Slow States Research Articles

Visualizing Thermally Activated Conical Intersections Governing Non-Radiative Triplet Decay in a Ni(II) Porphyrin-Nanographene Conjugate with Variable Temperature Transient Absorption Spectroscopy.

Metalloporphyrins based on open-shell transition metals, such as Ni(II), exhibit typically fast excited-state relaxation. In this work, we shed light into the nonradiative relaxation mechanism in a nanographene-Ni(II) porphyrin conjugate. Variable temperature transient absorption and global fit analysis are combined to produce a picture of the relaxation pathways. At room temperature, photoexcitation of the lowest π-π* transition is followed by vibrational cooling in 1.6 ps, setting a short 20 ps temporal window wherein a small fraction of relaxed singlets radiatively decay to the ground state before intersystem crossing proceeds. Following intersystem crossing, triplets relax rapidly to the ground state (S0) in a few tens of picoseconds. By performing measurements at low temperature, we provide evidence for a competition between two terminal relaxation pathways from the lowest (metal-centered) triplet to the ground state: a slow ground state relaxation process proceeding in time scales beyond 1.6 ns and a faster pathway dictated by a sloped conical intersection, which is thermally accessible at room temperature from the triplet state. The overall triplet decay at a given temperature is dictated by the interplay of these two contributions. This observation bears significance in understanding the underlying fast relaxation processes in Ni-based molecules and related transition metal complexes, opening avenues for potential applications for energy harvesting and optoelectronics.

Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.

Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe the long-time dynamics of such systems, which is responsible for their most informative characteristics, we can identify a few slow collective variables (CVs) while treating the remaining fast variables as thermal noise. This enables us to simplify the dynamics and treat it as diffusion in a free-energy landscape spanned by slow CVs, effectively rendering the dynamics Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J. J. Phys. Chem. Lett. 2023, 14(22), 5216-5220], explores this strategy to learn slow CVs by maximizing a spectral gap of a transition matrix. In this work, we introduce several advancements into our framework, using a high-dimensional reversible folding process of a protein as an example. We implement an algorithm for coarse-graining Markov transition matrices to partition the reduced space of slow CVs kinetically and use it to define a transition state ensemble. We show that slow CVs learned by spectral map closely approach the Markovian limit for an overdamped diffusion. We demonstrate that coordinate-dependent diffusion coefficients only slightly affect the constructed free-energy landscapes. Finally, we present how spectral maps can be used to quantify the importance of features and compare slow CVs with structural descriptors commonly used in protein folding. Overall, we demonstrate that a single slow CV learned by spectral map can be used as a physical reaction coordinate to capture essential characteristics of protein folding.

The effect of the loop on the thermodynamic and kinetic of single base pair in pseudoknot.

RNA pseudoknots are RNA molecules with specialized three-dimensional structures that play important roles in various biological processes. To understand the functions and mechanisms of pseudoknots, it is essential to elucidate their structures and folding pathways. The most fundamental step in RNA folding is the opening and closing of a base pair. The effect of flexible loops on the base pair in pseudoknots remains unclear. In this work, we use molecular dynamics simulations and Markov state model to study the configurations, thermodynamic and kinetic of single base pair in pseudoknots. We find that the presence of the loop leads to a trap state. In addition, the rate-limiting step for the formation of base pair is the disruption of the trap state, rather than the open state to the closed state, which is quite different from the previous studies on non-pseudoknot RNA. For the thermodynamic parameters in pseudoknots, we find that the entropy difference upon opening the base pair between this simulation and the nearest-neighbor model results from the different entropy of different lengths of loop in solution. The thermodynamic parameters of the stack in pseudoknot are close to the nearest-neighbor parameters. The bases on the loop have different distribution patterns in different states, and the slow transition states of the loop are determined by the orientation of the bases.

State estimation of singularly perturbed Semi-Markov jump coupled neural networks: A two-time-scale event-triggered approach

The state estimation problem of singularly perturbed semi-Markov jump coupled neural networks is investigated in this paper, in which a two-time-scale event-triggered mechanism is proposed to adapt the two-time-scale character of the singularly perturbed coupled neural networks such that mitigate the burden of data communication. A dual-rate sampling strategy is used for slow and fast states such that the proposed two-time-scale event-triggered mechanism contains two independent event-triggered conditions corresponding to the slow and fast states respectively. Based on the two-time-scale event-triggered mechanism, sufficient conditions for the H∞ performance analysis of singularly perturbed semi-Markov jump coupled neural networks are established. Furthermore, under the circumstances that the singularly perturbed parameter does not exceed the presupposed upper bound, a design algorithm is developed for the two-time-scale state estimator to ensure that the error dynamics are stochastically stable. Finally, an example is used to illustrate the effectiveness of this method used in singularly perturbed semi-Markov jump coupled neural networks.

