Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

BACKGROUND: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS: We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS: The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients.

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.

A comprehensive technology strategy for microbial identification and contamination investigation in the sterile drug manufacturing facility-a case study.

Objective: A comprehensive strategy for microbial identification and contamination investigation during sterile drug manufacturing was innovatively established in this study, mainly based on MALDI-TOF MS for the identification and complemented by sequencing technology on strain typing. Methods: It was implemented to monitor the bacterial contamination of a sterile drug manufacturing facility, including its bacterial distribution features and patterns. In three months, two hundred ninety-two samples were collected covering multiple critical components of raw materials, personnel, environment, and production water. Results: Based on our strategy, the bacterial profile across the production process was determined: 241/292 bacterial identities were obtained, and Staphylococcus spp. (40.25%), Micrococcus spp.(11.20%), Bacillus spp. (8.30%), Actinobacteria (5.81%), and Paenibacillus spp. (4.56%) are shown to be the most dominant microbial contaminants. With 75.8% species-level and 95.4% genus-level identification capability, MALDI-TOF MS was promising to be a first-line tool for environmental monitoring routine. Furthermore, to determine the source of the most frequently occurring Staphylococcus cohnii, which evidenced a widespread presence in the entire process, a more discriminating S. cohnii whole-genome SNP typing method was developed to track the transmission routes. Phylogenetic analysis based on SNP results indicated critical environment contamination is highly relevant to personnel flow in this case. The strain typing results provide robust and accurate information for the following risk assessment step and support effective preventive and corrective measures. Conclusion: In general, the strategy presented in this research will facilitate the development of improved production and environmental control processes for the pharmaceutical industry, and give insights about how to provide more sound and reliable evidence for the optimization of its control program.

Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification.

Objective: To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products. Methods: We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated in silico with a self-built genome database containing 158 thousand bacteria. The appropriate methodology for BCC detection using rt-RPA was evaluated by 58 strains in pure culture and 33 batches of artificially contaminated pharmaceutical and personal care products. Results: We identified the protein SecY and its protein-coding gene secY through the automatic comparison framework. The virtual evaluation of the conserved region of the secY gene showed more than 99.8% specificity from the genome database, and it can distinguish all known BCC species from other bacteria by phylogenetic analysis. Furthermore, the detection limit of the rt-RPA assay targeting the secY gene was 5.6 × 102 CFU of BCC bacteria in pure culture or 1.2 pg of BCC bacteria genomic DNA within 30 min. It was validated to detect <1 CFU/portion of BCC bacteria from artificially contaminated samples after a pre-enrichment process. The relative trueness and sensitivity of the rt-RPA assay were 100% in practice compared to the reference methods. Conclusion: The automatic comparison framework for molecular biomarker mining is straightforward, universal, applicable, and efficient. Based on recognizing the BCC-specific protein SecY and its gene, we successfully established the rt-RPA assay for rapid detection in pharmaceutical and personal care products.

Identification of indications for albumin administration in septic patients with liver cirrhosis.

BACKGROUND: Albumin infusion is the primary therapeutic strategy for septic patients with liver cirrhosis. Although recent studies have investigated the efficacy of albumin in the resuscitation stage of septic patients with liver cirrhosis, it remains unclear whether daily albumin administration can improve outcomes. Furthermore, the indications for initiating albumin therapy are not well defined. METHODS: Septic patients with liver cirrhosis were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV 2.0) database. Marginal structural Cox models were employed to investigate the association between daily albumin infusion and 28-day mortality. We also aimed to explore under what circumstances enrolled patients could benefit most from albumin administration, based on the clinical parameters collected on the day of albumin infusion, including serum albumin concentration, serum lactate concentration, mean arterial pressure (MAP), and vasopressor dosage. RESULTS: A total of 2265 patients were included in the final analysis, of whom 1093 (48.3%) had received albumin treatment at least once. The overall 28-day mortality was 29.6%. After marginal structural modeling, daily albumin infusion was associated with a reduced risk of 28-day death (hazard ratio, 0.76; 95% CI 0.61-0.94). We found that patients benefit most from albumin infusion when initiated on the day of serum albumin concentration between 2.5 and 3.0 g/dL, serum lactate concentration greater than or equal to 2 mmol/L, MAP less than 60 mmHg, or vasopressor dosage between 0.2 and 0.3 mcg/kg/min (norepinephrine equivalent, NEE). CONCLUSIONS: Albumin infusion is associated with a reduction in mortality in septic patients with liver cirrhosis under specific circumstances. Serum albumin concentration, serum lactate, MAP, and vasopressor dosage were found to be modifiers of treatment effectiveness and should be considered when deciding to initial albumin infusion.

