Complete genome sequence of Selenomonas species strain TAMA-11512, isolated from blood culture of a septic patient.

We report the complete sequence of Selenomonas species strain TAMA-11512, isolated from the blood culture of a septic patient. The phylogeny and average nucleotide identity show that the strain TAMA-11512 is considered a novel bacterial species in Selenomonas genus.

Effectiveness of primary series, first, and second booster vaccination of monovalent mRNA COVID-19 vaccines against symptomatic SARS-CoV-2 infections and severe diseases during the SARS-CoV-2 omicron BA.5 epidemic in Japan: vaccine effectiveness real-time surveillance for SARS-CoV-2 (VERSUS).

BACKGROUND: This study aimed to evaluate VE of primary, first, and second booster ancestral-strain monovalent mRNA COVID-19 vaccination against symptomatic infections and severe diseases in Japan. METHODS: We conducted a test-negative case-control study. We included medically attended episodes and hospitalizations involving individuals aged ≥16 with signs and symptoms from July to November 2022, when Omicron BA.5 was dominant nationwide. To evaluate VE, we calculated adjusted ORs of vaccination among test-positive versus test-negative individuals using a mixed-effects logistic regression. RESULTS: For VE against symptomatic infections among individuals aged 16 to 59, VE of primary vaccination at > 180 days was 26.1% (95% CI: 10.6-38.8%); VE of the first booster was 58.5% (48.4-66.7%) at ≤90 days, decreasing to 41.1% (29.5-50.8%) at 91 to 180 days. For individuals aged ≥60, VE of the first booster was 42.8% (1.7-66.7%) at ≤90 days, dropping to 15.4% (-25.9-43.2%) at 91 to 180 days, and then increasing to 44.0% (16.4-62.5%) after the second booster. For VE against severe diseases, VE of the first and second booster was 77.3% (61.2-86.7%) at ≤90 days and 55.9% (23.4-74.6%) afterward. CONCLUSION: mRNA booster vaccination provided moderate protection against symptomatic infections and high-level protection against severe diseases during the BA.5 epidemic in Japan.

Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

BACKGROUND: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). METHODS: We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. RESULTS: In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. CONCLUSIONS: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.