Psychosocial barriers and facilitators for cascade genetic testing in hereditary breast and ovarian cancer: a scoping review.

Despite increased awareness and availability of genetic testing for hereditary breast and ovarian cancer (HBOC) syndrome for over 20 years, there is still significant underuse of cascade genetic testing among at-risk relatives. This scoping review synthesized evidence regarding psychosocial barriers and facilitators of family communication and/or uptake of cascade genetic testing in relatives from HBOC families. Search terms included 'hereditary breast and ovarian cancer' and 'cascade genetic testing' for studies published from 2012-2022. Through searching common databases, and manual search of references, 480 studies were identified after excluding duplications. Each article was reviewed by two researchers independently and 20 studies were included in the final analysis. CASP, RoBANS 2.0, RoB 2.0, and MMAT were used to assess the quality of included studies. A convergent data synthesis method was used to integrate evidence from quantitative and narrative data into categories and subcategories. Evidence points to 3 categories and 12 subcategories of psychosocial barriers and facilitators for cascade testing: (1) facilitators (belief in health protection and prevention; family closeness; decisional empowerment; family support, sense of responsibility; self-efficacy; supportive health professionals); (2) bidirectional concepts (information; perception of genetic/cancer consequences; negative emotions and attitude); and (3) barriers (negative reactions from family and negative family dynamics). Healthcare providers need to systematically evaluate these psychosocial factors, strengthen facilitators and alleviate barriers to promote informed decision-making for communication of genetic test results and uptake of genetic testing. Bidirectional factors merit special consideration and tailored approaches, as they can potentially have a positive or negative influence on family communication and uptake of genetic testing.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Vitamin C-induced CO2 capture enables high-rate ethylene production in CO2 electroreduction.

High-rate production of multicarbon chemicals via the electrochemical CO2 reduction can be achieved by efficient CO2 mass transport. A key challenge for C-C coupling in high-current-density CO2 reduction is how to promote *CO formation and dimerization. Here, we report molecularly enhanced CO2-to-*CO conversion and *CO dimerization for high-rate ethylene production. Nanoconfinement of ascorbic acid by graphene quantum dots enables immobilization and redox reversibility of ascorbic acid in heterogeneous electrocatalysts. Cu nanowire with ascorbic acid nanoconfined by graphene quantum dots (cAA-CuNW) demonstrates high-rate ethylene production with a Faradaic efficiency of 60.7% and a partial current density of 539 mA/cm2, a 2.9-fold improvement over that of pristine CuNW. Furthermore, under low CO2 ratio of 33%, cAA-CuNW still exhibits efficient ethylene production with a Faradaic efficiency of 41.8%. We find that cAA-CuNW increases *CO coverage and optimizes the *CO binding mode ensemble between atop and bridge for efficient C-C coupling. A mechanistic study reveals that ascorbic acid can facilitate *CO formation and dimerization by favorable electron and proton transfer with strong hydrogen bonding.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Development of a web-based care networking system to support visiting healthcare professionals in the community.

BACKGROUND: The role of visiting health services has been proven to be effective in promoting the health of older populations. Hence, developing a web system for nurses may help improve the quality of visiting health services for community-dwelling frail older adults. This study was conducted to develop a web application that reflects the needs of visiting nurses. METHODS: Visiting nurses of public health centers and community centers in South Korea participated in the design and evaluation process. Six nurses took part in the focus group interviews, and 21 visiting nurses and community center managers participated in the satisfaction evaluation. Focus group interviews were conducted to identify the needs of visiting nurses with respect to system function. Based on the findings, a web application that can support the effective delivery of home visiting services in the community was developed. An artificial intelligence (AI) algorithm was also developed to recommend health and welfare services according to each patient's health status. After development, a structured survey was conducted to evaluate user satisfaction with system features using Kano's model. RESULTS: The new system can be used with mobile devices to increase the mobility of visiting nurses. The system includes 13 features that support the management of patient data and enhance the efficiency of visiting services (e.g., map, navigation, scheduler, protocol archives, professional advice, and online case conferencing). The user satisfaction survey revealed that nurses showed high satisfaction with the system. Among all features, the nurses were most satisfied with the care plan, which included AI-based recommendations for community referral. CONCLUSIONS: The system developed from the study has attractive features for visiting nurses and supports their essential tasks. The system can help with effective case management for older adults requiring in-home care and reduce nurses' workload. It can also improve communication and networking between healthcare and long-term care institutions.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

HIF-1α inhibition by MO-2097, a novel chiral-free benzofuran targeting hnRNPA2B1.

