Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. It is a high-mortality syndrome that is widespread globally. In this review, we explore the evolving understanding of the immunological features of sepsis, emphasizing the simultaneous occurrence of pro-inflammatory and anti-inflammatory responses during the disease progression. Early in the disease course, the host is typically in a pro-inflammatory state, characterized by excessive inflammatory responses and vascular damage. As the disease progresses, the host tends toward an immunosuppressive state, marked by immune suppression and secondary infections. This article outlines the immunological characteristics of these two states, including the reciprocal promotion of inflammatory storms and coagulation abnormalities, as well as the death and depletion of immune cells. The heterogeneity of sepsis presents a significant challenge to targeted therapy. A key future direction in sepsis immunology diagnosis and treatment is distinguishing endotypes among sepsis patients, identifying the immunological features and pathogenic mechanisms of each endotype, and enabling focused therapeutic interventions targeting specific sepsis endotypes.

This paper presents an automatic acquisition and analytic procedure of acupuncture manipulation based on optical navigation, aiming at solving the shortcomings of existing acquisition methods of acupuncture manipulation. An acquisition holder installed at the handle tail of filiform needle was designed to display the movement trajectory of the needle during acupuncture delivery by collecting the movement trajectory of holder. The 3-month old male Bama miniature pig was selected as the experimental subject, and 6 points, "Bojian" "Qiangfeng" "Housanli" "Xiaokua" "Huiyang" (BL35) and "Baihui" (GV20), were selected during acupuncture manipulation. The optical navigation system was used to collect the real-time data, and these data were per-processed and analyzed using mean filtering and Fourier transform. The acupuncture procedure was divided into 3 stages, inserting, lifting-thrusting, and twisting. The results showed that the accuracy was 96.3% at lifting-thrusting stage, and that was 100.0% at twisting stage. The decomposition effect of the entire procedure was satisfactory. This study provides a new approach to the quantitative analysis of acupuncture manipulation. In the future, it needs to further optimize the algorithm and expand the sample size so as to improve the accuracy of this analytic technique.
Acupuncture Therapy/methods* ; Male ; Animals ; Swine ; Acupuncture Points ; Humans ; Swine, Miniature ; Needles

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. It is a high-mortality syndrome that is widespread globally. In this review, we explore the evolving understanding of the immunological features of sepsis, emphasizing the simultaneous occurrence of pro-inflammatory and anti-inflammatory responses during the disease progression. Early in the disease course, the host is typically in a pro-inflammatory state, characterized by excessive inflammatory responses and vascular damage. As the disease progresses, the host tends toward an immunosuppressive state, marked by immune suppression and secondary infections. This article outlines the immunological characteristics of these two states, including the reciprocal promotion of inflammatory storms and coagulation abnormalities, as well as the death and depletion of immune cells. The heterogeneity of sepsis presents a significant challenge to targeted therapy. A key future direction in sepsis immunology diagnosis and treatment is distinguishing endotypes among sepsis patients, identifying the immunological features and pathogenic mechanisms of each endotype, and enabling focused therapeutic interventions targeting specific sepsis endotypes.

Objective To observe the efficacy of hepatic artery infusion chemotherapy(HAIC)combined with lenvatinib for treating Barcelona clinic liver cancer(BCLC)stage B or C hepatocellular carcinoma(HCC),and to explore the impact factors of patients'survival time.Methods Data of 104 patients with BCLC stage B or C HCC were retrospectively analyzed.The patients were divided into observation group(n=46,underwent HAIC combined with lenvatinib)and control group(n=58,underwent HAIC alone).The clinical efficacy and adverse reactions of treatments,as well as patients'overall survival(OS)and progression free survival(PFS)were recorded and compared between groups.Cox regressions were used to explore the impact factors of patients'survival time.Results Three months and 6 months after HAIC,the results of modified response evaluation criteria in solid tumors(mRECIST)in observation group were both better than those in control group(both P＜0.05),while no significant difference was found between groups one year after HAIC(P＞0.05).The overall survival rate in observation group was higher than that in control group(P＜0.05),while there was no significant difference of progression free survival rate between groups(P＞0.05).The incidence of rash in observation group was higher than that in control group(P＜0.05).Multiple Cox regression showed prolonged OS in HCC patients in observation group(hazard ratio[HR]=0.425,95%CI[0.255,0.791])compared with that in control group.Compared with pre-treatment Eastern Cooperative Oncology Group(ECOG)score 1,AFP≥400 μg/ml,the number of tumor foci≥3 and BCLC stage C,pre-treatment ECOG score 0,AFP＜400 μg/ml,the number of tumor foci≤2 and BCLC stage B were all independent protective factors of OS in HCC patients(all P＜0.05).Conclusion HAIC combined with lenvatinib was safe and effective for treating BCLC stage B or C HCC.Pre-treatment ECOG score,serum AFP level,the number of tumor foci and BCLC stage were all independent impact factors of OS.

