Peristomal Moisture-Associated Skin Damage Treatment: Use of Cyanoacrylate Liquid Skin Protectant: A Case Series.

BACKGROUND: Despite recent advances in ostomy care, the incidence of stoma and peristomal skin complications including peristomal moisture-associated skin damage (MASD) remains as high as 80% of patients living with ostomies. We evaluated a cyanoacrylate liquid skin protectant (CLSP) for the treatment and healing of peristomal MASD in patients with an ileostomy, ileal conduit, or colostomy. CASES: Five patients (24-85 years old) with peristomal MASD related to an ileostomy (n = 2), ileal conduit (n = 2), or colostomy (n = 1) were evaluated in this case study. All were treated with a CLSP in an attempt to reduce peristomal MASD caused by effluent leakage, which resulted in painful denudation of the peristomal skin. All patients received 1 to 2 applications of the CLSP prior to replacement of the pouching system. Prior to CLSP application, patients underwent assessment focusing on the causes of ostomy pouching system undermining and leakage. Interventions to prevent recurrent undermining and leakage, usually focused on modifications of the pouching system, were completed when indicated. CONCLUSIONS: For these 5 patients, complete resolution of peristomal MASD was observed at 2 to 8 days following CLSP treatment. More severe peristomal MASD cases required 7 to 8 days for complete resolution while less severe peristomal MASD resolved within 2 to 3 days. Patients showed less frequent pouching system changes, healing of peristomal skin, and reduced peristomal MASD associated with the CLSP treatment and addressing underlying etiology. On a pain scale of 0 to 10, patients reported less pain with an average of more than 7 out of 10 prior to the CLSP treatment and less than 4 out of 10 after treatment.

Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data.

Parkinson's disease is a disorder that affects the neurons in the human brain. The various symptoms include slowness of motor functions (bradykinesia), motor instability, speech impairment and in some cases, psychiatric effects such as hallucinations. Most of these, however, are also common side effects of natural aging. This makes an accurate diagnosis of Parkinson's disease a challenging task. Some breakthroughs have been made in recent years with the help of deep learning. This work aims at considering figure drawing data as a time series of coordinates, angles and pressure readings to train recurrent neural network models. In addition, the work compares two recurrent network models, Long Short-Term Memory and Echo State Networks, to explore the advantages and disadvantages of both architectures.

Multifunctional Surface, Subsurface, and Systemic Therapeutic (MS3T) Formulation for the Control of Citrus Canker.

A multifunctional surface, subsurface and systemic therapeutic (MS3T) formulation comprised of two bactericides, both didecyldimethylammonium chloride (DDAC) and a zinc (Zn)-chelate, was developed as an alternative to copper pesticides for crop protection. Agricultural grade chemicals were used to prepare MS3T formulations. Minimal inhibitory concentration (MIC) was determined to be tested in vitro against Xanthomonas alfalfae subsp. citrumelonis (herein called Xa), Escherichia coli (E. coli), and Pseudomonas syringae (Ps). Assessment of the phytotoxic potential was carried out on tomato under greenhouse conditions. Moreover, field trials were conducted during three consecutive years on grapefruit (Chrysopelea paradise) groves to evaluate efficacy against citrus canker (Xanthomonas citri subsp. citri), scab (Elsinoe fawcetti), and melanose (Diaporthe citri). In addition to disease control, improvements to both fruit yield and quality were observed likely due to the nutritional activity of MS3T via the sustained release of plant nutrients (Zn and nitrogen). Zn residues of leaf tissues were analyzed via atomic absorption spectroscopy (AAS) at various time points before and after MS3T foliar applications throughout the duration of the 2018 field trial. Field trial results demonstrated MS3T to be an effective alternative to copper (Cu)-based formulations for the control of citrus canker.

Significant cognitive decline in Parkinson's disease exacerbates the reliance on visual feedback during upper limb reaches.

While upper limb reaches are often made in a feed-forward manner, visual feedback during the movement can be used to guide the reaching hand towards a target. In Parkinson's disease (PD), there is evidence that the utilisation of this visual feedback is increased. However, it is unclear if this is due solely to the characteristic slowness of movements in PD providing more opportunity for incorporating visual feedback to modify reach trajectories, or whether it is due to cognitive decline impacting (feed-forward) movement planning ability. To investigate this, we compared reaction times and movement times of reaches to a target in groups of PD patients with normal cognition (PD-NC), mild cognitive impairment (PD-MCI) or dementia (PD-D), to that of controls with normal cognition (CON-NC) or mild cognitive impairment (CON-MCI). Reaches were undertaken with full visual feedback (at a 'natural' and 'fast-as-possible' pace); with reduced visual feedback of the reaching limb to an illuminated target; and without any visual feedback to a remembered target with eyes closed. The PD-D group exhibited slower reaction times than all other groups across conditions, indicative of less efficient movement planning. When reaching to a remembered target with eyes closed, all PD groups exhibited slower movement times relative to their natural pace with full visual feedback. Crucially, this relative slowing was most pronounced for the PD-D group, compared to the PD-MCI and PD-NC groups, suggesting that substantial cognitive decline in PD exacerbates dependence on visual feedback during upper limb reaches.

Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease.

Animal models of human disease provide an in vivo system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the potential to provide a valuable tool for drug development. Here, we have developed a zebrafish model of Parkinson's disease (PD) together with a novel method to screen for movement disorders in adult fish, pioneering a more efficient drug-testing route. Mutation of the PARK7 gene (which encodes DJ-1) is known to cause monogenic autosomal recessive PD in humans, and, using CRISPR/Cas9 gene editing, we generated a Dj-1 loss-of-function zebrafish with molecular hallmarks of PD. To establish whether there is a human-relevant parkinsonian phenotype in our model, we adapted proven tools used to diagnose PD in clinics and developed a novel and unbiased computational method to classify movement disorders in adult zebrafish. Using high-resolution video capture and machine learning, we extracted novel features of movement from continuous data streams and used an evolutionary algorithm to classify parkinsonian fish. This method will be widely applicable for assessing zebrafish models of human motor diseases and provide a valuable asset for the therapeutics pipeline. In addition, interrogation of RNA-seq data indicate metabolic reprogramming of brains in the absence of Dj-1, adding to growing evidence that disruption of bioenergetics is a key feature of neurodegeneration.This article has an associated First Person interview with the first author of the paper.

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input-output mappings. Our results show that ESNs can carry out effective classification and are competitive with existing approaches that have significantly longer training times, in addition to performing similarly with models employing conventional feature extraction strategies that require expert domain knowledge. This suggests that ESNs may prove beneficial in situations where predictive models must be trained rapidly and without the benefit of domain knowledge, for example on high-dimensional data produced by wearable medical technologies. This application area is emphasized with a case study of Parkinson's disease patients who have been recorded by wearable sensors while performing basic movement tasks.

A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson's Dyskinesia.

Parkinson's disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient's movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia.

Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms.

This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models.

Using epigenetic networks for the analysis of movement associated with levodopa therapy for Parkinson's disease.

Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa.

Which figure copy test is more sensitive for cognitive impairment in Parkinson's disease: Wire cube or interlocking pentagons?

BACKGROUND: Performance on figure copy tests has been shown to predict progressive cognitive decline in Parkinson's disease (PD). Historically, the interlocking pentagons from the Mini Mental State Exam (MMSE) have been the figure copy test most commonly used during cognitive screening evaluations. However, the wire cube from the Montreal Cognitive Assessment (MoCA) is increasingly being used. OBJECTIVE: To evaluate which of these figure copy tests is more sensitive for cognitive impairment in PD. METHODS: Sixty-three PD patients from UK and USA completed the MMSE and MoCA. Logistic regression and sensitivity/specificity analyses were used to evaluate the utility of each figure copy test for detecting global cognitive impairment. RESULTS: The wire cube was a significant indicator of cognitive impairment (OR=4.79, 95% CI=1.63-14.07, p=0.004), with a sensitivity/specificity of 0.74/0.63 in our sample. In contrast, interlocking pentagons were not a significant indicator of cognitive impairment (OR=1.88, 95% CI=0.54-6.50, p=0.32), with a sensitivity/specificity of 0.26/0.84. CONCLUSION: The wire cube is more sensitive to cognitive impairment in PD, most likely related to its greater complexity. The results have implications for clinicians who may have time for just one figure copying task as part of a brief screen for cognitive impairment in busy clinics and for researchers applying the PD mild cognitive impairment diagnostic criteria necessitating two tests of visuospatial function to be administered.

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease.

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.

Characterising neurological time series data using biologically motivated networks of coupled discrete maps.

Artificial biochemical networks (ABNs) are a class of computational dynamical system whose architectures are motivated by the organisation of genetic and metabolic networks in biological cells. Using evolutionary algorithms to search for networks with diagnostic potential, we demonstrate how ABNs can be used to carry out classification when stimulated with time series data collected from human subjects with and without Parkinson's disease. Artificial metabolic networks, composed of coupled discrete maps, offer the best recognition of Parkinsonian behaviour, achieving accuracies in the region of 90%. This is comparable to the diagnostic accuracies found in clinical diagnosis, and is significantly higher than those found in primary and non-expert secondary care. We also illustrate how an evolved classifier is able to recognise diverse features of Parkinsonian behaviour and, using perturbation analysis, show that the evolved classifiers have interesting computational behaviours.

