Hypoperfusion is the main cause of anastomotic leakage (AL) following colorectal surgery. The conventional method for evaluating anastomotic perfusion is to observe color change and active bleeding of the resection margin of the intestine and the pulsation of mesenteric vessels. However, the accuracy of this method is low, which may be due to insufficient observation time. A novel surgical procedure that separates the mesentery in advance at the intended transection site can delay the observation of anastomotic perfusion, and can potentially detect more anastomotic sites with insufficient vascular supply and reduce the rate of AL. This study aimed to investigate the effects of a novel surgical procedure on AL following sigmoid colon and rectal cancer surgeries. A total of 343 patients who underwent rectal and sigmoid colon cancer surgeries were included in the study. From August 2021 to June 2022, patients with sigmoid colon or rectal cancer underwent a new surgical procedure of pre-division of the mesentery (PDM) at the intended transection site (PDM group). Patients with colorectal cancer who underwent conventional surgical procedures from August 2018 to July 2021 were categorized as the non-PDM group. Symptomatic AL (SAL) within 30 days and other outcomes were retrospectively analyzed using propensity score matching and compared between the two groups. The incidences of SAL were 1.3% and 11.3% in the PDM and non-PDM groups, respectively. PDM significantly reduced the SAL rate in sigmoid colon and rectal cancer surgeries (P = 0.009). The incidence of total postoperative complications (P < 0.05) was significantly lower in the PDM group than that in the non-PDM group. There were no significant differences between the two groups for operative time (P = 0.662), intraoperative blood loss (P = 0.651), intraoperative blood transfusion (P = 0.316), and intensive care rate (P = 1). The length of postoperative hospital stay (P = 0.010) and first exhaust (P = 0.001) and defecation time (P < 0.05) were shorter in the PDM group than in the non-PDM group. PDM can effectively prevent AL, and this procedure can be safely performed in sigmoid colon and rectal cancer surgeries. Anastomotic leakage (AL) is one of the major postoperative complications of colorectal surgery, and is the main cause of increased postoperative morbidity and mortality , 2. AL nRisk factors for AL include male sex, obesity, low tumor location, large tumor diameter, advanced tumor stage, preoperative hypoproteinemia, neoadjuvant chemoradiotherapy, smoking, diabetes, poor nutritional status, and poor blood perfusion in the anastomotic area. Of these factors, anastomotic blood perfusion influences AL the most , 8, 9; tNo clinical studies have yet been conducted to confirm the effectiveness of this new surgical procedure in preventing AL. Propensity score matching (PSM) is a statistical method that reduces the imbalances in baseline data between experimental and control groups . FurtherThis study was approved by Ethics Committee of The First Affiliated Hospital of Chongqing Medical University  and all patients signed informed consents. We retrospectively analyzed 343 consecutive patients who underwent surgery for rectal and sigmoid colon cancers in the Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University from August 2018 to June 2022.We included patients with pathologically confirmed sigmoid colon or rectal cancer undergoing elective laparoscopic or robot-assisted laparoscopic surgery. The exclusion criteria were as follows : cases wBetween August 2021 and June 2022, all patients with sigmoid colon or rectal cancer underwent a new surgical procedure of pre-division of the mesentery (PDM) at the intended transection site to delay the observation of tissue perfusion at the intended transection site. This group of patients was categorized as the PDM group. Patients with colorectal cancer who underwent conventional surgical procedures from August 2018 to July 2021 were categorized as the control or non-PDM group.in vivo. After tumor specimens were resected, the proximal sigmoid colon and rectal stump were anastomosed using an end-to-end double stapler technique. Standard air leak tests were routinely performed, and the tissue rings were checked for integrity. When there was leakage of air or the anastomotic effect was not good, suturing was performed to reinforce the anastomosis. In the PDM group, the new procedure of delayed observation was performed; after ligation and dissection of the inferior mesenteric artery and dissociation of the proximal colon, the mesentery of the intended transection site with a length greater than 3 cm was separated, followed by the dissociation of the distal colon or rectum. The distal rectum was transected and the specimen was removed from the abdominal incision. Perfusion of the intended transection site of the bowel was observed prior to proximal resection. The time between the separation of the mesentery at the intended transection site and the beginning of observation was defined as delayed observation time. In the non-PDM group, conventional surgical procedures were performed; after ligation and dissection of the inferior mesenteric artery, sufficient length of colon or rectum was dissociated. Then, the distal rectum was transected, and the tissue perfusion at the planned transection level was observed immediately after the mesentery at the planned transection site and was separated . Grade B requires aggressive therapeutic intervention but does not require surgery, while grade C AL requires reoperation . Only SAThe sample size was estimated using the chi-square test with a significance level of 0.05 (double-sided) and power of 0.80. Previously published studies showed that the incidence of SAL in the control group was 13.7%. Based on previous findings, assuming a 2% incidence of SAL in the PDM group, we estimated that 65 cases need to be included in each matched group.To reduce selection bias caused by potential confounding factors, PSM was performed based on the following risk factors associated with AL: gender, age, body mass index (BMI), smoking history, physical condition according to the classification system of the American Society of Anesthesiologists, preoperative neoadjuvant therapy, tumor location, the use of transanal tube, tumor stage, diverting stoma, air leak test, and surgical approach. The nearest neighbor matching method was used for one-to-one matching, and the caliper size was set to 0.05 standard deviation of the logarithm of the estimated propensity score. Pearson’s chi square test and Mann–Whitney U test were used to compare categorical and continuous variables, respectively. Statistical analyses were performed using IBM SPSS version 26. All P values were two-sided, and P < 0.05 was considered significant.A total of 343 patients were included in this study. The baseline characteristics of the included patients are shown in Due to the heterogeneity of covariables between the PDM and non-PDM groups, PSM was performed. After matching (n =160), there were no significant differences between the PDM (n=80) and non-PDM groups (n=80) for all covariables  Table 4.The incidences of SAL  were 1.3% (1/80) and 11.3% (9/80) in the PDM and non-PDM groups (P = 0.009), respectively Table 3.Among patients with rectal cancer, the incidence of SAL was significantly lower in the PDM group (0%) than in the non-PDM group (11.1%) before matching (P = 0.009). After matching, all variables  were balanced between the two groups; there were 56 rectal cancer patients in each group. The incidences of SAL were 0% (0/56) and 10.7% (6/56) in the PDM and non-PDM groups, respectively. PDM significantly reduced the incidence of SAL (P = 0.012) and the incidence of total complications (P < 0.05) after rectal cancer surgery Table 6.Our study showed that PDM can effectively reduce the postoperative incidence of SAL following sigmoid colon and rectal cancer surgeries. In addition, compared with the control group, PDM did not increase the operative time, amount of intraoperative blood loss, or transfusion rate. Furthermore, when PDM was performed, the incidence of total postoperative complications and length of hospital stay were shortened, suggesting that PDM is a safe and feasible surgical procedure in preventing AL in sigmoid and rectal cancer surgeries.The estimated incidence of AL after colon surgery is between 6–8%, while it is as high as 7–20% for rectal surgery . AL is aTo prevent AL, several strategies have been proposed, but the effectiveness of most of them are still controversial. For example, indocyanine green (ICG) fluorescence angiography may be a potential strategy to prevent AL . SeveralThe risk factors of AL in this study were male sex, obesity, low tumor location, large tumor diameter, advanced tumor, preoperative hypoproteinemia, neoadjuvant chemoradiotherapy, smoking, diabetes, poor nutritional status, and poor blood perfusion in the anastomotic area , 8, 9. DOur novel surgical procedure has two advantages. First, no additional equipment or technical support is required; thus, our procedure does not impose any additional economic burden and is easy to promote. Second, it is simple and relatively easy to learn, without the addition of complex surgery steps. However, one drawback of our procedure is that it requires the surgeon to separate the mesentery in a predetermined cross section under a laparoscope, which may be difficult for inexperienced surgeons.There are some limitations to our study. First, this study was a single-center retrospective study. Second, the design of this study was non-random, and it was impossible to control for every deviation in the study. Therefore, PSM was used to reduce the deviations between groups. Third, grade A AL had little impact on the prognosis of patients with colorectal cancer, and no intervention was required. Contrast agent enema examination was not routinely performed; thus, our study did not evaluate the effect of PDM on grade A AL.In conclusion, our study indicated that PDM can effectively prevent AL, and can be safely performed in sigmoid colon and rectal cancer surgeries. Although the results of our study need to be further confirmed by RCTs, our study provided a reference for the development of a potential strategy for preventing AL.