Nitroglycerin combined with NSAIDs for prevention of post-ERCP pancreatitis: A meta-analysis of prospective, randomized, controlled trials.

BACKGROUND: Acute pancreatitis is the most common complication of endoscopic retrograde cholangiopancreatography (ERCP), with an incidence of approximately 9.7% according to some literature reviews. Recent clinical guidelines propose that glyceryl trinitrate (GTN) can reduce the incidence of post-ERCP pancreatitis (PEP). However, currently, no guidelines provide an exact opinion on GTN and nonsteroidal anti-inflammatory drugs (NSAIDs) to prevent post-ERCP pancreatitis. OBJECTIVE: A meta-analysis was performed of published, full-length, randomized controlled trials (RCTs) evaluating the effects of prophylactic use of GTN, including GTN alone or GTN in combination with NSAIDs, on the prevention of PEP. METHODS: Literature searches were conducted using PubMed, Embase, Web of Science, and The Cochrane Library. Search terms included "endoscopic retrograde cholangiopancreatography" OR "ERCP," "OR 'PEP' OR 'post-endoscopic retrograde cholangiopancreatography pancreatitis', pancreatitis," "GTN" OR "glyceryl trinitrate" OR "nitroglycerin," "NSAIDs" OR "Nonsteroidal Anti-inflammatory Drugs" and limited to RCT. RESULTS: A total of 10 RCTs comprising 3240 patients undergoing ERCP were included. Meta-analysis revealed that the administration of GTN was associated with a significant reduction in the overall incidence of PEP. Moreover, PEP incidence was significantly lower in the GTN combined with the NSAIDs group than in the GTN alone group. GTN alone or GTN combined with NSAIDs may not reduce the severity of PEP (risk ratio = 0.64; 95% confidence interval: 0.41-0.99; P = .04). The difference in incidence between the 2 groups is 1.01% (6/594) in the GTN with NSAIDs group and 2.36% (14/592) in the placebo group. CONCLUSION: GTN has a significant benefit in preventing postoperative ERCP pancreatitis (P < .001). And neither GTN nor GTN plus NSAIDs reduces the incidence of non-mild ERCP postoperative pancreatitis. These conclusions need to be confirmed by high-quality randomized controlled studies with multicenter, large samples, and long-term follow-up.

Natural Prenylflavonoids from Sophora flavescens Root Bark against Multidrug-Resistant Methicillin-Sensitive Staphylococcus aureus Targeting the Membrane Permeability.

The overuse of antibiotics in animal farming and aquaculture has led to multidrug-resistant methicillin-sensitive Staphylococcus aureus (MR-MSSA) becoming a common pathogen in foodborne diseases. Sophora flavescens Ait. serves as a traditional plant antibacterial agent and functional food ingredient. A total of 30 compounds (1-30) were isolated from the root bark of S. flavescens, consisting of 20 new compounds (1-20). In the biological activity assay, compound 1 demonstrated a remarkable inhibitory effect on MR-MSSA, with an MIC of 2 µg/mL. Furthermore, 1 was found to rapidly eliminate bacteria, inhibit biofilm growth, and exhibit exceptionally low cytotoxicity. Mechanistic studies have revealed that 1 possesses an enhanced membrane-targeting ability, binding to the bacterial cell membrane components phosphatidylglycerol (PG), phosphatidylethanolamine (PE), and cardiolipin (CL). This disruption of bacterial cell membrane integrity increases intracellular reactive oxygen species, protein and DNA leakage, reduced bacterial metabolism, and ultimately bacterial death. In summary, these findings suggest that compound 1 holds promise as a lead compound against MR-MSSA.

Two undescribed constituents from Salvia castanea Diels and evaluation of their cytotoxic activity.

One previously undescribed abietane diterpene alkaloid containing an oxazole ring (1), one unreported abietane diterpene (2), and nine known abietane diterpenes (3-11) were isolated from the roots and rhizomes of Salvia castanea Diels. Their structures and absolute configurations were elucidated by a combination of HRESIMS, 1D and 2D NMR, and ECD. All compounds were evaluated for their cytotoxic activity against several human cancer cell lines (HepG2, A549, H460, MCF7, PC3, and Hela). The results showed that 1 exhibited a moderate cytotoxic effect on HepG2 cells (IC50: 14.22 ± 1.05 µM) and was able to inhibit the cell growth of MCF7 and Hela cells by 35.08% and 47.26% respectively, at a concentration of 20 µM, while other compounds showed low cytotoxic activity.

