Along with the coming of aged society, the prevalence of heart valvular disease is significantly increasing, and the use of bioprosthetic valves for treating patients with severe valve disease has increased over the last two decades. As a consequence, a growing number of patients with surgical bioprosthesis degeneration is predicted in the near future. In this setting, valve-in-valve (ViV) transcatheter aortic/mitral valve replacement (TAVR/TMVR) has emerged as an alternative to redo surgery. A deep knowledge of the mechanism and features of the failed bioprosthetic heart valve is pivotal to plan an adequate procedure. Multimodal imaging is fundamental in the diagnostic and pre-procedural phases. The immediate and mid-term clinical and hemodynamic results have demonstrated the safety and feasibility of ViV techniques, but the development of these techniques faces several specific challenges, such as coronary obstruction, potential post-procedural mismatch and leaflet thrombosis. This article reviews the current status and prospects of ViV-TAVR technology in the treatment for biological valve degeneration, and suggests that ViV-TAVR should be promoted and implemented in existing medical centers with good surgical aortic valve replacement experience, so as to provide better treatment for patients.

As a global disease, heart failure affects at least 26 million people, and its prevalence is still rising. Besides, the mortality rate and readmission rate remain high. Advanced heart failure is the terminal stage of various heart diseases, and often requires some treatments other than drug intervention, such as heart transplantation which is the gold standard for treatment of heart failure. However, limited by the number of donors, the number of heart transplants in the world has reached a bottleneck. There is a huge gap between the number of patients who need heart transplants and patients who get hearts for survival successfully in reality. With the exploration and development of mechanical circulation support devices for more than half a century, they have become a wonderful treatment for patients with advanced heart failure. This article will introduce the latest progress of mechanical circulatory support devices at home and abroad from the aspects of temporary and long-term devices.

Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.
Algorithms ; Gene Expression Profiling ; Genes, Neoplasm ; Humans ; Lung ; Lung Neoplasms/genetics* ; Pharmaceutical Preparations

Myasthenia gravies is a common disease in the clinic. Extended thymectomy is an important way to treat myasthenia gravis. Median thoracotomy, thoracoscopy, and robots are important surgical methods. Da Vinci robot-assisted thoracoscopic surgery is more and more widely used in extended thymectomy, with high surgical safety and good stability. The surgical approach includes intercostal approach, subxiphoid approach, etc. Different surgical approaches have their own advantages, and their surgical effects are different. This article introduces the indications, technical steps, and effects of da Vinci robot-assisted thoracoscopic surgery, analyzes the advantages and limitations of treating myasthenia gravis, and looks forward to its development prospects.

OBJECTIVE: To explore the long-term effectiveness of arthroscopic partial repair in treatment of massive irreparable rotator cuff tears from both the radiological and clinical perspectives. METHODS: A retrospective analysis was conducted on the clinical data of 24 patients (25 sides) with massive irreparable rotator cuff tears who met the inclusion criteria between May 2006 and September 2014. Among them, there were 17 males (18 sides) and 7 females (7 sides) with an age range of 43-67 years (mean, 55.0 years). There were 23 cases of unilateral injury and 1 case of bilateral injuries. All patients were treated with the arthroscopic partial repair. The active range of motion of forward elevation and abduction, external rotation, and internal rotation, as well as the muscle strength for forward flexion and external rotation, were recorded before operation, at the first postoperative follow-up, and at last follow-up. The American Association of Shoulder and Elbow Surgeons (ASES) score, the University of California at Los Angeles (UCLA) shoulder scoring, and Constant score were used to evaluate shoulder joint function. And the visual analogue scale (VAS) score was used to evaluate shoulder joint pain. MRI examination was performed. The signal-to-noise quotient (SNQ) was measured above the anchor point near the footprint area (m area) and above the glenoid (g area) in the oblique coronal T2 fat suppression sequence. The atrophy of the supraspinatus muscle was evaluated using the tangent sign. The global fatty degeneration index (GFDI) was measured to assess fat infiltration in the supraspinatus muscle, infraspinatus muscle, teres minor muscle, upper and lower parts of the subscapularis muscle. The mean GFDI (GFDI-5) of 5 muscles was calculated. RESULTS: The incisions healed by first intention. All patients were followed up with the first follow-up time of 1.0-1.7 years (mean, 1.3 years) and the last follow-up time of 7-11 years (mean, 8.4 years). At last follow-up, the range of motion and muscle strength of forward elevation and abduction, ASES score, Constant score, UCLA score, and VAS score of the patients significantly improved when compared with those before operation ( P<0.05). Compared with the first follow-up, except for a significant increase in ASES score ( P<0.05), there was no significant difference in the other indicators ( P>0.05). Compared with those before operation, the degree of supraspinatus muscle infiltration worsened at last follow-up ( P<0.05), GFDI-5 increased significantly ( P<0.05), and there was significant difference in the tangent sign ( P<0.05); while there was no significant difference in the infiltration degree of infraspinatus muscle, teres minor muscle, and subscapularis muscle, upper and lower parts of the subscapularis muscle ( P>0.05). Compared with the first follow-up, the SNQm and SNQg decreased significantly at last follow-up ( P<0.05). At the first and last follow-up, there was no correlation between the SNQm and SNQg and the ASES score, Constant score, UCLA score, and VAS score of the shoulder ( P>0.05). CONCLUSION Arthroscopic partial repair is effective in treating massive irreparable rotator cuff tear and significantly improves long-term shoulder joint function. For patients with severe preoperative fat infiltration involving a large number of tendons and poor quality of repairable tendons, it is suggested to consider other treatment methods.
Male ; Female ; Humans ; Adult ; Middle Aged ; Aged ; Rotator Cuff Injuries/surgery* ; Retrospective Studies ; Shoulder Joint/surgery* ; Treatment Outcome ; Arthroscopy/methods* ; Range of Motion, Articular

