Urinary catheter placement and adverse urinary outcomes with a focus on elevated risk in men with indwelling Foley catheters.

BACKGROUND: Prior studies indicated a link between urinary catheter use and urinary complications, highlighting the need for comprehensive, gender-specific investigations. This study explored the association through a national retrospective cohort, emphasizing gender disparities and long-term outcomes. METHODS: Our study utilized data from the entire population covered by Taiwan's National Health Insurance Research Database from 2000 to 2017. We included 148,304 patients who had undergone Foley catheter placement and their propensity-scores matched controls in the study. We evaluated urinary complications, which encompassed urinary tract cancer, urolithiasis, urethral stricture, obstructive uropathy, reflux uropathy, fistula, diverticulum, caruncle, false passage, prolapsed urethral mucosa, urinary tract rupture, and urinary tract infection. These were assessed using the Fine and Gray sub-distribution proportional hazards model to compare between the Foley and non-Foley groups. Sensitivity analyses were conducted with different matching ratios. RESULTS: In the study, the non-Foley group presented a marginally higher mean age (75.24 ± 10.47 years) than the Foley group (74.09 ± 10.47 years). The mean duration of Foley catheterization was 6.1 ± 4.19 years. Men with Foley catheterization exhibited the highest adjusted sub-distribution hazard ratios for urinary tract cancer (6.57, 95% CI: 5.85-7.37), followed by women with Foley catheterization (4.48, 95% CI: 3.98-5.05), and men without catheterization (1.58, 95% CI: 1.39-1.8) in comparison with women without the procedure. Furthermore, men with Foley catheterization were found to be at the greatest risk for complications such as urolithiasis, urethral stricture, obstructive and reflux uropathy, fistula, diverticulum, caruncle, false passage, prolapsed urethral mucosa, and urinary tract rupture. Conversely, women with urinary catheterization were most susceptible to urinary tract infections. CONCLUSIONS: The evidence confirms that urinary catheterization significantly increases urinary complications, particularly among men. Our study underscores the crucial need for healthcare providers to carefully evaluate the necessity of catheterization, aim to shorten its duration whenever feasible, and strictly adhere to established protocols to minimize complications.

Andrographolide Relieves Post-Operative Wound Pain but Affects Local Angiogenesis.

Andrographolide (Andro), the major constituent of Andrographis paniculata Nees (Acanthaceae), is was known to reduces inflammatory reaction. In the current study, the ability of Andro to reduce pain sensation in a rat post-operative wound model was explored. The hind paws of 18 Sprague-Dawley rats (SD) bearing post-operative wounds received the following three treatments: Saline, Andro via direct injection into the paw (Andro-injected) and Tablet containing Andro + poly (lactic-co-glycolic acid) (PLGA) (Andro-tablet). Von Frey tests assessed mechanical allodynia at 1, 3, 5 h and 1-, 2-, 3-, 4-, and 5-days post-operation. Behavioral analyses were performed to measure reaction threshold and reaction frequencies. Immunoreactivity of p-ERK and GluR1 was examined in the dorsal horn of the spinal cord. Histopathological and immunostaining studies were conducted on paw epidermis to observe the gross morphology and angiogenesis. The threshold for inducing allodynia increased and the reaction frequency reduced in the Andro-injected group compared to the saline-group, at 3 h post-surgery and the effect lasted between 3-4 days. The threshold for inducing pain and reaction frequency for the Andro-tablet group did not differ from the saline-treated group. The levels of p-ERK and GluR1 in the dorsal horn were reduced after Andro treatment. No significant difference in wound healing index was observed between saline and Andro-injected groups, but CD-31 staining showed less angiogenesis in the Andro-injected group. Andro significantly reduced mechanical allodynia compared to saline treatment, both in shorter and longer time frames. Furthermore, Andro influenced the expression of p-ERK and GluR1 in the dorsal horn, and the angiogenesis process in the wound healing area.

Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.

RATIONALE AND OBJECTIVES: Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS: Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS: When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION: Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.

Association between the risk of heart failure hospitalization and end-stage renal disease with digoxin usage in patients with cardiorenal syndrome: A population-based study.

Background: The management of the coexistence of heart disease and kidney disease is increasingly challenging for clinicians. Chronic kidney disease (CKD) is not only a prevalent comorbidity of patients with heart failure but has also been identified as a noteworthy risk factor for all-cause mortality and poor clinical outcomes. Digoxin is one of the commonest treatments for heart disease. There are few trials investigating the role of digoxin in patients with cardiorenal syndrome (CRS). This study aims to examine the association between digoxin usage and clinical outcomes in patients with CRS in a nationwide cohort. Method: We conducted a population-based study that included 705 digoxin users with CRS; each patient was age, sex, comorbidities, and medications matched with three non-users who were randomly selected from the CRS population. Cox proportional hazards regression analysis was conducted to estimate the effects of digoxin on the incidence of all-cause mortality, congestive heart failure (CHF) hospitalization, coronary artery disease (CAD) hospitalization, and end-stage renal disease (ESRD). Results: The all-cause mortality rate was significantly higher in digoxin users than in non-users (adjusted hazard ratio [aHR] = 1.26; 95% confidence interval [CI] = 1.09-1.46, p = 0.002). In a subgroup analysis, there was significantly high mortality in the 0.26-0.75 defined daily dose (DDD) subgroup of digoxin users (aHR = 1.49; 95% CI = 1.23-1.82, p<0.001). Thus, the p for trend was 0.013. With digoxin prescription, the CHF hospitalization was significantly higher [subdistribution HR (sHR) = 1.17; 95% CI = 1.05-1.30, p = 0.004], especially in the >0.75 DDD subgroup (sHR = 1.19; 95% CI = 1.01-1.41, p = 0.046; p for trend = 0.006). The digoxin usage lowered the coronary artery disease (CAD) hospitalization in the > 0.75 DDD subgroup (sHR = 0.79; 95% CI = 0.63-0.99, p = 0.048). In renal function progression, more patients with CRS entered ESRD with digoxin usage (sHR = 1.34; 95% CI = 1.16-1.54, p<0.001). There was a significantly greater incidence of ESRD in the < 0.26 DDD and 0.26-0.75 DDD subgroups of digoxin users (sHR = 1.32; 95% CI = 1.06-1.66, p = 0.015; sHR = 1.44; 95% CI = 1.18-1.75; p for trend<0.001). Conclusion: Digoxin should be prescribed with caution to patients with CRS.

Deferoxamine accelerates endothelial progenitor cell senescence and compromises angiogenesis.

Senescence reduces the circulating number and angiogenic activity of endothelial progenitor cells (EPCs), and is associated with aging-related vascular diseases. However, it is very time-consuming to obtain aged cells (~1 month of repeated replication) or animals (~2 years) for senescence studies. Here, we established an accelerated senescence model by treating EPCs with deferoxamine (DFO), an FDA-approved iron chelator. Four days of low-dose (3 µM) DFO induced senescent phenotypes in EPCs, including a senescent pattern of protein expression, impaired mitochondrial bioenergetics, altered mitochondrial protein levels and compromised angiogenic activity. DFO-treated early EPCs from young and old donors (< 35 vs. > 70 years old) displayed similar senescent phenotypes, including elevated senescence-associated ß-galactosidase activity and reduced relative telomere lengths, colony-forming units and adenosine triphosphate levels. To validate this accelerated senescence model in vivo, we intraperitoneally injected Sprague-Dawley rats with DFO for 4 weeks. Early EPCs from DFO-treated rats displayed profoundly senescent phenotypes compared to those from control rats. Additionally, in hind-limb ischemic mice, DFO pretreatment compromised EPC angiogenesis by reducing both blood perfusion and capillary density. DFO thus accelerates EPC senescence and appears to hasten model development for cellular senescence studies.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.

Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas. Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient. Results: Gross total resection (Simpson grades I-III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003). Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.

Effects and safety of body positioning on back pain after transcatheter arterial chemoembolization in people with hepatocellular carcinoma: A randomized controlled study.

BACKGROUND: People with hepatocellular carcinoma who undergo transcatheter arterial chemoembolization usually experience back pain due to lie supine for at least 4 hours to avoid bleeding and hematoma. Body positioning is an effective and safe method for decreasing back pain in people with transfemoral cardiac catheterization; however, its effects and safety among patients with high bleeding tendency are unknown. OBJECTIVE: To investigate whether body positioning could decrease back pain without increasing the chance of bleeding after transcatheter arterial chemoembolization. DESIGN: A single-blind randomized controlled trial (ClinicalTrials.gov No.: NCT03784469). METHODS: A total of 78 people with liver cancer who had undergone chemoembolization through the femoral artery were enrolled. Each person was randomly assigned to either the control or intervention group (each consisted of 39 participants). The control group received the usual care, remaining flat and lying in a supine position, whereas the intervention group had their positions changed in the second and fourth hour after chemoembolization. Participants' pain level was rated by using numerical rating scale -11 (score from 0 to 10), bleeding was measured by using volume of blood (cc.) in gauze and hematoma size in diameter (cm), and satisfaction was self-rated from 1 to 5. Repeated-measure analysis of variance (ANOVA) was used to compare the difference in pain levels over time within each group and independent t test to compare the mean difference of pain between groups at 5 endpoints, both methods with Bonferroni adjustment. Independent t test, chi-squared test, and Fisher's exact test compared postembolization discomfort, puncture sites bleeding, satisfaction between groups. RESULTS: Significant changes of pain levels over time in both intervention [F(2.93, 111.20)=7.64, p<.001] and control groups [F(2.66, 101.17)=20.55, p<.001]. The intervention group had a significantly lower mean pain score in the second hour (t = -2.838, p = .006) and fourth hour (t = -4.739, p < .001) when patients turning to the side than did the control group lying supine. Furthermore, patients in the intervention group had significantly higher satisfaction than did those in the control group (t = -2.422, p = .018). No hematoma and significant difference of post-procedural bleeding between groups. CONCLUSION: Changing patients' body positions in bed after transcatheter arterial chemoembolization is a safe and effective method of decreasing back pain, and increasing patients' satisfaction, without increasing the complications of bleeding and hematoma. Clinicians should change the positions of people with hepatocellular carcinoma 2 hours after they receive transcatheter arterial chemoembolization.

Neuroprotection in the Acute Stage Enables Functional Recovery Following Repair of Chronic Cervical Root Transection After a 3-Week Delay.

BACKGROUND: Preganglionic cervical root transection (PCRT) is the most severe type of brachial plexus injury. In some cases, surgical procedures must be postponed for ≥3 wk until electromyographic confirmation. However, research works have previously shown that treating PCRT after a 3-wk delay fails to result in functional recovery. OBJECTIVE: To assess whether the immunosuppressive drug sirolimus, by promoting neuroprotection in the acute phase of PCRT, could enable functional recovery in cases of delayed repair. METHODS: First, rats received a left 6th to 8th cervical root transection, after which half were administered sirolimus for 1 wk. Markers of microglia, astrocytes, neurons, and autophagy were assessed at days 7 and 21. Second, animals with the same injury received nerve grafts, along with acidic fibroblast growth factor and fibrin glue, 3 wk postinjury. Sirolimus was administered to half of them for the first week. Mechanical sensation, grasping power, spinal cord morphology, functional neuron survival, nerve fiber regeneration, and somatosensory-evoked potentials (SSEPs) were assessed 1 and 23 wk postinjury. RESULTS: Sirolimus was shown to attenuate microglial and astrocytic proliferation and enhance neuronal autophagy and survival; only rats treated with sirolimus underwent significant sensory and motor function recovery. In addition, rats who achieved functional recovery were shown to have abundant nerve fibers and neurons in the dorsal root entry zone, dorsal root ganglion, and ventral horn, as well as to have SSEPs reappearance. CONCLUSION: Sirolimus-induced neuroprotection in the acute stage of PCRT enables functional recovery, even if surgical repair is performed after a 3-wk delay.

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas.

OBJECTIVES: A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs. METHODS: Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features. RESULTS: Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001). CONCLUSIONS: Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.

Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

RATIONALE AND OBJECTIVES: Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI. MATERIALS AND METHODS: Two datasets were used. The training set was MRI of 286 patients with unilateral breast cancer. The segmentation was done on the contralateral normal breasts. The ground truth for the breast and fibroglandular tissue (FGT) was obtained by using a template-based segmentation method. The U-net deep learning algorithm was implemented to analyze the training set, and the final model was obtained using 10-fold cross-validation. The independent validation set was MRI of 28 normal volunteers acquired using four different MR scanners. Dice Similarity Coefficient (DSC), voxel-based accuracy, and Pearson's correlation were used to evaluate the performance. RESULTS: For the 10-fold cross-validation in the initial training set of 286 patients, the DSC range was 0.83-0.98 (mean 0.95 ± 0.02) for breast and 0.73-0.97 (mean 0.91 ± 0.03) for FGT; and the accuracy range was 0.92-0.99 (mean 0.98 ± 0.01) for breast and 0.87-0.99 (mean 0.97 ± 0.01) for FGT. For the entire 224 testing breasts of the 28 normal volunteers in the validation datasets, the mean DSC was 0.86 ± 0.05 for breast, 0.83 ± 0.06 for FGT; and the mean accuracy was 0.94 ± 0.03 for breast and 0.93 ± 0.04 for FGT. The testing results for MRI acquired using four different scanners were comparable. CONCLUSION: Deep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI. It may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.

