Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism.

Critical to a sustainable energy future are microbial platforms that can process aromatic carbons from the largely untapped reservoir of lignin and plastic feedstocks. Comamonas species present promising bacterial candidates for such platforms because they can use a range of natural and xenobiotic aromatic compounds and often possess innate genetic constraints that avoid competition with sugars. However, the metabolic reactions of these species are underexplored, and the regulatory mechanisms are unknown. Here we identify multilevel regulation in the conversion of lignin-related natural aromatic compounds, 4-hydroxybenzoate and vanillate, and the plastics-related xenobiotic aromatic compound, terephthalate, in Comamonas testosteroni KF-1. Transcription-level regulation controls initial catabolism and cleavage, but metabolite-level thermodynamic regulation governs fluxes in central carbon metabolism. Quantitative 13C mapping of tricarboxylic acid cycle and cataplerotic reactions elucidates key carbon routing not evident from enzyme abundance changes. This scheme of transcriptional activation coupled with metabolic fine-tuning challenges outcome predictions during metabolic manipulations.

Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite.

In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its application for precise biosensing owing to tissue absorption and scattering. In this work, a PersL lifetime-based nanoplatform via deep learning was proposed for high-fidelity bioimaging and biosensing in vivo. The persistent luminescence imaging network (PLI-Net), which consisted of a 3D-deep convolutional neural network (3D-CNN) and the PersL imaging system, was logically constructed to accurately extract the lifetime feature from the profile of PersL intensity-based decay images. Significantly, the NIR PersL nanomaterials represented by Zn1+xGa2-2xSnxO4: 0.4 % Cr (ZGSO) were precisely adjusted over their lifetime, enabling the PersL lifetime-based imaging with high-contrast signals. Inspired by the adjustable and reliable PersL lifetime imaging of ZGSO NPs, a proof-of-concept PersL nanoplatform was further developed and showed exceptional analytical performance for hypochlorite detection via a luminescence resonance energy transfer process. Remarkably, on the merits of the dependable and anti-interference PersL lifetimes, this PersL lifetime-based nanoprobe provided highly sensitive and accurate imaging of both endogenous and exogenous hypochlorite. This breakthrough opened up a new way for the development of high-fidelity biosensing in complex matrix systems.

Heterothermic Cataluminescence Sensor System for Efficient Determination of Aldehyde Molecules.

A simple and stable cataluminescence (CTL) sensing platform based on a single sensing material for effective and rapid detection of aldehydes is an urgent need due to growing concerns for the environment, security, and health. Here, an effective and user-friendly identification method is successfully proposed to determine six common aldehydes of homologous compounds via a heterothermic CTL sensor system. Using Gd2O3 with excellent catalytic activity as a sensing material, thermodynamic and kinetic insights into the interactions between Gd2O3 and aldehydes at different temperatures were extracted and integrated to generate a unique constellation profile for each tested aldehyde, whereby achieving their effective and prompt determination. Moreover, the sensor system allowed the quantitative analysis of aldehydes with detection limits of 0.001, 0.009, 0.011, 0.011, 0.007, and 0.003 µg mL-1. Significantly, the sensor system had an excellent stability of up to 30 days. The CTL sensing platform was constructed based on a thermal regulation strategy that can provide a new approach to chemical agent identification.

Near-Infrared Catalytic Chemiluminescence System based on Zinc Gallate Nanoprobe for Hydrazine Sensing.

To the deep tissue penetration and ultra-low background, developing near-infrared (NIR) chemiluminescence probes for human health and environmental safety has attracted more and more attention, but it remains a huge challenge. Herein, a novel NIR chemiluminescence (CL) system was rationally designed and developed, utilizing Cr3+-activated ZnGa2O4 (ZGC) nanoparticles as a catalytic luminophore via hypochlorite (NaClO) activation for poisonous target (hydrazine, N2H4) detection. With superior optical performance and unique catalytic structure of ZGC nanoparticles, the fabricated ZGC-NaClO-N2H4 CL system successfully demonstrated excellent NIR emission centered at 700 nm, fast response, and high sensibility (limit of detection down to 0.0126 µM). Further experimental studies and theoretical calculations found the cooperative catalytic chemiluminescence resonance energy transfer mechanism in the ZGC-NaClO-N2H4 system. Remarkably, the ZGC-based NIR CL system was further employed for N2H4 detection in a complicated matrix involving bioimaging and real water samples, thereby opening a new way as a highly reliable and accurate tool in biomedical and environmental monitoring applications.

