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BACKGROUND: Exercise interventions for mild cognitive impairment (MCI) have been extensively studied. However, there is no bibliometric study on exercise interventions for MCI. This study aimed to identify the collaborative networks, research hotspots, evolution trends, and future directions. METHODS: Relevant documents were retrieved from the Web of Science Core Collection database. VOSviewer was used to analyze the co-authorship of the author, countries and institutions, and the keywords co-occurrence. CiteSpace was used to detect burst keywords' research trends. RESULTS: A total of 569 articles were included and showed an overall increasing trend in annual publications. The most influential subject categories, authors, journals, country, and institutions were "geriatrics gerontology," "Doi, Takehiko and Shimada, Hiroyuki," "Journal of Alzheimer's Disease," USA, and "Veterans Health Administration," respectively. The research hotspots are "effectiveness," "neural mechanism" and "correlation" of exercise interventions, and the emerging trend is "intervention quality." CONCLUSION: This area is in a rapid development phase, whereby research hotpots are focused and the research trend is clear. The highly productive authors and institutions have made outstanding contributions and the subject categories present an interdisciplinary trend. However, there is weak cooperation between countries and institutions, and a substantial research gap exists between developed and developing countries. Future research may highlight the intervention quality, emphasizing the combination with virtual reality technology.

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Stress-induced alterations in central neuron metabolism and function are crucial contributors to depression onset. However, the metabolic dysfunctions of the neurons associated with depression and specific molecular mechanisms remain unclear. This study initially analyzed the relationship between cholesterol and depression using the NHANES database. We then induced depressive-like behaviors in mice via restraint stress. Applying bioinformatics, pathology, and molecular biology, we observed the pathological characteristics of brain cholesterol homeostasis and investigated the regulatory mechanisms of brain cholesterol metabolism disorders. Through the NHANES database, we initially confirmed a significant correlation between cholesterol metabolism abnormalities and depression. Furthermore, based on successful stress mouse model establishment, we discovered the number of cholesterol-related DEGs significantly increased in the brain due to stress, and exhibited regional heterogeneity. Further investigation of the frontal cortex, a brain region closely related to depression, revealed stress caused significant disruption to key genes related to cholesterol metabolism, including HMGCR, CYP46A1, ACAT1, APOE, ABCA1, and LDLR, leading to an increase in total cholesterol content and a significant decrease in synaptic proteins PSD-95 and SYN. This indicates cholesterol metabolism affects neuronal synaptic plasticity and is associated with stress-induced depressive-like behavior in mice. Adeno-associated virus interference with NR3C1 in the prefrontal cortex of mice subjected to short-term stress resulted in reduced protein levels of NRIP1, NR1H2, ABCA1, and total cholesterol content. At the same time, it increased synaptic proteins PSD95 and SYN, effectively alleviating depressive-like behavior. Therefore, these results suggest that short-term stress may induce cholesterol metabolism disorders by activating the NR3C1/NRIP1/NR1H2 signaling pathway. This impairs neuronal synaptic plasticity and consequently participates in depressive-like behavior in mice. These findings suggest that abnormal cholesterol metabolism in the brain induced by stress is a significant contributor to depression onset.

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Stress triggers a comprehensive pathophysiological cascade in organisms. However, there is a substantial gap in the research regarding the effects of stress on liver function. This study aimed to investigate the impact of restraint stress on hepatocellular damage and elucidate the underlying molecular mechanisms. An effective mouse restraint stress model was successfully developed, and liver function analysis was performed using laser speckle imaging, metabolomics and serum testing. Alterations in hepatocyte morphology were assessed using haematoxylin and eosin staining and transmission electron microscopy. Oxidative stress in hepatocytes was assessed using lipid reactive oxygen species and malondialdehyde. The methylation status and expression of GSTP1 were analysed using DNA sequencing and, real-time PCR, and the expression levels of GPX4, TF and Nrf2 were evaluated using real-time quantitative PCR, western blotting, and immunohistochemical staining. A stress-induced model was established in vitro by using dexamethasone-treated AML-12 cells. To investigate the underlying mechanisms, GSTP1 overexpression, small interfering RNA, ferroptosis and Nrf2 inhibitors were used. GSTP1 methylation contributes to stress-induced hepatocellular damage and dysfunction. GSTP1 is involved in ferroptosis-mediated hepatocellular injury induced by restraint stress via the TF/Nrf2 pathway. These findings suggest that stress-induced hepatocellular injury is associated with ferroptosis, which is regulated by TF/Nrf2/GSTP1.

