ObjectiveTo investigate the clinical features and outcomes of recompensation in patients with autoimmune hepatitis （AIH）-related decompensated cirrhosis， to identify independent predictive factors， and to construct a nomogram prediction model for the probability of recompensation. MethodsA retrospective cohort study was conducted among the adult patients with AIH-related decompensated cirrhosis who were admitted to The Fifth Medical Center of PLA General Hospital from January 2015 to August 2023 （n=211）. The primary endpoint was achievement of recompensation， and the secondary endpoint was liver-related death or liver transplantation. According to the outcome of the patients at the end of the follow-up， the patients were divided into the recompensation group （n=16） and the persistent decompensation group（n=150）.The independent-samples t test was used for comparison of normally distributed continuous data with homogeneity of variance， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data with heterogeneity of variance； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups； the Kaplan-Meier method was used for survival analysis； the Cox proportional-hazards regression model was used to identify independent predictive factors， and a nomogram model was constructed and validated. ResultsA total of 211 patients were enrolled， with a median age of 55.0 years and a median follow-up time of 44.0 months， and female patients accounted for 87.2%. Among the 211 patients， 61 （with a cumulative proportion of 35.5%） achieved recompensation. Compared with the persistent decompensation group， the recompensation group had significantly higher white blood cell count， platelet count （PLT）， total bilirubin （TBil）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， total bile acid， prothrombin time， international normalized ratio （INR）， SMA positive rate， Model for End-Stage Liver Disease （MELD） score， Child-Pugh score， and rate of use of glucocorticoids （all P0.05）， as well as significantly lower age at baseline， number of complications， and death/liver transplantation rate （all P0.05）. At 3 and 12 months after treatment， the recompensation group had continuous improvements in AST， TBil， INR， IgG， MELD score， and Child-Pugh score， which were significantly lower than the values in the persistent decompensation group （all P0.05）， alongside with continuous increases in PLT and albumin， which were significantly higher than the values in the persistent decompensation group （P0.05）. The multivariate Cox regression analysis showed that baseline ALT （hazard ratio ［HR］=1.067， 95% confidence interval ［CI］： 1.010 — 1.127， P=0.021）， IgG （HR=0.463，95%CI：0.258 — 0.833， P=0.010）， SMA positivity （HR=3.122，95%CI：1.768 — 5.515， P0.001）， and glucocorticoid therapy （HR=20.651，95%CI：8.744 — 48.770， P0.001） were independent predictive factors for recompensation， and the nomogram model based on these predictive factors showed excellent predictive performance （C-index=0.87，95%CI：0.84 — 0.90）. ConclusionAchieving recompensation significantly improves clinical outcomes in patients with AIH-related decompensated cirrhosis. Baseline SMA positivity， a high level of ALT， a low level of IgG， and corticosteroid therapy are independent predictive factors for recompensation. The predictive model constructed based on these factors can provide a basis for decision-making in individualized clinical management.

Objective:To establish an artificial intelligence-based surgical selection system utilizing deep learning to assist in the decision-making process for lumbar endoscopic surgery.Methods:General data of 1,110 patients who underwent percutaneous transforaminal endoscopic discectomy, 804 patients who underwent percutaneous interlaminar endoscopic discectomy, 923 patients who underwent mobile microendoscopic discectomy and 623 patients who underwent unilateral biportal endoscopic in Tianjin Hospital from January 2018 to June 2023 were included in the study. Clinical outcomes were assessed using the visual analogue scale (VAS) for leg and back pain, the Oswestry disability index (ODI), and MacNab criteria both before surgery and 12 months postoperatively. Using a random number table method, patients were divided into a training dataset (2,768 cases) and a test dataset (692 cases) at a ratio of 4∶1. Patient clinical symptoms, physical signs, and multi-modal imaging data were input into a deep learning model. This model was structured into three main modules: intervertebral disc detection, surgical necessity identification, and surgical recommendation. The final surgical method was determined using a convolutional neural network incorporating U-Net for segmentation and ResNet for classification. The accuracy and recall rates of each module were evaluated using the test dataset.Results:Compared to preoperative values, all patients showed significant improvements at the 12-month postoperative follow-up. For patients who underwent percutaneous transforaminal endoscopic discectomy, percutaneous interlaminar endoscopic discectomy, mobile microendoscopic discectomy, and unilateral biportal endoscopic surgery, the VAS scores for leg pain decreased from 7.69±0.80, 7.82±0.88, 7.62±0.69, and 7.56±1.00 preoperatively to 1.44±1.09, 1.35±0.82, 1.51±1.08, and 1.43±0.91 postoperatively. Similarly, the VAS scores for back pain decreased from 5.73±0.83, 6.17±0.99, 6.11±0.88, and 6.46±0.95 to 0.93±0.75, 1.01±0.67, 1.40±0.72, and 1.27±0.70, respectively. Additionally, the ODI significantly decreased from 39.91%±4.50%, 40.05%±8.05%, 47.08%±9.50%, and 44.43%±4.71% preoperatively to 5.77%±2.22%, 6.05%±2.31%, 8.51%±2.16%, and 9.51%±3.70% postoperatively, with all differences being statistically significant ( P<0.05). The excellent rate according to the MacNab criteria was 93.12% (3,222/3,460). In the deep learning model, the multi-modal data of 2,768 patients were input in the training set for deep learning to form a surgical identification and operation recommendation system, and the preoperative data of 692 patients were input in the test set to compare with the final operation method. In the intervertebral disc location module, the accuracy of location and designation of the five lumbar intervertebral discs was 97.1%(672/692). In the module of intervertebral disc need for surgery, the accuracy was 94.8%(3,280/3,460) and the recall rate was 91.9%(636/692). As for patients, the accuracy rate was 91.9%(636/692). In the operation recommendation module, the accuracy rate of operation recommendation based on intervertebral disc was 89.5%(569/636), and the accuracy rate of surgical recommendation based on patient was 82.2%(569/692). Conclusion:In this study, an artificial intelligent surgical procedures selection system based on deep learning was established, which could effectively integrate relevant data and accurately guide the selection of lumbar endoscopic surgery.

