Bifidobacterium longum suppresses colorectal cancer through the modulation of intestinal microbes and immune function.

Colorectal cancer (CRC), one of the most common malignancies in the world, urgently requires more treatment strategies. Although there has been much research on probiotics, limited research has been done in treating cancer. The purpose of this study was to investigate the role of Bifidobacterium longum (B. longum) in the prevention and treatment of CRC. Through Cell Counting Kit-8 and Colony Formation Assays, 8 h and a B. longum count of 1 × 108 CFU/ml were chosen as the best cocultivation conditions with CRC cells. The role of B. longum in inhibiting the progression of CRC cells was verified by a series of functional and immunofluorescence assays. For instance, in vivo assays have verified that B. longum could alleviate CRC progression. In addition, according to the results of in vivo assays and clinical statistical analysis, B. longum could reduce diarrhea symptoms. Mechanistically, by 16S and RNA sequencing, it was found that B. longum could affect the development of CRC by regulating the composition of gut microbes and enhancing immune function. The B. longum might inhibit the occurrence and development of CRC and relieve diarrhea symptoms by regulating intestinal microbes and immune function.

Myeloperoxidase to high-density lipoprotein ratio: Potential predictor of severity and outcome in patients with acute ischemic stroke.

OBJECTIVE: As a new marker of inflammation and lipid metabolism, the ratio of myeloperoxidase to high density lipoprotein (MPO/HDL) has been reported in the field of cardiovascular disease. However, the effect of MPO/HDL on acute ischemic stroke (AIS) is not clear. The purpose of this study was to explore the prognostic value of MPO/HDL level in patients with AIS. METHODS: This study conducted a retrospective analysis of 363 patients diagnosed with AIS. Stroke severity was assessed by National Institutes of Health Stroke Scale (NIHSS). The short-term functional outcome was evaluated with modified Rankin Scale (mRS) 90 days after admission. Spearman correlation analysis was used to evaluate the correlation between MPO/HDL and NIHSS scores. The predictive value of MPO, HDL and MPO/HDL to AIS was evaluated by receiver operating characteristic curve (ROC). RESULTS: The level of MPO/HDL in patients with NIHSS score ≥ 4 was significantly higher than that in patients with NIHSS score < 4 (P < 0.001). MPO and MPO/HDL were positively correlated with NIHSS score (P < 0.001), while HDL was negatively correlated with NIHSS score (P < 0.001). During 90-day follow-up, multivariate Logistic regression analysis showed that increased MPO/HDL levels were associated with 90-day functional outcomes. ROC showed that compared with MPO and HDL, MPO/HDL had the highest predictive value for 90-day functional prognosis in patients with AIS (AUC = 0.9825). CONCLUSION: The level of serum MPO/HDL may be potential prognostic biomarker in AIS 90 days.

Experimental Study of Autologous Platelet-rich Plasma Combined With Sodium Hyaluronate on Tendon-bone Healing After Rotator Cuff Injury Repair in Rabbits.

OBJECTIVE: To investigate the therapeutic effects of autologous platelet-rich plasma (PRP) combined with sodium hyaluronate on tendon healing following rotator cuff injury repair in rabbits. METHODS: New Zealand white rabbits were randomly assigned to five groups: sham operation group, control group, PRP group, sodium hyaluronate group, and combined group, each comprising 12 rabbits. A rotator cuff injury model was established in all groups except the sham operation group. At 8 weeks post-surgery, 12 lateral rotator cuff specimens were taken from each group. Four specimens were randomly selected from each group for biomechanical testing, and analyses were conducted on the expression of vascular endothelial growth factor (VEGF), the fiber area ratio of COL-I and COL-III, and tissue morphology. RESULTS: The combined group exhibited the highest biomechanical strength in the cuff tissue of white rabbits (P < 0.05). There was no significant difference in VEGF levels among the five groups (F = 0.814, P = 0.523). However, a significant difference was observed in the ratio of fiber area between COL-I and COL-III groups (F = 11.600, P < 0.001), with the combined group scoring the highest (3.82 ± 0.47 minutes). The inflammatory infiltration in tendon-bone tissue was minimal, and histological morphology was optimal. CONCLUSION: The combination of PRP and sodium hyaluronate effectively promotes the repair of rotator cuff injuries and accelerates tendon-bone healing.

Risk factors for anxiety in patients with epilepsy: A meta-analysis.

