Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging

Multiplex immunofluorescence (MxIF) is an emerging technique that allows for staining multiple cellular and histological markers to stain simultaneously on a single tissue section. However, with multiple rounds of staining and bleaching, it is inevitable that the scarce tissue may be physically depleted. Thus, a digital way of synthesizing such missing tissue would be appealing since it would increase the useable areas for the downstream single-cell analysis. In this work, we investigate the feasibility of employing generative adversarial network (GAN) approaches to synthesize missing tissues using 11 MxIF structural molecular markers (i.e., epithelial and stromal). Briefly, we integrate a multi-channel high-resolution image synthesis approach to synthesize the missing tissue from the remaining markers. The performance of different methods is quantitatively evaluated via the downstream cell membrane segmentation task. Our contribution is that we, for the first time, assess the feasibility of synthesizing missing tissues in MxIF via quantitative segmentation. The proposed synthesis method has comparable reproducibility with the baseline method on performance for the missing tissue region reconstruction only, but it improves 40% on whole tissue synthesis that is crucial for practical application. We conclude that GANs are a promising direction of advancing MxIF imaging with deep image synthesis.

A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data

The Gut Cell Atlas (GCA), an initiative funded by the Helmsley Charitable Trust, seeks to create a reference platform to understand the human gut, with a specific focus on Crohn's disease. Although a primary focus of the GCA is on focusing on single-cell profiling, we seek to provide a framework to integrate other analyses on multi-modality data such as electronic health record data, radiological images, and histology tissues/images. Herein, we use the research electronic data capture (REDCap) system as the central tool for a secure web application that supports protected health information (PHI) restricted access. Our innovations focus on addressing the challenges with tracking all specimens and biopsies, validating manual data entry at scale, and sharing organizational data across the group. We present a scalable, cross-platform barcode printing/record system that integrates with REDCap. The central informatics infrastructure to support our design is a tuple table to track longitudinal data entry and sample tracking. The current data collection (by December 2020) is illustrated with types and formats of the data that the system collects. We estimate that one terabyte is needed for data storage per patient study. Our proposed data sharing informatics system addresses the challenges with integrating physical sample tracking, large files, and manual data entry with REDCap.

Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging

Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of "seeing is believing", MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ("missing tissue"). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ("missing stain"). In this work, we focus on the "missing stain" issue. It would be appealing to develop digital image synthesis approaches to restore missing stain images without losing more tissue physically. Herein, we aim to develop image synthesis approaches for eleven MxIF structural molecular markers (i.e., epithelial and stromal) on real samples. We propose a novel multi-channel high-resolution image synthesis approach, called pixN2N-HD, to tackle possible missing stain scenarios via a high-resolution generative adversarial network (GAN). Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed "N-to-N" strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e.g., "(N-1)-to-1", "(N-2)-to-2"); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF. Our results elucidate a promising direction of advancing MxIF imaging with deep image synthesis.

RAP-NET: COARSE-TO-FINE MULTI-ORGAN SEGMENTATION WITH SINGLE RANDOM ANATOMICAL PRIOR

Performing coarse-to-fine abdominal multi-organ segmentation facilitates extraction of high-resolution segmentation minimizing the loss of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models to perform single organ segmentation. We propose a coarse-to-fine pipeline RAP-Net, which starts from the extraction of the global prior context of multiple organs from 3D volumes using a low-resolution coarse network, followed by a fine phase that uses a single refined model to segment all abdominal organs instead of multiple organ corresponding models. We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model. To train and evaluate our method, a clinical research cohort consisting of 100 patient volumes with 13 organs well-annotated is used. We tested our algorithms with 4-fold cross-validation and computed the Dice score for evaluating the segmentation performance of the 13 organs. Our proposed method using single auto-context outperforms the state-of-the-art on 13 models with an average Dice score 84.58% versus 81.69% (p<0.0001).

