Although obesity is a well-known risk factor for hyperuricemia, it remains unclear whether obese subjects with metabolically healthy status have a decreased the risk of hyperuricemia and whether sex modifies the association of metabolically healthy obesity (MHO) with hyperuricemia risk. We aimed to investigate the sex-specific association between MHO and other obesity phenotypes and hyperuricemia, and to use Bayesian networks to determine and visualize the interactions among hyperuricemia and its related factors.This study was conducted using data from the China Health and Nutrition Survey 2009. Hyperuricemia was defined as serum uric acid ≥ 420 μmol/L in men and ≥ 360 μmol/L in women according to the guidelines. Body mass index (BMI) was used to define normal weight, overweight, and obese status in subjects, and metabolic health state was defined by the Adult Treatment Panel (ATP)-III and Visceral Adiposity Index (VAI) criteria, respectively. Subjects were categorized into six phenotypes according to their metabolic health and BMI level status.Of the 7,364 Chinese adult individuals included, the prevalence of hyperuricemia among MHO women was only 8.5% (95% CI 4.8 to 14.3%), but increased to 30.7% among MUO women, whereas the highest prevalence among men was found in the MUOW phenotype , compared to 15.4% for male subjects with MHO. After adjusting for confounders, the MHO phenotype was significantly associated with an increased risk of hyperuricemia compared with their MHNW counterparts in women  whereas a significant association was not found in men . A complex network structure was established by BNs and then used to find connections between hyperuricemia and its related factors, as well as their interrelationships. By using BN reasoning, the probability of having hyperuricemia was 0.076 among MHO men, while it reached 0.124 in MHO women.In conclusion, our results demonstrated that the MHO phenotype was significantly associated with the risk of hyperuricemia only in women, not in men. This sex-specific differences in the association may suggest a favorable condition of MHO for Chinese men with respect to hyperuricemia risk, meanwhile more attention should be paid to the increased risk of hyperuricemia among MHO women. Hyperuricemia is now becoming one of the most important public health concerns in international communities because of its rapid increase and significant impact on various clinical complications –3. RecenMetabolic syndrome (MetS) refers to a cluster of interrelated metabolic abnormalities such as increased blood pressure, hyperglycemia, central adiposity, insulin resistance, and dyslipidemia , 17. It Of note, there are clear sex-specific disparities regarding the prevalence of hyperuricemia and/or SUA concentration, owing to various factors. Potential explanations for sex differences in the SUA level include genetic predisposition related to susceptibility to DNA methylation, differences in immune response such as interleukin levels, hormonal influences, and environmental factors such as differential response to exposures . In contTo date, as far as we know, no consistent conclusions have been established regarding whether there is a sex-specific effect on the potential benefits of MHO status in terms of hyperuricemia risk in the Chinese population, and whether the degree and direction of the relationship between various obesity phenotypes and hyperuricemia differ by sex; therefore, this study aimed to investigate potential sex-specific aspects of the association of metabolic health and obesity with the risk of hyperuricemia in a population-level study of Chinese adults.This study used data from the China Health and Nutrition Survey (CHNS), a representative sample of the Chinese population. This survey is a large-scale longitudinal, household-based ongoing survey, and aims to examine the effects of health and nutrition across a set of large Chinese provinces. The comprehensive details of the CHNS and its sampling procedures have been described elsewhere . In brieThis survey involved self-administered and standardized questionnaires, physical examinations, and biochemical measurements. The standardized questionnaire was required to be completed by the participants in a face-to-face interview with well-trained personnel, and it was used to collect information on sociodemographic characteristics , behavioral factors , and their medications and self-reported family histories. Smoking status  and alcohol drinking  were defined according to participants’ smoking and alcohol drinking history.The physical examination followed standardized procedures and evaluated anthropometric measurements and BP measured by well-trained examiners. Body weight and height were measured with participants barefoot and in light clothing to the nearest 0.1 kg and 0.1 cm, respectively. Waist circumference (WC) was measured with an inelastic tape to the nearest 0.1 cm at a midpoint between the bottom of the rib cage and the top of the iliac crest, following exhalation. Hip circumference was measured over thin clothing at the point of the maximum circumference of the buttocks. Both circumferences were measured to the nearest 0.1 cm. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (meters). Systolic and diastolic blood pressures (SBP and DBP) were measured using a standardized mercury sphygmomanometer on an individual’s right arm. Three consecutive measurements of BP were taken on the same arm after a 10 min seated rest, and the mean of the three measurements was used for our analysis.After an overnight fast, fasting blood samples were collected in the morning from participants using a standardized process and then analyzed in a national central clinical laboratory in Beijing. Plasma and serum samples were then frozen, and stored at −86°C for later laboratory analysis. Serum levels of fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein-cholesterol concentrations (HDL-C), triglyceride (TG), and uric acid (UA) and other routine blood biochemical indices were measured by a biochemical autoanalyzer. Details of all laboratory analyses and measurements can be found elsewhere . Homeost≥ 420 μmol/L in men and ≥ 360 μmol/L in women according to the guidelines  the Adult Treatment Panel-III (ATP-III) definition of metabolic syndrome , and indAccording to the cross-classification of BMI categories and metabolic health status, study participants were categorized into one of the six following phenotypes: metabolically healthy normal-weight (MHNW); metabolically healthy overweight (MHOW); metabolically healthy obese (MHO); metabolically unhealthy normal-weight (MUNW); metabolically unhealthy overweight (MUOW); and metabolically unhealthy obese (MUO).B = , with G =  and a DAG defined by two sets: vertices or nodes (denoted by V) and arcs or directed edges (denoted by A), X = {iX, …,nX} a set of random variables and P a joint probability distribution. Specifically, the nodes are represented as circles representing the random variables and directed edges symbolize the relationships between the variables. Providing that there is an edge from node iX to node jX, iX is then referred to as the parent of jX, emphasizing a statistical dependence between the corresponding variables; similarly, jX is referred to as the child of iX. The distribution P can be written as the product of the local probability of each random variable, conditional on their parent variables in the graph G:A Bayesian network (BN) is a statistical model to describe probabilistic relationships and reasoning among a set of variables using a directed acyclic graph (DAG) and can be thought of as a fusion of incidence diagrams and Bayes’ theorem –44. A BNPa(iX) are the parents of iX in BN, and P(X) reflects the properties of BN  apply one of the most commonly used heuristic algorithms, Tabu , for graThe directions between certain variables were restricted by using a layering approach . First, P-value <0.05 was considered statistically significant. The bnlearn package  and 95% confidence intervals (CIs) calculated. The ORs and their 95% CIs for the association were adjusted sequentially for age, sex, urban/rural residence, smoking status, alcohol status, and metabolic health-obesity phenotypes (model 1), and then further adjusted for white blood cell, total cholesterol, LDL-C, hs-CRP, and diabetes variables (model 2). As there is no universal definition of metabolic health status, a sensitivity analysis was performed by using a new criteria based on the VAI , and met package  of the Rvs. 3.8%; alcohol intake: 61.1 vs. 8.7%, respectively). The occurrence of general obesity (including overweight) was similar between men and women (42.06 vs. 41.72%), whereas abdominal obesity was more common among women than men (56.9 vs. 49.6%). vs. 42.97%), MHOW (16.96 vs. 17.67%), MUNW (14.89 vs. 15.31%) and MUO (6.41 vs. 7.02%) were similar in men and women, respectively. However, a higher proportion of women were classified as MHO (2.66 vs. 3.6%), and a higher proportion of men were classified as MUOW (16.03 vs. 13.43%), when compared to their opposite sex counterparts   Table 2.In addition, the sex-specific prevalence of hyperuricemia, based on VAI criteria-based metabolic health status are shown in The overall and sex-specific associations of different obesity phenotypes with hyperuricemia are presented in In men, the age-adjusted OR for having hyperuricemia in MHO subjects was 1.26 (95% CI: 0.69–2.28), indicating that those subjects were unlikely to have a greater risk of hyperuricemia compared to their MHNW peers. This association was still non-significant when adjusting for various biomarkers (model 2), even though the magnitude of association raised to 1.46 (95% CI: 0.8–2.68) Table 3.Furthermore, when using VAI criteria-based metabolic health status , a highly similar pattern was found: the association between MHO and hyperuricemia was only significant in women, not in men, which justified a sex-specific effect on this association  since they restrict the directions of arcs.After demonstrating a sex-specific differences in the association between MHO and hyperuricemia, we further extended the investigation to understand how changes in metabolic health status, BMI levels, and other risk factors can affect the risk of hyperuricemia, as well as to simultaneously explore interrelationships among hyperuricemia and their related risk factors. Logistic regression modeling can only be used to evaluate the associated risk intensity between obesity phenotypes and hyperuricemia, but did not enable to reflect the complex relationship between metabolic health, BMI levels, risk factor, and outcomes because it required independence among variables; therefore, a BN modeling was finally used for this special purpose, as well as for estimating the conditional probability rather than selecting the significant variables Figure 3Then, a further examination of the arc strengths was performed by using a threshold of 0.85, and the resulting BN confirmed that 25 out of 37 arcs in the network appear with a frequency of at least 0.85. We can also use BNs to predict the conditional probability of having a hyperuricemia event given various evidence from specific nodes. In this nationally representative cross-sectional study using Chinese adults, we examined the association between various obesity phenotypes and hyperuricemia, with a specific focus on sex-specific effects. As one major finding, a significantly positive association between MHO and hyperuricemia was found in women only, after adjusting for potential confounders, whereas metabolically unhealthy status was associated with hyperuricemia regardless of sex. To the best of our knowledge, this is the first large and population-based study that investigated the sex-specific relationship between MHO and hyperuricemia in a Chinese population.vs. 2.90). Intriguingly, although MHO in men was significantly associated with hyperuricemia, women with the MHO phenotype still had a higher risk than men, with OR values of 4.6 and 1.6, respectively. In addition, the association of male MHO was just at the borderline of significance  based on the built network , 45. ForSeveral limitations of this study should be acknowledged. First, due to the nature of the cross-sectional design, we can only identify the association and its magnitude between MHO and hyperuricemia risk, and causality cannot be determined, even using BN modeling; further cohort studies are warranted to clarify our findings. In addition, the studied population included only Chinese subjects, so generalizability of the results to a wider population should be undertaken with caution. Nevertheless, the notable strengths and contributions of this study should be mentioned. First, the large sample size of our study guaranteed a reasonable statistical power that likely reflects real sex-specific associations. Second, our study offers a potential compelling application of BN in general medicine: BN not only provides an intuitive qualitative description of the relationship between hyperuricemia and its relevant factors, but also provides quantitative reasoning that can be used to explore the interrelationships among these factors and test novel hypotheses by simulation.In conclusion, by using a nationwide, population-based Chinese cohort, our study reports compelling evidence that the MHO phenotype was significantly associated with the risk of hyperuricemia, and more importantly, the association revealed sex differences that were only found in MHO women, not in men, which suggests a favorable condition of MHO for Chinese men with respect to hyperuricemia risk. In addition, we applied BN modeling to show the interrelationships between hyperuricemia and its related factors. BN modeling provides insights that will help to identify appropriate target populations and relevant risk factors for managing hyperuricemia risk.http://www.cpc.unc.edu/projects/china/data.Publicly available datasets were analyzed in this study. This data can be found here: The studies involving human participants were reviewed and approved by the University of North Carolina at Chapel Hill, the National Institute of Nutrition and Food Safety, Chinese Centers for Disease Control and Prevention, and the China-Japan Friendship Hospital. The patients/participants provided their written informed consent to participate in this study.Conceptualization: SZ and ST. Data acquisition: YL and AF. Statistical Analysis: ST. Investigation: SL and YL. Writing—original draft: ST and ZL. Writing—review and editing: SZ, YL, and AF. All authors contributed to the article and approved the submitted version.This research was funded by National Natural Science Foundation of China (81803329) and China Postdoctoral Science Foundation (2018M631780).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.


Recently discovered carbon quantum dots (CQDs) and graphene quantum dots (GQDs) with excellent optical properties sparked considerable interest in the scientific community. First of all, these CQDs and GQDs can be conveniently prepared using green protocols from virtually any carbon-rich media making them more attractive compared to other optical materials. Second, the emission spectrum of CQDs and GQDs can be tuned by controlling the reaction parameters or by incorporation of dopant ions. Finally, low-cost, non-toxic, inert, and photostable CQDs and GQDs already exhibited promising results in areas such as bioimaging, sensing and detection, photocatalysis, energy, and security printing.In this special issue, we placed a special focus on the synthesis of novel fluorescent carbon-based nanomaterials for bioimaging purposes. In addition to that, several works also covered other interesting areas such as sensing and detection. In total, one review article and four research articles were published in this special issue after a rigorous peer-review process.Younis et al. explicitly covers top-down and bottom-up strategies for the synthesis of GQDs. In addition, the authors covered fluorescence, two-photon, magnetic resonance, and dual-mode bioimaging applications of GQDs with selected examples from the scientific literature. Finally, toxicity issues, current challenges, and future prospects were deliberated.A review article titled “Recent Advances on Graphene Quantum Dots for Bioimaging Applications” prepared by Liu et al. prepared a multifunctional nanocomposite comprising of Fe3O4, upconversion nanoparticles, and hollow carbon sphere. Thanks to the presence of Fe3O4, obtained nanocomposite can be utilized as an MRI contrast agent with a high r2 value of 57.7 mM−1 s−1. Under 980 nm excitation, upconversion nanoparticles can generate green and red fluorescent signals suitable for deep and safe NIR-excitable bioimaging. Furthermore, the carbon sphere can convert the absorbed NIR radiation into heat making this nanocomposite suitable for photothermal therapy. Excellent biocompatibility and multimodality of prepared nanocomposite can be useful for the development of future contrast agents.Zhang et al. reported the synthesis of novel CQDs using the Rosa roxburghii biomass precursor. According to their results, CQDs with a mean size of 2.5 nm and quantum yield of 24.8% can be obtained. These CQDs showed good biocompatibility and were suitable for fluorescence imaging of Hep3B cells. Moreover, these CQDs can be also used for o-nitrophenol detection in the linear range of 0.08–40 μmol/L with a limit of detection of 15.2 nmol/L.Chen et al. for the synthesis of fluorescent CQDs with an average size of 4.5 nm and quantum yield of 12.45%. The authors demonstrated that prepared CQDs can be used for selective Fe3+ detection in the range of 8–80 μM with a limit of detection of 3.8 μM. Later on, the authors demonstrated that these CQDs can serve as a fluorescent label for bioimaging purposes and for intracellular Fe3+ detection inside fungal cells.The microwave-assisted method was used by Niu et al. utilized fluorescent GQDs with an average diameter of 5 nm for the detection of sunset yellow food additive using the electrochemiluminescence (ECL) method. Under optimized conditions, GCDs based sensor is suitable for sensitive detection of sunset yellow in the range of 0.0025–25 μM with a limit of detection of 7.6 nM. A proposed method can serve as a basis for the development of low-cost and rapid screening methods of some food additives.Thanks to their unique fluorescent properties, CQDs and GQDs have been extensively applied for bioimaging, sensing, catalytic, and solar light conversion purposes. In general, synthesis protocols, size and shape control, tuning of fluorescence emission has been advanced significantly in recent years. On the other hand, some important questions still need to be investigated. Among them, the origin of excitation-dependent emission, the position of dopant ions and their role in fluorescence emission, and the mechanism of upconversion emission. Therefore, this special issue collected recent promising studies that substantially improved the scientific knowledge and partially filled the existing gaps. On the other hand, some unresolved questions still remain, especially, the fundamental understanding of the fluorescence processes in CQDs and GQDs. We hope, that these important questions will be addressed in the near future.It was a great pleasure for us to edit such an exciting special issue for Frontiers in Chemistry journal. The guest editors hope that this collection of articles will gain considerable attention from the scientific community.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


Objective: Road traffic injuries are leading cause of death and economic losses, particularly in developing countries such as Iran. Thus, increased understanding of the causes of traffic accidents can help solve this problem.The primary goal of this study was to examine attentional bias, decision-making styles, and impulsiveness in drivers with safe or risky driving behaviors. The secondary purpose was to determine the variance of each variable among 2 groups of drivers.Method: This was a cross sectional design study, in which 120 male drivers aged 20-30 years  were recruited from Tehran using sampling technique. Barratt Impulsiveness Scale (BIS), Decision-Making Style Scale (DMSQ), Manchester Driver Behavior Questionnaire (MDBQ), Self-Assessment Manikin Scale (SAM), and Dot Probe Task were used. The analyses were performed using IBM SPSS version 22.Results: The mean age of participants was 26 years. Significant differences were found between impulsiveness  and decision-making styles (spontaneous and avoidant) between the 2 groups. Also, based on the results of discriminant function analysis (DFS), the subscales of impulsiveness and 2 decision-making styles explained 25% of the variance in the 2 groups of risky and safe drivers.Conclusion: Findings of this study indicated that impulsiveness and 2 decision-making styles were predominant factors. Therefore, not only is there a need for research to reduce traffic accidents, but studies can also be helpful in issuing driving licenses to individuals. Road traffic injuries are an important cause of physical, psychological, and financial injuries and are a major threat to the public health . Road trThe role of psychological factors is important in accidents. Recent studies have paid a special attention to the role of cognitive factors, including impulsiveness, attentional bias, and decision-making styles in driving behaviors and have found significant associations between these factors and risky driving behaviors -12. The Multiple studies have shown that impulsive personality has a major role in risky driving behaviors , 7, becaIn other words, impulsiveness refers to a tendency to act suddenly without any planning in advance according to external or internal stimuli, without considering the personal or social outcomes of the action .Barratt et al (1997) designed a comprehensive systematic theory involving biological, behavioral, environmental, and cognitive factors. They distinguished between 3 aspects of impulsiveness: (1) motor impulsiveness (tendency to act with stubbornness and without planning in advance), (2) attentional impulsiveness (inability to focus on immediate tasks or cognitive instability), and (3) non-planning impulsiveness (inability to plan or think carefully) .Decision-making is another important factor in driving behaviors. Making decisions during driving is a vital factor in driving behavior , and proAttentional bias is another cognitive factor with an important role in driving behavior . AttentiFrom the cognitive perspective, impulsivity, decision-making styles, and attentional bias have different meanings -26. For This was a cross sectional study in which drivers aged 20-60 years who met the inclusion criteria were recruited. Sampling was done among high-risk drivers. According to the literature review, the prevalence of high-risk driving behaviors was high among male drivers compared to females with high risk driving behavior. In this study, drivers who met the inclusion criteria for entering the high risk group, were male drivers aged 20-34 years; this was consistent with the research literature , 31. A tAt the first stage, the researcher intended to recruit drivers from car clearance centers (storages for seized vehicles). However, sampling was not possible in the Car Clearance Center because we needed a lap top to do it, but due to security reasons we were not allowed to carry or use lap tops in the center. Therefore, sampling was done at Pasargad Insurance, Iran Insurance, and Sadeghieh Occupational Medicine center in Tehran. The initial sample included 120 participants, but due to attrition, a total of 117 participants entered the analysis. After collecting the demographic information, first, Dot Probe Task and then Barratt Impulsiveness Scale (BIS), Manchester Driver Behavior Questionnaire (MDBQ), and Decision-Making Style Scale (DMSQ) were administered. The data were analyzed with independent samples t test, repeated measures ANOVA, and discriminant function analysis. All analyses were performed using SPSS, version 22.The following measures were taken to protect participants’ privacy: Written consent forms were obtained from all the participants, and they were allowed to leave the study at any time.The participants could leave the study at any time.The participants were reassured that their personal information would remain confidential and would only be used for research purposes.Codes were substituted with real names in questionnaires and the computerized task, and only the researcher kept the information.Risky Driving GroupTo examine the human factors of road