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This study was approved by Ethics Committee of The First Affiliated Hospital of Chongqing Medical University . The patients/participants provided their written informed consent to participate in this study.Conceptualization: Z-QW, FP, GT, and D-HZ. Data collection and analyses: FP, GT, D-HZ, and Y-HQ. Writing—original draft preparation: GT and FP. Writing—review and editing: Z-QW, FP, GT, D-HZ, and Y-HQ. Z-QW, FP, GT, D-HZ, and Y-HQ had primary responsibility for final content. All authors contributed to the article and approved the submitted version.This study was funded by Chongqing Key Diseases Research and Application Demonstration Program, No. 2019ZX003.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. 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Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months.The clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events . Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold.In total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points.Machine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery. Post-thrombotic syndrome (PTS) is a common sequela of deep vein thrombosis (DVT), which is caused by chronic venous insufficiency (CVI), secondary to prior DVT, and can affect up to 50% of patients with proximal DVT within 2 years , 2. HowePreventing PTS remains a huge challenge as symptoms of PTS change gradually during chronic progression. Many previous studies have identified predictors that may help in the risk stratification of patients with PTS. The baseline Villalta Scale score is usually identified as an independent predictor . ProximaMachine learning (ML) is a widely accepted computational technique that can overcome some of the limitations of current analytical approaches and capture high-dimensional, non-linear relationships among clinical features to make data-driven outcome predictions . ML can Clinical data of the derivation cohort included in this study were extracted from the ATTRACT study . A totalClinical data in the external validation cohort were obtained from the electronic record database of Sun Yat-sen Memorial Hospital. The inclusion criteria were as follows: patients diagnosed with lower-extremity DVT who were admitted to the hospital between 2010 and 2020, and the gold standard for diagnosis was thrombus filling defect detected by Doppler ultrasound in the deep iliofemoral or femoral popliteal veins. Other auxiliary diagnoses included clinical symptoms, D-dimer index, and relative clinical score. As this study was retrospective, the requirement for informed consent was waived under the ethical supervision of the center. The patients were followed up for 2 years, and their Villalta and venous clinical severity scores (VCSS) were calculated. The exclusion criteria were as follows: (1) patients who refused follow-up visits or forgot about their status; (2) patients who had not been followed up for 2 years and did not present with PTS events; (3) patients whose baseline data could not be found in the electronic record database; (4) The patient was diagnosed with DVT but also with small saphenous vein thrombosis, femoral-popliteal vein sclerosis, and others diagnosed using Doppler ultrasonography; and (5) Patients mainly treated with traditional Chinese medicine.5 U. Urokinase was continuously infused at a dose of 1–1.5 × 104 U/kg/d. Residual thrombi were evaluated daily using ultrasonography or venography. Thrombolysis should generally last for less than 7 days. If the patient experienced a mild, controllable bleeding event, PCDT was paused. If minor bleeding continued, the PCDT was permanently disabled. When anticoagulation was administered, an appropriate antagonist was used, if necessary. The center followed the guidelines of the American College of Chest Physicians for the diagnosis and treatment of DVT , vitamin K antagonists (VKA), and direct oral anticoagulants (DOAC). For patients with DVT, but not cancer, DOACs, such as rivaroxaban or LMWH with VKA, were preferred. For those with cancer, LMWH, as well as VKA or DOAC, was preferred. The use of retrievable inferior vena cava filters is generally recommended for patients at a high risk of pulmonary embolism (PE) . Physical pressure therapy includes the use of elastic stockings and intermittent pneumatic compression devices. The treatment plan for PCDT at our center was detailed in our previous study . The patt of DVT .In 2009, the International Society on Thrombosis and Hemostasis (ISTH) recommended the Villalta scale for PTS assessment 3–6 months following acute DVT . The VilThe VCSS score was also calculated for each patient, if possible. The VCSS score was calculated using 10 items, including pain, varicose veins, venous edema, skin pigmentation, inflammation, induration of active ulcers, number of active ulcers, active ulcer diameter, ulcer duration, and compression therapy, and scored on a scale from 0 (non-existent) to 3 (severe). The SOX-PTS score was also calculated based on the research by Rabinovich et al., which contained three items: iliofemoral DVT (1 score), BMI of ≥35 (2 scores), baseline Villalta score of ≥15 (2 scores), or baseline Villita score of 10–14 (1 score) , provoked DVT, and in-hospital diagnoses were obtained from the admission records. The clinical treatment plan was obtained from the doctor’s list. Height and weight were obtained from the nursing sheets. The DVT type and leg involved were obtained from the Doppler ultrasonography reports.Only variables that had missing value rate lower than 5% would be included in the model and filled with imputation. The missing rate of all variables is shown in Twenty-three variables were included in the structured dataset: basic demographic information, including age, sex, height, weight, and BMI; DVT-associated variables, including an extension to the iliac vein or isolated femoropopliteal, DVT leg, previous VTE, major surgery, hospitalization, plaster cast immobilization, childbirth, impatient qualifying DVT, baseline Villalta score, and complications, including hypertension, diabetes mellitus, high cholesterol, asthma, chronic obstructive pulmonary disease (COPD), angina or myocardial infarction (MI), congestive heart failure (CHF), DVT treatment type, and aspirin use. The treatment included PCDT with anticoagulation or base anticoagulation only.As there still exited imbalance in the derivation set , the synthetic minority over-sampling technique (SMOTE) was used to oversample the derivation set, which was intended to synthesize new samples and add them into the derivation cohort to ensure equality between the number of positive and negative examples. Our previous experimental results also had shown that the performance index of the models was improved after oversampling in both primary and secondary outcomes .To decrease the effect of non-normality on the model performance, the Shapiro-Wilk normality test was conducted to detect the normality of the continued variables in the derivation cohort  and none of them showed normality . In the To select a more suitable model that had a better matching degree with the data, 12 algorithms  were conducted to build models, and, finally, models with better performances in both derivation and external validation cohorts, which did not have too much overfitting, were chosen . At lastIn addition to the primary outcome, models for predicting secondary outcomes were established and validated.The relative importance of each feature in the four models was calculated and ranked to select the predictor with the greatest impact on each outcome. The feature importance was retrieved using the scikit-learn library and XGBoost package. Based on the different principle of the four model algorithms, we used different methods to calculate the feature importance. For