Caged Dexamethasone to Photo-control the Development of Embryos through Activation of the Glucocorticoid Receptor.

Efficient tools for controlling molecular functions with exquisite spatiotemporal resolution are much in demand to investigate biological processes in living systems. Here we report an easily synthesized caged dexamethasone for photo-activating cytoplasmic proteins fused to the glucocorticoid receptor. In the dark, it is stable in vitro as well as in vivo in both zebrafish (Danio rerio) and Xenopus sp, two significant models of vertebrates. In contrast, it liberates dexamethasone upon UV illumination, which has been harnessed to interfere with developmental steps in embryos of these animals. Interestingly, this new system is biologically orthogonal to the one for photo-activating proteins fused to the estrogen ERT receptor, which brings great prospect for activating two distinct proteins down to the single cell level.

A series of caged fluorophores for calibrating light intensity.

Absolute measurement of light intensity is sought for in multiple areas of chemistry, biology, physics, and engineering. It can be achieved by using an actinometer from analyzing the time-course of its reaction extent on applying constant light. However, most reported actinometers exploit the absorbance observable for reporting the reaction extent, which is not very sensitive nor relevant in imaging systems. In this work, we report a series of hydrophobic and hydrophilic caged fluorophores that overcome the preceding limitations. Based on the robust pyranine backbone, they can easily be synthesized on a large scale in one to a few steps. Their brightness increases over illumination and their uncaging cross-sections have been thoroughly characterized upon one- and two-photon excitation. As a demonstration of their use, we calibrated light intensity in various chemical and biological samples, which have been observed with epifluorescence and confocal imaging systems.

Fluorescence to measure light intensity.

Despite the need for quantitative measurements of light intensity across many scientific disciplines, existing technologies for measuring light dose at the sample of a fluorescence microscope cannot simultaneously retrieve light intensity along with spatial distribution over a wide range of wavelengths and intensities. To address this limitation, we developed two rapid and straightforward protocols that use organic dyes and fluorescent proteins as actinometers. The first protocol relies on molecular systems whose fluorescence intensity decays and/or rises in a monoexponential fashion when constant light is applied. The second protocol relies on a broad-absorbing photochemically inert fluorophore to back-calculate the light intensity from one wavelength to another. As a demonstration of their use, the protocols are applied to quantitatively characterize the spatial distribution of light of various fluorescence imaging systems, and to calibrate illumination of commercially available instruments and light sources.

meta-Fluorophores: an uncharted ocean of opportunities.

meta-Fluorophores (MFs) are unique ultra-light (in terms of molecular weight (MW)) fluorophores exhibiting luminescence with a wide colour gamut ranging from blue to the NIR. Single benzenic MFs are easy to synthesize, are quite bright (with photoluminescence quantum yield (PLQY) as high as 63%) and exhibit very large Stokes shift (as high as 260 nm (8965 cm-1)), with large solvatochromic shift (as high as 175 nm), and very long excited-state-lifetime (as high as 26 ns) for such ultra-light fluorophores. An emission maximum of ≥600 nm has been achieved with an MF in a polar medium having a MW of only 177 g mol-1 and in a nonpolar medium having MW of only 255 g mol-1; therefore, a large-sized π-conjugated para-fluorophore is no longer a prerequisite for red/NIR emission. Structurally varied MFs pave the way for creating an ocean of opportunities and are thus promising for replacing para-fluorophores for different applications, ranging from bioimaging to LEDs.

Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods.

Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single-cell angular laser-light scattering patterns (ALSP) can be used for label-free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP.

Groundwater quality enumeration and health risk in the extended part of Chhotanagpur granite gneiss complex of India.

The majority of people on the earth bank largely on groundwater to quench their thirst. In the era of rapid population growth, the over-exploitation of groundwater gives rise to water scarcity, and people find themselves in distress to manage safe drinking water. In this backdrop, the present study is carried out in the terrain of Pre-Cambrian igneous and high- to low-graded metamorphic rocks, to assess the groundwater potential zones (GWPZs) and evaluation of groundwater quality. The map of GWPZ is produced employing the multi-criteria decision-making model and geospatial technology. It unveils that around 29% area of the watershed enjoys good GWPZ, whereas around 43% area experiences low GWPZ. The overall accuracy of the simulated model is 92%. The water quality index indicates that 68% of water samples belong to excellent to good water quality. A significant proportion of water samples (24%) are found to be unsuitable for drinking, which may be due to groundwater contamination by the process of leaching of mineral-rich weathered rocks. The presence of fluoride (F-) beyond the maximum permissible limit (1.5 mg L-1) of WHO is recorded among 18% samples of the watershed, where 24,963 souls including 3457 children aged between 0 and 6 years lived and might have ingested F- through drinking water. Hence, the health risk of those people is quite high. Children are at a more non-carcinogenic health risk of F- than adults. The study also confirms no statistically significant difference (p ˃ 0.05) is observed between low and high GWPZ with respect to groundwater quality. The study recommends adopting a sustainable outlook to explore GWPZ, and an assessment of drinking water quality must be done before drinking.

