A five-category rurality variable was created using the percentage of a county’s population living in a rural area. Factor scores from Factor Analyses (FA) were used in subsequent Multiple Linear Regression (MLR) analyses stratified by rurality to identify associations between environmental factor scores and the prevalence of males and females meeting PA guidelines. Results: FA revealed a 13-variable, four-factor structure of natural, social, recreation, and transportation environments. MLR revealed that natural, social, and recreation environments were associated with PA for males and females, with variation by sex for social environment. The natural environment was associated with PA in all but urban counties; the recreation environment was associated with PA in the urban counties and the two most rural counties. Conclusions: Variations across the rural-urban continuum in environmental factors associated with adults’ PA, highlight the uniqueness of rural PA and the need to further study what succeeds in creating active rural places.Background: Rural U.S. adults’ prevalence of meeting physical activity (PA) guidelines is lower than urban adults, yet rural-urban differences in environmental influences of adults’ PA are largely unknown. The study’s objective was to identify rural-urban variations in environmental factors associated with the prevalence of adults meeting PA guidelines. Methods: County-level data for non-frontier counties ( Rural U.S. communities experience a multitude of impactful health disparities , includiTwo decades of PA research has accumulated evidence that multiple “environments” influence PA based on a social-ecological perspective , emphasiRural-urban comparisons are important so that appropriate interventions can be developed and undertaken. Recent research highlights some of the rural-urban differences in walking behaviors and perceived walkability ,11,12. FThe objective of this study was to answer the following question: how do environmental factors associated with the prevalence of adults meeting PA guidelines differ along the rural-urban continuum for males and females? n = 443), defined as counties with a population density of six or fewer people per square mile [Exclusively Rural (n = 389), Rural (n = 577), Somewhat Rural (n = 577), Somewhat Urban (n = 577), and Urban (n = 577).Data from 3140 counties and county-equivalents  (hereafter “counties”) were utilized for analyses. Rurality was defined as the county population living in rural areas according to 2010 Census data. Frontier counties . Data collection years ranged from 2010 to 2011 to match the timeframe of the collection of the primary dependent variable see .The ACS is a critical element in the Census Bureau’s reengineered decennial census program. The ACS collects and produces population and housing information every year and publishes both one-year and five-year estimates .The BRFSS is a state-based surveillance system that is operated by state health departments in collaboration with the Centers for Disease Control and Prevention (CDC). The BRFSS collects self-reported information by telephone on an assortment of behaviors and health outcomes among adults .The UCR Program consists of four data collections from roughly 17,000 law enforcement agencies in the U.S.: (1) the National Incident-Based Reporting System (NIBRS), (2) the Summary Reporting System (SRS), (3) the Law Enforcement Officers Killed and Assaulted (LEOKA) Program, and (4) the Hate Crime Statistics Program. The UCR Program publishes annual reports for each of these data collections, a preliminary semiannual report of summary data each winter, and statistical publications, such as the County-Level Detailed Arrest and Offense Data report that is utilized to create the County Health Rankings .The FARS is a census of fatal motor vehicle crashes with a set of data files documenting all qualifying fatalities that occurred within the 50 States, the District of Columbia, and Puerto Rico since 1975 .For more than a decade, the EPH Program has collected, integrated, and analyzed non-infectious disease and environmental data from a nationwide network of partners. The purpose of this program is to deliver information and data to protect the nation from health issues arising from or directly related to environmental factors .The decennial U.S. Census gathers data from all households in the United States and Island Areas related to name, gender, age, race, ethnicity, relationship, and home-ownership. The 2010 questionnaire was one of the shortest in history—asking just 10 questions—and is augmented by the U.S. Census Bureau’s annual ACS .CDC WONDER manages nearly 20 collections of public-use data for U.S. births, deaths, cancer diagnoses, tuberculosis cases, vaccinations, environmental exposures, and population estimates, among other topics. These data collections are available as online databases, which provide public access to ad-hoc queries, summary statistics, maps, charts, and data extracts. Most of the data are updated annually; some collections are updated monthly or weekly . For the current study, the county-level prevalence of meeting the PA guidelines by males and females over 18 years old, assessed via the 2011 BRFSS, was the outcome of interest. Individual respondents were asked about the frequency and time of each type of PA that they engaged in outside of work in the prior month if they engaged in any activity. The relative intensity of each type of activity engaged in was classified as moderate or vigorous based on the age and sex of the respondent. Each respondent was classified as meeting PA guidelines, insufficiently active, or inactive based on their responses. At the time of data collection for the 2011 BRFSS, the U.S. PA guidelines were for adults to achieve 150 min of moderate intensity PA per week, the equivalent in vigorous PA, or a combination of moderate and vigorous PA where one minute of vigorous PA is equivalent to two minutes of moderate PA. At the county level, the prevalence estimates  of adult females and males meeting the PA guideline were acquired from the Dwyer-Lindgren et al. online supplemental material . SpecifiThe independent/predictor variables were categorized into natural, social, recreation, and transportation environments in alignment with Sallis and colleagues’  framewor©  [All analyses were performed in SAS 9.4NC, USA)  with misFactor Analyses (FA) were used to establish model parsimony, variable redundancy, and obtain weighted factor scores prior to regression analyses on the dependent variables. First, all variables were standardized into z-scores, reviewed for non-normality, and log-transformed (when appropriate) to ensure statistical assumptions for inferential analyses were met. FA criteria used squared multiple correlations (SMC) as priors for communality estimation, an orthogonal (varimax) rotation, and loadings ≥40 considered “large” for factor extraction. Lastly, estimated factor scores were assigned to each observation using the linear composite from the observed variables .Multiple Linear Regression (MLR) analyses were used to identify associations between principal components (factor scores) and county-level prevalence of males and females meeting PA guidelines. MLR models were stratified by quintile and accounted for clustering effects by state. Standardized (β) beta coefficients and standard errors (SE) represent model association. Lastly, model assumptions as outlined by  were perUnivariate analyses are presented in Somewhat Rural, Rural, and Exclusively Rural counties. For this reason, and to further support model parsimony, these variables were not included in the final model.Factor loadings are reported in p 0.01), social environment , and recreation environment . Female PA was associated with the natural environment , social environment , and recreation environment . The transportation environment was not associated with PA for either sex . Results from the MLR analyses by sex are presented in p 0.01) except the Urban counties. The recreation environment was associated with meeting PA guidelines in the Urban category of counties and the two most rural categories, with a greater difference in β between rural and urban counties among males than females. The negative association of the social environment and PA was significant among men except in the two most rural categories of counties; among females, it was also significant in the Exclusively Rural counties . The transportation environment was associated with PA only in females in Somewhat Urban counties .Results from the MLR analyses by rurality and sex are also presented in Findings from this study address a critical research gap highlighted in reviews of the literature ,10 by idRural and Exclusively Rural counties. Within this factor, access to exercise opportunities (including private recreation facilities) did not load significantly in the two most rural categories of counties, suggesting the other variables—proximity to parks and elementary schools—are particularly important as rurality increases, in concordance with existing literature [Urban counties, access to places to exercise was of particular importance, demonstrating the primacy that private and/or indoor facilities may have in high-density urban areas. In combination with the natural environment results, these findings highlight the importance of creating opportunities for low-to-no-cost indoor activity, providing shaded outdoor places, and/or utilizing natural water assets  or swimming pools for rural PA, in concurrence with existing research [The association of the recreation environment with meeting PA guidelines was strongest in the terature ,9,10. Thterature . In the Most Rural counties; additional understanding of these relationships is needed.The social environment was negatively associated with meeting PA guidelines, with greater regression coefficients in urban counties than rural ones, and a coefficient for females twice that of males across all levels of rurality. This factor was composed of variables serving as proxy measures for safety and social barriers to PA, with the prevalence of violent crime and single-parent households loading most significantly on the factor across all levels of rurality and for both sexes. Our findings, using objective measures, add to the preponderance of evidence observing that safety is associated with PA in rural settings . The litSomewhat Urban counties. This suggests that commuting behaviors and living near work were not influential in meeting PA guidelines when considering a comprehensive model of multiple environmental factors. This is consistent with other data from rural areas where transportation PA is less likely [leisure time PA as measured by the BRFSS and did not include occupational, household, or transportation PA. Recent analyses of 2011–2016 National Health and Nutrition Examination Survey data by Whitfield and colleagues [The transportation environment factor had a nearly zero correlation with the adult prevalence of meeting PA guidelines, except for a negative association with PA among females in s likely ,10, but s likely . Howeverlleagues , using dThere are multiple limitations to the current study worth noting. First, the PA data utilized are 10 years old, based on a small area estimation, and include only aerobic and leisure-time PA. Though PA guidelines have since been revised to incorporate muscle-strengthening PA , and resThe findings of this study highlight multiple future needs, to (1) improve the granularity of PA surveillance in rural areas of the U.S., (2) identify the facilitators of and barriers to adults’ PA in rural areas of the US, and (3) utilize evidence-based strategies from rural research to address the PA disparities evident in rural areas of the U.S. Building this rural PA evidence will require a more detailed understanding of the variation of environmental factors associated with adults’ PA within rural communities. Positive Deviance methods have been used in under-resourced communities to identify promising practices in places that have better-than-expected outcomes (“Positive Outliers”) than peers so that the practices can be disseminated to peer communities ,45. UsedThis comprehensive, national-level comparison of environmental factors associated with adults’ PA in rural and urban areas in the U.S. addresses a gap previously identified in the literature ,10. Desp


