Assessing base-resolution DNA mechanics on the genome scale.

Intrinsic DNA properties including bending play a crucial role in diverse biological systems. A recent advance in a high-throughput technology called loop-seq makes it possible to determine the bendability of hundred thousand 50-bp DNA duplexes in one experiment. However, it's still challenging to assess base-resolution sequence bendability in large genomes such as human, which requires thousands of such experiments. Here, we introduce 'BendNet'-a deep neural network to predict the intrinsic DNA bending at base-resolution by using loop-seq results in yeast as training data. BendNet can predict the DNA bendability of any given sequence from different species with high accuracy. To explore the utility of BendNet, we applied it to the human genome and observed DNA bendability is associated with chromatin features and disease risk regions involving transcription/enhancer regulation, DNA replication, transcription factor binding and extrachromosomal circular DNA generation. These findings expand our understanding on DNA mechanics and its association with transcription regulation in mammals. Lastly, we built a comprehensive resource of genomic DNA bendability profiles for 307 species by applying BendNet, and provided an online tool to assess the bendability of user-specified DNA sequences (http://www.dnabendnet.com/).

B-cell lymphoma-2 family proteins in the crosshairs: Small molecule inhibitors and activators for cancer therapy.

The B-cell lymphoma-2 (BCL-2) family of proteins plays a crucial role in the regulation of apoptosis, offering a dual mechanism for its control. Numerous studies have established a strong association between gene disorders of these proteins and the proliferation of diverse cancer cell types. Consequently, the identification and development of drugs targeting BCL-2 family proteins have emerged as a prominent area in antitumor therapy. Over the last two decades, several small-molecules have been designed to modulate the protein-protein interactions between anti- and proapoptotic BCL-2 proteins, effectively suppressing tumor growth and metastasis in vivo. The primary focus of research has been on developing BCL-2 homology 3 (BH3) mimetics to target antiapoptotic BCL-2 proteins, thereby competitively releasing proapoptotic BCL-2 proteins and restoring the blocked intrinsic apoptotic program. Additionally, for proapoptotic BCL-2 proteins, exogenous small molecules have been explored to activate cell apoptosis by directly interacting with executioner proteins such as BCL-2-associated X protein (BAX) or BCL-2 homologous antagonist/killer protein (BAK). In this comprehensive review, we summarize the inhibitors and activators (sensitizers) of BCL-2 family proteins developed over the past decades, highlighting their discovery, optimization, preclinical and clinical status, and providing an overall landscape of drug development targeting these proteins for therapeutic purposes.

Chemoproteomics of Marine Natural Product Naamidine J Unveils CSE1L as a Therapeutic Target in Acute Lung Injury.

Acute lung injury is a devastating illness characterized by severe inflammation mediated by aberrant activation of macrophages, resulting in significant morbidity and mortality, highlighting the urgent need for novel pharmacological targets and drug candidates. In this study, we identified a novel target for regulating inflammation in macrophages and acute lung injury via chemical proteomics and genetics based on a marine alkaloid, naamidine J (NJ). The structures of NJ-related naamidine alkaloids were first confirmed or revised by a combination of quantum chemical calculations and X-ray diffraction analysis. NJ was found as a potential anti-inflammatory agent by screening our compound library, and CSE1L was identified by chemoproteomics as a main cellular target of NJ to inhibit inflammation in macrophages and protect against acute lung injury. Mechanistically, we demonstrated that NJ directly interacted with CSE1L on the sites of His745 and Phe903 and then inhibited the nuclear translocation and transcriptional activity of transcription factor SP1, thereby suppressing inflammation in macrophages and ameliorating acute lung injury. Taken together, these findings have uncovered a novel pharmacological target for the treatment of acute lung injury and have also provided a potential druggable pocket of CSE1L and a lead compound or an available chemical tool from marine sources for investigating CSE1L function and developing novel drug candidates against acute lung injury.

Germline variants of DNA repair and immune genes in lymphoma from lymphoma-cancer families.

