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Tissue scaffolds are one of the main components of the tissue engineering triad, playing a vital role in tissue engineering. However, their production procedures heavily rely on solvent-intensive and energy-demanding methods. This raises serious questions about industrial-scale manufacturability, residual solvent toxicity to living health, and sustainability for nature and the environment. Therefore, the main aim of this study is to identify robust scaffolds that provide a suitable microenvironment for resident cells and promote tissue regeneration, while reducing waste through environmentally friendly production methods. In this context, the scalable and ecologically friendly production methods emerge as necessary alternatives as biodegradable polymer scaffolds are used in more therapeutic settings. Clinically applicable and green synthesis-based supercritical carbon dioxide (scCO2) technologies, melt electrowriting (MEW), and solvent-free processing techniques are the main topics of this study for a critical analysis of biodegradable polymer scaffold production techniques. Scaffold structure-property correlations, polymer selection and interactions, production procedures, the benefits and drawbacks of existing fabrication technologies, and sustainability issues are discussed in detail. It aims to contribute a novel perspective and approach to literature by presenting and comparing production-oriented approaches as sustainable and green methods. The challenges in the development of biodegradable tissue scaffolds, along with the significance of green manufacturing techniques, are also revealed. The approach is designed to connect processing factors to scaffold features in addition to evaluating current technologies. This review tries to offer a framework for producing biodegradable polymer scaffolds in a sustainable and clinically implementable context.

Background: The dissolution of oral solid dosage forms is a key determinant of drug bioavailability, yet traditional testing methods do not capture the real-time surface dynamics of drug release. This study introduces a novel framework combining surface dissolution imaging (SDi2) with an interpretable, dual-wavelength convolutional neural network (CNN) to predict and understand dissolution behavior. Methods: Eight tablet formulations containing acetylsalicylic acid, sodium salicylate, or salicylamide, combined with either lactose or methylcellulose, were analyzed under two distinct, compendial conditions (pH 1.2 and pH 6.8). Results: Our final CNN model, which synergistically processes spectral images (280 nm for API release and 520 nm for structural changes), temporal data, and formulation composition, accurately predicted dissolution profiles, achieving a coefficient of determination of 0.89 and a root mean square error (RMSE) of 11.57. To overcome the "black-box" nature of deep learning, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret the model's predictions. The analysis revealed that the model focused on tablet edges at 280 nm, consistent with surface dissolution, and on bulk regions at 520 nm, reflecting structural changes including erosion and gel-layer growth. Conclusions: These findings suggest that integrating real-time imaging with explainable AI methods can support better understanding of dissolution processes in pharmaceutical formulation development.

Although ultrasound image segmentation has advanced significantly with deep learning, existing methods still suffer from a lack of prior knowledge guidance, partly due to the low-contrast, speckle-noise-corrupted nature from clinical ultrasound sensors. This paper proposes a novel ultrasound segmentation framework (RPFeaNet) that extracts progressive prompts from a low-to-high level prompt generation mechanism. Furthermore, the high-level prompt-guided feature interaction module (HPGFIM) fuses progressive prompt via Mamba blocks and stage-wise condition injection. The dynamic selective-frequency decoder (DSFD) combines dynamically selecting a strategy with the fusion of high-frequency details to suppress noise and refine edge details. Extensive experiments on six datasets demonstrate that RPFeaNet achieves state-of-the-art performance compared to existing methods, validating its strong generalization and robustness across diverse clinical ultrasound scenarios.

Background/Objectives: Wound management presents a substantial clinical challenge due to the rising incidence of chronic wounds, infections, and the limitations of conventional dressings in creating an ideal healing microenvironment. This review aims to provide a comprehensive overview of advanced smart hydrogel platforms designed to improve wound healing outcomes, focusing on their capacity to respond adaptively to physiological and external stimuli. Methods: This article analyzes the core characteristics of smart hydrogels, specifically examining stimuli-responsive systems (pH, temperature, enzyme, light, and electricity). The review evaluates advanced configurations-including injectable, self-healing, and 3D-printable systems-and functionalized hydrogels integrated with antimicrobials, drugs, and nanocomposites. Additionally, essential characterization methodologies, biological assessments, and regulatory considerations for clinical translation are synthesized. Results: The literature, which is predominantly preclinical in nature, indicates that functionalized hydrogels significantly enhance tissue regeneration, angiogenesis, and infection control compared to traditional methods. Conductive hydrogels utilizing bioelectrical signals show particular promise in accelerating the healing process. While current clinical applications and commercial products demonstrate efficacy, significant barriers remain regarding large-scale manufacturing and regulatory approval. Conclusions: Smart hydrogels represent a transformative approach to precision wound management, offering superior adaptability and therapeutic delivery. To achieve widespread clinical adoption, future research must address manufacturing scalability and focus on emerging trends, such as the integration of biosensors and AI-powered monitoring systems, to create fully intelligent wound care solutions.

BackgroundDetecting breast cancer, especially identifying microcalcifications in mammograms, is challenging due to the need for high sensitivity and efficient processing. This study presents a novel algorithm, Sigmoidal Slope Analysis and Aspect Ratio Evaluation (SAAR), designed for real-time application on edge devices. By employing a multi-step adaptive process with sigmoidal functions, SAAR enhances intensity contrast and prioritizes regions of interest, enabling fast, accurate detection of microcalcifications.ObjectiveThis study aims to develop and validate an efficient, edge-device-compatible method for detecting microcalcifications in mammographic images. The goal is to provide a tool that enhances diagnostic efficiency through real-time processing, thereby supporting early breast cancer detection in both clinical and remote settings.MethodsThe SAAR algorithm utilizes an adaptive slope detection technique based on the sigmoid function, dynamically adjusting to local intensity features. This approach allows for greater adaptability to image variations. The algorithm prioritizes regions of interest through a multi-step adaptive process, enhancing intensity differences to focus on potential microcalcifications.ResultsTesting on established mammography databases, such as MIAS, demonstrates the algorithm's effectiveness, with improved sensitivity compared to conventional methods. Designed for edge devices, the algorithm leverages their real-time processing capabilities, offering lower latency and enhanced privacy.ConclusionsThe integration of SAAR with edge devices represents a promising advancement in breast cancer detection. The adaptive nature of SAAR, coupled with the real-time processing capabilities of edge devices, provides a robust solution for enhancing microcalcification detection efficiency and sensitivity in mammography.

