Structural Similarity Research Articles

Melanoma image synthesis: a review using generative adversarial networks

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Ecological Diversity of Micromycetes in Aerial Environments of Russian Libraries

The structure of micromycete communities in the library aerial environment was investigated in 57 Russian cities located in seven federal districts (Northwestern, Central, Southern, Volga, Ural, Siberian, and Far Eastern). A total of 107 micromycete species belonging to 41 genera were isolated and identified. Due to mostly similar conditions, the aerial ecosystem of internal spaces of the library storage facilities was relatively stable, with moderate diversity and high evenness, as was confirmed by the relevant indices: the Shannon index varied from 2.7 to 3.4, the McIntosh diversity index, from 22.7 to 140.8, and the Menhinick index, from 2.1 to 3.0. The Simpson’s dominance index and the Berger–Parker index did not exceed 0.11 and 0.24, respectively. The McIntosh and Pielou’s evenness indices were 0.71–0.78 and 0.79–0.85, respectively. High similarity of the taxonomic structures, independent of climatic conditions of the studied regions, was revealed, as was confirmed by the values of the Stugren–Radulescu and Morisita–Horn coefficients: 0.08‒0.77 and 0.04‒0.47, respectively. The typical members of the studied mycobiota were Aspergillus versicolor (7.5‒14.3), Cladosporium cladosporioides (17.5‒40.5), C. herbarum (0.8‒53.6), and Penicillium aurantiogriseum (6.5‒32.4). Most other species were scarce, with frequencies of occurrence not exceeding 7.1%.

Structure of the human systemic RNAi defective transmembrane protein 1 (hSIDT1) reveals the conformational flexibility of its lipid binding domain.

In Caenorhabditis elegans, inter-cellular transport of the small non-coding RNA causing systemic RNAi is mediated by the transmembrane protein SID1, encoded by the sid1 gene in the systemic RNAi defective (sid) loci. SID1 shares structural and sequence similarity with cholesterol uptake protein 1 (CHUP1) and is classified as a member of the ChUP family. Although systemic RNAi is not an evolutionarily conserved process, the sid gene products are found across the animal kingdom, suggesting the existence of other novel gene regulatory mechanisms mediated by small non-coding RNAs. Human homologs of sid gene products-hSIDT1 and hSIDT2-mediate contact-dependent lipophilic small non-coding dsRNA transport. Here, we report the structure of recombinant human SIDT1. We find that the extra-cytosolic domain of hSIDT1 adopts a double jelly roll fold, and the transmembrane domain exists as two modules-a flexible lipid binding domain and a rigid transmembrane domain core. Our structural analyses provide insights into the inherent conformational dynamics within the lipid binding domain in ChUP family members.

TGF-β-Based Therapies for Treating Ocular Surface Disorders

The cornea is continuously exposed to injuries, ranging from minor scratches to deep traumas. An effective healing mechanism is crucial for the cornea to restore its structure and function following major and minor insults. Transforming Growth Factor-Beta (TGF-β), a versatile signaling molecule that coordinates various cell responses, has a central role in corneal wound healing. Upon corneal injury, TGF-β is rapidly released into the extracellular environment, triggering cell migration and proliferation, the differentiation of keratocytes into myofibroblasts, and the initiation of the repair process. TGF-β-mediated processes are essential for wound closure; however, excessive levels of TGF-β can lead to fibrosis and scarring, causing impaired vision. Three primary isoforms of TGF-β exist—TGF-β1, TGF-β2, and TGF-β3. Although TGF-β isoforms share many structural and functional similarities, they present distinct roles in corneal regeneration, which adds an additional layer of complexity to understand the role of TGF-β in corneal wound healing. Further, aberrant TGF-β activity has been linked to various corneal pathologies, such as scarring and Peter’s Anomaly. Thus, understanding the molecular and cellular mechanisms by which TGF-β1-3 regulate corneal wound healing will enable the development of potential therapeutic interventions targeting the key molecule in this process. Herein, we summarize the multifaceted roles of TGF-β in corneal wound healing, dissecting its mechanisms of action and interactions with other molecules, and outline its role in corneal pathogenesis.

