Method for Collecting Single Epithelial Cells from the Mouse Larynx.

OBJECTIVE: The larynx is lined by specialized epithelial cell populations. Studying molecular changes occurring in individual epithelial cell types requires a reliable method for removing these cells from the larynx. Our objective was to develop a method to harvest individual epithelial cells from the mouse larynx while minimizing contamination from non-laryngeal sites and non-epithelial laryngeal cells. METHODS: Mice were euthanized, and the larynx was carefully exposed and separated from non-laryngeal sites. A small dental brush was inserted into the laryngeal inlet and rotated to obtain epithelial cells. Cells were transferred to collection media, counted, and cytospin preparations stained for laryngeal epithelial (i.e., Pan-Keratin, EpCAM, NGFR, p63, K5, ß-tubulin, MUC5AC) and non-epithelial (i.e., vimentin) cell markers. Histopathology was completed on brushed laryngeal tissue sections to evaluate the depth of cell collection. Preliminary Single-cell RNA sequencing (scRNA-seq) was performed to confirm this method can capture diverse laryngeal cell types. RESULTS: We collected 6000-8000 cells from a single larynx and 35000-40000 cells from combining brushings from three tissues. Histopathology demonstrated brushing removed the epithelial layer of the larynx and some underlying tissue. Immunofluorescence staining demonstrated the phenotype of harvested cells was primarily epithelial. Preliminary scRNA-seq was successfully conducted and displayed nine unique cell clusters. CONCLUSION: We developed a reliable method of harvesting individual epithelial cells from the mouse larynx. This method will be useful for collection of laryngeal cells for a variety of downstream cellular and molecular assays, including scRNA-seq, protein analyses, and cell-culture-based experiments, following laryngeal injury. LEVEL OF EVIDENCE: NA Laryngoscope, 134:786-794, 2024.

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.

Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.

An overview of polymer surface coated synthetic quantum dots as therapeutics and sensors applications.

Quantum dots (QDs) are a class of remarkable materials that have garnered significant attention since their initial discovery. It is noteworthy to mention that it took approximately a decade for these materials to be successfully implemented in practical applications. While QDs have demonstrated notable optical properties, it is important to note that these attributes alone have not rendered them a feasible substitute for traditional organic dyes. Furthermore, it is worth noting that the substance under investigation exhibited inherent toxicity and instability in its initial state, primarily due to the presence of a heavy metal core. In the initial stages of research, it was observed that the integration of nanocomposites had a positive impact on the properties of QDs. The discovery of these nanocomposites was motivated by the remarkable properties exhibited by biocomposites found in nature. Recent discoveries have shed light on the potential utilization of QDs as a viable strategy for drug delivery, offering a promising avenue to enhance the efficacy of current pharmaceuticals and pave the way for the creation of innovative therapeutic approaches. The primary objective of this review was to elucidate the distinctive characteristics that render QDs highly suitable for utilization as nanocarriers. In this study, we will delve into the multifaceted applications of QDs as sensing nanoprobes and their utilization in diverse drug delivery systems. The focus of our investigation was directed toward the utilization of QD/polymer composites in sensing applications, with particular emphasis on their potential as chemical sensors, biosensors, and physical sensors.

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.

Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

An approach to reduce Descemet's membrane scrolling: Relevance to Descemet's membrane endothelial keratoplasty (DMEK).

Purpose: We aimed to determine whether Descemet's membrane (DM) scrolling occurs primarily along the vertical or horizontal axis and establish whether oval trephination along the axis of least scrolling can reduce the grade of the scroll. Methods: The longest limbus-to-limbus axis on 28 sclerocorneal discs was taken as the horizontal axis. The horizontal (n = 7) or (right angles to it) vertical (n = 6) axis was marked on DM before peeling it off. The direction and grade of scrolling was observed. Narrow strips (3-4 mm wide) were then cut along the two axes (n = 4 each) and the scrolling pattern was observed. Ellipses (7 × 9 mm) of DM were punched along the two axes (n = 6 each) and the scrolls graded. Immunofluorescent staining for elastin on horizontal and vertical tissue sections from three DM samples was performed. The intensity and thickness of elastin staining were measured. Results: Twenty-four (85.72%) DM samples showed scrolling along the horizontal axis, none showed scrolling along the vertical axis, and four (14.28%) samples showed a spiral scroll, regardless of which axis was marked (grade 3.7 and 3.6). Vertically oval discs showed significantly reduced scrolling (grade 1.2) compared to horizontally oval discs (grade 3.5). Narrow strips of DM showed a similar scrolling pattern. Immunohistology showed no difference in any of the parameters examined along the two axes or from the center to the periphery. Conclusion: DM scrolls primarily along the horizontal axis. Vertically oval DM samples show minimal scrolling, which can be an advantage in DMEK. Differential scrolling is not determined by the distribution of elastin.