The genetic predisposition to lymphoma is not fully understood. We identified 13 lymphoma-cancer families (2011-2021), in which 27 individuals developed lymphomas and 26 individuals had cancers. Notably, male is the predominant gender in lymphoma patients, whereas female is the predominant gender in cancer patients (p = .019; OR = 4.72, 95% CI, 1.30-14.33). We collected samples from 18 lymphoma patients, and detected germline variants through exome sequencing. We found that germline protein truncating variants (PTVs) were enriched in DNA repair and immune genes. Totally, we identified 31 heterozygous germline mutations (including 12 PTVs) of 25 DNA repair genes and 19 heterozygous germline variants (including 7 PTVs) of 14 immune genes. PTVs of ATM and PNKP were found in two families, respectively. We performed whole genome sequencing of diffuse large B cell lymphomas (DLBCLs), translocations at IGH locus and activation of oncogenes (BCL6 and MYC) were verified, and homologous recombination deficiency was detected. In DLBCLs with germline PTVs of ATM, deletion and insertion in CD58 were further revealed. Thus, in lymphoma-cancer families, we identified germline defects of both DNA repair and immune genes in lymphoma patients.

Glycosylation mode of phloretin affects the morphology and stress resistance of apple plant.

Phloretin has different glycosylation modes in plants. Phlorizin (phloretin 2'-O-glucoside) is one of the glycosylation products of phloretin, and accumulates abundantly in apple plants. However, it is still unclear whether phlorizin is more beneficial for apple plants compared with other glycosylation products of phloretin. We created transgenic apple plants with different glycosylation modes of phloretin. In transgenic plants, the accumulation of phlorizin was partly replaced by that of trilobatin (phloretin 4'-O-glucoside) or phloretin 3',5'-di-C-glycoside. Compared with wild type, transgenic plants with less phlorizin showed dwarf phenotype, larger stomatal size, higher stomatal density and less tolerance to drought stress. Transcriptome and phytohormones assay indicate that phlorizin might regulate stomatal development and behaviour via controlling auxin and abscisic acid signalling pathways as well as carbonic anhydrase expressions. Transgenic apple plants with less phlorizin also showed less resistance to spider mites. Apple plants may hydrolyse phlorizin to produce phloretin, but cannot hydrolyse trilobatin or phloretin 3',5'-di-C-glycoside. Compared with its glycosylation products, phloretin is more toxic to spider mites. These results suggest that the glycosylation of phloretin to produce phlorizin is the optimal glycosylation mode in apple plants, and plays an important role in apple resistance to stresses.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Children aged 0-14 years had a far lower mortality risk during the entire COVID-19 pandemic in four major industrial countries: an observational study.

The purpose of this study is to describe the morbidity and mortality of children during the entire COVID-19 pandemic. Age-disaggregated data of 108,003,741 cases and 560,426 deaths were collected from Canada, France, Germany, and Italy. The number of cases and deaths per million people per week, as well as case fatality rates (CFRs), were calculated for patients aged 0-14 and ≥ 15 years. During the first pandemic period in the four countries, starting from weeks 4 to 11 (in 2020) and ending at week 22 (in 2021), the number of deaths per million people per week and the CFRs in the ≥ 15 years age group were 500 to 2513 and 442 to 1662 times greater, respectively, than those in the 0-14 years age group. The number of deaths per million people per week was significantly lower in the first pandemic period than in the second pandemic period, which started at week 23 (2021) and ended from week 22 to week 25 (2023). During the second pandemic period in the four countries, the disparities between the ≥ 15 years and 0-14 years age groups decreased, and the number of deaths per million people per week in the ≥ 15 years age group was 76 to 180 times greater than it in the 0-14 years age group. CONCLUSION:  Children aged 0-14 years had a far lower mortality risk during the entire COVID-19 pandemic, and the impact of viral variants and/or vaccination on the mortality rate is difficult to distinguish. WHAT IS KNOWN: • Although extensive studies have focused on COVID-19-induced mortality, most of them are provisional reports performed during the unfolding of the pandemic and provide imprecise conclusion. WHAT IS NEW: • We described the morbidity and mortality for children aged 0-14 years using complete survey data recorded during the entire COVID-19 pandemic. • The number of deaths per million people per week was far lower in children aged 0-14 years, while the number of deaths per million people per week in children aged 0-14 years was significantly higher in the second period which starting from week 23 (2021) and ending at week 22 to 25 (2023) than in the first period which starting from week 1 to 11 (2020) and ending at week 22 (2021).

Multi-therapeutic-activity selenium nanodot toward preventing brain injury and restoring neurobehavioral functions following hemorrhagic stroke.

