LncRNA CamK-A Regulates Ca2+-Signaling-Mediated Tumor Microenvironment Remodeling.

Cancer cells entail metabolic adaptation and microenvironmental remodeling to survive and progress. Both calcium (Ca2+) flux and Ca2+-dependent signaling play a crucial role in this process, although the underlying mechanism has yet to be elucidated. Through RNA screening, we identified one long noncoding RNA (lncRNA) named CamK-A (lncRNA for calcium-dependent kinase activation) in tumorigenesis. CamK-A is highly expressed in multiple human cancers and involved in cancer microenvironment remodeling via activation of Ca2+-triggered signaling. Mechanistically, CamK-A activates Ca2+/calmodulin-dependent kinase PNCK, which in turn phosphorylates IκBα and triggers calcium-dependent nuclear factor κB (NF-κB) activation. This regulation results in the tumor microenvironment remodeling, including macrophage recruitment, angiogenesis, and tumor progression. Notably, our human-patient-derived xenograft (PDX) model studies demonstrate that targeting CamK-A robustly impaired cancer development. Clinically, CamK-A expression coordinates with the activation of CaMK-NF-κB axis, and its high expression indicates poor patient survival rate, suggesting its role as a potential biomarker and therapeutic target.

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.

The Current Status of the Use of Internet Hospitals for Outpatients With Pain: Retrospective Study.

BACKGROUND: The national "Internet +" policies and the emergence of internet hospitals have created a new direction for the management of pain outside of the hospital. Nevertheless, there are no consolidated studies conducted by pain physicians on the current state of internet hospital-based online medical services used by patients with pain outside of a hospital setting. OBJECTIVE: In this retrospective study, we aimed to examine the status of the use of internet hospitals by patients who experience pain. Moreover, we identified the factors that influenced patients' decisions to make an online visit through the internet hospital. METHODS: Detailed information was collected online and offline from outpatients with pain at the information technology center of West China Hospital of Sichuan University from February 2020 to April 2022. Binary logistic regression analysis was conducted to identify the determinants that influenced patients' decisions to make an online visit to the internet hospital. RESULTS: Over a 2-year period, 85,266 pain-related clinic visits were recorded. Ultimately, 39,260 patients were enrolled for the analysis, with 12.9% (5088/39,260) having online visits. Both online and offline clinics had a greater number of visits by women than men. The average age of patients attending the online clinic was 46.85 (SD 16.56) years, whereas the average age of patients attending the offline clinic was 51.48 (SD 16.12) years. The majority of online clinic visitors (3059/5088, 60.1%) were employed, and one of the most common occupations was farming (721/5088, 14.2%). In addition, 51.8% (2635/5088) of patients who participated in the online clinics lived outside the hospital vicinity. Young (odds ratio [OR] 1.35, 95% CI 1.01-1.81; P=.045) and middle-aged (OR 1.98, 95% CI 1.81-2.16; P<.001) patients, employed patients (OR 1.11, 95% CI 1.04-1.18; P=.002), nonlocal patients (OR 1.57, 95% CI 1.48-1.67; P<.001), and the ordinary staff (OR 1.19, 95%CI 1.01-1.39; P=.03) were more likely to have the intention to choose online visits through the internet hospitals. CONCLUSIONS: Internet hospitals are flourishing as a more efficient and promising method of pain management and follow-up for patients with pain outside the hospital. People with pain who are young, working, and not in the vicinity of hospitals are more likely to visit internet hospitals.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Electrical impedance tomography for titration of positive end-expiratory pressure in acute respiratory distress syndrome patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is one of most common comorbidities in acute respiratory distress syndrome (ARDS). There are few specific studies on the appropriate ventilation strategy for patients with ARDS comorbid with COPD, especially regarding on positive end-expiratory pressure (PEEP) titration. METHODS: To compare the respiratory mechanics in mechanical ventilated ARDS patients with or without COPD and to determine whether titration of PEEP based on electrical impedance tomography (EIT) is superior to the ARDSnet protocol. This is a single center, perspective, repeated measure study. ARDS patients requiring mechanical ventilation who were admitted to the intensive care unit between August 2017 and December 2020 were included. ARDS patients were divided according to whether they had COPD into a COPD group and a non-COPD group. Respiratory mechanics, gas exchange, and hemodynamics during ventilation were compared between the groups according to whether the PEEP level was titrated by EIT or the ARDSnet protocol. RESULTS: A total of twenty-seven ARDS patients including 14 comorbid with and 13 without COPD who met the study eligibility criteria were recruited. The PEEP levels titrated by EIT and the ARDSnet protocol were lower in the COPD group than in the non-COPD group (6.93 ± 1.69 cm H2O vs. 12.15 ± 2.40 cm H2O, P < 0.001 and 10.43 ± 1.20 cm H2O vs. 14.0 ± 3.0 cm H2O, P < 0.001, respectively). In the COPD group, the PEEP level titrated by EIT was lower than that titrated by the ARDSnet protocol (6.93 ± 1.69 cm H2O vs. 10.43 ± 1.20 cm H2O, P < 0.001), as was the global inhomogeneity (GI) index (0.397 ± 0.040 vs. 0.446 ± 0.052, P = 0.001), plateau airway pressure (16.50 ± 4.35 cm H2O vs. 20.93 ± 5.37 cm H2O, P = 0.001), dead space ventilation ratio (48.29 ± 6.78% vs. 55.14 ± 8.85%, P < 0.001), ventilation ratio (1.63 ± 0.33 vs. 1.87 ± 0.33, P < 0.001), and mechanical power (13.92 ± 2.18 J/min vs. 15.87 ± 2.53 J/min, P < 0.001). The cardiac index was higher when PEEP was treated by EIT than when it was titrated by the ARDSnet protocol (3.41 ± 0.50 L/min/m2 vs. 3.02 ± 0.43 L/min/m2, P < 0.001), as was oxygen delivery (466.40 ± 71.08 mL/min/m2 vs. 411.10 ± 69.71 mL/min/m2, P = 0.001). CONCLUSION: Titrated PEEP levels were lower in patients with ARDS with COPD than in ARDS patients without COPD. In ARDS patient comorbid with COPD, application of PEEP titrated by EIT was lower than those titrated by the ARDSnet protocol, which contributed to improvements in the ventilation ratio, mechanical energy, cardiac index, and oxygen delivery with less of an adverse impact on hemodynamics.