The area under the curve (AUC) values showed excellent test quality for both ML&DL and logistic regression methods  in the evaluation of prediction performance (p = 0.289). The affecting factors of the logistic regression method and the influence of the ML&DL method were not significantly different. The accuracy and performance of the ML&DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&DL algorithm could potentially predict the outcome of the MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice.This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method corresponded well with the feature importance of the six top-ranked factors in the ML&DL method. There was no significant difference when comparing the accuracy, F1-score, and error rate between ML&DL and logistic regression methods (accuracy = 0.89 and 0.91, F1 score = 0.89 and 0.90, error rate = 0.11 and 0.09;  Osteoarthritis (OA) is an important burden to the healthcare system because it has a higher prevalence with increasing age. The knee joint is the most common site affected by OA. It is a leading cause of disability and has effects on social and public health ,2. The pMMPRT has recently received greater attention because it has been associated with the development of excessive meniscal extrusion and accelerated degenerative changes . An impoFor the proper treatment of MMPRT, it would be necessary to establish an easy and accurate algorithm rather than rely on the surgeon’s subjective assessment for the standard approach. Nowadays, there has been much interest in using artificial intelligence (AI) in medical imaging techniques such as X-ray and magnetic resonance imaging (MRI) . More thWe conducted this study to identify whether ML&DL is suitable for determining the treatment algorithm for MMPRT similar to a logistic regression method that was previously determined by statistical analysis using affecting factors from the clinical data warehouse (CDW). The AI algorithm was set up with DL and machine learning (ML) using factors that were already analyzed from the CDW. Afterward, the accuracy and predicting performance of ML&DL were compared to those of the logistic regression. The purpose of this study was to validate the accuracy and predicting performance of the ML&DL method by comparing them to those of the logistic regression method. We hypothesized that the ML&DL method will show comparable accuracy and predicting performance with the logistic regression method.From July 2003 to May 2018, 640 patients who were diagnosed with MMPRT using MRI readings by MSK radiologists or outpatient chart review searching from the CDW in our hospital were included. The exclusion criteria were as follows: (1) a history of trauma such as a periarticular fracture or ligament injury, (2) development of infection, (3) inflammatory arthritis, and (4) previous meniscal injury and/or knee operation. First, affecting factors for the operation were evaluated using statistical analysis. Next, AI technology was introduced using X-ray and MRI machines. Finally, the accuracy and predicting performance were compared between ML&DL and logistic regression methods.The logistic regression method refers to a traditional statistical model. Based on previous articles about MMPRT, possible affecting factors of operation or OA progression were surveyed. These factors included age, body mass index (BMI), duration of attempted conservative treatment, malalignment as a weight-bearing line (WBL) ratio, proximal tibial morphology, Kellgren-Lawrence grading scale (K-L grade), bone marrow lesions (BMLs), and severity of meniscal extrusion ,12,13,14Radiologic factors were extracted from standing knee anterior–posterior, lateral, hip–knee–ankle (HKA) views, and MRI. WBL ratio, proximal tibial morphology, K-L grade, BMLs, and severity of meniscal extrusion were categorized as radiologic factors. Mechanical axis deviation was evaluated using the initial WBL and delta WBL ratios, while proximal tibial morphology was evaluated using tibial varus angle (TVA) and posterior tibial slope (PTS). Meanwhile, the severity of OA was assessed using the K-L grade and BMLs were evaluated using the MRI osteoarthritis knee score (MOAKS). The extent of medial meniscal extrusion was evaluated on MRI. INFINITT ver. 5.0.9.2  was used for all radiographic measurements. The WBL ratio was calculated by measuring the distance from the medial edge of the proximal tibia to the point where the WBL intersected the proximal tibia and then by dividing this measurement by the entire width. A percentage was calculated by multiplying this ratio by 100%. The delta WBL ratio was the difference between the initial WBL ratio and the WBL ratio just before the operation or the last follow-up in the outpatient department for patients who underwent conservative treatment. TVA was defined as the angle between the line perpendicular to the tibial shaft and the articular surface of the proximal tibia , while PLogistic regression models were applied to binary classification problems, such as the use of surgery or conservative treatment. The characteristics of the logistic regression method were interpretable and derived from coefficients such as odds ratio. The odds ratio was used to determine how each factor influenced the fate of MMPRT. All statistical analyses were performed using the Statistical Package for the Social Sciences . On a priori power analysis, at least 135 patients were required in each group .The dataset of the ML&DL method was shared with that of the logistic regression method. Preprocessing was performed using data cleaning, integration, transformation, reduction, and discretization. Meniscal extrusion and K-L grade were interpreted using the DL method with MRI and X-ray images, as opposed to the logistic regression method that used numerical data. Other numerical data such as age, BMI, conservative interval, TVA, PTS, MOAKS, and WBL ratio were modeled using ML. The results of the DL and ML models were summed using weighted voting and the probability was quantified . The detThe approach of the DL method was based on the CNN architecture. The detailed network structure for the CNN is summarized in Similar processes used in DL were also used in ML for preprocessing and data splitting. The boosting series XGBoost model, one of the three models frequently used in structured data analysis, was used for ML. The grid-searching method, which is the process of scanning the data to configure optimal parameters for a given model, was applied for hyperparameter tuning. Before running XGBoost, we set various types of parameters. The learning rate  was set to 0.3. In this model, the feature weights were reduced to make the boosting process more conservative. Gamma  was set to 0. As the maximum depth of the tree increased, the model became more complicated and the possibility of overfitting also increased. Therefore, the max_depth value applied was the default of 6. The subsample was set to 1, which was applied when making a tree , and was also used to prevent overfitting problems. Colsample_bytree, which was a family of parameters for subsampling of columns, was set to 1. As a weight parameter used in the classification model, scale_pos_weight was set to 1. ML was coded by numerical data, excluding meniscal extrusion and K-L grade, which were used differently in DL. The ML model was trained on 80% of the data set using a stratified threefold cross-validation method for initial model tuning, leaving out 20% for final model evaluation.We performed an analysis to determine those factors that affected the fate of MMPRT. The odds ratio of the logistic regression method and the feature importance of the AI method were compared to assess if they were affecting factors for the fate of MMPRT. The odds ratio was given a statistical significance, while the feature importance was a numerical value of relative influence. Affecting factors of the logistic regression method with statistically significant odds ratios were compared with the rankings of feature importance in the ML&DL method.Accuracy was compared through a confusion matrix using classification performance evaluation indicators including specificity, recall , precision , accuracy, F1 score, and error rate. Among the six parameters, accuracy, F1 score, and error rate were closely related to the accuracy evaluation. Therefore, these parameters were used for the accuracy comparison between logistic regression and ML&DL methods.  