Glycolysis­related lncRNA may be associated with prognosis and immune activity in grade II­III glioma.

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland.

OBJECTIVES: The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT. METHODS: A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model. RESULTS: The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively. CONCLUSIONS: The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland. CLINICAL RELEVANCE STATEMENT: This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application. KEY POINTS: • Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.

Urolithin a inhibits breast cancer progression via activating TFEB-mediated mitophagy in tumor macrophages.

INTRODUCTION: Urolithin A (UA) is a naturally occurring compound that is converted from ellagitannin-like precursors in pomegranates and nuts by intestinal flora. Previous studies have found that UA exerts tumor-suppressive effects through antitumor cell proliferation and promotion of memory T-cell expansion, but its role in tumor-associated macrophages remains unknown. OBJECTIVES: Our study aims to reveal how UA affects tumor macrophages and tumor cells to inhibit breast cancer progression. METHODS: Observe the effect of UA treatment on breast cancer progression though in vivo and in vitro experiments. Western blot and PCR assays were performed to discover that UA affects tumor macrophage autophagy and inflammation. Co-ip and Molecular docking were used to explore specific molecular mechanisms. RESULTS: We observed that UA treatment could simultaneously inhibit harmful inflammatory factors, especially for InterleuKin-6 (IL-6) and tumor necrosis factor α (TNF-α), in both breast cancer cells and tumor-associated macrophages, thereby improving the tumor microenvironment and delaying tumor progression. Mechanistically, UA induced the key regulator of autophagy, transcription factor EB (TFEB), into the nucleus in a partially mTOR-dependent manner and inhibited the ubiquitination degradation of TFEB, which facilitated the clearance of damaged mitochondria via the mitophagy-lysosomal pathway in macrophages under tumor supernatant stress, and reduced the deleterious inflammatory factors induced by the release of nucleic acid from damaged mitochondria. Molecular docking and experimental studies suggest that UA block the recognition of TFEB by 1433 and induce TFEB nuclear localization. Notably, UA treatment demonstrated inhibitory effects on tumor progression in multiple breast cancer models. CONCLUSION: Our study elucidated the anti-breast cancer effect of UA from the perspective of tumor-associated macrophages. Specifically, TFEB is a crucial downstream target in macrophages.

The Effects of Hypoxia-Preconditioned Dental Stem Cell-Derived Secretome on Tissue Regeneration.

Mesenchymal stroma cells derived from oral tissues are known as dental stem cells (DSCs). Owing to their unique therapeutic niche and clinical accessibility, DSCs serve as a promising treatment option for bone defects and oral tissue regeneration. DSCs exist in a hypoxic microenvironment in vivo, which is far lower than the current 20% oxygen concentration used in in vitro culture. It has been widely reported that the application of an oxygen concentration less than 5% in the culture of DSCs is beneficial for preserving stemness and promoting proliferation, migration, and paracrine activity. The paracrine function of DSCs involves the secretome, which includes conditioned media (CM) and soluble bioactive molecules, as well as extracellular vesicles extracted from CM. Hypoxia can play a role in immunomodulation and angiogenesis by altering the protein or nucleic acid components in the secretory group, which enhances the therapeutic potential of DSCs. This review summarizes the biological characteristics of DSCs, the influence of hypoxia on DSCs, the impact of hypoxia on the secretory group of DSCs, and the latest progress on the use of DSCs secretory group in tissue regeneration based on hypoxia pretreatment. We highlighted the multifunctional biological effect of hypoxia culture on tissue regeneration and provided a summary of the current mechanism of hypoxia in the pretreatment of DSCs.

ANXA9 facilitates S100A4 and promotes breast cancer progression through modulating STAT3 pathway.

Breast cancer has the highest global incidence and mortality rates among all cancer types. Abnormal expression of the Annexin family has been observed in different malignant tumors, including upregulated ANXA9 in breast cancer. We found highly expressed ANXA9 in metastatic breast cancer tissues, which is correlated with breast cancer progression. In vitro, the functional experiments indicated ANXA9 influenced breast cancer proliferation, motility, invasion, and apoptosis; in vivo, downregulation of ANXA9 suppressed breast cancer xenograft tumor growth and lung metastasis. Mechanically, on one side, we found that ANXA9 could mediate S100A4 and therefore regulate AKT/mTOR/STAT3 pathway to participate p53/Bcl-2 apoptosis; on the other side, we found ANXA9 transferred S100A4 from cells into the tumor microenvironment and mediated the excretion of cytokines IL-6, IL-8, CCL2, and CCL5 to participate angiogenesis via self- phosphorylation at site Ser2 and site Thr69. Our findings demonstrate significant involvement of ANXA9 in promoting breast cancer progression, thereby suggesting that therapeutic intervention via targeting ANXA9 may be effective in treating metastatic breast cancer.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization.

