A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise.

OBJECTIVES: Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed. DESIGN: Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments. RESULTS: The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM). CONCLUSIONS: This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

Au/Lum/RhB@Ag-DMcT ICP-Based Double-Ratio Colorimetric and Fluorometric Dual Mode Assay and Multi-Responsive Coffee Ring Chips for Point-of-Use Analysis of Phosphate Ions.

In this work, by fully exploring the stimulus response of the guest-functionalized infinite coordination polymers (ICPs), a double-ratio colorimetric and fluorometric dual mode assay and multi-responsive coffee ring chips for point-of-use analysis of phosphate ions (Pi) were proposed. First, the complex host-guest interactions were rationally designed to obtain Au/Lum/RhB@Ag-DMcT ICPs. The composite ICPs exhibited a purple-blue color resulted from the modulated localized surface plasmon resonance (LSPR) of the Au core, and a blue fluorescence color stemmed from the unique aggregation-induced-emission (AIE) of Luminol (Lum) and the aggregation-caused-quenching (ACQ) of rhodamine B (RhB). With the presence of Pi, the host-guest interactions of the shell within Au/Lum/RhB@Ag-DMcT ICPs were interrupted to release Au core, Lum, and RhB in a dispersed state. Consequently, the color of the solution changed to purple-red (the mixed color of the Au core and RhB guest), and the fluorescence color turned to orange-red (AIE of Lum decreased, while the ACQ of RhB recovered). This constituted the sensing mechanism for dual-mode Pi assay with the double ratiometric response. Second, during the stimulus response, the surface wettability/size/amount of Au/Lum/RhB@Ag-DMcT ICPs simultaneously altered. These changes were reflected in the form of the coffee ring deposition pattern variances on the glass substrate and served as signal readouts for the exploration of multi-responsive coffee ring chips for the first time. Quantitative Pi detection with high accuracy and reliability in real samples was thereby realized, which offered an opportunity for the point-of-use analysis of Pi in resources-limited areas in a high-throughput fashion.

Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy.

BACKGROUND: Many important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking. OBJECTIVE: The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data. MATERIAL AND METHODS: We extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively. RESULTS: This graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %). CONCLUSIONS: The results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.

The landscape of T and B lymphocytes interaction and synergistic effects of Th1 and Th2 type response in the involved tissue of IgG4-RD revealed by single cell transcriptome analysis.

OBJECTIVES: To investigate the landscape of T-B cell interaction, immune receptor profiles and effects of different types of immune responses in the involved tissues of IgG4-RD. METHODS: Single cell RNA sequencing, bulk sample RNA sequencing, immune receptor repertoire analysis (both BCR and TCR), multi-color flow cytometry, and in-vitro assays with model cells (e.g. EBV-immortalized B cells from IgG4-RD patient) and histologic methods were applied to investigate the immunopathological features of IgG4-RD from multiple aspects. RESULTS: Ectopic germinal center formation was observed in IgG4-RD patients at advanced disease stage, and a large part of B cells in involved tissue were germinal center B cell-like. Germinal center reaction in IgG4-RD led to the irregularities of both TCR and BCR clones in the involved tissues, and limited clonal overlaps among different samples. Enhanced Th1- and Th2-type responses were observed in involved tissues of IgG4-RD and patients with both increased Th1- and Th2-type response related cell subsets possessed more severe inflammatory indices. Analyses to the origin of IGHG4 transcripts in IgG4-RD indicated that IgG4 could be switched from IgM directly, or from other IgG subclasses. In vitro assays with EBV-immortalized B cells, fibroblasts and epithelial cells revealed the effects of Th1-type and Th2-type responses on germinal center reaction, ectopic expression of MHC-II molecules, and formation of tertiary lymphoid structures. CONCLUSIONS: Synergistic effects of Th1- and Th2-type responses were involved in the pathogenesis of IgG4-RD via their influences on both acute inflammatory processes and the chronicity and complexity of IgG4-RD.