LncRNA wires up Hippo and Hedgehog signaling to reprogramme glucose metabolism.

The Hippo pathway plays essential roles in organ size control and cancer prevention via restricting its downstream effector, Yes-associated protein (YAP). Previous studies have revealed an oncogenic function of YAP in reprogramming glucose metabolism, while the underlying mechanism remains to be fully clarified. Accumulating evidence suggests long noncoding RNAs (lncRNAs) as attractive therapeutic targets, given their roles in modulating various cancer-related signaling pathways. In this study, we report that lncRNA breast cancer anti-estrogen resistance 4 (BCAR4) is required for YAP-dependent glycolysis. Mechanistically, YAP promotes the expression of BCAR4, which subsequently coordinates the Hedgehog signaling to enhance the transcription of glycolysis activators HK2 and PFKFB3. Therapeutic delivery of locked nucleic acids (LNAs) targeting BCAR4 attenuated YAP-dependent glycolysis and tumor growth. The expression levels of BCAR4 and YAP are positively correlated in tissue samples from breast cancer patients, where high expression of both BCAR4 and YAP is associated with poor patient survival outcome. Taken together, our study not only reveals the mechanism by which YAP reprograms glucose metabolism, but also highlights the therapeutic potential of targeting YAP-BCAR4-glycolysis axis for breast cancer treatment.

Cytomegalovirus reactivation in immunocompetent mechanical ventilation patients: a prospective observational study.

