Lignin’s complex and highly branched architecture plays a critical role in biomass utilisation. Yet, unravelling the intricate relation between lignin’s measurable structural features and the underlying biosynthesis dynamics remains a major challenge. This difficulty stems from the variability in monolignol and bond type compositions, experimental datasets with typically distinct degrees of completeness, and extraction methods that differently impact molecular features. Together, this highlights the need for advanced numerical frameworks able to rationalise experimental data. We introduce LigninFit, a simulation software that integrates Lignin-KMC within an efficient and modular parameter optimisation procedure to infer lignin biosynthesis dynamics from bond distributions. Using experimental data from a curated set of 20 biomass samples, we reproduce their bond distributions following 3 versions of the fitting procedure, differing in the number of model parameters optimised. We then generate extensive in silico libraries of lignin structures for the best fit of each biomass and analysein depth, at both the ensemble and molecular levels, the properties of the resulting molecules in terms of bond distributions and branching degrees. Overall, LigninFit not only allows for predicting the complete bond distribution of incomplete datasets, and highlighting unexpected similarities and differences across biomass types, but also reveals which parameters most strongly impact the biosynthesis dynamics and which metrics are most suitable for discriminating among biomass types. Eventually, as a modular and open-source software, LigninFit paves the way for additional modelling developments, thereby contributing to guide experimental endeavours.
Background: Alström syndrome (ALMS) and Bardet-Biedl syndrome (BBS) are rare ciliopathies characterized by multisystem involvement, including obesity, insulin resistance, and type 2 diabetes. Systemic metabolic dysfunction may influence the oral microbiome; however, integrative analyses that combine microbial and metabolic profiles in these disorders remain limited. Methods: Saliva and gingival crevicular fluid (GCF) samples were collected from genetically confirmed ALMS and BBS patients, as well as from obesity and healthy control groups. Microbial communities were profiled using V3-V4 16S rRNA gene amplicon sequencing, and untargeted metabolomic profiling was performed by gas chromatography-mass spectrometry. Microbiome-metabolome associations were evaluated using Spearman's rank correlation analysis, followed by multi-omics integration using Multiple Co-Inertia Analysis (MCIA) and the supervised Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) framework (mixOmics). Results: Integrated analysis identified distinct microbiome-metabolome association patterns in ALMS and BBS. Compared with controls, the ALMS+BBS group showed enrichment of Prevotella, Enterococcus, and Eikenella, alongside reduced Lactobacillus abundance. Metabolomic profiling revealed alterations in amino acid, fatty acid, and carbohydrate metabolism. GCF exhibited structured associations between metabolites and Firmicutes, Proteobacteria, and Actinobacteriota, whereas saliva showed broader interaction networks. These associations were absent or markedly weaker in obesity and healthy controls. MCIA demonstrated coordinated variation across the oral microbiome, salivary metabolome, and GCF metabolome, while DIABLO identified a shared multi-omics signature. Conclusions: Coordinated shifts in amino acid, lipid, and central carbon metabolism can be linked to oral microbial reorganization in ALMS and BBS. Integrative multi-omics analyses identified coordinated microbiome-metabolome signatures across the oral microbiome, saliva, and GCF. These findings warrant validation in larger longitudinal and functional studies.
“Mirror life”, self-replicating organisms composed of nonnatural-chirality biomacromolecules, presents a future threat with potentially global consequences. Consequently, there is strong agreement among experts that it should not be created. However, there is some disagreement over how effective existing medical countermeasures might prove against mirror bacteria, in the event that they were created. Here, we leverage computational chemistry methods including docking and molecular dynamics to determine the likely binding efficacy of existing antibiotics against natural and mirror bacterial protein targets. We find that most existing antibiotics fail to bind to mirror bacterial protein targets, unlike their natural-chirality targets. This suggests altered binding of current medical countermeasures, which may impact antimicrobial activity against mirror bacteria if the latter were created.
