Search

Abstract Aims Patients with high‐flow arteriovenous (AV) access are at risk of developing high‐output cardiac failure (HOCF) and subsequent hospitalization. However, diagnosing HOCF is challenging and often requires invasive procedures. The role of systemic vascular resistance (SVR) in diagnosing HOCF is underestimated, and its predictive value is limited. Our study aims to identify non‐invasive risk factors for HOCF to facilitate early diagnosis and timely surgical interventions. Methods and results We included 109 patients with high‐flow AV access who underwent serial echocardiography. The retrospective cohort was divided into two groups based on their hospitalization due to HOCF. The two groups were matched for age and gender. After a mean follow‐up of 25.1 months, 19 patients (17.4%) were hospitalized due to HOCF. The two groups had similar baseline characteristics. However, the HOCF group had a higher value of vascular access blood flow (Qa) (2168 ± 856 vs. 1828 ± 617 mL/min; P = 0.045). Echocardiographic analysis revealed that the HOCF group had more pronounced left ventricular diastolic dysfunction (E/e′: 21.1 ± 7.3 vs. 16.2 ± 5.9; P = 0.002), more severe pulmonary hypertension (right ventricular systolic pressure: 41.4 ± 16.7 vs. 32.2 ± 12.8; P = 0.009), a higher Doppler‐derived cardiac index (CI) (4.3 ± 0.8 vs. 3.7 ± 1.1; P = 0.031), and a lower Doppler‐derived estimated SVR (eSVR) value (5.5 ± 0.3 vs. 6.9 ± 0.2; P = 0.002) than the non‐HOCF group. Using multivariable Cox regression analysis, a low eSVR value (

Background: Hemodialysis (HD) patients are particularly vulnerable to severe coronavirus disease 2019 (COVID-19) due to their immunocompromised state and comorbid conditions. Timely vaccination could be the most effective strategy to reduce morbidity and mortality. However, data on the survival benefit of the COVID-19 vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and death among HD patients are limited, especially during the Omicron-dominant period. Methods: In this prospective hospital-based cohort study, we identified HD patients from July 1, 2021, to April 29, 2022. The patients were divided into fully vaccinated and partially vaccinated groups. We compared the humoral response, risk of developing SARS-CoV-2 infection, and all-cause mortality between the two groups. Results: Among the 440 HD patients included, 152 patients were fully vaccinated, and 288 patients were partially vaccinated. Patients in the fully vaccinated group exhibited higher anti-spike protein receptor-binding domain (S protein RBD) antibody levels and lower risks of all-cause mortality (adjusted hazard ratio, 0.35; 95% confidence interval, 0.17–0.73; p = 0.005) than the partially vaccinated group. However, the risk for SARS-CoV-2 infection did not significantly differ between the two groups. Irrespective of the number of vaccinations, the risk of all-cause mortality was lower in patients with anti-S protein RBD antibody levels in the higher tertile. Conclusion: A third dose of the COVID-19 vaccine was associated with a decreased risk of all-cause mortality among HD patients during the Omicron-dominant period. A higher post-vaccination anti-S protein RBD antibody level was also associated with a lower risk of mortality.

Abstract Objectives Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings. Methods We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively. Results The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender. Conclusions Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.

Mesenchymal stem cells (MSCs) possess the capacity for self-renewal and multipotency. The traditional approach to manipulating MSC's fate choice predominantly relies on biochemical stimulation. Accumulating evidence also suggests the role of physical input in MSCs differentiation. Therefore, investigating mechanotransduction at the molecular level and related to tissue-specific cell functions sheds light on the responses secondary to mechanical forces. In this review, a new frontier aiming to optimize the cultural parameters was illustrated, i.e. spatial boundary condition, which recapitulates in vivo physiology and facilitates the investigations of cellular behavior. The concept of mechanical memory was additionally addressed to appreciate how MSCs store imprints from previous culture niches. Besides, different types of forces as physical stimuli were of interest based on the association with the respective signaling pathways and the differentiation outcome. The downstream mechanoreceptors and their corresponding effects were further pinpointed. The cardiovascular system or immune system may share similar mechanisms of mechanosensing and mechanotransduction; for example, resident stem cells in a vascular wall and recruited MSCs in the bloodstream experience mechanical forces such as stretch and fluid shear stress. In addition, baroreceptors or mechanosensors of endothelial cells detect changes in blood flow, pass over signals induced by mechanical stimuli and eventually maintain arterial pressure at the physiological level. These mechanosensitive receptors transduce pressure variation and regulate endothelial barrier functions. The exact signal transduction is considered context dependent but still elusive. In this review, we summarized the current evidence of how mechanical stimuli impact MSCs commitment and the underlying mechanisms. Future perspectives are anticipated to focus on the application of cardiovascular bioengineering and regenerative medicine.

Elevated circulating low-density lipoprotein cholesterol (LDL-C) is a major risk factor of atherosclerotic cardiovascular disease (ASCVD). Early control of LDL-C to prevent ASCVD later in life is important. The Taiwan Society of Lipids and Atherosclerosis in association with the other seven societies developed this new lipid guideline focusing on subjects without clinically significant ASCVD. In this guideline for primary prevention, the recommended LDL-C target is based on risk stratification. A healthy lifestyle with recommendations for foods, dietary supplements and alcohol drinking are described. The pharmacological therapies for LDL-C reduction are recommended. The aim of this guideline is to decrease the risk of ASCVD through adequate control of dyslipidemia in Taiwan.

