Valorisation of biomass and diaper waste into a sustainable production of the medical mushroom Lingzhi Ganoderma lucidum

Global solid waste is expected to increase by at least 70% annually until year 2050. The mixture of solid waste including food waste from food industry and domestic diaper waste in landfills is causing environmental and human health issues. Nevertheless, food and diaper waste containing high lignocellulose can easily degrade using lignocellulolytic enzymes thereby converted into energy for the development and growth of mushroom. Therefore, this study explores the potential of recycling biomass waste from coffee ground, banana, eggshell, tea waste, sugarcane bagasse and sawdust and diaper waste as raw material for Lingzhi mushroom (Ganoderma lucidum) cultivation. Using 2% of diaper core with sawdust biowaste leading to the fastest 100% mushroom mycelium spreading completed in one month. The highest production yield is 71.45 g mushroom; this represents about 36% production biological efficiency compared to only 21% as in commercial substrate. The high mushroom substrate reduction of 73% reflect the valorisation of landfill waste. The metabolomics profiling showed that the Lingzhi mushroom produced is of high quality with a high content of triterpene being the bioactive compounds that are medically important for treating assorted disease and used as health supplement. In conclusion, our study proposed a potential resource management towards zero-waste and circular bioeconomy for high profitable mushroom cultivation

Implementation of the compulsory universal testing scheme in Hong Kong: Mathematical simulations of a household-based pooling approach

This study aims to propose a pooling approach to simulate the compulsory universal RT-PCR test in Hong Kong and explore the feasibility of implementing the pooling method on a household basis. The mathematical model is initially verified, and then the simulation is performed under different prevalence rates and pooled sizes. The simulated population is based in Hong Kong. The simulation included 10,000,000 swab samples, with a representative distribution of populations in Hong Kong. The samples were grouped into a batch size of 20. If the entire batch is positive, then the group is further divided into an identical group size of 10 for re-testing. Different combinations of mini-group sizes were also investigated. The proposed pooling method was extended to a household basis. A representative from each household is required to perform the RT-PCR test. Results of the simulation replications, indicate a significant reduction (p < 0.001) of 83.62, 64.18, and 48.46% in the testing volume for prevalence rate 1, 3, and 5%, respectively. Combined with the household-based pooling approach, the total number of RT-PCR is 437,304, 956,133, and 1,375,795 for prevalence rates 1, 3, and 5%, respectively. The household-based pooling strategy showed efficiency when the prevalence rates in the population were low. This pooling strategy can rapidly screen people in high-risk groups for COVID-19 infections and quarantine those who test positive, even when time and resources for testing are limited

Clinical Outcomes of Left Atrial Appendage Occlusion Versus Switch of Direct Oral Antcoagulant in Atrial Fibrillation: A Territory‐Wide Retrospective Analysis

Background Left atrial appendage occlusion (LAAO) has emerged as an alternative to oral anticoagulation therapy for stroke prevention in atrial fibrillation, but data comparing LAAO with direct oral anticoagulant (DOAC) are sparse. Methods and Results This cohort study compared LAAO (with or without prior anticoagulation) with a switch of one DOAC to another DOAC by 1:2 propensity score matching. The primary outcome was a composite of all‐cause mortality, ischemic stroke, and major bleeding. A total of 2350 patients (874 in the LAAO group and 1476 in the DOAC switch group) were included. After a mean follow‐up of 1052±694 days, the primary outcome developed in 215 (24.6%) patients in the LAAO group and in 335 (22.7%) patients in the DOAC switch group (hazard ratio [HR], 0.94 [95% CI, 0.80–1.12]; P=0.516). The LAAO group had a lower all‐cause mortality (HR, 0.49 [95% CI, 0.39–0.60]; P<0.001) and cardiovascular mortality (HR, 0.49 [95% CI, 0.32–0.73]; P<0.001) but similar risk of ischemic stroke (HR, 0.83 [95% CI, 0.63–1.10]; P=0.194). The major bleeding risk was similar overall (HR, 1.18 [95% CI, 0.94–1.48], P=0.150) but was lower in the LAAO group after 6 months (HR, 0.71 [95% CI, 0.51–0.97]; P=0.032). Conclusions LAAO conferred a similar risk of composite outcome of all‐cause mortality, ischemic stroke, and major bleeding, as compared with DOAC switch. The risks of all‐cause mortality and cardiovascular mortality were lower with LAAO