RF, we used feature_importances_ property of RandomForestClassifier to calculate the feature importances, and the calculation method was based on impurity. For GBDT, we used feature_importances_ property of GradientBoostingClassifier, and the calculation method was based on impurity. For XGBoost, we used feature_importances_ property of XGBClassifier, and the calculation method was based on “gain,” which used the average gain across all splits of the feature. For LR, we used coef_ attribute of LogisticRegression, and the calculation method was based on regression coefficient.Moreover, to explain the interpretability of ML in more depth, permutation importance was calculated by ELI5 package of Python. Partial dependence plots (PDP) were drawn by the sklearn package of Python to show the marginal effect that each feature had on the predicted outcome of a model. Shapley additive explanations (SHAP) values were calculated by the SHAP package of Python, which used a game theoretic approach, to explain the output of ML models. The SHAP force plots and feature importance plots were also plotted.Evaluation of the model was internally validated with 10-fold cross-validation in the derivation cohort to investigate the stability of the model , and then external validation was conducted to investigate the generalization ability of the model. AUC and calibration plots were used to evaluate the discrimination and calibration. After determining the cutoff value of the prediction probability by the receiver operating characteristic (ROC) curve, calibration and risk classification results, F scores, negative predictive value, positive predictive value, sensitivity, and specificity were used to evaluate the risk stratification ability of the models. We also externally validated the SOX-PTS score in the derivation and external validation cohorts and used net reclassification improvement (NRI) and integrated discrimination improvement (IDI) to investigate the prediction ability improvement of ML models compared with SOX-PTS.Patients in the external validation cohort were divided into estimated risk deciles in accordance with the prediction probability yielded by the four models and then grouped into low-, intermediate-, and high-risk groups with thresholds reflecting clinically meaningful gradients in risk from one group to the next. The mean prediction probability and observed probability were calculated for each group.P < 0.05 was considered statistically significant. Data imputation and significance tests were conducted using R software (version 3.6.3). Data preprocessing, model development, and further evaluation and validation were conducted using Python (version 3.8.5).Continuous variables were represented by the median with interquartile range (IQR) and compared using the Kruskal–Wallis test. Categorical variables are expressed as percentages, compared with chi-square tests. A two-sided The funders of this research had no role in the study design, management, provision of study materials, data collection, data analysis, interpretation of the data, manuscript writing, preparation, review, and approval of this manuscript, or the decision to submit the manuscript for publication.P < 0.001]; 23.4 vs. 19.7% [P = 0.446]). The BMI in the derivation cohort was higher than that in the validation cohort , as were basic comorbidities and aspirin use . However, the DVT occurrence age was lower in the derivation cohort than in the external validation cohort . A higher prevalence of VTE occurrence, and basic comorbidities, as well as a higher BMI, might be associated with a higher prevalence of PTS occurrence.After filtration based on the exclusion criteria in this study, 555 patients from the ATTRACT study were finally included in the derivation cohort to build four models with ML methods comprising XGBoost, GBDT, LR, and RF. As for the external validation cohort, 428 patients were diagnosed with DVT between 2010 and 2020, 288 patients refused follow-up or forgot the details of body status, and 117 patients were finally included in the external validation cohort. The study pipeline is illustrated in The relative importance of the features in the four models was ranked, and a radar plot of the seven most important features for each model is shown in To explain the interpretability of ML in more depth and evaluate the effect of variables on outcome, permutation importance for 3 outcomes was calculated and is shown in When the models were evaluated and validated, the LR, based on gradient descent and L1 regularization, performed best in external validation (0.83 [95% CI:0.76–0.89]). In the derivation cohort, RF performed best (0.81 [95% CI:0.78–0.84]), whereas LR performed worst (0.73 [95% CI:0.70–0.76]). The ROC curves for the four models for predicting PTS are shown in The calibration plots of the four models for the three outcomes in both the derivation and external validation cohorts were also plotted, as shown in P < 0.05), as shown in For other ML performance indices, we paid more attention to the F2 score because the dataset had a degree of imbalance, and the potential cost of missed real cases was higher than that of missed cases. The results showed that all four models had a good predictive ability (F2 score:0.70–0.76 when the threshold was set to 0.3). LR performed best in predicting moderate-to-severe and severe PTS . Other performance indices  are shown in The ten divided groups based on the estimated risk deciles of the four models predicting PTS in the external validation cohort are shown in To evaluate the risk classification ability of the ML models, the XGBoost model was chosen as an example to investigate the association between risk groups and clinical features. As the derivation group was divided into low-risk, intermediate-risk, and high-risk groups based on the prediction probability of XGBoost, the prevalence of PTS increased . In addition, the related risk scores at different time points showed similar results, as shown in In this study, 555 patients with DVT were included to build models with different ML algorithms, and the models were validated in a Chinese cohort of 117 patients. The results showed that the models presented good prediction abilities for both the primary and secondary outcomes. When predicting PTS 2 years after acute DVT, LR based on gradient descent and L1 regularization had the highest AUC of 0.83 (95% CI:0.76–0.89) in external validation, whereas RF had the highest AUC of 0.81 (95% CI:0.78–0.84) in the derivation cohort. However, when considering the model performance in both the derivation and validation cohorts, the XGBoost and GBDT models had similar results and presented better stability and generalization. Compared with the SOX-PTS score, all ML models exhibited improved prediction ability, with NRI and IDI indices all significantly higher than zero. After dividing the external validation cohort into ten groups based on the estimated risk deciles of the models, three risk groups were identified. Moreover, a tendency of increase in the risk score and prevalence of PTS occurrence could be found as the risk increased, which indicated good clinical application of the models. Moreover, BMI, diabetes mellitus, baseline Villalta score, and treatment type were identified as important features using ML algorithms in this study. To the best of our knowledge, this study is the first to build models for predicting PTS using ML algorithms and confirm that ML can help improve the prediction ability.P = 0.56). This might be interpreted as the ATTRACT study, including both femoropopliteal and iliofemoral DVT, and femoropopliteal DVT showed a lower risk of developing PTS (P = 0.021) (P = 0.005)  . A recen= 0.021) . Percuta= 0.005) . With adMachine learning is currently an effective method to investigate high-dimensional and non-linear relations between features and outcomes and improves the prediction ability of the prognostic model . In thisThe chosen four ML models in this study both had their advantages and disadvantages. LR is a kind of discriminative models, which can be used in combination with regularization methods. The linear models have high interpretability compared to most classification algorithms. LR has the advantages of easy implementation and low computational cost. However, when there are a large number of features, LR performances are poor, and LR is easy to cause underfitting. It is mostly used to deal with binary classification problems, and the classes must be linearly separable. The non-linear characteristics need to be transformed before modeling. RF performs well on a lot of datasets. Firs, RF is suitable for highly dimensional features. Second, it has fast calculation speed and easy implementation. Third, the model has strong generalization ability. Fourth, RF has strong anti-interference capability and can also be used when there is a large amount of missing data. Fifth, it has a strong anti-overfitting ability. The disadvantages of RF are the following: poor performances in solving regression problems; the model is similar to the