Bioactivities of morroniside: A comprehensive review of pharmacological properties and molecular mechanisms.

Morroniside (MOR) is an iridoid glycoside and the main active principle of the medicinal plant, Cornus officinalis Sieb. This phytochemical is associated with numerous health benefits due to its antioxidant properties. The primary objective of the present study was to assess the pharmacological effects and underlying mechanisms of MOR, utilizing published data obtained from literature databases. Data collection involved accessing various sources, including PubMed/Medline, Scopus, Science Direct, Google Scholar, Web of Science, and SpringerLink. Our findings demonstrate that MOR can be utilized for the treatment of several diseases and disorders, as numerous studies have revealed its significant therapeutic activities. These activities encompass anti-inflammatory, antidiabetic, lipid-lowering capability, anticancer, trichogenic, hepatoprotective, gastroprotective, osteoprotective, renoprotective, and cardioprotective effects. MOR has also shown promising benefits against various neurological ailments, including Alzheimer's disease, Parkinson's disease, spinal cord injury, cerebral ischemia, and neuropathic pain. Considering these therapeutic features, MOR holds promise as a lead compound for the treatment of various ailments and disorders. However, further comprehensive preclinical and clinical trials are required to establish MOR as an effective and reliable therapeutic agent.

The status of implemented climate smart agriculture practices preferred by farmers of haor area as a climate resilient approach.

Bangladesh's Haor regions are famous for their natural resources and are unable to escape climate vulnerability. Triggered by climate vulnerabilities farmers are heading towards climate-resilient approaches. Hence, research was done in the haor area of Sunamganj district to analyze the status of adopted Climate-Smart Agriculture (CSA) techniques in Chhatak, Sunamganj, and Jagannathpur which are prone to severe flooding and climate conditions. Around 450 farmers were randomly selected and CSA adopters were contacted. A structured questionnaire was prepared with open-ended and closed-ended questions. The final questionnaire contained demographic questions and a list of adopted cropland and homestead CSA practices, and the survey proceeded with 115 finalized CSA adopters. MS Excel and SPSS were used to analyze the data. The data were expressed using frequency, percent, mean, and standard deviation. A t-test, analysis of variance, multiple linear regression, Pearson correlation, boxplot, and normal P-P plots were employed to test data normality. The analysis revealed that 30 CSA practices were identified to be practiced in cropland where major preferences were found for appropriate seed storage (100%), USG application (100%), IPM (98%), and good quality seed (95%) in cropland, whereas agroforestry (71%), organic fertilizer application (63%), perching (63%) and IPM (59%) were major CSA practices among the 18 identified practices in homesteads. The adoption level of CSA practices was found in the score category of 11-23 for cropland (90%) and up to 10 for homestead (68%). The results showed that the adoption status of CSA practices was inefficient for quick flood occurrence. CSA practices are not applied enough in haor areas' homesteads due to lack of knowledge, information access, and technical and financial resources. Thus, CSA should be implemented which necessitates working on barriers restricting CSA adoption through strengthening the infrastructure of technologies, supportive policies, and institutional framework.

An ab initio Study on the Mechanical Stability, Spin-Dependent Electronic Properties, Molecular Orbital Predictions, and Optical Features of Antiperovskite A3InN (A = Co, Ni).

Structural, mechanical, spin-dependent electronic, magnetic, and optical properties of antiperovskite nitrides A3InN (A = Co, Ni) along with molecular orbital diagram are investigated here by using an ab initio density functional theory (DFT). The mechanical stability, deformation, damage tolerance and ductile nature of A3InN are confirmed from elastic calculations. Different mechanical anisotropy factors are also discussed in detail. The spin dependent electronic properties such as the band structure and density of states (DOS) of A3InN are studied and, the dispersion curves and DOS at Fermi level are different for up and down spins only in case of Co3InN. These calculations also suggest that Co3InN and Ni3InN behave as ferromagnetic and nonmagnetic, respectively. The induced total magnetic moment of Co3InN is found 2.735 µB/cell in our calculation. Mulliken bond population analysis shows that the atomic bonds of A3InN are contributed by both ionic and covalent bonds. Molecular orbital diagrams of A3InN antiperovskites are proposed by analyzing orbital projected band structures. The formation of a molecular orbital energy diagram for Co3InN is similar to Ni3InN with respect to hybridization and orbital sequencing. However, the orbital positions with respect to the Fermi level (E F) and separations between them are different. The Fermi surface of A3InN is composed of multiple nonspherical electron and hole type sheets in which Co3InN displays a spin-dependent Fermi surface. The various ground-state optical functions such as real and imaginary parts of the dielectric constant, optical conductivity, reflectivity, refractive index, absorption coefficient, and loss function of A3InN are studied with implications. The reflectivity spectra reveal that A3InN reflects >45% of incident electromagnetic radiations in both the visible and ultraviolet region, which is an ideal feature of coating material for avoiding solar heating.

A deep learning framework for classifying microglia activation state using morphology and intrinsic fluorescence lifetime data.

Microglia are the immune cell in the central nervous system (CNS) and exist in a surveillant state characterized by a ramified form in the healthy brain. In response to brain injury or disease including neurodegenerative diseases, they become activated and change their morphology. Due to known correlation between this activation and neuroinflammation, there is great interest in improved approaches for studying microglial activation in the context of CNS disease mechanisms. One classic approach has utilized Microglia's morphology as one of the key indicators of its activation and correlated with its functional state. More recently microglial activation has been shown to have intrinsic NADH metabolic signatures that are detectable via fluorescence lifetime imaging (FLIM). Despite the promise of morphology and metabolism as key fingerprints of microglial function, they has not been analyzed together due to lack of an appropriate computational framework. Here we present a deep neural network to study the effect of both morphology and FLIM metabolic signatures toward identifying its activation status. Our model is tested on 1, 000+ cells (ground truth generated using LPS treatment) and provides a state-of-the-art framework to identify microglial activation and its role in neurodegenerative diseases.

