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This study examined the effects of a 24-week Tai Chi intervention on physical function in individuals with peripheral neuropathy. Twenty-five women and men with peripheral neuropathy were recruited. Plantar pressure detection threshold was assessed with a 5.07 gauge monofilament. Functional gait was assessed by the 6-min walk and timed up-and-go tests. Isokinetic leg strength and standing balance was also assessed. Twenty-four consecutive weeks of modified, group-based Tai Chi practice was completed, with testing repeated every six weeks throughout. No adverse events were observed and attendance was 17 ± 4 sessions per 6 weeks. After 6 weeks of Tai Chi, participants increased 6-min walk (P < 0.0001), timed up-and-go (P < 0.0001), and leg strength (P < 0.01) performance. Continued improvement was observed in the timed up-and-go. Plantar sensation improved (P = 0.003) following the Tai Chi intervention. Group-based Tai Chi is a safe, plausible, and effective intervention for those with PN.

Diabetes mellitus (DM) or hyperglycemia (in a more generalized term, high blood sugar) is a metabolic disorder that is now highly prevalent in the world population. Most of the food that people consume is converted into glucose, which enters the bloodstream following absorption–assimilation mechanisms. As a natural process, cells in our body utilize glucose for growth and energy. The glucose balance is maintained by a hormone called insulin that is secreted by the beta cells of pancreas. Hypotheses at the backdrop of DM occurrence are either (i) enough insulin is not produced and secreted resulting in increased level of glucose in blood, or (ii) insulin is insensitive to glucose, or (iii) insulin is non-targeted etc. If DM remains uncontrolled over time, it leads to serious damage to many of the body's systems, especially the nerves and blood vessels. This paper develops an enquiry into diabetes from many angles: (i) Diabetes as a disorder, its complications, causes, diagnostic tests, and treatment; (ii) Analysis of retinal and plantar images to characterize diabetes complications; (iii) How analysis of heart rate variability signals can depict diabetes; (iv) Biomedical engineering of the glucose–insulin regulatory system, and its employment in the modeling of the oral glucose tolerance test data, to detect diabetes as well as persons at risk of being diabetic; (v) Application of the glucose–insulin regulatory system to formulate an insulin delivery system for controlling blood sugar.

The recent years have witnessed an increase in the use of newer analytical tools in the field of medicine to assist in diagnostic procedure. Among the new tools, artificial neural networks (ANNs) have received particular attention because of their ability to analyze complex nonlinear data sets. This study suggests that ANNs can be used for the diagnosis of peripheral nerve disorders particularly the carpal tunnel syndrome (CTS) and neuropathy. This paper aims at building a classifier using a feed forward neural network that can distinguish between CTS, neuropathy, and normal controls using a reduced set of measurements or features from nerve conduction study (NCS) data. Three different ANN training algorithms, viz. Levenberg–Marquardt (LM), Conjugate gradient (CGB), and resilient back-propagation (RP) are used to see which algorithm produces better results and has faster training for the application under consideration. The data used were obtained from the Neurology Department, Kannur Medical College, Kerala, India. The obtained resultant confusion matrix indicated only a few misclassifications in all the three cases. The analysis showed that the CGB and RB algorithms provide faster convergence on pattern recognition problems, but the best performance in terms of accuracy is given by the LM algorithm. The accuracy obtained for the LM, CGB, and RB were 98.3%, 97.8%, and 97.2%, respectively. The respective sensitivities were 96.1%, 94.1%, and 94.1%, while the specificities were found to be equal to 99.4%, 98.8%, and 97.5%, respectively. The study aims at showing that ANNs may prove useful in combination with other systems in providing diagnostic and predictive medical opinions. However, it must always be kept in mind that ANNs represent only one form of computer-aided diagnosis, and the clinician's responsibility and overall control of patient care should never be underestimated.

This chapter covers the definition of peripheral neuropathy and the available non-invasive neurological assessments performed. Peripheral neuropathy is subdivided into motor, sensory and autonomic neuropathy. Only assessments for motor and sensory neuropathy will be described. All assessments for testing motor nerves and sensory nerves are non-invasive, inexpensive and patient friendly. The assessments for testing motor nerves are position sense, Romberg test, two-point discrimination, deep tendon reflexes for lower limbs, coordination and muscle tone test. The assessments for testing sensory nerves are light touch test, pinprick test, Semmes-Weinstein monofilament test, biothesiometer, neurothesiometer test, tuning fork and temperature test. These assessments are crucial in identifying patients suffering from peripheral neuropathy. Not all tests are required to be performed. Doctors and practitioners may be selective in neurological assessment. Correct diagnosis of peripheral neuropathy helps early prevention for serious complications such as neuropathic ulcers in the foot. The frequency of performing neurological assessment varies depending on the patient's current condition. Nevertheless, it is recommended to conduct a full neurological assessment on the patient on an annual basis.