Phosphate (PO4) pollution in irrigated catchments and their return-flow and drainage networks threatens water quality and agricultural sustainability, particularly under conditions of intensive fertilization and shallow groundwater. This study presents a predictive approach to estimate PO4 concentration using a Generalized Additive Model (GAM) based on daily monitoring data from the Akarsu Irrigation District in Türkiye's Lower Seyhan Plain. Here, the modeled variable is PO4 in irrigation return-flow/drainage water, measured at the main drainage outlet (L4), which integrates excess irrigation water that has passed through the agricultural landscape and collected surface runoff and subsurface drainage. Downstream of L4, drainage water is conveyed by the main drainage channel; part is reused for irrigation, and the remainder flows toward lagoon and wetland areas and ultimately the Mediterranean Sea. The dataset comprised 522 daily observations from the 2022-2023 water years and included nitrate (NO3), nitrite (NO2), electrical conductivity (EC), pH (hydrogen ion activity), flow rate (Q), and precipitation (P) as predictors. Despite weak pairwise correlations of PO4 with individual variables (maximum r = 0.1293 with NO3), the GAM captured nonlinear multivariate relationships and produced good agreement between predicted and measured PO4 at the L4 outlet (mean squared error (MSE) = 0.019966; root mean squared error (RMSE) = 0.1413 mg L-1; mean error = -0.00457 mg L-1; error SD = 0.14136 mg L-1), indicating minimal bias and stable performance. In benchmark comparisons using identical inputs and the same time-structured validation design (80/20 split; random splits were used only for sensitivity analysis), the GAM substantially outperformed linear regression (LR), artificial neural network (ANN), and support vector machine (SVM), which showed very low predictive skill (R2 ≈ 0.03-0.05). Predictive performance was evaluated primarily using error-based metrics; R2 was reported only as a goodness-of-fit measure. The L4 outlet drains an intensively managed agricultural catchment dominated by irrigated cropland. Model fit, expressed as explained variance values (training R2 = 0.832; testing R2 = 0.788), indicated consistent performance without evidence of substantial overfitting. Overall, the findings demonstrate that GAM-based estimation can reliably reproduce both peak and moderate PO4 concentrations and serve as a practical screening tool for nutrient monitoring at irrigated drainage/return-flow outlets. By leveraging routinely monitored variables, the model can reduce the frequency of laboratory PO4 assays-often requiring additional reagents, consumables, and handling time-thereby lowering analytical workload and spectrophotometric operating time while enabling near-real-time assessment of PO4 dynamics. These results support the use of data-driven estimation to inform nutrient management and reduce eutrophication risk in irrigated catchments by monitoring drainage exports. Phosphate pollution in irrigation areas, particularly in regions with shallow groundwater and intensive agriculture, poses serious environmental and agricultural risks, including eutrophication and water-quality degradation. Conventional methods for phosphate monitoring are often time-consuming, costly, and spatially limited, making them unsuitable for real-time applications. Furthermore, the complex, nonlinear interactions between phosphate concentrations and environmental variables, including nitrate, nitrite, pH, EC, flow rate, and precipitation, challenge traditional predictive approaches. While various machine learning models have been explored for phosphorus prediction, their computational demands and overfitting risks often limit their field-level applicability. Therefore, this study aimed to develop a robust, efficient, and interpretable method for predicting phosphate concentrations using a GAM and leveraging daily environmental data collected in a Mediterranean irrigation district in Türkiye. Daily water samples were collected at the outlet of the L4 agricultural catchment in the Akarsu Irrigation District (AID) on the Lower Seyhan Plain, Türkiye, during the 2022 and 2023 water years. The area is characterized by intensively managed irrigated cropland and shallow groundwater conditions. A total of 522 daily observations were compiled, including PO4, NO3, NO2, EC, pH, flow rate (Q), and precipitation (P). Laboratory analyses were performed using spectrophotometric methods for nutrients and electrochemical measurements of EC and pH, while discharge data were obtained from an on-site automatic monitoring and sampling system.A GAM was developed to represent nonlinear relationships between PO4 and the predictor variables using penalized smoothing functions. Because the dataset is a daily time series, temporal dependence was addressed by including a smoother for time (date/time index) and by fitting the model with an AR(1) residual correlation structure (GAMM). To ensure realistic model evaluation under temporal dependence, predictive skill was assessed primarily using a time-structured (blocked, contiguous) 80/20 split, with the earlier 80% of observations used for training and the later 20% for testing. To assess robustness to the choice of partition (sensitivity analysis only), we additionally repeated the split-fit-evaluate procedure over 100 independent randomized 80/20 splits. These random-split results are reported as a secondary check and are not interpreted as the main estimate of predictive skill under autocorrelation. Model predictive performance was primarily assessed using error-based metrics (MSE, RMSE, bias/mean error, and error SD), while R2 was reported only as explained variance (goodness-of-fit). Residual diagnostics, including inspection of the residual distribution and autocorrelation (ACF), were used to evaluate model assumptions, stability, and potential overfitting. This study developed a data-driven method for estimating PO4 concentrations at the L4 drainage outlet using a Generalized Additive Model. Although same-day Pearson correlations between PO4 and routinely monitored predictors (EC, pH, Q, P, NO2, NO3) were weak (maximum r = 0.1293 for NO3), the GAM captured nonlinear and conditional multivariate effects. It demonstrated strong agreement between predicted and measured PO4 values. Model performance was evaluated primarily using error-based metrics, yielding MSE = 0.019966; RMSE = 0.1413 mg L-1; mean error (bias) = -0.00457 mg L-1; and error SD = 0.14136 mg L-1. R2 was reported only as explained variance (goodness-of-fit): training R2 = 0.8319; testing R2 = 0.7875. Because the dataset is a daily time series, temporal dependence was addressed by fitting a GAMM with a smooth function of time and an AR(1) residual structure; and generalization was assessed using a time-structured (blocked or contiguous) train-test split to reduce information leakage from autocorrelation. Repeated random 80/20 splits were used only as a sensitivity analysis and showed consistent performance (mean R2 = 0.772, SD = 0.0166 across 100 trials). In benchmark comparisons, the GAM substantially outperformed traditional alternatives (LR, ANN, SVM), which showed very low predictive skill for PO4 (R2 ≈ 0.03-0.05), highlighting the need for a flexible nonlinear structure to reproduce the observed phosphate dynamics. The model reproduced the overall temporal pattern of PO4, while some underestimation remained for the highest short-duration peaks-consistent with the sparse nature of extreme events in the dataset. Overall, the results support the use of the proposed GAM/GAMM framework as an outlet-scale screening tool for near-real-time identification of periods with elevated PO4, thereby helping to prioritize laboratory sampling and monitoring efforts when direct PO4 measurements are costly or intermittent.

Despite making significant advancements, deep learning is still confronted with inherent challenges due to its black-box nature. Existing approaches usually employ explicit data distribution learning methods to enhance interpretability. However, such methods tend to overlook the relational constraints among tokens during the process of interpretation and fall short in natural language understanding tasks. In this paper, we propose a textual white-box transformer for natural language understanding, named TWT, which executes an optimization objective of sparse rate reduction based on token relational constraints. Specifically, the low-rank sparse embedding strategy (LSES) and the label-interacted mapping mechanism (LMM) in the preprocessing layer are used to utilize the feature of natural language. The multi-head subspace self-attention (MSSA) and the token-conditioned iterative shrinkage-thresholding algorithm (T-ISTA) in the transformer layer are employed to maximize rate reduction and sparsify feature representation. Extensive experimental results on four widely used text classification datasets demonstrate that our proposed method performs better than the state-of-the-art baselines, and shows consistent performance while maintaining simplicity and interpretability. Beyond downstream supervised classification, we further investigate a self-supervised pretraining setting for TWT, in which structured textual embeddings are learned without explicit labels, complementing standard transformer architectures for interpretable representation learning in natural language understanding.

The digital transformation of the labor market has increased the number of Online Job Advertisements, which offer valuable data on job requirements and skills. However, extracting skills from this data is challenging because it is unstructured and lacks standardized analysis. Lexicon-based and supervised methods use predefined, labeled skill sets, which limit adaptability. Unsupervised methods often lack robustness and semantic clarity. Large Language Models (LLMs) have opened new possibilities for text analysis, but their use is often ad hoc. To address this, we present the Skill-Oriented Extraction Engine (SKORE), a framework for extracting, normalizing, and categorizing skills from Online Job Advertisements. It combines the semantic power of LLMs with human-in-the-loop approaches. The framework is model-agnostic to support a wide range of domains and data sources. A proof-of-concept case study shows that it can capture both technical and behavioral skills from job-post data. This framework provides information for professionals who wish to enter a particular domain or transition careers. SKORE also assists in creating Online Job Advertisements that reflect the real needs of the market. Its applicability is envisioned for educational institutions (e.g., technical schools and universities) to respond to the market's dynamic nature on an ongoing basis.

