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Doxorubicin (DOX) is a cornerstone of chemotherapy for solid tumors and leukemias. DOX-induced cognitive impairment, termed chemo brain, has been reported in cancer survivors, whereas its mechanism remains poorly understood. Here we initially evaluated the cognitive impairments of mice treated with clinically relevant, long-term, low-dosage of DOX. Using HILIC-MS/MS-based targeted metabolomics, we presented the changes of 21 amino acids across six anatomical brain regions of mice with DOX-induced chemo brain. By mapping the altered amino acids to the human metabolic network, we constructed an amino acid-based network module for each brain region. We identified phenylalanine, tyrosine, methionine, and γ-aminobutyric acid as putative signatures of three regions (hippocampus, prefrontal cortex, and neocortex) highly associated with cognition. Relying on the reported mouse brain metabolome atlas, we found that DOX might perturb the amino acid homeostasis in multiple brain regions, similar to the changes in the aging brain. Correlation analysis suggested the possible indirect neurotoxicity of DOX that altered the brain levels of phenylalanine, tyrosine, and methionine by causing metabolic disorders in the liver and kidney. In summary, we revealed the region-specific amino acid signatures as actionable targets for DOX-induced chemo brain, which might provide safer treatment and improve the quality of life among cancer survivors.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Background Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. Methods An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient’s measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. Results A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. Conclusions This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.

Introduction: The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR (eGFR) for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation. Methods: Fifty-three CKD participants were recruited from the third affiliated hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC–MS/MS). All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst5.0 for one-way ANOVA, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) and confirmed the metabolites whose levels increased or decreased with mGFR and Variable Importance in Projection (VIP) values>1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient>0.8 and VIP >2 were identified. Results: We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites. Conclusion: We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

Accurate understanding of spatial distribution and variability of soil total nitrogen (TN) is critical for the site-specific nitrogen management. Based on 4337 newly obtained soil observations and 33 covariates, this study applied the random forest (RF) algorithm and modified regression kriging (RF combined with residual kriging: RFK, hereafter) model to spatially predict and map topsoil TN content in agricultural areas of Henan Province, central China. According to the RFK prediction, topsoil TN content ranged from 0.52 to 1.81 g kg−1, and the farmland with the topsoil TN contents of 1.00–1.23 g kg−1 and 0.80–1.23 g kg−1 accounted for 48.2% and 81.2% of the total farmland area, respectively. Spatially, the topsoil TN in the study area was generally higher in the west and lower in the east. By using the Boruta variable selection algorithm, soil organic matter (SOM) and available potassium contents in topsoil, nitrogen deposition, average annual precipitation, livestock discharges, and topsoil pH were identified as the main factors driving the spatial distribution and variation of soil TN in the study area. The RF and RFK models used showed the expected performance and achieved acceptable TN prediction accuracy. In comparison, RFK performed slightly better than the RF model. The R2 and RMSE achieved by the RFK model were improved by 4.5% and 4.5%, respectively, compared with that by the RF model. However, the results suggest that RFK was inferior to the RF model in quantifying prediction uncertainty and thus may have a slight disadvantage in model reliability.

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.

Currently, in-motion alignment for Strapdown inertial navigation system (SINS) is still a challenge, especially in underwater condition. This paper proposes an approach for DVL-aided SINS in-motion alignment from the perspective of observability. The core of the approach is an improved observability analysis method which overcomes the problems of existing methods. Based on numerical calculations, it can accurately and intuitively analyze the observability of the multi-dimensional complex navigation systems. In response to the problem that numerical calculations may introduce calculation errors, a solution based on prior knowledge of the system is presented. By utilizing the proposed observability analysis method to analyze the DVL-aided SINS in-motion alignment system, this paper has a further research on improving the alignment accuracy through observability incentives. Specifically, the factors which restrict the alignment accuracy are clarified, the reason for how the alignment accuracy is improved by trajectory design is explained, the question of whether the alignment accuracy can be further improved by more complex trajectories is studied, and the guidance for more efficient observability incentives is provided. Consequently, theoretical supports are presented to improve the alignment accuracy efficiently and purposefully. The effectiveness of the proposed approach is verified by simulations and semi-physical experiments.

