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Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries.

Atom manipulation by scanning tunneling microscopy was used to construct quantum dots on the InAs(111)A surface. Each dot comprised six ionized indium adatoms. The positively charged adatoms create a confining potential acting on surface-state electrons, leading to the emergence of a bound state associated with the dot. By lining up the dots into N-dot chains with alternating tunnel coupling between them, quantum-dot molecules were constructed that revealed electronic boundary states as predicted by the Su-Schrieffer-Heeger (SSH) model of one-dimensional topological phases. Dot chains with odd N were constructed such that they host a single end or domain-wall state, allowing one to probe the localization of the boundary state on a given sublattice by scanning tunneling spectroscopy. We found probability density also on the forbidden sublattice together with an asymmetric energy spectrum of the chain-confined states. This deviation from the SSH model arises because the dots are charged and create a variation in on-site potential along the chain—which does not remove the boundary states but shifts their energy away from the midgap position. Our results demonstrate that topological boundary states can be created in quantum-dot arrays engineered with atomic-scale precision.

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

Canine coronavirus (CCoV) is an important gastrointestinal pathogen that causes serious harm to pet dogs worldwide. Here, 1791 clinical samples were collected from pet dogs in different pet hospitals in Guangxi Province, southern China, for the 2021–2024 period and detected for CCoV by a multiplex RT-qPCR. The results showed that 8.43% (151/1791) of samples were positive for CCoV. Sixty-five positive samples were selected to amplify, sequence, and analyze S, M, and N genes. A sequence comparison revealed that the nucleotide and amino acid similarities of the S, M, and N genes were 94.86% and 94.62%, 96.85% and 97.80%, and 96.85% and 97.80%, respectively. Phylogenetic analysis indicated that 65 CCoV strains obtained in this study belonged to the CCoV-II genotype, of which 56 CCoV strains belonged to the CCoV-IIa subtype and 9 CCoV strains belonged to the CCoV-IIb subtype. A potential recombination event analysis of S gene sequences indicated that two CCoV strains, i.e., GXBSHM0328-34 and GXYLAC0318-35, have recombination signals. A Bayesian analysis indicated that the evolutionary rates of the S, M, and N genes were 1.791 × 10−3, 6.529 × 10−4, and 4.775 × 10−4 substitutions/site/year, respectively. The population size grew slowly before 1980 and then began to shrink slowly; it then shrank rapidly in 2005 and expanded sharply in 2020, leveling off thereafter. These results indicated the CCoV strains prevalent in Guangxi Province, southern China, showed a high level of genetic diversity and maintained continuous variation among clinical epidemic strains.

Feline coronavirus (FCoV), as one of the important pathogens of feline viral gastroenteritis, has been attracting great attention. A total of 1869 rectal and nasal swabs, feces, and ascites samples were collected from eight regions in Guangxi province during 2021–2024. The multiplex RT-qPCR established in our laboratory was used to test these samples for FCoV, and 17.66% (330/1869) of the samples were positive for FCoV. The S, M, and N genes of 63 FCoV-positive samples were amplified and sequenced, and the genetic and evolutionary characteristics were analyzed. Similarity analysis showed that the nucleotide and amino acid homologies of S, M, and N genes were 81.2–99.6% and 70.2–99.5%, 89.9–100% and 91.6–100%, and 90.1–100% and 91.5–100%, respectively. Phylogenetic analysis revealed that all 63 FCoV strains, based on S gene sequences, belonged to type I FCoV (FCoV-I), and were clustered with Chinese strains and the Netherlands UU strains. Recombinant signals were detected in the S gene of strains GXLZ03-2022, GXLZ08-2022, and CCoV GD/2020/X9. The results suggest that FCoV is still prevalent in the Guangxi province of southern China, and the prevalent FCoV strains show high genetic diversity and novel epidemic characteristics.