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

Construction of a Multi-Phase Contrast Computed Tomography Kidney Atlas

The Human BioMolecular Atlas Program (HuBMAP) seeks to create a molecular atlas at the cellular level of the human body to spur interdisciplinary innovations across spatial and temporal scales. While the preponderance of effort is allocated towards cellular and molecular scale mapping, differentiating and contextualizing findings within tissues, organs and systems are essential for the HuBMAP efforts. The kidney is an initial organ target of HuBMAP, and constructing a framework (or atlas) for integrating information across scales is needed for visualizing and integrating information. However, there is no abdominal atlas currently available in the public domain. Substantial variation in healthy kidneys exists with sex, body size, and imaging protocols. With the integration of clinical archives for secondary research use, we are able to build atlases based on a diverse population and clinically relevant protocols. In this study, we created a computed tomography (CT) phase-specific atlas for the abdomen, which is optimized for the kidney organ. A two-stage registration pipeline was used by registering extracted abdominal volume of interest from body part regression, to a high-resolution CT. Affine and non-rigid registration were performed to all scans hierarchically. To generate and evaluate the atlas, multiphase CT scans of 500 control subjects (age: 15 - 50, 250 males, 250 females) are registered to the atlas target through the complete pipeline. The abdominal body and kidney registration are shown to be stable with the variance map computed from the result average template. Both left and right kidneys are substantially localized in the high-resolution target space, which successfully demonstrated the sharp details of its anatomical characteristics across each phase. We illustrated the applicability of the atlas template for integrating across normal kidney variation from 64 cm³ to 302 cm³.

Phase identification for dynamic CT enhancements with generative adversarial network

PURPOSE

Dynamic contrast-enhanced computed tomography (CT) is widely used to provide dynamic tissue contrast for diagnostic investigation and vascular identification. However, the phase information of contrast injection is typically recorded manually by technicians, which introduces missing or mislabeling. Hence, imaging-based contrast phase identification is appealing, but challenging, due to large variations among different contrast protocols, vascular dynamics, and metabolism, especially for clinically acquired CT scans. The purpose of this study is to perform imaging-based phase identification for dynamic abdominal CT using a proposed adversarial learning framework across five representative contrast phases.

METHODS

A generative adversarial network (GAN) is proposed as a disentangled representation learning model. To explicitly model different contrast phases, a low dimensional common representation and a class specific code are fused in the hidden layer. Then, the low dimensional features are reconstructed following a discriminator and classifier. 36 350 slices of CT scans from 400 subjects are used to evaluate the proposed method with fivefold cross-validation with splits on subjects. Then, 2216 slices images from 20 independent subjects are employed as independent testing data, which are evaluated using multiclass normalized confusion matrix.

RESULTS

The proposed network significantly improved correspondence (0.93) over VGG, ResNet50, StarGAN, and 3DSE with accuracy scores 0.59, 0.62, 0.72, and 0.90, respectively (P < 0.001 Stuart-Maxwell test for normalized multiclass confusion matrix).

CONCLUSION

We show that adversarial learning for discriminator can be benefit for capturing contrast information among phases. The proposed discriminator from the disentangled network achieves promising results.

High-resolution 3D abdominal segmentation with random patch network fusion

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed tomography (CT) is a challenging topic, in part due to the limited memory provide by graphics processing units (GPU) and large number of parameters and in 3D fully convolutional networks (FCN). Two prevalent strategies, lower resolution with wider field of view and higher resolution with limited field of view, have been explored but have been presented with varying degrees of success. In this paper, we propose a novel patch-based network with random spatial initialization and statistical fusion on overlapping regions of interest (ROIs). We evaluate the proposed approach using three datasets consisting of 260 subjects with varying numbers of manual labels. Compared with the canonical "coarse-to-fine" baseline methods, the proposed method increases the performance on multi-organ segmentation from 0.799 to 0.856 in terms of mean DSC score (p-value < 0.01 with paired t-test). The effect of different numbers of patches is evaluated by increasing the depth of coverage (expected number of patches evaluated per voxel). In addition, our method outperforms other state-of-the-art methods in abdominal organ segmentation. In conclusion, the approach provides a memory-conservative framework to enable 3D segmentation on high-resolution CT. The approach is compatible with many base network structures, without substantially increasing the complexity during inference. Given a CT scan with at high resolution, a low-res section (left panel) is trained with multi-channel segmentation. The low-res part contains down-sampling and normalization in order to preserve the complete spatial information. Interpolation and random patch sampling (mid panel) is employed to collect patches. The high-dimensional probability maps are acquired (right panel) from integration of all patches on field of views.

