A systematic review and meta-analysis of the evidence on inflammation in depressive illness and symptoms in chronic and end-stage kidney disease.

BACKGROUND: Depression affects approximately 27% of adults with chronic kidney disease (CKD) and end-stage kidney failure (ESKF). Depression in this population is associated with impaired quality of life and increased mortality. The extent of inflammation and the impact on depression in CKD/ESKF is yet to be established. Through a systematic literature review and meta-analysis, we aim to understand the relationship between depression and inflammation in CKD/ESKF patients. METHODS: We searched nine electronic databases for published studies until January 2022. Titles and abstracts were screened against inclusion and exclusion criteria. Data extraction and study quality assessment was carried out independently by two reviewers. A meta-analysis was carried out where appropriate; otherwise a narrative review of studies was completed. RESULTS: Sixty studies met our inclusion criteria and entered the review (9481 patients included in meta-analysis). Meta-analysis of cross-sectional associations revealed significantly higher levels of pro-inflammatory biomarkers; C-reactive protein; Interleukin 6 (IL-6) and tumour necrosis factor-alpha in patients with depressive symptoms (DS) compared to patients without DS. Significantly lower levels of anti-inflammatory cytokine IL-10 were found in patients with DS compared to patients without DS. Considerable heterogeneity was detected in the analysis for most inflammatory markers. CONCLUSION: We found evidence for an association of higher levels of pro-inflammatory and lower anti-inflammatory cytokines and DS in patients with CKD/ESKF. Clinical trials are needed to investigate whether anti-inflammatory therapies will be effective in the prevention and treatment of DS in these patients with multiple comorbidities.

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB.

With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments.

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi.

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

Research on UAV Robust Adaptive Positioning Algorithm Based on IMU/GNSS/VO in Complex Scenes.

As an important component of autonomous intelligent systems, the research on autonomous positioning algorithms used by UAVs is of great significance. In order to resolve the problem whereby the GNSS signal is interrupted, and the visual sensor lacks sufficient feature points in complex scenes, which leads to difficulties in autonomous positioning, this paper proposes a new robust adaptive positioning algorithm that ensures the robustness and accuracy of autonomous navigation and positioning in UAVs. On the basis of the combined navigation model of vision/inertial navigation and satellite/inertial navigation, based on ESKF, a multi-source fusion model based on a federated Kalman filter is here established. Furthermore, a robust adaptive localization algorithm is proposed, which uses robust equivalent weights to estimate the sub-filters, and then uses the sub-filter state covariance to adaptively assign information sharing coefficients. After simulation experiments and dataset verification, the results show that the robust adaptive algorithm can effectively limit the impact of gross errors in observations and mathematical model deviations and can automatically update the information sharing coefficient online according to the sub-filter equivalent state covariance. Compared with the classical federated Kalman algorithm and the adaptive federated Kalman algorithm, our algorithm can meet the real-time requirements of navigation, and the accuracy of position, velocity, and attitude measurement is improved by 2-3 times. The robust adaptive localization algorithm proposed in this paper can effectively improve the reliability and accuracy of autonomous navigation systems in complex scenes. Moreover, the algorithm is general-it is not intended for a specific scene or a specific sensor combination- and is applicable to individual scenes with varied sensor combinations.

Clinical prediction models for progression of chronic kidney disease to end-stage kidney failure under pre-dialysis nephrology care: results from the Chronic Kidney Disease Japan Cohort Study.

BACKGROUND: Reliable prediction tools are needed to identify patients with chronic kidney disease (CKD) at greater risk of developing end-stage kidney failure (ESKF). We developed and validated clinical prediction models (CPMs) for CKD progression to ESKF under pre-dialysis nephrology care using CKD-Japan Cohort (CKD-JAC) data. METHODS: We prospectively followed up 2034 participants with CKD, defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2, aged 20-75 years for a mean of 3.15 years. We randomly divided the overall analysis set into development and validation cohorts. In the development cohort, CPMs were developed using Cox proportional hazard regression, and the goodness of fit was evaluated. In the validation cohort, discrimination and calibration of the developed CPMs were evaluated. We also validated developed CPMs in the dataset with the bootstrap method. RESULTS: ESKF onset was observed in 206 and 216 patients in the development (20.3%) and validation (21.2%) cohorts, respectively. Goodness of fit, discrimination, and calibration were worse for a simple model including age, sex, and eGFR than for a complicated model (plus albuminuria, systolic blood pressure, diabetes, serum albumin, and hemoglobin). The mean absolute difference between the observed and predictive probabilities of ESKF onset at 3 years was lower for the complicated model than for the simple model (1.57 vs. 1.87%). CONCLUSIONS: CPMs employing readily available data could precisely predict progression to ESKF in patients with CKD stage G3a to G5. These developed CPMs may facilitate more appropriate clinical care and shared decision-making between clinicians and patients.