Lab-on-a-ZnO-Submicron-Particle Sensor Array for Monitoring AD upon Cd2+ Exposure with CSF Tau441% as an Effective Hallmark.

In this study, based on the posttreatment strategy, blue-color-emissive ZnO submicron particles (B-ZnO SMPs) and red-color-emissive ZnO submicron particles (R-ZnO SMPs) were obtained from rationally designed Zn-infinite coordination polymer (ICP) precursors. After modification of thiol-containing aptamers, diverse spectral changes in the ultraviolet and visible regions of B- and R-ZnO SMPs toward different tau species were explored to construct a lab-on-a-ZnO-submicron-particle sensor array. Assisted by principal component analysis (PCA), the unique fingerprints of the sensor array enabled the simultaneous differentiation and quantitative detection of different tau species (tau381, tau410, and tau441) for the first time. Furthermore, the dynamic changes of tau441% (the ratio of the two most reported representative 4R isoform (full-length tau441) and 3R isoform (tau381)) in cerebrospinal fluid (CSF) during the Alzheimer's disease (AD) onset of Cd2+-exposed rats could also be monitored by the lab-on-a-ZnO-submicron-particle sensor array, which was supposed to be an effective hallmark and highly correlated with the formation of neurofibrillary tangles (NFTs). This study not only provides a further insight into the involvement of subchronic Cd2+ exposure in the tau etiology of AD but also offers more comprehensive and effective information about the asymptomatic stage of AD upon environmental risk, which has potential applications in the early diagnosis and therapy.

pH-Regulated H4TCPE@Eu/AMP ICP Sensor Array and Its Fingerprinting on Test Papers: Toward Point-of-Use Systematic Analysis of Environmental Antibiotics.

In this work, 1,1,2,2-tetra(4-carboxylphenyl)ethylene (H4TCPE) was selected as the guest and incorporated into a Eu/AMP ICP host to establish a "lab-on-an-AIE@Ln/ICP" sensor array for identifying and sensing environmental antibiotics simultaneously. First, on the basis of a theoretical study of the antenna effect and reductive photoinduced charge transfer between the as-prepared H4TCPE@Eu/AMP ICPs and antibiotics, respectively, the response from the sensitized time-resolved fluorescence of the host and the unique aggregation-induced emission (AIE) of the guest were selected as the main sensing elements for the sensor array. With the regulation of pH, the diverse fluorescence responses for antibiotics with either structural differences (flumequine, oxytetracycline, and sulfadiazine) or structural similarities (oxytetracycline, tetracycline, and doxycycline) were recorded and processed by principal component analysis; systematic analysis of environmental antibiotics was therefore realized. Encouraged by the superior anti-aggregation-caused quenching effect of H4TCPE@Eu/AMP ICPs on the test strip, the distinct fluorescence color changes of the "lab-on-an-AIE@Ln/ICP" sensor array were further explored with the aid of smartphones. The fingerprinting pattern of the sensor array on test paper eventually holds great potential for the point-of-use systematic analysis of environmental antibiotics even in complicated real samples.

Cu2+-Regulated reversible coordination interaction of GQD@Tb/GMP ICP nanoparticles: towards directly monitoring cerebrospinal acetylcholinesterase as a biomarker for cholinic brain dysfunction.

