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Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice.

Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip-dating approaches, including fossil data, for inference of time-scaled trees and rates of evolution. However, attention has largely focused on the improvement of models of morphological evolution and other analytical tools with much less discussion about data acquisition itself. Here we review past and current practice for describing and collecting morphological data for phylogenetic inference. We present a systematic review of 164 phylogenetic analyses conducted over the last 35 years and focused on a diverse group of extinct arthropods: trilobites. Trends in increasing matrix size, data type, and coding strategy are evident. Where present, polymorphic characters have been predominantly derived from discretized continuous characters, although increasingly practitioners are utilizing alternative approaches for the treatment of quantitative characters. Not surprisingly, traditional indices that describe character consistency are highly correlated with matrix size but show surprising variation at different taxonomic scales. More recent attempts to describe data quality using information theory imply that characters can have high information content even if data are missing for many tips, providing support against the exclusion of characters because of missing data. In consideration of this, as well as advances in the study of developmental biology and variational complexity, we identify several avenues for increasing the quality and quantity of morphological data going forward.

Phylogeny

Molecular characterization and biological characteristics of a highly pathogenic recombinant ALV-J strain (HUE2023) with cross-clade gp85 recombination.

Avian leukosis virus subgroup J (ALV-J) has undergone extensive diversification into phylogenetically distinct clades, yet whether recombination between these clades within the gp85 envelope glycoprotein generates variants with altered pathogenicity has received little direct investigation. A field strain (HUE2023) was recovered from breeding roosters displaying vascular tumors. The viral genome was sequenced and subjected to phylogenetic and recombination analyses. The three-dimensional structure of gp85 was predicted with AlphaFold3; electrostatic surface potentials and surface hydrophobicity were computed using the Adaptive Poisson-Boltzmann Solver and the Eisenberg hydrophobicity scale, respectively. Pathogenicity and immunosuppressive effects were assessed in Hy-Line Brown chickens. Recombination analysis revealed that HUE2023 is an inter-clade recombinant derived from Clade 1.1 (major parent: JS14NT01) and Clade 1.2 (minor parent: JS09GY3). A single-residue deletion at position 61 within receptor-binding domain 1 (RBD-1), unique to the recombinant, induced a localized conformational rearrangement that generated a concentrated electronegative surface patch and a contiguous hydrophobic pocket not observed in either parental gp85. Animal challenge showed that HUE2023 is highly pathogenic: female chickens in the high-dose group reached only 61% survival and displayed significant growth retardation (P&#x202f;<&#x202f;0.05) together with marked immunosuppression. The recombination in the RBD-1 led to local conformational rearrangement, resulting in a concentrated and negatively charged surface area as well as a continuous hydrophobic pocket, which were never present in any of the parental gp85 sequences. These results indicate that gp85 recombination across clades can yield variants with fundamentally altered receptor-binding surfaces and argue for integrating structural surveillance into ALV-J monitoring programmes.

Animals

Pairwise Comparative Safety and Effectiveness of Anti-TNF Blockers, Vedolizumab, and Ustekinumab During Pregnancy: A Systematic Review and Meta-Analysis.

PURPOSE: Biologic therapies, including tumor necrosis factor (TNF) blockers, vedolizumab (VDZ), and ustekinumab (UST), are generally considered safe during pregnancy in patients with inflammatory bowel disease (IBD), though comparative data remain limited. This meta-analysis examines their safety and effectiveness. METHODS: A systematic search of MEDLINE, EMBASE, CINAHL, Cochrane, and Web of Science was conducted through July 2025. Eligible studies reported maternal or neonatal outcomes in pregnant IBD patients treated with biologics. Studies were pooled using a random-effects model to calculate risk ratios (RRs) with 95% confidence intervals. Heterogeneity was assessed using I2. Primary outcomes were preterm birth and disease activity; secondary outcomes included pregnancy and neonatal outcomes. RESULTS: Nine observational studies (n&#x2009;=&#x2009;6,054) were included. Compared to TNF blockers, VDZ was associated with a higher risk of preterm delivery (RR&#x2009;=&#x2009;1.35, 95% CI 1.04-1.75, I2&#x2009;=&#x2009;0%) and active disease (RR&#x2009;=&#x2009;1.55, 95% CI 1.01-2.40, I2&#x2009;=&#x2009;50%). UST was associated with a higher risk of active disease (RR&#x2009;=&#x2009;1.30, 95% CI 1.06-1.60, I2&#x2009;=&#x2009;0%) and congenital anomalies (RR&#x2009;=&#x2009;2.08, 95% CI 1.30-3.32, I2&#x2009;=&#x2009;0%) compared to TNF blockers. Compared to UST, VDZ was linked to increased risks of preterm birth (RR&#x2009;=&#x2009;2.60, 95% CI 1.03-6.57, I2&#x2009;=&#x2009;0%) and low birth weight (RR&#x2009;=&#x2009;2.38, 95% CI 1.01-5.60, I2&#x2009;=&#x2009;0%). No significant differences were observed for live births, abortions, hospitalizations, or neonatal infections. CONCLUSION: TNF blockers showed a favorable safety and effectiveness profile, VDZ and UST performed broadly similar, and all three biological classes appeared compatible with safe use in pregnancy to maintain effective disease control. Observed differences reflect that VDZ and UST cohorts likely had longer disease duration, prior biologic exposure, and more active disease. The results of this meta-analysis support the continuation of biologic therapy for disease control in pregnant patients with IBD. Treatment decisions should be individualized and tailored to each patient's clinical context.

