This study presents a supervised machine learning approach using a Random Forest classifier to detect ischemia-reperfusion injury (IRI) in kidney tissue based on intravital two-photon microscopy data. A rodent model of unilateral renal IRI was used, with 30 min of pedicle occlusion followed by 15 min of reperfusion. Continuous imaging captured nuclear (Hoechst 33342), vascular (FITC-dextran), and mitochondrial (TMRM) changes in real time. From extracted video frames, 2000 manually segmented regions of interest (ROIs), 1000 control and 1000 injured segments, were analyzed using texture analysis. Five textural features were used as input: angular second moment (ASM) and inverse difference moment (IDM) from gray-level co-occurrence matrix (GLCM); short run emphasis (SRE) and long run emphasis (LRE) from run length matrix (RLM); and HH wavelet coefficient energy (EnHH) from discrete wavelet transform (DWT). All showed significant differences (p < 0.001) between injured and control tissue. The Random Forest model achieved 79.8% accuracy, a macro F1-score of 0.79, a Matthews Correlation Coefficient of 0.5959, and an ROC AUC of 0.83. These findings highlight the potential of AI-based texture analysis to detect early nuclear and vascular alterations during IRI. Future work should expand datasets, include 3D analyses, and incorporate multimodal imaging for greater generalizability.

Auditory evoked potentials (AEPs) are electroencephalographic (EEG) responses to auditory stimuli and are frequently used to evaluate auditory processing and cognitive integrity. Interpretation of AEPs today predominantly relies on standard linear techniques such as time-domain averaging and frequency-domain spectral decomposition. These approaches may not always capture nonlinear, nonstationary, and scale-free characteristics of EEG signals; therefore, in contemporary neurophysiology research, there may be a need for the utilization of additional nonlinear frameworks. Fractal analysis may be a powerful tool for the quantification of subtle changes in EEG and AEP complexity, irregularity, and variability. This approach is often overlooked due to methodological and conceptual limitations but nevertheless holds significant potential in revealing alterations in geometrical and spatial complexity of AEPs under various physiological conditions. Here, we discuss potential applications and shortcomings of fractal AEP analysis, as well as its possible integration with supervised machine learning algorithms. We also focus on novel artificial intelligence-based concepts that could, in theory, utilize the power of fractal AEP and EEG analysis to improve the classification and prediction of neurophysiological processes and phenomena.

Introduction/Aim: We aimed to propose a novel and innovative concept of a support vector machine learning algorithm that employs fractal and run-length matrix indicators of nuclear structure to identify malignant squamous epithelial cells in laryngeal cancer. Material and Methods: Regions of interest in micrographs of laryngeal cancer and chronic laryngitis were analyzed using the box-counting fractal and run-length matrix textural techniques. For each nucleus, we quantified fractal dimension values, lacunarity, long-run emphasis, and short-run emphasis. These features were used as input data for training and testing the support vector machine model in the "Scikit-learn" library for Python. Results: The support vector machine model produced relatively good performance indicators. The classification accuracy of the model was 0.83, indicating its adequate ability to distinguish cancer cells from non-cancer cells in our sample. The F1 score (the harmonic mean of precision and recall) was 0.83, suggesting a relatively good balance between these two metrics. The value of the Matthews Correlation Coefficient for this model was 0.65, which indicated moderate agreement between the predicted and actual labels and balanced performance across the two classes. Conclusion: The proposed model provides a solid foundation for further developing artificial intelligence systems for signal analysis in cancer research. If the limitations of this concept are addressed, future research can focus on developing a more comprehensive machine-learning model for identifying laryngeal epithelial cancer cells.

