A significant positive correlation was found between BMI and SBP (p = 0.000) and BMI and academic performance (p = 0.003). The linear regression analyses revealed that the gender , age , family income , residence , and type of family  had the strongest correlation with the BMI of the adolescents. Additionally, Mother’s education  had the strongest correlation with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age  and gender  of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity, and quality sleep among adolescents prevent and control childhood CVD risk factors such asoverweight and hypertension. The SEAL-MI may lead to age-appropriate development of adolescents as well as improve their academic performance and quality of life. Giving importance to adolescents from urban habitats, affluent, nuclear families, and catching them young will change the disease burden significantly.There are shreds of evidence of shared biological mechanisms between obesity and hypertension during childhood intoadulthood, and loads of research literature has proven that it will profoundly cost nations’ economies and health if neglected. The prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its’ complications. The study aims to assess the effect of school-based Exercise and Lifestyle Motivation Intervention (SEAL-MI) on adolescents’ cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents—520 and 485 were randomly selected for the control and study groups, respectively.A structured interview questionnaire was used to collect demographic details and data related to dietary habits, physical activity, sleep qualityand academic performance. The study group adolescents were given the SEAL-MI for six months, including a school-based rope exercise for 45 min per day for 5 days a week and a motivation intervention related to dietary habits, physical activity, and sleep. Post tests-1 and 2 were done after 3 and 6 months of intervention.The prevalence of overweight among adolescents was 28.73%, and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). There was a significant weight reduction in post-intervention B.P. ( Developing the full potential of adolescents and helping them to survive with good health is essential. Nevertheless, due to the constant development of economic level and corresponding lifestyle changes, we are facing a gradual increase in weight with the younger-age trend. In the United States, according to the Centre for Disease Control and Prevention (CDC), approximately 17%  of children and adolescents aged 2–19 years are obese, with the obesity prevalence in these groups having almost tripled since 1980 [Obesity causes various disorders, and it is of great public health concern across the world. According to the World Health Organization (WHO), childhood obesity has been strongly associated with a higher chance of premature death and disability in adulthood ,3. CentrThe behavioral risk factors such as an unhealthy diet are estimated to be related to about half of hypertension . Insufficient physical activity is the fourth leading risk factor for mortality. Physical inactivity is related to about 20% of hypertension, and obesity is also related to about 30%. More than two-thirds of the adolescents are physically inactive in India, as per WHO standards. Any preventive and promotive health interventions targeted in this age will be relatively cost-effective yet yield a lifetime gain, not only for individuals, also for societies and economies. Additionally, school-based childhood interventions are among the most cost-effective in saving the Disability Adjusted Life Years (DALYs) in their later life.Captivatingly, adolescents who have a low level of physical activity are more prone to be obese, depressed and more likely to have a lower grade point average (GPA) . AcademiFurther, depressed adolescents are more likely to develop and sustain obesity during their adolescence. Understanding the shared biological and social variables that link depressed mood and obesity could help with mutually prevention and therapy of both disorders. Depression in adolescents has also been linked to a higher body mass index in adulthood, though it is uncertain whether depression promotes obesity or obesity causes depression . Hence, For these reasons, school-based healthy lifestyle curriculum development is the need of the hour for adolescents as there is relative evidence that physical activity elevates adolescents’mood, improves memory, cognitive function, and prevents CVD risk factors. Additionally, school is an easily accessible and affordable ambience to adolescents, and teachers play a crucial role in children’s physical, psychological, social, and emotional development. Therefore, the main objectives of this study were to estimate the prevalence of overweight and hypertension among adolescents and to assess the effect of school-based Exercise and Lifestyle Motivation Intervention (SEAL-MI) on adolescents’ cardiovascular risk factors and Academic performance.This experimental study with before and after control group designwas conducted in the randomly selected high schools of Tiruvallur district, Tamilnadu, India.Adolescents with overweight and increased blood pressure were the target population of the study. The Tiruvallur educational District, Tamilnadu, India, had 2263 functioning Schools in both rural and urban zone, and data collection took place in the chosen high schools.The sample size was calculated based on the power analysis taking 95% confidence level and 80% power of the study with α = 5%, β = 20%. The sample size was estimated with the improvement of 20% gain from baseline 50% with 80% power of the study, 5% allowable error, and 10% dropout rate. The estimated sample size was rounded as 500 in each control and intervention group. An updated list of all public schools was used as the sampling frame, and stratified sampling method was used. The schools were stratified proportionately according to urban/rural distribution; after ensuring matching, eight rural zones and eight urban wards were selected, and they were randomly allotted into intervention and control groups using the lottery method. In the same way, two schools (for intervention and control group) from rural and urban wards were selected in the second stage. From the 16 selected schools from each urban and rural area, all the adolescents aged 13–14 years, who were willing to participate in the study, were screened for hypertension and overweight as a first phase .After obtaining approval from the Institutional Ethical Committee, the baseline information was assessed by the modified “WHO STEPS” instrument to monitor chronic disease risk factors . The adoThe adolescents diagnosed with pathological causes of hypertension such as renal diseases, cardiac conditions with any other co-morbidities and who were already on medication for overweight and hypertension were excluded. The adolescents who had significant complications associated with medical conditions, learning disabilities, breathing, hormonal and musculoskeletal limitations, on an exercise program, and unable to perform moderate to vigorous-intensity exercises were excluded.Initially, 12,895 adolescents from 16 rural and 16 urban schools were screened for obesity and hypertension in the first phase. Among them, 3705 were overweight, and 1195 were identified with both overweight and prehypertensive. Based on the inclusive and exclusive criteria, 1005 adolescents with overweight and prehypertensive were randomized and allotted by lottery method in the study (500) and control (485) group.Section A: Screening Tool, which was used to screen overweight adolescents with increased blood pressure, measuring height, weight, calculated body mass index, and blood pressure.Section B: Assessment tool was used to collect the demographic, lifestyle, and bio-physiological variables. It consists of three parts. Part A: Demographic variables of the adolescents consists of age, gender, religion, level of education, birth order in the family, number of siblings, number of persons in the family, monthly income of the family, mother and father’s education and working status, type of family, and nature of residence as well as family history of overweight and any other chronic illnesses. Part B: Lifestyle Variables, which was assessed by the modified tool, “WHO STEPS” instrument, used for the surveillance of chronic disease risk factors. It included dietary habits and physical activity. Part C: BEARS sleep screening test used to assess the adequacy of sleep among adolescents. Part D: Bio- Physiological variables include height, weight, Body Mass Index (BMI), and blood pressure.Using the BMI Percentile Chart for boys and girls by WHO (2007), if a child had a percentile ranking of 85th or greater was considered overweight. Interpretation of BP was done by following the 2004 guidelines produced by the National High Blood Pressure Education Working Group on High Blood Pressure in Children and Adolescents of the National Heart, Lung and Blood Institute blood pressure cut-points classified based on age, gender, and height percentile-specific systolic blood pressure (SBP) and diastolic blood pressure (DBP) levels. Age and height adjusted SBP and DBP was estimated for each participant according to the 4th report on Diagnosis, Evaluation and treatment of High B.P. in children and adolescents (2004) guidelines, and blood pressure percentiles were calculated for each child based on the expected blood pressure. In adolescents, the normal range of B.P. is determined by body size and age. This approach avoids misclassifying adolescents who are very tall or very short.The height percentile was determined using the newly revised WHO Growth Charts to use the tables in a clinical setting. The adolescents’ measured SBP and DBP are compared with the numbers provided in the table (boys or girls) according to the adolescent’s age and height percentile. The adolescent is normotensive if the B.P. is below the 90th percentile. If the B.P. is equal to or above the 90th percentile, the B.P. measurement was repeated at that visit to verify elevated B.P. The B.P. measurements between the 90th and 95th percentiles indicate prehypertension and warrant reassessment and consideration of other risk factors. In addition, if an adolescent’s B.P. is greater than 120/80 mmHg, the adolescent was considered prehypertensive even if this value is less than the 90th percentile. This B.P. level typically occurs for SBP at age 12 years and DBP at age 16 years. If the adolescent’s B.P. (systolic or diastolic) is at or above the 95th percentile, the adolescent may be hypertensive, and the measurement was repeated on at least two different occasions to confirm the diagnosis.Using the blood pressure tables: 1. Use the standard height charts to determine the height percentile, 2. Measure and record the child’s SBP and DBP, 3. Use the correct gender table for SBP and DBP, 4. Find the adolescent’s age on the left side of the table. Follow the age row horizontally across the table to the intersection of the line for the height percentile , and 5. Find the 50th, 90th, 95th, and 99th percentiles for SBP in the left columns and DBP in the right columns.B.P. less than the 90th percentile is normal.B.P. between the 90th and 95th percentile is prehypertension. In adolescents, B.P. equal to or exceeding 120/80 mmHg is prehypertension, even if this figure is less than the 90th percentile.B.P. greater than the 95th percentile may be hypertension.If the B.P. is greater than the 90th percentile, the B.P. was repeated twice at the same office visit, and an average SBP and DBP was used. If the B.P. is greater than the 95th percentile, B.P. was staged. If Stage 1 (95th percentile to the 99th percentile plus 5 mmHg), B.P. measurements were repeated on two more occasions. If B.P. was Stage 2 (>99th percentile plus 5 mmHg), a prompt referral was made for evaluation and treatment. If the adolescent was symptomatic, an immediate referral was made, and treatment was advised.The school-based Exercise and Life Style Motivation Intervention (SEAL-MI) incorporated planned teaching on knowledge regarding prevention and control of blood pressure, overweight, dietary modifications, rope exercise program, and activities to enhance sleep quality. The investigators administered the intervention, designed to change adolescents’ BMI and BP for 6 months. The teaching was given at the beginning of the intervention, and the rope exercise program was carried out for 6 months with a frequency of five times a week in the school after the school hours under the supervision of physical education teachers. The physical education teachers (PET) were given intensive training for 3 days on the rope exercise program, physical activity monitoring tools, exercise intensity, and calculations of maximum heart rate (MHR). A WhatsApp