Optimising Medications in Older Vascular Surgery Patients Through Geriatric Co-management.

BACKGROUND: Prescribing of potentially inappropriate medications and under-prescribing of guideline-recommended medications for cardiovascular risk modification have both been associated with negative outcomes in older adults. Hospitalisation represents an important opportunity to optimise medication use and may be achieved through geriatrician-led interventions. OBJECTIVE: We aimed to evaluate whether implementation of a novel model of care called Geriatric Comanagement of older Vascular (GeriCO-V) surgery patients is associated with improvements in medication prescribing. METHODS: We used a prospective pre-post study design. The intervention was a geriatric co-management model, where a geriatrician delivered comprehensive geriatric assessment-based interventions including a routine medication review. We included consecutively admitted patients to the vascular surgery unit at a tertiary academic centre aged ≥ 65 years with an expected length of stay of ≥ 2 days and who were discharged from hospital. Outcomes of interest were the prevalence of at least one potentially inappropriate medication as defined by the Beers Criteria at admission and discharge, and rates of cessation of at least one potentially inappropriate medication present on admission. In the subgroup of patients with peripheral arterial disease, the prevalence of guideline-recommended medications on discharge was determined. RESULTS: There were 137 patients in the pre-intervention group (median [interquartile range] age: 80.0 [74.0-85.0] years, 83 [60.6%] with peripheral arterial disease) and 132 patients in the post-intervention group (median [interquartile range] age: 79.0 (73.0-84.0) years, 75 [56.8%] with peripheral arterial disease). There was no change in the prevalence of potentially inappropriate medication use from admission to discharge in either group (pre-intervention: 74.5% on admission vs 75.2% on discharge; post-intervention: 72.0% vs 72.7%, p = 0.65). Forty-five percent of pre-intervention group patients had at least one potentially inappropriate medication present on admission ceased, compared with 36% of post-intervention group patients (p = 0.11). A higher number of patients with peripheral arterial disease in the post-intervention group were discharged on antiplatelet agent therapy (63 [84.0%] vs 53 [63.9%], p = 0.004) and lipid-lowering therapy (58 [77.3%] vs 55 [66.3%], p = 0.12). CONCLUSIONS: Geriatric co-management was associated with an improvement in guideline-recommended antiplatelet agent prescribing aimed at cardiovascular risk modification for older vascular surgery patients. The prevalence of potentially inappropriate medications was high in this population, and was not reduced with geriatric co-management.

An Appraisal of Immunohistochemical Stain Use in Hepatic Metastasis Highlights the Effectiveness of the Individualized, Case-Based Approach: Analysis of Data From a Tertiary Care Medical Center.

CONTEXT.­: Liver biopsy plays an important role in the clinical management of metastases and often requires workup using immunohistochemical (IHC) markers, but the approach varies among institutions. OBJECTIVE.­: To evaluate the utility of a morphologic pattern-based, individualized approach in the workup of hepatic metastases. DESIGN.­: All liver biopsies with metastasis between 2015 and 2018 were identified from our institutional database and were reviewed. The morphologic pattern of the metastasis and IHC markers used in each case were recorded. The final identification of primary site of the tumor was assessed based on all the available clinicopathologic data. The academic ranking and practice pattern of the pathologist signing out the case were also recorded. RESULTS.­: A total of 406 liver biopsies with metastasis were identified, and the cases were classified as adenocarcinoma (253 of 406; 62%), carcinoma not otherwise specified (12 of 406; 3%), neuroendocrine neoplasm (54 of 406; 13%), poorly differentiated carcinoma (43 of 406; 11%), nonepithelial tumor (24 of 406; 6%), and squamous cell carcinoma (20 of 406; 5%). The primary site was unknown in 39% (158 of 406) at the time of liver biopsy. A primary site was determined in 97% (395 of 406) of all cases, and only 3% (11 of 406) remained true carcinoma of unknown primary. The average number of IHC markers/case in patients with known primary was 2.6, compared with 5.9 with an initial unknown primary and 9.5 in cases of true carcinoma of unknown primary. CONCLUSIONS.­: An individualized, case-based approach seems to be highly cost-effective and uses fewer IHC markers compared with preset panels that often comprise 10 or more IHC markers.