The diagnostic technology of acute stroke by microwave imaging has the advantages of being non-ionizing, fast, small, and low-cost. Therefore, this technology is expected to become an auxiliary or alternative means to CT and MRI technology. As the signal transmitting and receiving device of the microwave imaging system, the antenna has an important influence on the performance of the imaging system. At present, there are many antennas with different performances used in imaging systems, but there is a lack of clear evaluation criteria for them. In this paper, several typical antennas were introduced, their advantages and disadvantages from the perspective of bandwidth and near-field were analyzed, and the common requirements of imaging systems for antennas and the performance indicators of various types of imaging systems were summarized. Moreover, the development trend of antenna technology for microwave imaging was pointed out to provide a reference for the study of stroke microwave imaging technology.

OBJECTIVE: To analyze the correlation of lipoprotein(a) [Lp(a)] with the clinical stability and severity of coronary artery stenosis in patients with coronary artery disease (CAD). METHODS: A total of 531 patients undergoing coronary angiography in Nanfang Hospital between January, 2013 and December, 2016 were enrolled in this study. At the cutoff Lp(a) concentration of 300 mg/L, the patients were divided into high Lp(a) group (=191) and low Lp(a) group (=340). In each group, the patients with an established diagnosis of CAD based on coronary angiography findings were further divided into stable angina pectoris (SAP) group and acute coronary syndrome (ACS) group. The correlation between the severity of coronary artery stenosis and Lp(a) was evaluated. RESULTS: The patients in high and low Lp(a) groups showed no significant differences in age, gender, body mass index, smoking status, hypertension, or diabetes (>0.05). Multivariate logistic regression analysis revealed that age, gender, and serum levels of low-density lipoprotein cholesterol (LDL-C) and Lp(a) were independent risk factors for CAD in these patients. A high Lp(a) level was associated with an increased risk of CAD (OR=2.443, 95%CI: 1.205-4.951, =0.013). The patients with a high Lp(a) level were at a significantly higher risk of CAD than those with a low Lp(a) level irrespective of a low or high level of LDL-C (=0.006 and 0.020). In the patients with CAD, the ACS group had a significantly higher Lp(a) level than the SAP group ( < 0.001); the proportion of the patients with high Gensini scores was significantly greater in high Lp(a) group than in low Lp(a) group (17.3% vs 5.6%, =0.026), and a linear relationship was found between Lp(a) level and Gensini score (R=0.130, =0.006). CONCLUSIONS Serum level of Lp(a) is an independent risk factor for CAD, and an increased Lp(a) is the residual risk for CAD. In patients with CAD, a high Lp(a) level is associated with the clinical instability and severity of coronary artery stenosis.
Acute Coronary Syndrome ; blood ; Angina Pectoris ; blood ; Cholesterol, LDL ; blood ; Coronary Angiography ; Coronary Artery Disease ; blood ; classification ; Coronary Stenosis ; blood ; pathology ; Humans ; Lipoprotein(a) ; blood ; Regression Analysis ; Risk Factors ; Severity of Illness Index

BACKGROUND:The greatest risk of osteoporosis in total knee arthroplastyisperioperative and long-term periprosthetic fractures. However, limited by the traditional concept of osteoarthritis patientswhousualy not associated with osteoporosis, domestic clinical trials have not given enough attention to the osteoporosis before total knee arthroplasty.
OBJECTIVE:To analyze the osteoporosis and its relative factors in osteoarthritis patients before total knee arthroplasty.
METHODS:Data of 81 cases (81 knees) of knee osteoarthritiswhounderwenttotal knee arthroplasty in the Department of Joint Surgery of Tianjin People’s Hospital from January 2012 to November 2014 were retrospectively analyzed. They received lumbar spine and hip bone mineral density examination before surgery. The correlation of bone mineral density with age, body mass index, knee motion range,and knee deformity was analyzed before surgery. The independent risk factors for osteoporosis before replacement were analyzed.
RESULTS AND CONCLUSION:(1) Of 81 patients, there were normal bone mineral density in 25 cases (31%), osteopenia in 35 cases (43%),andosteoporosis in 21 cases (26%). Al 10 males had no osteoporosis and 21 in 71 female cases suffered osteoporosis (30%). Theincidence of osteoporosis in females was significantly higher than in male patients (P=0.046). (2) There was a linear correlation of bone mineral density with age and body mass index (correlation coefficientr=-0.230, 0.225). (3) The age of≥65 years and body mass index≥25 kg/m2were independent risk factors of osteoporosis before replacement (P< 0.05). (4) Patients with knee osteoarthritis had higher incidence of osteopenia and osteoporosis before total knee arthroplasty. The degree of osteoporosis was negatively correlated with age, but positively correlated with body mass index. The age of≥65 years and body mass index < 25 kg/m2were independent risk factors for preoperative osteoporosis. Thus, bone mineral density examination is essential for those patients before total knee arthroplasty.