Self-powered deep ultraviolet photodetector based on p-CuI/n-ZnGa2O4 heterojunction with high sensitivity and fast speed.

Self-powered deep ultraviolet photodetectors (DUV PDs) are essential in environmental monitoring, flame detection, missile guidance, aerospace, and other fields. A heterojunction photodetector based on p-CuI/n-ZnGa2O4 has been fabricated by pulsed laser deposition combined with vacuum thermal evaporation. Under 260 nm DUV light irradiation, the photodetector exhibits apparent self-powered performance with a maximum responsivity and specific detectivity of 2.75 mA/W and 1.10 × 1011 Jones at 0 V. The photodetector exhibits high repeatability and stability under 260 nm periodic illumination. The response and recovery time are 205 ms and 133 ms, respectively. This work provides an effective strategy for fabricating high-performance self-powered DUV photodetectors.

Highly stable mixed-phase Cs-Cu-I films with tunable optoelectronic properties for UVB photodetector applications.

Ultraviolet (UV) photodetector plays an important role in military, civilian and people's daily life, and is an indispensable part of spectral detection. However, photodetectors target at the UVB region (280-320 nm) are rarely reported, and the devices detected by medium-wave UV light generally have problems such as low detection rate, low sensitivity, and poor stability, which are difficult to meet the market application needs. Herein, Cs-Cu-I films with mixed-phase have been prepared by vacuum thermal evaporation. By adjusting the proportion of evaporation sources (CsI and CuI), the optical bandgaps of mixed-phase Cs-Cu-I films can be tuned between 3.7 eV and 4.1 eV. This absorption cut-off edge is exactly at both ends of the UVB band, which indicating its potential application in the field of UVB detection. Finally, the photodetectors based on Cs-Cu-I/n-Si heterojunction are fabricated. The photodetector shows good spectral selectivity for UVB band, and has a photoresponsivity of 22 mA/W, a specific detectivity of 1.83*1011 Jones, an EQE over 8.7% and an on/off ratio above 20.

Deep-ultraviolet n-ZnGa2O4/p-GaN heterojunction photodetector fabricated by pulsed laser deposition.

Zinc gallium oxide (ZnGa2O4) has attracted considerable interest in deep-ultraviolet photodetectors, due to the ultrawide bandgap, high transmittance in the ultraviolet (UV) region, and excellent environmental stability. In this study, ZnGa2O4 thin films were deposited on p-GaN epi-layers using pulsed laser deposition, resulting in improved crystalline quality. The ZnGa2O4 film exhibited a bandgap of 4.93 eV, calculated through absorption spectra. A heterojunction photodetector (PD) was constructed, demonstrating a rectification effect, an on/off ratio of 12,697 at -5.87 V, a peak responsivity of 14.5 mA/W, and a peak detectivity of 1.14 × 1012 Jones (262 nm, -6 V). The PD exhibited a fast response time (39 ms) and recovery time (30 ms) under 262 nm illumination. The band diagram based on the Anderson model elucidates the photoresponse and carrier transport mechanism. This work paves the way for advancing next-generation optoelectronics.

Categorical linkage-data analysis.

Analysis of integrated data often requires record linkage in order to join together the data residing in separate sources. In case linkage errors cannot be avoided, due to the lack a unique identity key that can be used to link the records unequivocally, standard statistical techniques may produce misleading inference if the linked data are treated as if they were true observations. In this paper, we propose methods for categorical data analysis based on linked data that are not prepared by the analyst, such that neither the match-key variables nor the unlinked records are available. The adjustment is based on the proportion of false links in the linked file and our approach allows the probabilities of correct linkage to vary across the records without requiring that one is able to estimate this probability for each individual record. It accommodates also the general situation where unmatched records that cannot possibly be correctly linked exist in all the sources. The proposed methods are studied by simulation and applied to real data.

Cox regression with linked data.

Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third-party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France.

Overexpression of CuZn superoxide dismutase improves high-density lipoprotein function in swine.

