Label-free fluorescence lifetime imaging for the assessment of cell viability in living tumor fragments.

Significance: To enable non-destructive longitudinal assessment of drug agents in intact tumor tissue without the use of disruptive probes, we have designed a label-free method to quantify the health of individual tumor cells in excised tumor tissue using multiphoton fluorescence lifetime imaging microscopy (MP-FLIM). Aim: Using murine tumor fragments which preserve the native tumor microenvironment, we seek to demonstrate signals generated by the intrinsically fluorescent metabolic co-factors nicotinamide adenine dinucleotide phosphate [NAD(P)H] and flavin adenine dinucleotide (FAD) correlate with irreversible cascades leading to cell death. Approach: We use MP-FLIM of NAD(P)H and FAD on tissues and confirm viability using standard apoptosis and live/dead (Caspase 3/7 and propidium iodide, respectively) assays. Results: Through a statistical approach, reproducible shifts in FLIM data, determined through phasor analysis, are shown to correlate with loss of cell viability. With this, we demonstrate that cell death achieved through either apoptosis/necrosis or necroptosis can be discriminated. In addition, specific responses to common chemotherapeutic treatment inducing cell death were detected. Conclusions: These data demonstrate that MP-FLIM can detect and quantify cell viability without the use of potentially toxic dyes, thus enabling longitudinal multi-day studies assessing the effects of therapeutic agents on tumor fragments.

FTIR spectroscopy for assessment of hair from lung cancer patients and its application in monitoring the chemotherapy treatment effect.

Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.

Assessment of Fetal Exposure and Elimination of Perfluoroalkyl and Polyfluoroalkyl Substances: New Evidence from Paired Serum, Placenta, and Meconium Samples.

Multiple pieces of evidence have shown that prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) is closely related to adverse birth outcomes for infants. However, difficult access to human samples limits our understanding of PFASs transport and metabolism across the human placental barrier, as well as the accurate assessment of fetal PFASs exposure. Herein, we assess fetal exposure to 28 PFASs based on paired serum, placenta, and meconium samples. Overall, 21 PFASs were identified first to be exposed to the fetus prenatally and to be metabolized and excreted by the fetus. In meconium samples, 25 PFASs were detected, with perfluorooctane sulfonate and perfluorohexane sulfonic acid being the dominant congeners, suggesting the metabolism and excretion of PFASs through meconium. Perfluoroalkyl sulfonic acids might be more easily eliminated through the meconium than perfluorinated carboxylic acids. Importantly, based on molecular docking, MRP1, OATP2B1, ASCT1, and P-gp were identified as crucial transporters in the dynamic placental transfer of PFASs between the mother and the fetus. ATSC5p and PubchemFP679 were recognized as critical structural features that affect the metabolism and secretion of PFASs through meconium. With increasing carbon chain length, both the transplacental transfer efficiency and meconium excretion efficiency of PFASs showed a structure-dependent manner. This study reports, for the first time, that meconium, which is a noninvasive and stable biological matrix, can be strong evidence of prenatal PFASs exposure.

How does transition finance influence green innovation of high-polluting and high-energy-consuming enterprises? Evidence from China.

Under the impact of "double-carbon" target, transition finance has an important impact on green innovation of Chinese double-high enterprises. Using a sample of 4270 high-polluting and high-energy-consumption listed enterprises (referred to as double-high enterprises) in China from 2012 to 2021, this paper empirically examines the impact of transition finance on the green innovation of China's double-high enterprises by using a fixed-effects model. The study finds that transition finance can have a facilitating effect on green innovation in double-high enterprises. The intermediary mechanism test shows that transition finance can promote green innovation of double-high enterprises through alleviating financing constraints, increasing the level of green management, and enhancing the policy orientation effect. The heterogeneity test finds that transition finance promotes green innovation more significantly for the double-high enterprises that are state-owned, large-scale, and located in regions with high levels of intellectual property protection. Further research finds that the role of transition finance in promoting green innovation in double-high enterprises helps to promote the achievement of green development of double-high enterprises.

Impact of the digital economy on urban green growth: Empirical evidence from China.

Using the digital economy to empower urban economic green growth provides essential opportunities for China to achieve high-quality growth. This paper assesses the level of digital economy and green growth in Chinese cities, seeking to explore the mechanisms and effects of the digital economy on urban green growth in a unified framework. The results indicate that the digital economy can drive cities' green growth. This conclusion still holds after a set of robustness tests. Meanwhile, the green value of the digital economy is fully released among the eastern cities, major urban agglomerations, and high-level cities. Further research shows that the digital economy can indirectly enhance urban green growth in the neighboring regions through spatial spillover effects. Moreover, labor resource mismatch, capital resource mismatch and green technology innovation are significant mediating mechanisms. The findings could guide policymakers on green growth in emerging economies from a digital economy perspective.