group was maintained among PETs and the researchers for instant problem resolution if any. Before implementing the rope exercise program, a no harm certificate was received from a Paediatrician, a Physical therapist and a Clinical Nutritionist for ethical reasons.During the rope exercise program, participants warmed up for 5 min and completed eight cycles of rope jumping exercise. Each cycle consisted of rope jumping for 2 min followed by 2 min rest which lasted for around 30 min, followed by relaxation exercise for 5 min. To maintain the exercise intensity, changes in heart rate was observed. In the current study, the participant’s mean age was 13 years 2 months, and the estimated MHR employed was 210 bpm. Therefore, the MHR giving rise to a change in heart rate (as defined by aerobic work) is 210 × 60% MHR = 126 bpm. The heart rate of every child was recorded daily at the end of each 2-minute cycle of rope jumping, before and after warm-up to keep up the required advantage of the exercise and a diary was maintained for each child by the physical education teachers who have been trained for the same. The compliance was ensured through phone calls, WhatsApp and weekly direct visits to the intervention group adolescents by one investigator on rotation. The investigators maintained a 6-month physical activity, diet and sleep record with the help of physical education teachers for rope exercise and parents for diet and sleep patterns to monitor the progress and compliance. After 3 and 6 months, post-interventional data were collected.r = 0.92).The primary dependent variables were the students’ bio-physiological variables, including height, weight, Body Mass Index (BMI) and blood pressure. Adopting the BMI Percentile Chart for boys and girls ,13, if aTo assess the academic performance of adolescents, their self-reported academic performance was calculated by a question: “In the past 12 months, how has your average academic performance been?”. The answers were verified with their previous year school academic records, and the available responses were scored with a 5-point rating scale including 5 score for very good (average score above 80%) and 1 score for very poor (average score below 35%), respectively.Written consent was obtained from the Director, Directorate of Education, Tiruvallur District and Head of the Institution of all the schools selected for the study. Additionally, written consent from the parents and assent from the adolescents was obtained. The Institutional Ethical Committee approval was obtained from Meenakshi Academy of Higher Education and Research, Chennai.t’ test for continuous variables and chi-square test for categorical variables was used. Repeated measures ANOVA were performed to compare scores over a period of time. Multiple regression analysis was completedto assess the strength of the relationship between outcome variables (BMI and BP) and the predictor variables (demographic characteristics). All results from this study were presented as mean ± standard deviation. Statistical significance was set at p < 0.05 and the data were entered into an Excel sheet and analyzed through Statistical Package for Social Science/PC+Ver.17.Bivariate Analysis, Correlation Coefficient, Chi-square test, paired, unpaired to test, repeated-measures ANOVA, multiple regression analysis was performed to compare scores over a period of time, within and between groups to determine the effect, correlate and associate the variables. To compare groups, the ‘In the present study, a total of 12,895 adolescents aged between 13 and 14 years were screened by our team of investigators for overweight and hypertension. Among these adolescents, 3705 adolescents (28.73%) were found to be overweight, and 1195 adolescents (32.25%) were found to be overweight and prehypertensive.Among the 1195 overweight and prehypertension, 1005 adolescents and their parents who were willing to participate in the study were selected randomly for the intervention (520) and control (485) group by lottery method. Considering the gender, approximately equal number of the sample, 231 (44.4%) boys and 285 (55.6%) girls in the study, as well as 281 (57.9%) girls and 204 (42.06%) boys in the control group, participated in the project. At the end of the Post test—1 & 2, only 500 and 485 adolescents were in the intervention and reference group, respectively, and the attrition rate was 3.85%. It was due to various reasons such as changing school or residence, dropping out from school, etc. There was homogeneity among selected samples in both the groups tested through “Test of Goodness of Fit”.p = 0.000). After six months of intervention, 405 (81%) study group adolescents became normal weight, none of them was obese, and only 95 (19%) adolescents were overweight, which was statistically significant (p = 0.000). The comparison of blood pressure status for study and control group adolescents showed that 105 (20.2%) study group adolescents become normotensive in the post test-1. In the post test-2, 347 (69.4%) study group adolescents became normal, which were statistically significant (p = 0.000), whereas only 39 (8.3%) of the control group became normotensive after 6 months. In the post test-1, 145 (29%) adolescents from the study group became normal weight which was statistically significant . In contrast, the control group did not show any significant difference after 3 months but showed a significant difference after 6months (p = 0.03) due to the time effect. Similarly, the mean differed DBP was 0.09 with SD of 0.34 in pretest to post test-1 and 0.69 with SD of 1.26 in pretest to post test-2, which confirms that the intervention should be long term to bring desired change in DBP. Additionally, this confirms that the SEAL-MI was highly effective in reducing the B.P. among study group adolescents , there was a significant difference within the study group at rvention .p = 0.004) (p = 0.000), education (p = 0.000) (P = 0.000), number of family members (p = 0.006) (p = 0.003), type of family (p = 0.035) (p = 0.002), family history of obesity and chronic illness (p = 0.036) (p = 0.031), respectively, with BMI and systolic BP of the adolescents in study group  that uses an innovative approach to prevent overweight and hypertension among adolescents through promoting healthy lifestyles. The plan targets families and is implemented via schools. The study’s objectives were to find the correlation between BMI and BP and BMI with academic performance. One of the project’s major strengths was that we were able to conduct a 6 months intervention involving the school teachers and family. Another point of strength is the multi-faceted behavioral approach. A systematic review found that Family Based Intervention studies discovered significant changes in two or more behavioral aspects. This study focused on three behavioral domains  directly and depression indirectly. In addition, as this study was focusing on academic performance of the adolescent, there was much response from the parents and teachers and they acted as ‘change agents’.In the present study, the intervention group had 520 adolescents and the control group had 485 initially, with the dropout rate of 3.85% in the intervention group at the end of the study. Among the 12,895 adolescents screened, 28.73% were found to be overweight, and 9.26% were prehypertensive. Among the overweight adolescents, the prevalence of prehypertension was 32.25%, which was relatively very high. Many studies emphasized the same trend of overweight and hypertension among adolescents ,18,19,20t” test, showed that though it was not significant, the difference found during the study periods indicated that the SEAL-MI was highly effective in reducing the BMI and B.P. It also emphasizes that long-term intervention with appropriate reinforcement is needed to meet the desired change. It also indicates that it is easy to reduce BMI and B.P. initially; sustaining the reduction was the more significant challenge. According to a systematic review, the level of parental involvement appeared to have a positive impact on the effectiveness of interventions on children’s weight and energy balance-related behaviors [In this study, the reduction in the BMI and B.P. of the intervention group adolescents was statistically significant after 3 and 6 months of intervention. The comparison of BMI and B.P. in the study group, done by independent “ehaviors .Another ehaviors . With reA study by Lone DK et al., 2014 suggested that a chronic aerobic exercise intervention lasting 4–8 months effectively reduces blood pressure in overweight adolescents at high to moderate intensities . Ghosh Sp = 0.000 level after six months of intervention. According to Kim J et al., (2020), there were significant improvements following the 12-week exercise program for body fat%, waist circumference, systolic BP, blood glucose, and insulin levels. This study evidenced that rope exercise intervention can be a beneficial therapeutic intervention to improve CVD risk factors in obese adolescent girls with prehypertension [Regarding dietary habits, physical activity and sleep, there was a significant difference within the study group at rtension .An interesting fact emerged from the study that there was a negative association between the BMI and academic performance of the adolescents during the study periods. It shows that when BMI increases, the academic performance of the adolescents’ declines. A similar finding was reported in a few other studies as well ,31. ObesIn this study, there was a statistically significant association between the demographic variables such as gender, level of education, number of family members, type of family, family history of obesity and chronic illness with BMI and systolic B.P. of the adolescents. The present study results were consistent with other studies ,38. The The linear regression analyses revealed that the gender , age , family income , residence , and type of family  had the strongest correlate with the BMI of the adolescents. This suggests that these variables strongly influence adolescents’ BMI status and need to give importance to the adolescents from rural habitats, from poor, nuclear families and the most crucial point of catching them young will bring significant change in the disease burden. Additionally, the mother’s education  had the strongest correlate with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age  and gender  of the adolescents. Therefore, it is imperative to take these risk factors into consideration in order to enhance the efficacy of preventive actions.BMI and SBP showed a significant positive correlation in all the three study periods, proving that BMI is strongly correlated with blood pressure and the greatest need for early diagnosis. The treatment of overweight will have a significant reduction in hypertension status. Numerous studies have been performed across the globe on whether or not physical exercise has a significant positive correlation with weight and cardiovascular risk factors ,42,43,44One of the limitations was, although the current study allowed adolescents’ parents to attend initial education session, it may be beneficial for future similar interventions to involve parents intensively. Evidence suggests that involving families as well as adolescents in a childhood obesity intervention increases the likelihood of intervention success. Another limitation is that no covariates/confounding factors were examined, despite the fact that additional covariates could be useful in investigating individual-level changes.The earlier is the best for any preventive strategies. According to the findings of this study, interventions involving lifestyle and behavioral modifications such as physical activity, adaptation of a balanced diet, and improving sleep quality diet, and a longer duration of intervention are effective in the prevention and reduction of childhood obesity and hypertension. Additionally, a decrease in BMI is most closely associated with a decrease in blood pressure. As a result, school-based interventions, such as the weekly after-school clubs with parental supervision at home, may be an effective way to reduce CVD risk factors in adolescents in addition, with a positive bond with their academic performance.Medical experts can feel confident in advising parents to address their at-risk children’s food choices and exercise patterns instantly at the earliest rather than waiting for overweight and the patterns that promote it to resolve on their own. Identifying adolescent obesity allows healthcare professionals to intervene sooner to slow the trajectory of aberrant weight gain that leads to obesity-related illness and CVD risk factors. Preventing these will provide a more significant opportunity for the adolescents to grow to their fullest potential physically, psychologically, socially, and emotionally.