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A rare autosomal recessive genetic disease is spinal muscular atrophy with respiratory distress type 1 (SMARD 1; OMIM #604320), which is characterized by progressive distal limb muscle weakness, muscular atrophy, and early onset of respiratory failure. Herein, we report the case of a 4-month-old female infant with SMARD type 1 who was admitted to our hospital owing to unexplained distal limb muscle weakness and early respiratory failure. This report summarizes the characteristics of SMARD type 1 caused by heterozygous variation in the immunoglobulin mu DNA binding protein 2 (IGHMBP2) gene by analyzing its clinical manifestations, genetic variation characteristics, and related examinations, aiming to deepen clinicians' understanding of the disease, assisting pediatricians in providing medical information to parents and improving the decision-making process involved in establishing life support.

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Hairy cell leukemia (HCL) is an infrequent and persistent B-cell inert lymphoid leukemia. In this study, we present the case of a 71-year-old female patient with a previous diagnosis of variant HCL who experienced a severe herpes zoster infection leading to an extensive skin eruption. The patient's initial diagnosis of HCL occurred 7 years ago, and she underwent treatment with cladribine, interferon, COP (cyclophosphamide, vincristine, and prednisone), benztropine tablets + clarithromycin dispersible, and ibrutinib. Immune disorders resulting from repeated prior chemotherapy and targeted therapy may potentially precipitate herpes zoster infection. Despite an initial two-week period of unresponsiveness to antivirals and nerve nutrition treatments, the introduction of topical Coptis liquid to the treatment regimen yielded significant efficacy. This case report underscores the potential of Chinese medicine as an adjunct to conventional antiviral therapy in the management of herpes zoster infection in immunocompromised patients. This treatment protocol has the potential to enhance efficacy, enhance quality of life, and serve as a more robust foundation for clinical diagnosis and improved treatments.

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OBJECTIVE: Lymphoma is the most common malignancy of the haematological system. Jeduxiaoliu formula (JDXLF) exerts good therapeutic effects against lymphoma, however, the mechanisms underlying these effects remain unclear. Therefore, this study aimed to investigate the mechanism of action of JDXLF. METHOD: RNA-Seq was performed to examine the molecular mechanisms underlying the therapeutic effects of JDXLF against lymphoma. CCK-8 assay was performed to examine the effects of JDXLF on the proliferation of lymphoma cells. Electron microscopy was performed to examine the morphology of lymphoma cells. Flow cytometry was performed to examine the apoptosis and cell cycle of lymphoma cells. qPCR and Western blotting were performed to detect the expression of apoptotic genes and proteins. In vivo, the tumour-suppressive effect of JDXLF on lymphoma transplanted tumours was examined by establishing a subcutaneous transplantation tumour model in nude mice, and the expression of apoptotic proteins in tumour tissues was analysed via immunohistochemical staining. RESULTS: RNA-Seq revealed 71, 350 and 620 differentially expressed genes (DEGs) in the 1mg/mL, 4mg/mL and 8mg/mL JDXLF treatment groups, respectively. KEGG pathway analysis showed that the DEGs were significantly associated with apoptosis, TNF signalling and NF-κB signalling. In vitro experiments revealed that JDXLF inhibited the proliferation of lymphoma (Raji and Jeko-1) cells in a dose-dependent manner, induced apoptosis and upregulated the expression of Bax/Bcl-2 and caspase3. In vivo experiments revealed that JDXLF had a significant tumourshrinking effect on mice and increased the expression of the apoptosis-related proteins caspase3 and Bax/Bcl-2. CONCLUSIONS: This study indicates that JDXLF can induce apoptosis in lymphoma cells in vitro and in vivo. We suggest this may provide a direction for further research into lymphoma therapy.