Intra- and Interexaminer Repeatability of Diagnostic Peri-Implant Clinical Measurement: A Pilot Study.

Despite the crucial role of examiner reliability on quality research and practice, there is still limited literature analyzing factors affecting examiner variability of peri-implant clinical measurements. The present study investigated clinical peri-implant parameters to quantify their repeatability and investigate factors that may affect their accuracy. Thirty-three implants were examined by 4 operators. Peri-implant probing depth (PD), recession (REC), and gingival index (GI) were measured for agreement and included in the analysis. Agreement was quantified using intraclass correlation coefficients (ICCs; 95% confidence interval); mixed linear and logistic regressions were used to assess additional variables. The overall interexaminer agreement was comparable between PD (0.80) and REC (0.78) but significantly worse for GI (0.45; P < .001). Similarly, the intraexaminer agreement was similar for PD (0.81) and REC (0.80) but significantly worse for GI (0.57; P < .05). The magnitude of PD did not influence the agreement. In contrast, increasing disagreement was noted for positive REC (odds ratio [OR]: 3.0), negative REC (OR: 4.8), and lower GI (OR: 4.4). The incidence of bleeding on probing and severity of GI increased for deeper PD (0.113-unit increase per millimeter). Negative and positive values of recession and lower GI were associated with increasing disagreement. Radiographic bone loss, restoration contour, and implant diameter did not affect PD accuracy in this study. In conclusion, within the limitations of the study, GI measurements presented higher variability than PD and REC did. The PD and GI were associated with one another and increased after multiple measurements.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

Limbal stem cell deficiency secondary to systemic paclitaxel (Taxol) for breast cancer: a case report.

BACKGROUND: Paclitaxel (PTX) is an antineoplastic drug widely used in treatments for ovarian, breast, and small-cell lung cancer. Although ocular effects associated with PTX have been previously described, very few studies have specifically reported systemic PTX as a contributing factor for limbal stem cell deficiency (LSCD), which is characterized by the loss of stem cell and barrier function of the limbus leading to progressive pain and reduction in visual acuity. Described here is a unique case where a patient was diagnosed with LSCD secondary to PTX use for the treatment of breast cancer, at doses of PTX far lower than what is reported in current literature. CASE PRESENTATION: A 73-year-old woman with a previous diagnosis of breast cancer with liver metastasis presented with a complaint of increasing pain in the left eye more than the right, along with decreasing visual acuity in both eyes following 3 months of PTX therapy for recurrent liver metastases. Upon examination, best-corrected visual acuity was 20/100 in the right eye and counting fingers on the left. Peripheral neovascularization, stromal scarring, and features of limbal stem cell deficiency (LSCD) were noted on the right cornea. A central neurotrophic ulcer with thinning to 50% and 360 degrees of conjunctivalization were noted on the left. After the discontinuation PTX with doxorubicin as the substitute, there was no further progression of her LSCD, and stabilization of her ocular surface was achieved. CONCLUSION: Although chemotherapy induced LSCD is a relatively rare adverse event, it is essential for clinicians starting new chemotherapy agents to consider the potential ocular toxicities that may result in their use. Ophthalmology review is recommended for patients after starting PTX therapy to assess for signs of LSCD, particularly in patients where drug toxicity can be aggravated due to impaired hepatic function.

Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network.

This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a 'peak map' (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics. The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 s for a plan using 1500 spot beams with a consumer grade GPU. A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

Lognormal Distribution of Local Strain: A Universal Law of Plastic Deformation in Material.