Objective: To compare the effectiveness and safety of enhanced recovery after surgery (ERAS) in patients with hepatocellular carcinoma (HCC) undergoing laparoscopic hepatectomy.Methods: From September 2016 to June 2019, 282 patients were enrolled, and ERAS was implemented since March 2018. All indicators related to surgery, liver function, and postoperative outcomes were included in the analysis. Propensity score matching (PSM) identified 174 patients for further comparison.Results: After PSM, the clinicopathological baselines were well-matched. The group showed significantly less intraoperative blood loss , fewer days before abdominal drainage tube removal , shorter hospital stay after surgery , and reduced postoperative morbidity . The proportion of patients with a pain score ≥ 4 was significantly lower in the ERAS group within the first 2 days after surgery . Pringle maneuver was performed more frequently in the ERAS group , and a significantly higher postoperative alanine aminotransferase level was also observed . The 2-year recurrence-free survival was similar between the two groups .Conclusions: ERAS programs are feasible and safe and do not influence mid-term recurrence in HCC patients undergoing laparoscopic hepatectomy. Since enhanced recovery after surgery (ERAS) programs were introduced by Kehlet in the 1990's, they have been widely applied in gastrointestinal, urologic, gynecological, orthopedic surgery, and many other surgical fields to minimize perioperative pain and stress, reduce morbidity, and accelerate postoperative recovery worldwide . FurtherAlthough the implementation of ERAS programs in hepatic surgery was slightly later than in other surgical fields , many raHepatocellular carcinoma (HCC) is still one of the leading causes of cancer-related mortality, especially in China. Most cases of hepatitis B virus (HBV)-related HCC occur in patients with cirrhosis , 22. In Since the second international consensus conference for laparoscopic liver resection in 2014, laparoscopic minor hepatectomy was the standard surgical practice . In addiTherefore, this study aimed to compare the effectiveness and safety of ERAS in patients with HCC undergoing LH.The inclusion criteria were as follows: (1) patients were pathologically confirmed to have HCC after surgery; (2) all surgical procedures were successfully performed by laparoscopy; (3) radical resection was achieved; and (4) preoperative liver function was Child-Pugh A or B. The exclusion criteria were as follows: (1) patients underwent laparoscopic radiofrequency ablation alone, and (2) laparoscopic surgery was converted to open surgery for any reason. From September 2016 to June 2019, 282 eligible patients in the Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, were enrolled in the study retrospectively. ERAS programs were implemented in our center on March 1, 2018. All the patients who were suitable for undergoing LH routinely followed the ERAS protocol. Therefore, 108 patients from September 2016 to February 2018 were enrolled in the control group, and the subsequent 174 patients were enrolled in the ERAS group. All surgical operations were performed by the team of Prof. Lu Wang. This study was approved by the Research Ethics Committee of Shanghai Cancer Center, Fudan University.Enhanced recovery after surgery (ERAS) management measures at our center were introduced in our previous study  and are t-test or Mann–Whitney U-test was performed to compare quantitative variables. For data analyzed with a two-sample Student's t-test, the data were presented as mean ± standard error, and for data analyzed with Mann–Whitney U test, the data were presented as median (interquartile range). Pearson's χ2 test or Fisher's exact test was used to comparing qualitative variables. Statistical analyses were performed using the SPSS 22.0. Plot analysis was performed using GraphPad Prism 5 for Windows . Statistical significance was set at P < 0.05.Propensity score matching (PSM) analysis was performed in this study to reduce bias in patient selection using SPSS 22.0 for Windows . The variables in the clinicopathological baseline that were not balanced and might affect the results, namely, age, sex, underlying diseases, HBV infection, American Society of Anesthesiologists (ASA) score, body mass index (BMI), preoperative level of ALT, TB, PT, PAB, and type of hepatectomy were included in the calculation. The propensity score was generated using logistic regression with these variables, and the caliper value was set to 0.02. The patients were selected using nearest-neighbor matching without replacement at a ratio of 1:1. A two-sample Student's P = 0.025), patients with underlying diseases (P = 0.010), higher ASA scores (P = 0.017), and lower preoperative ALT levels (P = 0.032) were observed in the ERAS group.A total of 282 patients were recruited for this study. Among these, 108 patients (38.30%) received traditional perioperative care in the control group, and 174 patients (61.70%) received ERAS programs in the ERAS group. The clinicopathological characteristics of these cohorts are summarized in P = 0.009). Although the proportion of intraoperative blood transfusion was similar  in the two groups, the type of hepatectomy was different . The use of the Pringle maneuver was also significantly more frequent in the ERAS group . As an indicator of postoperative liver function recovery, the TB level was significantly lower in the ERAS group , while the ALT, PT, and PAB levels showed no significant difference between these two groups. The postoperative outcomes, namely, the days that semiliquid diet was allowed after surgery and hospital stay after surgery, were significantly less in the ERAS group . In terms of pain score, the proportion of patients with a score ≥ 4 was significantly lower in the ERAS group within the first 2 days after surgery . There was no significant difference in the abdominal drainage tube indwelling duration and the hospital costs between these two groups were also similar . The incidence of complications was not significantly different between the two groups . Furthermore, the 2-year RFS was similar between the two groups .The operative results and postoperative outcomes in the entire patient population are shown in P = 0.001). The proportion of patients with intraoperative blood transfusion was slightly more in the control group  and the type of hepatectomy between the two groups was similar . The operative duration and intraoperative blood transfusion did not demonstrate any obvious differences. The Pringle maneuver was performed more frequently in the ERAS group . Impressively, in the liver function recovery indicators, the ALT level in the ERAS group was significantly higher than that in the control group , which was completely consistent with the results of our previous study (P = 0.073). PT and PAB levels were also similar between the two groups.After PSM, the clinicopathological baselines of the two groups were well-matched . In the P < 0.001), abdominal drainage tube removal , and hospital stay after surgery  than in the control group. Similar to that before PSM, the proportion of patients with a pain score ≥ 4 was significantly lower in the ERAS group within the first 2 days after surgery . In the patient population after PSM, there was also no significant difference in the hospital costs between these two groups . Interestingly, the ERAS group demonstrated significantly less postoperative morbidity  after PSM. Furthermore, after PSM, 2-year RFS was similar in these two groups .In the postoperative outcomes, the ERAS group showed significantly fewer days that a semiliquid diet was allowed  has been considered as a landmark development in the progression of a surgical treatment since it was gradually introduced to cure liver lesions in the 1990s , 33. LH In this study, PSM was performed to minimize the confounding bias of the baselines due to the retrospective design. Early abdominal drainage tube removal, better pain control, shorter hospital stay, and lower postoperative morbidity after ERAS were confirmed. These results proved the effectiveness of ERAS programs in patients with HCC who had undergone LH. Furthermore, alterations in postoperative liver function and mid-term recurrence were also investigated in these patients. Several representative indicators, namely, ALT, TB, and PT were selected, and these indicators generally peaked on the 3rd day after surgery. The postoperative ALT level was significantly higher in the ERAS group. Conversely, the TB level was lower in the ERAS group, although the difference was not statistically significant. The PT levels were also not affected. Similar results were also observed in LH, but not open surgery, in our previous study . These iFew studies have explored the role of ERAS programs in long-term or mid-term survival. Recently, stage III gastric cancer patients undergoing laparoscopic radical gastrectomy were verified to have a survival benefit from ERAS implementation . In coloGenerally, in HCC patients undergoing LH, ERAS programs were verified to improve postoperative recovery significantly but did not show their role in 2-year recurrence. Although hepatic IRI might be enhanced, laparoscopic Pringle maneuver combined with low CVP might make LH safer by improving intraoperative blood loss, which should be considered as a step of ERAS programs. Although PSM was used to reduce confounding bias and make the conclusion more convincing, a following prospective RCT is still necessary to further confirm the conclusion.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Shanghai Cancer Center, Fudan University. The patients/participants provided their written informed consent to participate in this study.Study concept: AM and LW. Data collection: JZ and XH. First draft: JZ. Data analysis and review and editing: All authors.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.


