Measuring serum beta2-microglobulin to predict long-term mortality in hemodialysis patients using low-flux dialyzer reuse

Nguyen Huu Dung,1 Nguyen Trung Kien,2 Nguyen Thi Thu Hai,1 Phan The Cuong,1 Nguyen Thi Thu Huong,3 Dao Bui Quy Quyen,4 Nguyen Minh Tuan,4 Do Manh Ha,2 Truong Quy Kien,2 Nguyen Thi Thuy Dung,2 Pham Quoc Toan,2 Hoang Trung Vinh,2 Tomoko Usui,5 Le Viet Thang21Bach Mai Hospital, Ha Noi, Vietnam; 2Military Hospital 103, Ha Noi, Vietnam; 3Ha Noi Kidney Hospital, Ha Noi, Vietnam; 4Cho Ray Hospital, Ho Chi Minh, Vietnam; 5University of Tokyo Hospital, Tokyo, JapanPurpose: Beta2-microglobulin (β2-M) is recognized as a surrogate marker relating to the mechanisms of dialysis-associated amyloidosis. Few studies have evaluated the association of serum β2-M with clinical outcome in hemodialysis patients using high-flux type. However, study on patients using low-flux dialyzer reuse has not been done yet.Patients and methods: Using serum β2-M level on predicting long-term mortality of hemodialysis patients was examined in 326 prevalent hemodialysis patients (45.59±14.46 years, hemodialysis duration of 47.5 (26–79) months, 186 males and 140 females). The patients were divided into 3 groups with equal number of patients, according to their serum β2-M levels: group A (n=109, serum β2-M concentration ≤55.7 mg/L), group B (n=109, serum β2-M level from 55.8 mg/L to 75.4 mg/L) and group C (n=108, serum β2-M concentration >75.4 mg/L).Results: During the follow-up period of 5 years, there were 75 all-cause deaths (23.0%). Kaplan–Meier analysis revealed that all-cause mortality in the higher β2-M group was significantly higher compared to that in the lower β2-M groups (p<0.001). Serum β2-M level was a significant predictor for all-cause mortality (AUC =0.898; p<0.001; Cut-off value: 74.9 mg/L, Se=93.3%, Sp=92.9%).Conclusion: Serum β2-M levels were a significant predictor of long-term mortality in hemodialysis patients, who use only low-flux dialyzers and reuse 6 times.Keywords: Beta2-microglobulin, mortality, hemodialysi

Evolution and spatio-temporal dynamics of Enterovirus A71 subgenogroups in Vietnam

BackgroundEnterovirus A71 (EV-A71) is the major cause of severe hand, foot and mouth disease and viral encephalitis in children across the Asia-Pacific region, including in Vietnam which has experienced a high burden of disease in recent years. Multiple subgenogroups (C1, C4, C5 and B5) concurrently circulate in the region with a large variation in epidemic severity. The relative differences in their evolution and epidemiology were examined within Vietnam and globally. MethodsA total of 752 VP1 gene sequences were analysed (413 generated in this study combined with 339 obtained from GenBank), collected from patients in 36 provinces in Vietnam during 2003andndash;2013 along with epidemiological metadata. Globally representative VP1 gene datasets of subgenogroups were used to co-estimate time-resolved phylogenies and relative genetic diversity to infer virus origins and regional transmission network. ResultsDespite frequent virus migration between countries, the highest genetic diversity of individual subgenogroups was maintained independently for several years in specific Asian countries representing genogroup-specific sources of EV-A71 diversity. ConclusionThis study highlights a persistent transmission network of EV-A71, with specific Asian countries seeding other countries in the region and beyond, emphasising the need for improved EV-A71 surveillance and detailed genetic and antigenic characterisation.</p

