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Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function

  • Jianhua Li , Futao Zhang , Xinyan Lan , Feifei Li , Chunrui Tan and Wangkai Cao EMAIL logo
Published/Copyright: May 23, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Graphical abstract

Abstract

The main focus of this study was to compare the predictive value of coagulation, fibrinolysis, thromboelastography, stress response, and immune function in predicting the incidence of deep venous thrombosis (DVT) in lung cancer (LC) patients undergoing thoracoscopic LC resection vs thoracotomy LC resection. To do that, a prospective, single-center, case-control study involving 460 LC patients was conducted. The risk indicators affecting patients with DVT after LC resection in the testing cohort were determined using logistic regression and receiver operator characteristic (ROC) analyses. One validation cohort was used to assess the risk prediction models. DVT incidence was higher in the thoracoscopic group (18.7%) than in the thoracotomy group (11.2%) in the testing cohort (χ 2 = 4.116, P = 0.042). The final model to predict the incidence of DVT after thoracoscopic LC excision (1 day after surgery) was as follows: Logit(P) = 9.378 – 0.061(R-value) – 0.109(K value) + 0.374(α angle) + 0.403(MA) + 0.298(FIB) + 0.406(D-D) + 0.190(MDA) − 0.097(CD4+/CD8+). For thoracotomy LC resection, the final model (3 days after operation) was: Logit(P) = –2.463 − 0.026(R-value) − 0.143(K value) + 0.402(α angle) + 0.198(D-D) + 0.237(MDA) + 0.409(SOD). In the validation cohort, this risk prediction model continued to demonstrate good predictive performance. As a result, the predictive accuracy of postoperative DVT in patients who underwent thoracoscopic LC resection and thoracotomy LC resection was improved by risk prediction models.

Keywords: lung cancer resection; machine learning; prospective study; venous thrombosis; lung cancer

1 Introduction

Lung cancer (LC) is one of the most rapidly growing malignant tumors and poses the highest threat to human life and health in terms of morbidity and mortality [1]. The reported survival rate for patients diagnosed with LC at an early stage is over 90%; however, this rate decreases to less than 20% for those diagnosed at a later stage, which is directly correlated with tumor metastasis [2]. Patients with LC must continue to adhere to the treatment principles of early treatment to achieve the greatest clinical benefit. In the current standard LC treatment methodologies, surgical treatment can be divided into two categories based on the various surgical procedures, namely, thoracotomy LC resection and thoracoscopic LC resection.

Traditional thoracotomy LC resection has several benefits, including a distinct surgical field, a simple operation, an accurate resection range, and a generally low postoperative recurrence rate [3]. However, it has significant drawbacks, such as significant trauma, significant postoperative bleeding, and a strong postoperative stress response [3]. These factors will significantly hinder the recovery of LC patients following surgery. Patients who have undergone thoracotomy LC resection are susceptible to a variety of postoperative complications, such as incision infection and poor surgical incision healing, which further prolong the hospital stay [4]. Compared to thoracotomy LC resection, thoracoscopic surgery offers numerous benefits, including a smaller incision, less trauma, and less blood loss [5]. In addition, the gastrointestinal function of patients undergoing thoracoscopic operation recovers rapidly, facilitating the rapid reconstruction of enteral nutrition [5]. More significantly, thoracoscopic surgery can help to strengthen the body’s immune function and encourage patients to endure systemic chemotherapy or radiotherapy, resulting in greater clinical benefits [6].

With the widespread implementation of thoracoscopic operation and the accumulation of prognostic data, the closely related complications of thoracoscopic surgery have attracted people’s attention and are becoming an area of extensive investigation. Venous thromboembolism, which includes deep venous thrombosis (DVT) and pulmonary embolism, is a frequent postoperative complication associated with significant morbidity, mortality, and utilization of resources [7,8]. DVT is a disorder of venous return produced by abnormal clotting of blood in the deep veins of the lower extremities. According to studies, the incidence of DVT ranges from 10 to 40% [9,10]. The risk of DVT has been underestimated, although postoperative DVT is one of the more frequent complications in the perioperative phase of surgery. The low incidence of postoperative DVT observed in patients having thoracic surgery may be because perioperative or postoperative DVT can be asymptomatic [11]. The elevated disability and mortality rates associated with DVT necessitate serious consideration. This highlights the significance of both identifying at-risk patients and identifying those who have already acquired DVT. The results of a study comparing postoperative DVT incidence between thoracoscopic and thoracotomy LC resections are not yet known. To easily estimate DVT, we conducted a single-center prospective case-control study in this work to develop novel logistic regression models of thoracoscopic LC resection and thoracotomy LC resection based on the coagulation, fibrinolysis, thromboelastography, stress response, and immune function laboratory indicators. These models were then validated in one external validation cohort.

2 Materials and methods

2.1 General information on LC patients included in this study

From June 2017 to February 2019, a total of 602 LC patients diagnosed at the Chengyang District People’s Hospital were included in the study. Patients who met the inclusion and exclusion criteria of the study were evaluated for eligibility and then regularly contacted via telephone for follow-up. After excluding those who were lost to follow-up, 460 LC patients were enrolled in the study.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration, and has been approved by Chengyang District People’s Hospital’s ethics committees (as revised in 2013).

2.2 Inclusion and exclusion criteria

The inclusion criteria for the patients were as follows: (1) The patient was over the age of 18. (2) Pathological examination confirmed the patient’s diagnosis of LC. The pathology of the gross specimen of the patient was also diagnosed as LC. (3) The patient has no obvious surgical contraindications. (4) Patients can undergo radical LC resection. (5) For a preliminary evaluation of the patient’s condition, imaging examinations such as enhanced chest CT were performed prior to the operation. (6) An informed consent document for the procedure was signed by the patient and his family.