Multi-modal approaches to anatomy education improve learners' understanding and motivation. Gaming may enhance learning through improved engagement and motivation. This study explored experiences of learners using a novel anatomy card game. The purpose was to investigate experiences and perceptions of doctor of physical therapy (DPT) learners using the game. Forty learners participated in the game, which involved matching cards to create movement patterns. A survey of 10 statements, scored on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree), evaluated gameplay for: learning regional anatomy, muscle function, innervation, ease of gameplay, group dynamics, and learner preparedness. Open-ended questions explored: learners' feelings during gameplay, anatomy understanding, impact on motivation, and social aspects. Thirty-four learners responded to the survey. Likert scores (mean ± SD) are as follows: understanding regional anatomy 4.29 ± 0.84, understanding muscle anatomy 4.32 ± 0.77, understanding innervations 4.53 ± 0.66, improving group dynamics 4.29 ± 0.87, challenging game play 3.91 ± 1.03, fun to play 4.79 ± 0.41, utility 4.50 ± 0.83, include in course 4.53 ± 0.79, felt prepared to play 4.12 ± 0.77, felt prepared after play 4.29 ± 0.91. Nineteen learners responded to open questions (Q1 n = 4, Q2 n = 3, Q3 n = 4, Q4 n = 8). Key themes identified were: (1) competitive nature/strategizing improved motivation and (2) the social aspect allowed learners to work together. This study showed learners perceived a positive social and motivational impact of the game. Findings suggest interactive gaming methods can foster a more engaging and effective learning environment.

The cellular environment plays a critical role in shaping protein conformations, including aggregated states implicated in disease. One challenge to studying this relationship is that most techniques offering high-resolution insight into the nature of these aggregates cannot be deployed in living cells. Systematic mutagenesis presents an opportunity to bridge this gap but requires general and robust methods to detect protein aggregation across large numbers of variants. Here, we use clickable protein tags to generate FRET pairs in situ that can report protein aggregation in high throughput in living cells. We applied this strategy to probe the nature of cellular inclusions of α-synuclein in a popular yeast model. Our results demonstrate that cellular aggregates of α-synuclein in yeast are likely dominated by protein-membrane interactions, making the aggregation pathway in this cellular model very different than in many in vitro experiments. Furthermore, our comprehensive mutational data reveal the molecular determinants of membrane-induced aggregation. For example, residues that control membrane affinity have a profound effect on membrane-induced aggregation both in vitro and in cells. Furthermore, we discovered that glycine residues, particularly in the central region of the protein, act as gatekeepers to reduce membrane-induced aggregation. Mutational scanning with a clickable protein tag therefore provides high-resolution insights into cellular protein aggregates.

Background/Objectives: Dravet Syndrome (DS) is a severe form of epilepsy that typically manifests in the first year of life and often requires polytherapy with two or more antiseizure medications (ASMs) to achieve adequate seizure control. Whereas the combination of stiripentol (STP) and cannabidiol (CBD) has demonstrated clinical efficacy, it presents significant formulation challenges due to the low aqueous solubility and poor oral bioavailability of both compounds. Furthermore, the high daily dosages of STP (approximately 50 mg/kg/day or higher) and the oily nature of conventional CBD formulations often hinder patient compliance, as pediatric patients frequently reject these treatments due to unfavorable organoleptic properties. Methods: Nanostructured lipid carriers (NLCs) containing STP and CBD suspended in an aqueous medium were developed. The formulation was optimized using Response Surface Methodology (RSM) and subjected to comprehensive in vitro and in vivo characterization. Results: The optimized formulation exhibited a mean particle size of 175.3 nm, a polydispersity index (PDI) of 0.232, a zeta potential of -8.35 mV, and an encapsulation efficiency greater than 99% for both drugs. Physicochemical characterization via atomic force microscopy, differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy revealed spherical nanoparticles without aggregation, with the drugs molecularly dispersed within the lipid matrix. Both STP and CBD showed sustained release profiles and demonstrated oral pharmacokinetic profiles that were comparable or superior to current commercial products. Conclusions: This novel formulation represents a promising therapeutic alternative for DS, enabling the co-administration of STP and CBD while potentially enhancing CBD bioavailability and treatment adherence in pediatric populations.