accidents, we should distinguish between driving errors and violations. Error is the inability to exercise proper judgment or perform a series of actions designed to achieve a desirable result, which include slips and blunders. Violations include the behaviors that increase the risk of driving. There are 2 types of violations: unintentional violations and intentional violations. Unintentional violations include unintentional behaviors that lead to violation of rules, such as driving slowly in a narrow 2-sided road. Intentional violations include behaviors aimed at harming others or violating the rules, which are considered a form of vandalism . TherefoSafe Driving GroupThe drivers with safe driving behaviors were matched with those with risky driving behaviors by age and education. This group included drivers who had their driving license for at least 2 years, and had no history of risky driving behaviors, including intentional violations .The exclusion criteria for both groups included presence of the diagnostic criteria of an active psychiatric disorder at the time of interview and history of brain injury or epilepsy.First, the Dot Probe Task and then the other instruments were administered for the participants. Data were analyzed using independent samples t test, repeated measures ANOVA, and discriminant function analysis. All analyses were performed using SPSS, version 22.InstrumentsThe Barratt Impulsiveness Scale Version 11 (BIS-11): This 30-item scale was developed by Barratt in 1995. The items are answered on a 4-point scale, ranging from 1 (never) to 4  and assess 3 factors: motor impulsiveness (acting on the spur of the moment), attentional impulsiveness (tenancy to make quick decisions), and nonplanning impulsiveness (lack of foresight) (resight) . Differeresight) , 39. Numresight) . Thereforesight) . In the The Manchester Driver Behavior Questionnaire (MDBQ): This 50-item questionnaire was developed by Reason et al in 1990 at the University of Manchester. Validated in various European countries, the MDBQ is based on the idea that there are different psychological causes for violations and errors and that the 2 constructs should be distinguished : This 25-item questionnaire was developed by Scott and Bruce in 1995 to examine decision-making styles (g styles . The GDMg styles . Previoug styles . In Irang styles . In the The Dot Probe Task: This computer-based task was developed by MacLeod (1986) using words, and it is commonly used to assess attentional bias  and stayed on the screen until the proper key on the keyboard was pressed by the participant. The reaction time to the bright dot was assessed by the computer. The bright dot took the places of the neutral and emotional stimuli equally. Slower reaction times indicated lower levels of vigilance to the presented stimuli  . The SelThe Self-Assessment Manikin Scale (SAM): We used the paper-and-pencil, the 9-point version of the SAM developed by Lang in 1980 : This system was used to choose neutral images. Pictures showing risky driving behavior were shown to 10 drivers from Tehran and proper pictures were selected. For each picture that was presented, the drivers were asked to determine whether it showed a risky driving behavior or not. Then, they assessed the picture on a scale using the SAM. The pictures perceived by all of the 10 drivers as showing a risky driving behavior were selected. In addition, only those pictures were selected that were related to the highest level of arousal and the lowest level of pleasure. Finally, 32 pictures, including 16 neutral pictures and 16 pictures showing risky driving behavior were selected. The resolution of the pictures was set at 200x300 pixels.A total of 116 men participated in the present study. The mean (SD) age of participants was 26.43 years (SD = 3.87), and 69.3% of the participants were single and 30.8% were married. In addition, 44.5% of the participants did not complete high school, or had a high school diploma, or an associate degree; 41.4% had a bachelor’s degree, and 13.8% a master’s degree. Moreover, 68.4% of the participants had a full-time job, 15.4% had a part-time job, and 12.8% were students.No significant difference was found between the 2 groups in age, marital status, education, and job status . Independent samples t test was used to compare the 2 groups for attentional bias, impulsivity, and decision-making styles. No significant difference was found between the 2 groups in attentional bias and rational, intuitive, and dependent decision-making styles, but significant differences were found in attentional impulsiveness, motor impulsiveness, and nonplanning impulsiveness and spontaneous and avoidant decision-making styles .Repeated measures ANOVA was used to compare scores on impulsiveness and decision-making styles between drivers with safe or risky driving behaviors. In this study, independent t test was used to determine difference between groups for impulsivity and decision-making style. The repeated measure ANOVA was used to identify different forms of impulsiveness and decision-making style. Thus, the 2 groups (high-risk and safe drivers) were considered as intergroup variables while scores on decision-making style and impulsivity were assessed as an intragroup variable. Although there were 2 comparison groups, the researchers decided to use repeated measure ANOVA to minimize error. Consequently, each variable with subscales was presented and each of these subscales was considered as a level. Therefore, the 2 groups were compared at the same time based on different subscales. On the other hand, if the research team used independent t test for each subscale, they had to repeat the test for several time. Thus, repeated measure ANOVA as a strong and advanced analysis was considered. Significant differences were found in scores on the impulsiveness subscales between the 2 groups of drivers  and 2. IDiscriminant function analysis was used to determine which groups of the drivers was more similar to the study sample. Before conducting the discriminant function analysis, the assumption that the data were normal and continues was examined and confirmed. The results indicated that 25% of the variance of being a member of the 2 groups was explained by the 3 impulsiveness subscales and 2 decision-making styles. In addition, the calculated Wilks' Lambda was statistically significant .According to the results, means of 0.564 and -0.587 were found for the drivers with risky driving behaviors and those with safe driving behaviors, respectively. Therefore, a driver with a negative score would be a member of the safe driving group and a driver with a positive score would be a member of the risky driving group.According to the results, among the drivers with risky driving behaviors, 67.2% were correctly identified (true positive) and 32.8% were wrongly identified as drivers with safe driving behaviors . In addition, among the drivers with safe driving behaviors, 71.2% were correctly identified as drivers with safe driving behaviors (true negative) and 28.8% were wrongly identified as drivers with risky driving behaviors . The discriminant power was equal to .70. In other words, the discriminant function classified 70% of the cases correctly.The results indicted significant differences between the drivers with risky or safe driving behaviors in attentional, motor, and nonplanning impulsiveness (P < .05). In other words, drivers with risky driving behaviors had a higher mean on all the 3 subscales of impulsiveness. Previous studies have also indicated significant differences between the 2 groups of drivers in impulsiveness subscales . For exaThe present study revealed no significant difference among rational, intuitive, and dependent decision-making styles in the 2 groups. However, a significant relationship was found in avoidance and spontaneous decision-making style between the 2 groups. Although these results differs from some published studies , 54, theFrom a biological and neuropsychological perspective, impulsivity is associated with 2 basic motivational systems: behavioral inhibition system (BIS) and behavioral activation system (BAS). The BIS is responsible for controlling responses that lead to punishment and failure. On the other hand, BAS is responsible to control responses related to rewards and no punishment. Individuals with less active BIS are less likely to recognize unpleasant stimuli and evaluate them as a threat. Individuals with overactive BAS have difficulty in learning inhibitors because of strong motivation for reward. On the contrary, BIS with overactivity has high punishment-sensitivity . Reward  Numerous studies have been reported that decision-making as a major cognitive function is associated with behavioral inhibition . In factA number of limitations could have influenced the results of this study. First, sampling was not performed at car clearance centers because of the nature of the research and measurement of attentional bias by personal laptop. Second, accidents and driving violations is a multidimensional issue. Then, controlling nonhuman factors  and other human factors such as physical health was not possible. Third, the research team could not find high-risk women drivers, so men drivers were selected. However, as the number of women drivers is increasing, future studies should be conducted on driving behaviors of both sexes.In brief, the difference between impulsivity and decision-making styles between groups was explained by drivers' cognitive ability and increased frequency of high-risk driving behaviors. Direct or indirect effect of psychological factors on driving has led to unprincipled, low-quality and dangerous driving. All individuals who has obtained a driver's license are not qualified to drive and more psychological assessments should be performed on drivers to ensure their safe driving.


Myxine fernholmi  was analyzed by the primer walking method. Its mitogenome was 18,862 bp in total length and was composed of 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes. The gene content and order were congruent with those of typical vertebrates. In the phylogenetic tree, M. fernholmi showed the closest relationship to M. glutinosa in the same genus and subfamily and well separated from the other hagfish in the subfamily Eptatretinae.The full-length mitochondrial genome of the Fernholm’s hagfish,  Myxine fernholmi inhabits off southern Chile and the Falkland Islands  and regarded as the most primitive lineages of vertebrates. They have the sisterhood relationship to gnathostomes (or jawed vertebrates)  and deposited in the National Marine Biodiversity Institute of Korea (MABIK Lot No. 0016059). Genomic DNA was extracted according to Asahida et al. . They were aligned together with that of M. fernholmi in this study and refined manually. For the phylogenetic analysis, the nucleotide matrix of 12 protein-coding genes, excluding nad6 was created and divided into three partitions, consisting of 2998 bp each for the first, second and third bases of each codon triplet, respectively. A phylogenetic tree was reconstructed using RAxML 7.0.4 (Stamatakis Three mitogenomic sequences of the species belonging to the order Myxiniformes were retrieved from GenBank in NCBI (M. fernholmi was a circular molecule of 18,862 bp in total length, consisting of 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes, of which gene content is identical not only to that of the congeneric Myxine glutinosa (Delarbre et al. The mitogenome of M. fernholmi, a phylogenetic tree was reconstructed by the ML method, using the nucleotide sequence matrix from 12 protein-coding genes (Eptatretus species belonging to the subfamily Eptatretinae were consistently separated from two Myxine species belonging to the subfamily Myxininae. Myxine fernholmi in this study clustered with the congeneric Myxine glutinosa with 51% bootstrap value.With the full-length mitogenomic sequence of ng genes . In the M. fernholmi was analyzed for the first time, and the phylogenetic tree among hagfish was reconstructed with reference to their mitogenomic data. Our data will provide baseline data for the molecular identification, geographical distribution, population genetics and conservation biology for the rare natural living resource, M. fernholmi in the Southwest Atlantic Ocean.In this study, the complete mitogenome of


This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects  were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group . Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD. Attention deficit hyperactivity disorder (ADHD) is characterized by three major symptoms: attention deficits, hyperactivity, and impulsivity . The preWhile ADHD has a very high prevalence among children and adolescents, it is difficult to clinically diagnose precisely. ADHD has many comorbidities such as mood disorders, oppositional defiant disorder, conduct disorder, anxiety disorders, and sleep disorders, making ADHD diagnosis difficult . FurtherCurrently, there are three subtypes of ADHD classified using QEEG. The first is the maturational lag subtype characterized by increased slow wave and decreased fast wave activity ; EEG actResearch on the diagnostic accuracy of resting QEEG in ADHD has been conducted previously –12. HoweIndividuals who visited the child and adolescent psychiatric clinic at Daegu Catholic University Hospital from 2018 to 2020 were considered for the study. Participants between 7 and 12 years of age diagnosed with ADHD according to the DSM-5 criteria were included in the study. The ADHD diagnosis was based on a Korean version of the Diagnostic Interview Schedule for Children Version IV (DISC-IV), which is a structured interview tool, and these diagnoses were confirmed by multiple child and adolescent psychiatrists. If participants did not meet the ADHD diagnostic criteria of DSM-IV and DISC-IV, they were assigned to the Neurotypical (NT) group. Based on the results of the DISC-IV test, participants were assigned to the ADHD or Non-Other Specified (NOS) group. Patients who met the diagnostic criteria of ADHD in DSM-IV, but whose score did not exceed six, and had a score of more than three in the attention deficit or hyperactivity/impactivity scale of DISC-IV were included in the NOS group. Children with a history of brain damage, neurological disorders, genetic disorders, substance dependence, epilepsy, or any other mental disorder were excluded from participation. Children with an IQ of 70 or lower according to the Korean-Wechsler Intelligence Scale for Children (Fourth Edition) or who were receiving drug treatment were also excluded from this study. Based on the inclusion and exclusion criteria, 74 subjects  were enrolled in this study. Detailed information regarding the study was provided to the parents and children. Written consent for the medical use of the test results and participation of the children in this study were obtained from all of the participants’ parents. In addition, after receiving a detailed explanation of the study, all of children participated voluntarily and provided written consent for participation. This study was approved by the Institutional Review Board (IRB) of the Daegu Catholic University Medical Center  and was performed in accordance with the Declaration of Helsinki .The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents. Using DISC-IV, the presence of nine symptoms of attention/concentration issues and nine symptoms of hyperactivity–impulsivity over the past 6 months can be assessed. When symptoms were noted, detailed questions, such as whether the symptoms were observed at home or school, were asked. The DISC-IV was revised in 2000 by the U.S. National Institute of Mental Health. The present study used the Korean version of the DISC-IV, which was translated into Korean in 2007 and subsequently underwent reliability(κ = 0.67) and validity(κ = 0.65~1.00) verification [For screening purposes, the 18-item Korean version of the ADHD Rating Scale was used to assess ADHD behavior . The KARwww.braintrain.com) to obtain behavioral measures of attention. The IVA CPT results yielded standardized scores of attention and the Response Control Quotient for each visual and auditory stimulus based on normative data. Cronbach’s alpha index of that study was 0.72 and that of our study was 0.7 [For screening purposes, the CPT was used to assess the level of functioning of the attention/arousal system. We used the Intermediate Visual and Auditory Continuous Performance Test  amplifier and a 10–20 layout 64-channel Quick-Cap electrode-placement system . The EEG data were digitally recorded from 19 gold cup electrodes placed according to the international 10–20 system . The impedances were maintained below 5 kΩ, and the sampling rate was 1000 Hz. We used the linked mastoid reference and two additional bipolar electrodes to measure horizontal and vertical eye movements. During the recording, each participant laid in a dimly lit, electrically shielded, sound-attenuated room. Resting EEG recordings were recorded after three minutes with the participants’ eyes closed.We used MATLAB 7.0.1  and the EEGLAB toolbox  to pre-pFive frequency bands were defined for further analysis: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (12–30 Hz). We investigated the power spectra of the EEG data for each subject using the short-time Fourier transform ‘spectrogram.m’ function from the Signal Processing Toolbox in MATLAB. Time windows of 1,000 ms with an 800 ms overlap and Hamming window were used for the spectral analysis. Outliers that were far from the spectral value distribution of each frequency band at the 0.05 significance level were removed. Finally, the absolute powers were averaged over all the time windows and frequency bands for further analysis. The data from participants were converted to Z scores based on the means and standard deviations of the NeuroGuide normative database . The Z sThe MATLAB 7.0.1 Statistical Toolbox was used for the statistical analyses. All of the values are expressed as means and the standard deviations. First, an analysis of variance (ANOVA) was conducted on the QEEG results for the ADHD, ADHD NOS, and NT groups. Second, we used the statistical function named 'classify.m' in MATLAB to implement the classifiers of linear discriminant analysis (LDA). LDA is used to determine the linear combination of features that can better separate two or more classes . LDA entThere were 74 children  included in the analysis. There was no significant difference between the mean ages of the males and females , at 8.9±1.2 years and 8.7±1.2 years, respectively. The mean KARS score for all subjects was 29.47±12.57 for males and 23.19±14.40 for females, with no statistically significant difference between males and females . Based on the DISC-IV results, we identified the differences between the groups by categorizing them into three groups: 27 participants  in the ADHD group, 32 participants  in the ADHD Not Otherwise Specified (NOS) group, and 15 participants  in the Neurotypical (NT) group.Using ANOVA, the mean-to-mean difference between the three groups was investigated with respect to the absolute and relative power of the delta, theta, slow alpha, fast alpha and beta waves. Among the waves, the fast-alpha absolute power  and beta absolute power  showed a difference in meaningful scores between the three groups. Following the Scheffe post-hoc test, the ADHD and NOS groups were found to be significantly lower in fast alpha absolute power and beta absolute power than NT.Using cluster analysis, the QEEG results were divided into groups (a) to (d). The percentages of NT, NOS, and ADHD groups in each group were obtained and plotted on a circular graph . In addiThe topography of groups (a)-(d) divided through the cluster analysis was implemented through EEGLAB . Groups The present study classified ADHD children into NT, NOS, and ADHD groups using a structured interview (DISC-IV), and the results of their attention test and QEEG characteristics were assessed for each group. We also divided participants into four groups through cluster analysis of the QEEG results, determined the distribution of the ADHD, NOS, and NT group, and constructed topographical plot to identify the characteristics of the QEEG results according to group.The results showed that the demographic characteristics of the subjects did not differ in terms of age and gender. A gender ratio of 3:1 favoring males was found, similar to what has been observed in previous studies . Using cGroup (c) included 47% of ADHD groups and had elevated theta absolute power and low fast alpha and beta relative power . This reGroup (b), which comprised of 59% of participants from the NOS group, was characterized by the elevated alpha waves. Alpha waves are functionally associated with arousal , and eleLastly, group (d) included 62% of participants from the NT, and fast alpha and beta wave absolute power were elevated, but relative power was relatively equal compared to the other groups. These elevations in absolute power may be an error of the measurement tool employed. For EEG measurements, it is important to control for artifacts and resistance. A moderate amount of electrode paste is required to attach electrodes, and excessive amounts of paste can cause electric bridges with other electrodes, which distorts the distribution of EEG. Furthermore, using too little paste also increases and causes error in measurements. The measurement environment is also important, and if the room is too hot or humid, it may cause problems such as sweating which may affect resistance and artifacts . TherefoThis study has several limitations. First, we failed to fully consider the IQ of the subjects, although we excluded subjects below an IQ of 70. It is known that EEG can vary depending on an individual's IQ, so this variable should be controlled for. Second, the number of participants among the three groups  was inconsistent, especially the number of NT . Third, When diagnosing ADHD in clinical practice, QEEG is available as an auxiliary tool, providing additional information regarding the QEEG subtypes. An elevated delta power and decreased theta power, or an elevated theta power and decreased fast wave likely indicates a diagnosis of ADHD. On the other hand, if alpha waves are high, it is possible that attention-deficit symptoms may have been caused by childhood depression, or that a comorbidity such as childhood depression may be present rather than ADHD only. Finally, if absolute power is shown to be high overall, it is necessary to evaluate QEEG using relative power.S1 FigAbbreviations: ADHD, Attention-Deficit Hyperactivity Disorder; NT, Neurotypical; NOS, ADHD Not Otherwise Specified; K-ARS, Korean ADHD rating scale, IVA+Plus, Integrated visual and auditory test; H/I, Hyperactivity/Impulsivity; Inatt., Inattention.(TIF)Click here for additional data file.


Background: Early detection of breast cancer alters the prognosis and tools that can predict the risk for breast cancer in women will have a significant impact on healthcare systems in low- and middle-income regions, such as North Cyprus. Objective: In this study, we developed a simple breast cancer risk model for the women of North Cyprus. Methods: Data from 655 women, consisting of 318 breast cancer cases and 337 hospital-based controls, was used to develop and internally validate the model, external validation was carried out using, 653 women consisting of 126 cases and 527 controls. Data were obtained from medical records and interviews after informed consent. Results: A model was derived that consisted of age ≥50 years and <50 years and the presence and absence of >1 first-degree relatives (FDR) with breast cancer. From internal and external validations the model’s AUCs were, 0.66 (95% CI = 0.62–0.70) and 0.69 (95% CI = 0.63–0.74) respectively. Conclusions: A unique model for risk prediction of breast cancer was developed to aid in identifying high-risk women from North Cyprus that can benefit from mammogram screening. Further study on a large scale that includes environmental risk factors is warranted. Breast cancer is a disease that has remained the leading cause of death in women worldwide . In 2018Additionally, the prognosis is always poor for women living in low- and middle-income countries ,8, becauNorthern Cyprus, can be regarded as a middle-income society, small, enclosed, ideal for epidemiological research, has a typical western Mediterranean lifestyle, with living conditions and diets that should be favorable for good health ,10. PrevIn high-income countries, the incidence and mortality associated with breast cancer are decreasing. These may be accounted for because of access to mammogram screening, effective health services, and good execution of breast cancer prevention campaigns, as well as primary prevention policies and guidelines ,15,16. YDespite the fact, mammogram screening has been supported for the early detection of breast cancer, it is not cost-effective and feasible for developing countries, because of the large population of women, underfunding, infrastructure and human expertise needed ,22,23,24Risk prediction models can be used to identify high-risk women that will be eligible for mammogram screening. Thus, reducing the overload on the limited facilities available and narrowing the focus on the appropriate group can also reduce administering unnecessary radiation to women, who are not eligible, while at the same time reducing the economic burden on the government. Currently, several comprehensive breast cancer risk assessment tools exist that incorporate various risk factors for the calculation of breast cancer risk .Many risk factors play a role in the development of breast cancer. Environmental factors that contribute to interethnic variations in risk, age, lifestyle factors, endogenous and exogenous hormonal exposure, childbearing, breastfeeding, reduced physical activity, dietary consumption, certain reproductive factors , body size/obesity contribute to differences in incidence , family Breast cancer risk assessment models are categorized into empirical models or genetic models . A patteThis study was carried out in the hospital, Burhan Nalbantoglu Devlet Hastanesi, Lefkosa, North Cyprus. This hospital treats all breast cancer cases in North Cyprus. Ethical approvals were obtained from Near East University, scientific research evaluation ethics committee, and Burhan Nalbantoglu Devlet Hastanesi’s ethics committee, before the research was carried out (YDU/2018/55-523). All methods were performed following the relevant guidelines and regulations.The retrospective dataset of 655 women, collected from April 2018 to December 2018, was used to derive the model. A total of 318 women had newly confirmed breast cancer, and 337 women were without breast cancer. Women with a history of lobular or ductal carcinoma in-situ were excluded. Only participants between the ages of 30 to 84 years were included in the whole study groups. Informed consent to participate was obtained after the aims of the study were explained by a medical professional.Retrospective medical and demographic information of all participants were collected through interviews.The interview included: age, age at menarche, age at first delivery, menopausal status, presence or absence of benign breast disease, history of breast cancer in first-degree relatives or other relatives, history of hormone replacement therapy including estrogen/progestin and breast density.Sampling size was based on the following calculations:N = 121,257 (Women Population Size);t = t value = 1.96, (at α = 0.05);p =  = 91/100000 = 0.00091 (Expected Frequency);q = 1 − p = 0.99909;d = (Acceptable margin of Error) = 0.001;The required sample size = 317.8 women.The frequency of the risk factors of the study group was analyzed using descriptive statistics. An initial multivariable logistic regression was carried out. The significant variables were considered for further multivariable logistic regression. A forward multivariable logistic regression was used to access the final model. In the multivariable regression analysis, the categories that conferred protection against breast cancer were used as the reference. All statistical analysis was performed with IBM Spss .The whole dataset of 655 women, consisting of 318 breast cancer cases and 337 without breast cancer from the derived phase, was used to internally validate the model using bootstrap with 200 repetitions ,39. For Separate information of 653 women, consisting of 126 cancer cases and 527 women without breast cancer, collected between November 2018 to January 2020, were used to externally validate the model. Total scores for individuals were calculated based on the derived scoring scheme, and the c-statistic was then estimated.A total of 655 women were used to derive the model. Among them, 51.1% were above 50 years; 48.5% (318) of the women had breast cancer, while the rest reported with no breast cancer. A > 1 FDR with breast cancer was reported in 9.9% of the study population .A total of 10 variables were analyzed to access the risk model, After an initial logistic regression of all the variables and two successive forward multivariable logistic regression, the risk factors that were observed to be insignificant were eliminated at each step, then we arrived at two significant risk predictors that comprised the final model, that is >1 FDR with breast cancer OR = 3.0 (95% CI 1.6–5.4) and age above 50 years OR = 3.0 (95% CI 2.2–4.1). The c-statistic of the final model on internal validation was 0.66 (95% CI 0.62–0.70). The estimated coefficients of the two variables served as the basis for the scoring with a range of 0–2. The risk scores were stratified into three groups; low-risk (0) women <50 years and with no >1 FDR with breast cancer, moderate-risk (1) women with >1 FDR with breast cancer or ≥50 years and high-risk (2)—that is, women—≥50 years and with >1 FDR with breast cancer.From the internal validation the average Doriginal and Dboot were (0.328) and (0.350) respectively. The bias or optimism was (0.022).Separate information from 653 women, was used for external validation, consisting of 126 women with breast cancer and 527 women without breast cancer. From In comparison, to a recent validation study carried out on a large cohort, the model showed similar c-statistics, sensitivity, and specificity to the Gail, IBIS and BOADICEA models . The othInherited factors elucidate just about a quarter of breast cancer risk . Meta anAging plays a role in the pathogenesis of breast cancer because of genetic instability, telomere attrition, epigenetic alteration, stem cell exhaustion associated with aging.From 50 years to 70 years and above, the risk of breast cancer increases .The assessment of risk is vital for the management of breast cancer. High-risk women will not automatically have breast cancer, but they are strongly advised to visit cancer clinics. They should have a mammogram at least yearly, for this has been shown to increase detection rates and reduce mortality ,60,61,62We recommend that the women of North Cyprus categorized as high-risk for breast cancer can benefit from regular monitoring using mammograms, early detection, and preventive interventions such as healthy lifestyle and medication.Those women with moderate-risk of breast cancer, should undergo screening of breast cancer risk using mammograms biennially. For women above 50 years with no >1 FDR with breast cancer, while those women below 50 years with >1 FDR with breast cancer can consider the use of medications, such as tamoxifen or raloxifene to reduce the risk of developing breast cancer. Risk and benefits have to be assessed by a medical professional.Low-risk women are required to indulge in primary care since their risk is not different from the general population, but this status is liable to change in the presence of modifiable risk factors. Thus, these women are encouraged to maintain a healthy lifestyle and breast care. Increased awareness of existing and identified risk factors will aid in evaluating their current status and make the appropriate decision for mammogram screening if warranted. These recommendations are summarized in This model can serve as a simple, noninvasive, alternative screening for the identification of high-risk women, thus streamlining the focus of the limited mammogram resources to the right group in low- and middle-income countries such as North Cyprus. Using this model will also reduce unnecessary mammograms needed and radiation exposure to potentially low-risk women. The use of the risk prediction model has additional advantages, as it is not dependent on physical examination, easy to utilize and implement, being cost-effective and will enhance outcome and survival for women categorized as high-risk.The poor funding of health systems in low- and middle-income countries causes problems in the implementation of mammogram screening programs, thereby leaving many of the women out and only a few from the urban centers with insurance policies are privileged to participate.This model can serve as additional screening tools that will aid in identifying women at high-risk that will need an immediate mammogram, thus reducing the burden on the already constrained facilities and hence saving lives.The progress in mobile internet services in low- and middle-income countries has made online materials easily accessible to everyone. Educating and empowering women on how to use the risk prediction tools online will drastically reduce the number of high-risk women that cannot have access to early detection facilities. These will protect more women at the individual and population levels.This initiative is maybe similar to the mobile health  initiative launched by the world health organization in 2012, whereby mobile phones were used to improve the prevention, detection, and management of diseases in low-income countries . The risRisk prediction tools at the moment can serve as cost-saving tools. However, the benefits can only be maximized when all identified high-risk women can receive further confirmation screening and treatment. Therefore, identified high-risk women will still need to visit healthcare centers for counseling and prophylactic treatment.While the models intend to ascertain the risk for an individual, the risk factors utilized to depend on population risk from epidemiological investigations. Therefore, more studies have to be carried out among various populations of women in other to identify new lifestyle/environmental factors, biomarkers, genetic markers and incidence rates that are peculiar to that group, which can be incorporated into prospective risk models because the possibility of identifying those at high-risk would be enhanced by using a comprehensive risk model that integrates all known risk factors . BecauseThe weakness of our study is the fact that it was based on retrospectively collected data. However, the collecting process was done independently, so unlikely to have altered the results and caused bias. The AUC estimates are bound to be biased since our validation was carried out on a case-control group, but this was minimized . The intInformation on environmental risk factors was not collected, and this may have created a gap in the risk factors of the studied population.Despite these biases and limitations, the urgent need for a risk prediction model in providing relevant breast cancer control in developing societies such as North Cyprus outweighs the shortfalls.Our results demonstrated that this newly developed breast cancer risk prediction model is a simple, cost-effective, and noninvasive tool for the identification of high-risk women in North Cyprus that can be eligible for a mammogram. It may serve as a gatekeeper for a mammogram and a radiation saving tool for low-risk women, by reducing unnecessary mammograms and thereby decreasing health costs. This model is suitable for the prediction of late on-set breast cancer risk and further studies on a larger study group, including environmental risk factors will be needed to improve the model.


To evaluate the association between apolipoprotein B gene polymorphism and coronary heart disease in some populations at home and abroad by means of meta-analysis.Using the strict exclusion criteria for primary screening of the literature and applying the Hardy-Weinberg equilibrium to test the genetic balance of the selected literature. The corresponding models were selected according to the results of the heterogeneity test. The Begg’s test and Egger’s test were used to evaluate publication bias, and meta-analysis was performed using Stata 12.0.P = 0.811,OR = 0.95, 95%CI = 0.62–1.46), recessive genetic models , or dominant genetic models . Subgroup analysis based on ethnicity showed allelic comparison , recessive genetic models , and dominant genetic models  in Asians, allelic comparison , recessive genetic models ,dominant genetic models  in Caucasian;The study included twelve articles. In the literature, a total of 1596 patients with coronary heart disease and 1431 controls.Meta-analysis results showed no statistical value in the following three genetic models: allelic comparison  XbaI locus is not a risk factor when it comes to the development of coronary heart disease in the domestic and international populations included in this paper. In Caucasians, people carrying the aa genotype may be less susceptible to CHD (coronary heart disease). The results of recessive genetic models have to take the effect of heterogeneity and sample sizes into account. Further research may require a larger and more rigorous research design. As countries grow richer, cases of coronary heart disease(CHD) also rise rapidly, and coronary heart disease patients are becoming younger and younger. CHD is a major cause of both death and disability in developed countries, responsible for about one-third or more of all deaths in individuals over the age of 35 –3. The 2ApoB XbaI (rs693) is a kind of single-nucleotide polymorphism (SNP) resulting in the change of amino acids, which is located in exon 26 of the ApoB gene. Many studies have been done on the correlation between ApoB XbaI gene polymorphism and CHD in different countries, however, their conclusions go different ways. Different studies at home and abroad have disputed the relationship between ApoB XbaI gene polymorphism and coronary heart disease, mainly because of the small sample size and genetic differences among different ethnic groups. Due to the limitations of a single study regarding research methods, number of studies, ethnicity of the study population and other aspects, it is important to summarize the results of these individual studies to be able to make reliable conclusions concerning XbaI gene polymorphism. The purpose of this meta-analysis is to integrate the results of these case-control studies to comprehensively assess the association between ApoB XbaI gene polymorphisms and CHD.http://www.ncbi.nlm.nih.gov/pubmed), Google Scholar(http://www.scholar.google.com),Web of Science (http://www.isiknowledge.com), Cochrane Library (http://www.thecochranelibrary.com),Chinese VIP database (http://www.cqvip.com),Chinese Wanfang database (http://www.wanfangdata.com.cn),China National Knowledge Infrastructure (CNKI) database (http://www.cnki.net). The search was limited to Chinese and English articles,for all publications about the association between ApoB XbaI gene polymorphisms and CHD, while excepting reviews and critical articles. The following key words were used while searching:apolipoproteins B/ApoB, ApoB XbaI polymorphisms, and coronary heart disease/coronary &disease/coronary disease &heart/ coronary atherosclerotic heart disease/CHD/CAD.We have conducted a comprehensive search through the computer system in the following databases up until April 2019:MEDLINE database(Articles were identified based on the inclusion criteria as follows: (1) The original study type was a well-designed case-control study with strict controls;(2) The studies should have exact genotypic and allelic data of ApoB XbaI;(3)Cases in the CHD group were diagnosed and cases in control group were excluded by coronary angiography, based on the World Health Organization (WHO) 1979 CHD Criteria;(4)The studies had comprehensive statistical indicators, such as odds ratio, confidence interval, etc. The genotypes of the case group and the control group in each study were in line with the Hardy-Weinberg equilibrium (HWE) to verify the representativeness of the study population, and if HWE-p was bigger than 0.05,which meant the research group met HWE, then it was included. The exclusion criteria of these articles were as followed:(1)The article was not a case-control study,but a lecture, review, correspondence, editorial letter,etc.;(2) Unable to extract exact genotype data from the article;(3) If the HWE-p was smaller than 0.05,which meant that the research group didn’t meet the Hardy-Weinberg equilibrium, it was excluded.The quality of these case-control studies was assessed by a star rating system, Newcastle-Ottawa scale (NOS), and evaluation of the NOS score of the studies was averaged by two independent reviewers. Full score is 9 stars,while a score range from 0 to 4 stars is considered poor quality , 6,and aData of selected studies was extracted by two independent reviewers in a standard data collection form, which included the following items:(1)the basic information of the included studies,such as the first author’s name,the publication year, etc.;(2)the region of the study population;(3) genotype data and allele frequency of ApoB XbaI for both CHD cases and control cases;(4) HWE-p in healthy control groups. In case of disagreement, we settle it through discussion or refer to a third party for help.P < 0.05 was considered a significant departure from HWE. At the same time, I2 statistics were used to test the heterogeneity of each piece of literature  and were used in this meta-analysis with a total of 1596 CHD patients and 1431 healthy control subjects. The detailed screening process was shown in Fig. A total of 66 abstracts both in English and Chinese from the last 20 years on the association between apolipoprotein B Xbal polymorphisms and CHD susceptibility were identified, including 9 in Chinese and 57 in English. Whenever two studies largely overlapped , 13, onl2 statistic was used to assess the heterogeneity respectively by using STATA12.0. In the three genetic models , significant heterogeneity was found: allelic comparison, recessive genetic models,dominant genetic models , recessive genetic models , or dominant genetic models . Next, subgroup analysis was conducted  and dominant genetic models , so therefore random effects model was used. While fixed effects model was used in recessive genetic models.No statistical association was found to be significant in any genetic models: allelic comparison , recessive genetic models , or dominant genetic models .In Caucasians,random effects model was conducted because of significant heterogeneity in any genetic models. Statistical significance was found in recessive genetic models ,which meant that ApoB XbaI gene polymorphism had a certain correlation with CHD. However, no statistically significant difference was found in the other two genetic models, dominant genetic models ). The above data showed that among Caucasians, those carrying the aa genotype may be less susceptible to CHD.Based on the statistically significant heterogeneity which was found for the analysis of allelic comparison, recessive genetic models, and dominant genetic models in this meta, the random effects model was employed to merge data. The combined OR values were calculated and the forest plot of the association between ApoB XbaI gene polymorphisms and CHD was made by D-L method, shown in Figs. p-value was 0.451,while the result of Eggers linear regression test showed that the p-value was 0.49 . In the three gene models studied in this paper we tested the heterogeneity, and they all showed significant heterogeneity. Therefore, we used random effect models to combine data, and we performed subgroup analysis according to ethnicity to explore the sources of heterogeneity. Interestingly, in the recessive genetic models, the heterogeneity between Asians and Caucasians was very different, seemingly indicating that this subgroup was a source of heterogeneity. However, we could also see that in the Asian subgroup,two piece of literature [Meta-analysis conducted a secondary analysis of descriptive data has a scientific and rational aspect, but it is also affected by a variety of factors including the amount of selected literature, the size of the total sample size, publication bias, and flaws in the analytical method itself. Through the foregoing description, this study has been proven to be heterogeneous through various methods of heterogeneity test. The significant positive correlation between plasma apolipoprotein B and LDL-C indicates that it is often an important predictor of CHD, and the risk-related relevance between its polymorphisms and coronary heart disease requires more large-scale original studies to be further validated.


Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided. Consumer acceptance and food safety are key concerns for wholesalers and retailers of fresh fish and seafood products . Fish anReliable methods and techniques are required to evaluate the fish quality and spoilage . SensoryQuantitative chemical and physical techniques are correlated with sensory parameters to evaluate fish freshness and quality attributes. Chemical attributes are related to biochemical changes that occur in a fish after the harvest. Some of the most used parameters are specific spoilage organism (SSO) growth, lipid oxidation, peroxide value (PV), and the K value, based on adenosine triphosphate (ATP) degradation . PhysicaThe cited chemical and physical quantitative techniques have plenty of disadvantages, as they are destructive, expensive, time consuming, and require highly skilled operators. Moreover, they cannot be applied on-field or in on-line detection. Additionally, the fact that they are very time-consuming is detrimental, because fishes must often be put in the market the same day they are killed, given the fast spoilage time. The more innovative sensing techniques could overcome these problems, being rapid, cost-effective, and, except for biosensors, not invasive, permitting on-line detection to be achieved .The review presents and describes the state of the art and future perspectives of these indirect techniques. Particularly, the text covers: biosensor techniques, e-sensing techniques, methods based on the fish dielectric properties, nuclear magnetic resonance (NMR), and spectroscopic techniques, including fluorescence, infrared, Raman, and hyperspectral imaging. Moreover, the review also covers the statistical analyses utilized in most of the presented papers. These algorithms, mainly utilized for the electronic and spectroscopic techniques, have a great and increasing importance in the sensors field, because they allow experimental data to be processed to create qualitative and quantitative models, enabling a fast and non-invasive measurement process. Almost all these algorithms are based on principal component analysis (PCA): it changes the original data space (defined by the measured variables) to a new space defined by the directions that contain the majority of data variance, called principal components (PCs). This analysis greatly reduces the data dimensions  and allows a better visualization of clusters and trends in the data. Evolutions of PCA allow the creation of both classification and regression models, such as the soft independent model of class analogy (SIMCA) algorithm, which applies PCA to different classes of data and then predicts the class of new measurements . RegardiBiosensors are devices able to measure a chemical or a biological reaction and to convert the response into an electrical signal . These dConcerning fish applications, electrochemical biosensors have been investigated to assess the presence of some compounds derived from the deterioration process. Between these compounds, the main ones are purine based like xhantine (Xa), produced by the degradation of adenosin-triphosphate (ATP); uric acid (UA) and hypoxhantine (HxA), both products of the action of xanthine oxidase on Xa; and gas molecules like trimethylamine (TMA) and dimethylacetamide (DMA), created by the conversion of trimethylamine oxide (TMAO) in fresh meat by spoilage bacteria, which also decompose urea and amino acids into ammonia.Research performed by Tang et al.  showed tTo obtain a non-invasive measure of the content of ammonia in fishes, Heising et al.  monitoreA system based on differential pulse voltammetry (DPV) was proposed by Vishnu et al.  for the Starting from an electrode of copper phosphate, characterized by a rapid production time (within 20 min), Lee et al.  set up aA particular and well-diffused family of biosensors is the enzymatic biosensors, based on the measurement of the reaction between a substance of interest and an enzyme. In particular, amperometric enzymatic biosensors, which are composed of an enzyme as a biological substance and an electrode as a transducer, are one of the most popular biosensors. In general, an amperometric enzymatic biosensor is an analytical device that combines an electroactive substance of interest with an enzyme immobilized on the electrode. An electrical signal gives the information regarding the presence and concentration of the substance and the results are calculated by a standard curve . For fis−1) and a good prediction accuracy, as well as a recovery value of 96–97%. Additionally, the stability of the system was investigated. Acquisitions were performed on stable histamine solutions, over a total of 35 days, obtaining a final maintenance of the original signal of 87.7%. Finally, Yazdanparast et al. [−4 μM) and a linear behavior in two different ranges: 0.001–0.004 µM (R2 = 0.997) and 0.005–50.0 µM (R2 = 0.995). A recovery test was performed on fishes stored for 0, 2, and 8 days, obtaining mean recovery values that were always higher than 95%.In 2018, Torre et al.  researchInstead of carbon nanostructures, platinum nanoparticles were used by Chen et al.  to mimicAs described above, sensory evaluation techniques, like QIM, are the most used methods to classify fish according to freshness . HoweverElectronic nose is a technique aimed at mimicking the functions of the human nose, not in the transduction principles, based on receptors, but in the use of arrays of biochemical transducers, followed by data processing. Within the variety of e-noses designed and created in the last two decades, we can observe systems based on electrochemical gas, metal oxide, and conducting polymer sensors coupled with different feature extraction and data processing methods.A first application of the technique is represented by the prototype “FishNose”, from Haugen et al. . It cons2 values from 0.91 to 0.97 were observed for the two parameters. In the work by Liu et al. [TVC was also measured by a sensor based on four integrated metal oxide micro-sensors, developed for sardine discrimination according to different freshness stages  by using fishes stored for 2, 3, and 4 days, respectively . More reu et al. , the useElectronic tongue refers to a device made of sensors that, thanks to the transduction of a signal or a pattern of them, permits identification of tastes in (soluble) food, obtaining a rapid assessment of complex liquids, whereas the e-nose described above works with gaseous systems. In the literature, it is possible to find different electrochemical sensors used for the creation of these device. The most common are amperometric, potentiometric, and voltametric sensor arrays . E-tonguThe devices based on colorimetric sensor array technology monitor the process of chemical bonds creations  between different chemical agents . The creColorimetric systems differ from colorimetric sensor arrays in the subject of the investigation; instead of color changes strictly related to the creation of chemical bonds, they can analyze the samples without the need to induce chemical reactions. In fact, these devices are used to monitor changes in the whole sample, considering several visual and color parameters. For example, Quevedo et al.  set up aThe study of changes in the dielectric properties can be considered a fast and non-invasive way to monitor different parameters of fishes. This method is based on the evaluation of the impedance Z from the Ohm’s law V = ZI, thanks to the use of two electrodes that apply a current flow I and a voltage V through the fish sample .When an electric current is applied to the fish sample it can pass through two different types of pathways: one is a continuous extracellular fluid (ECF), considered to be purely resistive, whereas the second is a series of ECF and intracellular field (ICF) that includes the capacitive effect of the cell membrane, creating a complex impedance with a magnitude frequency dependence . This imOther than resistivity and conductivity, dielectric properties can be assessed by measuring the electrical permittivity, composed of two factors, the dielectric constant and the loss tangent. These two parameters are influenced by different food attributes, such as the moisture content , chemicaThe most investigated techniques are the open-ended coaxial probe, the transmission line, and the resonant cavity method. The first one consists in a coaxial line put in contact with the fish sample, measuring the reflection coefficient, related to the sample permittivity. The transmission line method is based on the use of an enclosed transmission line, filled with a solution of minced sample. Finally, for the resonant cavity method, a piece of sample, of a known geometry, is placed in a single-mode cavity, and the dielectric properties are computed from the changes in the reflected power and the frequency of resonance of the cavity .Gadus morhua), frozen Atlantic and Pacific hake , and chilled farmed Atlantic salmon . The measurement data were used as input for a PCA analysis, alongside other measured variables like the time of storage and temperature. Results showed that the age of whole gutted cod could be predicted with an error of 1.5 days, as well as the salmon one. The inclusion of measurements obtained with an electronic ‘nose’ sensor, as input for the PCA model, increased the precision up to ±1 day. Regarding hake, discrimination linear models were created for several variables, obtaining an R2 of 0.982 in validation, for the determination of an overall quality grade. Finally, discrimination models between different types and treatments of frozen fish (for hike and cod) were created, obtaining R2 values that were always higher than 0.83 in validation.An open-ended coaxial probe was used by Kent et al.  to asses2 values that were always higher than 0.977.Wang et al.  focused Sepia officinalis) and shortfin squid (Illex coindetii) stored in ice, with several analyses, including dielectric measurements carried out by two different fish freshness meters (Torrymeter type 295 by Distell Industries and RT-Freshmeter type RT-2E by Rafagnatækni Electronics). These devices are based on the presence of two pairs of concentrically arranged electrodes in a sensing head, applied directly onto the skin of the fishes. An alternating current is passed through the sample by the outer pairs of electrodes, whereas the inner pair senses the resulting voltage, acquiring in this manner measurements based on the skin impedance. Results showed that these instruments could be useful for the study of fish freshness, especially in the first days. Moreover, for both species, the less rigid areas were identified as the best sides for the acquisition: the ventral side for cuttlefish and the dorsal side for squid.In 2008, Vaz-Pires et al.  characteCyprinus carpio L.) to develop a technique proving the falsification of thawed meat as fresh. The obtained results showed a correct classification of 100%, proving the utility of dielectric property measurement for