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Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell labeling strategy aiming to capture a cell’s state introduced in 2013 to optimize and standardize image-based profiling. Cell Painting’s ability to capture cellular Show less

Three cytotoxic copper(ii) complexes – [Cu2(bipy)2L4] (1), [Cu2(phen)2L4] (2) and [Cu2(dmphen)2L4]·2H2O (3) – were synthesized based on 5-methyltetrazole (HL) and 2,2′-bipyridine/1,10-phenanthroline derivatives. Show less

This study presents a protein search framework with conformal prediction, enabling statistically reliable annotation of protein function. The method improves homology search, enzyme classification, and filters proteins for further characterization. Show less

Solvent effects play a critical role in ionic chemical reactions and have been a research topic for a long time. The solvent molecules in the first solvation shell of the solute are the most important solvating species. Consequently, manipulation of the structure of this shell can be used to control the reactivity and selectivity of ionic reactions. In this work, we report extensive experimental and insightful computational studies of the effects of adding diverse fluorinated bulky alcohols with different solvation abilities to the fluorination reaction of alkyl bromides with potassium fluoride promoted by 18-crown-6. We found that adding a stoichiometric amount of these alcohols to the acetonitrile solution has an important effect on the kinetics and selectivity. The most effective alcohol was 2-trifluoromethyl-2-propanol (TBOH-F3), and the use of 3 equiv of this alcohol to fluorinate a primary alkyl bromide led to a 78% fluorination yield in just 6 h of reaction time at a mild temperature of 82 °C, with 8% of E2 yield. The more challenging secondary alkyl bromide substrate obtained 44% fluorination yield and 56% E2 yield at 18 h of reaction time. More fluorinated alcohols with six or more fluorine atoms have resulted in relatively acidic alcohols, leading to large amounts of the corresponding ethers of these alcohols as side products. The widely used hexafluoroisopropanol (HFIP) was the least effective one for monofluorination, indicating that both acidity and bulkiness are important features of the alcohols for promoting fluorination using KF salt. Nevertheless, the ether of HFIP can be easily formed with the substrate, generating a highly fluorinated ether product. Theoretical calculations predict ΔG ^‡ in close agreement with the experiments and explain the higher selectivity induced by the fluorinated bulky alcohols in relation to the use of crown ether alone. Show less

Aminoacyl-thiols reacting selectively with RNA diols over amine nucleophiles and demonstration of chemically controlled formation of peptidyl-RNA in water at neutral pH suggest an important role for thiol cofactors before the evolution of enzymes. Show less

Cisplatin and its analogs are extensively utilized as metal-based anticancer agents in clinical settings due to their mechanism of action, which involves targeting genomic double-stranded DNA to induce cytotoxicity in cancer cells. However, the associated severe side effects and DNA damage repair-inducing drug resistance present significant challenges. In recent years, G-quadruplex nucleic acids, formed through the self-assembly of guanine-rich nucleic acid sequences, have emerged as a compelling target for the design of novel anticancer therapeutics. The strategic design of platinum complexes that selectively interact with, stabilize, or cleave G-quadruplex structures represents a promising approach for developing effective anticancer agents to overcome cisplatin resistance. This review will emphasize the advancements made over the past decade in interacting G-quadruplexes with platinum complexes as potential anticancer therapeutics. The ongoing development of platinum complexes spans from targeting nuclear DNA G-quadruplexes to mitochondrial DNA and cytoplasmic RNA G-quadruplexes, evolving from monotherapy approaches, such as chemotherapy and photodynamic therapy, to a combination of radiotherapy, immunotherapy, and more, highlighting the dynamic progress of platinum complexes. At the end, we have summarized 4 points of pending issues in this fast-growing field, which we hope can provide some help to the development of this field. Show less

In the healthcare industry, the ever-increasing volume of clinical trial data presents challenges for ensuring drug safety and detecting adverse drug reactions (ADRs). This study aims to address the challenge of accurately detecting Serious Adverse Events (SAEs) in pharmacovigilance, a critical component in ensuring drug safety during and after clinical trials. The key problem lies in the underreporting and delayed detection of Adverse Drug Reactions (ADRs) due to the heterogeneous nature of medical data, class imbalance, and the limited scope of traditional monitoring techniques. This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. The objective is to outperform traditional signal detection methods and provide interpretable predictions to aid clinicians in real-time. By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. Through meticulous feature engineering and the application of techniques to address data imbalance, our model demonstrates improved accuracy and interpretability in predicting ADRs. The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes.•The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data.•Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model's performance. Among the models tested, the CNN model with BERT achieved the highest accuracy, providing valuable insights into the potential of deep learning for enhancing pharmacovigilance practices.•These findings suggest that an inclusion of diverse clinical data when supplied to advanced ML and NLP techniques can significantly improve the detection of ADRs, leading to better alignment with the fundamental principles of Good Clinical Practice (GCP). Show less

