Mobile phone-based UV fluorescence microscopy for the identification of fungal pathogens.

BACKGROUND AND OBJECTIVE: Dermatophytes are fungi that cause infections in hair, skin, and nails. Potassium Hydroxide (KOH) microscopy is the most frequently used method for identifying dermatophytes. KOH helps in the visualization of the hyphae as it clears the debris present in the specimen but needs a trained eye for final diagnosis of the infection. Fluorescence microscopy using staining agents, such as calcofluor white (CFW) or blankophor, is a better method for identification of dermatophytes but is not used in clinics due to the cost and complexity of fluorescence microscopes. The objective of the present work is to develop a simple low-cost mobile phone-based device for the identification of fungal pathogens in skin samples. MATERIALS AND METHODS: A fluorescence spectrometer was used to establish the excitation/emission peaks of fluorescence intensity of CFW and KOH and Methyl Cellulose, a surrogate of fungi used for system development. A transillumination microscopy prototype was fabricated using off-the-shelf components, 3D printing and a mobile phone. The system was optically characterized using contrast resolution targets and verified using fungi isolate samples. An isolate of Trichophyton (T) rubrum was grown for 10-14 days for formation of fungal colonies. The surface of a single colony was gently scraped with a sterile loop and transferred to a glass slide. CFW with KOH was added to the T. rubrum and covered with cover slip for microscopic examination. The images of T. rubrum obtained with the prototype device were compared to those obtained using a commercial microscope. RESULTS: The excitation/emission wavelength pair for CFW was found to be 370/430 nm. The proposed device design is a transillumination microscopy setup using a mobile phone. It consists of a 365 nm LED as the excitation source, a 3V battery to power the LED, a slide to hold the sample, a lens for magnification and a phone to capture and store the images of the sample. The fabricated prototype has a resolution of 70 to 99 µm, a 2% to 30% distortion, and 60% contrast value for well illuminated samples. Images of T. rubrum samples obtained under brightfield illumination clearly show the long septate hyphae of the dermatophyte. As expected, images of the same samples with CFW and KOH show blue fluorescence, which results from the binding of the CFW to the chitin and cellulose in the fungal hyphae. These images are similar to those obtained with a commercial microscope. SUMMARY AND CONCLUSIONS: The concept and design of a mobile phone-based fluorescence microscope to identify dermatophytes has been demonstrated in a prototype and laboratory samples. The concept and design offer a simple, low-cost, compact but robust method for identification of fungal pathogens. This method is shown to be feasible for detecting fluorescence accurately and imaging the fungal structure at a resolution of 100 µm or better. Lasers Surg. Med. 51:201-207, 2019. © 2018 Wiley Periodicals, Inc.

UV fluorescence excitation imaging of healing of wounds in skin: Evaluation of wound closure in organ culture model.

BACKGROUND AND OBJECTIVE: Molecules native to tissue that fluoresce upon light excitation can serve as reporters of cellular activity and protein structure. In skin, the fluorescence ascribed to tryptophan is a marker of cellular proliferation, whereas the fluorescence ascribed to cross-links of collagen is a structural marker. In this work, we introduce and demonstrate a simple but robust optical method to image the functional process of epithelialization and the exposed dermal collagen in wound healing of human skin in an organ culture model. MATERIALS AND METHODS: Non-closing non-grafted, partial closing non-grafted, and grafted wounds were created in ex vivo human skin and kept in culture. A wide-field UV fluorescence excitation imaging system was used to visualize epithelialization of the exposed dermis and quantitate wound area, closure, and gap. Histology (H&E staining) was also used to evaluate epithelialization. RESULTS: The endogenous fluorescence excitation of cross-links of collagen at 335 nm clearly shows the dermis missing epithelium, while the endogenous fluorescence excitation of tryptophan at 295 nm shows keratinocytes in higher proliferating state. The size of the non-closing wound was 11.4 ± 1.8 mm and remained constant during the observation period, while the partial-close wound reached 65.5 ± 4.9% closure by day 16. Evaluations of wound gaps using fluorescence excitation images and histology images are in agreement. CONCLUSIONS: We have established a fluorescence imaging method for studying epithelialization processes, evaluating keratinocyte proliferation, and quantitating closure during wound healing of skin in an organ culture model: the dermal fluorescence of pepsin-digestible collagen cross-links can be used to quantitate wound size, closure extents, and gaps; and, the epidermal fluorescence ascribed to tryptophan can be used to monitor and quantitate functional states of epithelialization. UV fluorescence excitation imaging has the potential to become a valuable tool for research, diagnostic and educational purposes on evaluating the healing of wounds. Lasers Surg. Med. 48:678-685, 2016. © 2016 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?

