Potential utilization of ferulic acid and its derivatives in the management of metabolic diseases and disorders: An insight into mechanisms.

Metabolic diseases are abnormal conditions that impair the normal metabolic process, which involves converting food into energy at a cellular level, and cause difficulties like obesity and diabetes. The study aimed to investigate how ferulic acid (FA) and its derivatives could prevent different metabolic diseases and disorders and to understand the specific molecular mechanisms responsible for their therapeutic effects. Information regarding FA associations with metabolic diseases and disorders was compiled from different scientific search engines, including Science Direct, Wiley Online, PubMed, Scopus, Web of Science, Springer Link, and Google Scholar. This review revealed that FA exerts protective effects against metabolic diseases such as diabetes, diabetic retinopathy, neuropathy, nephropathy, cardiomyopathy, obesity, and diabetic hypertension, with beneficial effects on pancreatic cancer. Findings also indicated that FA improves insulin secretion by increasing Ca2+ influx through the L-type Ca2+ channel, thus aiding in diabetes management. Furthermore, FA regulates the activity of inflammatory cytokines (TNF-α, IL-18, and IL-1ß) and antioxidant enzymes (CAT, SOD, and GSH-Px) and reduces oxidative stress and inflammation, which are common features of metabolic diseases. FA also affects various signaling pathways, including the MAPK/NF-κB pathways, which play an important role in the progression of diabetic neuropathy and other metabolic disorders. Additionally, FA regulates apoptosis markers (Bcl-2, Bax, and caspase-3) and exerts its protective effects on cellular destruction. In conclusion, FA and its derivatives may act as potential medications for the management of metabolic diseases.

Food insecurity and its contributing determinants among rural households in the south-western region of Bangladesh, 2021: A cross-sectional study.

Introduction: The onset of the COVID-19 pandemic has disrupted food access, resulting in substantial consequences for food insecurity and contributing to adverse individual and public health outcomes. To comprehensively evaluate these challenges and grasp their implications for food security, this study aimed to evaluate the contributing determinants of food insecurity among rural households in the southwestern region of Bangladesh. Study design: A cross-sectional study was conducted using a validated questionnaire in selected 310 rural household respondents from the southwestern region of Bangladesh. Methods: Household food insecurity status was the outcome variable for the analysis. Multinomial logistic regression analysis was used to explore and predict risk factors correlated with food insecurity among southwestern Bangladeshi households. Results: We found that 59 % and 27.5 % of households were suffering from moderate food insecurity and severe food insecurity, respectively. The multinomial regression model revealed that respondents residing in Kusthia (RRR = 5.56 CI:2.67-8.4 and RRR = 6.65, CI:3.37-9.22) aged between 30 and 40 years (RRR = 2.32, 95 % CI:1.84-3.77 and RRR = 1.87, 95 % CI:1.48-3.97) and 40-50 years (RRR = 1.86 95 % CI:1.46-3.82 and RRR = 1.95, 95 % CI:1.75-3.26) were significantly associated with mild-to-moderate and severe food insecurity. Respondents with a monthly family income of <58.96 USD (3.38 times and 2.18 times), had ≥5 family members (2.68 times and 1.89 times), and had poor income during the pandemic (4.25 times and 2.75 times) more likely to be moderate and severe food insecure. Conclusion: The results emphasized that during the COVID-19 lockdown in Bangladesh, rural households faced diverse levels of food insecurity, ranging from moderate to severe. It suggests that efforts to raise awareness and implement support strategies for those at higher risk should not only focus on income but also consider additional factors such as family size, adults aged 30-40 years, and occupation.

Sustainable development of three distinct starch based bio-composites reinforced with the cotton spinning waste collected from fiber preparation stage.

Composites are new materials that combine two or more distinct components with diverse properties to create a new material with improved properties. The goal of this endeavor was to use fiber preparation wastes, or waste from cotton spinning mill blow room and carding, to produce bio composites based on starch. The matrix was prepared using the starches of potatoes, maize, and arrowroot, and any remaining reinforcing material was used. A hand layup technique was used to make the bio-composites. Tensile, bending, density, water absorbency, and SEM testing were among the studies used to illustrate the starch-based biodegradable materials. The maximum tensile strength of 0.49 MPa is displayed by sample AB. The resistive bending force of 3.71 MPa is greatest in Sample AB. The most uniform combination of reinforcing material (wastage cotton) and matrix is seen in PB's SEM picture. Among the samples, AB had the greatest density value, measuring 0.35 g/cm3. The sample PC had the highest absorption findings in both water and the 5 % HCl combination because carding waste had more fiber than blow room and fiber absorbs more water. The resultant bio-composites made of starch had the potential to replace Styrofoam.