Introduction: The 2019 Kidney Disease Outcome Quality Initiative (K/DOQI) guideline recommended evaluating arteriovenous fistula (AVF) malfunction risks primarily based on clinical monitoring, which can be assisted with the value of vascular access flow (Qa). Nevertheless, Qa thresholds recommended by different guidelines vary, ranging from 300 to 500 ml/min. This study investigated the optimal Qa threshold to predict future functional patency in AVFs with Qa

Background: Our aim was to characterize etiologic diagnoses obtained from bronchoalveolar lavage fluid (BALF) and blood specimens, and to identify risk factors for mortality in systemic lupus erythematosus (SLE) patients with pneumonia and respiratory failure. Methods: We conducted a retrospective analysis of SLE patients with pneumonia and respiratory failure. Clinical characteristics, laboratory profiles, and microbiology in BALF and blood samples were evaluated. We performed univariable analyses to identify mortality risk factors. Results: All 24 patients (F:M = 21:3, median age 46.5 years; disease duration 11 years) received mechanical ventilation (median duration: 11 days). Pathogens identified in BALF included Pneumocystis jiroveci (12 patients [50%]), cytomegalovirus (CMV, 7 patients [29.2%]), and bacteria (11 patients [45.8%]). Thirteen patients (54.2%) yielded pathogens in blood (CMV in 8 patients [33.3%] and Escherichia coli in 5 patients [20.8%]). Eight developed septic shock, and 9 died within 30 days. Univariable analysis identified thrombocytopenia (odds ratio [OR]: 8.0, 95% confidence interval [CI]: 1.23–52.25), bacteremia within 30 days before or after endotracheal intubation (OR: 8.0, 95% CI: 1.23–52.5), and P. jiroveci pneumonia (PJP, OR: 7.0, 95% CI: 1.04–46.95) as risk factors for 30-day mortality. Kaplan–Meier analysis confirmed an increased risk of 30-day mortality with thrombocytopenia and bacteremia. Conclusion: There are high prevalence rates of PJP and CMV infections as evidenced by BALF analyses in SLE patients with pneumonia and respiratory failure. BALF analysis can facilitate rescue therapy per pathogen. Thrombocytopenia, bacteremia, and PJP in SLE patients can increase their 30-day mortality, so warrant early and aggressive treatments.

BackgroundAnterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears. ObjectiveThis study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images. MethodsThe sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience. ResultsThe first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively. ConclusionsThis study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.

ObjectiveSLC12A3 (solute carrier family 12 member 3) gene variants are associated with diabetic nephropathy; however, their association with hypertensive nephropathy remains unknown. We aimed to investigate the association between SLC12A3 gene polymorphisms and renal function in patients with hypertension.MethodsParticipants from three non-diabetic hypertensive cohorts, including young-onset hypertension (cohort 1, n = 882), treatment-naïve hypertension (cohort 2, n = 90), and follow-up cohort (cohort 3, n = 166), underwent genotyping for single nucleotide polymorphisms in SLC12A3. Renal events were defined as a >25 and >50% decline in estimated glomerular filtration rate (eGFR).ResultsIn cohort 1, SLC12A3 rs16963397 C/C or C/G (P = 0.005), rs13334864 C/C or C/T (P = 0.020), and rs7187932 A/A or A/G polymorphisms (P = 0.014) had higher eGFRs compared to their counterparts, with similar findings observed in cohort 2. In cohort 3, over a mean follow-up of 5.8 ± 1.7 years, participants with either SLC12A3 rs16963397 C/C or rs13334864 C/C polymorphisms had more >25 and >50% eGFR decline than their counterparts (log-rank test, P = 0.058 and P = 0.038, respectively). Cox regression analysis revealed that SLC12A3 rs16963397 C/C and rs13334864 C/C polymorphisms were significantly associated with an increased risk of >25% [hazard ratio (HR), 3.294; 95% confidence interval (CI), 1.158–9.368; P = 0.025] and >50% decline in eGFR (HR, 18.630; 95% CI, 1.529–227.005, P = 0.022) than their counterparts.ConclusionSLC12A3 polymorphisms are associated with renal function in Chinese patients with hypertension.

Abstract Background Life-long peritoneal dialysis (PD) as a renal replacement therapy is limited by peritoneal fibrosis. Previous studies showed immunomodulatory and antifibrotic effects of adipose-derived mesenchymal stem cells (ADSCs) on peritoneal fibrosis. However, the role of the peritoneal macrophage in this process remains uninvestigated. Methods We examined the therapeutic effects of ADSC and bone marrow-derived mesenchymal stem cells (BM-MSC) in the rat model of dialysis-induced peritoneal fibrosis using methylglyoxal. In addition, treatment of macrophages with the conditioned medium of ADSC and BM-MSC was performed individually to identify the beneficial component of the stem cell secretome. Results In the in vivo experiments, we found dialysis-induced rat peritoneal fibrosis was attenuated by both ADSC and BM-MSC. Interestingly, ADSC possessed a more prominent therapeutic effect than BM-MSC in ameliorating peritoneal membrane thickening while also upregulating epithelial cell markers in rat peritoneal tissues. The therapeutic effects of ADSC were positively associated with M2 macrophage polarization. In the in vitro experiments, we confirmed that interleukin-6 (IL-6) secreted by MSCs upon transforming growth factor-β1 stimulation promotes M2 macrophage polarization. Conclusions In dialysis-induced peritoneal fibrosis, MSCs are situated in an inflammatory environment of TGF-β1 and secrete IL-6 to polarize macrophages into the M2 phenotype. Our findings reveal a previously unidentified role of tissue macrophage in this antifibrotic process. ADSC has the advantage of abundance and accessibility, making the application values extremely promising. Graphical abstract In dialysis-induced peritoneal fibrosis, peritoneal mesothelial cells secrete transforming growth factor-β1 (TGF-β1) when exposed to methylglyoxal (MGO)-containing peritoneal dialysate. When situated in TGF-β1, the inflammatory environment induces mesenchymal stem cells to secrete interleukin-6 (IL-6), IL-6 polarizes macrophages into the M2 phenotype. The dominant peritoneal tissue M2 macrophages, marked by upregulated Arg-1 expression, account for the attenuation of MGO-induced dedifferentiation of peritoneal mesothelial cells to maintain epithelial integrity.