Biomass directional pyrolysis based on element economy to produce high-quality fuels, chemicals, carbon materials - A review.

Biomass is regarded as the only carbon-containing renewable energy source and has performed an increasingly important role in the gradual substitution of conventional fossil energy, which also contributes to the goals of carbon neutrality. In the past decade, the academic field has paid much greater attention to the development of biomass pyrolysis technologies. However, most biomass conversion technologies mainly derive from the fossil fuel industry, and it must be noticed that the large element component difference between biomass and traditional fossil fuels. Thus, it's necessary to develop biomass directional pyrolysis technology based on the unique element distribution of biomass for realizing enrichment target element (i.e., element economy). This article provides a broad review of biomass directional pyrolysis to produce high-quality fuels, chemicals, and carbon materials based on element economy. The C (carbon) element economy of biomass pyrolysis is realized by the production of high-performance carbon materials from different carbon sources. For efficient H (hydrogen) element utilization, high-value hydrocarbons could be obtained by the co-pyrolysis or catalytic pyrolysis of biomass and cheap hydrogen source. For improving the O (oxygen) element economy, different from the traditional hydrodeoxygenation (HDO) process, the high content of O in biomass would also become an advantage because biomass is an appropriate raw material for producing oxygenated liquid additives. Based on the N (nitrogen) element economy, the recent studies on preparing N-containing chemicals (or N-rich carbon materials) are reviewed. Moreover, the feasibility of the biomass poly-generation industrialization and the suitable process for different types of target products are also mentioned. Moreover, the enviro-economic assessment of representative biomass pyrolysis technologies is analyzed. Finally, the brief challenges and perspectives of biomass pyrolysis are provided.

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov.

The Yellow River Basin in China has the world's most serious soil erosion problem. The Yellow River Basin in Sichuan Province (YRS), as the upper reaches of the Yellow River, and its water conservation (WC) capacity greatly affects the ecological environment of the downstream basin. In recent years, YRS has received more and more attention, and numerous policies have been developed to improve local WC. However, there is a vacancy in the long-term research of WC in the YRS due to the lack of in-situ data. This study quantitatively evaluated the WC of YRS from 2001 to 2020 through Google Earth Engine (GEE) and analyzed the spatio-temporal variations of WC and land cover (LC). CA-Markov predicted the LC and WC in 2025 under three scenarios to assess the contribution of different scenarios to WC. The WC in YRS fluctuated from 1.93 to 6.77 billion m3. The climate is the dominant factor of WC change, but the effect of LC on WC is also evident. The WC capacity increases with vegetation coverage and height. The WC capacity of forests per km2 exceeds 600 mm, while that of grasslands is about 250 mm, and barren can cause around 300 mm of WC loss. In 2025, the WC in YRS may exceed 7.5 billion m3, but the past ecological management mode should be transformed. Improving the quality of land use and converting grasslands to forests is better than reducing cropland to improve WC.

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.

BACKGROUND: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS: In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. FINDINGS: Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. INTERPRETATION: The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. FUNDING: National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.

The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination. METHODS: This study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images. RESULTS: The system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2. CONCLUSIONS: The results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.

Association of greenness with the disease burden of lower respiratory infections and mediation effects of air pollution and heat: a global ecological study.

Exposure to greenness is increasingly linked to beneficial health outcomes, but the associations between greenness and the disease burden of lower respiratory infections (LRIs) are unclear. We used the normalized difference vegetation index (NDVI) and the leaf area index (LAI) to measure greenness and incidence, death, and disability-adjusted life years (DALYs) due to LRIs to represent the disease burden of LRIs. We applied a generalized linear mixed model to evaluate the association between greenness and LRI disease burden and performed a stratified analysis, after adjusting for covariates. Additionally, we assessed the potential mediating effects of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and heat on the association between greenness and the disease burden of LRIs. In the adjusted model, one 0.1 unit increase of NDVI and 0.5 increase in LAI were significantly inversely associated with incidence, death, and DALYs due to LRIs, respectively. Greenness was negatively correlated with the disease burden of LRIs across 15-65 age group, both sexes, and low SDI groups. PM2.5, O3, and heat mediated the effects of greenness on the disease burden of LRIs. Greenness was significantly negatively associated with the disease burden of LRIs, possibly by reducing exposure to air pollution and heat.

How does green finance affect the innovation performance of enterprises? Evidence from China.