Patients were excluded based on the following criteria: (1) Before the operation, the patient got preoperative neoadjuvant chemotherapy, molecular targeted therapy, and other related treatments. (2) Before the surgery, the patient was examined and discovered to have organ malignancies other than LC. (3) Before undergoing an operation, the patient was examined and it was discovered that LC had metastasized. (4) During the operation, it was discovered that the patient’s tumor had widespread metastasis or implant metastasis in the abdominal cavity. (5) Thoracoscopic LC resection was converted to thoracotomy. (6) The patient had previously undergone resection for LC, and he was diagnosed with residual LC at this time. (7) The preoperative anesthesia risk score was ≥3 grade. (8) Prior to an operation, the patient had a history of orally administered anticoagulants. (9) Contraindications for thoracoscopic operation include patients with abdominal wall hernia, severe cardiopulmonary disease before operation and cardiopulmonary function that cannot improve in a short time, coagulation dysfunction before operation, and a history of related diseases. (10) The patient or family members did not sign the preoperative informed consent.

2.3 Surgical procedure and follow-up

The thoracoscopic and traditional thoracotomy LC resections were performed as previously described [12]. All patients were followed up by telephone for 30 days after operation.

2.4 Observation and detection indicators

Patient demographics were recorded, including age, gender, height, weight, preoperative problems, pathological findings, and a history of any preexisting conditions such as hypertension, coronary heart disease, diabetes, and others. A patient’s anesthetic type, duration, operation time, intraoperative blood loss, and length of hospital stay following surgery were also recorded.

Thromboelastography was carried out as previously described [13]. The primary parameters of thromboelastography are the K value, reaction time (R), maximum amplitude (MA), and α angle [14]. The K value measures the rate of clot formation and is equal to the time required to produce thrombin. The time needed for the blood sample to identify the initial clot formation is represented by the R-value, which is equal to the thromboplastin generation time. When coagulation factors are absent or anticoagulants are present, the R-value is prolonged; in the hypercoagulable condition, it is shortened. The fibrinogen (FIB) thrombin and platelet concentrations, as well as their quality or amount, have a significant impact on the α angle, a parameter that measures the rate of clot formation. The rate of blood clot formation is indicated by both the K number and the α angle. Hypercoagulability is indicated by abnormalities in any of the coagulation parameters (lower R value or K value, larger α angle, greater MA value) [15]. Principal indices for evaluating coagulation and fibrinolysis: Before surgery and 1 day, 3 days, and 5 days after surgery, a number of coagulation parameters, including prothrombin time (PT), activated partial thromboplastin time (APTT), FIB, and D-dimer (D-D), were measured in both groups. A prothrombin international normalized ratio (INR) calculation was made to determine the INR shift [16].

Identification of stress response and cellular immune function markers: 4 mL of fasting venous blood from the patient was drawn and collected in tubes A and B on 1, 3, and 5 days after the operation. Tube A was centrifuged at 720 × g for 10 min in preparation for the test, and the supernatant was kept at −20°C. ELISA (kit purchased from Shanghai Enzyme-linked Biotechnology Co., Ltd) was used to determine the concentrations of malondialdehyde (MDA), superoxide dismutase (SOD), interleukin 6 (IL-6), and C-reactive protein (CRP) in the blood [17]. The FACSCalibur flow cytometer (BD Company, USA) was used to detect CD4+ and CD8+ in tube B and to calculate the percentage of CD4+/CD8+ [18].

2.5 Testing cohort

Using logistic regression on the testing population, prediction models for DVT have been developed. The population tested comprised 374 individuals with LC. Using a random number method, patients were assigned to either thoracoscopic or open thoracotomy LC resection.

2.6 Developing the logistic regression model’s formula

The model for predicting DVT has the following formula: Logit (P = DVT) = X 0 + X 1 Y 1 + X 2 Y 2 + … + X n Y n = ln[p/(1 − p)], In this equation, “p” represents the estimated probability of DVT, “X” is the coefficient, and “Y” represents each indication.

2.7 Validation cohort

In a validation cohort, models of logistic regression prediction accuracy were evaluated and found to be accurate. The validation cohort for the present study consisted of 86 LC patients. Using a random number table, the 86 patients were equally divided into two groups: thoracoscopic (43 patients) and open (43 patients).

2.8 Experimental specimen collection, testing, and quality control principles

The venous blood collection method was utilized to obtain blood samples from both patient groups. Blood was obtained from both patient groups from the elbow vein. Blood samples were drawn at the same time from both groups of LC patients. Before operation and 1 day, 3 days, and 5 days after the operation, cubital venous blood was collected while the patient was fasting. The blood was drawn into the usual tube and the heparin-containing negative pressure tube for testing. The plasma was isolated for detection from the blood in the anticoagulation tube after centrifuging it for 20 min at 4°C in a refrigerator and 15 min at 3,000 rpm/centrifugal speed. The diagnostic equipment used on the two blood sample collections was identical. During the transportation and testing process, the same set of samples was tested at the same time, the experimental procedure was identical, and the testing was conducted by the same physician.

2.9 Statistical analysis

All experiments were performed in triplicate unless specified. The data were summarized using measures of central tendency and dispersion, such as the mean value ± standard deviation (SD) or the median and interquartile range (IQR) or the percentage. The Student’s t-test was used to compare normally distributed numeric variables, while the Mann–Whitney U-test was used to compare non-normally distributed variables. One-way analysis of variance (ANOVA) was used to compare groups where the variance was comparable, and the Kruskal–Wallis test was used to assess variables with non-normal distributions. The SNK analysis was utilized to compare the relevant categories. Receiver operator characteristic (ROC) curves were used to evaluate the efficacy of each indicator in detecting DVT by calculating the area under the ROC curve (AUC) and the 95% confidence interval (CI). All significant variables from the univariate study were included in the multivariate analysis of patients with post-operative DVT using a binary logistic regression model. All statistical testing was performed with SPSS version 22.0 (IBM Corporation). If the significance level is less than 0.05, the difference is statistically significant [19].