Traditional Chinese Medicine (TCM) diagnosis, particularly methods like tongue diagnosis, faces significant challenges in subjectivity and scalability. The application of Large Language Models (LLMs) to fundamental TCM tasks, such as syndrome differentiation and prescription generation, is significantly hampered by the difficulty of integrating visual tongue data with clinical text, and by the scarcity of suitable public datasets. To overcome these barriers, we introduce ViTCM-LLM, a novel framework that emulates an expert's diagnostic process by integrating multimodal language modeling with Retrieval-Augmented Generation (RAG). Employing a dual-component architecture, ViTCM-LLM integrates a fine-tuned Qwen2.5-VL model for visual analysis with a Qwen3-based RAG for clinical reasoning. The framework was developed and validated using MedTCM, a new large-scale multimodal dataset that we introduce specifically for advanced TCM research. To properly evaluate our framework's clinical accuracy, which existing metrics fail to capture, we also developed TDEU, a domain-specific evaluation metric. Evaluated using this metric alongside standard benchmarks, our comprehensive experiments on MedTCM demonstrate that ViTCM-LLM significantly outperforms leading models, including GPT-4o and Gemini 2.5 Flash. These findings not only establish the feasibility of a generalizable tongue diagnosis model but also validate the critical role of integrating visual data for advanced TCM diagnostics. The code and data can be found at https://github.com/jw-chae/ViTCM_LLM.

The desire to stay informed about others’ activities and the aversion to missing information shared on social media have become increasingly prominent in contemporary society. This study aimed to investigate the mediating role of personal characteristics in the relationship between Fear of Missing Out (FoMO), psychological distress, and flourishing. A sample of 6,403 participants (65.6

Objective. Thyroid dysfunction represents common disorder occurring very frequently among women of reproductive age, including pregnancy. The aim of this literature review was to determine in which way thyroid function during pregnancy is associated with GDM. Design. We conducted review of the literature following the basic principles of literature search. Methods. Two researcher independently searched PubMed in the period of last five years (2018-2023) to identify eligible studies regarding thyroid function and GDM. Results. From 51 papers initially found after the inserting key words in PubMed search field 30 were excluded after the title and abstract review. After reading full text of 21 articles, 15 were included in the review. Conclusions. Our review of literature showed not only that two most common disorders during pregnancy were GDM and thyroid dysfunction, but also indicated that they were in positive correlation.

Aim: The escalating demand for corneal transplants significantly surpasses the available supply. To bridge this gap, we concentrated on ethical and sustainable corneal grafting sources. Our objective was to create viable corneal scaffolds from preserved slaughterhouse waste. Materials & methods: Corneas were extracted and decellularized from eyeballs that had been refrigerated for several days. These scaffolds underwent evaluation through DNA quantification, histological analysis, surface tension measurement, light propagation testing, and tensile strength assessment. Results: Both the native and acellular corneas (with similar to 90% DNA removed using a cost-effective and environmentally friendly surfactant) maintained essential optical and biomechanical properties for potential clinical use. Conclusion: Our method of repurposing slaughterhouse waste, stored at 4 degrees C for several days, to develop corneal scaffolds offers a sustainable and economical alternative xenograft model.

Developing sustainable and effective treatments for chronic venous insufficiency (CVI) is crucial. In this study, we propose an innovative restorative approach utilizing hydrogels derived from the decellularized extracellular matrix (dECM) of cadaveric vascular tissues, adipose-derived stem cells (ADSCs), and gold nanoparticles (AuNPs). This therapeutic method leverages waste valorization by repurposing discarded cadaveric tissues from slaughterhouse livestock. The dECM hydrogels, enriched with ADSCs and AuNPs, offer a biocompatible scaffold that supports cellular differentiation and vascular integrity. Our approach addresses the limitations of current allo-, auto-, and xenograft methods by enhancing integration and functionality while potentially reducing costs through sustainable practices. This study explores functionalized hydrogel formulation solely generated from agri-food waste, gelation mechanisms, and preliminary cost-effectiveness, presenting a promising new avenue for treating early-stage varicose veins that can ultimately be translated to human models using discarded tissues.

Introduction/Aim: The aim of our study was to create a machine learning model, specifically a random forest model, which uses textural data from liver micrographs to differentiate between normal hepatic tissue and damaged tissue exposed to iron oxide nanoparticles. Material and Methods: Regions of interest in micrographs of hepatic tissue, obtained from mice treated with iron oxide nanoparticles and controls, were analyzed using the gray-level co-occurrence matrix (GLCM) method. The resulting GLCM features were employed as input data for the training and testing of the random forest model using the "Scikit-learn" library in the Python programming language. Additionally, a conventional decision tree model was developed, based on the classification and regression tree (CART) algorithm. Results: The random forest model outperformed the alternative CART decision tree approach in terms of classification accuracy, correctly predicting the class for 73.67% of the instances in the validation ROI dataset. The area under the receiver operating characteristic curve was 0.81, indicating relatively good discriminatory power. The F1 score for the model was 0.74, showcasing fairly good precision and recall, though not perfect. Conclusion: The data obtained from this study may be utilized for further development of artificial intelligence computation systems to identify physiological and pathophysiological changes in hepatic tissue. The results also serve as a starting point for additional research on the automation of histopathological analysis of liver tissue exposed to external toxic agents.

