Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy.

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Generalizable Links Between Borderline Personality Traits and Functional Connectivity.

BACKGROUND: Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents. METHODS: We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. RESULTS: Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA ppermuted = .001) and older adolescents (HCP-D ppermuted = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth. CONCLUSIONS: Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.

Similarity-based multimodal regression.

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

Generalizable links between symptoms of borderline personality disorder and functional connectivity.

Background |: Symptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. Methods |: We used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. Results |: Multivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; pperm = 0.001) and older adolescents (HCP-D; pperm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth. Conclusion |: Individual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data.

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Deconfounded Dimension Reduction via Partial Embeddings.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Immune aging in multiple sclerosis is characterized by abnormal CD4 T cell activation and increased frequencies of cytotoxic CD4 T cells with advancing age.

BACKGROUND: Immunosenescence (ISC) describes age-related changes in immune-system composition and function. Multiple sclerosis (MS) is a lifelong inflammatory condition involving effector and regulatory T-cell imbalance, yet little is known about T-cell ISC in MS. We examined age-associated changes in circulating T cells in MS compared to normal controls (NC). METHODS: Forty untreated MS (Mean Age 43·3, Range 18-72) and 49 NC (Mean Age 48·6, Range 20-84) without inflammatory conditions were included in cross-sectional design. T-cell subsets were phenotypically and functionally characterized using validated multiparametric flow cytometry. Their aging trajectories, and differences between MS and NC, were determined using linear mixed-effects models. FINDINGS: MS patients demonstrated early and persistent redistribution of naïve and memory CD4 T-cell compartments. While most CD4 and CD8 T-cell aging trajectories were similar between groups, MS patients exhibited abnormal age-associated increases of activated (HLA-DR+CD38+; (P = 0·013) and cytotoxic CD4 T cells, particularly in patients >60 (EOMES: P < 0·001). Aging MS patients also failed to upregulate CTLA-4 expression on both CD4 (P = 0·014) and CD8 (P = 0·009) T cells, coupled with abnormal age-associated increases in frequencies of B cells expressing costimulatory molecules. INTERPRETATION: While many aspects of T-cell aging in MS are conserved, the older MS patients harbour abnormally increased frequencies of CD4 T cells with activated and cytotoxic effector profiles. Age-related decreased expression of T-cell co-inhibitory receptor CTLA-4, and increased B-cell costimulatory molecule expression, may provide a mechanism that drives aberrant activation of effector CD4 T cells that have been implicated in progressive disease. FUNDING: Stated in Acknowledgements section of manuscript.

First-generation clinical dual-source photon-counting CT: ultra-low-dose quantitative spectral imaging.

OBJECTIVE: Evaluation of image characteristics at ultra-low radiation dose levels of a first-generation dual-source photon-counting computed tomography (PCCT) compared to a dual-source dual-energy CT (DECT) scanner. METHODS: A multi-energy CT phantom was imaged with and without an extension ring on both scanners over a range of radiation dose levels (CTDIvol 0.4-15.0 mGy). Scans were performed in different modes of acquisition for PCCT with 120 kVp and DECT with 70/Sn150 kVp and 100/Sn150 kVp. Various tissue inserts were used to characterize the precision and repeatability of Hounsfield units (HUs) on virtual mono-energetic images between 40 and 190 keV. Image noise was additionally investigated at an ultra-low radiation dose to illustrate PCCT's ability to remove electronic background noise. RESULTS: Our results demonstrate the high precision of HU measurements for a wide range of inserts and radiation exposure levels with PCCT. We report high performance for both scanners across a wide range of radiation exposure levels, with PCCT outperforming at low exposures compared to DECT. PCCT scans at the lowest radiation exposures illustrate significant reduction in electronic background noise, with a mean percent reduction of 74% (p value ~ 10-8) compared to DECT 70/Sn150 kVp and 60% (p value ~ 10-6) compared to DECT 100/Sn150 kVp. CONCLUSIONS: This paper reports the first experiences with a clinical dual-source PCCT. PCCT provides reliable HUs without disruption from electronic background noise for a wide range of dose values. Diagnostic benefits are not only for quantification at an ultra-low dose but also for imaging of obese patients. KEY POINTS: PCCT scanners provide precise and reliable Hounsfield units at ultra-low dose levels. The influence of electronic background noise can be removed at ultra-low-dose acquisitions with PCCT. Both spectral platforms have high performance along a wide range of radiation exposure levels, with PCCT outperforming at low radiation exposures.