Psychiatric diseases are often treated with several drugs. In addition, the risk of developing somatic comorbidities which may require drug therapy is higher in patients with than in patients without psychiatric diseases. Further on, the risk of drug-drug interactions (DDI) increases with the number of drugs taken. The aim of this study was to analyze whether already known DDI between psychiatric drugs and somatic medications still occur in everyday clinical practice. Show less

Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug−drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

In recent years, mostly spanning the past decade, the concept of immunometabolism has ushered with a novel perspective on carcinogenesis, tumor progression, and tumor response to therapy. It has become clear that the metabolic state of immune cells plays a significant role in shaping their antitumor or protumor activities within the cancer microenvironment. Consequently, the examination of tumor metabolic heterogeneity, including an exploration of immunometabolism, proves indispensable for enhancing prognostic tools and advancing the quest for personalized treatments. Here we have delved into how metabolic reprogramming profoundly influences the acquisition and maintenance of functional states, spanning from effector and cytotoxic profiles to regulatory and immunosuppressive phenotypes in both innate and adaptive immunity. These alterations wield considerable influence over tumor evolution and affect the outcome of cancer. Furthermore, we explore some of the cellular signaling mechanisms that underpin the metabolic and phenotypic flexibility of immune cells in response to external stimuli. Show less

Hyperthymesia has been described in individuals, who show superior retrieval capacities in autobiographical memory. This condition differs from superior memory, which refers to the supranormal ability to acquire and recall new information but not autobiographical information. The process responsible for hyperthymesia is still largely unknown and most knowledge come from case studies, showing individual with impressive superior capacities to retrieve autobiographical memories. Here, we describe a case of hyperthymesia with an objective as well as a subjective assessment of mental time travel abilities in different temporal distances. This is the first observation of hyperthymesia with a full evaluation of mental time travel capacities in different temporal distances, encompassing the individual capacity to retrieve personal events from the personal past as well as to foresee personal events in the future. This observation could pave the way to further research on superior autobiographical abilities, studied in the context of personal temporality. Show less

A novel bioorganometallic PNA conjugate (Ir-PNA) was synthesized by covalently bonding a model PNA tetramer to a luminescent bis-cyclometalated Ir(III) complex that acted as a photosensitizer under light irradiation to generate singlet oxygen (¹O₂). The conjugate was prepared using an Ir complex bearing the 1,10-phenanthroline ligand functionalized with either a free primary amine (Ir-NH₂) or a carboxyl group (Ir-COOH) for the conjugation to PNA. The photophysical studies on the Ir-COOH and the Ir-PNA demonstrated that the luminescent properties were maintained after the conjugation of the Ir fragment to PNA. Furthermore, the abilities to produce ¹O₂ of Ir-COOH and Ir-PNA were confirmed in a cuvette under visible light irradiation employing 1,5-dihydroxynaphthalene as a reporter, and the measured singlet oxygen quantum yield (Φ_Δ) supported the Ir-PNA conjugate efficacy as a photosensitizer (Φ_Δ = 0.54). Two-photon absorption microscopy on HeLa cells revealed that Ir-PNA localized in both the cytosol and nucleus, suggesting its potential as an intracellular carrier for PNA. Cytotoxicity assays by MTT tests showed that Ir-PNA was nontoxic in the absence of light, but induced cell death (EC₅₀ = 18 μM) after UV irradiation. Overall, the Ir-PNA conjugate represents a promising system for the intracellular delivery of the PNA and its application in PDT. Show less

Abstract The first examples of Ru(II) η 6 ‐arene (benzene and p ‐cymene) complexes containing a bidentate triazolylidene‐triazolide ligand have been prepared and fully characterized. Their antiproliferative effect has been investigated against tumour cells A2780 (ovarian carcinoma), HCT116 (colorectal carcinoma), and HCT116dox (colorectal carcinoma resistant to doxorubicin), and in human dermal fibroblasts. The Ru complex bearing the p ‐cymene arene group exhibited a stronger antiproliferative effect across all tested cell lines, while the benzene‐containing complex displayed higher selectivity toward tumor cells. Both complexes induced apoptosis, likely through ROS production (in the benzene complex), and inhibited tumorigenic processes, including cell migration and angiogenesis. In zebrafish models, they showed strong selectivity for cancer cells with minimal toxicity to healthy cells, effectively reducing the proliferation of HCT116 colorectal cancer cells. This study provides the first in vivo evidence of the anticancer potential of Ru triazolylidenes in zebrafish models. Show less

Predicting protein‒protein interactions (PPIs) plays a crucial role in understanding biological processes. Although biological experimental methods can identify PPIs, they are costly, time-consuming, labor-intensive, and often lack stability. In contrast, computational approaches for PPI prediction, particularly deep learning methods, can efficiently learn representations from protein sequences. However, the generalizability, robustness, and stability of computational PPI prediction models still need improvement, especially for species with limited verified PPI data. Protein embeddings generated by protein language models can extract features from protein sequences and reflect hierarchical biological structures, making them suitable for predicting PPIs. Therefore, in this study, we propose a novel protein sequence-based PPI prediction framework designed for generalized PPI assessment by integrating two protein language models (PLMs) and an enhanced deep neural network (MPIDNN-GPPI). Specifically, the sequences are embedded using two protein language models, Ankh and ESM-2. A deep neural network is then used to learn representations from the feature vectors produced by PLMs. Subsequently, a multi-head attention mechanism is introduced to capture long-range dependencies and fuse them with DNN-derived representations. Finally, a deep neural network is applied to assess the probability of interaction between two proteins. To evaluate the performance of MPIDNN-GPPI, nine PPI datasets were collected from the STRING database, covering a diverse set of species: five datasets from mammals (D. melanogaster, C. elegans, S. cerevisiae, H. sapiens, and M. musculus), and four datasets from plants (O. sativa, A. thaliana, G. max, and Z. mays). When trained on H. sapiens, MPIDNN-GPPI achieved AUC values of 0.959, 0.966, 0.954, and 0.916 on independent test sets for M. musculus, D. melanogaster, C. elegans, and S. cerevisiae, respectively. These results represent the best performance among all PPI models compared in this study. Similarly, when trained on O. sativa, the model achieved AUC values of 0.96, 0.95, and 0.913 on independent datasets for A. thaliana, G. max, and Z. mays, respectively. Ablation experiments demonstrated that models combining Ankh and ESM-2 outperformed those relying on a single protein language model. Furthermore, MPIDNN-GPPI, which incorporates multi-head attention and deep neural networks (DNN), achieved superior performance compared to models using DNN alone. These findings indicate that MPIDNN-GPPI possesses strong generalization capability for cross-species PPI prediction. The proposed model, trained on one species, can be effectively applied to accurately predict PPIs in other species. Show less

Chemical gardens are hollow precipitates with a plant-like appearance formed when a metal salt seed is immersed in an alkaline aqueous solution containing silicate, phosphate, or carbonate ions. Due to their potential to mimic biological and geological structures relevant to the understanding of life's emergence on Earth and Mars, the study of the nonequilibrium properties of chemical gardens has become increasingly important. Hence, in this article, the influence of gravity on the formation and growth of chemical gardens is investigated. To this end, experimental evidence of the influence of gravity on the formation and growth of chemical gardens is analyzed according to nonequilibrium sensitivity theory. The results obtained from the nonequilibrium sensitivity analysis show that the upward-growing pattern observed in chemical gardens, usually formed under Earth's gravity, is a consequence of symmetry breaking in the system's bifurcating solutions. Under these circumstances, the thermal fluctuations within the system become negligible, favoring the vertical growth of the chemical garden. Moreover, by exploiting the definition of nonequilibrium sensitivity, the minimum magnitude of the gravitational field necessary for the vertical growth of a chemical garden was estimated. The results indicate that the upward growth pattern emerges as the dominant dissipative structure for gravitational field magnitudes larger than 10^-5 m s^-2, provided fluctuations remain negligible. Show less

Academic Editor: Sabrina Venditti Received: 22 May 2025 Revised: 5 July 2025 N6-methyladenosine (m6A) represents the most common and thoroughly investigated RNA modification and exerts essential functions in regulating gene expression through influencing the RNA stability, the translation efficiency, alternative splicing, and nuclear export processes. The rapid development of high-throughput sequencing approaches, including miCLIP and MeRIP-seq, has profoundly transformed epitranscriptomics research. Show less