Female

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase

Systemic biomarkers of treatment response to methotrexate in people with painful knee osteoarthritis: A biological substudy of the PROMOTE randomised controlled clinical trial.

OBJECTIVE: Stratification of therapeutic responses may help identify efficacious therapies for osteoarthritis (OA). In the PROMOTE randomised trial, participants with elevated baseline high-sensitivity C-reactive protein (hs-CRP) showed greater pain reduction after methotrexate treatment. We set out to interrogate a broader panel of serum/plasma inflammatory response markers relevant to methotrexate actions as potential biomarkers of therapeutic effect. Our objectives were to: (i) characterize changes in these systemic markers during methotrexate treatment; determine whether (ii) baseline levels or (iii) changes in any marker during treatment were associated with treatment response; and (iv) compare these findings with the more established clinical inflammatory marker, hs-CRP. DESIGN: Plasma/serum samples from participants in PROMOTE's biological substudy were analysed for 35 inflammatory markers at baseline (pre-treatment) and at 6-months (post-treatment), by MesoScale V-plex multiplex assay. Those with paired biological and clinical data at both baseline and 6-months were included in the substudy analysis set. Relationships between markers and overall data structure were assessed by Pearson correlation and Principal Component analysis. Associations between markers (baseline levels or change over time) and change in average knee pain severity in past week (numerical rating scale, NRS) were evaluated by univariable linear regression, adjusting for baseline age, sex, and body mass index. Least Absolute Shrinkage and Selection Operator (LASSO) regression with bootstrap resampling enabled marker selection. Benjamini-Hochberg correction adjusted for multiple testing (Padj). RESULTS: 87 participants with paired blood marker and clinical data were eligible for substudy analysis. 18/35 markers were quantifiable and analysed. Systemic IL-8 and TNF-&#x3b1; levels decreased (Padj=0.015, 0.048 respectively) while IL-15 increased (Padj=0.033) with methotrexate treatment over 6-months. Analysing within this active treatment randomised arm, higher baseline IFN-&#x3b3; was associated with greater reduction in NRS pain change (0.66 [0.01, 1.31], P=0.047), as was decreasing TNF-&#x3b1; over 6-months (2.25 [0.00, 4.5], P=0.049). LASSO identified higher IFN-&#x3b3;, lower plasma IL-15 and IL-16, and younger age as the most important baseline predictors of pain improvement. hs-CRP was highly selected by LASSO for treatment response in both arms. In a secondary univariate treatment arm-by-biomarker interaction analysis, of the 19 markers, only hs-CRP showed consistent effects in adjusted models (at baseline, coeffic. 2.34 [0.53, 4.15], P=0.001; change over 6-months, (0.36 [0.06, 0.66], P=0.018). CONCLUSIONS: Blood measurement of IFN-&#x3b3;, TNF-&#x3b1;, IL-15 and IL-16 as well as hs-CRP could act as potential markers to stratify the treatment response by average knee pain to methotrexate in knee osteoarthritis.

Humans

Impact of Commercial Artificial Intelligence on Radiologist Reading Time for Pulmonary Nodule Evaluation at Chest CT.

Background Chest CT is a primary method for identifying pulmonary nodules, yet interpreting scans remains time-intensive and demanding. Currently, artificial intelligence (AI) is expected to reduce reading times, but the effect of AI on reporting times in this setting is unknown. Purpose To evaluate the impact of a commercial AI software on radiologists' reading time for pulmonary nodule assessment on chest CT scans within a real-world clinical setting. Materials and Methods This retrospective study included patients who underwent chest CT examinations at a tertiary medical center between September 2021 and May 2024. The study period was divided into pre- and post-AI phases. The primary outcome was radiology reporting time. The association between AI implementation and reporting time was evaluated using a multivariable parametric Weibull shared frailty survival model adjusted for reader function, examination type, patient location, and requesting specialty, with clustering at the radiologist level. Interaction analyses assessed heterogeneity across prespecified subgroups. An exploratory extrapolation estimated projected workforce and financial impact. Results This study included 19&#x2009;433 patients (mean age, 62 years &#xb1; 14.2 [SD]; 21&#x2009;814 men; 39&#x2009;323 chest CT examinations, 19&#x2009;190 pre-AI, and 20&#x2009;133 post-AI). AI implementation was associated with faster report completion (adjusted hazard ratio, 1.17; 95% CI: 1.14, 1.21; P < .001). The adjusted median reporting time decreased from 21.3 minutes pre-AI to 18.2 minutes post-AI (14.6% reduction; P < .001). Heterogeneity was observed across reader function (P < .001), examination type (P = .048), and requesting specialty (P = .03). The largest relative reductions were observed for CT thorax electrocardiogram-gated examinations (-41.1%; P < .001) and thoracic radiologists (-25.0%; P < .001), whereas emergency department examinations showed increased median reporting time (7.1%; P < .001). At institutional scan volumes (approximately 20&#x2009;000-22&#x2009;000 chest CT examinations annually), exploratory modeling suggested an approximate reduction of 0.5 full-time equivalent radiologist workload. Conclusion Implementation of commercial AI-assisted pulmonary nodule assessment on chest CT scans reduced radiologist reporting time in a real-world clinical setting. &#xa9; The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Iwasawa in this issue.

Humans

Facilitating Thought Progression via a Gamified Mobile Application for Depression: Possible Mediators of Outcomes.

Mobile health interventions represent a scalable and accessible alternative to traditional therapy, which often is out of reach due to high costs and societal stigma. Rumination is considered a key mechanism of emotional disorders and represents a potential treatment target for digital health interventions. The current study investigated the role of rumination as a mediator of the reduction in depression and anxiety reported after the use of a gamified mobile app based on the Facilitating Thought Progression (FTP) framework. One hundred-one adults with mild to moderate depression were randomized to the FTP intervention or a waitlist control group and completed weekly assessments of depression, anxiety, and rumination over 8 weeks. Multilevel structural equation modeling revealed that reduction in rumination significantly mediated decreases in depression and anxiety in the intervention group but not in the waitlist condition. These findings suggest that the FTP app targeted rumination and further highlights its role as a critical target for interventions for depression and anxiety.

Adult

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence

Long-term (>7-year) parental consumption of genetically modified maize (Cry1Ab/Cry2Aj and EPSPS) induces no adverse sperm DNA methylation alterations across two generations of cynomolgus monkeys.

This study assessed the long-term safety of genetically modified (GM) maize from a male reproductive perspective, using a non-human primate model. We analyzed the sperm DNA methylation profiles in cynomolgus monkeys fed GM maize, non-GM parental maize, or a conventional diet over two generations (F0/F1). Whole-genome bisulfite sequencing (WGBS) revealed no significant differences in global methylation levels among groups. The identified differentially methylated regions (DMRs) were short, enriched in non-regulatory genomic areas, and did not cluster after treatment. Functional enrichment analysis showed that DMR-associated genes were consistently involved in the same core biological pathways (e.g., mTOR and Wnt signaling) across all dietary comparisons. These findings indicate that GM maize consumption did not induce specific adverse epigenetic alterations in sperm, with the observed changes reflecting common physiological adaptations to dietary variations rather than GM-related effects.

Animals

Practical Saudi Guidelines on management of moderate-to-severe psoriasis: 2026 update.

BACKGROUND: Psoriasis is a chronic, immune-mediated inflammatory skin disease that affects approximately 5.3% of the population in the Kingdom of Saudi Arabia (KSA). Thus, we aim to develop updated evidence-based clinical practice guidelines for the management of adults and pediatric patients with moderate-to-severe plaque psoriasis in the KSA. METHODS: These guidelines followed the "Grading of Recommendations, Assessment, Development, and Evaluation" (GRADE) methodology. We conducted a systematic literature review of PubMed, EMBASE, and the Cochrane Library for high-quality evidence published between 2020 and 2026. The panel developed 31 PICO questions that address key treatment considerations for moderate-to-severe psoriasis. RESULTS: We established 27 evidence-based recommendations and 4 good-practice statements addressing key aspects of moderate-to-severe psoriasis management. These guidelines strongly recommend adopting the Psoriasis Area and Severity Index (PASI) 90 as the primary treatment goal over PASI 75. For adult patients, the guidelines recommend biologic therapies, including interleukin (IL)-17 inhibitors, IL-23 inhibitors, IL-12/23 inhibitors, and tumor necrosis factor (TNF)-&#x3b1; inhibitors, for better disease control. For pediatric patients, the guidelines recommend early initiation of biologic therapy, with etanercept, secukinumab, ixekizumab, and adalimumab as preferred options. CONCLUSION: These Saudi national guidelines offer a comprehensive, evidence-based framework for managing moderate-to-severe psoriasis in adults and pediatric patients.