BACKGROUND: Cytomegalovirus (CMV) reactivation is associated with adverse prognoses of critically ill patients. However, the epidemiology and predictors of CMV reactivation in immunocompetent patients receiving mechanical ventilation (MV) are not clear. The aim of this study was to investigate the epidemiology and predictors of CMV reactivation in immunocompetent patients requiring MV. METHODS: A single-center, prospective observational study (conducted from June 30, 2017 to July 01, 2018) with a follow-up of 90 days (September 29, 2018) that included 71 CMV-seropositive immunocompetent patients with MV at a 37-bed university hospital general intensive care unit (ICU) in China. Routine detection of CMV DNAemia was performed once a week for 28 days (Days 1, 7, 14, 21, and 28). CMV serology, laboratory findings, and clinical data were obtained during hospitalization. RESULTS: Among 71 patients, 13 (18.3%) showed CMV reactivation within 28 days in the ICU. The median time to reactivation was 7 days. CMV reactivation was related to various factors, including body mass index (BMI), sepsis, N-terminal pro-b-type natriuretic peptide (NT-proBNP), blood urea nitrogen (BUN), and hemoglobin (Hb) levels (P < 0.05). In the multivariate regression model, BMI, Hb level, and sepsis were independently associated with CMV reactivation patients (P < 0.05). Moreover, the area under the receiver operating characteristic (AUROC) of BMI, Hb, and BMI combined with Hb was 0.69, 0.70, and 0.76, respectively. The duration of MV, hospitalization expense, length of ICU stay, and 90 day all-cause mortality rate in patients with CMV reactivation was significantly higher than in those without CMV reactivation (P < 0.05). CONCLUSIONS: Among immunocompetent patients with MV, the incidence of CMV reactivation was 18.3%. CMV reactivation was associated with several adverse prognoses. BMI, Hb, and sepsis were independent risk factors for CMV reactivation. BMI and Hb may predict CMV reactivation.

Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China.

BACKGROUND: Crucial roles of hematologic and immunologic responses in progression of coronavirus disease 2019 (COVID-19) remain largely unclear. OBJECTIVE: We sought to address the dynamic changes in hematologic and immunologic biomarkers and their associations with severity and outcomes of COVID-19. METHODS: A retrospective study including 548 patients with COVID-19 with clarified outcome (discharged or deceased) from a national cohort in China was performed. Cross-sectional and longitudinal variations were compared and the associations with different severity and outcomes were analyzed. RESULTS: On admission, the counts of lymphocytes, T-cell subsets, eosinophils, and platelets decreased markedly, especially in severe/critical and fatal patients. Increased neutrophil count and neutrophils-to-lymphocytes ratio were predominant in severe/critical cases or nonsurvivors. During hospitalization, eosinophils, lymphocytes, and platelets showed an increasing trend in survivors, but maintained lower levels or dropped significantly afterwards in nonsurvivors. Nonsurvivors kept a high level or showed an upward trend for neutrophils, IL-6, procalcitonin, D-dimer, amyloid A protein, and C-reactive protein, which were kept stable or showed a downward trend in survivors. Positive correlation between CD8+ T-cell and lymphocytes count was found in survivors but not in nonsurvivors. A multivariate Cox regression model suggested that restored levels of lymphocytes, eosinophils, and platelets could serve as predictors for recovery, whereas progressive increases in neutrophils, basophils, and IL-6 were associated with fatal outcome. CONCLUSIONS: Hematologic and immunologic impairment showed a significantly different profile between survivors and nonsurvivors in patients with COVID-19 with different severity. The longitudinal variations in these biomarkers could serve to predict recovery or fatal outcome.

Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide. OBJECTIVE: To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status. METHODS: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities. RESULTS: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (1.61-4.17) among patients with two or more comorbidities. CONCLUSION: Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.

Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicentre) and outside Hubei (non-epicentre): a nationwide analysis of China.