Abstract Background Immunoglobulin M (IgM) mesangial deposition in pediatric minimal change disease (MCD) has been reported to be associated with steroid dependence and poor renal outcomes. However, the evidence linking the impacts of IgM mesangial deposition to the treatment prognosis in adult-onset MCD is still elusive. Methods In this retrospective cohort study, 37 adult patients with MCD received kidney biopsies from January 2010 to May 2020. Immunofluorescence microscopy was performed and the patients dichotomized according to IgM mesangial deposition (12 patients with positive IgM deposition; 25 patients with negative IgM deposition). We analyzed the clinical features, the dosage of immunosuppressive agents, and the response to treatment for 2 years between the two groups. Results Analysis of the clinical symptoms, the dosage of immunosuppressive treatment, and the time to remission revealed no statistical difference between the groups. However, compared to the negative IgM group, the frequency of relapses was significantly higher in the positive IgM group during the two-year follow-up period (the negative IgM group 0.25 episodes/year; the positive IgM group 0.75 episodes/year, p = 0.029). Furthermore, multivariate linear regression revealed that the positivity of IgM mesangial deposition is independently associated with the frequency of relapses (regression coefficient B 0.450, 95% CI 0.116–0.784, p = 0.010). Conclusions Our findings indicated that adult-onset MCD patients with IgM mesangial deposition have a high risk of relapses. Therefore, intensive monitoring of disease activity should be considered in MCD adults with IgM mesangial deposition.

Background/Purpose: End-stage kidney disease (ESKD) is a global burden that reflects each country's unique condition. We used the National Health Insurance Research Database (NHIRD) of Taiwan to decipher changes in the mortality and international survival rates and to determine the effectiveness of the pre-end-stage renal disease care program (pre-ESRD care program) to guide future health policies for ESKD. Methods: We conducted a retrospective cohort analysis of the NHIRD data along with records from the catastrophic illness certificate program of ESKD patients from 2010 to 2018. Results: From 2010 to 2018, the annual dialysis-related mortality rate in Taiwan increased from 10.6 to 11.8 deaths per hundred patient-years. The mortality rate for patients below 40 years appears to be decreasing, reflecting the improved quality of care for ESKD patients. Patients above 75 years showed increasing mortality, indicating the prolonged survival and aging of the ESKD population. Patients undergoing dialysis who participated in the pre-ESRD care program had a higher post-dialysis initiation life expectancy than those who did not participate. Among the program enrollees, the post-dialysis initiation life expectancy was higher in patients who had participated for more than one year. Taiwan has one of the highest ESKD patient survival rates globally. Conclusion: From 2010 to 2018, the reduced mortality in young patients and aging of the ESKD population might indicate that the quality of care in Taiwan for ESKD has improved. Furthermore, a better survival rate after dialysis initiation was observed in the pre-ESRD care program participants.

Abstract Background Cardiac surgery–associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence–based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. Methods A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. Results Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772–0.898), whereas the AUC (0.843, 95% CI 0.778–0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model. Conclusions In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

Abstract Background Diabetic nephropathy (DN) is the most prevalent cause of renal disease in type 2 diabetic patients and is usually diagnosed clinically. A kidney biopsy is considered when non-diabetic renal disease (NDRD) is suspected, such as rapid progression in renal function impairment and severe proteinuria. Still, there is yet no consensus on the timing of kidney biopsy in type 2 diabetic patients. This study aims to identify markers that can help differentiate between DN and NDRD and guide the decision of kidney biopsy. Methods We retrospectively reviewed patients with type 2 diabetes who received kidney biopsy from 2008 to 2017 at Taipei Veterans General Hospital. Ophthalmologist consultation and outpatient records, diagnosis of kidney biopsy, laboratory data, and clinical characteristics were collected. Results This study enrolled 160 type 2 diabetic patients, among which 120 (75%) had isolated DN and 40 (25%) had NDRD ± DN (26 had isolated NDRD, and 14 had NDRD superimposed on DN). In multivariate logistic regression analysis, DM duration (odds ratio [OR]: 0.907; 95% confidence interval [CI]: 0.842–0.977; P = 0.01), diabetic retinopathy (OR: 0.196; 95% CI: 0.061–0.627; P = 0.006), and urinary RBC (OR: 1.068; 95% CI: 1.024–1.115; P = 0.002) were independent predictors of NDRD. In patients with diabetic retinopathy (n = 112, 70%), the presence of proliferative diabetic retinopathy, pan-retinal photocoagulation, and hematuria were factors predicting NDRD; and in patients without diabetic retinopathy (n = 48, 30%), short DM duration and hematuria were factors predicting NDRD. Conclusions Using diabetic retinopathy, DM duration, and hematuria, we developed a 3-step approach to stratify patients into three categories with the different likelihoods of having NDRD. Then different strategies could be taken accordingly. Our stepwise approach is easy to follow and may serve as an appropriate and useful tool to help clinicians in making decisions of kidney biopsy in type 2 DM patients presenting with kidney diseases.

BackgroundHemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. ObjectiveWe aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. MethodsThree-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. ResultsTime series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. ConclusionsOur results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.

The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO2 and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.

Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor of nitric oxide synthase and plays a significant role in the pathogenesis of arteriovenous fistula (AVF) dysfunction. The aim of this study is to evaluate the effect of far-infrared (FIR) therapy on the maturation and patency of newly-created AVFs in patients with advanced diabetic kidney disease (DKD) as well as the concurrent change in plasma ADMA. The study enrolled 144 participants with advanced DKD where 101 patients were randomly allocated to the FIR therapy group (N = 50) and control group (N = 51). Patients receiving FIR therapy had a decreased AVF failure rate within 12 months (16% versus 35.3%; p = 0.027); decreased incremental change of ADMA concentration at the 3rd and 12th month; increased AVF blood flow at the 1st, 3rd, and 12th month; increased 3-month physiologic maturation rate (88% versus 68.6%; p = 0.034); increased 1-year unassisted AVF patency rate (84% versus 64.7%; p = 0.017); and increased clinical AVF maturation rate within 12 months (84% versus 62.7%; p = 0.029) compared to the control group. The study demonstrates that FIR therapy can reduce the incremental changes in plasma ADMA concentration, which may be associated with the improvement of AVF prognosis in patients with advanced DKD.

Abstract Background A complete infectious focus survey relies on a thorough physical examination as well as a pelvic examination. Tubo-ovarian abscess, though less likely to occur in senior women, may become a life-threatening disease requiring emergent surgery. Hence, clinical awareness and aggressive management are warranted to avoid delayed diagnosis and subsequent complications. Case presentation We report a post-menopausal woman presented with sepsis of unknown origin, which turned out to be a huge tubo-ovarian abscess. Although tubo-ovarian abscess mostly occurs in women of fertile age, it is likely that the immune status of our post-menopausal patient was compromised because of old age and uremia. Moreover, due to underlying dementia, she could not express her discomfort in the early stage. Her sepsis resolved after a unilateral salpingo-oophorectomy surgery and antibiotic treatment. It is crucial to exclude pelvic inflammatory disease (PID) if no specific source of infection can be identified. Conclusions Rupture of the tubo-ovarian abscess is a condition of high mortality rate. Although tubo-ovarian abscess is more likely to develop in patients aged 15–25 years old, the tubo-ovarian abscess should be listed as a differential diagnosis in all post-menopausal women, especially those who are immunocompromised or with a palpable pelvic mass, to enable timely management and better prognosis.

One of the critical factors of project success is the interface management. Past project interface management mainly relies on knowledge and experience of the project managers. The existing studies of project interface management also mainly focused on the definition and the classification of project interfaces. There is no workable and flexible framework for construction interface problem identification and solutions. This study conducted an analogic study of contradiction matrix at Theory of Inventive Problem Solving (TRIZ) to develop such a framework. This research collected around six hundred interface management cases from civil works and building projects. For comparison, this research defined an experimental group (TRIZ case) and a control group (experience-oriented solution case) based on the concept of clinical experiments in medical science to evaluate and improve the rationality of the matrix. Subsequently, based upon the theory of information retrieval (IR), this research conducted statistical evaluation and TRIZ features remedies to develop generalized construction interface problem identification and solutions. For solving project management interface problems, the TRIZ-analogic construction interface problem identification and solutions framework provides a systematic approach to develop more robust solutions for interface problems in construction engineering rather than just simply relying on intuitive experience.

Background and Objectives: Osteoporosis and anemia are prevalent among chronic kidney disease stage 5D (CKD stage 5D) patients. Osteoblasts are known as the niche cells of hematopoietic stem cells (HSCs) and stimulate HSCs to form blood-cell lineages within bone marrow microenvironments. We hypothesized that an inverse correlation may exist between mean corpuscular volume (MCV), a surrogate for ineffective hematopoiesis, and bone mineral density (BMD) in the CKD stage 5D population. Materials and Methods: This is a cross-sectional designed cohort study evaluating CKD stage 5D patients who have received dialysis therapy for over three months. Baseline clinical characteristics and laboratory data were prospectively collected. The dual-energy X-ray absorptiometry (DXA) method was used to measure BMD at five sites, which were bilateral femoral neck, total hip, and lumbar spine 1–4. The Pearson correlation test was initially adopted, and a multivariate linear regression model was further applied for potential confounder adjustments. Results: From September 2020 to January 2021, a total of 123 CKD stage 5D patients were enrolled. The Pearson correlation test revealed a significant inverse association between MCV and BMD at bilateral femoral neck and lumbar spine. The lowest T-score of the five body sites was determined as the recorded T-score. After adjustments for several potential confounding factors, the multivariate linear regression model found consistent negative associations between T-score and MCV. Conclusions: The present study found significant inverse correlations between MCV and BMD at specific body locations in patients on dialysis. A decreased T-score was also found to be associated with macrocytosis after adjustments for confounding variables. However, direct evidence for the causative etiology was lacking.

The juxta-anastomotic stenosis of an arteriovenous fistula (AVF) is a significant clinical problem in hemodialysis patients with no effective treatment. Previous studies of AV anastomotic angles on hemodynamics and vascular wall injury were based on computational fluid dynamics (CFD) simulations using standardized AVF geometry, not the real-world patient images. The present study is the first CFD study to use angiographic images with patient-specific outcome information, i.e., the exact location of the AVF stenotic lesion. We conducted the CFD analysis utilizing patient-specific AVF geometric models to investigate hemodynamic parameters at different locations of an AVF, and the association between hemodynamic parameters and the anastomotic angle, particularly at the stenotic location. We analyzed 27 patients who used radio-cephalic AVF for hemodialysis and received an angiographic examination for juxta-anastomotic stenosis. The three-dimensional geometrical model of each patient’s AVF was built using the angiographic images, in which the shape and the anastomotic angle of the AVF were depicted. CFD simulations of AVF hemodynamics were conducted to obtain blood flow parameters at different locations of an AVF. We found that at the location of the stenotic lesion, the AV angle was significantly correlated with access flow disturbance (r = 0.739; p < 0.001) and flow velocity (r = 0.563; p = 0.002). Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the AV angle determines the lesion’s flow disturbance with a high area under the curve value of 0.878. The ROC analysis also identified a cut-off value of the AV angle as 46.5°, above or below which the access flow disturbance was significantly different. By applying CFD analysis to real-world patient images, the present study provides evidence that an anastomotic angle wider than 46.5° might lead to disturbed flow generation, demonstrating a reference angle to adopt during the anastomosis surgery.