Magnetic resonance imaging(MRI) can obtain multi-modal images with different contrast, which provides rich information for clinical diagnosis. However, some contrast images are not scanned or the quality of the acquired images cannot meet the diagnostic requirements due to the difficulty of patient's cooperation or the limitation of scanning conditions. Image synthesis techniques have become a method to compensate for such image deficiencies. In recent years, deep learning has been widely used in the field of MRI synthesis. In this paper, a synthesis network based on multi-modal fusion is proposed, which firstly uses a feature encoder to encode the features of multiple unimodal images separately, and then fuses the features of different modal images through a feature fusion module, and finally generates the target modal image. The similarity measure between the target image and the predicted image in the network is improved by introducing a dynamic weighted combined loss function based on the spatial domain and K-space domain. After experimental validation and quantitative comparison, the multi-modal fusion deep learning network proposed in this paper can effectively synthesize high-quality MRI fluid-attenuated inversion recovery (FLAIR) images. In summary, the method proposed in this paper can reduce MRI scanning time of the patient, as well as solve the clinical problem of missing FLAIR images or image quality that is difficult to meet diagnostic requirements.
Humans ; Deep Learning ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods*

For the detection and identification of abnormal nodular tissues on the body surface, a microwave sensor structure loaded with a spiral resonator is proposed in this paper, a sensor simulation model is established using HFSS software, the structural parameters are optimized, and the actual sensor is fabricated. The S21 parameters of the tissue were obtained when nodules appeared by simulation, and the characteristic relationship between the difference of S21 parameters with position was analyzed and tested experimentally. The results showed that when nodules were present in normal tissues, the curve of S21 parameter difference with position change had obvious inverted bimodal characteristics, and the extreme value of S21 parameter difference appeared when the sensor was directly above the nodules, which was easy to identify the position of nodules. It provides an objective detection tool for the identification of abnormal nodular tissues on the body surface.
Microwaves ; Recognition, Psychology ; Computer Simulation ; Software

Interventional embolization therapy is widely used for procedures such as targeted tumour therapy, anti-organ hyperactivity and haemostasis. During embolic agent injection, doctors need to work under X-ray irradiation environment. Moreover, embolic agent injection is largely dependent on doctors' experience and feelings, and over-injection of embolic agent can lead to reflux, causing ectopic embolism and serious complications. As an effective way to reduce radiation exposure and improve the success rate of interventional embolization therapy, embolic agent injection robot is highly anticipated, but how to decide the injection flow velocity of embolic agent is a problem that remains to be solved. On the basis of fluid dynamics simulation and experiment, we established an arterial pressure-injection flow velocity boundary curve model that can avoid reflux, which provides a design basis for the control of embolic agent injection system. An in vitro experimental platform for injection system was built and validation experiments were conducted. The results showed that the embolic agent injection flow speed curve designed under the guidance of the critical flow speed curve model of reflux could effectively avoid the embolic agent reflux and shorten the embolic agent injection time. Exceeding the flow speed limit of the model would lead to the risk of embolization of normal blood vessels. This paper confirms the validity of designing the embolic agent injection flow speed based on the critical flow speed curve model of reflux, which can achieve rapid injection of embolic agent while avoiding reflux, and provide a basis for the design of the embolic agent injection robot.
Embolization, Therapeutic/methods*

Objective    To systematically evaluate the risk factors for postoperative pulmonary infection in patients with esophageal cancer. Methods    CNKI, Wangfang Data, VIP, CBM, PubMed, EMbase, The Cochrane Library were searched from inception to January 2021 to collect case-control studies, cohort studies and cross-sectional studies about risk factors for postoperative pulmonary infection in patients with esophageal cancer. Two researchers independently conducted literature screening, data extraction and quality assessment. RevMan 5.3 software and Stata 15.0 software were used for meta-analysis. Results    A total of 20 articles were included, covering 5 409 patients of esophageal cancer. The quality score of included studies was 6-8 points. Meta-analysis results showed that age (MD=1.99, 95%CI 0.10 to 3.88, P=0.04), age≥60 years (OR=2.68, 95%CI 1.46 to 4.91, P=0.001), smoking history (OR=2.41, 95%CI 1.77 to 3.28, P<0.001), diabetes (OR=2.30, 95%CI 1.90 to 2.77, P<0.001), chronic obstructive pulmonary disease (OR=3.69, 95%CI 2.09 to 6.52, P<0.001), pulmonary disease (OR=2.22, 95%CI 1.16 to 4.26, P=0.02), thoracotomy (OR=1.77, 95%CI 1.32 to 2.37, P<0.001), operation time (MD=14.08, 95%CI 9.64 to 18.52, P<0.001), operation time>4 h (OR=3.09, 95%CI 1.46 to 6.55, P=0.003), single lung ventilation (OR=3.46, 95%CI 1.61 to 7.44, P=0.001), recurrent laryngeal nerve injury (OR=5.66, 95%CI 1.63 to 19.71, P=0.006), and no use of patient-controlled epidural analgesia (PCEA) (OR=2.81, 95%CI 1.71 to 4.61, P<0.001) were risk factors for postoperative pulmonary infection in patients with esophageal cancer. Conclusion    The existing evidence shows that age, age≥60 years, smoking history, diabetes, chronic obstructive pulmonary disease, pulmonary disease, thoracotomy, operation time, operation time>4 h, single lung ventilation, recurrent laryngeal nerve injury, and no use of PCEA are risk factors for postoperative pulmonary infection in patients with esophageal cancer. Due to the limitation of the quantity and quality of included literature, the conclusion of this study still needs to be confirmed by more high-quality studies.

Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.
Image Processing, Computer-Assisted/methods*