Corneal diseases and injuries, with their substantial global prevalence and adverse effects on quality of life, demographics, occupations, and lifestyles, pose a pressing healthcare challenge worldwide. Limited treatment options are unable to halt the progression of end-stage conditions, where transplantation is the ideal solution. Unfortunately, the high demand and low supply of corneal tissues, donor-recipient mismatches, and host rejections leading to graft failure limit this ideal option. As a result, there is a critical need for alternative interventions. This article aims to establish a bioengineered corneal model generated from the cadaveric decellularized extracellular matrix (dECM) that can repurpose discarded human corneal tissues to potentially increase the supply of transplantable tissues. Comparable studies have primarily focused on reendothelialization and re-epithelialization. Therefore, we hypothesize devising a method to support scaffold reinnervation emanating from prominent nerve plexi spanning the stroma to the epithelium in a patient-centered manner, using peripheral blood mononuclear cells.

This study focuses on the development of machine learning models based on the features of the run length matrix (RLM) and wavelet analyses, with the potential to detect subtle alterations in hepatocyte chromatin organization due to iron oxide nanoparticle exposure. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

Regenerative MedicineAhead of Print CommentaryOpen AccessSlaughterhouse waste: a unique and sustainable source for dECM-based bioinksMeklit G Shibru, Zehara M Ali, Aliyaa S Almansoori, Jovana Paunovic, Igor V Pantic & Peter R CorridonMeklit G ShibruDepartment of Biomedical Engineering & Biotechnology, College of Medicine & Health Sciences, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates, Zehara M AliDepartment of Biomedical Engineering & Biotechnology, College of Medicine & Health Sciences, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates, Aliyaa S AlmansooriDepartment of Biomedical Engineering & Biotechnology, College of Medicine & Health Sciences, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates, Jovana PaunovicUniversity of Belgrade, Faculty of Medicine, Department of Pathophysiology, Dr. Subotica 9, RS-11129, Belgrade, Serbia, Igor V PanticUniversity of Belgrade, Faculty of Medicine, Department of Medical Physiology, Laboratory for Cellular Physiology, Visegradska 26/II, RS-11129, Belgrade, SerbiaUniversity of Haifa, 199 Abba Hushi Blvd, Mount Carmel, Haifa, IL, 3498838, IsraelDepartment of Pharmacology, College of Medicine & Health Sciences, Khalifa University of Science & Technology & Peter R Corridon *Author for correspondence: E-mail Address: peter.corridon@ku.ac.aehttps://orcid.org/0000-0002-6796-4301Department of Biomedical Engineering & Biotechnology, College of Medicine & Health Sciences, Khalifa University of Science & Technology, Abu Dhabi, United Arab EmiratesBiomedical Engineering & Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab EmiratesCenter for Biotechnology, Khalifa University of Science & Technology, Abu Dhabi, United Arab EmiratesPublished Online:15 Feb 2024https://doi.org/10.2217/rme-2023-0194AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail Keywords: 3D bioprintingBioinkdECMslaughterhouse wastesustainable regenerationEnd-stage tissue and organ failure, stemming from an array of factors such as diseases, injuries, and developmental anomalies, has evolved into major economic and healthcare problems. Currently, standard clinical approaches to tackle these issues rely on organ donation. However, the shortage of donors and growing recipient pools limit the reliance on transplantation [1]. In fact, back in 2011, studies estimated that merely a third of patients in need of transplantation would receive one, and the overall likelihood of someone needing a transplant far outweighed their ability to become a donor [2]. Sadly, modern-day statistics fail to provide a better outlook. The rising incidences of debilitating conditions, along with substantial logistical constraints that affect successful transplantation, support trends pointing to a growing mismatch between tissue/organ supply and demand. These pervasive issues emphasize the need for alternative solutions to tackle the substantial global shortage.To address this pressing medical necessity, tissue engineering approaches have emerged as viable solutions. These multidisciplinary fields combine knowledge and technologies from a diverse spectrum of areas, including biology, chemistry, engineering, medicine, pharmacology, materials science and, more recently, artificial intelligence [3]. Their primary goal is to develop treatments and innovations for repairing or replacing damaged tissues and organs [4]. Tissue engineering employs a wide range of techniques to design and create artificial/bio-artificial constructs. This process involves assembling biomimetic materials encompassing scaffolds, cells and signaling molecules to replicate natural tissue architectures and functions, thereby offering promising solutions to meet the growing demand for transplantation [5].One of the most promising techniques within the field of tissue engineering is 3D bioprinting because of its versatility, ease of use, and precision of the fabrication process. 