Using the heme peroxidase APEX2 to probe intracellular H2O2 flux and diffusion.

Currently available genetically encoded H2O2 probes report on the thiol redox state of the probe, which means that they reflect the balance between probe thiol oxidation and reduction. Here we introduce the use of the engineered heme peroxidase APEX2 as a thiol-independent chemogenetic H2O2 probe that directly and irreversibly converts H2O2 molecules into either fluorescent or luminescent signals. We demonstrate sensitivity, specificity, and the ability to quantitate endogenous H2O2 turnover. We show how the probe can be used to detect changes in endogenous H2O2 generation and to assess the roles and relative contributions of endogenous H2O2 scavengers. Furthermore, APEX2 can be used to study H2O2 diffusion inside the cytosol. Finally, APEX2 reveals the impact of commonly used alkylating agents and cell lysis protocols on cellular H2O2 generation.

Hydropersulfides inhibit lipid peroxidation and ferroptosis by scavenging radicals.

Ferroptosis is a type of cell death caused by radical-driven lipid peroxidation, leading to membrane damage and rupture. Here we show that enzymatically produced sulfane sulfur (S0) species, specifically hydropersulfides, scavenge endogenously generated free radicals and, thereby, suppress lipid peroxidation and ferroptosis. By providing sulfur for S0 biosynthesis, cysteine can support ferroptosis resistance independently of the canonical GPX4 pathway. Our results further suggest that hydropersulfides terminate radical chain reactions through the formation and self-recombination of perthiyl radicals. The autocatalytic regeneration of hydropersulfides may explain why low micromolar concentrations of persulfides suffice to produce potent cytoprotective effects on a background of millimolar concentrations of glutathione. We propose that increased S0 biosynthesis is an adaptive cellular response to radical-driven lipid peroxidation, potentially representing a primordial radical protection system.

A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time-Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses.

Clinical otorhinolaryngological presentation of COVID-19 patients in Saudi Arabia: A multicenter study.

OBJECTIVES: To assess the otorhinolaryngological (ORL) symptoms, including their prevalence, severity, and early presentations among coronavirus disease-19 (COVID-19) patients in the Saudi population. METHODS: This was a multicentric, cross-sectional study carried out on severe acute respiratory syndrome coronavirus-2 positive patients at 3 COVID-19 centres; Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, ALKhobar, Qatif Central Hospital, AlQatif, and Ohud Hospital, Al Madinah Al Munawarah, Saudi Arabia. The validated survey consisted of sociodemographic data, general symptoms of COVID-19, and ORL manifestations. RESULTS: A total of 1734 COVID-19 positive patients were included in the study (mean age: 37.7±11.6 years). Most of the cases were mild (51.8%), followed by moderate (45.6%), and severe (2.6%) cases. Approximately 33.7% were asymptomatic. Severity of symptoms was significantly associated with male gender (p=0.017), higher age-group (p=0.04), and smoking (p<0.001). The most common initial ORL presentations were sore throat (20.9%), anosmia (15.5%), hyposmia (10%), and loss of taste (11.4%). Overall, 79.5% showed ORL symptoms as the initial presenting symptoms for COVID-19, with anosmia and sore throat being the most common symptoms. CONCLUSION: This study confirmed the prevalence of ORL symptoms among COVID-19 patients in Saudi Arabia. Moreover, these symptoms could also be considered for early detection of COVID-19 as they might appear prior to other symptoms.

Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals.

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts' experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.

Bilateral simultaneous sino-nasal inverted papilloma; A report of two cases and literature review.

INTRODUCTION: Inverted Papilloma is a benign sinonasal tumor with a high recurrence rate and potential for malignant transformation, it typically presents as an obstructing unilateral nasal mass, atypical presentations include bilateral involvement which occurs in up to 5 % of cases. CASE PRESENTATION: Here we present two different cases of bilateral inverted papilloma, both presented complaining of bilateral nasal obstruction, the second case also had associated nasal polyposis and history of multiple previous endoscopic sinus surgeries, both cases were managed with endoscopic medial maxillectomy and tumor removal, with no signs of recurrence on follow up. CONCLUSION: Bilateral involvement is an uncommon, atypical presentation of inverted papilloma, endoscopic surgery is a safe, reliable approach and it is the mainstay of treatment, regular endoscopic and clinical follow up is important for detection of recurrence.

Complete Eye Ophthalmoplegia: the unusual initial presentation of Leptomeningeal Carcinomatosis.

Ophthalmoplegia is a paralysis or weakness of extraocular muscles that have a variety of different etiologies including and not limited to Leptomeningeal Carcinomatosis (LC). LC is caused mainly by metastatic cancers and can cause a wide variety of symptoms. We present a case of LC with no preexisting condition who presented with a unilateral ophthalmoplegia as initial presentation who was found to have LC secondary to large B-cell lymphoma.

Percutaneous Ankle Reconstruction of Lateral Ligaments.

Chronic ankle instability following ankle sprains causes pain and functional problems such as recurrent giving way. Within the 3 ligaments of the lateral ligament complex, 80% of patients tear the anterior talofibular ligament (ATFL), whereas the other 20% of patients tear the ATFL and calcaneofibular ligament (CFL). Rarely, the posterior talofibular ligament is involved. An incidence of 10% to 30% of patients will fail conservative treatment and result in chronic ankle instability that may require surgical treatment. To date, numerous open surgical procedures for anatomic repair or reconstruction of ATFL and/or CFL provide good clinical results.