Abnormal elevated levels of cytokines such as interferon (IFN), interleukin (IL), and tumor necrosis factor (TNF), are considered as one of the prognosis biomarkers for indicating the progression to severe or critical COVID‐19. Hence, it is of great significance to develop devices for monitoring their levels in COVID‐19 patients, and thus enabling detecting COVID‐19 patients that are worsening and to treat them before they become critically ill. Here, an intelligent aptameric dual channel graphene‐TWEEN 80 field effect transistor (DGTFET) biosensing device for on‐site detection of IFN‐γ, TNF‐α, and IL‐6 within 7 min with limits of detection (LODs) of 476 × 10−15, 608 × 10−15, or 611 × 10−15 m respectively in biofluids is presented. Using the customized Android App together with this intelligent device, asymptomatic or mild COVID‐19 patients can have a preliminary self‐detection of cytokines and get a warning reminder while the condition starts to deteriorate. Also, the device can be fabricated on flexible substrates toward wearable applications for moderate or even critical COVID‐19 cases for consistently monitoring cytokines under different deformations. Hence, the intelligent aptameric DGTFET biosensing device is promising to be used for point‐of‐care applications for monitoring conditions of COVID‐19 patients who are in different situations.
Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem.Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images.Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process.Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration.
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