"green" + "innovation" has become a new concept of development. The integration of the two can bring win-win for the environment and economy. This paper selects the annual data of 14309 A-share companies in Shanghai and Shenzhen Stock Exchange from 2012 to 2020 as the research sample. And it uses the two-way fixed effect model to empirically test the impact of green finance on enterprise innovation performance. The study found that the development of green finance can promote the improvement of enterprise innovation performance. The analysis of influence mechanism shows that the development of green finance can reduce the financing constraints of enterprises, and then improve the innovation performance of enterprises; the development of green finance can increase the R&D investment of enterprises, and then improve the innovation performance of enterprises; green finance development can increase corporate investment in environmental protection, which in turn can improve corporate innovation performance. The results of heterogeneity test analysis show that compared with the western region, private enterprises, small and medium-sized enterprises, and high energy consumption and high pollution enterprises, the development of green finance in the central and eastern regions, state-owned enterprises, large enterprises, and non double high enterprises has more obvious role in promoting enterprise innovation performance. Therefore, the government should issue relevant policies and actively promote green finance policies that can help improve environmental and economic issues.

Private placements of equity and accessibility of bank loans.

This study investigates the changes in quantity and cost of bank loans after a private placement of common stocks by A-share listed companies in China from 2011 to 2021. This research is derived from the signaling theory and is based on a difference-in-difference design. Through propensity score matching, the sample comprises companies that placed equity privately in the experiment group and companies that did not place equity privately in the control group. We find evidence that the increase in bank loans slowed down, and the cost of bank loans increased after the private placement. The signaling effect of private placements is robust to various additional tests. Further analysis indicates that when state-owned enterprises place equity privately, their access to bank loans is not affected. When institutional investors participate in the private placement, the company's access to bank credit does not go through significant changes. In addition, private placements by companies located in regions with higher levels of marketization of the financial market do not reduce the cost of bank loans.

Analysis of Physical Education Classroom Teaching after Implementation of the Chinese Health Physical Education Curriculum Model: A Video-Based Assessment.

This study assessed the Chinese health physical education curriculum model recently suggested to meet the recommended physical education curriculum reforms addressing the declining physical and mental health of students in China. We used video analyses of 41 physical education classroom teaching cases with a physical education classroom teaching behavior analysis system to provide quantitative and qualitative behavioral data. We established reference ranges for classroom teaching behavior indicators, summarized classroom teaching patterns, and assessed classroom discourse and the emotional climate. Notable findings included teachers in elementary schools using closed-ended questions, predictable responses, and general feedback significantly more often than teachers in senior high school, and ball sports instructors using demonstration and competition significantly more frequently than instructors in athletics. Overall, three teaching patterns were most commonly used-lecture, practice, and dialogue-with practice being dominant. Analysis of the top 50 most commonly spoken words by teachers identified five types of discourse-motivational, directive, specialized, transitional, and regulatory-with motivational words being most frequent. The classroom atmosphere was mainly positive. These findings provide evidence that the use of this curriculum model may bring positive changes to physical education classroom teaching methods in China and will inform subsequent innovative physical education classroom teaching practices.

Robust, Scalable, and Cost-Effective Surface Carbonized Pulp Foam for Highly Efficient Solar Steam Generation.

Recently, a solar-driven evaporator has been applied in seawater desalination, but the low stability, high cost, and complex fabrication limit its further application. Herein, we report a novel, low-cost, scalable, and easily fabricated pulp-natural rubber (PNR) foam with a unique porous structure, which was directly used as a solar-driven evaporator after facile surface carbonization. This surface carbonized PNR (CPNR) foam without interface adhesion or modification was composed of a top photothermal layer with light absorption ability and a bottom hydrophilic foam layer with a porous and interconnected network structure. Due to the strong light absorption ability (93.2%) of the carbonized top layer, together with the low thermal conductivity (0.1 W m K-1) and good water adsorption performance (9.9 g g-1) of the bottom layer, the evaporation rate and evaporation efficiency of the pulp foam evaporator under 1 sun of illumination attained 1.62 kg m-2 h-1 and 98.09%, respectively, which were much higher than those of most cellulose-based solar-driven evaporators. Furthermore, the CPNR foam evaporator with high cost-effectiveness presented high light-thermal conversion, heat localization, and good salt rejection properties due to the unique porous structure. Additionally, the CPNR foam evaporator exhibited potential applications in the treatments of simulated sewage, metal ion concentration, and seawater desalination. Its cost-effectiveness was clearly higher than that of most reported evaporators as well. Therefore, this novel, low-cost, and stable pulp foam evaporator demonstrated here can be a very promising solution for water desalination and purification.