3D bioprinting, also known as additive manufacturing, is a process of joining materials layer-by-layer to build a framework that mimics native tissue architectures. This innovative approach has many applications, such as generating skin grafts for burn victims, cartilage and bone replacements for orthopedic procedures, nervous system repair, cornea replacement and vascularized tissues [6].A key component of adaptive manufacturing is the bioink, which is a specialized natural or synthetic material infused with living cells, growth factors, and other bioactive compounds. A bioink should possess desired physicochemical properties, such as proper biomechanical, rheological, and biochemical characteristics of the target tissues. They are composed of a variety of materials, including natural polymers such as alginate and collagen, synthetic polymers like polyethylene glycol (PEG), and even the decellularized extracellular matrix (dECM) [7].The dECM is typically derived from cadaveric sources, as well as laboratory bovine, ovine, murine, porcine, and simian tissues that share anatomical and physiological similarities with humans, including. In recent years, dECM components have been used in models for biomaterials-based biofabrication [8]. These dECM components can be homogenized into solutions containing bioactive cues that recapitulate a natural cellular environment. Such an environment will synergistically provide physical barriers, anchorage sites, and pathways for cellular growth, migration, and differentiation – essential for morphogenesis and a basic set of fundamental criteria for designing functional human tissues and organs.We recognize the untapped potential of dECM derivatives found in animal tissues from livestock. Domestically raised animals, which are routinely slaughtered for food production, contribute to approximately 150 million tonnes of organic waste annually. This waste is notably rich in key bioink components like collagen, elastin, fibronectin, and hyaluronic acid [9]. As such, repurposing slaughterhouse waste offers a viable and sustainable method for producing hydrogels, tailored to specific tissue needs and cost-effective in nature. These hydrogels are adept at encapsulating desired cells and can be cross-linked or stabilized during or immediately after the bioprinting process, which is vital for achieving the precise shape, structure and architecture of the intended bioprinted construct [10].This commentary underscores the significant aspects of developing bioinks from slaughterhouse waste. It highlights the abundance of available starter materials, outlines formulation and classification techniques, and discusses the derivation of these materials from discarded dECM sources. The repurposing of such waste not only presents a solution to an environmental problem but also unlocks new possibilities in the realm of tissue engineering and regenerative medicine.A vast supply of starter materials for bioink productionDespite advancements in bioprinting technologies, finding suitable bioinks that align with the necessary mechanical, rheological, and biological requirements has remained a challenge to date [11]. The repurposing of slaughterhouse waste presents a promising solution with several advantages, offering a vast supply of tissues and organs. This organic waste from abattoirs is an abundant source that is ideal for biofabrication, supplementing laboratory and cadaveric sources.Financially, utilizing discarded slaughterhouse waste for bioinks is cost-effective compared with current market sources, ranging from $400–1000 for just 3 to 4 ml [12]. This affordability enhances the potential of this method for research and medical applications, providing researchers with ample raw materials for experimentation.However, this approach has several drawbacks as it requires rigorous sterilization to mitigate pathogen and toxin contamination, thorough quality control throughout the production process [13], and comprehensive biocompatibility testing [14]. Despite these challenges, repurposing abattoir tissues/organs for bioinks reduces waste and aligns with the circular economy model. It transforms slaughterhouse byproducts into valuable resources for the biomedical field, offering a sustainable and innovative solution to current bioprinting challenges.With global meat consumption escalating due to rising income levels and population growth, the meat industry generates 150 million tonnes of organic waste annually [10]. This waste predominantly comprises 50–60% of the discarded weights of bovine, ovine, and porcine livestock. Some of these remains are repurposed to create high-value products like animal feeds, fats and oils, fertilizers and biogas [9]. However, a significant amount of this waste is left unutilized, primarily due to cultural, religious, and health considerations. The conventional disposal methods for this waste, including landfilling, incineration and burial, pose substantial environmental challenges [15].To help address the significant waste in the meat industry, researchers are focused on transforming slaughterhouse byproducts into valuable biomaterials and scaffolds for use in regenerative medicine and adaptive manufacturing. This conversion of byproducts into bioinks, as opposed to traditional disposal methods, offers a range of unique advantages for bioprinting. These include sustainability through waste repurposing, efficient utilization of resources and cost–effectiveness. Additionally, these bioinks possess inherent biocompatibility ideal for tissue engineering, allow for customized formulations tailored to specific applications, and reduce reliance on animal-derived resources. Moreover, this approach stimulates innovation in materials science and aligns with the principles of waste reduction and resource optimization central to a circular economy.All of these advantages collectively can contribute to developing a comprehensive catalog of bioinks, each meticulously optimized for constructing cellular microenvironments. These microenvironments are designed to deliver precise biochemical and biomechanical cues critical for cellular functions. Such cues include cell surface recognition, growth, chemotaxis, and responses to mechanical stimuli such as shear stress, stretching, and compression. Additionally, they can mimic the natural matrix's elasticity and