This work demonstrates a new strategy for sensing cerebrospinal acetylcholinesterase (AChE) as a cholinergic biomarker for brain dysfunction based on graphene quantum dot (GQD)-functionalized lanthanide infinite coordination polymer (Ln-ICP) nanoparticles. The ICPs used in this work were comprised of two components, i.e. a supramolecular Ln-ICP host formed by the coordination between the GMP ligand and central metal ion Tb3+, and guest GQDs with abundant functional groups, which were utilized as antenna ligands to further sensitize the fluorescence of Tb/GMP. Upon excitation at 300 nm, the obtained GQD@Tb/GMP ICP nanoparticles exhibited enhanced green fluorescence from Tb/GMP. With the addition of Cu2+, the competitive coordination between Cu2+ and GQDs weakened the antenna effect, leading to a decrease in the fluorescence of GQD@Tb/GMP ICPs. However, in the presence of thiocholine (TCh), a thiol-containing compound hydrolyzed from acetylthiocholine (ATCh) by AChE, a stronger coordination interaction between Cu2+ and TCh occurred, resulting in the restoration of the fluorescence of GQD@Tb/GMP ICPs. Using the method established herein, the cerebrospinal AChE fluctuation of rats with acute organophosphorus pesticide (OP) poisoning or chronic Alzheimer's disease (AD) could be monitored. This study essentially provides a novel approach to realize the direct monitoring of a biomarker for brain dysfunction by regulating the competitive coordination interaction reversibly, which is critical in the early diagnosis and therapy of brain diseases.

Analysis of correlation between window duration for kurtosis computation and accuracy of noise-induced hearing loss prediction.

Kurtosis is considered an important metric for evaluating noise-induced hearing loss (NIHL). However, how to select window duration to calculate kurtosis remains unsolved. In this study, two algorithms were designed to investigate the correlation between window duration for kurtosis computation and the accuracy of NIHL prediction using a Chinese industrial database. Pure-tone hearing threshold levels (HTLs) and full-shift noise were recorded from each subject. In the statistical comparison, subjects were divided into high- and low-kurtosis groups based on kurtosis values computed over different window durations. Mann-Whitney U test was used to compare the difference in group HTLs to find the optimal window duration to best distinguish these two groups. In the support vector machine NIHL prediction model, kurtosis obtained from different window durations was used as a feature of the model for NIHL evaluation. The area under the curve was used to evaluate the performances of models. Fourteen window durations were tested for each algorithm. Results showed that 60 s was an optimal window duration that allows for both efficient computation and high accuracy for NIHL evaluation at test frequencies of 3, 4 and 6 kHz, and the geometric mean of kurtosis sequence was the best metric in NIHL evaluation.

Stimulus Response of TPE-TS@Eu/GMP ICPs: Toward Colorimetric Sensing of an Anthrax Biomarker with Double Ratiometric Fluorescence and Its Coffee Ring Test Kit for Point-of-Use Application.

In this work, by fully exploring the stimulus response of infinite coordination polymer nanoparticles (ICPs) and by taking advantage of the particular optical properties of ICP guest tetra(4-sulfophenyl)ethene (TPE-TS) with adjustable monomer emission (ME) and aggregation-induced emission (AIE), we demonstrated a novel sensing mechanism for an anthrax biomarker dipicolinic acid (DPA) based on the competitive coordination interaction regulating the structure of TPE-TS@Eu/GMP ICPs. The double ratiometric fluorescence stemmed from triple response of TPE-TS@Eu/GMP ICPs without spectral cross-interference (ME and AIE from TPE-TS and sensitized emission from Eu/DPA) and a corresponding blue-to-red fluorescent color change, which not only benefited the direct detection of DPA with high sensitivity and selectivity, but also offered a great opportunity to realize real-time monitoring of DPA released by Bacillus subtilis spores. Furthermore, the coffee ring deposition patterns on a test paper were innovatively tuned by the quantity and morphology changes of TPE-TS@Eu/GMP ICPs during their stimulus response toward DPA, which could be exploited as expanded signal channels. By integrating a multichannel responsive coffee ring test kit with image recognition and processing application installed on smartphones, point-of-use analysis of DPA could be realized in a low-cost and high-throughput fashion.

Electrochemical modeling and evaluation for textile electrodes to skin.