PredictiThe mean age of the patients was 58.09 years (range: 21–86), and the average follow-up period was 3.75 years (range: 2–11) to final treatment. The inter- (kappa = 0.816) and intraobserver (kappa = 0.853) reliabilities were acceptable for assessing X-rays and MRI. Detailed demographic and radiologic data are listed in p-values were 0.030, 0.029, 0.042, 0.024, 0.037, and 0.035, respectively). Odds ratios were 4.11, 0.43, 0.67, 7.88, 5.79, and 2.14, respectively. Age, MOAKS, PTS, and TVA showed odds ratios that were not statistically significant: 1.15, 1.22, 1.10, and 0.98, respectively   A. The coctively) . ROC anactively) B.Using the feature importance score of the ML&DL method, we investigated which factors affect the fate of MMPRT. The results suggest that the greater the feature importance score was, the more important the affecting factor was to the fate of MMPRT. The feature importance scores of the following parameters were ranked from highest to lowest: meniscal extrusion, MRI–Event, initial WBL, delta WBL, K-L grade, BMI, Age, MOAKS, PTS, and TVA  B. Classip = 0.010); recalls of the logistic regression and ML&DL methods were 0.86 and 0.83, respectively (p = 0.030); precisions of the logistic regression and ML&DL methods were 0.92 and 0.98, respectively (p = 0.010). There was no significant difference when comparing the accuracy, F1-score, and error rate between the logistic regression and ML&DL methods . The AUC values showed excellent test quality for both ML&DL and logistic regression methods . There was no significant difference between ML&DL and logistic regression methods in terms of evaluating the predicting performance (p = 0.289) (In terms of the comparison of influence (affecting factors) between ML&DL and logistic regression methods, six of the affecting factors that were statistically significant in the logistic regression method also had the highest feature importance scores in the ML&DL method. Classification performance evaluation indicators were as follows: specificities of the logistic regression and ML&DL methods were 0.92 and 0.98, respectively (= 0.289) .This study compared the accuracy and predicting performance between logistic regression and ML&DL methods. The principal findings of this study include the following: (1) important affecting factors of the logistic regression method for the fate of MMPRT, namely, meniscal extrusion, MRI–Event, initial WBL, delta WBL, K-L grade, and BMI, corresponded well with feature importance of the ML&DL method; (2) the accuracy, F1-score, and error rate of the logistic regression and ML&DL methods were not significantly different; (3) predicting performance was not significantly different between the two groups in terms of the AUC. Therefore, both of our hypotheses were verified.With regard to evaluating the influence of affecting factors, several studies tried to identify factors that would affect the fate of MMPRT based on treatment strategies. Ford et al. demonstrated significant associations between increasing BMI and meniscal tears, leading to surgical interventions . HashikaMany papers comparing the accuracy and performance of ML&DL and logistic regression methods, a similar area of study as ours, have been published. Accuracy, defined as the proportion of correct predictions, was often used as the result in these papers. However, care had to be taken when using this metric in highly imbalanced datasets. The F1-score was typically used instead of accuracy in cases of severe class imbalance in the dataset. In most papers, accuracy and F1 scores were used together or interchangeably . The DL Our study used DL and ML together to evaluate all important affecting factors. For the meniscal extrusion and K-L grade, convolution features taken from morphologic images of the X-ray and MRI were used to predict the fate of MMPRT. These factors are usually measured or interpreted by orthopedic or radiologic specialists in the clinic. However, our DL model could effectively recognize the convolution features, quantify the meniscal extrusion, and classify the K-L grade. In addition, the probability of fate was calculated by weighted voting using both DL and ML. Therefore, this finding implies that predicting the fate of MMPRT may be possible by obtaining only a few factors from the patient without expert involvement. This enables primary medical facilities to utilize reliable tools in predicting the fate of MMPRT with less involvement from tertiary medical institutions. It can also help to increase the reading efficiency and accuracy of large-scale MRI and X-ray results.This study has several strengths. First, we tried to establish a treatment algorithm that goes beyond the diagnosis, whereas most studies in the field of ML&DL are limited by focusing on diagnostic accuracy or performance only. It was significant that ML&DL could support clinicians in terms of arriving at a diagnosis and in choosing the best treatment. Second, we used the combined modalities of DL and ML for the evaluation of various kinds of affecting factors. This enabled us to assess all important factors without selection bias. Third, both images and numerical data used in this study were obtained from the CDW of a single institute and were long-term follow-up data. We were able to obtain broad-spectrum information and observe the serial changes in most patients. Finally, our method may be easy to use, and it enables us objectively compare the serial analysis regardless of the location or scale of the hospital.The current study also has some limitations to be considered. First, our study is limited to the MMRPT in OA that may have various etiologies, and MRI is a prerequisite for establishing the diagnosis of MMPRT. However, MMPRT is an important contributor to OA progression, and it also has a high incidence in the early stages of OA. Second, image data of the WBL was converted to numerical data and was operated in the ML method, which made full automatization impossible although meniscal extrusion and K-L grade used the DL method. It was technically difficult to establish the program to identify the WBL ratio automatically with a whole leg radiograph. Third, the number of data used in our ML&DL model was less than in another ML&DL study. The data numbers were small because we targeted patients with MMPRT as a precondition and not just patients with common OA. However, our dataset included a wide range of information and serial long-term follow-up, which can improve the performance of the ML&DL.The affecting factors of the logistic regression method and the influence of the ML&DL method were not significantly different. The accuracy and performance of the ML&DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&DL algorithm could potentially predict the fate of MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice.


ExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.Breast cancer is the second cause of cancer-associated death among women and seriously endangers women’s health. Therefore, early identification of breast cancer would be beneficial to women’s health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BC Breast cancer is a major kind of malignant tumor that seriously endangers women’s health. According to cancer statistics in 2018, breast cancer accounts for more than 10% of all new diagnoses and causes about 600,000 deaths every year (Plasma extracellular vesicles (EVs), such as exosomes and microvesicles, are mainly derived from cancer and hematopoietic cells in cancer patients, which host cell-information of their original tissues , 4. SincCircular RNA (circRNA) is a new type of RNA, which shows the remarkable feature of being covalently closed continuous loops without 5’ to 3’ polar structure . CircRNAIn this study, we first implemented genome-wide long RNA sequencing to determine the difference of RNA profiles in EVs of plasma between breast cancer patients and controls. In the training stage, the circRNAs with significantly different levels were selected and their relative levels were evaluated among 182 participants by quantitative real-time polymerase chain reaction (qPCR). Based on the relative levels, we then constructed the diagnosis classifier with multiple machine learning models, including SVM, LR, and LDA. According to the results of their cross-validation, the classifier with the largest AUC was selected, and its performance was further studied in the validation cohort.In total, we collected 259 plasma samples from two groups of individuals: breast cancer patients and controls . In brief, the plasma was prefiltered, then was mixed with 2x binding buffer. The mixture is added to the exoEasy membrane affinity to bind the EVs to the membrane. After centrifugation, the wash buffer was added to wash off non-specific material in the column. After enriching EVs, QIAZOL was added to the column to lyse the vesicles and chloroform was added to the lysate collected after centrifugation. After the aqueous phase is recovered and mixed with ethanol, and the sample-ethanol mixture is added to the RNeasy MinElute spin column and centrifuged. Washing the column with buffer RWT, then wash twice with buffer RPE. And finally elute RNA in water. The rRNAs in total RNAs were first removed using Ribo-Zero rRNA Removal Kits  and the libraries of RNA-sequencing were constructed with TruSeq Stranded Total RNA Library Prep Kit . Subsequently, quality and quantification of libraries were assessed using the BioAnalyzer 2100 system . Finally, 10 pM libraries were denatured as single-stranded DNA molecules, captured on Illumina flow cells, amplified The 3’ adapter of the raw read was trimmed, and the low-quality read was removed by using cutadapt software (v1.9.3). At first, the reads were aligned to the reference genome hg19) and transcriptome with STAR software (v2.5.1b) . Then, t and tran2 fold change| ≥ 2 and P-value < 0.05 (www.omicshare.com/tools).The dysregulated circRNAs were determined by the edgeR package of R software with a cutoff threshold of |loge < 0.05  and Omic–Δct, where ΔCt = Cttarget – Ctreference. Their primer sequence was shown in TaKaRa PrimeScript™ RT reagent was done with equal quality of input RNA. The qPCR for human circRNAs was done on an Applied Biosystems 7500 Real-Time PCR System using the TaKaRa TB Green™ Premix Ex Taq™ II. The value of the cycle threshold (Ct) was processed and exported by the software of Applied Biosystems SDS (v2.3.0). CircRNAs from the training and testing cohort were detected by qPCR with a human endogenous mRNA, U6, as a reference. Relative quantification was used and the levels of circRNAs were normalized against the level of reference by 2The workflow of classifier construction was shown in Since quite a number of studies have reported that discrete data may improve classifier performance , before 2 test were used for comparison of categorical variables. P-value < 0.05 for two-sided tests was considered to be statistically significant. Hierarchical clustering was applied to the circRNAs with significantly different levels, using the average‐linkage clustering algorithms in Cluster (ver. 3.0). Heat maps were plotted using the pheatmap package of R (version 3.0.1). The receiver operating characteristic curve (ROC) was drawn and the difference of the area under the curve (AUC) was calculated by using the pROC package , lncRNA  and mRNA ; By using public databases and literatures to annotate the circRNAs, we found that approximately 71.30% of circRNAs were novel circRNAs  was calculated by edgR  and accuracy=0.83] in the training cohort after LOOCV, displaying the maximum AUC of SVM model .Then, the performance of BCExoC) composed of nine circRNAs with the highest AUC was selected, and then it was verified in the validation cohort. The AUC of BCExoC was 0.82 [0.77-0.87] in all cohorts , and the heat map results showed distinct patterns between breast cancer patients and controls, indicating their potentials as early diagnostic biomarkers of breast cancer. In this study, we used circRNAs in plasma EVs, so our method is non-invasive, which could reduce the harm of biopsy to patients and avoid the heterogeneity of cancer. In addition, the circular structure of circRNAs was normally more stable than the linear RNA, therefore, they may be more suitable taken as disease biomarkers. However, our study also has some limitations: as all samples were merely collected from one center, the performance of our classifier needs to be validated with more independent cohorts prior to their clinical applications.By literature search, we found that three of the top 20 up-regulated circRNAs have been studied in breast cancer cells, and their functions were closely related to tumorigenesis , 30. ForExoC is a promising noninvasive