rigidity, ensuring the bioinks are tailored to meet the requirements of tissue-specific applications.Bioink formulation & classification techniquesThe bioink, an essential element in adaptive manufacturing technologies, is typically a hydrogel-based solution comprising one or more biomaterials [16,17]. In the bioprinting process, this solution encapsulates living cells, growth factors, and other vital biomolecules, including extracellular matrix components, which are integral for creating functional and mechanically robust constructs [11]. The bioink must exhibit specific biomaterial properties to ensure the printed tissues and organs possess the desired characteristics. These properties include printability, mechanical strength, biodegradability, and modifiable functional groups [18].Printability is influenced by factors such as the viscosity of the solution, surface tension of the bioink, and its ability for self-crosslinking, which is critical for maintaining the integrity of the 3D structure. The hydrophilic nature and viscosity of the bioink also play a significant role in the reliability of the printing process and the effective encapsulation of live cells. Controlled gelation and stabilization are crucial; these refer to the bioink's transition from a liquid to a gel-like state, a process that must be precise and maintained throughout the printing. Furthermore, bioinks must meet biological standards, including biocompatibility and cytocompatibility, ensuring they are safe for living cells and tissues and can support cell survival, proliferation and functionality during and post-printing.Bioinks can be broadly classified into natural and synthetic categories based on composition. Natural bioinks (collagen, agarose, gelatin, alginic acid, hyaluronic acid, chitosan, etc.) have central roles as bioinks for 3D printing tissues and organs due to their ability to mimic the structure of the ECM and their biocompatibility and biodegradability. On the other hand, synthetic bioinks are engineered using artificial polymers like polyethylene glycol (PEG), poly(L-lactic) acid (PLA), poly(lactic-co-glycolic) acid (PLGA) and polycaprolactone (PCL) [19]. While they offer properties such as controllability of mechanical stability, degree of photo crosslinking, pH, and temperature responsivity, which are not present in natural bioinks, synthetic bioinks represent only approximately 10% of the bioinks used in 3D printing [20]. This is because they lack sites for cellular recognition and proliferation, along with the challenges regarding the encapsulation of cells.These solutions can also be categorized as scaffolding or non-scaffolding based on whether they contain a supporting structure that provides cells within the printed construct. While scaffold-based bioinks provide a supportive framework that mimics the ECM, scaffold-free bioinks rely on cell–cell interactions and self-assembly to form a desired tissue. Scaffold-free bioprinting is viewed as a promising approach in tissue engineering because it allows for the replication of native tissues in a shorter timeframe compared with the more conventional method of using bioinks containing cells. These two distinctions play a big role in the applications of bioinks for 3D printing, tissue engineering, and regenerative medicine.Bioink derivation from discarded dECM sourcesFrom these wastes, it is possible to generate tissue-specific and ECM-based bioinks through a technique known as decellularization after unwanted components like fat are removed from the native tissue. Decellularization is the process of removing cellular and nuclear matter from the native tissue using several methods, such as chemical, physical and biological treatments, while minimizing any adverse effects on the composition, biological activity and mechanical integrity of the residual ECM. The first step in preparing bioinks is harvesting tissues from the slaughterhouse waste and then effectively eliminating cells and a minimum of 90% of the native DNA. The decellularization process varies based on tissue type, as well as factors like tissue density, lipid content, and ECM composition. Residual decellularization agents must be removed to prevent cytotoxicity, followed by sterilization with agents like peracetic acid or ethanol. The tissue is then lyophilized and pulverized into small particles using instruments like a cryomill or homogenizer.The resulting dECM powder can be solubilized using pepsin in an acidic environment, with pH adjustment to stop digestion followed by gelation at 37°C [21]. The ECM is an intricate network composed of an array of multidomain macromolecules organized in a cell-specific manner. It also acts as a dynamic repository for growth factors and bioactive molecules, profoundly influencing fundamental cellular behaviors such as proliferation, adhesion, migration, polarity, differentiation and apoptosis. Major components include collagens, proteoglycans, elastin and cell-binding glycoproteins, each with distinct physical and biochemical properties [22].The unique properties of dECM-based bioinks are seen as central to their role in revolutionizing tissue engineering and regenerative medicine. The blend of collagens, proteoglycans, glycoproteins, and bioactive molecules contained within the dECM bioinks ensures both biocompatibility and biomimicry. Since these tissues retain the microenvironment, they promote cell adhesion and foster tissue regeneration via structures such as collagen and fibronectin. Furthermore, their biodegradability stems from the composition of structural molecules, ensuring they are naturally absorbed over time [23]. Their remarkable versatility and ability to be precisely tailored for tissue-specific formulations set dECM-based bioinks apart from other inks used in adaptive manufacturing. Another key element is rheological