Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite sig Show more
Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite significant advances in targeted therapy and immunotherapy, the overall prognosis of patients with NSCLC remains poor. Hypoxia is a critical driving factor in tumor progression, influencing the biological behavior of tumor cells through complex molecular mechanisms. The present review systematically examined the role of the hypoxic microenvironment in NSCLC, demonstrating its crucial role in promoting tumor cell growth, invasion and metastasis. Additionally, it has been previously reported that the hypoxic microenvironment enhances tumor cell resistance by activating hypoxia-inducible factor and regulating exosome secretion. The hypoxic microenvironment also enables tumor cells to adapt to low oxygen and nutrient-deficient conditions by enhancing metabolic reprogramming, such as through upregulating glycolysis. Further studies have shown that the hypoxic microenvironment facilitates immune escape by modulating tumor-associated immune cells and suppressing the antitumor response of the immune system. Moreover, the hypoxic microenvironment increases tumor resistance to radiotherapy, chemotherapy and other types of targeted therapy through various pathways, significantly reducing the therapeutic efficacy of these treatments. Therefore, it could be suggested that early detection of cellular hypoxia and targeted therapy based on hypoxia may offer new therapeutic approaches for patients with NSCLC. The present review not only deepened the current understanding of the mechanisms of action and role of the hypoxic microenvironment in NSCLC but also provided a solid theoretical basis for the future development of precision treatments for patients with NSCLC. Show less
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-re Show more
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai . Show less
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of pr Show more
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. Show less
The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-acce Show more
The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100Â million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose. Show less
Abstract Significance: Mitochondria are the energetic, metabolic, redox, and information signaling centers of the cell. Substrate pressure, mitochondrial network dynamics, and cristae morphology Show more
Abstract Significance: Mitochondria are the energetic, metabolic, redox, and information signaling centers of the cell. Substrate pressure, mitochondrial network dynamics, and cristae morphology state are integrated by the protonmotive force Δ p or its potential component, Δ Ψ , which are attenuated by proton backflux into the matrix, termed uncoupling. The mitochondrial uncoupling proteins (UCP1–5) play an eminent role in the regulation of each of the mentioned aspects, being involved in numerous physiological events including redox signaling. Recent Advances: UCP2 structure, including purine nucleotide and fatty acid (FA) binding sites, strongly support the FA cycling mechanism: UCP2 expels FA anions, whereas uncoupling is achieved by the membrane backflux of protonated FA. Nascent FAs, cleaved by phospholipases, are preferential. The resulting Δ p dissipation decreases superoxide formation dependent on Δ p . UCP-mediated antioxidant protection and its impairment are expected to play a major role in cell physiology and pathology. Moreover, UCP2-mediated aspartate, oxaloacetate, and malate antiport with phosphate is expected to alter metabolism of cancer cells. Critical Issues: A wide range of UCP antioxidant effects and participations in redox signaling have been reported; however, mechanisms of UCP activation are still debated. Switching off/on the UCP2 protonophoretic function might serve as redox signaling either by employing/releasing the extra capacity of cell antioxidant systems or by directly increasing/decreasing mitochondrial superoxide sources. Rapid UCP2 degradation, FA levels, elevation of purine nucleotides, decreased Mg 2+ , or increased pyruvate accumulation may initiate UCP-mediated redox signaling. Future Directions: Issues such as UCP2 participation in glucose sensing, neuronal (synaptic) function, and immune cell activation should be elucidated. Antioxid. Redox Signal. 29, 667–714. Show less
Mitochondrial Ca 2+ uptake plays a pivotal role both in cell energy balance and in cell fate determination. Studies on the role of mitochondrial Ca 2+ signaling in pathophysiology have been favored Show more
Mitochondrial Ca 2+ uptake plays a pivotal role both in cell energy balance and in cell fate determination. Studies on the role of mitochondrial Ca 2+ signaling in pathophysiology have been favored by the identification of the genes encoding the mitochondrial calcium uniporter (MCU) and its regulatory subunits. Thus, research carried on in the last years on one hand has determined the structure of the MCU complex and its regulation, on the other has uncovered the consequences of dysregulated mitochondrial Ca 2+ signaling in cell and tissue homeostasis. Whether mitochondrial Ca 2+ uptake can be exploited as a weapon to counteract cancer progression is debated. In this review, we summarize recent research on the molecular structure of the MCU, the regulatory mechanisms that control its activity and its relevance in pathophysiology, focusing in particular on its role in cancer progression. Show less