Significance: Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF. Aims: We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM. Approach: We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with "short-separation regression" designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests. Results: We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment. Conclusions: Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy.

Significance: Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance. Aim: Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously. Approach: The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT. Results: The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation. Conclusions: The SS-DOT model improves the fNIRS image reconstruction quality.

Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.

Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.

Diffuse Reflectance Spectroscopy with Infrared Thermography for Accurate Prediction of Cellulitis.

Cellulitis is frequently misdiagnosed owing to its clinical mimickers, collectively known as pseudocellulitis. This study investigated diffuse reflectance spectroscopy (DRS) alone and in combination with infrared thermography (IRT) for the differentiation of cellulitis from pseudocellulitis. A prospective cohort study at an urban academic hospital was conducted from March 2017 to March 2018. Patients presenting to the emergency department with presumed cellulitis were screened for eligibility, and 30 adult patients were enrolled. Dermatology consultation conferred a final diagnosis of cellulitis or pseudocellulitis. DRS measurements yielded a spectral ratio between 556 nm (deoxyhemoglobin peak) and 542 nm (oxyhemoglobin peak), and IRT measurements yielded temperature differentials between the affected and unaffected skin. Of the 30 enrolled patients, 30% were diagnosed with pseudocellulitis. DRS revealed higher spectral ratios in patients with cellulitis (P = 0.005). A single parameter model using logistic regression on DRS measurements alone demonstrated a classification accuracy of 77.0%. A dual parameter model using linear discriminant analysis on DRS and IRT measurements combined demonstrated a 95.2% sensitivity, 77.8% specificity, and 90.0% accuracy for cellulitis prediction. DRS and IRT combined diagnoses cellulitis with an accuracy of 90%. DRS and IRT are inexpensive and noninvasive, and their use may reduce cellulitis misdiagnosis.

Towards Neuroscience of the Everyday World (NEW) using functional Near-Infrared Spectroscopy.

Functional Near-Infrared Spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional Magnetic Resonance Imaging (fMRI), expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and Electroencephalography (EEG) for routine, increasingly unconstrained, and mobile brain imaging is leading towards a new domain that we term "Neuroscience of the Everyday World" (NEW). In this light, we review recent advances in hardware, study design and signal processing, and discuss challenges and future directions towards achieving NEW.

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective.

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.

Contribution of speckle noise in near-infrared spectroscopy measurements.

Near-infrared spectroscopy (NIRS) is widely used in biomedical optics with applications ranging from basic science, such as in functional neuroimaging, to clinical, as in pulse oximetry. Despite the relatively low absorption of tissue in the near-infrared, there is still a significant amount of optical attenuation produced by the highly scattering nature of tissue. Because of this, designers of NIRS systems have to balance source optical power and source­detector separation to maximize the signal-to-noise ratio (SNR). However, theoretical estimations of SNR neglect the effects of speckle. Speckle manifests as fluctuations of the optical power received at the detector. These fluctuations are caused by interference of the multiple random paths taken by photons in tissue. We present a model for the NIRS SNR that includes the effects of speckle. We performed experimental validations with a NIRS system to show that it agrees with our model. Additionally, we performed computer simulations based on the model to estimate the contribution of speckle noise for different collection areas and source­detector separations. We show that at short source­detector separation, speckle contributes most of the noise when using long coherence length sources. Considering this additional noise is especially important for hybrid applications that use NIRS and speckle contrast simultaneously, such as in diffuse correlation spectroscopy.

Intrinsic fluorescence and mechanical testing of articular cartilage in human patients with osteoarthritis.

The degeneration of articular cartilage is the main cause of osteoarthritis (OA), a common cause of disability among elderly patients. The aim of this study is to understand the correlation between intrinsic fluorescence of articular cartilage and its biomechanical properties in patients with osteoarthritis. Cylindrical samples of articular cartilage 6 mm in diameter were extracted via biopsy punch from the femoral condyles of 6 patients with advanced OA undergoing knee replacement surgery. The mechanical stiffness and fluorescence of each cartilage plug were measured by indentation test and spectrofluorometry. Maps of fluorescence intensity, at excitation/emission wavelengths of 240-520/290-530 nm, were used to identify wavelengths of interest. The mechanical stiffness and fluorescence intensity were correlated using a Spearman analysis. The excitation/emission maps demonstrated three fluorescence peaks at excitation/emission wavelength pairs 330/390, 350/430 and 370/460 nm. The best correlation between the fluorescence intensity and stiffness of cartilage was obtained for the 330 nm excitation band [R=0.82, p=0.04]. The intrinsic fluorescence of articular cartilage may have application in optically assessing the state of cartilage in patients with osteoarthritis.

Skin Surface Temperatures Measured by Thermal Imaging Aid in the Diagnosis of Cellulitis.

Warmth is a characteristic but nondiagnostic feature of cellulitis. We assessed the diagnostic utility of skin surface temperature in differentiating cellulitis from pseudocellulitis. Adult patients presenting to the emergency department of a large urban hospital with presumed cellulitis were enrolled. Patients were randomized to dermatology consultation (n = 40) versus standard of care (n = 32). Thermal images of affected and unaffected skin were obtained for each patient. Analysis was performed on dermatology consultation patients to establish a predictive model for cellulitis, which was then validated in the other cohort. Of those evaluated by dermatology consultation, pseudocellulitis was diagnosed in 28%. Cellulitis patients had an average maximum affected skin temperature of 34.1°C, which was 3.7°C warmer than the corresponding unaffected area (95% confidence interval = 2.7-4.8°C, P < 0.00001). Pseudocellulitis patients had an average maximum affected temperature of 31.5°C, which was 0.2°C warmer than the corresponding unaffected area (95% confidence interval = -1.1 to 1.5°C, P = 0.44). Temperature differences between sites were greater in cellulitis patients than in pseudocellulitis patients (3.7 vs. 0.2°C, P = 0.002). A logistic regression model showed that a temperature difference of 0.47°C or greater conferred a 96.6% sensitivity, 45.5% specificity, 82.4% positive predictive value, and 83.3% negative predictive value for cellulitis diagnosis. When validated in the other cohort, this model gave the correct diagnosis for 100% of patients with cellulitis and 50% of those with pseudocellulitis. A difference threshold of 0.47°C or greater between affected and unaffected skin showed an 87.5% accuracy in cellulitis diagnosis.

Variations in the endogenous fluorescence of rabbit corneas after mechanical property alterations.

Keratoconus is an eye disease in which the cornea progressively deforms due to loss of cornea mechanical rigidity, and thus causes deterioration of visual acuity. Techniques to characterize the mechanical characteristics of the cornea are important to better monitor changes and response to treatments. To investigate the feasibility of using the endogenous fluorescence of cornea for monitoring alterations of its mechanical rigidity, linear tensiometry was used to quantitate stiffness and Young's modulus (YM) after treatments that increase cornea stiffness (collagen photocross-linking) or decrease stiffness (enzymatic digestion). The endogenous ultraviolet fluorescence of cornea was also measured before and after these treatments. The fluorescence excitation/emission spectral ranges were 280 to 430/390 to 520 nm, respectively. A correlation analysis was carried out to identify fluorescence excitation/emission pairs whose intensity changes correlated with the stiffness. A positive correlation was found between variations in fluorescence intensity of the 415-/485-nm excitation/emission pair and YM of photocross-linked corneas. After treatment of corneas with pepsin, the YM decreased as the fluorescence intensity at 290-/390-nm wavelengths decreased. For weakening of corneas with collagenase, only qualitative changes in the fluorescence spectrum were observed. Changes in the concentration of native or newly created fluorescent molecular species contain information that may be directly or indirectly related to the mechanical structure of the cornea.

Changes in endogenous UV fluorescence and biomechanical stiffness of bovine articular cartilage after collagenase digestion are strongly correlated.

A significant source of morbidity in the elderly population of the United States is osteoarthritis (OA), a disease caused by the breakdown and loss of articular cartilage. The exact causes of OA remain unknown, though biomechanical forces and biochemical alterations are important factors. There exists an unmet need for an imaging tool to identify early lesions of OA via metabolic, chemical or structural changes. Our work aims to characterize changes in the intensity of UV fluorescent bands associated with known structural proteins of cartilage. We employed an OA model in which bovine osteochondral plugs were digested in collagenase of varying concentrations. UV fluorescence before and after proteolytic digestion was measured using a spectrofluorimeter. The elastic modulus (EM) of each sample was measured using an indentation apparatus. Hydroxypyridinoline crosslink (330/390 nm) fluorescence intensity after digestion correlated with cartilage EM (R = 0.922, p = 0.026), as did tryptophan (290/350 nm) fluorescence intensity after digestion and EM (R = 0.949, p = 0.014) and tyrosine (290/310 nm) fluorescence intensity after digestion and EM (R = 0.946, p = 0.015). Loss of endogenous UV fluorescence correlated with cartilage degradation in an in-vitro model of OA, and may serve as a sensitive optical biomarker for the state of cartilage.