method for the early diagnosis of breast cancer. Our techniques required for circRNA detection, such as plasma collection, RNA extraction, and qPCR, are routinely used in clinics. What’s more, the cost of reagents and consumables is relatively low and the result of BCExoC is easy to be explained. Therefore, it is feasible to analyze BCExoC in clinical practice.In summary, our data showed that BCThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by Institutional review board of The First People’s Hospital of Foshan. The patients/participants provided their written informed consent to participate in this study.G-lY, Y-sW designed and supervised the study. LL and Z-wG analyzed and interpreted the data and prepared the manuscript. X-mZ, LL, B-w H, Z-j L and M-qC designed the study, provided samples and interpreted clinical data. X-xY, ML, KL, C-lZ and T-cL analyzed and interpreted the data. All authors contributed to the article and approved the submitted version.The work was supported by National Natural Science Foundation of China , Medical Scientific Research Foundation of Guangdong Province of China (B2017006), and China Postdoctoral Science Foundation funded project (2019M662998) and Special fund of Foshan Summit plan (2020G010).Author KL was employed by Guangzhou XGene Co., Ltd.The remaining 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.The reviewer LZ declared a shared affiliation, with no collaboration, with the authors to the handling editor at the time of the reviewAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Due to the current practice on colorectal cancer (CRC) management, chemoresistance is most often recognized at the end of the treatment. Therefore, effective and easy-to-use prognostic biomarkers are needed.We evaluated the prognostic significance of two novel CRC biomarkers: a) micronuclei frequency (MNf) in 55 metastatic CRC (mCRC) and 21 locally advanced rectal cancer (laRC) patients using cytokinesis block micronucleus assay (CBMN assay) and b) telomerase activity (TA) in 23 mCRC and five laRC patients using TRAP-ELISA. Both biomarkers were evaluated in peripheral blood lymphocytes (PBLs) before, at the middle, and at the end of the therapy  for mCRC patients before, at the end of the therapy, and after surgery for laRC patients.Overall, MNf demonstrated significant prognostic value since a decrease of MNf less than 29% between middle and initial MNf measurements can discriminate between progressive and stable/responsive disease with sensitivity of 36% and specificity of 87.0% while being able to identify responsive disease with sensitivity of 72.7% and specificity of 59.3%. On the other hand, TA presented a significant trend of increase (p = 0.07) in patients with progressive disease at the middle measurement.The findings of this study suggest that the MN frequency may serve as a promising prognostic biomarker for the monitoring of the treatment response of patients with CRC, while TA should be evaluated in a larger group of patients to further validate its significance. Colorectal cancer is considered as one of the leading causes of cancer-related morbidity and mortality worldwide for both sexes. It was estimated that in 2020 147,950 individuals would be diagnosed with CRC in the United States  while 53,200 patients would die from the disease .Micronuclei (MNs), also known as Howell–Jolly bodies, are particles formed during anaphase, since part of the genetic material fails to follow the rest of the chromosomes and therefore is not included in the daughter nucleus during telophase. This results in a smaller “nucleus” close to the main one. It is exhibited that MN formation can be attributed primarily to mitotic spindle failure, kinetochore damage, centromeric DNA hypomethylation, and defective control in the cell cycle system . Nonethevia upregulation of the T-STAR gene  , and a template, the telomerase RNA component (TERC). Notwithstanding that, the telomeres’ length is induced in each cell cycle in a lower rate, endorsing cellular senescence . In non-control) . Given tcontrol) . Interescontrol) , while icontrol) .From December 2016 to February 2021, 94 consecutive patients treated at the Department of Medical Oncology, University Hospital of Heraklion were evaluated for participation in this study. Inclusion criteria were: I) Patients with radiologic evidence of mCRC documented by computed tomography (CT) and/or magnetic resonance imaging (MRI) presenting measurable disease treated with first line systemic treatment according to the Hellenic Society of Medical Oncologists (HeSMO) guidelines Figure 1Based on the chemotherapeutic protocol that was selected, mCRC patients received one of the following therapies: I) folinic acid with 5-fluorouracil and oxaliplatin (FOLFOX), II) folinic acid with 5-fluorouracil and irinotecan (FOLFIRI), or III) folinic acid with 5-fluorouracil, oxaliplatin, and irinotecan (FOLFOXIRI). Based on the genetic profile of each patient, a biological factor could be used.Patients with laRC were informed about their therapeutic strategy: Induction chemotherapy and then operation for RC. Chemotherapeutic protocols were FOLFOX and capecitabine and oxaliplatin (CAPOX).The RECIST criteria version 1.1 were used as the gold standard for the evaluation of the treatment response . Using tPeripheral blood samples were collected at predetermined time-points. For mCRC patients, these were before the beginning of the treatment, in the middle of it, and at the end of treatment . For laRC patients, samples were taken before the beginning of chemotherapy and at the end. We also took blood samples two weeks postoperatively from 12 laRC patients. All blood samples were stored in 5°C until processing within 48 h from sampling.The control group was constituted by 25 healthy individuals with no medical condition after having normal colonoscopy. All individuals provided an informed written consent. Inclusion criteria were: age between 45 and 75 years old, medical history free of cancer, autoimmune diseases, diabetes mellitus type I or II, and chronic obstructive pulmonary disease (COPD), non-smokers or no smoking habits for the last ten years, and no consumption of immune-modifying medication. Exclusion criteria were the presence of the above-mentioned diseases, direct exposure at any time  to pesticides, herbicides, organic solvents, or any persistent organic pollutant; for women, additional exclusion criteria were the use of oral contraceptives and the will not to participate in the study.Laboratory preparation for MNf quantification follows the description we have presented earlier . Standarwhere M1, M2, M3, and M4 correspond to the number of cells with one, two, three, and four nuclei, respectively, and ‘N’ is the total number of cells. For CBPI calculation, 2,000 cells were counted. CBPI is a tool that is used in order to better understand BNMN results from cell cultures where cytochalasin B is used. Moreover, it is able to provide substantial information regarding possible cytotoxic effects  on the cell culture induced by any chemical agents. If CBPI remains close to the numeric value of one, then there is no cytotoxic event. Moreover, should it remain almost the same between time-points, then MNf results are comparable, and any fluctuation of MNf can be attributed solely to the parameter of interest (in our case CRC and/or the systemic treatment). These parameters were calculated, in order to determine the possible cytotoxic effects, as previously described –27.TA was measured in 28 patients, whose characteristics are presented in 2) was applied to estimate differences in proportions of patients’ and disease characteristics (e.g. mCRC vs IaRC), the Mann–Witney test was applied. Whereas, in order to examine TA differences and %DMNf for more than two groups (e.g. disease response) Kruskal–Wallis comparisons were applied. Comparison of counts of MNf was assessed using G-test when bivariate comparisons of before, middle, and after therapy sampling points were made. The Chi-squared test was used for the analysis of the CBPI values., Due to the small number of cases, a crude discrimination limit between responses was established using %DMNf as an indicator. The %DMNf definition between middle and before was set by the formulaFrequency data were analyzed using non-parametric statistics. Pearson’s Chi-square test  according to %DMNf.ROC curve analysis, corresponding diagrams of sensitivity https://www.openepi.com/Menu/OE_Menu.htm) were used for statistical analysis of data and sensitivity analysis. A level of 0.05 was set as level of acceptance.IBM SPSS Statistics 26.0 and OpenEpi 3.01 open source epidemiological program  and progressive/stable disease vs partial/complete response (scenario 2). For scenario 1, the best set of sensitivity and specificity was found at 29% difference between middle and initial MNf measurements (sensitivity 36% and specificity 87.0%), while the highest specificity (87.2%) was achieved at 31% reduction of MNf. For scenario 2, the best set of sensitivity and specificity was found again at 29% difference between middle and initial MNf measurements (sensitivity 72.7% and specificity 59.3%), and the highest specificity (59.6%) was found for 31% reduction of MNf  in PBLs isolated from patients with mCRC and laRC blood samples. Although our findings are hypothesis driven, given the relatively small number of patients we tested, our data provides further proof that MNf is not only significantly increased in CRC [as we have already shown ] but it pathways . A graphvia STAT3 inhibition (a potent activator of hTERT promoter)  and could mainly serve as hypothesis generating study for further evaluation.Finally, in order to thoroughly understand and interpret our results, it is important to know how these biomarkers are affected by the different therapeutic agents used in our study. To begin with, FOLFOX, FOLFIRI, and FOLFOXIRI regimens are the three principal first-line therapeutic regimens administered for stage IV CRC according to HeSMO guidelines . They shromoter) . However effects . HoweverThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation.The studies involving human participants were reviewed and approved by the Human Ethics Committee at the University Hospital of Heraklion. The patients/participants provided their written informed consent to participate in this study.TN conducted the experiments, interpreted the data, performed the analysis, and wrote the manuscript. EV conducted experiments. PS performed the analysis and interpreted the data. AA performed the statistical analysis, interpreted the data, and wrote the manuscript. AB conducted experiments. NR conducted experiments. JS provided blood samples, raised funds, edited, and critically revised the manuscript. AT raised funds, edited and critically revised the manuscript. JT conceived and designed the study, raised funds, edited, and critically revised the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by Gastrointestinal Cancer Study Group (GIC-SG), Hellenic Society Medical Oncology (HESMO) and ToxPlus S.A. The funder, ToxPlus S.A., was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit 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.