BACKGROUND: During the outbreak of coronavirus disease 2019 (COVID-19), consistent and considerable differences in disease severity and mortality rate of patients treated in Hubei province compared to those in other parts of China have been observed. We sought to compare the clinical characteristics and outcomes of patients being treated inside and outside Hubei province, and explore the factors underlying these differences. METHODS: Collaborating with the National Health Commission, we established a retrospective cohort to study hospitalised COVID-19 cases in China. Clinical characteristics, the rate of severe events and deaths, and the time to critical illness (invasive ventilation or intensive care unit admission or death) were compared between patients within and outside Hubei. The impact of Wuhan-related exposure (a presumed key factor that drove the severe situation in Hubei, as Wuhan is the epicentre as well the administrative centre of Hubei province) and the duration between symptom onset and admission on prognosis were also determined. RESULTS: At the data cut-off (31 January 2020), 1590 cases from 575 hospitals in 31 provincial administrative regions were collected (core cohort). The overall rate of severe cases and mortality was 16.0% and 3.2%, respectively. Patients in Hubei (predominantly with Wuhan-related exposure, 597 (92.3%) out of 647) were older (mean age 49.7 versus 44.9 years), had more cases with comorbidity (32.9% versus 19.7%), higher symptomatic burden, abnormal radiologic manifestations and, especially, a longer waiting time between symptom onset and admission (5.7 versus 4.5 days) compared with patients outside Hubei. Patients in Hubei (severe event rate 23.0% versus 11.1%, death rate 7.3% versus 0.3%, HR (95% CI) for critical illness 1.59 (1.05-2.41)) have a poorer prognosis compared with patients outside Hubei after adjusting for age and comorbidity. However, among patients outside Hubei, the duration from symptom onset to hospitalisation (mean 4.4 versus 4.7 days) and prognosis (HR (95%) 0.84 (0.40-1.80)) were similar between patients with or without Wuhan-related exposure. In the overall population, the waiting time, but neither treated in Hubei nor Wuhan-related exposure, remained an independent prognostic factor (HR (95%) 1.05 (1.01-1.08)). CONCLUSION: There were more severe cases and poorer outcomes for COVID-19 patients treated in Hubei, which might be attributed to the prolonged duration of symptom onset to hospitalisation in the epicentre. Future studies to determine the reason for delaying hospitalisation are warranted.

Clinical Features of Coronavirus Disease 2019 Patients With Mechanical Ventilation: A Nationwide Study in China.

OBJECTIVES: The outbreak of coronavirus disease 2019 is becoming a worldwide pandemic. Mechanical ventilation is lifesaving for respiratory distress, this study was designed to delineate the clinical features of the coronavirus disease 2019 patients with mechanical ventilation from a national cohort in China. DESIGN: Prospective observational study. SETTING: The rapid spread of severe acute respiratory syndrome coronavirus 2 has infected more than 7.7 million people and caused more than 423,000 deaths. PATIENTS: Adult hospitalized coronavirus disease 2019 patients with mechanical ventilation from 557 hospitals from China. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From a nationwide cohort, 141 coronavirus disease 2019 cases with mechanical ventilation were extracted from 1,590 cases. Cigarette smoke, advanced age, coexisting chronic illness, elevated systolic blood pressure, high body temperature, and abnormal laboratory findings are common in these ventilated cases. Multivariate regression analysis showed that higher odds of in-hospital death was associated with invasive mechanical ventilation requirement (hazard ratio: 2.95; 95% CI, 1.40-6.23; p = 0.005), and coexisting chronic obstructive pulmonary disease (hazard ratio, 4.57; 95% CI, 1.65-12.69; p = 0.004) and chronic renal disease (hazard ratio, 5.45; 95% CI, 1.85-16.12; p = 0.002). Compared with patients with noninvasive mechanical ventilation, patients who needs invasive mechanical ventilation showed higher rate of elevated D-dimer (> 1.5 mg/L) at admission (hazard ratio, 3.28, 95% CI, 1.07-10.10; p = 0.039). CONCLUSIONS: The potential risk factors of elevated D-dimer level could help clinicians to identify invasive mechanical ventilation requirement at an early stage, and coexisting chronic obstructive pulmonary disease or chronic renal disease are independent risk factors associated with fatal outcome in coronavirus disease 2019 patients with mechanical ventilation.