Cardiovascular diseases remain a major global health challenge, driving the search for plant‐derived bioactive systems with improved pharmaceutical performance. Plinia peruviana, a Brazilian native species, is a rich source of phenolic compounds with well‐documented biological activities. However, limitations related to physicochemical instability and low oral bioavailability hinder its practical application. Herein, a supramolecular complex between the ethanolic extract of P. peruviana branches and β‐cyclodextrin was developed to improve the biopharmaceutical properties of the extract. Total phenolic content was determined by visible spectrophotometry, while inclusion complex formation was confirmed by nuclear magnetic resonance and Raman spectroscopy. Vasodilatory activity was evaluated through ex vivo vascular reactivity assays using isolated mesenteric arterial beds, and oral safety was assessed in acute and subchronic exposure models following internationally accepted guidelines. In addition, simulated gastrointestinal digestion was employed to investigate phenolic stability and bioaccessibility. The supramolecular complex exhibited biologically relevant vasodilatory activity, showed low oral toxicity, and significantly enhanced the intestinal bioaccessibility of phenolic compounds. Overall, these findings demonstrate that cyclodextrin‐based supramolecular systems represent a promising pharmaceutical delivery strategy for improving the biopharmaceutical performance of phenolic‐rich plant extracts through enhanced intestinal bioaccessibility and a favorable oral safety profile. These characteristics support their further investigation as plant‐based delivery systems for future phytopharmaceutical development for cardiovascular applications.
Introduction: Electrical impedance tomography (EIT) is a noninvasive, radiation-free imaging modality that provides real-time information on regional lung ventilation from wearable sensors. Chronic obstructive pulmonary disease (COPD) is a highly prevalent respiratory disease causing persistent and progressive airway obstruction. The diagnosis is based primarily on spirometry, in which a Tiffeneau index (forced expiratory volume in 1 s/forced vital capacity ratio) below 0.7 is a key feature of the definitive diagnosis. Despite many advances in medicine, there is a lack of widely available methods for estimating airway obstruction using noninvasive bedside measurements, which could facilitate assessment in patients who are unable to perform standard spirometry, e.g., patients after laryngectomy and those with chronic tracheostomies. Moreover, the prevalence of COPD in this group of patients may be substantial. Methods: The study examined the relationship between EIT-derived signal features and the Tiffeneau index through a comprehensive statistical and machine learning analysis of patient data collected using the Dräger EIT system. Data from 15 adult patients who successfully completed conventional spirometry and EIT measurements were analyzed. Patients conducted the examination a couple of times; consequently, 30 measurements were collected. A total of 755 time-series features, complemented by physiological measurements, were extracted, analyzed, and evaluated using correlation metrics, P-value testing, categorical associations, and Shapley additive explanations explainability. Results: Frequency-domain features (fast Fourier transform angle coefficients), entropy measures, and autocorrelation-based descriptors have the strongest associations with the Tiffeneau index. Feature-selected machine learning models demonstrated that EIT-derived time-frequency features, combined with anthropometric variables (weight and body mass index), could approximate Tiffeneau index values in this small cohort (best model: R 2 = 0.72 on the testing set). Conclusion: These findings support the exploratory feasibility of EIT-based, noninvasive approaches for pulmonary function estimation in a general clinical cohort and motivate future validation studies in patients for whom spirometry cannot be performed.
Foundation vision encoders are rapidly emerging as the standard for retinal artificial intelligence. Yet, ophthalmology still lacks a comprehensive benchmark, leaving model selection for basic science and clinical translation as guesswork. Here, we present a large-scale comparison of 34 pretrained encoders on 39 classification tasks covering color fundus photography, optical coherence tomography, scanning laser ophthalmoscopy, and ultrawidefield imaging. Using a unified pipeline, we compare frozen-feature evaluation, linear probing, and end-to-end fine-tuning to determine which models translate into strong downstream performance. We show that ophthalmic transfer is highly task dependent: no single encoder dominates, and model rankings vary across datasets. Contrary to common expectations, retina-specific pretraining does not confer an advantage. Instead, several natural-image and cross-domain medical encoders match or surpass ophthalmology-specialized models, with the histopathology-pretrained Virchow achieving the strongest overall performance. In addition, pathology-pretrained encoders consistently place near the top, revealing the value of cross-domain pretraining for ophthalmic applications. We further show that inexpensive proxy evaluations are unreliable substitutes for full fine-tuning. Across fairness analyses, all encoders exhibit similar age- and sex-associated performance gaps, and larger models appear more sensitive to suboptimal learning rates, whereas smaller encoders are robust. Together, these findings provide an objective reference for encoder selection in ophthalmology and show that reliable retinal artificial intelligence depends not only on model scale or domain-specific pretraining but also on careful, protocol-aware evaluation. By releasing our code, splits, and benchmarking pipeline, we aim to establish a transparent foundation for future ophthalmic foundation-model research.
