Learnable sparse dictionary compressed sensing for channeled spectropolarimeter.

Channeled spectropolarimetry enables real-time measurement of the polarimetric spectral information of the target. A crucial aspect of this technology is the accurate reconstruction of Stokes parameters spectra from the modulated spectra obtained through snapshot measurements. In this paper, a learnable sparse dictionary compressed sensing method is proposed for channeled spectropolarimeter (CSP) spectral reconstruction. Grounded in the compressive sensing framework, this method defines a variable sparse dictionary. It can learn prior knowledge from the measured modulated spectra, continuously optimizing its own structure and parameters iteratively by removing redundant basis functions and refining the matched basis functions. The learned sparse dictionary, post-training, can provide a more accurate sparse representation of the Stokes parameters spectra, enabling the proposed method to achieve more precise reconstruction results. To assess the efficacy of the proposed method, simulations and experiments were conducted, both of which consistently demonstrated the superior performance of the proposed approach. The suggested method is well-positioned to enhance the efficiency and accuracy of polarimetric spectral information retrieval in CSP applications.

Comparative Cardiovascular Risks of Febuxostat and Allopurinol in Patients with Diabetes Mellitus and Chronic Kidney Disease.

BACKGROUND Hyperuricemia, which is common in chronic kidney disease and diabetes mellitus patients, raises health concerns. Febuxostat, a first-line urate-lowering agent, prompts cardiovascular risk questions, especially in high-risk patients. This study compared the effects of febuxostat and allopurinol on cardiovascular risk in diabetes mellitus and chronic kidney disease patients. MATERIAL AND METHODS This retrospective observational cohort study, conducted using Taiwan's National Health Insurance Research Database, focused on patients diagnosed with chronic kidney disease and diabetes between January 2012 and December 2017. The study population was divided into 2 groups: allopurinol users (n=12 901) and febuxostat users (n=2997). We performed 1: 1 propensity score matching, resulting in subgroups of 2997 patients each. The primary outcomes were assessed using a competing risk model, estimating hazard ratios (HR) for long-term outcomes, including the risks of all-cause hospitalization, hospitalization for heart failure, and hospitalization for cardiovascular interventions. RESULTS Febuxostat users, compared to allopurinol users, had higher all-cause hospitalization (HR: 1.33; 95% confidence interval [CI]: 1.25 to 1.42; P<.001), hospitalization for heart failure (HR: 1.62; 95% CI: 1.43 to 1.83; P<.001), and hospitalization for cardiovascular interventions (HR: 1.51; 95% CI: 1.32 to 1.74; P<.001). Moreover, the adverse effects of febuxostat on cardiac health were consistent across most subgroups. CONCLUSIONS Use of febuxostat in patients with diabetes mellitus and chronic kidney disease is associated with higher cardiovascular risks compared to allopurinol. Prudent evaluation is essential when recommending febuxostat for this at-risk group.

Association between urinary glyphosate levels and hand grip strength in a representative sample of US adults: NHANES 2013-2014.

Introduction: Glyphosate, a widely utilized herbicide globally, has been linked to various health issues, including cancer, birth abnormalities, and reproductive issues. Additionally, there is growing experimental support indicating potential harm to skeletal muscles. Despite this, the impact of glyphosate on human muscle health remains unclear. Methods: We examined information gathered from the 2013-2014 National Health and Nutrition Examination Survey (NHANES), which included 1466 adults aged 18 or older. Our primary aim was to investigate the relationship between glyphosate exposure and hand grip strength, as well as its influence on lean muscle mass. Results and discussion: Our investigation uncovered a detrimental correlation between glyphosate exposure and all measures of grip strength, except for the second test of the first hand. Specifically, we observed a statistically significant adverse association between glyphosate exposure and combined grip strength, which is calculated as the sum of the highest readings from both hands (ß coefficient of -2.000, S.E. = 0.891, p = 0.040). We did not observe a significant correlation between glyphosate levels, lean muscle mass, and the likelihood of reaching maximum grip strength meeting sarcopenia criteria. Additionally, we observed an interaction between age and glyphosate, as well as between body mass index (BMI) and glyphosate, concerning the association with combined grip strength. In this comprehensive analysis of NHANES data, our study reveals a potential association between glyphosate exposure and hand grip strength in the adult population. Our findings suggest the need for deeper exploration into the health effects of glyphosate exposure and its impact on muscle strength, shedding light on possible public health concerns.