This study pays more attention to the energy consumption saving, environmental pollution, and health efficiency improvement. We employ the Slack-based measure of Dynamic network Data Envelopment Analysis (DEA) model (DNSBM) to assess the impact of forestry area on annual and overall energy and health efficiency in 2 intertemporal stages, and also put forward on direction and magnitude to be improved respect to the slack variables. For the empirical study, this study employs the 13 countries in the Association of Southeast Asian Nations Plus Three Cooperation (hereinafter referred to as APT) during 2011-2015. From the empirical evidence, it is not easy to raise gross domestic product while reducing energy consumption and PM2.5 emissions to improve energy efficiency. What makes people neglect is the impact of reduced forestry area on health efficiency. Optimistically, all economies are able to adopt measures from policy and technical perspectives, for instance, appropriately adjust energy-related policies, energetically develop innovative energy technologies, and preserve forestry areas, to create a harmonious atmosphere featuring economic development, environmental conservation, and national health and well-being.

Acute kidney injury (AKI) causes a lot of harm to human health but is treated by only supportive therapy in most cases. Recent evidence shows that mesenchymal stem cells (MSCs) benefit kidney regeneration through releasing paracrine factors and extracellular vesicles (EVs) to the recipient kidney cells and are considered to be promising cellular therapy for AKI. To develop more efficient, precise therapies for AKI, we review the therapeutic mechanism of MSCs and MSC-derived EVs in AKI and look for a better understanding of molecular signaling and cellular communication between donor MSCs and recipient kidney cells. We also review recent clinical trials of MSC-EVs in AKI. This review summarizes the molecular mechanisms of MSCs’ therapeutic effects on kidney regeneration, expecting to comprehensively facilitate future clinical application for treating AKI.

Polyacrylonitrile (AN69) filter membranes adsorb cytokines during continuous venovenous hemofiltration (CVVH). Although high-volume hemofiltration has shown limited benefits, the dose-effect relationship in CVVH with AN69 membranes on severe sepsis remains undetermined. This multi-centered study enrolled 266 patients with sepsis-induced multiorgan dysfunction syndrome (MODS) who underwent CVVH with AN69 membranes between 2014 and 2015. We investigated the effects of ultrafiltration rates (UFR) on mortality. We categorized patients that were treated with UFR of 20–25 mL/kg/h as the standard UFR group (n = 124) and those that were treated with a UFR >25 mL/kg/h as the high UFR group (n = 142). Among the patient characteristics, the baseline estimated glomerular filtration rates (eGFR) 2, hemoglobin levels p = 0.005). We concluded that in patients with sepsis-induced MODS, SOFA scores ≥15 predicted a poor rate of survival. High UFR setting >25 mL/kg/h in CVVH with AN69 membranes may reduce the mortality risk in these high-risk patients.

Galectin-3 reportedly participates in the inflammatory process that causes insulin resistance in the target tissues. However, the role of high plasma galectin-3 levels as an indicator of protein-energy wasting (PEW) in patients undergoing maintenance hemodialysis remains unclear. This study included 240 hemodialysis patients (64.5 [55.3−74.0] years, 35.8% women) from a tertiary medical center. A baseline assessment of demographic and clinical data, biochemical parameters, and body composition was conducted. Plasma galectin-3 and other biomarkers were measured using a multiplex bead-based immunoassay. Participants were then divided into two subgroups depending on the median value of plasma galectin-3. Malnutrition was identified using the geriatric nutritional risk index (GNRI) and the criteria of the International Society of Renal Nutrition and Metabolism. Independent risk factors for elevated plasma galectin-3 and malnutrition were identified by multivariate logistic regression. The high galectin-3 group was more likely to be older, have lower lean tissue mass and GNRI scores, be diagnosed with PEW, dialyze through a tunneled catheter, and have higher circulating IL-6, TNF-α, and MCP-1 concentrations than the low galectin-3 group. After multivariate adjustment, only low mean arterial pressure, dialyzing with tunneled cuffed catheters, and elevated systemic inflammatory markers correlated with high galectin-3 levels. Plasma galectin-3 concentrations also increased significantly in hemodialysis patients with PEW. However, compared with other commonly used nutritional indicators, galectin-3 did not show superiority in predicting PEW. Although the plasma galectin-3 levels correlated with PEW severity, this correlation disappeared after adjustment for potential confounding variables (OR, 1.000; 95% CI, 0.999–1.001). In conclusion, plasma galectin-3 is a valuable biomarker for systemic inflammation but is less prominent for PEW in patients with maintenance hemodialysis. Further identification of novel biomarkers is required to detect patients at risk for malnutrition and implement appropriate interventions.

This study introduces the translation adjustment model of Seiford and Zhu (2002) into dynamic DEA models to measure and analyze the dynamic energy efficiency of Asia-Pacific Economic Cooperation (APEC) economies from 2010 to 2014. The APEC economies are divided into annual energy and overall energy efficiency ratings, and improvement directions are proposed for the different variables. With the proposal of magnitude, this study discusses the changes in intertemporal conversion variables and proposes suggestions for improvement. Finally, this study analyzes the implications of energy investment and the efficiency policies of APEC economies. The results show that economies with the lowest overall energy efficiency ratings have great potential for improvement. Reducing capital stock, labor, fossil fuel consumption, and CO2 emissions while increasing GDP can increase energy efficiency ratings. However, economies do not want to reduce the state’s capital stock, and labor and population birth adjustments are difficult. Energy efficiency can only start by adjusting the consumption of fossil fuels, CO2 emissions, and GDP. The results indicate that to improve energy efficiency and reduce fossil fuel consumption and CO2 emissions, economies are expected to increase their GDP unless they enact cuts through policy and technical approaches, appropriately adjust their energy policies, and actively develop new energy technologies to effectively reduce CO2 emissions and achieve optimal energy efficiency.

Protein-energy wasting (PEW) is associated with adverse outcomes in hemodialysis patients. This study compares the simplified creatinine index (SCI) and circulating inflammatory markers as nutritional screening tools for hemodialysis patients. Maintenance hemodialysis patients (230 total patients, 34.8% women, 64.0 ± 14.3 years old) from a tertiary medical center were assessed for demographic data, body composition analysis, biochemistry tests, and circulating inflammatory biomarkers. The SCI was calculated using Canaud’s formula. Reduced fat-free mass index (FFMI), a surrogate of lean body mass, was identified according to the European Society for Clinical Nutrition and Metabolism guidelines. Nutritional status was assessed by the geriatric nutritional risk index (GNRI) and International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Multivariate logistic regression revealed independent risk factors for low FFMI and malnutrition. Of the patients, 47.4% had low FFMI. Patients with a reduction in FFMI tended to be older females with lower body mass index, SCI, and GNRI scores but significantly higher levels of interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and IL-8. SCI was found to be an independent predictor for reduced FFMI (OR 0.57, 95% CI 0.40–0.81) and presence of PEW according to ISRNM criteria (OR 0.38, 95% CI 0.21–0.68). Although a positive association between systemic inflammatory markers and low FFMI was observed, this association disappeared in multivariate analysis. Moreover, the inflammatory markers examined in this study were not associated with malnutrition after adjusting for potential confounders. Compared with markers of systemic inflammation, SCI achieved better performance in assessing the nutritional status of hemodialysis patients.

Abstract BK virus nephropathy (BKVN) and allograft rejection are two distinct disease entities which occur at opposite ends of the immune spectrum. However, they coexist in renal transplant recipients. Predisposing factors for this coexistence remain elusive. We identified nine biopsy-proven BKVN patients with coexisting acute rejection, and 21 patients with BKVN alone. We retrospectively analyzed the dosage and blood concentrations of immunosuppressants during the 3-month period prior to the renal biopsy between the two patient groups. Compared to the BKVN alone group, renal function was noticeably worse in the coexistence group (p = 0.030). Regarding the dose and average drug level of immunosuppressants, there was no difference between the two groups. Interestingly, the coefficient of variance of tacrolimus trough blood level was noticeably higher during the 3-month period prior to the renal biopsy in the coexistence group (p = 0.010). Our novel findings suggest that a higher variability of tacrolimus trough level may be associated with the coexistence of BKVN and acute rejection. Since the prognosis is poor and the treatment is challenging in patients with coexisting BKVN and acute rejection, transplant clinicians should strive to avoid fluctuations in immunosuppressant drug levels in patients with either one of these two disease entities.

Objective: The objective of this study was to investigate the efficacy of second-line versus first-line antibiotics in the prevention of postoperative fever after tubeless percutaneous nephrolithotomy (PCNL). Methods: Three hundred consecutive tubeless PCNL procedures performed at our hospital between August 2010 and December 2013 were enrolled in this study. The efficacy of second-line antibiotics in the prevention PCNL-related urinary traction was reviewed by retrospective chart review. Results: The overall postoperative fever rate was 12% (36 of 300 procedures). The second-line antibiotic prophylaxis was provided in 32 procedures according to preoperative urine culture data or physician's judgment. The first-line prophylactic antibiotic was used in 268 procedures with postoperative fever in 29 procedures (10.8%). The second-line prophylactic antibiotic was used in 32 procedures with postoperative fever in seven procedures (21.9%). No significance difference of postoperative fever rate between first-line or second-line prophylactic antibiotic was found (P = 0.069). Conclusions: The second-line prophylactic antibiotic use for tubeless PCNL does not offer additional benefit over the first-line prophylactic antibiotic in the prevention of postoperative fever or shortening of the postoperative hospital stay.

BK polyomavirus nephropathy (BKVN) and allograft rejection are two closely-associated diseases on opposite ends of the immune scale in kidney transplant recipients. The principle of balancing the immune system remains the mainstay of therapeutic strategy. While patient outcomes can be improved through screening, risk factors identification, and rapid reduction of immunosuppressants, a lack of standard curative therapy is the primary concern during clinical practice. Additionally, difficulty in pathological differential diagnosis and clinicopathology’s dissociation pose problems for a definite diagnosis. This article discusses the delicate evaluation needed to optimize immunosuppression and reviews recent advances in molecular diagnosis and immunological therapy for BKVN patients. New biomarkers for BKVN diagnosis are under development. For example, measurement of virus-specific T cell level may play a role in steering immunosuppressants. The development of cellular therapy may provide prevention, even a cure, for BKVN, a complex post-transplant complication.