Remnant cholesterol (RC) mediates the progression of coronary artery disease, diabetic complications, hypertension, and chronic kidney disease. Limited information is available on the association of RC with nonalcoholic fatty liver disease (NAFLD). This study aimed to explore whether RC can be used to independently evaluate the risk of NAFLD in the general population and to analyze the predictive value of RC for NAFLD.The study included 14,251 subjects enrolled in a health screening program. NAFLD was diagnosed by ultrasound, and the association of RC with NAFLD was assessed using the receiver operating characteristic (ROC) curve and logistic regression equation.P trend< 0.001). There were significant differences in this association among sex, BMI and age stratification. Compared with men, women were facing a higher risk of RC-related NAFLD. Compared with people with normal BMI, overweight and obesity, the risk of RC-related NAFLD was higher in thin people. In different age stratifications, when RC increased, young people had a higher risk of developing NAFLD than other age groups. Additionally, ROC analysis results showed that among all lipid parameters, the AUC of RC was the largest , and the best threshold for predicting NAFLD was 0.54 in women and 0.63 in men.Subjects with elevated RC had a significantly higher risk of developing NAFLD after fully adjusting for potential confounding factors  in the general population, RC is independently associated with NAFLD but not with other risk factors. (2) Compared with traditional lipid parameters, RC has a better predictive ability for NAFLD in men.The online version contains supplementary material available at 10.1186/s12944-021-01573-y. In recent decades, the incidence of nonalcoholic fatty liver disease (NAFLD) has increased significantly globally with the prevalence of obesity and increased consumption of refined carbohydrates and saturated fats , 2. A reDyslipidemia is a recognized pathogenic factor of NAFLD and has been confirmed in several epidemiological and genetic studies , 8. DyslNAGALA  is a longitudinal survey launched in 1994 to assess risk factors for common chronic diseases in the general population. The NAGALA project continuously includes the general population who take part in a comprehensive health examination at Murakami Memorial Hospital. The details of the study design have been described elsewhere , in whicn = 20,944) and finally included 14,251 subjects who met the requirements according to the following exclusion criteria: (i) Missing baseline information of subjects (n = 863); (ii) Subjects who drank heavily  [n = 2321); (iv) Subjects with alcoholic hepatitis or viral hepatitis or diabetes or impaired fasting glucose (n = 1547); and (v) Subjects not enrolled in the study for unknown reasons (n = 10).This study was a post hoc analysis of the NAGALA to investigate the association of RC with NAFLD. According to the current research purpose, the researchers extracted the available data from 2004 to 2015 in the NAGALA project (n = 739) ; (iii) SAs described in a previous survey report , the subAbdominal ultrasound was used to diagnose NAFLD. The abdominal ultrasound procedure was performed by trained technicians, and then the gastroenterologist examined the ultrasound image without referring to the subject’s other personal data. The diagnosis of NAFLD was based on the four criteria of vascular blurring, deep attenuation, hepatorenal echo contrast and liver brightness .P value less than 0.05  was the significance criterion.In this study, the statistical software Empower Stats (version 2.0) and R language (version 3.4.3) were used for data analysis, and a First, the quintile of RC was calculated by a quantile function, the subjects were equally divided into five groups, and the differences among each group were compared by chi-square test, nonparametric test or one-way ANOVA. The distribution pattern of continuous variables was evaluated by a QQ plot and reported as the mean (standard deviation: SD) or median (interquartile range). Categorical variables were reported as percentages.Univariate analyses were used to initially assess the association between baseline variables and NAFLD. Then, the independent association of RC with NAFLD was evaluated by a multivariable logistic regression model, the odds ratio (OR) and 95% confidence interval (CI) of NAFLD were calculated, and the unadjusted and multivariable adjusted model analysis results were listed according to the recommendations of Strengthening the Reporting of Observational Studies in Epidemiology statement . Model 1Finally, to test the predictive value of lipid parameters for NAFLD, receiver operating characteristic (ROC) curves were plotted, the area under the curve (AUC) corresponding to each lipid parameter and its best threshold were calculated, and the DeLong test was used to check whether RC was significantly different from other lipid parameters.P < 0.05). Subjects in the highest RC group (Q5) had higher ages, LDL-C, AST, ALT, weight, height, GGT, WC, TC, BMI, TG, FPG, HbA1c, non-HDL-C and systolic/diastolic blood pressure (S/DBP) and more drinkers than other groups (Q1-Q4). Subjects with the highest RC (Q5) had lower HDL-C values than subjects with lower RC (Q1-Q4). In addition, the prevalence of NAFLD increased rapidly among the RC quintiles, and there was a significant difference, among which the prevalence rate of males was higher than that of females in all groups.Overall, 14,251 subjects met the inclusion and exclusion criteria for this study. Among these subjects, the prevalence of NAFLD was 17.59%. The baseline characteristics of the RC quintile groups by sex are shown in Table P trend< 0.001). In the age-, sex- and BMI-adjusted model (Model 1), the positive correlation between RC and NAFLD was slightly weaker than that in the unadjusted model, and the trend of the positive correlation remained unchanged. After further adjustment for sex, BMI, TC, FPG and SBP (Model 2), the degree of positive correlation between them changed slightly, and the risk of NAFLD increased by 88% for each increase in 1 SD by RC . Finally, after adjusting all non-collinear covariables, RC and NAFLD maintained a positive correlation, the degree of correlation between them decreased slightly, and the linear trend of RC from the lowest to the highest quintile was significant .First, the association between baseline variables and NAFLD was assessed by univariate analysis. It is evident from Table P-interaction = 0.0001). Compared with people with normal BMI, overweight and obesity, the risk of RC-related NAFLD was higher in thin people . In different age stratifications, when RC increased, young people had a higher risk of developing NAFLD than other age groups.The researchers also analyzed the association of RC with NAFLD after stratification by sex, BMI, age and habit of exercise and examined the differences among different subgroups by the likelihood ratio test Table . These rTo evaluate the predictive value of non-HDL-C, LDL-C, TG, HDL-C, TC and RC in NAFLD, ROC analysis was performed to calculate the AUC of each lipid parameter in different sexes. As shown in Table In this cross-sectional study based on the general population, it was observed that there was an independent positive correlation between RC and NAFLD. After fully adjusting for other covariates, the risk of NAFLD increased by 88% for every 1 SD increase in RC. This association also existed in different subgroups, and there were significant differences. In addition, the predictive value of RC for NAFLD was better than that of other lipid parameters in men.Dyslipidemia is a recognized pathogenic factor of NAFLD and has been confirmed in several epidemiological and genetic studies , 8. AmonRC is cholesterol-rich in TG lipoprotein, which consists of chylomicron remnants, intermediate-density lipoprotein and very-low-density lipoprotein . It can Another important finding of the current study is that there are significant differences in the corresponding NAFLD risk of RC in different age, sex and BMI stratifications. Compared with men, women have a higher risk of developing NAFLD with higher RC levels. However, the prevalence of NAFLD in men was still higher than that in women in the group with higher levels of RC in this study. Therefore, there may be some special reasons for the higher risk of NAFLD associated with RC in women. To explain this phenomenon, the researchers summarized the baseline characteristics according to sex in the group with the highest RC value (Supplementary Table The advantages of this study are as follows: (i) The sample size of this study is large, it has been strictly statistically adjusted, and the final conclusion can be considered to be relatively objective. (ii) This study found that RC is more valuable in predicting NAFLD than traditional lipid parameters in men. (iii) RC is simple to calculate and easy to obtain. It is especially suitable for chronic disease risk assessment and epidemiological investigation in the general population.A few limitations should also be highlighted: (i) Due to the cross-sectional design of this study, the causal association between RC and NAFLD needs to be explored in further longitudinal studies. (ii) In this study, NAFLD was evaluated and diagnosed according to ultrasound. Compared with liver biopsy, abdominal ultrasound may not be very sensitive to some mild hepatic steatosis . (iii) AIn conclusion, this study demonstrates that RC is an independent risk factor for NAFLD in the general population and is a better predictor of NAFLD risk than conventional lipid parameters in men. RC is a novel and clinically effective marker that is simple to calculate and easy to obtain and can be used to identify people at high risk of NAFLD in the early stage.Additional file 1: Supplementary Table 1. Collinearity diagnostics steps. Supplementary Table 2. Baseline characteristics of NAFLD patients in Q5 group of RC.