3 Results

3.1 Technical route

Figure 1 depicts the technical methodology of this investigation. The inclusion criteria were not met by 142 out of 602 LC patients, so they were excluded. The included 460 patients were randomly assigned to the testing cohort and the verification cohort. Basic information of patients in thoracoscopic LC resection group (n = 187) and laparotomy group (n = 187) in the testing cohort and thoracoscopic group (n = 43) and laparotomy group (n = 43) in the verification cohort is shown in Table 1.

Figure 1 The technical route of this study.
Figure 1

The technical route of this study.

Table 1

Comparison of patients’ general clinical indicators and surgery-related indicators (results are represented as the mean values ± SD or median [interquartile range; IQR] or percentage)

Indicators Testing cohort (n = 374) Verification cohort (n = 86)
Thoracoscopic LC resection (n = 187) Thoracotomy LC resection (n = 187) t/Z/χ 2 P Thoracoscopic LC resection (n = 43) Thoracotomy LC resection (n = 43) t/Z/χ 2 P
Gender (male/female) 126/61 137/50 1.550 0.213 32/11 29/14 0.508 0.476
Age 54 (26–78) 56 (29–77) 1.143 0.438 50 (25–73) 55 (31–75) 1.643 0.229
BMI (kg/m2) 24.11 ± 3.40 23.76 ± 3.04 1.206 0.228 23.91 ± 3.20 24.01 ± 3.34 −0.143 0.887
Hypertension (n, %) 49 (26.2) 43 (23.0) 0.519 0.471 9 (20.9) 7 (16.3) 0.307 0.579
Diabetes (n, %) 28 (15.0) 22 (11.8) 0.831 0.362 6 (14.0) 7 (16.3) 0.091 0.763
Hyperlipidemia (n, %) 31 (16.6) 26 (13.9) 0.517 0.472 8 (18.6) 10 (23.3) 0.281 0.596
Anticoagulation immediately after operation (n, %) 92 (49.2) 87 (46.5) 0.268 0.605 26 (60.5) 24 (55.8) 0.191 0.662
Tumor size (cm) 4.1 (1.2–7.3) 4.4 (1.1–7.6) 0.898 0.713 3.9 (0.9–6.9) 4.5 (1.0–7.7) 1.694 0.177
TNM staging (Ⅰ/Ⅱ/Ⅲ) 63/34/90 57/42/88 1.165 0.559 14/7/22 13/11/19 1.145 0.564
Operation time (min) 238.61 ± 36.53 195.90 ± 25.14 13.182 <0.001 226.42 ± 42.24 186.79 ± 30.71 4.976 <0.001
Intraoperative blood loss (mL) 188.83 ± 26.64 296.22 ± 80.8 −17.265 <0.001 175.73 ± 29.01 291.58 ± 77.42 −9.195 <0.001
Postoperative hospital stays (days) 9.71 ± 1.81 14.64 ± 2.30 −22.943 <0.001 9.32 ± 1.63 13.70 ± 2.46 −10.003 <0.001

Abbreviation: BMI: body mass index.

3.2 Comparison and analysis of laboratory indicators for coagulation, fibrinolysis, thromboelastography, stress response, and immune function between the thoracoscopic and thoracotomy LC resection groups in the testing cohort

The results of a comparison between the two groups using laboratory markers of coagulation, fibrinolysis, thromboelastography, stress response, and immunological function at different time intervals are presented in Table 2. There was no statistically significant difference between the two groups, despite the fact that both had comparable INR values before and after operation (all P > 0.05). The levels of the other markers, however, showed statistically significant changes at 1, 3, and 5 days after operation when compared to the same group prior to the procedure (all P < 0.05). Additionally, a pairwise comparison between groups was conducted. The thromoelastographic parameters of patients undergoing thoracoscopic LC resection compared to those undergoing thoracotomy LC resection differed significantly. Interestingly, we discovered that the most significant changes in the patients’ indicators occurred on day 1 after surgery in the thoracoscopic LC resection group, and on day 3 after surgery in the thoracotomy LC resection group (all P < 0.05). DVT incidence in LC was found to be associated with a shorter K-time, as well as a higher angle and MA [13]. In addition, MA, a well-known diagnostic marker for determining the risk of DVT, was most closely correlated with tumor stage, which reflects the contributions of platelet function and FIB deposition. As compared to the thoracotomy LC resection group at 3 days after surgery, K-time was considerably shorter and angle and MA were significantly higher in the study’s thoracoscopic LC resection group at 1 day after surgery. The result was highly correlated with previous investigations that identified LC using comparable values [20].

Table 2

Comparison of changes in thromboelastography, coagulation, fibrinolysis, stress response, and immune function indicators at different time points before and after operation in the testing cohort (n = 374) (results are represented as the mean values ± SD)