Abstract The multi-subunit homotypic fusion and vacuole protein sorting (HOPS) membrane-tethering complex is required for autophagosome-lysosome fusion in mammals, yet reconstituting the mammalian HOPS complex remains a challenge. Here we propose a “hook-up” model for mammalian HOPS complex assembly, which requires two HOPS sub-complexes docking on membranes via membrane-associated Rabs. We identify Rab39A as a key small GTPase that recruits HOPS onto autophagic vesicles. Proper pairing with Rab2 and Rab39A enables HOPS complex assembly between proteoliposomes for its tethering function, facilitating efficient membrane fusion. GTP loading of Rab39A is important for the recruitment of HOPS to autophagic membranes. Activation of Rab39A is catalyzed by C9orf72, a guanine exchange factor associated with amyotrophic lateral sclerosis and familial frontotemporal dementia. Constitutive activation of Rab39A can rescue autophagy defects caused by C9orf72 depletion. These results therefore reveal a crucial role for the C9orf72-Rab39A-HOPS axis in autophagosome-lysosome fusion.

Abstract Background Triple-negative breast cancer (TNBC) possesses special biological behavior and clinicopathological characteristics, which is highly invasive and propensity to metastasize to lymph nodes, leading to a worse prognosis than other types of breast cancer. Thus, the development of an effective therapeutic method is significant to improve the survival rate of TNBC patients. Results In this work, a liposome-based theranostic nanosystem (ILA@Lip) was successfully prepared by simultaneously encapsulating IR 780 as the photosensitizer and lenvatinib as an anti-angiogenic agent, together with banoxantrone (AQ4N) molecule as the hypoxia-activated prodrug. The ILA@Lip can be applied for the near-infrared (NIR) fluorescence diagnostic imaging of TNBC and its lymph node metastasis for multimodal therapy. Lenvatinib in ILA@Lip can inhibit angiogenesis by cutting oxygen supply, thereby leading to enhanced hypoxia levels. Meanwhile, large amounts of reactive oxygen species (ROS) were produced while IR 780 was irradiated by an 808 nm laser, which also rapidly exhausted oxygen in tumor cells to worsen tumor hypoxia. Through creating an extremely hypoxic in TNBC, the conversion of non-toxic AQ4N to toxic AQ4 was much more efficiency for hypoxia-activated chemotherapy. Cytotoxicity assay of ILA@Lip indicated excellent biocompatibility with normal cells and tissues, but showed high toxicity in hypoxic breast cancer cells. Also, the in vivo tumors treated by the ILA@Lip with laser irradiation were admirably suppressed in both subcutaneous tumor model and orthotopic tumor models. Conclusion Utilizing ILA@Lip is a profound strategy to create an extremely hypoxic tumor microenvironment for higher therapeutic efficacy of hypoxia-activated chemotherapy, which realized collective suppression of tumor growth and has promising potential for clinical translation.

BackgroundThe relationship between plaque psoriasis and both MASLD and lean MASLD has not been sufficiently explored in the current literature.MethodThis retrospective and observational study was carried out from January 2021 to January 2023 at The First Affiliated Hospital of Zhejiang Chinese Medical University. Patients diagnosed with plaque psoriasis and a control group consisting of individuals undergoing routine physical examinations were enrolled. The incidence of MASLD and lean MASLD among these groups was compared. Additionally, patients with plaque psoriasis were divided into those with MASLD, those with lean MASLD, and a control group with only psoriasis for a serological comparative analysis.ResultsThe incidence of MASLD in the observation group and the control group was 43.67% (69/158) and 22.15% (35/158), respectively (p

Pseudouridine (Ψ), the most prevalent modified base in cellular RNAs, has been mapped to numerous sites not only in rRNAs, tRNAs, and snRNAs but also mRNAs. Although there have been multiple techniques to identify Ψs, due to the recent development of sequencing technologies some reagents are not compatible with the current sequencer. Here, we show the updated Pseudo-seq, a technique enabling the genome-wide identification of pseudouridylation sites with single-nucleotide precision. We provide a comprehensive description of Pseudo-seq, covering protocols for RNA isolation from human cells, library preparation, and detailed data analysis procedures. The methodology presented is easily adaptable to any cell or tissue type with high-quality mRNA isolation. It can be used for discovering novel pseudouridylation sites, thus constituting a crucial initial step toward understanding the regulation and function of this modification.