Given the infinite diversity of microstructural inhomogeneity, the variation in spatial distribution of local strain could be infinite. However, this study finds that the statistical distribution of local strain universally follows a lognormal distribution irrespective of phase content and deformation mechanism. Moreover, this universal law is proved conditional upon the macroscopic homogeneity of deformation on the statistical window scale, equivalent to the equality between the macrostrain calculated from the displacements at the window corners and the average of the local strain. The discovery of a lognormal distribution law suggests the existence of a minimum statistical representative window (MSRW) size that is characteristic for each material. Explorations on the dependence of MSRW size on the microstructure, deformation mechanism, and strain magnitude are expected to add new dimensions to understanding of the relationship between microstructure and mechanical properties.

Relationship of Serum Progesterone and Progesterone Metabolites with Mammographic Breast Density and Terminal Ductal Lobular Unit Involution among Women Undergoing Diagnostic Breast Biopsy.

The association of progesterone/progesterone metabolites with elevated mammographic breast density (MBD) and delayed age-related terminal duct lobular unit (TDLU) involution, strong breast cancer risk factors, has received limited attention. Using a reliable liquid chromatography-tandem mass-spectrometry assay, we quantified serum progesterone/progesterone metabolites and explored cross-sectional relationships with MBD and TDLU involution among women, ages 40-65, undergoing diagnostic breast biopsy. Quantitative MBD measures were estimated in pre-biopsy digital mammograms. TDLU involution was quantified in diagnostic biopsies. Adjusted partial correlations and trends across MBD/TDLU categories were calculated. Pregnenolone was positively associated with percent MBD-area (MBD-A, rho: 0.30; p-trend = 0.01) among premenopausal luteal phase women. Progesterone tended to be positively associated with percent MBD-A among luteal phase (rho: 0.26; p-trend = 0.07) and postmenopausal (rho: 0.17; p-trend = 0.04) women. Consistent with experimental data, implicating an elevated 5α-pregnanes/3α-dihydroprogesterone (5αP/3αHP) metabolite ratio in breast cancer, higher 5αP/3αHP was associated with elevated percent MBD-A among luteal phase (rho: 0.29; p-trend = 0.08), but not postmenopausal women. This exploratory analysis provided some evidence that endogenous progesterone and progesterone metabolites might be correlated with MBD, a strong breast cancer risk factor, in both pre- and postmenopausal women undergoing breast biopsy. Additional studies are needed to understand the role of progesterone/progesterone metabolites in breast tissue composition and breast cancer risk.

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

Using Digital Pathology to Understand Epithelial Characteristics of Benign Breast Disease among Women Undergoing Diagnostic Image-Guided Breast Biopsy.

Delayed terminal duct lobular unit (TDLU) involution is associated with elevated mammographic breast density (MD). Both are independent breast cancer risk factors among women with benign breast disease (BBD). Prior digital analyses of normal breast tissues revealed that epithelial nuclear density (END) and TDLU involution are inversely correlated. Accordingly, we examined associations of END, TDLU involution, and MD in BBD clinical biopsies. This study included digitized images of 262 representative image-guided hematoxylin and eosin-stained biopsies from 224 women diagnosed with BBD, enrolled within the cross-sectional BREAST-Stamp project that were visually assessed for TDLU involution (TDLU count/100 mm2, median TDLU span and median acini count per TDLU). A digital algorithm estimated nuclei count per unit epithelial area, or END. Single X-ray absorptiometry of prebiopsy ipsilateral craniocaudal digital mammograms measured global and localized MD surrounding the biopsy region. Adjusted ordinal logistic regression models assessed relationships between tertiles of TDLU and END measures. Analysis of covariance examined mean differences in MD across END tertiles. TDLU measures were positively associated with increasing END tertiles [TDLU count/100 mm2, ORT3vsT1: 3.42, 95% confidence interval (CI), 1.87-6.28; acini count/TDLUT3vsT1, OR: 2.40, 95% CI, 1.39-4.15]. END was significantly associated with localized, but not, global MD. Relationships were most apparent among patients with nonproliferative BBD. These findings suggest that quantitative END reflects different but complementary information to the histologic information captured by visual TDLU and radiologic MD measures and merits continued evaluation in assessing cellularity of breast parenchyma to understand the etiology of BBD.