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (e.g., body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Herein, we explore secondary use of the CT imaging data to refine the risk profile of future diagnosis of T2DM. In this work, we delineate quantitative information and imaging slices of patient history to predict onset T2DM retrieved from ICD-9 codes at least one year in the future. Furthermore, we investigate the role of five different types of electronic medical records (EMR), specifically 1) demographics; 2) pancreas volume; 3) visceral/subcutaneous fat volumes in L2 region of interest; 4) abdominal body fat distribution and 5) glucose lab tests in prediction. Next, we build a deep neural network to predict onset T2DM with pancreas imaging slices. Finally, motivated by multi-modal machine learning, we construct a merged framework to combine CT imaging slices with EMR information to refine the prediction. We empirically demonstrate our proposed joint analysis involving images and EMR leads to 4.25% and 6.93% AUC increase in predicting T2DM compared with only using images or EMR. In this study, we used case-control dataset of 997 subjects with CT scans and contextual EMR scores. To the best of our knowledge, this is the first work to show the ability to prognose T2DM using the patients' contextual and imaging history. We believe this study has promising potential for heterogeneous data analysis and multi-modal medical applications.

Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Segmentation of abdominal computed tomography (CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, continued cross-validation on open datasets presents the risk of indirect knowledge contamination and could result in circular reasoning. Moreover, "real world" segmentations can be challenging due to the wide variability of abdomen physiology within patients. Herein, we perform two data retrievals to capture clinically acquired deidentified abdominal CT cohorts with respect to a recently published variation on 3D U-Net (baseline algorithm). First, we retrieved 2004 deidentified studies on 476 patients with diagnosis codes involving spleen abnormalities (cohort A). Second, we retrieved 4313 deidentified studies on 1754 patients without diagnosis codes involving spleen abnormalities (cohort B). We perform prospective evaluation of the existing algorithm on both cohorts, yielding 13% and 8% failure rate, respectively. Then, we identified 51 subjects in cohort A with segmentation failures and manually corrected the liver and gallbladder labels. We re-trained the model adding the manual labels, resulting in performance improvement of 9% and 6% failure rate for the A and B cohorts, respectively. In summary, the performance of the baseline on the prospective cohorts was similar to that on previously published datasets. Moreover, adding data from the first cohort substantively improved performance when evaluated on the second withheld validation cohort.

Patterns in Pediatric Dental Surgery under General Anesthesia across 7 State Medicaid Programs

OBJECTIVES

Children's access to dental general anesthesia (DGA) is limited, with highly variable wait times. Access factors occur at the levels of facility, dental provider, and anesthesia provider. It is unknown if these factors also influence utilization of dental surgery. We characterized patterns in DGA utilization by system, provider, population, and individual disease levels to explain variation.

METHODS

We conducted a cross-sectional analysis of Medicaid-enrolled children (≤9 y) who received DGA in Massachusetts, Maryland, Texas, Connecticut, Washington, Illinois, and Florida from 2011 to 2012. DGA events were characterized by the place of service, measures of disease burden, average reimbursements for dental provider and anesthesia provider, and average total expenditures.

RESULTS

A total of 10,149,793 children met study eligibility criteria. States with similar patterns of caries-related visits, such as Illinois (16% of Medicaid enrollees had a caries-related claim) and Washington (22%), had different DGA rates (1% and 17%, respectively). Reimbursement rates for dental providers, DGA services, and nonhospital places of services did not consistently align in states with higher DGA rates. Surgical extraction rates, as a proxy for the most severe disease, exceeded 75% in Maryland, which had the lowest DGA rate (0.3%).

CONCLUSIONS

Variation in DGA rates across states was not explained by reimbursements rates (provider, DGA services, place of service) or population or individual level of caries burden. Efforts to evaluate and alter utilization of DGA should consider factors such as dental and anesthesia provider capacity, health facility capacity (hospital vs. ambulatory surgery center vs. office), and population- and individual-level disease burden. Our negative findings suggest the presence of other social determinants of oral health that influence utilization of services (e.g., race/ethnicity, language preference, immigration status, policy and budget goals), which should be explored. Our findings also raise the specter that variation in surgical rates may represent instances of unmet needs or overtreatment.