ObjectiveWhether uric acid (UA) has an effect on renal function remains controversial. We aimed to investigate the association between serum UA with the decline in estimated glomerular filtration rate (eGFR) in middle-aged and elderly populations in the China Health and Retirement Longitudinal Study (CHARLS).DesignLongitudinal cohort study.SettingThis was a second analysis of a public dataset (CHARLS).ParticipantsIn this study, 4538 middle-aged and elderly individuals were screened after removing individuals younger than 45 years old, with kidney disease, malignant tumour and missing values.Outcome measuresBlood tests were performed both in 2011 and 2015. Decline in eGFR was defined as an eGFR decrease of more than 25% or deterioration of the eGFR stage during the 4-year follow-up period. Logistic models corrected for multiple covariables were used to analyse the association of UA with the decline in eGFR.ResultsThe median (IQR) concentrations of serum UA grouped by quartiles were 3.1 (0.6), 3.9 (0.3), 4.6 (0.4) and 5.7 (1.0) mg/dL, respectively. After multivariable adjustment, the OR of the decline in eGFR was higher for quartile 2 (3.5–ConclusionsOver a 4-year follow-up period, we found that elevated UA was associated with a decline in eGFR in the middle-aged and elderly individuals with normal renal function.

Portion size selection is an indicator of appetite and within younger adults, is predicted by factors such as expected satiety, liking and motivations to achieve an ideal sensation of fullness (i.e., implicit satiety goals). Currently, there is limited research available on the determinants of portion size selection within older adults. Therefore, the current study aimed to examine the relationship between individual differences in implicit satiety goals, food-related expectations, and portion size selection in older adults. Free-living older adult Singaporeans (N = 115; N

Efficient decontamination of radioactive Ba2+ is of great significance to human health and environmental safety. Herein, an adsorbent based on the sulfonic acid functionalized Zr-MOF has been successfully developed, which could efficiently decontaminate radioactive Ba2+ with excellent selectivity, recyclability, a high adsorption capacity up to 60.8 mg g−1 as well as a short adsorption kinetic time of less than 5 min. This outstanding adsorption performance is attributed to the strong affinity between Ba2+ and high density –SO3H active sites in MOFs which were introduced by an in situ ligand modification strategy during the assembly of MOFs.

Background. Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function. Methods. Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory. Results. From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study. Conclusions. Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.

Background: With the development of chronic kidney disease (CKD), there are various changes in metabolites. However, the effect of these metabolites on the etiology, progression and prognosis of CKD remains unclear.Objective: We aimed to identify significant metabolic pathways in CKD progression by screening metabolites through metabolic profiling, thus identifying potential targets for CKD treatment.Methods: Clinical data were collected from 145 CKD participants. GFR (mGFR) was measured by the iohexol method and participants were divided into four groups according to their mGFR. Untargeted metabolomics analysis was performed via UPLC-MS/MSUPLC–MSMS/MS assays. Metabolomic data were analyzed by MetaboAnalyst 5.0, one-way ANOVA, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) to identify differential metabolites for further analysis. The open database sources of MBRole2.0, including KEGG and HMDB, were used to identify significant metabolic pathways in CKD progression.Results: Four metabolic pathways were classified as important in CKD progression, among which the most significant was caffeine metabolism. A total of 12 differential metabolites were enriched in caffeine metabolism, four of which decreased with the deterioration of the CKD stage, and two of which increased with the deterioration of the CKD stage. Of the four decreased metabolites, the most important was caffeine.Conclusion: Caffeine metabolism appears to be the most important pathway in the progression of CKD as identified by metabolic profiling. Caffeine is the most important metabolite that decreases with the deterioration of the CKD stage.

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p &lt, 0.01), and of Hg was 0.318 (p &lt, 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.

