Search Results (530)

Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing. Full article

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. Full article

Background: Oral squamous cell carcinoma (OSCC) is one of the most serious forms of cancer in the world. The opportunities to decrease the mortality rate would lie in the possibility of earlier identification of this pathology, and at the same time, the immediate approach of anticancer therapy. Furthermore, new treatment strategies for OSCC are needed to improve existing therapeutic options. Bioactive compounds found in medicinal plants could be used to support these strategies. It is already known that they have an increased potential for action and a safety profile; therefore, they could improve the therapeutic effect of classical chemotherapeutic agents in combination therapies. Methodology: This research was based on an extensive review of recently published studies in scientific databases (PubMed, Scopus, and Web of Science). The selection criteria were based on experimental protocols investigating molecular mechanisms, synergistic actions with conventional anticancer agents, and novel formulation possibilities (e.g., nanoemulsions and mucoadhesive films) for the targeted delivery of bioactive compounds in OSCC. Particular attention was given to in vitro, in vivo, translational, and clinical studies that have proven therapeutic relevance. Results: Recent discoveries regarding the effect of bioactive compounds in the treatment of oral cancer were analyzed, with a view to integrating them into oncological practice for increasing therapeutic efficacy and reducing the occurrence of adverse reactions and treatment resistance. Conclusions: Significant progress has been achieved in this review, allowing us to appreciate that the valorization of these bioactive compounds is emerging. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

The discovery that human beings may endogenously produce ethanol is not new and dates back at the end of the 19th century; recently, however, it has become clear that through the proliferation of gut microorganisms that produce ethanol from sugars or other substrates, blood alcohol level may be greater than 0, despite Homo sapiens sapiens lacking the enzymatic pathways to produce it. Very rarely this can lead to symptoms and/or to a disease, named gut fermentation syndrome or auto-brewery syndrome (ABS). The list of microorganisms (mostly bacteria and fungi) is very long and contains almost 100 different strains, and many metabolic pathways are involved. Endogenous ethanol production is a neglected entity, but it may be suspected in patients in whom ethanol consumption may be firmly excluded. Nevertheless, due to the growing prevalence of NAFLD (now renamed as MAFLD) worldwide, an ethanol-producing microorganism responsible for endogenous ethanol production such as Klebsiella pneumoniae or Saccharomices cerevisiae is increasingly sought in NAFLD patients, or in patients with metabolic diseases such as diabetes mellitus, obesity, or metabolic syndrome, at least in selected instances. In the absence of standard diagnostic and therapeutic guidelines, ABS requires a detailed patient history, including dietary habits, alcohol consumption, and gastrointestinal symptoms, and a comprehensive physical examination to detect unexplained ethanol intoxication. It has been proposed to start the diagnostic protocol with a standardized carbohydrate challenge test, followed, if positive, by the use of antifungal agents or antibiotics; indeed, fecal microbiota transplantation might be the only way to cure a patient with refractory ABS. Scientific societies should produce internationally agreed recommendations for ABS and other conditions linked to excessive endogenous ethanol production. Full article

Lobelia nummularia Lam. is a traditional medicinal herb of which the anticancer mechanisms remain largely unexplored. Here, we demonstrated that its ethanolic extract (LNE) exerts potent anti-breast cancer activity by inducing ROS-dependent mitochondrial apoptosis in MDA-MB-231 cells, a mechanism confirmed via rescue experiments with the antioxidant N-acetylcysteine (NAC). This pro-apoptotic program is driven by a dual mechanism: potent suppression of the pro-survival EGFR/PI3K/AKT signaling pathway and simultaneous activation of the TP53-mediated apoptotic cascade, culminating in the cleavage of executor caspase-3. Phytochemical analysis identified numerous flavonoids, and quantitative HPLC confirmed that key bioactive compounds, including luteolin and apigenin, are substantially present in the extract. These mechanisms translated to significant in vivo efficacy, where LNE administration suppressed primary tumor growth and lung metastasis in a 4T1 orthotopic model in BALB/c mice. Furthermore, a validated molecular docking protocol provided a plausible structural basis for these multi-target interactions. Collectively, this study provides a comprehensive, multi-layered validation of LNE’s therapeutic potential, establishing it as a mechanistically well-defined candidate for natural product-based anticancer drug discovery. Full article

Schwann cells (SCs) play a crucial role in peripheral nerve repair by supporting axonal regeneration and remyelination. While extensive research has been conducted using rodent SCs, increasing attention is being directed toward human SCs due to species-specific differences in phenotypical and functional properties, and accessibility of human SCs derived from diverse sources. A major challenge in translating SC-based therapies for nerve repair lies in the inability to replicate human SC myelination in vitro, posing a significant obstacle to drug discovery and preclinical research. In this study, we compared the myelination capacity of human, rodent, and porcine SCs in various co-culture conditions, including species-matched and cross-species neuronal environments in a serum-free medium. Our results confirmed that rodent and porcine SCs readily myelinate neurites under standard culture conditions after treatment with ascorbic acid for two weeks, whereas human SCs, at least within the four-week observation period, failed to show myelin staining in all co-cultures. Furthermore, we investigated whether cell culture manipulation impairs human SC myelination by transplanting freshly harvested and predegenerated human nerve segments into NOD-SCID mice for four weeks. Despite supporting host axonal regeneration into the grafts, human SCs exhibited very limited myelination, suggesting an intrinsic species-specific restriction rather than a cell culture-induced defect. These observations suggest fundamental differences between human and rodent SCs and highlight the need for human-specific models and protocols to advance our understanding of SC myelination. Full article

Background: Metastatic prostate cancer (mPrC), with a median survival of under 2 years, represents an important unmet medical need which may benefit from the development of more effective targeted drug delivery systems. Several cell surface receptors have been identified as candidates for targeted drug delivery to mPrC cells; however, these receptors were selected for their overabundance on PrC cells rather than for their suitability for targeted delivery and uptake of cytotoxic drug payloads. Methods: We describe a novel, unbiased strategy to isolate peptides that fulfill functional criteria required for effective intracellular drug delivery and the specific cytotoxicity of PrC cells without prior knowledge of the targeted receptor. Phage clones displaying 7-mer cyclic peptides were negatively selected in vivo and then positively biopanned through a series of parent and drug-resistant mPrC cells. Peptides from the internalized clones were then subjected to a panel of biochemical and functional tests that led to the selection of several peptide candidates. Results: The selected peptides do not bind PSMA. Peptide-drug conjugates (PDCs) incorporating one of the peptides selectively killed wild-type and drug-resistant PrC cell lines and patient PrC cells but not normal prostate tissue cells in vitro. The PDC also halted the growth of PC3 tumors in a xenograft model. Conclusions: Our study demonstrates that adding unbiased, functional criteria into drug carrier selection protocols can lead to the discovery of novel peptides with appropriate properties required for effective targeted drug delivery into target cancer cells. Full article

Background: Zinc finger proteins (ZNFs), functioning as pervasive transcriptional modulators, serve as pivotal mediators of tumorigenesis and malignant advancement. However, the mechanistic contributions of these epigenetic orchestrators to lung adenocarcinoma pathogenesis remain incompletely characterized. Methods: To elucidate zinc finger proteins’ biological significance in lung adenocarcinoma (LUAD) pathogenesis, we first extracted relevant transcriptional data from TCGA. After preliminary screening with univariate Cox regression, a LASSO algorithm was applied to optimize the risk score model, incorporating key zinc finger protein markers. For independent validation, we accessed GEO dataset GSE68465, applying identical analytical protocols to confirm model generalizability. We performed multivariable Cox regression to identify independent predictors of clinical outcomes after adjusting for confounding variables. Cell-based validation included (1) comparative analysis of zinc finger protein expression across LUAD/normal cell models and (2) technical verification using standardized qRT-PCR protocols. Results: Following rigorous bioinformatics screening comprising differential expression and survival analysis, the final 21-zinc finger protein cohort was selected for risk score algorithm development aimed at clinical outcome prediction. Stratification based on computed risk scores revealed markedly superior survival outcomes in the low-risk cohort compared to high-risk patients. Comparative analysis revealed overall concordance in the transcriptional profiles of eight ZNFs (|coef| > 0.1) across experimental cell systems and TCGA datasets. Conclusions: Collectively, the prognostic framework incorporating zinc finger proteins demonstrates biomarker utility in lung adenocarcinoma survival prediction, while offering novel avenues for molecular target discovery in therapeutic strategies against this malignancy. Full article

Pathogenic bacteria such as the drug-resistant strains of Staphylococcus aureus dominate our medical and environmental landscape, causing hundreds of thousands of deaths from infections and life-threatening complications following surgeries. The availability of antibiotics and treatment protocols to control these microbes are becoming increasingly more limited as antibiotic resistance becomes more prevalent. In this article, a new family of small molecules referred to as N-organothio β-lactams is presented that have unique features and a mode of action against these pathogenic microbes, including multi-drug resistant strains, that may offer new options to address these concerns. This review gives an overview of the initial discovery, exploration and ongoing development of these synthetic antibacterial agents, with a focus on their unique properties and capabilities that provide fresh opportunities for combating pathogenic bacteria. Full article

The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a significant concern amid this urgency. To standardize the legal provision and usage of genetic resources, the United Nations Development Program (UNDP) introduced the Nagoya Protocol. Despite advancements in drug research, the production process remains tedious, complex and vulnerable to fraud. FairChain addresses this pressing challenge by creating a transparent ecosystem that builds trust among all stakeholders throughout the Drug Development Life Cycle (DDLC) by using decentralized, immutable, and transparent blockchain technology. This makes FairChain the first digital health tool to implement the principles of the UNDP’s Nagoya Protocol among all stakeholders throughout all DDLC stages, starting with sample collection, to discovery and development, to preclinical research, to clinical development, to regulator review, and ending with post-market monitoring. Therefore, FairChain allows pharmaceutical companies to document the entire drug production process, landowners to monitor bio-samples from their land, doctors to share clinical research, and regulatory agencies such as the Food and Drug Authority to oversee samples and authorize production. FairChain should enhance transparency, foster trust and efficiency, and ensure a fair and traceable DDLC. To date, no blockchain-based framework has addressed the integration of traceability, auditability, and Nagoya Protocol compliance within a unified system architecture. This paper introduces FairChain, a system that formalizes these requirements in a modular, policy-aligned, and verifiable digital trust infrastructure. Full article

Background: Newborns are referred primary carnitine deficiency (PCD) when a low free carnitine (C0) concentration (<10 μmol/L) is detected, leading to high false-positive referrals. To improve the follow-up protocol for PCD, various acylcarnitines and the summations were comprehensively evaluated in the present study. Methods: A retrospective study was performed using samples due to low C0 concentration. Data were available for 72 patients with genetically confirmed PCD, whereafter C0 with the selected sum of (butyrylcarnitine (C4) + isovalerylcarnitine (C5)) was validated in an additional cohort study including about 80,000 samples. Results: In the discovery study, C4, acetylcarnitine (C2) and C5 exhibited significant discriminant power in distinguishing PCDs from NoPCDs. The area under the ROC curve (AUC) was 99.792% (C4), 98.715% (C2) and 98.620% (C5). The excellent performances in sensitivity, specificity, negative predictive value, positive predictive value (PPV) and accuracy indexes suggested that C4, C2 and C5 would be ideal auxiliary indicators in improving the diagnostic performance of C0 for PCD. Multivariate ROC curve-based exploratory analysis showed that C5, C4 and C2 were the most top-ranked features in differentiating PCDs from NoPCDs. AUC for C4 + C5 was the highest with a cutoff required for 100% sensitivity at 0.181 μmol/L. In the validation cohort, adding C4 + C5 in the NBS program could elevate PPV from 0.75% to 1.54%. Conclusions: Our work revealed that C4 + C5 summation should be used as the auxiliary quantization indicator to reduce false-positive results for PCD. Full article

Effective postoperative pain management remains a major clinical challenge in spinal surgery, with poorly controlled pain affecting up to 50% of patients and contributing to delayed mobilization, prolonged hospitalization, and risk of chronic postsurgical pain. This review synthesizes current and emerging strategies in postoperative spinal pain management, tracing the evolution from opioid-centric paradigms to individualized, multimodal approaches. Multimodal analgesia (MMA) has become the cornerstone of contemporary care, combining pharmacologic agents, such as non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and gabapentinoids, with regional anesthesia techniques, including erector spinae plane blocks and liposomal bupivacaine. Adjunctive nonpharmacologic modalities like early mobilization, cognitive behavioral therapy, and mindfulness-based interventions further optimize recovery and address the biopsychosocial dimensions of pain. For patients with refractory pain, neuromodulation techniques such as spinal cord and peripheral nerve stimulation offer promising results. Advances in artificial intelligence (AI), biomarker discovery, and nanotechnology are poised to enhance personalized pain protocols through predictive modeling and targeted drug delivery. Enhanced recovery after surgery protocols, which integrate many of these strategies, have been shown to reduce opioid use, hospital length of stay, and complication rates. Nevertheless, variability in implementation and the need for individualized protocols remain key challenges. Future directions include AI-guided analytics, regenerative therapies, and expanded research on long-term functional outcomes. This review provides an evidence-based framework for pain control following spinal surgery, emphasizing integration of multimodal and innovative approaches tailored to diverse patient populations. Full article

Cell-free DNA (cfDNA), a fragmented DNA circulating in blood, is a promising biomarker for cancer diagnosis and monitoring. Standardization of cfDNA isolation to enhance the sensitivity of molecular analyses in prostate cancer (PCa) is required. Towards this goal, we optimized existing methods to obtain a high quantity and quality of cfDNA from low volumes of plasma. The protocol was applied to samples from healthy males and three patient categories: radical prostatectomy (RP), disease-free (>6 years post-RP), and metastatic castration-resistant PCa (mCRPC). The yield was significantly higher in mCRPC cases, and the size of fragments was shorter. We compared for the first time library preparation using two cfDNA inputs and low vs. high sequencing depth. Clonal events were observed irrespective of input and depth, but lower input showed more subclonal events. The clinical application of the refined protocols to cfDNA samples from an mCRPC patient showed no tumor fraction before RP, while it increased to 25% at the advanced stage. Among chromosomal changes and mutations, the androgen receptor gene amplification was detected. Altogether, this comprehensive study on improved cfDNA procedures is highly promising to enhance the quality of liquid biopsy-based research for discoveries and much-needed clinical applications. Full article