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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.

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: 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.

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 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.

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.