Recent advances in the chemistry and biology of oleanolic acid and its derivatives.

The pentacyclic triterpenes represent a significant class of plant bioactives with a variety of structures and a wide array of biological activities. These are biosynthetically produced via the mevalonate pathway although occasionally mixed pathways may also occur to introduce structural divergence. Oleanolic acid is one of the most explored bioactive from this class of compounds and possesses a broad spectrum of pharmacological and biological activities including liver protection, anti-cancer, atherosclerosis, anti-inflammation, antibacterial, anti-HIV, anti-oxidative, anti-diabetic etc. This review provides an overview of the latest research findings, highlighting the versatile medicinal and biological potential of oleanolic and its future prospects.

May I see what you see? Predicting visual features from neuronal activity.

Understanding brain response to audiovisual stimuli is a key challenge in understanding neuronal processes. In this paper, we describe our effort aimed at reconstructing video frames from observed functional MRI images. We also demonstrate that our model can predict visual objects. Our method constructs an autoencoder model for a set of training video segments to code video streams into their corresponding latent representations. Next, we learn a mapping from the observed fMRI response to the corresponding latent video frame representation. Finally, we pass the latent vectors computed using the fMRI response through the decoder to reconstruct the predicted image. We show that the representations of video frames and those constructed from corresponding fMRI images are highly clustered, the latent representations can be used to predict objects in video frames using just the fMRI frames, and fMRI responses can be used to reconstruct the inputs to predict the presence of faces.

Transformation of PMMA from sunlight-blocking to sunlight-activated coupled with DNH photocatalytic platform for oxidative coupling of amines and generation/regeneration of LDC/NADH.

The photocatalytic oxidation and generation/regeneration of amines to imines and leucodopaminechrome (LDC)/NADH are subjects of intense interest in contemporary research. Imines serve as crucial intermediates for the synthesis of solar fuels, fine chemicals, agricultural chemicals, and pharmaceuticals. While significant progress has been made in developing efficient processes for the oxidation and generation/regeneration of secondary amines, the oxidation of primary amines has received comparatively less attention until recently. This discrepancy can be attributed to the high reactivity of imines generated from primary amines, which are prone to dehydrogenation into nitriles. In this study, we present the synthesis and characterization of a novel polymer-based photocatalyst, denoted as PMMA-DNH, designed for solar light-harvesting applications. PMMA-DNH incorporates the light-harvesting molecule dinitrophenyl hydrazine (DNH) at varying concentrations (5%, 10%, 20%, 30%, and 40%). Leveraging its high molar extinction coefficient and slow charge recombination, the 30% DNH-incorporated PMMA photocatalyst proves to be particularly efficient. This photocatalytic system demonstrates exceptional yields (96.5%) in imine production and high generation/regeneration rates for LDC/NADH (65.27%/78.77%). The research presented herein emphasizes the development and application of a newly engineered polymer-based photocatalyst, which holds significant promise for direct solar-assisted chemical synthesis in diverse commercial applications.

2D Zinc Oxide - Synthesis, Methodologies, Reaction Mechanism, and Applications.

Zinc oxide (ZnO) is a thermally stable n-type semiconducting material. ZnO 2D nanosheets have mainly gained substantial attention due to their unique properties, such as direct bandgap and strong excitonic binding energy at room temperature. These are widely utilized in piezotronics, energy storage, photodetectors, light-emitting diodes, solar cells, gas sensors, and photocatalysis. Notably, the chemical properties and performances of ZnO nanosheets largely depend on the nano-structuring that can be regulated and controlled through modulating synthetic strategies. Two synthetic approaches, top-down and bottom-up, are mainly employed for preparing ZnO 2D nanomaterials. However, owing to better results in producing defect-free nanostructures, homogenous chemical composition, etc., the bottom-up approach is extensively used compared to the top-down method for preparing ZnO 2D nanosheets. This review presents a comprehensive study on designing and developing 2D ZnO nanomaterials, followed by accenting its potential applications. To begin with, various synthetic strategies and attributes of ZnO 2D nanosheets are discussed, followed by focusing on methodologies and reaction mechanisms. Then, their deliberation toward batteries, supercapacitors, electronics/optoelectronics, photocatalysis, sensing, and piezoelectronic platforms are further discussed. Finally, the challenges and future opportunities are featured based on its current development.