Indicators Thoracoscopic LC resection (n = 187) Thoracotomy LC resection (n = 187)
Preoperative 1 day after operation 3 days after operation 5 days after operation Preoperative 1 day after operation 3 days after operation 5 days after operation
Thromboelastography indicators
R value (min) 5.68 ± 1.17 4.87 ± 0.97a 5.34 ± 1.06a,b 5.39 ± 1.11a,b 5.69 ± 1.28 5.31 ± 1.15a 5.07 ± 1.08a,b 5.27 ± 1.13a,c
K value (min) 1.64 ± 0.50 1.19 ± 0.37a 1.47 ± 0.45a,b 1.59 ± 0.49b,c 1.61 ± 0.53 1.37 ± 0.42a 1.22 ± 0.31a,b 1.41 ± 0.44a,c
α angle (degree) 63.14 ± 5.06 77.18 ± 6.67a 73.27 ± 5.49a,b 71.50 ± 5.65a,b,c 63.17 ± 4.98 67.11 ± 5.11a 74.34 ± 5.89a,b 69.54 ± 6.03a,b,c
MA (mm) 66.19 ± 7.11 75.63 ± 7.33a 70.95 ± 7.13a,b 70.88 ± 7.46a,b 66.15 ± 7.24 70.05 ± 7.75a 76.83 ± 8.27a,b 69.87 ± 8.04a,c
Coagulation indicators
PT (s) 12.71 ± 1.22 10.84 ± 0.91a 12.04 ± 1.11a,b 12.73 ± 1.10b,c 12.68 ± 1.20 12.64 ± 1.11 10.68 ± 0.94a,b 11.93 ± 1.05a,b,c
APTT (s) 30.11 ± 4.11 29.27 ± 4.08a 29.99 ± 4.18a,b 30.07 ± 3.89b 30.15 ± 4.15 29.94 ± 4.02a 29.48 ± 4.16a,b 29.90 ± 4.11a,c
INR 1.01 ± 0.09 1.03 ± 0.06 1.02 ± 0.10 1.00 ± 0.09 1.03 ± 0.11 1.01 ± 0.12 1.02 ± 0.09 1.01 ± 0.13
FIB (g/L) 2.74 ± 0.42 4.48 ± 0.57a 3.84 ± 0.32a,b 3.79 ± 0.49a,b 2.78 ± 0.45 3.47 ± 0.46a 4.36 ± 0.53a,b 4.18 ± 0.39a,b,c
D-D (mg/L) 0.15 ± 0.05 0.75 ± 0.23a 0.42 ± 0.11a,b 0.35 ± 0.09a,b,c 0.14 ± 0.04 0.29 ± 0.07a 0.81 ± 0.16a,b 0.46 ± 0.13a,b,c
Stress response indicators
MDA (mmol/mL) 4.25 ± 1.03 7.42 ± 1.26a 5.87 ± 1.14a,b 5.09 ± 1.08a,b,c 4.21 ± 0.97 5.94 ± 1.02a 8.87 ± 1.24a,b 6.77 ± 1.11a,c
SOD (U/mL) 87.56 ± 11.07 67.25 ± 8.86a 75.15 ± 10.80a,b 86.98 ± 10.86b,c 87.47 ± 10.85 74.10 ± 9.37a 64.20 ± 7.97a,b 71.34 ± 9.11a,c
IL-6 (pg/mL) 8.52 ± 1.24 37.03 ± 9.05a 22.09 ± 5.38a,b 15.98 ± 5.89a,b,c 8.54 ± 1.22 19.87 ± 5.44a 35.12 ± 8.65a,b 23.15 ± 6.87a,c
CRP (ng/mL) 11.94 ± 2.24 39.21 ± 7.69a 18.40 ± 5.26a,b 18.33 ± 5.08a,b 11.90 ± 2.16 17.50 ± 4.82a 34.72 ± 6.97a,b 27.09 ± 5.42a,c
Immune function indicators
CD4+ (%) 44.57 ± 4.06 22.89 ± 4.82a 27.05 ± 4.96a,b 29.76 ± 5.03a,b,c 44.16 ± 3.89 28.19 ± 4.48a 23.17 ± 4.56a,b 28.20 ± 4.87a,c
CD8+ (%) 29.04 ± 2.78 17.36 ± 2.36a 18.04 ± 3.16a,b 19.54 ± 2.61a,b,c 29.11 ± 2.54 18.11 ± 3.03a 17.64 ± 2.51a,b 18.77 ± 2.54a,b,c
CD4+/CD8+ 1.53 ± 0.18 1.32 ± 0.14a 1.50 ± 0.13b 1.51 ± 0.16b 1.52 ± 0.15 1.56 ± 0.11 1.31 ± 0.15a,b 1.50 ± 0.14c

Abbreviation: R value: reaction time value; MA: maximum amplitude; PT: prothrombin time; APTT: activated partial thromboplastin time; FIB: fibrinogen; D-D: D-dimerization; IL-6: interleukin 6; MDA: malondialdehyde; SOD: superoxide dismutase; CRP: C-reactive protein.

3.3 Comparison of the DVT risk between the thoracoscopic and thoracotomy LC resection groups in the testing cohort

DVT occurred in 35% of patients who underwent thoracoscopic LC resection and in 21% of patients who underwent thoracotomy LC resection. In the testing cohort of patients having LC resection, the incidence of DVT was higher in the thoracoscopic group (18.7%) compared to the thoracotomy group (11.2%; χ 2 = 4.116, P = 0.042).

3.4 Comparison of coagulation, fibrinolysis, thromboelastography, stress response, and immune function laboratory markers between DVT and non-DVT groups

Table S1 shows the comparison between the DVT group and the non-DVT group based on the indicators at various time points, such as 1 day and 3 days following the procedure. It was discovered that the markers changed most significantly in the thoracoscopic LC resection group 1 day after surgery and in the thoracotomy LC resection group 3 days after surgery (all P < 0.05). Additionally, the DVT group had a reduced operating time, less blood loss during surgery, and a shorter hospital stay following surgery than the non-DVT group (all P < 0.05).

3.5 Logistic regression and ROC analyses of coagulation, fibrinolysis, thromboelastography, stress response, and immune function laboratory indicators in testing cohort

Univariate and multivariate logistic regression analyses were performed to better understand the factors that contributed to the substantially increased risk of DVT in the thoracoscopic group compared to the thoracotomy group. The results of logistic regression analysis are displayed in Tables 3 and 4. 1 day after surgery, the levels of R value, K value, angle, MA, FIB, D-D, MDA, and CD4+/CD8+ were independent predictors of DVT after thoracoscopic LC resection. Three days after a thoracotomy LC resection, risk variables for DVT included R value, K value, angle, D-D, MDA, and SOD values.