The majority of population rely on traditional medicine as a source of healthcare. Artemisia afra is a plant traditionally used for its medicinal values, including treatment of malaria in many parts of the world. Currently, it is also attracting attention because of a claim that a related species, Artemisia annua, is a remedy for the COVD-19 pandemic. The aim of the present study was to investigate toxic effects of A. afra on brain, heart and suprarenal glands in mice aged 8–12 weeks and weighing 25–30g.Leaves of A.afra were collected from Bale National Park, dried under shade, crushed into powder and soaked in distilled water to yield aqueous extract for oral administration. For acute toxicity study, seven treated and one control groups, with 3 female mice each, were used. They were given a single dose of 200mg/kg, 700mg/kg, 1200mg/kg, 2200mg/kg, 3200mg/kg, 4200mg/kg or 5000mg/kg b/wt of the extract. For the sub-acute toxicity study, two treated and one control groups, with 5 female and 5 male mice each, were used. They were daily treated with 600mg/kg or 1800mg/kg b/wt of extract.50 was found to be greater than 5000mg/kg indicating that the plant is relatively safe. In the sub-acute study, no signs of toxicity were observed in all treatment groups. On microscopic examination of the brain, heart and suprarenal glands no sign of cellular injury was observed.LDThe findings of this study suggest that the leaves extract of A. afra is relatively safe in mice. A. afra is a medium-size perennial herb, rarely exceeding 2m high. It is located in Ethiopia Kenya, Zimbabwe, Malawi, Angola and South Africa , College of Health Sciences, Departments of Anatomy and Physiology, and Ethiopian Public Health Institute (EPHI) from September 2014 to July, 2015.Collection of plant materials: A. afra was collected from Bale National Park, 400km southeast of Addis Ababa in Oromia regional state during the month of September 2014. The plant was identified by a taxonomist, and a few samples were deposited at the National Herbarium in the College of Natural and Computational Sciences, Addis Ababa University (AAU), with a voucher specimen number of 392/NKI/PHARM.Preparation of aqueous leaf extract: The plant leaves were cleaned, dried in shade, ground to powder (400g) and macerated with distilled water for 2hrs and 30minutes with intermittent agitation by orbital shaker. The supernatant part of agitated materials was decanted and filtered with 0.1 mm2 mesh gauze from the un-dissolved portion of the plant. The filtrate was freeze-dried to give 43g (10.75% yield) of crude extract.Experimental animals: The animals used in this study were bred and reared at the animal house of EPHI and transported to AAU, College of Health Sciences, Department of Physiology. Experiments were conducted on 54 healthy adult male and female mice aged 8–12 weeks and weighing 25–30g. Grouping of mice was done randomly. The animals were kept in separate polycarbonate cages and provided with bedding of clean paddy husk. The mice were acclimatized to laboratory conditions for one week prior to experimentation to minimize nonspecific stress  for aqueous leaf extracts of A. afra were determined using Protocol for LD50 determination  and 1800mg/kg (high dose) of the extract for 28 days, while another group was given vehicle (distilled water). The low dose was selected in reference to an efficacy study of A. afra in treatment of malaria which showed 400 mg/kg as effective dose . Ribbons of tissue sections were gently collected and placed onto the surface of a water bath heated at 40°C. They were then collected onto gelatin coated glass slides and placed in oven overnight. Sections were deparaffinized by xylene, hydrated through a down series of alcohols, and stained by Harris' hematoxylin. Slides were differentiated by 1% acid alcohol and counter stained in eosin. Stained sections were dehydrated with increasing concentrations of ethyl alcohols and cleared in xylene, mounted using DPX and covered with cover slips.Stained tissue sections of brain, heart and suprarenal glands were carefully examined under binocular compound light microscope . Tissue sections from the treated groups were examined for any evidence of histopathological changes by a pathologist with respect to those of the controls blindly. After examination, photomicrographs of selected sample sections of brain, heart and suprarenal glands from both treated and control mice were taken under a magnification of x20 objective by using automated built-in digital photo-camera .Data processing and analysis: All quantitative data were organized and analyzed using Statistical Package for Social Science (SPSS) version 21 statistical software. The values of body and organ weight including relative organ weights were analyzed, and the results were expressed as mean ±SEM (standard error of mean). Difference between treated and control groups were compared using one-way ANOVA. P-values <0.05 were considered statistically significant.Ethical approval: Ethical approval was obtained from the Research Review Committee of the Department of Anatomy, College of Health Sciences, AAU.Acute toxicity study: The single oral administration of aqueous extract of A. afra in mice did not show any mortality even with the highest dose which was 5000mg/kg. No signs of toxicity were observed at the lower four doses, i.e., 200mg/kg, 700mg/kg, 1200mg/kg and 2200mg/kg. However, signs of mild toxicity like anxiety and piloerection were seen at the doses of 3200, 4200mg/kg and 5000mg/kg. These symptoms gradually disappeared after some wash out periods over two weeks observation. Moreover, there was a gradual increase in body weight of treated mice though not statistically different among the different groups.Sub-acute ToxicityA. afra aqueous leaves extract on behavior and body weight of miceEffects of : During the period of 28 days of sub-acute toxicity study, mice treated orally with both low and high doses of the extract showed no noticeable change in their general behavior compared to the control group. There was also no significant difference between the two sexes, except that the males, in both treated and non-treated control groups, were more aggressive compared to the females  was observed in female and male treated mice groups .On examination of the sections of suprarenal glands, no signs of toxicity were observed on both cortical and medullary regions. The architecture of both cortex and medulla of mice treated with 600mg/kg and 1800mg/kg of the extract was identical with that of the control. In both treated and control mice, two cortical zones were observed with some mice showing additional zone (X-zone). However, no cortical lesion like degeneration (vacuolar or granular), necrosis or hemorrhage was observed .Evaluation of the pathological alterations induced in laboratory animals by novel treatment agents represents one of the safety assessments prior to conducting clinical trials. This preliminary assessment represents major contributions to the development of new treatments for humans and animals. Acute toxicity tests provide data on the relative toxicity likely to arise from a single or brief exposure. It is an initial assessment of toxic manifestations induced with the test substance/s .50 of the aqueous extract of A. afra was above 5000mg/kg which is grouped under category 5 or unclassified according to Globally Harmonized Classification System  like shrinkage of cell body, pyknosis of the nucleus, disappearance of the nucleolus and loss of Nissl substance with intense eosinophilia of the cytoplasm (red neurons) . In the Because of its high oxidative metabolic need, the heart can be injured by any compounds that interfere with its oxygen supply. On microscopic examination, myocardial damage can take the form of cytoplasmic alterations such as vacillation, pyknotic nucleus, karyrrhexis, and karyolysis in diffuse or localized area . In the Suprarenal glands are reported to be the most common endocrine organ associated with chemically induced lesions . These lA. afra in mice for a month is safe.In conclusion, the present findings suggest that administration of 600mg/kg and 1800mg/kg of body weight of aqueous leaves extract of


