Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks

Accurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body shapes. With recent advancements in machine learning, this study proposed a novel artificial neural network-based method for computing subject-specific trunk segment mass and center of mass (CoM) using only anthropometric measurements. We first developed, trained, and validated two artificial neural networks that used anthropometric measurements as input to predict body shape (ANN1) and tissue mass (ANN2). Then, we calculated trunk segmental mass for two volunteers using the predicted body shape and tissue mass. The body shape model (ANN1) was tested on 279 subjects, and maximum deviation between the predicted body shape and the original was 28 mm. The tissue mass model (ANN2) was evaluated on 223 subjects, which when compared to ground truth data, had a mean error of less than 0.51% in the head, trunk, legs, and arms. We also compared the two volunteer's trunk segment mass with experimental data and found similar trend and magnitude. Our findings suggested that the proposed method could serve as an effective and convenient tool for predicting trunk mass.

PPAR agonists as add-on treatment with metformin in management of type 2 diabetes: a systematic review and meta-analysis

The combination of metformin and the peroxisome proliferator-activated receptors (PPAR) agonists offers a promising avenue for managing type 2 diabetes (T2D) through their potential complementary mechanisms of action. The results from randomized controlled trials (RCT) assessing the efficacy of PPAR agonists plus metformin versus metformin alone in T2D are inconsistent, which prompted the conduct of the systematic review and meta-analysis. We searched MEDLINE and EMBASE from inception (1966) to March 2023 to identify all RCTs comparing any PPAR agonists plus metformin versus metformin alone in T2D. Categorical variables were summarized as relative risk along with 95% confidence interval (CI). Twenty RCTs enrolling a total of 6058 patients met the inclusion criteria. The certainty of evidence ranged from moderate to very low. Pooled results show that using PPAR agonist plus metformin, as compared to metformin alone, results in lower concentrations of fasting glucose [MD = - 22.07 mg/dl (95% CI - 27.17, - 16.97), HbA1c [MD = - 0.53% (95% CI - 0.67, - 0.38)], HOMA-IR [MD = - 1.26 (95% CI - 2.16, - 0.37)], and fasting insulin [MD = - 19.83 pmol/L (95% CI - 29.54, - 10.13)] without significant increase in any adverse events. Thus, synthesized evidence from RCTs demonstrates the beneficial effects of PPAR agonist add-on treatment versus metformin alone in T2D patients. In particular, novel dual PPARα/γ agonist (tesaglitazar) demonstrate efficacy in improving glycaemic and lipid concentrations, so further RCTs should be performed to elucidate the long-term outcomes and safety profile of these novel combined and personalized therapeutic strategies in the management of T2D.PROSPERO registration no. CRD42023412603.

Pore-scale physics of ice melting within unconsolidated porous media revealed by non-destructive magnetic resonance characterization

Melting of ice in porous media widely exists in energy and environment applications as well as extraterrestrial water resource utilization. In order to characterize the ice-water phase transition within complicated opaque porous media, we employ the nuclear magnetic resonance (NMR) and imaging (MRI) approaches. Transient distributions of transverse relaxation time T2 from NMR enable us to reveal the substantial role of inherent throat and pore confinements in ice melting among porous media. More importantly, the increase in minimum T2 provides new findings on how the confinement between ice crystal and particle surface evolves inside the pore. For porous media with negligible gravity effect, both the changes in NMR-determined melting rate and our theoretical analysis of melting front confirm that conduction is the dominant heat transfer mode. The evolution of mushy melting front and 3D spatial distribution of water content are directly visualized by a stack of temporal cross-section images from MRI, in consistency with the corresponding NMR results. For heterogeneous porous media like lunar regolith simulant, the T2 distribution shows two distinct pore size distributions with different pore-scale melting dynamics, and its maximum T2 keeps increasing till the end of melting process instead of reaching steady in homogeneous porous media.

Smart Biomaterials in Biomedical Applications: Current Advances and Possible Future Directions

Smart biomaterials with the capacity to alter their properties in response to an outside stimulus or from within the environment around them have picked up significant attention in the biomedical community. This is primarily due to the interest in their biomedical applications that may be anticipated from them in a considerable number of dynamic structures and devices. Shape-memory materials are some of these materials that have been exclusively used for these applications. They exhibit unique structural reconfiguration features they adapt as per the provided environmental conditions and can be designed for their enhanced biocompatibility. Numerous research initiatives have focused on these smart biocompatible materials over the last few decades to enhance their biomedical applications. Shape-memory materials play a significant role in this regard to meet new surgical and medical devices' requirements for special features and utility cases. Because of the favorable design variety, different biomedical shape-memory materials can be developed by modifying their chemical and physical behaviors to accommodate the desired requirements. In this review, recent advances and characteristics of smart biomaterials for biomedical applications are described. The authors also discuss about their clinical translations in tissue engineering, drug delivery, and medical devices.

