Did the first Movement Control Order impact healthcare seeking behaviour and healthcare access among Malaysians? Findings from a cross-sectional survey

Introduction: In March 2020, Malaysia initiated the first Movement Control Order (MCO) to curb the spread of COVID-19 infections. While healthcare services remained operational, fears of contracting COVID-19 may have impacted health seeking behaviours and healthcare access. A survey was conducted to investigate how the first MCO impacted actions of individuals experiencing upper respiratory tract infection versus other symptoms, routine follow-up visits, and refill prescription practices. Method(s): A cross-sectional survey among adult Malaysians was conducted from November to December 2020. A selfadministered questionnaire was developed, validated, and disseminated on social media and communication platforms. Result(s): 3001 participants responded to the survey. 486 (16.0%) of them reported being unwell during the MCO. Regardless of symptoms, actions taken to seek medical care were similar. The most common action on average was visiting a medical doctor (55.0%), followed by self-medicating at home (38.0%). 588 participants had a scheduled appointment for their medical condition during the MCO. 253 of them had their appointments affected by the lockdown, for examples, 85.0% postponed, 12% cancelled, 9.0% referred. Only 42.3% of these affected participants saw a doctor within 3 months after the first MCO. Out of 487 participants who regularly collected medications from the pharmacy, 69.0% had a prescription refill appointment during the lockdown. Only 15.0% of them did not collect their medications on the scheduled date. Conclusion(s): The first MCO did not severely affect health seeking behaviour and prescription refill practices. However, there are concerns over timely access to follow-up appointments due to extensive rescheduling and backlogs triggered by the pandemic.

Impact of COVID-19 pandemic on national cataract surgical rate: An interrupted time-series analysis

Introduction: Elective surgeries were suspended during the national lockdown implemented in March 2020 to curb the spread of the COVID-19 pandemic. Our study aimed to assess the impact of COVID-19 pandemic on national cataract surgical rates. Method(s): We conducted an interrupted time series analysis of cataract surgeries from 2017 to 2021 in Malaysia to evaluate the change in cataract surgical rates before and after the lockdown. Incidence rate ratios were estimated using a seasonally adjusted Poisson regression model. Stratified analyses were performed to establish whether the effect of the lockdown varied by COVID- 19 status of the hospital, sex, and age groups. Result(s): The mean monthly cataract surgical rates before lockdown was 14.1 per 100,000 population with an underlying trend of a 1.0% increase per month. The lockdown was associated with an abrupt 54.0% reduction in monthly rates (95%CI: 0.36-0.60;p<0.001). In May 2020, we observed a gradual recovery in the rates with a peak at 13.8 per 100,000 population in September 2020 although it has not rebounded to its pre-lockdown rate in December 2021. There was no evidence that the effect of the lockdown differed by COVID-status of the hospital, sex, or age groups. Conclusion(s): The initial lockdown period in March 2020 was associated with an immediate reduction in cataract surgical rates to nearly half of its baseline rate. Although cataract surgical rates have marginally trended upward after restrictions were eased, efforts should be taken to restore the delivery of cataract services to its pre-pandemic level to mitigate the negative effects caused by service disruption.

Research on the Influence of Teaching Methods on Mathematics Academic Achievement of College Students during Online Teaching

Affected by COVID-19, many mathematics courses in college are currently taught online. However, studies revealed its effect is unsatisfactory. For this problem, many scholars thought the teaching method of teachers is one of the main affecting factors and discussed many different teaching methods. Based on these teaching methods, we interviewed 273 college students majoring in mathematics with questionnaires in China and explored the effectiveness of different teaching methods with the way of neural network analysis. It could be found that there are twenty-one main teaching methods that can promote the mathematics academic achievement of college students during online teaching. Among them, the methods such as teachers uploading electronic teaching materials for students to learn before class, teachers using electronic equipment to demonstrate the derivation process to the students in class, teachers showing students universal errors through the network platform and giving correct answers, and teachers tutoring students individually through the network platform after class is the most influential four teaching methods. Meanwhile, there are four main teaching methods that can hinder the improvement of mathematics academic achievement of college students during online teaching, which are teachers use the voice function to teach knowledge in class, teachers play prefabricated teaching videos in class, teachers encourage students to use network technology to discuss with each other in class, teachers evaluate students' homework in text after class. © 2022 ACM.

