Nanopore sequencing has been widely used for the reconstruction of microbial genomes. Owing to higher error rates, errors on the genome are corrected via neural networks trained by Nanopore reads. However, the systematic errors usually remain uncorrected. This paper designs a model that is trained by homologous sequences for the correction of Nanopore systematic errors. The developed program, Homopolish, outperforms Medaka and HELEN in bacteria, viruses, fungi, and metagenomic datasets. When combined with Medaka/HELEN, the genome quality can exceed Q50 on R9.4 flow cells. We show that Nanopore-only sequencing can produce high-quality microbial genomes sufficient for downstream analysis.

Respiratory syncytial virus (RSV) is one of the most common respiratory viruses. It not only affects young children but also the elderly and immunocompromised patients. After the emergence of SARS-CoV-2 and the corona virus disease 2019 (COVID-19) era, a dramatic reduction in RSV activity was found, which coincided with the implementation of public health and social measures (PHSMs). However, the correlation is more complicated than we initially thought. After PHSMs were gradually lifted, a seasonality shift and a delayed RSV outbreak with greater number of infected patients were found in numerous countries, such as Israel, Australia, South Africa, New Zealand, France, United States, and Japan. Several hypotheses and possible reasons explaining the interaction between SARS-CoV-2 and RSV were mentioned. Since RSV vaccinations are still under investigation, administration of palivizumab should be considered in high-risk patients. In the post-COVID-19 era, greater attention should be paid to a further resurgence of RSV. In this narrative review, we conducted a thorough review of the current knowledge on the epidemiology of RSV during the COVID-19 era, the out-of-season outbreak of RSV, and the data on co-infection with RSV and SARS-CoV-2.

We aimed to investigate the association of gut microbiota with disease activity, inflammatory parameters, and auto-antibodies profile in rheumatoid arthritis (RA). A total of 138 RA patients and 21 healthy controls (HC) were enrolled. Fecal samples were collected for bacterial DNA extraction and 16S ribosome (r)RNA sequencing, followed by analyses of gut microbiota composition. Serum levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-17A were determined by using ELISA. Our results indicated that RA patients had lower diversity index, which reflects both evenness and richness of gut microbiota, compared to HC. The alpha-diversity was lower in anti-citrullinated peptide antibodies (ACPA)-positive patients than in HC. The phylum Verrucomicrobiae and genus Akkermansia were more abundant in patients compared to HC. There was increased relative abundance of Enterobacteriaceae as well as Klebsiella, and less abundance of Bifidobacterium in patients with high levels of TNF-α or IL-17A compared to those who had low levels of these cytokines. In addition, ACPA-positive patients had higher proportions of Blautia, Akkermansia, and Clostridiales than ACPA-negative patients. Gut dysbiosis in RA patients was presented as different microbial composition and its association with inflammatory parameters as well as ACPA seropositivity. These findings support the involvement of gut microbiota in RA pathogenesis.

The emergence of carbapenem-resistant Enterobacterales (CRE) has become a major public health concern. Moreover, its colonization among residents of long-term care facilities (LTCFs) is associated with subsequent infections and mortality. To further explore the various aspects concerning CRE in LTCFs, we conducted a literature review on CRE colonization and/or infections in long-term care facilities. The prevalence and incidence of CRE acquisition among residents of LTCFs, especially in California, central Italy, Spain, Japan, and Taiwan, were determined. There was a significant predominance of CRE in LTCFs, especially in high-acuity LTCFs with mechanical ventilation, and thus may serve as outbreak centers. The prevalence rate of CRE in LTCFs was significantly higher than that in acute care settings and the community, which indicated that LTCFs are a vital reservoir for CRE. The detailed species and genomic analyses of CRE among LTCFs reported that Klebsiella pneumoniae is the primary species in the LTCFs in the United States, Spain, and Taiwan. KPC-2-containing K. pneumoniae strains with sequence type 258 is the most common sequence type of KPC-producing K. pneumoniae in the LTCFs in the United States. IMP-11- and IMP-6-producing CRE were commonly reported among LTCFs in Japan. OXA-48 was the predominant carbapenemase among LTCFs in Spain. Multiple risk factors associated with the increased risk for CRE acquisition in LTCFs were found, such as comorbidities, immunosuppressive status, dependent functional status, usage of gastrointestinal devices or indwelling catheters, mechanical ventilation, prior antibiotic exposures, and previous culture reports. A high CRE acquisition rate and prolonged CRE carriage duration after colonization were found among residents in LTCFs. Moreover, the patients from LTCFs who were colonized or infected with CRE had poor clinical outcomes, with a mortality rate of up to 75% in infected patients. Infection prevention and control measures to reduce CRE in LTCFs is important, and could possibly be controlled via active surveillance, contact precautions, cohort staffing, daily chlorhexidine bathing, healthcare-worker education, and hand-hygiene adherence.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.