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OBJECTIVE: Microneedling with topical tranexamic acid (TXA) is a novel treatment option for melasma; however, the efficacy and safety of this combined administration therapy is in controversial. This study is conducted to address this issue of this technique in melasma. METHODS: An extensive literature review was performed to identify relevant trials, including randomized split-face studies, randomized controlled trials and prospective non-randomized split-face studies, comparing microneedling plus topical TXA to routine treatments or placebo. The primary outcomes were changes of the Melasma Area Severity Index (MASI)/modified MASI (mMASI)/hemi MASI between before and after treatment, as well as the changes between a particular treatment and microneedling plus TXA. The mean differences (MDs) and 95% confidence intervals (CIs) were calculated for the reduction of melasma severity scores from baseline to each time point. In contrast, the standard mean differences (SMDs) and 95% CIs were calculated for the differences in reduction in melasma severity scores between the experimental and control groups at each time point. RESULTS: A total of 16 trials were included in the systematic review and data synthesis. The pooled analysis demonstrated that MASI, mMASI, and hemiMASI scores decreased significantly at 4 weeks (MD = 1.85; 95% CI = 1.15-2.54), 8 weeks (MD = 3.28; 95% CI = 2.31-4.24), 12 weeks (MD = 4.73; 95% CI = 2.79-6.50), 16 weeks (MD = 3.18; 95% CI = 1.50-4.85), and 20 weeks (MD = 3.20; 95% CI = 1.95-4.46) after treatment when compared with baseline. The reduction in melasma severity scores of microneedling with TXA group at 4 weeks was more significant than the routine treatment group (SMD = 0.97; 95% CI = 0.09-1.86), while insignificant at 8 weeks (SMD = 1.21; 95% CI = -0.17 to 2.59), 12 weeks (SMD = 0.63; 95% CI = -0.03 to 1.29), 16 weeks (SMD = 0.61; 95% CI = -2.85 to 4.07), or 20 weeks (SMD = 1.04; 95% CI = -1.28 to 3.36). CONCLUSION: Despite the high heterogeneity across these studies, the current findings indicated that microneedling with topical TXA is an alternative treatment option for melasma treatment; and more well-designed studies are needed to confirm it.

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Recycled rubber concrete (RRC), a sustainable building material, provides a solution to the environmental issues posed by rubber waste. This research introduces a sophisticated hybrid random aggregate model for RRC. The model is established by combining convex polygon aggregates and rounded rubber co-casting schemes with supplemental tools developed in MATLAB and Fortran for processing. Numerical analyses, based on the base force element method (BFEM) of the complementary energy principle, are performed on RRC's uniaxial tensile and compressive behaviors using the proposed aggregate models. This study identified the interfacial transition zone (ITZ) around the rubber as RRC's weakest area. Here, cracks originate and progress to the aggregate, leading to widespread cracking. Primary cracks form perpendicular to the load under tension, whereas bifurcated cracks result from compression, echoing conventional concrete's failure mechanisms. Additionally, the hybrid aggregate model outperformed the rounded aggregate model, exhibiting closer peak strengths and more accurate aggregate shapes. The method's validity is supported by experimental findings, resulting In detailed stress-strain curves and damage contour diagrams.

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Subsequently to the publication of the above article, the authors have drawn to the attention of the Editorial Office that a few inadvertent errors were made during the assembly of Fig. 6 on p. 1802. In the first instance, the images selected to represent the A549 cell line in Fig. 6A were inadvertently shown as the data for the NCI­H460 cell line in Fig. 6C and vice versa, and so the data shown for Fig. 6A and C have been interchanged in the revised version of this figure. Moreover, the representative image for panel '3' in Fig. 6A of the above article (and so, now panel '3' in Fig. 6C of the corrected version) was wrongly copied across from that of panel '2' in Fig. 6C (now, panel '2' in Fig. 6A of the corrected version). The authors were able to re­examine their original data files, and realize how the errors were made during the assembly of this figure. The revised version of Fig. 6, with the data originally shown in Fig. 6A and C now interchanged, also showing the correct data for panel '3' in Fig. 6C, is shown on the next page. Note that the errors made in assembling this figure did not affect the overall conclusions reported in the paper. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this Corrigendum, and all the authors agree with its publication. They also apologize to the readership for any inconvenience caused. [Oncology Reports 37: 1793­1803, 2017; DOI: 10.3892/or.2017.5366].