Intracerebral hemorrhage is a lethal cerebrovascular disease, and the inevitable secondary brain injury (SBI) is responsible for serious disability and death. Perfect therapeutic goal is to minimize SBI and restore neurobehavioral functions. Recently, neuroprotection is highlighted to reduce SBI, but it still faces "Neuronal survival but impaired functions" dilemma. Herein, this work further proposes a novel combinational therapeutic strategy of neuroprotection and neurogenesis toward this goal. However, appropriate therapeutic agents are rarely reported, and their discovery and development are urgently needed. Selenium participates in various physiological/pathological processes, which is hypothesized as a potential targeting molecule. To explore this effect, this work formulates an ultra-small selenium nanodot with a seleno-amino acid derived carbon dot domain and a hydrophilic PEG layer, surprisingly finding that it increases various selenoproteins levels at perihematomal region, to not only exert multiple neuroprotective roles at acute phase but promote neurogenesis and inhibit glial scar formation at recovery phase. At a safe dose, this combinational strategy effectively prevents SBI and recovers neurobehavioral functions to a normal level. Furthermore, its molecular mechanisms are revealed to broaden application scopes in other complex diseases.

A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random Wind.

As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing.

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.

Caveolin-3 negatively regulates endocytic recycling of cardiac KATP channels.

Sarcolemmal ATP-sensitive potassium (KATP) channels play a vital role in cardioprotection. Cardiac KATP channels are enriched in caveolae and physically interact with the caveolae structural protein caveolin-3 (Cav3). Disrupting caveolae impairs the regulation of KATP channels through several signaling pathways. However, the direct functional effect of Cav3 on KATP channels is still poorly understood. Here, we used the cardiac KATP channel subtype, Kir6.2/SUR2A, and showed that Cav3 greatly reduced KATP channel surface density and current amplitude in a caveolae-independent manner. A screen of Cav3 functional domains revealed that a 25 amino acid region in the membrane attachment domain of Cav3 is the minimal effective segment (MAD1). The peptide corresponding to the MAD1 segment decreased KATP channel current in a concentration-dependent manner with an IC50 of ∼5 µM. The MAD1 segment prevented KATP channel recycling, thus decreasing KATP channel surface density and abolishing the cardioprotective effect of ischemic preconditioning. Our research identified the Cav3 MAD1 segment as a novel negative regulator of KATP channel recycling, providing pharmacological potential in the treatment of diseases with KATP channel trafficking defects.NEW & NOTEWORTHY Cardiac KATP channels physically interact with caveolin-3 in caveolae. In this study, we investigated the functional effect of caveolin-3 on KATP channel activity and identified a novel segment (MAD1) in the C-terminus domain of Caveolin-3 that negatively regulates KATP channel surface density and current amplitude by impairing KATP channel recycling. The peptide corresponding to the MAD1 segment abolished the cardioprotective effect of ischemic preconditioning.

Exosomal PD-L1 confers chemoresistance and promotes tumorigenic properties in esophageal cancer cells via upregulating STAT3/miR-21.

Chemotherapy resistance remains a major obstacle in the treatment of esophageal cancer. Previous researches have shown that an increase in exosomal PD-L1 expression was positively associated with a more advanced clinical stage, a poorer prognosis as well as drug resistance in patients with esophageal squamous cell carcinoma (ESCC). To explore the role of exosomal PD-L1 in ESCC, we performed bioinformatics analysis as well as several in vitro/in vivo functional experiments in a parental sensitive cell line EC-9706 and its derivative, a paclitaxel-resistant subline EC-9706R, and found that the exosomal PD-L1 from EC-9706R was higher than that from EC-9706. Moreover, exosomes from EC-9706R significantly increased invasion, migration and chemoresistance of EC-9706. Anti-PD-L1 treatment in combination with chemotherapy also led to reduced tumor burden in vivo. Inhibition of the release of exosomes by GW4869 or inhibition of STAT3 phosphorylation by stattic could effectively reverse the resistance to paclitaxel mediated by exosomal PD-L1. Furthermore, we found that PD-L1, miR-21, and multidrug resistance (MDR1) gene are involved in the process of exosomal transfer. Moreover, PD-L1 could enhance miR-21 expression by increasing the enrichment of STAT3 on miR-21 promoter. Our results suggested that exosomal PD-L1 may contribute to drug resistance to paclitaxel by regulating the STAT3/miR-21/PTEN/Akt axis and promote tumorigenic phenotype. This study provides a novel potential therapeutic approach to reverse chemoresistance and tumor progression through exosomal PD-L1 in ESCC patients.