Cardiovascular disease (CVD) has been the leading cause of death worldwide. As a chronic inflammatory disease, atherosclerosis (AS) acts as the initiating factor for CVD and reactive oxygen species (ROS) play a vital role in its development. Superoxide dismutases (SOD) can alleviate the detrimental effects of ROS and serve as the first line of defense through detoxifying the products derived from oxidative stress in vivo. Considering the potential preventive effects of high-density lipoprotein (HDL) on AS and the close relationship between CuZn superoxide dismutase (CuZnSOD) and HDL, the present work investigated whether CuZnSOD overexpression in swine could improve the function of HDL. Seven CuZnSOD transgenic swine, constructed by sperm and magnetic nanoparticles, demonstrated overexpressed CuZnSOD in the liver (P < 0.01) but comparable level to control in plasma (P > 0.05). CuZnSOD overexpression significantly down-regulated the levels of triglyceride (TG), apolipoprotein A-I (apoA-I) (P < 0.05), and high-density lipoprotein cholesterol (HDL-C) (P < 0.01) in plasma. In the presence of CuZnSOD overexpression, HDL3 significantly inhibited levels of IL-6 and TNF-α induced by oxidized low-density lipoprotein (oxLDL) (P < 0.05), indicating enhanced anti-inflammatory activity of HDL. At the same time, HDL-mediated cholesterol efflux did not decrease (P > 0.05). CuZnSOD overexpression improves the anti-inflammatory function of HDL despite decreased levels of HDL-C. In Conclusion, CuZnSOD overexpression improves HDL function in swine.

Integrated Optimization of Crystal Facets and Nanoscale Spatial Confinement toward the Boosted Catalytic Performance of Pd Nanocrystals.

Tuning the surface chemical property and the local environment of nanocrystals is crucial for realizing a high catalytic performance in various reactions. Herein, we aim to elucidate the structure sensitivity of Pd facets on the surface catalytic hydrogenation reaction and to identify what role the nanoconfinement effect plays in the catalytic properties of Pd nanocrystal catalysts. By controlling the coating structures of mesoporous silica (mSiO2) on Pd nanocrystals with different exposed facets that include {100}, {111}, and {hk0}, we present a series of Pd@mSiO2 nanoreactors in core-shell and yolk-shell structures and the discovery of a partial-coated structure, which can provide different types of nanoconfinement, and we propose a seed size-dominated growth mechanism. We demonstrate that a superior activity was exhibited in Pd nanocrystals enclosed by the {hk0} facet as compared to the Pd{100} and Pd{111} facets, and substantially enhanced efficiency and stability were achieved in Pd@mSiO2 particles with yolk-shell structures, indicating a crucial superiority of optimizing the configuration of crystal facets and nanoconfinement. Our study provides an efficient strategy to rationally design and optimize nanocatalysts for promoting catalytic performance.

Disproportionate Carbon Dioxide Efflux in Bacterial Metabolic Pathways for Different Organic Substrates Leads to Variable Contribution to Carbon-Use Efficiency.

Microbial organic matter turnover is an important contributor to the terrestrial carbon dioxide (CO2) budget. Partitioning of organic carbons into biomass relative to CO2 efflux, termed carbon-use efficiency (CUE), is widely used to characterize organic carbon cycling by soil microorganisms. Recent studies challenge proposals of CUE dependence on the oxidation state of the substrate carbon and implicate instead metabolic strategies. Still unknown are the metabolic mechanisms underlying variability in CUE. We performed a multiomics investigation of these mechanisms in Pseudomonas putida, a versatile soil bacterium of the Gammaproteobacteria, processing a mixture of plant matter derivatives. Our 13C-metabolomics data captured substrate carbons into different metabolic pathways: cellulose-derived sugar carbons in glycolytic and pentose-phosphate pathways; lignin-related aromatic carbons in the tricarboxylic acid cycle. Subsequent 13C-metabolic flux analysis revealed a 3-fold lower investment of sugar carbons in CO2 efflux compared to aromatic carbons, in agreement with reported substrate-dependent CUE. Proteomics analysis revealed enzyme-level regulation only for substrate uptake and initial catabolism, which dictated downstream fluxes through CO2-producing versus biomass-synthesizing reactions. Metabolic partitioning as shown here explained the substrate-dependent CUE calculated from reported metabolic flux analyses of other bacteria, further supporting a metabolism-guided perspective for predicting the microbial conversion of accessible organic matter to CO2 efflux.