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset

Abstract Background Predicting an individual’s risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy. Methods The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions. Results The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups. Conclusion The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction

Molecular detection and characterisation of domestic cat hepadnavirus (DCH) from blood and liver tissues of cats in Malaysia

Background: A new domestic cat hepadnavirus (DCH, family Hepadnaviridae) was first reported from whole blood samples of domestic cats in Australia in 2018, and from cat serum samples in Italy in 2019. The pathogenesis of DCH is unknown, but it was reported in cats with viraemia (6.5–10.8%), chronic hepatitis (43%) and hepatocellular carcinoma (28%). Recent reports suggest that DCH resembles the human hepatitis B virus (HBV) and its related hepatopathies. This study aims to detect and characterize DCH among domestic cats in Malaysia. A cross-sectional study was performed on 253 cats, of which 87 had paired blood and liver samples, entailing whole-genome sequencing and phylogenetic analysis of DCH from a liver tissue sample. Result: Among the 253 cats included in this study, 12.3% of the whole blood samples tested positive for DCH. The detection rate was significantly higher in pet cats (16.6%, n = 24/145) compared to shelter cats (6.5%, n = 7/108). Liver tissues showed higher a DCH detection rate (14.9%, n = 13/87) compared to blood; 5 out of these 13 cats tested positive for DCH in their paired liver and blood samples. Serum alanine transaminase (ALT) was elevated (> 95 units/L) in 12 out of the 23 DCH-positive cats (52.2%, p = 0.012). Whole-genome sequence analysis revealed that the Malaysian DCH strain, with a genome size of 3184 bp, had 98.3% and 97.5% nucleotide identities to the Australian and Italian strains, respectively. The phylogenetic analysis demonstrated that the Malaysian DCH genome was clustered closely to the Australian strain, suggesting that they belong to the same geographically-determined genetic pool (Australasia). Conclusions: This study provided insights into a Malaysian DCH strain that was detected from a liver tissue. Interestingly, pet cats or cats with elevated ALT were significantly more likely to be DCH positive. Cats with positive DCH detection from liver tissues may not necessarily have viraemia. The impact of this virus on inducing liver diseases in felines warrants further investigation

Doxorubicin and subsequent risk of cardiovascular diseases among survivors of diffuse large B-cell lymphoma in Hong Kong.

Evidence regarding the dose-related impact of doxorubicin on subsequent cardiovascular diseases (CVDs) in Asian patients with diffuse large B-cell lymphoma (DLBCL) without preexisting CVDs is lacking. From a territory-wide electronic database in Hong Kong, we identified adults who were diagnosed with DLBCL and treated with chemotherapy between 2000 and 2018. We evaluated the patients for incident CVDs (including ischemic heart disease, heart failure, and cardiomyopathy). We evaluated the cause-specific cumulative incidence (csCI) of CVD with levels of doxorubicin exposure by using flexible parametric competing risk analysis and adjusting for demographics, comorbidities, therapeutic exposure, cardiovascular risk factors, and lifestyle factors. Controls were age- and sex-matched to DLBCL patients. We analyzed 2600 patients and 13 000 controls. The adjusted cause-specific hazard ratio (HR) for CVD in patients treated with >500 mg doxorubicin compared with non-doxorubicin regimens was 2.65 (95% confidence interval [CI], 1.23-5.74; P = .013). The 5-, 10-, and 15-year csCIs were 8.2%, 11.3%, and 12.8% in patients vs 3.1%, 4.4%, and 5.2% in controls, respectively. Hypertension (HR, 6.20; 95% CI, 0.79-48.44; P = .082) and use of aspirin/angiotensin-converting enzyme inhibitor/beta-blocker at baseline (HR, 2.13-4.63; P 500 mg doxorubicin compared with non-doxorubicin regimens was 2.65 (95% confidence interval [CI], 1.23-5.74; P = .013). The 5-, 10-, and 15-year csCIs were 8.2%, 11.3%, and 12.8% in patients vs 3.1%, 4.4%, and 5.2% in controls, respectively. Hypertension (HR, 6.20; 95% CI, 0.79-48.44; P = .082) and use of aspirin/angiotensin-converting enzyme inhibitor/beta-blocker at baseline (HR, 2.13-4.63; P 500 mg), together with hypertension or baseline use of medication for cardiovascular risk factors, was found to be associated with an increase in csCIs of CVDs. Tailoring therapeutic strategies to underlying CVD risk factors and risk-adapted monitoring and follow-up of susceptible DLBCL patients are advisable