Interactive Multimedia for Promoting Cultural Heritage Tourism in Penang

Penang, also referred as the Pearl of the Orient, is one of the most well-known tourist destinations in Malaysia due to its unique cultural, architectural properties and diversity range of heritage arts and crafts. Research revealed that digital technologies in today's world are almost needed for presenting all types of cultural heritage and boosting the tourism sector especially after the imposition of Covid-19 anti-epidermic measures, resulting in the significant loss of tourists. The main aim of this research project is to promote cultural heritage tourism in Penang with the use of interactive multimedia tools. The methodological approach is formed through the examination and evaluation of a comprehensive reference and an online survey that distributed to the visitors in Penang to discuss the current issues, challenges, and the solutions in promoting the cultural heritage tourism in Penang. This is a concept paper that explores the benefits and methods to develop an interactive website that includes 360o virtual tours, detailed travel guides, animation for introducing the histories of the heritage building and video for promoting the cultural heritage tourism in Penang. © 2022 IEEE.

Nirmatrelvir increases blood tacrolimus concentration in COVID-19 patients as determined by UHPLC-MS/MS method.

OBJECTIVE: Transplant recipients have a higher risk of SARS-CoV-2 infection owing to the use of immunosuppressive drugs like tacrolimus (FK506). FK506 and nirmatrelvir (NMV) (an anti-SARS-CoV-2 drug) are metabolized by cytochrome P450 3A4 and may have potential drug-drug interactions. It is important to determine the effect of NMV on FK506 concentrations. PATIENTS AND METHODS: Following protein precipitation from blood, FK506 and its internal standard (FK506-13C,2d4) were detected by ultra-high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Total 22 blood samples (valley concentrations) from two coronavirus disease 2019 (COVID-19) patients were collected and analyzed for FK506 concentrations. RESULTS: Blood levels of FK506 (0.5-100 ng/mL) showed good linearity. The UHPLC-MS/MS method was validated with intra- and inter-batch accuracies of 104.55-107.85%, and 99.52-108.01%, respectively, and precisions of < 15%. Mean blood FK506 concentration was 12.01 ng/mL (range, 3.15-33.1 ng/mL). Five-day co-administration with NMV increased the FK506 concentrations from 3.15 ng/mL to 33.1 ng/mL, returning to 3.36 ng/mL after a 9-day-washout. CONCLUSIONS: We developed a simple quantification method for therapeutic drug monitoring of FK506 in patients with COVID-19 using UHPLC-MS/MS with protein precipitation. We found that NMV increased FK506 blood concentration 10-fold. Therefore, it is necessary to re-consider co-administration of FK506 with NMV.

Preparation and in vitro characterization of remdesivir-loaded liposomes for inhalation

Remdesivir-loaded liposomes for inhalation were prepared and the in vitro properties were evaluated. Firstly, preparation methods of remdesivir-loaded liposomes were screened, and single-factor experiments were conducted to optimize the prescription and preparation process. Then the physical property, deposition ratio and aerodynamic particle size distribution of remdesivir-loaded liposomes suspension for inhalation were comprehensively evaluated. As a result, the optimal liposomes were prepared by the thin-film dispersion method with pH 6. 5 phosphate-buffered saline as the hydration medium. In the prescription, the ratio of drug to DPPC was 1:20;the cholesterol accounted for 10% of total lipids;and 20% DSPE-mPEG 2000 was added as stabilizer. 4% trehalose was added as lyoprotectant when lyophilizing to obtain ideal appearance, good stability and a small particle size change after reconstitution. Remdesivir-loaded liposomes were spherical with smooth surface and uniform particle size distribution under transmission electron microscope. In vitro release tests showed no significant change for release curves of remdesivir-loaded liposomes suspension before and after nebulization. Deposition experiments indicated that the fine particles fraction of liposomes was 51. 4%, and the mass median aerodynamic diameter was less than 5 μm measured by next generation impactor. To sum up, remdesivir-loaded liposomes for inhalation with high encapsulation efficiency and stability can achieve a suitable particle size distribution to effectively deposit in the lung after nebulization, which provides a new approach for the treatment of COVID-19.

[Consensus of experts on the oral health management and medical risk prevention for the patients with chronic airway diseases (2022 edition)].

Today, there is greater awareness on the association between oral diseases and respiration diseases after the outbreak of COVID-19. However, confusion regarding the oral health management and medical risk prevention for patients with chronic airway diseases has been remained among dental clinicians. Therefore, the dental experts of the Fifth General Dentistry Special Committee, Chinese Stomatological Association, combined with the experts of respiratory and critical care medicine, undertook the formation of consensus on the oral health management of patients with chronic airway diseases in order to help dental clinicians to evaluate medical risks and make better treatment decision in clinical practice. In the present consensus report, the relationship of oral diseases and chronic airway diseases, the oral health management and the treatment recommendations of patients with chronic airway diseases are provided.

Design of Spraying Disinfection Robot Based on Video Teleoperation

Since December 2019, COVID-19 spread around the world and a number of epidemic protection robot products have emerged with outstanding applications. Combining with the rapid development of mobile network communication technology today, a disinfection robot with real-time remote control operation is designed in this paper. The motion chassis of the disinfection robot designed in this project adopts the wheeled chassis based on Ackerman steering. Based on the mobile network communication technology, operating personnel can remotely drive the robot into the contaminated area for operation, without the need to reach the contaminated area. The robot has the characteristics of flexibility, high efficiency and strong mobility. In addition, it has the ability of cross-region remote operation, high real-time, low delay video transmission ability. At the same time, the remote disinfection robot is equipped with a camera, which can observe the site environment remotely, so that the operator can judge the site environment and adopt the best spray disinfection operation method.

The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascular tissue from video - would be well suited for these situations. Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the 1st Vision for Vitals Challenge (V4V) presented a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. In this paper, we outline the evaluation protocol, the data used, and the results. V4V is to be held in conjunction with the 2021 International Conference on Computer Vision(1).

A high-performance humidity sensor based on alkalized MXenes and poly(dopamine) for touchless sensing and respiration monitoring

The typical mode of interaction between humans and machines in current intelligent equipment and personalized health care systems is mainly contact-type. However, there are severe problems associated with direct contact, such as uncomfortable wear and cross-infection of bacteria or viruses, especially under global pandemic conditions (e.g., COVID-19, MERS-CoV). In this study, a flexible humidity sensor is developed based on alkalized MXenes and polydopamine (PDA). The unique accordion-like hierarchical structure of the alkalized MXenes with large specific surface area and the chemical structure of the abundant water-absorbing functional groups of PDA wrapped on the MXene surface contribute synergistically to the outstanding performance of the sensor, which has high sensitivity, rapid response, and large detection range. The device was successfully used to control a noncontact switch system based on the distance of the fingertip and monitor different breathing patterns of a volunteer from a long range, proving its potential application in future noncontact human-machine interaction and human physiology monitoring.

Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges

The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users. 2021, Science Press. All right reserved.

Effectiveness of Non-pharmaceutical Interventions on Suppressing the 1st Wave of COVID-19 Epidemic in Shenzhen: An Agent-based Modelling Study

Many cities in China have adopted a series of Non-Pharmaceutical Interventions (NPIs) and rapidly suppressed the 1 st wave of COVID-19 epidemic in 2020. It is critical to evaluate the effectiveness of these NPIs for future epidemic control. However, as a variety of NPIs were applied together in practice, it is difficult to evaluate the effectiveness of a single type of intervention by epidemiological observation. Taking Shenzhen city as an example, this study used a spatially explicit agent-based model by integrating mobile phone location data, travel survey data, building survey data and other multi-source spatiotemporal big data to evaluate the effectiveness of different types of NPIs in the suppression of the 1 st wave of COVID-19 epidemic in Shenzhen. The simulation results show that the peak of the epidemic would have appeared on the 127 th day since Jan 1st of 2020, resulting in an average of 72.26% of the population to be infected without any interventions. In the 1 st wave of Shenzhen epidemic, except for the hospitalization of confirmed cases and intercity traffic restrictions, the stay-at-home order was the most effective one, followed by comprehensive isolation and quarantine measures (for close contacts, imported population and suspected cases), mask wearing, and orderly resumption of work. The stay-at-home order and comprehensive isolation and quarantine measures can effectively control the large-scale outbreak of the COVID-19, which are identified as the core measures;Mask wearing and orderly resumption of work can only reduce the overall infection size and delay the epidemic peak, which are identified as secondary measures. Considering the socioeconomic costs and the receding compliance to interventions in the post-epidemic period, this study suggests that the core measures and secondary measures should be combined to control the sporadic cases. Specifically, the local government can give the highest priority to isolation and quarantine measures for confirmed cases and high-risk individuals, complemented by mask wearing. In addition, our model can reveal the high-risk infection areas at a community level, which can help deploy control measures within an urban environment. In summary, this study demonstrated the advantages of integrating spatiotemporal big data and agent-based models to simulate the spread processes of infectious diseases in an urban environment: it can not only simulate the evolving processes of an epidemic at a fine-grained scale, but also evaluate the effectiveness of the NPIs at an individual level and for activity-travel behaviors, which can be useful for precise intervention. 2021, Science Press. All right reserved.