Validation of the Job Content Questionnaire among hospital nurses in Vietnam

OBJECTIVES: The aim of this study was to examine the reliability and validity of the Job Content Questionnaire (JCQ) in Vietnamese among hospital nursing staff. METHODS: The 22-items version of the JCQ was used. This includes four scales: (a) psychological demands (5 items); (b) job control (9 items); (c) supervisor support (4 items); and (d) coworker support (4 items). All 1258 nurses in a general hospital in Vietnam, excluding 11 who were due to retire, were invited to complete the cross-sectional survey. The internal consistency reliability was estimated using Cronbach's α. Construct validity was examined using exploratory factor analysis (EFA). Convergent validity was evaluated by calculating correlations between the JCQ scores and DASS 21 and overtime work. RESULTS: In total, 949 (75%) of the 1258 eligible nurses completed the survey. Cronbach's α values demonstrated acceptable internal consistency in two scales (supervisor support α = .87; coworker support α = .86), while Cronbach's α was below the acceptable threshold of 0.70 for job control (α = .45) and job demand (α = .50). EFA assuming a four-factor structure showed a factor structure that was almost identical to the original JCQ, with two items loading on other scales. The subscales of depression, anxiety, and stress response of DASS 21 and the subscales of JCQ were significantly correlated, as expected. CONCLUSION: The results suggest that the JCQ in Vietnamese can be used with some reliability and validity for examining psychosocial work environment among nurses. Further studies should be done to confirm and expand our findings in a variety of occupational groups and in other Asian low- and middle-income countries

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated

Applying machine learning to identify anti‐vaccination tweets during the covid‐19 pandemic

Anti‐vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti‐vaccination content widely available on social media, including Twitter. Being able to identify anti‐vaccination tweets could provide useful information for formulating strategies to reduce anti‐vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti‐vaccination tweets that were published during the COVID‐19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long shortterm memory networks with pre‐trained GLoVe embeddings (Bi‐LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi‐LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi‐LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti‐vaccination tweets in future studies

High prevalence of hospital-acquired infections caused by gram-negative carbapenem resistant strains in Vietnamese pediatric ICUs A multi-centre point prevalence survey

There is scarce information regarding hospital-acquired infections (HAIs) among children in resource-constrained settings. This study aims to measure prevalence of HAIs in Vietnamese pediatric hospitals. Monthly point prevalence surveys (PPSs) in 6 pediatric intensive care units (ICUs) in 3 referral hospitals during 1 year. A total of 1363 cases (1143 children) were surveyed, 59.9% male, average age 11 months. Admission sources were: other hospital 49.3%, current hospital 36.5%, and community 15.3%. Reasons for admission were: infectious disease (66%), noninfectious (20.8%), and surgery/trauma (11.3%). Intubation rate was 47.8%, central venous catheter 29.4%, peripheral venous catheter 86.2%, urinary catheter 14.6%, and hemodialysis/filtration 1.7%. HAI was diagnosed in 33.1% of the cases: pneumonia (52.2%), septicemia (26.4%), surgical site infection (2%), and necrotizing enterocolitis (2%). Significant risk factors for HAI included age under 7 months, intubation and infection at admission. Microbiological findings were reported in 212 cases (43%) with 276 isolates: 50 Klebsiella pneumoniae, 46 Pseudomonas aeruginosa, and 39 Acinetobacter baumannii, with carbapenem resistance detected in 55%, 71%, and 65%, respectively. Staphylococcus aureus was cultured in 18 cases, with 81% methicillin-resistant Staphylococcus aureus. Most children (87.6%) received antibiotics, with an average of 1.6 antibiotics per case. Colistin was administered to 96 patients, 93% with HAI and 49% with culture confirmed carbapenem resistance. The high prevalence of HAI with carbapenem resistant gram-negative strains and common treatment with broad-spectrum antibiotics and colistin suggests that interventions are needed to prevent HAI and to optimize antibiotic use.Funding Agencies|Swedish International Development Agency (Sida); Wellcome Trust (UK); Global Antibiotic Resistance Partnership (GARP)</p