Methane decomposition is a COx-free process for hydrogen generation that also produces valuable carbon nanostructures. The work herein focused on Fe-based catalysts supported on some common supports (Al2O3, ZrO2, SiO2) and on alumina modified by metal oxides (10SiAl, 10ZrAl, 10YAl, 10CeAl). The prepared catalysts were characterized in detail by XRD, BET, TEM, TPR, TGA, and Raman spectroscopy. Among the single-component supports, the 20Fe/Al2O3 (Fe-Al) catalyst exhibited the highest initial activity, with 69.4% CH4 conversion. Among all the catalysts studied, Fe-10ZrAl showed the best catalytic activity with a maximum CH4 conversion (76.5%) and H2 yield (74.1%). Furthermore, during regeneration cycles, the catalyst maintained CH4 conversion (90%) without detrimental carbon encapsulation. The performance of Fe-10ZrAl is attributed to the synergistic effect of the ZrO2 modifier which improves strong metal-support interactions, stabilizes active iron nanoparticles from thermal sintering, and allows a highly reversible in-situ carbide cycle. The Fe-10ZrAl catalyst is the most active, followed by the Fe-10YAl catalyst, owing to its improved reducibility. Our work demonstrates the key role of modified alumina structures for sustainable co-production of hydrogen and nanomaterials.
Retinothalamic and thalamocortical synapses are efficient in the sense that each synapse conveys as many bits per Joule as possible, but efficiency falls rapidly if synaptic conductance deviates from its natural value Harris et al. (2015, 2019). However, the manner in which efficiency falls with conductance remains unexplained. Recently, Malkin et al. (2026) showed that synaptic noise is minimised given the available energy, consistent with a minimal energy boundary. Here, this boundary is expressed in terms of Shannon’s information theory Shannon and Weaver (1949), which yields a model that predicts the efficiency values observed in Harris et al. (2015) across a 120-fold change in synaptic conductance ( R^2= 0.769 , p<0.001 ). This model also predicts that, for a synapse at its natural conductance, each pre-synaptic spike provides an average of 3.58 bits to its post-synaptic neuron, which is consistent with physiological values. Crucially, given the biophysical constraints that, a) synaptic efficiency is maximised at the natural conductance, and, b) synaptic noise variance is minimised in accordance with the minimal energy boundary, the proposed model contains no free parameters, so it is predictive rather than descriptive. The results presented here are consistent with the general principle that CNS neurons maximise information efficiency (bits per Joule), rather than information rate (bits per second).
Ten 5,5'-azotetrazolate salts with nitrogen-rich bases was prepared and evaluated as technologically accessible gas-generating materials. In addition to sufficient performance, the development focused on safety, reliable handling, and environmental responsibility, as essential factors for evaluating compounds intended for controlled gas generation. The azo and tetrazole framework combines high nitrogen content with favorable energetic characteristics, making it an attractive platform for applications requiring rapid, controlled gas release. The prepared salts were evaluated for their physicochemical and safety-relevant properties, including bulk density, hygroscopicity, and mechanical sensitivity. The structures of several compounds were determined by X-ray analysis of single-crystalline material. The ballistic properties were calculated for both the compounds themselves and their mixtures with the oxidizers potassium perchlorate and strontium nitrate, which ensure the required reaction rate while producing nontoxic gaseous and solid products. Several compounds showed advantageous combinations of high bulk density, low-to-moderate hygroscopicity, and favorable impact and friction sensitivities, resulting in performance comparable to that of currently used materials or commonly cited high-potential candidates for gas-generating components in various safety systems.
