Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.

Background: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods: A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results: The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion: A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.

Copper Difluorocarbene Enables Catalytic Difluoromethylation.

Despite the synthetic versatility of difluorocarbene, its high reactivity severely regulates widespread applications of difluorocarbene in organic synthesis. Here, we report a copper difluorocarbene-involved catalytic coupling, representing a new mode of the difluoromethylation reaction. This method allows difluoromethylation of a wide range of readily available allyl/propargyl electrophiles with NaBH3CN and low-cost difluorocarbene precursor BrCF2CO2K, featuring high cost-efficiency, high stereo- and regioselectivities, and high functional group tolerance, even with complex drug-like molecules. Applying the method led to the efficient synthesis of deuterated difluoromethylated compounds of medicinal interest. The resulting difluoromethylated allyl and allenyl products can serve as versatile synthons for diverse transformations, rendering the approach attractive for synthesizing complex fluorinated structures. Experimental mechanistic studies and computational calculations reveal that the formation of a difluoromethylcopper(I) intermediate through the nucleophilic attack of boron hydride on the copper(I) difluorocarbene is the key step in the reaction.

Advances in 2D Materials Based Gas Sensors for Industrial Machine Olfactory Applications.

The escalating development and improvement of gas sensing ability in industrial equipment, or "machine olfactory", propels the evolution of gas sensors toward enhanced sensitivity, selectivity, stability, power efficiency, cost-effectiveness, and longevity. Two-dimensional (2D) materials, distinguished by their atomic-thin profile, expansive specific surface area, remarkable mechanical strength, and surface tunability, hold significant potential for addressing the intricate challenges in gas sensing. However, a comprehensive review of 2D materials-based gas sensors for specific industrial applications is absent. This review delves into the recent advances in this field and highlights the potential applications in industrial machine olfaction. The main content encompasses industrial scenario characteristics, fundamental classification, enhancement methods, underlying mechanisms, and diverse gas sensing applications. Additionally, the challenges associated with transitioning 2D material gas sensors from laboratory development to industrialization and commercialization are addressed, and future-looking viewpoints on the evolution of next-generation intelligent gas sensory systems in the industrial sector are prospected.

Deep Spatial-Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection.

Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrary, this article proposes a novel deep spatial-spectral joint-sparse prior encoding network (JSPEN), which reasonably embeds the domain knowledge of hyperspectral target detection into the neural network, and has explicit interpretability. In JSPEN, the sparse encoded prior information with spatial-spectral constraints is learned end-to-end from hyperspectral images (HSIs). Specifically, an adaptive joint spatial-spectral sparse model (AS 2 JSM) is developed to mine the spatial-spectral correlation of HSIs and improves the accuracy of data representation. An optimization algorithm is designed for iteratively solving AS 2 JSM, and JSPEN is proposed to simulate the iterative optimization process in the algorithm. Each basic module of JSPEN one-to-one corresponds to the operation in the optimization algorithm so that each intermediate result in the network has a clear explanation, which is convenient for intuitive analysis of the operation of the network. With end-to-end training, JSPEN can automatically capture the general sparse properties of HSIs and faithfully characterize the features of background and target. Experimental results verify the effectiveness and accuracy of the proposed method. Code is available at https://github.com/Jiahuiqu/JSPEN.

Observation of quantum nonlocality in Greenberger-Horne-Zeilinger entanglement on a silicon chip.

Nonlocality is the defining feature of quantum entanglement. Entangled states with multiple particles are of crucial importance in fundamental tests of quantum physics as well as in many quantum information tasks. One of the archetypal multipartite quantum states, Greenberger-Horne-Zeilinger (GHZ) state, allows one to observe the striking conflict of quantum physics to local realism in the so-called all-versus-nothing way. This is profoundly different from Bell's theorem for two particles, which relies on statistical predictions. Here, we demonstrate an integrated photonic chip capable of generating and manipulating the four-photon GHZ state. We perform a complete characterization of the four-photon GHZ state using quantum state tomography and obtain a state fidelity of 0.729±0.006. We further use the all-versus-nothing test and the Mermin inequalities to witness the quantum nonlocality of GHZ entanglement. Our work paves the way to perform fundamental tests of quantum physics with complex integrated quantum devices.

AHNAK2 Regulates NF-κB/MMP-9 Signaling to Promote Pancreatic Cancer Progression.

