sEMG-based automatic characterization of swallowed materials.

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.

The prognostic value of seminal anti-sperm antibodies screening in men prepared for ICSI: a call to change the current antibody-directed viewpoint of sperm autoimmunity testing.

AIMS: We aimed to prospectively evaluate the prognostic value of seminal anti-sperm antibodies (ASA) screening in couples prepared for intracytoplasmic sperm injection (ICSI). METHODS: A prospective comparative study was conducted, including 184 non-azoospermic infertile men scheduled for ICSI. For each patient, detection of ASA in seminal plasma using the ELISA technique was done and its relation to standard semen parameters and ICSI outcomes, including fertilization, embryo development, and pregnancy rates, was analyzed. RESULTS: Sperm count, motility, and morphology were negatively affected by existence of seminal ASA (p = 0.012, 0.006, and 0.011, respectively). However, no statistically significant difference was detected between patients with positive and negative seminal ASA regarding the median values of fertilization (56.3% versus 66.7%, p = 0.091), percentage of couples with grade A embryo development (91.4% versus 89.9%, p = 0.520), and pregnancy rates (31.4% versus 32.2%, p = 0.98) after ICSI. CONCLUSION: ICSI seems able to overcome the problem of ASA in semen. The routine screening of ASA in men prepared for ICSI has no additional prognostic value and cannot be recommended for the time being, until more specific antigen-concerned testing can be developed.