ObjectivesFew studies have explored correlations between metabolic syndrome (MetS) alterations and renal deterioration in longitudinal cohorts. We aim to investigate associations between MetS recovery/development and rapid estimated glomerular filtration rate (eGFR) decline in the China Health and Retirement Longitudinal Study (CHARLS).DesignLongitudinal cohort study.SettingThis study is a secondary analysis of CHARLS.ParticipantsAfter excluding individuals with age 2and clinician-reported malignant tumour, heart disease, stroke or kidney disease at baseline, 4142 participants with complete data were selected from the CHARLS during the 4-year follow-up period (2011–2015).Outcome measuresMetS were measured at 2011 and 2015 in CHARLS. A rapid eGFR decline was defined as an average annual eGFR decline of >3 mL/min/1.73 m2. The associations between rapid eGFR decline and MetS recovery/development were analysed using multivariable adjusted logistic models.ResultsAccording to MetS baseline status and follow-up, participants were divided into four groups: (1) 2460 (59.4%) in the MetS-free group, (2) 361 (8.7%) in the MetS-developed group, (3) 499 (12.0%) in the MetS recovery group and (4) 822 (19.8%) in the MetS chronic group. When compared with the MetS chronic group, the multivariable adjusted OR of rapid eGFR decline in the MetS recovery group was 0.64 (OR: 0.64; 95% CI 0.45 to 0.90, p=0.01). In contrast, when compared with the MetS-free group, the multivariable adjusted OR of rapid eGFR decline in the MetS-developed group was 1.00 (OR: 1.00; 95% CI 0.73 to 1.38, p=0.98).ConclusionsOver the 4-year follow-up period, we found that MetS recovery was associated with a reduced risk of rapid eGFR decline in middle-aged and older adults, while MetS occurrence was not related to rapid eGFR decline. Recovery from MetS appeared to protect against a rapid decline in eGFR.

In this paper, we study a robust beamforming design for multi-user multiple-input multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet of Things devices under the non-Linear energy harvesting model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve energy harvesting fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method outperforms the traditional AN-aided method and CJ-aided method.

Efficient decontamination of radioactive Ba2+ is of great significance to human health and environmental safety. Herein, an adsorbent based on the sulfonic acid functionalized Zr-MOF has been successfully developed, which could efficiently decontaminate radioactive Ba2+ with excellent selectivity, recyclability, a high adsorption capacity up to 60.8 mg g-1 as well as a short adsorption kinetic time of less than 5 min. This outstanding adsorption performance is attributed to the strong affinity between Ba2+ and high density -SO3H active sites in MOFs which were introduced by an in situ ligand modification strategy during the assembly of MOFs.

Background Chronic kidney disease (CKD) is a global public health problem. Identifying sensitive filtration biomarkers is a key diagnostic value contributing to an understanding of CKD at the molecular level. A metabolomics study indicated a snapshot of the biochemical activity of the human body at a particular time in the progression of CKD. This metabolome-wide biomarker study verified whether plasma or serum metabolite profiles are significantly different in CKD at various stages and characterized potential markers to assess kidney function in the Chinese population.Methods: An analysis of plasma and serum metabolites using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on 198 participants (53 serum samples and 145 plasma samples) based on their measured GFR (by iohexol plasma clearance). Participants started recruiting in late 2019, and untargeted metabolomics assays (N ¼ 214) were conducted at the Calibra-Metabolon Joint Laboratory (Hangzhou, China) using Metabolon's HD4 Discovery untargeted metabolomics platform in early 2021.Results A large number of metabolomics related to the mGFR were selected as the top 30 metabolites by the random forest method, and we found 15 amino acids, 8 nucleotides, and 2 carbohydrates strongly related to kidney function in the combined group (serum and plasma). Thirteen amino acids, 9 nucleotides, and 3 carbohydrates were identified in the plasma group, while 13 amino acids, 7 nucleotides, and 3 carbohydrates were found in the serum group. We observed that 10 of the top 15 ranked metabolites were concordant between the plasma and serum groups. Major differences in metabolite profiles with increasing stage of CKD were observed, including altered tryptophan metabolism and pyrimidine metabolism.Conclusions Global metabolite profiling of plasma/serum uncovered potential biomarkers of stages of CKD. In addition, these novel biomarkers provide insight into possible pathophysiologic processes that may contribute to the progression of CKD and a higher risk of comorbidities and mortality.

Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equationMethods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy , precision and root mean square error(RMSE).Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all PP=0.10, 0.8 vs 0.78, P=0.19, respectively).Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

Background The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population. Methods The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age Results Compared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P Conclusions It is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Additional file 1. Determining the knot of spline of serum creatinine and serum cystatin C.

Additional file 2. Development of the ANN model.

Additional file 4. Sensitivity analysis of data splitting method.

Additional file 2. Development of the ANN model.

Additional file 4. Sensitivity analysis of data splitting method.

