Bilateral atrophic squirrhus of breast in neglected breast cancer: case report

The atrophic squirrhus carcinoma is an advanced form of breast cancer, which is most often neglected by patients. These days it has become very rare. The bilaterality of this form is even more exceptional. We present a case of atrophic squirrhus breast cancer of a 58 years old woman, rural origin, which is particular for its bilaterality and rapid evolution causing the death after 22 months from the first abnormal functional sign

Reversible Wernicke encephalopathy caused by hyperemesis gravidarum in the second trimester of pregnancy: a case report

Wernicke encephalopathy is a potentially life-threatening neurologic syndrome caused by acute thiamine (vitamin B1) deficiency. It is usually associated with excessive alcohol consumption. Less frequently, this syndrome can be caused by persistent vomiting. This is a case report of a 33-year-old woman diagnosed with Wernicke encephalopathy (WE) during the second trimester of pregnancy. The presence of neurological and ophthalmological symptoms in the context of hyperemesis gravidarum led us to evoke the diagnosis of WE, and it was confirmed when specific lesions were found in the brain magnetic resonance imaging (MRI). Luckily for our patient, WE was diagnosed promptly and the signs were reversible after thiamine supplementation. In conclusion, any first line care taker or midwife must know the symptoms of Wernicke encephalopathy because prompt diagnosis and treatment can lead to recovery

Chaotic Dingo Optimization Algorithm: Application in Feature Selection for Beamforming Aided Spectrum Sensing

International audienceSpectrum sensing based on Beamforming, like others classification problem, require feature selection to perform learning algorithms and enhance the classification task. This paper proposes a novel version of the Dingo Optimization Algorithm (DOA) to optimize feature selection for a Deep Neural Network (DNN) classifier. Two improvements are introduced to avoid the premature convergence problem and stagnation in the local optima of the original DOA. First, the chaos strategy is executed to produce a high level of diversification in the algorithm, which improves its ability to escape from potential local optimums. Second, the weight factor is introduced to boot up the search process to the global optima. Here, the aim is to improve the DOA for feature selection in the deep learning approach in order to enhance the performance of blind spectrum sensing based on Beamforming in the context of cognitive radio (CR). Through simulations results, we illustrate that our algorithm, called Chaotic Dingo Optimization Algorithm (CDOA), outperforms the original one and a set of state-of-the-art optimization algorithms (i.e., HS, BBO, PSO, and SA) for feature selection in the learning approach

A hybrid Modified Black Widow Optimization and PSO Algorithm: Application in Feature Selection for Cognitive Radio Networks

International audienceIn spectrum sensing issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature selection. This paper proposes a new hybrid optimization algorithm to optimize feature selection for a Deep Neural Network (DNN) classifier. To surpass the premature convergence problem and improve the exploitation ability of the original Black Widow Optimization Algorithm (BWO), we mix a modified version of BWO and Particle Swarm Optimization (PSO), called MBWPSO. The aim is to enhance the performance of a blind spectrum sensing approach in the context of cognitive radio (CR) for wireless communications. Computer simulations show that the MBWPSO algorithm outperforms the original one and a set of state-of-the-art algorithms (i.e., HS, BBO, PSO, and SA) algorithms. The MBWPSO also exhibits the best performance once applied for feature selection in the above contex

Smart Full-Exploitation of Beamforming Fusion assisted Spectrum Sensing for Cognitive Radio

International audienceThis paper proposes blind spectrum sensing (SS) in a narrowband context called Beamforming Fusion assisted Spectrum Sensing (BFSS). Considering a channel with angles of arrival (AoA), we jointly exploit beamforming algorithms to make decisions about the detection of users on frequency resources. The proposed method is totally blind and does not require knowledge of the noise power, the channel estimation, and the source signal. A state-of-the-art comparison of SS methods using beamforming is provided to validate our contribution in a shallow SNR region

Gonadal dysgenesis and the Mayer-Rokitansky-Kuster-Hauser Syndrome in a girl with a 46, XX karyotype: A case report and review of literature

Mayer-Rokitansky-Kuster-Hauser (MRKH) is a characteristic syndrome in which the Mullerian structures are absent or rudimentary. It is also associated with anomalies of the genitourinary and skeletal systems. Its association with gonadal dysgenesis is extremely rare and appears to be fortuitous, independent of chromosomal anomalies. We report such a case in a 21-year-old girl who presented primary amenorrhea and impuberism. The endocrine study revealed hypergonadotrophic hypogonadism. The karyotype was normal, 46, XX. No chromosome Y was detected at the fluorescence in situ hybridization (FISH) analysis. Internal genitalia could not be identified on the pelvic ultrasound and pelvic magnetic resonance imaging. Laparoscopy disclosed concomitant ovarian dysgenesis and MRKH syndrome. There were no other associated malformations. Hormonal substitution therapy with oral conjugated estrogens was begun. The patient has been under regular follow-up for the last two years and is doing well