A typology of internationally qualified dentists in the United Kingdom.

Introduction: The Global Strategy for Human Resources for Health 2030, requires member states to half their dependency on an international workforce by 2030. In order to design policies towards that goal, country-specific research on migration motivations of the health workforce is required. The United Kingdom (UK) is a net importer of health professionals and whilst there is a body of research on doctors' and nurses' migration, there is no research on the migration motivations of migrant dentists in the UK. This research explored the migration motivations of internationally qualified dentists (IQDs) in the UK and presents a typology to understand the global migration of dentists in the context of oral health workforce. Methods: The paper presents qualitative data from semi-structured interviews conducted between August 2014 and October 2017, of IQDs working in the United Kingdom. The topic guide for interviews was informed by the literature, with new themes added inductively. A phenomenological approach involving an epistemological stance of interpretivism, was used with framework analysis. Results: A total of 38 internationally qualified dentists (M = 18, F = 20), migrating from the five World Health Organization regions, and working in general practice, NHS hospitals and in community dental services across the four nations of the UK were interviewed. Seven types of internationally qualified dentists were identified working in the UK. They were livelihood migrants, career-orientated migrants, dependant migrants, backpacker migrants, commuter migrants, undocumented migrants, and education-tourist migrant. The categories were based on their migration motivations, which were complex, multifactorial, and included personal, professional, national, and international drivers. The typology, based on their migration motivations, offered a structured, comprehensive understanding of the migrant dental workforce. This typology involving dentists provides additional dimensions to commuter and undocumented migrants described in the context of other health professionals. The education-tourist migrant is a new category proposed as an extension to existing typology in health professional migration. Conclusions: The typology of internationally qualified dentists has congruency with other health professionals' typology in categories previously described and demonstrates that each of these categories are complex, fluid and change in response to policy changes. The new category of education-tourist migrant along with oral health dimensions of commuter and undocumented migrants adds to the existing typology in health professional migration.

Modulation of Nuclear Receptor 4A1 Expression Improves Insulin Secretion in a Mouse Model of Chronic Pancreatitis.

OBJECTIVES: Diabetes secondary to chronic pancreatitis (CP) presents clinical challenges due to lack of understanding on factor(s) triggering insulin secretory defects. Therefore, we aimed to delineate the molecular mechanism of ß-cell dysfunction in CP. MATERIALS AND METHODS: Transcriptomic analysis was conducted to identify endocrine-specific receptor expression in mice and human CP on microarray. The identified receptor (NR4A1) was overexpressed in MIN6 cells using PEI linear transfection. RNA-Seq analysis of NR4A1-overexpressed (OE) MIN6 cells on NovaSeq6000 identified aberrant metabolic pathways. Upstream trigger for NR4A1OE was studied by InBio Discover and cytokine exposure, whereas downstream effect was examined by Fura2 AM-based fluorimetric and imaging studies. Mice with CP were treated with IFN-γ-neutralizing monoclonal antibodies to assess NR4A1 expression and insulin secretion. RESULTS: Increased expression of NR4A1 associated with decreased insulin secretion in islets (humans: controls 9 ± 0.2, CP 3.7 ± 0.2, mice: controls 8.5 ± 0.2, CP 2.1 ± 0.1 µg/L). NR4A1OE in MIN6 cells (13.2 ± 0.1) showed reduction in insulin secretion (13 ± 5 to 0.2 ± 0.1 µg/mg protein per minute, P = 0.001) and downregulation of calcium and cAMP signaling pathways. IFN-γ was identified as upstream signal for NR4A1OE in MIN6. Mice treated with IFN-γ-neutralizing antibodies showed decreased NR4A1 expression 3.4 ± 0.11-fold ( P = 0.03), showed improved insulin secretion (4.4 ± 0.2-fold, P = 0.01), and associated with increased Ca 2+ levels (2.39 ± 0.06-fold, P = 0.009). CONCLUSIONS: Modulating NR4A1 expression can be a promising therapeutic strategy to improve insulin secretion in CP.

Survival after Trastuzumab Therapy in Patients with Locally Advanced or Metastatic HER2-Positive Gastric or Gastroesophageal Junction Cancer: A Meta-Analysis of Randomized Controlled Trials.