Noise2Variance: Dual networks with variance constraint for self‐supervised real‐world image denoising

AbstractImage denoising aims to restore a clean image from a noisy image. Traditional methods utilizing convolutional neural networks (CNN) for denoising are trained using pairs of noisy and clean images to comprehend the transformation from a noisy image to a clean one. However, the acquisition of such image pairs in real‐world scenarios presents a challenge. Hence, numerous self‐supervised denoising techniques have been developed that do not require clean images for training. This study demonstrates that a straightforward loss design, concentrating on variance, can effectively train a standard CNN denoiser in a self‐supervised fashion. A novel theoretical framework is introduced for training a basic CNN denoising model using three constraints: mean, variance, and augmentation. The variance constraint is crucial as it prevents the trained model from converging to trivial solutions such as identity or zero mapping. This theory provides valuable insights for the development of new self‐supervised denoising methods. Furthermore, a method that applies this theory to proposed dual networks is developed, which consist of two standard CNN models predicting both the clean image and the noise. This approach enhances model capacity during training while minimizing computational costs during inference. This method exemplifies the implementation of the variance constraint and introduces a data constraint for dual networks. Notably, the proposed method only assumes the presence of additive white noise, irrespective of the noise distribution. This minimal assumption enhances the model's robustness against noise with complex or unknown distributions in real‐world distorted images. Experimental results indicate that the proposed Noise2Variance method exhibits commendable performance on peak signal noise ratio and structural similarity metrics compared to existing self‐supervised denoising techniques. Visual comparison of results further substantiates the efficacy of the proposed method. A comparison of model complexity reveals that the method is efficient among the compared CNN‐based techniques.

UoC-7: A Bimetallic K-Zn-MOF with an Anionic Framework Based on Fluorinated Trimesate Ligands Exhibiting a Large CO2 Uptake.

In solvothermal reactions of Zn(NO3)2×6H2O with K(H2mF-BTC) or K(H2dF-BTC) in DMF/ethanol or DMA/ethanol solvent mixtures, single crystals of the MOFs UoC-7(1F) and UoC-7(2F) were obtained crystallizing in the hexagonal space group P63/m (no.176) (H3BTC: 1,3,5-benzenetricarboxylic acid; mF-/dF: mono-/difluoro; DMF: N,N-dimethylformamide; DMA: N,N-dimethylacetamide; UoC: University of Cologne). According to the general composition [(CH3)2NH2][K2Zn3(mF-/dF-BTC)3(H2O)]×solvent, UoC-7 consists of an anionic bimetallic framework. The charge is compensated by a (CH3)2NH2 + cation stemming from the (partial) hydrolysis of the solvent. The crystal structure shows large channels along the hexagonal [001] direction, which accommodate the cations as well as solvent molecules. Surface areas (SBET) of 2740 m2/g (UoC-7(1F)) and 1643 m2/g (UoC-7(2F)) were obtained from N2 sorption measurements. UoC-7 shows structural similarities to the MOF NKU-521 with a 5-(1H-tetrazol-5-yl)isophthalate linker. Both MOFs exhibit a 4,7,8T14 topology. Despite smaller channels in UoC-7 compared to NKU-521, the CO2 uptake is considerably higher (~164 cm3/g at 1 bar/293 K) being one of the highest CO2 uptakes observed up to now.

Using diffusion models to generate synthetic labeled data for medical image segmentation.

Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity. We trained diffusion models on the HyperKvasir dataset, comprising 1000 images of polyps in the human GI tract from 2008 to 2016. Qualitative expert review, Fréchet Inception Distance (FID), and Multi-Scale Structural Similarity (MS-SSIM) were tested for evaluation. Additionally, various segmentation models were trained with the generated data and evaluated using Dice score (DS) and Intersection over Union (IoU). Our pipeline produced images more akin to real polyp images based on FID scores. Segmentation model performance also showed improvements over GAN methods when trained entirely, or partially, with synthetic data, despite requiring less compute for training. Moreover, the improvement persists when tested on different datasets, showcasing the transferability of the generated images. The proposed pipeline produced realistic image and mask pairs which could reduce the need for manual data annotation when performing a machine learning task. We support this use case by showing that the methods proposed in this study enhanced segmentation model performance, as measured by Dice and IoU scores, when trained fully or partially on synthetic data.