Table 3

Univariate and multivariate logistic regression analyses of DVT in patients undergoing thoracoscopic LC resection

Univariate model a Multivariate model a,b
Indicators B SE χ 2 OR (95% CI) P β Standardized β χ 2 OR (95% CI) P
Operation time (min) 0.136 0.109 1.557 0.842 (0.925–1.419) 0.146
Intraoperative blood loss (mL) 0.023 0.124 0.034 1.023 (0.802–1.305) 0.853
R value (min), 1 day after operation −0.253 0.089 8.081 0.776 (0.652–0.924) 0.004 −0.061 0.020 9.303 0.941 (0.905–0.978) 0.002
K value (min), 1 day after operation −0.198 0.072 7.563 0.820 (0.712–0.945) 0.006 −0.109 0.041 7.068 0.897 (0.827–0.972) 0.008
α angle (degree), 1 day after operation 0.178 0.054 10.866 1.195 (1.075–1.328) 0.001 0.374 0.106 12.449 1.454 (1.181–1.789) <0.001
MA (mm), 1 day after operation 0.194 0.038 26.064 1.214 (1.127–1.308) <0.001 0.403 0.098 16.911 1.496 (1.235–1.813) <0.001
PT (s), 1 day after operation 0.202 0.118 2.930 1.224 (0.971–1.542) 0.087
FIB (g/L), 1 day after operation 0.487 0.219 4.945 1.627 (1.059–2.500) 0.026 0.298 0.112 7.079 1.347 (1.082–1.678) 0.008
D-D (mg/L), 1 day after operation 0.385 0.177 4.731 1.470 (1.039–2.079) 0.030 0.406 0.135 4.873 1.347 (1.034–1.755) 0.027
MDA (mmol/mL), 1 day after operation 0.359 0.144 6.181 1.430 (1.079–1.897) 0.013 0.190 0.086 12.007 1.347 (1.138–1.594) 0.001
IL-6 (pg/mL), 1 day after operation 0.292 0.163 3.209 1.339 (0.973–1.843) 0.073
CRP (ng/mL), 1 day after operation 0.158 0.077 4.210 1.171 (1.007–1.362) 0.040 0.503 0.167 3.184 1.347 (0.971–1.869) 0.074
CD4+/CD8+, 1 day after operation −0.099 0.045 4.840 0.906 (0.829–0.989) 0.028 −0.097 0.038 6.516 0.908 (0.842–0.978) 0.011

Abbreviation: R value: reaction time value; MA: maximum amplitude; PT: prothrombin time; FIB: fibrinogen; D-D: D-dimerization; IL-6: interleukin 6; MDA: malondialdehyde; CRP: C-reactive protein; OR: odds ratio; CI: confidence interval.

a The odds ratio corresponds to a unit increase in the variable; b The odds ratio was adjusted for all significant variables of the univariate logistic regression analysis.

Table 4

Univariate and multivariate logistic regression analyses of DVT in patients undergoing thoracotomy LC resection

Univariate modela Multivariate modela,b
Indicators B SE χ 2 OR (95% CI) P β Standardized β χ 2 OR (95% CI) P
R value (min), 3 days after operation −0.029 0.009 10.383 0.971 (0.954–0.989) 0.001 −0.026 0.012 4.694 0.974 (0.952–0.998) 0.030
K value (min), 3 days after operation −0.117 0.041 8.143 0.890 (0.821–0.964) 0.004 −0.143 0.037 14.937 0.869 (0.806–0.932) <0.001
α angle (degree), 3 days after operation 0.274 0.104 6.941 1.315 (1.073–1.613) 0.008 0.402 0.143 7.903 1.495 (1.129–1.978) 0.005
MA (mm), 3 days after operation 0.301 0.092 10.704 1.351 (1.128–1.618) 0.001 0.289 0.166 3.031 1.335 (0.964–1.848) 0.082
PT (s), 3 days after operation −0.069 0.111 0.386 0.933 (0.751–1.160) 0.534
FIB (g/L), 3 days after operation 0.271 0.132 4.215 1.311 (1.012–1.698) 0.040 0.472 0.253 3.481 1.603 (0.976–2.632) 0.062
D-D (mg/L), 3 days after operation 0.149 0.037 16.217 1.161 (1.079–1.248) <0.001 0.198 0.087 5.180 1.219 (1.028–1.446) 0.023
MDA (mmol/mL), 3 days after operation 0.239 0.087 7.547 1.270 (1.071–1.506) 0.006 0.237 0.074 10.257 1.267 (1.096–1.465) 0.001
SOD (U/mL), 3 days after operation 0.354 0.083 18.191 1.425 (1.211–1.676) <0.001 0.409 0.136 9.044 1.505 (1.153–1.965) 0.003
IL-6 (pg/mL), 3 days after operation 0.462 0.170 7.386 1.587 (1.137–2.215) 0.007 0.114 0.273 0.175 1.121 (0.656–1.914) 0.676

Abbreviation: R value: reaction time value; MA: maximum amplitude; PT: prothrombin time; FIB: fibrinogen; D-D: D-dimerization; IL-6: interleukin 6; MDA: malondialdehyde; SOD: superoxide dismutase; OR: odds ratio; CI: confidence interval.

a The odds ratio corresponds to a unit increase in the variable; b The odds ratio was adjusted for all significant variables of the univariate logistic regression analysis.

The risk variables for DVT following LC surgery were established through multivariate logistic regression studies. Subsequently, these factors were subjected to further analysis using the ROC approach. The AUCs for the R value, K value, angle, MA, FIB, D-D, MDA, and CD4+/CD8+ in thoracoscopic LC resection were 0.843, 0.684, 0.792, 0.747, 0.835, 0.797, 0.848, and 0.881, respectively (Figure 2 and Table 5). The AUCs for the R value, K value, angle, D-D, MDA, and SOD in thoracotomy LC resection were 0.864, 0.649, 0.893, 0.796, 0.817, and 0.697 respectively.