Early-stage diagnosis of paroxysmal atrial fibrillation (PAF) is challenging owing to its asymptomatic nature. However, the genetic factors underlying PAF and predictive utility of polygenic risk scores (PRSs) for PAF in Asian populations remain elusive. We aimed to explore the PAF-associated genetic variants in a Japanese cohort and evaluate the predictive performance of PAF-specific PRSs. This study included 2,604 participants. Following exclusion, quality control, and genotype imputation, a genome-wide association study (GWAS) was conducted. The predictive performance of 30 sets of PRS models constructed across various thresholds was evaluated using three machine learning methods. Model performance was assessed using area under the curve (AUC) and SHapley Additive exPlanations (SHAP). The GWAS using 1,038 PAF cases and 744 controls identified 82 genome-wide significant variants (P&#x2009;<&#x2009;5&#x2009;&#xd7;&#x2009;10-8), all on chromosome 4q25. Of these, 80 variants clustered upstream of PITX2, and two were located in LINC01438. Fine mapping identified two independent intergenic signals, with rs2200732 as the lead single-nucleotide polymorphism. The best PRS-only model achieved an AUC of >0.70, which was improved up to 0.737 in additive models incorporating both PRS and clinical variables. SHAP analysis consistently ranked PRS as the most influential predictor among the clinical variables included in this study. These results suggest that genetic risk, particularly at the established 4q25/PITX2 locus, contributes substantially to PAF susceptibility in this Japanese cohort and that PRS may improve early risk stratification when integrated with clinical risk factors.

Sepsis remains a major global health challenge due to its complex pathophysiology and limited therapeutic options. Nanomedicine offers innovative strategies to address these limitations by enabling diverse nanoparticle designs and mechanisms that modulate the septic response. This review examines the dynamic interactions between nanoparticles and the immune system, with a focus on how protein corona formation shapes nanoparticle behavior, biodistribution, and therapeutic efficacy. Disease-specific protein corona profiles can serve as pathology "fingerprints" for diagnosis and targeted delivery, and their controlled formation is now emerging as a therapeutic tool rather than only a diagnostic readout. While the protein corona is a spontaneous biomolecular layer, its composition can be rationally steered to support defined therapeutic goals. In this context, decoy nanoparticles are engineered to sequester pathogens or inflammatory mediators, such as cytokines, histones, and neutrophil extracellular traps, thereby mitigating inflammation and tissue damage. This review discusses how protein corona engineering can potentiate decoy strategies in sepsis diagnosis and therapy, highlighting key platforms including macrophage&#x2011;like nanoparticles that neutralize endotoxins and cytokines, histone&#x2011;binding hydrogels, and mesoporous silica nanoparticles that scavenge cell&#x2011;free DNA and inhibit Toll&#x2011;like receptor activation. We also address how Artificial Intelligence can improve prediction of protein corona dynamics and identification of disease&#x2011;specific protein signatures, enabling more personalized nanodecoy design. Given the highly dynamic and heterogeneous nature of sepsis, characterized by evolving circulating mediators and protein profiles, integrating protein corona control with decoy mechanisms offers a multifaceted route to limit immune dysregulation, enhance drug delivery, and reduce organ damage, paving the way toward precision nanomedicine in sepsis.

Background/Objectives: Glioblastoma multiforme (GBM) is the most infiltrative, treatment-resistant, and deadly brain tumor in adults. Given the extremely malignant phenotype of the GBM cells, the high intratumoral heterogeneity, and the limited efficacy of the vast majority of chemotherapeutics due to the restrictive nature of the blood-brain barrier, GBM remains largely incurable. Methods: Utilizing the U87, U251, and T98G GBM cell lines, diverse in vitro approaches (Western blotting, quantitative real-time PCR, flow cytometry, immunofluorescence, Luc-reporter analysis, microscopic examination, and scanning electron microscopy), and pharmacological inhibition, we investigated for the first time the cell death decisions in the GBM cells in response to the LCS1269 treatment. Results: We showed that LCS1269 collapsed the mitochondrial potential and triggered both intrinsic and extrinsic apoptosis. Importantly, our findings demonstrated that LCS1269-mediated apoptosis was paralleled by an induction of both MLKL-dependent necroptosis and caspase-3/GSDME-dependent pyroptosis. Using a combination of specific inhibitors, we further demonstrated that apoptosis, necroptosis, and pyroptosis provoked by LCS1269 occur simultaneously and orchestrate a peculiar form of programmed cell death, which is known as PANoptosis. We subsequently found that LCS1269-induced PANoptosis may be initiated either through the RIPK1-PANoptosome alone or through the integrated ZBP1-, AIM2-, and RIPK1-PANoptosomes. Additionally, we revealed that LCS1269-mediated PANoptosis may be closely related to micronuclei formation. Conclusions: Taken together, our results confirm that LCS1269 is a promising anti-glioblastoma agent that is capable of effectively promoting GBM cell death via PANoptosis.