Potential for Raman spectroscopy to provide cancer screening using a peripheral blood sample.

Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical, breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and non-cancer patients through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The PCA/LDA classifier gave a 65% sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75% with a specificity of 75% was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be implemented in the clinical setting.

An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease.

This paper presents a novel evolutionary algorithm inspired by protein/substrate binding exploited in enzyme genetic programming (EGP) and artificial immune networks. The immune network-inspired evolutionary algorithm has been developed in direct response to an application in clinical neurology, the diagnosis of Parkinson's disease. The inspiration for, and implementation of the algorithm is described and its performance to the application area considered.

The optimal management of the axillae of patients with microinvasive breast cancer in the sentinel lymph node era.

BACKGROUND: For patients with microinvasive breast cancer, the value of intraoperative analysis of sentinel lymph nodes (SLNs) and complete axillary lymph node dissection (CALND) is not well known. METHODS: All patients staged T1mic from 2001 to 2005 were analyzed. RESULTS: Among all 81 patients, 4 (5%) had SLN metastases detected with hematoxylin and eosin staining and 2 (2%) had metastases identified by immunohistochemistry staining only. Seventy-seven patients (95%) underwent SLN biopsy; 3 (4%) had hematoxylin and eosin SLN metastases and 2 (3%) had immunohistochemistry-detected metastases. One SLN metastasis was identified on frozen section analysis. No patient with a SLN metastasis had additional metastases on CALND. The patient charges for frozen section analyses were $39,578 for 77 patients. This prevented 1 reoperative CALND at a charge of $20,274. CONCLUSIONS: Frozen section analysis should be used only in select patients with microinvasive breast cancer and CALND is of limited value for these patients.

Intraoperative gamma probe localization of the ureters: a novel concept.

BACKGROUND: Ureteral stent placement to localize the ureters during operations is an invasive procedure. The aim of this study was to evaluate the feasibility of using the gamma probe to intraoperatively identify the ureters after intravenous injection of a radiopharmaceutical agent. STUDY DESIGN: Ten patients undergoing elective abdominal operations were prospectively enrolled in this study. An average dose of 4.5 mCi (range 2.8 to 5.3 mCi) of technetium Tc 99m-labeled diethylenetriamine pentaacetic acid ((99m)Tc-DTPA) was administered intravenously before localization of the ureters. The gamma probe was used to localize the ureters. Correct identification of the ureters was confirmed when gentle manipulation induced a typical ureteral peristaltic pattern. RESULTS: Gamma counts were significantly elevated in all ureters examined. Compared with background counts, gamma counts were increased over the ureter in all patients, with an average increase of 465%. The technique was modified after use in the first 3 patients and standardized for patients 4 through 10. Data from those seven patients were analyzed. Both ureters were correctly identified using the gamma probe at a mean of 15 minutes (median, 10 minutes) after a single (99m)Tc-DTPA injection (range 4 to 41 minutes). The mean background count was 80 counts per second (cps, range 50 to 130 cps). The mean ureter count was 393 cps (range 128 to 700 cps). The average percent increase of each ureter count compared with its specific background count was 465% (range 256% to 1,077%). The difference was statistically significant for all values (p < 0.001). CONCLUSIONS: This novel technique of gamma probe localization of the ureters may offer a noninvasive approach for ureteral identification.

Acute suppurative thyroiditis caused by Pasteurella multocida and associated with thyrotoxicosis.

Acute suppurative thyroiditis (AST) is an uncommon condition of the thyroid gland. Organisms of the staphylococcal and streptococcal species are the most commonly reported causative agents. Rarely, AST has been associated with transient hyperthyroidism. We report a unique case of AST that was caused by Pasteurella multocida and was associated with thyrotoxicosis in a previously healthy 51-year-old woman.

Solitary breast mass as initial presentation of clinically silent metastatic renal cell carcinoma.

Metastasis to the breast from extramammary tumors is rare. Breast metastases of renal cell carcinoma (RCC) origin have been described in sporadic case reports. We present a patient with a solitary breast mass representing the manifestation of clinically silent, metastatic RCC. A 76-year-old female was 12 years prior removed from radical nephrectomy for localized RCC. Her new breast mass was identified on physical examination. Pathology of the resected mass was diagnostic of metastatic RCC and subsequent imaging studies demonstrated a 1.9 cm renal mass in her solitary kidney. The patient elected subcutaneous Interleukin-2 immunotherapy as primary treatment for her recurrent RCC.