Preclinical evidence suggests that melatonin may affect cellular pathways involved in colorectal cancer (CRC). We sought to test whether melatonin use was associated with decreased risk of CRC using population-based data.We performed a nationwide cohort study using a new-user study design. We identified a total of 58,657 incident melatonin users aged 50 years and older from the Prescribed Drug Register, and matched them with 175,971 comparisons who did not use melatonin, on the ratio of 1:3. The Cox regression model was used to calculate hazard ratios and 95% confidence intervals.P < 0.001). The decrease of CRC risk was independent of tumor location and stage at diagnosis. When stratified by age groups, the inverse association was significant only among individuals aged 60 years and older.The incidence rate of CRC was 10.40 per 10,000 person-years for melatonin users, whereas the rate was 12.82 per 10,000 person-years in the nonusers. We found a significant negative association between melatonin use and risk of CRC . A test for trend showed a significant dose-response correlation (This population-based cohort study suggests that the use of melatonin was associated with a reduced risk of CRC. Further studies are needed to confirm the observed association and to explore the underlying mechanisms. Globally, colorectal cancer (CRC) remains the third most diagnosed cancer and the second leading cause of death through cancer. According to statistics from the International Agency for Research on Cancer, there were approximately 1.8 million new CRC cases and 900,000 deaths in 2018, accounting for about one-tenth of cancer cases and deaths . Low-dosMelatonin is a natural indolic compound mainly secreted by the pineal gland of humans and mammals, which regulates the circadian rhythm . ClinicaTherefore, the objective of this study was to investigate whether the use of melatonin was associated with a reduced CRC incidence in the elder Swedish population (age 50+ years). We hypothesized that melatonin might have a protective effect regarding the development of CRC.This retrospective nationwide cohort study was approved on February 6, 2013, by the Ethics Committee at Lund University (Dnr 2012/795), Sweden. We used information on individuals from Swedish population-based registers with national coverage. These registers were linked using each person's unique identification number replaced by a serial number to preserve confidentiality.The source population included 9,147,428 cancer-free Swedish-born residents registered from July 2005 through December 2015 in the Swedish Total Population Register, which includes detailed demographic information for nearly 100% of residents living in Sweden . The SweFor each melatonin user, 3 individuals without a prescription of melatonin were randomly selected from the source population and matched by birth year, sex, education level, and family history of CRC.International Classification of Diseases, Tenth Revision, Clinical Modification codes C18, C19, and C20. The Swedish Cancer Registry contains data on the TNM staging system, including the size of the tumor (T), nodal status (N), and presence of metastatic disease (M). By combining the T, N, and M categories, we can determine the stage at diagnosis of CRC, ranging from stage I (the least advanced) to stage IV (the most advanced) as follows: stage I , stage II , stage III , and stage IV (any M1)  . By furtThe follow-up was started on the date of the first prescription of melatonin for melatonin users or the index date for their comparisons (the same date as the corresponding melatonin user), ended at the first date of diagnosis of cancer, date of death from any cause, and the end of the study period , whichever came first.International Classification of Diseases-10 code E66, yes/no), chronic obstructive pulmonary disease  as a proxy for smoking, prescription of other medication , and Charlson Comorbidity Index . As having comorbidity is an important factor affecting the health condition and risk of cancer, we calculated the CCI based on a total of 17 categories.By retrieving data from the National Patient Register, Statistics Sweden's Total Population Register and Population Housing Census, and Swedish Multiple Generation Register, we extracted information on potential confounding factors, including birth year, sex , highest education  , family 2 test was used to test the difference of sociodemographic and clinical factors between individuals who used melatonin and the matched comparisons, and variables with P values < 0.1 were included in the regression model. The final model was adjusted for the history of inflammatory bowel disease, use of colonoscopy, outpatient visits, obesity, COPD, use of aspirin, use of statin, and use of metformin besides the matched factors. Moreover, we evaluated the association of melatonin use with the risk of the specific site of cancer  and specific stage of cancer (stage I or II and stage III or IV). In addition, we calculated the cumulative defined daily doses (cDDDs) of melatonin as the sum of DDDs for all prescriptions during the follow-up period using records from the Swedish Prescribed Drug Register. We then performed a dose-response analysis of melatonin use with CRC by modeling cDDD of melatonin as 3 groups: <30 cDDD, 30–89 cDDD, and ≥ 90 cDDD and tested for the trend by entering the cDDD as a continuous variable in the regression model. We further conducted several sensitivity analyses to explore the possibility of chance findings. First, we performed an analysis by lagging exposure to melatonin for 1 year to evaluate whether the results of the main analysis could be influenced by biological latency. Second, to minimize reverse causation, we excluded CRC diagnosed within 4 years after the baseline date. Third, we evaluated the risk of CRC among individuals aged 50 years and older, after excluding individuals with a history of benign colorectal tumor. Fourth, to increase confidence in the reported association, we evaluated a negative control outcome (incident diabetes). In addition, we performed a sensitivity analysis to evaluate the influence of potential misclassification of melatonin use, and the sensitivity and specificity were defined as 88% and 99%, respectively.We used a competing risk Cox regression model to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of CRC associated with melatonin use to control the competing risk of death . The χ2 All analyses were conducted using SAS, version 9.4 .P > 0.05). Compared with individuals without melatonin use, incident melatonin users had a higher proportion of inflammatory bowel disease, obesity, COPD, colonoscopy screening, aspirin, metformin and statin use, and higher CCIs . Melatonin use was inversely associated with CRC risk among older adults, with a crude HR of 0.80  and an adjusted HR of 0.82 . We found the inverse association was slightly stronger for rectal cancer  compared with distal colon cancer  and proximal colon cancer  and for earlier stages  compared with advanced stages  (Table P < 0.001).The results of stratified analyses are listed in Figure Sensitivity analyses' results are listed in Table As far as we know, this retrospective nationwide cohort study is the first population-based study to explore whether the use of melatonin was associated with a reduced CRC risk in older adults. We found a statistically significant inverse association between melatonin use and CRC incidence among older adults. In addition, the reduced risk was associated with an increased cumulative dose of melatonin use, and individuals with the highest doses have a 34% reduced risk of developing CRC. The association was apparent irrespective of CRC location and cancer stage at diagnosis. Our study highlights the possibility of melatonin as a potential chemopreventive agent for the primary prevention of CRC from nationwide real-world data.in vivo or in vitro models. First, melatonin presented an oncostatic effect in colon cancer cells, which might partly be because of its antioxidative and anti-inflammatory activities , whereas more serious side effects (increasing in systolic and diastolic blood pressure and heart rate) were seen at very high doses or in specific populations. Most untoward effects can be easily managed by dosing in accordance with natural circadian rhythms . Future The major strength of the current study was that it was performed based on the population at a national level. The cohort study design and large sample size ensured statistical power and avoided reversal causality. Data collected by continuously updated nationwide registers could efficiently eliminate recall bias and minimize selection bias. Register-based data also provided us with information on potential demographic and clinical confounding factors. We selected comparisons matched by birth year, sex, education level, and family history of CRC, while other confounders were included in the regression models. The design also allowed the assessment of a dose-response effect for melatonin exposure on CRC. A few limitations warrant consideration. First, the results might be affected by detection bias because individuals who used melatonin may be more likely to have healthier lifestyles; thus, they were more likely to screen for CRC and might be diagnosed with CRC at an earlier stage. However, our regression models adjusted colonoscopy, which is offered in Sweden in an opportunistic manner and can be seen as an indication of health behavior because people with higher levels of knowledge and higher health awareness are more likely to have colonoscopy . TherefoIn conclusion, this population-based retrospective cohort study suggests that the use of melatonin was associated with a reduced risk of CRC, and the decrease showed a dose-response correlation. Findings from this study need to be confirmed by well-designed randomized clinical trials in the future.Guarantor of the article: Jianguang Ji, MD, PhDSpecific author contributions: N.Z., J.S., K.S., and J.J.: study concept and design. J.S., K.S., and J.J.: obtained funding. K.S. and J.S.: data acquisition. N.Z.: statistical analysis and drafting the manuscript. All authors revised it for important intellectual content.Financial support: This work was supported by grants awarded to J.J. by the Swedish Research Council (2016–02373), Cancerfonden (CAN2017/340) and Crafoordska Stiftelsen and Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, to K.S. by the Swedish Research Council (2018–02400), and to J.S.  by the Swedish Research Council as well as by ALF funding from Region Skåne awarded to K.S., and J.J., and to N.Z. by China Scholarship Council (Grant No. 201906380063).Potential competing interests: None to report.✓ Experimental studies suggested that melatonin had anticancer activity.✓ Population-based evidence is poorly documented.✓ Melatonin use was associated with a reduced risk of colorectal cancer among older adults.✓ The chemopreventive effect showed a dose-response pattern.