Harmonizing functional connectivity reduces scanner effects in community detection.

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

Privacy-preserving harmonization via distributed ComBat.

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.

Pharmacologic prophylaxis and risk factors for intraoperative floppy-iris syndrome in phacoemulsification performed by resident physicians.

PURPOSE: To determine the incidence of intraoperative floppy-iris syndrome (IFIS) in patients taking tamsulosin who had surgery by resident physicians, the effect of prophylactic intracameral lidocaine-epinephrine on the incidence, and the relationship between preoperative dilated pupil diameter and the incidence. SETTING: Tertiary care hospital, Seattle, Washington, USA. METHODS: Charts of consecutive patients who had cataract extraction by resident physicians between January 2005 and July 2008 were reviewed. Operative notes for patients taking tamsulosin at the time of surgery were reviewed. Preoperative dilated pupil diameter; use of prophylactic intracameral lidocaine-epinephrine; and presence of billowing iris, iris prolapse, and pupil constriction were recorded. Intraoperative floppy-iris syndrome was defined as the occurrence of any of the 3 phenomena constituting the syndrome. RESULTS: Review of 1163 charts identified 59 patients (81 eyes) taking tamsulosin at the time of surgery. The overall incidence of IFIS was 29.6%. Of those who received prophylactic intracameral lidocaine-epinephrine, the incidence of IFIS was 38.5%. The incidence of IFIS was 44.8% in eyes with preoperative dilated pupil diameter smaller than 6.5 mm and 21.7% in eyes with a preoperative dilated pupil diameter larger than 6.5 mm. A preoperative dilated pupil diameter smaller than 6.5 mm was significantly associated with IFIS (P = .032). CONCLUSIONS: The incidence of IFIS was lower than previously reported. Use of prophylactic intracameral lidocaine-epinephrine did not reduce the incidence of IFIS. A preoperative dilated pupil diameter smaller than 6.5 mm was significantly associated with an increased incidence of IFIS.

Is there a role for FISH in the management and surveillance of patients with upper tract transitional-cell carcinoma?

BACKGROUND AND PURPOSE: Fluorescence in-situ hybridization (FISH) assay has been approved by the U.S. Food and Drug Administration for the detection of recurrent transitional-cell carcinoma (TCC) of the bladder and in the initial workup of hematuria. In this study, we retrospectively reviewed our initial 94 FISH specimens taken from patients monitored for upper-tract TCC. PATIENTS AND METHODS: Between 2004 and 2007, 43 patients had one or more FISH assays performed as part of the workup and management of upper-tract TCC. Of 94 specimens sent for FISH analysis, 25 voided specimens collected at an outpatient encounter and 40 specimens taken as a bladder wash or selective upper-tract washing under anesthesia were followed by upper-tract endoscopy. The sensitivity and specificity of the FISH assay for detecting urothelial lesions in this population were calculated and compared with cytology specimens from the same sources. RESULTS: Overall sensitivity of FISH in the detection of TCC in this population was 52%, compared with 26% for urinary cytology. Both FISH and cytology showed superior sensitivity for high-grade (79% and 50%, respectively) nu low-grade tumors (41% and 12%, respectively). Selective upper-tract washings were more sensitive and specific for upper-tract TCC than bladder washings or voided specimens. CONCLUSIONS: While the sensitivity of FISH for upper-tract TCC parallels its performance in bladder cancer, the preponderance of low-grade, recurrent disease in the population undergoing surveillance and minimally invasive therapy for upper-tract TCC may limit its usefulness in this setting. Until a high-sensitivity marker for low-grade urothelial lesions is developed, the surveillance of upper-tract TCC will continue to require vigilant direct visual inspection.

Biomechanics of the rabbit knee and ankle: muscle, ligament, and joint contact force predictions.

Mathematical models of small animals that predict in vivo forces acting on the lower extremities are critical for studies of musculoskeletal biomechanics and diseases. Rabbits are advantageous in this regard because they remodel their cortical bone similar to humans. Here, we enhance a recent mathematical model of the rabbit knee joint to include the loading behavior of individual muscles, ligaments, and joint contact at the knee and ankle during the stance phase of hopping. Geometric data from the hindlimbs of three adult New Zealand white rabbits, combined with previously reported intersegmental forces and moments, were used as inputs to the model. Muscle, ligament, and joint contact forces were computed using optimization techniques assuming that muscle endurance is maximized and ligament strain energy resists tibial shear force along an inclined plateau. Peak forces developed by the quadriceps and gastrocnemius muscle groups and by compressive knee contact were within the range of theoretical and in vivo predictions. Although a minimal force was carried by the anterior cruciate and medial collateral ligaments, force patterns in the posterior cruciate ligament were consistent with in vivo tibial displacement patterns during hopping in rabbits. Overall, our predictions compare favorably with theoretical estimates and in vivo measurements in rabbits, and enhance previous models by providing individual muscle, ligament, and joint contact information to predict in vivo forces acting on the lower extremities in rabbits.