The neural correlates of consciousness have been characterized primarily through temporal EEG features, while the spatial dynamics of cortical activity across consciousness states remain poorly understood. Here, we analyze the spatial mode structure of the Robinson corticothalamic neural field model (CTM) across five consciousness states: healthy wakefulness, emergence from minimally conscious state (eMCS), minimally conscious state (MCS), deep sleep (N3), and unresponsive wakefulness syndrome (UWS). We compute the noise-amplified spatial spectrum, which quantifies how the CTM filters spatiotemporal noise across spatial wavenumbers. We find systematic spectral narrowing with decreasing consciousness: the spectral centroid decreases by 48
A zinc-mediated catalyst-free cross-electrophile coupling of gem-difluoroenol sulfonates with N-(acyloxy)phthalimides is developed. It efficiently affords diverse α,α-difluoroketones with broad radical compatibility, good gram-scale scalability, and functional group tolerance, enabling access to complex amino acid and glycosyl derivatives.
A set of four H‐ZSM‐5‐supported Ni–Cu‐containing materials with 5% Ni‐1% Cu loadings was prepared. The effect of using different reduction protocols (non‐reduced reference; H2 reduction at Tred.: 300; 400 and 500 °C) during the materials’ preparation was monitored to investigate the effect of this parameter on their microstructural features and their catalytic properties in a hydrogenolysis reaction. The variation of the reduction temperature has a direct effect on the composition and thus the separation of Ni atoms within the bimetallic nanoparticles and this in‐turn affects their reactivities for the hydrogenolysis of benzyl phenyl ether (a lignin model compound). Alloy Cu/Ni‐containing nanoparticles (confirmed by p‐XRD) prepared by reduction at 400 °C containing relatively more separated Ni atoms (observed using transmission electron microscopy ‐ energy dispersive x‐ray spectroscopy (TEM‐EDX)) were found to be more active in the reaction of interest than catalysts prepared using a reduction at 500 °C which had higher Ni contents (but lower Ni within particle separations).
Neurological disorders (NDs) are characterized by substantial loss of specific neurons, with Alzheimer's and Parkinson's diseases being the most frequent NDs and nearly 99% of all “foreign substances” are prohibited from entering the brain by the blood‐brain barrier (BBB) and the blood‐cerebrospinal fluid barrier (CFB). These barriers, while crucial for brain protection, pose significant challenges for drug delivery, as they restrict the entry of many therapeutic agents into the brain, and this represents the primary manifestation of the absence of pathogenesis‐targeting therapeutics. With significant success across multiple cell transplantation research efforts, stem cell therapy has been utilized for decades to treat neurological disorders. They work by replacing injured or lost cells directly, releasing proliferation and neurotrophic factors through autocrine and paracrine actions, suppressing neurological inflammation, and activation of endogenous brain progenitor cells. Nanocarriers derived from stem cells represent an innovative and promising therapeutic strategy for combating neurological diseases, combining faculties of regeneration of stem cells with the precision of nanotechnology. These nanocarriers possess natural biocompatibility and can efficiently cross the BBB to transport the therapeutic agents directly to affected neural tissues, thereby promoting enhanced treatment efficacy while reducing off‐target effects. However, key challenges remain in large‐scale production, standardization, and long‐term safety. Thus, this review has examined the potential applications of stem cell extracellular vesicle (EV)‐nanocarriers, mainly exosomes and recent development in the treatment of neurological diseases.
Salvia balansae, an endemic Algerian species, remains poorly investigated despite the recognized medicinal importance of the Salvia genus. This study comprehensively evaluated its phytochemical composition, biological activities, and safety profile. Quantitative analyses revealed high levels of total phenolics, flavonoids, flavonols, condensed tannins, and triterpenoids, while LC–MS identified a distinctive Aurès‐region chemotype enriched in luteolin, rosmarinic acid, and flavonoid glycosides. The extract demonstrated strong antioxidant activity in free radical‐scavenging, metal‐chelating, and reducing‐power assays. Anti‐inflammatory activity was confirmed by inhibition of protein denaturation in vitro and significant suppression of croton oil‐induced ear edema and histopathological damage in vivo. Molecular docking predicted favorable binding of salvianolic acid, rutin, and apigenin‐7‐O‐glucoside to NLRP3 inflammasome components, suggesting a potential mechanism underlying the observed anti‐inflammatory effects. The extract also exhibited moderate inhibitory activity against cholinesterases and α‐amylase, indicating possible neuroprotective and antidiabetic potential. Acute oral toxicity testing at 2000 mg/kg showed no mortality, major clinical signs of toxicity, or apparent organ damage, although repeated‐dose studies are warranted. Collectively, these findings identify S. balansae as a polyphenol‐ and triterpenoid‐rich species with considerable promise for pharmaceutical and nutraceutical applications.
Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups. This article presents the Fairness-Aware Data Orchestration Pipeline (FADOP), a reusable workflow that analyzes biomedical datasets, trains baseline binary classifiers, audits subgroup error patterns, tests mitigation strategies, and generates a documented recommendation. Such a pipeline is intended for systematic evaluation before clinical translation, not as an automatic deployment tool. Two publicly available case studies illustrate its use: the HIV-related ACTG175 dataset was repurposed from a treatment-comparison trial into a 1-year baseline mortality-prediction task, with death by day 365 as the positive class rather than the cid AIDS/failure composite endpoint; then, a stroke-risk dataset was analyzed as direct event prediction. The case studies show how the same workflow can generate cohort, performance, fairness, mitigation, and recommendation evidence across different rare-event clinical datasets.
Cell populations grow, shrink, and reshuffle their composition as individual cells divide, arrest, and die. Because cell division is paced by progression through the cell cycle, cell-cycle control is the fundamental axis linking intracellular regulation to population-level dynamics. Quantitatively capturing how cell-cycle regulation affects population dynamics is thus central to understanding tissue homeostasis, tumor expansion, immune responses, and the performance of cell-based bioprocesses—from tissue engineering and regenerative applications to the manufacturing of vaccines and recombinant therapeutics. In this view, a broad spectrum of mathematical models has been proposed to describe cell population dynamics, ranging from phenomenological growth laws that treat net proliferation as a black box to structured descriptions that resolve single-cell heterogeneity and link population change to cell-cycle progression. In this review, we survey deterministic frameworks for modeling cell-cycle-informed population dynamics, highlighting how modeling choices map onto accessible experimental readouts and biomedical and biotechnological questions. In doing so, we aim to provide a theoretical roadmap for readers new to the field who seek to translate intracellular cell-cycle regulation into empirically grounded population-level models. We organize models along 2 conceptual dimensions: the representation of cell-to-cell heterogeneity through increasing levels of population structure and the level of mechanistic specification of cell division through cell-cycle regulation. In particular, we discuss practical approaches to couple cell-cycle regulation to population-level dynamics—from coarse-grained to explicit multiscale couplings—and how external perturbations enter such couplings. We conclude by outlining open challenges toward cell-cycle-aware population models that match mechanistic resolution with experimental identifiability.
Anchusa azurea has attracted increasing interest as a medicinal plant due to its phenolic and flavonoid compounds, which may contribute to its antioxidant and wound‐healing activities. In this study, aqueous (AW) and aqueous ethanolic (AE) extracts obtained from the aerial parts of Anchusa azurea by ultrasonic extraction were evaluated for their phytochemical composition, antioxidant activity, cytotoxicity, and wound‐healing efficacy using in vitro and in vivo models. The AE extract exhibited higher total phenolic and flavonoid contents and stronger antioxidant activity than the AW extract. Cytotoxicity was assessed on human skin fibroblasts, adult (HDFa) cells, using the MTT assay. Both extracts showed no significant cytotoxic effects. AW‐loaded nanoliposomes (AWNP) and AE‐loaded nanoliposomes (AENP) were successfully prepared, and scanning electron microscopy, transmission electron microscopy, and dynamic light scattering analyses confirmed nanoscale vesicles with negative zeta potential values. In vitro release studies demonstrated biphasic release profiles, with AENP exhibiting greater cumulative release than AWNP. In a rat excisional wound model, nanoliposomal formulations significantly enhanced wound contraction compared with crude extracts and controls. Histopathological evaluation demonstrated increased collagen deposition, neovascularization, and re‐epithelialization, particularly in the AE and AENP groups. These findings suggest that the phenolic‐rich aqueous ethanolic extract and its nanoliposomal formulation are promising candidates for wound‐healing applications.