this application. More recently, both studies conducted by Badiani et al.  and RutkNuclear magnetic resonance (NMR) spectroscopy is based on the spin of atoms with an odd number of neutrons or proton, like hydrogen. Samples are placed in a magnetic field, where they are subjected to radio waves that excite the nuclei into resonance, detected with sensitive radio receivers.This generated magnetic field changes the resonance frequency, thus giving access to details of the electronic structure of a molecule and its individual functional groups. As the fields are highly characteristic to individual compounds, NMR spectroscopy is particularly useful for the identification of monomolecular organic compounds. Usually, samples are dissolved in a solvent, because NMR analysis of solids requires dedicated components and may not give equally well-resolved spectra. Concerning fish applications, the most diffused NMR techniques are the low-field NMR (LF-NMR) and the high-resolution NMR (HR-NMR). The last technique appeared to be able in the discrimination between different species, geographical origin, production, and process history .1H-NMR). Results showed a slow increase of acidic amino acids and decrease of basic amino acids in the first days, due to protein autolysis, and a faster process afterward, due to microbial development. Moreover, it was observed that different storing temperatures cause differences in the amino acid trends starting at day 4. The storing process at different temperatures was also studied by Shulimina et al. [p < 0.05) was found between the quality grade obtained by the chemical and physical parameters  and the relaxation data obtained with NMR. Ciampa et al.  studied a et al. ,79 on saa et al.  to rapida et al.  assesseda et al.  evaluatea et al.  investigWith the term “optical”, we refer to a family of techniques that use electromagnetic waves in the ranges of ultraviolet (UV), visible (VIS), and infrared (IR), to obtain information regarding the food freshness and quality. Over the years, it became an interesting and attractive research area thanks to the decrease of instrument prices, the improvement of equipment, and the combination with chemometric tools.Regarding fish freshness, the main applications concern the detection of microbial growth and spoilage, the prediction of physicochemical and texture features, and the detection of issues due to different species and production methods. The main techniques taken into consideration are fluorescence spectroscopy, infrared spectroscopy, hyperspectral imaging, spectroscopy, and Raman spectroscopy. The physical effect of fluorescence is related to the presence in the food samples of substances called “fluorophores”. They absorb energy from the external ambient, in the form of electromagnetic waves ; then, they emit this energy in the form of light, at a higher frequency . Acquisi−1, where it showed a linear behavior, and was compared to a more standard gas chromatography technique, obtaining a ratio of 1.003 between the measurements of the two techniques. In the same year, a classification of shrimp samples, according to their geographical origins and species, was performed by Eaton et al. [2 of 0.85 in the first work and up to 0.94 in the second one, where an optic fiber probe was used both for sample excitation and spectra capture, obtaining an EEM in the range of 250 to 800 nm. Fiber probes were also used by Shibata et al. [2 value = 0.88; RMSECV = 0.97 μmol/g).The SFS technique was used by Liu et al.  to measun et al.  thanks tn et al. ,89 in twa et al.  to develPrincipal component analysis (PCA) was used in combination with the FFFS technique by Karoui and Hassoun  to study2 value of 0.92 (RMSECV = 3.5%).More recently, Omwange et al.  studied Spectroscopy in the near infrared and visible region (VIS/NIR) was historically explored in food analysis, due to the great presence of C–H, N–H, and O–H groups that vibrate in the VIS/NIR range of frequencies . Measure2 = 0.95 (RMSEC = 0.12 log(cfu)/g). More recently, Reis et al. [Tito et al.  research2 of 0.98 in prediction and a root mean square error in prediction (RMSEP) of 0.93 days whereas Agyekum et al. [2p of 0.980 (RMSEP = 5.1 mgN/100 g). A similar paper was published by the same research group in 2020, where FT-NIR was coupled with an ant colony PLS (ACO-PLS) algorithm to evaluate the K-value in silver carps, obtaining an R2p of 0.98 (RMSEP = 3.98). On the other hand, Alamprese et al. [2 value of 0.95 and an RMSEP of 0.081, followed by thiobarbituric acid (TBA) (0.95/0.107), TVB-N (0.93/2.1), and K-value (0.81/6.5).In the last couple of years, deep learning techniques have been used more and more in this field: for example, Wu et al.  coupled m et al.  studied e et al. . Several2 between 0.92 and 0.98, PLSR models). More recently, Fengou et al. [2p = 0.73 and RMSEP = 0.72), highlighting that this technique could be more suitable for whole samples, with respect to fillets. Finally, Saraiva et al. [2 of 0.8 and 0.9, PLSR models).The mid-infrared (MIR) region of the electromagnetic spectrum  is one of the most informative sections of the whole electromagnetic spectrum. Like near-infrared (NIR), the use of Fourier transforms has significantly improved its capabilities and applications, replacing dispersive spectrometry. Hernandez-Martinez et al.  worked ou et al.  combineda et al.  used a tThe term hyperspectral imaging (HSI) refers to the blending of two different techniques, computer vision and VIS/NIR spectroscopy. With a hyperspectral camera it is possible to acquire a full spectrum for every pixel of an image, obtaining three-dimensional data called a “hyperspectral cube” . After a2p (in prediction) of 0.94 (RMSEP = 5.21%). In 2016, the same research group expanded the previous work, using LS-SVM and multiple linear regression (MLR) to simultaneously determine the K-value, TVB-N, and thiobarbituric acid reactive substances (TBARSs), with the support of SPA and genetic algorithm (GA) for the determination of the optimal wavelengths [2p > 0.90, with five wavelengths found by SPA and six wavelengths found by GA. In 2017, they combined the GA with an innovative algorithm called physarum network (PN), focusing on the determination of the TVB-N [2p of 0.98 (RMSEP = 1.44 mgN/100 g). In 2016, Ivorra et al. [2CV  of 0.89 (RMSECV = 2.7 days). More recently, Khoshnoudi-Nia et al. [2p ≥ 0.856), with the best results coming from LS-SVM (R2p = 0.91). Cheng et al.  studied elengths . Both alhe TVB-N . PLSR ana et al.  developea et al.  studied 2p = 0.908 and RMSEP = 6.87%), where only four wavelengths  were selected. Another example is provided by the work conducted by Qu et al. [2p of 0.91 (RMSEP = 56.3 g/kg). Finally, Qin et al. [−1). Images were acquired on six different fish species , and used as input for 24 different machine learning techniques. In general, the VIS/NIR reflectance mode gave the best performance for both species and freshness inspection, proving to be the best wavelength range for the detection of fish fillet substitution and mislabeling.The HSI technique could be used to measure parameters not related with freshness. For example, Xu et al.  worked ou et al.  to map t−1 in frozen hake fillets. Janci et al. [−1. The resulting spectra were used as input for a PLSR analysis (R2p of 0.97 and RMSEP of 14.9 mg/kg). Histamine content was also the parameter analyzed by Xie et al. [−1 and compared with the results of a more traditional, but less cost and user-friendly, HPLC technique, obtaining good agreement and reproducibility. Another application of Raman spectroscopy is represented by the detection of unwanted chemical products in fishes. Deng et al. [−1, and an R2 of 0.99 calculated in the range of 10−7–10−5 mol/L, with an LOD of 20 ng/mL, was observed.Raman spectroscopy is based on the measurement of the vibrational energy modes thanks to the use of scattered light: A monochromatic laser beam irradiates the sample , causing a shift in the vibrational energy level of the molecules from the ground state to a state of high-energy collision. When these molecules quickly return to the ground state, the physical process of Raman scattering occurs, allowing structural and chemical information of the sample to be obtained at the same time . Measurei et al.  evaluatee et al.  that useg et al.  investig−1. The spectra were analyzed with a PCA that allowed successful discrimination between the different classes. More recently, Song et al. [−1. An algorithm called the fuzzy-rough set model based on the thermal-charge algorithm (FRSTCA) was applied to the resulting spectra to select the spectral band containing the major amount of information, which was 961–965 cm−1.Regarding classification problems, Velioglu et al.  investigg et al.  developeFrom these data, a support vector data description (SVDD) classification model was established, obtaining a detection performance of 90.5%. The main families of innovative systems for fish freshness assessment and spoilage monitoring were reviewed. Biosensors, electronic sensors, dielectric devices, and spectroscopy techniques were covered, with a secondary focus on the several algorithms  used for the set up of classification/regression models and chemical maps. Some of these devices, thanks to a fast and less invasive acquisition process, are much more suitable for in-line or on-line industrial monitoring with respect to traditional methods , which are costly, time consuming, and require high-skill operators. Spectroscopic devices could represent the best method for industrial application, being compact and not requiring contact with the fish. Between these techniques, hyperspectral imaging could be the most interesting for the huge amount of gathered information, although the acquisition speed of hyperspectral cameras must be increased before it can be used in practice. To obtain this important improvement, algorithms for the selection of few optimal wavelengths should be applied, optimizing and speeding up the measurement phase. Finally, biosensor applications in the food package could represent a good freshness monitoring method for products on the market.However, the main conclusion that can be reached by the review of the literature is that, nowadays, all the described techniques have specific advantages and disadvantages: none of them can perform a satisfactory measurement of the overall fish quality by themselves. For this reason, the mentioned devices have not yet had extensive and widespread use in practical applications. In addition, several of the examined solutions are destructive. So, in the near future, research could be conducted on the combination of different techniques, allowing an increase of the volume of information extracted from the sample and the limitations of each individual method to be overcome. The continuous innovations in computer science and in complex statistical analysis could allow the use of these techniques, especially the spectroscopic ones, in an increasing number of different applications.