In inertial navigation system (INS)/doppler velocity log (DVL) integrated navigation, DVL commonly provides the 3-D velocity of the vehicle to the navigation filter. Theoretically, this INS/DVL fusion is the loosely coupled approach, in which DVL requires sufficient beam measurements (at least three) to calculate the 3-D velocity. However, in cases that DVL only has limited (fewer than three) beam measurements, which is possible in the underwater environment, the loosely coupled navigation system can no longer work, and only leaves the INS to work alone. Therefore, the navigation error will accumulate with time. In contrast to the loosely coupled approach, the tightly coupled approach directly uses DVL beam measurements without transforming them to 3-D velocity. In this paper, a method based on the tightly coupled approach for INS/DVL integrated navigation with limited DVL beam measurements is proposed, in which depth updates from a pressure sensor generally equipped by most autonomous underwater vehicles (AUVs) are applied. Simulation results verify that the proposed method is efficient in most kinds of tasks of the AUV.

Background/Aims: Chronic cerebral hypoperfusion (CCH) is a high-risk factor for vascular dementia and Alzheimer’s disease. Autophagy plays a critical role in the initiation and progression of CCH. However, the underlying mechanisms remain unclear. In this study, we identified the effect of a microRNA (miR) on autophagy under CCH. Methods: A CCH rat model was established by two-vessel occlusion (2VO). Learning and memory abilities were assessed by the Morris water maze. The protein levels of LC3, beclin-1, and mTOR were detected by western blotting and immunofluorescence assays, miR-96 expression was assessed by real-time PCR, luciferase assays were used to determine the effect of miR-96 on the 3′ untranslated region (UTR) of mTOR, and the number of autophagosomes was examined by electron microscopy. Results: The level of miR-96 was significantly increased in 2VO rats, and inhibition of miR-96 ameliorated the cognitive impairment induced by 2VO. Furthermore, the number of LC3- and beclin-1-positive autophagosomes was increased in 2VO rats, and was decreased after miR-96 antagomir injection. However, the protein level of mTOR was reduced in 2VO rats, and it was down-regulated by miR-96 overexpression and up-regulated by miR-96 inhibition in 2VO rats and primary culture cells. Moreover, the luciferase activity of the 3′-UTR of mTOR was suppressed by miR-96, which was relieved by mutation of the miR-96 binding sites. Conclusion: Our study demonstrated that miR-96 may play a key role in autophagy under CCH by regulating mTOR; therefore, miR-96 may represent a potential therapeutic target for CCH.

Due to the random wave motions as well as the vehicle’s movement pattern changes, the attitude dynamics (including heading, pitch, and roll motions) of the vehicle induced degrades the accuracy of doppler velocity log (DVL), which is a key element in deciding the precision of strapdown inertial navigation system (SINS)/DVL integrated navigation system. In this paper, the effects of heading, pitch and roll dynamics are first analyzed separately. Then, the general error model for DVL measurement errors caused by attitude dynamics is established. In the modeling process, the changes of angular position and angular rate between the DVL signal transmission and reception instant are considered. Subsequently, by utilizing the established error model, a correction method based on the real-time attitude information from SINS/DVL system is presented. Finally, the performance of the proposed method is examined through numerical simulations in which typical scenarios with a series of attitude dynamics are used. Simulation results show that the errors can be effectively corrected by using the correction method. This contributes to an improved positioning accuracy of SINS/DVL navigation.

Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS.

In this paper, we show some new plurisubharmonic functions on Teichmuller space, related to extremal length and hyperbolic length. And we give a new compactification of Teichmuller space: the pluripotential compactification.

