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HighlightsA comprehensive review is presented on the chemical reactions of perovskite films under different environmental conditions and with charge transfer materials and metal electrodes in perovskite solar cells.The influence of chemical reactions on device stability is elucidated. Effective strategies for suppressing the degradation reactions are specified. Lead halide perovskite solar cells (PSCs) have become a promising next-generation photovoltaic technology due to their skyrocketed power conversion efficiency. However, the device stability issues may restrict their commercial applications, which are dominated by various chemical reactions of perovskite layers. Hence, a comprehensive illustration on the stability of perovskite films in PSCs is urgently needed. In this review article, chemical reactions of perovskite films under different environmental conditions (e.g., moisture, oxygen, light) and with charge transfer materials and metal electrodes are systematically elucidated. Effective strategies for suppressing the degradation reactions of perovskites, such as buffer layer introduction and additives engineering, are specified. Finally, conclusions and outlooks for this field are proposed. The comprehensive review will provide a guideline on the material engineering and device design for PSCs.

Transcription factors (TFs) specifically bind to DNA, recruit cofactor proteins and modulate target gene expression, rendering them essential roles in the regulation of numerous biological processes. Meanwhile, mutated or dysregulated TFs are involved in a variety of human diseases. As multiple signaling pathways ultimately converge at TFs, targeting these TFs directly may prove to be more specific and cause fewer side effects, than targeting the upfront conventional targets in these pathways. All these features together endue TFs with great potential and high selectivity as therapeutic drug targets. However, TFs have been historically considered “undruggable”, mainly due to their lack of structural information, especially about the appropriate ligand-binding sites and protein-protein interactions, leading to relatively limited choices in the TF-targeting drug design. In this review, we summarize the recent progress of TF-targeting drugs and highlight certain strategies used for targeting TFs, with a number of representative drugs that have been approved or in the clinical trials as examples. Various approaches in targeting TFs directly or indirectly have been developed. Common direct strategies include aiming at defined binding pockets, proteolysis-targeting chimaera (PROTAC), and mutant protein reactivation. In contrast, the indirect ones comprise inhibition of protein-protein interactions between TF and other proteins, blockade of TF expression, targeting the post-translational modifications, and targeting the TF-DNA interactions. With more comprehensive structural information about TFs revealed by the powerful cryo-electron microscopy technology and predicted by machine-learning algorithms, plus more efficient compound screening platforms and a deeper understanding of TF-disease relationships, the development of TF-targeting drugs will certainly be accelerated in the near future.

Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway serves as an immune sentinel for cytosolic DNA, recognizing double-stranded DNA (dsDNA) derived from abnormally localized nuclear DNA or mitochondrial DNA (mtDNA), and plays a pivotal role in innate immune responses and tumor immune surveillance. Conventional antitumor therapies induce genomic instability and mitochondrial stress, leading to the release of nuclear DNA and mtDNA into the cytosol, thereby activating the cGAS-STING pathway. This activation triggers the production of type I interferons (IFN-I) and pro-inflammatory cytokines, which reshape the tumor immune microenvironment (TIME). However, the complexity of TIME reveals a “double-edged sword” effect of cGAS-STING signaling: while it activates antitumor immune responses, it also promotes immune escape and metastasis through the regulation of immunosuppressive cells and stromal components. This review comprehensively delineates the differential regulatory mechanisms of the pathway within TIME constituents, highlighting its multifaceted roles in tumor immunity. Furthermore, it reviews recent advances and challenges in targeting the cGAS-STING pathway for cancer immunotherapy, with the aim of advancing cGAS-STING signaling modulation as a key therapeutic strategy to reprogram TIME and overcome immunosuppression in antitumor treatment.

Aim： Arctigenin, a phenylpropanoid dibenzylbutyrolactone lignan found in traditional Chinese herbs, has been determined to exhibit a variety of pharmacological activities, including anti-tumor, anti-inflammation, neuroprotection, and endurance enhancement. In the present study, we investigated the antioxidation and anti-fatigue effects of arctigenin in rats. Methods： Rat L6 skeletal muscle cell line was exposed to H202 （700 pmol/L）, and ROS level was assayed using DCFH-DA as a probe. Male SD rats were injected with arctigenin （15 mg.kg-1., ip） for 6 weeks, and then the weight-loaded forced swimming test （WFST） was performed to evaluate their endurance. The levels of antioxidant-related genes in L6 cells and the skeletal muscles of rats were analyzed using real-time RT-PCR and Western blotting. Results： Incubation of L6 cells with arctigenin （1, 5, and 20 pmol/L） dose-dependently decreased the H2O2-induced ROS production. WFST results demonstrated that chronic administration of arctigenin significantly enhanced the endurance of rats. Furthermore, molecular biology studies on L6 cells and skeletal muscles of the rats showed that arctigenin effectively increased the expression of the antioxidant-related genes, including superoxide dismutase （SOD）, glutathione reductase （Gsr）, glutathione peroxidase （GPX1）, thioredoxin （Txn） and uncoupling protein 2 （UCP2）, through regulation of two potential antioxidant pathways： AMPK/PGC-1α/PPARα in mitochondria and AMPK/p53/Nrf2 in the cell nucleus. Conclusion： Arctigenin efficiently enhances rat swimming endurance by elevation of the antioxidant capacity of the skeletal muscles, which has thereby highlighted the potential of this natural product as an antioxidant in the treatment of fatigue and related diseases.

The spin angular momentum (SAM) plays a significant role in light-matter interactions. It is well known that light carrying SAM can exert optical torques on micro-objects and drive rotations, but 3D rotation around an arbitrary axis remains challenging. Here, we demonstrate full control of the 3D optical torque acting on a trapped microparticle by tailoring the vectorial SAM transfer. To this end, we construct a theoretical relationship between the 3D SAM vector of a tightly focused field and the local polarization helicity of the incident field. In practice, a single-beam configuration is proposed for dynamic 3D SAM manipulation, facilitating time-varying vectorial SAM transfer to particles. Control of 3D optical torque on birefringent microparticles is validated by simulations, and dynamic 3D rotations of optically trapped particles around arbitrary axes are experimentally demonstrated. Our work paves the way for manipulating 3D optical torque and particle spinning, which is expected to boost new functionalities and applications of optical tweezers. Optical tweezers can apply 3D forces to trap and move microparticles, but application of 3D optical torques for controlled rotations remains challenging. Here, the authors report a technique for controlling 3D optical torque via a single beam, achieving dynamic 3D rotations around an arbitrary axis.

Exosomes derived from tumor cells play a key role in tumor development. In the present study, we identified the bioactivity of exosomes released from WERI-Rb1 retinoblastoma cells in tumor angiogenesis, as well as the underlying mechanism, through biochemical methods and animal experiments. Our in vitro data showed that exosomes could be engulfed by human vesicle endothelial cells (HUVECs), significantly promote cell viability and induce an inflammatory response in HUVECs by increasing the expression of a series of related genes, such as IL-1, IL-6, IL-8, MCP-1, VCAM1, and ICAM1. Significant increases in migration and tube formation were also observed in the HUVECs incubated with exosomes. Moreover, experiments with a nude mouse xenotransplantation model showed that exosomes injected near tumors could be strongly absorbed by tumor cells. The numbers of endothelial cells and blood vessels were significantly increased in tumor tissues treated with exosomes compared to control tissues. Furthermore, to reveal the mechanism underlying exosome-mediated angiogenesis in retinoblastoma, we analyzed the levels of 12 microRNAs in the exosomes. Specifically, our data showed that miR-92a-3p was enriched in RB exosomes. Accordingly, miR-92a-3p was increased in the HUVECs incubated with these exosomes. After treatment with a miR-92a-3p inhibitor, the promoting effect of exosomes on the migration and tube formation of HUVECs was significantly abrogated. The expression of the angiogenesis-related genes mentioned above was markedly decreased in HUVECs. Similarly, treatment with a microRNA mimic also demonstrated that miR-92a-3p was involved in the angiogenesis of HUVECs. More importantly, bioinformatics analysis predicted that Krüppel-like factor 2 (KLF2), a member of the KLF family of zinc-finger transcription factors, might be an active target of miR-92a-3p. Notably, this prediction was confirmed both in vitro and in vivo. Thus, our work suggests that exosomal miR-92a-3p is involved in tumor angiogenesis and might be a promising therapeutic candidate for retinoblastoma.

