Palpation is a clinical diagnosis method utilized by physicians to acquire valuable information about the pathological condition of an organ using the sense of touch. This method, however, is subjective. The accuracy depends on the physician's experience and skill. Therefore, to make palpation objective and minimize variability in prostate cancer diagnosis among physicians, an automated palpation system is required. This paper describes the design and experimental evaluation of a 2 Degrees of Freedom (2DoF) tendon driven robotic palpation probe. The probe's palpation motion is controlled by actuating driving tendons using a cable-differential pulley transmission system and a return spring. A kinematic model of the robotic probe was derived. Furthermore, a tendon path length model was geometrically determined, and an optimization method for guide arc center placement to minimize change in tendon length was presented. Preliminary experimental and theoretical results were compared to determine the positioning accuracy. The difference between theoretical pitch angles [0 o ,80 o ] and measured values for the yaw angle range of [0 o , 40 o ] was found to be in the range of 0.03 o ~ 5.06 o .Clinical Relevance-Diagnosis based on manual palpation is often subjective and palpation sensitivity depends on the physician's level of experience and skill . Therefore, an objective method for acquiring tactile information is relevant. Robotic palpation system provides objective and quantitative information for better understanding of the pathological and physiological changes in the tissue using mechanical properties as biomarkers.

Background Prostate Cancer screening based on manual palpation is subjective. Robotic palpation systems can objectively acquire stiffness conditions of the prostate. Methods A 2DoF cable driven robotic system for prostate palpation is proposed. An indirect method to estimate the contact force based on cable tension observation is presented. Kinematic models and a joint angle estimation method to determine the tip position of the probe are derived. Positioning accuracy was verified using an optical marker tracking system and by displacement measurement, respectively. The contact force estimation method was validated on silicone phantom samples. Results A good consistence between the estimated and measured contact force was observed. The contact force was correlated with the elastic modulus of each silicone phantom. There was also a good agreement between the theoretical and the measured tip position. Conclusion In the proposed palpation system, the indirect contact force estimation method is viable and holds potential for the stiffness assessment of the prostate. The tip position vital for palpation can be determined through estimated joint angles.

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Surges that have been observed at different periods in the number of COVID-19 cases are associated with the emergence of multiple SARS-CoV-2 (Severe Acute Respiratory Virus) variants. The design of methods to support laboratory detection are crucial in the monitoring of these variants. Hence, in this paper, we develop a semi-automated method to design both forward and reverse primer sets to detect SARS-CoV-2 variants. To proceed, we train deep Convolution Neural Networks (CNNs) to classify labelled SARS-CoV-2 variants and identify partial genomic features needed for the forward and reverse Polymerase Chain Reaction (PCR) primer design. Our proposed approach supplements existing ones while promoting the emerging concept of neural network assisted primer design for PCR. Our CNN model was trained using a database of SARS-CoV-2 full-length genomes from GISAID and tested on a separate dataset from NCBI, with 98% accuracy for the classification of variants. This result is based on the development of three different methods of feature extraction, and the selected primer sequences for each SARS-CoV-2 variant detection (except Omicron) were present in more than 95 % of sequences in an independent set of 5000 same variant sequences, and below 5 % in other independent datasets with 5000 sequences of each variant. In total, we obtain 22 forward and reverse primer pairs with flexible length sizes (18-25 base pairs) with an expected amplicon length ranging between 42 and 3322 nucleotides. Besides the feature appearance, in-silico primer checks confirmed that the identified primer pairs are suitable for accurate SARS-CoV-2 variant detection by means of PCR tests.

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