Analysis and prediction of economic cross-correlation under COVID-19 based on MF-LSTM and WNN

In the era of COVID-19, it is particularly important to analyze the correlation of economic indicators and propose corresponding policies. In this paper, a number of industry indicators that have an important impact on the economy are selected, and normalization, interpolation, and PCA operations are performed on them. Based on the MF-LSTM neural network, this paper analyzes the many-to-one correlation between industry indicators and macroeconomic indicators. Furthermore, based on the WNN neural network, wavelet analysis is used to predict the impact of macroeconomic indicators on people's livelihood indicators under time series. Based on the above model, the coupling relationship between industry indicators and macroeconomic indicators and the development trend of people's livelihood indicators for a period of time in the future have been obtained, and the accuracy of the model has also been verified. © 2021 ACM.

An audit on the diagnosis of primary CNS lymphoma

AIMS: Primary central nervous system lymphoma (PCNSL) is a rare form of non-Hodgkin lymphoma with exclusive manifestations in the central nervous system, leptomeninges and eyes. It forms around 5% of all primary brain tumours. It is an aggressive tumour which has a poor prognosis if left untreated. It is imperative that diagnosis is made timely so treatment can be started promptly. Therefore, we performed an audit looking into the speed of diagnostic process of PCNSL in our tertiary Neuro-oncology Unit. METHOD: Single-centre retrospective review of PCNSL cases referred to a tertiary Neuro-Oncology Unit over a six month period from June to November 2020. RESULTS: A total of 1309 cases were discussed in the Neuro-oncology MDT meeting over the study period. Fourteen cases (6 male, 8 female;median age [range] 66 [59-83] years) were identified as highly likely PCNSL. Neuroimaging suggested PCNSL as the likely diagnosis in twelve patients. Twelve patients were started on steroids after CT or MRI brain scans. Nine patients had a surgical target and proceeded to have diagnostic brain biopsy. Two patients had different working diagnoses and three patients were deemed unsuitable for brain surgery. One patient required repeat brain biopsy. A tissue diagnosis was made in twelve patients. One patient deteriorated rapidly and one patient had a brain lesion that was deemed too high risk for surgery. The median time between neuroimaging and biopsy was 25 days. The median time taken from first investigation to the pathological confirmation of PCNSL was 36 days (range 6-86 days). CONCLUSION: The chief reason for delay in diagnosis of PCNSL was that patients were started on steroids before diagnostic investigations were completed. Steroids caused the brain lesions to become smaller or disappear. Accordingly, time was needed to allow withdrawal of steroids before diagnostic investigations could be repeated. Diagnostic delays may have been exacerbated by logistical issues associated with COVID-19. We propose that there needs to be greater awareness of how early introduction of steroids can markedly delay the diagnosis of PCNSL.