Proapoptotic protein BIM as a novel prognostic marker in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is an aggressive B-cell lymphoma. Numerous studies have demonstrated many genetic aberrations in MCL in addition to the characteristic t(11:14), including frequent biallelic deletions of Bim, a proapoptotic member of the BCL-2 family. In mice, Bim deletion coupled with cyclin D1 overexpression generates pathologic and molecular features of human MCL. Since the regulation of apoptosis is crucial in MCL pathogenesis, we hypothesize that BIM expression may be associated with tumor cell survival. Clinical data and tissue from 100 nodal MCL cases between 1988 and 2009 were collected from three large academic medical centers. The average patient age of our MCL cohort was 65.5 years old (range, 42-97) with a 2:1 male to female ratio. Immunohistochemistry was performed with a validated anti-BIM antibody. Patients were separated into low and high BIM-expressing categories with a cutoff of 80%. As expected for a proapoptotic tumor suppressor, patients with high BIM expression were less likely to have progressive disease and more likely to have a complete response (P = .022). In addition, high BIM-expressing MCL tumors revealed a trend toward increased overall survival with this trend persisting in sub-analysis of Ann Arbor stages III and IV. No correlation between BIM expression, Ki-67 index, and MIPI score was observed, suggesting a role for BIM as a novel independent prognostic factor. While BIM is only one member of a complex family of apoptosis-regulating proteins, these findings may yield clinically relevant information for the prognosis and therapeutic susceptibility of MCL.

Relationship of circulating insulin-like growth factor-I and binding proteins 1-7 with mammographic density among women undergoing image-guided diagnostic breast biopsy.

BACKGROUND: Mammographic density (MD) is a strong breast cancer risk factor that reflects fibroglandular and adipose tissue composition, but its biologic underpinnings are poorly understood. Insulin-like growth factor binding proteins (IGFBPs) are markers that may be associated with MD given their hypothesized role in breast carcinogenesis. IGFBPs sequester IGF-I, limiting its bioavailability. Prior studies have found positive associations between circulating IGF-I and the IGF-I:IGFBP-3 ratio and breast cancer risk. We evaluated the associations of IGF-I, IGFBP-3, and six other IGFBPs with MD. METHODS: Serum IGF measures were quantified in 296 women, ages 40-65, undergoing diagnostic image-guided breast biopsy. Volumetric density measures (MD-V) were assessed in pre-biopsy digital mammograms using single X-ray absorptiometry. Area density measures (MD-A) were estimated by computer-assisted thresholding software. Age, body mass index (BMI), and BMI2-adjusted linear regression models were used to examine associations of serum IGF measures with MD. Effect modification by BMI was also assessed. RESULTS: IGF-I and IGFBP-3 were not strongly associated with MD after BMI adjustment. In multivariable analyses among premenopausal women, IGFBP-2 was positively associated with both percent MD-V (ß = 1.49, p value = 0.02) and MD-A (ß = 1.55, p value = 0.05). Among postmenopausal women, positive relationships between IGFBP-2 and percent MD-V (ß = 2.04, p = 0.003) were observed; the positive associations between IGFBP-2 and percent MD-V were stronger among lean women (BMI < 25 kg/m2) (ß = 5.32, p = 0.0002; p interaction = 0.0003). CONCLUSIONS: In this comprehensive study of IGFBPs and MD, we observed a novel positive association between IGFBP-2 and MD, particularly among women with lower BMI. In concert with in vitro studies suggesting a dual role of IGFBP-2 on breast tissue, promoting cell proliferation as well as inhibiting tumorigenesis, our findings suggest that further studies assessing the role of IGFBP-2 in breast tissue composition, in addition to IGF-1 and IGFBP-3, are warranted.