Figure 2 ROC analysis of coagulation, fibrinolysis, thromboelastography, stress response, and immune function laboratory indicators to predict the occurrence of DVT after LC resection in the testing cohort. (a) Thoracoscopic LC resection. (b) Thoracotomy LC resection. ROC: receiver operator characteristic; DVT: deep venous thrombosis.
Figure 2

ROC analysis of coagulation, fibrinolysis, thromboelastography, stress response, and immune function laboratory indicators to predict the occurrence of DVT after LC resection in the testing cohort. (a) Thoracoscopic LC resection. (b) Thoracotomy LC resection. ROC: receiver operator characteristic; DVT: deep venous thrombosis.

Table 5

Calculated performance values for different indicators in the testing cohort

Variable AUC 95% CI P Sensitivity (%) Specificity (%)
Thoracoscopic LC resection
R value (min), 1 day after operation 0.843 0.805–0.880 <0.001 90.1 62.3
K value (min), 1 day after operation 0.684 0.635–0.732 <0.001 52.4 93.4
α angle (degree), 1 day after operation 0.792 0.751–0.832 <0.001 77.8 66.2
MA (mm), 1 day after operation 0.747 0.702–0.792 <0.001 68.6 78.5
FIB (g/L), 1 days after operation 0.835 0.798–0.873 <0.001 68.8 98.6
D-D (mg/L), 1 day after operation 0.797 0.754–0.840 <0.001 72.9 86.7
MDA (mmol/mL), 1 day after operation 0.848 0.812–0.885 <0.001 54.1 91.3
CD4+/CD8+, 1 day after operation 0.881 0.849–0.912 <0.001 79.2 86.3
Model for thoracoscopic 0.934 0.909–0.958 <0.001 87.8 100.0
Thoracotomy LC resection
R value (min), 3 days after operation 0.864 0.829–0.899 <0.001 90.2 71.3
K value (min), 3 days after operation 0.649 0.600–0.697 <0.001 42.3 100.0
α angle (degree), 3 days after operation 0.697 0.649–0.744 <0.001 68.9 67.8
D-D (mg/L), 3 days after operation 0.893 0.864–0.922 <0.001 82.2 86.3
MDA (mmol/mL), 3 days after operation 0.796 0.755–0.836 <0.001 67.9 97.5
SOD (U/mL), 3 days after operation 0.817 0.778–0.857 <0.001 73.6 89.2
Model for thoracotomy LC resection 0.918 0.892–0.944 <0.001 80.7 100.0

Abbreviation: R value: reaction time value; MA: maximum amplitude; PT: prothrombin time; FIB: fibrinogen; D-D: D-dimerization; MDA: malondialdehyde; SOD: superoxide dismutase; AUC: area under curve; OR: odds ratio; CI: confidence interval.

3.6 Novel risk prediction models for DVT based on the testing cohort

The aforementioned laboratory parameters were included in logistic regression models. The final model for predicting the incidence of DVT following thoracoscopic LC resection (1 day after surgery) was Logit(P) = 9.378 − 0.061(R value) − 0.109(K value) + 0.374(α angle) + 0.403(MA) + 0.298(FIB) + 0.406(D-D) + 0.190(MDA) – 0.097(CD4+/CD8+). This model had high value in predicting the occurrence of DVT after thoracoscopic LC resection, with an AUC of 0.934 and an estimated probability of 0.715 (meaning that patients would be categorized as high risk of DVT if the chance were lower than 0.715). The final model for predicting the incidence of DVT 3 days following thoracotomy LC resection was as follows: Logit(P) = −2.463 – 0.026(R value) – 0.143(K value) + 0.402(α angle) + 0.198(D-D) + 0.237(MDA) + 0.409(SOD). This model is also helpful in identifying the occurrence of DVT following thoracotomy LC resection, with an AUC of 0.918 and an estimated probability of 0.686 (meaning that patients would be categorized as high risk of DVT if the chance was lower than 0.686).

3.7 Validation of the logistic regression models

A validation cohort analysis was conducted to evaluate the robustness of our logistic regression models. Table 6 contains information regarding the subjects. 86 individuals participated in the validation phase of the investigation.

Table 6

Comparison of changes in thromboelastography, coagulation, fibrinolysis, stress response, and immune function indicators at different time points before and after operation in the verification cohort (n = 86) (results are represented as the mean value ± SD)