Early metastasis of pancreatic cancer (PaC) is a major cause of its high mortality rate. Previous studies have shown that AHNAK2 is involved in the progression of some tumors and is predicted to be an independent prognostic factor for PaC; however, the specific mechanisms through which AHNAK2 regulates PaC remain unclear. In this study, we examined the role of AHNAK2 in PaC and its potential molecular mechanisms. AHNAK2 mRNA and protein expression in PaC tissues and cells were measured using qRT-PCR and western blot analysis. After AHNAK2 knockdown using small interfering RNA, PaC cells were subjected to CCK-8 scratch, and Transwell assays to assess cell proliferation, migration, and invasion, respectively. Furthermore, the validation of the mechanistic pathway was achieved by western blot analysis. AHNAK2 mRNA and protein levels were up-regulated in PaC and silencing AHNAK2 significantly inhibited the proliferation, migration, and invasion of PaC cells. Mechanistically, AHNAK2 knockdown decreased the expression of phosphorylated p65, phosphorylated IκBα, and matrix metalloproteinase-9 (MMP-9), suggesting that activation of the NF-κB/MMP-9 signaling pathway was inhibited. Importantly, activation of NF-κB reversed the effects of AHNAK2 knockdown. Our findings indicate that AHNAK2 promotes PaC progression through the NF-kB/MMP-9 pathway and provides a theoretical basis for targeting AHNAK2 for the treatment of PaC.

Assessing the association between a sedentary lifestyle and prevalence of primary osteoporosis: a community-based cross-sectional study among Chinese population.

OBJECTIVES: To reveal the association between a sedentary lifestyle and the prevalence of primary osteoporosis (POP). DESIGN: A community-based cross-sectional study was conducted. SETTING: This study was conducted in communities in Hefei city, Anhui province, China. PARTICIPANTS: A total of 1346 residents aged 40 and above underwent POP screening via calcaneus ultrasound bone mineral density (BMD) testing and completed a questionnaire survey. OUTCOME MEASURES: The average daily sitting time was included in the study variable and used to assess sedentary behaviour. The 15 control variables included general information, dietary information and life behaviour information. Logistic regression was used to analyse the association between the POP prevalence and study or control variables in different models. RESULTS: 1346 participants were finally included in the study. According to the 15 control variables, the crude model and 4 models were established. The analysis revealed that the average daily sitting time showed a significant correlation with the prevalence of POP in the crude model (OR=2.02, 95% CI=1.74 to 2.36, p<0.001), Model 1 (OR=2.65, 95% CI=2.21 to 3.17, p<0.001), Model 2 (OR=2.63, 95% CI=2.19 to 3.15, p<0.001), Model 3 (OR=2.62, 95% CI=2.18 to 3.15, p<0.001) and Model 4 (OR=2.58, 95% CI=2.14 to 3.11, p<0.001). Besides, gender, age and body mass index showed a significant correlation with the POP prevalence in all models. CONCLUSIONS: This study suggests a potential association between a sedentary lifestyle and the prevalence of POP within the Chinese population. Modifying sedentary behaviours could contribute to a reduction in POP risk. However, longitudinal cohort studies are necessary to confirm this hypothesis in the future.

Identification of UDP-glucuronosyltransferase (UGT) isoforms involved in the metabolism of Chlorophenols (CPs).

Chlorophenols (CPs) are a group of pollutants that pose a great threat to the environment, they are widely used in industrial and agricultural wastes, pesticides, herbicides, textiles, pharmaceuticals and plastics. Among CPs, pentachlorophenol was listed as one of the persistent organic pollutants (POPs) by the Stockholm convention. This study aims to identify the UDP-glucosyltransferase (UGT) isoforms involved in the metabolic elimination of CPs. CPs' mono-glucuronide was detected in the human liver microsomes (HLMs) incubation mixture with co-factor uridine-diphosphate glucuronic acid (UDPGA). HLMs-catalyzed glucuronidation metabolism reaction equations followed Michaelis-Menten or substrate inhibition type. Recombinant enzymes and chemical reagents inhibition experiments were utilized to phenotype the main UGT isoforms involved in the glucuronidation of CPs. UGT1A6 might be the major enzyme in the glucuronidation of mono-chlorophenol isomer. UGT1A1, UGT1A6, UGT1A9, UGT2B4 and UGT2B7 were the most important five UGT isoforms for metabolizing the di-chlorophenol and tri-chlorophenol isomers. UGT1A1 and UGT1A3 were the most important UGT isoforms in the catalysis of tetra-chlorophenol and pentachlorophenol isomers. Species differences were investigated using rat liver microsomes (RLMs), pig liver microsomes (PLMs), dog liver microsomes (DLMs), and monkey liver microsomes (MyLMs). All these results were helpful for elucidating the metabolic elimination and toxicity of CPs.

