A growing number of studies has implicated oxidative stress in the pathophysiology of psychiatric disorders including schizophrenia. The aim of this study was to explore the field of schizophrenia and oxidative stress-related research from a bibliometric perspective.All relevant publications on schizophrenia and oxidative stress were obtained from Web of Science Core Collection (WOSCC) database from its inception date to November 8, 2022. VOSviewer software was used to examine co-authorships and co-occurring keywords. R software was used to present the main characteristics of publications and cooperation frequency among countries. CiteSpace was used to investigate keywords with the strongest citation bursts.A total of 3,510 publications on schizophrenia and oxidative stress were included. The United States had the largest number of publications (26.1%), and international collaborations. University of Melbourne was the most productive institution, while Schizophrenia Research was the most productive journal in this field. Apart from “schizophrenia” and “oxidative stress”, the terms “prefrontal cortex”, “brain” and “nitric oxide” were among the most frequently used keywords.In conclusion, research on the association between oxidative stress and schizophrenia has received growing attention in the academic literature that is expected to continue its upward trajectory during the next two decades. Existing research suggests there has been a transition from research focused on pathways to animal models, and subsequently to clinical applications. Intervention studies on oxidative stress and schizophrenia are likely to be an important focus of related work in the near future. Schizophrenia is a severe psychiatric disorder characterized by positive symptoms  and negative symptoms  as well as a range of deficits in emotional, motivational, social, and cognitive functioning . The lifOxidative stress is a phenomenon caused by an imbalance between production and accumulation of oxygen reactive species in cells and tissues and the ability of a biological system to detoxify these reactive products . A meta-Bibliometric analysis is a widely used approach for performing quantitative and qualitative analyses of publications in specific research fields . The appWeb of Science (WOS) is the most commonly used academic information resource databases for bibliometric analysis. As recommended previously , relevanhttps://bibliometric.com/) were used for data analysis. The “Bibliometrix” (R (version 4.0.4) , VOSviewometrix”  package VOSviewer is a program for visualizing bibliometric maps. In such visualizations, a node represents an item , the size of the node reflects its importance , and the color of the node indicates the cluster that includes the node as a member. Network connectivity represents collaborations between institutions or authors . Line wihttps://bibliometric.com/) was used to visualize international collaborations between countries.CiteSpace software was used to examine the dynamic visual evolution of bibliometric networks over time . In thisA total of 3,510 articles on schizophrenia and oxidative stress were included, of which, 3,455 (98.4%) were published in English, 13 were published in Russian, 9 in German, 8 in French, 8 in Spanish, 6 in Turkish, 4 in Polish, 3 in Japanese, 3 in Portuguese, and 1 each in Chinese and Hungarian.2 = 0.9164). According to the prediction curve, the trend for annual publications on schizophrenia and oxidative stress shows an upward trajectory during the next 10 years; estimated annual numbers of papers are expected to increase to 276 by 2026 and to 316 by 2032.A total of 3,497 institutions contributed to research on schizophrenia and oxidative stress. As shown in Articles on schizophrenia and oxidative stress have been published in 762 journals. Citation analysis can reveal the influence of publications in a specific research field . SupplemA total of 11,548 keywords were obtained from the included publications. Using the criterion of at least 50 occurrences, 111 keywords were included for analyses . The sizN = 907), and “prefrontal cortex” (N = 328).This bibliometric analysis study examined trends in research on schizophrenia and oxidative stress. Analyses indicated publications on schizophrenia and oxidative stress have shown a steady increase during the past several decades in line with findings of previous “stand-alone” bibliometric studies on schizophrenia research  and oxidThis bibliometric analysis indicated that, to date, the U.S. has generated more publications than other countries on schizophrenia and oxidative stress in line with similar findings from bibliometric analyses conducted in fields of Psychiatry , NeuroscN = 6) in this subset, while the top two most productive institutions were in Australia  and Canada , respectively. For authors, Do, Kim Quang  from Switzerland was the most productive author, while Berk, Michael  from Australia was the author with the most h-index article. From a global perspective, scientific research collaborations between institutions and authors is greatly influenced by region  or European countries of the Netherlands (N = 3) or Germany (N = 1); the two remaining top 10 journals originated in the U.S. In contrast, no active publishers were located in China, Brazil, or Canada, although these were among the most productive countries. Schizophrenia Research was the most productive journal, consistent with findings of other bibliometric analyses on schizophrenia related cognitive behavioral therapy  predisposes an individual susceptible to develop the disease, while a second “hit”  induces the full-blown syndrome; both of these hits could cause damage to the central nervous system through common pathways, including inflammation and oxidative stress . Recent This is the first bibliometric analysis to have explored the field of schizophrenia and oxidative stress-related research. Although this study helps to illustrate trends in research on oxidative stress and schizophrenia, its main limitations should be acknowledged. First, although WOS is a reliable, widely used bibliometric database, its use could have led to the omission of some studies, particularly from non-English language sources. Second, some research institutions have different names at different points in history, and such changes cannot be recognized in current bibliometric analysis approaches.In conclusion, research on the association between oxidative stress and schizophrenia has received growing attention in the academic literature that is expected to continue its upward trajectory during the next two decades. To date, the United States has been the most productive region in generating associated research although international collaborations have contributed to the quantity and impact of studies in this field. In addition, the keyword analysis from this study suggested there has been a shift away from pathways between schizophrenia and oxidative stress to animal models, and, more recently, to clinical applications. As such, intervention studies on oxidative stress and schizophrenia are likely to be an important focus of related work in the near future.The original contributions presented in the study are included in the article/Study design: M-YC, X-HL, and Y-TX. Data collection, analysis, and interpretation: M-YC, QZ, Y-FL, W-YZ, TS, ZS, and TC. Drafting of the manuscript: M-YC and Y-TX. Critical revision of the manuscript: TJ. Approval of the final version for publication: all authors. All authors contributed to the article and approved the submitted version.


Acute cholecystitis (AC) and acute appendicitis (AA), independently, are among the most commonly diagnosed conditions in the emergency department (ED). However, their synchronous presentation is very rare. Here, we present a 31-year-old man with worsening right flank abdominal pain, nausea, and vomiting. Physical examination results were significant for moderate to severe right upper abdominal quadrant pain with a positive Murphy’s sign and right lower quadrant pain with rebound. Workup in the ED revealed leukocytosis with a left shift, and the abdominal ultrasound and computerized tomography scan showed AA and AC. A literature review revealed a paucity of publications on concomitant AA and AC. Reporting new cases will contribute to improving our understanding of the biology, natural history, and management of this rare pathological combination. Acute appendicitis (AA) and acute cholecystitis (AC), independently, are common diagnosed conditions, with a yearly estimate of approximately 350,000 AA and 200,000 AC cases diagnosed in the United States ,2. IndivSeparately diagnosing AC and AA is straightforward and usually supported and confirmed by imaging technologies, including ultrasound, commonly used for the initial diagnosis of AC, and computerized tomography (CT), most often performed for possible AA ,3,6,7. LA 31-year-old otherwise healthy man presented to the emergency department (ED) reporting worsening right flank abdominal pain over the past several days, associated with nausea, vomiting, and fever. The patient denied any medical, surgical, or known family health history. He stated that the pain was severe and mainly in the RUQ and RLQ.2) of 98%, and blood pressure of 138/81 mmHg. He had a non-distended abdomen with moderate to severe tenderness in the RUQ with a positive Murphy's sign and in the RLQ with punctum maximum at McBurney's point. The remainder of the physical examination results were unremarkable.The patient's physical examination findings are significant for a body temperature of 38.4 °C (101.2 °F), heart rate of 103 bpm, respiratory rate of 18, oxygen saturation  at the Palmer’s point. After establishing pneumoperitoneum, three additional 5 mm trocars were placed . The 5 mm OPTIVIEW® laparoscopic trocar was upgraded to a 12 mm port. The patient was initially positioned in Fowler's position with the right side up in preparation for laparoscopic cholecystectomy. Significant distention of the gallbladder was noted, and the drainage with an endoscopic aspiration needle revealed empyema/abscess of the gallbladder.After decompressing the gallbladder, an uneventful laparoscopic cholecystectomy was performed, and the gallbladder was sent to pathology as a permanent specimen. The same port access sites were used for the appendectomy portion. The patient was repositioned in the Trendelenburg position with the right side up. Significant inflammatory changes in the RLQ were caused by gangrenous appendicitis with local serous-fibrinous periappendicitis. An uneventful appendectomy was performed, and a specimen was sent to pathology as a permanent specimen. The final surgical pathology report was consistent with acute on chronic cholecystitis with cholelithiasis and gangrenous appendicitis.The patient tolerated and recovered from the procedure well. He was discharged home on postoperative day two without post-surgical complications, with well-controlled, mild post-surgical pain at the port site incisions, tolerating a regular diet, and passing flatus and bowel movement. As expected, the patient had a follow-up visit in the outpatient surgery clinic three weeks after the procedure with an uneventful recovery course.The classic presentations of AA and AC have been extensively described in the literature; however, patients with AA and AC do not always present with classic symptoms that may lead to diagnostic and management challenges -6,9. TheWe found similarities and differences in the patients’ presentations compared to our case. The patients diagnosed with concurrent AA and AC primarily presented with right-sided pain - usually both RUQ and RLQ - and almost always with concurrent nausea and non-bilious vomiting. Physical examination results typically showed tenderness to palpation and Murphy’s sign. Many of the physical examinations had a positive McBurney’s point in the patient, and CT scans and ultrasound confirmed the combined AA and AC diagnosis, hence suggesting laparoscopic procedures in the patients -19. ThisMurphy’s sign is commonly seen as a diagnostic feature in the case studies of concurrent AA and AC, which is consistent with the findings of one study showing the sensitivity and specificity of Murphy’s sign in AC as 97% and 48%, respectively . The durCT is the most commonly used imaging technique in diagnosing suspected AA, and laparoscopic appendectomy remains the first-line treatment in most cases . Our patConsidering concurrent AA and AC in patients presenting with generalized right-sided abdominal pain, regardless of the length of symptoms upon presentation, is imperative. A thorough medical history, physical examination, and appropriate imaging will reveal the concurrent pathology. Reporting cases of combined AA and AC will help us better understand the biology and natural history and help practitioners of all specialties recognize this rare condition.


