Genome Sequence of the Pathogenic Intestinal Spirochete Brachyspira hyodysenteriae Reveals Adaptations to Its Lifestyle in the Porcine Large Intestine

Brachyspira hyodysenteriae is an anaerobic intestinal spirochete that colonizes the large intestine of pigs and causes swine dysentery, a disease of significant economic importance. The genome sequence of B. hyodysenteriae strain WA1 was determined, making it the first representative of the genus Brachyspira to be sequenced, and the seventeenth spirochete genome to be reported. The genome consisted of a circular 3,000,694 base pair (bp) chromosome, and a 35,940 bp circular plasmid that has not previously been described. The spirochete had 2,122 protein-coding sequences. Of the predicted proteins, more had similarities to proteins of the enteric Escherichia coli and Clostridium species than they did to proteins of other spirochetes. Many of these genes were associated with transport and metabolism, and they may have been gradually acquired through horizontal gene transfer in the environment of the large intestine. A reconstruction of central metabolic pathways identified a complete set of coding sequences for glycolysis, gluconeogenesis, a non-oxidative pentose phosphate pathway, nucleotide metabolism, lipooligosaccharide biosynthesis, and a respiratory electron transport chain. A notable finding was the presence on the plasmid of the genes involved in rhamnose biosynthesis. Potential virulence genes included those for 15 proteases and six hemolysins. Other adaptations to an enteric lifestyle included the presence of large numbers of genes associated with chemotaxis and motility. B. hyodysenteriae has diverged from other spirochetes in the process of accommodating to its habitat in the porcine large intestine

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting

Detection of Toscana virus from an adult traveler returning to Australia with encephalitis

Toscana virus (TOSV) is identified in sandflies, animals, and humans around the Mediterranean Sea. TOSV has not been reported in Australia. During investigations of cerebrospinal fluid samples from patients with encephalitis, TOSV genetic sequences were identified in a traveler returning to Australia from Europe. TOSV should be considered, especially during May to October, in travelers to Australia who embarked in countries in and around the Mediterranean Sea and who subsequently present for medical care because of neurological symptoms

Toscana virus tree showing H.sapiens-wt/Italy/2014/Genotype-A_BB8 segments detected in a returned Australian traveller

Toscana virus (TOSV) is identified in sandflies, animals and humans around the Mediterranean Sea. TOSV has not been reported in Australia. During investigations of cerebrospinal fluid samples from patients with encephalitis, a TOSV was identified in a traveller returning to Australia from Europe. <br>TOSV should be considered, especially during May to October, in travellers to Australia who embarked in countries in and around the Mediterranean Sea and who subsequently present for medical care because of neurological symptoms.<br><div><p>LEGEND.</p><p>Phylogenetic representation of complete and near complete Toscana virus (TOSV) lineage L (1a), M (1b) and S (1c) gene nucleotide segments compared to a Sandfly fever Naples virus (SFNV). Lineage A and B strains are indicated by blue (solid outline) and green boxes (dashed outline), respectively. A circle indicates the TOSV strain H.sapiens-wt/Italy/2014/Genotype-A_BB8, described here. MEGA7 was used to construct Neighbor-Joining trees with distances calculated using the p-distance method (500 bootstraps; percentage replicate tree branching patterns are shown next to initial branches). </p></div

Murine and related chapparvoviruses are nephro-tropic and produce novel accessory proteins in infected kidneys.

Mouse kidney parvovirus (MKPV) is a member of the provisional genus Chapparvovirus that causes renal disease in immune-compromised mice, with a disease course reminiscent of polyomavirus-associated nephropathy in immune-suppressed kidney transplant patients. Here we map four major MKPV transcripts, created by alternative splicing, to a common initiator region, and use mass spectrometry to identify "p10" and "p15" as novel chapparvovirus accessory proteins produced in MKPV-infected kidneys. p15 and the splicing-dependent putative accessory protein NS2 are conserved in all near-complete amniote chapparvovirus genomes currently available (from mammals, birds and a reptile). In contrast, p10 may be encoded only by viruses with >60% amino acid identity to MKPV. We show that MKPV is kidney-tropic and that the bat chapparvovirus DrPV-1 and a non-human primate chapparvovirus, CKPV, are also found in the kidneys of their hosts. We propose, therefore, that many mammal chapparvoviruses are likely to be nephrotropic

Genetic, morphological and antigenic relationships between mesonivirus isolates from Australian mosquitoes and evidence for their horizontal transmission

The Mesoniviridae are a newly assigned family of viruses in the order Nidovirales. Unlike other nidoviruses, which include the Coronaviridae, mesoniviruses are restricted to mosquito hosts and do not infect vertebrate cells. To date there is little information on the morphological and antigenic characteristics of this new group of viruses and a dearth of mesonivirus-specific research tools. In this study we determined the genetic relationships of recent Australian isolates of Alphamesonivirus 4 (Casuarina virus—CASV) and Alphamesonivirus 1 (Nam Dinh virus—NDiV), obtained from multiple mosquito species. Australian isolates of NDiV showed high-level similarity to the prototype NDiV isolate from Vietnam (99% nucleotide (nt) and amino acid (aa) identity). Isolates of CASV from Central Queensland were genetically very similar to the prototype virus from Darwin (95–96% nt and 91–92% aa identity). Electron microscopy studies demonstrated that virion diameter (≈80 nm) and spike length (≈10 nm) were similar for both viruses. Monoclonal antibodies specific to CASV and NDiV revealed a close antigenic relationship between the two viruses with 13/34 mAbs recognising both viruses. We also detected NDiV RNA on honey-soaked nucleic acid preservation cards fed on by wild mosquitoes supporting a possible mechanism of horizontal transmission between insects in nature

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting