Reviewing the effect of aggregates in Rhodamine 6G aqueous solution on fluorescence quantum efficiency.

Rhodamines constitute a class of dyes extensively investigated and applied in various contexts, primarily attributed to their high luminescence quantum yield. This study delves into the impact of aggregation on the thermal and optical properties of Rhodamine 6G (R-6G) solutions in distilled water. Examined properties encompass thermal diffusivity (D), temperature coefficient of the refractive index (dn/dT), fluorescence quantum efficiency (η), and energy transfer (ET). These parameters were assessed through thermal lens (TL) and conventional absorption and emission spectroscopic techniques. The dimerization of R-6G solutions was revisited, revealing that an increase in R-6G concentration alters the features of absorption and emission spectra due to dimer formation, resulting in unexpected behavior of η. Consequently, we introduce a novel model for the fraction of absorbed energy converted into heat (φ), which accounts for emissions from both monomers and dimers. Employing this model, we investigate and discuss the concentration-dependent behaviors of η for monomers (ηm) and dimers (ηd). Notably, our findings demonstrate that ηm values necessitate ηd = 0.2, a relatively substantial value that cannot be disregarded. Additionally, applying the Förster theory for dipole-dipole electric ET, we calculate microparameters for ET between monomers (CDD) and monomer-dimer (CDA). Critical ranges for ET in each case are quantified. Microparameter analysis indicates that ET between monomer-monomer and monomer-dimer species of R-6G dissolved in distilled water holds significance, particularly in determining ηm. These results bear significance, especially in scenarios involving high dye concentrations. While applicable to R-6G in water, similar assessments in other media featuring aggregates are encouraged.

Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images.

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.

Towards a comprehensive characterization of spatio-temporal dependence of light-induced electromagnetic forces in dielectric liquids.

The interaction of localized light with matter generates optical electrostriction within dielectric fluids, leading to a discernible change in the refractive index of the medium according to the excitation's light profile. This optical force holds critical significance in optical manipulation and plays a fundamental role in numerous photonic applications. In this study, we demonstrate the applicability of the pump-probe, photo-induced lensing (PIL) method to investigate optical electrostriction in various dielectric liquids. Notably, the thermal and nonlinear effects are observed to be temporally decoupled from the electrostriction effects, facilitating isolated observation of the latter. Our findings provide a comprehensive explanation of optical forces in the context of the recently introduced microscopic Ampère electromagnetic formalism, which is grounded in the dipolar approximation of electromagnetic sources within matter and characterizes electrostriction as an electromagnetic-induced stress within the medium. Here, the optical force density is re-obtained through a new Lagrangian approach.

MDF-Net for abnormality detection by fusing X-rays with clinical data.

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible( https://github.com/ChihchengHsieh/multimodal-abnormalities-detection ).

Optical detection of the ultrasound-induced pulsed thermal lens close to the ice-water phase transition.

Piezo-optic and thermo-optic coefficients are important material properties that play a critical role in the design and optimization of many optical devices. The ability to accurately measure and control these coefficients is essential for achieving high performance and reliability in a wide range of applications. In this article, we use the optical detection of the ultrasound-induced thermal lens effect to investigate these properties for water at low temperatures. The results show that the anomalous behavior of water around 4°C is easily observed. The thermal lens method is used to determine the temperature dependence of the piezo-optic and thermo-optic coefficients.

Effects including photobiomodulation of galactomannan gel from Cassia grandis seeds in the healing process of second-degree burns.

The epithelium recovery of skin-burned wounds has been currently achieved by several therapies, for example, hydrogel-based dressings and photobiomodulation therapy (PBMT). Thus, this work aimed to evaluate the healing activity of Cassia grandis seeds' galactomannan gel, associated or not with PBMT, in second-degree burns. Sixty male Wistar rats were assigned to four groups: Control (CG), Gel (GG), Laser/PBMT (LG), and Laser+Gel (GLG). Burns were made with an aluminum bar (90 °C), and submitted to clinical observations diary and area measurements at specific days. Microscopic analysis was based on histological criteria. The results showed that GG, LG, and GLG had a higher contraction rate (p < 0.05) than CG on the 14th experimental day, not differing from each other (∼95 %). At 21 days, all groups showed complete contraction (p > 0.05). Considering the histological results, LG and GLG showed excellent pro-wound healing properties after 14 days; at 21 days, all groups showed wound recovery compared to previous days. In view of the macroscopic and microscopic observations, the isolated treatments (Gel or Laser) effectively accelerated healing; however, the association (Laser+Gel) promoted re-epithelialization and stromal remodeling with better evolution of epithelium recovery due to the positive synergistic effect, thus emerging as a promising therapeutic alternative in the repair of burns.

BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions.

In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient.

Model-Agnostic Temporal Regularizer for Object Localization Using Motion Fields.

Video analysis often requires locating and tracking target objects. In some applications, the localization system has access to the full video, which allows fine-grain motion information to be estimated. This paper proposes capturing this information through motion fields and using it to improve the localization results. The learned motion fields act as a model-agnostic temporal regularizer that can be used with any localization system based on keypoints. Unlike optical flow-based strategies, our motion fields are estimated from the model domain, based on the trajectories described by the object keypoints. Therefore, they are not affected by poor imaging conditions. The benefits of the proposed strategy are shown on three applications: 1) segmentation of cardiac magnetic resonance; 2) facial model alignment; and 3) vehicle tracking. In each case, combining popular localization methods with the proposed regularizer leads to improvement in overall accuracies and reduces gross errors.

CyCoSeg: A Cyclic Collaborative Framework for Automated Medical Image Segmentation.

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans.

One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization.

This paper proposes a novel approach for the non-rigid segmentation of deformable objects in image sequences, which is based on one-shot segmentation that unifies rigid detection and non-rigid segmentation using elastic regularization. The domain of application is the segmentation of a visual object that temporally undergoes a rigid transformation (e.g., affine transformation) and a non-rigid transformation (i.e., contour deformation). The majority of segmentation approaches to solve this problem are generally based on two steps that run in sequence: a rigid detection, followed by a non-rigid segmentation. In this paper, we propose a new approach, where both the rigid and non-rigid segmentation are performed in a single shot using a sparse low-dimensional manifold that represents the visual object deformations. Given the multi-modality of these deformations, the manifold partitions the training data into several patches, where each patch provides a segmentation proposal during the inference process. These multiple segmentation proposals are merged using the classification results produced by deep belief networks (DBN) that compute the confidence on each segmentation proposal. Thus, an ensemble of DBN classifiers is used for estimating the final segmentation. Compared to current methods proposed in the field, our proposed approach is advantageous in four aspects: (i) it is a unified framework to produce rigid and non-rigid segmentations; (ii) it uses an ensemble classification process, which can help the segmentation robustness; (iii) it provides a significant reduction in terms of the number of dimensions of the rigid and non-rigid segmentations search spaces, compared to current approaches that divide these two problems; and (iv) this lower dimensionality of the search space can also reduce the need for large annotated training sets to be used for estimating the DBN models. Experiments on the problem of left ventricle endocardial segmentation from ultrasound images, and lip segmentation from frontal facial images using the extended Cohn-Kanade (CK+) database, demonstrate the potential of the methodology through qualitative and quantitative evaluations, and the ability to reduce the search and training complexities without a significant impact on the segmentation accuracy.

Antioxidant and Antiulcerogenic Activity of the Dry Extract of Pods of Libidibia ferrea Mart. ex Tul. (Fabaceae).

Ethnomedicinal studies in the Amazon community and in the Northeast region of Brazil highlight the use of Libidibia ferrea fruits for the treatment of gastric problems. However, there are no data in the literature of this pharmacological activity. Thus, the aim of this paper is to provide a scientific basis for the use of the dry extract of L. ferrea pods (DELfp) for the treatment of peptic ulcers. Phytochemical characterization was performed by HPLC/MS. In vitro antioxidant activity was assessed using DPPH, ABTS, phosphomolybdenum, and superoxide radical scavenging activity. The gastroprotective activity, the ability to stimulate mucus production, the antisecretory activity, and the influence of -SH and NO compounds on the antiulcerogenic activity of DELfp were evaluated. The healing activity was determined by the acetic acid-induced chronic ulcer model. Anti-Helicobacter pylori activity was investigated. HPLC/MS results identified the presence of phenolic compounds, gallic acid and ellagic acid, in DELfp. The extract showed antioxidant activity in vitro. In ulcers induced by absolute ethanol and acidified ethanol, the ED50 values of DELfp were 113 and 185.7 mg/kg, respectively. DELfp (100, 200, and 400 mg/kg) inhibited indomethacin-induced lesions by 66.7, 69.6, and 65.8%, respectively. DELfp (200 mg/kg) reduced gastric secretion and H+ concentration in the gastric contents and showed to be independent of nitric oxide (NO) and dependent on sulfhydryl (-SH) compounds in the protection of the gastric mucosa. In the chronic ulcer model, DELfp reduced the area of the gastric lesion. DELfp also showed anti-H. pylori activity. In conclusion, DELfp showed antioxidant, gastroprotective, healing, and antiulcerogenic activities. The mechanism of these actions seems to be mediated by different pathways and involves the reduction of gastric secretion and H+ concentration, dependence on sulfhydryl compounds, and anti-H. pylori activity. All these actions support the medicinal use of this species in the management of peptic ulcers.