Rural child health in India: the persistent nature of deprivation, undernutrition and the 2030 Agenda.

The 2030 Agenda for sustainable development was launched to achieve Sustainable Development Goals (SDGs) across the globe. This paper is based on the primary database to assess the nutritional status of 5-10 years children and the incidence of deprivation in their households of a backward district (Purulia), India in the context of the first two SDGs, e.g., no poverty and zero hunger. We conclude that around 74% of children are undernourished. The proportion of households multidimensionally deprived is 90%, and the majority of them live on less than $1.25 a day. Results reveal that the BMI of mother and the education of father are the two most statistically significant predictors of child malnutrition. Purulia has long been witnessing the persistent nature of deprivation, which is well reflected in the child's health. The district is quite far from the national targets in achieving the SDGs. Government, private sector, and civil society must come together to accelerate the progress of SDGs.

Gully erosion vulnerability modelling, estimation of soil loss and assessment of gully morphology: a study from cratonic part of eastern India.

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha-1 y-1. Around 29% of the study area experiences high to very high (57 to > 147 t ha-1 y-1) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha-1 y-1). The study of gully morphology depicts gully depth ranging from < 1 to 5 m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation.

Visible-Light-Driven Photocatalytic Degradation of Tetracycline Using Heterostructured Cu2O-TiO2 Nanotubes, Kinetics, and Toxicity Evaluation of Degraded Products on Cell Lines.

This study first reports on the tetracycline photodegradation with the synthesized heterostructured titanium oxide nanotubes coupled with cuprous oxide photocatalyst. The large surface area and more active sites on TiO2 nanotubes with a reduced band gap (coupling of Cu2O) provide faster photodegradation of tetracycline under visible light conditions. Cytotoxicity experiments performed on the RAW 264.7 (mouse macrophage) and THP-1 (human monocytes) cell lines of tetracycline and the photodegraded products of tetracycline as well as quenching experiments were also performed. The effects of different parameters like pH, photocatalyst loading concentration, cuprous oxide concentration, and tetracycline load on the photodegradation rate were investigated. With an enhanced surface area of nanotubes and a reduced band gap of 2.58 eV, 1.5 g/L concentration of 10% C-TAC showed the highest efficiency of visible-light-driven photodegradation (∼100% photodegradation rate in 60 min) of tetracycline at pH 5, 7, and 9. The photodegradation efficiency is not depleted up to five consecutive batch cycles. Quenching experiments confirmed that superoxide radicals and hydroxyl radicals are the most involved reactive species in the photodegradation of tetracycline, while valance band electrons are the least involved reactive species. The cytotoxicity percentage of tetracycline and its degraded products on RAW 264.7 (-0.932) as well as THP-1 (-0.931) showed a negative correlation with the degradation percentage with a p-value of 0.01. The toxicity-free effluent of photodegradation suggests the application of the synthesized photocatalyst in wastewater treatment.

Valorization of wastewater to recover value-added products: A comprehensive insight and perspective on different technologies.

In recent years, due to rapid globalization and urbanization, the demand for fuels, energy, water and nutrients has been continuously increasing. To meet the future need of the society, wastewater is a prominent and emerging source for resource recovery. It provides an opportunity to recover valuable resources in the form of energy, fertilizers, electricity, nutrients and other products. The aim of this review is to elaborate the scientific literature on the valorization of wastewater using wide range of treatment technologies and reduce the existing knowledge gap in the field of resource recovery and water reuse. Several versatile, resilient environmental techniques/technologies such as ion exchange, bioelectrochemical, adsorption, electrodialysis, solvent extraction, etc. are employed for the extraction of value-added products from waste matrices. Since the last two decades, valuable resources such as polyhydroxyalkanoate (PHA), matrix or polymers, cellulosic fibers, syngas, biodiesel, electricity, nitrogen, phosphorus, sulfur, enzymes and a wide range of platform chemicals have been recovered from wastewater. In this review, the aspects related to the persisting global water issues, the technologies used for the recovery of different products and/or by-products, economic sustainability of the technologies and the challenges encountered during the valorization of wastewater are discussed comprehensively.

