Comparison of automated corneal endothelial cell analysis in healthy and postoperative eyes with phakic intraocular lens: a cross-sectional study and literature review.

PURPOSE: Measurement of corneal endothelial cells is critical for postoperative evaluation of phakic intraocular lens (pIOL) surgery. However, inter-instrument differences in corneal endothelial cell density (ECD) after pIOL implantation have not yet been reported. This study aimed to compare automated corneal endothelial cell analysis between CellChek-20 (Konan Medical, Hyogo, Japan) and EM-4000 (Tomey, Nagoya, Japan) in healthy and postoperative eyes with pIOL. METHODS: We retrospectively analyzed 154 healthy and 236 postoperative eyes after pIOL surgery. Endothelial cell measurements were performed using CellChek-20 and EM-4000 with autofocusing and automated image analysis. ECD, percentage of hexagonal cells (HEX), coefficient of variation in cell size (CoV), and central corneal thickness (CCT) were compared between the two devices. RESULTS: The ECDs of the two devices were highly correlated in both healthy (Spearman's correlation coefficient [r] = 0.805; p < 0.001) and postoperative (r = 0.901; p < 0.001) groups. ECD from CellChek-20 was higher than EM-4000 in both healthy (mean difference = 228.9 cells/mm2; p < 0.001) and postoperative (mean difference = 115.6 cells/mm2; p < 0.001) groups. The CCT values also showed a strong correlation in healthy eyes (r = 0.974; p < 0.001) and in postoperative eyes (r = 0.936; p < 0.001); however, significant inter-instrument differences were observed. HEX and CV showed significant differences and relatively weak correlations (r < 0.7) between the two devices in both healthy and postoperative groups. CONCLUSION: The ECD values between the two instruments were correlated, but that of the CellChek-20 was significantly higher than that of the EM-4000 in both healthy and postoperative eyes after pIOL surgery. Most previous studies have also shown that the Konan software overestimated the ECD compared to other products in automatic measurement mode. The possibility of measurement bias should be considered when replacing equipment used for corneal endothelial cell measurements.

Comparison of early visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 for myopia: a retrospective matched case-control study.

VISUMAX 800 was introduced to improve the patient experience and clinical outcomes of small incision lenticule extraction (SMILE). This was a retrospective, matched, and case-control study (1:2) controlled for preoperative central corneal thickness and refractive error that compared early refractive and visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 to treat myopia. We included 50 eyes that underwent the VISUMAX 800 SMILE and 100 eyes that underwent the VISUMAX 500 SMILE. SMILE using VISUMAX 800 was performed using the CentraLign aid for vertex centration. Cyclotorsion was controlled by an OcuLign assistant in the VISUMAX 800 group after corneal marking. Corneal higher-order aberrations (HOAs) were evaluated using a Pentacam 1 month after surgery. No differences were observed in the pre- and post-operative refractive and visual outcomes at 1 day, 1 month, and 6 months after surgery. VISUMAX 800 induced less total HOAs than VISUMAX 500 (P = 0.036). No statistically significant differences were observed in the amounts of induced spherical aberrations or vertical and horizontal comas. No differences were observed in the 1 month and 6 months refractive and visual outcomes between two SMILE procedures, except for VISUMAX 800, which resulted in lower postoperative total HOAs than VISUMAX 500.

Comparison of implantation of posterior chamber phakic IOL implantation and laser vision correction in terms of corneal endothelial cells: 3-year observational paired-eye study.

PURPOSE: To compare the postoperative endothelial cell counts of EVO-implantable collamer lenses (ICLs) with a central hole (V4c and V5) and laser vision correction surgery (laser in situ keratomileusis or photorefractive keratectomy). SETTING: B&VIIT Eye Center, Seoul, South Korea. DESIGN: Retrospective observational and paired contralateral study. METHODS: 62 eyes of 31 patients who underwent EVO-ICLs with a central hole implantation in one eye (phakic intraocular lens [pIOL] group) and laser vision correction in the contralateral eye (LVC group) to correct refractive errors were retrospectively reviewed. Central endothelial cell density (ECD), percentage of hexagonal cells (HEX), coefficient of variation (CoV) in cell size, and adverse events were evaluated for at least 3 years. The endothelial cells were observed using a noncontact specular microscope. RESULTS: All surgeries were performed, without complications during the follow-up period. The mean ECD loss values compared with the preoperative measurements were 6.65% and 4.95% during the 3 years after pIOL and LVC, respectively. There was no significant difference in ECD loss compared with the preoperative values (paired t test, P = .188) between the 2 groups. No significant loss in ECD was observed at any timepoint. The pIOL group showed higher HEX ( P = .018) and lower CoV ( P = .006) values than the LVC group at the last visit. CONCLUSIONS: According to the authors' experience, the EVO-ICL with a central hole implantation was a safe and stable vision correction method. Moreover, it did not induce statistically significant changes in ECD at 3 years postoperatively compared with LVC. However, further long-term follow-up studies are required to confirm these results.

