Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Significance: During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim: In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach: We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results: We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions: DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.

Enhancing Diagnostic Capabilities for Occupational Lung Diseases Using LIBS Imaging on Biopsy Tissue.

Laser-induced breakdown spectroscopy (LIBS) imaging continues to gain strength as an influential bioanalytical technique, showing intriguing potential in the field of clinical analysis. This is because hyperspectral LIBS imaging allows for rapid, comprehensive elemental analysis, covering elements from major to trace levels consistently year after year. In this study, we estimated the potential of a multivariate spectral data treatment approach based on a so-called convex envelope method to detect exotic elements (whether they are minor or in trace amounts) in biopsy tissues of patients with occupational exposure-related diseases. More precisely, we have developed an approach called Interesting Features Finder (IFF), which initially allowed us to identify unexpected elements without any preconceptions, considering only the set of spectra contained in a LIBS hyperspectral data cube. This task is, in fact, almost impossible with conventional chemometric tools, as it entails identifying a few exotic spectra among several hundred thousand others. Once this detection was performed, a second approach based on correlation was used to locate their distribution in the biopsies. Through this unique data analysis pipeline to processing massive LIBS spectroscopic data, it was possible to detect and locate exotic elements such as tin and rhodium in a patient's tissue section, ultimately leading to a possible reclassification of their lung condition as an occupational disease. This review will thus demonstrate the potential of this new diagnostic tool based on LIBS imaging in addressing the shortcomings of approaches developed thus far. The proposed data processing approach naturally transcends this specific framework and can be leveraged across various domains of analytical chemistry, where the detection of rare events is concealed within extensive data sets.

Application of fiber loop ringdown spectroscopy technique for a new approach to beta-amyloid monitoring for Alzheimer Disease's early detection.

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1-42 (Aß42) for Alzheimer's Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aß42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment. Here, the measurement is based on the total optical loss detection when specially fabricated sensor heads were immersed into ACSF solutions with and without different concentrations of Aß42 biomarkers since the bulk refractive index change was performed. Baseline stability and the reference ring down times of the sensor head were measured in the air as 0.87% and 441.6µs ± 3.9µs, respectively. Afterward, the total optical loss of the system was measured when the sensor head was immersed in deionized water, ACSF solution, and ACSF solutions with Aß42 in different concentrations. The lowest Aß42 concentration of 2 ppm was detected by LOR. Results showed that LOR fabricated by single-mode fibers for FLRDS system design are promising candidates to be utilized as fiber optic biosensors after sensor head modification and have a high potential for early detection applications of not only AD but possibly also several fatal diseases such as diabetes and cancer.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Low cost sample preparation method using ultrasound for the determination of environmentally critical elements in seaweed.

In this study, ultrasound (US) was evaluated for As, Cd, Pb, Mn, Sr and V extraction from seaweed samples. The following parameters of ultrasound-assisted extraction (UAE) using an US bath were: frequency (25 to 130 kHz), amplitude (30 to 100%), temperature (30 to 80 °C), sample mass (50 to 200 mg), extractant concentration (1 to 3 mol L-1 of HNO3) and treatment time (5 to 30 min). Acoustic density and power density distribution were calculated using the calorimetric method and mapping of the acoustic pressure distribution was also evaluated. The optimized UAE conditions were 200 mg of sample in 10 mL of 2 mol L-1 HNO3 and 30 min of sonication in a 25 kHz US bath (37.2 ± 4.0 W L-1) at 70% of amplitude and 70 °C. Analytes were quantified using inductively coupled plasma mass spectrometry and results were compared with values obtained using "silent" conditions (magnetic or mechanical stirring at 500 rpm, and without stirring), and a reference method based on microwave-assisted wet digestion (MAWD). The UAE method demonstrated the best extraction efficiency (higher than 95%) for all analytes, especially for As, Cd and V, with lower standard deviations (up to 5%) and lower blank values in comparison with the silent conditions. The proposed UAE method was more advantageous than the reference method, being faster, simpler, safer, more environmentally friendly, and with higher detectability (lower limits of quantification, from 0.0033 to 1.34 µg g-1). In addition, negligible blank values were obtained for UAE and no interference were observed in the determination step. Furthermore, the optimized UAE method was applied for Antarctic seaweed samples and comparison with results obtained by MAWD was satisfactory. In this sense, UAE is demonstrated to be a suitable option for sample preparation of seaweed samples and further determination of environmentally critical elements avoiding the use of concentrated reagents as in the MAWD reference method.

Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy.

Core needle biopsy is a part of the histopathological process, which is required for cancerous tissue examination. The most common method to guide the needle inside of the body is ultrasound screening, which in greater part is also the only guidance method. Ultrasound screening requires user experience. Furthermore, patient involuntary movements such as breathing might introduce artifacts and blur the screen. Optically enhanced core needle biopsy probe could potentially aid interventional radiologists during this procedure, providing real-time information on tissue properties close to the needle tip, while it is advancing inside of the body. In this study, we used diffuse optical spectroscopy in a custom-made core needle probe for real-time tissue classification. Our aim was to provide initial characteristics of the smart needle probe in the differentiation of tissues and validate the basic purpose of the probe of informing about breaking into a desired organ. We collected optical spectra from rat blood, fat, heart, kidney, liver, lungs, and muscle tissues. Gathered data were analyzed for feature extraction and evaluation of two machine learning-based classifiers: support vector machine and k-nearest neighbors. Their performances on training data were compared using subject-independent k-fold cross-validation. The best classifier model was chosen and its feasibility for real-time automated tissue recognition and classification was then evaluated. The final model reached nearly 80% of correct real-time classification of rat organs when using the needle probe during real-time classification.

Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery.

Significance: Accurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis. Aim: We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach: DRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k-nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches. Results: Tumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations. Conclusions: Automatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.

Significance: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, µa and reduced scattering coefficient, µs'. Aim: We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra. Approach: We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where µa=0.44 to 2.45 cm-1, and µs'=6.53 to 9.58 cm-1. We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model. Results: When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for µa and 1.53% for µs', compared to the MCI's 50.9% for µa and 24.6% for µs'. Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for µa and µs', respectively. The errors for MCI were about eight times higher. Conclusions: The WIR model presents reliable use-error-robust optical property predictions from DRS data.

Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. The authors investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex-vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time. METHODS: Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex-vivo colorectal tissue. Spectral data were acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve. RESULTS: A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap. CONCLUSION: A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex-vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in-vivo studies are needed to ensure integration into a surgical workflow.

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization.

Significance: Breast cancer ranks second in the world in terms of the number of women diagnosed. Effective methods for its early-stage detection are critical for facilitating timely intervention and lowering the mortality rate. Aim: Polarimetry provides much useful information on the structural properties of breast cancer tissue samples and is a valuable diagnostic tool. The present study classifies human breast tissue samples as healthy or cancerous utilizing a surface-illuminated backscatter polarization imaging technique. Approach: The viability of the proposed approach is demonstrated using 95 breast tissue samples, including 35 healthy samples, 20 benign cancer samples, 20 grade-2 malignant samples, and 20 grade-3 malignant samples. Results: The observation results reveal that element m23 in the Mueller matrix of the healthy samples has a deeper color and greater intensity than that in the breast cancer samples. Conversely, element m32 shows a lighter color and reduced intensity. Finally, element m44 has a darker color in the healthy samples than in the cancer samples. The analysis of variance test results and frequency distribution histograms confirm that elements m23, m32, and m44 provide an effective means of detecting and classifying human breast tissue samples. Conclusions: Overall, the results indicate that surface-illuminated backscatter polarization imaging has significant potential as an assistive tool for breast cancer diagnosis and classification.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications.

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Laser absorption spectroscopy measurements of different pulmonary oxygen gas concentrations in transmittance and remittance geometry: phantom study.

