The design and validation of a low-cost trans perineal (TP) prostate biopsy simulator for training: improving trainees' confidence and cognitive targeting skills.

PURPOSE: The aim of this research was to create a novel and low-cost TP prostate biopsy simulator that has face, content and construct validity with high educational value. METHODS: This research developed a trans perineal prostate (TP) biopsy simulator using 3D-printed moulds and tissue-mimicking materials. Important regions (anterior, mid, and posterior zones) were coded with different colours. Ultrasound visible abnormal lesions were embedded in the prostate phantom. Expert and novice participants in TP biopsies were recruited. Essential skills were identified through the consensus of six experts. These skills were assessed through tasks performed by participants. This included the accuracy and timing of systematic and target biopsies. Immediate feedback was determined by the colour of the biopsy cores taken. A survey was distributed to evaluate its realism and educational value. RESULTS: The material cost of one simulator was £7.50. This simulator was proven to have face, content, and construct validity. There was a significant difference (p = 0.02) in the accuracy of systematic biopsies between both experts and novices. Significant difference was also observed (p = 0.01), in accurately identifying target lesion on ultrasound between both groups. Participants rated the overall realism of the simulator 4.57/5 (range 3-5). 100% of the experts agreed that introducing this simulator to training will be beneficial. 85.7% of the participants strongly agree that the simulator improved their confidence in TP biopsies. CONCLUSION: There is value in integrating this proof-of-concept TP prostate biopsy simulator into training. It has highly rated educational value and has face, content, and construct validity.

Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.

STATEMENT OF PROBLEM: The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking. PURPOSE: The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion. MATERIAL AND METHODS: Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute's Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist. RESULTS: Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience. CONCLUSIONS: Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.

Ex Vivo Antiplatelet and Thrombolytic Activity of Bioactive Fractions from the New-Fangled Stem Buds of Ficus religiosa L. with Simultaneous GC-MS Examination.

Different parts of Ficus religiosa are the common components of various traditional formulations for the treatment of several blood disorders. The new-fangled stem buds' powder was extracted with 80% ethanol and successively fractionated by chloroform and methanol. Chloroform and methanol fractions of Ficus religiosa (CFFR and MFFR) were tested for antiplatelet, antithrombotic, thrombolytic, and antioxidant activity in ex vivo mode. The MFFR was particularly investigated for GC-MS and toxicity. The antiplatelet activity of the CFFR, MFFR, and standard drug aspirin at 50 µg/mL was 54.32%, 86.61%, and 87.57%, and a significant delay in clot formation was noted. CFFR at different concentrations did not show a significant effect on the delay of clot formation, antiplatelet, and free radical scavenging activity. The most possible marker compounds for antiplatelet and antioxidant activity identified by GC-MS in the MFFR are salicylate derivatives aromatic compounds such as benzeneacetaldehyde (7), phenylmalonic acid (13), and Salicylic acid (14), as well as Benzamides derivatives such as carbobenzyloxy-dl-norvaline (17), 3-acetoxy-2(1H)-pyridone (16), and 3-benzylhexahydropyrrolo [1,2-a] pyrazine-1,4-dione (35). A toxicity study of MFFR did not show any physical indications of toxicity and mortality up to 1500 mg/kg body weight and nontoxic up to 1000 mg/kg, which is promising for the treatment of atherothrombotic diseases.

Articular cartilage repair using autologous collagen-induced chondrogenesis (ACIC): a pragmatic and cost-effective enhancement of a traditional technique.

PURPOSE: The autologous collagen-induced chondrogenesis technique is described, and the results of a 6-year follow-up clinical study using this technique are presented. METHODS: 30 patients with International Cartilage Repair Society (ICRS) Grade III/IVa symptomatic chondral defects of the knee treated with enhanced microdrilling using atelocollagen were prospectively examined in this clinical series. The median age of the patients was 39.0 years (range 19-61 years). Patients were followed up to 72 months. Clinical evaluation was performed using functional knee scores and radiologically. Both quantitative and qualitative assessments were performed. RESULTS: Statistically significant and clinically relevant improvement was observed in 2 years and was sustained for the 6 years of the study observation. At 6 years, the mean Lysholm score was 79.7 (SD 6.8) compared to 52.6 (SD 10.7) pre-operatively (p < 0.05). The symptomatic Knee Injury and Osteoarthritis Outcome Score (KOOS) improved from 68.3 (SD 11.4) to 90.2 (SD 4.3) (p < 0.05). The subjective International Knee Documentation Committee (IKDC) also showed improvement from 39.1 (SD 4.1) to 81.6 (SD 7.8) (p < 0.05). The calculated T2* relaxation times were 26.0 (SD 4.2) seconds and 30.3 (SD 6.2) seconds for the repair tissue and native cartilage, respectively. The average magnetic resonance observation of cartilage repair tissue (MOCART) score was 78.5 (SD 9.6) for all lesions. CONCLUSION: The enhanced microdrilling using atelocollagen is an enhancement of the traditional microfracture method using an off-the-shelf product. When used to treat moderate to severe chondral lesions, this enhancement produces hyaline-like cartilage with a corresponding improvement in symptoms. LEVEL OF EVIDENCE: IV.

Feasibility of robotic surgery in a developing country, a public sector Perspective.

OBJECTIVE: To assess the clinical feasibility of robotic platform and to calculate cost for sustaining it.. METHODS: The study was conducted at Sindh Government Qatar Hospital and Civil Hospital Karachi from October 11, 2011, to August 30, 2017. Feasibility was assessed in terms of clinical outcome i.e. surgical complications, duration of stay and readmissions. The cost of doing an individual procedure was calculated along with the projected cost for 150, 200 and 250 cases per year. SPSS 23 was used for data analysis.. RESULTS: Of the 119 patients, 45(37.8%) were males and 74(62.2%) were females. Overall mean age was 42.10}13.40 years (range: 21-80 years. Mean hospital stay was 3.59}3 (range: 1-19 days). Complications occurred in 17(14.3%) patients ranging from wound infection 7(5.9%), bleeding 5(4.2%), intra-abdominal abscess 3(2.5%) and recurrence 2(1.7%). The total cost of robotic platform was Rs320 million with an annual maintenance contract of 10% of the total cost. The mean cost of performing a robotic procedure was Rs.389,263.05}39,249.63 (range: Rs252,823.09- Rs456,842.79).. CONCLUSIONS: Robotic surgery was found to be a feasible and viable option. The major hindrance was the cost involved in setting up the system and recurring costs in terms of disposables.

Engineered Cardiac Microtissue Biomanufacturing Using Human Induced Pluripotent Stem Cell Derived Epicardial Cells.

Epicardial cells are a crucial component in constructing in vitro 3D tissue models of the human heart, contributing to the ECM environment and the resident mesenchymal cell population. Studying the human epicardium and its development from the proepicardial organ is difficult, but induced pluripotent stem cells can provide a source of human epicardial cells for developmental modeling and for biomanufacturing heterotypic cardiac tissues. This study shows that a robust population of epicardial cells (approx. 87.7% WT1+) can be obtained by small molecule modulation of the Wnt signaling pathway. The population maintains WT1 expression and characteristic epithelial morphology over successive passaging, but increases in size and decreases in cell number, suggesting a limit to their expandability in vitro. Further, low passage number epicardial cells formed into more robust 3D microtissues compared to their higher passage counterparts, suggesting that the ideal time frame for use of these epicardial cells for tissue engineering and modeling purposes is early on in their differentiated state. Additionally, the differentiated epicardial cells displayed two distinct morphologic sub populations with a subset of larger, more migratory cells which led expansion of the epicardial cells across various extracellular matrix environments. When incorporated into a mixed 3D co-culture with cardiomyocytes, epicardial cells promoted greater remodeling and migration without impairing cardiomyocyte function. This study provides an important characterization of stem cell-derived epicardial cells, identifying key characteristics that influence their ability to fabricate consistent engineered cardiac tissues.

Impact of Medicaid Expansion on Surgical Care and Outcomes for Hepatobiliary Malignancies.