Condition optimization of iron-air fuel cell to treat phosphate-containing wastewater regarding sustainable development.

Increasing use of phosphorus products and excessive exploitation of phosphorus resources become two major problems in perspective of phosphorus sustainable development. Phosphorus recovery is the shortcut to solve this dilemma. Combining electrochemistry, an iron-air fuel cell was adopted to recover phosphate and electricity from phosphate-containing wastewater in our previous studies. The present study focused on investigating the effects of catholyte/anolyte conductivity, external resistance, and anolyte pH on the performance of iron-air fuel cell, and obtaining the optimized conditions. Furthermore, the electrochemical methods of phosphate recovery were compared and assessed, and it is concluded that iron-air fuel cell has great potential for energy recovery. The phosphate removal efficiencies and vivianite yield roughly positively correlated with the catholyte conductivity and the anolyte pH, but negatively correlated with the external resistance and the anolyte conductivity. The electricity generation roughly positively correlated with the catholyte conductivity and anolyte conductivity, but showed limitations in the test range of anolyte pH and external resistance. To pursue high phosphate removal efficiencies and vivianite yield, the catholyte conductivity, external resistance, anolyte pH and anolyte conductivity were suggested to be 35 g-NaCl/L, 10 Ω, 8 and 0 g-NaCl/L. While if electricity generation was the primary goal, these parameters should be 35 g-NaCl/L, 220 Ω, 5 and 70 g-NaCl/L. The optimized conditions will help to improve the phosphate removal efficiency, vivianite yield and electricity generation, and to promote the development of iron-air fuel cell technology.

Internet of things-enabled real-time health monitoring system using deep learning.

Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes' life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes' conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.

Digital Economy, Environmental Regulation, and Ecological Well-Being Performance: A Provincial Panel Data Analysis from China.

China is currently in a strategic opportunity period for green and high-quality development, and developing the digital economy is an important choice to achieve environmental pollution control, improve regional ecological efficiency, and enhance social welfare. In this context, the impact of the digital economy on ecological well-being performance and the role of environmental regulation need to be examined. In this study, the super-efficiency SBM-DEA model was used to measure the level of ecological well-being performance in 30 provinces of China from 2011 to 2019. On this basis, the mediating effect model and spatial Durbin model were adopted to explore the transmission mechanism and regional heterogeneity of the impact of the digital economy on ecological well-being performance. The empirical results show that the digital economy significantly contributes to regional ecological well-being performance in China, and there is significant spatial spillover as well. Moreover, the findings still hold under robustness tests. The results also show that environmental regulation is an important transmission path for the digital economy to enhance regional ecological well-being performance, and the impact of environmental regulation on ecological well-being performance varies by region; specifically, the impact in eastern China is positive but not significant. However, the digital economy plays a significant positive role in promoting ecological well-being performance in the central and western regions, and is more obvious in the central region. Finally, suggestions are put forward to enhance the role of the digital economy in regional ecological well-being performance, which is of great significance for promoting green economic growth and high-quality development.

Fabrication of a label-free electrochemical cell-based biosensor for toxicity assessment of thiram.

Thiram has been widely used in agriculture and may invades the food chain, posing a threat to human health. In this research, a label-free electrochemical cell-based biosensor was presented for in vitro toxicity assessment of thiram. HepG2 cells were cultured on poly-l-lysine@gold nano-flowers functionalized indium tin oxide coated glass electrode (PLL@AuNFs/ITO) to serve as biorecognition elements. AuNFs were electrodeposited on ITO to provide an enlarged specific surface area and benefited the output signal amplification. PLL was selected as an effective biocompatible coating material to facilitate cell adhesion and proliferation, thereby realizing one-step recording of electrochemical signals from thiram-treated cells. With the aid of the differential pulse voltammetry method, the fabricated biosensor was applied to assess the cytotoxicity of thiram. Results showed that the cytotoxicity measured by the fabricated biosensor exhibited a linear relationship related to thiram concentration ranging from 5 to 50 µM with a detection limit of 2.23 µM. The IC50 of thiram obtained by the biosensor was 29.5 µM, which was close to that of conventional MTT assay (30.8 µM). The effects of thiram on HepG2 cells were also investigated via SEM and flow cytometry. Meanwhile, the proposed biosensor was used to evaluate the toxicity of thiram in fruit samples. Results indicated that the toxicity of thiram cannot be ignored even at a low residual concentration in food (≤5 mg/kg). In conclusion, the developed sensor showed excellent sensitivity, stability, and reliability, which provided a great capacity for the convenient toxicity evaluation of thiram residue in food.