Significance: The gas in scattering media absorption spectroscopy (GASMAS) technique has the potential for continuous, clinical monitoring of preterm infant lung function, removing the need for X-ray diagnosis and reliance on indirect and relatively slow measurement of blood oxygenation. Aim: We aim to determine the optimal source-detector configuration for reliable pathlength calculation and to estimate the oxygen gas concentration inside the lung cavities filled with humidified gas with four different oxygen gas concentrations ranging between 21% and 100%. Approach: Anthropomorphic optical phantoms of neonatal thorax with two different geometries were used to acquire GASMAS signals, for 30 source-detector configurations in transmittance and remittance geometry of phantoms in two sizes. Results: The results show that an internal light administration is more likely to provide a high GASMAS signal-to-noise ratio (SNR). In general, better SNRs were obtained with the smaller set of phantoms. The values of pathlength and O2 concentrations calculated with signals from the phantoms with optical properties at 820 nm exhibit higher variations than signals from the phantoms with optical properties at 764 nm. Conclusion: Our study shows that, by moving the source and detector over the thorax, most of the lung volumes can potentially be assessed using the GASMAS technique.

Single-cell force spectroscopy of fluid flow-tuned cell adhesion for dissecting hemodynamics in tumor metastasis.

Cell adhesion plays an important role in regulating the metastasis of cancer cells, and atomic force microscopy (AFM)-based single-cell force spectroscopy (SCFS) has become an important method to directly measure the adhesion forces of individual cells. Particularly, bodily fluid flow environments strongly affect the functions and behaviors of metastatic cells for successful dissemination. Nevertheless, the interactions between fluidic flow medium environment and cell adhesion remain poorly understood. In this work, AFM-based SCFS was exploited to examine the effects of fluidic flow environment on cellular adhesion. A fluidic cell culture medium device was used to simulate the fluidic flow environment experienced by cancer cells during metastasis, which was combined with AFM-based SCFS assay. A single living cancer cell was attached to the AFM tipless cantilever to prepare the single-cell probe for performing SCFS experiments on the mesothelial cells grown under the fluidic flow medium conditions, and the effects of experimental parameters (retraction speed, contact time, loading force) on the measured cellular adhesion forces were analyzed. Experimental results of SCFS assay show that cellular adhesion forces significantly decrease after growth in fluidic flow medium, whereas cellular adhesion forces increase after growth in static culture medium. Experiments performed with the use of spherical probes coated with cell adhesion-associated biomolecules also show the weakening of cell adhesion after growth in fluidic flow cell culture medium, which was subsequently confirmed by the confocal fluorescence microscopy experiments of cell adhesion molecules, vividly illustrating the remarkable effects of fluidic flow environment on cellular adhesion. The study provides a new approach to detect adhesion force dynamics involved in the interactions between cells and the fluidic flow environment at the single-cell level, which will facilitate dissecting the role of hemodynamics in tumor metastasis.

Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy.

The adulteration of olive oil is a crucial matter for food safety authorities, global organizations, and consumers. To guarantee olive oil authenticity, the European Union (EU) has promoted the labeling of olive oils with the indices of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), while food security agencies are also interested in newly emerging technologies capable of operating reliably, fast, and in real-time, either in situ or remotely, for quality control. Among the proposed methods, photonic technologies appear to be suitable and promising for dealing with this issue. In this regard, a laser-based technique, namely, Laser-Induced Breakdown Spectroscopy (LIBS), assisted via machine learning tools, is proposed for the real-time detection of olive oil adulteration with lower-quality oils (i.e., pomace, soybean, sunflower, and corn oils). The results of the present work demonstrate the high efficiency and potential of the LIBS technique for the rapid detection of olive oil adulteration and the detection of adulterants.

Contenido de arsénico, conocimiento y actitud en el consumo del agua de pozo familiar / Arsenic content, knowledge and attitude in family well water consumption / Conteúdo de arsênico, conhecimento e atitude em relação ao consumo doméstico de água de poço