INTRODUCTION: Hypoxia-inducible factor 1 (HIF-1) is a transcriptional activator mediating adaptive responses to hypoxia. It is up-regulated in the tumor microenvironment and recognized as an effective anticancer drug target. Previously, we discovered that the natural compound moracin-O and its synthetic derivative MO-460 inhibited HIF-1α via hnRNPA2B1. OBJECTIVES: This study aimed to develop novel HIF-1 inhibitors for cancer chemotherapy by harnessing the potential of the natural products moracins-O and P. METHODS: In an ongoing search for novel HIF-1 inhibitors, a series of nature-inspired benzofurans with modifications on the chiral rings of moracins-O and P were synthesized. They showed improved chemical tractability and were evaluated for their inhibitory activity on HIF-1α accumulation under hypoxic conditions in HeLa CCL2 cells. The most potent derivative's chemical-based toxicities, binding affinities, and in vivo anti-tumorigenic effects were evaluated. Further, we examined whether our compound, MO-2097, exhibited anticancer effects in three-dimensional cultured organoids. RESULTS: Herein, we identified a novel synthetic chiral-free compound, MO-2097, with reduced structural complexity and increased efficiency. MO-2097 exhibited inhibitory effects on hypoxia-induced HIF-1α accumulation in HeLa CCL2 cells via inhibition of hnRNPA2B1 protein, whose binding affinities were confirmed by isothermal titration calorimetry analysis. In addition, MO-2097 demonstrated in vivo efficacy and biocompatibility in a BALB/c mice xenograft model. The immunohistochemistry staining of MO-2097-treated tissues showed decreased expression of HIF-1α and increased levels of apoptosis marker cleaved caspase 3, confirming in vivo efficacy. Furthermore, we confirmed that MO-2097 works effectively in cancer patient-based organoid models. CONCLUSION: MO-2097 represents a promising new generation of chemotherapeutic agents targeting HIF-1α inhibition via hnRNPA2B1, requiring further investigation.

Functionally similar genes exhibit comparable/similar time-course expression kinetics in the UV-induced photoaged mouse model.

Skin photoaging induced by ultraviolet (UV) irradiation contributes to the formation of thick and coarse wrinkles. Humans are exposed to UV light throughout their lives. Therefore, it is crucial to determine the time-sequential effects of UV on the skin. In this study, we irradiated the mouse back skin with UV light for eight weeks and observed the changes in gene expressions via microarray analysis every week. There were more downregulated genes (514) than upregulated genes (123). The downregulated genes had more functional diversity than the upregulated genes. Additionally, the number of downregulated genes did not increase in a time-dependent manner. Instead, time-dependent kinetic patterns were observed. Interestingly, each kinetic cluster harbored functionally enriched gene sets. Since collagen changes in the dermis are considered to be a major cause of photoaging, we hypothesized that other gene sets contributing to photoaging would exhibit kinetics similar to those of the collagen-regulatory genes identified in this study. Accordingly, co-expression network analysis was conducted using 11 well-known collagen-regulatory seed genes to predict genes with similar kinetics. We ranked all downregulated genes from 1 to 504 based on their expression levels, and the top 50 genes were suggested to be involved in the photoaging process. Additionally, to validate and support our identified top 50 gene lists, we demonstrated that the genes (FN1, CCDC80, PRELP, and TGFBR3) we discovered are downregulated by UV irradiation in cultured human fibroblasts, leading to decreased collagen levels, which is indicative of photoaging processes. Overall, this study demonstrated the time-sequential genetic changes in chronically UV-irradiated skin and proposed 50 genes that are involved in the mechanisms of photoaging.

Long-Term Efficacy and Safety of Eculizumab in Patients With Paroxysmal Nocturnal Hemoglobinuria and High Disease Burden: Real-World Data From Korea.