Chronic kidney disease (CKD) has long been known to cause significant digestive tract pathology. Of note, indoxyl sulfate is a gut microbe-derived uremic toxin that accumulates in CKD patients. Nevertheless, the relationship between gut microbiota, fecal indole content, and blood indoxyl sulfate level remains unknown. In our study, we established an adenine-induced CKD rat model, which recapitulates human CKD-related gut dysbiosis. Synbiotic treatment in CKD rats showed a significant reduction in both the indole-producing bacterium Clostridium and fecal indole amount. Furthermore, gut microbiota diversity was reduced in CKD rats but was restored after synbiotic treatment. Intriguingly, in our end-stage kidney disease (ESKD) patients, the abundance of indole-producing bacteria, Bacteroides, Prevotella, and Clostridium, is similar to that of healthy controls. Consistently, the fecal indole tends to be higher in the ESKD patients, but the difference did not achieve statistical significance. However, the blood level of indoxyl sulfate was significantly higher than that of healthy controls, implicating that under an equivalent indole production rate, the impaired renal excretion contributes to the accumulation of this notorious uremic toxin. On the other hand, we did identify two short-chain fatty acid-producing bacteria, Faecalibacterium and Roseburia, were reduced in ESKD patients as compared to the healthy controls. This may contribute to gut dysbiosis. We also identified that three genera Fusobacterium, Shewanella, and Erwinia, in the ESKD patients but not in the healthy controls. Building up gut symbiosis to treat CKD is a novel concept, but once proved effective, it will provide an additional treatment strategy for CKD patients.

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R -peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

Background/Aims: Hyperparathyroidism (HPT) is a common complication of chronic kidney disease and contributes to hypertension and cardiovascular risks. Successful kidney transplantation corrects abnormal mineral metabolism, but persistent HPT is still observed in up to 25% of patients one year after transplantation despite renal function improvement. The purpose of this study was to examine the long-term effects of parathyroidectomy (PTX) on blood pressure (BP) and graft function in patients with persistent post-transplant HPT. Methods: This is a retrospective study of renal allograft recipients at a single institute. Records from all the patients who received kidney transplantation at the Taipei Veterans General Hospital between 2004 and 2012 were reviewed and enrolled 19 patients who underwent PTX for persistent post-transplant HPT. Preoperative and postoperative clinical and biochemical data were compared. Matched controls (n = 19) in the corresponding time span were enrolled for graft function comparisons. Results: The mean systolic BP (127.7 ± 14.3 to 119.5 ± 12.7 mmHg, p = 0.028, 1 year after PTX; 127.7 ± 14.3 to 117 ± 12.4 mmHg, p = 0.007, 2 years after PTX) and pulse pressure (PP) (51.3 ± 10.7 to 44.3 ± 11.6, p = 0.019 1 year after PTX; 51.3 ± 10.7 to 44.9 ± 12.5 mmHg, p = 0.028, 2 years after PTX) reduced significantly at 1 year and 2 years of follow-up. However, no significant change of diastolic BP was observed. The improvement of SBP, DBP and PP was not correlated with the reduction of serum calcium level 1 year after PTX. The estimated glomerular filtration rate decreased significantly from 74.0 ± 20.5 mL/min/1.73m2 preoperatively to 68.2 ± 24.8 mL/min/1.73 m2 12 months after PTX but recovered at 15 months and lasted to 2 years after PTX. The all-cause hospitalization rate 1 year after PTX tended to be higher than that 1 year before PTX (105.3 versus 47.4 per 100 patient-years; RR, 2.22; 95 % CI: 0.97-5.54), but there was no significant difference between them. Conclusions: Our study demonstrated systolic BP and PP reduced 2 years after PTX and there was no significant difference between the peri-operative all-cause hospitalization rates. In addition, kidney allograft function impaired temporarily 12 months after PTX, but recovered 15 months after PTX.

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog−digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer−Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

Ischemia-reperfusion injury (IRI) plays a significant role in the pathogenesis of acute kidney injury (AKI). The complicated interaction between injured tubular cells, activated endothelial cells, and the immune system leads to oxidative stress and systemic inflammation, thereby exacerbating the apoptosis of renal tubular cells and impeding the process of tissue repair. Stem cell therapy is an innovative approach to ameliorate IRI due to its antioxidative, immunomodulatory, and anti-apoptotic properties. Therefore, it is crucial to understand the biological effects and mechanisms of action of stem cell therapy in the context of acute ischemic AKI to improve its therapeutic benefits. The recent finding that treatment with conditioned medium (CM) derived from stem cells is likely an effective alternative to conventional stem cell transplantation increases the potential for future therapeutic uses of stem cell therapy. In this review, we discuss the recent findings regarding stem cell-mediated cytoprotection, with a focus on the anti-inflammatory effects via suppression of oxidative stress and uncompromised immune responses following AKI. Stem cell-derived CM represents a favorable approach to stem cell-based therapy and may serve as a potential therapeutic strategy against acute ischemic AKI.

A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert’s law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.

Background: Secondary hyperparathyroidism is associated with vascular calcification and arterial stiffness in patients with end-stage renal disease. The aim of this study was to analyze the frequency of intradialytic hypotension (IDH) and cardiovascular function before and after parathyroidectomy (PTX) in maintenance hemodialysis patients. Methods: We compared predialytic and intradialytic blood pressure, left and right ventricular ejection fraction (LVEF and RVEF), and cardiothoracic ratio 1 month before PTX, and 6 and 12 months after PTX. IDH was defined as a decrease in systolic blood pressure ≥ 20 mmHg or a decrease in mean arterial pressure ≥ 10 mmHg. Results: At the time of PTX, the mean age of the patients was 57.4 ± 12.0 years, and the mean dialysis vintage was 12.2 ± 5.8 years. At baseline, 6 months, and 12 months after PTX, the average numbers of sessions disturbed by IDH during 13 dialysis sessions (1 month) were 6.4, 3.9 (p vs. baseline), and 4.0 sessions (p vs. baseline, p = 0.801 vs. 6 months), respectively. LVEF and RVEF were improved significantly after PTX. Furthermore, volume status was also improved, as evidenced by the significantly greater ultrafiltration volume and reduced cardiothoracic ratio. Conclusions: Hemodialysis patients with severe secondary hyperparathyroidism are more likely to achieve normotensive and euvolemic status after PTX, probably through improved heart function and reduced IDH episodes.

