Endometriosis is a debilitating condition characterized by high recurrence rates. The etiology and pathogenesis remain unclear. Typically, endometriosis causes pain and infertility, although 20-25% of patients are asymptomatic. The principal aims of therapy include relief of symptoms, resolution of existing endometriotic implants, and prevention of new foci of ectopic endometrial tissue. Current therapeutic approaches are far from being curative; they focus on managing the clinical symptoms of the disease rather than fighting the disease. Specific combinations of medical, surgical, and psychological treatments can ameliorate the quality of life of women with endometriosis. The benefits of these treatments have not been entirely demonstrated, particularly in terms of expectations that women hold for their own lives. Although theoretically advantageous, there is no evidence that a combination medical-surgical treatment significantly enhances fertility, and it may unnecessarily delay further fertility therapy. Randomized controlled trials are required to demonstrate the efficacy of different treatments.

Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

A synapse is a biological structure, which connects two neurons enabling specific and unidirectional information flow (excitation or inhibition) from one neuron to another. Synaptic connections are the key elements of the neuronal networks and their plasticity underlies learning and memory. Recent progress in building artificial neuronal networks is largely based on the elements mimicking features of natural synapses in silico or in electrico. [1][2][3] Hybrid networks, in which braincomputer systems read and control the activity of live cells also require interphase devices with cellular resolution. Use