Carbene-Assisted Arene Ring-Opening.

Despite the significant achievements in dearomatization and C-H functionalization of arenes, the arene ring-opening remains a largely unmet challenge and is underdeveloped due to the high bond dissociation energy and strong resonance stabilization energy inherent in aromatic compounds. Herein, we demonstrate a novel carbene assisted strategy for arene ring-opening. The understanding of the mechanism by our DFT calculations will stimulate wide application of bulk arene chemicals for the synthesis of value-added polyconjugated chain molecules. Various aryl azide derivatives now can be directly converted into valuable polyconjugated enynes, avoiding traditional synthesis including multistep unsaturated precursors, poor selectivity control, and subsequent transition-metal catalyzed cross-coupling reactions. The simple conditions required were demonstrated in the late-stage modification of complex molecules and fused ring compounds. This chemistry expands the horizons of carbene chemistry and provides a novel pathway for arene ring-opening.

Ten-year observation of corneal densitometry and associated factors following small incision lenticule extraction.

AIM: To investigate the long-term changes of corneal densitometry (CD) and its contributing elements after small incision lenticule extraction (SMILE). METHODS: Totally 31 eyes of 31 patients with mean spherical equivalent of -6.46±1.50 D and mean age 28.23±7.38y were enrolled. Full-scale examinations were conducted on all patients preoperatively and during follow-up. Visual acuity, manifest refraction, axial length, corneal thickness, corneal higher-order aberrations, and CD were evaluated. RESULTS: All surgeries were completed successfully without complications or adverse events. Ten-year safety index was 1.17±0.20 and efficacy 1.04±0.28. CD value of 0-6 mm zones in central layer was statistically significantly lower 10y postoperatively, compared with preoperative values (0-2 mmΔ=-1.62, 2-6 mmΔ=-1.24, P<0.01). There were no correlations between CD values and factors evaluated. CONCLUSION: SMILE is a safe and efficient procedure for myopia on a long-term basis. CD values get lower 10y postoperatively, whose mechanism is to be further discussed.

Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer.

BACKGROUND: This study aimed to explore the expression, molecular mechanism and its biological function of potassium two pore domain channel subfamily K member 1 (KCNK1) in bladder cancer (BC). METHODS: We integrated large numbers of external samples (n = 1486) to assess KCNK1 mRNA expression levels and collected in-house samples (n = 245) for immunohistochemistry (IHC) experiments to validate at the KCNK1 protein level. Single-cell RNA sequencing (scRNA-seq) analysis was performed to further assess KCNK1 expression and cellular communication. The transcriptional regulatory mechanisms of KCNK1 expression were explored by ChIP-seq, ATAC-seq and ChIA-PET data. Highly expressed co-expressed genes (HECEGs) of KCNK1 were used to explore potential signalling pathways. Furthermore, the immunoassay, clinical significance and molecular docking of KCNK1 were calculated. RESULTS: KCNK1 mRNA was significantly overexpressed in BC (SMD = 0.58, 95% CI [0.05; 1.11]), validated at the protein level (p < 0.0001). Upregulated KCNK1 mRNA exhibited highly distinguishing ability between BC and control samples (AUC = 0.82 [0.78-0.85]). Further, scRNA-seq analysis revealed that KCNK1 expression was predominantly clustered in BC epithelial cells and tended to increase with cellular differentiation. BC epithelial cells were involved in cellular communication mainly through the MK signalling pathway. Secondly, the KCNK1 transcription start site (TSS) showed promoter-enhancer interactions in three-dimensional space, while being transcriptionally regulated by GRHL2 and FOXA1. Most of the KCNK1 HECEGs were enriched in cell cycle-related signalling pathways. KCNK1 was mainly involved in cellular metabolism-related pathways and regulated cell membrane potassium channel activity. KCNK1 expression was associated with the level of infiltration of various immune cells. Immunotherapy and chemotherapy (docetaxel, paclitaxel and vinblastine) were more effective in BC patients in the high KCNK1 expression group. KCNK1 expression correlated with age, pathology grade and pathologic_M in BC patients. CONCLUSIONS: KCNK1 was significantly overexpressed in BC. A complex and sophisticated three-dimensional spatial transcriptional regulatory network existed in the KCNK1 TSS and promoted the upregulated of KCNK1 expression. The high expression of KCNK1 might be involved in the cell cycle, cellular metabolism, and tumour microenvironment through the regulation of potassium channels, and ultimately contributed to the deterioration of BC.

