Objective:To investigate the efficacy and safety of acute stent implantation during endovascular treatment for patients with emergent large vessel occlusion due to intracranial atherosclerotic stenosis.Methods:A retrospective analysis was carried out on 46 patients with emergent large vessel occlusion due to intracranial atherosclerotic stenosis who received endovascular treatment at the Strategic Support Force Medical Center from January 2015 to August 2022. Twenty-seven patients underwent balloon angioplasty alone and 19 patients underwent acute stent implantation. The baseline characteristics, modified thrombolysis in cerebral infarction (mTICI) score of the responsible vessels, modified Rankin scale (mRS) score 90 days after operation, incidence of symptomatic intracranial hemorrhage and mortality of the two groups were evaluated.Results:The proportion of effective recanalization of the offending vessels (mTICI≥2b) in the acute stenting group was slightly higher than that in the balloon angioplasty group (16/19 vs. 81.5%), but the difference was not statistically significant ( P>0.05). Besides, there was no significant difference in the median of mRS between the acute stenting group [3.0(0, 4.0)] and the balloon angioplasty group [4.0(1.0, 5.0)] 90 days after operation ( P>0.05). In terms of safety, the incidence of symptomatic intracranial hemorrhage and mortality were comparable between the two groups ( P>0.05). Conclusions:The effect of acute stent implantation during endovascular treatment for patients with emergent large vessel occlusion due to intracranial atherosclerotic stenosis is not inferior to that of balloon angioplasty, and it does not increase the risk of intracranial bleeding complications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Traditional medications used for treating autoimmune diseases often come with a wide range of adverse effects. Current treatments focus mainly on symptom management, resulting in significant health issues and financial burdens for patients. Recently, clinical research has demonstrated the potential of helminths and their derivatives as effective therapies for autoimmune disorders. Helminths, being a near-natural immunomodulator, exhibit milder effects than broad-spectrum immunosuppressants and corticosteroids, thereby presenting a promising alternative for the treatment of autoimmune diseases. However, different helminths' therapeutic efficacy and mechanisms and their derivatives in treating autoimmune diseases may vary. Therefore, we aim to review recent clinical advancements in the use of helminths and their derivatives for treating inflammatory bowel disease, multiple sclerosis, and autism spectrum disorder, with a view to offering novel clinical treatment approaches.
Animals ; Humans ; Autism Spectrum Disorder ; Autoimmune Diseases/drug therapy* ; Helminths ; Inflammatory Bowel Diseases

Objective:To evaluate the safety and efficacy of intra-arterial tirofiban infusion during endovascular reperfusion therapy in patients with acute cardiogenic cerebral embolism.Methods:Clinical data of 72 patients with acute cardiogenic cerebral embolism caused by large artery occlusion were retrospectively analyzed in Department of Neurology, Strategic Support Force Medical Center from August 2015 to August 2020.Among those, 52 patients were treated with intra-arterial tirofiban, the other 20 patients were treated with control medication. The baseline characteristics, modified thrombolysis in cerebral infarction (mTICI) score of responsible vessels, modified Rankin scale (mRS) score 90 days after operation, incidence of symptomatic intracranial hemorrhage and mortality were evaluated and compared in two groups.Results:The proportion of effective recanalization of the offending vessels (mTICI≥2b) in tirofiban group was higher than that in control group (92.3% vs. 75.0%), but the difference was not statistically significant ( P=0.104). At 90 days after operation, the rate of patients with good prognosis (mRS≤2) in tirofiban group (61.5%) was significantly higher than that in control group (35.0%) ( P<0.05). The incidence of symptomatic intracranial hemorrhage and mortality were comparable between the two groups ( P>0.05). Conclusion:Intra-arterial tirofiban infusion in patients with acute cardiogenic cerebral embolism is effective and feasible, which improves the prognosis without increasing the risk of intracranial bleeding complications.

With the research progress on the biology and pathogenesis of cancer, immune checkpoint inhibitors (ICIs) have come into being, bringing a new hope for the survival of patients with advanced cancer and opening a new era of cancer immunotherapy. However, with the wide application of immunotherapy in clinical practice, ICI-related adverse events (irAEs) have gradually emerged and are widely known by first-line clinicians. ICIs primarily activate T cells that can attack normal tissues and organs in the body and cause a variety of adverse reactions. Checkpoint inhibitor pneumonitis (CIP) is one of the rare complications with poor prognosis in irAEs. This article reviews the therapeutic mechanism of some ICIs; the incidence, risk factors, pathogenesis, and clinical and imaging manifestations of CIP; and the classification and treatment management of CIP.

The number of the patients with double primary cancers is increasing. Breast cancer and lung cancer are the two most common malignant tumors in women, seriously threatening the health of the majority of women. Although the lung is one of the most common metastatic sites of breast cancer, in recent years, there have been increasing cases of primary lung cancer in patients with breast cancer. The incidence of double primary cancers with breast cancer as the first primary cancer is much higher than that with lung cancer as the first primary cancer. Primary breast cancer is closely related to the occurrence and development of lung cancer. This article reviews the epidemiology, clinicopathological features, diagnosis and treatment of double primary breast cancer and lung cancer with breast cancer as the first cancer.

This study proposes an image segmentation method based on bottleneck detection and watershed algorithm to solve the problem of overlapping cervical cell image. First, we use polygon approximation to get all feature points on the cell contour and then use bottleneck detection and ellipse fitting to obtain the correct split point pairs. Therefore, the approximate range of the overlapping region was determined. The watershed algorithm was used to obtain the internal boundary information for the gradient image of the region. Finally, the segmentation results of the overlapped cells were obtained by superimposing with the outer contour. The experimental results show that this algorithm can segment the contour of a single cell from the overlapping cervical cell images with good accuracy and integrity. The segmentation result is close to that of doctors' manual marking, and the segmentation result is better than other existing algorithms.
Algorithms ; Cervix Uteri/cytology* ; Female ; Humans ; Image Processing, Computer-Assisted