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PurposeThis research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.Design/methodology/approachThis paper adopts a two-state Markov switching model to characterize the bull and bear markets spanning from 1994 to 2019 and then conduct a bear stock market predictability test by running regressions between the filtered probabilities of bear markets and a series of macroeconomic variables in turn at different horizons of 1, 3, 6, 12 and 24 months.FindingsThis paper shows that inflation rates, changes in real exchange rates, and foreign currency reserve growth are key predictors of bear markets in China, while term spreads, unemployment rates and foreign reserve growth are major factors that can predict bear markets in Taiwan. Remarkably, industrial production growth does not have predictive power for bear markets, which may suggest emerging markets are driven by fund flows rather than real economic activities. Besides, the impact directions of foreign currency reserve growth are opposite, which may be due to different proportions of the financial accounts in their balance of payments.Practical implicationsIn practical respect, this paper provides market participants the usefulness, impact direction and implications of bear market predictors when building their market-timing strategies in China and Taiwan stock markets. The government institutions may also thereby make appropriate policies to prevent huge stock market downturns and serious drawbacks.Originality/valueIt highlights the “fund-driven market hypothesis” and “foreign currency reserve effects” that commonly dominate Taiwan and China stock markets since both are highly affected by international funds.

Background. Dengue is the most common arboviral infection of humans. There are currently no specific treatments for dengue. Balapiravir is a prodrug of a nucleoside analogue (called R1479) and an inhibitor of hepatitis C virus replication in vivo. Methods. We conducted in vitro experiments to determine the potency of balapiravir against dengue viruses and then an exploratory, dose-escalating, randomized placebo-controlled trial in adult male patients with dengue with <48 hours of fever. Results. The clinical and laboratory adverse event profile in patients receiving balapiravir at doses of 1500 mg (n = 10) or 3000 mg (n = 22) orally for 5 days was similar to that of patients receiving placebo (n = 32), indicating balapiravir was well tolerated. However, twice daily assessment of viremia and daily assessment of NS1 antigenemia indicated balapiravir did not measurably alter the kinetics of these virological markers, nor did it reduce the fever clearance time. The kinetics of plasma cytokine concentrations and the whole blood transcriptional profile were also not attenuated by balapiravir treatment. Conclusions. Although this trial, the first of its kind in dengue, does not support balapiravir as a candidate drug, it does establish a framework for antiviral treatment trials in dengue and provides the field with a clinically evaluated benchmark molecule. Clinical Trials Registration. NCT01096576.

One of the common problems in strawberry (Fragaria × ananassa) micropropagation is the vitrification phenomenon (succulent plantlets, brittle stems, yellow leaves, etc.) leading to the reduction of plantlets quality and low survival rate in the greenhouse. In this study, the effects of silver nanoparticles (AgNPs) on explant disinfection, in vitro growth (shoot multiplication, and root formation), runner formation as well as ethylene accumulation during micropropagation of strawberry were investigated. The results showed that leaf explants treated with 200 mg/L AgNPs solution for 20 min was more effective in explant disinfection and shoot regeneration than using 1 g/L HgCl2. In addition, AgNPs stimulated the growth of shoot and plantlet and as well shortened the duration of root formation (4 days) as compared to those in control without AgNPs during micropropagation. Besides, AgNPs reduced the ethylene gas accumulation in the culture’s vessels of shoots (0.66 ppm) and plants (0.06 ppm) compared to controls (1.77 ppm; 0.15 ppm; respectively). Moreover, AgNPs combination with culture period (5; 10 or 15 days) effect root formation stage and acclimatization in the greenhouse. The plantlets that cultured on MS medium supplemethed with 0.5 mg/L AgNPs during 10 days showed higher survival rate (93.33%) after 15 days as well as runner formation per plant (8.00 runners) after 60 days in greenhouse than those in control.Key messageAgNPs improved explant disinfection and in vitro growth. AgNPs improved runner formation in the greenhouse. AgNPs limited ethylene accumulation during micropropagation.