Humans

First insights into the role of evolutionary history in shaping venom composition of Vipera ammodytes.

Understanding intraspecific venom variation requires distinguishing the contributions of neutral population history from natural selection. This study aims to determine whether venom variation in Vipera ammodytes species complex is structured across eight phylogenetic groups. Despite a complex evolutionary history, venom composition did not differ among phylogenetic groups within the analytical framework used, suggesting that shared ancestry alone does not explain venom variation. Whether local adaptation to environmental conditions explains the observed variation remains an open question for future studies.

Animals

Molecular mechanisms of natural de novo shoot organogenesis and their applications.

Natural de novo shoot organogenesis (DNSO) is the spontaneous regeneration of shoots from wound sites outside the shoot apical region through endogenous developmental programs. This regenerative capacity enables plants to recover from severe tissue damage by re-establishing the shoot-root axis. Here, we review current knowledge about the molecular mechanisms of natural DNSO, focusing on transcriptomic and physiological studies in model plants. Accumulating evidence suggests that natural DNSO proceeds through three sequential phases: (i) early wound responses, characterized by the activation of the WIND1-ESR1 module and the establishment of apical-basal auxin asymmetry; (ii) cellular proliferation driven by metabolic and cell-cycle reprogramming; and (iii) cytokinin-mediated establishment of shoot apical meristem identity. We also discuss how these mechanistic insights have been harnessed for practical applications, including tissue culture-free transformation systems such as the cut-dip-budding (CDB) method, and developmental reprogramming strategies that employ ectopic expression of developmental regulator (DR) genes to induce DNSO in otherwise recalcitrant species. Together, these advances illustrate how understanding natural regeneration can guide the development of simplified, broadly applicable plant transformation technologies.

Plant Shoots

Meta-analysis of source identification and apportionment in soil: A systematic review of analytical procedures, receptor modeling, and environmental applications.

Soil pollution poses significant risks to ecosystems and human health, necessitating accurate source identification and apportionment to guide mitigation strategies. This systematic review evaluates the application of Positive Matrix Factorization (PMF) and other receptor models in soil pollution studies, focusing on analytical procedures, tracer indicators, and environmental applications. This review aims to provide a comprehensive framework for conducting soil source apportionment studies, aiding policymakers in designing effective, region-specific environmental management strategies by compiling global trends and methodological insights. The study addresses sampling protocols, emphasizing representativeness and quality control. Data from 500 peer-reviewed publications highlight the dominance of research in China, Eastern Europe, and South Asia, with agricultural soils being the most frequently studied. Key findings reveal that traffic emissions (20.8 %) and industrial activities (19.4 %) are the primary global contributors to soil contamination, with regional variations such as coal combustion in cold climates and agricultural inputs in developing regions. Policy recommendations include stricter industrial regulations, sustainable agricultural practices, and targeted remediation efforts based on source-specific risks.

Soil Pollutants

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans

Applications of metal-organic frameworks in smart packaging for food freshness indication: a comprehensive review.

Smart packaging is extensively studied for its multifunctional capabilities in antimicrobial activity, preservation, and atmosphere modification. Recently emerged metal-organic frameworks (MOFs) freshness-indicating packaging becomes a key research direction in smart packaging owing to its distinctive functions and physicochemical properties. As multifunctional materials, the unique porous structure and tunable properties of MOFs provide a distinctive approach for developing food packaging applications dedicated to food freshness indication. Existing MOFs-based smart packaging still faces potential safety risks and technical challenges in practical applications, and there remains a lack of integrated discussion that combines synthesis strategies, packaging design, optimization, and safety assessment. This review elaborates on the application of MOFs in freshness-indicating smart packaging, focusing on diverse MOFs synthesis strategies, the formats of smart packaging, types of indicator signals, and qualitative/quantitative analytical methods. It also delves into the methodology concepts of MOFs-based smart packaging and evaluates MOFs safety in food packaging by addressing potential risks. Studies show that MOFs-based smart packaging achieves qualitative and semi-quantitative analysis of food freshness through multiple signal modalities such as visible color change, fluorescence, and photothermal effects. This review emphasizes that safe MOFs design is critically important and should comply with the overall migration limit of <10 mg/dm2 specified in Regulation (EC) No 1935/2004, lanthanide element limit of <0.05 mg/kg, and FDA threshold of 1.5 &#x3bc;g/person/day. Comprehensive safety assessment and intelligent sensing platforms will constitute pivotal directions for advancing MOFs-based smart packaging toward practical application.

Food Packaging

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals