To investigate the relationship between mean platelet volume (MPV) level and carotid atherosclerosis and prognosis in patients with acute cerebral infarction

. A retrospectively included 160 patients with acute cerebral infarction classified by TOAST classification as aortic atherosclerosis as the observation group. To analyze the relationship between MPV and carotid atherosclerosis, and use receiver operating characteristic (ROC) curves to analyze the role of MPV in predicting the prognosis of acute cerebral infarction in the observation group, grouping patients with different MPV by cut-off value, and analyze the differences in factors between the two groups of patients.

MPV has a positive correlation with carotid atherosclerosis in patients with acute cerebral infarction. Multivariate logistic regression analysis revealed that increased MPV was an independent predictor of poor functional outcome in patients with acute cerebral infarction (Odds Ratio (OR) 6.152, 95% CI 2.385-13.625,

< 0.01). ROC curve analysis showed that the area under the curve for MPV to predict poor prognosis was 0.868 (95% CI 0.787-949,

< 0.01). The cutoff value, sensitivity, and specificity were 12.65, 76.2%, and 87.6%. Compared with patients with MPV < 12.65 at admission, patients with higher MPV levels (MPV ≥ 12.65) at admission have larger infarct size, more severe carotid artery stenosis, poor short-term prognosis, and higher mortality.

MPV level is closely related to the degree of carotid atherosclerosis in patients with acute cerebral infarction, and it is also an independent predictor of poor prognosis in patients with acute cerebral infarction at 3 months.

MPV level is closely related to the degree of carotid atherosclerosis in patients with acute cerebral infarction, and it is also an independent predictor of poor prognosis in patients with acute cerebral infarction at 3 months.

Diabetes mellitus (DM) is a common disease that has an adverse impact on most orthopedic surgeries, and its prevalence has gradually increased in recent years. We aim to investigate the influence of DM on comorbidities and complications of patients undergoing primary total lower extremity arthroplasty.

PubMed, Embase, Cochrane Library, Medline, and Web of Science were systematically searched for relevant studies published before December 2019. Demographic data, comorbidities, and postoperative complications after primary total hip arthroplasties (THA) or primary total knee arthroplasties (TKA) were assessed between DM and non-DM patients. Meta-analysis was conducted using Review Manager 5.3, and forest plots were drawn for each variable.

A total of 1,560,461 patients (215,916 patients with DM and 1,344,545 patients without DM) from 23 studies were included in this meta-analysis. The incidences of several preoperative comorbidities (hypertension (HTN), kidney disease, cardiac and cerebrovascular disease) were generally higher in patients with DM. Moreover, DM patients had a higher rate of postoperative complications (superficial and deep infection, deep vein thrombosis (DVT), and in-hospital mortality) compared to non-DM patients.

DM patients were more likely to suffer from comorbidities and had a higher risk of complications in total lower extremity arthroplasty compared to non-DM patients. It is necessary to identify DM and control hyperglycemia in the perioperative period to prevent postoperative complications in patients with DM.

DM patients were more likely to suffer from comorbidities and had a higher risk of complications in total lower extremity arthroplasty compared to non-DM patients. It is necessary to identify DM and control hyperglycemia in the perioperative period to prevent postoperative complications in patients with DM.

We identified adult CVST patients in our centers. Functional outcome and prevention strategy were extracted from medical records. Modified Rankin Scale (mRS) ≤ 1 is considered a good functional outcome.

A total of 113 patients were identified. The most common presenting symptoms were headache (86.72%) and nausea/vomiting (56.63%); the top two identified risk factors were local head/neck infection (27.43%) and pregnancy/puerperal period (19.47%). NMS-873 ic50 The medical encounter lag time was 0.04 d-120 d. Four enrolled patients were diagnosed as CVST again, and the interval time was 3-8 years from the first time. Thrombus was most frequently seen at superior sagittal sinus (53.10%) and sigmoid sinus (50.44%). 94 (83.19%) of the patients had good outcomes. In the acute phase, 91 (80.53%) patients received low molecular weight heparin, 29 (25.66%) took aspirin, 7 (6.19%) patients were put on low molecular weight heparin and aspirin together. During our follow up (6-24 m), there were 10 (8.85%) patients who suffered from thrombotic event recurrence. For the patients > 40 years old, they tended to suffer from neurological deficit (25.00%) and stupor/coma (16.67%) (

> 0.05), with a higher rate of hemorrhage (20.83%) and death (4.16%) when compared with the younger patients (10.77% and 1.53%, separately) (

> 0.05).

Functional outcome after CVST appears good. For the patients over 40-year-old, neurological deficit and altered consciousness were more common, accompanied by a higher rate of hemorrhage and mortality. The recurrent rate of CVST was low, longer-term follow up needed. The prevention strategy after CVST was uncertain, further studies needed.

Functional outcome after CVST appears good. For the patients over 40-year-old, neurological deficit and altered consciousness were more common, accompanied by a higher rate of hemorrhage and mortality. The recurrent rate of CVST was low, longer-term follow up needed. The prevention strategy after CVST was uncertain, further studies needed.Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when medical images parameters are trained using convolutional neural networks. Lung tumors in chest CT image based on nonnegative, sparse, and collaborative representation classification of DenseNet (DenseNet-NSCR) are proposed by this paper firstly, initialization parameters of pretrained DenseNet model using transfer learning; secondly, training DenseNet using CT images to extract feature vectors for the full connectivity layer; thirdly, a nonnegative, sparse, and collaborative representation (NSCR) is used to represent the feature vector and solve the coding coefficient matrix; fourthly, the residual similarity is used for classification. The experimental results show that the DenseNet-NSCR classification is better than the other models, and the various evaluation indexes such as specificity and sensitivity are also high, and the method has better robustness and generalization ability through comparison experiment using AlexNet, GoogleNet, and DenseNet-201 models.