An evaluation of two signal-processing hearing aids

Various forms of signal processing are used in modern ASP hearing aids. The present study investigated the relationship between sentence recognition ability and two types of signal processing used in commercially available hearing aids. Results indicated a significant improvement in sentence recognition ability employing an instrument with adaptive compression (variable release time) versus an instrument with an adaptive high-pass filter with short attack and release times. Data were obtained for a single-talker competing message at several message-to-competition ratios. The adaptive compression system may prove to be beneficial to hearing-impaired listeners in certain background noise environments.     

Molecular genetics and transfusion management in a child with Bernard Soulier syndrome.

We present a case of Bernard Soulier syndrome in a 9-year-old boy caused by a novel genetic mutation. This child was shown to be homozygous for a single nucleotide deletion (c.1077delG) in the GP1BA gene not previously reported. Clinically, the boy has become refractory to platelet transfusions with both allo-antibodies and iso-antibodies and a massive transfusion requirement for ongoing haemorrhage. We describe the critical role that the blood product transfusion continues to play in the management of Bernard Soulier syndrome and discuss therapeutic options in these patients.     

荧光原位杂交技术分析人结肠菌群方法研究

建立荧光原位杂交技术分析人体内结肠菌群的方法。取受试者新鲜粪便 ,选用 5种特异性的 16SrRNA寡核苷酸探针 ,检测粪便样本收集后的保存时间、温度 ,离心条件及样本固定液存放时间对杂交计数结果的影响。结果建立最佳实验条件为 :粪便样本收集后应尽快在 4℃下保存 ,放置时间不要超过 12小时即作处理 ;样本的适宜离心条件为 70 0g 2分钟 ;样本用多聚甲醛固定后在 - 80℃下存放时间不要超过 5个月。该方法具有较好的稳定性 ,可以有效地检出个体之间结肠菌群的差异。     

Altered gastric emptying in the head-injured patient: relationship to feeding intolerance

Most patients with moderate to severe head injury initially do not tolerate enteral feedings postinjury. This intolerance is more prolonged than that found in patients suffering other types of trauma. The authors prospectively evaluated 12 patients with moderate to severe head injury (Glasgow Coma Scale score between 4 and 10) throughout their hospitalization for liquid gastric emptying as a possible mechanism for intolerance to enteral feeding. During Week 1, the majority of patients displayed a delay in gastric emptying. Patients also displayed an abnormal biphasic response (gastric emptying faster than normal during the early stage but prolonged later). By Week 2, many patients still had delayed and abnormal biphasic responses to gastric emptying. By Week 3, an improvement was observed with the majority of patients exhibiting rapid gastric emptying, but delays and abnormal biphasic responses were still seen. Patients who initially had rapid or normal gastric emptying tolerated full-strength full-rate feedings significantly earlier compared with those who experienced delayed gastric emptying (8.5 +/- 0.5 days vs. 13.7 +/- 3.2 days, p less than 0.001). All patients tolerated full-strength full-rate feedings by Day 16 postinjury (range 7 to 16 days) except the two patients who displayed delayed gastric emptying for prolonged periods of time (mean 25 days). This is the first study to longitudinally evaluate gastric emptying following head injury. The authors suggest that patients with moderate to severe head injury often experience alterations in gastric emptying which may affect their ability to tolerate enteral feedings.     

Using an artificial neural network to diagnose hepatic masses

Using abdominal ultrasonographic data and laboratory tests, radiologists often find differential diagnoses of hepatic masses difficult. A computerized second opinion would be especially helpful for clinicians in diagnosing liver cancer because of the difficulty of such diagnoses. A back-propagation neural network was designed to diagnose five classifications of hepatic masses: hepatoma, metastic carcinoma, abscess, cavernous hemangioma, and cirrhosis. The network input consisted of 35 numbers per patient case that represented ultrasonographic data and laboratory tests. The network architecture had 35 elements in the input layer, two hidden layers of 35 elements each, and 5 elements in the output layer. After being trained to a learning tolerance of 1%, the network classified hepatic masses correctly in 48 of 64 cases. An accuracy of 75% is higher than the 50% scored by the average radiology resident in training but lower than the 90% scored by the typical board-certified radiologist. When sufficiently sophisticated, a neural network may significantly improve the analysis of hepatic-mass radiographs.