Low-intensity online intervention (LiON) for distressed young people in Hong Kong: Preliminary findings

Previous studies have demonstrated that low-intensity intervention is effective in improving mental health in young people. Whilst interventions have majorly been offered online during COVID-19 pandemic, it is not known whether low-intensity interventions delivered online can also help reduce the level of distress experienced by young people. The study aimed to determine whether a low-intensity online intervention (LiON) can reduce levels of distress in young people greater than those in similar initial distress levels but received no intervention. Young people aged 15 to 24 were recruited for the 4-weekly LiON intervention consisting of three modules namely sleep and relaxation, stress-coping and problem-solving. The reductions in distress level after intervention were compared to those that occurred over a period of 3 months among community young people with similar baseline K6 distress levels. Seventy-four young people (mean age 20.24 [SD 2.26] years, 71.6% female) received the LiON intervention from December 2021 to July 2022. We observed a greater improvement in their distress levels after receiving the intervention than those with no intervention in the community (beta -4.13, 95%CI -5.12, -3.07, p < .001, Cohen's f2 0.027). The findings offered evidence that the LiON intervention significantly reduced young people's distress level in addition to the improvement that may occur naturally. The use of LiON is adaptable to a wider variety of frontline community organizations. Future evaluation of its cost-effectiveness is warranted.

Heterologous prime-boost viral vector vaccines for the treatment of a P1A-expressing BGL-1 glioblastoma model

Glioblastoma is an extremely aggressive and difficult cancer to treat, which may partly be due to its limited ability to induce T-cell responses. However, combining viral vector vaccines with other therapies to generate tumor-specific T cells may provide a meaningful benefit to patients. Here, we investigated whether heterologous prime-boost vaccination with chimpanzee-derived adenoviral vector ChAdOx1 and modified vaccinia Ankara (MVA) vaccines could generate therapeutically effective CD8+ T-cell responses against a model antigen P1A, a mouse homolog of human tumorassociated Melanoma Antigen GenE (MAGE)-type antigens, expressed by a BGL-1 mouse glioblastoma cell line. We demonstrated that heterologous prime-boost vaccination with ChAdOx1/MVA vaccines targeting P1A generated a high magnitude of CD8+ T cells specific for the P1A35-43 epitope presented by the MHC class I molecule H-2Ld . Prophylactic vaccination with ChAdOx1/MVA-P1A significantly prolonged the survival of syngeneic mice subcutaneously challenged with P1A-expressing BGL-1 tumors. Furthermore, different vaccination schedules significantly impact the magnitude of antigen-specific CD8+ T-cell responses and may impact protective efficacy. However, the substantial induction of myeloid-derived suppressor cells (MDSCs) by this tumor model presents a significant challenge in the therapeutic setting. Future work will investigate the efficacy of this vaccination strategy on intracranial P1A-expressing BGL-1 models.

Active Learning Opportunities Outside Classroom and Laboratory

This paper presented a case study for the foundation year subject Greenhouse Gases and Life (ABCT1D09) launched in PolyU in 2019/2020 semester one. We investigated the implementation of blended learning with outside classroom learning component and TAL pedagogies (technology-assisted laboratory classes, virtual lab and remote lab) in the traditional face-to-face (F2F) lectures with the use of institutional virtual learning environment (Blackboard LMS) to improve students' learning experience by enhancing students' engagement in this large GE class (90 students). Feedbacks from survey and students' reflective journal (i.e. 91% of students satisfied with the designed class activities and 75% of students found the learning experience was enjoyable), as well as the students' academic performance suggest this model brings positive impact to students' learning. The results obtained in the present study may offer more new learning opportunities in tertiary all-round education and inform the design of "new-normal” learning after the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Epidemiology and virology of SARS-CoV-2

This chapter addresses the basic virological and epidemiologic characteristics of severe acute respiratory syndrome–associated coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). SARS-CoV-2 is a member of the seven human coronaviruses and is one of the three coronaviruses that cause acute respiratory distress syndrome in humans, along with Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV. It is an airborne virus although vertical transmission is suspected. Existing evidence shows that children play a small role in the transmission and that newborns have a higher risk of mortality. © 2023 Elsevier Inc. All rights reserved.

Clinical Management of Pediatric COVID-19: An International Perspective and Practical Guide

Clinical Management of Pediatric COVID-19: An International Perspective and Practical Guide provides the most current international research and clinical characteristics of pediatric patients with SARS-CoV-2 infection. Coverage ranges from epidemiology including origin, route of transmission, incubation period, mortality and susceptibility risk factors;to pathogenesis, including difference between the adult and pediatric populations. Diagnosis is covered with special attention to the difference between adult and pediatric patients as well as the differences between newborns, children and adolescents. The book presents current complications, including multisystemic inflammatory syndrome as well as treatment therapies including antiviral and immunomodulatory therapies for this age group. Finally, immunization efficacy and safety are examined. This is the perfect reference to provide guidance to pediatricians on the diagnosis and treatment of SARS-CoV-2, as well as a valuable source of the latest research about the pediatric population for further study. © 2023 Elsevier Inc. All rights reserved.