There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients.Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model.CI: 0.893–0.926) and an AUPRC of 0.43 (95% CI: 0.363–0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802–0.917) and AUPRC of 0.30 (95% CI: 0.265–0.358), respectively.In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision–recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% According to the study results, there was no conspicuous decrease in the model’s performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer. HRs) of 2.3 (95% CI: 2.2–2.4) and 1.5 (95% CI: 1.3–1.7) for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), respectively  plays an important role in analyzing complex medical data, and its superiority has been shown in studies on omics, electronic health records, and image processing , 9. VTE Therefore, this study aimed to develop and validate ML models that predict the incidence of VTE among Chinese patients of lung cancer, and improve on the limitations in those studies as much as possible and design a more comprehensive and robust ML model that is suitable for lung cancer patients.Data were collected from Chongqing University Cancer Hospital of China, an oncology-specific Grade 3A institution. Inclusion criteria were as follows: (1) Patients aged ≥18 years; (2) A record of at least one time of hospitalization; (3) With newly diagnosis of lung cancer by pathology; (4) Index date from January 2013 to December 2019. The exclusive criteria listed as (1) VTE had occurred before the diagnosis of lung cancer and (2) died within 48 h after admission. All available data on the database were used to maximize the power and generalizability of the results. Those patients with incomplete information were not included. Patients with multiple hospitalizations were only counted once.via internal jugular vein cannulation, subclavian vein, and femoral vein; and biomarker: platelet count, albumin concentration, hemoglobin, creatinine, platelet count, leukocyte count, and D-dimer. All laboratory tests were performed within 90 days before treatment. In the case of repeated tests, the results closest to the time of the treatment were used. We diagnosed VTE and comorbidities using ICD-10 codes.We then verified outliers manually and corrected them. The primary endpoint was the diagnosis of VTE during index hospitalization. The presence or absence of VTE was decided blinded to the predictor variables since the data were collected retrospectively. Investigators, blinded to both predictor variables and outcome, reviewed and classified standardized data collection forms. Risk factors were chosen based on known risk variables and those previously described in the literature. We chose the Karnofsky Performance Status (KPS) as a performance metric. The following variables were taken into consideration: patient-related factors: age, sex, weight, height, KPS, comorbidities , VTE history, history of malignant tumor other than lung cancer; cancer-related factors: tumor stage, pathological classification, clinical stage (according to AJCC 8th edition staging system) ; treatmeFirst, missing values were treated differently for numerical and categorical features. Missing values in numerical features were imputed iteratively with the Bayesian ridge regression estimator; while for all categorical features, missing values were considered and treated as additional categories. After that, the one-hot-encoder was performed on categorical variables like “pathological type” to get a binary representation, while an ordinal encoder was used on categorical features like “stage of cancer.” Numerical variables were then scaled to zero mean and unit variance. All continuous variables were described by their count, mean, and standard deviation, while categorical variables are described by their count.We divided the data into training (64%), validation (16%), and test (20%) sets randomly. Random splitting maximizes the assurance that the results of the validation and training sets are unbiased when evaluating the model. To account for the imbalance between target classes, the Synthetic Minority Over-sampling (SMOTE) algorithm was utilized at the training stage.Then several models were compared with their performance . A best-performed classifier was selected to make predictions, in our case, it was Random Forest.Hyperparameters were tuned with Bayesian optimization on our validation set to avoid overfitting. An entire process of 5-fold cross-validation was performed with each set of hyperparameters and used the validation set to evaluate the hyperparameters. Since we aimed to correctly predict more patients with VTE, the true positive rate was valued slightly more during hyperparameter tuning by weighting it more heavily in the objective function that the Bayesian optimizer was trying to minimize. We combined the previous training and validation sets to form a new training set to train the final model with the optimal hyperparameters we found after the hyperparameter tuning.We also investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in the selected model. The impurity-based feature importance score makes it easy to gauge each feature’s contribution. This score is based on the number of times a feature is used as a node and how much that mode reduces entropy. In conjunction with permutation importance , which mFor the reason that an effective clinical prediction model requires fewer features to predict, we simplified the model features based on features ranking, literature review, and clinical experiences to achieve the best balance between model performance and model real clinical applicability.To evaluate the power of the ML models, we calculated and compared the area under the receiver operating characteristic (AUROC) curves. Since AUROC can be interpreted as the probability that the model would rank a randomly selected patient with VTE higher than a randomly selected patient without VTE, the higher the AUROC, the better the model’s performance . HoweverCI was also calculated for each performance assessment measurement to account for the uncertainty of the model by bootstrapping a sample (60%) from the training set and test set 1000 times with replacements.At the same time, test accuracy, sensitivity and specificity were also recorded. The 95% In total, 3,398 patients were included in our analysis, 125 of whom experienced VTE during their hospital stay. The variables’ characteristics and the statistical analysis between the non-VTE and VTE subgroups are summarized in As shown in CI: 0.893–0.926) and an AUPRC of 0.43 (95% CI: 0.363–0.500). Random forest model has a test accuracy score of 0.957 (95% CI: 0.934–0.975), sensitivity of 0.714 (95% CI: 0.614–0.762) and specificity of 0.965 (95% CI: 0.941–0.985). Besides the model performance, we ranked features according to their permutation feature importance during both the training stage and the test stage, as shown in CI of 0.802–0.917 and 0.265 and 0.358. Compared to To generate a more easy-to-use model with less required input features, we selected the following five features: KPS, A history of VTE, Recombinant human endostatin, EGFR-TKI and Platelet count, and re-trained a random forest classifier. These five features were obtained by taking the union of the top three features from each of the three rankings  mentioned above, with “Stage of Cancer” being removed because of its high missing rate. As shown in Cis were widened to the left direction, which were 0.896–0.968 and 0.900–0.977, respectively. Since there was no conspicuous decrease in the model’s performance, with the fact that it requires fewer features to predict, we conclude that it would be more applicable in real-life clinical settings.In addition, this model’s sensitivity score of 0.714 did not change (0.619–0.762). Yet, both the test accuracy score (0.929) and the specificity scores (0.936) were lowered, and their A simplified and applicable ML model with five most relevant features, KPS, A history of VTE, Recombinant human endostatin, EGFR-TKI, and Platelet count, to predict VTE incidence in lung cancer patients was developed and validated in this study. In terms of the importance ranking of variables, our results are consistent with previous studies.OR: 15.4, 95% CI: 15.22–15.6) in a cohort study of 4,159,400 patients.The published data have suggested that prior history of VTE history of VTE increases the risk of VTE recurrence by 2 to 7-fold after cancer diagnosis . Also, SThe KPS is a simple and rapid method to assess patients’ performance in activities of daily living which has been mostly used in medical oncology . It is cHR of VTE occurrence is 2.808  in patients with EGFR-TKI treatment relative to patients without the treatment  of patients with primary lung cancer. A high prevalence (32%) of elevated platelet counts was observed in patients with lung cancer .Recombinant human endostatin is a kind of the antiangiogenic drugs independently developed in our country and has been suggested to treat NSCLC. It inhibits angiogenesis by blocking the pro-angiogenic activities of vascular endothelial growth factor (VEGF) and fibroblast growth factors (FGF)-basic, which is a risk factor for thrombosis .Regarding ML models, in this specific study and dataset with a limited number of patients and variables, deep learning models were not used because the possibility of overfitting is high for such complicated models. In addition, there are many