To Study the Correlation of Clinical Severity and Cytokine Storm in COVID-19 Pulmonary Embolism Patients by Using Computed Tomography Pulmonary Angiography (CTPA) Qanadli Clot Burden Scoring System.

BACKGROUND: Pulmonary embolism (PE) is a fatal form of venous thromboembolism (VTE), with an overall untreated mortality of up to 30%. Greater than 50% of patients with lower extremity proximal DVT have concurrent PE at presentation. VTE has been seen in up to one-third of patients with COVID-19 infections requiring intensive care unit (ICU) admission. The objective of this study is to determine the correlation between CT pulmonary angiography, pulmonary embolism clot burden, and the Qanadli scoring system with clinically severe COVID-19 pneumonia and cytokine storm. MATERIAL AND METHOD: 153 COVID-19 hospitalized patients who underwent CT pulmonary angiography (CTPA) for likely PE on pretest probability modified Wells criteria were enrolled. COVID-19 pneumonia was classified as URTI (upper respiratory tract infection), mild, severe, and critical COVID pneumonia. For data analysis, we categorized into two groups: (1) the non-severe group included URTI and mild pneumonia, and (2) the severe group included severe and critical pneumonia. We used the Qanadli scoring system to assess the PE percentages of pulmonary vascular obstruction using CTPA.  Results: 41.8% (64) of COVID-19 patients were diagnosed with pulmonary embolism (PE) on CTPA. The majority of 51.6% of pulmonary vascular occlusions using the Qanadli scoring system for pulmonary embolism were at segmental arterial levels. Out of 104 COVID-19 cytokine storm patients, 45 (43%) were associated with pulmonary embolism. Overall, a 25% (16) mortality rate was observed in COVID-19 patients with pulmonary embolism. DISCUSSION: The pathogenesis of hypercoagulability in COVID-19 may include direct endothelial cell invasion by the virus, microvascular inflammation, endothelial exocytosis, and endotheliitis. A meta-analysis of 71 studies to investigate the occurrence of PE on CTPA in COVID-19 patients found 48.6% in ICU settings and 65.3% of patients have clots in the peripheral pulmonary vasculature. CONCLUSIONS: There is a significant correlation between pulmonary embolism and high clot burden Qanadli CTPA scores, as well as between the severity of COVID-19 pneumonia and mortality. The association between critically ill COVID-19 pneumonia and pulmonary embolism may result in higher mortality and a poor prognostic marker.

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia.

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

The pre-Descemet's layer (Dua's layer, also known as the Dua-Fine layer and the pre-posterior limiting lamina layer): Discovery, characterisation, clinical and surgical applications, and the controversy.

The pre-Descemet's layer/Dua's layer, also termed the Dua-Fine layer and the pre-posterior limiting lamina layer, lies anterior to the Descemet's membrane in the cornea, is 10 µm (range 6-16) thick, made predominantly of type I and some type VI collagen with abundant elastin, more than any other layer of the cornea. It has high tensile strength (bursting pressure up to 700 mm of Hg), is impervious to air and almost acellular. At the periphery it demonstrates fenestrations and ramifies to become the core of the trabecular meshwork, with implications for intraocular pressure and glaucoma. It has been demonstrated in some species of animals. The layer has assumed considerable importance in anterior and posterior lamellar corneal transplant surgery by improving our understanding of the behaviour of corneal tissue during these procedures, improved techniques and made the surgery safer with better outcomes. It has led to the innovation of new surgical procedures namely, pre-Descemet's endothelial keratoplasty, suture management of acute hydrops, DALK-triple and Fogla's mini DALK. The discovery and knowledge of the layer has introduced paradigm shifts in our age old concepts of Descemet's membrane detachment, acute corneal hydrops in keratoconus and Descemetoceles, with impact on management approaches. It has been shown to contribute to the pathology and clinical signs observed in corneal infections and some corneal dystrophies. Early evidence suggests that it may have a role in the pathogenesis of keratoconus in relation to its elastin content. Its contribution to corneal biomechanics and glaucoma are subjects of current investigations.

Anterolateral versus posterolateral decompression and spinal stabilization in thoracolumbar Potts spine: a comparative study.

AIM: To assess and compare the clinical, radiological, and functional outcomes of anterolateral and posterolateral decompression and spinal stabilization in the thoracolumbar tuberculous spine. MATERIAL AND METHODS: 30 patients with thoracolumbar spinal tuberculosis were treated surgically between September 2014 and 2018. Fifteen patients underwent anterolateral decompression and spinal stabilization from September 2014 to September 2016. These patients were studied retrospectively (group A). Fifteen patients underwent posterolateral decompression by costotransversectomy and spinal stabilization from September 2016 to September 2017 were studied prospectively. Neurological recovery, correction of kyphotic deformity, pain (visual analog score) and ESR, and duration of stay were assessed. Neurological outcome was assessed using Frankel grading, and pain was assessed using visual analog scale. RESULTS: The average follow-up period in both the groups is 12 months. There was a statistically significant difference in the kyphotic angle correction between anterolateral and posterolateral groups at the end of 12 months (follow up). No statistically significant difference was found between the two groups for ESR, visual analog scale for pain, and neurological recovery (Frankel's grading) at the end of 12 months. CONCLUSION: Both anterolateral and posterolateral approaches are sufficient thoracic and thoracolumbar tuberculous spine but, the posterolateral approach allows a significant correction of kyphotic angle, better improvement of pain and lesser duration of stay.

Preemptive Diagnosis of Alzheimer's Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques.

Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data.

Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients' quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.

A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy.

Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist's experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures' identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.

Host Defense Peptides at the Ocular Surface: Roles in Health and Major Diseases, and Therapeutic Potentials.

Sight is arguably the most important sense in human. Being constantly exposed to the environmental stress, irritants and pathogens, the ocular surface - a specialized functional and anatomical unit composed of tear film, conjunctival and corneal epithelium, lacrimal glands, meibomian glands, and nasolacrimal drainage apparatus - serves as a crucial front-line defense of the eye. Host defense peptides (HDPs), also known as antimicrobial peptides, are evolutionarily conserved molecular components of innate immunity that are found in all classes of life. Since the first discovery of lysozyme in 1922, a wide range of HDPs have been identified at the ocular surface. In addition to their antimicrobial activity, HDPs are increasingly recognized for their wide array of biological functions, including anti-biofilm, immunomodulation, wound healing, and anti-cancer properties. In this review, we provide an updated review on: (1) spectrum and expression of HDPs at the ocular surface; (2) participation of HDPs in ocular surface diseases/conditions such as infectious keratitis, conjunctivitis, dry eye disease, keratoconus, allergic eye disease, rosacea keratitis, and post-ocular surgery; (3) HDPs that are currently in the development pipeline for treatment of ocular diseases and infections; and (4) future potential of HDP-based clinical pharmacotherapy for ocular diseases.

Lesion Localization and Prognosis Using Electrodiagnostic Studies in Facial Diplegia: A Rare Variant of Guillain-Barre Syndrome.

Background The etiology of facial nerve palsy is diverse and includes herpes zoster virus, Guillain-Barre syndrome (GBS), otitis media, Lyme disease, sarcoidosis, human immunodeficiency virus, etc. The lower motor neuron type facial nerve palsy is usually caused by an ipsilateral facial nerve lesion; however, it may be caused by a central lesion of the facial nerve nucleus and tract in the pons. Facial diplegia is an extremely rare condition that occurs in approximately 0.3% to 2.0% of all facial palsies. Electrodiagnostic studies including direct facial nerve conduction, facial electromyography (EMG), and blink reflex studies are useful for the prognosis and lesion localization in facial nerve palsy. Methodology This retrospective, observational study was conducted at the Neurophysiology Unit, Hamad General Hospital, Doha, Qatar. This study included 11 patients with bilateral facial weakness who visited for electrodiagnostic studies in the neurophysiology laboratory. Results In total, eight (72.7%) patients had facial diplegia, eight (72.7%) had hypo/areflexia, seven (63.6%) had facial numbness, and five (45.5%) had cerebrospinal fluid albuminocytological dissociation. The most frequent cause of facial diplegia in this study was GBS (81.9%). Direct facial nerve conduction stimulation showed that nine (81.8%) patients had bilateral facial nerve low compound muscle action potential amplitudes. The bilateral blink reflex study showed that eight (88.8%) patients had absent bilateral evoked responses. Finally, the EMG study showed that five (55.5%) patients had active denervation in bilateral sample facial muscles. Conclusions Bilateral facial nerve palsy is an extremely rare condition with a varied etiology. Electrodiagnostic studies are useful in detecting the underlying pathophysiologic processes, prognosis, and central or peripheral lesion localization in patients with facial diplegia.

Evaluation of double lung point sign as a marker for transient tachypnoea of the newborn in preterm.