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

Spinal lymphatic dysfunction aggravates the recovery process after spinal cord injury.

We aimed to evaluate the role of the spinal lymphatic system in spinal cord injury and whether it has an impact on recovery after spinal cord injury. Flow cytometry was used to evaluate the changes in the number of microvesicles after spinal cord injury. Evans blue extravasation was used to evaluate the function of the lymphatic system. Evans blue extravasation and immunofluorescence were used to evaluate the permeability of blood spinal cord barrier. The spinal cord edema was evaluated by dry and wet weight.Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay was used to evaluate apoptosis after spinal cord injury. Nuclear factor-kappa B pathway was detected by Western blot. Behavioral tests were used to evaluate limb function. Microvesicles released after spinal cord injury can enter the thoracic duct and then enter the blood through the lymph around the spine. After ligation of the thoracic duct, it can aggravate the neuropathological manifestations and limb function after spinal cord injury. The potential mechanism may involve nuclear factor-kappa B pathway.

Development of Sustainable and Active Food Packaging Materials Composed by Chitosan, Polyvinyl Alcohol and Quercetin Functionalized Layered Clay.

In order to solve the problems of insufficient active functions (antibacterial and antioxidant activities) and the poor degradability of traditional plastic packaging materials, biodegradable chitosan (CS)/polyvinyl alcohol (PVA) nanocomposite active films reinforced with natural plant polyphenol-quercetin functionalized layered clay nanosheets (QUE-LDHs) were prepared by a solution casting method. In this study, QUE-LDHs realizes a combination of the active functions of QUE and the enhancement effect of LDHs nanosheets through the deposition and complexation of QUE and copper ions on the LDHs. Infrared and thermal analysis results revealed that there was a strong interface interaction between QUE-LDHs and CS/PVA matrix, resulting in the limited movement of PVA molecules and the increase in glass transition temperature and melting temperature. With the addition of QUE-LDHs, the active films showed excellent UV barrier, antibacterial, antioxidant properties and tensile strength, and still had certain transparency in the range of visible light. As QUE-LDHs content was 3 wt%, the active films exhibited a maximum tensile strength of 58.9 MPa, representing a significant increase of 40.9% compared with CS/PVA matrix. Notably, the UV barrier (280 nm), antibacterial (E. coli) and antioxidant activities (DPPH method) of the active films achieved 100.0%, 95.5% and 58.9%, respectively. Therefore, CS/PVA matrix reinforced with QUE-LDHs has good potential to act as an environmentally and friendly active packaging film or coating.

Human periodontal ligament stem cell sheets activated by graphene oxide quantum dots repair periodontal bone defects by promoting mitochondrial dynamics dependent osteogenic differentiation.

BACKGROUND: Bone defects in the maxillofacial region restrict the integrity of dental function, posing challenges in clinical treatment. Bone tissue engineering (BTE) with stem cell implants is an effective method. Nanobiomaterials can effectively enhance the resistance of implanted stem cells to the harsh microenvironment of bone defect areas by promoting cell differentiation. Graphene oxide quantum dots (GOQDs) are zero-dimensional nanoscale derivatives of graphene oxide with excellent biological activity. In the present study, we aimed to explore the effects of GOQDs prepared by two methods (Y-GOQDs and B-GOQDs) on the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs), as well as the effect of gelatin methacryloyl (GelMA)-encapsulated GOQD-induced hPDLSC sheets on the repair of mandibular periodontal defects in rats. We also explored the molecular biological mechanism through which GOQD promotes bone differentiation. RESULTS: There were significant differences in oxygen-containing functional groups, particle size and morphology between Y-GOQDs and B-GOQDs. Y-GOQDs promoted the osteogenic differentiation of hPDLSCs more effectively than did B-GOQDs. In addition, GelMA hydrogel-encapsulated Y-GOQD-induced hPDLSC cell sheet fragments not only exhibited good growth and osteogenic differentiation in vitro but also promoted the repair of mandibular periodontal bone defects in vivo. Furthermore, the greater effectiveness of Y-GOQDs than B-GOQDs in promoting osteogenic differentiation is due to the regulation of hPDLSC mitochondrial dynamics, namely, the promotion of fusion and inhibition of fission. CONCLUSIONS: Overall, Y-GOQDs are more effective than B-GOQDs at promoting the osteogenic differentiation of hPDLSCs by regulating mitochondrial dynamics, which ultimately contributes to bone regeneration via the aid of the GelMA hydrogels in vivo.