Focal Segmental Glomerulosclerosis Followed by Acute Hepatitis A Infection: Case Report.

Background and Objectives: Chronic viral hepatitis such as hepatitis B or hepatitis C is frequently related to nephropathies, yet acute hepatitis A virus (HAV) infection is an exception. Materials and Methods: A 43-year-old male presented with jaundice accompanied by nausea and vomiting. The patient was diagnosed with acute HAV infection. Although the liver function improved after conservative treatment, various symptoms such as proteinuria, hypoalbuminemia, generalized edema and pleural effusion persisted. Due to nephrotic syndrome, the patient was referred to the clinic of the nephrology department and a renal biopsy was performed. Results: The result of the renal biopsy was focal segmental glomerulosclerosis (FSGS) based on histology, electron microscopy and immunohistochemistry. Therefore, based on the clinical history and biopsy results, the patient was diagnosed as having FSGS aggravated by acute HAV infection. Proteinuria, hypoalbuminemia and generalized edema were improved after prednisolone treatment. Conclusions: Although less common, acute HAV infection can also present with an extrahepatic manifestation, for example, FSGS. Hence, clinical attention is required if proteinuria or hypoalbuminemia persists in patients with acute HAV infection.

Predicting Postoperative Anterior Chamber Angle for Phakic Intraocular Lens Implantation Using Preoperative Anterior Segment Metrics.

Purpose: The anterior chamber angle (ACA) is a critical factor in posterior chamber phakic intraocular lens (EVO Implantable Collamer Lens [ICL]) implantation. Herein, we predicted postoperative ACAs to select the optimal ICL size to reduce narrow ACA-related complications. Methods: Regression models were constructed using pre-operative anterior segment optical coherence tomography metrics to predict postoperative ACAs, including trabecular-iris angles (TIAs) and scleral-spur angles (SSAs) at 500 µm and 750 µm from the scleral spur (TIA500, TIA750, SSA500, and SSA750). Data from three expert surgeons were assigned to the development (N = 430 eyes) and internal validation (N = 108 eyes) datasets. Additionally, data from a novice surgeon (N = 42 eyes) were used for external validation. Results: Postoperative ACAs were highly predictable using the machine-learning (ML) technique (extreme gradient boosting regression [XGBoost]), with mean absolute errors (MAEs) of 4.42 degrees, 3.77 degrees, 5.25 degrees, and 4.30 degrees for TIA500, TIA750, SSA500, and SSA750, respectively, in internal validation. External validation also showed MAEs of 3.93 degrees, 3.86 degrees, 5.02 degrees, and 4.74 degrees for TIA500, TIA750, SSA500, and SSA750, respectively. Linear regression using the pre-operative anterior chamber depth, anterior chamber width, crystalline lens rise, TIA, and ICL size also exhibited good performance, with no significant difference compared with XGBoost in the validation sets. Conclusions: We developed linear regression and ML models to predict postoperative ACAs for ICL surgery anterior segment metrics. These will prevent surgeons from overlooking the risks associated with the narrowing of the ACA. Translational Relevance: Using the proposed algorithms, surgeons can consider the postoperative ACAs to increase surgical accuracy and safety.

Clinical outcomes of the immediate reapplication of small-incision lenticule extraction without adjusting the surgical parameters after suction loss.