Mechanism and Data-Driven Dual Estimation of Coupling Hydraulic-Thermal States for Steam Heating Networks Considering Multi-Time-Scale Characteristics.

For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.

Selective atomic sieving across metal/oxide interface for super-oxidation resistance

Surface passivation, a desirable natural consequence during initial oxidation of alloys, is the foundation for functioning of corrosion and oxidation resistant alloys ranging from industrial stainless steel to kitchen utensils. This initial oxidation has been long perceived to vary with crystal facet, however, the underlying mechanism remains elusive. Here, using in situ environmental transmission electron microscopy, we gain atomic details on crystal facet dependent initial oxidation behavior in a model Ni-5Cr alloy. We find the (001) surface shows higher initial oxidation resistance as compared to the (111) surface. We reveal the crystal facet dependent oxidation is related to an interfacial atomic sieving effect, wherein the oxide/metal interface selectively promotes diffusion of certain atomic species. Density functional theory calculations rationalize the oxygen diffusion across Ni(111)/NiO(111) interface, as contrasted with Ni(001)/NiO(111), is enhanced. We unveil that crystal facet with initial fast oxidation rate could conversely switch to a slow steady state oxidation.

Dendritic cell force-migration coupling on aligned fiber networks

Dendritic cells (DCs) are antigen-presenting cells that reside in peripheral tissues and are responsible for initiating adaptive immune responses. As gatekeepers of the immune system, DCs need to continuously explore their surroundings, for which they can rapidly move through various types of connective tissue and basement membranes. DC motility has been extensively studied on flat 2D surfaces, yet the influences of a contextual 3D fibrous environment still need to be described. Using ECM-mimicking suspended fiber networks, we show how immature DCs (iDCs) engage in migratory cycles that allow them to transition from persistent migration to slow migratory states. For a subset of iDCs with high migratory potential, we report the organization of protrusions at the front of the cell body, which reverses upon treatment with inflammation agent PGE2. We identify an unusual migratory response to aligned fiber networks, whereby iDCs use filamentous protrusions to attach laterally and exert forces on fibers to migrate independent of fiber alignment. Increasing the fiber diameter from 200 to 500 nm does not significantly affect the migratory response; however, iDCs respond by forming denser actin bundles around larger diameters. Overall, the correlation between force-coupling and random migration of iDCs in aligned fibrous topography offers new insights into how iDCs might move in fibrous environments in vivo.

Predicting transient dynamics in a model of reed musical instrument with slowly time-varying control parameter.

When playing a self-sustained reed instrument (such as the clarinet), initial acoustical transients (at the beginning of a note) are known to be of crucial importance. Nevertheless, they have been mostly overlooked in the literature on musical instruments. We investigate here the dynamic behavior of a simple model of reed instrument with a time-varying blowing pressure accounting for attack transients performed by the musician. In practice, this means studying a one-dimensional non-autonomous dynamical system obtained by slowly varying in time the bifurcation parameter (the blowing pressure) of the corresponding autonomous systems, i.e., whose bifurcation parameter is constant. In this context, the study focuses on the case for which the time-varying blowing pressure crosses the bistability domain (with the coexistence of a periodic solution and an equilibrium) of the corresponding autonomous model. Considering the time-varying blowing pressure as a new (slow) state variable, the considered non-autonomous one-dimensional system becomes an autonomous two-dimensional fast-slow system. In the bistability domain, the latter has attracting manifolds associated with two stable branches of the bifurcation diagram of the system with constant parameter. In the framework of the geometric singular perturbation theory, we show that a single solution of the two-dimensional fast-slow system can be used to describe the global system behavior. Indeed, this allows us to determine, depending on the initial conditions and rate of change of the blowing pressure, which manifold is approached when the bistability domain is crossed and to predict whether a sound is produced during transient as a function of the musician's control.

Eugenol and lidocaine inhibit voltage-gated Na+ channels from dorsal root ganglion neurons with different mechanisms.

Eugenol (EUG) is a bioactive monoterpenoid used as an analgesic, preservative, and flavoring agent. Our new data show EUG as a voltage-gated Na+ channel (VGSC) inhibitor, comparable but not identical to lidocaine (LID). EUG inhibits both total and only TTX-R voltage-activated Na+ currents (INa) recorded from VGSCs naturally expressed on dorsal root ganglion sensory neurons in rats. Inhibition is quick, fully reversible, and dose-dependent. Our biophysical and pharmacological analyses showed that EUG and LID inhibit VGSCs with different mechanisms. EUG inhibits VGSCs with a dose-response relationship characterized by a Hill coefficient of 2, while this parameter for the inhibition by LID is 1. Furthermore, in a different way from LID, EUG modified the voltage dependence of both the VGSC activation and inactivation processes and the recovery from fast inactivated states and the entry to slow inactivated states. In addition, we suggest that EUG, but not LID, interacts with VGSC pre-open-closed states, according to our data.

