A Novel Multi-Parameter Calibrator for Ventilator Tester Based on Reciprocating Plunger

Ventilators are widely needed when the COVID-19 is a global outbreak, and they are used to provide mechanical ventilation for patients who are physically unable to breathe, or breathe insufficiently. A ventilator tester is an instrument used to verify and calibrate ventilation parameters of ventilators like gas flow, tidal volume, frequency. As a measuring device, the ventilator tester also needs to be calibrated periodically. And different parameters are usually calibrated with different devices. In order to improve calibration efficiency and accuracy, a novel multi-parameter calibrator for ventilator tester based on reciprocating plunger is proposed in this work. The system composition is introduced and mathematical models are deduced. According to calibrating regulation, different calibrating modes are simulated and realized. © FLOMEKO 2022.All rights reserved

AUTOANTIBODIES RELATED TO RHEUMATIC DISEASES AFTER COVID-19 INFECTION

BackgroundSARS-CoV-2(Severe acute respiratory syndrome coronavirus 2) has been circulating worldwide for three years. It mainly causes upper respiratory tract infection, which can manifest as pulmonary infection and even respiratory distress syndrome in severe cases. Different autoantibodies can be detected in patients infected with COVID-19.ObjectivesTo explore autoantibodies related to rheumatic diseases after COVID-19 infection.MethodsNinety-eight inpatients were tested for antinuclear antibodies (ANA), antibodies to extractable nuclear antigens(ENA), anti-neutrophil cytoplasmic antibodies（ANCA), anticardiolipin antibodies,a-β2GPI (IgG/IgM). They were from a tertiary hospital in Guangzhou during the COVID-19 epidemic. Data were described statistically.ResultsNinety-eight hospitalized patients were tested for relevant antibodies. The average age was 50.64±19.54;67 (68.4%) were male, 64 (65.3%) were COVID-19 positive, 90 (90.9%) had rheumatic diseases, and 56 of them were COVID-19 positive patients with rheumatic diseases.There were 76 patients tested for antinuclear antibodies;29 （38.16%）were negative, 18 （23.68%）had a 1/80 titre, and 29（28.16%） had a titre greater than 1:80. The 31 covid patients were positive for ANA. In the high-titer group, 19 patients with rheumatic diseases were positive for COVID-19, and 12 patients had an exacerbation of the rheumatic diseases (6 of whom had previously had pulmonary fibrosis). Of 31 covid patients, only two were non-rheumatic patients, and both were elderly, aged 85 and 100, respectively.Fifty-six patients had ENA results, and 29 for positive antibodies, 8 for ds-DNA antibodies, 2 for anti-Sm antibodies, 6 for anti-nucleosome antibodies, 12 for anti-U1RNP antibodies, 2 for anti-Scl-70 antibodies, 12 for anti-SS-A antibodies, 3 for anti-mitochondrial M2 antibodies, 2 for anti-centromere antibodies, 1 for anti-Po antibodies, and one for anti-Jo-1 antibody. All 56 patients had rheumatic diseases, and no new patients were found.There were 62 patients with ANCA data. P-ANCA was positive in 12 cases(19.35%), and MPO-ANCA was positive in 2 cases. An 85-year-old non-rheumatic COVID-19 patient was P-ANCA positive. She had a history of hypertension, colon cancer, CKD3, coronary heart disease, and atrial flutter.In the anticardiolipin antibodies group, there were 62 patients;only 6 were positive, and 2 were rheumatic patients infected with COVID-19. Antiphospholipid antibodies were detected in 33 patients, and a-β2GPI was tested in one patient, an 82-year-old COVID-19 patient with gout, diabetes, and cerebral infarction in the past. We did not find a statistical difference in the above results.ConclusionWe have not found a correlation between SARS-CoV-2 and serum autoantibodies of rheumatic immune diseases. It needs large samples and an extended follow-up to research.AcknowledgementsThis work was supported by Scientific and Technological Planning Project of Guangzhou City [202102020150], Guangdong Provincial Basic and Applied Basic Research Fund Project [2021A1515111172], National Natural Science Foundation of China Youth Fund [82201998] and Third Affiliated Hospital of Sun Yat-Sen University Cultivating Special Fund Project for National Natural Science Foundation of China [2022GZRPYQN01].Disclosure of Interestsone declared.

The effect of eating restaurant prepared food on BMI: evidence from China during the COVID-19 pandemic

Leveraging the unexpected variation in the frequency of eating restaurant prepared food due to the COVID-19 pandemic, we seek to identify and estimate the causal relationship between the frequency of eating restaurant prepared food and people's BMI. We use first-differencing and instrumental variable approaches to correct for potential endogeneity bias due to both the time-invariant and time-varying unobserved factors. Our results show eating more restaurant prepared food has a positive and statistically significant effect on BMI, and in addition to other channels that have been identified in the literature, mood-boosting is another channel through which eating restaurant prepared food leads to weight gain. Heterogeneous effect analysis further shows that eating restaurant prepared food is more likely to have an impact on those people who lead a more stressful lifestyle before the pandemic.

Analyzing the Stock Volatility Spillovers in Chinese Financial and Economic Sectors

By regarding the Chinese financial and economic sectors as a system, this article studies the stock volatility spillover in the system and explores its effects on the overall performance of the macroeconomy in China. The recent outbreak of COVID-19, U.S.-China trade friction, and three historical financial turbulences are involved to distinguish the changes in the spillover in these distinct crises, which has seldom been unveiled in the literature. By considering that the stock volatility spillover may vary over distinct timescales, the spillovers are disclosed through innovatively constructing the multi-scale spillover networks, followed by connectedness computation, based on variational mode decomposition (VMD) and generalized vector autoregression (GVAR) process. Our empirical analysis first demonstrates the different levels of increases in the total sectoral volatility spillover and changes in the roles of the sectors in the system under the aforementioned crises. Besides, the increases in the sectoral spillover in the long-term are verified to negatively impact the macroeconomy and can thereby act as warning signals.

DeCoP: Deep Learning for COVID-19 Prediction of Survival

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.

Molecular epidemiological analysis of COVID-19 imported cases in Ruili from July to November, 2021

Objective To analyze the epidemiological and genomic characteristics of COVID-19 cases imported by land in Ruili, and to provide reference for border epidemic prevention and control in Yunnan Province. Methods We collected information about SARS-CoV-2 infected individuals from overseas land in Ruili, Yunnan from July to November, 2021. The epidemiological characteristics were statistically analyzed. The second-generation sequencing platform of Illumina was used to conduct high-through-put sequencing on the selected 40 positive samples and to analyze their genotyping and variation characteristics. Results During the study period,Ruili City reported 796 COVID-19 cases from abroad.The median age of COVID-19 cases was 28.5 years (Interquantile range 10, range 1–85). The gender ratio between men and women was 4.61 : 1, Most of these infected individuals engaged in business services, accounting for 49.75% (396/796) , 95.60% of COVID-19 cases were mild and moderate cases. The sequencing results of 34 cases can be divided into three clades according to Nextstrain typing method, including 24 cases belong to 21A(Delta) clade, 9 cases belong to 21I(Delta) clade and 1 case belongs to 20I (Alpha V1) clade. Conclusions The virus genotypes of the cases in this study were mainly divided into three branches and there were some differences among them, most of which were Delta mutants.We should continue to implement border control measures and continue to monitor the virus mutation of imported cases, so as to evaluate the threat of the mutant strain to the current situation of epidemic prevention and control in Yunnan Province. © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Assessment of a pandemic emergency financing facility

The pandemic bond issued by the World Bank (WB) in 2017 is a financial innovation enabling the transfer of the pandemic risk from the underdeveloped/developing countries to the financial market. It covers perils of various diseases that could overwhelm the global health systems and adversely impact the world economy. If all the triggers are activated, the bond's principal and coupons are used to finance coordinated, swift and resilient medical response to safeguard the well-being of the populace. This product, however, is criticised for its onerous trigger requirements. We examine the WB's pandemic-bond pricing framework, which requires inputs that are only partially available. From a rather unstructured COVID-19 data set, an information database is created and customised for pandemic-bond valuation. A vector auto-regressive moving average model is utilised to jointly describe the triggers dynamics. Our modelling simulations of risk triggers reveal that the bond payout could be made in less than half of the WB's earliest opportunity of 85 days. © 2023 The Authors

Structural design and performance study of corrugated fibreboard with a laminated structure

Corrugated box is an important transport packaging container in the global logistics. The research on its raw material, that is, corrugated fibreboard, has always been an important topic in the packaging field. However, the research objects nearly focus on the corrugated fibreboard of traditional corrugated medium. The innovative research on the structure of corrugated fibreboard is also still rare. Since the pandemic COVID-19 in 2020, the international corrugated packaging industry has faced new dilemma. The development of new corrugated boxes and fibreboard is the way to break the situation in innovation and transformation. This study aims to design a new fibreboard with laminated structure medium for obtaining a new type of corrugated fibreboard for packaging with better cushioning and anti-vibration performance than traditional corrugated fibreboard. The verification experiments show that the new fibreboard has better recovery and structural stability due to its unique three-layer structure. The static and dynamic cushioning performance and anti-vibration performance of the proposed fibreboard have been greatly improved compared with those of the traditional one. © 2023 John Wiley & Sons Ltd.