Studies of successive vaccination suggest that immunological memory against past influenza viruses may limit responses to vaccines containing current strains. The impact of memory induced by prior infection is rarely considered and is difficult to ascertain, because infections are often subclinical. This study investigated influenza vaccination among adults from the Ha Nam cohort (Vietnam), who were purposefully selected to include 72 with and 28 without documented influenza A(H3N2) infection during the preceding 9 years (Australian New Zealand Clinical Trials Registry 12621000110886). The primary outcome was the effect of prior influenza A(H3N2) infection on hemagglutinin-inhibiting antibody responses induced by a locally available influenza vaccine administered in November 2016. Baseline and postvaccination sera were titrated against 40 influenza A(H3N2) strains spanning 1968–2018. At each time point (baseline, day 14 and day 280), geometric mean antibody titers against 2008–2018 strains were higher among participants with recent infection (34 (29–40), 187 (154–227) and 86 (72–103)) than among participants without recent infection (19 (17–22), 91 (64–130) and 38 (30–49)). On days 14 and 280, mean titer rises against 2014–2018 strains were 6.1-fold (5.0- to 7.4-fold) and 2.6-fold (2.2- to 3.1-fold) for participants with recent infection versus 4.8-fold (3.5- to 6.7-fold) and 1.9-fold (1.5- to 2.3-fold) for those without. One of 72 vaccinees with recent infection versus 4 of 28 without developed symptomatic A(H3N2) infection in the season after vaccination ( P  = 0.021). The range of A(H3N2) viruses recognized by vaccine-induced antibodies was associated with the prior infection strain. These results suggest that recall of immunological memory induced by prior infection enhances antibody responses to inactivated influenza vaccine and is important to attain protective antibody titers. Recent prior influenza A infection is associated with elevated hemagglutinin-inhibiting antibody responses and greater breadth of reactivity to influenza strains following vaccination, suggesting that infection history boosts vaccine responses.

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Background Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. Methods Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. Results Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. Conclusions Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Background Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in  a low resource ICU. Methods This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool. Results The average accuracy of beginners’ LUS interpretation was 68.7% [95% CI 66.8–70.7%] compared to 72.2% [95% CI 70.0–75.6%] in intermediate, and 73.4% [95% CI 62.2–87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2–100.0%], which was significantly better than beginners, intermediate and advanced users ( p  < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6–73.9%] to 82.9% [95% CI 79.1–86.7%], ( p  < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9–78.2%] to 93.4% [95% CI 89.0–97.8%], ( p  < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5–20.6) to 5.0 s (IQR 3.5–8.8), ( p  < 0.001) and clinicians’ median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool. Conclusions AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.

This paper employs a range of carefully controlled experiments to develop a detailed understanding of the role of the structure, crystallinity, and chemical composition of polytetrafluoroethylene (PTFE) in driving catalytic reactions during sonication. The new findings demonstrate the significantly enhanced production of hydrogen, hydrogen peroxide, carbon monoxide, and nitrate from water, CO2, and nitrogen in the presence of PTFE during the application of ultrasound. The critical role of PTFE in the degradation of Rhodamine B and para‐nitrophenol, which are important examples of synthetic dyes and nitroaromatic compounds, respectively is demonstrated. By understanding the mechanism and optimization of the catalytic conditions, the system achieves the highest hydrogen production yield reported to date among tribocatalytic, contact‐electrocatalytic, and piezocatalytic systems, where fine‐scale PTFE particles formed during ultrasound contribute to the enhanced activity. Importantly, the impact of PTFE's physical and chemical properties, including hydrophobicity, crystallinity, and atomic composition, on its catalytic performance is investigated. The underlying mechanism of sono‐contact‐electrocatalysis is outlined by examining reactive species generated under various gas environments. These findings provide new insights into the broad applicability of PTFE in redox reactions and highlight key factors influencing its catalytic behavior in aqueous systems for environmental remediation and energy conversion. This paper employs a range of carefully controlled experiments to understand the role of the structure, crystallinity, and chemical composition of polytetrafluoroethylene (PTFE) in driving catalytic reactions during sonication. These findings provide new insights into the applicability of PTFE in redox reactions and highlight key factors influencing its catalytic behavior in aqueous systems for environmental remediation and energy conversion.