Multiscale investigation of pore structure heterogeneity in carbonate rocks using digital imaging and SCAL measurements: A case study from Upper Jurassic limestones, Abu Dhabi, UAE

This study presents a comprehensive analysis of rock properties for a selected group of six carbonate reservoir rock samples revealing complex structures at various length scales. Experimental laboratory methods as well as image analysis techniques were conducted in this study to characterize the macro- and micro-pores in mud- and grain-dominated limestones samples from the Upper Jurassic Arab Formation (Arab D member). Mercury Injection Capillary Pressure (MICP), porosimeter, and permeameter lab measurements were employed to assess the pore network heterogeneity and complexity. In addition, a multiscale rock imaging approach was implemented to detect grain and pore phases at several length scales using Thin Sections (TS), Scanning Electron Microscopy (SEM), Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), as well as 3D X-ray Computed Tomography (CT), and micro-computed tomography images (MCT). Furthermore, the multifractal analysis method was applied on the MICP and FIB-SEM to characterize quantitatively the heterogeneity of the pores in the studied samples. Heterogeneous samples 3R, 4M, 5W, and 6M display the highest non-uniformity degree Δα values, falling within the range of [1.21, 1.39] based on FIB-SEM images. Samples 1G, 2R, 3R, and 5W exhibit more heterogeneous pore structure, with Δα values ranging from 0.73 to 1.49 based on the MICP experiments. The results and findings confirm the effectiveness of multifractal parameters Δα and the asymmetry degree in the vertical axis Δf(α) in quantifying and characterizing rock heterogeneity.

The cellular zeta potential: cell electrophysiology beyond the membrane

The standard model of the cell membrane potential Vm describes it as arising from diffusion currents across a membrane with a constant electric field, with zero electric field outside the cell membrane. However, the influence of Vm has been shown to extend into the extracellular space where it alters the cell's ζ-potential, the electrical potential measured a few nm from the cell surface which defines how the cell interacts with charged entities in its environment, including ions, molecules, and other cells. The paradigm arising from surface science is that the ζ-potential arises only from fixed membrane surface charge, and has consequently received little interest. However, if the ζ-potential can mechanistically and dynamically change by alteration of Vm, it allows the cell to dynamically alter cell-cell and cell-molecule interactions and may explain previously unexplained electrophysiological behaviours. Whilst the two potentials Vm and ζ are rarely reported together, they are occasionally described in different studies for the same cell type. By considering published data on these parameters across multiple cell types, as well as incidences of unexplained but seemingly functional Vm changes correlating with changes in cell behaviour, evidence is presented that this may play a functional role in the physiology of red blood cells, macrophages, platelets, sperm, ova, bacteria and cancer. Understanding how these properties will improve understanding of the role of electrical potentials and charges in the regulation of cell function and in the way in which cells interact with their environment. Insight  The zeta (ζ) potential is the electrical potential a few nm beyond the surface of any suspensoid in water. Whilst typically assumed to arise only from fixed charges on the cell surface, recent and historical evidence shows a strong link to the cell's membrane potential Vm, which the cell can alter mechanistically through the use of ion channels. Whilst these two potentials have rarely been studied simultaneously, this review collates data across multiple studies reporting Vm, ζ-potential, electrical properties of changes in cell behaviour. Collectively, this points to Vm-mediated ζ-potential playing a significant role in the physiology and activity of blood cells, immune response, developmental biology and egg fertilization, and cancer among others.