Background More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective To analyze the characteristic microbes in AA. Methods Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results The abundance of Prevotella sp900557255 , Alistipes putredinis , and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus , Felixounavirus , and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D , Prevotella sp900557255 , CAG-180 sp000432435 , Phocaeicola plebeiuA , Teseptimavirus , Svunavirus , Felixounavirus , and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing. Highlights The bacteria (including Prevotella sp900557255 , Alistipes putredinis , Megamonas funiformis , etc. ) and viruses (including Lilyvirus , Felixounavirus , and Drulisvirus , etc.) existed differences in AA. And there were correlations between AA and basic information, lipid index and serological index. Prediction models based on bacteria and viruses were established to distinguish AA, and the accuracy reached 87.27% and 83.33%. A new typing method was established based on bacteria and viruses to divide gut microbes into 4 subtypes. Prediction models after typing had higher accuracy (100% in B1V1, 96.34% in B1V2, 100% in B2V1, 98.46% in B2V2).

Long non-coding RNAs (lncRNAs) are a group of non-protein coding RNAs with a length of more than 200 bp. The lncRNA taurine up-regulated gene 1 ( TUG1 ) is abnormally expressed in many human malignant cancers, where it acts as a competitive endogenous RNA (ceRNA), regulating gene expression by specifically sponging its corresponding microRNAs. In the present review, we summarised the current understanding of the role of lncRNA TUG1 in cancer cell proliferation, metastasis, angiogenesis, chemotherapeutic drug resistance, radiosensitivity, cell regulation, and cell glycolysis, as well as highlighting its potential application as a clinical biomarker or therapeutic target for malignant cancer. This review provides the basis for new research directions for lncRNA TUG1 in cancer prevention, diagnosis, and treatment.

Little is known about the epidemiology of myopia-related behavior patterns among adolescents in economically developed regions of China, and their associations with myopia.This prospective cohort study included 1945 adolescents aged 12 to 17 years from 2019 to 2023. Lifestyle indicators (sleep time, moderate to vigorous physical activity (MVPA), outdoor time, screen time, extra-study time) were investigated by self-reported questionnaire annually, and trajectory groups were generated using group-based multi-trajectory models. The main outcome measures were noncycloplegic refractions and axial length (AL), corneal radius (CR).In the cohort of‌ 1945 participants, we identified three lifestyle trajectory groups based on the distribution of lifestyle indicators. The “General” group accounted for 38.2%, exhibiting a longer sleep duration of about 8 h, approximately 3 days of MVPA ≥ 1 h weekly, daily outdoor time of at least 2 h, around 2 h of daily screen time, and weekly extra-study time of 0.5 h. The “Rapidly declining sleep time and prolonged extra-study time” (52.2%) group was characterized by a rapidly declining sleep duration and 2.5 h of weekly extra-study time. The “Persistently low MVPA and prolonged extra-study time” group (8.8%) demonstrated minimal physical activity, averaging only 0.6 days of MVPA ≥ 1 h weekly and 2 h of weekly extra-study time. Compared to “General”, the “Rapidly declining sleep time and prolonged extra-study time” lifestyle was associated with myopia(OR:1.30; 95%CI = 1.01 to 1.67), rapid SE progression(OR = 1.35; 95%CI = 1.06 to 1.72), and higher myopia degree(OR = 1.10; 95%CI = 1.01 to 1.20); longer AL(β coefficient: 0.17; 95%CI = 0.05 to 0.29) and positive AL/CR ratio(β coefficient: 0.02; 95%CI = 0.01 to 0.03), but not associated with AL progression or AL/CR ratio progression. For middle school students in China, there were 3 different patterns of myopia-related behaviors. The lifestyle characterized by prolonged extra-study time and rapidly declining sleep time was associated with an elevated risk for myopia.