A rapidly aging population, a shifting disease burden and the ongoing threat of infectious disease outbreaks pose major concerns for Vietnam's health care system. Health disparities are evident in many parts of the country, especially in rural areas, and the population faces inequitable access to patient-centered health care. Vietnam must therefore explore and implement advanced solutions to the provision of patient-centered care, with a view to reducing pressures on the health care system simultaneously. The use of digital health technologies (DHTs) may be one of these solutions. This study aimed to identify the application of DHTs to support the provision of patient-centered care in low- and middle-income countries in the Asia-Pacific region (APR) and to draw lessons for Vietnam. A scoping review was undertaken. Systematic searches of 7 databases were conducted in January 2022 to identify publications on DHTs and patient-centered care in the APR. Thematic analysis was conducted, and DHTs were classified using the National Institute for Health and Care Excellence evidence standards framework for DHTs (tiers A, B, and C). Reporting was in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Of the 264 publications identified, 45 (17%) met the inclusion criteria. The majority of the DHTs were classified as tier C (15/33, 45%), followed by tier B (14/33, 42%) and tier A (4/33, 12%). At an individual level, DHTs increased accessibility of health care and health-related information, supported individuals in self-management, and led to improvements in clinical and quality-of-life outcomes. At a systems level, DHTs supported patient-centered outcomes by increasing efficiency, reducing strain on health care resources, and supporting patient-centered clinical practice. The most frequently reported enablers for the use of DHTs for patient-centered care included alignment of DHTs with users' individual needs, ease of use, availability of direct support from health care professionals, provision of technical support as well as user education and training, appropriate governance of privacy and security, and cross-sectorial collaboration. Common barriers included low user literacy and digital literacy, limited user access to DHT infrastructure, and a lack of policies and protocols to guide the implementation and use of DHTs. The use of DHTs is a viable option to increase equitable access to quality, patient-centered care across Vietnam and simultaneously reduce pressures on the health care system. Vietnam can take advantage of the lessons learned by other low- and middle-income countries in the APR when developing a national road map to digital health transformation. Recommendations that Vietnamese policy makers may consider include emphasizing stakeholder engagement, strengthening digital literacy, supporting the improvement of DHT infrastructure, increasing cross-sectorial collaboration, strengthening governance of cybersecurity, and leading the way in DHT uptake.

Attention deficit hyperactivity disorder (ADHD) is a mental health disorder commonly in children. This study aimed to examine the prevalence of ADHD and risk factors among first-year pupils in Vietnam's urban city. A cross-sectional study was conducted in four randomly selected primary schools. Information on 525 pupils in grade 1 (ages 6 to 7 years) was collected from 525 parents/caregivers and 28 teachers. We used the Vanderbilt Assessment Scales with two separate versions for parents and teachers to screen children with ADHD symptoms. Among the total of 525 pupils, 24 (4.6%) were found to have ADHD symptom types (boy: 6.5%; girl: 2.1%). The combined ADHD type accounted for the highest proportion of 3.4%, followed by predominantly inattentive and predominantly hyperactivity type. ADHD prevalence rated by teachers was higher than those rated by parents. High agreement between parents and teachers was reported (κ > 0.6). The risk of ADHD increased in male participants (aOR=4.90, 95% CI 1.51-15.85), those having a first-degree relative with ADHD (aOR=85.2, 95% CI 1.66-4371.8), in-utero exposure to tobacco smoke (aOR=4.78, 95% CI 1.31-17.44), and prenatal alcohol drinking (aOR=8.87, 95% CI 2.29-34.42). These findings suggest the importance of ADHD screening for pupils attending elementary schools, particularly those with a family history of ADHD. Public health programs should reduce prenatal exposure to the potential risk factors of ADHD (smoking and alcohol consumption).