Purpose: The study was conducted to diagnose transient tachypnoea of the newborn (TTN) in the early stage using ultrasonography and to estimate the sensitivity and specificity of double lung point (DLP) sign in diagnosing TTN. Material and methods: The study population included premature neonates with respiratory distress, admitted in the neonatal intensive care unit from December 2017 to June 2019, who fulfilled the inclusion criteria. A total of 100 patients were included in the study, and they underwent lung ultrasonography within 6 hours of birth. Inclusion criteria were as follows: preterm babies born < 37 weeks of gestation presenting with respiratory distress within 6 hours, clinically diagnosed with TTN and other causes like respiratory distress syndrome and pneumonia. Term neonates and neonates with congenital malformations and trauma were excluded from the study. Preterms with respiratory distress were enrolled in the study. Based on the clinical findings and laboratory investigations, clinical diagnosis was made by the paediatrician. After obtaining informed consent, ultrasonography of bilateral chest was performed using a Philips HD7 XE and a Sonoscape S2 portable ultrasound machine with a linear transducer (6-12 MHz) within 6 hours of birth. Results: The mean gestational age was 33.0 ± 1.9 weeks. Double lung point sign was present in 55 preterm neonates in our study. It was most common in bilateral posterior lung fields. The sensitivity and specificity of DLP in diagnosing TTN was 85% and 100%, respectively. Conclusions: It was found in our study that double lung point sign is the primary ultrasonographic characteristic of TTN, and ultrasonography can almost accurately diagnose TTN in early stages.

Acute Neurological Manifestations of COVID-19 Patients From Three Tertiary Care Hospitals in Qatar.

Introduction Worldwide, there are more than 424 million confirmed cases of COVID-19. Most of the hospitalized critical COVID-19 patients manifested neurological signs and symptoms and higher mortality. The majority of COVID-19 fatalities occurred mostly in patients with advanced age and underlying medical comorbidities. This is the first local retrospective study in Qatar, which reported neurologic manifestations (48.5%) of hospitalized COVID-19 patients. The primary objective of this study is to evaluate acute neurological manifestations in COVID-19 hospitalized patients in the country. Methods This is a retrospective, observational study of 413 hospitalized COVID-19 patients. They were admitted to three different COVID-19 designated hospitals (Hazm Mebaireek, Ras Laffan, and Cuban tertiary care Hospitals) under the Hamad Medical Corporation, Qatar from 1st January 2020, to 31 January 2021. We evaluated electronic medical records of these patients and data were collected while their neurological manifestations were confirmed by two trained neurologists. These neurologic manifestations were categorized into three major groups: central nervous system (CNS), peripheral nervous system (PNS), and neuromuscular system. Results Of 413 patients, 94% (389) were male and 6% (24) were female; the mean age was 52 years. Among all different nationalities of COVID-19 patients, 20.3% (84) were Indian, 12.5% (52) were Bangladeshi, 10.1% (42) were Qatari and 9.2% (38) were Nepali. The most common symptoms at the onset of COVID-19 illness were as follows: 77.5% (321) had a fever, 67.4% (279) experienced cough, 58.7% (243) experienced shortness of breath and 26.1% (108) developed a sore throat. Overall 48.5% (201) patients developed different neurologic manifestations. The most common neurologic symptoms were myalgia (28%; 116), headache (10.4%; 43), dizziness (5.8%; 24) and hemiparesis due to strokes (5.3%; 22). In this study, the most common risk factors were hypertension (47.6%), diabetes (46.9%), obesity (21%), chronic kidney disease (10%), ischemic heart disease (9.7%), and smoking (6.8%). About 45.2% (187) patients were admitted to MICU and 8.5% (35) died due to COVID-19 complications. Significant other extrapulmonary multiorgan system involvement were skeletal muscle injury (39.4%), kidney injury (36.7%), liver injury (27.5%), myocardial injury (23.9%), rhabdomyolysis (15.7%) heart failure (11.4%) and acute pancreatitis (11.1%). Discussion The most common neurologic signs and symptoms were myalgia, headache, dizziness, and strokes, mainly due to large vessel thrombosis, lacunar, and posterior circulation strokes. Conclusions Patients with COVID-19 are at high risk of developing neurological manifestations. The most common COVID-19-related acute neurological manifestations were myalgia, headache, dizziness, and acute ischemic stroke. Prompt recognition, early diagnosis, and appropriate management of these manifestations could potentially lead to better patient outcomes in COVID-19 patients.

Host Defence Peptides: A Potent Alternative to Combat Antimicrobial Resistance in the Era of the COVID-19 Pandemic.

One of the greatest challenges facing the medical community today is the ever-increasing trajectory of antimicrobial resistance (AMR), which is being compounded by the decrease in our antimicrobial armamentarium. From their initial discovery to the current day, antibiotics have seen an exponential increase in their usage, from medical to agricultural use. Benefits aside, this has led to an exponential increase in AMR, with the fear that over 10 million lives are predicted to be lost by 2050, according to the World Health Organisation (WHO). As such, medical researchers are turning their focus to discovering novel alternatives to antimicrobials, one being Host Defence Peptides (HDPs). These small cationic peptides have shown great efficacy in being used as an antimicrobial therapy for currently resistant microbial variants. With the sudden emergence of the SARS-CoV-2 variant and the subsequent global pandemic, the great versatility and potential use of HDPs as an alternative to conventional antibiotics in treating as well as preventing the spread of COVID-19 has been reviewed. Thus, to allow the reader to have a full understanding of the multifaceted therapeutic use of HDPs, this literature review shall cover the association between COVID-19 and AMR whilst discussing and evaluating the use of HDPs as an answer to antimicrobial resistance (AMR).

Introduction and expression of PIK3CAE545K in a papillary thyroid cancer BRAFV600E cell line leads to a dedifferentiated aggressive phenotype.

Anaplastic thyroid cancer (ATC) is a rare, aggressive form of undifferentiated thyroid cancer, which exhibits rapid progression and is almost universally fatal. At least a subset of ATC is thought to arise from pre-existing well-differentiated thyroid cancer, most frequently papillary thyroid cancer (PTC). While PIK3CA mutations are rare in PTC, they are common in ATC and tend to co-occur with BRAF mutations. This provided the rationale for our study to identify the potential role of PIK3CA mutations in the progression from well-differentiated to undifferentiated thyroid cancer. We introduced PIK3CAE545K into the LAM1 PTC cell line, which carries a BRAFV600E mutation. In culture, the engineered cell line (LAM1:PIK3CAE545K) proliferated faster and demonstrated increased clonogenic potential relative to the parental line carrying an empty vector (LAM1EV). Both the LAM1EV and LAM1:PIK3CAE545K edited lines were implanted into hind flanks of athymic nude mice for in vivo determination of disease progression. While tumour weights and volumes were not significantly higher in LAM1:PIK3CAE545K mice, there was a decrease in expression of thyroid differentiation markers TTF-1, thyroglobulin, PAX8 and B-catenin, suggesting that introduction of PIK3CAE545K led to dedifferentiation in vivo. Collectively, this study provides evidence of a role for PIK3CAE545K in driving disease progression from a well-differentiated to an undifferentiated thyroid cancer; however, over-expression was not a determinant of an accelerated growth phenotype in ATC.

Real-world experience of using ciclosporin-A 0.1% in the management of ocular surface inflammatory diseases.

PURPOSE: To report the real-world experience of using topical ciclosporin, Ikervis, in the management of ocular surface inflammatory diseases (OSIDs). METHODS: This was a retrospective study of patients treated with Ikervis for OSIDs at the Queen's Medical Centre, Nottingham, between 2016 and 2019. Relevant data, including demographics, indications, clinical parameters, outcomes and adverse events, were collected and analysed for patients who had completed at least 6 months follow-up. For analytic purpose, clinical outcome was categorised as 'successful' (resolved or stable disease), 'active disease' and 'drug intolerance'. RESULTS: 463 patients were included; mean age was 51.1±21.6 years, with a 59.0% female predominance. Mean follow-up was 14.6±9.2 months. The most common diagnosis was dry eye disease (DED; 322, 69.5%), followed by allergic eye disease (AED; 53, 11.4%) and ocular mucous membrane pemphigoid/Steven-Johnson syndrome (OMMP/SJS; 38, 8.2%). Successful treatment was achieved in 343 (74.1%) patients, with 44 (9.5%) requiring additional treatment and 76 (16.4%) reporting drug intolerance. The efficacy of Ikervis was highest in DED (264, 82.0%), followed by OMMP/SJS (25, 65.8%) and post-keratoplasty (7, 50.0%; p<0.001). Logistic regression analysis demonstrated age <70 years (p=0.007), AED (p=0.002) and OMMP/SJS (p=0.001) as significant predictive factors for Ikervis intolerance. AED and post-keratoplasty were 8.16 times (95% CI, 2.78 to 23.99) and 13.98 times (95% CI, 4.22 to 46.28), respectively, more likely to require additional treatment compared with DED. CONCLUSIONS: Ikervis is a useful steroid-sparing topical treatment for managing OSIDs in the real-world setting. Preparations with improved tolerability are needed to benefit a larger number of patients.