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The objective of this study was to evaluate the effect of environmental endocrine disrupting chemicals by a germ–somatic cell coculture model in vitro . Testicular cells of 18-day-old chicken embryos were dispersed and cultured in different media. Results showed that somatic cells formed a monolayer to which germ cells adhered in the medium supplemented with insulin (Ins), transferrin (Tf), and selenite (Se) (ITS medium). However, the medium without ITS or single subtraction of Ins, Tf, or Se could not maintain cell survival in culture because many germ cells manifested apoptosis. Three known endocrine disrupters were selected to test the feasibility of this model. Aroclor 1254 (A1254, 10 μ g/mL) induced condensed nuclei and vacuolated cytoplasm in germ cells, which was further confirmed by a cell proliferation assay. However, after culture for 48 h, the number of germ cells displayed a significant augment stimulated by A1254 (0.1–10 μ g/mL) ( P [ABSTRACT FROM AUTHOR]

Abstract: Continuing modifications of fuels like gasoline should include evaluations of the proposed constituents for their potential to damage environmental resources such as subsurface water supplies. Consequently, we developed a screening model to estimate well water concentrations and transport times for gasoline components migrating from underground fuel tank (UFT) releases to typical at-risk community water supply wells. Representative fuel release volumes and hydrogeologic characteristics were used to parameterize the transport calculation. Subsurface degradation processes were neglected in the model in order to make risk-conservative assessments. The model was tailored to individual compounds based on their abundances in gasoline, gasoline–water partition coefficients (K gw), and organic matter–water partition coefficients (K om). Transport calculations were conducted for 20 polar and 4 nonpolar compounds found in gasoline, including methyl tert-butyl ether (MTBE) and other ether oxygenates, ethanol, methanol, and some aromatic hydrocarbons. With no calibration, the screening model successfully captured the reported magnitude of MTBE contamination of at-risk community supply wells. Such screening indicates that other oxygenates would cause similar widespread problems unless they were biodegradable. Stochastic analysis of field parameter variability concluded that community supply well contamination estimates had order-of-magnitude reliability. This indicated that such pre-manufacturing analyses may reasonably anticipate widespread environmental problems and/or inspire focused investigations into chemical properties (e.g., biodegradability) before industrial adoption of new fuel formulations. [Copyright &y& Elsevier]

This paper describes the design and implementation of a field study to understand dispersion in an urban area, characterized by buildings whose heights are less than 5 m. The study site, Barrio Logan, is located on the San Diego shore line, where most of the industry is located. The predominant wind from the SW brings emissions from the industrial area into the residential area located downwind in the NW. The field study was conducted during the period 21–31 August 2001 on 5 days. The tracer, SF6, was released at a height of 5 m from a shipyard on the shoreline, and the concentrations of the tracer were sampled on four arcs at 200, 500, 1000, and 2000 m from the source during 10 h of each day starting at 10 am. This experiment was designed to provide data during both daytime and nighttime conditions. The meteorological conditions that governed dispersion were measured using sonic anemometers and a SODAR with a range of 200 m in the vertical.The maximum concentrations (normalized by the emission rate) measured during the experiments ranged from 100 μs m-3 at 200 m to 1 μs m-3 at 2000 m. An analysis of the concentration observations indicated that the maximum concentrations followed: where the dilution velocity is given by Udil, a measure of the dilution capability of the boundary layer, varied between 0.1 and 0.2 m s-1 during most of the field study hours. [Copyright &y& Elsevier]

Gas-phase atmospheric deposition was evaluated in a screening level model of the multimedia environmental distribution of toxics (MEND-TOX). Algorithmic additions to MEND-TOX for the estimation of gas-phase deposition velocity over vegetated surfaces were analyzed using recently published dry deposition flux measurements for nitric acid. Model outputs are compared to similar estimates from the NOAA multilayer dry deposition model. Results of the evaluation indicate that MEND-TOX performs well as a screening level model for the estimation of gas-phase dry deposition velocity of nitric acid over soybeans. The present study expands previous laboratory results for organic species to include an inorganic species and open field and dry leaf, conditions. [ABSTRACT FROM AUTHOR]

The steroid 5α-reductase isoenzymes (5αR) transform testosterone into 17β-hydroxy-5α-androstan-3-one (5-dihydrotestosterone, DHT), which exerts a much stronger biological activity than does testosterone. Briefly, the two 5αR isoenzymes are differentially expressed in the two major target organs of steroid action, the prostate (isoenzyme 2, 5αR2) and the skin (isoenzyme 1, 5αR1). We analysed the potential of a human epidermal tissue reconstituted by cell culture (RHE, provided by SkinEthic Laboratories, Nice, France) as a model for assessing 5αR activity. The epidermal model was found to express the type-1 (skin) isoform of 5αR and thus could be used as an enzyme source for the screening of 5αR modulators for dermatological/cosmetic purposes. A reproducible and convenient assay method was developed, allowing both the evaluation of testosterone transformation into DHT (5αR activity) and an outlook on the general metabolism process of testosterone. This could be important for the detection of any compound that could act mainly on another target enzyme than 5αR. The assay gave evidence of the inhibitory activity of finasteride against type-1 5αR, which is now established both in vitro and in clinical studies. In addition to enzyme inhibitors, this in situ cellular assay can detect transcriptional modulators of 5αR gene expression, or any compound that could modulate enzyme processing or post-translational activation. RT-PCR analysis of RNA samples from RHE failed to show any notable effect of finasteride, testosterone, or DHT treatment on the expression of 5αR1 at the transcriptional level. [ABSTRACT FROM AUTHOR]

Background: Hypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80-90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening., Objective: We aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM., Methods: Between April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model., Results: Among 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818-0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812-0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825-0.841]. A browser-based calculator was generated accordingly., Conclusion: The pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Guo, Gao, Yang, Ma, Zhou, Liu, Shao, Wang, Hu, Zhao, Zhang, Guo, Huang, Cui, Song, Sun, Liu, Zhang and Tao.)

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ji, Xue, Zhang, Yao and Wang.)

Few works study the manufacturer's optimal decisions in a supply chain considering the downstream retailer's fairness concern with asymmetric demand information. Assuming that the retailer possesses private demand information and shows fairness concern behavior and that the manufacturer produces products with or without green quality, this paper derives the manufacturer's optimal contract to screen the retailer's private information. A basic model in which the manufacturer produces nongreen products is examined. Then, comparisons are conducted to further investigate the influences of the retailer's fairness concern behavior and the greenness degree of products on the decision making within the supply chain, the manufacturer's expected profits and the retailer's expected utilities. The results show that the manufacturer can always benefit from producing green products. The retailer's fairness concern behavior and the greenness degree of products can induce the manufacturer to change the optimal contract design strategy. Our findings can provide several managerial references for green supply chain management. [ABSTRACT FROM AUTHOR]

• New screening model for predicting pressure buildup and CO 2 plume migration for CO 2 geologic sequestration requiring limited information. • Can assist project developers during the early days of project planning help regulators perform simple checks against detailed numerical models. • Model uses new correlation for injectivity index in terms of the slope of the CO 2 fractional flow curve, to calculate pressure buildup. • Second correlation for total storage efficiency as a function of gravity number and fractional flow curve slope, used to calculate plume radius. We present a new screening model for predicting injection well pressure buildup and CO 2 plume migration for CO 2 geologic sequestration projects. The model requires only limited information and is quite accurate when compared to detailed simulation results. Such models can assist project developers during the early days of project planning (e.g., for 45Q related projects), and also help regulators perform simple checks against detailed numerical models. The screening model consists of two correlations: one for injectivity index in terms of the slope of the CO 2 fractional flow curve, and the second for total storage efficiency within the footprint of plume as a function of gravity number and slope of the CO 2 fractional flow curve. Using these two correlations, as well as a knowledge of some basic reservoir characteristics and estimates of fluid properties from standard correlations, the injection-well pressure buildup and CO 2 plume extent in the formation can be readily estimated. The new correlations show a good match with the results of the underlying simulations, and also provide good agreement with independent calculations for two example problems. [ABSTRACT FROM AUTHOR]

Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.

With the mortality rate of invasive aspergillosis caused by Aspergillus fumigatus reaching almost 100% among some groups of patients, and with the rapidly increasing resistance of A. fumigatus to available antifungal drugs, new antifungal agents have never been more desirable than now. Numerous bioactive compounds were isolated and characterized from marine resources. However, only a few exhibited a potent activity against A. fumigatus when compared to the multitude that did against some other pathogens. Here, we review the marine bioactive compounds that display a bioactivity against A. fumigatus. The challenges hampering the discovery of antifungal agents from this rich habitat are also critically analyzed. Further, we propose strategies that could speed up an efficient discovery and broaden the dimensions of screening in order to obtain promising in vivo antifungal agents with new modes of action. [ABSTRACT FROM AUTHOR]

Background: Distinguishing biliary atresia from non-biliary atresia in patients with cholestasis is challenging, as these conditions have a similar clinical presentation. We developed and externally validated a screening model for biliary atresia and devised a web-based calculator for use in clinical settings., Methods: A screening model was developed based on data from 227 cholestatic infants (82 and 145 with and without biliary atresia, respectively) and validated in 234 infants (90 and 144 with and without biliary atresia, respectively) admitted to three hospitals. Variables were selected from routine examination results using the least absolute shrinkage and selection operator method and entered into a logistic regression model to construct a biliary-atresia-risk-predicting equation. Cutoff values for risk stratification were estimated using model sensitivity, derived from the receiver-operating characteristic curves., Results: The final screening model included seven variables (i.e., weight at admission, clay-colored stools, γ-glutamyl transpeptidase and albumin levels at admission, abnormal gallbladder, triangular cord sign, and change in total bilirubin levels). The model generated an area under the curve of 0.94 with a sensitivity of 91.46 and specificity of 86.62 in the derivation cohort. This was confirmed in the validation cohort, as we found an area under the curve of 0.93 with a sensitivity of 93.1 and specificity of 80.15. Patients were stratified into three risk groups (low-, moderate-, and high-risk groups). Biliary atresia was excluded in the low-risk group. The high-risk group showed a higher detection rate of biliary atresia compared to the stool color screening method alone. This model was integrated into a user-friendly web-based system., Conclusions: The screening tool had a high predictive value and may help in decision-making by physicians at tertiary and community hospitals., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tp-20-170). The authors have no conflicts of interest to declare., (2021 Translational Pediatrics. All rights reserved.)

A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010-2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system.

We investigated the effect of cisplatin given at different treatment intervals on growth of the s.c.-implanted M 5076 reticulum sarcoma and the number of liver metastases at the end of the experiment (day 24). The later the treatment was started, the smaller was the tumour-inhibiting effect of cisplatin on primary tumour growth. Initiation of cisplatin treatment before day 14 after tumour implantation inhibited liver metastases completely. Treatment starting from day 14 or later did not influence the number of liver metastases. With regard to the clinical situation, the data imply that in the search for new leads in anticancer compounds, experimental conditions should concentrate on the inhibition of metastatic tumour growth. [ABSTRACT FROM AUTHOR]

The adverse outcome pathway (AOP) framework is a conceptual construct that mechanistically links molecular initiating events to adverse biological outcomes through a series of causal key events (KEs) that represent the perturbation of the biological system. Quantitative, predictive AOPs are necessary for screening emerging contaminants and potential substitutes to inform their prioritization for testing. In practice, they are not widely used because they can be costly to develop and validate. A modular approach for assembly of quantitative AOPs, based on existing knowledge, would allow for rapid development of biological pathway models to screen contaminants for potential hazards and prioritize them for subsequent testing and modeling. For each pair of KEs, a quantitative KE relationship (KER) can be derived as a response-response function or a conditional probability matrix describing the anticipated change in a KE based on the response of the prior KE. This transfer of response across KERs can be used to assemble a quantitative AOP. Here we demonstrate the use of proposed approach in two cases: inhibition of cytochrome P450 aromatase leading to reduced fecundity in fathead minnows and ionic glutamate receptor mediated excitotoxicity leading to memory impairment in rodents. The model created from these chains have value in characterizing the pathway and the potential or relative level of toxicological effect anticipated. This approach to simplistic, modular AOP models has wide applicability for rapid development of biological pathway models.

Background: Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications., Objective: To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults., Subjects and Methods: Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS)., Results: The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively., Conclusions: Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries., (Copyright © 2018 SEEN y SED. Publicado por Elsevier España, S.L.U. All rights reserved.)

Objective: To evaluate the caries-predictive validity of a screening Cariogram model without saliva tests in Chinese young adults with past caries experience., Methods: Two-hundred and fifteen Chinese young adults seeking dental care were included in this observational study, with informed consent obtained. All participants were 18-29 years of age and with past caries experience. A caries risk assessment was made at baseline using a full-blown Cariogram model and a screening Cariogram model without saliva tests; this assessment included bacterial count, saliva secretion and buffer capacity. Participants were divided into five risk groups according to 'the chance of avoiding caries' expressed in Cariogram software with two models. Each participant was required to receive re-examination after 2 years and then the actual caries increment (ΔDMFS) was calculated. The correlation between ΔDMFS and the ratings of caries risk was analysed using Spearman rank correlation. Differences between the full-blown and screening models were expressed as area under the receiver-operating characteristics (ROC) curve (AUC)., Results: One-hundred and ninety-two participants finished the 2-year follow-up study, after 23 dropped out. The mean 2-year caries increment was 0.67 ± 1.03. Both Cariogram models displayed a statistically significant relationship with caries development (P < 0.001): more new caries was found among those assessed with high risk compared with those assessed with low risk. No statistically significant difference of the AUC was found between the two Cariogram models (P > 0.05)., Conclusions: The Cariogram model without saliva tests does not significantly decrease the caries-predictive ability in young adults with a history of caries. This screening model is a potential tool for rapid caries risk assessment for such populations., (© 2018 FDI World Dental Federation.)

The development of new screening models for cancer therapy is indispensable for the improvement of cancer treatment, and for the creation of alternative possibilities in the field of chemotherapy. Screening models are routinely used to reduce the cost and resources involved in anticancer drug development. The value of any screening model will ultimately be ascertained by its ability to reliably predict the clinical response. Traditional in vivo screening models have been replaced with cell-based screening assays, and these cell-based models are under constant development to better mimic in vivo conditions. In this review, the developmental phases of anticancer screening models are discussed and exemplified, including the two-dimensional, threedimensional, cancer stem cell, and non-mammalian screening models. In addition, the use of new virtual screening tools as a model for anticancer drug development is highlighted., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: In the past few years, great of attention has been paid to the identification and characterization of selective and potent inhibitors of the first identified histone demethylase LSD1, which may erase mono- and di-methylated histone 3 lysine 4 and 9. As the aberrant overexpression of LSD1 is involved in various pathological processes, especially cancer, obtaining selective and potent LSD1 inhibitors has emerged as a crucial issue in medicinal chemistry research., Method: Until now, several LSD1 inhibitor screening models have been established, including enzyme coupled assay, LC-MS based assay, and FRET based assay. Nevertheless, due to some special instrument requirement and additional costs of LC-MS and FRET, the enzyme coupled assay is the most widely applied method for LSD1 inhibitor screening., Result: We summarized and compared several reported in vitro LSD1 inhibitor screening models. Each of them has distinct advantages and disadvantages, and none of these methods is perfect. In order to exclude the false positive results, at least one additional method should be applied to screen LSD1 inhibitors., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

The effects of a known antipsychotic agent (haloperidol) were assessed using both a behavioral model and a prolactin (PRL) model for screening potential antipsychotic drugs in the rat. The PRL model was found to be highly reliable and more sensitive than the behavioral model. [ABSTRACT FROM AUTHOR]