Despite anthocyanidins have anti-inflammatory and antioxidant properties, no studies have researched association between dietary intake of anthocyanidins and heart failure.We enrolled 15,869 participants from the National Health and Nutrition Examination Survey (NHANES) (2007–2010 and 2017–2018) in this cross-sectional study. We examined baseline data and prevalence of heart failure in different quartile groups of anthocyanin intake (Q1-4). Three models were established through logistic regression to evaluate the protective effect of Q4 (highest anthocyanidins intake) on heart failure. The protective effect of high anthocyanidins intake on heart failure was further evaluated in different subgroups.p = 0.02). After adjusting for possible confounding factors, compared with the Q1 group, the highest anthocyanidins intake (Q4) was independently related to lower presence of heart failure . And this association was still stable in subgroups of female, ≥45 years, smoker, non-Hispanic White or without diabetes, stroke and renal failure.Participants with the highest anthocyanidins intake (Q4) had the lowest prevalence of heart failure (Q1:2.54%, Q2:2.33%, Q3:2.43%, Q4:1.57%, Dietary intake of anthocyanidins had negative association with the presence of heart failure. Heart failure (HF) is the terminal manifestation of cardiovascular disease . In recevia activation of Nrf2-ARE pathway  is a series of surveys designed on the basis of cross-sections to investigate the health status of all U.S. populations, which conducted by National Center for Health Statistics (NCHS). The survey included demographic information, dietary information, various physical examination indicators and health related data. All information and survey methods are available online.This study mainly collects the intake of flavonoids in foods and beverages, which are usually onions, potatoes, celery, etc. According to the food code from Nutrient Database for Dietary Studies (FNDDS), the food types were refined. Different codes represent different flavonoid contents. Versions of the FNDDS that are suitable for each survey cycle were utilized: version 4.1 was used for 2007–2008, while version 5.0 was used for 2009–2010 and 2017–2018  and no (never). Diabetes, hypertension and hyperlipidemia were all diagnosed by doctors. The systolic and diastolic blood pressure, body mass index (BMI), and waist measured by experts using conventional physical examination techniques. In a typical laboratory, the level of triglycerides (TG), total cholesterol (TC), high-density cholesterol (HDL), low-density cholesterol (LDL), fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), and creatinine were all measured. We calculated the estimated glomerular filtration rate (eGFR) through the creatinine equation. Information about the specific techniques and quality of the determination of all covariate control methods were accessible from Website of NHANES.p < 0.05. Analysis method was referred to previously NHANES-based articles  were used. Statistical significance was determined to two-tailed, articles . Particiarticles .p = 0.02)  HF patients . The ave = 0.02) , but thep < 0.0001). Moreover, the five anthocyanidins subclasses  also showed the same trends (p < 0.001) in the unadjusted model. p = 0.003). Additionally, we transformed intake of dietary anthocyanidins into a categorical variable (Q1-4), both the unadjusted (p for trend <0.001) and adjusted (p for trend = 0.005) models showed significant p for trends.We compared the dietary intake of flavonoids between non-HF and HF. Surprisingly, although there was no difference in the intake of total flavonoids and other five flavonoids, the intake of anthocyanidins in non-HF was higher than that in HF  and HF were found to be negatively correlated in the NHANES 2007–2010 and 2017–2018. When dietary anthocyanidins intake was in the Q4 group, the incidence of HF was lowered by 50% after adjusting probable confounding factors. This negative correlation is still stable in subgroups of female, ≥45 years, former smoker, non-Hispanic White or without diabetes, stroke, renal failure. Moreover, compared with anthocyanidins intake between HF and non-HF for each quartile, it was found that only anthocyanins intake of Q4 was different between HF and non-HF, the intake of anthocyanins in non-HF was higher than that in HF .p < 0.001), it was also found that dietary anthocyanidins may have a more significant protective effect on total CVD mortality in women  exceeds the internal defense capacity of cells, excess ROS will attack cells, leading to protein and lipid peroxidation, DNA damage, and ultimately cell death . AnthocyNevertheless, there were some limitations in this study. First, as a cross-sectional study, this study was unable to confirm the causal relationship between dietary intake of anthocyanidins and HF. Second, this study only evaluated the effect of anthocyanidins in food, but whether the purified anthocyanidins had the same effect still needs further randomized controlled experiments. Third, the NHANES database does not provide brain natriuretic peptide and echocardiography data, so this study cannot further evaluate the relationship between dietary intake of anthocyanidins and the severity of HF. Moreover, uncontrollable confounding variables may also need further analysis, such as physical activity and nutritional supplements. Finally, the subjects of this study were adult Americans, excluding adolescents and children, which would affect the promotion of the research results.In conclusion, dietary intake of anthocyanidins was associated with HF negatively, people can decrease the presence of HF by increasing anthocyanidins in their daily diets. To determine their clear relationship, more cellular, animal, and human investigations are necessary.The 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 National Health and Nutrition Examination Survey (NHANES). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.ZT conceived and designed the study. RZ, WZ, and ZJ were responsible for the management and retrieval of data, contributed to initial data analysis, and interpretation. ZT drafted the initial manuscript. XC, RH, and XL revised the manuscript and were the guarantors of this work and had full access to all the data in the study. GM take responsibility for its integrity and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.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.All 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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Family environment is the primary environment for adolescent growth and development, which is believed to have an important impact on the occurrence of non-suicidal self-injury (NSSI) behavior in adolescents. This study aimed to explore the effects of family environment cognition and cognitive differences perceived by adolescents and their parents on the treatment effects of NSSI in adolescents and to provide more potential perspectives for NSSI treatment.A one-year prospective longitudinal sub-cohort investigation was carried out among 199 adolescents engaged in NSSI and one of their important guardians from the Longitudinal Psychosomatic Disease Study (LoPDS). The NSSI behaviors of adolescents were evaluated at 3 months, 6 months and 1 year after enrollment. The family environment scale (FES) and NSSI Behavior Questionnaire were used as assessment tools for family environment and adolescents NSSI behaviors. Multiple linear regression was used to investigate the role of family environment perception difference in the treatment effect of adolescent NSSI.After one year of follow-up, the perceived self-injury impulse score in recent 2 weeks, self-injury impulse frequency in recent 2 weeks, total number of self-injury in recent 2 weeks decreased significantly. The higher the adolescent family cohesion , parental family expressiveness  and parental family active-recreational orientation score , the better the treatment effect. However, higher adolescent family conflict  were associated with lower treatment outcomes. The greater the cognitive difference between parents and adolescents in family cohesion  and family conflict, the worse the therapeutic effect of NSSI might be.There were certain differences in the cognition of family relationships between parents and adolescents, and subjective family relationship cognition and cognitive differences had a significant effect on the treatment effect of NSSI in adolescents. Helping them identify the cause of cognitive differences and conducting systematic family therapy from the points of difference may be another perspective to improve the treatment effect of NSSI in adolescents. The incThe prevalence of NSSI peaks in mid-adolescence (approximately age 15–16 years) and decreases in late adolescence (approximately age 18 years) . In addiPreviously, NSSI was considered a symptom of borderline personality disorder . HoweverEnvironmental factors, especially the family environment, are the primary factors for adolescent growth and development, which have an important effect on adolescent mental health and are closely related to adolescent self-awareness, behaviors, and psychological symptoms . PreviouThus, we conducted a 1 year prospective cohort study on patients with NSSI. This study collected the family environment cognition data of adolescents and their parents and the treatment effects of NSSI. This study aimed to explore the effects of family environment cognition and cognitive differences perceived by adolescents and their parents on the treatment effects of NSSI in adolescents and to provide more potential perspectives for NSSI treatment.2.2.1.The present study was embedded in the Longitudinal Psychosomatic Disease Study (LoPDS), an ongoing psychosomatic disease cohort study in adolescents conducted in Chengdu that aims to determine the relative contributions of biological factors and the environment to the development and prognosis of psychosomatic diseases . This study was approved by the Ethics Committee of the Fourth People’s Hospital of Chengdu (No. 201830). This prospective sub-cohort study was conducted at the Fourth People’s Hospital of Chengdu and recruited all adolescents who engaged in NSSI behaviors between January 2019 and December 2020. Written informed consent was obtained from all participants and their parents or guardians. Patients with generalised developmental disorders, intellectual disability, schizophrenia pedigree, drug abuse, or other severe somatic diseases were excluded