BackgroundMaking the diagnosis of acute appendicitis is difficult, and is important for preventing perforation of the appendix and negative appendectomy results. Ultrasound and clinical scoring systems are very helpful in making the diagnosis. Ultrasound is non-invasive, available and cost-effective, and can accomplish more than CT scans. However, there is no certainty about its effect on the clinical outcomes of patients, and it is operator dependent. Counting the neutrophils as a parameter of the Alvarado Scale is not routine in many laboratories, so we decided to evaluate the diagnostic value of the Modified Alvarado Scaling System (MASS) by omitting the neutrophil count and ultrasonography.MethodsAfter ethical approval of methodology in Tehran University of Medical Sciences ethical committee, we collected the data. During 9 months, 75 patients with right lower quadrant pain were enrolled in the study, and underwent abdominal ultrasonography and appendectomy, with pathological evaluation of the appendix. The MASS score was calculated for these patients and compared with pathology results.ResultsFifty-five male and 20 female patients were assessed. Of these patients 89.3% had acute appendicitis. The sensitivity, specificity, PPV, NPV and accuracy rate of ultrasonography was 71.2%, 83.3%, 97.4%, 25% and 72.4%, respectively. By taking a cutoff point of 7 for the MASS score, a sensitivity of 65.7%, specificity of 37.5%, PPV of 89.8%, NPV of 11.5% and accuracy of 62.7% were calculated. Using the cutoff point of 6, a sensitivity of 85.1%, specificity of 25%, PPV of 90.5%, NPV of 16.7% and accuracy of 78.7% were obtained.ConclusionUltrasound provides reliable findings for helping to diagnose acute appendicitis in our hospital. A cutoff point of 6 for the MASS score will yield more sensitivity and a better diagnosis of appendicitis, though with an increase in negative appendectomy.

Borrelia burgdorferi is a tick-borne bacterium responsible for approximately 300,000 annual cases of Lyme disease (LD) in the United States, with increasing incidences in other parts of the world. The debilitating nature of LD is mainly attributed to the ability of B. burgdorferi to persist in patients for many years despite strong anti-Borrelia antibody responses. Antimicrobial treatment of persistent infection is challenging. Similar to infection of humans, B. burgdorferi establishes long-term infection in various experimental animal models except for New Zealand White (NZW) rabbits, which clear the spirochete within 4 to 12 weeks. LD spirochetes have a highly evolved antigenic variation vls system, on the lp28-1 plasmid, where gene conversion results in surface expression of the antigenically variable VlsE protein. VlsE is required for B. burgdorferi to establish persistent infection by continually evading otherwise potent antibodies. Since the clearance of B. burgdorferi is mediated by humoral immunity in NZW rabbits, the previously reported results that LD spirochetes lose lp28-1 during rabbit infection could potentially explain the failure of B. burgdorferi to persist. However, the present study unequivocally disproves that previous finding by demonstrating that LD spirochetes retain the vls system. However, despite the vls system being fully functional, the spirochete fails to evade anti-Borrelia antibodies of NZW rabbits. In addition to being protective against homologous and heterologous challenges, the rabbit antibodies significantly ameliorate LD-induced arthritis in persistently infected mice. Overall, the current data indicate that NZW rabbits develop a protective antibody repertoire, whose specificities, once defined, will identify potential candidates for a much-anticipated LD vaccine.

No abstract

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy --- the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), Gamma and Zeta (Brazil) variants in the GISAID dataset. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large datasets.