Predictive efficacy of immature granulocytes in acute complicated appendicitis.

Objective: Acute appendicitis is the most common cause of acute abdomen. It is important to differentiate between complicated and uncomplicated appendicitis before surgery in the emergency department. Recently, immature granulocytes have become one of the biomarkers used as predictors of inflammation.

Atypical presentation of ACCES syndrome resembling dominant Spondyloepiphyseal dysplasia tarda.

Aplasia Cutis Congenita with Ectrodactyly Skeletal Syndrome (ACCES, OMIM #619959) is an extremely rare multiple congenital anomalies syndrome caused by haploinsufficiency of the UBA2 gene. This syndrome presents with growth retardation, dysmorphic facial features, neurodevelopmental delay, skeletal problems including ectrodactyly, developmental dysplasia of the hip (DDH) and scoliosis, skin findings such as aplasia cutis, and some internal organ abnormalities. Our 13-year-old female patient and her 38-year-old father had a skeletal dysplasia phenotype with disproportionate short stature, bilateral DDH, mild epiphyseal involvement, scoliosis, and increased lumbar lordosis.

The relation between thrombus burden and early mortality risk in inpatients diagnosed with COVID-19-related acute pulmonary embolism: a retrospective cohort study.

Background: COVID-19-related acute pulmonary thromboembolism (APE) is associated with poor outcomes in patients with COVID-19. There are studies investigating the association between thrombus burden and high risk of early mortality in the pre-COVID-19 period. This study aimed to evaluate the relationship between clot burden and early mortality risk in COVID-19-related APE patients.

The evaluation of subcutaneous and visceral adipose tissue changes by computed tomography in coronavirus disease 2019 and comparison with quantitative analysis of lung involvement.

Background: This study aims to reveal the relationship between lung involvement and visceral adipose tissue changes between chest-computed tomography (CT) scans taken in short intervals in COVID-19 patients.

Methods: The retrospective study included 52 patients who tested positive for SARS-CoV-2. All patients had two chest CT exams.

Early effects of inactivated (CoronaVac) SARS-CoV-2 vaccine on retrobulbar vascular blood flow and retinal vascular density.

Aims: We aimed to investigate the early effects of inactivated SARS-CoV-2 vaccine on retrobulbar vascular blood flow and retinal vascular density in healthy subjects.

Methods: Thirty-four eyes of 34 healthy volunteers who received the CoronaVac (Sinovac Life Sciences, China) were included in this prospective study. Resistive index (RI), pulsatility index (PI) and peak systolic velocity (PSV) of the ophthalmic artery (OA), central retinal artery (CRA), and the temporal and nasal posterior ciliary arteries (PCA) were evaluated with color Doppler ultrasonography (CDUS) before vaccination, at the 2nd and 4th weeks after vaccination.

Unilateral Axillary Lymphadenopathy after the Inactivated SARS-COV-2 (CoronaVac) Vaccine: Ultrasonographic Imaging.

Background: Currently, unilateral clinical and subclinical axillary adenopathy cases associated with the Pfizer-BioNTech and Moderna vaccines are increasingly reported. However, only one study on axillary adenopathy due to the CoronaVac vaccine is published.

Aims: To present the incidence, severity, and ultrasonographic findings of axillary adenopathy that developed in healthcare professionals in Turkey after they were vaccinated with CoronaVac against coronavirus disease-19.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

Background: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission.

Methods: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test.