to reduce confusion.Patients aged <12 or > 18 years were not included in this study. Those who were unable to participate in follow-ups and were unable to conduct the standardised questionnaire assessment were considered to have dropped out. Simultaneously, we invited one of the adolescents’ parents/guardians to complete the questionnaires at baseline. All adolescents needed to complete the subsequent three follow-ups at 3 months, 6 months, and 1 year thereafter. At each follow-up visit, patients were interviewed in the outpatient clinic or by telephone to determine their current condition. A total of 350 adolescents with NSSI behaviors as part of the LoPDS study were initially recruited for this subgroup study. After excluding parent–adolescent pairs who did not meet the inclusion criteria or did not complete a standardised questionnaire assessment, suicide, or were unable to participate in follow-up, the final analysis included 199 parent–adolescent pairs with NSSI, which can meet the quantity for statistical analysis.2.2.Standardised questionnaires  were useThe Family Environment Scale-Chinese Version (FES-CV)  was usedThe NSSI behavior questionnaire  was used2.3.According to the 5th Edition of the Diagnostic and Statistical Manual of Mental Disorders , 28, NSS2.4.Recently, there are no unified international treatment guidelines for NSSI. At our hospital, the commonly used therapeutic methods in clinical practice are mainly divided into psychological, drug, and physical therapies . In our 2.5.t-test, least significant difference Student’s t-test, one-way analysis of variance, or non-parametric test. Multiple linear regression analysis was performed to explore the association between the treatment effects of NSSI and parental/adolescent family environment cognition or the difference between them. Covariates were selected according to the significantly different variables in the univariate analysis and factors reported in previous studies that would affect the dependent variable. All tests were two-tailed, and statistical significance was set at p < 0.05.All statistical analyses were performed using the Statistical Package for the Social Sciences version 25.0 . The chi-squared or Fisher’s exact test was used to assess categorical data, which were reported as counts and percentages. The means and standard deviations of continuous variables were calculated using Student’s 3.The selection process of the study population is shown in p < 0.001)  .p < 0.05). There was no significant difference in parent–adolescent’s cognition of family achievement orientation and intellectual–cultural orientation  lower than their parents, but they had a higher perception of family conflict than their parents (entation .p = 0.032), parental family expressiveness , and parental family active–recreational orientation score , the better the treatment effect. However, higher adolescent family conflict  was associated with lower treatment outcomes  and family conflict  would reduce the treatment effect. The greater the cognitive difference between parents and adolescents in family cohesion and conflict, the worse the therapeutic effect of NSSI  on the therapeutic effects of NSSI. After adjusting for age, sex, family history of psychosis, and left-behind children, parent–adolescent perceptions of family cohesion and family conflict were significantly related to the treatment effect. Differences between parents and adolescents in family cohesion , family history of psychosis , and left-behind children  increased the risk of suicide by 6.9, 4.0, and 3.8, respectively. Moreover, the risk of suicide was reduced by 27.9% when age increased by 1 year . Regarding the association between the occurrence of suicide and parental and adolescent family cognition, the risk of suicide reduced by 26.0 and 37.3% when adolescent family independence and parental family expressiveness increased by 1 score, respectively. However, the risk of suicide increased by 39.6% when adolescent family conflict increased by 1 score . Written informed consent was obtained from all participants and their parents or guardians.YalL and XL designed the research protocol, analysed the data, and drafted the manuscript. ND conducted the study and provided the funding resources. YuL, YX, CL, JC, YaoL, LL, DS, JJ, HC, and TL critically revised the manuscript. All authors contributed to the article and approved the submitted version.Financial support of this work was provided by Sichuan Medical Association (Q20017), Chengdu Municipal Health Commission (2021103 and 2022441), and Sichuan Provincial Department of Science and Technology (2023YFS0219 and 2023YFS0228). The funding agencies did not have any role in the design of the study, collection, analysis, and interpretation of data, and in writing the manuscript.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.All 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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1183916/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.Click here for additional data file.Click here for additional data file.


In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to diagnose stenosis. As a result, they face various challenges which include high workloads, long processing times and human error. Computer-aided segmentation and classification of coronary arteries, as to whether stenosis is present or not, significantly reduces the workload of cardiologists and human errors caused by manual processes. Moreover, deep learning techniques have been shown to aid medical experts in diagnosing diseases using biomedical imaging. Thus, this study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models. In the case of segmentation, the comparative analysis of the three models has shown that U-Net achieved the highest score with a 0.8467 Dice score and 0.7454 Jaccard Index in comparison with UNet++ and ResUnet-a. Evaluation of the classification model’s performances has shown that DenseNet201 performed better than other pretrained models with 0.9000 accuracy, 0.9833 specificity, 0.9556 PPV, 0.7746 Cohen’s Kappa and 0.9694 Area Under the Curve (AUC). Coronary Artery Disease (CAD) is one of the most common forms of cardiovascular disease affecting the human population globally. CAD is the leading cause of death in both developed and developing countries . About 3The coronary arteries supply blood to the heart muscles. This is an important function for the heart muscle and thus for the body. The Left Main Coronary Artery (LMCA) and the Right Coronary Artery (RCA) are the primary types of coronary arteries. The LMCA carries blood to the left part of the heart, whereas the RCA is mainly responsible for carrying blood to the right side of the heart. LMCA is composed of two major branches. The Left Anterior Descending artery (LAD) is one of the major branches of the LMCA that transports blood to the left anterior side of the heart. Another branch of the LMCA is the Circumflex Artery (Cx) which surrounds the heart muscle (myocardium). Through this artery, the outside and the back part of the heart are supplied with blood ,4. Most heart diseases are caused by atherosclerosis, which causes the arteries to narrow or be blocked due to the build-up of plaque. CAD occurs when plaque (fatty deposits) builds up, narrowing (stenosis) the passageway and interfering with the heart’s supply of blood, oxygen, and other vital nutrients. Plaque build-up can block the coronary arteries and prevent nutrients and oxygen from reaching the heart muscle, which can lead to myocardial infraction (heart attacks) and sometimes death. When plaque is deposited on the wall of a coronary artery, the lumen of that artery is affected. Therefore, it is noteworthy that the diagnosis of stenosis can be made by measuring the lumen diameter with an angiography imaging system ,4. The dEarly diagnosis of CAD significantly reduces mortality and morbidity rates . In factOne of these procedures is Invasive Coronary Angiography (ICA) uses a catheter, and X-ray imaging and remains the “gold standard” for the diagnosis of coronary artery stenosis ,8. The pIn addition, ICA is a decision-making aid for revascularization treatments such as stent placement and coronary artery bypass surgery . Cardiologists view frames in angiographic videos with their naked eyes during angiography procedures in order to evaluate various problems affecting blood vessels, such as stenosis, plaque and blockage. Based on their experience, cardiologists can diagnose the stenosis of coronary arteries and decide medical treatment options for CAD . However, this direct method, which requires more accuracy, objectivity, and consistency, is heavily influenced by human factors . On the other hand, automated segmentation of the cardiovascular system has been reported to reduce the error rate in the diagnosis of stenosis. In fact, an image segmentation method has already been proposed to extract blood vessels from images so that cardiologists can quickly diagnose plaque and stenosis. Recently, with the development of Deep Learning (DL), various Convolutional Neural Network (CNN) architectures have been proposed and used for image segmentation . CNNs arThe main research question of this study revolves around how segmentation and classification models can help increase stenosis classification performance and thus relieve the workload of cardiologists in Northern Cyprus hospitals. Consequently, the objective of this study is tailored toward applying these models to the classification of patient images. It is essential to mention that in this study, no artificial data were generated by data augmentation. On the contrary, all images used were clinical images and none were synthesized. Instead of developing a network from scratch, which requires a substantial amount of data, pretrained models (based on transfer learning) are used only for classification tasks, where learned parameters are transferred to solve a problem in another task. .It is noteworthy to emphasize that the system proposed in this study is intended to aid cardiologists in conducting an automated and accuracy-increased diagnosis of stenosis. In the healthcare field, it is vital to minimize diagnostic errors. For this reason, decision accuracy can be significantly improved in the case that the experience and instinct of the doctors are complemented with novel automated technologies, such as Artificial Intelligence (AI). An automated diagnosis system can provide a second  opinion/verification. It should be noted that using