Quantitative regional associations between remodeling, modeling, and osteocyte apoptosis and density in rabbit tibial midshafts.

Evidence suggests that osteocyte apoptosis is involved in the adaptive response of bone, although the specific role of osteocytes in the signaling mechanism is unknown. Here, we examined and correlated regional variability in indices of remodeling, modeling, osteocyte apoptosis, and osteocyte density in rabbit tibia midshafts. Histomorphometric analysis indicated that remodeling parameters (BMU activation frequency, osteon density, forming osteon density, and resorption cavity density) were lower in the cranial region compared to other quadrants. In addition, pericortical subregions displayed less remodeling relative to intracortical and endocortical ones. Modeling indices also demonstrated regional variability in that periosteal surfaces exhibited a greater extent of bone forming surface than endosteal ones across all anatomic quadrants. In contrast, endosteal surfaces demonstrated significantly greater surface mineral apposition rates compared to periosteal surfaces in caudal, medial, and lateral but not cranial quadrants. Using TUNEL analysis to detect osteocytes undergoing apoptosis, the density of apoptotic osteocytes was found to be lower in cranial quadrants relative to medial ones. In addition, the densities of osteocyte lacunae, empty lacunae, and total osteocytes were higher in lateral fields relative to caudal quadrants. There was a strong, statistically significant linear correlation between the remodeling indices and apoptotic osteocyte density, supporting the theory that osteocytes undergoing apoptosis produce signals that attract or direct bone remodeling. In contrast, the modeling parameters did not exhibit a correlation with apoptotic osteocytes, although there was a strong correlation between the modeling indices and the density of empty osteocyte lacunae, corroborating previous studies that have found that osteocytes inhibit bone formation. It was found that osteocyte density and osteocyte lacunar density did not significantly correlate with modeling or remodeling parameters, suggesting that cell viability should be examined in studies correlating bone turnover parameters with the functional role of osteocytes in bone adaptation.

Generating models of surgical procedures using UMLS concepts and multiple sequence alignment.

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient's anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation.

Diabetes mellitus as the presenting feature of extrahepatic cholangiocarcinoma in situ: case report and review of literature.

OBJECTIVE: To report a case of newly recognized diabetes, manifested by hyperglycemic crisis, as the presenting feature of an extrahepatic cholangiocarcinoma in situ. METHODS: We summarize the initial clinical manifestations and pertinent laboratory, radiologic, and pathologic findings in a patient with hyperglycemic emergency and a biliary carcinoma in situ. A review of the literature involving cholangiocarcinoma, pancreatic tumors, and diabetes mellitus is also presented. RESULTS: An 85-year-old woman with no prior history of hyperglycemia presented to the hospital in hyperglycemic crisis, without identifiable precipitants. Further work-up disclosed a tumor in the common bile duct. Pathologic analysis, after pancreatoduodenectomy, demonstrated a carcinoma in situ without extension to nearby structures. Adjacent pancreatic islet cells appeared normal. Screening for all relevant islet cell autoantibodies was negative. After tumor removal, mild hyperglycemia persisted, although without insulin requirements. CONCLUSION: Extrahepatic cholangiocarcinoma and diabetes are not usually associated, and to our knowledge, this is the first reported case of a hyperglycemic emergency with this specific type of tumor. The cause-and-effect relationship between the patient's biliary carcinoma in situ and diabetes obviously cannot be confirmed; however, in the absence of other identifiable conditions, it is reasonable to speculate that some factor (or factors) produced by the tumor had a role in the metabolic decompensation. Such a relationship has been considered by others concerning the well-described association between diabetes and carcinoma of the pancreas, in which the underlying pathophysiologic process seems to be insulin resistance. This unusual case of secondary diabetes emphasizes the importance of considering the precise "cause" of the hyperglycemia when the presentation is atypical, as it was in this older, lean patient without risk factors for diabetes.