Early sepsis care with the National Early Warning Score 2-guided Sepsis Hour-1 Bundle in the emergency department: hybrid type 1 effectiveness-implementation pilot stepped wedge randomised controlled trial (NEWS-1 TRIPS) protocol

Introduction Early sepsis treatment in the emergency department (ED) is crucial to improve patient survival. Despite international promulgation, the uptake of the Surviving Sepsis Campaign (SSC) Hour-1 Bundle (lactate measurement, blood culture, broad-spectrum antibiotics, 30 mL/kg crystalloid for hypotension/lactate ≥4 mmol/L and vasopressors for hypotension during/after fluid resuscitation within 1 hour of sepsis recognition) is low across healthcare settings. Delays in sepsis recognition and a lack of high-quality evidence hinder its implementation. We propose a novel sepsis care model (National Early Warning Score, NEWS-1 care), in which the SSC Hour-1 Bundle is triggered objectively by a high NEWS-2 (≥5). This study aims to determine the feasibility of a full-scale type 1 hybrid effectiveness-implementation trial on the NEWS-1 care in multiple EDs.Methods and analysis We will conduct a pilot type 1 hybrid trial and prospectively recruit 200 patients from 4 public EDs in Hong Kong cluster randomised in a stepped wedge design over 10 months. All study sites will start with an initial period of standard care and switch in random order at 2-month intervals to the NEWS-1 care unidirectionally. The implementation evaluation will employ mixed methods guided by the Reach, Effectiveness, Adoption, Implementation and Maintenance framework, which includes qualitative and quantitative data from focus group interviews, staff survey and clinical record reviews. We will analyse the 14 feasibility outcomes as progression criteria to a full-scale trial, including trial acceptability to patients and staff, patient and staff recruitment rates, accuracy of sepsis screening, protocol adherence, accessibility to follow-up data, safety and preliminary clinical impacts of the NEWS1 care, using descriptive statistics.Ethics and dissemination The institutional review boards of all study sites approved this study. This study will establish the feasibility of a full-scale hybrid trial. We will disseminate the findings through peer-reviewed publications, conference presentations and educational activities.Trial registration number NCT05731349

Long-term spill-over impact of COVID-19 on health and healthcare of people with non-communicable diseases: a study protocol for a population-based cohort and health economic study

INTRODUCTION: The COVID-19 pandemic has a significant spill-over effect on people with non-communicable diseases (NCDs) over the long term, beyond the direct effect of COVID-19 infection. Evaluating changes in health outcomes, health service use and costs can provide evidence to optimise care for people with NCDs during and after the pandemic, and to better prepare outbreak responses in the future. METHODS AND ANALYSIS: This is a population-based cohort study using electronic health records of the Hong Kong Hospital Authority (HA) CMS, economic modelling and serial cross-sectional surveys on health service use. This study includes people aged ≥18 years who have a documented diagnosis of diabetes mellitus, hypertension, cardiovascular disease, cancer, chronic respiratory disease or chronic kidney disease with at least one attendance at the HA hospital or clinic between 1 January 2010 and 31 December 2019, and without COVID-19 infection. Changes in all-cause mortality, disease-specific outcomes, and health services use rates and costs will be assessed between pre-COVID-19 and-post-COVID-19 pandemic or during each wave using an interrupted time series analysis. The long-term health economic impact of healthcare disruptions during the COVID-19 pandemic will be studied using microsimulation modelling. Multivariable Cox proportional hazards regression and Poisson/negative binomial regression will be used to evaluate the effect of different modes of supplementary care on health outcomes. ETHICS AND DISSEMNIATION: The study was approved by the institutional review board of the University of Hong Kong, the HA Hong Kong West Cluster (reference number UW 21–297). The study findings will be disseminated through peer-reviewed publications and international conferences