Traditional bacterial identification methods take one to two days to complete, relying on large bacteria colonies for visual identification. In order to decrease this analysis time in a cost-effective manner, a method to sort and concentrate bacteria based on the bacteria’s characteristics itself is needed. One example of such a method is dielectrophoresis, which has been used by researchers to separate bacteria from sample debris and sort bacteria according to species. This work presents variations in which dielectrophoresis can be performed and their associated drawbacks and benefits specifically to bacterial identification. In addition, a potential microfluidic design will be discussed.
Globally, fifteen million babies are born preterm each year, affecting 1 in 8 pregnancies in the US alone. Cervical
remodeling includes a biochemical cascade of changes that ultimately result in the thinning and dilation of the cervix
for passage of a fetus. This process is poorly understood and is the focus of this study. Our group is utilizing Raman
spectroscopy to evaluate biochemical changes occurring in the human cervix throughout pregnancy. This technique
has high molecular specificity and can be performed in vivo, with the potential to unveil new molecular dynamics
essential for cervical remodeling.
Many groups have used Raman spectroscopy for diagnosing cervical dysplasia; however, there have been few studies looking at the effect of normal physiological variations on Raman spectra. We assess four patient variables that may affect normal Raman spectra: Race/ethnicity, body mass index (BMI), parity, and socioeconomic status. Raman spectra were acquired from a diverse population of 75 patients undergoing routine screening for cervical dysplasia. Classification of Raman spectra from patients with a normal cervix is performed using sparse multinomial logistic regression (SMLR) to determine if any of these variables has a significant effect. Results suggest that BMI and parity have the greatest impact, whereas race/ethnicity and socioeconomic status have a limited effect. Incorporating BMI and obstetric history into classification algorithms may increase sensitivity and specificity rates of disease classification using Raman spectroscopy. Studies are underway to assess the effect of these variables on disease.
KEYWORDS: Tumors, Tissues, Breast, Raman spectroscopy, Signal to noise ratio, Sensors, Natural surfaces, Tissue optics, Composites, Monte Carlo methods
The risk of local recurrence for breast cancers is strongly correlated with the presence of a tumor within 1 to 2 mm of the surgical margin on the excised specimen. Previous experimental and theoretical results suggest that spatially offset Raman spectroscopy (SORS) holds much promise for intraoperative margin analysis. Based on simulation predictions for signal-to-noise ratio differences among varying spatial offsets, a SORS probe with multiple source-detector offsets was designed and tested. It was then employed to acquire spectra from 35 frozen-thawed breast tissue samples in vitro. Spectra from each detector ring were averaged to create a composite spectrum with biochemical information covering the entire range from the tissue surface to ∼2 mm below the surface, and a probabilistic classification scheme was used to classify these composite spectra as "negative" or "positive" margins. This discrimination was performed with 95% sensitivity and 100% specificity, or with 100% positive predictive value and 94% negative predictive value.
Preterm birth is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect changes in the cervix during pregnancy indicative of labor. Since Raman spectroscopy has been used to detect cancers in vivo in organs like the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy
levels. We believe significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this
study, Raman spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman will enter labor.
Preterm labor is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed
development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention
and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into
any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect
changes in the cervix during pregnancy. Since Raman spectroscopy has been used to detect cancers in vivo in organs like
the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that
fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy levels. We believe
significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this study, Raman
spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical
softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman
will enter labor.
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