Enhancing structural diversity through chemical engineering of Ambrosia tenuifolia extract for novel anti-glioblastoma compounds

Natural products are an unsurpassed source of leading structures in drug discovery. The biosynthetic machinery of the producing organism offers an important source for modifying complex natural products, leading to analogs that are unattainable by chemical semisynthesis or total synthesis. In this report, through the combination of natural products chemistry and diversity-oriented synthesis, a diversity-enhanced extracts approach is proposed using chemical reactions that remodel molecular scaffolds directly on extracts of natural resources. This method was applied to subextract enriched in sesquiterpene lactones from Ambrosia tenuifolia (Fam. Asteraceae) using acid media conditions (p-toluenesulfonic acid) to change molecular skeletons. The chemically modified extract was then fractionated by a bioguided approach to obtain the pure compounds responsible for the anti-glioblastoma (GBM) activity in T98G cell cultures. Indeed, with the best candidate, chronobiological experiments were performed to evaluate temporal susceptibility to the treatment on GBM cell cultures to define the best time to apply the therapy. Finally, bioinformatics tools were used to supply qualitative and quantitative information on the physicochemical properties, chemical space, and structural similarity of the compound library obtained. As a result, natural products derivatives containing new molecular skeletons were obtained, with possible applications as chemotherapeutic agents against human GBM T98G cell cultures.

High-quality AFM image acquisition of living cells by modified residual encoder-decoder network

Atomic force microscope enables ultra-precision imaging of living cells. However, atomic force microscope imaging is a complex and time-consuming process. The obtained images of living cells usually have low resolution and are easily influenced by noise leading to unsatisfactory imaging quality, obstructing the research and analysis based on cell images. Herein, an adaptive attention image reconstruction network based on residual encoder-decoder was proposed, through the combination of deep learning technology and atomic force microscope imaging supporting high-quality cell image acquisition. Compared with other learning-based methods, the proposed network showed higher peak signal-to-noise ratio, higher structural similarity and better image reconstruction performances. In addition, the cell images reconstructed by each method were used for cell recognition, and the cell images reconstructed by the proposed network had the highest cell recognition rate. The proposed network has brought insights into the atomic force microscope-based imaging of living cells and cell image reconstruction, which is of great significance in biological and medical research.

Voxel-Wise Medical Image Generalization for Eliminating Distribution Shift

Currently, the medical field is witnessing an increase in the use of machine learning techniques. Supervised learning methods adopted in classification, prediction, and segmentation tasks for medical images always experience decreased performance when the training and testing datasets do not follow the independent and identically distributed assumption. These distribution shift situations seriously influence machine learning applications’ robustness, fairness, and trustworthiness in the medical domain. Hence, in this article, we adopt the CycleGAN (generative adversarial network) method to cycle train the computed tomography data from different scanners/manufacturers. It aims to eliminate the distribution shift from diverse data terminals based on our previous work [ 14 ]. However, due to the model collapse problem and generative mechanisms of the GAN-based model, the images we generated contained serious artifacts. To remove the boundary marks and artifacts, we adopt score-based diffusion generative models to refine the images voxel-wisely. This innovative combination of two generative models enhances the quality of data providers while maintaining significant features. Meanwhile, we use five paired patients’ medical images to deal with the evaluation experiments with structural similarity index measure metrics and the segmentation model’s performance comparison. We conclude that CycleGAN can be utilized as an efficient data augmentation technique rather than a distribution-shift-eliminating method. In contrast, the denoising diffusion the denoising diffusion model is more suitable for dealing with the distribution shift problem aroused by the different terminal modules. The limitation of generative methods applied in medical images is the difficulty in obtaining large and diverse datasets that accurately capture the complexity of biological structure and variability. In our following research, we plan to assess the initial and generated datasets to explore more possibilities to overcome the above limitation. We will also incorporate the generative methods into the federated learning architecture, which can maintain their advantages and resolve the distribution shift issue on a larger scale.

