Keystone Design Sliding Skin Flap for the Management of Small Full Thickness Burns

Deep dermal burns and full thickness burns are generally managed by excision and split thickness skin grafting. The skin graft may lead to unacceptable colour changes and be aesthetically unacceptable. Also, there may be a contour defect and, furthermore, it is followed by varying degrees of contracture. The keystone design sliding flap, first described in 2003, avoids the need for grafting and is not associated with any skin graft problems. We report two cases of the use of this flap as the primary surgery in reconstruction of small full thickness burn defects.

Sustainable Clinical Academic Training Pathways: A framework for implementation in Oman

Clinical academics—medical doctors with additional training in basic science or clinical research—play a pivotal role in translating biomedical research into practical bedside applications. However, international studies suggest that the proportion of clinical academics relative to the medical workforce is dwindling worldwide. Although efforts to reverse this trend are ongoing in many countries, there is little perceptible dialogue concerning these issues in Oman. This article explores the current status of clinical academic training pathways worldwide, concluding with a framework for the implementation of a dual-degree medical-research training programme in Oman in order to stimulate and develop a sustainable national clinical academic workforce.Keywords: Training Programs; Undergraduate Medical Education; Graduate Medical Education; Internship and Residency; Medical Students; Research; Oman

Hazard analysis and critical control point (HACCP) in seafood processing: an analysis of its application and use in regulation in the Sultanate of Oman

When considering the supply of fish products to consumers, the adoption of food safety management systems throughout the 'net to plate' continuum is of a paramount importance. It is essential to safeguard consumers and to facilitate regional and international trade. This study has assessed the technical barriers and benefits associated with the implementation of management system incorporating HACCP and related pre-requisite programmes in the seafood processors in the Sultanate of Oman. A survey, using qualitative surveys and interviews, was conducted out to verify the level of implementation of the seafood safety and quality requirements. A total of 22 (92% returned) HACCP processors, and 15 (83% returned) non-HACCP processors and 15 (75%) officials completed the questionnaires. Differences between processors operating with or without a HACCP system in place have been identified. The survey of local officials provided an additional perspective on the issues involved. The implications of handling practices in the seafood supply chain, seafood trade and the cost implications of implementing HACCP-based food safety management systems were also assessed. In comparison to the non-HACCP processors, the results indicated that HACCP firms were more diversified in their export markets and were able to target the more lucrative markets such as EU, Japan and America. However, the processors felt that the main barrier for exporting to these markets was the restriction imposed by the government on exporting certain species which reduced their ability to meet contracts with these countries. The study has also shown inadequate execution of prerequisite programmes due mainly to lack of training delivered to food handlers and a poor knowledge of food safety concepts. In particular there is an overreliance on the use of CCPs to control hazards when prerequisite programmes would be more appropriate is many situations. When considering whether to implement HACCP-based control systems, the seafood processors identified barriers linked to costs as their main concerns. However, whilst recognising this issue, the officials also highlighted barriers linked to the lack of expertise, skills and commitment of the staff. In general, the study highlighted significant gaps which undermine the effectiveness and success of implementing safety and quality requirements to meet national legislative obligations. These include: poor attitudes and understanding toward HACCP and its pre-requisite programmes, lenient enforcement by the authorities, the lack of training and consultancy organizations in the country, a lack of awareness. The overlapping structure of the regulatory authorities in the country and the distribution of national inspection resources have also been identified as an issue of concern

First record of Gymnocranius griseus (Temminck & Schlegel, 1843) (family Lethrinidae) from southern Oman, Western Indian Ocean