A novel KRIT1/CCM1 mutation accompanied by a NOTCH3 mutation in a Chinese family with multiple cerebral cavernous malformations.

Family cerebral cavernous malformations (FCCMs) are mainly inherited through the mutation of classical CCM genes, including CCM1/KRIT1, CCM2/MGC4607, and CCM3/PDCD10. FCCMs can cause severe clinical symptoms, including epileptic seizures, intracranial hemorrhage (ICH), or functional neurological deficits (FNDs). In this study, we reported a novel mutation in KRIT1 accompanied by a NOTCH3 mutation in a Chinese family. This family consists of 8 members, 4 of whom had been diagnosed with CCMs using cerebral MRI (T1WI, T2WI, SWI). The proband (II-2) and her daughter (III-4) had intracerebral hemorrhage and refractory epilepsy, respectively. Based on whole-exome sequencing (WES) data and bioinformatics analysis from 4 patients with multiple CCMs and 2 normal first-degree relatives, a novel KRIT1 mutation, NG_012964.1 (NM_194456.1): c.1255-1G > T (splice-3), in intron 13 was considered a pathogenic gene in this family. Furthermore, based on 2 severe and 2 mild CCM patients, we found an SNV missense mutation, NG_009819.1 (NM_000435.2): c.1630C > T (p.R544C), in NOTCH3. Finally, the KRIT1 and NOTCH3 mutations were validated in 8 members using Sanger sequencing. This study revealed a novel KRIT1 mutation, NG_012964.1 (NM_194456.1): c.1255-1G > T (splice-3), in a Chinese CCM family, which had not been reported previously. Moreover, the NOTCH3 mutation NG_009819.1 (NM_000435.2): c.1630C > T (p.R544C) might be a second hit and associated with the progression of CCM lesions and severe clinical symptoms.

The Arabidopsis thaliana onset of leaf death 12 mutation in the lectin receptor kinase P2K2 results in an autoimmune phenotype.

BACKGROUND: Plant immunity relies on the perception of immunogenic signals by cell-surface and intracellular receptors and subsequent activation of defense responses like programmed cell death. Under certain circumstances, the fine-tuned innate immune system of plants results in the activation of autoimmune responses that cause constitutive defense responses and spontaneous cell death in the absence of pathogens. RESULTS: Here, we characterized the onset of leaf death 12 (old12) mutant that was identified in the Arabidopsis accession Landsberg erecta. The old12 mutant is characterized by a growth defect, spontaneous cell death, plant-defense gene activation, and early senescence. In addition, the old12 phenotype is temperature reversible, thereby exhibiting all characteristics of an autoimmune mutant. Mapping the mutated locus revealed that the old12 phenotype is caused by a mutation in the Lectin Receptor Kinase P2-TYPE PURINERGIC RECEPTOR 2 (P2K2) gene. Interestingly, the P2K2 allele from Landsberg erecta is conserved among Brassicaceae. P2K2 has been implicated in pathogen tolerance and sensing extracellular ATP. The constitutive activation of defense responses in old12 results in improved resistance against Pseudomonas syringae pv. tomato DC3000. CONCLUSION: We demonstrate that old12 is an auto-immune mutant and that allelic variation of P2K2 contributes to diversity in Arabidopsis immune responses.

Diagnostic markers and potential therapeutic agents for Sjögren's syndrome screened through multiple machine learning and molecular docking.

Primary Sjögren's syndrome (pSS) is a chronic inflammatory autoimmune disease, which mainly damages patients' exocrine glands. Sensitive early diagnostic indicators and effective treatments for pSS are lacking. Using machine learning methods to find diagnostic markers and effective therapeutic ways for pSS is of great significance. In our study, first, 1643 differentially expressed genes (DEGs; 737 were upregulated and 906 were downregulated) were ultimately screened out and analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes based on the datasets from the Gene Expression Omnibus. Then, support vector machine, least absolute shrinkage and selection operator regression, random forest, and weighted correlation network analysis were used to screen out feature genes from DEGs. Subsequently, the intersection of the feature genes was taken to screen 10 genes as hub genes. Meanwhile, the analysis of the diagnostic efficiency of 10 hub genes showed their good diagnostic value for pSS, which was validated through immunohistochemistry on the paraffin sections of the labial gland. Subsequently, a multi-factor regulatory network and correlation analysis of hub genes were performed, and the results showed that ELAVL1 and IGF1R were positively correlated with each other but both negatively correlated with the other seven hub genes. Moreover, several meaningful results were detected through the immune infiltration landscape. Finally, we used molecular docking to screen potential therapeutic compounds of pSS based on the hub genes. We found that the small molecules DB08006, DB08036, and DB15308 had good docking scores with ELAVL1 and IGF1R simultaneously. Our study might provide effective diagnostic biomarkers and new therapeutic ideas for pSS.