Discovery of Anti-Hypercholesterolemia Agents Targeting LXRα from Marine Microorganism-Derived Natural Products.

A strategy integrating in silico molecular docking with LXRα and phenotypic assays was adopted to discover anti-hypercholesterolemia agents in a small library containing 205 marine microorganism-derived natural products, collected by our group in recent years. Two fumitremorgin derivatives, 12R,13S-dihydroxyfumitremorgin C (1) and tryprostatin A (3), were identified as potential LXRα agonists, by real-time qPCR and Western blot (WB) analysis, together with a surface plasmon resonance (SPR) assay. The anti-hypercholesterolemic effects of 1 and 3, together with their mechanisms, were investigated in depth using different cell and mouse models, among which the study of LXRα is of crucial importance. Compound 1 or 3 exhibited the capacity to effectively reverse excessive lipid accumulation in a hepatic steatosis cell model and significantly reduce liver damage and blood cholesterol levels in high cholesterol diet (HCD)-fed wild-type mice, whereas those beneficial effects were completely nullified in HCD-fed LXRα-knockout mice. Furthermore, 1 and 3 outperformed common LXRα agonists by suppressing the expression of sterol regulatory element-binding protein 1 (SREBP1) in HCD-fed mice, mitigating lipotoxicity. Thus, this study highlights the discovery of two marine microorganism-derived anti-hypercholesterolemia agents targeting LXRα.

A collaborative model between dialysis clinics and a hospital center improves the quality of vascular access care and intervention for hemodialysis patients.

BACKGROUND: This study reports the outcomes of a collaborative program between dialysis clinics and a referral hospital, which consisted of clinical monitoring and supplementary routine surveillance, for improving the quality of vascular access care. METHODS: This retrospective observational study was performed at five dialysis clinics as part of a 2-year collaborative program (2019-2020) in conjunction with a hospital-based dialysis access management center. A total of 392 hemodialysis patients (arteriovenous fistula [AVF], n = 339 and arteriovenous graft [AVG], n = 53) were included. Outcome measures included the prognosis of vascular access, clinic satisfaction, and referral rate to the hospital. RESULTS: Increased vascular access flow was observed and critical flow events decreased from the first to the second year (AVF: 18.3% vs. 12.7%, p < 0.001; AVG: 26.2% vs. 20.1%, p = 0.30). There were fewer percutaneous transluminal angioplasty events in the AVG group (0.77 per person-year vs. 0.51 per person-year, p = 0.005). New AVF or AVG creation events also remained low. All dialysis clinics were satisfied with the program. The overall referral rate from the participating clinics increased (65.7% vs. 72.0%) during the study period independently of the physical distance between the dialysis clinic and the hospital. CONCLUSION: The collaboration between dialysis clinics and a referral hospital for improving the quality of vascular access care was successful in this study, and the model can be used by other clinics and hospitals looking to improve care coordination in dialysis patients.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Association of mono-2-ethylhexyl phthalate with adverse outcomes in chronic hemodialysis patients.

Phthalate exposure is widespread and has a global impact. Growing evidence shows that mono-2-ethylhexyl phthalate (MEHP) exposure has a negative impact on human health. However, whether MEHP exposure is associated with mortality and other adverse outcomes in hemodialysis patients remains unknown. This study prospectively enrolled 217 patients on maintenance hemodialysis from June 30, 2021, to August 16, 2022. Baseline serum MEHP, di-2-ethylhexyl phthalate (DEHP), and indoxyl sulfate (IS) concentrations were measured. Primary endpoints were all-cause mortality or composite adverse outcomes, including all-cause death plus hospitalization due to cardiovascular disease, heart failure, stroke, infection, or cancer. Serum MEHP concentrations were positively associated with DEHP but not indoxyl sulfate concentrations in hemodialysis patients. Additionally, serum MEHP concentrations were significantly and independently associated with all-cause mortality and composite adverse outcomes (adjusted hazard ratios [HRs], 1.04 and 1.03 per ng/mL, 95% confidence intervals [CIs], 1.01-1.07 and 1.00-1.05; p = 0.016 and 0.015, respectively). We found a cutoff value of MEHP for predicting both endpoints. Patients with serum MEHP concentrations of ≥ 41.8 ng/mL had much higher risks for all-cause mortality and composite adverse outcomes (adjusted HRs, 39.2 and 13; 95% CIs, 2.44-65.7 and 2.74-61.4; p = 0.011 and 0.001, respectively). MEHP exposure is significantly associated with higher risks for all-cause mortality and composite adverse outcomes. Hemodialysis patients with serum MEHP concentrations above 41.8 ng/mL had much poorer prognoses regarding both outcomes.

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.

Background and objectives: Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5. Design: A retrospective cohort study. Setting and participants: A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021. Methods: This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM). Main outcome measurement: Subsequent eGFR level after three consecutive laboratory data assessments. Results: Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM. Conclusion: The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.

A New Predictive Equation for Estimating Serum Ionized Calcium Levels in Patients on Chronic Hemodialysis.

BACKGROUND Circulating calcium mainly carries out its physiologic function in its ionized form (iCa). Clinically, iCa is usually estimated by multiplying the total calcium (TCa) level by 0.5 in the general population, but this method is not accurate when applied to patients on long-term hemodialysis (CHD). Accordingly, this study aimed to develop a predictive function for iCa in patients on CHD by incorporating TCa and other additional variables. MATERIAL AND METHODS This was a retrospective cross-sectional study consisting of 2 cross-sectional datasets: a derivation set including 469 CHD patients in June 2019, and a validation set including 446 CHD patients in September 2019. The derivation set's data were analyzed using the stepwise model selection of machine learning with 10-fold cross-validation to develop a predictive function for iCa. This predictive function was then applied to the validation set's data, and the predictive function's estimated iCa was compared with the actual laboratory iCa by using the paired-samples t test and intraclass correlation coefficient. RESULTS After analyzing the routine laboratory data parameters of patients in the derivation set, the following 5 variables were included in the predictive function of iCa: blood urea nitrogen, creatinine, phosphate, TCa, and albumin. This predictive function was applied to the validation set to yield an estimated iCa level that was not significantly different from the laboratory-measured iCa level of the validation dataset (P=0.676) with an excellent ICC of 0.905. CONCLUSIONS We developed a new predictive function that accurately measures the iCa in patients on CHD by using routine laboratory data.

Impact of Pre-Transplant Parathyroidectomy on Graft Survival: A Comparative Study of Renal Transplant Patients (2005-2015).

BACKGROUND Hyperparathyroidism poses significant risks for patients prior to kidney transplantation. However, the outcomes of patients who undergo parathyroidectomy before renal transplantation compared to those without such a procedure remain uncertain. This real-world data study aimed to examine the clinical outcomes of both patient groups. MATERIAL AND METHODS Using the Taiwan National Health Insurance Research Database, we conducted a retrospective cohort study on patients who underwent renal transplantation between January 2005 and December 2015. The patients were divided into two groups: a case group (n=294) with parathyroidectomy and a control group (n=588) without the need for parathyroidectomy before kidney transplantation. The groups were matched based on age, sex, dialysis vintage, and baseline characteristics at a 1:2 ratio. Hazard ratios (HR) were estimated using the Cox regression model. The main outcomes assessed were graft failure, mortality, and major adverse cardiovascular events (MACE) recorded until December 2019. RESULTS During a mean follow-up period of 6 years, a significant difference was observed in graft failure (HR 1.40; 95% confidence interval 1.10-1.79, p=0.007) between the two groups. After further adjustment, graft failure remained significant (HR 1.52; 95% CI 1.07-2.15, p=0.019). Additionally, machine learning-based feature selection identified the importance of parathyroidectomy (ranked 9 out of 11) before kidney transplantation in predicting subsequent graft failure. CONCLUSIONS Our study demonstrates that severe hyperparathyroidism requiring parathyroidectomy before kidney transplantation may contribute to poor post-transplant graft outcomes compared to patients who do not require parathyroidectomy.

Polarization-selective narrow band dual-toroidal-dipole resonances in a symmetry-broken dielectric tetramer metamaterial.

Here we propose a metasurface consisting of symmetry-broken dielectric tetramer arrays, which can generate polarization-selective dual-band toroidal dipole resonances (TDR) with ultra-narrow linewidth in the near-infrared region. We found, by breaking the C4v symmetry of the tetramer arrays, two narrow-band TDRs can be created with the linewidth reaching ∼ 1.5 nm. Multipolar decomposition of scattering power and electromagnetic field distribution calculations confirm the nature of TDRs. A 100% modulation depth in light absorption and selective field confinement has been demonstrated theoretically by simply changing the polarization orientation of the exciting light. Intriguingly, it is also found that absorption responses of TDRs on polarization angle follow the equation of Malus' law in this metasurface. Furthermore, the dual-band toroidal resonances are proposed to sense the birefringence of an anisotropic medium. Such polarization-tunable dual toroidal dipole resonances with ultra-narrow bandwidth offered by this structure may find potential applications in optical switching, storage, polarization detection, and light emitting devices.

High intact fibroblast growth factor 23 levels associated with low hemoglobin levels in patients on chronic hemodialysis.

Introduction: A negative association between C-terminal fibroblast growth factor 23 (cFGF23) and hemoglobin (Hb) levels has been reported in patients with predialysis chronic kidney disease. In dialysis patients, the dominant form of serum FGF23 is intact FGF23 (iFGF23); however, its association with the Hb level remains unclear. Therefore, simultaneously monitoring iFGF23 and cFGF23 levels is crucial. In this study, we investigated the associations between both forms of FGF23 (iFGF23 and cFGF23) and renal anemia in chronic hemodialysis (CHD) patients. Methods: We included 166 CHD patients from two hospitals in this cross-sectional, observational study. The primary predictors were serum iFGF23, cFGF23, and iFGF23/cFGF23 levels. The main outcome was the Hb level. Results: Among the CHD patients included, 60.8% were men with a mean age of 59.4 ± 12.7 years. In the crude analysis, iFGF23 and iFGF23/cFGF23 levels showed a significant negative association (-0.27, p = 0.004 and -0.22, p = 0.034, respectively) with the Hb level. Even after adjusting for multiple variables (a parsimonious model), every increment of natural log transformation by 1 for (ln)iFGF23 and ln(iFGF23/cFGF23) levels showed a negative correlation with the Hb level (estimate: -0.27 [95%CI: -0.44, -0.10, p = 0.001]; -0.19 [95%CI: -0.37, -0.01, p = 0.042], respectively), whereas both were positively associated with erythropoietin-stimulating agent (ESA) hyporesponsiveness (odds ratio [OR]: [95%CI: 2.30, 1.26-4.17], p = 0.006; 1.95 [95%CI: 1.08-3.50], p = 0.025). Moreover, these abovementioned associations were more dominant in patients with diabetes who used angiotensin receptor blockers. Discussion: In conclusion, a negative association between serum iFGF23 or iFGF23/cFGF23 level and the Hb level was observed in our CHD patients. Meanwhile, a higher iFGF23 or iFGF23/cFGF23 level may predispose patients to ESA hyporesponsiveness.

Serum Intact Fibroblast Growth Factor 23 Levels Are Negatively Associated with Bone Mineral Density in Chronic Hemodialysis Patients.

(1) Background: Fibroblast growth factor 23 (FGF23) is predominantly secreted from bone and plays an important role in mineral balance in chronic kidney disease. However, the relationship between FGF23 and bone mineral density (BMD) in chronic hemodialysis (CHD) patients remains unclear. (2) Methods: This was a cross-sectional observational study that involved 43 stable outpatients on CHD. A linear regression model was used to determine risk factors for BMD. Measurements included serum hemoglobin, intact FGF23 (iFGF23), C-terminal FGF23 (cFGF23), sclerostin, Dickkopf-1, α-klotho, 1,25-hydroxyvitamin D, intact parathyroid hormone levels and dialysis profiles. (3) Results: Study participants had a mean age of 59.4 ± 12.3 years, and 65% were male. In the multivariable analysis, cFGF23 levels showed no significant associations with the BMD of the lumbar spine (p = 0.387) nor that of the femoral head (p = 0.430). However, iFGF23 levels showed a significant negative association with the BMD of the lumbar spine (p = 0.015) and that of the femoral neck (p = 0.037). (4) Conclusions: Among patients on CHD, higher serum iFGF23 levels, but not serum cFGF23 levels, were associated with lower BMD values of the lumbar spine and femoral neck. However, further research is required to validate our findings.