The Use of Amoxicillin (500 Mg) Plus Metronidazole (500 Mg) for 7 Days Adds Adjunctive Benefits for Nonsurgical Periodontal Therapy, but Limited Evidence Supports Higher/Longer Dose.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Optimal dose and duration of amoxicillin-plus-metronidazole as an adjunct to nonsurgical periodontal therapy: A systematic review and meta-analysis of randomized, placebo-controlled trials. McGowan K, McGowan T, Ivanovski S. J Clin Periodontol 2018;45:56-67. SOURCE OF FUNDING: Information not available. TYPE OF STUDY/DESIGN: Systematic review with meta-analysis of data.

Non-Seminomatous Germ Cell Tumor Presenting with Superior Vena Cava Syndrome.

BACKGROUND Primary mediastinal non-seminomatous germ cell tumors (NSGCTs) are aggressive and carry a poor five-year disease free survival rate even with aggressive treatment. We describe a young adult male with primary mediastinal NSGCT presenting with airway obstruction and superior vena cava syndrome (SVCS). CASE REPORT The patient presented with four weeks of nonproductive cough, weight loss, and right-sided pleuritic chest pain. Chest computed topography (CT) imaging demonstrated a right-sided mediastinal mass determined as a yolk sac tumor on biopsy. The patient underwent induction chemotherapy with etoposide and cisplatin for stage III NSGCT. In the interim, he developed SVCS warranting a second cycle of chemotherapy along with intravenous steroids, with notable improvement in symptoms. However, serial alpha-fetoprotein (AFP) measurements showed progressively increasing levels up to a maximum of 18,781 ng/mL indicating treatment failure. He is currently on salvage chemotherapy. CONCLUSIONS Obstruction of the SVC by external compression is often a manifestation of a malignant process in the thorax. SVCS is a medical emergency and occurs in 6% of patients with mediastinal GCTs. Historically, irradiation was initiated without a histologic diagnosis to relieve the life-threatening obstruction. However, newer data suggest that it is acceptable to defer therapy until a full diagnostic workup is completed. This case highlights the malignant nature of primary mediastinal NSGCTs. In addition, inasmuch as SVCS is dramatic in presentation, it is important to recognize that symptomatic obstruction often develops over weeks or longer. In a hemodynamically stable patient, an accurate histologic diagnosis prior to starting treatment is essential in guiding therapy.

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.

PURPOSE: To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer. MATERIALS AND METHODS: In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. RESULTS: On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. CONCLUSION: DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.

Microbial Typing by Machine Learned DNA Melt Signatures.

There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.

Novel in vivo mouse model of implant related spine infection.

Post-operative spine infections are a challenge, as hardware must often be retained to prevent destabilization of the spine, and bacteria form biofilm on implants, rendering them inaccessible to antibiotic therapy, and immune cells. A model of posterior-approach spinal surgery was created in which a stainless steel k-wire was transfixed into the L4 spinous process of 12-week-old C57BL/six mice. Mice were then randomized to receive either one of three concentrations (1 × 102 , 1 × 103 , and 1 × 104 colony forming units (CFU)) of a bioluminescent strain of Staphylococcus aureus or normal saline at surgery. The mice were then longitudinally imaged for bacterial bioluminescence to quantify infection. The 1 × 102 CFU group had a decrease in signal down to control levels by POD 25, while the 1 × 103 and 1 × 104 CFU groups maintained a 10-fold higher signal through POD 35. Bacteria were then harvested from the pin and surrounding tissue for confirmatory CFU counts. All mice in the 1 × 104 CFU group experienced wound breakdown, while no mice in the other groups had this complication. Once an optimal bacterial concentration was determined, mice expressing enhanced green fluorescent protein in their myeloid cells (Lys-EGFP) were utilized to contemporaneously quantify bacterial burden, and immune response. Neutrophil fluorescence peaked for both groups on POD 3, and then declined. The infected group continued to have a response above the control group through POD 35. This study, establishes a noninvasive in vivo mouse model of spine implant infection that can quantify bacterial burden and host inflammation longitudinally in real time without requiring animal sacrifice. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:193-199, 2017.