Background: Venous thromboembolism (VTE) is an important complication in patients with malignant tumors. Its exact diagnosis and treatment are still lacking. We used a high-sensitive chemiluminescence method to detect thrombin–antithrombin III complex (TAT), plasmin-α2-plasmininhibitor complex (PIC), thrombomodulin (TM), and tissue plasminogen activator–inhibitor complex(t-PAIC) in combination with D-dimer and fibrin degradation product (FDP) to analyze their diagnostic and prognostic value in patients with malignant tumors. Methods: In total, 870 patients with confirmed malignant tumors were included, 82 of whom had diagnosed VTE; 200 healthy individuals were classified as the control group. The TAT, PIC, TM, and t-PAIC were detected using Sysmex HISCL5000 automated analyzers, whereas FDP and D-dimer were detected using Sysmex CS5100 coagulation analyzer. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficiency. Survival probabilities were determined using Kaplan–Meier analysis, and multivariate analyses were performed using a Cox regression model. Results: Compared with healthy controls, patients with malignant tumors showed significantly elevated TAT, PIC, TM, t-PAIC, D-dimer, and FDP. Similarly, compared with patients in the non-thrombosis group, those in the thrombosis group showed significantly elevated levels of the above mentioned markers. Logistic regression analysis showed that TAT, PIC, TM, t-PAIC, D-Dimer, and FDP were all associated with VTE. ROC analysis showed that “TAT+PIC+TM+t-PAIC+D-dimer+FDP”showed the highest sensitivity and specificity. Patients with elevated TAT, PIC, TM, and t-PAIC had a significantly shorter survival. Multivariate Cox survival analysis showed that TM and t-PAIC were significantly associated with poor prognosis. In addition, the incidence of VTE was significantly lower in patients with malignant tumors who were treated with low-molecular-weight heparin (LMWH), and their survival period was significantly longer than that of patients with malignant tumors who were not treated with LMWH. Conclusion: TAT, PIC, TM, and t-PAIC combined with D-dimer and FDP were better than the application of a single marker in the diagnosis of VTE in patients with malignant tumors. TAT and PIC can be used as sensitive markers in the diagnosis of VTE but not as prognostic markers. TM and t-PAIC might be independent prognostic indicators in patients with malignant tumors, regardless of the state of thrombus.

Glutaminolysis is the metabolic pathway that lyses glutamine to glutamate, alanine, citrate, aspartate, and so on. As partially recruiting reaction steps from the tricarboxylic acid (TCA) cycle and the malate-aspartate shuttle, glutaminolysis takes essential place in physiological and pathological situations. We herein developed a sensitive, rapid, and reproducible liquid chromatography-tandem mass spectrometry method to determine the perturbation of glutaminolysis in human plasma by quantifying 13 involved metabolites in a single 20-min run. A pHILIC column with a gradient elution system consisting of acetonitrile-5 mM ammonium acetate was used for separation, while an electrospray ionization source (ESI) operated in negative mode with multiple reaction monitoring was employed for detection. The method was fully validated according to FDA's guidelines, and it generally provided good results in terms of linearity (the correlation coefficient no less than 0.9911 within the range of 0.05-800 μg/mL), intra- and inter-day precision (less than 18.38%) and accuracy (relative standard deviation between 89.24 and 113.4%), with lower limits of quantification between 0.05 and 10 μg/mL. The new analytical approach was successfully applied to analyze the plasma samples from 38 healthy volunteers and 34 patients with type 2 diabetes (T2D). Based on the great sensitivity and comprehensive capacity, the targeted analysis revealed the imperceptible abnormalities in the concentrations of key intermediates, such as iso-citrate and cis-aconitate, thus allowing us to obtain a thorough understanding of glutaminolysis disorder during T2D. Graphical abstract ᅟ.

We describe the effect of Fenchel-Nielsen coordinates under elementary move for hyperbolic surfaces with geodesic boundaries, punctures and cone points, which generalize Okai's result for surfaces with geodesic boundaries. The proof relies on the parametrization of the Teichmuller space of surface of type (1,1) or (0,4) as a sub-locus of an algebraic equation in $\mathbf{R}^3$. As an application, we show that the hyperbolic length functions of closed curves are asymptotically piecewise linear functions with respect to the Fenchel Nielsen coordinates in the Teichmuller spaces of surfaces with cone points.

Carboxypeptidase E (CPE) is one of the most important carboxypeptidases involved in biosynthesis of numerous peptide hormones and neurotransmitters and has an important role in endocrine regulation. A splice variant of CPE (CPE-ΔN) has been detected and the mechanism of CPE-ΔN action in tumorigenesis has been studied in many different cancers. The aim of this study was to examine CPE-ΔN expression in human colorectal cancer (CRC) and to evaluate its possible use as a potential prognostic marker. Two hundred nineteen primary colorectal tumors and corresponding normal tissues were included in the study. We have analyzed CPE-ΔN isoform expression by qRT-PCR and Western blot in 219 CRC patients. Correlations between CPE-ΔN mRNA expression and clinicopathological variables were determined with chi-square tests. Survival probabilities were determined using Kaplan–Meier analysis, and univariate and multivariate analyses of the prognostic factors were performed with a Cox regression model. Our results show that CPE-ΔN is overexpressed in colorectal tumor tissue and that high CPE-ΔN mRNA expression is closely correlated with tumor differentiation, pT classification, pN classification, tumor recurrence, and lymph node metastasis (P = 0.042, 0.036, 0.031, 0.006, and 0.008, respectively). However, no correlation was observed between CPE-ΔN expression and age, gender, tumor localization, gross features, and the tumor size. In addition, patients with high CPE-ΔN expression had a significantly shorter survival (P