Remodeling of anti-tumor immunity with antibodies targeting a p53 mutant.

BACKGROUND: p53, the most frequently mutated gene in cancer, lacks effective targeted drugs. METHODS: We developed monoclonal antibodies (mAbs) that target a p53 hotspot mutation E285K without cross-reactivity with wild-type p53. They were delivered using lipid nanoparticles (LNPs) that encapsulate DNA plasmids. Western blot, BLI, flow cytometry, single-cell sequencing (scRNA-seq), and other methods were employed to assess the function of mAbs in vitro and in vivo. RESULTS: These LNP-pE285K-mAbs in the IgG1 format exhibited a robust anti-tumor effect, facilitating the infiltration of immune cells, including CD8+ T, B, and NK cells. scRNA-seq revealed that IgG1 reduces immune inhibitory signaling, increases MHC signaling from B cells to CD8+ T cells, and enriches anti-tumor T cell and B cell receptor profiles. The E285K-mAbs were also produced in the dimeric IgA (dIgA) format, whose anti-tumor activity depended on the polymeric immunoglobulin receptor (PIGR), a membrane Ig receptor, whereas that of IgG1 relied on TRIM21, an intracellular IgG receptor. CONCLUSIONS: Targeting specific mutant epitopes using DNA-encoded and LNP-delivered mAbs represents a potential precision medicine strategy against p53 mutants in TRIM21- or PIGR-positive cancers.

Stereoselective Synthesis of Polysubstituted Dihydropyrroles via 1,5-Addition and N-1,4-Addition Cascade.

An unprecedented 1,5-addition/N-1,4-addition cascade reaction is established via palladium hydride catalysis. A variety of polysubstituted dihydropyrrole skeletons are constructed in high yield and with exclusively >20 : 1 diastereoselectivity. An enantioselective protocol of this design is also developed to provide a novel access to enantioenriched dihydropyrroles.

Dimensional and Doping Engineering of Chiral Perovskites with Enhanced Spin Selectivity for Green Emissive Spin Light-Emitting Diodes.

Chiral perovskites play a pivotal role in spintronics and optoelectronic systems attributed to their chiral-induced spin selectivity (CISS) effect. Specifically, they allow for spin-polarized charge transport in spin light-emitting diodes (LEDs), yielding circularly polarized electroluminescence at room temperature without external magnetic fields. However, chiral lead bromide-based perovskites have yet to achieve high-performance green emissive spin-LEDs, owing to limited CISS effects and charge transport. Herein, we employ dimensional regulation and Sn2+-doping to optimize chiral bromide-based perovskite architecture for green emissive spin-LEDs. The optimized (PEA)x(S/R-PRDA)2-xSn0.1Pb0.9Br4 chiral perovskite film exhibits an enhanced CISS effect, higher hole mobility, and better energy level alignment with the emissive layer. These improvements allow us to fabricate green emissive spin-LEDs with an external quantum efficiency (EQE) of 5.7% and an asymmetry factor |gCP-EL| of 1.1 × 10-3. This work highlights the importance of tailored perovskite architectures and doping strategies in advancing spintronics for optoelectronic applications.

Nomogram for predicting pathological response to neoadjuvant treatment in patients with locally advanced gastric cancer: Data from a phase III clinical trial.

PURPOSE: This study aimed to establish a nomogram using routinely available clinicopathological parameters to predict the pathological response in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant treatment. MATERIALS AND METHODS: We conducted this study based on the ongoing Neo-CRAG trial, a prospective study focused on preoperative treatment in patients with LAGC. A total of 221 patients who underwent surgery following neoadjuvant chemotherapy (nCT) or neoadjuvant chemoradiotherapy (nCRT) at Sun Yat-sen University Cancer Center between June 2013 and July 2022 were included in the analysis. We defined complete or near-complete pathological regression and ypN0 as good response (GR), and determined the prognostic value of GR by Kaplan-Meier survival analysis. Eventually, a nomogram for predicting GR was developed based on statistically identified predictors through multivariate logistic regression analysis and internally validated by the bootstrap method. RESULTS: GR was confirmed in 54 patients (54/221, 24.4%). Patients who achieved GR had a longer progression-free survival and overall survival. Then, five independent factors, including pretreatment tumor differentiation, clinical T stage, monocyte count, CA724 level, and the use of nCRT, were identified. Based on these predictors, the nomogram was established with an area under the curve (AUC) of 0.777 (95% CI, 0.705-0.850) and a bias-corrected AUC of 0.752. CONCLUSION: A good pathological response after neoadjuvant treatment was associated with an improved prognosis in LAGC patients. The nomogram we established exhibits a high predictive capability for GR, offering potential value in devising personalized and precise treatment strategies for LAGC patients.