KNOWLEDGE TRANSFER STATEMENT

The results of this study can be used by clinicians and policy makers as they address policy and clinical interventions to influence children with severe caries. Interventions to change utilization of surgical services on a population level may need to include state-specific factors that extend beyond reimbursement, disease burden, anesthesia provider type, or facility type.

Contrast Phase Classification with a Generative Adversarial Network

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contrast enhanced CT is that phase discrepancies are latent in different tissues due to contrast protocols, vascular dynamics, and metabolism variance. Previous studies with deep learning frameworks have been proposed for classifying contrast enhancement with networks inspired by computer vision. Here, we revisit the challenge in the context of whole abdomen contrast enhanced CTs. To capture and compensate for the complex contrast changes, we propose a novel discriminator in the form of a multi-domain disentangled representation learning network. The goal of this network is to learn an intermediate representation that separates contrast enhancement from anatomy and enables classification of images with varying contrast time. Briefly, our unpaired contrast disentangling GAN(CD-GAN) Discriminator follows the ResNet architecture to classify a CT scan from different enhancement phases. To evaluate the approach, we trained the enhancement phase classifier on 21060 slices from two clinical cohorts of 230 subjects. The scans were manually labeled with three independent enhancement phases (non-contrast, portal venous and delayed). Testing was performed on 9100 slices from 30 independent subjects who had been imaged with CT scans from all contrast phases. Performance was quantified in terms of the multi-class normalized confusion matrix. The proposed network significantly improved correspondence over baseline UNet, ResNet50 and StarGAN's performance of accuracy scores 0.54. 0.55, 0.62 and 0.91, respectively (p-value<0.0001 paired t-test for ResNet versus CD-GAN). The proposed discriminator from the disentangled network presents a promising technique that may allow deeper modeling of dynamic imaging against patient specific anatomies.

Development and control of a hybrid active mount module for precision stages

In recent years, precision stages, which are widely used in many industrial fields, have been required to have a higher speed, larger size, and higher precision to help realize higher productivity and product quality. High-performance positioning techniques for inspection and production equipment are classified as one of the most challenging technologies. Vibration control is crucial to realize high-precision positioning technologies. In a precision system, various vibrations exist, which act as disturbances and can degrade the system performance. Minimizing the vibrations generated by the system can, thus, help improve the accuracy of system positioning. This paper proposes a hybrid active mount module for a precision stage. The developed module improves stage performance by reducing the base vibration arising from the floor, minimizing the vibration caused by the driving linear motors of the precision stage, and reducing the settling time by compensating the offset displacement due to the nonlinearity of the passive mount during stage driving. The prototype design is presented herein, and the experimental results demonstrate the potential of the developed device. The developed system is expected to effectively improve the stage performance by controlling the various causes of vibration.

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores (e.g. "successful" versus "unsuccessful") can be generated. Unfortunately, the precious use of resources for human in-the-loop QA scores are not typically reused in medical image machine learning, especially to train a deep neural network for image segmentation. Herein, we perform a pilot study to investigate if the QA labels can be used as supplementary supervision to augment the training process in a semi-supervised fashion. In this paper, we propose a semi-supervised multi-organ segmentation deep neural network consisting of a traditional segmentation model generator and a QA involved discriminator. An existing 3-D abdominal segmentation network is employed, while the pre-trained ResNet-18 network is used as discriminator. A large-scale dataset of 2027 volumes are used to train the generator, whose 2-D montage images and segmentation mask with QA scores are used to train the discriminator. To generate the QA scores, the 2-D montage images were reviewed manually and coded 0 (success), 1 (errors consistent with published performance), and 2 (gross failure). Then, the ResNet-18 network was trained with 1623 montage images in equal distribution of all three code labels and achieved an accuracy 94% for classification predictions with 404 montage images withheld for the test cohort. To assess the performance of using the QA supervision, the discriminator was used as a loss function in a multi-organ segmentation pipeline. The inclusion of QA-loss function boosted performance on the unlabeled test dataset from 714 patients to 951 patients over the baseline model. Additionally, the number of failures decreased from 606 (29.90%) to 402 (19.83%). The contributions of the proposed method are three-fold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional "true/false", and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method. The use of QA-inspired loss functions represents a promising area of future research and may permit tighter integration of supervised and semi-supervised learning.