such systems on their own can result in misdiagnosis in problematic/rare cases since they do not have human-like instincts ,11. Thus, this study suggests automated diagnosis technologies be used in conjunction with health professionals.The application of UNet, Unet++ and ResUNet-a for the automatic segmentation of coronary angiograms.Comparison of model’s performances for both segmentation and classification tasks.One of the major contributions of this study is the application of DL-based transferred learning using several pretrained models  for the classification of coronary angiograms.Comparison between the performance of models trained on the coronary artery small datasets can aid cardiologists in the selection of the best-performing model and also aid them in making appropriate decision-making.Another contribution of this study is the use of raw/unaltered data that are obtained from real patients by the cardiology department of Near East University Hospital, instead of using a dataset curated from an online repository system. We have noticed that a significant number of images available from online repository systems have been altered  to aid the segmentation and classification performance of coronary arteries. However, this is not the case in clinical applications.Performance evaluation of models based on accuracy, sensitivity, specificity, precision, Dice Score (F1 Score), Jaccard Index and Matthews correlation coefficient (MCC), negative predictive value (NPV), Cohen’s kappa, Area Under Curve (AUC) and Receiver Operating Characteristic (ROC) curve.In this study, based on the U-Net model and its variants (ResUNet-a and UNet++), we applied the DL approach to segment the major coronary arteries, including the RCA, LMCA, LAD, and Cx. Finally, five different pretrained models are used to automatically classify angiographic images with or without stenosis of the major coronary arteries. The study’s main contributions are highlighted below:This section briefly reviews previous methods relevant to our work, including conventional medical image segmentation of coronary arteries, DL-based segmentation, and image classification and diagnosis of coronary stenosis. Various methods have been proposed for the automatic segmentation of blood vessels based on traditional techniques. The following methods are commonly used in image processing: Filtering, thresholding, tracking-based and region growing. To illustrate, typical filtering methods utilize Gaussian and Gabor filters to extract information about edges iteratively . MoreoveIn addition, Kerkeni et al.  presenteAlthough these traditional image segmentation methods have improved the segmentation of vessel structures in image analysis, they could be less efficient in clinical practice due to the high computational complexity of pixel-by-pixel analysis. DL techniques using CNNs have recently gained more attention in medical imaging due to their promising results in vessel segmentation ,19,20,21There are various CNN architectures that have been proposed for image segmentation. The most commonly used architectures are U-Net and SegNet models. SegNet is a DL semantic segmentation model that was originally developed for modeling scene segmentors and pixel-wise classification layers. It is an efficient architecture, especially for road and building image segmentation ,23. Zhao et al.  proposedThe U-Net model is considered the most successful architecture for medical image segmentation competitions . The U-NEver since, several models have been developed and tested using ImageNet datasets. Some of the models that achieved high accuracy scores include VGGNet, Inception, ResNet, DenseNet, etc. In comparison with traditional machine learning (ML) models, DL models can automatically learn features and patterns, and classify different types of images with higher generalization. DL models have been shown to aid healthcare experts in terms of classifying medical images of diseases such as different kinds of cancers, tuberculosis and pneumonia .Furthermore, data augmentation is often used to expand the range of data available to CNNs. For example, Antczak and Liberadzki  proposedThe study conducted by Lee et al.  applied Jamil and Roy  proposedConsidering the fact that the manual approach for quantitatively assessing the stenosis severity of coronary arteries is very tedious and is subject to the experience of cardiologists; therefore, developing automatic quantitative coronary analysis (QCA) is crucial. In order to address this concern, Liu et al.  proposedThe proposed system of this study contains two main stages. The first stage consists of image pre-processing and segmentation and the second stage is comprised of image classification. The dataset is evaluated based on K-fold cross-validation set. In the first stage, U-Net, ResUNet-a, and UNet++, models are used to segment the coronary arteries. In the second stage, the coronary arteries are classified using five different pretrained models according to the presence of stenosis based on the original images of the patients. These pretrained models are evaluated using five-fold cross-validation. DenseNet201, EfficientNet-B0, Mobilenet-v2, and ResNet101 models adjust the input size of the RGB images to 224 × 224 × 3, whereas for the Xception model, the input images are resized to 229 × 229 × 3. The block diagram of the proposed system is presented in The dataset used in this study consisted of 170 coronary angiograms from 22 patients, including 68 narrowed (with stenosis) and 102 normal (without stenosis), acquired using the GE  Innova 3100 Cath/Angio System (Angiography machine) at the Department of Cardiology, Near East University Hospital, Nicosia, TRNC. Image examples of coronary arteries with and without stenosis are shown in In this study, a cardiologist and an angiography technician from the Near East University Hospital analyzed the images using Syngo QCA clinical software. Catheter calibration was used as the reference standard to analyze the QCA (Quantitative coronary angiography). It should be mentioned that the QCA is one of the most commonly used tools in clinical practice to assess coronary artery stenosis. The QCA was developed to objectively measure the lumen diameter of the coronary artery . The scale currently proposed by the Society of Cardiovascular Computed Tomography for grading the severity of stenosis is used to rate the grade according to a standardized reporting system for patients with coronary Computed Tomography Angiography (CTA) images. The percentage of narrowing of the luminal diameter of the coronary arteries can be classified according to specific grades. According to the Coronary Artery Disease Reporting and Data System (CAD-RADS) , if therImage pre-processing was used in this study to prepare images before coronary artery segmentation. First, the images in the dataset were converted from RGB to grayscale. In addition, the images were resized to 256 × 256 to meet memory requirements by reducing training time.Cross-validation is a popular technique used in AI to select parameters and evaluate learning parameters and predictive performance. In this technique, the original sample is randomly divided into groups of equal size in K-fold cross-validation. Then, the training of the dataset is repeated several times for K . Finally, the average performance of the training and testing datasets is calculated as an evaluation index for the models. This approach is very effective, especially when dealing with a limited number of images, as it uses the entire dataset ,39. In tRonneberger et al.  proposedThe modification of U-Net gives rise to ResUNet and is categorized under the semantic segmentation neural networks. ResUnet which combines the characteristics of U-Net and the residual networks . The namU-Net++ is proposed by Zhou et al. which applied the model for the segmentation of medical images. U-Net++ is another form of the U-net model derived from the DenseNet model, using a dense network of skip connections between the expansive and the contracting paths. The architecture is known for its encoder and decoder sub-networks which are linked via a series of nested, dense skip pathways. As an updated or modified version of U-Net, U-Net++ is designed with skip pathways that link the decoder and the encoder network. Another variation between the two is that the feature map in the encoder network is straightly transmitted to the decoder network in U-Net while the feature map of the encoder network is mapped to the decoder network via dense convolution blocks through the redesigned skip pathways in U-Net++. In essence, the feature graph semantic level in the encoder is proximate to the feature graph semantic level in the decoder via dense convolution blocks .The pretrained CNN model is already trained using millions of images (ImageNet) of different objects such as pencils, coffee mugs, cats, dogs, keyboards, etc. Most pretrained CNN models are trained on a subset of the ImageNet database, used in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The architecture is designed to classify up to 1000 categories ,46.In this study, five different pretrained CNN models were used to classify coronary angiography images. DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models were used for the classification system. This section briefly summarizes these pretrained models.Increasing the model’s depth through blocks and layers has been shown to increase efficiency. DenseNet is one of these models along with ResNet with higher depths. Densely Connected Convolutional Networks (DenseNet) shared a lot of similarities with ResNet, one of the main differences between the 2 is that ResNet utilized an additive approach in which the model takes previous output as an input for future layers while DenseNet takes in all the entire previous output as an input for a future layer. DenseNet is stacked with narrow layers , which add a lesser set of new feature maps. Currently, there are several variants of DenseNet with the latest one as DenseNet264 followed by DenseNet201, DenseNet169 and the oldest as DenseNet121. The computational cost of this architecture is lower compared to ResNet which owes to the fact that each layer in the DenseNet model has uninterrupted access to the original input as well as a gradient from the loss function. This specific characteristic makes DenseNet one of the superior DL architectures for image classification tasks ,48. For EfficientNet is a DL model developed by Mingxing Tan and Quoc V. Le . The