Pathogenesis of Painful Diabetic Neuropathy

The prevalence of diabetes is rising globally and, as a result, its associated complications are also rising. Painful diabetic neuropathy (PDN) is a well-known complication of diabetes and the most common cause of all neuropathic pain. About one-third of all diabetes patients suffer from PDN. It has a huge effect on a person’s daily life, both physically and mentally. Despite huge advances in diabetes and neurology, the exact mechanism of pain causation in PDN is still not clear. The origin of pain could be in the peripheral nerves of the central nervous system. In this review, we discuss various possible mechanisms of the pathogenesis of pain in PDN. We discuss the role of hyperglycaemia in altering the physiology of peripheral nerves. We also describe central mechanisms of pain

Diagnosis and Treatment of a Typical Painful Neuropathy Due to "Insulin Neuritis" in Patients with Diabetes

Diabetes is very common and its global prevalence is rising day by day. As a result we are seeing more complications related to diabetes. In order to prevent micro vascular and macro vascular complications such as retinopathy, nephropathy, erectile dysfunction, neuropathy, myocardial infarction and stroke health care professionals are keen to have better glycaemic control. When dealing with newly diagnosed or poorly controlled diabetes patients are encouraged to bring down glycated haemoglobin (HbA1c). Diabetic painful neuropathy (DPN) is one of the well-known complications associated with long- term poor glycaemic control. However, on the other hand rapid control of high blood sugar can precipitate painful neuropathy known as “insulin neuritis”. The rapid tight glycaemic control with either insulin or oral hypoglycaemic agents on poorly controlled diabetic patients cause flux of blood glucose and metabolic shift resulting in structural changes at nerve endings (endoneural blood vessels) which resemble the retinopathy changes in retina. It causes steal effect and hypoxia in the nerves and hence precipitates neuropathic pain. It lasts for about 6 months and responds well to standard treatment of painful neuropathy. Health professionals need to be aware of this condition and consider gentle glycaemic control when aiming for Target HbA1c. This review outlines the disease, the symptoms, the types and treatment

An Observational Study on Lignocaine Infusion for Intractable Chronic Pain

Objective: This study assessed the efficacy of lignocaine infusion as a treatment for painful diabetic neuropathy (PDN) groups compared to chronic pain (non-PDN group) in challenging cases where conventional treatment is not helpful. Methods: A total 11 patients participated in the study with 7 patients referred from pain clinic (non-PDN group) and 4 for patients referred from diabetic foot clinic (PDN group) for lignocaine infusion as a treatment for chronic pain. Both groups of participants were on multiple combination of pain medication with minimal help. All the subjects gave consent for participation and filled out McGill short form (SF) questionnaire before and after the lignocaine infusion. Results: The results show 33% reduction of visual analogue pain score after lignocaine infusion in PDN group compared to 11% reduction of visual analogue pain score in non-PDN group. These data were statistically significant (p0.05). All 11 patients had no reported adverse effects and their observations were in normal limits throughout the lignocaine infusion. Conclusion: Overall, the study showed that lignocaine infusion is both effective and safe in reducing chronic intractable pain when conventional treatments are intolerable or not helpful. It is more effective in painful diabetic neuropathy patients compared to other causes of chronic pain

Empowering local communities to make lifestyle changes: is the Health Mela a potential solution?

Background: Health Melas are community-led public health events held in the North West of England that provide health information and free health checks. This descriptive observational study evaluates whether Health Melas are able to identify undiagnosed cardiovascular disease (CVD) risk factors in hard-to-reach communities and encourage individuals to make lifestyle changes. Methods: Attendees ≥18 years at three separate Health Melas in 2016–2017 were invited to participate in screening and counselling for CVD risk factors as part of a Health MOT. Information was collected about demographics, CVD risk factors, blood pressure, total cholesterol, blood sugar and attendees’ feedback. QRISK2 scoring system was used to estimate CVD risk. Results: 375 attendees completed a questionnaire. The highest proportion (36.9%) of attendees were from areas of the lowest Index of Health Deprivation and Disability quintile; 38.8% were of South Asian ethnicity. Of the attendees who were eligible for a free National Health Service Health Check, 9.1% had received one. Overall, 57.5% of all attendees had a QRISK2 score ≥10% (of whom 56.9% were not on statins), 92.2% of attendees believed the Health Mela will help them to make lifestyle changes, 98.2% said they had improved their understanding of their health, and 99.6% thought the Health Mela was useful. 73.6% of those who had received a previous Health MOT reported making lifestyle changes. There was a positive correlation between South Asian ethnicity and QRISK2 score. Conclusion: This study suggests the Health Melas successfully involve South Asian populations and people from a lower Index of Health Deprivation and Disability. Attendees felt the events were useful, improved understanding of their health needs and encouraged them to make lifestyle changes. High rates of modifiable CVD risk factors were newly identified and a high proportion of attendees were found to be at intermediate to high risk of CVD

Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices

Current practice for Diabetic Foot Ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In this work, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this dataset by outlining the region of interest of DFU with an annotator software. Using 5-fold cross-validation, overall, Faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91.8%, the speed of 48 ms for inferencing a single image and with a model size of 57.2 MB. To demonstrate the robustness and practicality of our solution to real-time prediction, we evaluated the performance of the models on a NVIDIA Jetson TX2 and a smartphone app. This work demonstrates the capability of deep learning in real-time localization of DFU, which can be further improved with a more extensive dataset

Diabetic Foot Infections and Problems in Guyana

Diabetes mellitus (DM) is a major global metabolic disorder currently affecting more than 465 million people. If diagnosed late or left untreated, DM can induce a number of long-term complications which are due to DM-induced hyperglycaemia leading to nephropathy, cardiomyopathy, neuropathy, retinopathy, impotence, foot ulcers and amputations. Diabetic foot problems (DFB) are major concerns for the patients as they affect their quality of life and exert great financial burden on them, especially for people from a low income developing country as Guyana. In 2019, it was estimated that the expenditure for diagnosis, treatment and care for diabetic foot patients was USAD$75 million in Guyana. Both obesity and diabetes are on the increase in Guyana with almost 11 - 12% of the adult population have DM, due to obesity (diabesity) and more women compared to men. Moreover, there is an unacceptable high rate of amputations due to lack of organised foot-care programme and the absence of national guidelines in its management. There are various aspects of good diabetic foot care and this article now reviews what has been achieved and what needs prioritisation to improve the management of diabetic foot problems in Guyana among patients with diabetes mellitus. ’The Guyana Diabetes and Foot Care Project” has made significant improvement in this area but the lack of specialist vascular surgical service is obvious

DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification

Globally, in 2016, 1 out of 11 adults suffered from diabetes mellitus. Diabetic foot ulcers (DFU) are a major complication of this disease, which if not managed properly can lead to amputation. Current clinical approaches to DFU treatment rely on patient and clinician vigilance, which has significant limitations, such as the high cost involved in the diagnosis, treatment, and lengthy care of the DFU. We collected an extensive dataset of foot images, which contain DFU from different patients. In this DFU classification problem, we assessed the two classes as normal skin (healthy skin) and abnormal skin (DFU). In this paper, we have proposed the use of machine learning algorithms to extract the features for DFU and healthy skin patches to understand the differences in the computer vision perspective. This experiment is performed to evaluate the skin conditions of both classes that are at high risk of misclassification by computer vision algorithms. Furthermore, we used convolutional neural networks for the first time in this binary classification. We have proposed a novel convolutional neural network architecture, DFUNet, with better feature extraction to identify the feature differences between healthy skin and the DFU. Using 10-fold cross validation, DFUNet achieved an AUC score of 0.961. This outperformed both the traditional machine learning and deep learning classifiers we have tested. Here, we present the development of a novel and highly sensitive DFUNet for objectively detecting the presence of DFUs. This novel approach has the potential to deliver a paradigm shift in diabetic foot care among diabetic patients, which represent a cost-effective, remote, and convenient healthcare solution