Colorectal cancer (CRC) exhibits significant diversity and heterogeneity, posing a requirement for novel therapeutic targets. Polysulfides are associated with CRC progression and immune evasion, but the underlying mechanisms are not fully understood. Sulfide: quinone oxidoreductase (SQR), a mitochondrial flavoprotein, catalyzes hydrogen sulfide (H₂S) oxidation and polysulfides production. Herein, we explored its role in CRC pathogenesis and its potential as a therapeutic target. Our findings revealed that SQR knockout disrupted polysulfides homeostasis, diminished mitochondrial function, impaired cell proliferation, and triggered early apoptosis in HCT116 CRC cells. Moreover, the SQR knockout led to markedly reduced tumor sizes in mice models of colon xenografts. Although the transcription of glycolytic genes remained largely unchanged, metabolomic analysis demonstrated a reprogramming of glycolysis at the fructose-1,6-bisphosphate degradation step, catalyzed by aldolase A (ALDOA). Both Western blot analysis and enzymatic assays confirmed the decrease in ALDOA levels and activity. In conclusion, the study establishes the critical role of SQR in mitochondrial function and metabolic regulation in CRC, with its knockout leading to metabolic reprogramming and diminished tumor growth in HCT116 tumor xenografts. These insights lay a foundation for the development of SQR-targeted therapies for CRC. Show less

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies. Additionally, advancements like AlphaFold highlight AI's capacity to address intricate challenges in drug development. However, the field faces significant challenges, including the management of large-scale genomic datasets and ethical concerns surrounding AI deployment in healthcare. This review underscores the promise of data-centric AI approaches and emphasizes the necessity of continued innovation and interdisciplinary collaboration. Together, AI and genomics are charting a path toward more precise, efficient, and transformative cancer therapeutics. Show less

Ferredoxins (FDXs) are evolutionarily conserved iron-sulfur (Fe-S) proteins that serve as master regulators of mitochondrial redox homeostasis, governing critical processes including electron transfer, energy metabolism, Fe-S cluster biogenesis, and steroidogenesis. In humans, the mitochondrial isoforms FDX1 and FDX2 exhibit specialized yet complementary functions: FDX1 directs steroidogenesis, protein lipoylation, and copper redox cycling, while FDX2 is a core factor in Fe-S cluster assembly. Crucially, dysregulation of these proteins disrupts mitochondrial integrity, impairs redox balance, and activates multiple programmed cell death (PCD) pathways such as cuproptosis, ferroptosis, apoptosis, and autophagic cell death. This review systematically analyzes their isoform-specific roles in mitochondrial electron transport, Fe-S cluster dynamics, metabolic regulation, and summarizes major advances in understanding how FDX1 and FDX2 orchestrate mitochondrial-PCD crosstalk. The work further examines their critical functions in PCD execution, including FDX1-mediated cuproptosis through Cu+-dependent aggregation of lipoylated proteins and FDX2-deficiency-driven ferroptosis via Fe-S cluster collapse and iron overload. Disease mechanisms across multiple pathologies, including cancer, neurodegeneration, cardiovascular disease, endocrine disorders, and genetic syndromes, are explored, highlighting links to FDX dysfunction, with emerging therapeutic strategies targeting FDXs also addressed. By elucidating the synergistic roles of FDX1 and FDX2 as metabolic-death gatekeepers, this review establishes a foundation for developing isoform-targeted therapies against diverse pathologies. Show less

A series of cyclometalated platinum-(II) complexes bearing neutral isocyanide or acyclic diaminocarbene ancillary ligands were designed and developed. Their photophysical properties were systematically studied in different polymer systems: poly-(methyl methacrylate), polystyrene, poly-(isobornyl acrylate), and copolymers based on them. The dependence of luminescent characteristics on the concentration of the doped complex (0.5-10 wt %), composition, and properties of the polymer material was investigated as key factors for the measurement of quantum yields, excited-state lifetimes, and spectral profiles in routine studies. Show less

The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals. We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in engineering and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating `AGI' as the north-star goal of AI research. Show less

AbstractA series of xylose‐based ligands was obtained using a convenient approach, in a few steps from D‐xylose. The complexation properties of these ligands towards Au3+ cations have been studied through different methods (multinuclear NMR, mass spectrometry, elemental analysis). The biological properties (antibacterial and anti‐tumoral) of all the isolated xyloside Au(III) complexes were investigated in vitro. The xyloside Au(III) complexes gave the highest activities against E. coli (vs P. aeruginosa, S. aureus and S. epidermidis). The study also revealed that the nature of the sugar may play an important role in determining the selectivity of the antibacterial effect. Preliminary anti‐tumoral evaluations showed that one complex containing a polyamine chain, exhibited interesting anti‐proliferative activities on breast tumor cell lines MDA‐MB‐231 and BT‐20. The anti‐migratory effect of this complex also showed an average 35 % reduction in cell migration on the same two cancer cell lines. Show less