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The paper discussed the use of machine learning (ML) and quantum chemistry calculations to predict the transition state and yield of copper-catalyzed P-H insertion reactions. By analyzing a dataset of 120 experimental data points, the transition state was determined using density functional theory (DFT). ML algorithms were then applied to analyze 16 descriptors derived from the quantum chemical transition state to predict the product yield. Among the algorithms studied, the Support Vector Machine (SVM) achieved the highest prediction accuracy of 97%, with over 80% correlation in Leave-One-Out Cross-Validation (LOOCV). Sensitivity analysis was performed on each descriptor, and a comprehensive investigation of the reaction mechanism was conducted to better understand the transition state characteristics. Finally, the ML model was used to predict reaction plans for experimental design, demonstrating strong predictive performance in subsequent experimental validation.

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In this study, Ti particles reinforced Mg AZ31/Al 6082 composite sheets were successfully prepared by hot rolling, with the aim of revealing the effect of Ti particles addition on the mechanical behavior and microstructure of Mg AZ31/Al 6082 composite sheets. The results showed that Ti particles were uniformly distributed at the interface of the Mg/Al-Ti composite sheets, which could greatly reduce the amount of Mg-Al intermetallic compounds during annealing treatment. Compared to the Mg/Al sheet, the tensile strength and elongation of the Mg/Al-Ti sheet could be improved simultaneously after the annealing treatment. Ti particles addition hardly affected the grain size, texture type, and tensile fracture morphology of the Mg layer and Al layer in the composite sheets before and after annealing. This present study provides a new perspective on the mechanical behavior and microstructure of Mg/Al composites through the addition of metal particles.

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The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.

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Visualizing chiral structures in solid materials is crucial but difficult in chiral analysis. The three-dimensional structures in the helicoidal nano-assemblies in cellulose nanocrystal (CNC) films were visualized using a Mueller matrix microscope (MMM). Optical analysis of the assembly of CNCs through structural reconstruction by optical simulation revealed the complex structures in CNC films.

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In the field of metal matrix composites, it is a great challenge to improve the strength and elongation of magnesium matrix composites simultaneously. In this work, xTC4/AZ31 (x = 0.5, 1, 1.5 wt.%) composites were fabricated by spark plasma sintering (SPS) followed by hot extrusion. Scanning electron microscopy (SEM) showed that nano-TC4 (Ti-6Al-4V) was well dispersed in the AZ31 matrix. We studied the microstructure evolution and tensile properties of the composites, and analyzed the strengthening mechanism of nano-TC4 on magnesium matrix composites. The results showed that magnesium matrix composites with 1 wt.%TC4 had good comprehensive properties; compared with the AZ31 matrix, the yield strength (YS) was increased by 20.4%, from 162 MPa to 195 MPa; the ultimate tensile strength (UTS) was increased by 11.7%, from 274 MPa to 306 MPa, and the failure strain (FS) was increased by 21.1%, from 7.6% to 9.2%. The improvement in strength was mainly due to grain refinement and good interfacial bonding between nano-TC4 and the Mg matrix. The increase in elongation was the result of grain refinement and a weakened texture.

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OBJECTIVES: This study aims to analyze and summarize the characteristics of supernumerary teeth by using cone-beam computed tomography (CBCT). METHODS: A total of 718 patients with 1 138 supernumerary teeth were retrospectively collected. Age, gender, number, location, morphology, eruption status, and accompanying symptoms of the supernumerary teeth were statistically analyzed. The relationship relative to jaws, gender, and eruption status were analyzed and discussed. RESULTS: The average age of the patients was 9.54±5.32 years, and the male to female ratio was 2.88∶1. About 77.02% of the patients sought medical advice during the mixed dentition period, and 50.70% had one supernumerary tooth. These supernumeraries were most commonly conical in shape, and 85.76% of them were in the incisor region, 92.09% in the upper jaw, 46.75% in inverted position, and 86.20% unerupted. Overall, 65.29% of them had fully developed roots, and 60.63% had an impact on adjacent structures. Significant differences were found in eruption status, morphology, zoning, direction, root development, and impact on adjacent structures between the supernumerary teeth located in the upper and lower jaws (P<0.05). Significant differences were also detected in gender, morphology, zoning, orientation, root development, and impact on adjacent structures between erupted and unerupted teeth (P<0.05). The incidence of supernumerary teeth in the incisor region was higher in males than that in females. Moreover, the root of supernumeraries was more completely developed in males than in females (P<0.05). CONCLUSIONS: For supernumerary teeth, CBCT images can provide accurate three-dimensional radiographic data and are valuable for clinical diagnosis and treatment planning.