Pharmacological Activities of Plant-Derived Fraxin with Molecular Mechanisms: A Comprehensive Review.

Fruits and vegetables serve not only as sources of nutrition but also as medicinal agents for the treatment of diverse diseases and maladies. These dietary components are significant resources of phytochemicals that demonstrate therapeutic properties against many illnesses. Fraxin is a naturally occurring coumarin glycoside mainly present in various species of Fraxinus genera, having a multitude of therapeutic uses against various diseases and disorders. This study focuses to investigate the pharmacological activities, botanical sources, and biopharmaceutical profile of the phytochemical fraxin based on different preclinical and non-clinical studies to show the scientific evidence and to evaluate the underlying molecular mechanisms of the therapeutic effects against various ailments. For this, data was searched and collected (as of February 15, 2024) in a variety of credible electronic databases, including PubMed/Medline, Scopus, Springer Link, ScienceDirect, Wiley Online, Web of Science, and Google Scholar. The findings demonstrated favorable outcomes in relation to a range of diseases or medical conditions, including inflammation, neurodegenerative disorders such as cerebral ischemia-reperfusion (I/R) and depression, viral infection, as well as diabetic nephropathy. The phytochemical also showed protective effects such as osteoprotective, renoprotective, pulmoprotective, hepatoprotective, and gastroprotective effects due to its antioxidant capacity. Fraxin has a great capability to diminish oxidative stress-related damage in different organs by stimulating the antioxidant enzymes, downregulating nuclear factor kappa B and NLRP3, and triggering the Nrf2/ARE signaling pathways. Fraxin exhibited poor oral bioavailability because of reduced absorption and a wide distribution into tissues of different organs. However, extensive research is required to decipher the biopharmaceutical profiles, and clinical studies are necessary to establish the efficacy of the natural compound as a reliable therapeutic agent.

Anticonvulsant effect of (±) citronellal possibly through the GABAergic and voltage-gated sodium channel receptor interaction pathways: In vivo and in silico studies.

This study aimed to investigate the anticonvulsant effects of citronellal (CIT) and possible underlying mechanisms through an isoniazid (INH)-induced seizure (convulsion) via in vivo and in silico studies. For this, convulsions were induced by the oral administration of INH (300 mg/kg) to the mice. The animals were treated orally with different doses of CIT (50, 100, and 200 mg/kg). Vehicle served as a negative control (NC), while diazepam (DZP) (2 mg/kg) and carbamazepine (CAR) (80 mg/kg) were provided (p.o.) as positive controls (PC). A combination therapy of CIT (middle dose) with DZP and CAR was also given to two separate groups of animals to estimate the synergistic or antagonistic effects. Molecular docking and visualization of ligand-receptor interactions are also estimated through different computational tools. The results of the in vivo study showed that CIT dose-dependently significantly (p < 0.05) exhibited a higher onset of seizures while reducing the frequency and duration of seizures in mice compared to the NC group. Besides these, in combination therapy, CIT significantly antagonized the activity of CAR and DZP, leading to a reduction in the onset of seizures and an increase in their frequency and duration compared to treatment with CAR and DZP alone. Additionally, molecular docking revealed that the CIT exhibited a moderate binding affinity (-5.8 kcal/mol) towards the GABAA receptor and a relative binding affinity (-5.3 kcal/mol) towards the voltage-gated sodium channel receptor by forming several bonds. In conclusion, CIT showed moderate anticonvulsant activity in INH-induced convulsion animals, possibly by enhancing GABAA receptor activity and inhibiting the voltage-gated sodium channel receptor.

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity.

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

Anxiolytic-like Effects by trans-Ferulic Acid Possibly Occur through GABAergic Interaction Pathways.

