Quantitative Biophotonics for Tissue Characterization and Function

胎盘氧合在健康怀孕中起着至关重要的作用，确保胎儿获得足够的氧气供应。胎盘中的缺陷影响其氧饱和度，可导致母亲和婴儿的不良妊娠结局，包括先兆子痫、宫内胎儿生长受限、胎儿缺氧、窒息和脑瘫。此外，灌注减少可引起产时窒息，最严重的灌注减少可导致胎儿死亡。翻译生物光子学组开发了一种可穿戴光学装置，用于连续监测胎盘组织中的氧饱和度。该胎盘氧合监测仪采用深度分辨近红外光谱（NIRS），具有6个源探测器分离，范围从10 - 60 mm，这有助于针对不同的组织深度。近红外探头是灵活和可弯曲的，这使得该设备可以适当地安装在弯曲的表面，如腹部。该设备能够以0.5 Hz的数据采集速率实时测量组织氧饱和度。将组织氧饱和度测量值与商用系统（TRS-41系统，Hamamatsu photonics, Japan）进行比较，以验证性能。胎盘氧合监测仪的氧饱和度水平与商用设备接近（R2 = 0.94），平均误差为2.7% 1.8%。根据韦恩州立大学人类调查委员会机构审查委员会（IRB）批准的#090717MP4E方案，在底特律医疗中心（DMC, Detroit, Michigan， USA）围产期研究分院高级产科护理和研究中心（Nguyen et al., Biomed）使用胎盘氧合监测仪测量12名妊娠晚期单胎孕妇胎盘组织氧饱和度。光学实验，2021)。其中5名妇女有妊娠并发症。我们的初步结果表明，与正常妊娠的同龄人（75.0% 5.8%）相比，复杂妊娠患者胎盘组织氧饱和度（69.4% 6.7%）明显较低。分娩后，12名参与者中的10名胎盘被送到DMC的病理部门检查病变。5个胎盘被发现有慢性或急性病变，其中4个属于产妇妊娠并发症的参与者。我们进一步发现，无病变胎盘患者的胎盘氧饱和度（74.2% 5.8%）明显高于有病变患者（68.7% 5.6%）。这些结果提示胎盘氧饱和度与妊娠并发症和胎盘病理之间的关系。

Placental oxygenation plays a crucial role in a healthy pregnancy, ensuring the fetus receives an adequate supply of oxygen. Defects in the placenta that affects its oxygen saturation level can lead to poor pregnancy outcomes for both the mother and baby, including preeclampsia, intrauterine fetal growth restriction, fetal hypoxia, asphyxia, and cerebral palsy. Additionally, reduced perfusion could cause intrapartum asphyxia and the most severe reduction of perfusion could lead to fetal death. The Section on Translational Biophotonics has developed a wearable optical device to monitor tissue oxygen saturation in the placenta continuously. This placental oxygenation monitor utilizes depth-resolved near-infrared spectroscopy (NIRS) and features six source-detector separations ranging from 10 - 60 mm, which is helpful to target different tissue depths. The NIRS probe is flexible and bendable, which allows the device to fit properly on a curved surface like an abdomen. This device can measure real-time tissue oxygen saturation at a data acquisition rate of 0.5 Hz. Measurements of tissue oxygen saturation were compared with a commercial system (TRS-41 system, Hamamatsu photonics, Japan) to validate performance. The placental oxygenation monitor yields close oxygen saturation levels with the commercial device (R2 = 0.94) with an averaged error of 2.7% 1.8%. Under protocol #090717MP4E, which was approved by the Wayne State University Human Investigations Committee Institutional Review Board (IRB), the placental oxygenation monitor was used to measure tissue oxygen saturation in the placenta of 12 singleton pregnant women in their third trimester at the Center for Advanced Obstetrical Care and Research of the Perinatology Research Branch, located at the Detroit Medical Center (DMC, Detroit, Michigan, USA) (Nguyen et al., Biomed. Optic Exp., 2021). Five of these women had maternal pregnancy complications. Our preliminary results have indicated a significantly lower tissue oxygen saturation level in the placenta of patients with complicated pregnancies (69.4% 6.7%) compared to their peers with normal pregnancies (75.0% 5.8%). After delivery, 10 of the 12 participants placentas were delivered to the pathology department at the DMC to inspect for lesions. Five placentas were found to have chronic or acute lesions, four of which belonged to participants with maternal pregnancy complications. We further found that patients with lesion-free placentas presented a significantly higher placental oxygen saturation (74.2% 5.8%) than patients with lesions (68.7% 5.6%). These results suggest a relationship between the placental oxygen saturation and pregnancy complications and placental pathology. Based on the original plancental oxygenation monitor, we have developed a new wireless and wearable multimodal placental device with an embeded accelerometer. The biosensor has a data acquisition rate of 20 Hz, which allows collection of high frequency signals such as maternal and fetal heart rates. The accelerometer was added to detect fetal movement. The wireless placenta-device has recently been deployed to perform a one-time measurement of 24 pregnant women at their second and third trimesters at DMC (Detroit, Michigan, USA). Ten of the women