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Simple summary: Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects. Background: Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer. Methods: Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds. Results: The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients. Conclusions: The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

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Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

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Alzheimer's disease (AD) is the most common cause of dementia worldwide, posing a considerable economic burden to patients and society as a whole. Exercise has been confirmed as a non-drug intervention method in the related literature on AD. However, at present, there are still few bibliometric studies on AD exercise research. In order to fill the gap, this paper aims to intuitively analyze the growth in AD exercise literature published from 1998 to 2021 using bibliometrics, providing historical insights for scientific research circles. The main source of literature retrieval is the Web of Science database. Using the Boolean operator tools "OR" and "AND" combined with keywords related to "exercise" and "Alzheimer's disease", we conducted a title search and obtained 247 documents. Using Microsoft Excel, Datawrapper, and Biblioshiny, this study carried out a bibliometric analysis of countries, institutions, categories, journals, documents, authors, and keyword plus terms. The study found that the number of papers published from 2016 to 2021 had the greatest increase, which may have been influenced by the Global Dementia Report 2015 and COVID-19. Interdisciplinary cooperation and the research results published in high-scoring journals actively promoted research and development in the AD exercise field. The United States and the University of Minnesota system play a central role in this field. In future, it will be necessary to explore the effectiveness and feasibility of multi-mode interventions on an active lifestyle, including exercise, in different groups and environments worldwide. This study may provide a direction and path for future research by showing the global overview, theme evolution, and future trends of research results in the AD exercise field.

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The World Health Organization has identified nervous system diseases as one of the biggest public health problems, including autism spectrum disorder (ASD). Considering the extensive benefits of physical activity (PA), the literature on the PA research of ASD has increased each year, but there is a lack of bibliometric analyses in this field. To investigate the research achievements worldwide, this paper adopts bibliometrics to analyze the trend in the academic literature on the PA research of ASD published from 1980 to 2021. The documents were retrieved from the Web of Science database, and the search strategy was to combine the keywords related to "physical activity" and "autism spectrum disorder" by using the Boolean operator tools "OR" and "AND" in the title. A total of 359 English documents were retrieved. Microsoft Excel, Data Wrapper, VOSviewer, and Biblioshiny were used for the visual analysis. We found that the number of published documents increased the fastest from 2017 to 2021, which may be due to the promulgation of the Global Action Plan for Physical Activity 2018-2030 and the influence of COVID-19 on the world. The United States and the University of California systems are in the leading position in this field. Cooperation among countries with different levels of development will help to jointly promote the PA research progress on ASD. The focus themes include "individual effect", "social support" and "activity dose". The analysis of the frontier topic points out that researchers are paying increasing attention to how to improve the health and physical fitness of this group through PA. This research clearly puts forward a comprehensive overview, theme focus, and future trends in this field, which may be helpful to guide future research.

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Air pollution is a serious problem. Refractory thiophene sulfides, which cause air pollution, bring great challenges to their rapid and accurate identification. In this work, we propose a fluorescent sensor array based on two perovskite nanocrystals (CsPbBr3 NCs and CsPbBr3/SiO2 NCs) to distinguish different thiophene sulfides. The hydrogen bonding force between the thiophenics of thiophene sulfides and the amino groups of the perovskite NCs results in the weakening of the fluorescence signals of the perovskite NCs. The diverse interactions between thiophene sulfides and two perovskite NCs provide rich information, which can be obtained on the sensor array and identified by linear discriminant analysis. Five thiophene sulfides (i.e., benzothiophene, dibenzothiophene, 2-methylbenzothiophene, 3-methylthiophene, and thiophene) were discriminated by the sensor array at concentrations of 10-50 ppm. The effectiveness of the sensor array was further verified in the discrimination of blinded samples, in which all 10 samples were correctly identified. In addition, it is gratifying that even binary mixtures of thiophene sulfides could be distinguished by the proposed sensor array.

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