COVID-19 Biomarkers Comparison: Children, Adults and Elders.

Background and Objectives: this study aimed to research links between C-reactive protein (CRP), lactate dehydrogenase (LDH), creatinekinase (CK), 25-OH vitamin D (25-OHD), ferritin (FER), high-density lipoprotein cholesterol (HDL)cholesterol and clinical severity in patients from the western part of Romania, and compare their potential use as biomarkers for intensive care units (ICU) admission and death in children, adults and elders. Materials and Methods: this study is a retrospective cohort study, performed on patients positively diagnosed with COVID-19. Available CRP, LDH, CK 25-OH vitamin D, ferritin, HDL cholesterol and clinical severity were recorded. The following were assessed: median group differences, association, correlation and receiver operating characteristic. Results: 381 children, 614 adults and 381 elders were studied between 1 March 2021 and 1 March 2022. Most children and adults presented mild symptomatology (53.28%, 35.02%, respectively), while most elders presented severe symptomatology (30.04%). ICU admission was 3.67% for children, 13.19% for adults and 46.09% for elders, while mortality was 0.79% for children, 8.63% for adults and 25.1% for elders. With the exception of CK, all other biomarkers showed some significant associations with clinical severity, ICU admission and death. Conclusions: CRP, LDH, 25-OH vitamin D, ferritin and HDL are important biomarkers for COVID-19 positive patients, especially in the pediatric population, while CK was mostly within normal ranges.

ACUTE VARICOCELE THROMBOSIS AFTER VACCINATION WITH MRNA SARS-COV-2 VACCINE- A CASE REPORT

The dilatation of the pampiniform plexus, the venous system that drains the testicle, causes varicocele. Varicocele thrombosis is rare, with few reported cases in the medical literature [1-6]. Patients may present with acute scrotal pain, mimicking a testicular torsion or strangulated hernia. Diagnosis is difficult, therefore, when based solely on clinical history and examination. Our objective is to present an unusual case of varicocele thrombosis, after Covid 19 vaccination. Ultrasonography (US) with Doppler interrogation is the first-line imaging choice for diagnosis [1]. The therapeutical management is primarily conservative;however, some cases might require surgery [1,6]. The Coronavirus 2019 pandemic stimulated the development of vaccines with unprecedented speed and employing novel technologies. Serious adverse effects remained low after worldwide vaccination [7,8]. We report the case of a 35-year-old male patient who presented in the Urology Department accusing intense, continuous scrotal pain and swelling, with onset the next day after receiving the second dose of an mRNA Covid-19 vaccine (BNT162b2, Cominarty, Pfizer/BioNTech). There were no associated urinary signs and no fever. The patient was a healthy young man, with no known malignancy or blood dyscrasia. He could not recall suffering any local trauma between the vaccination and the appearance of the symptoms. The clinical exam revealed a tender, scrotal swelling inferior to the left testicle. The ultrasound exam demonstrated homogenous, normal echogenic testicles without changes in vascularity on Doppler US. Multiple variceal dilatations with a spontaneous diameter of up to 5 mm were observed around the left testis. The lumen of several dilated veins appeared filled with echogenic debris. The blood flow was slow in the remaining veins, with a sluggish aspect. Continuous, progressive probe compression was applied, with no complete venous collapse observed. The greyscale aspect was consistent with partially obstructing thrombi. The venous filling defects were confirmed on Colour Doppler. No signs of thrombosis were present at the level of the spermatic cord or the inguinal canal. Pampiniform plexus thrombosis should be considered in the differential diagnosis of acute testicular pain. This case report reveals an unprecedented etiology of varicocele thrombosis, as a side effect of an mRNA SARS-COV2 vaccine. [ FROM AUTHOR] Copyright of Ultrasound in Medicine & Biology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.