BACKGROUND: Epilepsy is a very common neurological disease, and it is important to focus on both controlling seizures and alleviating the psychological problems associated with this disease.Anxiety is an important risk factor for epilepsy and seriously affects the quality of life of patients with epilepsy (PWE). However, several risk factors for anxiety in PWE are relatively controversial and understudied. This meta-analysis was performed to identify potential risk factors for anxiety in PWE with the aim of reducing the incidence of anxiety and improving the quality of life among the individuals. METHOD: The PubMed, Embase and Cochrane Library databases were systematically searched up to July 2023 to find eligible original English studies. All the search results were reviewed based on our inclusion and exclusion criteria. We calculated the combined odds ratios (ORs), standard mean differences (SMDs) and their corresponding 95% confidence intervals (CIs) to evaluate the effect of the included risk factors on anxiety in PWE. RESULTS: Twenty-four studies involving 5,403 PWE were ultimately included. The pooled results of our meta-analysis showed that female sex (OR = 1.67; 95 % CI: 1.30,2.15; p < 0.001), unmarried/divorced/widowed (OR = 0.83; 95 % CI: 0.72,0.96; p = 0.011), low socioeconomic status (OR = 0.47; 95 % CI: 0.33,0.67; p < 0.001), education levels below high school (OR = 1.74; 95 % CI: 1.36,2.23; p < 0.001), a history of trauma (OR = 2.53; 95 % CI: 1.69,3.78; p < 0.001), monotherapy (OR = 0.49; 95 % CI: 0.39,0.62; p < 0.001), AED-induced psychiatric side effects (OR = 2.45; 95 % CI: 1.20,4.98); p = 0.014), depression (OR = 5.45 95 % CI: 2.49,11.94; p < 0.001), a history of suicide (OR = 3.56; 95 % CI: 1.72,7.38; p = 0.001), and illness-related shame (OR = 2.76; 95 % CI: 2.17,3.52; p < 0.001) were risk factors for anxiety. CONCLUSION: This meta-analysis showed that female, unmarried, low socioeconomic status, education level below senior high school, a history of trauma, monotherapy, AED-induced psychiatric side effects, depression, a history of suicide, and shame were risk factors for anxiety in PWE. However, further research is needed to determine the effect of other potential risk factors on anxiety in PWE. In addition, most of the studies included in this meta-analysis were not uniform in scale, and the risk factors were not comprehensive; therefore, larger prospective studies in different countries are needed to further investigate these risk factors.

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning.

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more practical but challenging scenario, the annotator may miss the ground-truth and provide a wrong candidate set, which is known as the noisy PLL problem. To remedy this problem, we propose the PiCO+ framework that simultaneously disambiguates the candidate sets and mitigates label noise. Core to PiCO+, we develop a novel label disambiguation algorithm PiCO that consists of a contrastive learning module along with a novel class prototype-based disambiguation method. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. To handle label noise, we extend PiCO to PiCO+, which further performs distance-based clean sample selection, and learns robust classifiers by a semi-supervised contrastive learning algorithm. Beyond this, we further investigate the robustness of PiCO+ in the context of out-of-distribution noise and incorporate a novel energy-based rejection method for improved robustness. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy.

Cardiac oxidative stress is a significant phenotype of myocardial infarction disease, a leading cause of global health threat. There is an urgent need to develop innovative therapies. Nanosized extracellular vesicle (nEV)-based therapy shows promise, yet real-time monitoring of cardiomyocyte responses to nEVs remains a challenge. In this study, a dynamic and label-free cardiomyocyte biosensing system using microelectrode arrays (MEAs) was constructed. Cardiomyocytes were cultured on MEA devices for electrophysiological signal detection and treated with nEVs from E. coli, gardenia, HEK293 cells, and mesenchymal stem cells (MSC), respectively. E. coli-nEVs and gardenia-nEVs induced severe paroxysmal fibrillation, revealing distinct biochemical communication compared to MSC-nEVs. Principal component analysis identified variations and correlations between nEV types. MSC-nEVs enhanced recovery without inducing arrhythmias in a H2O2-induced oxidative stress injury model. This study establishes a fundamental platform for assessing biochemical communication between nEVs and cardiomyocytes, offering new avenues for understanding nEVs' functions in the cardiovascular system.

Variable stiffness soft robotic gripper: design, development, and prospects.

The advent of variable stiffness soft robotic grippers furnishes a conduit for exploration and manipulation within uncharted, non-structured environments. The paper provides a comprehensive review of the necessary technologies for the configuration design of soft robotic grippers with variable stiffness, serving as a reference for innovative gripper design. The design of variable stiffness soft robotic grippers typically encompasses the design of soft robotic grippers and variable stiffness modules. To adapt to unfamiliar environments and grasp unknown objects, a categorization and discussion have been undertaken based on the contact and motion manifestations between the gripper and the things across various dimensions: points contact, lines contact, surfaces contact, and full-bodies contact, elucidating the advantages and characteristics of each gripping type. Furthermore, when designing soft robotic grippers, we must consider the effectiveness of object grasping methods but also the applicability of the actuation in the target environment. The actuation is the propelling force behind the gripping motion, holding utmost significance in shaping the structure of the gripper. Given the challenge of matching the actuation of robotic grippers with the target scenario, we reviewed the actuation of soft robotic grippers. We analyzed the strengths and limitations of various soft actuation, providing insights into the actuation design for soft robotic grippers. As a crucial technique for variable stiffness soft robotic grippers, variable stiffness technology can effectively address issues such as poor load-bearing capacity and instability caused by the softness of materials. Through a retrospective analysis of variable stiffness theory, we comprehensively introduce the development of variable stiffness theory in soft robotic grippers and showcase the application of variable stiffness grasping technology through specific case studies. Finally, we discuss the future prospects of variable stiffness grasping robots from several perspectives of applications and technologies.

Understanding the Role of Titanium Metal-Organic Framework Nanosheets in Modulating Anode Chemistry for Aqueous Zinc-Ion Batteries.

Aqueous zinc-ion batteries have attracted a continually increasing level of interest for large-scale energy storage because they are highly safe and have high energy density and abundant reserves. However, Zn anodes face significant challenges such as severe dendrite growth and hydrogen evolution reaction (HER). We here propose an efficient Zn2+ sieve strategy for modulating the anode chemistry using two-dimensional NH2-MIL-125 (Ti) metal-organic framework (MOF) nanosheets. Theoretical investigations reveal the crucial role of the Ti MOF in regulating Zn2+ solvation structures for fast diffusion and uniform deposition and decreasing HER reactivity. The structure of the nanosheets enables abundant accessible desolvation sites and shortened ionic pathways. As a result, the MOF nanosheet-protected Zn anode exhibited greatly improved cycling stability in both symmetric cells and full cells. Operando optical monitoring and postmortem analysis revealed effective suppression of dendrite growth and HER by Ti MOF nanosheets. This anti-HER MOF-enabled Zn2+ sieve strategy provides a viable Zn anode and provides new insights for optimizing aqueous batteries.

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points.

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.

Repairing Behaviors of Cracked Steel Plates Based on Bolted Fiber-Reinforced Polymer Plates.

The use of FRP materials to repair cracked/damaged steel structures has gradually been adopted by researchers. This paper investigates the repairing effect of bolted FRP plates for cracked steel plates based on experimental and numerical simulation methods. In the experimental investigation, the tensile strengths of six specimens, including three repaired specimens and three pure cracked steel specimens, were evaluated. The test outcomes indicated that the bolt repairing method significantly enhanced the tensile strengths of the cracked steel plates. As an example, the failure of a pure steel plate with a 1 mm width crack occurred at 813 N, whereas after being repaired, a tensile strength of 1298 N was observed. Based on finite element (FE) analysis, the influence of bolt preloads and interfacial friction coefficients were verified. The stress-relative ratio for specimens was contingent on the bolt preload magnitude and gradually decreased as the preload was augmented. By exploring the repairing effect for varied friction coefficients, it was concluded that using a higher bolt preload can aid in eliminating the performance discrepancy of the overall component caused by interface treatment errors.

Packing bimodal magnetic particles to fabricate highly dense anisotropic rare earth bonded permanent magnets.

Highly dense and magnetically anisotropic rare earth bonded magnets have been fabricated via packing bimodal magnetic particles using a batch extrusion process followed by compression molding technology. The bimodal feedstock was a 96 wt% magnet powder mixture, with 40% being anisotropic Sm-Fe-N (3 µm) and 60% being anisotropic Nd-Fe-B (100 µm) as fine and coarse particles, respectively; these were blended with a 4 wt% polyphenylene sulfide (PPS) polymer binder to fabricate the bonded magnets. The hybrid bonded magnet with an 81 vol% magnet loading yielded a density of 6.15 g cm-3 and a maximum energy product (BH)m of 20.0 MGOe at 300 K. Scanning electron microscopy (SEM) indicated that the fine-sized Sm-Fe-N particles filled the gap between the large Nd-Fe-B particles. Rietveld analysis of the X-ray diffraction data showed that the relative contents of the Nd2Fe14B and Sm2Fe17N3 phases were 61% and 39%, respectively, in the hybrid bonded magnet. The PPS binder coated most of the magnetic particles homogeneously. Compared with the magnetic properties of the initial Nd-Fe-B and Sm-Fe-N powders, the reduction in the remanence, from the demagnetization curve, is ascribed to the dilution effect of the binder, the non-perfect alignment, and the internal magnetic stray field.