On the low-frequency dispersion observed in dielectrophoresis spectra

The foundation of dielectrophoresis (DEP) as a tool for biological investigation is the use of the Clausius-Mossotti (C-M) factor to model the observed behaviour of cells experiencing DEP across a frequency range. Nevertheless, it is also the case that at lower frequencies, the DEP spectrum deviates from predictions; there exists a rise in DEP polarisability, which varies in frequency and magnitude with different cell types and medium conductivities. In order to evaluate the origin of this effect, we have studied DEP spectra from five cell types (erythrocytes, platelets, neurons, HeLa cancer cells and monocytes) in several conditions including medium conductivity and cell treatment. Our results suggest the effect manifests as a low-pass dispersion whose cut-off frequency varies with membrane conductance and capacitance as determined using the DEP spectrum; the effect also varies as a logarithm of medium conductivity and Debye length. These together suggest that the values of membrane capacitance and conductance depend not only on the impedance of the membrane itself, but also of the surrounding double layer. The amplitude of the effect in different cell types compared to the C-M factor was found to correlate with the depolarisation factors for the cells' shapes, suggesting that this ratio may be useful as an indicator of cell shape for DEP modelling.

Temporal patterns of pre- and post-natal target organ damage associated with hypertensive pregnancy: a systematic review

AIMS

Hypertensive pregnancy is associated with increased risks of developing a range of vascular disorders in later life. Understanding when hypertensive target organ damage first emerges could guide optimal timing of preventive interventions. This review identifies evidence of hypertensive target organ damage across cardiac, vascular, cerebral, and renal systems at different time points from pregnancy to postpartum.

METHODS AND RESULTS

Systematic review of Ovid/MEDLINE, EMBASE, and ClinicalTrials.gov up to and including February 2023 including review of reference lists. Identified articles underwent evaluation via a synthesis without meta-analysis using a vote-counting approach based on direction of effect, regardless of statistical significance. Risk of bias was assessed for each outcome domain, and only higher quality studies were used for final analysis. From 7644 articles, 76 studies, including data from 1 742 698 pregnancies, were identified of high quality that reported either blood pressure trajectories or target organ damage during or after a hypertensive pregnancy. Left ventricular hypertrophy, white matter lesions, proteinuria, and retinal microvasculature changes were first evident in women during a hypertensive pregnancy. Cardiac, cerebral, and retinal changes were also reported in studies performed during the early and late post-partum period despite reduction in blood pressure early postpartum. Cognitive dysfunction was first reported late postpartum.

CONCLUSION

The majority of target organ damage reported during a hypertensive pregnancy remains evident throughout the early and late post-partum period despite variation in blood pressure. Early peri-partum strategies may be required to prevent or reverse target organ damage in women who have had a hypertensive pregnancy.

A machine learning driven monogram for predicting chronic kidney disease stages 3-5

Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven monogram approach for early identification of individuals at risk for developing CKD stages 3-5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3-5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based monogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations.

Interpreting and validating complexity and causality in lesion-symptom prognoses

This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data. The first implication is that, because of these constraints, lesion-symptom mapping needs to focus on probabilistic relationships between Lesion and Symptom, with Lesion as a multivariate spatial pattern, Symptom as a time-dependent behavioural profile and evidence that Lesion raises the probability of Symptom. The second implication is that in order to assess the strength of probabilistic causality, we need to distinguish between causal lesion sites, incidental lesion sites, spared but dysfunctional sites and intact sites, all of which might affect the accuracy of the predictions and prognoses generated. We then formulate lesion-symptom mappings in logical notations, including combinatorial rules, that are then used to evaluate and better understand complex brain-behaviour relationships. The logical and theoretical framework presented applies to any type of neurological disorder but is primarily discussed in relationship to stroke damage. Accommodating the identified constraints, we discuss how the 1965 Bradford Hill criteria for inferring probabilistic causality, post hoc, from observed correlations in epidemiology-can be applied to lesion-symptom mapping in stroke survivors. Finally, we propose that rather than rely on post hoc evaluation of how well the causality criteria have been met, the neurobiological and methodological constraints should be addressed, a priori, by changing the experimental design of lesion-symptom mappings and setting up an open platform to share and validate the discovery of reliable and accurate lesion rules that are clinically useful.