Ordered intermetallic compounds combining precious metals and transition metals for electrocatalysis.

Ordered intermetallic alloys with significantly improved activity and stability have attracted extensive attention as advanced electrocatalysts for reactions in polymer electrolyte membrane fuel cells (PEMFCs). Here, recent advances in tuning intermetallic Pt- and Pd-based nanocrystals with tunable morphology and structure in PEMFCs to catalyze the cathodic reduction of oxygen and the anodic oxidation of fuels are highlighted. The fabrication/tuning of ordered noble metal-transition metal-bonded intermetallic PtM and PdM (M = Fe, Co) nanocrystals by using high temperature annealing treatments to promote the activity and stability of electrocatalytic reactions are discussed. Furthermore, the further improvement of the efficiency of this unique ordered intermetallic alloys for electrocatalysis are also proposed and discussed. This report aims to demonstrate the potential of the ordered intermetallic strategy of noble and transition metals to facilitate electrocatalysis and facilitate more research efforts in this field.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages.

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

fIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

To study the contamination of microorganisms in the food industry, pharmaceutical industry, clinical diagnosis, or bacterial taxonomy, accurate identification of species is a key starting point of further investigation. The conventional method of identification by the 16S rDNA gene or other marker gene comparison is not accurate, because it uses a tiny part of the genomic information. The average nucleotide identity calculated between two whole bacterial genomes was proven to be consistent with DNA-DNA hybridization and adopted as the gold standard of bacterial species delineation. Furthermore, there are more bacterial genomes available in public databases recently. All of those contribute to a genome era of bacterial species identification. However, wrongly labeled and low-quality bacterial genome assemblies, especially from type strains, greatly affect accurate identification. In this study, we employed a multi-step strategy to create a type-strain genome database, by removing the wrongly labeled and low-quality genome assemblies. Based on the curated database, a fast bacterial genome identification platform (fIDBAC) was developed (http://fbac.dmicrobe.cn/). The fIDBAC is aimed to provide a single, coherent, and automated workflow for species identification, strain typing, and downstream analysis, such as CDS prediction, drug resistance genes, virulence gene annotation, and phylogenetic analysis.

Highly efficient enrichment and identification of pathogens using a herringbone microfluidic chip and by MALDI-TOF mass spectrometry.

Bacterial infections cause considerable morbidity and expensive healthcare costs. The prescription of broad-spectrum antimicrobial drugs results in failure of treatment or overtreatment and exacerbates the spread of multidrug-resistant pathogens. There is an emergent demand for rapid and accurate methods to identify pathogens and conduct personalized therapy. Here, we develop a herringbone microfluidic chip integrated with vancomycin modified magnetic beads (herringbone-VMB microchip) to enrich pathogens. The enriched pathogens are identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The herringbone-VMB microchip applies passive mixing of bacterial samples by generating microvortices, which significantly enhances the interaction between bacteria and vancomycin modified magnetic beads and leads to more efficient enrichment compared to in-tube extraction. Four common pathogens in urinary tract infections are utilized to validate the method, and the capture efficiency of the bacteria from urine is up to 90%. The whole procedure takes 1.5 hours from enrichment to identification. This method shows potential in shortening the turnaround time in the clinical diagnosis of bacterial infections.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. DESIGN: Retrospective observational study. SETTING: We developed our model on the shared ICUs publicly data and verified on the full hidden populations for challenge scoring. PATIENTS: Public database included 40,336 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (hospital system A) and Emory University Hospital (hospital system B). A total of 24,819 patients from hospital systems A, B, and C (an unidentified hospital system) were sequestered as full hidden test sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 168 features were extracted on hourly basis. Explainable artificial intelligence sepsis predictor model was trained to predict sepsis in real time. Impact of each feature on hourly sepsis prediction was explored in-depth to show the interpretability. The algorithm demonstrated the final clinical utility score of 0.364 in this challenge when tested on the full hidden test sets, and the scores on three separate test sets were 0.430, 0.422, and -0.048, respectively. CONCLUSIONS: Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.

A Comprehensive Multilocus Sequence Typing Scheme for Identification and Genotyping of Staphylococcus Strains.