The usage of urine protein/creatinine ratio to estimate daily urine protein excretion is prevalent, but relatively little attention has been paid to the influence of urine concentration and its impact on test accuracy. We took advantage of 24-hour urine collection to examine both urine protein/creatinine ratio (UPCR) and daily urine protein excretion, with the latter as the reference standard. Specific gravity from a concomitant urinalysis of the same urine sample was used to indicate the urine concentration.During 2010 to 2014, there were 540 adequately collected 24h urine samples with protein concentration, creatinine concentration, total volume, and a concomitant urinalysis of the same sample. Variables associated with an accurate UPCR estimation were determined by multivariate linear regression analysis. Receiver operating characteristic (ROC) curves were generated to determine the discriminant cut-off values of urine creatinine concentration for predicting an accurate UPCR estimation in either dilute or concentrated urine samples.Our findings indicated that for dilute urine, as indicated by a low urine specific gravity, UPCR is more likely to overestimate the actual daily urine protein excretion. On the contrary, UPCR of concentrated urine is more likely to result in an underestimation. By ROC curve analysis, the best cut-off value of urine creatinine concentration for predicting overestimation by UPCR of dilute urine (specific gravity ≦ 1.005) was ≦ 38.8 mg/dL, whereas the best cut-off values of urine creatinine for predicting underestimation by UPCR of thick urine were ≧ 63.6 mg/dL (specific gravity ≧ 1.015), ≧ 62.1 mg/dL (specific gravity ≧ 1.020), ≧ 61.5 mg/dL (specific gravity ≧ 1.025), respectively. We also compared distribution patterns of urine creatinine concentration of 24h urine cohort with a concurrent spot urine cohort and found that the underestimation might be more profound in single voided samples.The UPCR in samples with low or high specific gravity is more likely to overestimate or underestimate actual daily urine protein amount, respectively, especially in a dilute urine sample with its creatinine below 38.8 mg/dL or a concentrated sample with its creatinine above 61.5 mg/dL. In particular, UPCR results should be interpreted with caution in cases that involve dilute urine samples because its overestimation may lead to an erroneous diagnosis of proteinuric renal disease or an incorrect staging of chronic kidney disease.

Hemodialysis (HD) patients are under recurrent circulatory stress, and hemodialysis has a high mortality rate. The characteristics of plasma proteomes in patients surviving long-term HD remain obscure, as well as the potential biomarkers in predicting prognoses. This study reports the proteome analyses of patient plasma from non-diabetic long-term HD (LHD, dialysis vintage 14.9±4.1 years, n = 6) and the age/sex/uremic etiology-comparable short-term HD (SHD, dialysis vintage 5.3±2.9 years, n = 6) using 2-DE and mass spectrometry. In addition, a 4-year longitudinal follow-up of 60 non-diabetic HD patients was subsequently conducted to analyze the baseline plasma proteins by ELISA in predicting prognosis. Compared to the SHD, the LHD survivors had increased plasma vitamin D binding proteins (DBP) and decreased clusterin, apolipoprotein A-IV, haptoglobin, hemopexin, complement factors B and H, and altered isoforms of α1-antitrypsin and fibrinogen gamma. During the 45.7±15 months for follow-up of the 60 HD patient cases, 16 patients died. Kaplan-Meier analysis demonstrated that HD patients with the lowest tertile of the baseline plasma DBP level have a significantly higher mortality rate. Multivariate Cox regression analysis further indicated that DBP is an independent predictor of mortality. In summary, the altered plasma proteins in LHD implicated accelerated atherosclerosis, defective antioxidative activity, increased inflammation/infection, and organ dysfunction. Furthermore, lower baseline plasma DBP in HD patients is related to mortality. The results suggest that the proteomic approach could help discover the potential biomarker in HD prognoses.

Ankle-brachial index (ABI) is an accurate tool to diagnose peripheral arterial disease. The aim of this study was to evaluate whether ABI is also a good predictor of renal outcome and cardiovascular events in patients with chronic kidney disease (CKD). We enrolled 436 patients with stage 3–5 CKD who had not been undergoing dialysis. Patients were stratified into two groups according to the ABI value with a cut point of 0.9. The composite renal outcome, including doubling of serum creatinine level and commencement of dialysis, and the incidence of cardiovascular events were compared between the two groups. After a median follow-up period of 13 months, the lower ABI group had a poorer composite renal outcome (OR=2.719, P=0.015) and a higher incidence of cardiovascular events (OR=3.260, P=0.001). Our findings illustrated that ABI is a powerful predictor of cardiovascular events and renal outcome in patients with CKD.

The purpose of this study is to analyze whether there are differences in the impact of corporate social responsibility on its financial performance and stock performance in different industries. The research period is from 2016 to 2018. The samples are selected from the corporate social responsibility awardwinning companies selected by Commonwealth Magazine and Vision Magazine, and the financial information comes from Taiwan Economic News (TEJ). The empirical results show that traditional heavy industries have been established for a long time and the equipment is relatively old, so it needs to pay a relatively high cost to improve. Therefore, in this type of industry, corporate social responsibility has a negative impact on its financial impact, and even if it is a reputation, it has won the evaluation of the public, but its stock characteristics are not easy to fluctuate, so the impact on its stock performance is not significant. On the contrary, in the other two industries, corporate social responsibility will not burden its financial performance, but will positively help its stock performance. Among them, in the electronics industry, corporate social responsibility will positively help its financial performance. It may be that this type of industry has a relatively high bargaining power for products and relatively advanced technology, so it can transfer the cost of corporate responsibility invested to the selling price of the product. [ABSTRACT FROM AUTHOR]