Background: In the Trastuzumab for Gastric Cancer study, it was found that trastuzumab combined with doublet chemotherapy (fluoropyrimidine and platinum) was the gold-standard treatment for gastroesophageal adenocarcinoma (GEA) that was locally advanced, unresectable, or metastatic (HER2+). Materials and Methods: We performed a meta-analysis of randomized phase II/III studies testing trastuzumab in combination or alone. Results: This meta-analysis's findings involved 2048 patients in total. The treatment arm and hormone receptor status were used to stratify the combined HR. Overall, the PFS (Random model) HR [0.80] and 95% confidence intervals (CI) [0.68-0.95] were significantly higher for regimens containing trastuzumab, fluoropyrimidine, and platinum compared to regimens containing fluoropyrimidine and platinum. Conclusions: The results of this meta-analysis provide additional support for trastuzumab's use in treating HER2-positive GEA, particularly in cases where the disease lacks a HER2+ receptor.

Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds.

Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction on neuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives.

Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images.

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.

Detection and mitigation of coordinated cyber-physical attack in CPPS.

Cyber-Physical Power System (CPPS) refers to a system in which the elements of the internet and the physical power system communicate and work together. With the use of modern communication and information technology, grid monitoring and control have improved. However, the components of a cyber system are extremely vulnerable to cyberattacks via cyber connections due to inadequate cyber security measures. Therefore, an adaptive defence strategy is required for the analysis and mitigation of the coordinated attack. The conventional approach of using an offline controller requires tuning for changes in the operating conditions of the system, which is inappropriate for the modern CPPS. To counter the coordinated attack, a framework that integrates STATCOM based Adaptive Model Predictive Controller with RPME and time delay compensator is proposed. This paper addresses attack impact, detection, and mitigation methods in CPPS. In both time domain and frequency domain simulations the case studies are conducted for three distinct situations namely physical attack, cyberattack, and coordinated attack. Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), and K Nearest Neighbour (KNN) are four data-driven methods used for the detection of anomalies in PMU measurement data. Simulation studies show that CNN performs better in anomaly detection than other classifiers based on assessed performance metrics. For coordinated attack mitigation the proposed STATCOM based Adaptive Model Predictive Controller with RPME quickly recovers the system than the STATCOM based conventional lead-lag controller. The efficacy of the proposed strategy is validated on the WSCC 3 machine 9 bus system.

Association of Cardiovascular Events with COVID-19 Vaccines using Vaccine Adverse Event Reporting System (VAERS): A Retrospective Study.

BACKGROUND: COVID-19 vaccines have played a crucial role in reducing the burden of the global pandemic. However, recent case reports have indicated the association of the COVID- 19 vaccines with cardiovascular events but the exact association is unclear so far. OBJECTIVE: Therefore, the objective of the current study is to find out the association of cardiovascular events with COVID-19 vaccines. METHODS: The COVID-19 Vaccine Knowledge Base (Cov19VaxKB) tool was used to query the Vaccine Adverse Event Reporting System (VAERS) database. The proportional reporting ratio [PRR (≥2)] with associated chi-squared value (>4), and the number of cases > 0.2% of total reports, was used to assess the association of COVID-19 vaccines with cardiovascular events. RESULTS: A total of 33,754 cases of cardiovascular events associated with COVID-19 vaccines were found in the Cov19VaxKB tool. The cases were observed in different age groups (18-64, and 65 years and above) and gender. The disproportionality measures indicate a statistically significant association between cardiovascular events and COVID-19 vaccines. CONCLUSION: The current study identified a signal of various cardiovascular events with the COVID-19 vaccines. However, further causality assessment is required to confirm the association.

Nogo-A reduces ceramide de novo biosynthesis to protect from heart failure.

