Diabetes Distress Among Patients Undergoing Surgery for Diabetic Retinopathy and Associated Factors: A Cross-Sectional Survey.

Background: Diabetes distress (DD) is a negative emotion related to diabetes management and a predictor of depression; it affects diabetic retinopathy (DR) patients' quality of life and disease outcomes. The prevalence of DD was higher in patients undergoing surgery for DR. However, few studies have been conducted on DD in DR surgery patients. The present study aims to investigate the status of DD in DR surgery patients and identify factors associated with DD. Methods: Using a convenience sampling method, 210 DR surgery patients who were admitted to 2 tertiary-level hospitals in Wenzhou City (Zhejiang Province) and Zhengzhou City (Henan Province) from February to June 2023 were selected as research subjects. A questionnaire collecting demographic and disease-related information, the Diabetes Distress Scale, the Summary of Diabetes Self-Management Activities, the Family Care Index Scale, and the Social Support Rating Scale were used to collect data. Statistical analyses included descriptive statistics, t tests, ANOVAs, Pearson's correlation analyses and stepwise multiple linear regression. This study is reported according to the STROBE guidelines. Results: In total, 156 out of 210 (74.29%) DR surgery patients experienced DD, with an average score of 2.13±0.63. The results of the stepwise multiple regression analysis showed that residential location, employment status, self-management level, family support, and social support were significantly associated with DD. These variables accounted for 30.6% of the total variation in DD. Conclusions: DR surgery patients exhibit moderate levels of distress. Health care professionals should pay attention to DD in DR surgery patients and develop targeted interventions to improve the self-management ability of these patients, increase their family support and social support to reduce their DD levels.

Time-series analysis of the association between air pollution exposure and outpatient visits for dry eye disease: a case study in Zhengzhou, China.

Background: Dry eye disease (DED) is a prevalent ocular surface disease that significantly impacts patients' quality of life. The association between air pollution and the risk of dry eye disease remains uncertain. Methods: Data on outdoor air pollutants, meteorological information, and outpatient visits for DED were collected from July 1, 2014, to December 31, 2019. The relationship between ambient air pollutants and DED outpatient visits was analyzed using a generalized additive model with a Poisson distribution. Results: Among the 5,204 DED patients included in the study, 63.76% were female and 36.24% were male. The single-pollutant model revealed a significant association between a 10 µg/m3 increase in concentrations of fine-particulate matter with a median aerometric diameter of less than 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) and outpatient visits for DED. Fine-particulate matter with a median aerometric diameter of less than 2.5 µm (PM2.5) showed a significant association with DED outpatient visits in males and the 19-59 years age group. The strongest associations between air pollutants and outpatient visits were observed in male patients and during the cold season. Conclusion: The noteworthy correlation between air pollutants and DED outpatient visits can offer evidence for policy makers and underscore the significance of reinforcing environmental protection.

Factors influencing nurses' post-traumatic growth during the COVID-19 pandemic: Bayesian network analysis.

Objective: During the COVID-19 pandemic, nurses, especially if females and working in intensive care units or emergencies unit, were much more at risk than other health-workers categories to develop malaise and acute stress symptoms. This study aimed to examine the nurses' post-traumatic growth and associated influencing factors during the COVID-19 pandemic. Methods: A cross-sectional study using an online survey was conducted at Henan Provincial People's Hospital to gather data from nurses. A set of questionnaires was used to measure the participants' professional identity, organizational support, psychological resilience and post-traumatic growth. Univariate, correlation, and multiple linear regression analyses were used to determine significant factors influencing post-traumatic growth. A theoretical framework based on the Bayesian network was constructed to understand post-traumatic growth and its associated factors comprehensively. Results: In total, 1,512 nurses participated in the study, and a moderate-to-high level of post-traumatic growth was reported. After screening, the identified variables, including psychological counseling, average daily working hours, average daily sleep duration, professional identity, organizational support, and psychological resilience, were selected to build a Bayesian network model. The results of Bayesian network showed that professional identity and psychological resilience positively affected post-traumatic growth directly, which was particularly pronounced in low- and high-scoring groups. While organizational support positively affected post-traumatic growth indirectly. Conclusion: Although this study identified a moderate-to-high level of nurses' post-traumatic growth, proactive measures to improve psychological resilience fostered by professional identity and organizational support should be prioritized by hospitals and nursing managers.

Relationship between renal and liver function with diabetic retinopathy in patients with type 2 diabetes mellitus: a study based on cross-sectional data.

This study aims to explore the relationship between abnormal renal- and liver-function and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). A total of 994 T2DM patients who received inpatient treatment in the Endocrinology Department of Henan Province People's Hospital were included in the study. Logistic regression was performed to identify the relationship between abnormal renal and liver function with DR. Receiver operator characteristic analysis was performed to explore the efficacy of risk factors in predicting DR. Higher urine albumin [OR(95%CI) = 3.344(1.921-5.822), P < 0.001] and urine albumin/creatinine ratio [OR (95%CI) = 2.901(1.911-5.822), P < 0.001] were closely related to the occurrence of DR. People with low TP had a 1.624-times higher risk (95%CI: 1.008-2.617) of developing DR than those with normal total protein (P = 0.046). The more risk factors that are present, the greater the risk of DR. For every one-point incremental increase in the risk-factor score, the risk of DR increased by 31.0% (P < 0.001). The area under receiver operating curve of risk-factor score was 0.839 (0.812, 0.866), with a sensitivity of 81.9% and a specificity of 74.8%. The risk of developing DR increased with an increased risk-factor score. These findings are potentially valuable for DR screening and early diagnosis in patients with T2DM.

Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients.

AIM: To explore the efficacy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients. METHODS: Data were obtained from 549 T2DM patients who visited the Fundus Disease Center at Henan Provincial People's Hospital from 2018/10-2020/09. DR identification and grading were conducted by two retina specialists, EyeWisdom®DSS and EyeWisdom®MCS, with ophthalmologist grading as reference standard, efficacy of EyeWisdom was evaluated according to sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: Ophthalmologists detected 324 DR cases. Among them, there were 43 of mild non-proliferative DR (NPDR), 79 of moderate NPDR, 61 of severe NPDR, and 141 of proliferative DR (PDR). EyeWisdom®DSS detected 337 DR and EyeWisdom®MCS detected 264 DR. Sensitivity and specificity of EyeWisdom®DSS were 91.0%(95 %CI: 87.3%-93.8%) and 81.3% (95 %CI: 75.5%-86.1%), while EyeWisdom®MCS correctly identified 76.2%(95 %CI: 71.1%-80.7%) of patients with DR and 92.4%(95 %CI: 87.9%-95.4%) of patients without DR. EyeWisdom®DSS showed 76.5%(95 %CI: 69.6%-82.3%) sensitivity and 78.4%(95 %CI: 73.7%-82.5%) specificity for detecting NPDR and 64.5%(95 %CI: 56.0%-72.3%) sensitivity and 93.1%(95 %CI: 90.1%-95.3%) specificity for diagnosing PDR. CONCLUSION: EyeWisdom®DSS is effective in screening for DR, and the accuracy of EyeWisdom®MCS was higher for identifying patients without DR. It is valuable to carry out AI-based DR screening in poorer regions.

Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases.

The study aims to explore the distribution characteristics and influencing factors of diabetic retinopathy (DR) in diabetes mellitus (DM) patients and association rules of eye diseases in these patients. Data were obtained from 1284 DM patients at Henan Provincial People's Hospital. Association rules were employed to calculate the probability of the common occurrence of eye-related diseases in DM patients. A web visualization network diagram was used to display the association rules of the eye-related diseases in DM patients. DR prevalence in people aged < 40 years (≥ 58.5%) was higher than that in those aged 50-60 years (≤ 43.7%). Patients with DM in rural areas were more likely to have DR than those in urban areas (56.2% vs. 35.6%, P < 0.001). DR prevalence in Pingdingshan City (68.4%) was significantly higher than in other cities. The prevalence of DR in patients who had DM for ≥ 5 years was higher than in other groups (P < 0.001). About 33.07% of DM patients had both diabetic maculopathy and DR, and 36.02% had both diabetic maculopathy and cataracts. The number of strong rules in patients ≥ 60 years old was more than those in people under 60 in age, and those in rural areas had more strong rules than those in urban areas. DM patients with one or more eye diseases are at higher risks of other eye diseases than general DM patients. These association rules are affected by factors such as age, region, disease duration, and DR severity.

Exposure misclassification in propensity score-based time-to-event data analysis.

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses' Health Study. The proposed methodology can be applied using our user-friendly R program published online.

Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study.

PURPOSE: To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting. METHODS: Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel. RESULTS: Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, specificity and area under curve for detection of RDR were 84.6% (95% CI: 54.6- 98.1%), 98.0% (95% CI: 94.3-99.6%) and 0.913 (95% CI: 0.797-1.000), respectively. For detection of any DR, the upper indicators were 90.0% (95% CI: 68.3-98.8), 96.6% (95% CI: 92.1-98.9) and 0.933 (95% CI: 0.933-1.000), respectively. CONCLUSION: The AI system showed relatively good consistency with ophthalmologist diagnosis in DR grading, high specificity and acceptable sensitivity for identifying RDR and any DR. TRANSLATIONAL RELEVANCE: It is feasible to apply AI-based DR screening in community. PRECIS: Deployed in community real-world clinic setting, AI-based DR screening system showed high specificity and acceptable sensitivity in identifying RDR and any DR. Good DR diagnostic consistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval.

Efficient vaccination strategies for epidemic control using network information.

BACKGROUND: Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness. METHODS: We simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks. RESULTS: In regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals' degrees at three was as effective as using complete networks. CONCLUSIONS: Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required.

Recent Advances in the Discovery of HIF-1α-p300/CBP Inhibitors as Anti-Cancer Agents.

Hypoxia-inducible factor-1 (HIF-1), a heterodimeric (containing α and ß subunits) transcription factor, is involved in hypoxia response pathway that regulates the expression of many tumorrelated genes. The stabilized HIF-1 heterodimer couples to the general co-activators p300/CBP (CREB binding protein), forming an active transcription factor to initiate hypoxic responses. Inhibiting the transcription factor-coactivator HIF-1α-p300/CBP interaction represents an attractive approach for blocking hypoxia pathway in tumors. Recently, diverse HIF-1α-p300/CBP inhibitors have been designed and their anti-tumor activities have been evaluated. The developments of inhibitors of HIF-1α- p300/CBP are discussed in this review. An outline of structures and biological activities of these inhibitors can be traced, along with the approaches for inhibitors discovery. The challenges in identifying novel and selective potent inhibitors of HIF-1α-p300/CBP are also put forward.

Efficient Monte Carlo evaluation of resampling-based hypothesis tests with applications to genetic epidemiology.

Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this article, we first investigate the asymptotic properties of the pooling resampling-based method and then propose a novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method. Theorems as well as simulations show that the proposed method can give smaller or comparable root mean squared errors and bias with much less computing time, thus can be strongly recommended especially for evaluating highly computationally intensive hypothesis testing procedures in genetic epidemiology.

Design, synthesis, and evaluation of benzofuran derivatives as novel anti-pancreatic carcinoma agents via interfering the hypoxia environment by targeting HIF-1α pathway.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common type of pancreatic cancer, and has still been the medicinal mystery. New drugs and treatment strategies are urgently needed. In this study, 32 benzofuran derivatives are designed, synthesized and evaluated as potential agents against the pancreatic cancer. Among them, compound 9o with the best physicochemical and pharmacokinetic properties exhibited excellent cytotoxicity against many tumor cell lines. In vivo study showed that compound 9o dramatically suppressed the tumor growth of nude mice. Furthermore, compound 9o could affect the hypoxia environment through Hif-1α/VEGF pathway, resulting in the anti-angiogenic activity. These studies indicated that compound 9o was a promising candidate for the treatment of PDAC, deserving further studies.

Synthesis and evaluation of N-(benzofuran-5-yl)aromaticsulfonamide derivatives as novel HIF-1 inhibitors that possess anti-angiogenic potential.

Hypoxia-inducible factor-1 (HIF-1) as a key mediator in tumor metastasis, angiogenesis, and poor patient prognosis has been recognized as an important cancer drug target. A novel series of N-(benzofuran-5-yl)aromaticsulfonamide derivatives were synthesized and evaluated as HIF-1 inhibitor. Among these compounds, 7q exhibited specific inhibitory effects on HIF-1 by downregulating the expression of HIF-1α under hypoxic conditions. It inhibited the HIF-1 transcriptional activity (IC50=12.5±0.7µM) and secretion of VEGF (IC50=18.8µM) in MCF-7 cells. Meanwhile, it also significantly suppressed hypoxia-induced migration of HUVEC cells in nontoxic concentrations. Additionally, tube formation assay demonstrated its anti-angiogenesis activity. Finally, the in vivo study indicated that compound 7q could retard angiogenesis in CAM model. These findings supported the HIF-1 inhibitory effect and anti-angiogenic potential of this class of compounds as HIF-1 inhibitor.

NRF2 promotes breast cancer cell proliferation and metastasis by increasing RhoA/ROCK pathway signal transduction.

Nuclear factor erythroid 2-related factor (NRF2) is an important transcription factor in oxidative stress regulation. Overexpression of NRF2 is associated with human breast carcinogenesis, and increased NRF2 mRNA levels predict poor patient outcome for breast cancer. However, the mechanisms linking gain of NRF2 expression and poor prognosis in breast cancer are still unclear. Here, we provide evidence that NRF2 deletion inhibits proliferation and metastasis of breast cancer cells by down-regulating RhoA. Restoration of RhoA in MCF7 and MDA-MB-231 cells induced NRF2 knockdown-suppressed cell growth and metastasis in vitro, and NRF2 silencing suppressed stress fiber and focal adhesion formation leading to decreased cell migration and invasion. Mechanistic studies showed that NRF2 binds to the promoter region of estrogen-related receptor α (ERR1) and may function as a silencer. This may enhance RhoA protein stability and lead to RhoA overexpression in breast cancer cell. Our findings indicate that NRF2 silencing-mediated reduction of RhoA expression contributes, at least in part, to the poor outcome of breast cancer patients with high NRF2 expression.

Discovery and optimization of new benzofuran derivatives against p53-independent malignant cancer cells through inhibition of HIF-1 pathway.

p53-independent malignant cancer is still severe health problem of human beings. HIF-1 pathway is believed to play an important role in the survival and developing progress of such cancers. In the present study, with the aim to inhibit the proliferation of p53-independent malignant cells, we disclose the optimization of 6a, the starting compound which is discovered in the screening of in-house compound collection. The structure-activity relationship (SAR) is summarized. The most potent derivative 8d, inhibits the proliferation of both p53-null and p53-mutated cells through inhibition of HIF-1 pathway. Our findings here provide a new chemotype in designing potent anticancer agent especially against those p53-independent malignant tumors.

Escape, or Vanish: Control the Fate of p53 through MDM2-Mediated Ubiquitination.

p53 protein is a prominent tumor suppressor to induce cell cycle arrest, apoptosis and senescence, which attracts significant interest to cancer treatment. Therefore, it would be particularly important to restore the wild-type p53 that retains latent functions in the approximately 50% of tumors. MDM2 (murine double minute 2), the principal cellular antagonist of p53, has long been believed to suppress p53 activity through two main mechanisms: promoting degradation via its E3 ligase activity and masking p53 transcriptional activation by direct binding. Targeting MDM2 E3 ligase activity is becoming a potential antitumor strategy resulting from MDM2's decisive role in controlling the fate of p53: p53 is going to degradation when entrapped into MDM2-mediated ubiquitination, where p53 can escape by abrogating MDM2 E3 ligase activity using regulators. The intensive focus on regulating MDM2 ubiquitin E3 ligase activity has led to the rapid progress of its inhibitors, which may be possible to help p53 escape from degradation and restore its function to control tumor growth. This review summarizes the current inhibitors of MDM2 E3 ligase in cancer therapy based on the understanding the regulation of MDM2 E3 ubiquitin ligase activity, including post-translational modification, interactions between MDM2 and its cofactors, and regulation of MDM2 stability.

[Progress in research of the structural optimization of natural product-like Garcinia caged xanthones].

Designing of natural product-like compounds using natural products as template structures is an important strategy for the discovery of new drugs. Gambogic acid (GA), which is a Garcinia natural product with a unique caged xanthone scaffold, inhibits potent antitumor activity both in vitro and in vivo. This review summarized the researches on the identification of the antitumor pharmacophore of GA, and the design, structural optimization and structure-activity relationship (SAR) of natural product-like caged xanthones based on it.

Synthesis and bioevaluation of a series of α-pyrone derivatives as potent activators of Nrf2/ARE pathway (part I).

When exposed to electrophiles, human colorectal cancer cells (HCT116) counteract oxidative stress through activating NF-E2-related factor 2 (Nrf2)/antioxidant response element (ARE) pathway. To identify new activators, luciferase reporter gene assay was used to screen in-house database of our laboratory, leading to a novel α-pyrone compound 1 as a hit. 2 with 2-fluoro phenyl group exhibited the strongest ARE inductive activity in the first round structure-activity relationship (SAR) study. Biological studies showed the compound induced nuclear translocation of Nrf2 preceded by phosphorylation of ERK1/2. The data encouraged us to use 2 as lead and 20 derivatives were synthesized to discuss a more detailed SAR, leading to a more potent compound 9, which can be the starting compound for further modification.

Garcinia xanthones as orally active antitumor agents.

Using a newly developed strategy whose key step is the regioselective propargylation of hydroxyxanthone substrates, 99 structurally diverse Garcinia natural-product-like xanthones based on gambogic acid were designed and synthesized and their in vitro antitumor activity was evaluated. A set of 40 related compounds was chosen for determination of their physicochemical properties including polar surface area, log D7.4, aqueous solubility, and permeability at pH 7.4. In the light of the in vitro antitumor activity and the physicochemical properties, two compounds were advanced into in vivo efficacy experiments. The antitumor activity of compound 112, administered po, showed more potent in vivo oral antitumor activity than gambogic acid.

Studies on chemical-structure modification and structure-activity relationship of gambogic acid derivatives at carbon(34).

Gambogic acid (GA), a natural product, was identified as a promising antitumor agent. To further explore the structure-activity relationship of GA and discover novel GA derivatives as antitumor agents, 19 novel GA derivatives modified at C(34) were synthesized and evaluated against A549, BGC-823, U251, HepG2, and MB-231 cancer cell lines by cellular assays. Among them, 15 compounds were found to be more potent than GA against some cancer cell lines. Notably, compound 3 possessed potent inhibitory activities against five cell lines with IC(50) values ranging between 0.24 and 1.09 µM. Compounds 9 and 18 were seven to eightfold more active than GA against A549 cell line. Chemical modification at C(34) of GA by introducing of hydrophilic aliphatic amines resulted in increased activity and improved drug-like properties. These findings will enhance our understanding of the SAR of GA and can lead to the discovery of novel GA derivatives as potential antitumor agents.