High-Resolution Melting Genotyping of Enterococcus faecium Based on Multilocus Sequence Typing Derived Single Nucleotide Polymorphisms

We have developed a single nucleotide polymorphism (SNP) nucleated high-resolution melting (HRM) technique to genotype Enterococcus faecium. Eight SNPs were derived from the E. faecium multilocus sequence typing (MLST) database and amplified fragments containing these SNPs were interrogated by HRM. We tested the HRM genotyping scheme on 85 E. faecium bloodstream isolates and compared the results with MLST, pulsed-field gel electrophoresis (PFGE) and an allele specific real-time PCR (AS kinetic PCR) SNP typing method. In silico analysis based on predicted HRM curves according to the G+C content of each fragment for all 567 sequence types (STs) in the MLST database together with empiric data from the 85 isolates demonstrated that HRM analysis resolves E. faecium into 231 “melting types” (MelTs) and provides a Simpson's Index of Diversity (D) of 0.991 with respect to MLST. This is a significant improvement on the AS kinetic PCR SNP typing scheme that resolves 61 SNP types with D of 0.95. The MelTs were concordant with the known ST of the isolates. For the 85 isolates, there were 13 PFGE patterns, 17 STs, 14 MelTs and eight SNP types. There was excellent concordance between PFGE, MLST and MelTs with Adjusted Rand Indices of PFGE to MelT 0.936 and ST to MelT 0.973. In conclusion, this HRM based method appears rapid and reproducible. The results are concordant with MLST and the MLST based population structure

Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Background and Aims: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed. Methods: We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (n=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). Results: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59–0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC curve of 0.95 (range, 0.92–0.96) without image-preprocessing and an AUROC curve of 0.96 (range, 0.93–0.98) after color normalization. Conclusions: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using hematoxylin and eosin-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens

Concurrent Analysis of Nose and Groin Swab Specimens by the IDI-MRSA PCR Assay Is Comparable to Analysis by Individual-Specimen PCR and Routine Culture Assays for Detection of Colonization by Methicillin-Resistant Staphylococcus aureus

The IDI-MRSA assay (Infectio Diagnostic, Inc., Sainte-Foy, Quebec, Canada) with the Smart Cycler II rapid DNA amplification system (Cepheid, Sunnyvale, CA) appears to be sensitive and specific for the rapid detection of nasal colonization by methicillin-resistant Staphylococcus aureus (MRSA). We assessed the sensitivity and specificity of this assay under conditions in which both the nose and cutaneous groin specimens were analyzed together and compared the accuracy of this PCR approach to that when these specimens were tested separately and by culture assays in an inpatient population with known high rates (12 to 15%) of MRSA colonization. Of 211 patients screened, 192 had results assessable by all three methods (agar-broth culture, separate nose and groin IDI-MRSA assay, and combined nose-groin IDI-MRSA assay), with MRSA carriage noted in 31/192 (16.1%), 41/192 (21.4%), and 36/192 (18.8%) patients by each method, respectively. Compared to agar culture results, the sensitivity and specificity of the combined nose-groin IDI-MRSA assay were 88.0% and 91.6%, respectively, whereas when each specimen was processed separately, the sensitivities were 90.0% (nose) and 83.3% (groin) and the specificities were 91.7% (nose) and 90.2% (groin). IDI-MRSA assay of a combined nose-groin specimen appears to have an accuracy similar to that of the current recommended PCR protocol, providing results in a clinically useful time frame, and may represent a more cost-effective approach to using this assay for screening for MRSA colonization

Superbugs in the supermarket? Assessing the rate of contamination with third-generation cephalosporin-resistant gram-negative bacteria in fresh Australian pork and chicken

Abstract Background Antibiotic misuse in food-producing animals is potentially associated with human acquisition of multidrug-resistant (MDR; resistance to ≥ 3 drug classes) bacteria via the food chain. We aimed to determine if MDR Gram-negative (GNB) organisms are present in fresh Australian chicken and pork products. Methods We sampled raw, chicken drumsticks (CD) and pork ribs (PR) from 30 local supermarkets/butchers across Melbourne on two occasions. Specimens were sub-cultured onto selective media for third-generation cephalosporin-resistant (3GCR) GNBs, with species identification and antibiotic susceptibility determined for all unique colonies. Isolates were assessed by PCR for SHV, TEM, CTX-M, AmpC and carbapenemase genes (encoding IMP, VIM, KPC, OXA-48, NDM). Results From 120 specimens (60 CD, 60 PR), 112 (93%) grew a 3GCR-GNB (n = 164 isolates; 86 CD, 78 PR); common species were Acinetobacter baumannii (37%), Pseudomonas aeruginosa (13%) and Serratia fonticola (12%), but only one E. coli isolate. Fifty-nine (36%) had evidence of 3GCR alone, 93/163 (57%) displayed 3GCR plus resistance to one additional antibiotic class, and 9/163 (6%) were 3GCR plus resistance to two additional classes. Of 158 DNA specimens, all were negative for ESBL/carbapenemase genes, except 23 (15%) which were positive for AmpC, with 22/23 considered to be inherently chromosomal, but the sole E. coli isolate contained a plasmid-mediated CMY-2 AmpC. Conclusions We found low rates of MDR-GNBs in Australian chicken and pork meat, but potential 3GCR-GNBs are common (93% specimens). Testing programs that only assess for E. coli are likely to severely underestimate the diversity of 3GCR organisms in fresh meat