ways to evaluate a model’s performance, which depends on the dataset and its usage, which makes determining the better model difficult. In this study, we chose a random forest model for the following reasons.The random forest classifier consists of an ensemble of decision trees and is based on the bagging method. This means that the model trains with a set of decision trees and takes the main voting to be the final output. The advantage of this method is that it is very stable and it can reduce the chance of overfitting, which is suitable for our relatively small dataset. This would, therefore, increase not only the test accuracy, but also the accuracy of the potential patients’ samples when the model is used in the future. Although it is widely accepted that random forest classifiers can produce good results without systematically selecting hyperparameters, we still performed hyperparameter tuning to further avoid overfitting and to increase the true positive rate.This study, to our knowledge, is by far a novel study for a prediction model using ML method for VTE prediction in Chinese patients of lung cancer. One of its great strengths is in the large number of patients included in a real-world setting. The another strength of this study is that we selected five easily available variables to build the model, which has strong clinical applicability.However, we acknowledge the following limitations. First, some patient information may not be completely collected, for example, some VTE risk factors were recorded in clinical notes , which need specific natural language processing tool for information extraction, this part of data may be included in the modeling after data are well curated in future. Secondly, though some previous studies have shown that adenocarcinoma and VTE were more closely related than patients with squamous cell lung carcinoma, due to data incompleteness on the pathological types in this study, adenocarcinoma was classified into NSCLC. Thirdly, this study was a single-center, retrospective study using real-world data, which inevitably leads to bias. Another, in terms of the criteria of choosing risk factors, since acute comorbidities are hard to confirm through the timeline in this dataset, such as surgery or fracture and heart failure within 1 month, we chose chronic comorbidities for risk factors. Besides, our model still needs to be verified in multi-center populations prospectively in the future.In this retrospective, real-world, Chinese cohort study, we developed and validated a novel model using ML algorithms with minimal model input of 5 commonly used clinical variables, with an overall good performance AUROC of 0.87, AUPRC of 0.30, accuracy of 0.929, sensitivity of 0.714,and specificity of 0.936, allow identifying high-risk populations and preventing the development of VTE in patients with lung cancer.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.HLei, HLiu, YW, and NH conceived and designed the study. CL, MZ, and XL collected and assembled data and were responsible for privacy management. ZW and WZ cleaned the data, analyzed and implemented the algorithm. BL, MG, GW, and YW interpreted the data. JM and HZ were responsible for literature review. HLei, MZ, and ZW drafted the manuscript. All authors revised the manuscript for important intellectual content.MZ, ZW, CL, JM, HZ, MG, and NH was employed by company Digital Health China. 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.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.


K-Nearest Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD.Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network’s optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM),  GFR [Chronic kidney disease is a disorder that occurs when a patient’s kidney function deteriorates. As a result, their overall quality of life suffers. Chronic kidney disease affects one out of every 10 people worldwide (CKD). CKD is on the rise, and by 2040, it is expected to be the fifth leading cause of death worldwide . The numage. GFR  is the mThe GFR  is also The machine learning technique provides valid decision-making approaches for computer-assisted automatic disease identification . MachineThe ability of Artificial Neural Networks (ANN) to learn about fault tolerance, generalization, and the environment is becoming more widespread in the area of medical diagnostics.In many cases, the Neural Networks (NN) method outperforms standard machine learning techniques. The resource learning architecture can be enhanced to boost its performance even more. The neural networks ,27,28,29The main contributions of this paper are:Deep neural networks have been proposed to detect and diagnose CKD.Apply feature selection to improve efficiency and efficacy of deep neural network.The computational accuracy of the proposed model is compared with existing classification methods from the literature.Furthermore, the performance is evaluated through the various performance measures.The following is a breakdown of the paper structure: The related works on machine learning approaches in the fields of CKD are presented in Machine learning models have been shown to be effective in predicting and diagnosing serious diseases. Early detection of chronic diseases, particularly the search for new treatments for chronic kidney disease, has gotten a lot of attention from doctors and researchers in recent years. Several recent research has demonstrated that machine learning and deep learning models may be used to successfully diagnose chronic kidney disease (CKD). KNN, SVM, and Soft independent modeling of class analogy. In comparison to the other two models, the SVM model processed noise within the data set better. In this comparison, the SVM accuracy was 99%. The author of [KNN, Naive Bayes, LDA, random subspace, and tree-based classification techniques on the CRF data set from the UCI repository. The random subspace with the KNN classifier has a 94% accuracy rate, according to the researchers. The authors of another study [KNN to detect CKD. The authors developed a ranking algorithm to choose features. With a score of 99.75, the decision tree outperformed three alternative machine learning methods. The authors of [Z. Chen, X.et al. proved the reliability of multivariate models in clinical practice risk assessment for patients with CKD . The Chruthor of  developeer study  created er study . The auter study  created er study  publishethors of  presenteK-means technique. On processed data, the KNN, SVM, and Naive Bayes classification algorithms were used. Classification algorithms produced the greatest accuracy of 97.8%. Almasoud and Ward [KNN, random forest and decision tree algorithms were compared.As a baseline, the authors used naive Bayes, logistic regression, decision trees, and random forests classifiers. Within each patient, the proposed classification approach discovered severe cases. A chronic renal disease diagnosis system was proposed in  to diagnand Ward  reportedand Ward  recommenKrishnamurthy S. et al.  developeVasquez-Morales et al.  used larKNN, and feed-forward neural network to develop their models. The most accurate classification model was random forest, which had a 99.75% accuracy rate. Z. Segal et al. [J. Qin et al.  presentel et al.  presentel et al.  developePolat, H. et al.  presenteThe University of California Irvine (UCI) Repository was used to gather CKD data. There are 400 patient records in the data set, and some values are missing. It comprises 24 clinical qualities that emerge in the prognosis of chronic kidney disease, with one class attribute serving as a result of the patient’s presence of chronic renal failure being predicted. There are two types of values in the expected feature diagnostic: “ckd” and “notckd.” The data set contains 250 values of the “ckd” class (62.5%) and 150 values of the “notckd” class (37.5%). The characteristics of the UCI CKD data collection are listed in The estimation of missing values and the removal of noise such as outliers, as well as the normalization and validation of unbalanced data, were all part of the preprocessing stages. When assessing a patient, some measurements could be missing or incomplete.There are 158 completed cases in the data collection, with the remainder missing. Ignoring records is the simplest technique to deal with missing values; however, this is not practical for small data sets. The data set is examined during the data preparation process to see whether any attribute values are missing. The missing values for numerical features were estimated using the statistical technique of mean imputation. The mode technique was used to replace the missing values of nominal features.Because most machine learning algorithms only accept numeric values as input, category values must be encoded into numerical values. The binary values “0” and “1” are used to represent the characteristics of categories such as “no” and “yes”.Data transformation is the process of transforming numbers on the same scale so that one variable does not dominate the others. Otherwise, learning algorithms perceive larger values as higher and smaller values as lower, regardless of the unit of weight. Data transformations alter the values in a data set so that they can be processed further . To imprThe standardization formula is given below:w = Standardized scorex = Observed valueσ = Standard deviationOutliers are observation points that are isolated from the rest of the data. An outlier could be caused by measurement variability or signal an error in the experiment. An outlier can distort and mislead the learning process of the machine learning algorithm. It leads to longer training times, less accuracy in the model, and ultimately to poorer results. This paper uses the Interquartile Range (IQR)  based apRecursive Feature Elimination (RFE) removes features recursively, building a model based on other features . It applThe SVM constructs a separation hyperplane that splits the labeled data into classes and determines whether a new data value belongs above or below the line. There may be several hyperplanes, and the one with the largest margin between data points is chosen. However, the equation of the maximum-margin hyperplane can be writteni is the support vector, and iy is the training instance a(i) class value. The learning algorithm determines the numeric value b and i,α respectively.Here, KNN algorithm recognizes similarities between new and previous data points and categorizes fresh test points into existing related groups. The KNN method is a slow learning algorithm since it is not parametric. This means that instead of learning from the training data set, it should be secured. It uses K to categorize the data. The distance between the new location and the saved training point was determined using the Euclidean distance. K-Nearest neighbor classification based on K values.The KNN algorithm searches t training data set with minimum distance to the testing set.Decision trees are a nonparametric method of supervised learning . This isThe random forest algorithm is based on ensemble learning, improving the model’s performance, and solving complex problems by combining several classifiers. A classifier named after the algorithm that contains multiple decision trees averaged over a database subset to improve predictions. In the forecasting process, it does not rely on a single decision tree, and the random forest algorithm creates forecasts from each decision tree that predicts the conclusion based on the majority of decision votes. The usage of several trees decreases the possibility of the model overfitting. To predict the classes in the database, the algorithm includes many decision trees, some of which can predict the proper outcome while others cannot. As a result, there are two assumptions regarding the prediction’s accuracy. To forecast a more accurate outcome than an estimate, the algorithm must first include the actual value of the feature variable. Second, there must be an extremely low correlation between the forecasts for each tree. As a result, there are two requirements for high forecast accuracy. This phase applied different methods such as handling missing values, categorical data encoding, data transformation, removing outliers and extreme values, and feature selection. The CKD data set is separated into training and testing data sets after being preprocessed. Only a few features are selected using Recursive Feature Elimination out of a total of 24 features in this study. The RFE algorithm evaluates each feature’s value based on its significance, which helps to lower the method’s processing complexity. Finally, redundant and unrelated characteristics are filtered away. The learning model is then fed with the most important characteristics. There are 12 layers in the proposed model architecture: an input layer, five dense layers, five drop layers, and an output dense classifier layer. In The CNN model has several hyperparameters that need to be optimized. The optimal hyperparameters selection process is experimental; however, it is time-consuming and difficult. Adam ,53 optimAdam uses adaptive assessment to determine individual learning rates for various hyperparameter grades ranging from first to second-order gradients. Stochastic Gradient Optimization (SGD)  is less The proposed model was created using data from a variety of situations. The configuration of the system of the developing model is shown in The proposed model accuracy was calculated by making the CKD class value positive and the notCKD class value negative. The confusion matrix was utilized to evaluate the performance by using True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) . AccordiIt refers to the proportion of correct guesses to total predictions. Accuracy can be described as the ability to accurately predict the outcome of a situation.The recall calculates the proportion of accurately predicted positive observations to the total number of observations in the class, as shown in the following equation.The specificity estimates the number of well-scored negative patterns. The higher the specificity value, the more negative the classifier. It can be defined as:As stated in the equation below, this metric represents the proportion of accurately predicted positive observations to total predictive positive observations.Precision and Recall are weighted averaged in the F-measure . False pThe F-Measure values lie from 0 to 1.The findings of the proposed model are presented in this section. The CKD data sets are split into 75% training and 25% test data sets. The hyperparameter settings for the proposed model are shown in KNN, SVM, Decision tree, and Random forest. No parameter adjustments were made for these algorithms to show the improved performance of the proposed model. Therefore, the default value for a parameter was used in scikit-learn. All models are evaluated using the F1-score. The proposed model is compared with other classifier algorithms, including logistic regression, KNN, SVM, Naïve Bayes, Decision tree, logistic regression, and the proposed model is 92%, 92%, 95%, 97%, 99% and 100%, respectively. The proposed model was found to be the most accurate, with a 100% accuracy rate. Because it optimally identified positive samples as 250 samples (TP) and all 150 samples as negative samples, the suggested model appropriately classifies all positive and negative samples (TN). True Positive samples were graded 99%, 92%, 95%, 92%, and 97% by Logistic Regression, KNN, Nave Bayes, SVM, and Decision Tree, respectively, with a margin of error of 1%, 8%, 5%, 8%, and 3%, respectively. The results of all five classifiers are shown in The accuracy of The proposed model outperforms the other classifiers by scoring 100% on all measures. The F1-score, accuracy, precision, and recall of the Logistic regression were all 99%, 99%, 100%, and 98%, respectively. Then Decision Tree obtained an F1-score, Accuracy, Precision, and Recall of 97%, 97%, 95%, and 100%, respectively. The Naïve Bayes F1-score, Accuracy, Precision, and Recall values were 95%, 95%, 92%, and 100%, respectively. The F1-score, Accuracy, Precision, and Recall values of Naïve Bayes were 92%, 92%, 88%, and 98%, respectively. The Support Vector Machines classifier performed the lowest with F1-score, Accuracy, Precision, and Recall values of 92%, 92%, 87%, and 96%, respectively.KNN by M Senan et al. [n et al. , Convolun et al. , SVM by n et al. , and SAEn et al. . The proThis section of the paper presents the most important feature selected by the RFE algorithm based on their ranking. The figure shows the chosen features and their importance during the classification of CKD disease. The most critical risk factors are Hemoglobin, Serum Creatinine, Specific Gravity, Packed Cell Volume, Red Blood Cell Count, Hypertension, and Albumin, as presented in KNN, and Naïve Bayes respectively. The proposed model achieved the highest AUC score value 1.0.The bottom of the square and the ROC curve define the area of the AUC. AUC scores closer to 1 indicate good performance, whereas AUC scores closer to 0.50 indicate poor performance. K-Nearest Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The accuracy of KNN, SVM, Naïve Bayes, Decision tree, Random Forest, logistic regression is 92%, 92%, 95%, 97%, and 99%, respectively.A deep learning model for the early diagnosis of chronic disease is presented in this work. In this research, the authors looked at the Recursive Feature Elimination approach to identify which features are the most important for prediction. The most essential CKD features are packed red blood cell count, albumin, cell volume, serum creatinine, specific gravity, hemoglobin, and hypertension. Classification algorithms are fed with a set of features. Different metrics, including classification accuracy, recall, precision, and f-measure, are used to estimate the comparative analysis. The proposed deep neural model outperformed the other five classifiers (Support Vector Machine (SVM), KNN by M Senan et al. [The performance of the proposed model compared with several recent scholarly studies, such as Ant Colony-based Optimization Classifier by Elhoseny et al. , Neural n et al. , Convolun et al. , SVM by n et al. , and SAEn et al. . The exiThe limitation of the proposed model was that it had been tested on small data sets. To improve the model performance, significant volumes of increasingly sophisticated and representative CKD data will be collected in the future to detect disease severity. The clinical data to be collected from pathologist’s experts. The performance of the proposed model will be evaluated on a large clinical data set based on acid-base parameters, hyperparathyroidism, inorganic phosphorus concentration, and night urination in the future. Additionally, new features will be applied to get a broader perspective on the informative parameters related to CKD disease to test the prediction accuracy.