Doses-Based BCS Solubility Classification of Dexamethasone

OBJECTIVE: To study the dose-based BCS (biopharmaceutical classification system) classification of dexamethasone for different indications. METHODS: Saturation shake-flask method was utilized with the conditions of shaking water bath at 37℃ and 120 r•min-1, somewhat excess solids added into saturate systems at pH 1.2, 4.5 and 6.8 buffers respectively. And high-performance liquid chromatography was used for saturation concentration measurement. In this study, the dosages of dexamethasone for both classic and new indications were collected, such as covid-19, which were divided into low-dose, intermediate-dose and high-dose. Then the dissolution volumes (DSVs) were calculated and the indication-based BCS classifications of dexamethasone was studied. RESULTS: At the low-dose, the BCS classification of dexamethasone was high solubility;at the high-dose, the BCS classification of dexamethasone was low;and at the intermediate-dose, the BCS classification of dexamethasone was on the edge of low solubility and high solubility. CONCLUSION: This study provides basic data for the BCS classification of dexamethasone;dose-related solubility classification has guiding significance for the BCS classification of dexamethasone for new indications, and provides refine reference for the rationality of the BE wavier for solid oral dosage forms in the consistency evaluation of generic drugs. Copyright 2021 by the Chinese Pharmaceutical Association.

Satisfaction and reimbursement of telemedicine in head and neck surgery

Introduction: The COVID-19 pandemic accelerated telemedicine efforts throughout otolaryngology. This study examines the impact of telemedicine visits on surgical yield, financial reimbursement, and patient satisfaction within the head and neck division at a tertiary referral center. Method: This is a retrospective review of new head and neck patients seen via telemedicine between January 2020 and December 2020. Chart review was conducted to identify the reason for the visit and surgical yield. Patient satisfaction was assessed using Press-Ganey surveys. Chi-squared tests were used to compare satisfaction scores, and Wilcoxon rank-sum tests and t tests were used to compare reimbursements. Results: In 2020 the head and neck division saw 1157 new patients in-person and 123 new patients via telemedicine. Telemedicine visits led to 52 (42%) surgeries, and 58 (47%) of patients seen were requesting a second opinion. Financial reimbursement data were available for 42 telemedicine and 202 in-person visits. Average reimbursement for telemedicine visits was 86%. No differences were seen between government and commercial payers in the charges (P = .22) and reimbursements (P = .42) for in-person visits. For the telemedicine visits, charges (P < .01) and reimbursements (P = .03) were significantly less for government payers. Of the telemedicine visits, 91% were rated as 'very good' in patients' likeliness to recommend, compared with 94% of in-person visits (P = .30). Some 91% of telemedicine patients felt that the ability of the care team to explain the condition was 'very good,' compared with 92% of in-person patients (P = .83). For 77% and 78% of patients, a 'very good' rating was given on the ease of contacting and scheduling telemedicine visits, which was not significantly different than the 79% and 83% of patients rating in-person visits (P = .90, P =.55). Conclusion: Definitive decisions about surgical planning can be made effectively by patients and providers in a virtual setting. Patient satisfaction rates are generally high for telemedicine visits and similar to those for in-person visits, but reimbursement for telemedicine visits was notably lower in government payers.

Laryngoscope8: Laryngeal image dataset and classification of laryngeal disease based on attention mechanism

Laryngeal disease is a common disease worldwide. However, currently there are no public laryngeal image datasets, which hinders the development of automatic classification of laryngeal disease. In this work, we build a new laryngeal image dataset called Laryngoscope8, which comprises 3057 images of 1950 unique individuals, and the images have been labeled with one of eight labels (including seven pathological labels and one normal label) by professional otolaryngologists. We also propose a laryngeal disease classification method, which uses attention mechanism to obtain the critical area under the supervision of image labels for laryngeal disease classification. That is, we first train a CNN model to classify the laryngeal images. If the classification result is correct, the region with strong response is most likely a critical area. The regions with strong responses are used as training data to train an object localization model that can automatically locate the critical area. Given an image for classification, the trained object localization model is employed to locate the critical area. Then, the located critical area is employed for image classification. The entire process only requires image-level labels and does not require manual labeling of the critical area. Experiment results show that the proposed method achieves promising performance in laryngeal disease classification. (C) 2021 Elsevier B.V. All rights reserved.

[Summary and prospect of early warning models and systems for infectious disease outbreaks].

This paper summarizes the basic principles and models of early warning for infectious disease outbreaks, introduces the early warning systems for infectious disease based on different data sources and their applications, and discusses the application potential of big data and their analysing techniques, which have been studied and used in the prevention and control of COVID-19 pandemic, including internet inquiry, social media, mobile positioning, in the early warning of infectious diseases in order to provide reference for the establishment of an intelligent early warning mechanism and platform for infectious diseases based on multi-source big data.