BACKGROUND: As part of the Patient Protection and Affordable Care Act, some states expanded Medicaid eligibility to adults with incomes below 138% of the federal poverty line. While this resulted in an increased proportion of insured residents, its impact on the diagnosis and treatment of hepatopancreaticobiliary (HPB) cancers has not been studied. STUDY DESIGN: The National Cancer Database (NCDB) from 2010 to 2017 was used. Patients diagnosed with HPB malignancies in states which expanded in 2014 were compared to patients in non-expansion states. Subset analyses of patients who underwent surgery and those in high-risk socioeconomic groups were performed. Outcomes studied included initiation of treatment within 30 days of diagnosis, stage at diagnosis, care at high volume or academic center, perioperative outcomes, and overall survival. Adjusted difference-in-differences analysis was performed. RESULTS: A total of 345,684 patients were included, of whom 55% resided in non-expansion states and 54% were diagnosed with pancreatic cancer. Overall survival was higher in states with Medicaid expansion (HR .90, 95% CI [.88-.92], P < .01). There were also better postoperative outcomes including 30-day mortality (.67 [.57-.80], P < .01) and 30-day readmissions (.87 [.78-.97], P = .02) as well as increased likelihood of having surgery in a high-volume center (1.42 [1.32-1.53], P < .01). However, there were lower odds of initiating care within 30 days of diagnosis (.77 [.75-.80], P < .01) and higher likelihood of diagnosis with stage IV disease (1.09 [1.06-1.12], P < .01) in expansion states. CONCLUSION: While operative outcomes and overall survival from HPB cancers were better in states with Medicaid expansion, there was no improvement in timeliness of initiating care or stage at diagnosis.

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

Surface Reconstruction with Aprotic Trimethylsulfonium Iodide for Efficient and Stable Perovskite Solar Cells.

Organic ammonium salts are widely used for surface passivation to enhance the photovoltaic (PV) performance and stability of perovskite solar cells (PSCs). However, the protic nature of ammonium units results in the quick degradation of perovskites due to the hydrogen bonding interaction with water molecules. Recently, organo-sulfur compounds have attracted growing interest as passivation layers on three-dimensional perovskites due to their moisture-resistive behavior. Herein, trimethylsulfonium iodide (TMSI), an aprotic S-based organic compound, is employed for surface modification of methylammonium lead iodide-based PSCs to impede moisture penetration, improve charge transfer, and passivate surface defects. The TMSI effectively passivates uncoordinated Pb through Pb···S interactions, and the optimized PSC exhibits a power conversion efficiency (PCE) of 21.03% with an open-circuit voltage of ca. 1.13 V under one-sun illumination, while it reached up to 37.58 and 37.69% under low-intensity indoor illuminations, 1000 and 2000 lx with LED 5000 K, respectively. TMSI-treated cells display enhanced device stability by retaining 92.7% of their initial PCE after 50 days of storage in ambient conditions. This study provides a novel and effective surface reconstruction strategy with aprotic materials to improve PV performance and device stability in PSCs.

Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.

Purpose This study aims to document the early stages of development of an unsupervised, deep learning-based clinical annotation and segmentation tool (CAST) capable of isolating clinically significant teeth in both intraoral photographs and their corresponding oral radiographs. Methods The dataset consisted of 172 intraoral photographs and 424 dental radiographs, manually annotated by two operators, augmented to yield 6258 images for training, 183 for validation, and 98 for testing. The training involved the use of an object detection model ('YOLOv8') combined with a feature extraction system ('Segment Anything Model'). This combination enabled the auto-annotation and segmentation of tooth-related features and lesions in both types of images without operator intervention. Outputs were further processed using a data relabelling tool ('X-AnyLabeling') enabling the option to manually reannotate erroneous data outputs through reinforcement learning. Results The trained object detection model achieved a mean average precision (mAP) of 77.4%, with precision and recall rates of 75.0% and 72.1%, respectively. The model was able to segment features from oral images annotated by polygonal boundaries better than radiological images annotated using bounding boxes. Conclusion The development of the auto-annotation and segmentation tool showed initial promise in automating the image labelling and segmentation process for intraoral images and radiographs. Further work is required to address the limitations.

A 3D printed electronic wearable device to generate vertical, horizontal and phono-articulatory jaw movement parameters: A concept implementation.

PURPOSE: The current research aimed to develop a concept open-source 3D printable, electronic wearable head gear to record jaw movement parameters. MATERIALS & METHODS: A 3D printed wearable device was designed and manufactured then fitted with open-source sensors to record vertical, horizontal and phono-articulatory jaw motions. Mean deviation and relative error were measured invitro. The device was implemented on two volunteers for the parameters of maximum anterior protrusion (MAP), maximum lateral excursion (MLE), normal (NMO), and maximum (MMO) mouth opening and fricative phono-articulation. Raw data was normalized using z-score and root mean squared error (RMSE) values were used to evaluate relative differences in readings across the two participants. RESULTS: RMSE differences across the left and right piezoresistive sensors demonstrated near similar bilateral movements during normal (0.12) and maximal mouth (0.09) opening for participant 1, while varying greatly for participant 2 (0.25 and 0.14, respectively). There were larger differences in RMSE during accelerometric motion in different axes for MAP, MLE and Fricatives. CONCLUSION: The current implementation demonstrated that a 3D printed electronic wearable device with open-source sensor technology can record horizontal, vertical, and phono-articulatory maxillomandibular movements in two participants. However, future efforts must be made to overcome the limitations documented within the current experiment.

Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs.

PURPOSE: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics. METHODS: The study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated. RESULTS: Overall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x's prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising. CONCLUSION: The current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.

Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.

Purpose: This study assessed the impact of intraoral scanner type, operator, and data augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also evaluated the validation accuracy of an unsupervised machine-learning model trained with these scans. Methods: Twenty-two dental casts were scanned using two handheld intraoral scanners and one laboratory scanner, resulting in 110 3D cast scans across five independent groups. The scans underwent uniform augmentation and were validated using Hausdorff's distance (HD) and root mean squared error (RMSE), with the laboratory scanner as reference. A 3-factor analysis of variance examined interactions between scanners, operators, and augmentation methods. Scans were divided into training and validation sets and processed through a pretrained 3D visual transformer, and validation accuracy was assessed for each of the five groups. Results: No significant differences in HD and RMSE were found across handheld scanners and operators. However, significant changes in RMSE were observed between native and augmented scans with no specific interaction between scanner or operator. The 3D visual transformer achieved 96.2% validation accuracy for differentiating upper and lower scans in the augmented dataset. Native scans lacked volumetric depth, preventing their use for deep learning. Conclusion: Scanner, operator, and processing method did not significantly affect the dimensional accuracy of 3D scans for unsupervised deep learning. However, data augmentation was crucial for processing intraoral scans in deep learning algorithms, introducing structural differences in the 3D scans. Clinical Significance. The specific type of intraoral scanner or the operator has no substantial influence on the quality of the generated 3D scans, but controlled data augmentation of the native scans is necessary to obtain reliable results with unsupervised deep learning.

Preparation and Evaluation of Nanoemulsion of Citronella Essential Oil with Improved Antimicrobial and Anti-Cancer Properties.

The development of new pharmaceutical solutions for treating various diseases results from a growing understanding of the benefits of using essential oils. One of the most often used volatile materials among essential oils is the oil of the citronella plant, termed citronella essential oil (CITEO), which has potential for use in food and medicine. Its wide use is limited due to lipophilicity, high volatility and poor physicochemical stability. With this background, the present study aims to evaluate the properties of CITEO-nanoemulsion (CITEO-NE) by analyzing its antimicrobial activities against Staphylococcus aureus (S. aureus) and Candida albicans (C. albicans) and its anticancer activity against, human skin adenocarcinoma cell line (A431). The CITEO-NE was prepared and evaluated for the size range of 130 ± 5 nm, polydispersity index (PDI) of 0.127 and zeta potential -12.6 mV. The percentage % of entrapment efficiency (%EE) of nanoemulsions loaded with CIT was very high at the beginning of the study, at 95.5 ± 4.775%. The MIC was observed to be 500 µg/mL for CITEO and 250 µg/mL for CITEO-NE against S. aureus and 250 µg/mL for CITEO and 125 µg/mL for CITEO-NE against C. albicans. The time-kill assay also suggests the effectiveness of CITEO-NE against the test pathogens as a novel alternative therapy. The IC50 values of CITEO and CITEO-NE exhibited significant cytotoxic properties against the A431 cell line, with 41.20 µg/mL and 37.71 µg/mL, respectively. Hence, our findings revealed that encapsulation of CITEO increased the pharmacological properties.

Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model.

BACKGROUND: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. METHODS: A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The "you only look once" algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP). RESULTS: The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs. CONCLUSION: A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.

Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation.

The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = - 0.01 (10), mean difference = - 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68-0.87, sensitivity of 1.00, precision of 0.50-0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.

Mycetoma in north-western Yemen: Clinico-epidemiological and histopathological study.

Background Mycetoma is widespread in Yemen; however, there are only a few documented reports on the entity from this geographical area. Methods A prospective study of 184 cases of mycetoma (male 145 and female 39) from different regions of north-western Yemen was conducted between July 2000 and May 2014. Clinical profile was recorded in a standardized protocol. The diagnosis was based on clinical features, X-ray studies, examination of grains, and histopathology. Results Eumycetoma was diagnosed in 129, caused by Madurella mycetomatis in 124, Leptosphaeria senegalensis in one and pale grain fungus in four, whereas actinomycetoma occurred in 55, caused by Streptomyces somaliensis in 29, Actinomadura madurai in nine, Actinomadura pelletieri in one, and Nocardia in sixteen. Eumycetoma cases were treated with prolonged course of antifungal drugs, mostly ketoconazole, with itraconazole being used in four patients, along with excision or debulking. Results were better when antifungal drugs were given two to three months before surgery and in those who received itraconazole. Actinomycetoma cases were initially treated with co-trimoxazole monotherapy; later streptomycin was added in 30 cases. Six patients who did not show adequate improvement and two others from the start were treated with modified Welsh regimen and with good results. Limitations Identification of different causative agents was done by histopathology and could not be reconfirmed by culture. Conclusion Mycetoma is widespread in north-western Yemen with a higher incidence of eumycetoma and a majority of the cases were caused by Madurella mycetomatis. Modified Welsh regimen in actinomycetoma and itraconazole with excision in eumycetoma showed the best results.

COVID-19 Vaccination-Induced Rash: Does the Choice of Vaccine Matter?

With the introduction of large-scale vaccination programmes against the coronavirus disease 2019 (COVID-19), the world has now begun to visualise a possible end to the ongoing pandemic. As with any vaccination programme, reports of side effects have begun to emerge in the wake of vaccinations. Initial reports were about mild side effects, such as local inflammation, pain, and fever. However, as a significant number of the population began to receive various COVID-19 vaccines, reports of various other moderate to severe side effects have now started to emerge. Although these side effects seem to be rare, the symptoms can be severe, and information and guidelines on how to manage them are scarce. In this case series, we discuss the incidence of widespread rashes that develop in some individuals after receiving COVID-19 vaccines by both AstraZeneca (AstraZeneca plc, Cambridge, UK) and Pfizer-BioNTech (Pfizer Inc., Brooklyn, NY; BioNTech SE, Mainz, Germany). The systemic skin reaction varied from maculopapular rashes to papules and patches that were widespread and not simply localised to the vaccine injection site. Further clinical information, awareness, and guidelines for practicing clinicians need to be exigently provided as vaccination programmes approach completion and the incidences of moderate to severe side effects of COVID-19 vaccination are becoming more apparent and pervasive.

Fracture of tibial tuberosity in an adult with Paget's disease of the bone - An interesting case and review of literature.

INTRODUCTION: Paget's disease of the bone is a non-malignant skeletal disorder characterized by focal abnormalities in bone remodelling at one or more skeletal sites. Pathological fractures occurring from trivial injuries are a well-known clinical presentation in patients with Paget's disease. An avulsion fracture of the tibial tuberosity is an infrequent injury and has an extremely low occurrence in adults, with only a few cases reported in literature. We describe a case of a patient with undiagnosed Paget's Disease of the bone, sustaining a pathological avulsion fracture of the tibial tuberosity. CASE REPORT: A 54-year-old male presented with right knee pain after his knee gave way whilst standing in the goal area during a game of football, twisting his right ankle and falling. Plain radiographs of the knee revealed an avulsion fracture of the tibial tuberosity with abnormal modelling of the proximal half of the tibia. An MRI confirmed a diagnosis of Paget's disease of the bone. The patient underwent open reduction internal fixation. At 3 months follow up, the patient had good knee range of motion from 0 to 100 degrees and by 6 months he had returned to his usual activities. CONCLUSION: We describe a unique case of tibial tuberosity avulsion fracture in an adult with PDB. Treatment was successful with cannulated screws and tension band wiring. Patients with PDB who fracture present with diagnostic and operative challenges, it is vital to progress with caution in the postoperative rehabilitation phase.

Ultrastructural morphology of lymphocytes of the Arabian oryx (Oryx leucoryx).

An ultrastructural study on the lymphocytes from peripheral blood samples from 20 healthy adult Arabian oryx (Oryx leucocoryx) was undertaken. Small lymphocytes ranged in size from 2-3.5 µm and exhibited the typical repertoire of organelles found in small lymphocytes of other animals but showed no evidence of azurophilic granules. Medium-sized lymphocytes were 5-6.5 µm in diameter and occasionally demonstrated azurophilic granules. Microvilli were a common finding of lymphocytes. Of particular interest was the presence of multivesicular bodies, which have previously only been described in human lymphocytes. Both small- and medium-sized lymphocytes of the Arabian oryx were smaller than those reported for other animals. Small lymphocytes exhibited short, thick microvilli, whereas medium-sized lymphocytes had long thin microvilli, a single nucleolus and occasionally azurophilic granules and multivesicular bodies.