Knockdown of NR3C1 inhibits the proliferation and migration of clear cell renal cell carcinoma through activating endoplasmic reticulum stress-mitophagy.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is closely associated with steroid hormones and their receptors affected by lipid metabolism. Recently, there has been growing interest in the carcinogenic role of NR3C1, the sole gene responsible for encoding glucocorticoid receptor. However, the specific role of NR3C1 in ccRCC remains unclear. The present study was thus developed to explore the underlying mechanism of NR3C1's carcinogenic effects in ccRCC. METHODS: Expression of NR3C1 was verified by various tumor databases and assessed using RT-qPCR and western blot. Stable transfected cell lines of ccRCC with NR3C1 knockdown were constructed, and a range of in vitro and in vivo experiments were performed to examine the effects of NR3C1 on ccRCC proliferation and migration. Transcriptomics and lipidomics sequencing were then conducted on ACHN cells, which were divided into control and sh-NR3C1 group. Finally, the sequencing results were validated using transmission electron microscopy, mitochondrial membrane potential assay, immunofluorescence co-localization, cell immunofluorescent staining, and Western blot. The rescue experiments were designed to investigate the relationship between endoplasmic reticulum stress (ER stress) and mitophagy in ccRCC cells after NR3C1 knockdown, as well as the regulation of their intrinsic signaling pathways. RESULTS: The expression of NR3C1 in ccRCC cells and tissues was significantly elevated. The sh-NR3C1 group, which had lower levels of NR3C1, exhibited a lower proliferation and migration capacity of ccRCC than that of the control group (P < 0.05). Then, lipidomic and transcriptomic sequencing showed that lipid metabolism disorders, ER stress, and mitophagy genes were enriched in the sh-NR3C1 group. Finally, compared to the control group, ER stress and mitophagy were observed in the sh-NR3C1 group, while the expression of ATF6, CHOP, PINK1, and BNIP3 was also up-regulated (P < 0.05). Furthermore, Ceapin-A7, an inhibitor of ATF6, significantly down-regulated the expression of PINK1 and BNIP3 (P < 0.05), and significantly increased the proliferation and migration of ccRCC cells (P < 0.05). CONCLUSIONS: This study confirms that knockdown of NR3C1 activates ER stress and induces mitophagy through the ATF6-PINK1/BNIP3 pathway, resulting in reduced proliferation and migration of ccRCC. These findings indicate potential novel targets for clinical treatment of ccRCC.

Production and Reconfiguration of Double Emulsions by Temperature Control.

Double emulsions are of great importance for both science and engineering. However, the production of multicore double-emulsion droplets is challenging and normally requires sophisticated microfluidic devices, which limits their availability to broader communities. Here, we propose a simple, precise, and scalable batch method for producing double emulsions with monodispersed multicores at milliliter per minute rates, using the most common means in laboratory, temperature. By rapidly cooling liquid crystal emulsions, the introduced temperature gradient around the emulsion droplets leads to the injection of monodispersed guest droplets to form double-emulsion droplets. The number of injected water droplets can be precisely controlled by adjusting the thermally induced mechanical force through the temperature difference and the cooling rate. In contrast to conventional microfluidic fabrication, this method processes all emulsion droplets simultaneously in a noncontact and in situ manner. Therefore, it has great flexibility, allows multiple processing of double emulsions of arbitrary shape, has good capacity for mass production, and offers excellent compatibility with technologies such as microfluidics. Finally, we demonstrate that temperature changes can also be used to release the inner droplets from the double emulsion. The proposed method offers a reversible tool for processing double emulsions with minimal cost and expertise and is applicable to droplet-based microsystems in materials science, photonics, sensors, pharmaceuticals, and biotechnology.

Treatment with a flow diverter-assisted coil embolization for ruptured blood blister-like aneurysms of the internal carotid artery: a technical note and analysis of single-center experience with pooled data.