Aggregation-Enabled Electrochemistry in Confined Nanopore Capable of Complementary Faradaic and Non-Faradaic Detection.

Electrochemistry that empowers innovative nanoscopic analysis has long been pursued. Here, the concept of aggregation-enabled electrochemistry (AEE) in a confined nanopore is proposed and devised by reactive oxygen species (ROS)-responsive aggregation of CdS quantum dots (QDs) within a functional nanopipette. Complementary Faradaic and non-Faradaic operations of the CdS QDs aggregate could be conducted to simultaneously induce the signal-on of the photocurrents and the signal-off of the ionic signals. Such a rationale permits the cross-checking of the mutually corroborated signals and thus delivers more reliable results for single-cell ROS analysis. Combined with the rich biomatter-light interplay, the concept of AEE can be extended to other stimuli-responsive aggregations for electrochemical innovations.

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study.

INTRODUCTION: The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. METHODS: Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. RESULTS: We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. CONCLUSIONS: The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.

[The Generative Artificial Intelligence Revolution in Nursing: A New Chapter in Enhancing Care Quality and Education].

Artificial intelligence (AI) represents a recent major breakthrough in technology development and, in recent years, generative AI has emerged as another trendsetter. The application of generative AI technologies in the healthcare sector has not only opened new possibilities for improving the efficiency of medical diagnoses but also provided healthcare professionals with more-accurate patient monitoring capabilities and optimized care processes. Combining generative AI with nursing expertise holds out the potential of creating a more valuable model of nursing care. The impact of generative AI on the nursing profession poses both challenges and opportunities. By applying appropriate strategies, it is possible to create more advanced and humane nursing values that enhance overall nursing efficiencies and align the nursing field with modern technological advancements. In this article, the development of AI and generative AI is reviewed, and the potential for their application to nursing care is discussed, with the goal of stimulating innovative thinking and new strategies for interdisciplinary collaboration between technology and nursing.

The next-generation DNA vaccine platforms and delivery systems: advances, challenges and prospects.

Vaccines have proven effective in the treatment and prevention of numerous diseases. However, traditional attenuated and inactivated vaccines suffer from certain drawbacks such as complex preparation, limited efficacy, potential risks and others. These limitations restrict their widespread use, especially in the face of an increasingly diverse range of diseases. With the ongoing advancements in genetic engineering vaccines, DNA vaccines have emerged as a highly promising approach in the treatment of both genetic diseases and acquired diseases. While several DNA vaccines have demonstrated substantial success in animal models of diseases, certain challenges need to be addressed before application in human subjects. The primary obstacle lies in the absence of an optimal delivery system, which significantly hampers the immunogenicity of DNA vaccines. We conduct a comprehensive analysis of the current status and limitations of DNA vaccines by focusing on both viral and non-viral DNA delivery systems, as they play crucial roles in the exploration of novel DNA vaccines. We provide an evaluation of their strengths and weaknesses based on our critical assessment. Additionally, the review summarizes the most recent advancements and breakthroughs in pre-clinical and clinical studies, highlighting the need for further clinical trials in this rapidly evolving field.

Overexpression of miR-927-5p suppresses stalky expression and negatively reduces the spermatid production in Zeugodacus cucurbitae.

