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Background Hearing deficit is one of the side effects of 1st generation iron chelators in β-thalassemia, however the risk of hearing deficits following 2nd generation iron chelators is not well known. Aim To assess hearing status of Transfusion Dependent β-thalassemia children on oral iron chelating agents and detect risk factors for hearing impairment. Methods This is a cross-sectional study recruited sixty children and adolescent with confirmed diagnosis of transfusion dependent β-thalassemia. Demographic and clinical characteristics collected, audiological testing were performed by the same audiologist using the same equipment for all patients including tympanometry, pure tone audiometry, speech audiometry, transient evoked otoacoustic emissions and distortion product otoacoustic emissions. Results Recruited children and adolescents with transfusion dependent β-thalassemia were 32 (53.3%) boys and 28 (46.7%) girls and their mean age was 11.34 ± 3.08, majority of patients 48 (80%) were on single Deferasirox. Our study revealed that among the 60 children evaluated, 16.6% exhibited some form of hearing loss. Sensorineural hearing loss (SNHL) was observed in 6.6% of the participants, while 10% had conductive hearing loss (CHL). Bilateral SNHL in 5% and bilateral CHL in 8.3% of all the cases. Hearing impairment was mild in nature, but predominantly affected high-frequency ranges, the most affected frequencies being 4000 Hz and 8000 Hz. There was no significant difference between studied thalassemia children with and without hearing impairment regarding gender, age at study entry, age at diagnosis, duration of disease and duration or dose of chelating agent ( P  > 0.05). Our study revealed significant difference between studied thalassemia children with and without hearing impairment regarding age of starting blood transfusion ( p -value = 0.024), affected patients started blood transfusion at older age, also statistically significant difference in both groups regarding median serum ferritin was found ( p -value = 0.028), lower levels were found in affected patients. Conclusion No significant effect of using oral iron chelation drugs was observed on frequency and type of hearing loss among the studied patients but instead the age at starting regular blood transfusion did. Screening of such group of patients for hearing impairment at diagnosis and at regular periods is recommended.

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

A study in Qatar assessed the effectiveness of previous infection, vaccination, and both against symptomatic SARS-CoV-2 caused by omicron BA.1 and BA.2 and against severe, critical, or fatal Covid-19.

Background Flipped learning excessively boosts the conceptual understanding of students through the reversed arrangement of pre-learning and in classroom learning events and challenges students to independently achieve learning objectives. Using a gamification method in flipped classrooms can help students stay motivated and achieve their goals.  Methods This study adopted a randomized controlled study design with a pre-test and post-test and involved 128 nursing students at Mansoura University. This study randomly divided the students into the study and control groups. Data were collected at three time points using six tools . In the intervention group, Moodle was gamified for 6 weeks.  Results A significant difference in the students’ self-confidence ( p  = 0.021), skills knowledge ( p  < 0.001), intensity of preparation ( p  < 0.001), and motivation ( p  < 0.001) was observed between the two groups; however, no difference in the students’ skills performance ( p  = 0.163) was observed between the two groups after using gamified flipped classrooms. Conclusions Compared with the traditional flipped classrooms, gamified flipped classrooms improved nursing students’ motivation, intensity of preparation, skills knowledge, and self-confidence during laboratory clinical practice. Thus, gamification is a learning approach that can be implemented in conjunction with the flipped classroom model to motivate students to participate in the learning process. Trial registration. Prospectively registered with ClinicalTrials.gov on 26/04/2021; registration number NCT04859192.

Using a national Covid-19 database in Qatar, investigators found that previous SARS-CoV-2 infection provided protection against subsequent reinfection that ranged from 85% to 92% for the alpha, beta, and delta strains and was approximately 60% protective against the omicron variant. Previous infection also appeared to protect against severe disease, hospitalization, and death.

Epidemiologic data from Qatar show that any previous SARS-CoV-2 infection was 35% effective in preventing reinfection with omicron BA.4 and BA.5 subvariants and previous omicron infection was 76% effective.

The 10-μg dose of BNT162b2 led to modest, rapidly waning protection against Covid-19 in children 5 to 11 years old. The 30-μg dose in adolescents gave greater, more durable protection, more so in 12-to-14-year-olds than in 15-to-17-year-olds.

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. However, the accurate measurement of shrimp size is challenged by factors such as their naturally curved body posture, frequent overlapping among individuals, and their tendency to blend with the background, all of which hinder precise size estimation. Traditional methods for measuring the size of shrimp involve manual sampling, which is labor-intensive and time consuming. In contrast, image processing and classical computer vision techniques provide some reasonable results but often suffer from inaccuracies, making them unsuitable for large-scale monitoring. To address this problem, this paper proposes a dual-segmentation deep learning-based framework for accurately estimating shrimp size. It integrates instance segmentation using the RTMDet-m model with an enhanced semantic segmentation model to effectively predict the centerline of the shrimp’s body, enabling precise size measurements. The first stage employs the RTMDet-m model for the instance segmentation of shrimp, achieving an average precision (AP50) of 96% with fewer parameters and the highest frames per second (FPS) count among state-of-the-art models. The second stage utilizes our custom segmentation model for centerline predictive module, attaining the highest FPS and F1-score of 88.3%. The proposed framework achieves the lowest mean absolute error of 1.02 cm and a root mean square error of 1.27 cm in shrimp size estimation compared to the baseline methods discussed in comparative study sections. Our proposed dual-segmentation framework outperforms both traditional and deep learning based methods used for measuring shrimp size.