Pulmonary Thromboendarterectomy Without Circulatory Arrest

Abstract Introduction: Here we describe our technique and results of beating heart pulmonary thromboendarterectomy (PTE) with cardiopulmonary bypass (CPB) in four patients for treatment of chronic thromboembolic pulmonary hypertension (CTEPH). Methods: Retrospective analysis of data from patients who underwent PTE for CTEPH between January 2019 and September 2020. Patients were followed up with clinical assessment, 2D echocardiography, and computed tomography pulmonary angiogram. Results: Four patients were operated for CTEPH using our technique. Moderate tricuspid regurgitation (TR) and severe TR were found in two patients each. Severe right ventricular (RV) dysfunction was found in all cases. Thrombi were classified as Jamieson type II in three cases and type I in one case. Postoperative median direct manometric pulmonary artery (PA) pressures decreased (from 46.5 mmHg to 23.5 mmHg), median CPB time was 126 minutes, and median temperature was 33.35 °C. Mechanical ventilation was for a median of 19.5 hours. There was one re-exploration. Median intensive care unit stay was 7.5 days. There was no mortality. Postoperative 2D echocardiography revealed decrease in median PA systolic pressures (from 85 mmHg to 33 mmHg), improvement in RV function by tricuspid annular plane systolic excursion (median 14 mm vs. 16 mm), and improved postoperative oxygen saturations (88.5% vs. 99%). In follow-up (ranging between 2-15 months), all patients reported improvement in quality of life and were in New York Heart Association class I. Conclusion: With our described simple modifications, advances in perfusion, and blood conservation technologies, one can avoid the need for deep hypothermic circulatory arrest during PTE.

Pulmonary Thromboendarterectomy Without Circulatory Arrest.

INTRODUCTION: Here we describe our technique and results of beating heart pulmonary thromboendarterectomy (PTE) with cardiopulmonary bypass (CPB) in four patients for treatment of chronic thromboembolic pulmonary hypertension (CTEPH). METHODS: Retrospective analysis of data from patients who underwent PTE for CTEPH between January 2019 and September 2020. Patients were followed up with clinical assessment, 2D echocardiography, and computed tomography pulmonary angiogram. RESULTS: Four patients were operated for CTEPH using our technique. Moderate tricuspid regurgitation (TR) and severe TR were found in two patients each. Severe right ventricular (RV) dysfunction was found in all cases. Thrombi were classified as Jamieson type II in three cases and type I in one case. Postoperative median direct manometric pulmonary artery (PA) pressures decreased (from 46.5 mmHg to 23.5 mmHg), median CPB time was 126 minutes, and median temperature was 33.35 °C. Mechanical ventilation was for a median of 19.5 hours. There was one re-exploration. Median intensive care unit stay was 7.5 days. There was no mortality. Postoperative 2D echocardiography revealed decrease in median PA systolic pressures (from 85 mmHg to 33 mmHg), improvement in RV function by tricuspid annular plane systolic excursion (median 14 mm vs. 16 mm), and improved postoperative oxygen saturations (88.5% vs. 99%). In follow-up (ranging between 2-15 months), all patients reported improvement in quality of life and were in New York Heart Association class I. CONCLUSION: With our described simple modifications, advances in perfusion, and blood conservation technologies, one can avoid the need for deep hypothermic circulatory arrest during PTE.

Biodegradation of cyclophosphamide and etoposide by white rot fungi and their degradation kinetics.

The biodegradation of cyclophosphamide and etoposide by Trametes versicolor (AH05), Ganoderma lucidum (MTCC-1039), and Phanerochaete chrysosporium (MTCC-787) were tested for 3, 6, 9, 12, and 15 days, respectively. G. lucidum achieved the highest degradation efficiency of cyclophosphamide (71.5%) and etoposide (98.4%) after 6 days of treatment. The degradation efficiency of T. versicolor and P. chrysosporium for etoposide was 79.8% and 76.8%, respectively. However, no degradation of cyclophosphamide was achieved with P. chrysosporium, although it showed the highest sorption efficiency for cyclophosphamide (23.7%). Trametes versicolor achieved only 1.4% degradation of cyclophosphamide, that includes both biodegradation and biosorption. The pseudo first-order degradation kinetics explained the degradation of etoposide and cyclophosphamide with t1/2 values of 1.32 and 4.43 days and 'k' constant of 0.16 and 0.54 day-1, respectively.

An efficient CNN based algorithm for detecting melanoma cancer regions in H&E-stained images.

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.

An unsupervised method for histological image segmentation based on tissue cluster level graph cut.

While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.

Skin Lesion Segmentation Using Deep Learning with Auxiliary Task.

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of 79.46, Accuracy (ACC) of 94.32, SEN of 88.76 with only one integrated model, which can be learned in an end-to-end manner.

Integration of light scattering with machine learning for label free cell detection.

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.

Automated proliferation index calculation for skin melanoma biopsy images using machine learning.

The Proliferation Index (PI) is an important diagnostic, predictive and prognostic parameter used for evaluating different types of cancer. This paper presents an automated technique to measure the PI values for skin melanoma images using machine learning algorithms. The proposed technique first analyzes a Mart-1 stained histology image and generates a region of interest (ROI) mask for the tumor. The ROI mask is then used to locate the tumor regions in the corresponding Ki-67 stained image. The nuclei in the Ki-67 ROI are then segmented and classified using a Convolutional Neural Network (CNN), and the PI value is calculated based on the number of the active and the passive nuclei. Experimental results show that the proposed technique can robustly segment (with 94 % accuracy) and classify the nuclei with a low computational complexity and the calculated PI values have less than 4 % average error.