Objective:To compare the parameters for infra-acetabular screw placement between men and women using a digital Chinese anatomical model of the pelvis and acetabulum.Methods:The normal pelvic CT data were collected from the 163 adult patients who had been admitted to the Traditional Chinese Medicine Hospital of Xinjiang Uygur Autonomous Region from January 2018 to December 2021. There were 61 males and 102 females with an age of 53.0 (45.0, 60.0) years. Mimics 21.0 software was used to reconstruct the three dimensional pelvis which was then imported into Autodesk maya 2022 software before the model was flattened. Polygonal modeling tools were used to create a cylinder to simulate an infra-acetabular screw for length and angle measurements of the screw. The diameters of the infra-acetabular screws were measured by axial fluoroscopy in Mimics 21.0 software. The maximum diameters and maximum lengths of the infra-acetabular bone channel were compared between males and females, and the angles between the axis of the infra-acetabular screw and the anterior pelvic plane and the median sagittal plane were also compared between genders.Results:The maximum diameters of the left and right infra-acetabular corridors were 5.24 (4.26, 6.38) mm and 5.04 (4.50, 6.57) mm in males, and 3.99 (3.81, 4.51) mm and 3.89 (3.65, 4.90) mm in females; the maximum lengths of the left and right infra-acetabular corridors were (98.43±4.42) mm and (98.01±5.08) mm in males and 87.73 (84.22, 90.98) mm and 87.51 (84.59, 90.15) mm in females. The left and right angles between the infra-acetabular screw axis and the median sagittal plane were -0.98°±4.79° and -1.08°±4.91° in men, and 6.20° (3.34°, 11.16°) and 6.44° (3.77°, 11.85°) in women. The differences in the above data between men and women were statistically significant ( P<0.05). There was no statistically significant difference between men and women in the angle between the infra-acetabular screw axis and the anterior pelvic plane ( P>0.05). Conclusions:The length and diameter of the infra-acetabular corridor in males are greater than those in females, the angle between the infra-acetabular corridor and the sagittal plane in males is smaller than that in females, and the infra-acetabular corridor in males is more parallel to the sagittal plane. Therefore, the fluoroscopy angle should be adjusted for males to reduce the difficulty in screw placement when an infra-acetabular screw is placed during surgery.