Outlier Guided Optimization of Abdominal Segmentation

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary greatly among the population and within an individual over time. While continuous integration of novel datasets into the training set provides potential for better segmentation performance, collection of data at scale is not only costly, but also impractical in some contexts. Moreover, it remains unclear what marginal value additional data have to offer. Herein, we propose a single-pass active learning method through human quality assurance (QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e.g., exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars for which the baseline algorithm worked). The new models were trained using the augmented datasets with 5-fold cross-validation (for outlier data) and withheld outlier samples (for inlier data). Manual labeling of outliers increased Dice scores with outliers by 0.130, compared to an increase of 0.067 with inliers (p<0.001, two-tailed paired t-test). By adding 5 to 37 inliers or outliers to training, we find that the marginal value of adding outliers is higher than that of adding inliers. In summary, improvement on single-organ performance was obtained without diminishing multi-organ performance or significantly increasing training time. Hence, identification and correction of baseline failure cases present an effective and efficient method of selecting training data to improve algorithm performance.

The mental health burden in acne vulgaris and rosacea: an analysis of the US National Inpatient Sample

BACKGROUND

Little is known about the mental health (MH) hospitalization among patients with acne and rosacea.

AIMS

To determine the MH disorders and cost burden associated with acne and rosacea.

METHODS

Data were examined from the 2002-2012 US National Inpatient Sample, comprising a sample of ~20% of all US paediatric and adult hospitalizations (n = 87 053 155 admissions).

RESULTS

A diagnosis of ≥ 1 MH disorder was much more common among all inpatients with vs. those without a diagnosis of acne (43.7% vs. 20.0%, respectively) and rosacea (35.1% vs. 20.0%, respectively). In multivariable logistic regression models controlling for sex, age, race/ethnicity and insurance status, acne (adjusted OR = 13.02; 95% CI 11.75-14.42) and rosacea (adjusted OR = 1.70; 95% CI 1.56-1.95) were associated with significantly higher odds of a primary admission for an MH disorder (13 and 8, respectively, of 15 MH disorders examined). Both acne and rosacea were associated with higher risk of mood, anxiety, impulse control and personality disorders, and with > $2 million of excess mean annual costs of hospitalization for MH disorders in the USA.

CONCLUSION

In this study, inpatients with acne or rosacea had increased odds of comorbid MH disorders. In particular, there was an increased number of hospital admissions secondary to a primary MH disorder with coexistent acne/rosacea. MH comorbidities were associated with considerable excess costs among inpatients with acne or rosacea.

Changes in Bone Mineral Density After Kidney Transplantation

BACKGROUND

Numerous studies have shown that osteoporosis is common in kidney transplant recipients. However, the change in bone mineral density after kidney transplantation (KT) is not fully understood.

METHODS

Thirty-nine kidney transplant recipients with bone densitometry at pretransplant and 24 months after KT were reviewed.

RESULTS

The recipients' median age (44.5 ± 10.7 years) and dialysis duration before KT (4.2 ± 3.4 years) were recorded. The T-scores of the lumbar spine and femur neck at 24 months after KT were positively associated with the respective pretransplant T-score (P < .001 in the lumbar spine and P < .001 in the femur neck). However, the T-score after KT did not show significant change (P = .680 in lumbar spine, P = .093 in femur neck). Changes in the T-scores of the lumbar spine and femur neck over 24 months (delta T-score) were negatively associated with the respective pretransplant T-scores (P = .001 in lumbar spine, P = .026 in femur neck). Changes in the T-scores of the lumbar spine and femur neck over 24 months (delta T-score) were also associated with the pretransplant T-scores after the adjustment of other variables.

CONCLUSION

The change of bone mineral density was related with pretransplant bone mineral density. Careful follow-up of bone densitometry for KT recipients was needed.

Impact of Cigarette Smoking on Living Kidney Donors

BACKGROUND

Smoking is known to result in a decline in renal allograft function and survival of recipients; however, the effect of smoking on living kidney donors remains unknown. In this study we evaluated the impact of cigarette smoking on renal function of kidney donors.

