Intraoperative characterization of cardiac tissue: the potential of light scattering spectroscopy.

Significance: Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions. Aim: To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization. Approach: A custom LSS probe was used to gather spectral data from locations across 14 excised human pediatric nodal tissue samples (8 sinus nodes, 6 atrioventricular nodes). The LSS spectra were used to train linear and neural-network-based regressor models to predict tissue composition characteristics derived from the 3D models. Results: Nodal tissue region nuclear densities were reported. A linear model trained to regress nuclear density from spectra achieved a prediction r-squared of 0.64 and a concordance correlation coefficient of 0.78. Conclusions: These methods build on previous studies suggesting that LSS measurements combined with machine learning signal processing can provide clinically relevant cardiac tissue composition.

Potential role of physical labor and cultural views of menstruation in high incidence of pelvic organ prolapse in Nepalese women: a comparative study across the menstrual cycle.

Introduction: Pelvic organ prolapse (POP) is a significant health concern for young Nepali women, with potential risk factors including pelvic floor trauma from vaginal delivery and heavy lifting. The prevalence of symptomatic POP (SPOP) among nulliparous women in Nepal is 6%, while the general population of Nepali women aged 15-49 years reports a prevalence of 7%. Surprisingly, the average age of SPOP onset in Nepal is 27 years, challenging the assumption that postmenopausal age and vaginal delivery are the sole risk factors. This study aims to investigate the influence of increased intra-abdominal pressure (IAP) during lifting tasks on pelvic organ descent in Nepali women across different menstrual cycle stages. Methods: The study included 22 asymptomatic Nepali women aged 18-30 years who regularly engage in heavy lifting. Intra-abdominal pressure was measured intra-vaginally during typical and simulated lifting tasks, which encompassed various scenarios such as ballistic lifting, ramped lifting, and pre-contraction of pelvic floor muscles, as well as coughing, Valsalva maneuver, and pelvic floor contractions. Pelvic floor displacement was recorded using transperineal ultrasound during menstruation, ovulation, and the mid-luteal phase. Results: Results indicated that pelvic floor displacement was greater during menstruation than ovulation when performing a simulated ballistic lifting task (6.0 ± 1.6 mm vs. 5.1 ± 1.5 mm, p = 0.03, d = 0.6). However, there was no significant difference in pelvic floor displacement during lifting when the pelvic muscles were pre-contracted. Conclusion: These findings suggest that lifting heavy loads during menstruation may increase the risk of stretching and injuring pelvic floor supportive tissues, potentially contributing to SPOP in young Nepali women. Pre-contracting pelvic floor muscles during lifting tasks may offer a protective effect. Understanding these factors could aid in developing targeted preventive measures and raising awareness about the impact of heavy lifting on pelvic floor health among Nepali women.

3D models of the cardiac conduction system in healthy neonatal human hearts.

Iatrogenic damage to the cardiac conduction system (CCS) remains a significant risk during congenital heart surgery. Current surgical best practice involves using superficial anatomical landmarks to locate and avoid damaging the CCS. Prior work indicates inherent variability in the anatomy of the CCS and supporting tissues. This study introduces high-resolution, 3D models of the CCS in normal pediatric human hearts to evaluate variability in the nodes and surrounding structures. Human pediatric hearts were obtained with an average donor age of 2.7 days. A pipeline was developed to excise, section, stain, and image atrioventricular (AVN) and sinus nodal (SN) tissue regions. A convolutional neural network was trained to enable precise multi-class segmentation of whole-slide images, which were subsequently used to generate high- resolution 3D tissue models. Nodal tissue region models were created. All models (10 AVN, 8 SN) contain tissue composition of neural tissue, vasculature, and nodal tissues at micrometer resolution. We describe novel nodal anatomical variations. We found that the depth of the His bundle in females was on average 304 µm shallower than those of male patients. These models provide surgeons with insight into the heterogeneity of the nodal regions and the intricate relationships between the CCS and surrounding structures.