characterization, which ensures the printability of these bioinks. While components such as glycosaminoglycans and proteoglycans ensure hydration of the ECM and endure compressive forces, fibrous matrix proteins such as collagen and laminin provide resistance to tensile forces [24]. This combination entails significant variations of rheological properties related to low viscosity or poor consistency that translates into limited printability. Consequently, this is associated with poor intrinsic mechanical properties such as low modulus, viscoelasticity and yield stress [25].To solve these problems, researchers use a framework printed with high mechanical strength biomaterials like PCL or silicone rubber, which can be used to maintain the structure of the dECM. Another effective strategy is to combine the dECM with other synthetic polymers or active molecules [26]. For instance, a hydrogel bioink containing porcine cardiac acellular extracellular matrix (cdECM), Laponite-XLG nanoclay and poly (ethylene glycol)-diacrylate (PEG-DA) components exhibited more shear-thinning behavior making the bioink suitable for extrusion through bioprinter nozzles during the printing process. Importantly, it quickly regains its original viscosity after reducing shear stress, ensuring structural integrity and cell viability. This also enabled it to support cell-laden up to 7 days post-print, indicating that it could withstand shear conditions [25].Nevertheless, there exist several obstacles in the widespread adoption of dECM-based bioinks. These challenges encompass issues like inconsistent and non-standardized decellularization procedures, limited control over the printability and mechanical stability of dECM bioinks [19], and concerns related to immune responses, which could pose substantial difficulties in ensuring long-term in vivo safety. In addition to these biological challenges, the impediments related to large-scale production, influenced by the tissue-specific nature of dECM from various sources, require a long time, making the products costly. Ethical considerations also play pivotal roles in shaping the progress and implementation of dECM-derived bioinks [7]. To this end, further research is required to optimize protocols, refine characterization strategies, and assess long-term functionality to advance bioinks derived from slaughterhouse waste.ConclusionRegenerative medicine strategies, like tissue engineering, have emerged as transformative fields in healthcare and biotechnology, potentially revolutionizing our approach to the shortage of organs available for transplantation. Innovative solutions are essential given the critical scarcity of organ donors and the continually expanding list of individuals awaiting life-saving transplants. Among various methods, 3D bioprinting is an advantageous technique for fabricating tissue engineering scaffolds, with bioinks being a pivotal component. Biomaterials discarded from slaughterhouses can be repurposed to generate tissue-specific bioinks derived from acellular tissue matrices. These bioinks are prized for their biocompatibility, biodegradability, and ability to support cell attachment and proliferation, marking them as promising candidates for tissue engineering applications. Still, further research is required to enhance production standards that support adequate structural and functional capacities needed for the long-term viability of slaughterhouse-sourced bioinks.AcknowledgmentsThe authors would like to thank M Corridon for reviewing the manuscript.Financial disclosureThe author disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The project was supported by funds granted to PC from Khalifa University of Science and Technology, Grant Numbers: FSU-2020-25, RC2-2018–022 (HEIC), ESIG-2023-005, RIG-2023-015 (Principal Investigated for this Dr. Mayssa Hachem, fund code: 8474000575), and the Center for Biotechnology and the College of Medicine and Health Sciences. The project was also funded by the support PC received from the Abu Dhabi Automated Slaughterhouse, Municipality of the City of Abu Dhabi. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Competing interests disclosureThe authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.Writing disclosureNo writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Creative Commons Attribution 4.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/References1. Caplan AL. Finding a solution to the organ shortage. 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Biotechnol. 10, 905438 (2022).MedlineGoogle ScholarFiguresReferencesRelatedDetails Ahead of Print STAY CONNECTED Metrics History Received 29 September 2023 Accepted 9 January 2024 Published online 15 February 2024 Information© 2024 The AuthorsKeywords3D bioprintingBioinkdECMslaughterhouse wastesustainable regenerationAcknowledgmentsThe authors would like to thank M Corridon for reviewing the manuscript.Financial disclosureThe author disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The project was supported by funds granted to PC from Khalifa University of Science and Technology, Grant Numbers: FSU-2020-25, RC2-2018–022 (HEIC), ESIG-2023-005, RIG-2023-015 (Principal Investigated for this Dr. Mayssa Hachem, fund code: 8474000575), and the Center for Biotechnology and the College of Medicine and Health Sciences. The project was also funded by the support PC received from the Abu Dhabi Automated Slaughterhouse, Municipality of the City of Abu Dhabi. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Competing interests disclosureThe authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.Writing disclosureNo writing assistance was utilized in the production of this manuscript.Open accessThis work is licensed under the Creative Commons Attribution 4.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/PDF download

