Sera from Breakthrough Infections with SARS-CoV-2 BA.5 or BF.7 Showed Lower Neutralization Activity against XBB.1.5 and CH.1.1.

From December 2022 to January 2023, SARS-CoV-2 infections caused by BA.5 and BF.7 subvariants of B.1.1.529 (Omicron) spread in China. It is urgently needed to evaluate the protective immune responses in the infected individuals against the current circulating variants to predict the future potential infection waves, such as the BQ.1.1, XBB.1.5, and CH1.1 variants. In this study, we constructed a panel of pseudotyped viruses for SARS-CoV-2 for the past and current circulating variants, including D614G, Delta, BA.1, BA.5, BF.7, BQ.1.1, XBB.1.5 and CH.1.1. We investigated the neutralization sensitivity of these pseudotyped viruses to sera from individuals who had BA.5 or BF.7 breakthrough infections in the infection wave of last December in China. The mean neutralization ID50 against infected variants BA.5 and BF.7 are 533 and 444, respectively. The highest neutralizing antibody level was observed when tested against the D614G strain, with the ID50 of 742, which is about 1.52-folds higher than that against the BA.5/BF.7 variant. The ID50 for BA.1, Delta and BQ.1.1 pseudotyped viruses were about 2-3 folds lower when compared to BA.5/BF.7. The neutralization activities of these serum samples against XBB.1.5 and CH.1.1 decreased 7.39-folds and 15.25-folds when compared to that against BA.5/BF.7. The immune escape capacity of these two variants might predict new infection waves in future when the neutralizing antibody levels decrease furtherly.

Associations between mother's depressive symptoms during pregnancy and newborn's brain functional connectivity.

Depression during pregnancy is common and the prevalence further increased during the COVID pandemic. Recent findings have shown potential impact of antenatal depression on children's neurodevelopment and behavior, but the underlying mechanisms are unclear. Nor is it clear whether mild depressive symptoms among pregnant women would impact the developing brain. In this study, 40 healthy pregnant women had their depressive symptoms evaluated by the Beck Depression Inventory-II at ~12, ~24, and ~36 weeks of pregnancy, and their healthy full-term newborns underwent a brain MRI without sedation including resting-state fMRI for evaluation of functional connectivity development. The relationships between functional connectivities and maternal Beck Depression Inventory-II scores were evaluated by Spearman's rank partial correlation tests using appropriate multiple comparison correction with newborn's gender and gestational age at birth controlled. Significant negative correlations were identified between neonatal brain functional connectivity and mother's Beck Depression Inventory-II scores in the third trimester, but not in the first or second trimester. Higher depressive symptoms during the third trimester of pregnancy were associated with lower neonatal brain functional connectivity in the frontal lobe and between frontal/temporal lobe and occipital lobe, indicating a potential impact of maternal depressive symptoms on offspring brain development, even in the absence of clinical depression.

Application of remote fetal heart rate monitoring via internet in late pregnancy during the COVID-19 pandemic.

BACKGROUND: Internet-related technologies have rapidly developed and started to impact the traditional medical practices, which combined wireless communication technology as well as "cloud service" technology with electronic fetal heart monitoring have become a mainstream tendency. OBJECTIVE: To investigate the clinical application value of remote fetal heart rate monitoring mode (RFHRM) on late pregnancy during the coronavirus disease (COVID-19) pandemic. METHODS: From March 2021 to February 2022, we recruited 800 cases of pregnant women received prenatal examination at the Anhui Province Maternity and Child Healthcare Hospital. These pregnant women were randomly divided into two groups: the control group (n= 400), which was given traditional management, and the observation group (n= 400), which received remote monitoring technology on this basis. The two groups were compared with neonatal asphyxia, pregnancy outcomes, Edinburgh postnatal depression scale scores (EPDS), prenatal examination expenses and total time consumption. RESULTS: There were no statistically significant differences between the groups in pregnancy outcome and neonatal outcome (P> 0.05). However, total EPDS score of 12.5% pregnant women in the observation group were higher than 12. The TPE group had significantly higher mean EPDS scores compared with the RFHRM group (7.79 ± 3.58 vs 5.10 ± 3.07; P< 0.05). The results showed a significant difference in maternity expenses (2949.83 ± 456.07 vs 2455.37 ± 506.67; P< 0.05) and total time consumption (42.81 ± 7.60 vs 20.43 ± 4.16; P< 0.05) between the groups. CONCLUSION: Remote fetal heart rate monitoring via internet served as an innovative, acceptable, safe and effective reduced-frequency prenatal examination model without affecting the outcome of perinatology of pregnant women with different risk factors.