METHODS

Among 1056 donors who underwent nephrectomy, 612 completed the 6-month follow-up protocol and were enrolled in the study. The association of smoking status, including pack-years smoking history, and postoperative renal function was evaluated.

RESULTS

Among donors, 68.1% had never smoked, 8% were former smokers, and 23.9% were current smokers. Donors who never smoked were older than former and current smokers (42.3 ± 11.8, 41.9 ± 11.1, and 38.3 ± 10.9 years, respectively; P < .001). There was no difference in preoperative renal function between groups; however, postoperative estimated glomerular filtration rate (eGFR) was lower in former and current smokers than in those who never smoked (64.6 ± 13.8, 64.7 ± 12.3, and 67.8 ± 13.1 mL/min/1.73 m², respectively; P = .023). In former and current smokers, pack-years smoking history was negatively associated with pre- and postoperative eGFR (r = -0.305 and -0.435, P < .001), and correlated with postoperative percent eGFR decline (r = 0.248, P < .001). Smoking history was associated with postoperative development of chronic kidney disease (CKD). Especially in former smokers, a smoking history of more than 12 pack-years was strongly associated with development of CKD (odds ratio = 7.5, P = .003).

CONCLUSION

Even if they no longer smoke, donors with a smoking history require close observation due to increased risk of CKD development after kidney donation. A detailed pack-years smoking history should be obtained, and smoking cessation strategies should be implemented in kidney donors.

Incidence of psoriasiform diseases secondary to tumour necrosis factor antagonists in patients with inflammatory bowel disease: a nationwide population-based cohort study

BACKGROUND

There are increasing reports of paradoxical psoriasiform diseases secondary to anti-tumour necrosis factor (TNF) agents.

AIMS

To determine the risks of paradoxical psoriasiform diseases secondary to anti-TNF agents in patients with inflammatory bowel disease (IBD).

METHODS

A nationwide population study was performed using the Korea National Health Insurance Claim Data. A total of 50 502 patients with IBD were identified between 2007 and 2016. We compared 5428 patients who were treated with any anti-TNF agent for more than 6 months (anti-TNF group) and 10 856 matched controls who had never taken anti-TNF agents (control group).

RESULTS

Incidence of psoriasis was significantly higher in the anti-TNF group (36.8 per 10 000 person-years) compared to the control group (14.5 per 10 000 person-years) (hazard ratio [HR] 2.357, 95% confidence interval [CI] 1.668-3.331). Palmoplantar pustulosis (HR 9.355, 95% CI 2.754-31.780) and psoriatic arthritis (HR 2.926, 95% CI 1.640-5.218) also showed higher risks in the anti-TNF group. In subgroup analyses, HRs for psoriasis by IBD subtype were 2.549 (95% CI 1.658-3.920) in Crohn's disease and 2.105 (95% CI 1.155-3.836) in ulcerative colitis. Interestingly, men and younger (10-39 years) patients have significantly higher risks of palmoplantar pustulosis (HR 19.682 [95% CI 3.867-100.169] and HR 14.318 [95% CI 2.915-70.315], respectively), whereas women and older (≥40 years) patients showed similar rates between the two groups.

CONCLUSIONS

The risks of psoriasiform diseases are increased by anti-TNF agents in patients with IBD. Among psoriasiform diseases, the risk of palmoplantar pustulosis shows the biggest increase particularly in male and younger patients.

Long-term Follow-up of Living Kidney Donors With Chronic Kidney Disease at 1 Year After Nephrectomy

BACKGROUND

Although renal function recovery of living kidney donors has been reported in a number of studies, many patients show poor recovery, and the long-term prognosis of these patients has not been well studied. In this investigation we explored the long-term prognosis of renal function in patients with chronic kidney disease (CKD) at 1 year after nephrectomy.

METHODS

Patients who underwent donor nephrectomy during the period from March 2006 to April 2014, with a follow-up creatinine study at 1 year postoperatively and more than 3 years of follow-up, were included in the study. Creatinine and estimated glomerular filtration rate (eGFR, using the Modification of Diet in Renal Disease formula) before and after surgery were studied. Age, sex, history of hypertension or diabetes, body mass index, blood pressure, complete blood count, preoperative routine serum chemistry, and urine study results were reviewed.

