Association Between Monocyte Levels and Breast Lump Detected By Ultrasound Among Chinese Women: A Longitudinal Study

Background Few studies have focused on the relationship between monocyte and breast lump. To explore whether absolute monocyte count (AMC) or monocyte percent (%MONO) could be used as a new circulation tumor marker for breast lump detection by ultrasonography among Chinese women. Methods A total of 3,231 women who had at least two breast ultrasonography examinations were followed up from January 2014 to December 2019. Adjusted Cox proportional hazards regression models were used to evaluate the relationships between AMC and %MONO and the incidence of breast lump. Results During a total of 6,037 person-years of follow-up, 803 participants developed a breast lump. In the final multivariable adjusted models, using the lowest quartile as the reference group, the HRs (95%CIs) of breast lump were 1.18 (0.95, 1.45), 1.33 (1.08, 1.65), and 1.28 (1.02, 1.61), respectively, for AMC in the 2nd, 3rd, and 4th quartiles (Ptrend < 0.001). The corresponding HRs (95%CIs) for %MONO in the 2nd, 3rd, and 4th quartiles were 1.03 (0.83, 1.28), 1.28 (1.03, 1.29), and 1.62 (1.30, 2.02, Ptrend< 0.001), respectively. The multivariable adjusted HRs for breast lump per unit increase of AMC and %MONO were 3.19 (1.38, 7.38; P = 0.007) and 1.14 (1.08, 1.21; P < 0.001), respectively. The effect of high monocyte levels on increased risks of breast lump were more remarkable in younger women. Conclusion This study demonstrated that increased monocyte levels can be used as an indicator of the incidence of breast lump, especially for younger women.

Optical-Tactile Sensor for Lump Detection Using Pneumatic Control

Soft tactile sensors are an attractive solution when robotic systems must interact with delicate objects in unstructured and obscured environments, such as most medical robotics applications. The soft nature of such a system increases both comfort and safety, while the addition of simultaneous soft active actuation provides additional features and can also improve the sensing range. This paper presents the development of a compact soft tactile sensor which is able to measure the profile of objects and, through an integrated pneumatic system, actuate and change the effective stiffness of its tactile contact surface. We report experimental results which demonstrate the sensor’s ability to detect lumps on the surface of objects or embedded within a silicone matrix. These results show the potential of this approach as a versatile method of tactile sensing with potential application in medical diagnosis.

Neural coding of passive lump detection in compliant artificial tissue

Here, we investigate the neural mechanisms of detecting lumps embedded in artificial compliant tissues. We performed a combined psychophysical study of humans performing a passive lump detection task with a neurophysiological study in nonhuman primates ( Macaca mulatta) where we recorded the responses of peripheral mechanoreceptive afferents to lumps embedded at various depths in intermediates (rubbers) of varying compliance. The psychophysical results reveal that human lump detection is greatly degraded by both lump depth and decreased compliance of the intermediate. The neurophysiology results reveal that only the slowly adapting type 1 (SA1) afferents provide a clear spatial representation of lumps at all depths and that the representation is affected by lump size, depth, and compliance of the intermediate. The rapidly adapting afferents are considerably less sensitive to the lump. We defined eight neural response measures that we hypothesized could explain the psychophysical behavior, including peak firing rate, spatial spread of neural activity, and additional parameters derived from these measures. We find that peak firing rate encodes the depth of the lump, and the neural spatial spread of the SA1 response encodes for lump size but not lump shape. We also find that the perception of lump size may be affected by the compliance of the intermediate. The results show that lump detection is based on a spatial population code of the SA1 afferents, which is distorted by the depth of the lump and compliance of the tissue.