Indicators Thoracoscopic LC resection (n = 43) Thoracotomy LC resection (n = 43)
Preoperative 1 day after operation 3 days after operation 5 days after operation Preoperative 1 day after operation 3 days after operation 5 days after operation
Thromboelastography indicators
R value (min) 5.83 ± 1.22 4.93 ± 0.99a 5.49 ± 1.11a,b 5.79 ± 1.17b,c 5.80 ± 1.19 5.22 ± 1.16a 4.98 ± 1.03a 5.51 ± 1.19a,c
K value (min) 1.67 ± 0.49 1.22 ± 0.38a 1.45 ± 0.43a,b 1.56 ± 0.44b 1.69 ± 0.52 1.46 ± 0.39a 1.21 ± 0.30a,b 1.47 ± 0.45a,c
α angle (degree) 63.34 ± 4.98 78.32 ± 6.74a 72.16 ± 5.38a,b 72.19 ± 5.16a,b 63.28 ± 5.01 69.41 ± 5.65a 75.25 ± 5.90a,b 69.53 ± 5.82a,c
MA (mm) 67.08 ± 7.03 74.25 ± 7.45a 69.78 ± 6.87a,b 70.43 ± 7.03a,b 67.14 ± 7.09 72.13 ± 7.26a 75.76 ± 7.84a 69.12 ± 7.51c
Coagulation indicators
PT (s) 12.83 ± 1.16 11.01 ± 0.88a 11.78 ± 1.04a,b 12.15 ± 1.13a,b 12.80 ± 1.19 12.57 ± 1.21a 10.77 ± 0.98a,b 12.48 ± 1.09a,c
APTT (s) 30.17 ± 4.09 29.30 ± 3.94a 29.87 ± 4.14b 30.16 ± 3.99b 30.16 ± 4.02 29.91 ± 4.00 29.24 ± 3.57a,b 29.96 ± 4.10c
INR 1.00 ± 0.09 1.02 ± 0.07 1.02 ± 0.11 1.01 ± 0.12 1.01 ± 0.10 1.01 ± 0.09 1.02 ± 0.07 1.00 ± 0.11
FIB (g/L) 2.79 ± 0.41 4.36 ± 0.49a 3.81 ± 0.30a 3.77 ± 0.46a,b 2.77 ± 0.42 3.41 ± 0.40a 4.47 ± 0.55a,b 4.36 ± 0.29a,b
D-D (mg/L) 0.13 ± 0.03 0.81 ± 0.14a 0.46 ± 0.13a,b 0.43 ± 0.10a,b 0.12 ± 0.02 0.32 ± 0.08a 0.77 ± 0.18a,b 0.51 ± 0.14a,b,c
Stress response indicators
MDA (mmol/mL) 4.34 ± 1.02 7.13 ± 1.14a 6.47 ± 1.03a,b 5.17 ± 0.97a,b,c 4.26 ± 0.99 5.89 ± 0.98a 8.65 ± 1.34a,b 6.58 ± 1.16a,b,c
SOD (U/mL) 84.17 ± 10.64 66.37 ± 8.35a 77.04 ± 10.03a,b 82.56 ± 10.55b 85.32 ± 10.27 75.23 ± 9.64a 63.26 ± 6.88a,b 73.30 ± 9.18a,c
IL-6 (pg/mL) 8.47 ± 1.20 38.15 ± 8.77a 23.34 ± 5.29a,b 20.44 ± 4.63a,b 8.51 ± 1.17 17.95 ± 5.06a 36.98 ± 7.43a,b 24.04 ± 6.25a,b,c
CRP (ng/mL) 10.38 ± 1.98 34.76 ± 7.58a 21.43 ± 5.78a,b 16.36 ± 5.23a,b,c 10.66 ± 2.05 19.53 ± 4.87a 33.88 ± 6.86a,b 26.40 ± 5.83a,b,c
Immune function indicators
CD4+ (%) 47.46 ± 4.11 21.47 ± 4.36a 28.24 ± 4.73a,b 31.69 ± 4.98a,b 45.37 ± 3.93 29.20 ± 4.53a 20.38 ± 4.17a,b 24.36 ± 3.67a
CD8+ (%) 30.05 ± 2.67 16.64 ± 2.47a 18.46 ± 3.05a,b 19.93 ± 2.50a,b 29.27 ± 2.43 18.96 ± 2.87a 16.17 ± 2.44a 16.46 ± 2.54a
CD4+/CD8+ 1.56 ± 0.14 1.29 ± 0.13a 1.53 ± 0.10b 1.59 ± 0.12b 1.55 ± 0.13 1.54 ± 0.14 1.26 ± 0.11a,b 1.48 ± 0.13c

Abbreviation: R value: reaction time value; MA: maximum amplitude; PT: prothrombin time; APTT: activated partial thromboplastin time; FIB: fibrinogen; D-D: D-dimerization; IL-6: interleukin 6; MDA: malondialdehyde; SOD: superoxide dismutase; CRP: C-reactive protein.

The calculated probability of thoracoscopic LC resection was 0.715 in one group (n = 8) and less than 0.715 in another group (n = 35). Outcomes are shown in Figure 3a. Patients with an estimated probability ≥0.715 had a significantly higher incidence of DVT than those with an estimated probability of 0.715 (χ 2 = 43.420, P < 0.001). 7 out of 8 LC patients had probabilities less than 0.715, while 34 out of 35 patients had probabilities greater than 0.715 when using the algorithm. The model’s sensitivity and specificity for predicting DVT after thoracoscopic LC resection were 87.5% and 97.1%, respectively (Figure 4a).

Figure 3 Validation of the logistic regression models in the validation cohort. (a) Comparison of the incidence of DVT between the group of estimated probability ≥0.715 and the group of estimated probability <0.715 in the thoracoscopic LC resection group. (b) Comparison of the incidence of DVT between the group of estimated probability ≥0.686 and the group of estimated probability <0.686 in the thoracotomy LC resection group. DVT: deep venous thrombosis.
Figure 3

Validation of the logistic regression models in the validation cohort. (a) Comparison of the incidence of DVT between the group of estimated probability ≥0.715 and the group of estimated probability <0.715 in the thoracoscopic LC resection group. (b) Comparison of the incidence of DVT between the group of estimated probability ≥0.686 and the group of estimated probability <0.686 in the thoracotomy LC resection group. DVT: deep venous thrombosis.

Figure 4 The scatter diagram of the established risk prediction models for the occurrence of DVT after LC resection in the validation cohort. (a) Thoracoscopic LC resection. (b) Thoracotomy LC resection. DVT: deep venous thrombosis.
Figure 4

The scatter diagram of the established risk prediction models for the occurrence of DVT after LC resection in the validation cohort. (a) Thoracoscopic LC resection. (b) Thoracotomy LC resection. DVT: deep venous thrombosis.

As depicted in Figure 3b, patients undergoing thoracotomy for LC resection were divided into two groups: those with an estimated probability of 0.686 or less (n = 7) and those with an estimated probability of 0.687 or greater (n = 36). Patients in the group with an estimated probability of 0.686 had a significantly higher incidence of DVT than those in the group with an estimated probability of 0.026 (χ 2 = 49.500, P < 0.001). According to the formula, all the 36 LC patients had odds greater than 0.686, whereas 6 of the 7 LC patients had odds less than 0.686. The model had 85.7% sensitivity and 100% specificity for predicting the presence of DVT after thoracoscopic LC excision (Figure 4b).