Factors associated with intraoperative extracorporeal membrane oxygenation support during lung transplantation.

BACKGROUND: Intraoperative Extracorporeal membrane oxygenation (ECMO) is increasingly being applied as life-support for lung transplantation patients. However, factors associated with this procedure in lung transplantation patients have not yet been characterized. The aim of this study was to identify preoperative factors of intraoperative ECMO support during lung transplantation and to evaluated the outcome of lung transplantation patients supported with ECMO. METHODS: Patients underwent lung transplantation treated with and without ECMO in Guangzhou Institute of Respiratory Diseases between January 2015 to August 2018 were retrospectively reviewed. Patient demographics and clinical variables were collected and analyzed. Multivariate logistic regression was performed to identify factors independently associated with intraoperative extracorporeal membrane oxygenation support during lung transplantation. RESULTS: During the study period, 138 patients underwent lung transplantation at our institution, the mean LAS was (56.63 ± 18.39) (range, 32.79 to 88.70). Fourty four patients were treated with veno-venous/veno-arterial ECMO. Among the patients, 32 patients wean successfully ECMO after operation, 12 patients remain ECMO after operation, and 32 patients (62.74%) survived to hospital discharge. In multiple analysis, the following factors were associated with intraoperative ECMO support: advanced age, high PAP before operation, duration of mechanical ventilation before operation, a higher APACHE II and primary diagnosis for transplantation. The overall survival rates at 1, 3, and 12 months were 90.91, 72.73, and 56.81% in the ECMO group, and 95.40, 82.76, and 73.56% in the non-ECMO group, respectively (log-rank P = 0.081). Patients who underwent single lung transplant had a lower survival rates in ECMO group as compared with non-ECMO group at 1, 3, and 12 months (90.47% vs 98.25, 71.43% vs 84.21, and 52.38% vs 75.44%) (log-rank P = 0.048). CONCLUSIONS: The preoperative factors of intraoperative ECMO support during lung transplantation included age, high PAP before operation, preoperative mechanical ventilation, a higher APACHE II and primary diagnosis for transplantation based on multivariate analysis.

Pharmacokinetics of and maintenance dose recommendations for vancomycin in severe pneumonia patients undergoing continuous venovenous hemofiltration with the combination of predilution and postdilution.

PURPOSE: Therapeutic vancomycin levels are difficult to maintain in severe pneumonia patients who are receiving IV vancomycin therapy while on continuous venovenous hemofiltration (CVVH). The objective of this study was to determine the pharmacokinetics and maintenance dose recommendations of vancomycin in severe pneumonia patients receiving CVVH. METHODS: A prospective study was conducted in the intensive care unit of a university hospital. Ten severe pneumonia patients receiving vancomycin and CVVH treatment were determined the initial and steady-state pharmacokinetics of vancomycin. CVVH was performed in mixed predilution and postdilution mode with a blood flow rate of 180 mL/min and an ultrafiltrate flow rate of 30-40 mL/kg/h. Group A received an initial dose of 500 mg only, whereas group B received 500 mg every 12 h until steady state is achieved. Serum and ultrafiltrate were collected over 12 h after infusion of vancomycin. RESULTS: After initial dosing, the mean sieving coefficient (SC) was 0.72 ± 0.02, and CVVH clearance (CLCVVH, 1.35 ± 0.03 L/h) constituted 60.55% ± 13.69% of total vancomycin clearance (CLtot, 2.36 ± 0.72 L/h). When steady state was reached, the SC of the patients was 0.71 ± 0.03, and the CLCVVH (1.34 ± 0.06 L/h) accounted for 66.96% ± 6.05% of the CLtot (2.03 ± 0.27 L/h). The recommended maintenance dose for vancomycin in severe pneumonia patients was 400-650 mg every 12 h, which was calculated based on CLtot, to achieve a trough concentration of 15-20 mg/L at steady state. CONCLUSIONS: Single administration or multiple administration does not affect SC and CLCVVH. Owing to therapeutic vancomycin levels is difficult to maintain in severe pneumonia patients who are receiving IV vancomycin therapy while on CVVH, close monitoring of serum trough concentrations is required.