La contaminación por arsénico del agua de consumo humano, es un problema de salud pública, porque produce diversas enfermedades cancerígenas y de piel. Objetivo. Determinar niveles de arsénico en el agua de pozos, y evaluar el grado de conocimiento y actitud sobre el consumo del agua familiar. Materiales y métodos. Se utilizaron 96 muestras del agua de pozos del distrito de Juliaca. Las cuales se analizaron en laboratorio de Unidad de Servicios de Análisis Químicos de la Facultad de Química - Universidad Nacional Mayor de San Marcos-Lima, mediante la técnica Espectrofotometría de Absorción Atómica con Horno de Grafito. Los datos de arsénico fueron procesados estadísticamente mediante el diseño completamente al azar. Asimismo, la descripción del conocimiento y actitud del consumo de agua familiar se realizó aplicando la técnica de la encuesta y el instrumento fue un cuestionario de 11 ítems para la variable conocimiento y 7 para la variable actitud, con respuestas de alto, medio y bajo para conocimiento y buena, regular y mala calificadas con escala de Likert. Resultados. La concentración promedio fue 0.031 mg de As/L de agua y entre zonas hubo semejanza (p>0.05). En el grado de conocimiento sobre contaminación con arsénico en el agua de consumo humano respondieron el 40.81 % con calificación alta y el 59.19% están entre medio y bajo; en actitud, la calificación buena obtuvo menos del 50 % de encuestados y el resto están entre regular y mala. Conclusiones. El contenido arsenical en el agua de pozos supera los límites máximos permisibles según Organización Mundial de Salud y más del 50% se exponen al agua contaminada.

Arsenic contamination of drinking water is a public health problem, because it causes various carcinogenic and skin diseases. Objective. To determine arsenic levels in well water, and to evaluate the degree of knowledge and attitude about family water consumption. Materials and methods. Ninety-six samples of well water from the district of Juliaca were used. These were analyzed in the laboratory of the Chemical Analysis Services Unit of the Faculty of Chemistry - Universidad Nacional Mayor de San Marcos-Lima, using the technique Atomic Absorption Spectrophotometry with Graphite Furnace. The arsenic data were statistically processed using a completely randomized design. Likewise, the description of the knowledge and attitude of family water consumption was carried out by applying the survey technique and the instrument was a questionnaire of 11 items for the knowledge variable and 7 for the attitude variable, with answers of high, medium and low for knowledge and good, regular and bad rated with a Likert scale. Results. The average concentration was 0.031 mg As/L water and there was similarity between zones (p>0.05). In the degree of knowledge about arsenic contamination in drinking water, 40.81% responded with high qualification and 59.19% were between medium and low; in attitude, the good qualification obtained less than 50% of respondents and the rest were between regular and bad. Conclusions. The arsenic content in well water exceeds the maximum permissible limits according to the World Health Organization and more than 50% are exposed to contaminated water.

A contaminação da água potável por arsênico é um problema de saúde pública, pois causa várias doenças de pele e carcinogênicas. Objetivo. Determinar os níveis de arsênico na água de poço e avaliar o grau de conhecimento e atitude em relação ao consumo doméstico de água. Materiais e métodos. Foram utilizadas 96 amostras de água de poço do distrito de Juliaca. Elas foram analisadas no laboratório da Unidade de Serviços de Análises Químicas da Faculdade de Química da Universidade Nacional Mayor de San Marcos-Lima, usando a técnica de Espectrofotometria de Absorção Atômica com Forno de Grafite. Os dados sobre arsênico foram processados estatisticamente usando um desenho completamente aleatório. Da mesma forma, a descrição do conhecimento e da atitude do consumo familiar de água foi realizada aplicando a técnica de pesquisa e o instrumento foi um questionário de 11 itens para a variável conhecimento e 7 para a variável atitude, com respostas de alto, médio e baixo para o conhecimento e classificado como bom, regular e ruim em uma escala Likert. Resultados. A concentração média foi de 0,031 mg As/L de água e houve similaridade entre as zonas (p>0,05). Quanto ao grau de conhecimento sobre a contaminação por arsênico na água potável, 40,81% responderam com uma pontuação alta e 59,19% ficaram entre médio e baixo; quanto à atitude, a pontuação boa foi obtida por menos de 50% dos entrevistados e o restante ficou entre regular e ruim. Conclusões. O teor de arsênico na água de poço excede os limites máximos permitidos de acordo com a Organização Mundial da Saúde e mais de 50% estão expostos à água contaminada.

Investigation of the Ability to Detect Electrolyte Disorder Using PET with Positron Annihilation Lifetime Spectroscopy.

Various concentrations (8-300 mmol/L) of NaCl, KCl, and NaCl + KCl aqueous solutions were investigated using positron annihilation lifetime spectroscopy (PALS). A strong dependence of the o-Ps intensity as a function of the solution concentration was demonstrated. On this basis, the mean positron lifetime and the sum of counts in a selected time interval were proposed as reliable parameters for detecting disturbances in the ion balance of living organisms. The use of these parameters for differentiating healthy and cancerous tissues allows for the development of auxiliary diagnostic methods in a new generation of PET scanners equipped with a PALS detection module.

Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers.

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide, representing 11.3% of the diagnosed cancer cases and resulting in 10.2% (0.88 million) of the cancer related deaths in 2020. CRCs are typically detected at the late stage, which leads to high mortality and morbidity. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications. Patient survival could be increased by improving precision in surgical resection using accurate surgical guidance tools based on diffuse reflectance spectroscopy (DRS). DRS enables real-time tissue identification for potential cancer margin delineation through determination of the circumferential resection margin (CRM), while also supporting non-invasive and label-free approaches for laparoscopic surgery to avoid short-term complications of open surgery as suitable. In this study, we have estimated the scattering properties and chromophore concentrations based on 2949 DRS measurements of freshly excised ex vivo specimens of 47 patients, and used this estimation to classify normal colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630 µm source-detector distance (SDD; probe 1) and 2500 µm SDD (probe 2) to measure tissue layers ∼0.5-1 mm and ∼0.5-2 mm deep, respectively. By using the 5-fold cross-validation of machine learning models generated with the classification and regression tree (CART) algorithm, we achieved 95.9 ± 0.7% sensitivity, 98.9 ± 0.3% specificity, 90.2 ± 0.4% accuracy, and 95.5 ± 0.3% AUC for probe 1. Similarly, we achieved 96.9 ± 0.8% sensitivity, 98.9 ± 0.2% specificity, 94.0 ± 0.4% accuracy, and 96.7 ± 0.4% AUC for probe 2.

Measuring and imaging of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy.

Significance: Evaluation of biological chromophore levels is useful for detection of various skin diseases, including cancer, monitoring of health status and tissue metabolism, and assessment of clinical and physiological vascular functions. Clinically, it is useful to assess multiple different chromophores in vivo with a single technique or instrument. Aim: To investigate the possibility of estimating the concentration of four chromophores, bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin from diffuse reflectance spectra in the visible region. Approach: A new diffuse reflectance spectroscopic method based on the multiple regression analysis aided by Monte Carlo simulations for light transport was developed to quantify bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin. Three different experimental animal models were used to induce hyperbilirubinemia, hypoxemia, and melanogenesis in rats. Results: The estimated bilirubin concentration increased after ligation of the bile duct and reached around 18 mg/dl at 50 h after the onset of ligation, which corresponds to the reference value of bilirubin measured by a commercially available transcutaneous bilirubin meter. The concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin decreased and increased, respectively, as the fraction of inspired oxygen decreased. Consequently, the tissue oxygen saturation dramatically decreased. The time course of melanin concentration after depilation of skin on the back of rats was indicative of the supply of melanosomes produced by melanocytes of hair follicles to the growing hair shaft. Conclusions: The results of our study showed that the proposed method is capable of the in vivo evaluation of percutaneous bilirubin level, skin hemodynamics, and melanogenesis in rats, and that it has potential as a tool for the diagnosis and management of hyperbilirubinemia, hypoxemia, and pigmented skin lesions.

Peptide Orientation Strongly Affected by the Nanoparticle Size as Revealed by Sum Frequency Scattering Spectroscopy.

The orientation of proteins at interfaces has a profound effect on the function of proteins. For nanoparticles (NPs) in a biological environment, protein orientation determines the toxicity, function, and identity of the NP. Thus, understanding how proteins orientate at NP surfaces is a critical parameter in controlling NP biochemistry. While planar surfaces are often used to model NP interfaces for protein orientation studies, it has been shown recently that proteins can orient very differently on NP surfaces. This study uses sum frequency scattering vibrational spectroscopy of the model helical leucine-lysine (LK) peptide on NPs of different sizes to determine the cause for the orientation effects. The data show that, for low dielectric constant materials, the orientation of the helical LK peptide is a function of the coulombic forces between peptides across different particle volumes. This finding strongly suggests that flat model systems are only of limited use for determining protein orientation at NP interfaces and that charge interactions should be considered when designing medical NPs or assessing NP biocompatibility.