A review on microfluidic-assisted nanoparticle synthesis, and their applications using multiscale simulation methods

Recent years have witnessed an increased interest in the development of nanoparticles (NPs) owing to their potential use in a wide variety of biomedical applications, including drug delivery, imaging agents, gene therapy, and vaccines, where recently, lipid nanoparticle mRNA-based vaccines were developed to prevent SARS-CoV-2 causing COVID-19. NPs typically fall into two broad categories: organic and inorganic. Organic NPs mainly include lipid-based and polymer-based nanoparticles, such as liposomes, solid lipid nanoparticles, polymersomes, dendrimers, and polymer micelles. Gold and silver NPs, iron oxide NPs, quantum dots, and carbon and silica-based nanomaterials make up the bulk of the inorganic NPs. These NPs are prepared using a variety of top-down and bottom-up approaches. Microfluidics provide an attractive synthesis alternative and is advantageous compared to the conventional bulk methods. The microfluidic mixing-based production methods offer better control in achieving the desired size, morphology, shape, size distribution, and surface properties of the synthesized NPs. The technology also exhibits excellent process repeatability, fast handling, less sample usage, and yields greater encapsulation efficiencies. In this article, we provide a comprehensive review of the microfluidic-based passive and active mixing techniques for NP synthesis, and their latest developments. Additionally, a summary of microfluidic devices used for NP production is presented. Nonetheless, despite significant advancements in the experimental procedures, complete details of a nanoparticle-based system cannot be deduced from the experiments alone, and thus, multiscale computer simulations are utilized to perform systematic investigations. The work also details the most common multiscale simulation methods and their advancements in unveiling critical mechanisms involved in nanoparticle synthesis and the interaction of nanoparticles with other entities, especially in biomedical and therapeutic systems. Finally, an analysis is provided on the challenges in microfluidics related to nanoparticle synthesis and applications, and the future perspectives, such as large-scale NP synthesis, and hybrid formulations and devices.

In vitro α-glucosidase inhibitory activity of Tamarix nilotica shoot extracts and fractions

α-glucosidase inhibitors represent an important class of type 2 antidiabetic drugs and they act by lowering postprandial hyperglycemia. Today, only three synthetic inhibitors exist on the market, and there is a need for novel, natural and more efficient molecules exhibiting this activity. In this study, we investigated the ability of Tamarix nilotica ethanolic and aqueous shoot extracts, as well as methanolic fractions prepared from aqueous crude extracts to inhibit α-glucosidase. Both, 50% ethanol and aqueous extracts inhibited α-glucosidase in a concentration-dependent manner, with IC₅₀ values of 12.5 μg/mL and 24.8 μg/mL, respectively. Importantly, α-glucosidase inhibitory activity observed in the T. nilotica crude extracts was considerably higher than pure acarbose (IC₅₀ = 151.1 μg/mL), the most highly prescribed α-glucosidase inhibitor on the market. When T. nilotica crude extracts were fractionated using methanol, enhanced α-glucosidase inhibitory activity was observed in general, with the highest observed α-glucosidase inhibitory activity in the 30% methanol fraction (IC₅₀ = 5.21 μg/mL). Kinetic studies further revealed a competitive reversible mechanism of inhibition by the plant extract. The phytochemical profiles of 50% ethanol extracts, aqueous extracts, and the methanolic fractions were investigated and compared using a metabolomics approach. Statistical analysis revealed significant differences in the contents of the crude extracts and fractions and potentially identified the molecules that were most responsible for these observed variations. Higher α-glucosidase inhibitory activity was associated with an enrichment of terpenoids, fatty acids, and flavonoids. Among the identified molecules, active compounds with known α-glucosidase inhibitory activity were detected, including unsaturated fatty acids, triterpenoids, and flavonoid glycosides. These results put forward T. nilotica as a therapeutic plant for type 2 diabetes and a source of α-glucosidase inhibitors.

Nanomaterials-Based Sensors for Respiratory Viral Detection: A Review

Contagious diseases are the principal cause of mortality, particularly respiratory viruses, a real menace for public health and economic development worldwide. Therefore, timely diagnosis and treatments are the only life-saving strategy to overcome any epidemic and particularly the ongoing prevailing pandemic COVID-19 caused by SARS-CoV-2. A rapid identification, point of care, portable, highly sensitive, stable, and inexpensive device is needed which is exceptionally satisfied by sensor technology. Consequently, the researchers have directed their attention to employing sensors targeting multiple analyses of pathogenic detections across the world. Nanostructured materials (nanoparticles, nanowires, nanobundles, etc.), owing to their unique characteristics such as large surface-to-volume ratio and nanoscale interactions, are widely employed to fabricate facile sensors to meet all the immediate emerging challenges and threats. This review is anticipated to foster researchers in developing advanced nanomaterials-based sensors for the increasing number of COVID-19 cases across the globe. The mechanism of respiratory viral detection by nanomaterials-based sensors has been reported. Moreover, the advantages, disadvantages, and their comparison with conventional sensors are summarized. Furthermore, we have highlighted the challenges and future potential of these sensors for achieving efficient and rapid detection.