Research Progress on the Geomechanical Properties of Block-in-Matrix Rocks.

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock.

LINC01572 promotes triple-negative breast cancer progression through EIF4A3-mediated ß-catenin mRNA nuclear exportation.

Long noncoding RNAs have been reported to be involved in the development of breast cancer. LINC01572 was previously reported to promote the development of various tumors. However, the potential biological function of LINC01572 in breast cancer remains largely unknown. R language was used to perform bioinformatic analysis of The Cancer Genome Atlas data. The expression level of RNAs was examined by RT-qPCR. The effect of knocking down or overexpression LINC01572 in triple-negative breast cancer (TNBC) cell lines was evaluated by detecting cell proliferation, migrant action. RNA immunoprecipitation assay and RNA pull-down assay were performed to explore the regulatory relationship between LINC01572, EIF4A3, and ß-catenin. Bioinformatics analysis identifies LINC01572 as an oncogene of breast cancer. LINC01572 is over-expressed in TNBC tissues and cell lines, correlated with poor clinical prognosis in BC patients. Cell function studies confirmed that LINC01572 facilitated the proliferation and migration of TNBC cells in both vivo and vitro. Mechanistically, ß-catenin mRNA and EIF4A3 combine spatially to form a complex, LINC01572 helps transport this complex from the nucleus to the cytoplasm, thereby facilitating the translation of ß-catenin. Our findings confirm that LINC01572 acts as a tumor promoter and may act as a biomarker in TNBC. In addition, novel molecular regulatory relationships involving LINC01572/EIF4A3/ß-catenin are critical to the development of TNBC, which led to a new understanding of the mechanisms of TNBC progression and shows a new target for precision treatment for TNBC.

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches.

Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.

Impact of molar teeth distalization by clear aligners on maxillary alveolar bone thickness and root resorption: a three­dimensional study.

OBJECTIVE: This study aimed to evaluate the impact of molar teeth distalization movement by clear aligners on changes in the alveolar bone thickness and orthodontically induced inflammatory root resorption (OIIRR) in maxillary molars using conebeam computed tomography (CBCT). MATERIALS AND METHODS: Three-dimensional CBCT scans of 35 adult patients (one hundred forty maxillary molars) with pre-designed selection criteria and a mean age of 24.4 ± 7.1 years were included. The measured parameters, including alveolar bone thickness for maxillary molars and root resorption (OIIRR), were analyzed using pre-and post-treatment CBCT (T0 and T1, respectively) with Invivo 6.0 software. RESULT: Post-treatment, relevant statistically significant changes included deposition of bone in the average palatal surface of the 1st molars. The reduction of bone was seen in the average buccal surface of the first molars and both surfaces of the second molars. Regarding root length after treatment, the average maxillary 1st molar roots showed significant OIIRR (p < 0.001). CONCLUSION: Clear aligner treatment could effectively reduce the incidence of alveolar bone thickness reduction and OIIRR in treating Class II malocclusions compared to conventional braces, as shown in previous studies. This research will aid in fully grasping the benefits of clear aligners.

The two-year prognosis of multinodular goiter following radiofrequency ablation: based on all nodule burdens.

OBJECTIVE: Few studies used all nodule burdens to specify the prognosis of multinodular goiter (MNG) following radiofrequency ablation (RFA), so this study addresses this question for MNG after completely ablating dominant nodules. METHODS: The RFA indications for MNG included a total of 2-5 benign nodules with over 50% normal tissue on ultrasound, 1-3 well-defined benign dominant nodules on cytology, largest diameter ≥20 mm and/or with clinical complaints, and patient refusal or unable of surgery. A retrospective study of 185 MNG patients with completely ablated dominant nodules in a single-session RFA was conducted. The efficacy and complications were evaluated at 1, 6, 12 months and yearly thereafter. Based on retreatment risks, progressive disease (PD), stable disease (SD) and complete relief (CR) were introduced to assess all nodule load changes. PD was clarified as having new/non-target nodules newly appeared to ACR TI-RADS≥4, or new/enlarged non-target nodules ≥1 cm. RESULTS: The initial ablation ratios of target nodules were 100% at one month. During a mean 22.38±13.75 months (range, 12-60 months), the VRR of ablated nodules was 98.25% at 24 months without regrowth. Cosmetic and symptomatic scores decreased to 1 and 0, respectively, after 48 months. 9.7% of patients (18/185) had PD and the retreatment rate was 2.2% (4/185). The complication rate was 2.7% (5/185). CONCLUSIONS: RFA provides cosmetic and symptomatic relief for an average of two years. RFA is an useful minimally invasive treatment modality for selected MNG patients.