Research progress on photocatalysis systems for inactivation of microbial aerosol

Now in the context of the novel coronavirus pneumonia outbreak, the control and removal of microbial aerosols has once again attracted academic attention, while conventional air purification methods such as filtration, chemical agents and UV have their own defects and deficiencies. With the advantages of high efficiency, wide spectrum, green, no residue, dynamic continuous disinfection, photocatalysis has broad application prospects. In this paper, the research on the inactivation of microbial aerosols with photocatalysis system is summarized and analyzed from the aspects of the types of photocatalysts, the load of photocatalysts, the light source and the structure and operation of reactors. TiO2 or its derivative materials are selected as photocatalysts in most studies, and more novel and efficient photocatalysts should be applied. Porous, multi-channel and large surface area catalyst carriers can effectively improve the efficiency of photocatalysis system. The light source still depends on UV light, and the application of visible light needs more research. There are few studies on improving the photocatalysis system by optimizing the reactor structure, and the most commonly used is the ring reactor. Researchers have developed photocatalytic air purifiers or combined photocatalysis systems with indoor air duct systems. In the future, photocatalysis system will become an important means for indoor microbial aerosol control. © 2023 Chemical Industry Press. All rights reserved.

Molecular epidemiological analysis of COVID-19 imported cases in Ruili from July to November, 2021.

Objective To analyze the epidemiological and genomic characteristics of COVID-19 cases imported by land in Ruili, and to provide reference for border epidemic prevention and control in Yunnan Province. Methods We collected information about SARS-CoV-2 infected individuals from overseas land in Ruili, Yunnan from July to November, 2021. The epidemiological characteristics were statistically analyzed. The second-generation sequencing platform of Illumina was used to conduct high-through-put sequencing on the selected 40 positive samples and to analyze their genotyping and variation characteristics. Results During the study period,Ruili City reported 796 COVID-19 cases from abroad.The median age of COVID-19 cases was 28.5 years (Interquantile range 10, range 1-85). The gender ratio between men and women was 4.61 : 1, Most of these infected individuals engaged in business services, accounting for 49.75% (396/796) , 95.60% of COVID-19 cases were mild and moderate cases. The sequencing results of 34 cases can be divided into three clades according to Nextstrain typing method, including 24 cases belong to 21A(Delta) clade, 9 cases belong to 21I(Delta) clade and 1 case belongs to 20I (Alpha V1) clade. Conclusions The virus genotypes of the cases in this study were mainly divided into three branches and there were some differences among them, most of which were Delta mutants.We should continue to implement border control measures and continue to monitor the virus mutation of imported cases, so as to evaluate the threat of the mutant strain to the current situation of epidemic prevention and control in Yunnan Province.Copyright © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Efficient Online and Privacy-preserving Medical Pre-diagnosis Based on Growing Learning Vector Quantization

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-Learning Vector Quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate. © 2022 IEEE.

Engagement Estimation using Time-series Facial and Body Features in an Unstable Dataset

The global education sector has been deeply shaken by COVID-19 and forced to shift to an online teaching model. However, the lack of face-to-face communication and interaction in online learning is critical to high-quality teaching and learning. Research on engagement is a crucial part of solving this problem. Because engagement is of time-series data with an ongoing change, research datasets used for engagement analysis need a certain preprocessing method to capture time-series related engagement features. This research proposed a novel deep learning preprocessing method for improving engagement estimation using time-series facial and body information to restore traditional scenes in online learning environments. Such information includes head pose, mouth shape, eye movement, and body distance from the screen. We conducted a preliminary experiment on the DAiSEE dataset for engagement estimation. We applied skipped moving average in data preprocessing to reduce the influence of the extracted noises and oversampled the low engagement level data to balance the engaged/unengaged data. Since engagement is continuous and cannot be captured at a particular instant in time or single images, temporal video classification generally performs better than static classifiers. Therefore, we adopted long short-term memory (LSTM) and Quasi-recurrent neural networks (QRNNs)sequence models to train models and achieved the correct rate of 55.7% (LSTM) and 51.1% (QRNN) using the original key points extracted from OpenPose. Finally, we proposed the optimization structure network achieved the engagement estimation correct rate of 68.5% in proposed LSTM models and 64.2% in QRNN models. The achieved correct rate is 10% higher than the baseline in the DAiSEE dataset. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Retrospective Analysis of the Duration of Hospitalization of Imported Patients Infected with SARS-CoV-2 in Guangzhou, 2021

The pandemic of coronavirus disease 2019 is "not over,"in fact, the "dynamic clearing"policy for SARS-CoV-2 control and prevention in China has been firmly enforced. This study aimed to analyze the clinical symptoms and dynamic viral RNA changes in 2021 at Guangzhou Eighth People's Hospital. This study showed that 31.4% of the patients (695/2212) tested negative for viral RNA from admission to the final release from quarantine. Of all negative cases, 86.5% (601/695) remained in the hospital for no more than 5 days and were asymptomatic or mild. Among the remaining 402 patients who stayed for no more than 5 days, 76.4% (307/402) were viral RNA retest positive during the isolation stage. However, 96.4% of the peak viral RNA (296/307) was over Ct = 33 cycles during the isolation stage. © Wolters Kluwer Health, Inc. All rights reserved.

Effects of convalescent plasma infusion on the ADAMTS13-von Willebrand factor axis and endothelial integrity in patients with severe and critical COVID-19.

Background: Convalescent plasma infusion (CPI) was given to patients with COVID-19 during the early pandemic with mixed therapeutic efficacy. However, the impacts of CPI on the ADAMTS13-von Willebrand factor (VWF) axis and vascular endothelial functions are not known. Objectives: To determine the impacts of CPI on the ADAMTS13-VWF axis and vascular endothelial functions. Methods: Sixty hospitalized patients with COVID-19 were enrolled in the study; 46 received CPI and 14 received no CPI. Plasma ADAMTS13 activity, VWF antigen, endothelial syndecan-1, and soluble thrombomodulin (sTM) were assessed before and 24 hours after treatment. Results: Patients with severe and critical COVID-19 exhibited significantly lower plasma ADAMTS13 activity than the healthy controls. Conversely, these patients showed a significantly increased VWF antigen. This resulted in markedly reduced ratios of ADAMTS13 to VWF in these patients. The levels of plasma ADAMTS13 activity in each patient remained relatively constant throughout hospitalization. Twenty-four hours following CPI, plasma ADAMTS13 activity increased by ∼12% from the baseline in all patients and ∼21% in those who survived. In contrast, plasma levels of VWF antigen varied significantly over time. Patients who died exhibited a significant reduction of plasma VWF antigen from the baseline 24 hours following CPI, whereas those who survived did not. Furthermore, patients with severe and critical COVID-19 showed significantly elevated plasma levels of syndecan-1 and sTM, similar to those found in patients with immune thrombotic thrombocytopenic purpura. Both syndecan-1 and sTM levels were significantly reduced 24 hours following CPI. Conclusion: Our results demonstrate the relative deficiency of plasma ADAMTS13 activity and endothelial damage in patients with severe and critical COVID-19, which could be modestly improved following CPI therapy.

Various pathogen-laden aerosol deposition in the realistic human airway during inhalation

Studying the deposition of different pathogens with various sizes and shapes is vital for understanding various respiratory infectious diseases. Few studies focus on the deposition of pathogen-laden aerosol during inhalation, especially for different respiratory infectious pathogens. This paper studied the depositions of H3N2, SAR-CoV-2, Ebola virus, Escherichia coli, and different sizes of droplets in the realistic human respiratory airway during inhalation. And results show that large droplets are mainly deposited in the upper respiratory tract, while most of the small particles, especially viruses, will transmit to somewhere further than bronchi-G7 and be deposited into the deep lobes of the lungs. Over 90% of single virus particles will inhale into lobes. The deposition efficiency of pathogens in the right lobes is significantly higher than that in the left, and this phenomenon is more obvious in the superior lobes, which may also explain why lung carcinomas are more likely to develop in the right lung. Compared with other viruses, SARS-CoV-2 is more inhaled into the right superior lobe, which should be paid attention to. This paper may help learn about various respiratory infectious diseases and provide references for treatment methods and drug delivery locations. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Longitudinal Assessment of Plasma Syndecan-1 Predicts 60-Day Mortality in Patients with COVID-19.

Background: Endotheliopathy is a common pathologic finding in patients with acute and long COVID-19. It may be associated with disease severity and predispose patients to long-term complications. Plasma levels of a proteoglycan, syndecan-1, are found to be significantly elevated in patients with COVID-19, but its roles in assessing disease severity and predicting long-term outcome are not fully understood. Methods: A total of 124 consecutive hospitalized patients with SARS-CoV-2 infection were prospectively enrolled and blood samples were collected on admission (T1), 3−4 days following treatment (T2), and 1−2 days prior to discharge or death (T3). Plasma levels of syndecan-1 were determined using an immunosorbent assay; various statistical analyses were performed to determine the association between plasma syndecan-1 levels and disease severity or the 60-day mortality rate. Results: Compared with those in the healthy controls, plasma levels of syndecan-1 in patients with critical COVID-19 were significantly higher (p < 0.0001). However, there was no statistically significant difference among patients with different disease severity (p > 0.05), resulting from large individual variability. Longitudinal analysis demonstrated that while the levels fluctuated during hospitalization in all patients, plasma syndecan-1 levels were persistently elevated from baseline in critical COVID-19 patients. Cox proportional hazard regression analyses revealed that elevated plasma levels of syndecan-1 (>260 ng/mL at T1, >1018 ng/mL at T2, and >461 ng/mL at T3) were significantly associated with the 60-day mortality rate. Conclusions: Endotheliopathy, marked by glycocalyx degradation and elevated plasma syndecan-1, occurs in nearly all hospitalized patients with SARS-CoV-2 infection; elevated plasma syndecan-1 is associated with increased mortality in COVID-19 patients.

TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19

The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.

Low Serum Albumin at Admission Is Associated with Mortality among Inpatients with Cirrhosis: A Multi-National Experience

Background: MELD and Child-Pugh scores have traditionally been used as prognostic indicators in patients with cirrhosis. Albumin infusions in outpatients have been associated with improved outcomes, but not in transplant waitlisted patients or inpatients. This aim of this study was to assess whether low serum albumin (sAlb) on admission alone is a poor prognostic indicator among cirrhotic inpatients from a new multi-national cohort. Method(s): The CLEARED study is a global study that enrolled consecutive non-electively admitted inpatients without organ transplant or COVID-19 from 6 continents. Admission demographics, medical history, laboratory data, inpatient course, death/hospice transfer and mortality at 30 days post-discharge were recorded. Patients were divided into 3 groups: sAlb <28gm/L(A), sAlb >=28 but <35gm/L (B), and sAlb>=35gm/L (C) were compared. Multi-variable logistic regression was performed using inpatient mortality and overall 30-day mortality as outcomes. Result(s): 2429 patients were enrolled from 21 countries worldwide. The distribution was A:49%, B:39%, C:12%. Gp A patients were significantly younger (54yrs vs. 57yrs vs 58yrs p<0.0001) but with similar gender distribution, and higher MELD-Na score of 25 vs. 20 vs. 17 (p<0.0001). Gp A patients were more likely to have alcohol as etiology of cirrhosis (49% vs. 45% vs 38%, p=0.004), and were more likely to have either infection (27% vs. 18% vs. 13%, p<0.0001), HE (39% vs. 33% vs. 23%, p=0.005) or fluid related issues as a reason for admission (p<0.0001). More patients in Gp A received albumin infusion during their hospital stay (120gm vs. 100gm vs. 100gm p=0.0004), mostly for the indications of AKI (47% vs. 49% vs. 47%, p=0.79) and performance of large volume paracentesis (44% vs. 42% vs. 41%, p=0.80), followed by bacterial peritonitis indication (22% vs. 17% vs. 11%, p=0.01). Group A patients had longer hospital stays (9 days vs. 8 days vs. 7 days (p<0.001), but similar ICU transfer (23% vs. 22% vs. 20%, p=0.55). group A patients were more likely to die while inpatients (19% vs. 11% vs. 5%, p<0.0001), or by 30 days post-discharge (29% vs. 20% vs. 9%, p<0.0001). Table shows the admission variables associated with a poor outcome. Conclusion(s): Hypoalbuminemia is extremely common among admitted cirrhotic patients, with sAlb of <28gm/L occurring in almost half. Together with MELD-Na score and infection at admission, a low sAlb is associated with a poor outcome in these patients. Future studies will need to validate these findings and to assess whether albumin infusions will improve the outcome of these patients. (Figure Presented).