African swine fever (ASF) is a highly contagious and lethal viral disease that causes severe hemorrhagic fever in pigs. It keeps spreading around the world, posing a severe socioeconomic risk and endangering biodiversity and domestic food security. ASF first outbroke in China in 2018, and has spread to most provinces nationwide. Genotypes I and II ASF virus (ASFV) as the etiological pathogens have been found in China. In this study, three pairs of specific primers and probes targeting the ASFV B646L gene, F1055L gene, and E183L gene were designed to detect universal, genotype I, and genotype II strains, respectively. A triplex crystal digital PCR (cdPCR) was established on the basis of optimizing various reaction conditions. The assay demonstrated remarkably sensitive with low limits of detection (LODs) of 5.120, 4.218, 4.588 copies/reaction for B646L, F1055L, and E183L gene, respectively; excellent repeatability with 1.24–2.01% intra-assay coefficients of variation (CVs) and 1.32–2.53% inter-assay CVs; good specificity for only detection of genotypes I and II ASFV, without cross-reactivity with PCV2, PRV, SIV, PRRSV, PEDV, FMDV, and CSFV. The triplex cdPCR was used to test 1,275 clinical samples from Guangxi province of China, and the positivity rates were 5.05, 3.22, and 1.02% for genotype I, genotype II, and co-infection of genotypes I and II, respectively. These 1,275 clinical samples were also detected using a reported reference triplex real-time quantitative PCR (qPCR), and the agreements of detection results between these two methods were more than 98.98%. In conclusion, the developed triplex cdPCR could be used as a rapid, sensitive, and accurate method to detect and differentiate genotypes I and II strains of ASFV.

Pulmonary Mucormycosis is a fatal infectious disease with high mortality rate. The occurrence of Mucormycosis is commonly related to the fungal virulence and the host’s immunological defenses against pathogens. Mucormycosis infection and granulation tissue formation occurred in the upper airway was rarely reported. This patient was a 60-year-old male with diabetes mellitus, who was admitted to hospital due to progressive cough, sputum and dyspnea. High-resolution computed tomography (HRCT) and bronchoscopy revealed extensive tracheal mucosal necrosis, granulation tissue proliferation, and severe airway stenosis. The mucosal necrotic tissue was induced by the infection of Rhizopus Oryzae, confirmed by metagenomic next-generation sequencing (mNGS) in tissue biopsy. This patient was treated with the placement of a covered stent and local instillation of amphotericin B via bronchoscope. The tracheal mucosal necrosis was markedly alleviated, the symptoms of cough, shortness of breath, as well as exercise tolerance were significantly improved. The placement of airway stent and transbronchial microtube drip of amphotericin B could conduce to rapidly relieve the severe airway obstruction due to Mucormycosis infection.

IntroductionHepatocellular carcinoma (HCC) is an aggressive malignancy, and CCL18, a marker of M2 macrophage activation, is often associated with tumor immune suppression. However, the role of CCL18 and its signaling pathway in HCC is still limited. Our study focuses on investigating the prognostic impact of CCL18 and its signaling pathway in HCC patients and biological functions in vitro.MethodsHCC-related RNA-seq data were obtained from TCGA, ICGC, and GEO. The 6 hub genes with the highest correlation to prognosis were identified using univariate Cox and LASSO regression analysis. Multivariate Cox regression analysis was performed to assess their independent prognostic potential and a nomogram was constructed. In vitro experiments, including CCK8, EdU, RT-qPCR, western blot, and transwell assays, were conducted to investigate the biological effects of exogenous CCL18 and 6 hub genes. A core network of highly expressed proteins in the high-risk group of tumors was constructed. Immune cell infiltration was evaluated using the ESTIMATE and CIBERSORT packages. Finally, potential treatments were explored using the OncoPredict package and CAMP database.ResultsWe identified 6 survival-related genes (BMI1, CCR3, CDC25C, CFL1, LDHA, RAC1) within the CCL18 signaling pathway in HCC patients. A nomogram was constructed using the TCGA_LIHC cohort to predict patient survival probability. Exogenous CCL18, as well as overexpression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1, can promote proliferation, migration, invasion, stemness, and increased expression of PD-L1 protein in LM3 and MHCC-97H cell lines. In the high-risk group of patients from the TCGA_LIHC cohort, immune suppression was observed, with a strong correlation to 21 immune-related genes and suppressive immune cells.ConclusionExogenous CCL18 promotes LM3 and MHCC-97H cells proliferation, migration, invasion, stemness, and immune evasion. The high expression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1 can serve as a biomarkers for immune evasion in HCC.

