LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data.

Background: In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene.

Essential oils as capsule disruptors: enhancing antibiotic efficacy against multidrug-resistant .

Background: Multidrug-resistant (MDR-) poses a significant global health threat due to its involvement in severe infections and high mortality rates. The emergence of MDR strains necessitates the exploration of alternative therapeutic strategies.

Methods: isolates were obtained from human and animal sources.

Successful open repair of syphilitic thoracoabdominal aneurysm.

Most syphilitic aneurysms involve the ascending aorta. Those involving the descending aorta are less common, and those involving the abdominal aorta are unusual. Rarer yet, we present the case of a 40-year-old man with HIV and a history of syphilis with a thoracoabdominal aneurysm.

Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security.

Differentiation of the Intradural Extramedullary Spinal Tumors, Schwannomas, and Meningiomas Utilizing the Contrast Ratio as a Quantitative Magnetic Resonance Imaging Method.

Background: Schwannomas and meningiomas are the most common intradural extramedullary spinal tumors; however, differentiating between them using magnetic resonance imaging (MRI) is a frequent challenge. In this study, we aimed to investigate the use of the contrast ratio (CR) as a quantitative MRI method in the differentiation of schwannomas and meningiomas.

Methods: We analyzed the data of patients with intradural extramedullary spinal tumors who underwent surgery and were diagnosed with either schwannomas or meningiomas by histopathological analysis.

Selecting drill size for post space preparation based on final endodontic radiographs: An in vitro study.

Background: For placement of intraradicular posts the intracanal filling material has to be removed. If drills are employed for this purpose, extra widening of the canal, incomplete cracks or root perforation are probable when inappropriate size of drill is used.

Objective: This in vitro study assessed the efficacy of radiographs taken after completion of root canal therapy in selecting the appropriate-sized Peeso reamer for post space preparation.

Evaluation of Epiconus and Conus Medullaris Disorders due to Thoracolumbar Vertebral Fracture using Motor-evoked Potentials.

Study Design: A retrospective case-control study.

Objective: To characterize the motor evoked potential (MEP) when the epiconus or conus medullaris is compressed by a fracture of the T12 or L1 vertebra.

Summary Of Background Data: Although the characteristics of compressive cervical and thoracic myelopathy with transcranial magnetic stimulation MEP have been reported, the MEP parameters in compressive disorders of the epiconus and conus medullaris have not yet been characterized.

Development of a novel animal model of lumbar vertebral endplate lesion by intervertebral disk injection of monosodium iodoacetate in rats.

Purpose: Vertebral endplate lesions (EPLs) caused by severe disk degeneration are associated with low back pain. However, its pathophysiology remains unclear. In this study, we aimed to develop a vertebral EPL rat model mimicking severe intervertebral disk (IVD) degeneration by injecting monosodium iodoacetate (MIA) into the IVDs and evaluating it by assessing pain-related behavior, micro-computed tomography (CT) findings, and histological changes.

Executive Summary of Clinical Practice Guideline on Immunotherapy for Inhalant Allergy.

Objective: Allergen immunotherapy (AIT) is the therapeutic exposure to an allergen or allergens selected by clinical assessment and allergy testing to decrease allergic symptoms and induce immunologic tolerance. Inhalant AIT is administered to millions of patients for allergic rhinitis (AR) and allergic asthma (AA) and is most commonly delivered as subcutaneous immunotherapy (SCIT) or sublingual immunotherapy (SLIT). Despite its widespread use, there is variability in the initiation and delivery of safe and effective immunotherapy, and there are opportunities for evidence-based recommendations for improved patient care.

Clinical Practice Guideline: Immunotherapy for Inhalant Allergy.

Objective: Allergen immunotherapy (AIT) is the therapeutic exposure to an allergen or allergens selected by clinical assessment and allergy testing to decrease allergic symptoms and induce immunologic tolerance. Inhalant AIT is administered to millions of patients for allergic rhinitis (AR) and allergic asthma (AA) and is most commonly delivered as subcutaneous immunotherapy (SCIT) or sublingual immunotherapy (SLIT). Despite its widespread use, there is variability in the initiation and delivery of safe and effective immunotherapy, and there are opportunities for evidence-based recommendations for improved patient care.

Short-segment spinal fusion for chronic low back pain with bone marrow edema adjacent to the vertebral endplate in adult spinal deformity.

Purpose: Corrective long spinal fusion is a widely accepted surgical method for patients with adult spinal deformities. However, instrumented long fusion is associated with a significant risk of complications. Therefore, we aimed to assess the success of short-segment spinal fusion, particularly for bone marrow edema (BME) adjacent to the vertebral endplate, in patients with low back pain (LBP) and spinal deformity.

Vertebral osteomyelitis due to Mycobacterium abscessus subsp. massiliense with paravertebral abscess: A case report and review.

The incidence of vertebral osteomyelitis (VO) caused by non-tuberculosis mycobacteria (NTM) without immunocompetence is extremely rare. Herein, we reported on a case of VO caused by NTM. A 38-year-old man was admitted to our hospital with persisting low back and leg pain which had lasted for a year.

Security Risks and User Perception towards Adopting Wearable Internet of Medical Things.

The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include smartwatches and fitness bands. This evolving phenomenon due to the IoT has become prevalent in managing health and poses severe security and privacy risks to personal information.

Differentiating Neurodegenerative Disease From Compressive Cervical Myelopathy Using Motor-Evoked Potentials.

Study Design: A retrospective case-control study.

Objective: To differentiate neurodegenerative diseases from compressive cervical myelopathy (CCM) using motor-evoked potentials (MEPs).

Summary Of Background Data: When considering surgery for CCM, it may be necessary to differentiate the condition from a neurodegenerative disease.

Differentiation Between Compressive Cervical and Thoracic Myelopathy Using the Central Motor Conduction Time Ratio.

Purpose: Thoracic myelopathy is a rare condition whose diagnosis is often missed or delayed. This study aimed to differentiate between cervical and thoracic myelopathy using motor-evoked potential testing.

Methods: The authors included 835 patients with compressive cervical myelopathy and 94 patients with compressive thoracic myelopathy.

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods.

Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based predictive model to detect adolescent diabetes.

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification.

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves.

Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage.

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis.

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System.

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations.

A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network.

This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera.

Crowd density estimation using deep learning for Hajj pilgrimage video analytics.

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms.

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline.

Hearing deficiency is the world's most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain's cortex area through an auditory stimulus.

A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services.

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication.

The road to optimal acceleration of Dixon imaging and quantitative T2-mapping in the ankle using compressed sensing and parallel imaging.

Objective: This study aimed to find the optimal acceleration factor achievable with CS-SENSE for a clinical ankle protocol while maintaining comparable image quality.

Methods: We explored the optimal acceleration achievable with factor CS-SENSE, for an ankle protocol with T2-weighted, PD-weighted TSE-Dixon (coronal, axial and sagittal) and T2-mapping (sagittal) sequences, on a 3 T MRI-scanner. This study contained three steps: (1) phantom test, (2) pilot test on healthy volunteers, (3) anatomical assessment on a cohort of healthy volunteers and a quantitative analysis.

Dental education in the Arabic language vs English language: A survey among Arab dentists.

Objective: The aim of this study was to explore the language difficulties encountered by Arabic dental students and dentists in some aspects of their dental education and to determine their attitude towards the Arabization of the medical curriculum.

Materials And Methods: A web-based self-administered questionnaire with 14 multiple-choice questions was designed and distributed online via google forms in the Arabic language. The online link was sent randomly to Arabic dental students, dental academic staff, and dental practitioners.