention with neutralizing antibodies is considered to be effective in preventing disease progression in patients with mild to moderate COVID-19 infection. Elderly patients are the most susceptible and at a higher risk of COVID-19 infection. The present study aimed to assess the necessity and possible clinical benefits of the early administration of Amubarvimab/Romlusevimab (BRII-196/198) in the elderly population. Methods: The present study was designed as a retrospective, multi-center cohort study conducted with 90 COVID-19 patients aged over 60, who were divided into two groups based on the timing of the administration of BRII-196/198 (administration at ≤ 3 days or > 3 days from the onset of infection symptoms). Results: The ≤ 3 days group exhibited a greater positive effect (HR 5.94, 95% CI, 1.42-24.83; P < 0.01), with only 2 patients among 21 patients (9.52%) exhibiting disease progression, compared to the 31 patients among the 69 patients (44.93%) of the > 3 days group who exhibited disease progression. The multivariate Cox regression analysis revealed low flow oxygen support prior to BRII-196/198 administration (HR 3.53, 95% CI 1.42-8.77, P < 0.01) and PLT class (HR 3.68, 95% CI 1.37-9.91, P < 0.01) as independent predictors of disease progression. Conclusions: In elderly patients with mild or moderate COVID-19 disease, who do not require oxygen support and had the risk factors for disease progression to severe COVID-19 disease, the administration of BRII-196/198 within 3 days resulted in a beneficial trend in terms of preventing disease progression.

Can Spectral Information Work While Extracting Spatial Distribution?-An Online Spectral Information Compensation Network for HSI Classification.

In the past few years, deep learning-based methods have shown commendable performance for hyperspectral image (HSI) classification. Many works focus on designing independent spectral and spatial branches and then fusing the output features from two branches for category prediction. In this way, the correlation that exists between spectral and spatial information is not completely explored, and spectral information extracted from one branch is always not sufficient. Some studies also try to directly extract spectral-spatial features using 3D convolutions but are accompanied by the severe over-smoothing phenomenon and poor representation ability of spectral signatures. Unlike the above-mentioned approaches, in this paper, we propose a novel online spectral information compensation network (OSICN) for HSI classification, which consists of a candidate spectral vector mechanism, progressive filling process, and multi-branch network. To the best of our knowledge, this paper is the first to online supplement spectral information into the network when spatial features are extracted. The proposed OSICN makes the spectral information participate in network learning in advance to guide spatial information extraction, which truly processes spectral and spatial features in HSI as a whole. Accordingly, OSICN is more reasonable and more effective for complex HSI data. Experimental results on three benchmark datasets demonstrate that the proposed approach has more outstanding classification performance compared with the state-of-the-art methods, even with a limited number of training samples.

Plasma cell-free DNA promise monitoring and tissue injury assessment of COVID-19.

Coronavirus 2019 (COVID-19) is a complex disease that affects billions of people worldwide. Currently, effective etiological treatment of COVID-19 is still lacking; COVID-19 also causes damages to various organs that affects therapeutics and mortality of the patients. Surveillance of the treatment responses and organ injury assessment of COVID-19 patients are of high clinical value. In this study, we investigated the characteristic fragmentation patterns and explored the potential in tissue injury assessment of plasma cell-free DNA in COVID-19 patients. Through recruitment of 37 COVID-19 patients, 32 controls and analysis of 208 blood samples upon diagnosis and during treatment, we report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA fragmentation characteristics reflect patient-specific physiological changes during treatment. Further analysis on cfDNA tissue-of-origin tracing reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, our work demonstrates and extends the translational merit of cfDNA fragmentation pattern as valuable analyte for effective treatment monitoring, as well as tissue injury assessment in COVID-19.

Ferroptosis, pyroptosis and necroptosis in acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is an acute and uncontrolled pulmonary inflammation caused by various insults. Cell death is a critical mechanism in the pathogenesis of ARDS. Ferroptosis, a novel form of cell death defined as iron-mediated lipid peroxidation, has been shown to play a role in the pathogenesis of ARDS. Additionally, pyroptosis and necroptosis are also involved in the pathophysiological process of ARDS. The crosstalk among ferroptosis, pyroptosis, and necroptosis is getting increasing attention. Therefore, this review will mainly summarize the molecular mechanisms and central pathophysiological role of ferroptosis in ARDS. We will also discuss our understanding of pyroptosis and necroptosis as they pertain to the pathogenesis of ARDS. Furthermore, we also describe the pathological processes that engage crosstalk among ferroptosis, pyroptosis, and necroptosis. We consider that individual pathways of ferroptosis, pyroptosis, and necroptosis are highly interconnected and can compensate for one another to promote cell death.