BACKGROUND: The melon fly, Zeugodacus cucurbitae Coquillett, is one of the major pests attacking Cucurbitaceae crops. Identifying critical genes or proteins regulating fertility is essential for sustainable pest control and a research hotspot in insect physiology. MicroRNAs (miRNAs) are short RNAs that do not directly participate in protein translation, but instead function in post-transcriptional regulation of gene expression involved in male fertility. RESULTS: We found that miR-927-5p is highly expressed in the testes and investigated its function in spermatogenesis in Z. cucurbitae. Fluorescence in situ hybridization (FISH) showed miR-927-5p in the transformation and maturation region of the testis, and overexpression of miR-927-5p reduced the number of sperms by 53%. In continuation, we predicted 12 target genes of miR-927-5p using bioinformatics combined with transcriptome sequencing data, and found that miR-927-5p targets the new gene Stalky in insects, which was validated by quantitative real-time PCR, RNA pull-down and dual luciferase reporter assays. FISH also confirmed the co-localization of miR-927-5p and the transcript Stalky_1 in the testis. Moreover, silencing of Stalky_1 by RNA interference reduced the number of sperms by 32% and reduced sperm viability by 39% in physiologically mature male adults. Meanwhile, the silencing of Stalky_1 also resulted in low hatchability. CONCLUSION: Our work not only presents a new, so far unreported mechanism regulating spermatogenesis by miR-927-5p targeting a new unknown target, Stalky, which is providing new knowledge on the regulatory network of insect spermatogenesis, but also lays a foundation for the development of SIT against important tephritid fly pests. © 2024 Society of Chemical Industry.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer.

BACKGROUND/OBJECTIVES: Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4. SUBJECTS/METHODS: In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays. RESULTS: The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4. CONCLUSIONS: Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis.

BACKGROUND: A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control. METHODS: A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated. RESULTS: A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results. CONCLUSION: When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Salvirrane A-F, six undescribed nordrimane sesquiterpene derivatives from Salvia castanea Diels f. tomentosa Stib and their cytotoxic activities.

Six undescribed nordrimane sesquiterpene derivatives, salvirrane A-F (1-6), were isolated from the roots and rhizomes of Salvia castanea Diels f. tomentosa Stib. Comprehensive spectral analysis and a quantum chemical calculation strategy were employed to determine their structures. These compounds represent four previously unreported nordrimane carbon skeletal types in Salvia genus, including 15-nor-drimane, 11,15-di-nor-drimane, 14,15-di-nor-drimane, and 11,14,15-tri-nor-drimane sesquiterpenes. All compounds were evaluated for their cytotoxic activities against several human cancer cell lines (A549, H460, Hep3B, MCF7, PC3, and HeLa). The results showed that 3 exhibited low activity against MCF7 cells (IC50,72.72 ± 6.95 µM) and moderate activity against HeLa cells (IC50, 9.80 ± 0.64 µM). Moreover, the EdU (5-ethynyl-2'-deoxyuridine) assay demonstrates that 3 displays dose-dependent efficacy in suppressing the proliferation of HeLa cells. Network pharmacology and molecular docking technology implied that 3 may potentially bind to Src (proto-oncogene tyrosine-protein kinase) to exert anti-proliferative activity.

(±)-Salvicatone A: A Pair of C27-Meroterpenoid Enantiomers with Skeletons from the Roots and Rhizomes of Salvia castanea Diels f. tomentosa Stib.

(±)-Salvicatone A (1), a C27-meroterpenoid featuring a unique 6/6/6/6/6-pentacyclic carbon skeleton with a 7,8,8a,9,10,10a-hexahydropyren-1 (6H)-one motif, was isolated from the roots and rhizomes of Salvia castanea Diels f. tomentosa Stib. Its structure was characterized by comprehensive spectroscopic analyses along with computer-assisted structure elucidation, including ACD/structure elucidator and quantum chemical calculations with 1H/13C NMR and electronic circular dichroism. Biogenetically, compound 1 was constructed from decarboxylation following [4 + 2] Diels-Alder cycloaddition reaction between caffeic acid and miltirone analogue. Bioassays showed that (-)-1 and (+)-1 inhibited nitric oxide production in lipopolysaccharide-induced RAW264.7 macrophage cells with an IC50 value of 6.48 ± 1.25 and 15.76 ± 5.55 µM, respectively. The structure-based virtual screening based on the pharmacophores in ePharmaLib, as well as the molecular docking and molecular dynamics simulations study, implied that (-)-1 and (+)-1 may potentially bind to retinoic acid receptor-related orphan receptor C to exert anti-inflammatory activities.

Two new sesquiterpenoids with glycosides from the leaves and stems of Schisandra chinensis (Turcz.) Baill.

Two previously undescribed glycosidic bisnorsesquiterpenoids A - B (1 - 2), together with two known compounds (3 - 4), were isolated from the leaves and stems of Schisandra chinensis. Their structures were elucidated by extensive spectroscopic data (1D, 2D NMR, and HRESIMS). The anti-inflammatory activity, ABTS+ radical scavenging activity, and DPPH radical scavenging activity of compounds 1 - 4 were tested. However, all of these compounds showed only weak anti-inflammatory or antioxidant effects.

A network meta-analysis of risk factors of infection among close contacts of COVID-19.

Objective: We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts. Methods: We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA). Results: 25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %). Conclusion: The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

YAP nuclear translocation induced by HIF-1α prevents DNA damage under hypoxic conditions.

Maladaptive repair of acute kidney injury (AKI) is associated with a high risk of developing chronic kidney disease deemed irremediable even in present days. When AKI arises from ischemia-reperfusion injury, hypoxia usually plays a major role. Although both hypoxia-inducible factor-1α (HIF-1α) and yes-associated protein (YAP) have been proven to promote renal cell survival under hypoxia, there is a lack of research that studies the crosstalk of the two and its effect on kidney repair. In studying the crosstalk, CoCl2 was used to create a mimetic hypoxic environment. Immunoprecipitation and proximity ligation assays were performed to verify protein interactions. The results show that HIF-1α interacts with YAP and promotes nuclear translocation of YAP at a high cell density under hypoxic conditions, suggesting HIF-1α serves as a direct carrier that enables YAP nuclear translocation. This is the first study to identify HIF-1α as a crucial pathway for YAP nuclear translocation under hypoxic conditions. Once translocated into a nucleus, YAP protects cells from DNA damage and apoptosis under hypoxic conditions. Since it is unlikely for YAP to translocate into a nucleus without HIF-1α, any treatment that fosters the crosstalk between the two holds the potential to improve cell recovery from hypoxic insults.

Development and external validation of a prediction model for brain metastases in patients with metastatic breast cancer.

BACKGROUND: Breast cancer patients with brain metastasis (BM) have a poor prognosis. This study aims to identify the risk factors of BM in patients with metastatic breast cancer (MBC) and establish a competing risk model for predicting the risk of brain metastases at different time points along the course of disease. METHODS: Patients with MBC admitted to the breast disease center of Peking University First Hospital from 2008 to 2019 were selected and retrospectively analyzed to establish a risk prediction model for brain metastases. Patients with MBC admitted to eight breast disease centers from 2015 to 2017 were selected for external validation of the competing risk model. The competing risk approach was used to estimate cumulative incidence. Univariate Fine-Gray competing risk regression, optimal subset regression, and LASSO Cox regression were used to screen potential predictors of brain metastases. Based on the results, a competing risk model for predicting brain metastases was established. The discrimination of the model was evaluated using AUC, Brier score, and C-index. The calibration was evaluated by the calibration curves. The model was assessed for clinical utility by decision curve analysis (DCA), as well as by comparing the cumulative incidence of brain metastases between groups with different predicted risks. RESULTS: From 2008 to 2019, a total of 327 patients with MBC in the breast disease center of Peking University First Hospital were admitted into the training set for this study. Among them, 74 (22.6%) patients developed brain metastases. From 2015 to 2017, a total of 160 patients with MBC in eight breast disease centers were admitted into the validation set for this study. Among them, 26 (16.3%) patients developed brain metastases. BMI, age, histological type, breast cancer subtype, and extracranial metastasis pattern were included in the final competing risk model for BM. The C-index of the prediction model in the validation set was 0.695, and the AUCs for predicting the risk of brain metastases within 1, 3, and 5 years were 0.674, 0.670, and 0.729, respectively. Time-dependent DCA curves demonstrated a net benefit of the prediction model with thresholds of 9-26% and 13-40% when predicting the risk of brain metastases at 1 and 3 years, respectively. Significant differences were observed in the cumulative incidence of brain metastases between groups with different predicted risks (P < 0.05 by Gray's test). CONCLUSIONS: In this study, a competing risk model for BM was innovatively established, with the multicenter data being used as an independent external validation set to confirm the predictive efficiency and universality of the model. The C-index, calibration curves, and DCA of the prediction model indicated good discrimination, calibration, and clinical utility, respectively. Considering the high risk of death in patients with metastatic breast cancer, the competing risk model of this study is more accurate in predicting the risk of brain metastases compared with the traditional Logistic and Cox regression models.