Purpose This study aimed to investigate the incidence, location, and related factors of preoperative deep venous thrombosis (DVT) in patients with isolated patellar fractures. Methods Patients with an isolated patellar fracture, admitted between January 2013 and December 2019 at our institution, were retrospectively analyzed. Upon admission, patients underwent routine Doppler ultrasound scanning (DUS) of the bilateral lower extremities to detect DVT; those with DVT were assigned to the case group and those without DVT to the control group. Patients in both groups did not perform preoperative off-bed weight-bearing exercises. Data on demographics, comorbidities, and laboratory test results upon admission were extracted. Variables were evaluated between the two groups using univariate analyses, and independent risk factors associated with DVT were identified by logistic regression analysis. Results During the study period, 827 patients were included, of whom 5.8% (48/827) were found to have preoperative DVT. In DVT patients, 85.4%(41/48) were injured, 8.3%(4/48) were not injured, and 6.3%(3/48) were lower limbs. Multivariate analysis showed that male (male vs. female, odds ratio, OR = 2.25), delayed from injury to DUS (in each day, OR = 1.29), and elevated plasma D-dimer level (> 0.5 µg/mL, OR = 2.47) were independent risk factors associated with DVT. Conclusions Despite the low prevalence of DVT after an isolated patellar fracture, this study underscores the importance of identifying those with a high risk of DVT, especially those with multiple identifiable factors, and encourage the early targeted use of anti-thromboembolic agents to reduce DVT occurrence.

Objective This study aims to investigate the incidence, occurrence timing and locations of preoperative DVT and identify the associated factors in this group. Methods A retrospective analysis of collected data in young and middle-aged (18–59 years) patients who presented with hip fracture between October 2015 and December 2018 was conducted. Before operation, patients were routinely examined for DVT by Duplex ultrasonography (DUS). Electronic medical records were retrieved to collect the data, involving demographics, comorbidities, injury and laboratory biomarkers after admission. Multivariate logistic regression analysis was performed to identify factors that were independently associated with DVT. Results Eight hundred and fifty-seven patients were included, and 51 (6.0%) were diagnosed with preoperative DVT, with 2.5% for proximal DVT. The average age of patients with DVT is 48.7 ± 9.4 year, while that of patients without DVT is 45.0 ± 10.9 year. The mean time from injury to diagnosis of DVT was 6.8 ± 5.5 days, 43.1% cases occurring at day 2–4 after injury. Among 51 patients with DVT, 97 thrombi were found. Most patients had thrombi at injured extremity (72.5%), 19.6% at uninjured and 7.8% at bilateral extremities. There are significantly difference between patients with DVT and patients without DVT in term of prevalence of total protein (41.2% vs 24.4%, P = 0.008), albumin (54.9% vs 25.6%, P = 0.001), low lactate dehydrogenase (51.0% vs 30.3%, P = 0.002), lower serum sodium concentration (60.8% vs 29.9%, P = 0.001), lower RBC count (68.6% vs 37.0%, P = 0.001), lower HGB (51.0% vs 35.1%, P = 0.022), higher HCT (86.3% vs 35.1%, P = 0.022) and higher platelet count (37.3% vs 11.3%, P = 0.001). The multivariate analyses showed increasing age in year (OR 1.04, 95% CI; P = 0.020), delay to DUS (OR, 1.26; P = 0.001), abnormal LDH (OR, 1.45; P = 0.026), lower serum sodium concentration (OR, 2.56; P = 0.007), and higher HCT level (OR, 4.11; P = 0.003) were independently associated with DVT. Conclusion These findings could be beneficial in informed preventive of DVT and optimized management of hip fracture in specific group of young and mid-aged patients.

Diagnosis of numerous cancers has been closely linked to the expression of certain long non-coding RNAs. This study aimed to evaluate levels of plasma FEZ family zinc finger 1 antisense RNA 1 (FEZF1-AS1) relative to non-small-cell lung carcinoma (NSCLC) diagnosis. The level of FEZF1-AS1 in the blood plasma of 126 NSCLC patients and 62 healthy controls was examined by quantitative real-time polymerase chain reaction. Plasma FEZF1-AS1 of the NSCLC group was increased compared with that in the control group (P < .0001). Plasma FEZF1-AS1 could distinguish patients with NSCLC from healthy individuals via the area under the ROC curve (AUC) of 0.855 (95% CI = 0.800–0.909; P = .000). FEZF1-AS1 combined with neuron-specific enolase increased the area under the (ROC) curve to 0.932 (95% CI = 0.897–0.968; P = .018). A high expression level of plasma FEZF1-AS1 was associated with some clinical features of NSCLC. Increased expression of FEZF1-AS1 greatly improved the risk of NSCLC (adjusted OR = 2.42; 95% CI = 1.23–4.76). A significant concentration–dependent relationship was noted between risk of NSCLC and higher FEZF1-AS1 expression (P for trend <.001). Plasma FEZF1-AS1 could potentially be used as a biomarker for NSCLC diagnosis.

BackgroundInhibin subunit beta A (INHBA) is a member of the TGF-beta (transforming growth factor-beta) superfamily proteins, which plays a fundamental role in various cancers. However, there is little systematical analysis on the exact role of INHBA in patients with gastric cancer (GC). Herein, we explored the exact role and the underlying mechanisms of INHBA regarding GC using multiple bioinformatic approaches. MethodsThe expression levels of INHBA in GC were analyzed in TIMER, GEPIA2, GEO, Oncomine and UALCAN databases. Protein and PCR test were performed to verify the expression states of INHBA in GC tissues. The correlation of INHBA and prognosis of GC was analyzed based on Kaplan Meier plotter database. Besides, the relationship between INHBA expression and immune infiltration levels and the type markers of immune cells in GC was explored in the TIMER database. What’s more, we studied INHBA mutations, promoter methylation, functional enrichment analysis in GC patients based on cBioportal, MEXPRESS, Metascape and LinkedOmics databases. Besides, we performed immunohistochemistry (IHC) and polymerase chain reaction (qRT-PCR ) verification in tissues from patients with gastric cancerResultsINHBA was elevated in GC and high expression level of INHBA in GC was significantly related to the unfavorable prognosis. Protein and PCR test verified the highly expression states of INHBA in GC tissues. Further analysis showed that INHBA was negatively correlated with B cell while positively correlated with the marker type of CD8+ T cells, macrophage, neutrophil and dendritic cell infiltration. High INHBA expression level had a poor prognosis in different enriched immune cells subgroups in GC. And there is week significant methylation level change between tumor and normal tissues. Moreover, INHBA mainly enriched cancer-related signaling pathways, including TGF-beta signaling pathway, ECM-receptor signaling pathway, PID ALK1/2 pathway, and AGE-RAGE signaling pathway. ConclusionsThe present study implies that INHBA may serve as a potential biomarkers for predicting prognosis in GC patients. High INHBA expression in GC may affect prognosis through immune infiltration.

In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%.