Validation of the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) in a Colombian Population.

BACKGROUND: Cognitive impairment has been recognized as an important factor in multiple sclerosis (MS) in the past few years. One brief, reliable and valid tool to assess cognition in MS is the BICAMS, which encompasses the Symbol Digit Modalities Test (SDMT), the California Verbal Learning Test (CVLT II), and the Brief Visuospatial Memory Test - Revised (BVMT-R). Continuing with the international initiative to validate the BICAMS in different countries, here we present the results obtained from the efforts in validating such test in the Colombian population. METHOD: 100 healthy controls and 50 MS patients participated in the study, group matched for age, education and gender. Subjects completed all three tests of the BICAMS. Instead of the CVLT-II, the Colombian validated form PAMCL (Prueba de Aprendizaje y Memoria con Codificación Libre) was used. Test-retest measures were obtained for 16 patients in order to test for reliability. RESULTS: Evidence of criterion validity was obtained, MS group performing significantly worse than HC group in all three tests (SDMT: p= .001, d= 0.59; PAMCL: p= .03, d= 0.38; BVMT-R: p<.001, d= 0.58). Test-retest was also obtained, finding significant correlations for all three tests (SDMT: r=0.932, p<.00; BVMT-R: r=0.863, p<.001; PAMCL: r=0.889, p<.001). Standardization of raw scores to uncontrolled scaled scores was done and these scores were then adjusted for age and years of schooling using a multiple linear regression. CONCLUSIONS: The BICAMS proved to be a valid and sensitive tool to screen for cognitive impairment in MS patients.

Multimodal Multiplexed Immunoimaging with Nanostars to Detect Multiple Immunomarkers and Monitor Response to Immunotherapies.

The overexpression of immunomarker programmed cell death protein 1 (PD-1) and engagement of PD-1 to its ligand, PD-L1, are involved in the functional impairment of cluster of differentiation 8+ (CD8+) T cells, contributing to cancer progression. However, heterogeneities in PD-L1 expression and variabilities in biopsy-based assays render current approaches inaccurate in predicting PD-L1 status. Therefore, PD-L1 screening alone is not predictive of patient response to treatment, which motivates us to simultaneously detect multiple immunomarkers engaged in immune modulation. Here, we have developed multimodal probes, immunoactive gold nanostars (IGNs), that accurately detect PD-L1+ tumor cells and CD8+ T cells simultaneously in vivo, surpassing the limitations of current immunoimaging techniques. IGNs integrate the whole-body imaging of positron emission tomography with high sensitivity and multiplexing of Raman spectroscopy, enabling the dynamic tracking of both immunomarkers. IGNs also monitor response to immunotherapies in mice treated with combinatorial PD-L1 and CD137 agonists and distinguish responders from those nonresponsive to treatment. Our results showed a multifunctional nanoscale probe with capabilities that cannot be achieved with either modality alone, allowing multiplexed immunologic tumor profiling critical for predicting early response to immunotherapies.

Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin.

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC=0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models.

Raman microspectroscopy differentiates perinatal pathogens on ex vivo infected human fetal membrane tissues.

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a major cause of chorioamnionitis and neonatal sepsis. This study evaluates Raman spectroscopy (RS) to identify spectral characteristics of infection and differentiate GBS from Escherichia coli and Staphylococcus aureus during ex vivo infection of human fetal membrane tissues. Unique spectral features were identified from colonies grown on agar and infected fetal membrane tissues. Multinomial logistic regression analysis accurately identified GBS infected tissues with 100.0% sensitivity and 88.9% specificity. Together, these findings support further investigation into the use of RS as an emerging microbiologic diagnostic tool and intrapartum screening test for GBS carriage.

Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy.

Staphylococcus aureus ( S. aureus) is a leading cause of hospital-acquired infections, such as bacteremia, pneumonia, and endocarditis. Treatment of these infections can be challenging since strains of S. aureus, such as methicillin-resistant S. aureus (MRSA), have evolved resistance to antimicrobials. Current methods to identify infectious agents in hospital environments often rely on time-consuming, multistep culturing techniques to distinguish problematic strains (i.e., antimicrobial resistant variants) of a particular bacterial species. Therefore, a need exists for a rapid, label-free technique to identify drug-resistant bacterial strains to guide proper antibiotic treatment. Here, our findings demonstrate the ability to characterize and identify microbes at the subspecies level using Raman microspectroscopy, which probes the vibrational modes of molecules to provide a biochemical "fingerprint". This technique can distinguish between different isolates of species such as Streptococcus agalactiae and S. aureus. To determine the ability of this analytical approach to detect drug-resistant bacteria, isogenic variants of S. aureus including the comparison of strains lacking or expressing antibiotic resistance determinants were evaluated. Spectral variations observed may be associated with biochemical components such as amino acids, carotenoids, and lipids. Mutants lacking carotenoid production were distinguished from wild-type S. aureus and other strain variants. Furthermore, spectral biomarkers of S. aureus isogenic bacterial strains were identified. These results demonstrate the feasibility of Raman microspectroscopy for distinguishing between various genetically distinct forms of a single bacterial species in situ. This is important for detecting antibiotic-resistant strains of bacteria and indicates the potential for future identification of other multidrug resistant pathogens with this technique.

Applying Full Spectrum Analysis to a Raman Spectroscopic Assessment of Fracture Toughness of Human Cortical Bone.

A decline in the inherent quality of bone tissue is a † Equal contributors contributor to the age-related increase in fracture risk. Although this is well-known, the important biochemical factors of bone quality have yet to be identified using Raman spectroscopy (RS), a nondestructive, inelastic light-scattering technique. To identify potential RS predictors of fracture risk, we applied principal component analysis (PCA) to 558 Raman spectra (370-1720 cm-1) of human cortical bone acquired from 62 female and male donors (nine spectra each) spanning adulthood (age range = 21-101 years). Spectra were analyzed prior to R-curve, nonlinear fracture mechanics that delineate crack initiation (Kinit) from crack growth toughness (Kgrow). The traditional ν1phosphate peak per amide I peak (mineral-to-matrix ratio) weakly correlated with Kinit (r = 0.341, p = 0.0067) and overall crack growth toughness (J-int: r = 0.331, p = 0.0086). Sub-peak ratios of the amide I band that are related to the secondary structure of type 1 collagen did not correlate with the fracture toughness properties. In the full spectrum analysis, one principal component (PC5) correlated with all of the mechanical properties (Kinit: r = - 0.467, Kgrow: r = - 0.375, and J-int: r = - 0.428; p < 0.0067). More importantly, when known predictors of fracture toughness, namely age and/or volumetric bone mineral density (vBMD), were included in general linear models as covariates, several PCs helped explain 45.0% (PC5) to 48.5% (PC7), 31.4% (PC6), and 25.8% (PC7) of the variance in Kinit, Kgrow, and J-int, respectively. Deriving spectral features from full spectrum analysis may improve the ability of RS, a clinically viable technology, to assess fracture risk.