Facebook is one the most popular social networking sites in the world. The social aspect and perceiving oneself in the context of relationships seem to be crucial in the analysis of Facebook use. In recent years the number of Facebook users has been increasing, which is related to the problem of Facebook intrusion. Machiavellianism and selected interdependence agency indicators (conformity, relatedness, and autonomy) are associated with social functioning, which is an important aspect of Facebook intrusion. The aim of the study was to check how Machiavelianism and the interdependent agency indicators mentioned above affect Facebook intrusion and whether Facebook intrusion decreases satisfaction with life. The participants in the online study were 715 Polish Facebook users. We administered the Facebook Intrusion Scale, Conformity, Relatedness, and Autonomy Scales, and the Satisfaction With Life Scale. The results show that the selected interdependence agency indicators predict Facebook intrusion. Our results revealed that a high level of Machiavellianism was related to a high level of Facebook intrusion in women. They also show that conformity is positively and autonomy negatively related to Facebook intrusion and that Facebook decreases reduces satisfaction with life. The presented research thus contributes to knowledge about Facebook usage patterns.

Facebook is one of the most popular social networking sites. However, Facebook intrusion or addiction is a growing concern as it involves an excessive attachment to Facebook, which disrupts daily functioning. To date, few studies have examined whether cross-cultural differences in the measurement of Facebook addiction exist. The aim of this study was to investigate the cross-cultural validity and measurement invariance of the Facebook Intrusion Questionnaire (FIQ), one of the most widely used measures of Facebook addiction, across 25 countries (N = 12,204, 62.3% female; mean age = 25 years). Multigroup confirmatory factor analyses (MGCFA) assessed cross-cultural validity as well as invariance. Additionally, individual confirmatory factor analyses evaluated the factorial structure and measurement invariance across genders in each country. The FIQ demonstrated partial metric invariance across countries and metric (13 countries), scalar (11 countries) or residual (10 countries) invariance across genders within individual countries. A one-factor model indicated a good fit in 18 countries. Cronbach's alpha for the entire sample was .85. Our findings suggest that the FIQ may provide an adequate assessment of Facebook addiction that is psychometrically equivalent across cultures. Moreover, the questionnaire seems to be universal and suitable for studying different social media in distinct cultural environments. Consequently, this robust tool can be used to explore behaviours related to specific media that are particularly popular in any given country.