This study was to analyze the clinical outcomes of immediate reapplication of small-incision lenticule extraction (SMILE) without adjusting the surgical parameters after suction loss and to compare the outcomes with contralateral eyes that underwent uneventful SMILE. A total of 74 patients who underwent uneventful SMILE in one eye (Uneventful group) and immediate reapplication of SMILE without adjusting the surgical parameters after suction loss in the contralateral eye (Suction loss group) were included. Suction loss occurred during the posterior lenticule surface cut in 39 eyes (53%) and the cap cut in 35 eyes (47%). Surgical outcomes, including visual acuity, manifest refraction, keratometry, and corneal wavefront aberrations, were evaluated at 6 months postoperatively. The mean uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), and spherical equivalent were - 0.02 ± 0.07, - 0.04 ± 0.04, and - 0.10 ± 0.46 diopters (D), respectively, in the Suction loss group and - 0.02 ± 0.07, - 0.04 ± 0.05, and - 0.19 ± 0.53 D, respectively (P = 0.965, 0.519, and 0.265, respectively), in the Uneventful group. Changes between the preoperative and 6-month postoperative total corneal aberrations, spherical aberrations, and horizontal and vertical coma did not significantly differ between the Suction loss and Uneventful groups. Immediate reapplication of SMILE without adjusting the surgical parameters after suction loss resulted in good surgical outcomes that were comparable with those of uneventful SMILE.

Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.

PURPOSE: Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography. METHODS: This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively. RESULTS: By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness. CONCLUSION: Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.

Urinary C5b-9 as a Prognostic Marker in IgA Nephropathy.

C5b-9 plays an important role in the pathogenesis of immunoglobin A nephropathy (IgAN). We evaluated C5b-9 as a prognostic marker for IgAN. We prospectively enrolled 33 patients with biopsy-proven IgAN. We analyzed the correlation between baseline urinary C5b-9 levels, posttreatment changes in their levels, and clinical outcomes, including changes in proteinuria, estimated glomerular filtration rate (eGFR), and treatment response. Baseline urinary C5b-9 levels were positively correlated with proteinuria (r = 0.548, p = 0.001) at the time of diagnosis. Changes in urinary C5b-9 levels were positively correlated with changes in proteinuria (r = 0.644, p < 0.001) and inversely correlated with changes in eGFR (r = -0.410, p = 0.018) at 6 months after treatment. Changes in urinary C5b-9 levels were positively correlated with time-averaged proteinuria during the follow-up period (r = 0.461, p = 0.007) but were not correlated with the mean annual rate of eGFR decline (r = -0.282, p = 0.112). Baseline urinary C5b-9 levels were not a significant independent factor that could predict the treatment response in logistic regression analyses (odds ratio 0.997; 95% confidence interval, 0.993 to 1.000; p = 0.078). Currently, urinary C5b-9 is not a promising prognostic biomarker for IgAN, and further studies are needed.

Myostatin/Appendicular Skeletal Muscle Mass (ASM) Ratio, Not Myostatin, Is Associated with Low Handgrip Strength in Community-Dwelling Older Women.

BACKGROUND/AIMS: Elevated levels of serum myostatin have been proposed as a biomarker for sarcopenia. Recent studies have shown that elevated level of serum myostatin was associated with physical fitness and performance. This study aimed to examine the significance of myostatin in the association between muscle mass and physical performance in the elderly. METHODS: This cross-sectional study is based on the Korean Frailty and Aging Cohort study involving 1053 people aged 70 years or over. Anthropometric, physical performance, and laboratory data were collected. RESULTS: The mean age of the participants was 75.8 years, and 50.7% of them were female. Serum myostatin levels in men (3.7 ± 1.2 vs. 3.2 ± 1.1 ng/mL, p < 0.001) were higher compared with that in women. Serum myostatin level was associated with appendicular skeletal muscle mass (ASM) index and eGFR by cystatin C. Serum myostatin/ASM ratio was associated with handgrip strength in women. CONCLUSION: Higher serum myostatin levels were related with higher muscle mass and better physical performances in the elderly. Serum myostatin/ASM ratio may be a predictor for physical performance rather than myostatin.

Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images.