Deep Active Shape Model for Robust Object Fitting.

Object recognition and localization is still a very challenging problem, despite recent advances in deep learning (DL) approaches, especially for objects with varying shapes and appearances. Statistical models, such as an Active Shape Model (ASM), rely on a parametric model of the object, allowing an easy incorporation of prior knowledge about shape and appearance in a principled way. To take advantage of these benefits, this paper proposes a new ASM framework that addresses two tasks: (i) comparing the performance of several image features used to extract observations from an input image; and (ii) improving the performance of the model fitting by relying on a probabilistic framework that allows the use of multiple observations and is robust to the presence of outliers. The goal in (i) is to maximize the quality of the observations by exploring a wide set of handcrafted features (HOG, SIFT, and texture templates) and more recent DL-based features. Regarding (ii), we use the Generalized Expectation-Maximization algorithm to deal with outliers and to extend the fitting process to multiple observations. The proposed framework is evaluated in the context of facial landmark fitting and the segmentation of the endocardium of the left ventricle in cardiac magnetic resonance volumes. We experimentally observe that the proposed approach is robust not only to outliers, but also to adverse initialization conditions and to large search regions (from where the observations are extracted from the image). Furthermore, the results of the proposed combination of the ASM with DL-based features are competitive with more recent DL approaches (e.g. FCN [1], U-Net [2] and CNN Cascade [3]), showing that it is possible to combine the benefits of statistical models and DL into a new deep ASM probabilistic framework.

Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI.

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy - this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). We also aim to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.

Evaluation of gastroprotective and ulcer healing activities of yellow mombin juice from Spondias mombin L.

Spondias mombin L. (yellow mombin) is a tree with a nutritional fruit that is commonly consumed in the North and Northeast of Brazil, as the juice of its pulp is rich in antioxidant compounds. This study aimed to investigate the gastroprotective and ulcer healing activities of yellow mombin juice (YMJ) in Wistar rats, and to elucidate the possible involved mechanisms. Phytochemical characterization of the lyophilized fruit juice was performed by high-performance liquid chromatography (HPLC). The gastroprotective activity of YMJ was investigated in ethanol (25, 50, and 100% YMJ) and indomethacin (100% YMJ) models of acute gastric ulcer in rats. Then, the effect of YMJ on mucus production and gastric secretions, and the involvement of non-protein sulfhydryl groups and prostaglandins in the gastroprotective process were examined. Moreover, the ulcer healing effect of YMJ was investigated in a model of acetic acid-induced chronic ulcer through histological and immunohistochemical analyses. HPLC results identified the presence of epicatechin (7.1 ± 1.6 µg/mL) and quercetin (17.3 ± 2.5 µg/mL) in YMJ. Ethanol-induced gastric lesions were inhibited by YMJ (25, 50, and 100%) by 42.42, 45.09, and 98.21% respectively, and indomethacin-induced lesions were inhibited by YMJ (100%) by 58.96%, compared to control group. Moreover, YMJ reduced gastric content and total acidy by 57.35 and 71.97%, respectively, compared to the control group. Treatment with YMJ also promoted healing of chronic ulcer, regeneration of the gastric mucosa, and restoration of mucus levels in glandular cells, as confirmed by histological analysis. It also increased cellular proliferation, as demonstrated by high reactivity to Ki-67 and bromodeoxyuridine. In conclusion, YMJ was found to possess gastroprotective and ulcer healing activities that are correlated to its antisecretory action. These results support the commercial exploration of YMJ as a functional food.

Fast segmentation of the left ventricle in cardiac MRI using dynamic programming.

BACKGROUND AND OBJECTIVE: The segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging is a necessary step for the analysis and diagnosis of cardiac function. In most clinical setups, this step is still manually performed by cardiologists, which is time-consuming and laborious. This paper proposes a fast system for the segmentation of the LV that significantly reduces human intervention. METHODS: A dynamic programming approach is used to obtain the border of the LV. Using very simple assumptions about the expected shape and location of the segmentation, this system is able to deal with many of the challenges associated with this problem. The system was evaluated on two public datasets: one with 33 patients, comprising a total of 660 magnetic resonance volumes and another with 45 patients, comprising a total of 90 volumes. Quantitative evaluation of the segmentation accuracy and computational complexity was performed. RESULTS: The proposed system is able to segment a whole volume in 1.5 seconds and achieves an average Dice similarity coefficient of 86.0% and an average perpendicular distance of 2.4 mm, which compares favorably with other state-of-the-art methods. CONCLUSIONS: A system for the segmentation of the left ventricle in cardiac magnetic resonance imaging is proposed. It is a fast framework that significantly reduces the amount of time and work required of cardiologists.