The dynamic change of serotype distribution and antimicrobial resistance of pneumococcal isolates since PCV13 administration and COVID-19 control in Urumqi, China.

Objective: This study aims to analyze the serotype distribution and drug resistance of Streptococcus pneumoniae isolated from children aged 8 days to 7 years in Urumqi, China, between 2014 to 2021, during which PCV13 was introduced in the private sector's immunization program and COVID-19 control was administrated in the last 2 years. Methods: Serotypes of S. pneumoniae isolates were determined by Quellung reaction, and their susceptibility against 14 antimicrobials were tested. According to the start year of PCV13 administration (2017) and COVID-19 control (2020), the study period was divided into three stages: 2014-2015, 2018-2019, and 2020-2021. Results: A total of 317 isolates were involved in this study. The most common serotypes were type 19F (34.4%), followed by 19A (15.8%), 23F (11.7%), 6B (11.4%), and 6A(5.0%). The coverage rate of both PCV13 and PCV15 was 83.0%. The coverage of PCV20 was a little higher at 85.2%. The resistance rate against penicillin was 28.6% according to the breakpoints of oral penicillin, which would reach up to 91.8% based on the breakpoints of parenteral penicillin for meningitis. The resistance rates to erythromycin, clindamycin, tetracycline, and sulfamethoxazole-trimethoprim were 95.9%, 90.2%, 88.9%, and 78.8%, respectively. The PCV13 isolate was more resistant to penicillin than the non-PCV13 ones. There was not any significant change found in the serotype distribution since the PCV13 introduction and the COVID-19 control. The resistance rate against oral penicillin slightly elevated to 34.5% in 2018-2019 from 30.7% in 2014-2015 and then decreased significantly to 18.1% in 2020-2021 (χ 2 = 7.716, P < 0.05), while the resistance rate to ceftriaxone (non-meningitis) continuously declined from 16.0% in 2014-2015 to 1.4% in 2018-2019 and 0% in 2020-2021 (Fisher = 24.463, P < 0.01). Conclusion: The common serotypes of S. pneumoniae isolated from children in Urumqi were types 19F, 19A, 23F, 6B, and 6A, which we found to have no marked change since the PCV13 introduction and the COVID-19 control However, the resistance rate to oral penicillin and ceftriaxone significantly declined in the COVID-19 control stage.

Supply forecasting and profiling of urban supermarket chains based on tensor quantization exponential regression for social governance.

Background: During the COVID-19 pandemic, the accurate forecasting and profiling of the supply of fresh commodities in urban supermarket chains may help the city government make better economic decisions, support activities of daily living, and optimize transportation to support social governance. In urban supermarket chains, the large variety of fresh commodities and the short shelf life of fresh commodities lead to the poor performance of the traditional fresh commodity supply forecasting algorithm. Methods: Unlike the classic method of forecasting a single type of fresh commodity, we proposed a third-order exponential regression algorithm incorporating the block Hankle tensor. First, a multi-way delay embedding transform was used to fuse multiple fresh commodities sales to a Hankle tensor, for aggregating the correlation and mutual information of the whole category of fresh commodities. Second, high-order orthogonal iterations were performed for tensor decomposition, which effectively extracted the high-dimensional features of multiple related fresh commodities sales time series. Finally, a tensor quantization third-order exponential regression algorithm was employed to simultaneously predict the sales of multiple correlated fresh produce items. Results: The experiment result showed that the provided tensor quantization exponential regression method reduced the normalized root mean square error by 24% and the symmetric mean absolute percentage error by 22%, compared with the state-of-the-art approaches.

Engineering viral genomics and nano-liposomes in microfluidic platforms for patient-specific analysis of SARS-CoV-2 variants.

New variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are continuing to spread globally, contributing to the persistence of the COVID-19 pandemic. Increasing resources have been focused on developing vaccines and therapeutics that target the Spike glycoprotein of SARS-CoV-2. Recent advances in microfluidics have the potential to recapitulate viral infection in the organ-specific platforms, known as organ-on-a-chip (OoC), in which binding of SARS-CoV-2 Spike protein to the angiotensin-converting enzyme 2 (ACE2) of the host cells occurs. As the COVID-19 pandemic lingers, there remains an unmet need to screen emerging mutations, to predict viral transmissibility and pathogenicity, and to assess the strength of neutralizing antibodies following vaccination or reinfection. Conventional detection of SARS-CoV-2 variants relies on two-dimensional (2-D) cell culture methods, whereas simulating the micro-environment requires three-dimensional (3-D) systems. To this end, analyzing SARS-CoV-2-mediated pathogenicity via microfluidic platforms minimizes the experimental cost, duration, and optimization needed for animal studies, and obviates the ethical concerns associated with the use of primates. In this context, this review highlights the state-of-the-art strategy to engineer the nano-liposomes that can be conjugated with SARS-CoV-2 Spike mutations or genomic sequences in the microfluidic platforms; thereby, allowing for screening the rising SARS-CoV-2 variants and predicting COVID-19-associated coagulation. Furthermore, introducing viral genomics to the patient-specific blood accelerates the discovery of therapeutic targets in the face of evolving viral variants, including B1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), c.37 (Lambda), and B.1.1.529 (Omicron). Thus, engineering nano-liposomes to encapsulate SARS-CoV-2 viral genomic sequences enables rapid detection of SARS-CoV-2 variants in the long COVID-19 era.

Pharmacologic therapies of ARDS: From natural herb to nanomedicine.

Acute respiratory distress syndrome (ARDS) is a common critical illness in respiratory care units with a huge public health burden. Despite tremendous advances in the prevention and treatment of ARDS, it remains the main cause of intensive care unit (ICU) management, and the mortality rate of ARDS remains unacceptably high. The poor performance of ARDS is closely related to its heterogeneous clinical syndrome caused by complicated pathophysiology. Based on the different pathophysiology phases, drugs, protective mechanical ventilation, conservative fluid therapy, and other treatment have been developed to serve as the ARDS therapeutic methods. In recent years, there has been a rapid development in nanomedicine, in which nanoparticles as drug delivery vehicles have been extensively studied in the treatment of ARDS. This study provides an overview of pharmacologic therapies for ARDS, including conventional drugs, natural medicine therapy, and nanomedicine. Particularly, we discuss the unique mechanism and strength of nanomedicine which may provide great promises in treating ARDS in the future.

Increases in stress hormone levels in a UK population during the COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Research suggests that psychological factors may influence vulnerability to SARS-CoV-2 infection, although the mechanisms are unclear. PURPOSE: We examined whether the hypothalamic-pituitary-adrenal axis may be a possible mechanism, by measuring the relationship between indices of psychological distress and cortisone in hair (hairE) in a UK cohort during the COVID-19 pandemic. METHODS: Participants (N = 827) provided two 3 cm hair samples over a 6-month period between April-September 2020. Samples reflected hairE in the 3 months prior to the collection date. RESULTS: HairE in the first samples (T1: commenced April 2020) did not differ significantly from pre-pandemic population norms. However, hairE in the second samples (T2: commenced July 2020) were significantly higher than T1 and pre-pandemic population norms, with a 23% increase between T1 and T2. Linear regressions, controlling for age and gender, demonstrated that at both timepoints, hairE levels were greatest in people with a history of mental health difficulties. In addition, stress reported at T1 predicted greater hairE at T2 and a greater change in hairE between T1 and T2. CONCLUSIONS: These findings demonstrate that during the COVID-19 pandemic hairE was substantially elevated across a large community cohort, with greatest levels in those with a history of mental health difficulties and greatest changes in those reporting greatest levels of stress early in the pandemic. Further research is required with verified SARS-CoV-2 outcomes to determine whether the HPA axis is among the mechanisms by which a history of mental health difficulties and stress influence SARS-CoV-2 outcomes.

The psychological impact of COVID-19 among newly diagnosed patients with breast cancer when cancer care was returning to normal.

OBJECTIVE: We aim to evaluate anxiety, depression and fear of cancer progression in newly diagnosed patients with breast cancer when cancer care was returning to normal after COVID-19 by comparing them with the pre-COVID patients and explore the association of worries about further cancer care and loneliness with them. METHODS: Two hundred and eighteen newly diagnosed patients with breast cancer during the pandemic were surveyed using questionnaires, and 153 patients before the pandemic were included in the control group. Logistic regression analyses were used. RESULTS: There were 51.8%, 44.0% and 30.7% of patients during the pandemic reported anxiety symptoms, depressive symptoms and clinically significant fear of cancer progression, respectively. The risks of anxiety symptoms (OR 2.24, 95% CI 1.43-3.51), depressive symptoms (1.61, 1.04-2.50) and clinically significant fear of cancer progression (4.65, 2.49-8.70) were higher in patients during the pandemic than pre-COVID patients. Worries about further cancer care and loneliness were associated with 1.40-2.52 times higher risks of these psychological problems among the patients during the pandemic. CONCLUSIONS: The newly diagnosed patients with breast cancer during COVID-19 are at elevated risks of depression, anxiety and fear of cancer progression, and those who are worried about further cancer care and felt loneliness during the pandemic were more likely to experience psychological problems.

Assessing quality of online learning platforms for in-service teachers’ professional development: The development and application of an instrument

To help optimize online learning platforms for in-service teachers’ professional development, this study aims to develop an instrument to assess the quality of this type of platforms on teacher satisfaction. After reliability and validity tests and expert empowerment, the 27-item instrument was formed. Based on the information systems (IS) success model, this instrument was designed to measure teacher perceptions of the quality of online learning platforms from three dimensions, namely, content quality, technical quality, and service quality. Moreover, the developed instrument was used to analyze the effects of the National Teacher Training Platform amid the COVID-19 outbreak in China. The findings revealed that the improvement of the platform’s style, tool function, operating efficiency, and teaching methods could enhance teachers’ experience of online training.

Optimization of the Prodrug Moiety of Remdesivir to Improve Lung Exposure/Selectivity and Enhance Anti-SARS-CoV-2 Activity.

COVID-19 patients with severe symptoms still lack antiviral treatment options. Although remdesivir is the only FDA-approved drug for those patients, its efficacy is limited by premature hydrolysis to nucleoside (NUC), low accumulation in the disease-targeted tissue (lungs), and low antiviral potency. In this study, we synthesized a new series of remdesivir analogues by modifying the ProTide moiety. In comparison with remdesivir, the lead compound MMT5-14 showed 2- to 7-fold higher antiviral activity in four variants of SARS-CoV-2. By reducing premature hydrolysis in hamsters, MMT5-14 increased the prodrug concentration by 200- to 300-fold in the plasma and lungs but also enhanced lung accumulation of the active metabolite triphosphate nucleosides (NTP) by 5-fold. Compared to remdesivir, MMT5-14 also increased the intracellular uptake and activation in lung epithelial cells by 4- to 25-fold. These data suggest that MMT5-14 could be a potential antiviral drug to treat COVID-19 patients with severe symptoms.

The mechanism of metal-based antibacterial materials and the progress of food packaging applications: A review.