CO Oxidation at Near-Ambient Temperatures over TiO2-Supported Pd-Cu Catalysts: Promoting Effect of Pd-Cu Nanointerface and TiO2 Morphology

Significant improvement of the catalytic activity of palladium-based catalysts toward carbon monoxide (CO) oxidation reaction has been achieved through alloying and using different support materials. This work demonstrates the promoting effects of the nanointerface and the morphological features of the support on the CO oxidation reaction using a Pd-Cu/TiO2 catalyst. Pd-Cu catalysts supported on TiO2 were synthesized with wet chemical approaches and their catalytic activities for CO oxidation reaction were evaluated. The physicochemical properties of the prepared catalysts were studied using standard characterization tools including SEM, EDX, XRD, XPS, and Raman. The effects of the nanointerface between Pd and Cu and the morphology of the TiO2 support were investigated using three different-shaped TiO2 nanoparticles, namely spheres, nanotubes, and nanowires. The Pd catalysts that are modified through nanointerfacing with Cu and supported on TiO2 nanowires demonstrated the highest CO oxidation rates, reaching 100% CO conversion at temperature regime down to near-ambient temperatures of ~45 °C, compared to 70 °C and 150 °C in the case of pure Pd and pure Cu counterpart catalysts on the same support, respectively. The optimized Pd-Cu/TiO2 nanowires nanostructured system could serve as efficient and durable catalyst for CO oxidation at near-ambient temperature.

Enzyme-Loaded Flower-Shaped Nanomaterials: A Versatile Platform with Biosensing, Biocatalytic, and Environmental Promise

As a result of their unique structural and multifunctional characteristics, organic-inorganic hybrid nanoflowers (hNFs), a newly developed class of flower-like, well-structured and well-oriented materials has gained significant attention. The structural attributes along with the surface-engineered functional entities of hNFs, e.g., their size, shape, surface orientation, structural integrity, stability under reactive environments, enzyme stabilizing capability, and organic-inorganic ratio, all significantly contribute to and determine their applications. Although hNFs are still in their infancy and in the early stage of robust development, the recent hike in biotechnology at large and nanotechnology in particular is making hNFs a versatile platform for constructing enzyme-loaded/immobilized structures for different applications. For instance, detection- and sensing-based applications, environmental- and sustainability-based applications, and biocatalytic and biotransformation applications are of supreme interest. Considering the above points, herein we reviewed current advances in multifunctional hNFs, with particular emphasis on (1) critical factors, (2) different metal/non-metal-based synthesizing processes (i.e., (i) copper-based hNFs, (ii) calcium-based hNFs, (iii) manganese-based hNFs, (iv) zinc-based hNFs, (v) cobalt-based hNFs, (vi) iron-based hNFs, (vii) multi-metal-based hNFs, and (viii) non-metal-based hNFs), and (3) their applications. Moreover, the interfacial mechanism involved in hNF development is also discussed considering the following three critical points: (1) the combination of metal ions and organic matter, (2) petal formation, and (3) the generation of hNFs. In summary, the literature given herein could be used to engineer hNFs for multipurpose applications in the biosensing, biocatalysis, and other environmental sectors.

A Review on New 3-D Printed Materials' Geometries for Catalysis and Adsorption: Paradigms from Reforming Reactions and CO2 Capture

"Bottom-up" additive manufacturing (AM) is the technology whereby a digitally designed structure is built layer-by-layer, i.e., differently than by traditional manufacturing techniques based on subtractive manufacturing. AM, as exemplified by 3D printing, has gained significant importance for scientists, among others, in the fields of catalysis and separation. Undoubtedly, it constitutes an enabling pathway by which new complex, promising and innovative structures can be built. According to recent studies, 3D printing technologies have been utilized in enhancing the heat, mass transfer, adsorption capacity and surface area in CO2 adsorption and separation applications and catalytic reactions. However, intense work is needed in the field to address further challenges in dealing with the materials and metrological features of the structures involved. Although few studies have been performed, the promise is there for future research to decrease carbon emissions and footprint. This review provides an overview on how AM is linked to the chemistry of catalysis and separation with particular emphasis on reforming reactions and carbon adsorption and how efficient it could be in enhancing their performance.