BACKGROUND: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare hematologic disorder characterized by uncontrolled terminal complement activation. Eculizumab, a monoclonal antibody C5 inhibitor was introduced in Korea in 2009 and has been the standard treatment option for PNH. METHODS: This study assessed the long-term efficacy/safety of eculizumab in PNH using real-world data from the Korean Health Insurance Review and Assessment Service. Eighty patients who initiated eculizumab from 2009-2020 were enrolled. RESULTS: At eculizumab initiation, the median age was 51.5 years, lactate dehydrogenase (LDH) 6.8 × upper limit of normal, and granulocyte clone size 93.0%. All patients had at least one PNH-related complication before eculizumab initiation, including renal failure (n = 36), smooth muscle spasm (n = 24), thromboembolism (n = 20), and pulmonary hypertension (n = 15). The median (range) duration of eculizumab treatment was 52.7 (1.0, 127.3) months (338.6 total treated patient-years). Despite high disease activity in the study population before treatment initiation, overall survival was 96.2% and LDH levels were stabilized in most patients during treatment. PNH-related complications at treatment initiation were resolved in 44.4% of patients with renal failure, 95.8% with smooth muscle spasm, 70.0% with thromboembolism, and 26.7% with pulmonary hypertension. Extravascular hemolysis occurred in 28.8% of patients (n = 23; 0.09 per patient-year) and breakthrough hemolysis in 18.8% (n = 15; 0.06 per patient-year). No treatment discontinuation cases related to eculizumab were observed. CONCLUSION: These data provided evidence for the long-term efficacy and safety of eculizumab in Korean PNH patients with high disease burdens.

Gender Differences in the Association between Workplace Bullying and Depression among Korean Employees.

Workplace bullying is a prevalent issue with a significant impact on employees' mental health. This study aimed to explore the relationship between workplace bullying and the prevalence of depression, with a particular focus on the role of gender. A total of 12,344 Korean employees aged 19-65 years were included in the study. They completed the Center for Epidemiologic Studies Depression Scale (CES-D) and a score of 16 or higher in CES-D indicated depression. The association between workplace bullying and depression was analyzed using logistic regression analyses. The average CES-D scores were higher for both male and female employees who experienced bullying than for those who did not (p < 0.001). The association between the experience of workplace bullying and the prevalence of depression was statistically significant for both genders, with a stronger correlation observed among male employees (p for interaction < 0.001). Organizations are urged to address workplace bullying, particularly for male employees, through the implementation of anti-bullying strategies and policies, as well as the provision of mental health resources and support.

Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer-Design, Synthesis, and In Vitro Evaluation.

The epidermal growth factor receptor (EGFR) is vital for regulating cellular functions, including cell division, migration, survival, apoptosis, angiogenesis, and cancer. EGFR overexpression is an ideal target for anticancer drug development as it is absent from normal tissues, marking it as tumor-specific. Unfortunately, the development of medication resistance limits the therapeutic efficacy of the currently approved EGFR inhibitors, indicating the need for further development. Herein, a machine learning-based application that predicts the bioactivity of novel EGFR inhibitors is presented. Clustering of the EGFR small-molecule inhibitor (∼9000 compounds) library showed that N-substituted quinazolin-4-amine-based compounds made up the largest cluster of EGFR inhibitors (∼2500 compounds). Taking advantage of this finding, rational drug design was used to design a novel series of 4-anilinoquinazoline-based EGFR inhibitors, which were first tested by the developed artificial intelligence application, and only the compounds which were predicted to be active were then chosen to be synthesized. This led to the synthesis of 18 novel compounds, which were subsequently evaluated for cytotoxicity and EGFR inhibitory activity. Among the tested compounds, compound 9 demonstrated the most potent antiproliferative activity, with 2.50 and 1.96 µM activity over MCF-7 and MDA-MB-231 cancer cell lines, respectively. Moreover, compound 9 displayed an EGFR inhibitory activity of 2.53 nM and promising apoptotic results, marking it a potential candidate for breast cancer therapy.

Cantharidin-Based Verbenone Derivatives as a Novel Insecticide against Plutella xylostella: Design, Synthesis, Insecticidal Activity Evaluation, and 3D QSAR Study.