Increasing clinical significance of coagulase-negative staphylococci requires effective methods for species identification and genotyping. In this study, six housekeeping genes (femA, ftsZ, gap, pyrH, rpoB, and tuf) with extensive allelic polymorphisms were identified and evaluated to develop a comprehensive multilocus sequence typing (MLST) scheme. Selected primers were capable of amplification of the six loci from all of the 180 Staphylococcus strains belonging to 18 different species. Sequence analysis of each locus (44-63 alleles) revealed higher nucleotide diversity than 16S rRNA (28 alleles). Phylogenetic analysis of the concatenated sequences (3054 bp) of the six loci provided accurate species identification and highly discriminatory typing for all the strains. Multilocus allelic analysis of the 180 Staphylococcus strains generated 103 different sequence profiles, suggesting high genetic diversity of the strains. For example, 30 S. aureus, 37 S. epidermidis, 32 S. haemolyticus, and 14 S. hominis strains were typed into 15, 21, 11, and 10 sequence profiles, respectively. Compared with published MLST schemes that restrict on a few particular species, this new scheme both achieved similar discrimination for typing S. aureus, S. epidermidis, S. haemolyticus, and S. hominis and provided sufficient discriminatory power for typing additional opportunistic species, such as S. cohnii, S. capitis, and S. warneri. Importantly, the comprehensive MLST scheme for Staphylococcus strains provides a better genotyping tool for understanding the phylogeny of coagulase-positive Staphylococcus aureus strains.

Genomic and transcriptomic analyses reveal distinct biological functions for cold shock proteins (VpaCspA and VpaCspD) in Vibrio parahaemolyticus CHN25 during low-temperature survival.

BACKGROUND: Vibrio parahaemolyticus causes serious seafood-borne gastroenteritis and death in humans. Raw seafood is often subjected to post-harvest processing and low-temperature storage. To date, very little information is available regarding the biological functions of cold shock proteins (CSPs) in the low-temperature survival of the bacterium. In this study, we determined the complete genome sequence of V. parahaemolyticus CHN25 (serotype: O5:KUT). The two main CSP-encoding genes (VpacspA and VpacspD) were deleted from the bacterial genome, and comparative transcriptomic analysis between the mutant and wild-type strains was performed to dissect the possible molecular mechanisms that underlie low-temperature adaptation by V. parahaemolyticus. RESULTS: The 5,443,401-bp V. parahaemolyticus CHN25 genome (45.2% G + C) consisted of two circular chromosomes and three plasmids with 4,724 predicted protein-encoding genes. One dual-gene and two single-gene deletion mutants were generated for VpacspA and VpacspD by homologous recombination. The growth of the ΔVpacspA mutant was strongly inhibited at 10 °C, whereas the VpacspD gene deletion strongly stimulated bacterial growth at this low temperature compared with the wild-type strain. The complementary phenotypes were observed in the reverse mutants (ΔVpacspA-com, and ΔVpacspD-com). The transcriptome data revealed that 12.4% of the expressed genes in V. parahaemolyticus CHN25 were significantly altered in the ΔVpacspA mutant when it was grown at 10 °C. These included genes that were involved in amino acid degradation, secretion systems, sulphur metabolism and glycerophospholipid metabolism along with ATP-binding cassette transporters. However, a low temperature elicited significant expression changes for 10.0% of the genes in the ΔVpacspD mutant, including those involved in the phosphotransferase system and in the metabolism of nitrogen and amino acids. The major metabolic pathways that were altered by the dual-gene deletion mutant (ΔVpacspAD) radically differed from those that were altered by single-gene mutants. Comparison of the transcriptome profiles further revealed numerous differentially expressed genes that were shared among the three mutants and regulators that were specifically, coordinately or antagonistically modulated by VpaCspA and VpaCspD. Our data also revealed several possible molecular coping strategies for low-temperature adaptation by the bacterium. CONCLUSIONS: This study is the first to describe the complete genome sequence of V. parahaemolyticus (serotype: O5:KUT). The gene deletions, complementary insertions, and comparative transcriptomics demonstrate that VpaCspA is a primary CSP in the bacterium, while VpaCspD functions as a growth inhibitor at 10 °C. These results have improved our understanding of the genetic basis for low-temperature survival by the most common seafood-borne pathogen worldwide.

Characterization of Fe3O4/SiO2/Gd2O(CO3)2 core/shell/shell nanoparticles as T1 and T2 dual mode MRI contrast agent.

Core/shell/shell structured Fe3O4/SiO2/Gd2O(CO3)2 nanoparticles were successfully synthesized. Their properties as a new type of T1-T2 dual model contrast agent for magnetic resonance imaging were investigated. Due to the introduce of a separating SiO2 layer, the magnetic coupling between Gd2O(CO3)2 and Fe3O4 could be modulated by the thickness of SiO2 layer and produce appropriate T1 and T2 signal. Additionally, the existence of Gd(3+) enhances the transverse relaxivity of Fe3O4 possibly because of the magnetic coupling between Gd(3+) and Fe3O4. The Fe3O4/SiO2/Gd2O(CO3)2 nanoparticles exhibit good biocompatibility, showing great potential for biomedical applications.

