Application of ultrasound in combination with drug therapy was effective in reducing tumor growth rate, irrespective of which frequency was employed.

Non-obstructive azoospermia (NOA) is a severe form of infertility accounting for 10% of infertile men. Microdissection testicular sperm extraction (microTESE) includes a set of clinical protocols from which viable sperm are collected from patients (suffering from NOA), for intracytoplasmic sperm injection (ICSI). Clinical protocols associated with the processing of a microTESE sample are inefficient and significantly reduce the success of obtaining a viable sperm population. In this review we highlight the sources of these inefficiencies and how these sources can possibly be removed by microfluidic technology and single-cell Raman spectroscopy.

Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.

We report a novel sperm trapping microfluidic chip that can trap sperm and then transfer entrapped sperm to individual collectors (cryotips) that can be detached from the chip. We were able to collect sperm in numbers ranging from 3–12 with good recovery of sperm from the cryotip after transfer. Based on our knowledge, this is the first time a microfluidic chip has been designed to enable entrapment of low numbers of cells while provisioning in the design the recovery and subsequent observation of those few cells off the chip. We also discuss the critical design and operational challenges that need to be taken into consideration for the chip to deliver its outcome. The chip has applications in non-invasive analysis and cryopreservation of a few sperm for treatment of severe cases of male infertility.

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