Essential proteins play a vital role in biological processes, and the combination of gene expression profiles with Protein-Protein Interaction (PPI) networks can improve the identification of essential proteins. However, gene expression data are prone to significant fluctuations due to noise interference in topological networks. In this work, we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation. We then proposed the Pearson Jaccard coefficient (PJC) that consisted of continuous and discrete similarities in the gene expression data. Using the graph theory as the basis, we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins. This strategy exhibited a high recognition rate and good specificity. We validated the new similarity coefficient PJC on PPI datasets of Krogan, Gavin, and DIP of yeast species and evaluated the results by receiver operating characteristic analysis, jackknife analysis, top analysis, and accuracy analysis. Compared with that of node-based network topology centrality and fusion biological information centrality methods, the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC, IC, Eigenvector centrality, subgraph centrality, betweenness centrality, closeness centrality, NC, PeC, and WDC. We also compared the PJC coefficient with other methods using the NF-PIN algorithm, which predicts proteins by constructing active PPI networks through dynamic gene expression. The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Abstract Aims Given recent suggestions that serum levels of insulin‐like growth factor‐binding protein 7 (IGFBP7) may identify patients who derive greater cardiorenal benefits from treatment with sodium‐glucose transport 2 inhibitors (SGLT2i), this exploratory sub‐analysis of the EMPA‐HEART CardioLink‐6 randomized controlled trial evaluated the association between serum levels of IGFBP7 and empagliflozin‐mediated left ventricular mass regression. Methods and results The EMPA‐HEART CardioLink‐6 trial used gold‐standard cardiac magnetic resonance imaging to detect change in left ventricular mass indexed to body surface area (LVMi) following 6 months of treatment with empagliflozin or matching placebo in 97 patients with type 2 diabetes and coronary artery disease. Serum samples were collected at baseline and analysed for IGFBP7 using an enzyme‐linked immunosorbent assay. A multivariate linear regression model was used to assess the association between IGFBP7 and baseline LVMi. A linear model adjusting for baseline differences in LVMi was used to test the relationship between baseline IGFBP7 level, change in LVMi over 6 months, and treatment arm. Of the 97 patients enrolled, 74 had complete covariate data and were included in our analysis. No association between baseline IGFBP7 and baseline LVMi was found [baseline LVMi: 0.14 g/m2 (95% CI: −0.29 g/m2 to 0.57 g/m2) per 1 ng/mL higher baseline IGFBP7]. In addition, no difference between patients treated with empagliflozin versus matching placebo was found when evaluating the association between serum IGFBP7, 6 month change in LVMi, and treatment arm [empagliflozin 6 month change in LVMi: 0.25 g/m2 (95% CI: −0.17 g/m2 to 0.67 g/m2) per 1 ng/mL higher IGFBP7 vs. matching placebo 6 month change in LVMi: 0.07 g/m2 (95% CI: −0.21 g/m2 to 0.35 g/m2) per 1 ng/mL higher IGFBP7; Pinteraction = 0.49]. Additional sensitivity analysis assessing IGFBP7 as a categorical variable (above/below the median) showed no significant association between IGFBP7, 6 month change in LVMi, and treatment arm. Conclusions Our study provides insight into the generalizability of IGFBP7 as a surrogate marker of cardiac remodelling in patients with type 2 diabetes and coronary artery disease. Our results suggest that SGLT2i‐mediated reverse cardiac remodelling may be independent of IGFBP7 levels. Further investigations evaluating the association between IGFBP7 and SGLT2i are suggested to understand if and how IGFBP7 levels may modulate benefits received from SLGT2i.

