Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning

Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision-making, and decrease healthcare costs. In this study, a machine learning-based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross-validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37-94.29) and 91.10 (95% CI: 88.16-94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.

Enhanced detection of viruses for improved water safety

Human viruses pose a significant health risk in freshwater environments, but current monitoring methods are inadequate for detecting viral presence efficiently. We evaluated a novel passive in-situ concentration method using granular activated carbon (GAC). This study detected and quantified eight enteric and non-enteric, pathogenic viruses in a freshwater recreational lake in paired grab and GAC passive samples. The results found that GAC passive sampling had a higher detection rate for all viruses compared to grab samples, with adenovirus found to be the most prevalent virus, followed by respiratory syncytial virus, norovirus, enterovirus, influenza A, SARS-CoV-2, and rotavirus. GAC in-situ concentration allowed for the capture and recovery of viral gene copy targets that ranged from one to three orders of magnitude higher than conventional ex-situ concentration methods used in viral monitoring. This simple and affordable sampling method may have far-reaching implications for reducing barriers associated with viral monitoring across various environmental contexts.

Kinetic modelling of ultrasound-triggered chemotherapeutic drug release from the surface of gold nanoparticles

Therapeutic ultrasound can be used to trigger the on-demand release of chemotherapeutic drugs from gold nanoparticles (GNPs). In the previous work, our group achieved doxorubicin (DOX) release from the surface of GNPS under low-intensity pulsed ultrasound (LIPUS) exposure. However, the specific release kinetics of ultrasound-triggered DOX release from GNPs is not known. Here, we present a release kinetics study of DOX from GNPs under ultrasound exposure for the first time. A novel dialysis membrane setup was designed to quantify DOX release from LIPUS-activated GNPs at 37.0 °C and 43.4 °C (hyperthermia temperature range). Contributions of thermal and non-thermal mechanisms of LIPUS-triggered DOX release were also quantified. Non-thermal mechanisms accounted for 40 ± 7% and 34 ± 5% of DOX release for 37.0 °C and 43.4 °C trials, respectively. DOX release under LIPUS exposure was found to follow Korsmeyer-Peppas (K-P) kinetics, suggesting a shift from a Fickian (static) to a non-Fickian (dynamic) release profile with the addition of non-thermal interactions. DOX release was attributed to an anomalous diffusion release mechanism from the GNP surface. A finite element model was also developed to quantify the acoustic radiation force, believed to be the driving force of non-thermal DOX release inside the dialysis bag.

SARS-CoV-2 pathogenesis in an angiotensin II-induced heart-on-a-chip disease model and extracellular vesicle screening

Adverse cardiac outcomes in COVID-19 patients, particularly those with preexisting cardiac disease, motivate the development of human cell-based organ-on-a-chip models to recapitulate cardiac injury and dysfunction and for screening of cardioprotective therapeutics. Here, we developed a heart-on-a-chip model to study the pathogenesis of SARS-CoV-2 in healthy myocardium established from human induced pluripotent stem cell (iPSC)-derived cardiomyocytes and a cardiac dysfunction model, mimicking aspects of preexisting hypertensive disease induced by angiotensin II (Ang II). We recapitulated cytopathic features of SARS-CoV-2-induced cardiac damage, including progressively impaired contractile function and calcium handling, apoptosis, and sarcomere disarray. SARS-CoV-2 presence in Ang II-treated hearts-on-a-chip decreased contractile force with earlier onset of contractile dysfunction and profoundly enhanced inflammatory cytokines compared to SARS-CoV-2 alone. Toward the development of potential therapeutics, we evaluated the cardioprotective effects of extracellular vesicles (EVs) from human iPSC which alleviated the impairment of contractile force, decreased apoptosis, reduced the disruption of sarcomeric proteins, and enhanced beta-oxidation gene expression. Viral load was not affected by either Ang II or EV treatment. We identified MicroRNAs miR-20a-5p and miR-19a-3p as potential mediators of cardioprotective effects of these EVs.