The incidence, risk factors and prognosis of acute kidney injury in severe and critically ill patients with COVID-19 in mainland China: a retrospective study.

BACKGROUND: The clinical correlates, prognosis and determinants of acute kidney injury (AKI) in patients with coronavirus disease 2019 (Covid-19) remain largely unclear. METHODS: We retrospectively reviewed medical records of all adult patients with laboratory-confirmed Covid-19 who were admitted to the intensive care unit (ICU) between January 23rd 2020 and April 6th 2020 at Wuhan JinYinTan Hospital and The First Affiliated Hospital of Guangzhou Medical University. RESULTS: Among 210 patients, 131 were males (62.4%). The median Age was 64 years (IQR: 56-71). Of 92 (43.8%) patients who developed AKI during hospitalization, 13 (14.1%), 15 (16.3%) and 64 (69.6%) were classified as being at stage 1, 2 and 3, respectively. 54 patients (58.7%) received continuous renal replacement therapy. Age, sepsis, nephrotoxic drug, invasive mechanical ventilation and elevated baseline serum creatinine levels were associated with the occurrence of AKI. Renal recovery during hospitalization was identified among 16 patients with AKI (17.4%), who had a significantly shorter time from admission to AKI diagnosis, lower incidence of right heart failure and higher ratio of partial pressure of oxygen to the fraction of inspired oxygen. Of 210 patients, 93 deceased within 28 days of ICU admission. AKI stage 3, critical disease, greater Age and the lowest ratio of partial pressure of oxygen to the fraction of inspired oxygen being < 150 mmHg were independently associated with death. CONCLUSIONS: Among patients with Covid-19, the incidence of AKI was high. Our findings of the risk factors of the development of AKI and factors associated with renal function recovery may inform clinical management of patients with critical illness of Covid-19.

Changes in erythrocyte polyunsaturated fatty acids and plasma eicosanoids level in patients with asthma.

BACKGROUND: To investigate the changes of polyunsaturated fatty acids (PUFAs) and their downstream eicosanoids in patients with asthma, the levels of erythrocyte membrane lipids and plasma lipid metabolites were examined. METHODS: Erythrocyte membrane lipids were extracted and esterificated, and then fatty acid compositions were determined by gas chromatography. The concentrations of six eicosanoids of PGE2, TXA2, LTB4, PGE1, 6-k-PGF1α and PGF2α in plasma were measured by ELISA. RESULTS: The results showed that the contents of erythrocyte membrane fatty acids in patients with asthma were mainly composed of C16:0, C18:0, C18:1, C18:2(n-6), and C20:4(n-6). The ratio n-6/n-3 PUFAs in patients and health persons were (4.42 ± 1.33):1 and (3.21 ± 0.79):1 (p < 0.01), showing statistically significant differences. ELISA results showed that the levels of plasma PGE2, TXB2, and PGE1 in patients were higher than health persons; and the levels of eicosanoids of PGF2α and 6-k-PGF1α were significantly lower in patient group than healthy group (p < 0.05), but LTB4 was no obvious difference (p = 0.09). Increased ratio of n-6/n-3 PUFAs is consistent to the increased levels of pro-inflammatory PGE2 and TXB2 and anti-inflammatory PGE1 originated from C20:4(n-6) and C18:2(n-6), indicating that increased ratio of n-6/n-3 PUFAs and eicosanoids from n-6 PUFAs might promote the progress of airway inflammation of asthma. CONCLUSION: Changes of erythrocyte fatty acids, n-6/n-3 PUFAs ratio and the levels of plasma PGE2, TXB2, and PGE1 in patients with asthma were relevant to airway inflammation in some extent. Therefore, it could be proposed that increase of n-3/n-6 PUFAs ratio by diet supplementation of n-3 PUFAs might effectively improve airway inflammation in asthma.