N = 302, Mage = 17.67, range = 17–19) completed a survey evaluating basic psychological needs , life skills , and subjective well-being . Data were analyzed by using descriptive statistics, correlation, and the Structural Equation Model (SEM). The model’s goodness of fit was df = 2.78, TLI = 0.90, CFI = 0.90, RMSEA = 0.077 , and SRMR = 0.085. The three basic psychological needs were positively related to life skills and subjective well-being. In addition, life skills had a mediation effect between the three basic psychological needs and subjective well-being. The interpretation of the results indicated that life skills development and well-being depend on basic psychological needs. Thus, coaches should encourage a PYD climate to satisfy their athletes’ psychological needs.Various theories in the field of positive youth development (PYD) through sport argue that student athletes’ satisfaction with basic psychological needs, life skills development, and well-being are closely related to each other. This study identified the structural relationship among three basic psychological needs, life skills, and subjective well-being. Korean Taekwondo student athletes ( The theory of positive youth development (PYD) through sports has contributed to changing and developing the perspective about student athletes. Sport PYD emphasizes the psychosocial skills and inner assets acquired and developed by student athletes while participating in sports. Experts of PYD pay attention to the multidimensional skills and inner assets acquired and developed by student athletes while participating in sports ,2. ThesePYD researchers have conducted many studies over the past 20 years to establish the theoretical foundation of life skills ,4,6,7. FBasic psychological needs consist of three factors: autonomy, competence, and relatedness. These three needs are directly involved in the intrinsic psychological and cognitive processes of humans and are major variables affecting actual behaviors. Autonomy means the desire to choose and act by themselves or a fundamental inclination related to an individual’s free will. Competence is the desire to prove one’s value by overcoming various challenges and demonstrating capability. Relatedness is the relief that comes from the sense of belonging and refers to the desire to belong to a social group required for achieving peace of mind ,13.Recent studies have argued that student athletes in an environment that guarantees the satisfaction of basic psychological needs are better at developing and transferring life skills ,4,7. In The ultimate goal of satisfying these basic psychological needs and developing life skills is to improve the “well-being” of student athletes. Deci and Ryan  reportedThere are not enough studies on the role of basic psychological needs despite the logical argument that it is important for developing life skills . MoreoveAs mentioned above, earlier researchers have argued that the satisfaction of the three basic psychological needs is important for developing the life skills of student athletes and their well-being ,3,4. ParMage = 17.67) after excluding data from 11 participants, which were determined unreliable. They had exercised for 5.44 years (SD = 2.35) on average and all had competed in national competitions. The study participants were 313 Taekwondo athletes between 17 and 19 years old who were registered as athletes with the Korean Olympic Committee. This study analyzed the data of 302 participants , and SRMR = 0.083, which were within suitable levels [Factors hindering the fitness of the model were removed after reviewing content while considering the regression coefficients and modification indices (MI) while conducting a confirmatory factor analysis for the basic psychological need scale. This study deleted No. 6 of autonomy, No. 1 of competence, and No. 2 of relatedness. The standardized regression coefficient (𝛽) for each item of three sub-factors ranged between 0.531 and 0.908. The model’s goodness of fit was 𝜘e levels . The Cro2/df = 1.804, CFI = 0.939, TLI = 0.922, RMSEA = 0.078 , and SRMR = 0.063, indicating a suitable level [This study used the Korean Life Skills Scale for Sport (KLSSS) , a modifle level . The Cro2/df = 2.805, CFI = 0.918, TLI = 0.908, RMSEA = 0.077 , and SRMR = 0.056. The Cronbach’s alpha of the subjective well-being scale was 0.86, 0.92, and 0.92 for life satisfaction, positive affect, and negative affect, respectively.The subjective well-being scale is composed by integrating the satisfaction with life scale (SWLS)  and the Following approval from the university’s ethics committee (IRB), participants were recruited from several high school Taekwondo teams. This study was conducted from October 2019 to May 2020. The researchers contacted team officials  to recruit study participants. The objectives and methods were explained to the officials in detail and consent to participate in the study was obtained from athletes. Researchers visited in person according to the schedule of each team. Just before conducting the survey, researchers explained the objective, survey method, and data processing procedures  of the study to the athletes once again. After that, the athletes, who voluntarily expressed their intention to participate in the study, filled out the consent form and questionnaire. The completed questionnaire was collected immediately. It was analyzed after the data cleaning process.r) was 0.8 or less, it was determined that there was no multicollinearity issue [2/df, CFI, TLI, RMSEA, and SRMR were used as indices for the model’s goodness of fit. The criterion of “very good fit” is 3 ≥ 𝜘2/df, 0.9 ≤ TLI, 0.9 ≤ CFI, 0.08 ≥ RMSEA, and 0.08 ≥ SRMR [The collected data were analyzed using SPSS (version 23) and AMOS (version 23) statistical programs. First, normality was tested to conduct structural equation modeling analysis. Skewness and kurtosis values were checked if they met the standard using descriptive statistics to examine whether normality assumptions were violated. It was determined that a variable met normality assumptions when the skewness value was 2 or less and the kurtosis value was 8 or less . Second,ty issue . Third, 8 ≥ SRMR . Fourth,8 ≥ SRMR .r = 0.619, p < 0.01), followed by the correlation between the competence (CP) factor and the leadership (LD) factor  and that between the CP factor and the goal setting (GS) factor . On the other hand, the autonomy (AU) factor and the time management (TM) factor showed the lowest correlation coefficient .The results of correlation analysis showed that three subfactors constituting basic psychological needs, five subfactors of life skills, and three subfactors of subjective well-being had significant correlations in most cases. In the correlation between basic psychological needs and life skills, the relatedness (RD) factor and the social skill (SS) factor showed the highest correlation coefficient , followed by the correlation between the CP factor and the life satisfaction (LS) factor  and that between the CP factor and the PA factor . On the other hand, negative affect (NA) showed a significant adverse correlation with the three subfactors of basic psychological needs. Especially, it had the highest negative correlation coefficient with the AU factor .In the correlation between basic psychological needs and subjective well-being, the RD factor and the positive affect (PA) factor showed the highest correlation coefficient , followed by the correlation coefficient between the GS factor and the PA factor  and that between the LD factor and the LS factor . Meanwhile, among the subfactors of life skills, GS, SS, and LD had a significant adverse correlation with NA.Lastly, in the relationship between life skills and subjective well-being, the GS factor and the LS factor showed the highest correlation coefficient . The model’s goodness of fit was 𝜘2/df = 2.78, TLI = 0.90, CFI = 0.90, RMSEA = 0.077 , and SRMR = 0.085. When model fit indices were evaluated comprehensively, the SEM was judged to be good [The results of SEM analysis showed that chi-square was 611.889, which was significant . The B1–B3 paths indicate the relationship between basic psychological needs and subjective well-being. Among the three basic psychological needs, autonomy (B1 path) and competence (B2 path) significantly influenced subjective well-being, but the effect of relatedness needs (B3 path) was not significant. The last C1 represents the relationship between life skills and subjective well-being. This path was significant with β = 0.505. The standardized regression coefficients of paths are shown in Paths A1’–A3’ paths represent the indirect path of the three basic psychological needs .Bootstrapping was performed to test whether the mediating effect of life skills was significant in the relationship between the three basic psychological needs and subjective well-being. eeds see . As a reThe purpose of this study was to (a) evaluate the structural relationship among the three basic psychological needs, life skills, and subjective well-being and (b) confirm the mediating effect of life skills in the relationship between the basic psychological needs and subjective well-being of student athletes. The results of this study showed that the three basic psychological needs  had significant relationships with life skills and subjective well-being. Moreover, the relationship between life skills and subjective well-being was also significant. Additionally, the analysis results of the mediating effect using bootstrapping revealed that life skills mediated the relationship between the three basic psychological needs and subjective well-being. These results are supported by Kendellen and Camiré  and CronBased on the results of this study, all three basic psychological needs are important for developing the life skills of athletes and promoting their subjective well-being. However, contrary to the expectation or argument of other researchers ,16 that The results of this study implied that relatedness needs would play a more important role in the development of life skills and the promotion of subjective well-being than autonomy needs. Holt, Tink, Mandigo, and Fox  argued tMoreover, many studies have proved that various types of relationships experienced by athletes are important for their psychological and emotional development . ParticuMeanwhile, it was confirmed that the relatedness needs in the study model affected subjective well-being indirectly through life skills, although they did not influence subjective well-being directly. In other words, the relationship among relatedness, life skills, and subjective well-being was presented using a full mediation model. The result can be interpreted that relatedness needs affect life satisfaction or emotional stability through the development of life skills. The indirect effect of relatedness was greater than that of autonomy or competence. This seems to be related to the measurement factor regarding life skills. Three of the five subfactors of life skills examined in this study  include the concept of social relationship or caring, and they are closely related to relatedness needs . In factLastly, competence is not only involved in the acquisition and development of life skills, but also very closely related to the generalization of life skills ,37. In gThe content of competence is also well described in the LDI theory , the theIn summary, the three basic psychological needs of high school Taekwondo athletes were important factors in developing life skills and improving subjective well-being. The results support the results of numerous previous studies that theoretically presented the relationship between basic psychological needs, life skills, and well-being ,3,4,7,15This study examined a theoretical model consisting of basic psychological needs, life skills, and subjective well-being for high school Taekwondo athletes. The results of this study showed that (a) three basic psychological needs, life skills, and life skill utilization were significantly related and (b) life skills mediated the relationship between three basic psychological needs and subjective well-being. In conclusion, this study statistically proved that athletes with a higher satisfaction level of three basic psychological needs were more advantageous in improving life skills and subjective well-being. Therefore, significant others around athletes should use a deliberate and systematic strategy to satisfy their basic psychological needs.Future researchers shall refer to the following information for the expansion of sports PYD. First, it is limited to generalize the results of this study to other sports because this study only evaluated Taekwondo athletes. Since the concept of basic psychological needs and life skills is highly dependent on the subject and environment, the generalization of the results to entire sports shall be performed very carefully. The environment of Taekwondo is very unique because it is an individual sport but athletes train in a group or live as a team at the same time. If a study evaluates only individual sports , the results of the study can be different. In fact, this study was aimed at student athletes of all sports at the design stage, but it ended up focusing on Taekwondo athletes due to data reliability and validity issues that arose in the preliminary stage. It is believed that the data reliability and validity issues were found in the initial study design stage because of the unique characteristics of each sport. Therefore, future studies shall study these properties after classifying various sports into similar groups or by characteristics. Second, although this study identified that basic psychological needs affected life skills, it is still necessary to find out whether the life skills acquired and developed in the sports environment are utilized in real life. The model developed by Hodge et al. , the keyFinally, the development and transfer of life skills need to be viewed from a long-term perspective. Changes in an individual do not occur at any one moment, but they occur when one person continuously experiences factors such as a specific event, participation in a systematically designed program, and the help or support of a significant other . Therefo