black box, which has poor interpretability; and it may not produce good classification results in small samples or low dimensional data. GBDT has good prediction performances and is suitable for low-dimensional data. It can flexibly handle various types of data and has strong robustness to outliers. However, due to the dependency between weak learners, it is difficult to carry out parallel computing. The calculation complexity will be increased when the data dimension is high. XGBoost is based on GBDT. Compared with GBDT, XGBoost has the following advantages: first, adding the complexity of tree models to the regularization term, the generalization ability is better; second, it uses Taylor expansion on the loss function to accelerate the optimization speed; and third, XGBoost supports parallel processing .The performance metrics for four ML models in predicting different outcomes were shown in Area under the curve is an important index to evaluate the discrimination of the models; however, the cut-off value of prediction probability should also be focused on because risk stratification and prevalence identification in each risk group are important for clinical decision-making. Based on the prediction results of the XGboost, three risk groups were identified. PTS occurrence reached up to 81% in the high-risk group and only 14.8% in the low-risk group. Other ML performance indices can also be calculated after the threshold is determined to reflect the effectiveness of the models. The results showed that all four models had good predictive ability (F2 score:0.70–0.76 when the threshold was set to 0.3). LR performed best in predicting moderate-severe and severe PTS (0.76 and 0.91 when the threshold was set as 0.4). The threshold may vary in different populations as the prevalence of disease may differ.P = 0.02)  , 44. Hyp = 0.02) , 46. HowThis study has several limitations. First, it had a retrospective design, and the derivation cohort in the ATTRACT study was not designed to build models in the original study. Consequently, some important factors were not included in the model because they were not included prospectively or too many values were missing. For example, some laboratory induces, such as D-dimers, were not included because >20% of the values were missing, which might affect the ability of the model if they were filled using mean values or single imputation. Moreover, previous studies have indicated that previous varicose vein surgery is a strong predictor . HoweverIn conclusion, we developed and validated models using ML algorithms in large cohorts. This study demonstrated that the ML models had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Informed patient consent was exempted, and the study was approved by the Ethics Committee of Sun Yet-sen Memorial Hospital. Additionally, the study was conducted in accordance with the ethical principles for medical research involving human subjects set out in the Declaration of Helsinki. Data associated with ATTRACT research were requested from the Biologic Specimen and Data Repository Information Coordinating Center (BIOLINCC). According to the purposes of the study, the clinical research protocol was written in advance and reviewed by the Ethics Committee of Sun Yat-sen Memorial Hospital (SYSEC-KY-KS-2020-188) and the Independent Committee of BIOLINCC (BIOLINCC ID:9456). Related baseline data and outcomes were consolidated and extracted using BIOLINCC software.TY, RS, GY, and KH: conception and design. TY and KH: administrative support. RS, GY, LL, DZ, and JX: provision of study materials or patients. RS and LL: collection and assembly of data. RS, GY, LL, SK, XW, and ZX: data analysis and interpretation. All authors manuscript writing, final approval of manuscript, agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated, and resolved.


N = 363) who participated in four waves of data collection over a year and a half of the COVID-19 pandemic. The mental health indicators were represented by general levels of depression and anxiety as well as COVID-related stress and anxiety. While there were only minor changes in depression and anxiety, the dynamic in COVID-related stress and especially anxiety was more noteworthy. Besides some exceptions, the results hold even after controlling for the socioeconomic situation. The gender differences in the mental health trends were found to be negligible. Overall, we observed no substantial deterioration in the mental health indicators across the four waves of the study.The changes in people’s mental health have become one of the hot topics during the COVID-19 pandemic. Parents have been said to be among the most vulnerable groups in terms of the imposed anti-pandemic measures. The present paper analyzes the trends in mental health indicators in a sample of Slovak parents ( Since the beginning of the COVID-19 pandemic, its impact on mental health has been studied intensively . There The objective economic situation of many families has worsened significantly as a result of the pandemic. There has been a global increase in unemployment ; note thVarious studies have confirmed an association between both objective and subjective economic status and mental health issues e.g., –19]. The. The19)]Parents’ struggles, such as an increased reliance on negative coping strategies  or mentaIn order to address these gaps, complex and especially longitudinal evidence is needed. In contrast to cross-sectional data, a longitudinal design means it is possible to study the dynamics of mental health indicators and subsequently help to understand the rate of change. The present study thus aims to analyze the changes in Slovak parents’ mental health over a year and a half of the COVID-19 pandemic, while also accounting for the effect of the objective, as well as subjective, economic situation. In particular, we will examine the trends will be examined in four mental health indicators out of which two are general (depression and anxiety), and the other two are COVID-related (COVID-related anxiety and COVID-related stress). The study focuses on a sample of parents from a representative sample of the Slovak population across four time points. The study also examines differences between mothers and fathers in these trends.A longitudinal design with four waves of online data collection  was implemented. The first three waves were intentionally carried out during a period of lockdown or very strict measures. The fourth wave took place during a period of eased  restrictive measures.https://osf.io/vjmfd/.Before the first wave of data collection, the total number of positive cases in Slovakia since the beginning of the pandemic was approximately 10,000. By October 2020, when the first wave of data collection was carried out, the number of new daily cases had grown rapidly, and at the end of October, the total number of positive cases was almost 60,000. In December 2020, during the second wave of data collection, this number exceeded 150,000 . In November and December 2021, after a relatively stable period, another increase of new positive cases emerged. During the third wave of data collection (December 2021) the total number of positive cases in Slovakia since the beginning of the pandemic had reached 840,000 (end of December). Up to March 2022, when the fourth data collection was carried out, this number had continued to grow rapidly. At the end of March, the total number of positive cases crossed 1,700,000. After that, the situation stabilized , 43. TheN = 363) is a subset of a representative sample  of Slovak inhabitants and consists of parents who, at the time of the third wave, had (or were taking care of) at least one child younger than 18 years. The participants took part in all four waves of data collection. About 52% of the participants were women and the mean age of the sample at T1 was 45.4 years (SD = 10.8). For more details on the demographic characteristics see The current sample .The subjective socioeconomic status (SES) was measured using a visual ladder . The participants evaluated how often they had been bothered by individual symptoms  during the last 2 weeks.Anxiety was measured using the Generalized Anxiety Disorder Screener-7  which aCovid-related anxiety was measured using the Covid-related anxiety stress scale , and coCovid-related stress was measured using the Covid-related stress scale . The itN = 363, alpha level of 0.05, and the usage of two-sided test, our design had 48% power to detect an effect of r = 0.10, 97% power to detect an effect of r = 0.2, and almost 100% power to detect correlations larger than 0.3. For the mental health indicators, as well as for the socioeconomic indicators, a trend line was visualized for each participant and an average trend line was plotted (without any smoothing). In order to answer the research question , three unconstrained linear growth models were run for each mental health indicator. The first of the three models did not include any of the covariates. In terms of accounting for one’s socio-economic situation, a time-varying covariate