Photoinduced Remote C(sp3)-H Phosphonylation of Amides.

The present study reports an unprecedented protocol for the phosphonylation of unactivated C(sp3)-H bonds. By utilizing 1 mol % 4DPAIPN (1,2,3,5-tetrakis(diphenylamino)-4,6-dicyanobenzene) as the catalyst, satisfactory yields of γ-phosphonylated amides are obtained through a visible-light-induced reaction between N-((4-cyanobenzoyl)oxy)alkanamides and 9-fluorenyl o-phenylene phosphite at room temperature. This protocol demonstrates broad substrate scope and wide functional group compatibility.

Importance of AIM2 as a serum marker for reflecting severity and predicting a poor outcome of human severe traumatic brain injury: A prospective longitudinal cohort study.

BACKGROUND: Absent in melanoma 2 (AIM2) participates in neuroinflammation. Here, the prognostic significance of serum AIM2 was explored in severe traumatic brain injury (sTBI). METHODS: A total of 135 sTBI patients and 80 healthy controls were recruited in this prospective cohort study. Serum C-reactive protein (CRP) and AIM2 levels were measured. Glasgow Coma Scale (GCS) and Rotterdam computed tomography (CT) classification were recorded as the severity indicators. Prognostic parameters were posttraumatic six-month extended Glasgow outcome scale (GOSE) scores and poor outcome (GOSE scores of 1-4). RESULTS: As opposed to controls, there were significantly elevated serum AIM2 levels after sTBI. Serum AIM2 levels were independently correlated with serum CRP levels, GCS scores, Rotterdam CT scores, GOSE scores and poor outcome. Also, serum AIM2 levels were efficiently predictive of poor outcome under the receiver operating characteristic (ROC) curve. Under the restricted cubic spline, serum AIM2 levels were linearly correlated with risk of poor outcome. Using subgroup analysis, serum AIM2 levels did not significantly interact with other indices, such as age, gender, alcohol drinking, cigarette smoking, etc. Also, combination model, in which serum AIM2, GCS scores and Rotterdam CT scores were merged, was outlined using nomogram and performed well under calibration curve, ROC curve and decision curve. CONCLUSIONS: Raised serum AIM2 levels after sTBI, in intimate correlation with systemic inflammation and trauma severity, are independently discriminative of posttraumatic six-month neurological outcome, substantializing serum AIM2 as an inflammatory prognostic biomarker of sTBI.

Reversal of the Regioselectivity of Iron-Promoted Hydrogenation and Hydrohalogenation of gem-Difluoroalkenes.

The reaction regioselectivity of gem-difluoroalkenes is dependent on the intrinsic polarity. Thus, the reversal of the regioselectivity of the addition reaction of gem-difluoroalkenes remains a formidable challenge. Herein, we described an unprecedented reversal of regioselectivity of hydrogen atom transfer (HAT) to gem-difluoroalkenes triggered by Fe-H species for the formation of difluoroalkyl radicals. Hydrogenation of the in situ generated radicals gave difluoromethylated products. Mechanism experiments and theoretical studies revealed that the kinetic effect of the irreversible HAT process resulted in the reversal of the regioselectivity of this scenario, leading to the formation of a less stable α-difluoroalkyl radical regioisomer. On basis of this new reaction of gem-difluoroalkene, the iron-promoted hydrohalogenation of gem-difluoroalkenes for the efficient synthesis of aliphatic chlorodifluoromethyl-, bromodifluoromethyl- and iododifluoromethyl-containing compounds was developed. Particularly, this novel hydrohalogenation of gem-difluoroalkenes provided an effect and large-scale access to various iododifluoromethylated compounds of high value for synthetic application.

Immunotherapeutic value of NUSAP1 associated with bladder cancer through a comprehensive analysis of 33 human cancer cases.