RESULTS

Among 841 patients who had donor nephrectomy, 362 were included in the study. There were 111 patients (30.6%) with eGFR <60 mL/min/1.73 m² at 1 year postsurgery, and the median follow-up period was 62.8 months (interquartile range [IQR] 42.0-86.3 months). The maximum eGFR after 3-year follow-up was studied, and 48 patients (43.2%) never recovered eGFR to >60 mL/min/1.73 m². Age, history of hypertension, preoperative eGFR, and eGFR at 1 year were predictive factors at univariate analysis. Multivariate analysis of these factors was studied, and age (52.5 [IQR 47-55.7] vs 47 [IQR 7-53] years, odds ratio [OR] 1.1, 95% confidence interval [CI] 1.02-1.15, P = .007), history of hypertension (16.7% vs 1.6%, OR 10.0, 95% CI 1.09-92.49, P = .042), and eGFR at 1 year (53.9 [IQR 50.3-56.0] vs 57.0 [IQR 54.2-58.4] mL/min/1.73 m², OR 0.8, 95% CI 0.72-0.92, P = .002) remained as significant risk factors.

CONCLUSION

Of all living donors, 15.7% had CKD after >3 years of follow-up. Close observation is warranted when donors have CKD after 1 year follow-up, as 43.2% fail to recover renal function. Patients who are older, have a history of hypertension, and have low eGFR at 1-year follow-up are especially at risk.

Delayed Recovery of Renal Function After Donor Nephrectomy

BACKGROUND

Many living kidney donors are still at risk of chronic kidney disease (CKD) 1 year after nephrectomy. Although some donors still experience poor renal function, many exhibit delayed recovery of renal function afterwards. We studied the factors related to delayed recovery of renal function in patients with CKD at 1 year after nephrectomy.

METHODS

Patients who underwent donor nephrectomy from March 2006 to April 2014 with a follow-up creatinine study at 1 month, 6 months, 1 year, and after 3 years of follow-up were included in the study. Age, sex, history of hypertension or diabetes, body mass index, blood pressure, complete blood cell count, preoperative routine serum chemistry, and urine study results were reviewed.

RESULTS

Among 275 donors, 83 (30.2%) who had an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m² at 1 year of follow-up were included in the study, and the eGFR was observed during a median follow-up of 62.0 months (interquartile range [IQR], 48.9-83.1 months). Those who had improvements in eGFR of >5 mL/min/1.73 m² were included in the recovery group (n = 48 [57.8%]), and those who did not were included in the nonrecovery group (n = 35 [42.2%]). The preoperative and 1-year follow-up eGFR did not differ significantly between the 2 groups, and the maximum eGFR after 3 years was higher in the recovery group (68.68 mL/min/1.73 m² [IQR, 61.81-75.64 mL/min/1.73 m²] vs 55.63 mL/min/1.73 m² [IQR, 51.73-58.29 mL/min/1.73 m²]; P < .001). The recovery group was more likely to have a history of hypertension (4.2% vs 20%; P = .032), a lower body mass index (24.11 kg/m² [IQR, 22.04-25.20 kg/m²] vs 25.25 kg/m² [IQR, 23.23-26.44 kg/m²]; P = .01), and a lower preoperative uric acid level (4.7 mg/dL [IQR, 3.8-5.4 mg/dL] vs 5.3 mg/dL [IQR, 4.4-6.2 mg/dL]; P = .031). After multivariate logistic regression analysis, history of hypertension (odds ratio, 0.131; P = .022) and uric acid level (odds ratio, 0.641; P = .036,) remained as significant factors.

CONCLUSIONS

Although 30.2% of donors had CKD at 1 year after nephrectomy, 57.8% reported improved renal function. Those with a history of hypertension and high preoperative uric acid levels were less likely to have improvements in renal function and required close follow-up.