[Impurity profile study of cefalotin sodium by two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry].

A two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry (2D-LC-QTOF MS) method to profile the impurities of cefalotin sodium was developed. A Symmetry C18 column (250 mm x 4.6 mm, 5 µm) was used in the first dimensional chromatography, with gradient elution using pH 2.5 phosphate buffer and acetonitrile as the mobile phases. The column temperature was maintained at 40 degrees C with an ultraviolet detection of 220 nm for analysis. An ACQUITY UPLC BEH C18 column (50 mm x 2.1 mm, 1.7 µm) was used in the second dimensional chromatography, with gradient elution using water containing 0.1% (v/v) formic acid and acetonitrile containing 0.1% (v/v) formic acid as the mobile phases. The column temperature was maintained at 40 degrees C. An HLB C18 column (30 mm x 2.1 mm, 20 µm) was used as the trap column. The data were collected in positive ion mode. The ion source temperature was set at 100 degrees C and the electrospray ionization (ESI) needle voltage was set at 1 000 V. The nebulizer gas temperature was set at 500 degrees C. The molecular formulas of the impurities were determined by their exact masses and isotope distributions. And the structures were determined by the protonated molecular ions and the manufacturing process of cefalotin sodium. Six impurities of cefalotin sodium were characterized and the origination of the impurities was deduced. Three of them were unknown impurities to the best of our knowledge. It was confirmed that the Chinese Pharmacopoeia 2010 has mistaken impurity A of cefalotin sodium. The results indicated that the 2D-LC-QTOF MS method could be used to investigate the impurity profile of cefalotin sodium, and it is simple and sensitive.

[Study on the proficiency testing program of pharmaceutical preparation analysis].

This proficiency testing program is established to evaluate the pharmaceutical preparation analysis capacity of laboratories recommended by 18 countries and economies. It was authorized by Asia Pacific Laboratory Accreditation Cooperation (APLAC), and organized by Shanghai Institute for Food and Drug Control (SIFDC) and China National Accreditation Service for Conformity Assessment (CNAS). The 0.3sigma test is used to evaluate the homogeneity and stability of the proficiency testing sample. The results of the laboratories were assessed by Z-score. The robust average and the robust standard deviation of the participants' results were calculated as assigned value and standard deviation for performance assessment of hydrochlorothiazide and captopril using robust statistics. Thirty-three of 38 laboratories recommended by 18 countries and economies sent their results back. Twenty-four laboratories' results were observed as satisfactory. Five laboratories were identified as having reported at least one questionable result. Four laboratories were identified as having reported at least one unsatisfactory result.

A photoelectrochemical immunosensor based on Au-doped TiO2 nanotube arrays for the detection of α-synuclein.

α-Synuclein (α-SYN) is a very important neuronal protein that is associated with Parkinson's disease. In this paper, we utilized Au-doped TiO(2) nanotube arrays to design a photoelectrochemical immunosensor for the detection of α-SYN. The highly ordered TiO(2) nanotubes were fabricated by using an electrochemical anodization technique on pure Ti foil. After that, a photoelectrochemical deposition method was exploited to modify the resulting nanotubes with Au nanoparticles, which have been demonstrated to facilitate the improvement of photocurrent responses. Moreover, the Au-doped TiO(2) nanotubes formed effective antibody immobilization arrays and immobilized primary antibodies (Ab(1)) with high stability and bioactivity to bind target α-SYN. The enhanced sensitivity was obtained by using {Ab(2)-Au-GOx} bioconjugates, which featured secondary antibody (Ab(2)) and glucose oxidase (GOx) labels linked to Au nanoparticles for signal amplification. The GOx enzyme immobilized on the prepared immunosensor could catalyze glucose in the detection solution to produce H(2)O(2), which acted as a sacrificial electron donor to scavenge the photogenerated holes in the valence band of TiO(2) nanotubes upon irradiation of the other side of the Ti foil and led to a prompt photocurrent. The photocurrents were proportional to the α-SYN concentrations, and the linear range of the developed immunosensor was from 50 pg mL(-1) to 100 ng mL(-1) with a detection limit of 34 pg mL(-1). The proposed method showed high sensitivity, stability, reproducibility, and could become a promising technique for protein detection.