Background/Objectives: Maziren-Wan (MZRW) is a traditional herbal prescription that has been used for the treatment of chronic constipation and is currently available in the form of granules or decoctions. Given its multi-component nature and various dosage forms, evaluating the chemical consistency of MZRW preparations is important for pharmaceutical quality assessment. The aim of the present study was to compare formulation-dependent chemical profiles of different MZRW preparations using a multi-component analytical approach. Methods: An excipient-free reference extract and two commercially available MZRW extract granule products were analyzed using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method operating in multiple reaction monitoring mode. Thirty marker compounds derived from the constituent herbs were simultaneously quantified, and their levels were statistically compared among the preparations. Results: Quantitative analysis revealed formulation-dependent variation in the abundance of several marker compounds. Amygdalin and magnoloside A exhibited markedly higher levels in the excipient-free reference extract than in the commercial granule products, whereas sennoside A showed relatively consistent levels across the preparations. Conclusions: The results indicate that MZRW preparations sharing an identical herbal composition can exhibit formulation-dependent differences in chemical profiles. Comparative evaluation based on multiple marker compounds may provide useful information for assessing chemical consistency and supporting quality assessment of MZRW preparations formulated under different conditions.

Background: The key parameters determining the bioavailability of an active pharmaceutical ingredient are its solubility/dissolution rate in physiological fluids and permeability across biological membranes. Highly accurate in vitro prediction of bioavailability is a key issue that typically arises during the development of new drug formulations and the improvement of existing ones. Objectives: The objective of the present work is to study the dissolution/release and permeation of olanzapine (OLZ) from two- and three-component solid dispersions (SDs) with sulfobutylether-&#x3b2;-cyclodextrin (SBE-&#x3b2;-CD) and several pharmaceutical adjuvants as solubilizing agents. Methods: Solid dispersions were prepared by mechanical grinding and characterized with X-ray Phase analysis (PXRD), Fourier Transform Infrared (FTIR) and Raman spectroscopy, Differential Scanning Calorimetry (DSC), and Scanning Electron Microscopy (SEM). Results: Raman spectroscopy was shown to be the best for revealing the interactions of OLZ with SBE-&#x3b2;-CD and &#x3b3;-aminobutyric acid (GABA) in the three-component SD. The kinetic dependences of OLZ release and diffusion through the cellulose membrane were thoroughly described by quantitative parameters and classified according to the drug release mechanism. Significant improvement of release rate, OLZ concentration, and permeation with SDs compared to the pure OLZ was demonstrated. Conclusions: It was shown that the selected dispersions were stable when stored under normal conditions but underwent changes upon exposure to elevated temperature and humidity. The nature of these changes was determined by the properties of the components and their mutual interactions.