Automated Diagnosis of Bone Metastasis by Classifying Bone Scintigrams Using a Self-defined Deep Learning Model.

BACKGROUND: Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images. OBJECTIVE: To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model. METHODS: A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis). RESULTS: Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively. CONCLUSION: The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of 99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.

One-step synthesis of phospho-rich, silica-enhanced chitosan aerogel for the efficient adsorption of uranium(VI).

In this study, an amorphous silica reinforced, phosphoric-crosslinked chitosan foam (P-CTS@SixOy) was prepared. The introduction of amorphous silica not only increased the affinity of the adsorbent for uranium, but also improved the stability of the material. The number of active sites of P-CTS@SixOy was increased by the introduction of phosphate groups. The material exhibited excellent uranium adsorption performance with the removal capacity and efficiency of 850.5 mg g-1 and 98.1 %, respectively. After regenerations, the morphology of P-CTS@SixOy still maintained, and the uranium adsorption efficiency remained above 90 %, manifesting the excellent cycle performance of P-CTS@SixOy. In the dynamic adsorption experiment, P-CTS@SixOy successfully concentrated the volume of uranium-containing solution, and exhibited excellent uranium adsorption performance. The analysis of kinetics, isotherms, and thermodynamics manifested that the uranium adsorption behavior of P-CTS@SixOy was a spontaneous, endothermic, monolayer chemical adsorption process. X-ray photoelectron spectroscopy, Scanning Electron Microscope, and Fourier Transform Infrared Spectrometer were used to characterized the P-CTS@SixOy before and after adsorption, which demonstrated that the main interaction mechanism between uranium and P-CTS@SixOy was the complexation. These studies indicated the huge application prospect of P-CTS@SixOy in the treatment of large-scale uranium-containing wastewater.

Enhancement and Broadening of the Internal Electric Field of Hole-Transport-Layer-Free Perovskite Solar Cells by Quantum Dot Interface Modification.

Hole-transport-layer-free perovskite solar cells have attracted strong interest due to their simple structure and low cost, but charge recombination is serious. Built-in electric field engineering is an intrinsic driver to facilitate charge separation transport and improve the efficiency of photovoltaic devices. However, the enhancement of the built-in electric field strength is often accompanied by the narrowing of the space charge region, which becomes a key constraint to the performance improvement of the device. Here, we propose an effective regulation method, the component engineering of quantum dots, to enhance the strength of the built-in electric field and broaden the range of space charge. By using all inorganic CsPbBrxI3-x (x = 0, 1, 2, 3) quantum dot interface modification to passivate the defects of MAPbI3 perovskite films, the regulation law of quantum dot components on the work function of perovskite films was revealed, and the mechanism of their influence on the internal electric field intensity and space charge region distribution was further clarified, thereby fundamentally solving the serious problem of charge recombination. As directly observed by electron-beam-induced current (EBIC), the introduction of CsPbBr2I quantum dots can effectively enhance the interfacial electric field intensity, widening the space charge region from 160 to 430 nm. Moreover, the efficiency of the hole-free transport layer perovskite solar cells modified by CsPbBr2I quantum dots was also significantly enhanced by 1.5 times. This is an important guideline for electric field modulation and efficiency improvement within photovoltaic devices with other simplified structures.

Exo70 Promotes the Invasion of Pancreatic Cancer Cells via the Regulation of Exosomes.

Pancreatic cancer (PC) is an aggressive and fatal malignant tumor, and exosomes have been reported to be closely related to PC invasion and metastasis. Here we found that Exo70, a key subunit of the exocyst complex, promoted PC metastasis by regulating the secretion of tumor exosomes. Clinical sample studies showed that Exo70 was highly expressed in PC and negatively correlated with patients' survival. Exo70 promoted PC cell lines' invasion and migration. Interestingly, knockdown of Exo70, or using an Exo70 inhibitor (ES2) inhibited the secretion of tumor exosomes and increased the accumulation of cellular vesicles. Furthermore, Exo70 was found to accumulate in the exosomes, which then fused with neighboring PC cells and promoted their invasion. Moreover, Exo70 increased the expression of exosomal PD-L1, leading to the immune escape of PC cells. In vivo, knockdown of Exo70 or treatment with ES2 both decreased the tumor metastasis of PC cells in mice. This study provides new insight into the mechanism of invasion and metastasis in PC and identifies Exo70 as a potential prognostic factor and therapeutic target for PC.