The curvature quantification of wave I in auditory brainstem responses detects cochlear synaptopathy in human beings.

PURPOSE: Patients with age-related hearing loss complain often about reduced speech perception in adverse listening environment. Studies on animals have suggested that cochlear synaptopathy may be one of the primary mechanisms responsible for this phenomenon. A decreased wave I amplitude in supra-threshold auditory brainstem response (ABR) can diagnose this pathology non-invasively. However, the interpretation of the wave I amplitude in humans remains controversial. Recent studies in mice have established a robust and reliable mathematic algorithm, i.e., curve curvature quantification, for detecting cochlear synaptopathy. This study aimed to determine whether the curve curvature has sufficient test-retest reliability to detect cochlear synaptopathy in aging humans. METHODS: Healthy participants were recruited into this prospective study. All subjects underwent an audiogram examination with standard and extended high frequencies ranging from 0.125 to 16 kHz and an ABR with a stimulus of 80 dB nHL click. The peak amplitude, peak latency, curvature at the peak, and the area under the curve of wave I were calculated and analyzed. RESULTS: A total of 80 individuals with normal hearing, aged 18 to 61 years, participated in this study, with a mean age of 26.4 years. Pearson correlation analysis showed a significant negative correlation between curvature and age, as well as between curvature and extended high frequency (EHF) threshold (10-16 kHz). Additionally, the same correlation was observed between age and area as well as age and EHF threshold. The model comparison demonstrated that the curvature at the peak of wave I is the best metric to correlate with EHF threshold. CONCLUSION: The curvature at the peak of wave I is the most sensitive metric for detecting cochlear synaptopathy in humans  and may be applied in routine diagnostics to detect early degenerations of the auditory nerve.

[Advance of research on Hereditary spastic paraplegia type 4].

Spastic paraplegia type 4 (SPG4) is the most common type of autosomally inherited spastic paraplegia. Its main clinical features include typical simple hereditary spastic paraplegia, with neurological impairments limited to lower limb spasticity, hypertonic bladder dysfunction, and mild weakening of lower limb vibration sensation, without accompanying features such as nerve atrophy, ataxia, cognitive impairment, seizures, and muscle tone disorders. SPAST is the main pathogenic gene underlying SPG4, and various pathogenic SPAST variants have been discovered. This disease has featured a high degree of clinical heterogeneity, and the same pathogenic variant can have different age of onset and severity among different patients and even within the same family. There is a lack of systematic research on the correlation between the genotype and phenotype of SPG4, and the pathogenic mechanism has remained controversial. This article has provided a review for the clinical characteristics, pathogenic gene characteristics, correlation between the genotype and phenotype, and pathogenic mechanism of this disease, with an aim to provide reference for its clinical diagnosis and treatment.

DsrC is involved in fermentative growth and interacts directly with the FlxABCD-HdrABC complex in Desulfovibrio vulgaris Hildenborough.

DsrC is a key protein in dissimilatory sulfur metabolism, where it works as co-substrate of the dissimilatory sulfite reductase DsrAB. DsrC has two conserved cysteines in a C-terminal arm that are converted to a trisulfide upon reduction of sulfite. In sulfate-reducing bacteria, DsrC is essential and previous works suggested additional functions beyond sulfite reduction. Here, we studied whether DsrC also plays a role during fermentative growth of Desulfovibrio vulgaris Hildenborough, by studying two strains where the functionality of DsrC is impaired by a lower level of expression (IPFG07) and additionally by the absence of one conserved Cys (IPFG09). Growth studies coupled with metabolite and proteomic analyses reveal that fermentation leads to lower levels of DsrC, but impairment of its function results in reduced growth by fermentation and a shift towards more fermentative metabolism during sulfate respiration. In both respiratory and fermentative conditions, there is increased abundance of the FlxABCD-HdrABC complex and Adh alcohol dehydrogenase in IPFG09 versus the wild type, which is reflected in higher production of ethanol. Pull-down experiments confirmed a direct interaction between DsrC and the FlxABCD-HdrABC complex, through the HdrB subunit. Dissimilatory sulfur metabolism, where sulfur compounds are used for energy generation, is a key process in the ecology of anoxic environments, and is more widespread among bacteria than previously believed. Two central proteins for this type of metabolism are DsrAB dissimilatory sulfite reductase and its co-substrate DsrC. Using physiological, proteomic and biochemical studies of Desulfovibrio vulgaris Hildenborough and mutants affected in DsrC functionality, we show that DsrC is also relevant for fermentative growth of this model organism and that it interacts directly with the soluble FlxABCD-HdrABC complex that links the NAD(H) pool with dissimilatory sulfite reduction.