E. coli (Escherichia coli) and its deactivated controls, K. pneumoniae (Klebsiella pneumoniae) and S. aureus (Staphylococcus aureus), were analyzed with fluorescence excitation by a 266 nm laser. By analyzing the results, it was found that the dominant fluorescence peaks of bacterial solutions at 335~350 nm were contributed by tryptophan, and the subfluorescence peaks at 515.9 nm were contributed by flavin; besides, K. pneumoniae and S. aureus had their own fluoresces characteristics, such as tyrosine contributing to sub-fluorescence peaks at 300 nm. The three species of bacteria can be differentiated with whole fluorescence spectrum by statistically analysis (p < 0.05), for various concentrations of aromatic amino acids and flavin in different bacteria. The experimental results also proved that the inactivation operation did not alter the spectral properties of E. coli. The indexes of fluorescence intensity and FIR  can be used to retrieve the bacteria concentration as well as for bacteria differentiation using the index of slopes. The detection limit of bacteria is less than ~105 cell/mL using laser induced fluorescence methods in the paper. The study demonstrated the rapid detection capability of the LIF bacterial detection system and its great potential for rapid quantitative analysis of bacteria. This may bring new insight into the detection of common bacteria in water in situ.As an important part of environmental water quality monitoring, efficient bacterial detection has attracted widespread attention. Among them, LIF (laser-induced fluorescence) technology has the characteristics of high efficiency and sensitivity for bacterial detection. To simplify the experimental process of bacterial detection, fluorescence emission spectra of  Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), and Staphylococcus aureus (S. aureus) are usually regarded as pathogenic bacteria. E. coli is a conditionally pathogenic bacterium, distributed widely in the intestinal tract of humans and animals and considered to be one of the most important indicators of sanitation and water quality. K. pneumoniae, a rare cause of community-acquired pneumonia, but accounting for a higher proportion of pneumonia acquired in hospitals and gram-negative, is the most important group of bacteria in the Klebsiella genus of the Enterobacteriaceae. S. aureus is gram-positive, a common foodborne pathogenic microorganism, which is ubiquitous in the environment. Fast detection of the distribution, concentration, and species of these bacteria is very important for preventing relative disease spread, and patients can be effectively cured in a very short time. In previous studies, the detection methods were mainly enzyme substrate assays, multitube fermentation assays, mass spectrometry, polymerase chain reaction, and Raman spectroscopy; however, false positives, high experimental requirements, tedious experimental operations, and expensive reagents and equipment are still insurmountable problems to overcome.Bacteria are one of the key indexes for water quality evaluation and land source pollution tracing. Among them, 3 CFU/mL. Based on Simões et al.’s research, Wu et al. [E. coli and obtained the characteristic fluorescence intensity change curves at 332 and 425 nm contributed by tryptophan, tyrosine, nucleic acid, and reduced form of nicotinamide adenine dinucleotide phosphate (NADH), which proved that fluorescence emission spectroscopy is sensitive, fast, and stable for the detection of E. coli. The important role of amino acid and flavin fluorescence in the characterization of bacteria was also considered by Mao et al., who concluded that amino acids contributed most to the intrinsic fluorescence of the bacteria [Numerous studies have shown that fluorescence is highly related to water quality indicators, and fluorescence spectroscopy has become a common method for characterizing organic matter in water. Aromatic amino acids, nucleic acids, and coenzymes with characteristic fluorescence peaks are natural fluorophores that can be used as indicators of endogenous cellular fluorescence, especially universal markers for biological materials , and plau et al.  further bacteria . In addibacteria . The typEscherichia coli and its inactivated solutions, Klebsiella pneumoniae and Staphylococcus aureus in waters, to simplify the experimental process of bacterial detection and to improve the efficiency and accuracy of bacterial identification, for rapid detection and quantification of bacteria in water.Although excitation-emission matrices (EEMs) have the advantages of comprehensive information and lower detection limits, it can be strongly interfered by non-targeted substances in the environment, and the detection sensitivity is affected by the bacterial species . In contE. coli and its hyperbaric inactivated controls, K. pneumoniae and S. aureus, and deionized water were kept and provided by the Deep Sea Biology Research Laboratory, Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences. The samples were inoculated in sterilized culture broth under aseptic conditions, incubated at 37 °C for 20 h at 150 r·min−1, and then centrifuged at 1500 r·min−1 for 15 min, and the supernatant was taken as a reserve. The bacterial supernatant was also diluted with deionized water and labelled 1:10, 1:20, 1:40, 1:80, 1:160, 1:320, 1:640, and 1:1280 according to their dilution ratios. The absorbance of E. coli, K. pneumoniae, and S. aureus at 1/10 dilution was measured by Ultraviolet-visible (UV/Vis) spectrophotometer (Shimadzu UV-2550), respectively, and the uninoculated culture medium at 1/10 dilution was used as a blank control.Solutions of An all-solid-state UV laser  was used as the excitation light source to excite the sample to be measured in the system’s optical chamber and the fluorescence signal was collected by the spectrometer . The integration time ranged from 8 ms to 15 min, which can be set as required, and in this paper, the integration time was 100 ms. The band of the spectrometer ranged from 220 to 1008 nm, of which 220~700 nm was used for the analysis in the paper.Detection of laser-induced fluorescence of bacteria was conducted using a UV laser to excite dilutions containing bacteria and simultaneously receive fluorescence with a spectrometer. The spectrum of fluorescence was stored for further analysis to differentiate species and concentration gradients of bacteria, respectively. The LIF instrument contains many modules, such as lasers (used as light sources), spectrometer (used as fluorescence receiver), and control module, among others see . Data ofp < 0.05).The fluorescence spectrum of the bacteria was analyzed with an average of 10 spectra. The results are shown in K. pneumoniae and S. aureus was mainly contributed by tyrosine. By reviewing the literature, we suggested that the spectral features at 360~370 nm  was set to 1, the relative concentration of the sample with the minimum dilution multiple (1/10) was set to 128, and subsequently the variation of bacterial fluorescence characteristic spectra with concentration was obtained, as shown in 335~350/I515.9) can describe the difference in fluorescence emission of tryptophan and flavin in bacteria. As shown in The FIR curve of the T-peak versus 515.9 nm  [E. coli, K. pneumoniae, and S. aureus at 600 nm were 0.1668, 0.1339, and 0.1454, respectively, and the limits of detection were 5.5313, 1.2401, and 1.5447 (×105 cell/mL), respectively, as measured by UV/Vis spectrophotometry. However, laser-induced fluorescence methods are more efficient, and can be used for the fast detection of bacteria in water in situ.Some research has shown that there was a good linear relationship between bacterial count and absorbance at 600 nm, with absorbance values between 0.032 and 0.828, and the regression equation was y = 1437.3x − 27.344 (R= 0.998) . In thisRapid identification and quantification of the concentration of pathogenic bacteria in water are essential for water quality evaluation and protection of human health. The LIF technique in this paper can help to achieve large-scale, real-time in situ detection while avoiding cumbersome detection steps, and improving the efficiency of detection and instant results. Although the method in this paper is more efficient, it is still difficult to achieve accurate detection and prediction of biohazards with a single technique. Therefore, the combination of multiple detection methods is a tendency in the future. Despite the promising development of LIF technology, a great deal of work still needs to be done before LIF truly becomes a viable alternative for the detection of bacteria in water in situ. Most factors, such as the integration of proteins, polarity of the solvent, local environment of fluorophores , experimIn the paper, an LIF system was developed for the detection of bacteria in water in situ, and some conclusions were drawn as follows: p < 0.05).(i) The maximum fluorescence peaks contributed by tryptophan were all located at 335-350 nm; however, the dominant fluorescence peaks were statistically different when bacterial species varied (2 > 0.95).(ii) The maximum fluorescence peaks’ height was linearly related to the bacterial concentration over a range of concentrations  The inactivation operation of (iv) FIR and normalized curves can be used for bacterial species identification and concentration analysis.In summary, the LIF system developed in the paper can be used for accurate, rapid identification of bacteria and concentration evaluation in natural water in situ.


Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs’ edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 ( The most recent statistics from the American Cancer Society showed that prostate cancer (PC) is the most prevalent type of cancer with 248,530 (26%) cases, and it is also the second leading cause of cancer-related death with 34,130 (26%) [The GP practically ranges from GP1 through GP5. The GP1 (stroma) and GP2 (benign) represent the nonepithelium tissue. While GP3, GP4, and GP5 represent the epithelium tissue. GP3 indicates moderately differentiated glands compared with that of GP5, which represents poorly differentiated cells [The GS grading system is often divided into only three categories, 6, 7, and 8–10 . This clThe discordance in GG diagnosis by different pathologists using the same biopsy is between 30–50% ,8. AgreeIn recent years, extensive research work was developed to diagnose the tumorous lesions for many organs, especially the prostate ,12,13,14According to the previous studies, the general technique to classify the GG,  is thatThe rest of the paper is structured as follows. This work’s primary objective is to develop a CAD system for accurate GG diagnosis of PBSs. The proposed overall CAD system performs GP classification, as well as identifies the GS and GG. The GP classification pipeline consists of three stages: a DL system consisting of fusion three convolution neural networks (CNN); namely, a pyramidal CNN. Also, a patch and pixel-wise classification that divided the original image into patches and labeled them according to GP, the majority voting techniques is used in this step to merge the patches images into the original size, see The CNN plays an essential role in many fields of medical image analysis, especially in the segmentation ,20,21, aAlgorithm 1: Preprocessing step of developing input data for a convolution neural network (CNN). Value of N is 100, 150, or 200.Input: Prostate biopsy specimens digitized. Output: Classified selected patches into Gleason pattern labels.1.Apply the histogram equalization and edge enhancement on the PBSs.2.Divide the PBSs into overlapping patches, with size N × N pixels and 75% overlapping.3.Calculate the majority voting for the pathological patch, WL ← Winning Label.Estimate two variables for corresponding label path, RB←ratio of Background and CV ←Center value.If  Select the patchElse Remove the patchSelecting appropriate patches for training The pyramidal CNN is composed of three CNNs, and each one has a different patch size, as shown in In the CL, the image is convolved with kernels (multiple kernels in each layer) to extract prominent features that describe the object of interest in the input patch; these features are called feature maps. Therefore, each CL results in a volume of feature maps. In our implementation, we use kernels of size 3 × 3. In MPLs, the spatial dimensions are reduced by a factor of two. Benefits are twofold: firstly, keeping only the most prominent features, discarding those less essential, and secondly, reducing computational cost and training time. In our implementation, the stride is equal to one for all layers.The CNN contains four convolution layers, and the number of CLs filters is 16, 32, 64, and 128. The dropout layers weight 0.1, 0.1, 0.3, 0.3. The number of units for the FCLs is 64 and 512, respectively. The training seeks to minimize the cross-entropy loss between the predicted probabilities and the ground truth labels. The dropouts layers follow FCLs to minimize network overfitting, and the dropout rate is set to 0.15 for both layers. The total and trainable parameters for Our DL model has numerous parameters. Therefore, we utilized a hyper-parameter tuning with a random search (RS) technique that helps to reduce overfitting and improves our model’s performance. Our system’s accuracy is assessed by performing training, validation, and testing for the patches. The curves for the accuracy and loss of training the The goal for the patch-wise technique is to label all patches generated from the digitized PBS. We apply the patch-wise classification for all three CNNs individually during the test to assign each overlapped patch a label from one to five as GP categories. After that, a pixel-wise classification is applied to obtain three images of the same size as the original. The pixel-wise technique utilizes the output from the patch-wise classification, labeled patches generated from the three CNNs, to give all pixels that contain this patch the same label. Most of the pixels appear in several batches due to overlapped batches. Therefore, overlapped pixels have multiple labels depending on their position in the image. The majority voting is the core approach for the pixel-wise classification to get a single label for each overlapped pixel. The two techniques, patch- and pixel-wise, are used on the output of the three CNNs. The results after applying those techniques are three images of the same size as the original (each pixel has three labels); then, we applied majority voting again on those three images to obtain the final pixel-based classification.The identification of the GG label is considered our goal in this work. Each digitized PBSs has labels between 1–5 according to its GP. We utilize The proposed framework is trained, validated, and tested on 416, 96, and 96, respectively, with whole slide images (WSIs) of the digitized PBSs from the Radboud University Medical Center, USA, and it was analyzed by the University of Louisville Hospital, USA ,27. A seThe total number of patches for each Our goal for this automated system is to identify the GG. Therefore, there are many steps, and each one has its results before reaching our target. We show first the accuracy for each CNN, pyramidal CNN, especially the patch-wise classification, see The overall diagnostic classification accuracy is evaluated using the accuracy metrics precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged ,29. The The patch-wise classification results for our proposed pyramidal CNN are reported in d VGG-16 ,30. The After applying the patch- and pixel-wise classification for each CNN, we merge the CNN outputs to obtain the production of the digitized PBS as the exact size of the original one. The new digitized PBS defines the results for GP from our automated system. We identify the GS and GG, the goal of our pipeline, using the fundamental converting that presents in the The overall GG diagnostic accuracy is summarized in The treatment for prostate cancer over the years improved for men with low-risk diseases. Notably, for patients with localized prostate cancer, active surveillance is safer compared with radical prostatectomy, as verified by many trials . AccordiFor that purpose, we developed an automated DL architecture for classifying the GG of digitized PBS. The ground truth of our datasets was performed using many experienced urologic subspecialists. They have around 25 years of experience with diverse backgrounds, and accessed several histologic sections and immunohistochemically stained sections for every specimen. The overall accuracy for our CAD system showed a similar rate compared with that of general pathologists, which is 70%. According to ,35, the Developing a framework with high accuracy is our ultimate goal in which DL, patch-, and pixel-wise classification were performed. The accuracy of the diagnostic results for the proposed framework using pyramidal CNN presents that d VGG-16 ,30, showThe proposed CAD system can be helpful in healthcare systems in several ways, such as decreasing the consultation-associated costs, enhancing grading consistency, and reducing treatment-related morbidity for men with low-risk diseases. The performance metrics for GG estimation are higher for G1 and G2 grades compared with that of G3, G4, G5. Therefore, our automated system could be accurately classifying low-risk cases that are eligible for more conservative management.The GG classification plays an essential role in prostate cancer treatment ,36. StilDespite the promising results, our automated DL system has some limitations. Firstly, our DL model was trained and tested from a single institution. Therefore, using an external test dataset for the different centers and WSI with various staining protocols should further enhance the robustness of our automated system. Secondly, our DL models, as well the pathologists who made the ground truth labeling, treated each biopsy as an independent sample. In clinical practice, multiple biopsies are sampled from various regions of the prostate. Therefore, an update to our model could take multiple biopsies into account and give a grade group prediction at the patient level. Finally, our study concentrated on the grading of acinar adenocarcinoma in prostate biopsies. However, prostate biopsies can contain other tumor types and foreign tissue, such as colon glands. The biopsies could also include additional prognostic information, such as the detection of intraductal carcinoma .This paper introduced a Deep Learning-based CAD system to classify the grade groups (GG) system using digitized prostate biopsy specimens (PBSs) using pyramidal CNN, with patch- and pixel-wise classifications. The proposed pipeline results highlight our system’s potential to classify all five GG of the PBSs in comparison with that of other standard CNNs. The agreement between our CAD system and pathologists is comparable to inter-rater reliability among pathologists. In future work, because the digitized PBSs do not have the same direction, adding a new preprocessing step to overcome this challenge will fit our results. This processing rotates the overlapped patches with angles 45 and 90, and flipping them will enhance our pyramidal CNN accuracy. In addition, to highlight our model, we will try to test our model with a dataset from another institution.