Abstract Background Angiogenesis and tissue repair in chronic non-healing diabetic wounds remain critical clinical problems. Engineered MSC-derived exosomes have significant potential for the promotion of wound healing. Here, we discuss the effects and mechanisms of eNOS-rich umbilical cord MSC exosomes (UCMSC-exo/eNOS) modified by genetic engineering and optogenetic techniques on diabetic chronic wound repair. Methods Umbilical cord mesenchymal stem cells were engineered to express two recombinant proteins. Large amounts of eNOS were loaded into UCMSC-exo using the EXPLOR system under blue light irradiation. The effects of UCMSC-exo/eNOS on the biological functions of fibroblasts and vascular endothelial cells in vitro were evaluated. Full-thickness skin wounds were constructed on the backs of diabetic mice to assess the role of UCMSC-exo/eNOS in vascular neogenesis and the immune microenvironment, and to explore the related molecular mechanisms. Results eNOS was substantially enriched in UCMSCs-exo by endogenous cellular activities under blue light irradiation. UCMSC-exo/eNOS significantly improved the biological functions of cells after high-glucose treatment and reduced the expression of inflammatory factors and apoptosis induced by oxidative stress. In vivo, UCMSC-exo/eNOS significantly improved the rate of wound closure and enhanced vascular neogenesis and matrix remodeling in diabetic mice. UCMSC-exo/eNOS also improved the inflammatory profile at the wound site and modulated the associated immune microenvironment, thus significantly promoting tissue repair. Conclusion This study provides a novel therapeutic strategy based on engineered stem cell-derived exosomes for the promotion of angiogenesis and tissue repair in chronic diabetic wounds. Graphic Abstract

The oxidation of benzylic alcohols is an important transformation in modern organic synthesis. A plethora of photoredox protocols have been developed to achieve the aerobic oxidation of alcohols into carbonyls. Recently, several groups described that ultraviolet (UV) or purple light can initiate the aerobic oxidation of benzylic alcohols in the absence of an external catalyst, and depicted different mechanisms involving the photoinduction of •O2− as a critical reactive oxygen species (ROS). However, based on comprehensive mechanistic investigations, including control experiments, radical quenching experiments, EPR studies, UV–vis spectroscopy, kinetics studies, and density functional theory calculations (DFT), we elucidate here that HOO•, which is released via the H2O2 elimination of α-hydroxyl peroxyl radicals [ArCR(OH)OO•], serves as the real chain carrier for the autocatalytic photooxidation of benzylic alcohols. The mechanistic ambiguities depicted in the precedent literature are clarified, in terms of the crucial ROS and its evolution, the rate-limiting step, and the primary radical cascade. This work highlights the necessity of stricter mechanistic analyses on UV-driven oxidative reactions that involve aldehydes’ (or ketones) generation.

The variant porcine epidemic diarrhea virus (PEDV) has caused considerable economic losses to the global pig industry since 2010. In this study, a total of 5859 diarrhea samples were collected from different pig farms in China’s Guangxi province during January 2020 and March 2024 and tested for PEDV using RT-qPCR. The positivity rate of PEDV was 11.90% (697/5859). Ninety-two PEDV-positive samples were selected based on sampling time, and the sampling region for amplification, sequencing, and analysis of the S1, M, and N genes. Phylogenetic analysis of the S1 gene revealed that all strains from Guangxi province were distributed in three subgroups, i.e., 81.5% (75/92) in the G2a subgroup, 4.3% (4/92) in the G2b subgroup, and 14.1% (13/92) in the G2c subgroup. The sequence analysis revealed that the S1 gene sequences from Guangxi province had higher homology with the variant strains than with the classical strains, showing as high as 99.2% with the variant strain AJ1102 and only 94.3% with the classical strain CV777. Recombination analysis revealed that the GX-BS08-2023 strain (G2c) from Guangxi province originated from inter-lineage recombination between the GX-BS09-2023 (G2a) and CH-JN547228-2011 (G1a) strains. In addition, the S1 gene of the G2a and G2b subgroup strains shared many mutations and insertions. There were common mutations of N143D and P235L in the G2a subgroup. Evolutionary analysis revealed that all Guangxi strains belonged to the G2 genotype. These strains have spread rapidly since the PEDV variant strains that emerged in 2010, weakened until 2021, and then remained stable. In conclusion, the results revealed the latest genetic evolution of circulating PEDV strains in Guangxi province in recent years, providing important information for preventing and controlling PEDV infection. Currently, the G2a subgroup strains are the predominant strains circulating in pig herds in Guangxi province, southern China.

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.