Corrigendum: Construction and validation of a robust prognostic model based on immune features in sepsis.

[This corrects the article DOI: 10.3389/fimmu.2022.994295.].

Changes of blood gas analysis in moderate-to-severe acute respiratory distress syndrome patients during long-term prone position ventilation: a retrospective cohort study.

Background: Prone position ventilation (PPV) has been recommended for patients with acute respiratory distress syndrome (ARDS) to improve oxygenation. However, whether prolonged prone ventilation will aggravate hyperoxia and whether abdominal compression will aggravate permissive hypercapnia acidosis are topics of concern. We carried out a retrospective analysis to investigate the issues above. Methods: Clinical data were collected from 97 moderate-to-severe ARDS patients who received PPV as part of their treatment in the intensive care unit (ICU) of the First Affiliated Hospital of Guangzhou Medical University from November 2015 to May 2021. We collected arterial blood gas of patients according to the 3 periods: supine position ventilation (SPV), PPV early stage (within 4 hours), and PPV middle and late stage (6 hours or later). We established a linear mixed-effects models with "body position changes, times of PPV, gender, age, baseline SOFA, and baseline APACHE II" as fixed effects, and individual and the number of prone positions as random intercept and random slope to investigate the effect of body position changes on blood gas analysis. Results: Among the 97 patients received PPV included, 51 were ICU survivors. Arterial partial pressure of oxygen (PaO2) and PaO2/fraction of inspired oxygen (FiO2) ratio were significantly higher at the early, middle and late stages of PPV than those in SPV [PFR (mmHg): 158 (118.00, 203.00) vs. 161 (129.00, 202.75) vs. 123 (91.75, 163.00), P<0.05]. Despite the synchronized reduction of FiO2, the incidence of hyperoxia in the prone position was still significantly higher than that in the supine position [hyperoxia (%):33.33 vs. 33.56 vs. 12.42, P<0.05]; there was no significant change in arterial carbon dioxide partial pressure (PaCO2) at each stage of PPV, but there was a significant increase in PH at PPV middle and late stages than those at early stage [PH: 7.39 (7.34, 7.42) vs. 7.37 (7.31, 7.41), P<0.05]. Conclusions: Although PPV improves the patients' oxygenation, the associated incidence of hyperoxia exceeds 33%. Down-regulate FiO2 more sharply after PPV is necessary, if oxygenation conditions permit. PPV may alleviate the acidosis associated with permissive hypercapnia in ARDS patients treated with lung protective ventilation strategy (LPVS).

Different Doping of VSe2 Monolayers as Adsorbent and Gas Sensing Material for Scavenging and Detecting SF6 Decomposed Species.

The application of intrinsic and transition metals (TM)-doped VSe2 monolayers for the detection of faulty gases in SF6 electrical insulated equipment is investigated based on first-principles calculations. The electron density difference, density of state, and adsorption energy are analyzed to further clarify the reaction mechanism. The results show that the intrinsic VSe2 monolayer has weak adsorption performance for SO2 and SOF2 molecules, but the adsorption properties of the system are significantly improved after doping TM atoms. Among them, the TM-doped VSe2 monolayer has better sensing performance for SO2 than for SOF2 molecules. Furthermore, the modulating effect of biaxial strain on the gas-sensitive properties of TM-doped VSe2 system is also analyzed. Finally, the recovery time of the gas molecules on the solid adsorbent is evaluated. The results confirm that the TM-doped VSe2 monolayer can be used as a novel sensing material or scavenger to ensure the normal operation of SF6 electrical insulated equipment. This will provide a prospective insight for experimenters to implement VSe2-based sensing materials or scavengers.

Construction and validation of a robust prognostic model based on immune features in sepsis.

Purpose: Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis. Materials and methods: The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis. Results: A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002). Conclusions: Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.