To investigate the correlation between nucleolar spindle-associated protein 1 (NUSAP1) and cancer immunotherapy across 33 different types of human cancers. We conducted an analysis of The Cancer Genome Atlas (TCGA) database to retrieve gene expression data and clinical characteristics for 33 different cancer types. The immunotherapy cohorts encompassed GSE67501, GSE78220, and IMvigor210. Relevant information was extracted from the gene expression repository. We assessed the prognostic significance of NUSAP1 by examining various clinical parameters. The single-sample gene-set enrichment analysis (ssGSEA) method was utilized to gauge NUSAP1 activity and to contrast NUSAP1 transcriptome and protein levels. We delved into the correlation between NUSAP1 and various immune processes and components to gain insights into NUSAP1's role. We also discussed coherent pathways associated with NUSAP1 signal transduction and its impact on immunotherapy biomarkers. To authenticate and validate the differential expression patterns of NUSAP1 in bladder tumor tissues versus normal bladder counterparts, we utilized Western blotting (WB), real-time quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry (IHC) techniques. NUSAP1 exhibits overexpression across a spectrum of malignancies, and its expression levels correlate with overall survival (OS), disease-specific survival, and tumor stage in specific cancer types. Furthermore, NUSAP1 expression is linked to mutations, methylation patterns, and immunotherapy responses in human cancers. Meanwhile, our experiments, involving WB, RT-qPCR, and IHC, consistently demonstrated significantly higher NUSAP1 expression in bladder tumor tissues compared to normal controls. Our study underscores the potential of NUSAP1 as a promising prognostic indicator and immunotherapeutic target for a range of malignant tumors.

Advanced Dielectric Materials for Triboelectric Nanogenerators: Principles, Methods, and Applications.

Triboelectric nanogenerator (TENG) manifests distinct advantages such as multiple structural selectivity, diverse selection of materials, environmental adaptability, low cost, and remarkable conversion efficiency, which becomes a promising technology for micro-nano energy harvesting and self-powered sensing. Tribo-dielectric materials are the fundamental and core components for high-performance TENGs. In particular, the charge generation, dissipation, storage, migration of the dielectrics, and dynamic equilibrium behaviors determine the overall performance. Herein, a comprehensive summary is presented to elucidate the dielectric charge transport mechanism and tribo-dielectric material modification principle toward high-performance TENGs. The contact electrification and charge transport mechanism of dielectric materials is started first, followed by introducing the basic principle and dielectric materials of TENGs. Subsequently, modification mechanisms and strategies for high-performance tribo-dielectric materials are highlighted regarding physical/chemical, surface/bulk, dielectric coupling, and structure optimization. Furthermore, representative applications of dielectric materials based TENGs as power sources, self-powered sensors are demonstrated. The existing challenges and promising potential opportunities for advanced tribo-dielectric materials are outlined, guiding the design, fabrication, and applications of tribo-dielectric materials.

Silver-coated hollow fiber surface plasmon resonance sensor for glucose detection with enhanced limit of detection.

A fiber-optic surface plasmon resonance (SPR) biosensor based on a silver-coated hollow fiber (HF) structure for glucose detection is presented. The sensor surface was immobilized with 4-mercaptophenylboronic acid (PMBA) acting as a glucose recognition monolayer. Then, gold nanoparticles (AuNPs) modified with 2-aminoethanethiol (2-AET) and PMBA were introduced onto the sensor surface after glucose was captured to enhance the wavelength shift of the SPR phenomenon excited by the light transmitted in the wall of the HF sensor. Instead of the conventional one-step sensitization pretreatment commonly used in the deposition process of silver films for fiber-optic SPR sensors, a sensitization-activation two-step activation method was adopted in the fabrication of the proposed sensor. Experiments for glucose detection were performed on the fabricated sensors in the concentration range of 1 nM-1 mM. Results showed that the sensor fabricated by the two-step activation method has a much larger shift of resonance wavelength than the sensor fabricated using the one-step sensitization method. The resonance wavelength shift was found to be linear to the logarithm of the concentration in the range of 1 nM-1 mM. The sensor achieved a limit of detection (LOD) of as low as 1 nM, which is at least an order of magnitude lower than that of other fiber-optic sensors for glucose detection reported previously. The presented HF glucose sensor has the potential for biosensing applications and provides a large reference value in the study of optical fiber SPR sensors for biosensing.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses.

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

[Evolution and Influencing Factors of Spatial Correlation Network of Construction Carbon Emission in China from the Perspective of Whole Life Cycle].

Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region.