The diamondback moth is a detrimental insect pest of brassicaceous crops which was among the first crop insects to be reported as DDT resistant. It has since proven to be significantly resistant to nearly every synthetic insecticide used in the field in many crucifer-producing regions. Due to insecticide control failures in some parts of the world, economically viable crucifer production is now all but impossible. As a result, there has been an increasing effort to identify new compounds with strong pesticidal activity. Cantharidin is one such compound that has been shown to be highly effective against a variety of insect pests. However, its chemical synthesis and potential toxicity to non-target organisms have been a major source of concern. Herein, using rational design approaches, a new series of cantharidin-based verbenone derivatives were synthesized and evaluated for their insecticidal activities against the diamondback moth. Among different compounds screened, compounds 6a, 6h, 6i, and 6q emerged as the most potent compounds exhibiting 100% mortality at a concentration of 100 mg/L after four days. These compounds demonstrated a good anti-feeding effect against the diamondback moth on cabbage leaves. Subsequently, a 3D QSAR study was carried out to identify the key structural features of the synthesized compounds and their correlation with insecticidal activity.

Self-compassion is associated with the superior longitudinal fasciculus in the mirroring network in healthy individuals.

Self-compassion (SC) involves taking an emotionally positive attitude towards oneself when suffering. Although SC has positive effects on mental well-being as well as a protective role in preventing symptoms in healthy individuals, few studies on white matter (WM) microstructures in neuroimaging studies of SC has been studied. Brain imaging data were acquired from 71 healthy participants. WM regions of mirroring network were analyzed using tract-based spatial statistics. After the WM regions associated with SC were extracted, exploratory correlation analysis with the self-forgiveness scale, the coping scale, and the world health organization quality of life scale abbreviated version was performed. We found that self-compassion scale total scores were negatively correlated with the fractional anisotropy (FA) values of the superior longitudinal fasciculus (SLF) in healthy individuals. The self-kindness and mindfulness subscale scores were also negatively correlated with FA values of the same regions. These FA values were negatively correlated with the total scores of self-forgiveness scale, and self-control coping strategy and confrontation coping strategy. Our findings suggest levels of SC may be associated with WM microstructural changes of SLF in healthy individuals. These lower WM microstructures may be associated with positive personal attitudes, such as self-forgiveness, self-control and active confrontational strategies.

Occupational stress and depression of Korean employees: Moderated mediation model of burnout and grit.

BACKGROUND: The present study investigated an interpersonal model of development of depressive symptoms in employees, where occupational stress affects burnout, which in turn affects risk for depression, and whether this mediation is moderated by grit. METHODS: A total of 11,421 participants, aged 19 to 65 years, who were employees of 18 private companies and local government organizations in Korea were included. They completed questionnaires, including the Korean version of occupational stress scale, Oldenburg Burnout Inventory, Center for Epidemiologic Studies Depression scale and Grit scale. Mediation and moderation analyses were carried out in the Statistical Package for the Social Sciences PROCESS macro. RESULTS: The association between occupational stress and depressive symptoms was mediated by exhaustion (b = -0.256, 95 % CI [0.244, 0.268]) and disengagement (b = -0.052, 95 % CI [0.039, 0.065]). Moreover, the effect of exhaustion on depressive symptoms was moderated by each grit, with the effect being stronger for employees with low grit (b = 0.939, p < 0.001 for passion and b = 0.629, p < 0.001 for perseverance) than for those with high grit (b = 0.944, p < 0.001 for passion and b = 0.686, p < 0.001 for perseverance). LIMITATIONS: The cross-sectional design of the study does not allow causal inferences. CONCLUSIONS: These findings contribute to the understanding of how occupational stress predicts depressive symptoms in the workplace and provide practical implications for preventing burnout and nurturing grit to protect employees' mental health in the workplace.

Design, Synthesis and Biological Evaluation of Novel MDH Inhibitors Targeting Tumor Microenvironment.

