Evaluation of the Oral Microcirculation in Patients Undergoing Anti COVID-19 Vaccination: A Preliminary Study.

Videocapillaroscopy allows the study of both the morphological and architectural structure of the microcirculation and its hemodynamic conditions; these parameters are directly involved in autoimmune and/or inflammatory pathologies. The purpose of this research, based on capillaroscopy, is to establish whether a patient who receives an anti-COVID 19 vaccine has any changes in their oral microcirculation. A complete capillaroscopic mapping of the oral cavity of the subjects examined was made; the investigated mucosa sites were the following: cheek, labial, chewing-gingival and back of the tongue. This study showed an increase in capillary density from the comparison between the mean labial capillary density of vaccinated patients and the reference mean capillary density value of the literature. The increase in capillary density is a sign that can be attributed to an increase in angiogenic activity. The EMA, GACVS and MHRA have reviewed the risk of thrombosis after vaccination, agreeing that the benefits outweigh the risks.

Data driven health monitoring of Peltier modules using machine-learning-methods.

Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.