4 Discussion

LC is still effectively treated with surgery. A comprehensive treatment plan based on surgery has substantially improved the 5 year survival rate and quality of life of patients, particularly those with advanced LC [21]. Current standard surgical procedures for LC resection include thoracotomy and thoracoscopic approaches. The advantages of thoracoscopic surgery, which include less trauma, speedier recovery, and shorter hospital stays, have led to its widespread acceptance [5].

Postoperative complications are frequently one of the most influential factors on the short- and long-term clinical outcomes of patients. Common complications following LC surgery include postoperative infection, pleural effusion, and postoperative DVT, the most dangerous of which is DVT [22]. In previous research, the formation of DVT was attributed to three factors: stagnant venous blood flow, venous endothelial injury, and blood hypercoagulability [23–25]. Among these, venous blood flow is frequently associated with the hemodynamics (R value, K value, α angle, and MA) of the blood in the vein [25]. Venous endothelial damage is related to stress response and immune function [26,27]. Blood hypercoagulability can be measured by coagulation (PT and APTT) and fibrinolytic (FIB and D-D) functions [27,28]. However, the impact of thoracoscopic and thoracotomy LC resection on patients with postoperative DVT has not been thoroughly investigated. This study compared the factors related to coagulation, fibrinolysis, thromboelastography, stress response, and immune function between the two groups of surgical procedures to determine their impact on postoperative DVT. As a product of fibrin degradation, D-D levels appear to be a useful diagnostic indicator in cases of suspected DVT. Elevated D-D levels can be used as a strong indicator of hypercoagulability because they typically signify an increase in the coagulation and fibrinolysis systems [29]. The findings of this study revealed that the thoracoscopic group had higher D-D levels 1 day after surgery than the thoracotomy group on the third day, suggesting a higher incidence of DVT in the thoracoscopic group. A prior study also suggested that a higher D-D level is associated with a higher chance of developing DVT [30]. Indicators of stress response, such as IL-6, MDA, and SOD, were also examined. The results showed that the thoracoscopic group had higher levels of IL-6 and MDA and lower levels of SOD at 1 day after surgery than the thoracotomy group, which had a similar outcome but on the third day after surgery. This shows that the group who underwent thoracoscopic surgery was at higher risk for DVT. The previous work for IL-6, MDA, and SOD in determining the risk for DVT in patients showed similar findings [31]. Similarly, immune-related markers decreased in the thoracoscopic group 1 day after surgery, while they decreased in the thoracotomy group 3 days after surgery, indicating the LC prognosis. Similar findings have been documented in the literature, where decreased CD4+ and CD8+ values signify a higher incidence of LC [32].

In thoracoscopic LC resection, the establishment of pneumoperitoneum is crucial to the execution of the procedure and the formation of the surgical field of vision. In recent years, however, it has been reported that the establishment of CO2 pneumoperitoneum, operation duration, and patient body position during thoracoscopic surgery may cause significant changes in hemodynamics and coagulation function, thereby promoting thrombus formation [33–35]. Establishment of CO2 pneumoperitoneum can significantly increase the diameter of the femoral vein and diminish the venous blood flow velocity of the lower extremities [34]. In addition, pneumoperitoneum can compress the inferior vena cava and iliac veins on both sides, resulting in an increase in venous blood flow resistance and a decrease in blood flow, which will impede venous blood return [35]. Additionally, the pneumoperitoneum has the ability to lift the diaphragm and raise the pressure in the thoracic cavity. As a result, the filling of the heart’s ventricles during diastole is reduced, and the inferior vena cava’s resistance is increased [36]. The aforementioned factors can directly or indirectly obstruct the venous blood of the lower extremities and impede blood return. Consistent with the prior theoretical foundation, the testing cohort in this research found that the incidence of DVT was significantly higher in the thoracoscopic LC resection group than in the thoracotomy LC resection group [35,36]. The risk of DVT was modeled following the two surgical procedures because of the substantial differences in the mechanism of causing DVT between the two.

Based on a comparison of the changes in the laboratory indicators of the patients before the operation and 1 day, 3 days, and 5 days after the operation, the most significant laboratory indicators were incorporated into the logistic regression models for this study. It was discovered that the two final regression models had a prediction accuracy of 87.8 and 80.7% and a specificity of 100% for postoperative DVT in patients with thoracoscopic LC resection and thoracotomy LC resection. In addition, these models were validated in a single validation cohort. The outcomes demonstrated that these two models still had a high level of predictive efficiency (for thoracoscopic LC resection, the false-positive rate was 12.5% and the false-negative rate was 2.9%). For thoracotomy LC resection, the false-positive rate was 14.3% and the false-negative rate was 0%, indicating that these established models can predict DVT in patients after thoracoscopic and thoracotomy LC resection.

In conclusion, the predictive accuracy of postoperative DVT in patients undergoing thoracoscopic and thoracotomy LC resections may be improved with the use of current risk prediction models, which include laboratory indicators for coagulation, fibrinolysis, thromboelastography, stress response, and immune function.

Patients undergoing thoracoscopic or thoracotomy LC resection may benefit from an updated, more accurate risk prediction model for postoperative DVT prediction. Evaluation of DVT-risk and significance of prophylactic measures is still considered to be lacking and future efforts with prospective study models are required. Future investigations should assess postoperative complications using validated scales. However, this investigation was conducted as a prospective case-control study at a single center, which limits its applicability and dissemination. In the future, it is hoped that this model will be further validated in a significant, multi-center, retrospective validation study that will help Chinese patients.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: J.L. and F.Z. designed the experiment. J.L., F.L., and X.L. conducted the analysis. C.T. and W.C. prepared the manuscript with the contribution of all co-authors. All authors have read and approved the final version of the manuscript. The authors applied the SDC approach for the sequence of authors.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-02-10
Revised: 2023-04-10
Accepted: 2023-04-26
Published Online: 2023-05-23

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
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  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
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  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
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  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0617
Keywords for this article
lung cancer resection; machine learning; prospective study; venous thrombosis; lung cancer
Creative Commons
BY 4.0

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  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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