从肾虚血瘀辨治前列腺增生症合并勃起功能障碍

勃起功能障碍和前列腺增生症是中老年男性最为常见的2种非肿瘤性疾病,且发病率随着年龄的增长而升高.现代研究表明,勃起功能障碍和前列腺增生之间有着密切相关性,两者可同时发病,但是目前中医对于勃起功能障碍合并前列腺增生的研究较少,且临床上尚无令人满意的治疗方法.中医学认为年老肾虚,瘀血内阻为其主要的病因病机,肾虚为本,瘀血为标,因此,临证以补肾益气,活血化瘀为基本治法,往往能够取得较好的效果.故从"肾虚血瘀"论治前列腺增生合并勃起功能障碍值得从临床方面来作进一步的探讨.     

Do men with normal testosterone–oestradiol ratios benefit from letrozole for the treatment of male infertility?

Research question

Previous studies of aromatase inhibitors on male infertility have focused on men with low testosterone–oestradiol ratio of less than 10. Can aromatase inhibitors improve spermatogenesis in men with idiopathic male infertility with normal testosterone–oestradiol ratio?

Chest radiography in acute aortic syndrome: pearls and pitfalls

Acute aortic syndrome is a group of life-threatening diseases of the thoracic aorta that usually present to the emergency department. It includes aortic dissection, aortic intramural hematoma, and penetrating aortic ulcer. Rare aortic pathologies of aorto-esophageal fistula and mycotic aneurysm may also be included in this list. All these conditions require urgent treatment with complex clinical care and management. Most patients who present with chest pain are evaluated with a chest radiograph in the emergency department. It is important that maximum diagnostic information is extracted from the chest radiograph as certain signs on the chest radiograph are extremely useful in pointing towards the diagnosis of acute aortic syndrome.     

Recurrent wheeze and cough in young children: is it asthma?

A clinical diagnosis of asthma is often considered when a child presents with recurrent cough, wheeze and breathlessness. However, there are many other causes of wheeze in a young child. These range from recurrent viral infections to chronic suppurative lung disease, gastro-oesophageal reflux disease and rare structural abnormalities. Arriving at a diagnosis includes taking into consideration the symptomatology, triggers, atopic features, family history, absence of red flags and therapeutic trial, where indicated.     

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

Background contextVariability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns.PurposeTo use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM.Study designThis is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion.Patient sampleA total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification.Outcome measuresFor each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded.MethodsA Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map.ResultsThe topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types.ConclusionsAn AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.