BACKGROUND: With the development of wearable health-monitoring technologies, a variety of textile electrodes have been produced and applied by researchers. However, there are no universal and effective methods even testing platforms for evaluating the skin-electrode electrochemical interface for textile electrodes because different human bodies have different skin characteristics. METHODS: An electrochemical modeling and evaluation for textile electrodes to skin was proposed, and two electrochemical evaluation platforms (EEP) were set up based on two simulated skin models (SSM). First, skin-electrode electrochemical interface (SEEI) models for traditional wet electrodes and textile electrodes were analyzed. Based on the SEEI models and YY/T 0196-2005 (Chinese YY/T pharmaceutical industry standard for disposable ECG electrode), three skin-electrode electrochemical characteristics (SEEC), including skin-electrode static impedance (SESI), skin-electrode alternating current impedance (SEAI), and skin-electrode polarization voltage (SEPV), were proposed. Then, three electrochemical evaluation methods for textile electrodes to skin were proposed and analyzed, which were the correlation between SEEC and skin-electrode contact pressure (SECP), skin-electrode relative movement (SERM), and conduction loss of active signals (CLAS). Finally, an electrochemical evaluation platform was set up based on an active simulated skin model (ASSM) and passive simulated skin model (PSSM). RESULTS: 9 feature parameters based on the passive electrochemical evaluation platform (PEEP) and 11 feature parameters based on the active electrochemical evaluation platform (AEEP) were obtained for evaluating textile electrodes. And four kinds of textile electrode characteristics including SEEC, SECP, SERM, and CLAS were quantitatively measured based on the electrochemical evaluation platform, and the testing accuracy and range for these characteristics were measured separately. Finally, correlation between SEEC and SECP for 10 kinds of textile electrode samples was studied, and 14 electrochemical characteristics and four skin-electrode contact pressure characteristics were extracted. Experimental results showed that significant correlations were found between six SEEC characteristics and SECP characteristics, and the correlation coefficient between ACI_3 and USECP was the highest. And the polarization voltages of most dry electrode samples showed a downward trend with the increase of contact pressure. CONCLUSIONS: The electrochemical evaluation platform yielded effective experimental data and could provide strong support for the evaluation and application of textile electrodes, which was also effective in evaluating other bioelectric electrodes such as 3M electrode, stainless steel electrode, dry electrode and microneedle electrode.

Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.

BACKGROUND: Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications. OBJECTIVE: The aim of this study is to provide a privacy-preserving machine learning protocol for multiple data providers and researchers (eg, logistic regression). This protocol allows researchers to train models and then evaluate them on medical data from multiple sources while providing privacy protection for both the sensitive data and the learned model. METHODS: We adapted a novel threshold homomorphic encryption scheme to guarantee privacy requirements. We devised new relinearization key generation techniques for greater scalability and multiplicative depth and new model training strategies for simultaneously training multiple models through x-fold cross-validation. RESULTS: Using a client-server architecture, we evaluated the performance of our protocol. The experimental results demonstrated that, with 10-fold cross-validation, our privacy-preserving logistic regression model training and evaluation over 10 attributes in a data set of 49,152 samples took approximately 7 minutes and 20 minutes, respectively. CONCLUSIONS: We present the first privacy-preserving multiparty logistic regression model training and evaluation protocol based on threshold homomorphic encryption. Our protocol is practical for real-world use and may promote multicenter medical research to some extent.

A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study.

BACKGROUND: The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. OBJECTIVE: The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. METHODS: Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. RESULTS: DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients' demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro-area under the curve were all above 0.71 in each scenario. CONCLUSIONS: DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data.

BACKGROUND: Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. METHODS: In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. RESULTS: Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. CONCLUSION: In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

A smartphone-based platform for point-of-use determination of alkaline phosphatase as an indicator of water eutrophication.

