Relationship between functional status and fatigue after COVID-19 infection: a multicenter study from Türkiye.

Background/aim: Symptoms of COVID-19 may persist for months. One of the persistent symptoms of COVID-19 is fatigue, which reduces functional status. The relationship between fatigue, functional status, and various other factors has received little attention, which this study aims to address.

ACE2-Decorated Virus-Like Particles Effectively Block SARS-CoV-2 Infection.

Purpose: Over the past three years, extensive research has been dedicated to understanding and combating COVID-19. Targeting the interaction between the SARS-CoV-2 Spike protein and the ACE2 receptor has emerged as a promising therapeutic strategy against SARS-CoV-2. This study aimed to develop ACE2-coated virus-like particles (ACE2-VLPs), which can be utilized to prevent viral entry into host cells and efficiently neutralize the virus.

Patient Survival With Extended Home Hemodialysis Compared to In-Center Conventional Hemodialysis.

Introduction: More frequent and/or longer hemodialysis (HD) has been associated with improvements in numerous clinical outcomes in patients on dialysis. Home HD (HHD), which allows more frequent and/or longer dialysis with lower cost and flexibility in treatment planning, is not widely used worldwide. Although, retrospective studies have indicated better survival with HHD, this issue remains controversial.

[Segmentation of acute pulmonary embolism in computed tomography pulmonary angiography using the deep learning method].

Abstract: Segmentation of acute pulmonary embolism in computed tomography pulmonary angiography using the deep learning method

Introduction: Pulmonary embolism is a type of thromboembolism seen in the main pulmonary artery and its branches. This study aimed to diagnose acute pulmonary embolism using the deep learning method in computed tomographic pulmonary angiography (CTPA) and perform the segmentation of pulmonary embolism data.

Materials And Methods: The CTPA images of patients diagnosed with pulmonary embolism who underwent scheduled imaging were retrospectively evaluated.

Learning Curve for Temporary Hemodialysis Catheter Placement.

Background: Training is essential for the safe and uncomplicated placement of hemodialysis catheters. This study explores the learning curve of this procedure.

Methods: In this prospective study, 60 patients who needed emergency hemodialysis without vascular access were included.

Multiple sessions of therapeutic electrical stimulation using implantable thin-film wireless nerve stimulators improve functional recovery after sciatic nerve isograft repair.

Introduction/aims: Although therapeutic electrical stimulation (TES) of injured peripheral nerve promotes axon regeneration and functional recovery, clinical applications of this therapy are limited to the intraoperative timeframe. Implantable, thin-film wireless nerve stimulators offer a potential solution to this problem by enabling delivery of electrical stimuli to an injured nerve over a period of several days post-surgery. The aim of this study was to determine the optimal time course of stimulation for maximizing functional recovery in a rat sciatic nerve isograft repair model.

Segmentation of orbital and periorbital lesions detected in orbital magnetic resonance imaging by deep learning method.

Purpose: Magnetic resonance imaging (MRI) has a special place in the evaluation of orbital and periorbital lesions. Segmentation is one of the deep learning methods. In this study, we aimed to perform segmentation in orbital and periorbital lesions.

A bioresorbable peripheral nerve stimulator for electronic pain block.

Local electrical stimulation of peripheral nerves can block the propagation of action potentials, as an attractive alternative to pharmacological agents for the treatment of acute pain. Traditional hardware for such purposes, however, involves interfaces that can damage nerve tissue and, when used for temporary pain relief, that impose costs and risks due to requirements for surgical extraction after a period of need. Here, we introduce a bioresorbable nerve stimulator that enables electrical nerve block and associated pain mitigation without these drawbacks.

A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs.

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations.

Subject And Methods: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs.

Characteristics and Survival Results of Peritoneal Dialysis Patients Suffering from COVID-19 in Turkey: A Multicenter National Study.

Introduction: We aimed to study the characteristics of peritoneal dialysis (PD) patients with coronavirus disease-19 (COVID-19), determine the short-term mortality and other medical complications, and delineate the factors associated with COVID-19 outcome.

Methods: In this multicenter national study, we included PD patients with confirmed COVID-19 from 27 centers. The baseline demographic, clinical, laboratory, and radiological data and outcomes at the end of the first month were recorded.

Post-COVID-19 outcomes of non-dialysis dependent chronic kidney disease patients: a national, multicenter, controlled study.

Purpose: Coronavirus disease 2019 (COVID-19) has a higher mortality in the presence of chronic kidney disease (CKD). However, there has not been much research in the literature concerning the outcomes of CKD patients in the post-COVID-19 period. We aimed to investigate the outcomes of CKD patients not receiving renal replacement therapy.

Comparison of Initial CT Findings and CO-RADS Stage in COVID-19 Patients with PCR, Inflammation and Coagulation Parameters in Diagnostic and Prognostic Perspectives.

Objectives: This study aims to determine whether COVID-19 patients with different initial reverse transcriptase-polymerase chain reaction (RT-PCR), computed tomography (CT) and laboratory findings have different clinical outcomes.

Materials And Methods: In this multi-center retrospective cohort study, 895 hospitalized patients with the diagnosis of COVID-19 were included. According to the RT-PCR positivity and presence of CT findings, the patients were divided into four groups.

Middle-term outcomes in renal transplant recipients with COVID-19: a national, multicenter, controlled study.

Background: In this study, we evaluated 3-month clinical outcomes of kidney transplant recipients (KTR) recovering from COVID-19 and compared them with a control group.

Method: The primary endpoint was death in the third month. Secondary endpoints were ongoing respiratory symptoms, need for home oxygen therapy, rehospitalization for any reason, lower respiratory tract infection, urinary tract infection, biopsy-proven acute rejection, venous/arterial thromboembolic event, cytomegalovirus (CMV) infection/disease and BK viruria/viremia at 3 months.

Dual mTORC1/2 inhibition compromises cell defenses against exogenous stress potentiating Obatoclax-induced cytotoxicity in atypical teratoid/rhabdoid tumors.

Atypical teratoid/rhabdoid tumors (AT/RT) are the most common malignant brain tumors of infancy and have a dismal 4-year event-free survival (EFS) of 37%. We have previously shown that mTOR activation contributes to AT/RT's aggressive growth and poor survival. Targeting the mTOR pathway with the dual mTORC1/2 inhibitor TAK-228 slows tumor growth and extends survival in mice bearing orthotopic xenografts.

The Longitudinal Evolution of Post-COVID-19 Outcomes Among Hemodialysis Patients in Turkey.

Introduction: Hemodialysis (HD) patients have increased risk for short-term adverse outcomes of COVID-19. However, complications and survival at the post-COVID-19 period have not been published extensively.

Methods: We conducted a national, multicenter observational study that included adult maintenance HD patients recovered from confirmed COVID-19.

A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs.

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.

Objectives: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer.

Methods: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs.

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images.

Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training.

Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system.

Objectives: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography.

Methods: One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskişehir, Turkey and based on a deep CNN method, was proposed to be evaluated.

An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs.

Background: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs.

Predicting the outcome of COVID-19 infection in kidney transplant recipients.

Background: We aimed to present the demographic characteristics, clinical presentation, and outcomes of our multicenter cohort of adult KTx recipients with COVID-19.

Methods: We conducted a multicenter, retrospective study using data of patients hospitalized for COVID-19 collected from 34 centers in Turkey. Demographic characteristics, clinical findings, laboratory parameters (hemogram, CRP, AST, ALT, LDH, and ferritin) at admission and follow-up, and treatment strategies were reviewed.

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs.

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.

Methods And Materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images.