Food packages have been detected carrying novel coronavirus in multi-locations since the outbreak of COVID-19, causing major concern in the field of food safety. Metal-based supported materials are widely used for sterilization due to their excellent antibacterial properties as well as low biological resistance. As the principal part of antibacterial materials, the active component, commonly referred to Ag, Cu, Zn, etc., plays the main role in inhibiting and killing pathogenic microorganisms by destroying the structure of cells. As another composition of metal-based antibacterial materials, the carrier could support and disperse the active component, which on one hand, could effectively decrease the usage amount of active component, on the other hand, could be processed into various forms to broaden the application range of antibacterial materials. Different from other metal-based antibacterial reviews, in order to highlight the detailed function of various carriers, we divided the carriers into biocompatible and adsorptable types and discussed their different antibacterial effects. Moreover, a novel substitution antibacterial mechanism was proposed. The coating and shaping techniques of metal-based antibacterial materials as well as their applications in food storage at ambient and low temperatures are also comprehensively summarized. This review aims to provide a theoretical basis and reference for researchers in this field to develop new metal-based antibacterial materials.

Geotemporal analysis of perinatal care changes and maternal mental health: an example from the COVID-19 pandemic.

Our primary objective was to document COVID-19 induced changes to perinatal care across the USA and examine the implication of these changes for maternal mental health. We performed an observational cross-sectional study with convenience sampling using direct patient reports from 1918 postpartum and 3868 pregnant individuals collected between April 2020 and December 2020 from 10 states across the USA. We leverage a subgroup of these participants who gave birth prior to March 2020 to estimate the pre-pandemic prevalence of specific birthing practices as a comparison. Our primary analyses describe the prevalence and timing of perinatal care changes, compare perinatal care changes depending on when and where individuals gave birth, and assess the linkage between perinatal care alterations and maternal anxiety and depressive symptoms. Seventy-eight percent of pregnant participants and 63% of postpartum participants reported at least one change to their perinatal care between March and August 2020. However, the prevalence and nature of specific perinatal care changes occurred unevenly over time and across geographic locations. The separation of infants and mothers immediately after birth and the cancelation of prenatal visits were associated with worsened depression and anxiety symptoms in mothers after controlling for sociodemographic factors, mental health history, number of pregnancy complications, and general stress about the COVID-19 pandemic. Our analyses reveal widespread changes to perinatal care across the US that fluctuated depending on where and when individuals gave birth. Disruptions to perinatal care may also exacerbate mental health concerns, so focused treatments that can mitigate the negative psychiatric sequelae of interrupted care are warranted.

The Impact of Data Vulnerability in Online Health Communities: An Institutional Assurance Perspective

In the last few years, online health communities (OHCs) have experienced rapid development due to advances in technology and the COVID-19 pandemic. However, the sensitive nature of medical information has also raised concerns from users about their privacy and reduced their intention to use OHCs. Considering the critical role of data privacy, this study explored the effect of data vulnerability on OHC users. Using online survey data collected from 438 OHC users in China, we found that data vulnerability significantly reduced psychological comfort, while the latter enhanced continuance intention. We also found that psychological comfort negatively mediated the impact of data vulnerability on continuance intention. Institutional assurance approaches, namely privacy policy, privacy protection technology, industry self-regulation, and government legislation, were also found to mitigate the negative impact of data vulnerability on psychological comfort. This study not only contributes to the data privacy, psychological comfort, and institutional assurance literature but also offers suggestions for OHC stakeholders.

All-printed soft human-machine interface for robotic physicochemical sensing.

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.

Improving antibody affinity through in vitro mutagenesis in complementarity determining regions.

High-affinity antibodies are widely used in diagnostics and for the treatment of human diseases. However, most antibodies are isolated from semi-synthetic libraries by phage display and do not possess in vivo affinity maturation, which is triggered by antigen immunization. It is therefore necessary to engineer the affinity of these antibodies by way of in vitro assaying. In this study, we optimized the affinity of two human monoclonal antibodies which were isolated by phage display in a previous related study. For the 42A1 antibody, which targets the liver cancer antigen glypican-3, the variant T57H in the second complementarity-determining region of the heavy chain (CDR-H2) exhibited a 2.6-fold improvement in affinity, as well as enhanced cell-binding activity. For the I4A3 antibody to severe acute respiratory syndrome coronavirus 2, beneficial single mutations in CDR-H2 and CDR-H3 were randomly combined to select the best synergistic mutations. Among these, the mutation S53P-S98T improved binding affinity (about 3.7 fold) and the neutralizing activity (about 12 fold) compared to the parent antibody. Taken together, single mutations of key residues in antibody CDRs were enough to increase binding affinity with improved antibody functions. The mutagenic combination of key residues in different CDRs creates additive enhancements. Therefore, this study provides a safe and effective in vitro strategy for optimizing antibody affinity.

Survival of SARS-CoV-2 in artificial seawater and on the surface of inanimate materials.

There is a potential risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread through human contact with seafood and the inanimate materials contaminated by the virus. In this study, we examined the stability of the virus in artificial seawater (ASW) and on the surface of selected materials. SARS-CoV-2 (3.75 log10 TCID50 ) in ASW at 22℃ maintained infectious about 3 days and at 4℃ the virus survived more than 7 days. It should be noticed that viable virus at high titer (5.50 log10 TCID50 ) may survive more than 20 days in ASW at 4℃ and for 7 days at 22℃. SARS-CoV-2 on stainless steel and plastic bag maintained infectious for 3 days, and on nonwoven fabric for 1 day at 22℃. In addition, the virus remained infectious for 9 days on stainless steel and non-woven fabric, and on plastic bag for 12 days at 4℃. It is important to highlight the role of inanimate material surfaces as a source of infection and the necessity for surface decontamination and disinfection.

New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system.

According to the report presented by the World Health Organization, a new member of viruses, namely, coronavirus, shortly 2019-nCoV, which arised in Wuhan, China, on January 7, 2020, has been introduced to the literature. The main aim of this paper is investigating and finding the optimal values for better understanding the mathematical model of the transfer of 2019-nCoV from the reservoir to people. This model, named Bats-Hosts-Reservoir-People coronavirus (BHRPC) model, is based on bats as essential animal beings. By using a powerful numerical method we obtain simulations of its spreading under suitably chosen parameters. Whereas the obtained results show the effectiveness of the theoretical method considered for the governing system, the results also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model.

Malnutrition predicts poor outcomes in diabetic COVID-19 patients in Huangshi, Hubei.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread throughout the world, which becomes a global public health emergency. Undernourishment prolongs its convalescence and has an adverse effect on its prognosis, especially in diabetic patients. The purpose of this study was to evaluate the prevalence and characteristics of undernourishment and to determine how it is related to the prognostic outcomes in the diabetic patients with coronavirus disease 2019 (COVID-19). A retrospective, multicenter study was conducted in 85 diabetic COVID-19 patients from three hospitals in Hubei Province. All patients were assessed using the European Nutritional Risk Screening 2002 (NRS-2002) and other nutritional assessments when admitted. Of them, 35 (41.18%) were at risk of malnutrition (NRS score ≥3). Severe COVID-19 patients had a significantly lower level of serum albumin and prealbumin and higher NRS score than non-severe patients. Multivariate logistic regression analysis showed that serum prealbumin and NRS score increased the likelihood of progression into severe status ( P<0.05). Meanwhile, single factor and multivariate analysis determined that grade of illness severity was an independent predictor for malnutrition. Furthermore, prealbumin and NRS score could well predict severe status for COVID-19 patients. The malnutrition group (NRS score ≥3) had more severe illness than the normal nutritional (NRS score <3) group ( P<0.001), and had a longer length of in-hospital stay and higher mortality. Malnutrition is highly prevalent among COVID-19 patients with diabetes. It is associated with severely ill status and poor prognosis. Evaluation of nutritional status should be strengthened, especially the indicators of NRS-2002 and the level of serum prealbumin.