AIMS: Growing evidence correlate the accrual of the sphingolipid ceramide in plasma and cardiac tissue with heart failure (HF). Regulation of sphingolipid metabolism in the heart and the pathological impact of its derangement remain poorly understood. Recently, we discovered that Nogo-B, a membrane protein of endoplasmic reticulum, abundant in the vascular wall, down-regulates the sphingolipid de novo biosynthesis via serine palmitoyltransferase (SPT), first and rate liming enzyme, to impact vascular functions and blood pressure. Nogo-A, a splice isoform of Nogo, is transiently expressed in cardiomyocyte (CM) following pressure overload. Cardiac Nogo is up-regulated in dilated and ischaemic cardiomyopathies in animals and humans. However, its biological function in the heart remains unknown. METHODS AND RESULTS: We discovered that Nogo-A is a negative regulator of SPT activity and refrains ceramide de novo biosynthesis in CM exposed to haemodynamic stress, hence limiting ceramide accrual. At 7 days following transverse aortic constriction (TAC), SPT activity was significantly up-regulated in CM lacking Nogo-A and correlated with ceramide accrual, particularly very long-chain ceramides, which are the most abundant in CM, resulting in the suppression of 'beneficial' autophagy. At 3 months post-TAC, mice lacking Nogo-A in CM showed worse pathological cardiac hypertrophy and dysfunction, with ca. 50% mortality rate. CONCLUSION: Mechanistically, Nogo-A refrains ceramides from accrual, therefore preserves the 'beneficial' autophagy, mitochondrial function, and metabolic gene expression, limiting the progression to HF under sustained stress.

PHLPPs: Emerging players in metabolic disorders.

That reversible protein phosphorylation by kinases and phosphatases occurs in metabolic disorders is well known. Various studies have revealed that a multi-faceted and tightly regulated phosphatase, pleckstrin homology domain leucine-rich repeat protein phosphatase (PHLPP)-1/2 displays robust effects in cardioprotection, ischaemia/reperfusion (I/R), and vascular remodelling. PHLPP1 promotes foamy macrophage development through ChREBP/AMPK-dependent pathways. Adipocyte-specific loss of PHLPP2 reduces adiposity, improves glucose tolerance,and attenuates fatty liver via the PHLPP2-HSL-PPARα axis. Discoveries of PHLPP1-mediated insulin resistance and pancreatic ß cell death via the PHLPP1/2-Mst1-mTORC1 triangular loop have shed light on its significance in diabetology. PHLPP1 downregulation attenuates diabetic cardiomyopathy (DCM) by restoring PI3K-Akt-mTOR signalling. In this review, we summarise the functional role of, and cellular signalling mediated by, PHLPPs in metabolic tissues and discuss their potential as therapeutic targets.

Mammalian Target of Rapamycin Pathway Assessment in Antiphospholipid Antibody-Positive Patients with Livedo.

OBJECTIVE: In antiphospholipid antibody (aPL) nephropathy, activation of the mammalian target of rapamycin (mTOR) contributes to endothelial cell proliferation, a key finding of aPL microvascular disease. Here, we examined mTOR activation in the skin of aPL-positive patients with livedo. METHODS: Three patient groups with livedo were studied: (1) persistently aPL-positive with systemic lupus erythematosus (SLE); (2) persistently aPL-positive without SLE; and (3) aPL-negative SLE (control). After collecting aPL-related medical history, two 5-mm skin biopsies of livedo were performed on each patient: (1) peripheral (erythematous-violaceous lesion); and (2) central (nonviolaceous area). We stained specimens for phosphorylated protein kinase B (p-AKT) and phosphorylated S6 ribosomal protein (p-S6RP) as mTOR activity markers, CD31 to identify endothelial cells, and Ki-67 to show cellular proliferation. We counted cells in the epidermis and compared mTOR-positive cell counts between peripheral and central samples, and between patient groups, using Freidman test and Wilcoxon signed-rank test. RESULTS: Ten patients with livedo reticularis were enrolled: 4 aPL-positive without SLE (antiphospholipid syndrome [APS] classification met, n = 3), 4 aPL-positive SLE (APS classification met, n = 3), and 2 aPL-negative SLE (control). In all aPL-positive patients, epidermal p-AKT and p-S6RP staining were significantly increased in both peripheral and central skin samples when compared to aPL-negative SLE controls; both were more pronounced in the lower basal layers of epidermis. CONCLUSION: Our study demonstrates increased mTOR activity in livedoid lesions of aPL-positive patients with or without SLE compared to aPL-negative patients with SLE, with more prominent activity in the lower basal layers of the epidermis. These findings may serve as a basis for further investigating the mTOR pathway in aPL-positive patients.

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.

Accreditation and professional integration experiences of internationally qualified dentists working in the United Kingdom.

INTRODUCTION: Regulatory processes for Oral health care professionals are considered essential for patient safety and to ensure health workforce quality. The global variation in their registration and regulation is under-reported in the literature. Regulatory systems could become a barrier to their national and international movement, leading to loss of skilled human resources. The General Dental Council is the regulatory authority in the UK, one of the nine regulators of health care overseen by the Professional Standards Authority. AIM: The aim of this paper is to present the professional integration experiences of internationally qualified dentists (IQDs) working in the UK, against the background of regulation and accreditation nationally. METHODS: Registration data were obtained from the General Dental Council to inform the sampling and recruitment of research participants. Semi-structured interviews of 38 internationally qualified dentists working in the United Kingdom were conducted between August 2014 and October 2017. The topic guide which explored professional integration experiences of the dentists was informed by the literature, with new themes added inductively. A phenomenological approach involving an epistemological stance of interpretivism, was used with framework analysis to detect themes. RESULTS: Internationally qualified dentist's professional integration was influenced by factors that could be broadly classified as structural (source country training; registration and employment; variation in practising dentistry) and relational (experiences of discrimination; value of networks and support; and personal attributes). The routes to register for work as a dentist were perceived to favour UK dental graduates and those qualifying from the European Economic Area. Dentists from the rest of the world reported experiencing major hurdles including succeeding in the licensing examinations, English tests, proving immigration status and succeeding in obtaining a National Health Service performer number, all prior to being able to practice within state funded dental care. CONCLUSION: The pathways for dentists to register and work in state funded dental care in UK differ by geographic type of registrant, creating significant inconsistencies in their professional integration. Professional integration is perceived by an individual IQD as a continuum dictated by host countries health care systems, workforce recruitment policies, access to training, together with their professional and personal skills. The reliance of the UK on internationally qualified dentists has increased in the past two decades, however, it is not known how these trends will be affected by UK's exit from the European Union and the COVID-19 pandemic.

Discernment on assistive technology for the care and support requirements of older adults and differently-abled individuals.

Assistive technology for the differently abled and older adults has made remarkable achievements in providing rehabilitative, adaptive, and assistive devices. It provides huge assistance for people with physical impairments to lead a better self-reliant daily life, in terms of mobility, education, rehabilitation, etc. This technology ranges from simple hand-held devices to complex robotic accessories which promote the individual's independence. This study aimed at identifying the assistance required by differently-abled individuals, and the solutions proposed by different researchers, and reviewed their merits and demerits. It provides a detailed discussion on the state of art assistive technologies, their applications, challenges, types, and their usage for rehabilitation. The study also identifies different unexplored research areas related to assistive technology that can improve the daily life of individuals and advance the field. Despite their high usage, assistive technologies have some limitations which have been briefly described in the study. This review, therefore, can help understand the utilization, and pros and cons of assistive devices in rehabilitation engineering and assistive technologies.

Smart access development for classifying lung disease with chest x-ray images using deep learning.

Recently the world has come across a pandemic disease known as covid-19. The presence of symptoms of covid-19 and pneumonia may be alike to other types of lung illnesses. So, because of this, it is difficult for the affected person or medical experts to identify the condition. Chest x-ray provides general orientation which can be an initial investigative study in the analysis of lung diseases. Information from retenogram studies help the finding of covid-19 and pneumonia affecting the lungs. We use a Convolution Neural Network (CNN) in Tensor Flow and Keras based covid-19, pneumonia classification. The best fit model of CNN is then deployed in the Django framework for providing a better user interface and predicting the output.

Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images.

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

Migration, Retention and Return Migration of Health Professionals Comment on "Doctor Retention: A Cross-sectional Study of How Ireland Has Been Losing the Battle".

Medical education and training of health professionals are linked with their recruitment and retention. Practising as a competent health professional requires life-long continuous training and therefore training structures in health systems appear to influence doctors job satisfaction, their well-being and their intentions to remain in that health system. The commentary critiques aspects of the paper on doctors retention in Ireland, while drawing some parallels with the United Kingdom. There appears to be an emerging type of health professional migrants 'education tourists' who travel to other countries to obtain medical education creating new routes of migration and this presents new challenges to source and destination countries. The global shortage of doctors and other health professionals further exacerbates health inequalities as seen in the present pandemic and therefore the increased need for research into health professionals' migration and their integration.

Profiles of registrant dentists and policy directions from 2000 to 2020.

INTRODUCTION: The National Health Service's reliance on overseas doctors and nurses, unlike dentists, has been widely reported. As the United Kingdom (UK) leaves the European Union, an understanding of the migration trends and possible influences are important to inform future planning. AIM: To examine trends in the profile of UK registered dentists in the context of key events and policy changes from 2000 to 2020. METHOD: Data were obtained from the General Dental Council via annual reports, and under 'freedom of information' communications; details of policy initiatives were obtained from the government and professional websites. RESULTS: Over a 20-year period (2000-2019), the number of registered dentists increased from 31,325 to 42,469, a net increase of 36% (11,144 dentists), the majority of whom were international graduates (58%; n = 6,416) such that by December 2019, 28% of all registered dentists had qualified outside of the UK. Similarly, regarding new registrants, there were increases of graduates from UK (18%), EEA countries (214%) and, via the Overseas Registration Examination route (621%); and a decrease from countries with bilateral agreements for recognition (43%), in line with changes in health and immigration policies. CONCLUSIONS: International dental graduates increasingly contribute to the UK dental workforce and there is an urgent need for research into dentist migration and retention in the UK in support of patient access to dental care. IMPACT: The United Kingdom (UK) dental workforce is increasingly reliant on international dental graduates representing 28% of current registrants compared with 18% in 2000. Health policies and immigration policies were the main drivers that influenced dental workforce migration to the UK along with wider events, such as EU expansions, global recession and Brexit. Pre-existing lack of research into dental workforce could add to the uncertainties of post COVID-19 oral health care access and delivery.

Toxic metabolite profiling of Inocybe virosa.

Wild mushroom foraging involves a high risk of unintentional consumption of poisonous mushrooms which is a serious health concern. This problem arises due to the close morphological resemblances of toxic mushrooms with edible ones. The genus Inocybe comprises both edible and poisonous species and it is therefore important to differentiate them. Knowledge about their chemical nature will unambiguously determine their edibility and aid in an effective treatment in case of poisonings. In the present study, the presence of volatile toxic metabolites was verified in Inocybe virosa by gas chromatography. Methyl palmitate, phenol, 3,5-bis (1,1-dimethyl ethyl) and phytol were the identified compounds with suspected toxicity. The presence of the toxin muscarine was confirmed by liquid chromatography. The in vitro study showed that there was negligible effect of the digestion process on muscarine content or its toxicity. Therefore, the role of muscarine in the toxicity of Inocybe virosa was studied using a bioassay wherein metameters such as hypersalivation, immobility, excessive defecation, heart rate and micturition were measured. Administration of muscarine resulted in an earlier onset of symptoms and the extract showed a slightly stronger muscarinic effect in comparison to an equivalent dose of muscarine estimated in it. Further, the biological fate of muscarine was studied by pharmacokinetics and gamma scintigraphy in New Zealand white rabbits. Significant amount of the toxin was rapidly and effectively concentrated in the thorax and head region. This study closely explains the early muscarinic response such as miosis and salivation in mice. By the end of 24 h, a relatively major proportion of muscarine administered was accumulated in the liver which stands as an explanation to the hepatotoxicity of Inocybe virosa. This is one of the rare studies that has attempted to understand the toxic potential of muscarine which has previously been explored extensively for its pharmaceutical applications.

Research dissertation to published paper: the journey to a successful publication.

Background Many dental professionals are now completing higher degrees that involve a research project. However, many of those research projects, although worthwhile, are not written up for publication in a peer-reviewed journal.Aim To encourage and assist novice authors in transferring their project report into a paper to submit for publication.Discussion The relationship between the authors and contributors is considered, and advice is given on how to focus on the specific research question and produce a succinct paper within the target journal's word limit. Emphasis is placed on choosing the right journal for submission and the need to follow the 'instructions to authors', as well as what happens post-submission, post-acceptance and post-publication. Furthermore, some of the difficulties that the authors have encountered on their own publication journeys are highlighted.Conclusion Great satisfaction will be derived if the novice researcher makes the most of their opportunity of undertaking a research project and subsequently getting it published in a peer-reviewed journal. Publishing gives the author recognition within their professional community, a feeling of personal achievement, can create better career perspectives and allows others to build on the work.

Carotid artery ultrasound image analysis: A review of the literature.

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.