Analysis of Mechanical Properties and Fatigue Life of Microturbine Angular Contact Ball Bearings under Eccentric Load Conditions.

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the radial clearance of the bearing. The increase in clearance also affects the stiffness of the bearing, which in turn affects the natural frequency and fatigue life of the bearing. At present, there are few studies on the influence of bearing wear (variation of clearance) on life. In this paper, the finite element model based on the theory of contact mechanics is established for the angular contact ball bearing with medium- and high-speed rotation, and the mechanical properties and fatigue life influenced by the internal action of the bearing are analyzed. The effects of radial load and deflection angle on the mechanical properties and fatigue life of the bearing are also studied. Based on the analysis results of bearing contact mechanical properties and clearance changes, the calculation method of bearing life under rolling element wear is established. The influence of the variation of clearance and preload clearance on bearing life is analyzed, and the optimal preload is obtained. The research results of this paper can provide a theoretical basis for optimizing the installation of angular contact ball bearings, reasonably determining the service conditions, and prolonging the service life of bearings, which is necessary for engineering practice.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel-Ziv Complexity under Varying Conditions.

The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel-Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0-1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel-Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel-Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel-Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Increasing diversity in connectomics with the Chinese Human Connectome Project.

Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain-mind studies.

Achieving single nucleotide sensitivity in direct hybridization genome imaging.

Direct visualization of point mutations in situ can be informative for studying genetic diseases and nuclear biology. We describe a direct hybridization genome imaging method with single-nucleotide sensitivity, single guide genome oligopaint via local denaturation fluorescence in situ hybridization (sgGOLDFISH), which leverages the high cleavage specificity of eSpCas9(1.1) variant combined with a rationally designed guide RNA to load a superhelicase and reveal probe binding sites through local denaturation. The guide RNA carries an intentionally introduced mismatch so that while wild-type target DNA sequence can be efficiently cleaved, a mutant sequence with an additional mismatch (e.g., caused by a point mutation) cannot be cleaved. Because sgGOLDFISH relies on genomic DNA being cleaved by Cas9 to reveal probe binding sites, the probes will only label the wild-type sequence but not the mutant sequence. Therefore, sgGOLDFISH has the sensitivity to differentiate the wild-type and mutant sequences differing by only a single base pair. Using sgGOLDFISH, we identify base-editor-modified and unmodified progeroid fibroblasts from a heterogeneous population, validate the identification through progerin immunofluorescence, and demonstrate accurate sub-nuclear localization of point mutations.

Preparation of Wax-Based Warm Mixture Additives from Waste Polypropylene (PP) Plastic and Their Effects on the Properties of Modified Asphalt.

The production of high-performance, low-cost warm mix additives (WMa) for matrix asphalt remains a challenge. The pyrolysis method was employed to prepare wax-based WMa using waste polypropylene plastic (WPP) as the raw material in this study. Penetration, softening point, ductility, rotational viscosity, and dynamic shear rheological tests were performed to determine the physical and rheological properties of the modified asphalt. The adhesion properties were characterized using the surface free energy (SFE) method. We proved that the pyrolysis temperature and pressure play a synergistic role in the production of wax-based WMa from WPPs. The product prepared at 380 °C and 1.0 MPa (380-1.0) can improve the penetration of matrix asphalt by 61% and reduce the viscosity (135 °C) of matrix asphalt by 48.6%. Furthermore, the modified asphalt shows favorable elasticity, rutting resistance, and adhesion properties; thus, it serves as a promising WMa for asphalt binders.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals.

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.

Solid Base Assisted Dual-Promoted Heterogeneous Conversion of PVC to Metal-Free Porous Carbon Catalyst.

Polyvinyl chloride (PVC) is widely used in daily life, but its waste has become a serious environmental problem. A solid base assisted low-temperature solvothermal dehalogenation was developed in this work to sustainably and efficiently transform PVC into high-value dimethylamine hydrochloride (DMACl) chemical and N,O co-doped carbon monolith with hierarchically porous structure. The synergistic promotion of solid-base catalyst and solvent decomposition with the removal of HCl can shift forward the chemical equilibrium to promote the dechlorination of PVC and increase the carbon yield. Meanwhile, the solid-base catalyst can also act as a pore-forming additive to fabricate the carbon monolith with hierarchical pores. Induced by the high specific surface area, hierarchical pores and N,O co-doped structure, the generated carbon monolith exhibits superior electrocatalytic performance towards H2 evolution. These discoveries shed light on the design of synergistically coupled solvent and solid catalyst to promote the heterogeneous conversion of waste chlorinated plastics into high-value chemicals for a sustainable future.