Experiences of and Responses to COVID-19 in East Asia: The Cases of Japan and Korea

Although the outbreak of COVID-19 has spread around the globe to become a pandemic, differences between the West and the East are observed. In the case of East Asia, represented by Japan and Korea, relatively low prevalence and death rates are reported. This chapter therefore aims to explain the phenomenon with reference to the knowledge of social sciences, with specific focus on the healthcare measures and initiatives on the older adults. Possible reasons of the effectiveness would also be illustrated. It is expected that this chapter may provide a lesson learned in these two East Asian countries fighting against the epidemic situation. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided.

The real-world impact of the COVID-19 pandemic on patients with cancer: a multidisciplinary cross-sectional survey.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented disruptions to cancer care worldwide. We conducted a multidisciplinary survey of the real-world impact of the pandemic, as perceived by patients with cancer. METHODS: A total of 424 patients with cancer were surveyed using a 64-item questionnaire constructed by a multidisciplinary panel. The questionnaire examined patient perspectives regarding COVID-19-related effects (eg, social distancing measures) on cancer care delivery, resources, and healthcare-seeking behaviour, along with the physical and psychosocial aspects of patient well-being and pandemic-related psychological repercussions. RESULTS: Overall, 82.8% of respondents believed that patients with cancer are more susceptible to COVID-19; 65.6% expected that COVID-19 would delay anti-cancer drug development. Although only 30.9% of respondents felt that hospital attendance was safe, 73.1% expressed unaltered willingness to attend scheduled appointments; 70.3% of respondents preferred to receive chemotherapy as planned, and 46.5% were willing to accept changes in efficacy or side-effect profile to allow an outpatient regimen. A survey of oncologists revealed significant underestimation of patient motivation to avoid treatment interruptions. Most surveyed patients felt that there was an insufficient amount of information available concerning the impact of COVID-19 on cancer care, and most patients reported social distancing-related declines in physical, psychological, and dietary wellness. Sex, age, education level, socio-economic status, and psychological risk were significantly associated with patient perceptions and preferences. CONCLUSION: This multidisciplinary survey concerning the effects of the COVID-19 pandemic revealed key patient care priorities and unmet needs. These findings should be considered when delivering cancer care during and after the pandemic.

Acupuncture for olfactory dysfunction in infected COVID-19 patients: Study protocol for a randomized, sham-controlled clinical trial

Background: Olfactory dysfunction (OD) is a common symptom of Corona Virus Disease 2019 (COVID-19). It is defined as the reduced or distorted ability to smell during sniffing (orthonasal olfaction) and represents one of the early symptoms in the clinical course of COVID-19 infection. A large online questionnaire-based survey has shown that some post-COVID-19 patients had no improvement 1 month after discharge from the hospital. Objective: To explore the efficacy of acupuncture for OD in COVID-19 infected patients and to determine whether acupuncture could have benefits over sham acupuncture for OD in post-COVID-19 patients. Methods: This is a single-blind, randomized controlled, cross-over trial. We plan to recruit 40 post-COVID-19 patients with smell loss or smell distortions lasting for more than 1 month. Qualified patients will be randomly allocated to the intervention group (real acupuncture) or the control group (sham acupuncture) at a 1:1 ratio. Each patient will receive 8 sessions of treatment over 4 weeks (Cycle 1) and a 2-week follow-up. After the follow-up, the control group will be subjected to real acupuncture for another 4 weeks (Cycle 2), and the real acupuncture group will undergo the 4-week sham acupuncture. The primary outcomes will be the score changes on the questionnaire of olfactory functioning and olfaction-related quality of life at week 6, 8, 12, and 14 from the baseline. The secondary outcomes will be the changes in the olfactory test score at week 6 and 12 from the baseline measured by using the Traditional Chinese version of the University of Pennsylvania Smell Identification Test (UPSIT-TC). Discussion: The results of this trial will help to determine the effectiveness of acupuncture for OD in post-COVID-19 patients. This may provide a new treatment option for patients.

Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning

Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.

Visualization and Visual Knowledge Discovery from Big Uncertain Data

In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.

Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning

Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.

Trustworthy Explanations for Knowledge Discovered from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. Embedded in the big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Many of these techniques apply "opaque box"approaches to make accurate predictions. However, these techniques may not be crystal clear to the users. As the users not necessarily be able to clearly view the entire knowledge discovery (e.g., prediction) process, they may not easily trust the discovered knowledge (e.g., predictions). Hence, in this paper, we present a system for providing trustworthy explanations for knowledge discovered from e-health records. Specifically, our system provides users with global explanations for the important features among the records. It also provides users with local explanations for a particular record. Evaluation results on real-life e-health records show the practicality of our system in providing trustworthy explanations to knowledge discovered (e.g., accurate predictions made). © 2022 IEEE.

Prediction of Hospital Status of COVID-19 Patients from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 610 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 2.5 years since COVID-19 has declared as a pandemic. As some of these cases require hospitalization. it is important to estimate the demand in hospitalization. Moreover, different levels of hospitalization may require different types of resources (e.g., hospital beds, medical staff). For example, patients admitted into the intensive care unit (ICU) may require assisted ventilation. Hence, in this paper, we present models to make predictions based on e-health records. Specifically, our binary model predicts whether a patient require hospitalization, whereas our multi-class model predicts what level of hospitalization (e.g., regular ward, semi-ICU, ICU) is required by the patient. Our models uses few-shot learning (and may use multi-task learning) with autoencoders (comprising encoders and decoders) and a predictor. Evaluation results on real-life e-health records show the practicality of our models in predicting hospital statuses of COVID-19 cases and the benefits of these models towards effective allocation of resources (e.g., hospital facilities, staff). © 2022 IEEE.

OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided. © 2022 IEEE.

The Impact of Vaccine Hesitancy on Epidemic Spreading

The COVID-19 pandemic has devastated the world in an unprecedented way, causing enormous loss of life. Time and again, public health authorities have urged people to become vaccinated to protect themselves and mitigate the spread of the disease. However, vaccine hesitancy has stalled vaccination levels in the United States. This study explores the effect of vaccine hesitancy on the spread of disease by introducing an SIRS-V, model, with compartments of susceptible (S), infected (I), recovered (R), and vaccinated (V). We leverage the concept of carrying capacity to account for vaccine hesitancy by defining a vaccine confidence level n, which is the maximum number of people that will become vaccinated during the course of a disease. The inverse of vaccine confidence is vaccine hesitance, W. We explore the equilibria of the SIRSV, model and their stability, and illustrate the impact of vaccine hesitance on epidemic spread analytically and via simulations.

A Community-Designed Qr Code-Based Animation Helps Patients Prepare for Endoscopies

Aim: Conventionally, patients are gathered to watch an introductory video at the clinic before endoscopic investigations take place. This may arouse practical issues under the COVID pandemic. Our centre, in collaboration with students from a local secondary school, has designed a set of animations which patients can easily access to with a QR code using their own mobile devices, so as to avoid patient gathering and increase their flexibility to read the information. This study aims to evaluate patients' perception of the QR code-based introductory animations of esophagogastroduodenoscopy (OGD) and colonoscopy (CLN). Method(s): A QR code linking to the animation was attached to the appointment sheet. Patients were asked to watch the animation with their own mobile devices before the endoscopy. A questionnaire with 5 questions was distributed after completion of their procedures. Result(s): A total of 144 patients undergoing OGD and CLN were recruited in May-June, 2022 at Tin Shui Wai Hospital. The response rate was 91.7%. Positive feedback was received. 12 patients (8.3%) did not gain access to the animation. A majority of patients agreed or totally agreed that the animation offered them more flexibility to understand the procedures before their OGD (75.4%) and CLN (79.1%). The QR code-based animation was deemed easy-to-use (80.3%), appealing (78.0%) and educational (81.0%). Conclusion(s): With increasing accessibility to mobile devices, patient education in preparation for medical procedures is no longer confined to the hospital setting. QR code-based animation is shown to be an effective and welcoming tool to prepare patients for endoscopies.

Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases

Health informatics is an interdisciplinary area where computer science and related disciplines meet to address problems and support healthcare and medicine. In particular, computer has played an important role in medicine. Many existing computer-based systems (e.g., machine learning models) for healthcare applications produce binary prediction (e.g., whether a patient catches a disease or not). However, there are situations in which a non-binary prediction (e.g., what is hospitalization status of a patient) is needed. As a concrete example, over the past two years, people around the world have been affected by the coronavirus disease 2019 (COVID-19) pandemic. There have been works on binary prediction to determine whether a patient is COVID-19 positive or not. With availability of alternative methods (e.g., rapid test), such a binary prediction has become less important. Moreover, with the evolution of the disease (e.g., recent development of COVID-19 Omicron variant), multi-label prediction of the hospitalization status has become more important when compared with binary prediction on the confirmation of cases. Hence, in this paper, we present a multi-label prediction system for computer-based medical applications. Our system makes use of autoencoders (consisting of encoders and decoders) and few-shot learning to predict the hospitalization status (e.g., ICU, semi-ICU, regular wards, or no hospitalization). The prediction is important for allocation of medical resources (e.g., hospital facilities and medical staff), which in turn affect patient lives. Experimental results on real-life open datasets show that, when training with only a few data, our multilabel prediction system gave a high F1-score when predicting hospitalization status of COVID-19 cases. © 2022 IEEE.

Principles of drug use and management in pregnancy

The antenatal and postnatal care of women is becoming increasingly complex, especiallyduring the COVID-19 pandemic. The increased use of medications during pregnancymakes it a challenging area for healthcare professionals. The role of the obstetric clinicalpharmacist has evolved from supply and prescription screening to that of a moreadvanced practitioner. The pharmacist is now required to be actively involved in patientcare by collaborating closely with the multidisciplinary team and adopting an evidence-based approach. The specialised pharmacist also participates in guideline development,research and development, service improvement in the multidisciplinary team setting andcritical evaluations of unlicensed drugs use. With these emerging clinical leadership roles,obstetric clinical pharmacy has become a rewarding and exciting career for pharmacistswho have a special interest in this field and who enjoy working with a dedicated team ofdoctors, midwives, nurses and other healthcare staff.