modMobileNet is CNN developed by a team in Google with the aim of minimizing the amount of memory used for computing and simultaneously producing high performance. The CNN architecture was trained using the large ImageNet dataset which contains thousands of categories. As the name implies, the model is designed for mobile phones or devices with lower computational power. The layers of MobileNet-v1 are divided into 2; the first layer is a depthwise convolutional layer designed for lightweight filtering  while the second layer is a pointwise (1 × 1 convolution) layer designed for building new features through computing linear combinations of input channels. The MobileNet-v2 model is comprised of two residual blocks, the first block is designed with a stride of 1 and the second block with a stride of 2 where their main function is for downsizing. Each of these residual blocks is equipped with three main layers which include a convolutional layer (1 × 1) with a rectified linear unit (ReLU), followed by a depthwise convolution and a third of convolutional layer (1 × 1) without non-linearity. The MobileNet-v2 architecture comprises a fully convolutional layer with 32 filters followed by 19 residual bottleneck blocks ,54.ResNet is a kind of DL based on residual learning. The ResNet model was crowned the best-performing model in the 2015 ILSVRC competition. The model was developed by a team working at Microsoft. Currently, there are a handful of ResNet variants designed with a different number of layers. Some of these variants include ResNet1202, ResNet164, ResNet152, ResNet110, ResNet101, ResNet50, ResNet34 and ResNet18. Among these variants, ResNet101 is utilized in this study. There are three main layers in the ResNet101 model: convolutional layers, pooling layers, and fully connected layers. The model is designed with 101 layers with 33 residual blocks. The main function of the residual blocks of ResNet101 is to enable shortcuts or skip connections to other layers ,56,57.In order to improve the training efficiency of neural networks, Google’s team first developed the Inception model back in 2014. The introduction of 1 × 1 convolutional kernels after 3 × 3 convolutional kernels, 5 × 5 convolutional kernels and 3 × 3 pooling kernels significantly minimizes the number of parameters and simultaneously increases the width of the model and optimizes the model’s adaptability to scale. The modification of the Inception model gives rise to Xception (with better performance) due to the introduction of both depthwise separable convolution and residual structure. Thus, the Xception model makes more efficient use of its parameters compared to the Inception model despite having the same number of parameters. The architecture of Xception CNN consists of 36 convolutional layers which form the feature extraction base of the CNN. In this study, the evaluation method will solely investigate image classification of CAD and thus, the convolutional base will be coupled with a logistic regression layer. Another option is to add fully connected layers before adding the logistic regression layer ,59.All our experiments were conducted on a computer with 32 GB RAM, an NVIDIA GeForce RTX 2080 Ti graphics card, and an Intel i7–8th Generation CPU. We implemented all experiments with DL networks in the MATLAB 2021b programming language. For the training process, the Adam optimizer and five-fold cross-validation are employed to train and test the dataset. Coronary artery segmentation is performed using 0.0001, 100, and 8 parameters for learning rate, epoch, and batch size, respectively. The training parameters for classifying coronary arteries with and without stenosis are set to 0.0001, 100, and 32.The efficiency of the image segmentation and classification system is evaluated using a variety of performance metrics ,61. In tSensitivity measures the ability of the system to segment coronary artery pixels, as seen in Equation (2).specificity represents the segments of the background pixels and is described in Equation (3).In contrast to sensitivity, Precision  represents the ratio of correctly classified coronary artery pixels to all pixels classified as coronary arteries by the system. This led the researchers to analyze the system’s success in generating true positives compared to all pixels classified as arteries, as defined in Equation (4).Dice score, which ignores the correct classification of negative samples, such as background pixels. The equation of the Dice score is given in Equation (5) [Image segmentation algorithms are often evaluated using the tion (5) .Dice score measures the pixel similarity between the segmented image and the ground truth that ranges from 0 (no similarity) to 1  [entical) .(5)DiceJaccard Index is calculated as the overlap ratio between the predicted and ground truth segmentation. This is the union area between the predicted and ground truth segmentation, as given in Equation (6) [The tion (6) .(6)JaccMCC). This metric was first introduced in 1975 by a biochemist known as Brian W. Matthews who was working on the prediction of enzyme structure. As a marginal measure unaffected by the unbalanced datasets issue, the MCC is a contingency matrix approach to calculating the Pearson product-moment correlation coefficient between predicted and actual values. MCC is represented mathematically in the equation below:Mathews Correlation Coefficient , true negatives (TN), false positives (FP), and false negatives (FN) [TP correctly identifies segmented coronary arteries, while FP identifies segmented background pixels as arteries. TN indicates background pixels classified as background, but FN incorrectly identifies arterial pixels segmented as background.A confusion matrix is a popular approach for estimating the performance of a model for segmentation and classification based on the number of true positives (ves (FN) ,67. TP cPPV), and negative predictive value (NPV), F1 score, MCC, Cohen’s kappa, Area Under Curve (AUC) and Receiver Operating Characteristic (ROC) curve to evaluate image classification. Accuracy indicates the overall success rate of the system in classifying images with and without coronary artery stenosis. The sensitivity metric is the ability of an algorithm to detect coronary artery stenosis. Specificity refers to the power of a classification technique to see images with non-stenotic coronary arteries. The PPV or precision defines the probability of coronary stenosis in the images when the stenosis is present. The NPV indicates the probability that the result is negative when stenosis is not present. The equation of the NPV is given in Equation (8).This study used accuracy, sensitivity, specificity, positive predictive value κ=pA−pE is the hypothetical probability of agreement by chance (with data labels randomly assigned).κ = 1 corresponds to the case of perfect agreementκ = 0 indicates no agreement other than what would be expected by chanceAUC-score summarizes the Receiver Operating Characteristic (ROC) curve or is a metric that is used to represent the area under the ROC curve. The higher the score, the better the performance of the model and its ability to discriminate between positive and negative cases.AUC is regarded as one of the most significant performance metrics for evaluating the performance of ML models, especially classification tasks. AUC is represented as: [Mathematically, nted as: (10)AUC=AUC score is showcased as a random classification score which is represented by a diagonal ROC curve in the unit square. The value can range between 0.5 to 1, where 1 represents a perfect classification score. Nevertheless, a score range of 0.7–0.8 is acceptable while any score below 0.7 is considered as poor performance [FPR) and the true-positive rate (TPR) [formance . The ROCte (TPR) .The results of our segmentation study and the steps that led to these results are shown in We performed K-fold cross-validation to evaluate coronary artery segmentation and classify non-stenotic and stenotic coronary arteries using DL models. The parameter K was set to 5 in our experiments. In our dataset, for each fold, 136 images were used for the training set and 34 for the test set. All the images  in our dataset were tested. Training and evaluation of U-Net yield an average accuracy of 0.9918, average Sensitivity of 0.8599, average Specificity of 0.9957, precision of 0.8423, average Dice score of 0.8467, average Jaccard Index of 0.7454 and average MCC of 0.8448 as shown in Training and evaluation of ResUNet-a yield an average accuracy of 0.9915, average Sensitivity of 0.8101, average Specificity of 0.9968, precision of 0.8716, average Dice score of 0.8348, average Jaccard Index of 0.7285 and average MCC of 0.8337 as shown in Training and evaluation of UNet++ yield an average accuracy of 0.9881, average Sensitivity of 0.8974, average Specificity of 0.9908, precision of 0.7274, average Dice score of 0.7966, average Jaccard Index of 0.6740 and average MCC of 0.7988 as shown in The performance measures for classifying coronary arteries with and without stenosis using five different pretrained classification models with 5-fold cross-validation are presented in The AUC/ROC for the classification of coronary arteries is presented in Training and evaluation of the DenseNet201 model yield an average accuracy of 0.9000, average Sensitivity of 0.7699, average Specificity of 0.9833, average PPV of 0.9556, average NPV of 0.8694, average F1 score of 0.8507, average MCC of 0.7879 and average Cohen’s Kappa of 0.7746 as shown in Training and evaluation of the EfficientNet-B0 model yield an average accuracy of 0.8882, average Sensitivity of 0.8092, average Specificity of 0.9399, average PPV of 0.8889, average NPV of 0.8963, average F1 score of 0.8357, average MCC of 0.7655 and average Cohen’s Kappa of 0.7525 as shown in Training and evaluation of the MobileNet-v2 model yield an average accuracy of 0.8824, average Sensitivity of 0.7663, average Specificity of 0.9635, average PPV of 0.9233, average NPV of 0.8576, average F1 score of 0.8340, average MCC of 0.7545 and average Cohen’s Kappa of 0.7422 as shown in Training and evaluation of the ResNet101 model yield an average accuracy of 0.8941, average Sensitivity of 0.8021, average Specificity of 0.9540, average PPV of 0.9025, average NPV of 0.8840, average F1 score of 0.8473, average MCC of 0.7710 and average Cohen’s Kappa of 0.7652 as shown in Training and evaluation of the Xception model yield an average accuracy of 0.8941, average Sensitivity of 0.8206, average Specificity of 0.9472, average