Repair of the lacertus fibrosus as an adjunct to distal biceps tendon repair strengthens the repair in the laboratory setting. Clinical testing is needed to verify that this increased strength improves clinical results. Surgeons should be cautioned to protect the underlying neurovascular structures during repair of the lacertus fibrosus and to avoid an overly tight repair.

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Respiratory signals collected from young adults using Biopac's abdominal strain gauge were properly filtered, amplified and digitized. An algorithm that combined Autoregressive (AR) and modified zero-crossing models was used to extract signal parameters such as the energy and frequency of the underlying signal. These parameters were used in a classification scheme based on fuzzy logic. Because of the variability of respiration signals, fuzzy logic provides a more natural classification as opposed to threshold based method [3]-[4]. Experimental results show that fuzzy logic presents a flexible and adaptable classificatory mechanism, which shows in percentage to which a segment of respiration signal belongs to one of the following categories: normal respiration, respiration with artifacts or apnea. It can be effectively used to reduce false alarms and improve classification of ambiguous cases.

Methemoglobinemia, defined as hemoglobin's impaired oxygen-carrying capacity due to oxidation from the ferrous (Fe2+) state to the ferric (Fe3+) state, has many well-documented etiologies. One example of an uncommon cause of acquired methemoglobinemia is the ingestion of nail polish remover, which can contain methemoglobin generators such as nitroethane, N,N-dimethyl-p-toluidine, and isobutyl nitrite. We present a case of methemoglobinemia in an 81-year-old male following accidental ingestion of isobutyl nitrite-containing nail polish remover, commonly used as a recreational inhalant. Furthermore, we review potentially toxic substances found in commercially available nail products. This case was designed to identify and efficiently treat a rather uncommon cause of methemoglobinemia induced in this case by a common household item, nail polish remover.

In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the concept of binary neural network and geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. It's a semisupervised based approach, the training samples are semi-labelled i.e. for some samples, labels are known and for some samples data labels are not known. The method starts with classification, which is done by using the concept of ETL algorithm. In classification process various classes are formed. These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.