In Vivo Real-Time Discrimination Among Glioma, Infiltration Zone, and Normal Brain Tissue via Autofluorescence Technology.

BACKGROUND: Surgery is the first-line therapy for glioblastoma. There is evidence that extent of resection is significantly associated with patient survival. Unfortunately, optimal surgical resection is usually limited because of the difficulty in discriminating tumor-infiltrated region and normal brain tissue. This study aimed to develop a tool to distinguish between infiltration zone and normal tissue in real time during glioma surgery. METHODS: In an in vivo study, C6 glioma cells were implanted into the left cerebral hemispheres of 6 rats to mimic tumorigenesis. A newly designed optical fiber-embedded needle probe was used to measure the autofluorescence of both cerebral hemispheres at various depths 5 days after the implantation. These rats were then sacrificed, and both cerebral hemispheres were removed for histopathologic analysis. RESULTS: Comparative analyses of corresponding areas by histopathology and autofluorescence revealed highly significant (P < 0.001) differences among the normal tissue, infiltration zone, tumors, and the contralateral cerebral hemispheres. The area of the receiver operating characteristic curve was 0.978, and the sensitivity and specificity of tumor delineation were 93.9% and 94.4%, respectively. CONCLUSIONS: The newly designed in vivo fiber-optic probe can distinguish tumor-infiltration zones from normal brain tissue in this in vivo study. Therefore, it may help neurosurgeons to increase extent of resection without damaging normal brain tissue and thus potentially improve the patients' survival and quality of life.

Which obesity index is the best predictor for high cardiovascular disease risk in middle-aged and elderly population?

BACKGROUND AND AIMS: The aim was to investigate the relationships between visceral fat rating scale (VFR), waist circumference (WC), body mass index (BMI) and cardiovascular disease (CVD) risk. METHODS: In this cross-sectional, community-based study, participants completed questionnaire that included personal and medical history, and underwent anthropometric measurement and blood sampling. The 2008 general CVD risk model was used to predict CVD risk. Associations between CVD risk and VFR, WC, BMI were evaluated by means of analysis of covariance (ANCOVA) with gender as covariate, Chi-squared test, Pearson's correlation, Cochran-Armitage test, multivariate logistic regression and receiver operating characteristic curves. RESULTS: A total of 377 people were enrolled. A significant association was identified between VFR, WC, BMI, and CVD risk, with coefficient of determination (r2) of 0.32 (p < 0.001), 0.18 (p < 0.001) and 0.03 (p = 0.001), respectively. There was a trend toward increasing prevalence of high CVD risk as VFR, WC, and BMI increased (all p values <0.05). Multivariate logistic regression revealed VFR (OR = 1.21; 95%CI = 1.02-1.24), WC (OR = 1.07; 95%CI = 1.04-1.11) and BMI (OR = 1.11; 95%CI = 1.02-1.21) to be independent predictors of high CVD risk. In male, the area under curves of VFR and WC are greater than BMI: 0.641, 0.647 and 0.562. In female, the area under curves of VFR and WC are also greater than BMI: 0.656, 0.688 and 0.601. CONCLUSIONS: VFR and WC were more strongly associated with high CVD risk than BMI among middle-aged and elderly persons in Taiwan.

Decreased incidence of diabetes in patients with gout using benzbromarone.

Objective: Insulin resistance is inversely correlated with the clearance rate of uric acid, which may indicate that improvement in the clearance rate of uric acid could reduce insulin resistance. Considering the increased prevalence of diabetes mellitus (DM) in the gout population, this study evaluated the effects of benzbromarone, a uricosuric agent, on the incidence of DM in the gout population. Methods: We used data from the Taiwan National Health Insurance program. The benzbromarone user cohort included 8678 patients; each patient was age- and sex-matched with one benzbromarone non-user who was randomly selected from the gout population. The Cox proportional hazard regression analysis was conducted to estimate the effects of benzbromarone on the incidence of DM in the gout population. Results: The incidence of DM was significantly lower in benzbromarone users than in benzbromarone non-users [adjusted hazard ratio (HR) = 0.86; 95% CI: 0.79, 0.94]. The HR for the incidence of DM was lower in male benzbromarone users (adjusted HR = 0.77; 95% CI: 0.69, 0.86) than in benzbromarone non-users. An analysis of three age groups (<40, 40-59 and ⩾60 years) indicated that the HRs of the age groups of 40-59 years (adjusted HR = 0.86; 95% CI: 0.76, 0.98) and ⩾60 years (adjusted HR = 0.82; 95% CI: 0.71, 0.94) were significantly lower among benzbromarone users than among benzbromarone non-users. Conclusion: In the gout population, the incidence of DM was lower in benzbromarone users than in benzbromarone non-users.