Instant Fingerprint Discrimination for Military Explosive Vapors by Dy3+ Doping a La2O3-Based Cataluminescence Sensor System.

With the intensification of explosive-based terrorism attack and environmental concerns, the innovation of high-efficiency and portable sensors for facile, rapid, and reliable monitoring of explosives has become one of the major demands in societies. Herein, a reliable and easy-to-use cataluminescence sensor system based on Dy3+ doping La2O3 nanorod catalysts has been developed for the identification and detection of six types of military explosive vapors, including homologous compounds and even isomers. The efficient discrimination is to make full use of the thermodynamic and kinetic information that can be extracted from the catalytic oxidation process of explosive molecules on various sensing materials, that is, the response signal and response time to generate the fingerprint of each target compound, while the rapid detection of the strategy can be manifested in response toward six military explosive vapors within 2.5 s and recover within 4 s. Meanwhile, the quantitative analysis of the explosives by the sensor system was realized based on 0.8%Dy:La2O3 with optimal catalytic activity, and the detection limits of NB, m-MNT, m-DNB, PNT, DNT, and TNT can reach 0.62, 0.49, 0.63, 0.38, 0.023, and 0.067 µg mL-1. In this research, we also constructed a novel sensor device and detection platform for explosive monitoring, which is of great significance for providing a new sensing principle for the efficient identification of explosives.

Integrated transcriptomic and metabolomic profiles reveal adaptive responses of three poplar varieties against the bacterial pathogen Lonsdalea populi.

Different poplar varieties vary in their tolerance to certain pathogens. However, knowledge about molecular regulation and critical responses of resistant poplars during pathogen infection remains scarce. To investigate adaptive responses to canker disease caused by the bacterium Lonsdalea populi, we screened three poplar varieties with contrasting tolerance, including Populus deltoides. 'Zhonglin 2025' (2025), Populus × Euramericana. '74/76' (107) and Populus tomentosa cv 'henan' (P. tomentosa). Transcriptomic analysis revealed significant changes in the expression levels of defence-related genes in different poplar varieties in response to infection, which reshaped the PTI and ETI processes. Intriguingly, photosynthesis-related genes were found to be highly expressed in the resistant variety, whereas the opposite was observed in the susceptible variety. Susceptible poplars maintained the activation of defence-related genes during early period of onset, which restricted the expression of photosynthesis-related and auxin signal-related genes. Furthermore, combined with metabolomic analysis, differences in the content of antibacterial substances and key differentially expressed genes in phenylpropane and flavonoid biosynthesis pathways were identified. Delayed induction of catechin in the susceptible variety and it's in vitro antibacterial activity were considered to be one of the important reasons for the differences in resistance to L. populi compared with the resistant variety, which is of practical interest for tree breeding. Moreover, the trade-off between growth and defence observed among the three poplar varieties during infection provides new insights into the multilevel regulatory circuits in tree-pathogen interactions.

Decreased syntaxin17 expression contributes to the pathogenesis of acute pancreatitis in murine models by impairing autophagic degradation.

Acute pancreatitis (AP) is an inflammatory disease of the exocrine pancreas. Disruptions in organelle homeostasis, including macroautophagy/autophagy dysfunction and endoplasmic reticulum (ER) stress, have been implicated in human and rodent pancreatitis. Syntaxin 17 (STX17) belongs to the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) subfamily. The Qa-SNARE STX17 is an autophagosomal SNARE protein that interacts with SNAP29 (Qbc-SNARE) and the lysosomal SNARE VAMP8 (R-SNARE) to drive autophagosome-lysosome fusion. In this study, we investigated the role of STX17 in the pathogenesis of AP in male mice or rats induced by repeated intraperitoneal injections of cerulein. We showed that cerulein hyperstimulation induced AP in mouse and rat models, which was characterized by increased serum amylase and lipase activities, pancreatic edema, necrotic cell death and the infiltration of inflammatory cells, as well as markedly decreased pancreatic STX17 expression. A similar reduction in STX17 levels was observed in primary and AR42J pancreatic acinar cells treated with CCK (100 nM) in vitro. By analyzing autophagic flux, we found that the decrease in STX17 blocked autophagosome-lysosome fusion and autophagic degradation, as well as the activation of ER stress. Pancreas-specific STX17 knockdown using adenovirus-shSTX17 further exacerbated pancreatic edema, inflammatory cell infiltration and necrotic cell death after cerulein injection. These data demonstrate a critical role of STX17 in maintaining pancreatic homeostasis and provide new evidence that autophagy serves as a protective mechanism against AP.

