Assessment of antibody responses against SARS-CoV-2 in unvaccinated individuals and vaccinees from Omicron-BA.2 infection in Zhaoqing, Guangdong Province, China.

Currently, the majority of the global population has been vaccinated with the COVID-19 vaccine, and characterization studies of antibodies in vivo from Omicron breakthrough infection and naive infection populations are urgently needed to provide pivotal clues about accurate diagnosis, treatment, and next-generation vaccine design against SARS-CoV-2 infection. We showed that after infection with Omicron-BA.2, the antibody levels of specific IgM against the Wuhan strain and specific IgG against Omicron were not significantly elevated within 27 days of onset. Interestingly, in this study, the levels of humoral immunity against Omicron-specific IgM were significantly increased after breakthrough infection, suggesting that the detection of Omicron-specific IgM antibodies can be used as a test criterion of Omicron breakthrough infection. In addition, we observed that serums from unvaccinated individuals and the majority of vaccinated infections possessed only low or no neutralizing activity against Omicron at the onset of Omicron breakthrough infections, and at the later stage of Omicron-BA.2 breakthrough infection, levels of neutralization antibody against the Wuhan and Omicron strains were elevated in infected individuals. The findings of this study provide important clues for the diagnosis of Omicron breakthrough infections, antibody characterization studies and vaccine design against COVID-19.

Molecular Epidemiological Analysis of ST11-K64 Extensively Drug-Resistant Klebsiella pneumoniae Infections Outbreak in Intensive Care and Neurosurgery Units Based on Whole-Genome Sequencing.

Klebsiella pneumoniae (Kp) is the primary causative bacteria for nosocomial infections and hospital outbreaks. In particular, extensively drug-resistant K. pneumoniae (XDRKp) causes severe clinical infections in hospitalized patients. Here, we used pulsed-field gel electrophoresis (PFGE), drug susceptibility tests, and the whole-genome sequencing (WGS) technology to examine genetic relatedness and phenotypic traits of the strains isolated during an outbreak period. Based on PFGE, a distinct clones cluster comprised of eight XDRKp was observed. These strains were confirmed as ST11-K64 via multiple-locus sequence typing database of Kp. The strains also had genes related to the regulation of biofilm biosynthesis (type 1 & 3 fimbriae, type IV pili biosynthesis, RcsAB, and type VI secretion system) and multiple drug resistance (ß-lactamase and aminoglycoside antibiotic resistance). WGS data based on core-single nucleotide polymorphisms and epidemiological investigation showed that the neurosurgery unit was likely the source of the outbreak, the strain was likely to have been transmitted to the ICU through patients. In addition, the two highly probable transmission routes were in the ICU (exposure through shared hospital beds) and the neurosurgery units (all cases were treated by the same rehabilitation physician and were most likely infected during the physical therapy). Notably, the bed mattress had played a crucial transmission role of this outbreak, served as a pathogen reservoir.

A priori knowledge and probability density based segmentation method for medical CT image sequences.

This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.

A hybrid method for pancreas extraction from CT image based on level set methods.

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.