in this cohort had maternal complications including chronic hypertension, asthma, type II diabetes, renal failure with dyalisis, and prolactinoma, and five had preeclampsia with severe features. The experimental procedure was perfomed in the same way as the previous measurement, where the participant lied down on the examination bed in a supine position. After delivery, the placentas of 22 participants were sent to a pathology labratory to examine for lesions. Seventeen placentas had issues including acute and/or chronic inflammartory lesions, acute funisitis and vasculitis, placental infarct, and lesions associated with maternal vascualar malperfusion. In general, all 24 participants had either maternal complications, placental issues, and/or neonatal complications. The average oxygen saturation of the placenta of all participants were 68.9 4.2%, which was similar to the oxygen saturation level found in the group of pregnant women with maternal complications and/or placental lesions in the first measurment. In order to test the multimodal biosensor in a larger population, we are collaborating with Dr. Guoyang Luo at the INOVA Fairfax Hospital to develop a clinical protocol to monitor pregnancy health in more than 1000 pregnant women. This research was recently awarded with the Scientific Director Award for FY 2023 & FY 2024. In addition, a provisional patent application (Title: System and protocol for monitoring pregnancy health, No. E-198-2022-0-US-01) is associated with the multimodal biosensor. An additional provisional patent application (Title: Single source-detector separation approach to calculate tissue oxygen saturation, No. E-037-2023-0-US-01) is associated with the methodology of the study. DFFOCT: In tandem with our research into the impact of placental oxygenation using the NIRS device, we are concurrently developing an algorithm that assesses the metabolic behavior of placental cells based on varying oxygen levels. This is accomplished through the utilization of the DFFOCT (Dynamic Full-field Optical Coherence Tomography) system. Our experimentation involves employing HeLa cells, which are similar to placental cells and the results of the study can be widely applied. The DFFOCT system is a label-free, non-invasive, and non-destructive method that analyzes the dynamic activity based on the movement of the scattering body within the cell. Capitalizing on the strengths of DFFOCT, we have devised a methodology for scrutinizing the dynamic activity traits of cells (Park, et al. 12(10), 6431-6441, Biomedical Optics Express, 2021), alongside an automated viability evaluation technology (Park, et al. 13(6), 3187-3194, Biomedical Optics Express, 2022). In 2023, we successfully constructed a customized incubator for cell cultivation in conjunction with the DFFOCT system. This incubator addresses past limitations associated with maintaining optimal 'temperature', 'gas concentration control', and the 'continuous supply of nutrients' during extended cell observations using the DFFOCT system. Notably, the incubator's systematic control over the supplied O2 concentration enables a more methodical exploration of the impact of placental oxygen levels on fetal development. Our current efforts revolve around the ongoing acquisition of dynamic activity data from HeLa cells exposed to varying O2 concentrations. Multimodal biosensor: The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to continuously monitor patients with respiratory infectious disease and classify breathing patterns in real-time using the measured data and Machine Learning.In a collaboration with Dr. Babak Shadgan at the University of British Columbia, Canada, data were collected in 21 healthy adult volunteers during different breathing exercises through a clinical protocol approved by the Clinical Research Ethics Board at the University of British Columbia. Collected data was used to classify breathing patterns using Random Forest algorithm, which results in a classification accuracy of 87% (Mah et al., 2022) and using Pre-ResNet, which results in a classification accuracy of 92.4% (Park et al., 2023).