Deep Learning on Sparse Manifolds for Faster Object Segmentation.

We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning-based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i.e., a training that does not require a vast amount of annotated images to estimate robust appearance and shape models) and a fast search process during inference. Therefore, it is desirable that the dimensionality of this rigid transformation space is as small as possible in order to enhance the advantages brought by the aforementioned division of the problem. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection space. Furthermore, we propose the use of deep belief networks to allow for a training process that can produce robust appearance models without the need of large annotated training sets. We test our approach in the segmentation of the left ventricle of the heart from ultrasound images and lips from frontal face images. Our experiments show that the use of sparse manifolds and deep belief networks for the rigid detection stage leads to segmentation results that are as accurate as the current state of the art, but with lower search complexity and training processes that require a small amount of annotated training data.

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

Healing activity evaluation of the galactomannan film obtained from Cassia grandis seeds with immobilized Cratylia mollis seed lectin.

Galactomannan films from Cassia grandis seeds, associated or not with Cramoll 1,4, were used on topical wounds of rats for the evaluation of the healing process during 14days. All of the films were evaluated by cytotoxic assay, FTIR and lectin hemagglutinating activity (HA). Forty-five male rats were submitted to aseptic dermal wounds (Ø=0.8cm) and divided in groups (n=15): control, test 1, and test 2, treated respectively with saline, galactomannan film and film with immobilized Cramoll 1,4. Macroscopic evaluations were performed by clinical observations and area measurements, and microscopic analysis by histological criteria. Epithelial cell proliferation and differentiation was immunohistochemically assessed using CK14 and PCNA. The presence of CO peaks in the FTIR spectrum confirmed the immobilization of Cramoll 1,4 in the film, while the residual HA confirmed the stability of the lectin after immobilization with 90.94% of the initial HA. The films presented non-cytotoxicity and cell viability exceeding 80%. All of the animals presented re-epithelization around 10days, furthermore test 2 group showed a diffuse response at the stromal tissue and the basal layer associated with wounds completely closed with 11days of experiment. The results suggest a promising use of the films as topical wound curatives.

Time resolved spectroscopy of infrared emitting Ag2S nanocrystals for subcutaneous thermometry.

We report a systematic investigation on the temperature dependence of fluorescence decay dynamics of infrared emitting colloidal Ag2S nanocrystals (NCs) with different surface coatings. The drastic lifetime reduction in the biological temperature range (20-50 °C) makes Ag2S NCs outstanding candidates for high sensitivity subcutaneous lifetime-based thermal sensing in the second biological window (1000-1400 nm). Indeed, the lifetime thermal sensitivity of Ag2S NCs has been found to be as large as 3-4% °C-1 at an operating wavelength of 1250 nm. Their application for lifetime-based luminescence nanothermometry has been demonstrated through simple ex vivo experiments specially designed to elucidate the magnitude of subcutaneous thermal gradients. Experimental data were found to be in excellent agreement with numerical simulations.

Lack of detection of human papillomavirus DNA in prostate carcinomas in patients from northeastern Brazil.

Prostate cancer is the second most common cancer among men in western populations, and despite its high mortality, its etiology remains unknown. Inflammatory processes are related to the etiology of various types of tumors, and prostate inflammation, in particular, has been associated with prostate cancer carcinogenesis and progression. Human papillomavirus (HPV) is associated with benign and malignant lesions in the anogenital tract of both females and males. The possible role of HPV in prostate carcinogenesis is a subject of great controversy. In this study, we aimed to examine the prevalence of HPV infections in prostate carcinomas of patients from northeastern Brazil. This study included 104 tissue samples from primary prostate carcinoma cases. HPV DNA was purified and then amplified using MY09/11 and GP5+/GP6+ degenerate primer sets that detect a wide range of HPV types, and with specific PCR primers sets for E6 and E7 HPV regions to detect HPV 16. None of the samples showed amplification products of HPV DNA for primer sets MY09/11 and GP5+/GP6+, or the specific primer set for the E6 and E7 HPV regions. HPV infection, thus, does not seem to be one of the causes of prostate cancer in the population studied.