A smartphone-based detection platform for the determination of alkaline phosphatase (ALP) is described. The method is based on the rational design of the stimulus-response of 7-methoxycoumarin-3-carboxylic acid (7-MC-3-COOH)-functionalized Eu-AMP infinite coordination polymer (ICP) nanoparticles. The blue fluorescence of 7-MC-3-COOH at 403 nm was suppressed, while the red fluorescence of Eu3+ at 615 nm was sensitized after the formation of 7-MC-3-COOH@Eu-AMP ICP. Upon exposure to ALP, the dephosphorylation of AMP resulted in the destruction of 7-MC-3-COOH@Eu-AMP ICP, and thereby, the blue fluorescence of 7-MC-3-COOH recovered; in the meantime, the sensitized red fluorescence was quenched. With the fluorescence intensity ratio F615/F430 as the signal readout, ALP can be detected within a concentration range 0.001 to 0.15 U mL-1, and the limit of detection (LOD) was 0.00035 U mL-1. Moreover, fluorescence color changes from red to blue could also be recognized by a portable device with the smartphone as a signal reader, and direct point-of-use testing (POUT) for ALP within a concentration range 0.005 to 0.7 U mL-1 could be realized, with LOD of 0.0015 U mL-1. Endowed with high sensitivity and superior reliability, the assay enabled direct monitoring of P-related water eutrophication in a freshwater lake with ALP as an indicator. Graphical abstract A smartphone-based platform for point-of-use determination of alkaline phosphatase.

Rational design of an ionic liquid dispersive liquid-liquid micro-extraction method for the detection of organophosphorus pesticides.

In this study, a functionalized ionic liquid (IL), 3-methyl-1-(ethoxycarbonylmethyl) imidazolium bis(trifluoromethylsulfonyl)-imide ([MimCH2COOCH3][NTf2]), was rationally designed and explored as an extraction solvent in dispersive liquid-liquid microextraction (DLLME) combined with ultra-high performance liquid chromatography (UHPLC) for the sensitive determination of organophosphorus pesticides (OPs). As π-π stacking interaction between the parent imidazolium cation core and the OPs is one of the most important factors, the proposed IL exhibited a high extraction efficiency. Moreover, during the DLLME process, a cloudy solution containing fine drops of [MimCH2COOCH3][NTf2] allowed for a larger contact area between the OPs and the IL, which accelerated the mass transfer, and therefore the enrichment could be realized in a rapid fashion. Under the optimal extraction conditions, the developed method was successfully applied in the analysis of OPs in environmental water samples with a high enrichment factor (more than 400), good recovery and reproducibility.

Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer.

BACKGROUND: An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients. METHODS: Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions. RESULTS: Based on univariate and multivariate analysis, some prognostic factors, such as "TNM stage", "tumor size" and "age at diagnosis," have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]). CONCLUSIONS: Based on this study, it's recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

In-situ generation of gold nanoparticles on MnO2 nanosheets for the enhanced oxidative degradation of basic dye (Methylene Blue).

In this work, the gold nanoparticles (Au-NPs) were in-situ generated on the surface of MnO2 nanosheets to form MnO2/Au-NPs nanocomposite in a simple and cost-effective way. Multiple experiments were carried out to optimize the oxidation of basic dye (Methylene Blue (MB)), including the molar ratio of MnO2 to chloroauric acid (HAuCl4), the pH of the solution and the effect of initial material. Under the optimal condition, the highest degradation efficiency for MB achieved to 98.9% within 60 min, which was obviously better than commercial MnO2 powders (4.3%) and MnO2 nanosheets (74.2%). The enhanced oxidative degradation might attribute to the in-situ generation of ultra-small and highly-dispersed Au-NPs which enlarged the synergistic effect and/or interfacial effect between MnO2 nanosheets and Au-NPs and facilitated the uptake of electrons by MnO2 from MB during the oxidation, thus validating the application of MnO2/Au-NPs nanocomposite for direct removal of organic dyes from wastewater in a simple and convenient fashion.

ß-cyclodextrin-ionic liquid polymer based dynamically coating for simultaneous determination of tetracyclines by capillary electrophoresis.