Despite the improved ability to detect mutations in recent years, tissue specimens cannot always be procured in a clinical setting, particularly from patients with recurrence of tumors or metastasis. Therefore, the aim of this study was to investigate whether plasma is able to be used for mutation analysis instead of tissue specimens. We collected plasma from 62 patients with colorectal cancer (CRC) prior to treatment. DNA extracted from plasma and matched tumor tissues were obtained. Mutations in KRAS were amplified from the tissue specimens and sequenced by regular polymerase chain reaction (PCR) and co-amplification at lower denaturation temperature (COLD)-PCR. Plasma KRAS gene mutation on codon 12 (GGT>GAT) was detected using a nested COLD-PCR/TaqMan® -MGB probe. Mutations in plasma and matched tumors were compared. KRAS mutation on codon 12 (GGT>GAT) was found in 13 (21.0%) plasma specimens and 12 (19.4%) matched tumor tissues. The consistency of KRAS mutations between plasma and tumors was 75% (9/12), which indicated a high correlation between the mutations detected in plasma DNA and the mutations detected in the corresponding tumor DNA (P

Branched-chain amino acids (BCAAs) and branched-chain α-keto acids (BCKAs) play significant biological roles as they are involved in protein and neurotransmitter synthesis as well as energy metabolism pathways. To routinely and accurately study the dynamics of BCAAs and BCKAs in human diseases, e.g. cerebral infarction, a novel liquid chromatography–tandem mass spectrometry (LC–MS/MS) method has been developed and validated. The plasma samples were deproteinized with acetonitrile, and then separated on a reversed phase C18 column with a mobile phase of 0.1 % formic acid (solvent A)–methanol (solvent B) using gradient elution. The detection of BCAAs and BCKAs was conducted in multiple reaction monitoring with positive/negative electrospray ionization switching mode. Biologically relevant isomers such as leucine and isoleucine were individually quantified by combining chromatographic separation and fragmentation. Good linearity (R 2 > 0.99) was obtained for all six analytes with the limits of detection from 0.1 to 0.2 µg/mL. The intra-day and inter-day accuracy ranged from 93.7 to 108.4 % and the relative standard deviation (RSD) did not exceed 15.0 %. The recovery was more than 80 % with RSD less than 14.0 %. The main improvements compared to related, state-of-the-art methods included enhanced sensitivity, enhanced separation of isomers, and reduced complexity of sample processing. Finally, the validated method was applied to analyze the plasma samples of healthy volunteers and patients suffering cerebral infarction, and significant differences in the concentration levels of BCAAs and BCKAs were observed.

Introduction: This study examines the effectiveness of double-marker analysis for α-fetoprotein (AFP) and β-human chorionic gonadotropin (β-hCG) combined with measurement of nuchal fold thickness (NT) in the detection of Down’s syndrome (DS) in Mainland Chinese subjects during second trimester prenatal screening. Materials and Methods: We examined pregnant women with a singleton pregnancy between 15 and 21 weeks of gestation who underwent second trimester screening for DS using double-marker analysis for AFP and β-hCG combined with ultrasound measurement of NT. The combined risk of DS was calculated. A cut-off of 1/270 was used to defi ne a pregnancy at high-risk of DS. Amniocentesis was offered to all patients with high-risk pregnancies. Results: Using double-marker analysis for AFP and β-hCG in combination with measurement of NT, the detection rate of DS increased from 66.7% to 77.8% when compared with double-marker analysis alone with similar false-positive rates (4.35%, 4.83% respectively). Using receiver operating characteristic curve (ROC) analysis, we determined that the double-marker analysis combined with measurement of NT exhibited an increased area under the curve (AUC) of 0.835 (95% CI: 0.743 to 0.927) when compared to double-marker analysis alone, which had an AUC of 0.748 (95% CI: 0.635 to 0.860). In addition, both methods were more effective than any other single test such as AFP, free β-hCG or NT measurement. Conclusion: Second trimester prenatal screening using double-marker analysis for AFP and β-hCG combined with measurement of NT is effective for the detection of DS in Mainland Chinese pregnancies. Key words: Down’s syndrome, Prenatal screening, Second trimester