In this study, the targeted phytochemical profiles and multi‐target biological activities of Mandragora autumnalis L. and Spartium junceum L. were evaluated using liquid chromatography–tandem mass spectrometry (LC‐MS/MS), antioxidant, enzyme inhibition, and antimicrobial assays. LC‐MS/MS analysis showed that M. autumnalis predominantly contained low‐molecular‐weight phenolic acids, particularly protocatechuic acid (0.6850 µg/mg), whereas S. junceum was richer in flavonoids and isoflavonoids, especially genistein (1.8280 µg/mg) and cynaroside (1.5240 µg/mg). M. autumnalis showed stronger antioxidant activity, with a DPPH IC 50 value of 60.16 µg/mL and a FRAP absorbance of 2.098 ± 0.042 at 45 µg/mL, whereas S. junceum showed weak DPPH radical scavenging activity (IC 50 : 813.50 µg/mL). M. autumnalis also exhibited stronger acetylcholinesterase and carbonic anhydrase inhibition, on the other hand S. junceum displayed a butyrylcholinesterase‐oriented inhibitory profile. Preliminary antimicrobial screening revealed inhibition zones of 11–13 mm against selected bacterial strains. Overall, the findings suggest that low‐molecular‐weight phenolic acids may contribute to the biological activity of M. autumnalis , whereas flavonoids and isoflavonoids may support the characteristic inhibitory profile of S. junceum , highlighting the importance of qualitative metabolite composition in shaping extract bioactivity.
Wearable sensors have shown great potential in sports motion monitoring, exercise evaluation, and intelligent training owing to their ability to provide continuous and real‐time biomechanical information. Here, a multinetwork PD hydrogel‐based triboelectric nanogenerator (PD‐TENG) is developed as a self‐powered wearable sensing platform for national fitness motion monitoring and intelligent evaluation. The device adopts a contact–separation configuration with pPTFE and nylon as the triboelectric pair and PD hydrogel as the flexible conductive electrode. Benefiting from the synergistic coupling of polymer chains, ionic species, and dynamic intermolecular interactions, the hydrogel exhibits favorable flexibility, mechanical compliance, and interfacial adaptability. The optimized PD‐TENG delivers an open‐circuit voltage (VOC) of 876 V, a short‐circuit current (ISC) of 40 μA, a transferred charge (QSC) of 260 nC, and a maximum output power of 598 μW. It also maintains stable electrical performance under varying frequencies, separation distances, temperature, humidity, and external force. As a wearable sensor, it enables joint‐angle monitoring and distinguishes walking, running, and jumping, demonstrating promising potential for intelligent motion recognition and scoring.
Finite-dimensional Koopman MPC for nonlinear controlled systems requires care when a learned LTI lift is used as a finite-horizon surrogate. We recast the memristive Hindmarsh–Rose benchmark through an input-exact Lie-lifting certificate. Because the stimulation vector field is g=e_1 , the augmented polynomial dictionary is closed under ℒ_g ; the pure stimulation flow is represented exactly by a nilpotent matrix exponential. Moreover, the drift–input commutator cascade terminates after three input commutators, so the controlled Koopman error can be written as a finite shifted-drift defect rather than an uncontrolled truncation heuristic. The resulting theory supports a bilinear, stimulation-aligned surrogate and places the affine EDMDc-MPC implementation in a conservative finite-horizon deterministic setting. Paired comparisons with Hermite and SINDy polynomial baselines, controlled-pulse prediction, measurement-noise stress tests, and affine-versus-bilinear Lie-MPC evaluations show that the bilinear Lie model gives the lowest controlled-prediction error, closed-loop RMSE, and control energy in the deterministic benchmark.