Primer registre de Gymnocranius griseus (Temminck & Schlegel, 1843) (família Lethrinidae) del sud d’Oman, oest de l’oceà Índic Es va recol·lectar un únic espècimen (285 mm longitud estàndard) de Gymnocranius griseus (Temminck & Schlegel, 1843) a la ciutat de Salalah (Oman), a la costa del mar d’Aràbia. És el primer registre d’aquesta espècie a les aigües d’Oman. Presenta característiques específiques: cos alt (2,17 vegades la longitud estàndard); els perfils dorsal i ventral del cap són uniformement convexos; el perfil de la part ventral del cos és recte; la vora inferior de l’ull se situa lleugerament per sota de la línia que uneix la part anterior de la boca amb el centre de l’aleta caudal lobulada; l’ull és relativament ample, de diàmetre pràcticament igual o lleugerament superior a les distàncies preorbitària i interorbitària; la boca és relativament petita i la part posterior dels maxil·lars arriba pràcticament al nivell dels orificis nasals anteriors; presenta tres parells de fines canines a la part anterior del maxil·lar superior i un parell a la part anterior de l’inferior, com també altres dents vil·liformes que adquireixen forma cònica a les parts laterals. L’espècimen va ser identificat com un G. griseus atès que les seves característiques corresponen a la descripció diagnòstica de Carpenter & Allen (1989). Palabras clave: Gymnocranius griseus, Salalah, Mar de Arabia, Primer registro.A single specimen (285 mm SL) of Gymnocranius griseus (Temminck & Schlegel, 1843) was collected from Salalah, Arabian Sea coast of Oman. It is the first record of this species from the Omani waters. It shows specific characters: deep body (2.17 times SL); evenly convex dorsal and ventral profile of head; ventral part of body profile straight; lower edge of eye slightly above a line from tip of snout to middle of caudal fin fork; eye relatively large, its diameter about equal to or slightly larger than preorbital and interorbital widths; mouth relatively small, posterior part of jaws reaching to about level of anterior nostrils; three pair and one pair of slender canines at front of upper and lower jaw, respectively, other teeth villiform, becoming conical on lateral sections. The specimen was identified as G. griseus as these characters fit the diagnostic description of Carpenter & Allen (1989). Key words: Gymnocranius griseus, Salalah, Arabian Sea, First record.Primer registre de Gymnocranius griseus (Temminck & Schlegel, 1843) (família Lethrinidae) del sud d’Oman, oest de l’oceà Índic Es va recol·lectar un únic espècimen (285 mm longitud estàndard) de Gymnocranius griseus (Temminck & Schlegel, 1843) a la ciutat de Salalah (Oman), a la costa del mar d’Aràbia. És el primer registre d’aquesta espècie a les aigües d’Oman. Presenta característiques específiques: cos alt (2,17 vegades la longitud estàndard); els perfils dorsal i ventral del cap són uniformement convexos; el perfil de la part ventral del cos és recte; la vora inferior de l’ull se situa lleugerament per sota de la línia que uneix la part anterior de la boca amb el centre de l’aleta caudal lobulada; l’ull és relativament ample, de diàmetre pràcticament igual o lleugerament superior a les distàncies preorbitària i interorbitària; la boca és relativament petita i la part posterior dels maxil·lars arriba pràcticament al nivell dels orificis nasals anteriors; presenta tres parells de fines canines a la part anterior del maxil·lar superior i un parell a la part anterior de l’inferior, com també altres dents vil·liformes que adquireixen forma cònica a les parts laterals. L’espècimen va ser identificat com un G. griseus atès que les seves característiques corresponen a la descripció diagnòstica de Carpenter & Allen (1989). Palabras clave: Gymnocranius griseus, Salalah, Mar de Arabia, Primer registro

Filtration‐histogram based magnetic resonance texture analysis (Mrta) for the distinction of primary central nervous system lymphoma and glioblastoma

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre‐treatment MRI sequences (T1‐weighted contrast‐enhanced (T1CE), T2‐weighted (T2), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2–6 mm) and unfil-tered (SSF = 0) histogram parameters were compared using Mann‐Whitney U non‐parametric test-ing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permit-ted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T1CE‐derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross‐sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images