Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas.

Purpose: For locoregionally recurrent head and neck squamous cell carcinoma (HNSCC), appropriate therapeutic decisions remain unclear. We examined the treatment outcomes of a national cohort to determine suitable treatments for and prognostic factors in patients with locoregionally recurrent HNSCCs at different stages and sites.

Immune interferon (IFN), also known as IFN-γ, promotes not only immunomodulation but also antimicrobial and anticancer activity. After IFN-γ binds to the complex of IFN-γ receptor (IFNGR) 1-IFNGR2 and subsequently activates its downstream signaling pathways, IFN-γ immediately causes transcriptional stimulation of a variety of genes that are principally involved in its biological activities. Regarding IFN-γ-dependent immunosurveillance, IFN-γ can directly suppress tumorigenesis and infection and/or can modulate the immunological status in both cancer cells and infected cells. Regarding the anticancer effects of IFN-γ, cancer cells develop strategies to escape from IFN-γ-dependent cancer immunosurveillance. Immune evasion, including the recruitment of immunosuppressive cells, secretion of immunosuppressive factors, and suppression of cytotoxic T lymphocyte responses, is speculated to be elicited by the oncogenic microenvironment. All of these events effectively downregulate IFN-γ-expressing cells and IFN-γ production. In addition to these extrinsic pathways, cancer cells may develop cellular tolerance that manifests as hyporesponsiveness to IFN-γ stimulation. This review discusses the potential escape mechanisms from IFN-γ-dependent immunosurveillance in tumorigenesis.

Methylibium petroleiphilum PM1 is a methylotroph distinguished by its ability to completely metabolize the fuel oxygenate methyl tert-butyl ether (MTBE). Strain PM1 also degrades aromatic (benzene, toluene, and xylene) and straight-chain (C₅ to C₁₂) hydrocarbons present in petroleum products. Whole-genome analysis of PM1 revealed an ∼4-Mb circular chromosome and an ∼600-kb megaplasmid, containing 3,831 and 646 genes, respectively. Aromatic hydrocarbon and alkane degradation, metal resistance, and methylotrophy are encoded on the chromosome. The megaplasmid contains an unusual t-RNA island, numerous insertion sequences, and large repeated elements, including a 40-kb region also present on the chromosome and a 29-kb tandem repeat encoding phosphonate transport and cobalamin biosynthesis. The megaplasmid also codes for alkane degradation and was shown to play an essential role in MTBE degradation through plasmid-curing experiments. Discrepancies between the insertion sequence element distribution patterns, the distributions of best BLASTP hits among major phylogenetic groups, and the G+C contents of the chromosome (69.2%) and plasmid (66%), together with comparative genome hybridization experiments, suggest that the plasmid was recently acquired and apparently carries the genetic information responsible for PM19s ability to degrade MTBE. Comparative genomic hybridization analysis with two PM1-like MTBE-degrading environmental isolates (∼99% identical 16S rRNA gene sequences) showed that the plasmid was highly conserved (ca. 99% identical), whereas the chromosomes were too diverse to conduct resequencing analysis. PM19s genome sequence provides a foundation for investigating MTBE biodegradation and exploring the genetic regulation of multiple biodegradation pathways in M. petroleiphilum and other MTBE-degrading beta-proteobacteria.

This study aims to investigate the controversial association between metformin use and diabetes-associated dementia in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate the potential protective effects of metformin, as well as its intensity of use and dose-dependency, against dementia in this population. The study employed a time-dependent Cox hazards model to evaluate the effect of metformin use on the incidence of dementia. The case group included elderly patients with T2DM (≥60 years old) who received metformin, while the control group consisted of elderly patients with T2DM who did not receive metformin during the follow-up period. Our analysis revealed a significant reduction in the risk of dementia among elderly individuals using metformin, with an adjusted hazard ratio (aHR) of 0.34 (95% CI: 0.33 to 0.36). Notably, metformin users with a daily intensity of 1 defined daily dose (DDD) or higher had a lower risk of dementia, with an aHR (95% CI) of 0.46 (0.22 to 0.6), compared to those with a daily intensity of less than 1 DDD. Additionally, the analysis of cumulative defined daily doses (cDDD) of metformin showed a dose-response relationship, with progressively lower aHRs across quartiles (0.15, 0.21, 0.28, and 0.53 for quartiles 4, 3, 2, and 1, respectively), compared to never metformin users (P for trend < 0.0001). Metformin use in elderly patients with T2DM is significantly associated with a substantial reduction in the risk of dementia. Notably, the protective effect of metformin demonstrates a dose-dependent relationship, with higher daily and cumulative dosages of metformin showing a greater risk reduction.