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines.

The application of an ultra-wideband (UWB) positioning system in a Global Positioning System (GPS) denial environment such as an underground coal mine, mainly focuses on position information and rarely involves information such as direction attitude. Position accuracy is often affected by multipath, non-visible ranges, base station layout, and more. We proposed an IMU-assisted UWB-based positioning system for the provision of positioning and orientation services to coal miners in underground mines. The Error-State Kalman Filter (ESKF) is used to filter the errors in the measured data from the IMU-assisted UWB positioning system to obtain the best estimate of the error for the current situation and correct for inaccuracies due to approximations. The base station layout of the IMU-assisted UWB positioning system was also simulated. The reasonable setting of the reference base station location can suppress multi-access interference and improve positioning accuracy to a certain extent. Numerous simulation experiments have been conducted in GPS denial environments, such as underground coal mines. The experimental results show the effectiveness of the method for determining the position, direction, and attitude of the coal miner under the mine, which provides a better reference value for positioning and orientation in a GPS rejection environment such as under the mine.

Ethical Challenges in Dialysis and Transplantation: Perspectives From the Developing World.

Renal replacement therapies including dialysis and transplantation for patients with end-stage kidney failure are treatment options beyond the reach of a large segment of the population, particularly in resource-constrained settings. Health care professionals practicing within developing countries face unique ethical issues in the provision of these treatment options despite the existence of free treatment at different centers. Apart from issues of accessibility of dialysis services, initiation of treatment can have disastrous consequences for the entire family unit, which is magnified in collectivist societies. Several cost-cutting measures also may have to be used that raise moral dilemmas for physicians. Although transplantation is considered the most cost-effective solution in developing countries, leading to significantly better quality of life, issues of consent from biologically related living donors and the use of marginal donors may place physicians in a quandary. Policy making in developing countries must consider the socioeconomic implications of treatment choices that extend far beyond the treatment cost.

Obesity anthropometric indicators associated with cardiometabolic risk in Portuguese children and adolescents.

The purpose of this study was to determine the association between cardiometabolic risk with body mass index and skinfold independently or in combination in youth. This cross-sectional study comprised a convenience sample of 450 children and adolescents (255 girls), aged 10 to 18 years old. Indicators of body composition were measured, and hemodynamic assessment completed. The association between body mass index and/or sum of skinfolds and cardiometabolic risk (z score of the sum of triglycerides/high density lipoprotein cholesterol, waist circumference, and mean blood pressure), was calculated using Generalized Linear Models Regression. The results showed that youngsters classified as overweight or obese with the highest skinfold measurements had the strongest association with cardiometabolic risk (< beta >: 2.60; IC 95%: 2.25-3.0) when compared with those exhibiting normal skinfold thickness (< beta >: 1.78; IC 95%: 1.30-2.20). Body mass index was most strongly associated with cardiometabolic risk (< beta >: 1.78; IC 95%: 1.3-2.2), in comparison to skinfold thickness, which was associated to a lesser extent (< beta >: 0.41; IC 95%: 0.34-0.49). Results of this cross-sectional study indicate that body mass index is more strongly associated with cardiometabolic risk than skinfold thickness. However when these two measures of overweight/obesity are combined, prediction of cardiometabolic risk is further improved. It is therefore important that public health professionals consider both body mass index and sum of skinfolds to better predict cardiometabolic risk in overweight and obese youth. Implications for future research include the use of longitudinal designs and inclusion of children from other racial/ethnic groups.