Decreased incidence of gout in diabetic patients using pioglitazone.

Objective: The incidence and prevalence of gout are increasing, but the management is poor. Considering the increased prevalence of gout in the diabetic population, this study evaluated the effects of pioglitazone, an insulin resistance inhibitor, on the incidence of gout in the diabetic population. Methods: We used data from the National Health Insurance program in Taiwan. The pioglitazone cohort contained 30 100 patients and each patient was age and sex matched with three non-pioglitazone users who were randomly selected from the diabetic population. Cox proportional hazards regression analysis was conducted to estimate the effects of pioglitazone on the incidence of gout in the diabetic population. Results: The incidence of gout was significantly lower in pioglitazone users than in non-pioglitazone users [adjusted hazard ratio (aHR) 0.81 (95% CI 0.78, 0.85)]. The HR for the incidence of gout was lower in both male [aHR 0.80 (95% CI 0.75, 0.85)] and female [aHR 0.83 (95% CI 0.78, 0.88)] pioglitazone users than in non-pioglitazone users. An analysis of three age groups (<40, 40-59 and ⩾60 years) revealed that the HRs of both the 40-59 years [aHR 0.78 (95% CI 0.73, 0.83)] and the ⩾60 years [aHR 0.85 (95% CI 0.80, 0.91)] age groups were significantly lower among pioglitazone users than non-pioglitazone users. Conclusion: Compared with the non-pioglitazone users, the incidence of gout in the diabetic population using pioglitazone was less.

Association between self-reported sleep duration and serum lipid profile in a middle-aged and elderly population in Taiwan: a community-based, cross-sectional study.

OBJECTIVES: The association between sleep duration and serum lipid profile in the middle-aged and the elderly is unclear. The aim of this study was to investigate and evaluate the relationships between sleep duration and levels of serum total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C) and triglycerides in these populations. DESIGN: Cross-sectional observational study. SETTING: Community-based investigation in Guishan Township of northern Taiwan. PARTICIPANTS: A total of 400 community-dwelling middle-aged and elderly individuals were enrolled. All participants underwent a baseline assessment in 2014, which included anthropometrics, blood samples and self-administered questionnaires. Participants were classified into three groups based on their sleep duration. OUTCOME MEASURES: Multivariate logistic regression was used to obtain ORs and 95% CIs to assess the relationship between sleep duration and lipid profiles. RESULTS: Participant mean age was 64.5 years and 35.3% were men. Subjects with longer (>7 hours) and shorter (<6 hours) nightly sleep duration had a higher prevalence of low HDL-C levels (HDL <40 mg/dL) than those with moderate sleep duration (6-7 hours). Multivariate logistic regression revealed that, compared with individuals with sleep duration of 6-7 hours, the ORs of having low HDL-C were 3.68 (95% CI 1.59 to 8.49) greater for individuals with sleep duration of <6 hours and 2.89 (95% CI 1.10 to 7.61) greater for individuals with sleep duration of >7 hours. CONCLUSIONS: There was a U-shaped relationship between sleep duration and HDL-C levels. Sleep duration >7 hours or <6 hours increased the risk of low serum HDL-C levels.

Data on the expression of leptin and leptin receptor in the dorsal root ganglion and spinal cord after preganglionic cervical root avulsion.

Leptin (Lep) is mainly, although not exclusively, secreted by adipocytes. In addition to regulating lipid metabolism, it is also a proinflammatory factor and involved in the development of neuropathic pain after peripheral nerve injuries (PNI) (Lim et al., 2009; Maeda et al., 2009) [1,2]. Leptin or its messenger ribonucleic acid expression has been found in various brain regions normally and in the dorsal horn after PNI (Lim et al., 2009; Ur et al., 2002; La Cava et al., 2004; White et al., 2004) [1,[3], [4], [5]]. However, the expression pattern of Lep and Leptin receptor (LepR) after preganglionic cervical root avulsion (PCRA) is still unknown. We provide data in this article related to Chang et al. (2017) [6]. Here, our data showed a profound Lep and LepR expression in the neurons of dorsal root ganglion (DRG) after PCRA. Moreover, the expression of Lep and LepR were also identified in significant portions of the neurons and microglia located in the dorsal horn. The roles of these increased expressions in the development of neuropathic pain after PCRA deserve further study.