Six-Coordinated CoII Single-Molecule Magnets: Synthetic Strategy, Structure and Magnetic Properties.

The pursuit of molecule-based magnetic memory materials contributes significantly to high-density information storage research in the frame of the ongoing information technologies revolution. Remarkable progress has been achieved in both transition metal (TM) and lanthanide based single-molecule magnets (SMMs). Notably, six-coordinated CoII SMMs hold particular research significance owing to the economic and abundant nature of 3d TM ions compared to lanthanide ions, the substantial spin-orbit coupling of CoII ions, the potential for precise control over coordination geometry, and the air-stability of coordination-saturated structures. In this review, we will summarize the progress made in six-coordinated CoII SMMs, organized by their coordination geometry and molecular structure similarity. Valuable insights, principles, and new mechanism gleaned from this research and remaining issues that need to be addressed will also be discussed to guide future optimization.

Characterization of medaka (Oryzias latipes) AHRs and the comparison of two model fishes—Medaka vs. zebrafish: The subform-specific sensitivity to dioxin

Dioxins and the emerging dioxin-like compounds (DLCs) have recruited increasing concerns about their environmental contamination, toxicity, health impacts, and mechanisms. Based on the structural similarity of dioxins and many DLCs, their toxicity was predominantly mediated by the dioxin receptor (aryl hydrocarbon receptor, AHR) in animals (including human), which can be different in expression and function among species and then possibly produce the species-specific risk or toxicity. To date, characterizing the AHR of additional species other than human and rodents can increase the accuracy of toxicity/risk evaluation and increase knowledge about AHR biology. As a key model, the medaka AHR has not been clearly characterized. Through genome survey and phylogenetic analysis, we identified four AHRs (olaAHR1a, olaAHR1b, olaAHR2a, and olaAHR2b) and two ARNTs (olaARNT1 and olaARNT2). The medaka AHR pathway was conserved in expression in nine tested tissues, of which olaAHR2a represented the predominant subform with greater abundance. Medaka AHRs and ARNTs were functional and could be efficiently transactivated by the classical dioxin congener 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), although olaAHR1a did not seem to cooperate with olaARNT2. In terms of function/sensitivity, the EC50 values of medaka olaAHR1a (9.01 ± 1.43 nM), olaAHR1b (4.00 ± 1.10 nM), olaAHR2a (8.75 ± 3.34 nM), and olaAHR2b (3.06 ± 0.81 nM) showed slight differences; however, they were all at the nM level. The sensitivity of four medaka AHRs to TCDD was similar to that of zebrafish dreAHR2 (the dominant form, EC50 = 3.14 ± 4.19 nM), but these medaka AHRs were more sensitive than zebrafish dreAHR1b (EC50 = 27.05 ± 18.51 nM). The additional comparison also indicated that the EC50 values in various species were usually within the nM range, but AHRs of certain subforms/species can vary by one or two orders of magnitude. In summary, the present study will enhance the understanding of AHR and help improve research on the ecotoxicity of dioxins/DLCs.

Study on the Mass Spectrometry Cleavage Pattern of Quinolone Antibiotics.