MDH1 and MDH2 enzymes play an important role in the survival of lung cancer. In this study, a novel series of dual MDH1/2 inhibitors for lung cancer was rationally designed and synthesized, and their SAR was carefully investigated. Among the tested compounds, compound 50 containing a piperidine ring displayed an improved growth inhibition of A549 and H460 lung cancer cell lines compared with LW1497. Compound 50 reduced the total ATP content in A549 cells in a dose-dependent manner; it also significantly suppressed the accumulation of hypoxia-inducible factor 1-alpha (HIF-1α) and the expression of HIF-1α target genes such as GLUT1 and pyruvate dehydrogenase kinase 1 (PDK1) in a dose-dependent manner. Furthermore, compound 50 inhibited HIF-1α-regulated CD73 expression under hypoxia in A549 lung cancer cells. Collectively, these results indicate that compound 50 may pave the way for the development of next-generation dual MDH1/2 inhibitors to target lung cancer.

Dual role of enhancer of zeste homolog 2 in the regulation of ultraviolet radiation-induced matrix metalloproteinase-1 and type I procollagen expression in human dermal fibroblasts.

Abnormalities in the extracellular matrix (ECM) caused by ultraviolet (UV) radiation are mediated by epigenetic mechanisms. Enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase that is implicated in inflammation, immune regulation, and senescence. However, its role in controlling UV-induced ECM alterations in the skin remains elusive. Here, we investigated the role of EZH2 in UV-induced expression of matrix metalloproteinase (MMP)-1 and type I procollagen. We found that UV induced EZH2 expression in human skin in vivo and in human dermal fibroblasts (HDFs). EZH2 knockdown reduced the expression and promoter activity of MMP-1 and increased those of type I procollagen, whereas EZH2 overexpression had the opposite effects. Mechanistically, EZH2 increased NF-κB activity, and p65 and p50 expression and promoter activity. Intriguingly, chromatin immunoprecipitation assays revealed that the EZH2/p65/p50 complex was recruited and bound to the MMP-1 promoter after UV irradiation, independent of its histone methyltransferase activity. In contrast, EZH2-induced DNA methyltransferase 1 (DNMT1) formed a complex with EZH2 and enhanced the enrichment of H3K27me3 on the COL1A2 promoter following UV irradiation. These findings indicate that EZH2 plays a dual role in regulating MMP-1 and type I procollagen expression and improve our understanding of how this epigenetic mechanism contributes to UV-induced skin responses and photoaging. This study shows that inhibiting EZH2 is a potential anti-aging strategy for preventing UV-induced skin aging by reducing MMP-1 expression and inducing type I procollagen expression.

Novel Hybrid Indole-Based Caffeic Acid Amide Derivatives as Potent Free Radical Scavenging Agents: Rational Design, Synthesis, Spectroscopic Characterization, In Silico and In Vitro Investigations.

Antioxidant small molecules can prevent or delay the oxidative damage caused by free radicals. Herein, a structure-based hybridization of two natural antioxidants (caffeic acid and melatonin) afforded a novel hybrid series of indole-based amide analogues which was synthesized with potential antioxidant properties. A multiple-step scheme of in vitro radical scavenging assays was carried out to evaluate the antioxidant activity of the synthesized compounds. The results of the DPPH assay demonstrated that the indole-based caffeic acid amides are more active free radical scavenging agents than their benzamide analogues. Compared to Trolox, a water-soluble analogue of vitamin E, compounds 3a, 3f, 3h, 3j, and 3m were found to have excellent DPPH radical scavenging activities with IC50 values of 95.81 ± 1.01, 136.8 ± 1.04, 86.77 ± 1.03, 50.98 ± 1.05, and 67.64 ± 1.02 µM. Three compounds out of five (3f, 3j, and 3m) showed a higher capacity to neutralize the radical cation ABTS•+ more than Trolox with IC50 values of 14.48 ± 0.68, 19.49 ± 0.54, and 14.92 ± 0.30 µM, respectively. Compound 3j presented the highest antioxidant activity with a FRAP value of 4774.37 ± 137.20 µM Trolox eq/mM sample. In a similar way to the FRAP assay, the best antioxidant activity against the peroxyl radicals was demonstrated by compound 3j (10,714.21 ± 817.76 µM Trolox eq/mM sample). Taken together, compound 3j was validated as a lead hybrid molecule that could be optimized to maximize its antioxidant potency for the treatment of oxidative stress-related diseases.