Risk Factors for New Detection of Vancomycin-Resistant Enterococci in Acute-Care Hospitals That Employ Strict Infection Control Procedures

Accurate assessment of the risk factors for colonization with vancomycin-resistant enterococci (VRE) among high-risk patients is often confounded by nosocomial VRE transmission. We undertook a 15-month prospective cohort study of adults admitted to high-risk units (hematology, renal, transplant, and intensive care) in three teaching hospitals that used identical strict infection control and isolation procedures for VRE to minimize nosocomial spread. Rectal swab specimens for culture were regularly obtained, and the results were compared with patient demographic factors and antibiotic exposure data. Compliance with screening was defined as “optimal” (100% compliance) or “acceptable” (minor protocol violations were allowed, but a negative rectal swab specimen culture was required within 1 week of becoming colonized with VRE). Colonization with VRE was detected in 1.56% (66 of 4,215) of admissions (0.45% at admission and 0.83% after admission; the acquisition time was uncertain for 0.28%), representing 1.91% of patients. No patients developed infection with VRE. The subsequent rate of new acquisition of VRE was 1.4/1,000 patient days. Renal units had the highest rate (3.23/1,000 patient days; 95% confidence interval [CI], 1.54 to 6.77/1,000 patient days). vanB Enterococcus faecium was the most common species (71%), but other species included vanB Enterococcus faecalis (21%), vanA E. faecium (6%), and vanA E. faecalis (2%). The majority of isolates were nonclonal by pulsed-field gel electrophoresis analysis. Multivariate analysis of risk factors in patients with an acceptable screening suggested that being managed by a renal unit (hazard ratio [HR] compared to the results for patients managed in an intensive care unit, 4.6; 95% CI, 1.2 to 17.0 [P = 0.02]) and recent administration of either ticarcillin-clavulanic acid (HR, 3.6; 95% CI, 1.1 to 11.6 [P = 0.03]) or carbapenems (HR, 2.8; 95% CI, 1.0, 8.0 [P = 0.05]), but not vancomycin or broad-spectrum cephalosporins, were associated with acquisition of VRE. The relatively low rates of colonization with VRE, the polyclonal nature of most isolates, and the possible association with the use of broad-spectrum antibiotics are consistent with either the endogenous emergence of VRE or the amplification of previously undetectable colonization with VRE among high-risk patients managed under conditions in which the risk of nosocomial acquisition was minimized

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer

Pathological lymph node regression following neoadjuvant chemotherapy predicts recurrence and survival in esophageal adenocarcinoma: a multicentre study in the United Kingdom

Background: there is limited evidence regarding the prognostic effects of pathological lymph node (LN) regression after neoadjuvant chemotherapy for esophageal adenocarcinoma, and a definition of LN response is lacking. This study aimed to evaluate how LN regression influences survival after surgery for esophageal adenocarcinoma.Methods: multicentre cohort study of patients with esophageal adenocarcinoma treated with neoadjuvant chemotherapy followed by surgical resection at five high-volume centres in the United Kingdom. LNs retrieved at esophagectomy were examined for chemotherapy response and given a LN regression score (LNRS) – LNRS 1, complete response; 2, &lt;10% residual tumor; 3, 10-50% residual tumor; 4, &gt;50% residual tumor; 5, no response. Survival analysis was performed using Cox regression adjusting for confounders including primary tumor regression. The discriminatory ability of different LN response classifications to predict survival was evaluated using Akaike’s information criterion and Harrell’s C-index.Results: 17,930 LNs from 763 patients were examined. LN response classified as: complete LN-response (LNRS 1 ≥1 LN, no residual tumor in any LN; n=62, 8.1%), partial LN-response (LNRS 1-3 ≥1 LN, residual tumor ≥1 LN; n=155, 20.3%), poor/no LN-response (LNRS 4-5; n=303, 39.7%), or LN negative (no tumor/regression; n=243, 31.8%) demonstrated superior discriminatory ability. Mortality was reduced in patients with complete LN-response (HR 0.35, 95%CI 0.22-0.56), partial LN-response (HR 0.72, 95%CI 0.57-0.93) or negative LNs (HR 0.32 95%CI 0.25-0.42) compared to those with poor/no LN-response. Primary tumor regression and LN regression were discordant in 165 patients (21.9%).Conclusion: pathological LN regression following neoadjuvant chemotherapy was a strong prognostic factor and provides important information beyond pathological TNM staging and primary tumor regression grading. LN regression should be included as standard in the pathological reporting of esophagectomy specimens.<br/