PPV of 0.9088, average NPV of 0.8884, average F1 score of 0.8548, average MCC of 0.7815 and average Cohen’s Kappa of 0.7711 as shown in Previous studies have shown that early detection of CAD is crucial for the timely treatment and for prolonging patients’ lives. Since heart disease is the leading cause of death around the world, cardiologists are entrusted with diagnosing and treating many patients. In addition, most healthcare facilities rely on invasive coronary angiography procedures. However, despite its high accuracy and efficiency, reviewing these angiographic images and interpreting many images can be tedious for cardiologists and can also lead to misdiagnosis ,3,4,6. TIn line with existing studies, this study used pretrained models to classify angiographic images. However, unlike previous studies that made use of datasets from various online repositories and many clinical datasets, this study used datasets generated by cardiologists at Near East University Hospital in North Cyprus. In addition, the classification of angiographic images  was achieved with five pretrained CNN models as shown in The growing field of AI is changing the landscape of several fields ranging from computer and electronics, medicine, agriculture, banking and finance, business, and advertisements, etc. Over the last decade, scientists have developed several platforms that integrated artificial intelligence in healthcare. Computer-aided systems are currently in use for modeling biological components (such as patient’s organs), assisted surgery (using robots), prediction of diseases outbreak, gene profiling, drug invention and repurposing and computer-aided diagnosis . The useAnother method proposed by scientists that can be used to resolve the issue of the limited amount of data is the use of data augmentation techniques which include zooming, rotation, cropping, mirroring, flipping, etc. Lastly, the last five years have seen the rise of hybrid and ensemble models. Extracting features and training using fused models have been shown to result in high performance. Moreover, the use of classifiers such as Support Vector Machine (SVM), K-nearest Neighbor (KNN) Decision Tree (DT) and Random Forrest (RF) instead of SoftMax is another dimension integrated by scientists in evaluating models with higher performances .Based on the comparison segmentation models with the U-Net model and its variants presented in Based on the comparison between different pretrained classification models as presented in The need to develop accurate diagnosis approaches is crucial for minimizing diagnostic errors and increasing treatment efficiency. In line with this statement, we proposed a system that includes U-Net and its variants and pretrained CNN models for the segmentation and classification of coronary angiograms. Another contribution of this research was the use of raw data acquired from real patients instead of using a dataset collected from online repositories. This is associated with the claim that a significant number of images available in online repositories have been altered , to facilitate coronary artery segmentation and classification. However, this is not the case for clinical applications. This work employed U-Net, ResUNet-a, and UNet++ models for the segmentation of coronary angiography images. Consequently, we used five different pretrained models  to classify coronary arteries into binary classes, e.g., with or without stenosis. The main goal of this research was to implement an automatic segmentation and classification system for coronary arteries. We believe that this will help doctors make more accurate diagnoses, reduce workload, and minimize the risk of error.It should be mentioned that one of the study’s limitations is using a small number of datasets for training and validation. In addition, the study is limited to binary classification. Therefore, future studies should focus on improving diversity in multiple coronary artery stenosis classifications according to their specific grades. Moreover, future studies will also focus on the precise measurement of coronary artery stenosis diameter using angiography images based on AI and image processing techniques. Furthermore, future studies will focus on a large number of datasets curated from various hospitals in Northern Cyprus. It is also suggested to develop an IoT/AI-based system that will allow cardiologists to upload images via online systems and obtain results in real-time.


Interventions lasting over 14 weeks with a minimum frequency of three sessions per week, each lasting 60 min, were deemed most effective. (4) Conclusion: An optimal training intensity was achieved with 30 min of aerobic exercise at 75% of the heart rate reserve, whereas sets of 10 repetitions at 75% of one repetition maximum produced the best outcomes in strength training.(1) Background: Non-pharmacological interventions have demonstrated efficacy in the prevention, management, and control of hypertension. Multicomponent training confers a host of benefits to the general populace. The aim of this research was to assess the impact of multicomponent training on the blood pressure of adults with hypertension and ascertain the nature of the dose–response relationship. (2) Methods: This systematic review adhered to the PRISMA guidelines and was registered in PROSPERO. Eight studies were included, following a literature search across PubMed, Web of Science, Cochrane, and EBSCO. Randomized controlled trials implementing multicomponent training interventions on adults with hypertension were considered for inclusion. A quality assessment was performed using the PEDro scale, with a random-effects model utilized for all analyses. (3) Results: Multicomponent training yielded a significant reduction in systolic (MD = −10.40,  Cardiovascular disease is the leading cause of premature death worldwide, and hypertension is one of its most prevalent risk factors ,2. ApproNon-pharmacological interventions have been shown to be effective in the prevention, management, and control of hypertension . The WorA description of the combined effect of both cardiovascular and strength training has been attempted. Multicomponent training has been shown to be more effective than aerobic training for improving some variables  and has Three similar systematic reviews have been conducted thus far. However, some aspects should be taken into consideration. The study by Corso et al.  includedOn the other hand, many differences have been observed between the dosage of the training protocols applied  ,21,22,23The present systematic review and meta-analysis hypothesises that (a) multicomponent training will be effective in reducing systolic and diastolic hypertension levels in subjects with hypertension, and (b) the multicomponent training protocol that will show the greatest benefits for blood pressure will apply moderate to vigorous intensities in cardiovascular work and moderate to high intensities in strength training. In addition, the present systematic review with a meta-analysis aims (a) to evaluate the effects of multicomponent training on SBP and DBP levels of adults with hypertension, and (b) to identify the optimal dosage of multicomponent training to manage hypertension in adults.This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . In addiThe inclusion criteria for the articles were (a) a randomised controlled trial (RCT) design, (b) the study of adults (30–80 years old) with hypertension, (c) a multicomponent training intervention, (d) the inclusion of a control group that did not engage in any form of exercise, (e) 100% supervision, and (f) articles written in English, Spanish, or Portuguese.The exclusion criteria were (a) multicomponent training that did not include physical exercise, (b) a sample composed of pregnant women and/or patients with hypertensive and with severe diseases that precluded safe physical exercise , and (c) short communications, notes, letters, review articles, or brief reports.Four electronic databases  were used for the search, which ended in April 2022. No limitations for the starting year were defined in the search. The following search terms were used: hypertension, blood pressure, systolic, diastolic, and hypertensive, which were combined using the Boolean AND operator, with the following: exercise, training, sports, physical*, rehabilitat*, fit*, train*, strength*, aerobic*, endurance*, weight*, HIIT, MICT, fitness, resistance, combined, and multicomponent. The search strategy used in each database is detailed in Two reviewers (I.L.R. and N.G.G.) independently screened the literature in the selected databases using the search terms, considering the inclusion and exclusion criteria. In the case of any discrepancy regarding the inclusion of a given study, the data extraction or assessment were repeated without looking at the reviewer’s previous information.A Physiotherapy Evidence Database (PEDro) score was used to assess the individual study of the quality. The score obtained showed a strong validity and inter-rater reliability for the assessment of RCTs ,27,28. AData extraction  and a qu2), Higgins’ I2, and significance (p) to determine the appropriateness of applying a fixed or random effects model for the pooled analysis. If there was evidence of between-study heterogeneity , random-effect estimates were described [R software, version 3.6.0., Copyright (C) 2019 , was used with the metacont package to perform the meta-analysis. The forest plots were created using the forestplot package. For continuous data, the changes in the mean (M) and standard deviation (SD) between the baseline and final (pre–post-intervention) measurements of SBP and DBP values were used. Some studies had more than one experimental group and were treated as other subgroups in the analysis. We used the DerSimonian–Laird (Cohen’s) clustering method and assessed heterogeneity using Cochrane’s Q test (Chiescribed .p < 0.05. To infer the pooled estimated standardised mean difference (MD), a meta-analysis with a random effects model was carried out . The DerThe mean age of the sample was 60.7 ± 10.4 years, the mean duration of the intervention was 12 ± 8.3 weeks (range 4–32), and all interventions were supervised .p < 0.05) in SBP and DBP levels in participants from the experimental group when compared to the control group; two of the studies [All the included studies showed significant improvements in SBP and DBP levels ,20,21,23 studies ,22 did np < 0.001) and DBP  levels  levels .p = 0.043, DBP: MD = 0.40, p = 0.024). The meta-analysis