Highly Stretchable, Soft, and Clear Viscoelastic Film with Good Recoverability for Flexible Display.

The advancement of flexible electronic devices has prompted new material development for the display application. For flexible display, a suitable clear viscoelastic film (CVF) is essential to bond different layers in a display stack in order to improve the visualization and durability during the repeated folding process. However, it is challenging to integrate different properties in the CVF by overcoming many contradictory requirements, such as low modulus/glass transition temperature (Tg) and high adhesion or high recoverability and good stress-relaxation. In this work, a CVF was prepared using an interpenetrating polymer network (IPN) with bimodal chain length distribution, and it exhibited several favorable properties. The bimodal elastomer was composed of short-chain polyurethane (PU) and long-chain polyacrylate. The long-chain polyacrylate network provided a large amount of entanglement that conferred stretchability, adhesion, and stress-relaxation, whereas the short PU chain network acted as an entropy spring and contributed mostly to the recoverability. The experimental data suggested the presence of a hydrogen-bonding interaction and interlocked polymer chains between the two networks. When the components of the IPN are adjusted, the CVF can simultaneously achieve good stress-relaxation, high strain recovery at large strain (1000%), high toughness, clarity, and adhesion. Moreover, the CVF displayed low glass transition temperature (-57 °C) and low storage modulus (20 to 30 kPa at room temperature). To the best of our knowledge, this is the first report using the IPN concept to prepare a CVF with well-balanced properties.

Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation.

Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers' attention in recent years. This paper provides a new framework to evaluate the hydroplaning potential based on emerging 3D laser scanning technology and water-film thickness estimation. The 3D information of the road surface was captured using a vehicle-mounted Light Detection and Ranging (LiDAR) system and then processed by a wavelet-based filter to remove the redundant information (surrounding environment: trees, buildings, and vehicles). Then, the water film thickness on the given road surface was estimated based on a proposed numerical algorithm developed by the two-dimensional depth-averaged Shallow Water Equations (2DDA-SWE). The effect of the road surface geometry was also investigated based on several field test data in Shanghai, China, in January 2021. The results indicated that the water-film is more likely to appear on the rutting tracks and the pavement with local unevenness. Based on the estimated water-film, the hydroplaning speeds were then estimated to represent the hydroplaning risk of asphalt pavement in rainy weather. The proposed method provides new insights into the water-film estimation, which can help drivers make effective decisions to maintain safe driving.

Outcome Analysis of Transition From Peritoneal Dialysis to Hemodialysis: A Population-Based Study.

If a technical failure occurs during peritoneal dialysis (PD), the patients undergoing PD may be transitioned to hemodialysis (HD). However, the clinical outcomes of patients who have undergone such a transition are under studied. This study assessed whether patients undergoing HD who have transitioned from PD have the same clinical outcomes as HD-only patients. This research was a retrospective cohort study by searching a National Health Insurance research database for data on patients in Taiwan who had undergone HD between January 2006 and December 2013. The patients were divided into two groups, namely a case group in which the patients were transitioned from PD to HD and a HD-only control group, through propensity score matching at a ratio of 1:4 (n = 1,100 vs. 4,400, respectively). We used the Cox regression model to estimate the hazard ratios (HRs) for all-cause death, all-cause hospitalization, infection-related admission, and major adverse cardiac events (MACE). Those selected patients will be followed until death or the end of the study period (December, 2017), whichever occurs first. Over a mean follow-up of 3.2 years, 1,695 patients (30.8%) died, 3,825 (69.5%) required hospitalization, and 1,142 (20.8%) experienced MACE. Patients transitioning from PD had a higher risk of all-cause death (HR: 1.36; 95% CI: 1.21-1.53) than HD-only patients. However, no significant difference was noted in terms of MACE (HR: 0.91; 95% CI: 0.73-1.12), all-cause hospitalization (HR: 1.07; 95% CI: 0.96-1.18), or infection-related admission (HR: 0.97, 95% CI: 0.80-1.18) between groups. Because of the violation of the proportional hazard assumption, the piecewise-HRs showed that the risk of mortality in the case group was significant within 5 months of the transition (HR: 2.61; 95% CI: 2.04-3.35) not in other partitions of the time axis. In conclusion, patients undergoing HD who transitioned from PD had a higher risk of death than the HD-only patients, especially in the first 5 months after transition (a 161% higher risk). Therefore, more caution and monitoring may be required for patients undergoing HD who transitioned from PD.

Mycobacterium tuberculosis Invasion of Main Pulmonary Artery.

ABSTRACT: Infectious causes of pulmonary artery invasion are extremely rare. A 71-year-old man had history of progressive dyspnea with abnormal salty taste. Contrast CT showed a filling defect mass in the main pulmonary artery with extraluminal extension to the heart and mediastinum. FDG PET/CT revealed a mass lesion with intense FDG uptake in the main pulmonary artery and peripheral. There were lesions with intense FDG uptake in the middle mediastinum besides the ascending aorta. Malignant tumor was suspected. Later, he received tumor debulking excision. Pathology reports showed necrotizing granulomatous inflammation, positive interferon-gamma release assays, and positive Mycobacterium tuberculosis culture.

Corrigendum: The Protective Effects of Lipid-Lowering Agents on Cardiovascular Disease and Mortality in Maintenance Dialysis Patients: Propensity Score Analysis of a Population-Based Cohort Study.

[This corrects the article DOI: 10.3389/fphar.2019.00079.].

Convolutional neural network-based spectrum reconstruction solver for channeled spectropolarimeter.

Channeled spectropolarimetry is a snapshot technique for measuring the spectra of Stokes parameters of light by demodulating the measured spectrum. As an indispensable part of the channeled spectropolarimeter, the spectrometer module is far from being perfect to reflect the real modulation spectrum, which further reduces the polarimetric reconstruction accuracy of the channeled spectropolarimeter. Since the modulation spectrum is composed of many continuous narrow-band spectra with high frequency, it is a challenging work to reconstruct it effectively by existing methods. To alleviate this issue, a convolutional neural network (CNN)-based spectral reconstruction solver is proposed for channeled spectropolarimeter. The key idea of the proposed method is to first preprocess the measured spectra using existing traditional methods, so that the preprocessed spectra contain more spectral features of the real spectra, and then these spectral features are employed to train a CNN to learn a map from the preprocessed spectra to the real spectra, so as to further improve the reconstruction quality of the preprocessed spectra. A series of simulation experiments and real experiments were carried out to verify the effect of the proposed method. In simulation experiments, we investigated the spectral reconstruction accuracy and robustness of the proposed method on three synthetic datasets and evaluate the effect of the proposed method on the demodulation results obtained by the Fourier reconstruction method. In real experiments, system matrices are constructed by using measured spectra and reconstructed spectra respectively, and the spectra of Stokes parameters of incident light are estimated by the linear operator method. Several other advanced demodulation methods are also used to demodulate the measured spectrum in both simulation and real experiments. The results show that compared with other methods, the accuracy of the demodulation results can be much more improved by employing the CNN-based solver to reconstruct the measured spectrum.

Protective Effects of Angiotensin Receptor Blockers on the Incidence of Dementia in Patients with Chronic Kidney Disease: A Population-Based Nationwide Study.

Chronic kidney disease (CKD) is associated with dementia. Angiotensin receptor blockers (ARBs) have been widely used for delaying CKD progression; however, their effect on dementia prevention in patients with CKD remains unclear. We designed a retrospective cohort study to investigate the effects of ARBs on the incidence of dementia in patients with CKD. We selected 21,208 patients from the Taiwan nationwide database from 1 January 2006 to 31 December 2006. We identified ARB users (n = 17,466) and ARB non-users (n = 3742) and their medication possession ratio (MPR). The Cox proportional hazard model was used to estimate hazard ratios (HRs) for the incidence of dementia in ARB users in the CKD population. During the 11-year follow-up period, 2207 dementia events were recorded; multivariate-adjusted hazard ratios for dementia by ARB usage and ARB usage per MPR were 0.578 (95% CI: 0.52-0.643) and 0.996 (95% CI: 0.995-0.998), respectively. This association was observed in almost all subgroups. Dose frequency effect of ARBs was noted; patients with higher MPRs of ARBs generally had higher protection from dementia. Patients with hypertension and CKD who received ARBs had a decreased risk of dementia. Protective effects of ARBs on dementia increased with the frequency of ARB use.