Publications by authors named "Hadjiandreou M"

Syndactyly release aims to address skin deficits by resurfacing web spaces and sides of digits to allow independent digital motion while minimizing the risk of web creep and scar contractures. Conventional methods include the use of a dorsal and interdigitating flaps with full-thickness skin grafts. More recently, there have been several descriptions of "graftless" syndactyly release without skin grafts, thus avoiding a further (usually distant) donor site.

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Facial transplantations have become a clinical reality as the last reconstructive option in severely disfigured patients. To date, clinical outcomes remain unclear. The purpose of this paper was to analyse the outcomes in facial transplantation (FT) and determine the risks and benefits of FT based on short- and long-term outcomes.

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Background: The Exoscope is a novel high-definition digital camera system. There is limited evidence signifying the use of exoscopic devices in microsurgery. This trial objectively assesses the effects of the use of the Exoscope as an alternative to the standard operating microscope (OM) on the performance of experts in a simulated microvascular anastomosis.

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Mobile computing devices (MCDs), such as smartphones and tablets, are revolutionizing medical practice. These devices are almost universally available and offer a multitude of capabilities, including online features, streaming capabilities, high-quality cameras, and numerous applications. Within the surgical field, MCDs are increasingly being used for simulations.

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Introduction: Microvascular anastomosis with coupler devices has revolutionized microsurgery practice. Couplers are considered easier to apply and offer improved operating time while maintaining success rates. This study aims to map the learning curve, skill acquisition, and decay of novice microsurgeons in performing coupler anastomosis.

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Medulloblastoma is the most common solid primary brain tumor in children. Remarkable advancements in the understanding of the genetic and epigenetic basis of these tumors have informed their recent molecular classification. However, the genotype/phenotype correlation of the subgroups remains largely uncharacterized.

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Background: Electronic patient records (EPRs) allow efficient and accurate medical documentation. Diagrams have traditionally been used to document clinical signs in patient notes. The interpretation of these diagrams may vary among doctors across a range of specialties, but this has never been tested previously.

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Tumorigenesis is a complex, multistep process that depends on numerous alterations within the cell and contribution from the surrounding stroma. The ability to model macroscopic tumor evolution with high fidelity may contribute to better predictive tools for designing tumor therapy in the clinic. However, attempts to model tumor growth have mainly been developed and validated using data from xenograft mouse models, which fail to capture important aspects of tumorigenesis including tumor-initiating events and interactions with the immune system.

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We propose a sequential probabilistic mixture model for individualized tumor growth forecasting. In contrast to conventional deterministic methods for estimation and prediction of tumor evolution, we utilize all available tumor-specific observations up to the present time to approximate the unknown multi-scale process of tumor growth over time, in a stochastic context. The suggested mixture model uses prior information obtained from the general population and becomes more individualized as more observations from the tumor are sequentially taken into account.

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In this work, we present how optimized treatment interruptions during chemotherapy may be used to control drug-resistance, a major challenge for clinicians worldwide. Specifically, we examine resistance in cancer and HIV/AIDS. For each disease, we use mathematical models alongside real data to represent the respective complex biological phenomena and optimal control algorithms to design optimized treatment schedules aiming at controlling disease progression and patient death.

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The combination of mathematical modeling and optimal control techniques holds great potential for quantitatively describing tumor progression and optimal treatment planning. Hereby, we use a Gompertz-type growth law and a pharmacokinetic-pharmacodynamic approach for modeling the effects of drugs on tumor progression in tumor bearing mice, and we combine these in order to design optimal therapeutic patterns. Specifically, we describe colon cancer progression in both untreated mice as well as mice treated with widely used anticancer agents.

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This study involves the mathematical modelling of long-term HIV dynamics. The proposed model is able to predict the entire trajectory of the disease: initial viremia in the early weeks of the infection, latency, and progression to AIDS; a range spanning approximately ten years. The model outcomes were compared to clinical data and significant agreement was achieved.

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