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Abstract Acute lung injury (ALI) is a severe respiratory disease with a high mortality rate. The integrity of the pulmonary endothelial barrier influences the development and prognosis of ALI. Therefore, it has become an important target for ALI treatment. Extracellular vesicles (EVs) are promising nanotherapeutic agents against ALI. Herein, endothelium‐derived engineered extracellular vesicles (eEVs) that deliver microRNA‐125b‐5p (miRNA‐125b) to lung tissues exerting a protective effect on endothelial barrier integrity are reported. eEVs that are modified with lung microvascular endothelial cell‐targeting peptides (LET) exhibit a prolonged retention time in lung tissues and targeted lung microvascular endothelial cells in vivo and in vitro. To improve the efficacy of the EVs, miRNA‐125b is loaded into EVs. Finally, LET‐EVs‐miRNA‐125b is constructed. The results show that compared to the EVs, miRNA‐125b, and EVs‐miRNA‐125b, LET‐EVs‐miRNA‐125b exhibit the most significant treatment efficacy in ALI. Moreover, LET‐EVs‐miRNA‐125b is found to have an important protective effect on endothelial barrier integrity by inhibiting cell apoptosis, promoting angiogenesis, and protecting intercellular junctions. Sequencing analysis reveals that LET‐EVs‐miRNA‐125b downregulates early growth response‐1 (EGR1) levels, which may be a potential mechanism of action. Taken together, these findings suggest that LET‐EVs‐miRNA‐125b can treat ALI by protecting the endothelial barrier integrity.

Abstract Background Sulfur mustard (SM) is a highly toxic chemical warfare agent that has caused numerous casualties during wars and conflicts in the past century. Specific antidotes or therapeutic strategies are rare due to the complicated mechanism of toxicity, which still awaits elucidation. Clinical data show that acute lung injury (ALI) is responsible for most mortality and morbidity after SM exposure. Extracellular vesicles are natural materials that participate in intercellular communication by delivering various substances and can be modified. In this study, we aim to show that extracellular vesicles derived from human umbilical cord mesenchymal stromal cells (hucMSC-EVs) could exert therapeutic effects on SM-induced ALI, and to explain the underlying mechanism of effects. Methods MiR-146a-5p contained in hucMSC-EVs may be involved in the process of hucMSC-EVs modulating the inflammatory response to SM-induced ALI. We utilized miR-146a-5p delivered by extracellular vesicles and further modified hucMSCs with a miR-146a-5p mimic or inhibitor to collect miR-146a-5p-overexpressing extracellular vesicles (miR-146a-5p+-EVs) or miR-146a-5p-underexpressing extracellular vesicles (miR-146a-5p−-EVs), respectively. Through in vivo and in vitro experiments, we investigated the mechanism. Results The effect of miR-146a-5p+-EVs on improving the inflammatory reaction tied to SM injury was better than that of hucMSC-EVs. We demonstrated that miR-146a-5p delivered by hucMSC-EVs targeted TRAF6 to negatively regulate inflammation in SM-induced ALI models in vitro and in vivo. Conclusion In summary, miR-146a-5p delivered by hucMSC-EVs targeted TRAF6, causing hucMSC-EVs to exert anti-inflammatory effects in SM-induced ALI; thus, hucMSC-EVs treatment may be a promising clinical therapeutic after SM exposure. Graphical Abstract

In recent years, the integration of robots in minimally invasive surgery has gained significant traction in clinical practice. However, conventional contact-based human-computer interaction poses the risk of bacterial infection, significantly limiting the role of robots in surgery. To address this limitation, we propose an innovative interaction method rooted in gestures and visual tags, allowing surgeons to control and fine-tune surgical robots without physical contact with the environment. By encoding the six gestures collected using LeapMotion, we can effectively control the surgical robot in a non-contact manner. Moreover, utilizing Aruco technology, we have accurately identified the 3D spatial position of the visual label, and developed 12 fine-tuning operations to refine surgical instruments. To evaluate the applicability of our proposed system in surgery, we designed a relevant experimental setup. In the experiment, we achieved enough precision. These results demonstrate that our system meets the clinical standard, providing doctors with a non-contact and flexible means of interacting with robots during surgery.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.

IntroductionBreast cancer (BC) is the most common malignancy in the world and has a high cancer-related mortality rate. Basement membranes (BMs) guide cell polarity, differentiation, migration and survival, and their functions are closely related to tumor diseases. However, few studies have focused on the association of basement membrane-related genes (BMRGs) with BC. This study aimed to explore the prognostic features of BMRGs in BC and provide new directions for the prevention and treatment of BC.MethodsWe collected transcriptomic and clinical data of BC patients from TCGA and GEO datasets and constructed a predictive signature for BMRGs by using univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The reliability of the model was further evaluated and validated by Kaplan-Meier survival curves and receiver operating characteristic curves (ROC). Column line plots and corresponding calibration curves were constructed. Possible biological pathways were investigated by enrichment analysis. Afterward, we assessed the mutation status by tumor mutational burden (TMB) analysis and compared different subtypes using cluster analysis. Finally, we examined drug treatment sensitivity and immunological correlation to lay the groundwork for more in-depth studies in this area.ResultsThe prognostic risk model consisted of 7 genes (FBLN5, ITGB2, LAMC3, MMP1, EVA1B, SDC1, UNC5A). After validation, we found that the model was highly reliable and could accurately predict the prognosis of BC patients. Cluster analysis showed that patients with cluster 1 had more sensitive drugs and had better chances of better clinical outcomes. In addition, TMB, immune checkpoint, immune status, and semi-inhibitory concentrations were significantly different between high and low-risk groups, with lower-risk patients having the better anti-cancer ability.DiscussionThe basement membrane-related gene signature that we established can be applied as an independent prognostic factor for BC and can provide a reference for individualized treatment of BC patients.