Hypothetical protein predicted to be tumor suppressor: a protein functional analysis

Litorilituus sediminis is a Gram-negative, aerobic, novel bacterium under the family of Colwelliaceae, has a stunning hypothetical protein containing domain called von Hippel-Lindau that has significant tumor suppressor activity. Therefore, this study was designed to elucidate the structure and function of the biologically important hypothetical protein EMK97_00595 (QBG34344.1) using several bioinformatics tools. The functional annotation exposed that the hypothetical protein is an extracellular secretory soluble signal peptide and contains the von Hippel-Lindau (VHL; VHL beta) domain that has a significant role in tumor suppression. This domain is conserved throughout evolution, as its homologs are available in various types of the organism like mammals, insects, and nematode. The gene product of VHL has a critical regulatory activity in the ubiquitous oxygen-sensing pathway. This domain has a significant role in inhibiting cell proliferation, angiogenesis progression, kidney cancer, breast cancer, and colon cancer. At last, the current study depicts that the annotated hypothetical protein is linked with tumor suppressor activity which might be of great interest to future research in the higher organism.

Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD.

In this paper, we develop a deep neural network based joint classification-regression approach to identify microglia, a resident central nervous system macrophage, in the brain using fluorescence lifetime imaging microscopy (FLIM) data. Microglia are responsible for several key aspects of brain development and neurodegenerative diseases. Accurate detection of microglia is key to understanding their role and function in the CNS, and has been studied extensively in recent years. In this paper, we propose a joint classification-regression scheme that can incorporate fluorescence lifetime data from two different autofluorescent metabolic co-enzymes, FAD and NADH, in the same model. This approach not only represents the lifetime data more accurately but also provides the classification engine a more diverse data source. Furthermore, the two components of model can be trained jointly which combines the strengths of the regression and classification methods. We demonstrate the efficacy of our method using datasets generated using mouse brain tissue which show that our joint learning model outperforms results on the coenzymes taken independently, providing an efficient way to classify microglia from other cells.

Rhesus monkeys as a translational model for late-onset Alzheimer's disease.

Age is a major risk factor for late-onset Alzheimer's disease (AD) but seldom features in laboratory models of the disease. Furthermore, heterogeneity in size and density of AD plaques observed in individuals are not recapitulated in transgenic mouse models, presenting an incomplete picture. We show that the amyloid plaque microenvironment is not equivalent between rodent and primate species, and that differences in the impact of AD pathology on local metabolism and inflammation might explain established differences in neurodegeneration and functional decline. Using brain tissue from transgenic APP/PSEN1 mice, rhesus monkeys with age-related amyloid plaques, and human subjects with confirmed AD, we report altered energetics in the plaque microenvironment. Metabolic features included changes in mitochondrial distribution and enzymatic activity, and changes in redox cofactors NAD(P)H that were shared among species. A greater burden of lipofuscin was detected in the brains from monkeys and humans of advanced age compared to transgenic mice. Local inflammatory signatures indexed by astrogliosis and microglial activation were detected in each species; however, the inflamed zone was considerably larger for monkeys and humans. These data demonstrate the advantage of nonhuman primates in modeling the plaque microenvironment, and provide a new framework to investigate how AD pathology might contribute to functional loss.

Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia.

Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in in vivo animal models and possible future clinical adaptations such as neurodegenerative pathologies. With the ANN-based NAD(P)H FLIM analysis approach, we found that microglia in cell culture mixed with other glial cells can be identified with more than 0.9 True Positive Rate (TPR). We also extended our approach to identify microglia in fixed brain tissue with a TPR of 0.79. In both cases the False Discovery Rate was around 30%. This method can be further extended to potentially study and better understand microglia's role in neurodegenerative disease with improved detection accuracy.

Optical fiber-based dispersion for spectral discrimination in fluorescence lifetime imaging systems.

The excited state lifetime of a fluorophore together with its fluorescence emission spectrum provide information that can yield valuable insights into the nature of a fluorophore and its microenvironment. However, it is difficult to obtain both channels of information in a conventional scheme as detectors are typically configured either for spectral or lifetime detection. We present a fiber-based method to obtain spectral information from a multiphoton fluorescence lifetime imaging (FLIM) system. This is made possible using the time delay introduced in the fluorescence emission path by a dispersive optical fiber coupled to a detector operating in time-correlated single-photon counting mode. This add-on spectral implementation requires only a few simple modifications to any existing FLIM system and is considerably more cost-efficient compared to currently available spectral detectors.

Cortex-wide neural interfacing via transparent polymer skulls.

Neural computations occurring simultaneously in multiple cerebral cortical regions are critical for mediating behaviors. Progress has been made in understanding how neural activity in specific cortical regions contributes to behavior. However, there is a lack of tools that allow simultaneous monitoring and perturbing neural activity from multiple cortical regions. We engineered 'See-Shells'-digitally designed, morphologically realistic, transparent polymer skulls that allow long-term (>300 days) optical access to 45 mm2 of the dorsal cerebral cortex in the mouse. We demonstrate the ability to perform mesoscopic imaging, as well as cellular and subcellular resolution two-photon imaging of neural structures up to 600 µm deep. See-Shells allow calcium imaging from multiple, non-contiguous regions across the cortex. Perforated See-Shells enable introducing penetrating neural probes to perturb or record neural activity simultaneously with whole cortex imaging. See-Shells are constructed using common desktop fabrication tools, providing a powerful tool for investigating brain structure and function.