A rare subcutaneous periorbital zoonotic infection in a 42-year-old female patient.

Human dirofilariasis represents a zoonotic infectious disorder caused by parasites belonging to the Dirofilaria genus, which includes numerous species with a large variability regarding the host specificity, life cycle, and clinical manifestations. This disease appears to be a vector-borne parasitosis that is spread through insects - intermediate hosts (usually mosquitos) - and affects several carnivores as definitive hosts. Humans represent an unsuitable host for the parasite to complete its life cycle, being unable to release microfilariae in the blood as the inoculated larvae fail to reach sexual maturity. Therefore, humans are unable to transmit the infection to other humans, neither directly nor through an intermediate host. The current case report indicates a rare encounter of a Dirofilaria repens infection in a 42-year-old female patient living in an urban area (Craiova, Romania), who developed intermittent right periorbital edema after a previous trip to Greece. Over the course of one month, the right periorbital edema gradually remitted, and a firm, round lump developed in the external inferior right periorbital region. There were no similar clinical manifestations among other family members. Her medical history showed no other complaints or any serious general health problems. Also, the patient denied having any pets at home. The blood tests did not reveal any abnormalities. The exact source of infection could not be identified, but the chances for this infection to be related to the history of traveling to Greece are high, given that this region is one of the most important endemic areas in Europe.

Should PI-RADS 3 be Subclassified According to ADC Values in the Transition Zone?

The premise of this paper starts from the fact that a more precise definition related to the intensity of the lesions on the ADC sequence can lead to a new subclassification of PI-RADS 3 lesions in the transitional zone and to an improvement of the specificity of the PI-RADS classification. The study was retrospective and included only patients who, based on prostate MRI examinations, contained exclusively PI-RADS 3 lesions, without other PI-RADS 4 or 5 lesions. The number of cases that meet all these conditions was reduced to 18, where a series of characteristics were noted for each one: PI-RADS 3 lesion area on the ADC sequence, the minimum and average ADC value of the lesion, the average ADC value of the transitional zone outside the lesion, PSA, prostatic volume, PSA density and biopsy result. The average ADC value of the negative lesions was 865(165) m2/sec, and of the positive ones was 869(118) m2/sec, which denies the hypothesis that there could be a value limit ADC to delimit PI-RADS 3 from PI-RADS 2 or 4 in this sample of patients. Furthermore, we reported the average ADC value of each such lesion to the adjacent unchanged transitional zone and obtained a greater difference of 432(163) m2/sec between the negative lesions and their adjacent transitional zone, compared to the difference of 399(127) m2/sec between the positive lesions and their adjacent ones.

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.