BACKGROUND AND OBJECTIVE: The purpose of the present study was to investigate low-shot deep learning models applied to conjunctival melanoma detection using a small dataset with ocular surface images. METHODS: A dataset was composed of anonymized images of four classes; conjunctival melanoma (136), nevus or melanosis (93), pterygium (75), and normal conjunctiva (94). Before training involving conventional deep learning models, two generative adversarial networks (GANs) were constructed to augment the training dataset for low-shot learning. The collected data were randomly divided into training (70%), validation (10%), and test (20%) datasets. Moreover, 3D melanoma phantoms were designed to build an external validation set using a smartphone. The GoogleNet, InceptionV3, NASNet, ResNet50, and MobileNetV2 architectures were trained through transfer learning and validated using the test and external validation datasets. RESULTS: The deep learning model demonstrated a significant improvement in the classification accuracy of conjunctival lesions using synthetic images generated by the GAN models. MobileNetV2 with GAN-based augmentation displayed the highest accuracy of 87.5% in the four-class classification and 97.2% in the binary classification for the detection of conjunctival melanoma. It showed an accuracy of 94.0% using 3D melanoma phantom images captured using a smartphone camera. CONCLUSIONS: The present study described a low-shot deep learning model that can detect conjunctival melanomas using ocular surface images. To the best of our knowledge, this study is the first to develop a deep learning model to detect conjunctival melanoma using a digital imaging device such as smartphone camera.

Validation of an international prediction model including the Oxford classification in Korean patients with IgA nephropathy.

BACKGROUND: Recently, a new international risk prediction model including the Oxford classification was published which was validated in a large multi-ethnic cohort. Therefore, we aimed to validate this risk prediction model in Korean patients with IgA nephropathy. METHODS: This retrospective cohort study was conducted with 545 patients who diagnosed IgA nephropathy with renal biopsy in three medical centers. The primary outcome was defined as a reduction in estimated glomerular filtration rate (eGFR) of >50% or incident end-stage renal disease (ESRD). Continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) were used to validate models. RESULTS: During the median 3.6 years of follow-up period, 53 (9.7%) renal events occurred. In multivariable Cox regression model, M1 (hazard ratio [HR], 2.22; 95% confidence interval [CI], 1.02-4.82; p = .043), T1 (HR, 2.98; 95% CI, 1.39-6.39; p = .005) and T2 (HR, 4.80; 95% CI, 2.06-11.18; p < .001) lesions were associated with increased risk of renal outcome. When applied the international prediction model, the area under curve (AUC) for 5-year risk of renal outcome was 0.69, which was lower than previous validation and internally derived models. Moreover, cNRI and IDI analyses showed that discrimination and reclassification performance of the international model was inferior to the internally derived models. CONCLUSION: The international risk prediction model for IgA nephropathy showed not as good performance in Korean patients as previous validation in other ethnic group. Further validation of risk prediction model is needed for Korean patients with IgA nephropathy.

Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level.

Purpose: Recently, laser refractive surgery options, including laser epithelial keratomileusis, laser in situ keratomileusis, and small incision lenticule extraction, successfully improved patients' quality of life. Evidence-based recommendation for an optimal surgery technique is valuable in increasing patient satisfaction. We developed an interpretable multiclass machine learning model that selects the laser surgery option on the expert level. Methods: A multiclass XGBoost model was constructed to classify patients into four categories including laser epithelial keratomileusis, laser in situ keratomileusis, small incision lenticule extraction, and contraindication groups. The analysis included 18,480 subjects who intended to undergo refractive surgery at the B&VIIT Eye center. Training (n = 10,561) and internal validation (n = 2640) were performed using subjects who visited between 2016 and 2017. The model was trained based on clinical decisions of highly experienced experts and ophthalmic measurements. External validation (n = 5279) was conducted using subjects who visited in 2018. The SHapley Additive ex-Planations technique was adopted to explain the output of the XGBoost model. Results: The multiclass XGBoost model exhibited an accuracy of 81.0% and 78.9% when tested on the internal and external validation datasets, respectively. The SHapley Additive ex-Planations explanations for the results were consistent with prior knowledge from ophthalmologists. The explanation from one-versus-one and one-versus-rest XGBoost classifiers was effective for easily understanding users in the multicategorical classification problem. Conclusions: This study suggests an expert-level multiclass machine learning model for selecting the refractive surgery for patients. It also provided a clinical understanding in a multiclass problem based on an explainable artificial intelligence technique. Translational Relevance: Explainable machine learning exhibits a promising future for increasing the practical use of artificial intelligence in ophthalmic clinics.

Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study.

Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient's charts due to their aseptic hands. To improve the conditions in surgical time-outs, we introduce a deep learning-based smart speaker to confirm the surgical information prior to cataract surgeries. This pilot study utilized the publicly available audio vocabulary dataset and recorded audio data published by the authors. The audio clips of the target words, such as left, right, cataract, phacoemulsification, and intraocular lens, were selected to determine and confirm surgical information in the time-out speech. A deep convolutional neural network model was trained and implemented in the smart speaker that was developed using a mini development board and commercial speakerphone. To validate our model in the consecutive speeches during time-outs, we generated 200 time-out speeches for cataract surgeries by randomly selecting the surgical statuses of the surgical participants. After the training process, the deep learning model achieved an accuracy of 96.3% for the validation dataset of short-word audio clips. Our deep learning-based smart speaker achieved an accuracy of 93.5% for the 200 time-out speeches. The surgical and procedural accuracy was 100%. Additionally, on validating the deep learning model by using web-generated time-out speeches and video clips for general surgery, the model exhibited a robust and good performance. In this pilot study, the proposed deep learning-based smart speaker was able to successfully confirm the surgical information during the time-out speech. Future studies should focus on collecting real-world time-out data and automatically connecting the device to electronic health records. Adopting smart speaker-assisted time-out phases will improve the patients' safety during cataract surgeries, particularly in relation to wrong-site surgeries.

Differences in Perspectives of Medical Device Adverse Events: Observational Results in Training Program Using Virtual Cases.

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of 'patient use'. The phases of 'patient use' are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of 'patient use,' 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on 'patient use' point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.

Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted using subjects who had visited between 2016 and 2017. External validation (5279 subjects) was performed using subjects who had visited in 2018. The best model, i.e., the ensemble classifier, had a high prediction performance with the area under the receiver operating characteristic curves of 0.983 (95% CI, 0.977-0.987) and 0.972 (95% CI, 0.967-0.976) when tested in the internal and external validation set, respectively. The machine learning models were statistically superior to classic methods including the percentage of tissue ablated and the Randleman ectatic score. Our model was able to correctly reclassify a patient with postoperative ectasia as an ectasia-risk group. Machine learning algorithms using a wide range of preoperative information achieved a comparable performance to screen candidates for corneal refractive surgery. An automated machine learning analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery.

An immunohistochemical panel consisting of EZH2, C-KIT, and CD205 is useful for distinguishing thymic squamous cell carcinoma from type B3 thymoma.

Type B3 thymoma and thymic squamous cell carcinoma (SqCC) often cause a diagnostic problem due to their histological similarities. The aim of this study is to identify EZH2 as a novel and powerful biomarker that can effectively distinguish thymic SqCC from type B3 thymoma, and find optimal combinations among 11 markers. A total of 53 patients, comprising 26 with type B3 thymoma and 27 with thymic SqCC, were allocated to the discovery or validation cohorts, and immunohistochemical staining was performed and analyzed. The expression level of each marker was scored, and receiver-operator characteristic curve analysis was performed to evaluate their diagnostic accuracies. This analysis identified EZH2, C-KIT, and CD205 as useful markers for distinguishing thymic SqCC, and a combined panel approach using them further improved diagnostic accuracy in both the discovery and validation cohorts. In the combined cohorts analysis, EZH2 was the single best marker with 88.9% sensitivity and 100% specificity [area under the curve (AUC) = 0.967]. The sensitivity and specificity were 85.2% and 100% (AUC = 0.962) for C-KIT, and 100% and 73.1% (AUC = 0.844) for CD205. The combined panel had the highest sensitivity and specificity at 96.3% and 100%, which was significantly or marginally higher than those of EZH2, C-KIT, and CD205 alone (P = 0.071, 0.034, and 0.005, respectively). The present findings indicate that EZH2 is useful as a novel diagnostic marker for distinguishing thymic SqCC and that the panel approach can be used as an effective differential diagnostic tool in daily practice.

The 24-Month Changes in Body Fat Mass and Adipokines in Patients Starting Peritoneal Dialysis.

