In Vitro Assessment of Yeasts Strains with Probiotic Attributes for Aquaculture Use

This study aimed to investigate in vitro the probiotic potential of three yeasts strains (BB06, OBT05, and MT07) isolated from agro-food natural sources. Screening was performed, including several functional, technological, and safety aspects of the yeast strains, in comparison to a reference Saccharomyces boulardii, to identify the ones with suitable probiotic attributes in aquaculture. The yeast strains were identified by 5.8S rDNA-ITS region sequencing as Metschnikowia pulcherrima OBT05, Saccharomyces cerevisiae BB06, and Torulaspora delbrueckii MT07. All yeast strains were tolerant to different temperatures, sodium chloride concentrations, and wide pH ranges. S. cerevisiae BB06 showed a strong and broad antagonistic activity. Moreover, the S. cerevisiae strain exhibited a high auto-aggregation ability (92.08 ± 1.49%) and good surface hydrophobicity to hexane as a solvent (53.43%). All of the yeast strains have excellent antioxidant properties (>55%). The high survival rate in the gastrointestinal tract (GIT) can promote yeast isolates as probiotics. All yeast strains presented a resistance pattern to the antibacterial antibiotics. Non-hemolytic activity was detected. Furthermore, freeze-drying with cryoprotective agents maintained a high survival rate of yeast strains, in the range of 74.95−97.85%. According to the results obtained, the S. cerevisiae BB06 strain was found to have valuable probiotic traits.

Prevention and Control of Porcine Epidemic Diarrhea: The Development of Recombination-Resistant Live Attenuated Vaccines

Porcine epidemic diarrhea (PED), causing up to 100% mortality in neonatal pigs, is a highly contagious enteric disease caused by PED virus (PEDV). The highly virulent genogroup 2 (G2) PEDV emerged in 2010 and has caused huge economic losses to the pork industry globally. It was first reported in the US in 2013, caused country-wide outbreaks, and posed tremendous hardship for many pork producers in 2013–2014. Vaccination of pregnant sows/gilts with live attenuated vaccines (LAVs) is the most effective strategy to induce lactogenic immunity in the sows/gilts and provide a passive protection via the colostrum and milk to suckling piglets against PED. However, there are still no safe and effective vaccines available after about one decade of endeavor. One of the biggest concerns is the potential reversion to virulence of an LAV in the field. In this review, we summarize the status and the major obstacles in PEDV LAV development. We also discuss the function of the transcriptional regulatory sequences in PEDV transcription, contributing to recombination, and possible strategies to prevent the reversion of LAVs. This article provides insights into the rational design of a promising LAV without safety issues.

COVID-19 mRNA Vaccines Are Generally Safe in the Short Term: A Vaccine Vigilance Real-World Study Says

Background

The prophylactic vaccination of COVID-19 mRNA vaccines is the first large-scale application of this kind in the human world. Over 1.8 million doses of the COVID-19 vaccine had been administered in the US until December 2020, and around 0.2% submitted AE reports to the Vaccine Adverse Event Reporting System (VAERS). This study aimed to evaluate the AEs following immunization (AEFIs) and analyze the potential associations based on the information from the VAERS database.

Methods

We searched the VAERS database recorded AEFIs after COVID-19 vaccines in December 2020. After data mapping, we summarized demographic and clinical features of reported cases. Fisher exact test was used to comparing the clinical characteristics among AE groups with an anaphylactic response, concerning neurological disorders and death.

Results

VAERS reported 3,908 AEFIs of COVID-19 vaccines in December 2020. Most (79.68%) were reported after the first dose of the vaccine. Among the reported cases, we found that general disorders (48.80%), nervous system disorders (46.39%), and gastrointestinal disorders (25.54%) were the most common AEFIs. The allergy history was more frequent in vaccine recipients with anaphylactic reactions than those without (64.91% vs. 49.62%, OR = 1.88, P <0.017). History of anxiety or depression was more common in subjects reporting severe neurological AEFIs than those reporting other AEFIs (18.37% vs. 7.85%, OR = 2.64, P <0.017). Cases reporting death were significantly older (79.36 ± 10.41-year-old vs. 42.64 ± 12.55-year-old, P <0.01, 95% CI 29.30-44.15) and more likely experienced hypertension (50.00% vs. 11.42%, OR = 7.76, P <0.01) and neurological disorders (50.00% vs. 5.36%, OR = 17.65, P <0.01) than other vaccine recipients. The outpatient and emergency room visit rates were 11.92 and 22.42% for AEFIs, and 2.53% of cases needed hospitalization.

Conclusion

AEFIs of COVID-19 mRNA vaccines were generally non-severe local or systemic reactions. A prior allergy history is the risk factor for anaphylaxis, while a history of anxiety may link with severe neurological AEs. Such vaccine recipients need further evaluation and monitor.

Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study

Background

Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy.

Objective

In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being.

Methods

This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared.

Results

Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021.

Conclusions

iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers.

Trial Registration

UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284

International Registered Report Identifier (IRRID)

DERR1-10.2196/24799