Numerous previous studies reported that ferulic acid exerts anxiolytic activity. However, the mechanisms have yet to be elucidated. The current study aimed to investigate the anxiolytic effect of trans-ferulic acid (TFA), a stereoisomer of ferulic acid, and evaluated its underlying mechanism using in vivo and computational studies. For this, different experimental doses of TFA (25, 50, and 75 mg/kg) were administered orally to Swiss albino mice, and various behavioral methods of open field, hole board, swing box, and light-dark tests were carried out. Diazepam (DZP), a positive allosteric modulator of the GABAA receptor, was employed as a positive control at a dose of 2 mg/kg, and distilled water served as a vehicle. Additionally, molecular docking was performed to estimate the binding affinities of the TFA and DZP toward the GABAA receptor subunits of α2 and α3, which are associated with the anxiolytic effect; visualizations of the ligand-receptor interaction were carried out using various computational tools. Our findings indicate that TFA dose-dependently reduces the locomotor activity of the animals in comparison with the controls, calming their behaviors. In addition, TFA exerted the highest binding affinity (-5.8 kcal/mol) to the α2 subunit of the GABAA receptor by forming several hydrogen and hydrophobic bonds. Taken together, our findings suggest that TFA exerts a similar effect to DZP, and the compound exerts moderate anxiolytic activity through the GABAergic interaction pathway. We suggest further clinical studies to develop TFA as a reliable anxiolytic agent.

Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes.

It is now known that mammalian brains leverage plasticity of both chemical and electrical synapses (ES) for collocating memory and processing. Unlike chemical synapses, ES join neurons via gap junction ion channels that permit fast, threshold-independent, and bidirectional ion transport. Like chemical synapses, ES exhibit activity-dependent plasticity, which modulates the ionic conductance between neurons and, thereby, enables adaptive synchronization of action potentials. Many types of adaptive computing devices that display discrete, threshold-dependent changes in conductance have been developed, yet far less effort has been devoted to emulating the continuously variable conductance and activity-dependent plasticity of ES. Here, we describe an artificial electrical synapse (AES) that exhibits voltage-dependent, analog changes in ionic conductance at biologically relevant voltages. AES plasticity is achieved at the nanoscale by linking dynamical geometrical changes of a host lipid bilayer to ion transport via gramicidin transmembrane ion channels. As a result, the AES uniquely mimics the composition, biophysical properties, bidirectional and threshold-independent ion transport, and plasticity of ES. Through experiments and modeling, we classify our AES as a volatile memristor, where the voltage-controlled conductance is governed by reversible changes in membrane geometry and gramicidin channel density. Simulations show that AES plasticity can adaptively synchronize Hodgkin-Huxley neurons. Finally, by modulating the molecular constituents of the AES, we show that the amplitude, direction, and speed of conductance changes can be tuned. This work motivates the development and integration of ES-inspired computing devices for achieving more capable neuromorphic hardware.

Author Correction: Dynamical nonlinear memory capacitance in biomimetic membranes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamical nonlinear memory capacitance in biomimetic membranes.

Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain's efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes. We demonstrate that the nonlinear dynamics and memory are governed by two implicitly-coupled, voltage-dependent state variables-membrane radius and thickness. Further, our system is capable of tuneable signal processing and learning via synapse-like, short-term capacitive plasticity. These findings will accelerate the development of low-energy, biomolecular neuromorphic memelements, which, in turn, could also serve as models to study capacitive memory and signal processing in neuronal membranes.

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes.

The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages. To overcome these limitations, we have developed and characterized a biomolecular memristor that mimics the composition, structure, and switching characteristics of biological synapses. Here, we describe the process of assembling and characterizing biomolecular memristors consisting of a 5 nm-thick lipid bilayer formed between lipid-functionalized water droplets in oil and doped with voltage-activated alamethicin peptides. While similar assembly protocols have been used to investigate biophysical properties of droplet-supported lipid membranes and membrane-bound ion channels, this article focuses on key modifications of the droplet interface bilayer method essential for achieving consistent memristor performance. Specifically, we describe the liposome preparation process and the incorporation of alamethicin peptides in lipid bilayer membranes, and the appropriate concentrations of each constituent as well as their impact on the overall response of the memristors. We also detail the characterization process of biomolecular memristors, including measurement and analysis of memristive current-voltage relationships obtained via cyclic voltammetry, as well as short-term plasticity and learning in response to step-wise voltage pulse trains.

Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current-voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.