represented by one’s subjective perception of SES and the objective economic situation was included in the second and the third model, respectively. From each estimated model, the latent intercept, latent slope, their variances and intercept-slope covariance, including the SEs and p-values of these parameters were extracted. The models were estimated using the MLR estimator (Maximum Likelihood with Robust standard errors) and the missing data were imputed using FIML (Full Information Maximum Likelihood). The analysis was performed in R, using the lavaan package, version 0.6-8  at least one child under 18 years of age were selected. This item was available in the third wave of data collection. The data were screened for improbable values and careless participants as part of general data wrangling for the purposes of the research project APVV-20-0319 “Behavioural aspects of COVID-19: Mapping the COVID-related behaviours, and psychological, social, and economic consequences of the pandemic”). The reliabilities of the scales were checked using the omega total coefficient. The estimated omega total coefficients ranged from 0.84 to 0.96. We proceeded with calculations of a sum score for each of the mental health constructs at each timepoint and divided it by the number of items forming the construct. For descriptive purposes, descriptive statistics were also calculated, and the correlation matrix examined. To examine the sensitivity of our design to a range of hypothetical effect sizes, we carried out a power analysis and found that, given the sample size of on 0.6-8 .The descriptive statistics of the scales are available in In order to answer our research question , three linear growth models were run for each mental health indicator.p = 0.006). This became more visible when controlling for SES . On the other hand, a very small increase in anxiety was observed across the four time points . This increase was more pronounced when the model controlled for income . There was a substantial decrease in COVID-related anxiety observed in the model without any covariates  as well as for the model that controlled for SES . The decrease was slower for parents with a high initial level of COVID-anxiety . Conversely, a notable increase was observed in COVID-related stress . This was further amplified when a covariate was added into the model . For more detailed results, see The results of the linear growth models suggest the following patterns. There was a very small decrease observed in the level of depression , the group differences in changes in the mental health indicators over the course of the study were negligible. After accounting for SES, notable exceptions in this pattern occurred. In particular, depression in mothers decreased more sharply (slope difference = −0.10), anxiety decreased (slope difference -0.13), while the increase in COVID-related stress was less steep compared to fathers (slope difference = −0.11). After controlling for one’s household income, mothers got lower scores in depression (intercept difference = −0.17) and anxiety (intercept difference = 0.52) compared to fathers. Fathers’ scores for COVID-related anxiety (slope difference = 0.38) and COVID-related stress (slope difference = 0.69) rose compared to mothers. The observed increase was very steep, especially in the case of COVID-related stress. More detailed results are available in Although the initial evidence has suggested a severe impact of the COVID-19 pandemic on mental health in the general population . How. How66)]Valero-Moreno et al.  have fouSecondly, although stabilization was probably the most common mental health response to the pandemic, some subgroups may have also experienced worsening or improving mental health issues . As baseThirdly, the measurement of mental health indicators should be taken into account. Many studies that have found modest changes in mental health during the COVID-19 pandemic assessed mental health using general measures e.g., , 73, 74], 7473, 7rs > 0.50) between the constructs across all time points.The present findings indicate a substantially higher dynamic of COVID-related mental health constructs compared to generally oriented depression or anxiety. In particular, the findings indicate that COVID-related anxiety started to decrease significantly after the first two waves of data collection. On the contrary, COVID-related stress increased over time, with its peak being observed in the third wave of data collection (late 2021). Stressors, their intensity as well as their qualitative aspects have changed during the pandemic see , 77, and, and77, As COVID-related worries are determined by different stressors of different severity during different phases of the pandemic, COVID-related stress tends to get higher in the early phases of the pandemic or at its peak . In the We further analyzed the trends in mental health also after controlling for the subjective economic status and equivalized household income. The results indicate that a decrease in depression became more visible when the model controlled for the subjective SES. This result corresponds with the findings of Hoebel et al.  who haveIn terms of the bivariate correlations between the constructs, it can be concluded that the relationships between the economic situation and mental health issues are almost uniformly negative. Valero-Moreno et al.  have notGiven the aim of the study, gender differences can be discussed in the context of changes in depression, anxiety and stress separately. The results have confirmed that mothers tend to score higher in most measurements (models without covariates) and this corresponds to epidemiological data from 23 EU countries which all report a higher incidence of depression in women . On the The limitations of the present study can be interpreted on several levels such as limits related to the design of data collection, parental characteristics, social and personal context, and children’s characteristics. Participants were not asked about their pre-morbid status such as pre-existing mental health issues, medication or using supplements. Therefore, the evaluation of changes in people with pre-existing mental health issues [e.g., worsening of symptoms; recurrence, etc.;  is lackiThe presented findings provide several fundamental proposals that could help improve health and social policies. We highly recommend paying more attention to parents’ mental health and especially to those who have a high baseline level of mental health issues. Moreover, parents’ mental health is mirrored in the well-being of their children. In terms of social policies, it seems to be of importance to pay extra attention to parents’ feelings and worries as their level of stress increases once they get concerned about their children’s safety, work-life balance and household income , 36, 84.Throughout the COVID-19 pandemic, societies and individuals have had to deal with a long-term crisis and intensive demands on safety and quality of life. This period of time has been accompanied by financial insecurity, job loss, or income reductions that could have occurred as a consequence of the pandemic. As parents’ mental health can have a wider impact, especially on their children , the curhttps://osf.io/epmt7/.Data and the analytic script can be found at The studies involving human participants were reviewed and approved by Ethics Committee at the Faculty of Arts, University of Presov. The patients/participants provided their written informed consent to participate in this study.LV: data curation, conceptualization, investigation, writing – original draft, and writing – review and editing. GM: conceptualization, investigation, writing – original draft, and writing – review and editing. DF: conceptualization, investigation, supervision, writing – original draft, and writing – review and editing. ML: investigation, writing – original draft, and writing – review and editing. JB: conceptualization, writing – original draft, and writing – review and editing. MŠ: writing – original draft and writing – review and editing. PB: data curation, conceptualization, and writing – review and editing. IR: formal analysis, methodology, and writing – review and editing. MA: conceptualization, investigation, supervision, formal analysis, methodology, and writing – review and editing. All authors contributed to the article and approved the submitted version.