Single-cell analysis of p53 transitional dynamics unravels stimulus- and cell type-dependent signaling output motifs.

BACKGROUND: To understand functional changes of complex biological networks, mathematical modeling of network topologies provides a quantitative measure of the way biological systems adapt to external stimuli. However, systemic network topology-based analysis often generates conflicting evidence depending on specific experimental conditions, leading to a limited mechanistic understanding of signaling networks and their differential dynamic outputs, an example of which is the regulation of p53 pathway responses to different stress stimuli and in variable mammalian cell types. Here, we employ a network motif approach to dissect key regulatory units of the p53 pathway and elucidate how network activities at the motif level generate context-specific dynamic responses. RESULTS: By combining single-cell imaging and mathematical modeling of dose-dependent p53 dynamics induced by three chemotherapeutics of distinct mechanism-of-actions, including Etoposide, Nutlin-3a and 5-fluorouracil, and in five cancer cell types, we uncovered novel and highly variable p53 dynamic responses, in particular p53 transitional dynamics induced at intermediate drug concentrations, and identified the functional roles of distinct positive and negative feedback motifs of the p53 pathway in modulating the central p53-Mdm2 negative feedback to generate stimulus- and cell type-specific signaling responses. The mechanistic understanding of p53 network dynamics also revealed previously unknown mediators of anticancer drug actions and phenotypic variations in cancer cells that impact drug sensitivity. CONCLUSIONS: Our results demonstrate that transitional dynamics of signaling proteins such as p53, activated at intermediate stimulus levels, vary the most between the dynamic outputs of different generic network motifs and can be employed as novel quantitative readouts to uncover and elucidate the key building blocks of large signaling networks. Our findings also provide new insight on drug mediators and phenotypic heterogeneity that underlie differential drug responses.

Efficient Photoinduced Thermocatalytic Chemiluminescence System Based on the Z-Scheme Heterojunction Ag3PO4/Ag/Bi4Ti3O12 for H2S Sensing.

Cataluminescence as a highly efficient gas transduction principle has attracted wide attention among research in environmental monitoring and clinical diagnosis with increasing awareness of human safety. Nowadays, the development of innovation sensing systems and the construction of the sensing mechanism to improve the analytical performance of compounds remain a major challenge. Herein, we construct an advanced photoinduced thermocatalytic chemiluminescence (PI-TC-CL) gas-sensing system via the introduction of a Z-scheme heterojunction Ag3PO4/Ag/Bi4Ti3O12 to achieve higher efficient detection of H2S. The unique electron transport path of the Z-scheme heterojunction and the LSPR effect of Ag nanoparticles fascinate the generation of the photoinduced electron-hole pair on the surface of catalysts when stimulated by LED lamps and slow down the recombination of electron-hole pairs under thermal conditions. Thus, based on the cooperative effect of the Z-scheme heterojunction AgPO/Ag/BTO and PI-TC-CL system, we have successfully established an efficient H2S CTL detection system, which has a response three times higher than that on the traditional CTL system and even 45 times higher than that on BTO and ranges among the best of the state-of-the-art CTL performance in H2S detection with the linear range of 0.095-8.87 µg mL-1 and a limit of detection of 0.0065 µg mL-1. Besides, to explore the gas-sensing mechanism, the synergetic effects of photoinduction and thermal catalysis are investigated thoroughly via conductivity and electrochemical experiments. This research provides a new perspective of engineering highly efficient catalysts and ingenious sensor systems through designing the nanostructure of materials and synergism catalytic mechanism.