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The consumption of food has a significant impact on the environment, individuals and public health. This study aims to investigate the integrative effects of consumers’ personal and situational factors on their attitude and purchase behavior of organic meat. The consumption of this product has been widely regarded as contributing towards sustainable food practices. The study was conducted in an emerging market economy, i.e., Vietnam. Data were collected using a customized and validated survey instrument from a sample of 609 organic meat consumers at four food outlets in Hanoi. The findings suggested that consumers’ concerns regarding the environment, health, food safety and their knowledge of organic food, all significantly impacted their attitude towards the purchase behavior of organic meat. Interestingly, their positive attitude did not necessarily translate into their actual purchase of organic meat. Additionally, food stores’ green marketing practices significantly enhanced consumers’ actual purchase behavior. Conversely, premium prices of organic meat were certainly a deterrent for the actual purchase of organic meat. The findings of this study have several important implications for organic food producers, retailers, policy makers and socio-environmental organizations that seek to develop intervention strategies aimed at increasing organic meat consumption in Vietnam.

•Lead-DBS v3.0 features an end-to-end solution for DBS-based neuroimaging analysis.•New preprocessing tools include WarpDrive and algorithms for electrode localization.•Three novel tools allow mapping of DBS effects to local, tract and network level.•Lead-DBS v3.0 features pathway activation modeling.•Updates are demonstrated in single patients and a retrospective cohort of 51 patients. Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

The rapid advancement of wearable biosensors has revolutionized healthcare monitoring by screening in a non-invasive and continuous manner. Among various sensing techniques, field-effect transistor (FET)-based wearable biosensors attract increasing attention due to their advantages such as label-free detection, fast response, easy operation, and capability of integration. This review explores the innovative developments and applications of FET-based wearable biosensors for healthcare monitoring. Beginning with an introduction to the significance of wearable biosensors, the paper gives an overview of structural and operational principles of FETs, providing insights into their diverse classifications. Next, the paper discusses the fabrication methods, semiconductor surface modification techniques and gate surface functionalization strategies. This background lays the foundation for exploring specific FET-based biosensor designs, including enzyme, antibody and nanobody, aptamer, as well as ion-sensitive membrane sensors. Subsequently, the paper investigates the incorporation of FET-based biosensors in monitoring biomarkers present in physiological fluids such as sweat, tears, saliva, and skin interstitial fluid (ISF). Finally, we address challenges, technical issues, and opportunities related to FET-based biosensor applications. This comprehensive review underscores the transformative potential of FET-based wearable biosensors in healthcare monitoring. By offering a multidimensional perspective on device design, fabrication, functionalization and applications, this paper aims to serve as a valuable resource for researchers in the field of biosensing technology and personalized healthcare.

Hidden beneath the Sylvian fissure and sometimes considered as the fifth lobe of the brain, the insula plays a multi-modal role from its strategic location. Previous structural studies have reported cortico-cortical connections with the frontal, temporal, parietal and occipital lobes, but only a few have looked at its connections with subcortical structures. The insular cortex plays a role in a wide range of functions including processing of visceral and somatosensory inputs, olfaction, audition, language, motivation, craving, addiction and emotions such as pain, empathy and disgust. These functions implicate numerous subcortical structures, as suggested by various functional studies. Based on these premises, we explored the structural connectivity of insular ROIs with the thalamus, amygdala, hippocampus, putamen, globus pallidus, caudate nucleus and nucleus accumbens. More precisely, we were interested in unraveling the specific areas of the insula connected to these subcortical structures. By using state-of-the-art HARDI tractography algorithm, we explored here the subcortical connectivity of the insula.

Cisplatin is a widely used chemotherapeutic agent used to treat solid tumours, such as ovarian, head and neck, and testicular germ cell. A known complication of cisplatin administration is acute kidney injury (AKI). The development of effective tumour interventions with reduced nephrotoxicity relies heavily on understanding the molecular pathophysiology of cisplatin-induced AKI. Rodent models have provided mechanistic insight into the pathophysiology of cisplatin-induced AKI. In the subsequent review, we provide a detailed discussion of recent advances in the cisplatin-induced AKI phenotype, principal mechanistic findings of injury and therapy, and pre-clinical use of AKI rodent models. Cisplatin-induced AKI murine models faithfully develop gross manifestations of clinical AKI such as decreased kidney function, increased expression of tubular injury biomarkers, and tubular injury evident by histology. Pathways involved in AKI include apoptosis, necrosis, inflammation, and increased oxidative stress, ultimately providing a translational platform for testing the therapeutic efficacy of potential interventions. This review provides a discussion of the foundation laid by cisplatin-induced AKI rodent models for our current understanding of AKI molecular pathophysiology.