Malnutrition, dynapenia, and sarcopenia are prevalent conditions among patients with maintenance hemodialysis (MHD). They are related to numerous adverse health outcomes. The aim of this study was to compare the effect of three nutritional screening tools on predicting the risk of dynapenia and sarcopenia in patients with MHD.From July 2020 to April 2021, a total of 849 patients with MHD were enrolled at seven different healthcare facilities in Shanghai, China in this multi-center cross-sectional study. Geriatric nutritional risk index (GNRI), malnutrition inflammation score (MIS), and creatinine (Cr) index were used for nutritional assessment. The cutoff values of muscle mass and strength to define dynapenia, pre-sarcopenia, and sarcopenia were based on the consensus by the Asia Working Group of Sarcopenia in 2019.Among 849, almost 60% were malnourished with the majority suffering from dynapenia (27.7%), followed by sarcopenia (22.7%), and pre-sarcopenia (6.2%).The area under the receiver–operating characteristic curve for GNRI was 0.722 [95% confidence interval (CI) = 0.684–0.760] and 0.723 (95% CI = 0.663–0.783) in predicting sarcopenia and pre-sarcopenia. The GNRI , MIS , and the Cr index  were all significantly associated with the risk of sarcopenia. More importantly, the sarcopenia predictability of the GNRI appears greater than the MIS and Cr index, while MIS was similar to the Cr index. Similarly, the superiority of GNRI prediction was also found in pre-sarcopenia, but not in dynapenia.All the three nutritional screening tools were significantly associated with an increased risk of sarcopenia. The sarcopenia predictability of the GNRI was greater than the MIS and Cr index. Sarcopenia is a clinical condition characterized by an aged-related decrease in skeletal muscle mass and low muscle strength and/or physical performance . Muscle The patients with MHD are related to a range of causes of muscle mass and function, such as decreased physical activity and nutrition intake, hormone dysfunction, and chronic inflammation . Among tIn addition, few studies have compared the usefulness and predictive ability of these three nutritional indexes  regarding dynapenia, pre-sarcopenia and sarcopenia in this population. It is clinically important to directly compare these three indexes in the hemodialysis population comprising a relatively large number of patients with MHD. Thus, the aim of this study was to evaluate the ability of the three nutritional screening tools  to identify dynapenia, pre-sarcopenia, and sarcopenia in patients with MHD.n = 11); (2) unable to complete the handgrip strength test because of hand disability (n = 2); (3) unable to walk due to disability or complete wheelchair dependence and unable to perform gait speed tests (n = 6); and 4) absence of results of relevant nutritional blood tests (n = 12). The remaining 849 patients were included in the final analytic sample  age ≥ 18 years, (2) receiving maintenance hemodialysis for at least 3 months, (3) able to provide informed consent, while the exclusion criteria included (1) missing data on diagnostic criteria for sarcopenia, (2) did not complete date on the nutrition assessments, (3) clinical instability , and (4) unable to communicate with the researchers or refusal to participate in this study. We excluded patients who: (1) refused to undergo body composition examinations .All the patients were invited to a face-to-face interview to answer a standardized questionnaire. Covariates included socio-demographic characteristics and chronic disease status. Demographic characteristics comprised of age, sex, post-dialysis weight, dialysis vintage, and education level. The International Physical Activity Questionnaire (IPAQ)  was used2 in man and women, respectively. The dynamometer  on the non-fistula hand before a dialysis session to evaluate the muscle strength.A direct segmental multi-frequency bioelectrical impedance analysis  was used to measure muscle mass . Low musFor patients with an indwelling dialysis catheter, we used the dominant hand to test muscle strength. Low muscle strength was defined as handgrip strength < 28 or < 18 kg in males and females. Usual gait speed was measured to assess poor-physical performance, which was less than 1.0 m/s in both males and females. All the patients were measured for these tests using the same measurement by a physical therapist at each facility on dialysis day before dialysis treatment.Dynapenia was defined as patients with decreased muscle strength and normal muscle mass . A low SThe Malnutrition–Inflammation Score (MIS) is a comprehensive scoring system that is associated with expected hospitalization rates, mortality rates, and nutritional measures of patients with MHD . The 10 In the several previous studies, Geriatric Nutritional Risk Index (GNRI) has been shown to be an important predictor of morbidity and mortality and associated with a variety of nutrition-related markers , 20. The2.Ideal body weight was defined as having a body mass index (BMI) value of 22 kg/mCreatinine (Cr) index was calculated based on known sex differences as follows , 22:t-test (numeric variables) or by chi-square test  were used to compared patients with and without sarcopenia. Numeric variables with a normal distribution are expressed as the mean ± SD, while with a non-normal distribution are expressed as the median, with the 25–75% interquartile ranges given in parentheses. Categorical variables used an absolute number and proportions. The receiver operating characteristic curve (ROC) was a graph of sensitivity plotted against (1–specificity) overall possible diagnostic cut points. The cutoff points of dynapenia, pre-sarcopenia and sarcopenia were determined by the maximal Youden's index, which was calculated as (sensitivity + specificity −1) and the greatest combination of sensitivity and specificity. Logistic regression analyses were used to assess the relationships between three nutritional screening tools  and dynapenia, pre-sarcopenia, or sarcopenia. Adjusted model included age, sex, Kt/V, IPAQ, CCI, Cr, PTH, and phosphorus. We further used Harrell's C-statistics to confirm which nutritional markers were best for identifying dynapenia, pre-sarcopenia, and sarcopenia. P < 0.05 was considered statistically significant. IBM SPSS Statistics v26.0  was used to perform all the statistical analyses.Independent P < 0.05, Among 849 participants , and there were 235 (27.7%) patients with dynapenia, followed by 193 (22.7%) patients with sarcopenia, and 53 (6.2%) with pre-sarcopenia, respectively. The ROC curves were created to quantify sensitivity, specificity, areas under the ROC curves (AUC), and optimal cutoff points of three nutritional screening tools. The relationships between the three nutritional screening tools and dynapenia, pre-sarcopenia, and sarcopenia are shown in P = 0.001; GNRI vs. Cr index: P = 0.001), and there was no significant difference between the MIS and the Cr index in the values of the Harrell's C statistic for sarcopenia  , as wellConsistently, previous studies found that malnutrition was significantly associated with sarcopenia in patients with MHD . With reIn our study, the cut-off value for estimating GNRI for sarcopenia was 103. In a previous study of Japanese dialysis patients, the cut-off value for GNRI was 90 when the outcome was mortality . The cutThis study has important implications for clinical practice and future research. First, it highlights that screening poor nutrition status is usually highly predictive of sarcopenia in patients with MHD. Second, it is strongly recommended to maintain sufficient calorie and protein intake especially physical therapy to avoid or reverse sarcopenia . HoweverSome limitations should be considered in our study. First, it is not possible to elucidate clear causal associations between three nutritional screening tools and sarcopenia in the patients with MHD because this study is a cross-sectional design. Second, the prevalence of malnutrition in this population is relatively low and few patients had severe malnutrition. Future studies with longitudinal designs are required to confirm the causal association between different nutritional screening tools and sarcopenia.In conclusion, MIS, GNRI, and Cr indexes were almost equally and significantly associated with an increased risk of dynapenia, pre-sarcopenia, and sarcopenia. The sarcopenia predictability of the GNRI was greater than the Cr index and MIS, while the Cr index similar to MIS. Our findings provide further evidence for the selection of the nutritional screening tool to predict sarcopenia in patients with MHD. If further confirmed, GNRI as a simple tool could be used to identify patients with MHD at high risk of sarcopenia. Multiple interventions targeting these high-risk patients and implementing early intensification can help reduce their risk of sarcopenia and other clinical complications in the future.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences. The patients/participants provided their written informed consent to participate in this study.XC, PH, and QG: study concept and design. PS, YZ, HZ, CY, JN, WD, and JZ: acquisition, analysis, and interpretation of data. XC and PH: drafting of the work. XZ, LZ, HQ, SZ, and QG: critical revision of the manuscript. All the authors contributed to the article and approved the submitted version.This work was supported by the funding of the National Natural Science Foundation of China (82172552), Shanghai Sailing Program (22YF1417900), the Clinical Trial Incubation Program From Tongji Hospital of Tongji University [ITJ(ZD)1808], and the Minhang District Medical Characteristic Specialty Construction Project (2020MWTZA01).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.


KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score . Accordingly, we constructed two drug–gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (The online version contains supplementary material available at 10.1186/s12920-023-01444-8. As genereatment , Kim et itioning , and Gre methods . In such methods , 6, wher methods . Studyin methods , 8.Ideally, the study of drug-related genes in both categories should extend to all human genes without any limitations. However, conventional studies have been limited to specific genes and specific drugs related to only a few diseases, and they have focused on only drug targets or drug signatures. Because such studies have investigated specific genes and drugs, the characteristics of all human genes in living cells are not typically taken into account. Additionally, for these reasons, conventional drug development studies may show effects on the phenotype of interest, and the presence of treated subjects is typically perceived as a cause of bias in genome-wide association studies . TherefoIn general, drugs affect the activity of proteins that correspond to target genes. During drug treatment, biological networks are disturbed and the expression of many other genes is significantly changed by unexpected responses to the drug. Thus, the pharmaceutical and adverse effects of the drug occur through complex relationships among drug targets and drug signatures . ChangesKDTN) and microarray signature information for drug-sensitive score (KDSN). KDTN represents the degree to which the protein corresponding to the gene binds to a large number of drugs, whereas KDSN represents the degree of the gene expression response following stimulation by drugs. Overall, we explored the network-based genome-wide landscape by comparing the cellular and functional characteristics of drug targets and drug signatures using the two variables KDTN and KDSN.To elucidate genome-wide inter-relationships between drug-target genes and signatures, we selected genes corresponding to targets and signatures for drugs that have both protein binding information for drug-target score  and 435 edges. As shown in Fig. DSG, DTSG, and DTG sets. Through GO analysis, the cellular components, biological processes, and molecular functions associated with each gene set were investigated. In the DTG set, 257 genes were associated with 13 cellular components, 28 biological processes, 20 molecular functions, and 11 KEGG pathways . In the DSG set, 8,770 genes were associated with 41 cellular components, 63 biological processes, 21 molecular functions, and 8 KEGG pathways . In the DTSG set, 463 genes were associated with 24 cellular components, 95 biological processes, 35 molecular functions, and 28 KEGG pathways .GO/KEGG pathway  analysisGO analysis revealed that most proteins synthesized by drug-sensitive genes were located in inner cellular zones such as the nuclear chromosome, nuclear pore, and nucleosome rather than in outer cellar zones such as the cell wall. The proteins synthesized by drug-sensitive genes were shown to be involved in gene transcription, gene expression regulation, and DNA replication, and to function in DNA, RNA, and protein binding. In contrast, GO analysis showed that most proteins synthesized by drug-target genes were located in outer cellular zones and played roles, for example, in receptor complexes, voltage-gated channel complexes, synapses, and cell junctions. Most proteins synthesized by drug-target genes were found to be involved in the catabolic process of cGMP and cAMP and in transmission and transport processes; they played roles in ion channels and enzyme activity.As shown in Fig. In gene set analysis, it is important not only to characterize the gene set but also to identify the number and type of TFs as this can help to improve understanding of gene regulatory networks. Thus, we examined whether there were differences in the number of TFs involved in each gene set Fig. b. We useKDT and KDS for the DTG and DSG sets was examined by applying a peeling algorithm. Each network that included > 50 genes was analyzed according to m-core. m-core of network is defined as a maximal connected subgraph of network in which all vertices have a degree of at least m [m-core. In cellular component analysis, the core genes of each DSN and DTN showed exclusive distributions. Proteins synthesized by core genes of the DSN were located in the cytosol, cytoplasm, nuclear chromosome, and nucleosome. Conversely, proteins synthesized by the core genes of the DTN were located in the synapses, dendrites, plasma membrane, and axon terminus. In molecular function analysis, the core genes of each DTN and DSN were also exclusively distributed. Proteins synthesized by the core genes of the DSN functioned during cell–cell adhesion and in protein heterodimerization activity by binding proteins and cadherin. The proteins synthesized by the core genes of the DTN functioned in ion binding, hormone binding, chemical receptor activity, and enzyme activity functions  the distribution between KDTN and KDSN, (2) GO and KEGG pathway analysis, (3) the number and type of TFs, and (4) GO analysis with m-coreDSN and m-coreDTN according to KDSN and KDTN.Here, we investigated the relationships between two major gene groups, i.e., target and signature genes, according to various perspectives related to drug discovery and development. Our purpose was not only to investigate the relationship between DTG and DSG sets but also to demonstrate the genome-wide landscape of drug–gene multidimensional networks based on relationships between DTNs and DSNs. We classified genes into three sets based on their operational roles such as their physical binding and/or response to drugs. First, we constructed DTNs and DSNs from experimental data such as drug-target and drug-signature data. Subnetworks were constructed using experimental data from drug targets and drug signatures. In a multidimensional network model constructed by merging each subnetwork, the KDTN, KDSN, and subnetwork visualization showed that the relationship between the DTG and DSG sets was exclusive. Each degree of KDTN and KDSN showed a power-law distribution and their relationship was reciprocal. This is indirect evidence that the response of cells to drugs is structured and organized systematically [Sorting gene processes using D1 and D3 showed that drug targets are not usually affected by the drug as reported in a previous conventional study . Therefoatically , 18.The relationships of GO terms  and KEGG pathways between the DTG, DSG, and DTSG sets was also reciprocal. This shows that the DTG and DSG sets have distinct functional differences in cells. Thus, studies to investigate target genes, not signature genes, should be conducted according to aspects of GO and KEGG pathways, as shown in the current study.TF analysis of each gene set showed that the average binding frequency of TFs involved in the DSG set was six times per gene. The TFs involved in each gene set were also exclusive and different. Thus, genes acting as drug signatures seem to be regulated with binding frequencies three-fold greater than those of genes acting as drug targets. Consequently, genes regulated with 1–3 TFs would be good candidates for drug-target genes.m-core of each network showed that the functional and spatial characteristics of the target gene core and the signature gene core differ. Mutually exclusive characteristics were also exhibited.GO analysis using the In conclusion, the expression of target genes was barely affected by drug treatments. Therefore, the pharmaceutical effect of drugs was due to the DSGs for which expression levels were significantly changed by drug treatment rather than the direct action of DTGs. These complex drug–gene relationships can produce drug side effects as well as therapeutic effects. This study provides a potential new approach to discovering drugs. However, further studies are needed to identify the therapeutic effects and adverse drug reactions associated with the relationship between the DTN and DSN.http://dsigdb.tanlab.org/DSigDBv1.0/) [The drug and genome database DSigDB DBv1.0/)  is an op.0/  is aFDA-approved drugs for which both protein binding data and microarray experiment data were available were used from the DSigDB database. Following these principles, the D1 and D3 datasets were used and matched to their drug and gene ID terms.https://pubchem.ncbi.nlm.nih.gov/) [https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html) [KDTN and KDSN, based on the relationship between drugs and genes as follows:KDTN and KDSN was from 1 to 186 and from 1 to 104, respectively, and the number of genes with KDTN ≥ 1 and KDSN ≥ 1 was 720 and 9,233, respectively. Among these genes, 463 simultaneously had both KDTN ≥ 1 and KDSN ≥ 1. From the relationship between the KDTN and KDSN of genes, the genes were divided into three groups as shown in Fig. In the process of matching terms from each domain, the Pubchem compound term (ih.gov/)  and Entrfs.html)  were usementclass2pt{minimFigure https://david.ncifcrf.gov/) [KDTN of D1, KDSN of D3, and KDTN and KDSN of DTSG were compared using the ggplot package in R. We then constructed a drug-target bipartite network for the drugs and genes involved in the DTSG set using Cytoscape. For network visualization, 19 drugs and 170 genes were used; these 19 drugs represented about 5% of the 371 drugs included in the DTSG set. Furthermore, network analysis was conducted using the concept of m-core decomposition to analyze the central function according to KDTN and KDSN. A “peeling algorithm” aims to characterize a network hub and elucidate the relationships between nodes based on network connectivity [m-core [m-coreDSN and m-coreDTN are defined as the maximal connected subgraph of the DSN and DTN, respectively, in which all genes have a degree of KDSN and KDTN greater than the m value.Gene enrichment analysis and network analysis were conducted in three groups of genes  using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses via DAVID (rf.gov/) . The topectivity . In a mu [m-core . SpecifiAdditional file 1. Supplementary figure 1. Molecular function and biological process analysis of the core genes from each gene set.Additional file 2. Supplementary figure 2. Whole drug-gene network of DTSG set.


Augmented and virtual reality  are becoming promising tools in neurosurgery. AR and VR can reduce challenges associated with conventional approaches via the simulation and mimicry of specific environments of choice for surgeons. Awake craniotomy (AC) enables the resection of lesions from eloquent brain areas while monitoring higher cortical and subcortical functions. Evidence suggests that both surgeons and patients benefit from the various applications of AR and VR in AC. This paper investigates the application of AR and VR in AC and assesses its prospective utility in neurosurgery. A systematic review of the literature was performed using PubMed, Scopus, and Web of Science databases in accordance with the PRISMA guidelines. Our search results yielded 220 articles. A total of six articles consisting of 118 patients have been included in this review. VR was used in four papers, and the other two used AR. Tumour was the most common pathology in 108 patients, followed by vascular lesions in eight patients. VR was used for intraoperative mapping of language, vision, and social cognition, while AR was incorporated in preoperative training of white matter dissection and intraoperative visualisation and navigation. Overall, patients and surgeons were satisfied with the applications of AR and VR in their cases. AR and VR can be safely incorporated during AC to supplement, augment, or even replace conventional approaches in neurosurgery. Future investigations are required to assess the feasibility of AR and VR in various phases of AC.The online version contains supplementary material available at 10.1007/s10143-022-01929-7. Neurosurgery has always been at the forefront of incorporating technological advances to better surgical management, with the incorporation of augmented reality (AR) and virtual reality (VR) in recent years , 59, 82.Computer-generated realistic images, sounds, and other sensations in VR immerse users in artificial and synthetic environments that simulate a desired situation , 40, 65.Incorporating relevant information into the surgical field has been proposed for several years , 37. SinVR can be used in the simulation for the training of surgeons, improving understanding of complex anatomical structures, and expanding clinical assessments , 65, 82.A difference between the two modalities is that in an AR setup, computer-generated 2-D and 3-D images are superimposed into the vision of the real world , 62, wheWe conducted our systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  to identWe considered articles eligible for our systematic review if they met the following conditions: (1) original articles, (2) published in English only, (3) used at least one aspect of AR and/or VR in AC, and (4) exclusively involving human subjects. The exclusion criterion was defined as (1) studies which investigated neurosurgical interventions other than AC, for example, minimally invasive procedures such as burr holes in deep brain stimulation , 56.Extracted data are presented in Tables Details of our search results are shown in Fig. The total sample size was 118, with the largest sample size of 55 patients reported by Roethe et al. , and then = 108, 91.53%), followed by vascular lesions  . Three studies reported preoperative symptoms in their patients, with seizure being the most common symptom recorded in 26 patients (22.03%) [The majority of pathologies operated on by AC were tumours (%) Table . The pat22.03%) , 61. Fin2.03% , 6(22.03%) , social Applications of AR and VR were specified with four papers studying VR , 18, 61 Two studies reported the mean surgery time, which were recorded as 4 h and 12 min  , and 4 hFifteen patients in three studies had previous experience using VR , 9, 18. While brain mapping during AC is mainly focused on language and motor areas , 95, VR OK, thumbs up, and clap. Bernand and colleagues concluded their study with the finding that VR could imitate complex social interactions in patients undergoing AC. The advantage of using VR to map social cognition is a fast and definite response by patients compared to other mapping approaches, such as story movies, comic strips, or interactive games, which are not compatible with a fast duration of direct electrical stimulation within a few seconds [Bernard et al.  used VR  seconds . FurtherDO-80 language denomination test, where patients are asked to name 80 objects presented to them, has been extensively used for mapping language areas , 91, andCasanova et al.  used theVR sickness is accompanied by experiences such as nausea and headache , and mayAR can be used for neurosurgical training. Surgical treatment of eloquent gliomas requires sufficient knowledge of the white matter to preserve these structures during tumour resection , 38, 80.P < 0.001). Navigation pointer was mainly used in the AR group for verifying positions during early phases of the surgery and for estimating the brain shift during advanced phases of the surgery. Head-up or head-mounted displays of AR can provide continuous integrated display, enabling pointer-less navigation. Roethe and colleagues also demonstrated that AR is most suited for deep-seated lesions (> 1 cm from the cortex), particularly during targeting small structures, defining the tumour borders, and identification of eloquent areas [Roethe et al.  investignt areas .P < 0.001), the visual accuracy of the overlay , and visual comprehensibility , in conventional navigation, due to perhaps familiarity with conventional navigation compared to AR [P = 0.060) and relevance of visualisation , between the two groups. Furthermore, important anatomic structures were blocked completely or partially and were not visible in 43.6% of cases using AR. Despite such challenges, 66.7% of surgeons found AR helpful for their surgical cases, and 76.9% of those cases were categorised as deep-seated lesions [Neurosurgeons rated higher the spatial understanding of information , head-mounted display (i.e. devices which are worn on the head or as part of a helmet), monitor- and tablet-based visualisation, and image insertion into the surgical field, have been investigated , 81, 93.AR in neurosurgery involves an overlay of the necessary information, such as CT/MRI and functional information, augmented onto the surgical field . TherebyThere are some challenges associated with AR and VR use in neurosurgery, including registration errors, depth perception errors, temporal asynchrony in visual and tactile modalities, and blocking of visual fields .The quality of AR visualisation is significantly dependent on the quality of the CT and MRI data as well as successful image fusion , 75. ResFade-in display of surgical information and pointer-free navigation may pose some challenges, such as completely or partially blocking the view of the surgical field and impairing depth assessment. One study demonstrated that most otolaryngology expert participants preferred the display of information in the periphery over AR visualisation in the focus level due to the distraction effect and visual occlusion . Other rFurthermore, mapping more complex cognitive functions, such as emotions in AC, ideally requires preoperative screening to establish the baseline characteristics of the patient as well as postoperative monitoring and rehabilitation . It has This systematic review is subject to some limitations. AR and VR in AC are relatively novel concepts reflected in the limited number of publications. Our review was limited to papers published in English, and there was heterogeneity in the articles reviewed. The patient numbers were low, and the number of participants in each study and data collection period varied widely. AR and VR technologies are not widely used in all neurosurgical settings, especially in countries with resource restraints. This was reflected in the fact that all studies included in this review were from high-income countries. Furthermore, participants in included studies were patients and surgeons, and the studies reviewed had different inclusion and exclusion criteria. No study investigated both AR and VR in the same group of participants. Studies focused on different phases of AC, namely preoperative and intraoperative, and no study was done at the postoperative phase of AC. Therefore, future studies are required to evaluate AR and VR at all phases of AC. Despite such limitations, the current review can be a useful addition to understanding AR and VR in neurosurgical procedures and prompt further research.The technologies concerning AR and VR are rapidly advancing in neurosurgery, and they can be used at different phases of AC by patients and surgeons. As the technological capability of AR and VR advances, neurosurgeons are finding more applications in training and patient interaction, within and out of the operating theatre. AR and VR have been tested against existing protocols and have been shown to generate equal or better outcomes in some studies. While the benefits of AR and VR certainly outweigh their risks, multiple factors, such as case selection, technology availability, visualisation, and technical limitations, should be considered when using AR and VR in the neurosurgical operating theatre. Results from studies included in this review can only be indicative of certain domains in AR and VR, and further technical improvements are required to eliminate existing problems with AR and VR. Development of future AR and VR environments can be customised for patients and surgeons to assess specific networks and domains that they desire to be preserved. In addition, more training is required to ensure competencies are achieved among neurosurgeons and trainees. Future prospective multi-centre studies with long-term follow-up and larger sample sizes are required to assess patient outcomes in AR and VR compared to conventional approaches.Supplementary file1 (DOCX 30 KB)Supplementary file2 (DOCX 15 KB)Supplementary file3 (DOCX 17 KB)Below is the link to the electronic supplementary material.