♦ BACKGROUND: Peritoneal dialysis (PD) is characterized by a gain in fat mass. The fat tissue is a complex endocrine organ that releases various adipokines. In this study, we prospectively examined serial changes of fat composition and adipokines in patients undergoing PD. ♦ METHODS: Body composition was assessed by computed tomography (CT). Nutrition status and adipokines (leptin, adiponectin, interleukin [IL]-6, and tumor necrosis factor [TNF]-α) were assessed on the 7th day and 6 months, 12 months, and 24 months after the start of PD. ♦ RESULTS: Fifty-four patients (28 men), with a mean age of 53.2 ± 13.2 years, were enrolled. Baseline fat mass, especially subcutaneous fat mass, was correlated with baseline leptin (ρ = 0.612), adiponetin (ρ = -0.477), and interleukin-6 (IL-6) (ρ = 0.391). Visceral fat mass was correlated with leptin (ρ = 0.545) and adiponectin (ρ = -0.514). Baseline adiponectin was negatively correlated with baseline leptin (ρ = -0.363). While body weight and leptin increased during the 24 months, serum adiponectin decreased in that period. The changes in visceral and subcutaneous fat mass were greater in the first 12 months and 6 months, respectively. There was no difference in IL-6 and TNF-α. Eight patients died during the follow-up period (mean 47.4 months). Twenty-seven patients continued PD. Increased baseline and serial change of IL-6 level were risk factors for mortality. After adjusting for age, sex, diabetes mellitus (DM), and coronary vascular disease (CVD), the significance of the IL-6 level disappeared. ♦ CONCLUSIONS: Baseline subcutaneous fat in patients starting PD is correlated with baseline adipokine levels rather than visceral fat. The increase in subcutaneous fat was greatest in the first 6 months. While leptin and adiponectin increased and decreased respectively, IL-6 did not change in the first 24 months.

Novel polymer composites from waste ethylene-propylene-diene-monomer rubber by supercritical CO2 foaming technology.

In this study, a composite has been prepared by mixing waste rubber, such as ethylene-propylene-diene-monomer and low-density poly ethylene foaming, with supercritical carbon dioxide. In order to optimise the foaming process of the waste ethylene-propylene-diene-monomer-low-density poly ethylene composite, the variations of pressure and temperature on the foamed Microcell formation were studied. As indicated in scanning electron microscope photographs, the most uniform microcellular pattern was found at 200 bar and 100 °C using 30% by weight of waste ethylene-propylene-diene-monomer. Carbon dioxide could not be dissolved uniformly during foaming owing to extensive cross-linking of the waste ethylene-propylene-diene-monomer used for the composite. As a result the presence of un-uniform microcells after foaming were observed in the composite matrix to impart inferior mechanical properties of the composite. This problem was solved with uniform foaming by increasing the cross-link density of low-density poly ethylene using 1.5 parts per hundred dicumyl peroxide that enhances composite tensile and compressive strength up to 57% and 15%, respectively. The composite has the potential to be used as a foaming mat for artificial turf.

Effect of treatment on urinary kidney injury molecule-1 in IgA nephropathy.

BACKGROUND: Kidney injury molecule-1 (KIM-1) is a biomarker useful for detecting early tubular damage and has been recently reported as a useful marker for evaluating kidney injury in IgA nephropathy (IgAN). We therefore investigated whether treatment decreases urinary KIM-1 excretion in IgAN. METHODS: We prospectively enrolled 37 patients with biopsy-proven IgAN. Urinary KIM-1 was assessed before and after treatment, which included low salt diet, blood pressure control, pharmacotherapy with angiotensin receptor blockers and/or angiotensin converting enzyme inhibitors, and immunosuppressive agents as necessary. The median treatment duration was 24 months. RESULTS: Urinary KIM-1/creatinine (Cr) was significantly decreased in patients with IgAN after treatment compared to baseline (P < 0.0001, 1.16 [0.51-1.83] vs 0.26 [0.12-0.65] ng/mg). There was a decrease in the amount of proteinuria after treatment, but it was not statistically significant (P = 0.052, 748.1 [405-1569.7] vs 569.2 [252.2-1114] g/d). Estimated glomerular filtration rate (eGFR) did not change with treatment (P = 0.599, 79.28 ± 30.56 vs 80.98 ± 32.37 ml/min/1.73 m2). Urinary KIM-1 was not correlated with proteinuria baseline or follow up (pre-: R = - 0.100, P = 0.577, post-: R = 0.001, P = 0.993). In patients with higher baseline urinary KIM-1, both urinary KIM-1 level and proteinuria were significantly decreased following treatment. CONCLUSIONS: Treatment decreases urinary KIM-1/Cr in patients with IgAN. It also reduces proteinuria in patients with higher baseline urinary KIM-1. These results suggest a potential role for urinary KIM-1 as a biomarker for predicting treatment response in IgAN, however, further study is needed to verify this.