for SBP  and DBP Similar results were observed for the duration of the training session in relation to SBP, with the most significant improvements observed for a duration of 30 min or longer. In relation to DBP, both protocols showed significant reductions, with no differences between them.p = 0.013). With respect to DBP, an intensity lower or higher than 75% of 1 RM produced significant improvements with a tendency towards significance, with no differences between the two. Any number of repetitions produced significant improvements in both SBP and DBP levels. However, a lower number of repetitions (≤10) resulted in significantly greater improvements in SBP than a higher number of repetitions (>10) (p = 0.005).In the strength component, a work intensity of less than 75% of one repetition maximum (1 RM)  did not produce significant improvements in SBP though higher intensities did, with a significant difference observed between the two intensities used (p = 0.007); however, the difference between groups was not significant. For DBP, significant improvements with no differences between them were found for both cases.The programmes that used aerobic work first and strength work second did not show a significant improvement in SBP values; those that applied the components in reverse did show a significant reduction , the heterogeneity of the protocols, the types of aerobic and strength training exercises, and the small sample sizes in some studies. These aspects limit the comparisons between the different intervention groups and indicate the need for new studies to provide new data that allows for the generalisation of results.However, the studies analysed herein also had strengths, among which we must underline that they obtained scores between 8 and 10 on the PEDRO scale, a ranking considered “high quality” .The results of this study indicate that multicomponent training programmes significantly improved the SBP and DBP levels of adults with hypertension. The greatest benefits were found in programmes lasting longer than 14 weeks with a frequency of at least three sessions per week and sessions lasting approximately 60 min. The intensity of aerobic training should be around 75% of the reserve HR, and the intensity of strength training should be greater than 75% of 1 RM, with 10 repetitions or less per set. This research provides relevant information for professionals who prescribe physical activity to subjects with hypertension, providing key guidelines for its implementation. In this regard, there are several future areas for research in this field. It is necessary to investigate if there are differences in the results of the application of two protocols with training that apply the different components in different sessions or in the same session. It is also considered relevant to investigate the influence of the type of activity during cardiovascular work.


Telemedicine has the potential to make healthcare more efficient, organized, and available and is a more beneficial technology that can ease preventive treatment and improve long-term health management. This is especially essential for those who face financial or regional reasons to get quality treatment. Telemedicine in Iran is a new medical field and a noble way to access medical facilities for populations living in deprived areas, and the primary healthcare workers in these deprived medical centers are the implementers of telemedicine in those areas; we aimed to investigate the awareness and attitude towards telemedicine among all the healthcare workers in these centers.  This is a descriptive cross-sectional study at the Health Centers of Raz County in North Khorasan Province, Iran, and 149 healthcare workers were included. For collecting information, we used a questionnaire that consisted of two parts. The first part contains the demographic data of health care workers, and the second part includes the 5-point Likert scale questionnaire . p < 0.05). The awareness of healthcare workers using continuing education, articles, workshops, or conferences was significantly higher (p < 0.05). The attitude scores for most questions are above 3.4 and reflect a positive attitude about telemedicine. Attitudes did not show a significant relation to gender, age, marital status, or work experience.  Most participants (51%) were male, and 69.8% were married. The most frequent sources of information about telemedicine are colleagues (40.3%), continuing education (24.7%), and social media and the internet (10.1%). Awareness did not significantly relate to gender, age, marital status, or work experience, but awareness of physicians and midwives is higher than other groups ( Using telemedicine in developing countries, rural or urban areas have a high potential to improve epidemiological investigations, disease control, and clinical case management. Providing healthcare professionals with more information about new technologies in healthcare, such as telemedicine, can help get a more realistic picture of their perceptions. Telemedicine is a health-related service using telecommunications and electronic information technology. Over the past decade, advanced technologies with quality network services have enabled individuals to improve healthcare delivery and make it accessible to more patients. It is an essential and beneficial technology that can ease preventive treatment and improve long-term health. In addition, it can make healthcare more efficient, organized, and available. This is increasingly being adopted to bring specialty-palliative care into the homes of seriously ill patients, improve chronic disease management, aid in emergency medicine, and as a critically essential service during pandemics –5.Current trends in medical education observe the development of information and communication technologies, where mobile technology and health applications have shown great potential to influence the timely diagnosis and treatment of diseases and aid in diagnostic evaluations at the clinical level. Due to the increase in the elderly population, the decrease in visits to health and medical centers due to the COVID-19 pandemic, and the lack of specialized health-work staff in the deprived and border areas of the country, it is necessary to produce new, accurate, and affordable methods to help physicians. Telemedicine has improved healthcare providers' capacity to care for many patients without physical presence. It has many advantages, including online consultation, remote monitoring, remote nursing, and physical and psychiatric rehabilitation. It enables better healthcare choices, increases the quality and efficiency of emergency services, reduces the time needed to establish a diagnosis, and reduces financial health-related burdens by optimizing streamlined clinical processes and reducing hospital travel costs , 4, 6–12Telemedicine will be conducted by text, audio, and video, allowing an individual risk assessment to determine if the patient has an appropriate case definition. Studies stated that patients who receive care by telemedicine are typically satisfied with the convenience, efficacy, and time-saving. With the experience of the COVID-19 pandemic, telemedicine has become a vital service for patients to help mitigate and control the spread of epidemics and increase personal protection in addition to meeting the medical needs of patients. Research in this area is still preliminary, but it is expanding. This is especially true for those who face financial or regional challenges to get quality treatment. In Iran, most telemedicine research has been conducted on healthcare workers in referral hospitals, and the awareness and attitude of telemedicine in rural and border medical centers, which are the main goals of its establishment, have yet to be studied –17.Considering that telemedicine in Iran is a new medical field and a noble way to access medical facilities for populations living in deprived areas is one of its goals. Moreover, the healthcare workers in these deprived medical centers are the implementers of telemedicine in those areas; we conducted this study to investigate the awareness and attitude towards telemedicine among all the healthcare workers in these centers.This is a descriptive cross-sectional study at the Health Centers of Raz County, Bojnord, Iran. The study population included physicians, nurses, midwives, health personnel, and laboratory and radiological technicians. The sampling method in this study was a census, and all health network staff members who met the entry criteria were invited to participate. Out of 162 healthcare staff, 149 (91.9%) ultimately completed the study. The study's inclusion criteria are all members of the Health Centers of Raz County and treatment network workers. The exclusion criteria were lack of cooperation of health care workers and failure to complete the questionnaire.For collecting information, we used a questionnaire that consisted of two parts. The first part contains the demographic data of health care workers (age-gender-marriage-education-work experience), and the second part includes the 5-point Likert scale questionnaire (questions on telemedicine awareness (8 questions), attitude (19 questions), and self-report readiness (3 questions)).A questionnaire with a 5-point Likert scale was implemented to collect information on telemedicine awareness, attitudes, and self-reported readiness. The questions were established based on a review of existing literature. To ensure the validity of the questionnaire, feedback was acquired from five specialists in medical and clinical informatics and health information. They assessed the format, clarity, and meaning of the questions and response options. The reliability of the questionnaire was investigated by having 30 staff members complete it and calculating Cronbach's alpha, which resulted in a value of 0.85 .Scoring of awareness based on the Likert scale was assigned as very little (1 point), little (2 points), some (3 points), enough (4 points), and very much (5 points). Attitude scores were to disagree (1 point) completely, disagree (2 points), agree somewhat (3 points), agree (4 points), and completely agree (5 points). In this study, a questionnaire was used based on the analysis of Sheikhtaheri et al. in 2016, conducted on Mashhad Hospital employees .p value less than 0.05 was considered significant.The Statistical Package for Social Sciences (SPSS) for Windows version 20.0  was used for all statistical analyses. Continuous variables were expressed as mean ± standard