This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

We hypothesize that the Gray-Level Co-occurrence Matrix (GLCM) and the Run-Length Matrix (RLM) techniques can effectively quantify discrete changes in EEG signals, and that the features extracted from these matrices can be utilized to train a Random Forest (RF) model. Our contribution includes the development of a robust code in sci-kit learn for a hypothetical model that, after adequate training and testing, could be used to detect and remove artifacts as well as differentiate between physiological and pathological EEG signals. Moreover, our approach envisions the RF model as a powerful tool capable of differentiating between normal and abnormal EEG signals. This approach could lead to the development of more potent AI tools that enhance clinical decision-making in neurology and psychiatry.

In this study, we propose an innovative concept that applies an AI-based approach using the random forest algorithm integrated with fractal and discrete wavelet transform features of nuclear chromatin. This strategy could be employed to identify subtle structural changes in cells that are in the early stages of programmed cell death. The code for the random forest model is developed using the Scikit-learn library in Python and includes hyperparameter tuning and cross-validation to optimize performance. The suggested input data for the model are chromatin fractal dimension, fractal lacunarity, and three wavelet coefficient energies obtained through high-pass and low-pass filtering. Additionally, the code contains several methods to assess the performance metrics of the model. This model holds potential as a starting point for designing simple yet advanced AI biosensors capable of detecting apoptotic cells that are not discernible through conventional microscopy techniques.

Acute kidney injury (AKI) relates to an abrupt reduction in renal function resulting from numerous conditions. Morbidity, mortality, and treatment costs related to AKI are relatively high. This condition is strongly associated with damage to proximal tubule cells (PTCs), generating distinct patterns of transcriptional and epigenetic alterations that result in structural changes in the nuclei of this epithelium. To this date, AKI-related nuclear chromatin redistribution in PTCs is poorly understood, and it is unclear whether changes in PTC chromatin patterns can be detected using conventional microscopy during mild AKI, which can progress to more debilitating forms of injury. In recent years, gray level co-occurrence matrix (GLCM) analysis and discrete wavelet transform (DWT) have emerged as potentially valuable methods for identifying discrete structural changes in nuclear chromatin architecture that are not visible during the conventional histopathological exam. Here we present findings indicating that GLCM and DWT methods can be successfully used in nephrology to detect subtle nuclear morphological alterations associated with mild tissue injury demonstrated in rodents by inducing a mild form of AKI through ischemia-reperfusion injury. Our results show that mild ischemic AKI is associated with the reduction of local textural homogeneity of PTC nuclei quantified by GLCM and the increase of nuclear structural heterogeneity indirectly assessed with DWT energy coefficients. This rodent model allowed us to show that mild ischemic AKI is associated with the significant reduction of textural homogeneity of PTC nuclei, indirectly assessed by GLCM indicators and DWT energy coefficients.

In this work, we demonstrate that it is possible to create supervised machine-learning models using a support vector machine and random forest algorithms to separate yeast cells exposed to hyperosmotic stress from intact cells. We performed fractal, gray level co-occurrence matrix (GLCM), and discrete wavelet transform analyses on digital micrographs of nuclear regions of interest of a total of 2000 Saccharomyces cerevisiae cells: 1000 exposed to hyperosmotic environments and 1000 control cells. For each nucleus, we calculated values for fractal dimension, angular second moment, inverse difference moment, textural contrast, correlation feature, textural variance, and discrete wavelet coefficient energy. The support vector machine achieved an acceptable classification accuracy of 71.7% in predicting whether the cell belonged to the experimental or control group. The random forest model performed better than the support vector machine, with a classification accuracy of 79.8%. These findings can serve as a starting point for developing AI-based methods that use GLCM, fractal, and wavelet data to classify damaged and healthy cells and make predictions about various physiological and pathological phenomena associated with osmotic stress.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality.