An Integrated Imaging Approach to the Study of the Oxidant Effects of Air Pollutions on Human Lung Cells

Air pollution is one of the most common environmental exposures imposed on humans in urban areas on a daily basis. Molecular toxicology studies of the inflammatory effects of ambient air pollutants typically focus on the activation of signaling events that lead to the transcriptional activation of relevant inflammatory genes. While there is a growing body of evidence that oxidative stress plays a critical role in adverse responses induced by a broad array of environmental agents, an integration of the study of oxidant effects in mechanistic studies is hampered by methodological shortcomings and limitations such as sensitivity and specificity. The causative relationship between signaling pathways and adverse outcomes stimulated by specific ambient contaminants has been described. In addition, the generation of oxidative stress has been implicated as an initiating event that leads to adverse responses triggered by exposure to environmental toxicants. However, due to their transient nature, reactivity, and low abundance, detection of reactive oxygen species (ROS) is methodologically challenging. The application of new genetically encoded reporters provides the opportunity to interface real-time measurement of oxidative stress into mechanistic studies with increased temporal and spatial resolution. The studies herein are aimed at integrating imaging analyses of oxidative stress endpoints in molecular signaling of the inflammatory effects of environmental oxidants. We conducted a study using zinc and 1,2-naphthoquinone as model toxicants to (1) develop an integrated imaging method for measurement of redox potential, ROS levels, and mitochondrial dysfunction; (2) examine the role of oxidative stress in the initiation of signaling events that lead to inflammatory gene expression; and (3) create an advanced imaging method to perform simultaneous measurements of redox changes and ROS production

Live-cell imaging approaches for the investigation of xenobiotic-induced oxidant stress

Oxidant stress is arguably a universal feature in toxicology. Research studies on the role of oxidant stress induced by xenobiotic exposures have typically relied on the identification of damaged biomolecules using a variety of conventional biochemical and molecular techniques. However, there is increasing evidence that low-level exposure to a variety of toxicants dysregulates cellular physiology by interfering with redox-dependent processes

Development of the Risk Management Mechanism of an Enterprise Resource Planning System based on Work System Method

This study collects 24 risk-management-relevant research papers published between 2000 and 2010 to elicit significant risk factors and thus develop the risk management mechanism of an enterprise resource planning (ERP) system. The study adopts the grounded theory and conducts an expert questionnaire in order to report its findings on 49 risk factors. Based on the work system method, the identified factors are classified into nine categories and a risk management mechanism is developed thereafter. Finally, to examine the feasibility of the mechanism, two case studies are further investigated. The developed mechanism is found to be a convenient, quick, and proper ERP system risk management tool that can assist enterprises in identifying, analyzing, assessing, and responding to potential risks

The association of XRCC1 gene single nucleotide polymorphisms with response to neoadjuvant chemotherapy in locally advanced cervical carcinoma

<p>Abstract</p> <p>Background</p> <p>Platinum-based neoadjuvant chemotherapy (NAC) is new therapeutic strategy for locally advanced cervical carcinoma, but the variables used to predict NAC response are still infrequently reported. The aim of our study was to investigate the association between <it>XRCC1 </it>gene single nucleotide polymorphisms (SNPs) and NAC response.</p> <p>Methods</p> <p>Seventy patients with locally advanced cervical carcinoma who underwent NAC were collected. SNPs of <it>XRCC1 </it>(at codon 194 and 399) and XRCC1 protein expression were detected. The association of <it>XRCC1 </it>gene SNPs and protein expression with NAC response were analyzed.</p> <p>Results</p> <p>Response to NAC was not statistically significant in three genotypes, Arg/Arg, Arg/Trp, Trp/Trp of <it>XRCC1 </it>at codon 194(X²= 1.243, P = 0.07), while responses were significantly different in genotypes Arg/Arg, Arg/Gln, Gln/Gln of <it>XRCC1 </it>at codon 399 (X²= 2.283, P = 0.020). The risk of failure to chemotherapy in the patients with a Gln allele(Arg/Gln+Gln/Gln) was significantly greater than that with Arg/Arg(OR = 3.254, 95%CI 1.708 ~ 14.951). The expression level of XRCC1 protein was significantly associated with response to NAC. Moreover, the genotype with the Gln allele(Arg/Gln+Gln/Gln) at codon 399, but not codon at 194, presented a significantly higher level of XRCC1 protein expression than that with Arg/Arg genotype (F = 2.699, p = 0.009).</p> <p>Conclusion</p> <p>SNP of <it>XRCC1 </it>gene at codon 399 influences the response of cervical carcinoma to platinum-based NAC. This is probably due to changes in expression of XRCC1 protein, affecting response to chemotherapy.</p

Prediction of 5-year cardiovascular disease risk in people with type 2 diabetes mellitus:derivation in Nanjing, China and external validation in Scotland, UK

BACKGROUND: To use routinely collected data to develop a five-year cardiovascular disease (CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry. METHODS: People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymized research extract of data from the First Affiliated Hospital of Nanjing Medical University (NMU). Five-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including 6 and 11 predictors respectively. The risk prediction models were internally validated and externally validated in a Scottish population–based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell’s C-statistic and calibration plots, respectively. RESULTS: Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, respectively, with 53.5% and 56.9% men. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU-Diabetes cohort and 8,763 (7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C-statistic of 0.723 [0.721–0.724] in internal validation and 0.716 [0.713–0.719] in external validation. CONCLUSIONS: It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score could be used to improve CVD prevention in people with diabetes

Effect of Free-range Rearing on Meat Composition, Physical Properties and Sensory Evaluation in Taiwan Game Hens

Experiments were conducted to evaluate the effect of an outdoor-grazed raising model on meat composition, physical properties and sensory attributes of Taiwan game hens. Six hundred 1-d old female chicks were raised on a floor for 8 weeks. On day 57, 600 healthy birds, with similar body weight, were selected and randomly assigned to three treatment groups (cage, floor-pen and free-range). The results showed that different feeding models had no effect on drip loss, cooking loss, moisture, crude protein, crude fat, crude ash, zinc and calorie contents in breast meat and moisture content in thigh meat. The free-range group had the lowest fat content in both breast and thigh meat, and the lowest calorie content in thigh meat. The firmness and toughness in both thigh and breast of the free-range group were the highest values (p<0.05). The crude protein, total collagen, zinc and iron contents in thigh meat and total collagen content in breast meat of the free-range group were significantly higher than those of the cage-feeding group (p<0.05). The meat sensory scores of flavor, chewiness and overall acceptability of both thigh and breast meat of the free-range group were significantly (p<0.05) better than those of the other two groups. Moreover, the current findings also indicate that the Taiwan game hens of the free-range feeding model displayed well-received carcass traits and meat quality, with higher scores for flavor, chewiness and overall acceptability for greater sensory satisfaction in both breast and thigh meat. In addition, the thigh meat contained high protein and total collage but low fat, offering a healthier diet choice

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

Objective: Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients. // Materials and Methods: The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case–control cohorts (17 491 patients) selected from 149 596 T2DM patients’ EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA). // Results: The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model. // Conclusion: The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model’s potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients

Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review

BACKGROUND: People with type 2 diabetes have a higher risk of cardiovascular disease morbidity and mortality. We aim to distil the evidence, summarize the developments, and identify the gaps in relevant research on predicting cardiovascular disease in type 2 diabetes people using AI techniques in the last ten years. METHODS: A systematic search was carried out for literature published between 1st January 2010 and 30th May 2021 in five medical and scientific databases, including Medline, EMBASE, Global Health (CABI), IEEE Xplore and Web of Science Core Collection. All English language studies describing AI models for predicting cardiovascular diseases in adults with type 2 diabetes were included. The retrieved studies were screened and the data from included studies were extracted by two reviewers. The survey and synthesis of extracted data were conducted based on predefined research questions. IJMEDI checklist was used for quality assessment. RESULTS: From 176 articles identified by the search, 5 studies with sample sizes ranging from 560 to 203,517 met our inclusion criteria. The models predicted the risk of multiple cardiovascular diseases over 5 or 10 years. Ensemble learning, particularly random forest, is the most used algorithm in these models and consistently provided competitive performance. Commonly used features include age, body mass index, blood pressure measurements, and cholesterol measurements. Only one study carried out external validation. The area under the receiver operating characteristic curve for derivation cohorts varied from 0.69 to 0.77. AI models achieved better performance than conventional models in some specific scenarios. CONCLUSIONS: AI technologies seem to show promising performance (AUROC in external validation: 0.75 compared to 0.69 from conventional risk scores) for cardiovascular disease prediction in type 2 diabetes people. However, only one of the reviewed models conducted an external validation. Quality of reporting was low in general, and all models lack reproducibility and reusability

[2,2′-(1,1′-Binaphthyl-2,2′-diyldiimino)­diethanol-κ3 N,N′,O]dichloridocopper(II)

In the title complex, [CuCl2(C24H24N2O2)], the CuII cation is N,N′,O-chelated by a 2,2′-(1,1′-binaphthyl-2,2′-diyldiimino)­diethanol ligand and coordinated by two chloride anions in a distorted square-pyramidal geometry. In the diethanol ligand, the two naphthalene ring systems are twisted with respect to each other at a dihedral angle of 68.30 (9)°. The uncoord­inated hy­droxy group links with a coordinated chloride anion via an intra­molecular O—H⋯Cl hydrogen bond. Inter­molecular N—H⋯O and N—H⋯Cl hydrogen bonds occur in the crystal structure