Quinolone antibiotics are extensively used clinically for human treatment and in agriculture. However, improper and excessive use can lead to the persistence of quinolone residues in animal tissues, potentially accumulating in the human body and posing health risks. Investigating the correlation between mass spectrometry cleavage patterns and molecular structural features enhances the analytical framework for detecting trace or unknown impurities in quinolones. To collect data, we employed triple quadrupole linear ion trap mass spectrometry in electrospray positive ion mode. Primary mass spectrometry scanning was utilized to confirm parent ions, while secondary mass spectrometry scanning enabled the observation of fragment ions. The cleavage characteristics and pathways of the compounds were inferred from accurate mass-to-charge ratios obtained from both primary and secondary mass spectrometry. Under soft ionization conditions, the compounds generally exhibited characteristic fragment ions of [M+H-H2O]+, [M+H-CO]+, and [M+H-H2O-CO]+. Additionally, subtle variations were observed in each compound due to differences in modifying groups. For instance, upon deacidification, the piperazine ring structure underwent breakage and rearrangement, yielding fragment ion peaks devoid of neutral molecules such as C2H5N, C3H7N, or C4H8N. Notably, compounds featuring a cyclopropyl substituent group at the N-1 position typically exhibited characteristic fragments resulting from the loss of the cyclopropyl radical (⋅C3H5). Moreover, substituents at the N-1 and C-8 positions, when linked to form a six-membered carbocyclic ring, were prone to cleavage, releasing the neutral C3H6 molecule. Quinolone antibiotics share structural similarities in their parent nuclei, leading to partially similar cleavage pathways. Nevertheless, distinct cleavage patterns emerge due to variations in functional groups. According to the difference of mass spectrometry cleavage patterns, it can provide an identification basis for the measured detection of antibiotics.

Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes-the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

Molecular mimicry of SARS-COV-2 antigens as a possible natural anti-cancer preventive immunization

Molecular mimicry of SARS-COV-2 antigens as a possible natural anti-cancer preventive immunization

Structure of the human systemic RNAi defective transmembrane protein 1 (hSIDT1) reveals the conformational flexibility of its lipid binding domain.

In C. elegans , inter-cellular transport of the small non-coding RNA causing systemic RNA interference (RNAi) is mediated by the transmembrane protein SID1, encoded by the sid1 gene in the systemic RNA interference-defective ( sid ) loci. SID1 shares structural and sequence similarity with cholesterol uptake protein 1 (CHUP1) and is classified as a member of the cholesterol uptake family (ChUP). Although systemic RNAi is not an evolutionarily conserved process, the sid gene products are found across the animal kingdom, suggesting the existence of other novel gene regulatory mechanisms mediated by small non-coding RNAs. Human homologs of sid gene products - hSIDT1 and hSIDT2 - mediate contact-dependent lipophilic small non-coding dsRNA transport. Here, we report the structure of recombinant human SIDT1. We find that the extra- cytosolic domain (ECD) of hSIDT1 adopts a double jelly roll fold, and the transmembrane domain (TMD) exists as two modules - a flexible lipid binding domain (LBD) and a rigid TMD core. Our structural analyses provide insights into the inherent conformational dynamics within the lipid binding domain in cholesterol uptake (ChUP) family members.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Donkey Gelatin and Keratin Nanofibers Loaded with Antioxidant Agents for Wound Healing Dressings.

Acute and chronic wounds present a significant healthcare challenge, requiring innovative solutions for effective treatment. The exploitation of natural by-products with advanced cell regeneration potential and plant-based materials, which possess bioactive properties, is an innovative topic in wound management. This study investigates the potential of donkey gelatin and keratin for blending with natural bioactive extracts such as sumac, curcumin, and oak acorn to fabricate antioxidant and antimicrobial nanofibers with accelerated wound healing processes. The fabricated nanofibers possess good in vitro biocompatibility, except for the sumac-based donkey nanofibers, where cell viability significantly dropped to 56.25% (p < 0.05 compared to non-treated cells). The nanofiber dimensions showed structural similarities to human extracellular matrix components, providing an ideal microenvironment for tissue regeneration. The donkey nanofiber-based sumac and curcumin extracts presented a higher dissolution in the first 10 min (74% and 72%). Curcumin extract showed similar antimicrobial and antifungal performances to rivanol, while acorn and sumac extracts demonstrated similar values to each other. In vitro tests performed on murine fibroblast cells demonstrated high migration rates of 89% and 85% after 24 h in the case of acorn and curcumin nanofibers, respectively, underscoring the potential of these nanofibers as versatile platforms for advanced wound care applications.