Suspected Frequent Relapsing IgG4-related Lung Disease in Kidney Transplant Patient: A Case Report

Besides the initial description of IgG4-related pancreatic disease, other sites are now commonly involved. However, occurrence of IgG4-related disease is rare in organ transplanted patients. A 57-year-old man who received a kidney transplantation presented with recurrent dyspnea on exertion. A computed tomography scan of the chest revealed bilateral interlobular septal thickening and multiple tubular and branching small nodular lesions in the right upper lobe, and mass-like consolidation of the left middle lobe. Despite no elevation of serum IgG4 level, a percutaneous core needle biopsy on consolidative mass showed interstitial fibrosis and infiltration of IgG4-positive plasma cells to be more than > 20 per high power field. After treatment with glucocorticoids and rituximab, the consolidative mass of the left middle lobe disappeared.

Renoprotective Effects of Carbon Monoxide-Releasing Molecule 3 in Ischemia-Reperfusion Injury and Cisplatin-Induced Toxicity

BACKGROUND

We investigated the effects of a soluble carbon monoxide-releasing molecule (CORM) in cisplatin-induced cytotoxicity and ischemia-reperfusion injury (IRI) in vitro.

METHODS

The effects of CORM-3 (12.5-200 μM) were assessed in normal kidney epithelial cells (HK-2, LLC-PK1) and renal cancer cells (Caki-1, Caki-2) subjected to cisplatin (50-200 μM) or IRI. To induce IRI, cells were placed in an anaerobic chamber (37°C, 95% nitrogen, 5% carbon dioxide) for 48 hours. Cells were transferred to complete medium and incubated at 37°C, 5% carbon dioxide for 6 hours. Cell viability (CCK assays), tumor necrosis factor (TNF)-α messenger RNA (mRNA) levels (quantitative reverse-transcriptase polymerase chain reaction), and protein expression of cleaved-caspase 3 and oxidative stress markers (including Erk1/2, JNK, and P38; Western blot) were assessed.

RESULTS

Viability after IRI was approximately 40% of control. Protective effects of CORM-3 in the IRI model were dose-dependent. Cell viability was 40% recovered in 200-μM CORM-3-pretreated cells compared with control. The protective effects of CORM-3 in cells exposed to cisplatin for 24 hours were weaker than in the IRI model. TNF-α mRNA was induced by stimulated IRI or cisplatin exposure; CORM-3 pretreatment attenuated the rise in TNF-α mRNA. IRI or cisplatin-induced activated oxidative stress markers decreased in CORM-3-pretreated cells. CORM-3 reduced expression of the apoptotic marker cleaved-caspase 3.

CONCLUSION

Our data demonstrate the protective effects of CORM-3 in cisplatin cytotoxicity and IRI in both normal kidney cells and renal cancer cells in vitro. CORM-3 exerts these effects by ameliorating inflammatory and oxidative stress pathways.

An animal model of co-existing sarcopenia and osteoporotic fracture in senescence accelerated mouse prone 8 (SAMP8)

Sarcopenia and osteoporotic fracture are common aging-related musculoskeletal problems. Recent evidences report that osteoporotic fracture patients showed high prevalence of sarcopenia; however, current clinical practice basically does not consider sarcopenia in the treatment or rehabilitation of osteoporotic fracture. There is almost no report studying the relationship of the co-existing of sarcopenia and osteoporotic fracture healing. In this study, we validated aged senescence accelerated mouse prone 8 (SAMP8) and senescence accelerated mouse resistant 1 (SAMR1) as animal models of senile osteoporosis with/without sarcopenia. Bone mineral density (BMD) at the 5th lumbar and muscle testing of the two animal strains were measured to confirm the status of osteoporosis and sarcopenia, respectively. Closed fracture was created on the right femur of 8-month-old animals. Radiographs were taken weekly post-fracture. MicroCT and histology of the fractured femur were performed at week 2, 4 and 6 post-fracture, while mechanical test of both femora at week 4 and 6 post-fracture. Results showed that the callus of SAMR1 was significantly larger at week 2 but smaller at week 6 post-fracture than SAMP8. Mechanical properties were significantly better at week 4 post-fracture in SAMR1 than SAMP8, indicating osteoporotic fracture healing was delayed in sarcopenic SAMP8. This study validated an animal model of co-existing sarcopenia and osteoporotic fracture, where a delayed fracture healing might be resulted in the presence of sarcopenia.