Electronic effect-dependent intramolecular non-covalent interactions on the activity of 4,4-dimethylimidazolidin-2-one pharmacophore-based androgen receptor antagonists.

Because androgen receptor (AR) signalling is important for the development and progression of prostate cancer (PC), AR antagonists are utilized in clinical practices to treat PC and are referred to as androgen deprivation therapy (ADT). However, continued administration of AR antagonists often results in the development of resistance, known as castration-resistant prostate cancer (CRPC). Despite castration, it has been demonstrated that AR signalling continues to be fundamental to tumour growth. In this regard, a series of readily synthesizable 4,4-dimethylimidazolidine-2-one pharmacophore-based AR antagonists (FAR01-FAR11) were designed and synthesized. Androgen-dependent LNCaP PC cell line was used to test the AR-antagonist activity of these compounds in vitro and compared with the U.S. Food and Drug Administration (FDA) approved second-generation enzalutamide. In our previous work, rigid thiohydantoin pharmacophore in enzalutamide is replaced by the flexible 4,4-dimethylimidazolidin-2-one. In order to improve the flexibility further, one methylene group is introduced between the pharmacophore and one of the aromatic ring. Despite the fact that the amide functional group is a crucial characteristic for building AR antagonists, this class of molecules lacks one. FAR06 has the exact same activity as enzalutamide (IC50 : 0.782 µM) with an IC50 value of 0.801 µM among the series of compounds.

Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs.

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.

Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes.

Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.

A geometric approach to characterize the functional identity of single cells.

Single-cell transcriptomic data has the potential to radically redefine our view of cell-type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma subtypes with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

Synthesis and evaluation of the 2,4-diaminoquinazoline series as anti-tubercular agents.

The 2,4-diaminoquinazoline class of compounds has previously been identified as an effective inhibitor of Mycobacterium tuberculosis growth. We conducted an extensive evaluation of the series for its potential as a lead candidate for tuberculosis drug discovery. Three segments of the representative molecule N-(4-fluorobenzyl)-2-(piperidin-1-yl)quinazolin-4-amine were examined systematically to explore structure-activity relationships influencing potency. We determined that the benzylic amine at the 4-position, the piperidine at 2-position and the N-1 (but not N-3) are key activity determinants. The 3-deaza analog retained similar activity to the parent molecule. Biological activity was not dependent on iron or carbon source availability. We demonstrated through pharmacokinetic studies in rats that good in vivo compound exposure is achievable. A representative compound demonstrated bactericidal activity against both replicating and non-replicating M. tuberculosis. We isolated and sequenced M. tuberculosis mutants resistant to this compound and observed mutations in Rv3161c, a gene predicted to encode a dioxygenase, suggesting that the compound may act as a pro-drug.

A comparative analysis of three non-invasive human-machine interfaces for the disabled.

In the framework of rehabilitation robotics, a major role is played by the human-machine interface (HMI) used to gather the patient's intent from biological signals, and convert them into control signals for the robotic artifact. Surprisingly, decades of research have not yet declared what the optimal HMI is in this context; in particular, the traditional approach based upon surface electromyography (sEMG) still yields unreliable results due to the inherent variability of the signal. To overcome this problem, the scientific community has recently been advocating the discovery, analysis, and usage of novel HMIs to supersede or augment sEMG; a comparative analysis of such HMIs is therefore a very desirable investigation. In this paper, we compare three such HMIs employed in the detection of finger forces, namely sEMG, ultrasound imaging, and pressure sensing. The comparison is performed along four main lines: the accuracy in the prediction, the stability over time, the wearability, and the cost. A psychophysical experiment involving ten intact subjects engaged in a simple finger-flexion task was set up. Our results show that, at least in this experiment, pressure sensing and sEMG yield comparably good prediction accuracies as opposed to ultrasound imaging; and that pressure sensing enjoys a much better stability than sEMG. Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claim that this HMI could represent a valid alternative/augmentation to sEMG to control a multi-fingered hand prosthesis.