Cyperus amuricus (Cyperaceae) has exhibited potential anticancer activity against hepatocellular carcinoma (HCC), yet its molecular mechanisms and phytoconstituent interactions with oncogenic pathways remain underexplored. This study integrates in vitro cytotoxicity assays and molecular docking to evaluate the effects of C. amuricus fractionated extracts on HCC, focusing on PI3K/AKT/mTOR signaling axis. The ethyl acetate (EA) fraction selectively inhibited HepG2 cell proliferation (IC 50 = 159.76 µg/mL) with minimal toxicity to normal fibroblasts. Apoptotic features—cell shrinkage, membrane blebbing, nuclear condensation, and DNA fragmentation—were confirmed through DAPI staining and gel electrophoresis. Western blot analysis revealed dose-dependent suppression of phosphorylated Akt and p70S6K, indicating pathway inhibition. Molecular docking identified strong binding affinities between Cyperaceae-derived compounds and PI3K/AKT/mTOR targets, with luteolin 7-O-β-D-glucuronopyranoside-6″-methyl ester blocked PI3K activation, vitexin bound AKT’s allosteric site, and digitoxin targeted mTOR’s ATP-binding pocket, showing comparable binding energies to reference ligands. These findings suggest C. amuricus as a promising candidate for natural product-based HCC therapy.

We propose two numerical algorithms in the fully nonconvex setting for the minimization of the sum of a smooth function and the composition of a nonsmooth function with a linear operator. The iterative schemes are formulated in the spirit of the proximal alternating direction method of multipliers and its linearized variant, respectively. The proximal terms are introduced via variable metrics, a fact that allows us to derive new proximal splitting algorithms for nonconvex structured optimization problems, as particular instances of the general schemes. Under mild conditions on the sequence of variable metrics and by assuming that a regularization of the associated augmented Lagrangian has the Kurdyka–Łojasiewicz property, we prove that the iterates converge to a Karush–Kuhn–Tucker point of the objective function. By assuming that the augmented Lagrangian has the Łojasiewicz property, we also derive convergence rates for both the augmented Lagrangian and the iterates.

The Grignard reaction has a storied place in the development of organic chemistry. Recognized by the Nobel Prize more than a century ago, this coupling of organomagnesium halides with carbonyl compounds remains a widely used route to carbon-carbon bonds. Nguyen et al. review an emerging alternative protocol that replaces the sensitive magnesium reagent with a catalytically activated olefin and a reductant such as hydrogen or an alcohol. In addition to safety and efficiency considerations, this class of reactions benefits from the high abundance and low cost of the olefins. Science , this issue p. 300 Metal-catalyzed reductive coupling of olefin-derived nucleophiles: Reinventing carbonyl addition α-Olefins are the most abundant petrochemical feedstock beyond alkanes, yet their use in commodity chemical manufacture is largely focused on polymerization and hydroformylation. The development of byproduct-free catalytic C–C bond–forming reactions that convert olefins to value-added products remains an important objective. Here, we review catalytic intermolecular reductive couplings of unactivated and activated olefin-derived nucleophiles with carbonyl partners. These processes represent an alternative to the longstanding use of stoichiometric organometallic reagents in carbonyl addition.

Segmented deep brain stimulation leads in the subthalamic nucleus have shown to increase therapeutic window using directional stimulation. However, it is not fully understood how these segmented leads with reduced electrode size modify the volume of tissue activated (VTA) and how this in turn relates with clinically observed therapeutic and side effect currents. Here, we investigated the differences between directional and omnidirectional stimulation and associated VTAs with patient-specific therapeutic and side effect currents for the two stimulation modes. Nine patients with Parkinson's disease underwent DBS implantation in the subthalamic nucleus. Therapeutic and side effect currents were identified intraoperatively with a segmented lead using directional and omnidirectional stimulation (these current thresholds were assessed in a blinded fashion). The electric field around the lead was simulated with a finite-element model for a range of stimulation currents for both stimulation modes. VTAs were estimated from the electric field by numerical differentiation and thresholding. Then for each patient, the VTAs for given therapeutic and side effect currents were projected onto the patient-specific subthalamic nucleus and lead position. Stimulation with segmented leads with reduced electrode size was associated with a significant reduction of VTA and a significant increase of radial distance in the best direction of stimulation. While beneficial effects were associated with activation volumes confined within the anatomical boundaries of the subthalamic nucleus at therapeutic currents, side effects were associated with activation volumes spreading beyond the nucleus' boundaries. The clinical benefits of segmented leads are likely to be obtained by a VTA confined within the subthalamic nucleus and a larger radial distance in the best stimulation direction, while steering the VTA away from unwanted fiber tracts outside the nucleus. Applying the same concepts at a larger scale and in chronically implanted patients may help to predict the best stimulation area.

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.