Objective:To investigate the impact of the early infusion rate on prognosis and the factors of influencing the infusion rate in patients with severe burns and inhalation injury.Methods:This study was a retrospective case series research. From January 2015 to December 2020, 220 patients with severe burns and inhalation injury meeting the inclusion criteria were admitted to 7 burn treatment centers in China, including 13 cases in the Fourth People's Hospital of Dalian, 26 cases in the First Affiliated Hospital of Naval Medical University, 73 cases in Guangzhou Red Cross Hospital of Jinan University, 21 cases in the 924 th Hospital of PLA, 30 cases in the First Affiliated Hospital of Jiangxi Medical College of Nanchang University, 30 cases in Tongren Hospital of Wuhan University & Wuhan Third Hospital, and 27 cases in Zhengzhou First People's Hospital. There were 163 males and 57 females, and their ages ranged from 18 to 91 years. The patients were divided into survival group and death group according to the survival within 28 d post injury. The following data of patients in the 2 groups were collected, including basic information (gender, age, body weight, body temperature, etc.), the injury characteristics (total burn area, post-injury admission time, etc.), the underlying diseases, the post-injury fluid resuscitation condition (infusion rate and ratio of infused electrolyte solution to colloid solution in the first 24 h post injury, etc.), the results of laboratory tests on admission (blood urea nitrogen, blood creatinine, albumin, pH value, base excess, blood lactate, oxygenation index, etc.), and treatment condition (inhaled oxygen volume fraction, hospitalization day, renal replacement therapy, etc.). After adjusting covariates using univariate Cox regression analysis, the multivariate Cox regression analysis was performed to evaluate the impact of infusion rate in the first 24 h post injury on patient death. The receiver operator characteristic curve for the infusion rate in the first 24 h post injury to predict the risk of death was plotted, and the maximum Youden index was calculated. Patients were divided into 2 groups according to the cutoff value (2.03 mL·kg -1·% total body surface area (TBSA) -1) for predicting risk of death by the infusion rate in the first 24 h post injury determined by the maximum Youden index, and the risk of death was compared between the 2 groups. The correlation between the previously mentioned clinical data and the infusion rate in the first 24 h post injury was analyzed; after the univariate linear regression analysis was used to screen the independent variables, the multivariate linear regression analysis was performed to screen the independent influential factors on the infusion rate in the first 24 h post injury. Results:Compared with those in survival group, patients in death group had significantly higher age and total burn area (with Z values of 12.08 and 23.71, respectively, P<0.05), the infusion rate in the first 24 h post injury, inhaled oxygen volume fraction, and blood urea nitrogen, blood creatinine, blood lactic acid on admission (with Z values of 7.99, 4.01, 11.76, 23.24, and 5.97, respectively, P<0.05), and the proportion of patients treated with renal replacement therapy ( P<0.05) were significantly higher, the albumin, pH value, and base excess on admission were significantly lower ( t=2.72, with Z values of 8.18 and 9.70, respectively, P<0.05), and the hospitalization day was significantly reduced ( Z=85.47, P<0.05). After adjusting covariates, the infusion rate in the first 24 h post injury was the independent influential factor on death (with standardized hazard ratio of 1.69, 95% confidence interval of 1.21-2.37, P<0.05). Patients in infusion rate ≥2.03 mL·kg -1·%TBSA -1 group had a significantly higher risk of death than those in infusion rate <2.03 mL·kg -1·% TBSA -1 group (with hazard ratio of 3.47, 95% confidence interval of 1.48-8.13, P<0.05). There was a significant correlation between total burn area, body weight, inhaled oxygen volume fraction, body temperature, post-injury admission time, the ratio of infused electrolyte solution to colloid solution in the first 24 h post injury, and oxygenation index <300 on admission and the infusion rate in the first 24 h post injury (with r values of -0.192, -0.215, 0.137, -0.162, -0.252, and 0.314, respectively, Z=4.48, P<0.05). After screening the independent variables, total burn area, body weight, post-injury admission time, and oxygenation index <300 on admission were the independent influential factors on the infusion rate in the first 24 h post injury (with standardized β values of -0.22, -0.22, -0.19, and 0.46, respectively, 95% confidence intervals of -0.34 to 0.09, -0.34 to 0.10, -0.32 to 0.06, and 0.22 to 0.71, respectively, P<0.05). Conclusions:The infusion rate in the first 24 h post injury in patients with severe burns and inhalation injury is the independent factor of influencing death, and patients with infusion rate ≥2.03 mL·kg -1·%TBSA -1 in the first 24 h post injury have a significantly increased risk of death. The total burn area, body weight, post-injury admission time, and oxygenation index <300 on admission were the independent factors of influencing the infusion rate in the first 24 h post injury in patients with severe burns and inhalation injury.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

The principles for surgical treatment of femoral neck fracture are anatomical reduction, rigid fixation and reduction of iatrogenic tissue damage to maintain sufficient blood supply and reduce the risk of complications such as avascular necrosis of the femoral head. In the evolution of internal fixation structures for femoral neck fracture, a variety of new products have been developed, such as the neck-shaft angle stabilization structure represented by dynamic hip screw, the multi-screw structure represented by three cannulated screws, and the plate-screw structure represented by multi-screw structure combined with a locked plate. These internal fixation structures have their own advantages and disadvantages in terms of stability and reduced risk of complications. However, none of them can perfectly meet the requirements of all the surgical principles. Femoral neck system (FNS) was firstly applied in clinic practice in 2017 to further improve the internal fixation of femoral neck fracture. In recent years, its mechanical properties and clinical effects have been widely reported in an attempt to further improve the implantation of this internal fixation device. This article reviews the researches on the mechanical properties and clinical efficacy of FNS and the suggestions put forward by orthopedic surgeons to improve the implantation methods of FNS.