deviation (SD), and median and categorical variables were expressed as counts (percentage). The data is analyzed using SPSS-20 software using the Mann–Whitney, Kruskal-Wallis, and Spearman correlation tests. Descriptive statistics indicators of mean, median, and standard deviation will describe data from tables and graphs. A Informed consent was obtained from all individual participants, and a copy of the written consent is available for review by the editor of this journal. The purpose of this study was completely explained to the participants, who were assured that all the information would be kept confidential by the authors. This study was conducted in line with the principles of the Declarations of Helsinki. The ethical committee of North Khorasan University of Medical Sciences approved (approved ethic number: IR.NKUMS.REC.1401.089).Most participants (51%) were male, and 69.8% were married. Age ranged from 22 to 61 years old, and the mean age was 33.73 ± 7.74 years old. The most common educational level of the participants (57%) was a bachelor's degree, and the mean occupational experience was 5.81 ± 5.04 years old.One hundred forty-three had no experience with telemedicine programs, and six had telemedicine experience. The most frequent sources of information about telemedicine are colleagues (40.3%), continuing education (24.7%), and social media and the internet (10.1%). Other sources include articles (9.2%), workshops (7.8%), formal education (5.5%), and conferences (2.4%).p < 0.05). The awareness of healthcare workers using continuing education, articles, workshops, or conferences was significantly higher (p < 0.05) , and it did not significantly relate to gender, age, marital status, or work experience, but awareness of physicians and midwives is higher than other groups ( < 0.05) .p = 0.16 and p = 0.37 for marriage and p = 0.69 and p = 0.19 for gender).The Mann–Whitney test did not show a significant difference between the average scores of attitude and awareness based on marital status and gender (p = 0.42 and p = 0.1 for work experience and p = 0.62 and p = 0.99 for age.)Spearman's test did not indicate a significant relationship between work experience or age in the knowledge and attitude scores (p < 0.05).The attitude scores for most questions are above 3.4 and reflect a positive attitude about telemedicine. Attitudes did not show a significant relation to gender, age, marital status, or work experience . The morTelemedicine technologies have been touted as an elegant solution to the challenges of providing equitable, cost-effective, and efficient healthcare services for over two decades. This is especially true for those living in rural, remote, or urban areas, especially in developing countries, who have limited healthcare options or cannot afford other forms of health. The sensory aspects of healthcare and the physician-patient relationship mediated by telemedicine significantly contribute to success, failure, or unintended consequences , 12–18.p < 0.05). The awareness of healthcare workers using continuing education, articles, workshops, or conferences was significantly higher (p < 0.05). Telemedicine technology is a new method in Iran; it does not have a sufficient place in the educational curricula of medical sciences, and on the other hand, many primary healthcare staff have not completed courses related to it during their education. In addition, the lack of necessary infrastructure and internet network in these areas, along with the distance from the capitals of the provinces and the country, the lack of trained healthcare staff, and the absence of comprehensive guidelines have led to a low level of awareness among healthcare staff in primary healthcare centers. Furthermore, it should be added that the geographical locations and the restrictions for the employees of deprived health centers have helped to intensify these factors. A low level of awareness of the healthcare staff regarding telemedicine and its positive capabilities can lead to disruption in the implementation process and an increase in cost and time for its administration. This lack of awareness can also decrease its performance and active role even after implementation. Increasing training programs for healthcare staff in these areas and solving other fundamental obstacles  can be a suitable way to increase its efficiency in deprived areas. The attitude scores for most questions are above 3.4 and reflect a positive attitude about telemedicine. Attitudes did not show a significant relation to gender, age, marital status, or work experience [In our study, the most common educational level of the participants (57%) was a bachelor's degree, and the most frequent sources of information about telemedicine are colleagues (40.3%), continuing education (24.7%), and social media and the internet (10.1%). In the study by Ghazi and Tanhapour in 2022, they stated that 57.3% of providing training and 17.4% of consulting were the best uses of telemedicine. Also, 60% of the specialists considered the lack of technical workers and 17.02% of primary costs as the biggest obstacles to using telemedicine in the hospital and assuming the existence of a favorable executive culture and the existing appropriate infrastructure and the current level of awareness among the specialists of this medical center. In our study, awareness of telemedicine among healthcare workers is low (2.6 out of 5). Awareness did not show a significant relation to gender, age, marital status, or work experience, but awareness of doctors and midwives is higher than other groups .The study by Sheikhtaheri et al. reported that the awareness score is 13 ± 5.5 out of 35, indicating low awareness of telemedicine. Only 43.7% answered that they had heard about telephone counseling. Awareness of other types of telemedicine services was even lower. The most commonly used sources of information about telemedicine were friends (51.4%) and public media (30.3%). Attitudes towards telemedicine were generally positive (63.42 ± 9.5 of 95). We found a significant positive correlation between attitudes and awareness  and suspected COVID-19. Controlling for individual and community-level characteristics, Black patients had 0.6 times the adjusted odds (95% CI: 0.58–0.63) of accessing telemedicine care compared to White patients. Although telemedicine has not solved all the challenges and problems caused by the pandemic and home lockdown, it has improved care and medical services in America , 18–26.Moreover, during the COVID-19 pandemic, numerous hospitals were dedicated to treating COVID-19 patients, and fewer hospital beds were available to other patients, so telemedicine became more critical. This issue led to an increased need for interdisciplinary cooperation. During this period, many specialists were required to use telemedicine and multidisciplinary collaboration to treat patients. These new collaborations increased the quality of medical services to patients, especially those in rural areas who needed instant or continuous follow-ups. This cooperation would not have been possible without the availability of telemedicine technologies .Several studies have been conducted on the obstacles to establishing and implementing telemedicine in developing countries. According to the systematic review study conducted by Al-Samarraie H.,et al.2020, the barriers in developing countries in the Middle East were divided into six categories. These include (1) cultural , (2) financial , (3) organizational , and broadband Internet connection in rural areas), (4) legal and regulatory , (5) individual , and (6) technological  .One of the positive results of telemedicine is reducing costs related to transporting patients and increasing the speed of making decisions and treating patients. In our study, 73.9%, 92.7%, and 92.6% of participants had a positive attitude towards improving clinical decisions, clinical workflow, and an increase in the speed of medical services. In line with our result, a study by Atmojo JT.,et al.2020, found that telemedicine can reduce patient transportation costs and speed treatment decision-making. In addition, it ensures patient satisfaction across a range of parameters, including improved medical outcomes, ease of use, improving patient satisfaction through improved communication, reduced travel time to the hospital, improved access, and reduced readmissions , 19–24.There are no specific courses or guidelines on this technology for clinical health workers in Iran. Graduates of clinical fields have not received extensive training in this regard. The results show that the most positive attitude is related to quick information access and improving information quality. Better access to patient records can improve document quality and timeliness of information. Physicians believe that telemedicine will be useful if it improves patient care, documentation, and timeliness and enables low-cost and high-accuracy patient monitoring , 29–33.Limited information about telemedicine has affected clinicians' perception of this technology. Therefore, before implementing this technology, it is essential to increase users' knowledge about it and demonstrate its capabilities and benefits. Sufficient knowledge and a positive perception of technology are the main factors that encourage users to use technology in the future , 29–33.In all countries, most of the research has been done on healthcare workers and physicians in hospitals, and the importance of telemedicine awareness in rural health centers and borders, which are the primary targets of telemedicine, has yet to be studied. In our study, the awareness and attitudes of health center workers in border and developing areas were investigated, which is one of the strengths of our research. We suggest that the development of guidelines and improvement in the information technology (IT) skills of clinical health workers could increase readiness to accept telemedicine. The limitation of our study is that patients' attitude and satisfaction was not investigated, and there is not enough information in this regard.In recent years, the role of telemedicine in postoperative care has attracted attention as it has demonstrated excellent clinical outcomes, increased patient satisfaction, increased accessibility, reduced waiting times, and saved costs for patients and the health system. Providing healthcare professionals with more information about new technologies in healthcare, such as telemedicine, can help get a more realistic picture of their perceptions.