Tetracyclines are a group of broad spectrum antibiotics widely used in animal husbandry to prevent and treat diseases. However, the improper use of tetracyclines may result in the presence of their residues in animal tissues or waste. Recently, great attention has been drawn towards the green solvents ionic liquids. Ionic liquids have been employed as a coating material to modify the electroosmotic flow in capillary electrophoresis. In this study, a functionalized ionic liquid, mono-6-deoxy-6-(3-methylimidazolium)-ß-cyclodextrin tosylate, was synthesized and used for the simultaneous separation and quantification of tetracyclines by capillary electrophoresis. Good separation efficiency could be achieved due to the multiple functions of ß-cyclodextrin derived ionic liquid, including the electrostatic interaction, the hydrogen bonding, and the cavity structure in ß-cyclodextrin ionic liquid which can entrap the tetracyclines to form inclusion complex. After optimization, baseline separation achieved in 25 min with the running buffer consisted of 10 mmol/L, pH 7.2 phosphate buffer and 20 mmol/L ß-cyclodextrin ionic liquid. The satisfied result demonstrated that the ß-cyclodextrin ionic liquid is an ideal background electrolyte modifier in the separation of tetracyclines with high stability and good reproducibility. And it is an effective strategy to design and synthesize specific ILs as additive applied in separation.

An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management.

OBJECTIVE: Chronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home. METHODS: We proposed an ontology-based framework to integrate patient data, medical domain knowledge, and patient assessment criteria for chronic disease patient follow-up assessments. A clinical decision support system was developed to implement this framework for automatic selection and adaptation of standard assessment protocols to suit patient personal conditions. We evaluated our method in the case study of type 2 diabetic patient follow-up assessments. RESULTS: The proposed framework was instantiated using real data from 115,477 follow-up assessment records of 36,162 type 2 diabetic patients. Standard evaluation criteria were automatically selected and adapted to the particularities of each patient. Assessment results were generated as a general typing of patient overall condition and detailed scoring for each criterion, providing important indicators to the case manager about possible inappropriate judgments, in addition to raising patient awareness of their disease control outcomes. Using historical data as the gold standard, our system achieved a rate of accuracy of 99.93% and completeness of 95.00%. CONCLUSIONS: This study contributes to improving the accessibility, efficiency and quality of current patient follow-up services. It also provides a generic approach to knowledge sharing and reuse for patient-centered chronic disease management.

Stimulus Response of Au-NPs@GMP-Tb Core-Shell Nanoparticles: Toward Colorimetric and Fluorescent Dual-Mode Sensing of Alkaline Phosphatase Activity in Algal Blooms of a Freshwater Lake.

In this study, we demonstrate a colorimetric and fluorescent dual-mode method for alkaline phosphatase activity (APA) sensing in freshwater lake with stimuli-responsive gold nanoparticles@terbium-guanosine monophosphate (Au-NPs@GMP-Tb) core-shell nanoparticles. Initially, the core-shell nanoparticles were fabricated based on Au-NPs decorated with a fluorescent GMP-Tb shell. Upon being excited at 290 nm, the as-formed Au-NPs@GMP-Tb core-shell nanoparticles emit green fluorescence, and the decorated GMP-Tb shell causes the aggregation of Au-NPs. However, the addition of ALP destroys GMP-Tb shell, resulting in the release of Au-NPs from the shell into the solvent. As a consequence, the aggregated Au-NPs solubilizes with the changes in the UV-vis spectrum of the dispersion, and in the meantime, the fluorescence of GMP-Tb shell turns off, which constitutes a new mechanism for colorimetric and fluorescent dual-mode sensing of APA. With the method developed here, we could monitor the dynamic change of APA during an algal bloom of a freshwater lake, both by the naked eye and further confirmed by fluorometric determination. This study not only offers a new method for on-site visible detection of APA but also provides a strategy for dual-mode sensing mechanisms by the rational design of the excellent optical properties of Au-NPs and the adaptive inclusion properties of the luminescent infinite coordination polymers.