Background and Objectives: Low back pain (LBP) is a leading cause of disability worldwide and is frequently associated with intervertebral disc degeneration (IVDD). Current therapeutic strategies are primarily symptomatic and do not restore native disc biology, largely due to the avascular nature of the intervertebral disc and the hostile inflammatory and mechanical microenvironment that characterizes degeneration. The aim of this study is to provide an updated and clinically oriented overview of the pathophysiology of IVDD and to evaluate the current evidence on mesenchymal stem cells (MSCs) and platelet-rich plasma (PRP)-based therapies. Materials and Methods: A focused narrative literature review was performed to evaluate current evidence on MSC- and PRP-based therapies for intervertebral disc degeneration (IVDD). The search was conducted in PubMed. Only studies in English were considered eligible. Results: Mesenchymal stem cells (MSCs) demonstrated regenerative and immunomodulatory effects primarily through paracrine mechanisms, enhancing extracellular matrix synthesis and reducing inflammation and apoptosis. MSC-derived extracellular vesicles emerged as a promising cell-free alternative, potentially overcoming limitations related to cell survival and safety. Platelet-rich plasma (PRP) showed anabolic and anti-inflammatory properties, promoting disc cell proliferation and matrix production, particularly in early-stage degeneration. Clinical studies, including randomized trials, reported significant improvements in pain and function for both MSC and PRP therapies, with favourable safety profiles. However, heterogeneity in treatment protocols and limited long-term data remain significant limitations. Orthobiologic therapies represent a minimally invasive option for patients with discogenic low back pain refractory to conservative treatment. Patient selection is crucial and should consider degeneration stage, disc viability, and clinical presentation. PRP is primarily indicated in early-stage degeneration (Pfirrmann II-III), whereas MSC-based therapies may be considered in selected patients with more advanced but still viable discs. Based on current evidence, a stepwise approach is proposed, progressing from conservative management to PRP, MSCs, and ultimately surgery. Orthobiologics should be integrated within a multimodal strategy including rehabilitation. Conclusions: MSCs and PRP represent a promising and, eventually, complementary orthobiologic therapies for IVDD. PRP is primarily effective in early degenerative stages as a biologic stimulator, whereas MSCs may provide regenerative benefits in more advanced but still viable discs. Further studies are necessary to standardize protocols and confirm long-term efficacy and safety.

While the Blood-Brain Barrier (BBB) is essential for the protection and function of the Central Nervous System (CNS), it also represents a challenge for drug delivery in the treatment of CNS disorders due to its limited permeability and high expression of efflux transporters. Crossing the BBB becomes even more difficult when dealing with biomolecular therapeutics (e.g., monoclonal antibodies and Antisense Oligonucleotides) due to their hydrophilic nature and high molecular weight. Over the years, different strategies have been developed in order to maximize the ability of biopharmaceuticals to cross the BBB and be delivered to the CNS. Both non-invasive techniques, mainly consisting of developing innovative vectors or using non-conventional routes of administration (e.g., intranasal delivery), and invasive methods, such as intracerebroventricular/intrathecal administration, have been tested individually and in combination. Given the improvements achieved nowadays with both approaches, here, we plan to compare the advances in invasive techniques, such as those based on the use of device-assisted strategies, and the employment of the intranasal route of administration. We are also interested in reporting the applicability of both strategies in the treatment of aggressive forms of cancer, such as glioblastoma, as well as neurodegenerative diseases, in order to determine which technique can be considered a better choice in each specific case.

The environmental and political problems caused by climate change are well known and widely debated, whereas the psychological fears arising from the eco-crisis have been less openly discussed. However, a wealth of empirical data indicates a high prevalence of anxiety and the loss of future perspectives, particularly among young people. The relevant psychological aspects involved are poorly understood. Dealing with eco-related fears is important, as the motivation to work for change depends on the psychological maturity with which multiple challenges of the eco-crisis are faced. The key idea of this study is to propose that the deep-seated fears triggered by the eco-crisis resemble the developmentally early, non-verbal anxieties of human infants. This analogy may help explain why eco-anxiety is difficult to comprehend. Addressing the resistant affects related to eco-crisis may facilitate the acceptance of early, unconscious components of anxiety, thereby reducing its psychological burden. Psychoanalytical perspectives derived from the works of Donald Winnicott, Wilfred Bion, and Sigmund Freud are presented to demonstrate how a psychoanalytical approach can be used to confront their resistance to accepting a crisis and the fears associated with it. The concept of a safe prior was introduced to illuminate the real and developmental nature of the early sense of security, which forms the cornerstone for maintaining psychological safety and cognitive functioning. A brief clinical vignette is presented to highlight how impingement on psychological safety in early infancy can be followed by resistant paranoid fears in adult life, particularly when more mature methods of mastering the experiences of loss are lacking. At the end, the study highlights the sense of omnipotence reflected in the denial exhibited by political leaders who refuse to submit to reality testing in the face of current environmental challenges.