Treatment of blood blister-like aneurysms (BBAs) of the supraclinoid internal carotid artery (ICA) with flow diverters (FDs) has become widespread in recent years. However, ruptured blood blister-like aneurysm (BBA) of ICA treatment with flow diverter-assisted coil embolization (FDAC) remains controversial. Moreover, limited direct comparative studies have been conducted between the two treatment modalities, FDs and FDAC, for BBAs. The purpose of this study was to document our experience and evaluate the effectiveness and safety of FDAC. We conducted a retrospective analysis of clinical and radiological information from ten patients who experienced ruptured BBAs of the supraclinoid ICA at our center from January 2021 to February 2023. The technical details of FDAC for ruptured BBAs were described, and the technical steps were named "pipeline embolization device (PED)-Individualized shaping(microcatheter)-Semi deploying-Rivet(coils)-Massage(microwire)" as the PEISSERM technique. Clinical outcomes were assessed using the modified Rankin Scale (mRS), whereas radiological results were determined through angiography. A pooled analysis was implemented, incorporating data from literature sources that reported perioperative and long-term clinical and angiographic outcomes of ruptured BBAs treated with FD and FDAC strategies, along with our data. Data in our analysis pool were categorized into FD and FDAC strategy groups to explore the preferred treatment modalities for BBAs. The PEISSERM technique was utilized to treat ten patients, seven males, and three females, with an average age of 41.7 years. A single PED was deployed in conjunction with coils in all ten patients. All PEDs were documented to have good wall apposition. The immediate postoperative angiograms demonstrated Raymond grade I in ten aneurysms. Angiographic follow-up of nine patients at 4-25 months showed total occlusion of the aneurysms. At the most recent follow-up, the mRS scores of nine patients hinted at a good prognosis. Pooled analysis of 233 ICA-BBA cases of FD revealed a technical success rate of 91% [95% confidence interval (CI), 0.88 to 0.95], a rate of complete occlusion of 79% (95% CI, 0.73 to 0.84), a recurrence rate of 2% (95% CI, 0.00 to 0.04), a rebleed rate of 2% (95% CI, 0.00 to 0.04), and the perioperative stroke rate was 8% (95% CI, 0.04 to 0.11). The perioperative mortality was 4% (95% CI, 0.01 to 0.07). The long-term good clinical outcome rate was 85% (95% CI, 0.80 to 0.90). The mortality rate was 6% (95% CI, 0.03 to 0.09). Results from the subgroup analysis illustrated that the FDAC strategy for BBAs had a significantly higher immediate postoperative complete occlusion rate (P < 0.001), total occlusion rate (P = 0.016), and a good outcome rate (P = 0.041) compared with the FD strategy. The FDAC strategy can yield a higher rate of good outcomes than the FD strategy. The PEISSERM technique employed by the FDAC is a reliable and effective treatment approach as it can minimize the hemodynamic burden of BBA's fragile dome, thereby achieving an excellent occlusion rate. The PEISSERM technique in the FDAC strategy contributes to understanding the BBA's treatment and offers a potentially optimal treatment for BBA.

Research on Mindfulness-Based Stress Reduction in Breast Cancer Patients Undergoing Chemotherapy: An Observational Pilot Study.

Objective: Mindfulness-Based Stress Reduction (MBSR) therapy has been very effective intervention across worldwide. Herein we aimed to investigate the effect of MBSR intervention on anxiety, depression among breast cancer patients undergoing postoperative chemotherapy. Methods: 225 breast cancer patients in our hospital were divided into two groups, 106 patients in the MBSR group received Mindfulness-Based Stress Reduction intervention, while 111 patients in the control group were given routine nursing. The Self-rating Anxiety Scale (SAS), self-rating depression scale (SDS), and functional assessment of cancer therapy-breast cancer (FACT-B) were used to assess the effect of MBSR intervention on breast cancer patients undergoing postoperative chemotherapy. Results: There were significant differences in the scores of physiological statuses, social and family status, emotional status, functional status, additional attention and total score after intervention between two groups (P < .05). The difference between SDS and SAS were statistically significant between the two groups (P < .05). The score of SDS and SAS were significantly improved in the MBSR group compared with the control group (P < .05). Conclusion: MBSR therapy could effectively improve the quality of life of patients with breast cancer patients with chemotherapy, mainly focusing on psychological aspects, while the effect of the physiological intervention was not significant.