Over the past 2 years, the world has witnessed the devastating effects of the COVID-19 pandemic on humanity. Fortunately, stem cell therapy is widely used in clinical practice for the treatment of COVID-19 and has saved the lives of many critically ill patients. A bibliometric analysis of this field can analyze research hotspots and predict the research trends. This research analyzed documents from Web of Science between the years 2020–2022. The bibliometrics software bibliometrix, VOSviewer, and CiteSpace were used to complete the visual analysis of publications, authors, countries, documents, organizations, collaborative networks, and keywords clustering. 896 publications on COVID-19 stem cell therapy were included in the analysis, including 451 articles and 445 review articles. The field grew at the average growth rate of 103.17% between 2020 and 2021. The United States had the highest number of publications and citations. Many developing countries had also contributed significantly to the field. The journal with the most articles was Stem Cell Research and Therapy. The most cited journal was Stem Cell Reviews and Reports. The published documents were focused on five themes: “Cell Biology”, “Medicine Research Experimental”, “Cell Tissue Engineering”, “Immunology”, and “Pharmacology Pharmacy”. The bibliometric analysis revealed that current clinical trials had validated stem cell therapy's remarkable potential in treating COVID-19 and its complications. It is foreseeable that future research in this area will continue to increase. With the help of bibliometric analysis, researchers can identify the current state of research and potential research hotspots.

Breast cancer (BC), the most common malignancy in women, has a high cancer-related mortality. Endoplasmic reticulum stress (ERS), a response to the accumulation of unfolded proteins, has emerging roles in tumorigenesis, including invasion, metastasis, immune escape, etc. However, few studies have focused on the correlation between ERS with long non-coding RNAs (lncRNAs) in BC. We attempted to construct an ERS-related lncRNA prognostic signature and study its value in BC from tumor mutational burden (TMB), tumor immune microenvironment (TIME), cluster, clinical treatment, and so on. In the present study, transcriptomic and clinical data of BC patients were extracted from The Cancer Genome Atlas (TCGA) database. Correlation test, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) method were performed to determine an ERS-related lncRNA prognostic signature. Survival and predictive performance were analyzed according to Kaplan–Meier curves and receiver operating characteristic (ROC) curves, while nomograms and calibration curves were established. Then, an enrichment analysis was performed to study the functions and biological processes of ERS-related lncRNAs. TMB and TIME were also analyzed to assess the mutational status and immune status. Additionally, by using consensus cluster analysis, we compared differences among tumor subtypes. Drug sensitivity analysis and immunologic efficacy evaluations were performed together for further exploration. We identified a novel prognostic signature consisting of 9 ERS-related lncRNAs. High-risk patients had worse prognoses. The signature had a good predictive performance as an independent prognostic indicator and was significantly associated with clinicopathological characteristics. Enrichment analysis showed that metabolic pathways were enriched in high-risk patients, while immune pathways were more active in low-risk patients. Low-risk patients had lower TMB, higher immune scores, and stronger immune functions. Cluster analysis clarified that cluster 2 had the most active immune functions and was sensitive to more drugs, which may have the best clinical immunological efficacy. A clinical efficacy evaluation revealed that patients in the low-risk group may benefit more from chemotherapy, targeted therapy, and immunotherapy. The novel signature has significant clinical implications in prognosis prediction for BC. Our study clarifies that there is a potential connection between the ERS-related lncRNAs and BC, which may provide new treatment guidelines for BC.

Biomineralization induced by microorganisms has become a hot spot in the field of carbonate sedimentology; however, the mechanisms involved still need to be explored. In this study, the bacterium Bacillus subtilis J2 (GenBank MG575432) was used to induce the precipitation of calcium carbonate minerals at Mg/Ca molar ratios of 0, 3, 6, 9, and 12. Bacillus subtilis J2 bacteria released ammonia to increase pH, but the ammonia released only made the pH increase to 8.25. Carbonic anhydrase was also produced to catalyze the hydration of carbon dioxide, and this process released carbonate and bicarbonate ions that not only increased pH but also elevated carbonate supersaturation. The biominerals formed at a Mg/Ca molar ratio of 0 were spherulitic, elongated, dumbbell-shaped, and irregularly rhombohedral calcite; at a Mg/Ca molar ratio of 3, the biominerals were calcite and aragonite, the weight ratio of calcite decreased from 26.7% to 15.6%, and that of aragonite increased from 73.3% to 84.4% with increasing incubation time. At higher Mg/Ca molar ratios, the biominerals were aragonite, and the crystallinity and thermal stability of aragonite decreased with increasing Mg/Ca molar ratios. FTIR results showed that many organic functional groups were present on/within the biominerals, such as C–O–C, N–H, C=O, O–H, and C–H. HRTEM-SAED examination of the ultra-thin slices of B. subtilis J2 bacteria showed that nano-sized minerals with poor crystal structure had grown or been adsorbed on the EPS coating. The EPS of the B. subtilis J2 strain contained abundant glutamic acid and aspartic acid, which could be deprotonated in an alkaline condition to adsorb Ca2+ and Mg2+ ions; this made EPS act as the nucleation sites. This study may provide some references for further understanding of the mechanism of biomineralization induced by microorganisms.