Metasurface‐Based Mueller Matrix Microscope

AbstractIn conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state‐of‐the‐art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, the study demonstrates a metasurface‐based MMM, i.e., Meta‐MMM, which is equipped with a chip‐integrated, single‐shot metasurface polarization state analyzer (Meta‐PSA). The Meta‐MMM is featured with high‐speed measurement (≈2s per Muller matrix (MM) image), superior operation stability, dual‐color operation, and high measurement accuracy (measurement error 1–2%) for MM imaging. The Meta‐MMM is applied to nanostructure characterization, surface morphology analysis, and discovering birefringent structures in honeybee wings. The Meta‐MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, and material characterization to industry inspection and space exploration.

A Reinterpretation of the Relationship Between Persistent and Resurgent Sodium Currents.

The resurgent sodium current (INaR) activates on membrane repolarization, such as during the downstroke of neuronal action potentials. Due to its unique activation properties, INaR is thought to drive high rates of repetitive neuronal firing. However, INaR is often studied in combination with the persistent or non-inactivating portion of sodium currents (INaP). We used dynamic clamp to test how INaR and INaP individually affect repetitive firing in adult cerebellar Purkinje neurons from male and female mice. We learned INaR does not scale repetitive firing rates due to its rapid decay at subthreshold voltages, and that subthreshold INaP is critical in regulating neuronal firing rate. Adjustments to the Nav conductance model used in these studies revealed INaP and INaR can be inversely scaled by adjusting occupancy in the slow inactivated kinetic state. Together with additional dynamic clamp experiments, these data suggest the regulation of sodium channel slow inactivation can fine-tune INaP and Purkinje neuron repetitive firing rates.

Evaluating Hydrogeomorphic Condition Across Ecosystem States in a Non-tidal, Brackish Peat Marsh of the Florida Coastal Everglades, USA

Emergent marsh and open water have been identified as alternate stable states in tidal marshes with large, relative differences in hydrogeomorphic conditions. In the Florida coastal Everglades, concern has been raised regarding the loss of non-tidal, coastal peat marsh via dieback of emergent vegetation and peat collapse. To aid in the identification of alternate stable states, our objective was to characterize the variability of hydrogeomorphic and biologic conditions using a field survey and long-term monitoring of hydrologic and geomorphic conditions across a range of vegetated (emergent, submerged) and unvegetated (open water) communities, which we refer to as “ecosystem states,” in a non-tidal, brackish peat marsh of the coastal Everglades. Results show (1) linear relationships among field-surveyed geomorphic, hydrologic, and biologic variables, with a 35-cm mean difference in soil surface elevation between emergent and open water states, (2) an overall decline in soil elevation in the submerged state that was related to cumulative dry days, and (3) a 2× increase in porewater salinity during the dry season in the emergent state that was also related to the number of dry days. Coupled with findings from previous experiments, we propose a conceptual model that describes how seasonal hydrologic variability may lead to ecosystem state transitions between emergent and open water alternate states. Since vegetative states are only moderately salt tolerant, as sea-level rise pushes the saltwater front inland, the importance of continued progress on Everglades restoration projects, with an aim to increase the volume of freshwater being delivered to coastal wetlands, is the primary management intervention available to mitigate salinization and slow ecosystem state shifts in non-tidal, brackish peat marshes.

Pre-Steady-State and Steady-State Kinetic Analysis of Butyrylcholinesterase-Catalyzed Hydrolysis of Mirabegron, an Arylacylamide Drug.

The β-adrenergic drug Mirabegron, a drug initially used for the treatment of an overactive bladder, has new potential indications and is hydrolyzed by butyrylcholinesterase (BChE). This compound is one of the only arylacylamide substrates to be catabolized by BChE. A steady-state kinetic analysis at 25 °C and pH 7.0 showed that the enzyme behavior is Michaelian with this substrate and displays a long pre-steady-state phase characterized by a burst. The induction time, τ, increased with substrate concentration (τ ≈ 18 min at maximum velocity). The kinetic behavior was interpreted in terms of hysteretic behavior, resulting from a slow equilibrium between two enzyme active forms, E and E'. The pre-steady-state phase with the highest activity corresponds to action of the E form, and the steady state corresponds to action of the E' form. The catalytic parameters were determined as kcat = 7.3 min-1 and Km = 23.5 μM for the initial (burst) form E, and kcat = 1.6 min-1 and Km = 3.9 μM for the final form E'. Thus, the higher affinity of E' for Mirabegron triggers the slow enzyme state equilibrium toward a slow steady state. Despite the complexity of the reaction mechanism of Mirabegron with BChE, slow BChE-catalyzed degradation of Mirabegron in blood should have no impact on the pharmacological activities of this drug.

Early Identification and Dynamic Stability Evaluation of High-Locality Landslides in Yezhi Site Area, China by the InSAR Method

In mountainous regions, high-locality landslides have the characteristics of a latent disaster process with a wide disaster range, which can easily cause large casualties. Therefore, early landslide identification and dynamic stability evaluation are significant. We first used multi-temporal synthetic aperture radar data to detect potential landslides at Yezhi Site Area during the 2015–2020 period, identifying and mapping a total of 18 active landslides. The study area was found to have an average deformation rate between −15 and 10 mm/y during the period. Then, time series and spatiotemporal deformation characteristics of landslides were examined using interferogram stacking and small baseline interferometry techniques. The results show that the majority of the landslide deformations detected exhibit a periodic variation trend, and the study area was in a slow deformation state before 2017. Finally, combined with detection results, Google Earth optical images, and field investigations, it is concluded that the main factors affecting the time series deformation and spatial distribution of landslides in the study area are rainfall, geological factors, and engineering activities. The results of this study provide valuable technical references and support for early identification and dynamic stability evaluation of regional active landslides in complex terrain, especially for high-locality landslides.

Abstract LT05: METTL8-MEDIATED EPITRANSCRIPTOMIC REGULATION OF GBM CYCLING STATE IS DEPENDENT ON RTK/PI3K/AKT SIGNALING

Abstract Glioblastoma (GBM) is a lethal brain tumor with limited treatment options. Treatment failures are often attributed to the existence of glioma stem cells (GSCs), which confer intratumoral heterogeneity and therapy resistance. Contrary to common belief that cancer cells employ aerobic glycolysis (Warburg effect) to sustain their metabolic requirements, GSCs rely on oxidative phosphorylation (OXPHOS) for energy production. As a result, GSCs are uniquely sensitive to OXPHOS inhibitors (Gboxin), as well as mitochondrial translation inhibitors which disrupt OXPHOS. METTL8 is a newly discovered mitochondrial methyltransferase which catalyzes m3C32 modifications in mitochondrial tRNAsThr/Ser(UCN) (mt-tRNAs). This post-transcriptional modification ensures the optimal structure of mt-tRNAs to facilitate efficient translation of mitochondrial proteins involved in electron transport chain (ETC). Although the epitranscriptomic functions of METTL8 have been well characterized, its roles in cancers (i.e. glioblastoma) remain largely unexplored. In this study, we showed that METTL8 is overexpressed in GBM and high METTL8 expression portends poor patient prognosis. In GSC, METTL8 is under the transcriptional regulation of HIF-1α, and the chromatin accessibility at METTL8 promoter is facilitated by the deposition of histone variant H2AZ. METTL8 depletion downregulated m3C32 modifications in mt-tRNAsThr/Ser(UCN) in GSC, leading to impaired mitochondrial translation and OXPHOS defects. Phenotypically, METTL8 knockdown (KD) compromised GSCs’ self-renewal, proliferation, invasiveness and tumorigenicity. Interestingly, METTL8-KD GSC acquired a slow cycling state which is linked to reduced receptor tyrosine kinase (RTK) signaling. Consistently, METTL8-KD GSC showed lower Akt phosphorylation and exhibited increased sensitivity to mTOR/Akt inhibitors. Finally, our mechanistic findings led to the identification of a novel HIF1-α/Akt inhibitor drug combination which faithfully recapitulates the phenotypes of METTL8-KD GSCs. In conclusion, our findings provide the first in-depth study of METTL8 roles in GBM pathogenesis, and highlights a novel drug combination which could potentially be used to treat this dreadful disease. Citation Format: Bernice Woon Li Lee, You Heng Chuah, Jeehyun Yoon, Oleg V. Grinchuk, Jia Feng, Yajing Liang, Jayshree L Hirpara, Ya Ting Shen, Loo Chien Wang, Yan Ting Lim, Tianyun Zhao, Radoslaw M Sobota, Tan Boon Toh, Shazib Pervaiz, Zhewang Lin, Derrick Sek Tong Ong. METTL8-MEDIATED EPITRANSCRIPTOMIC REGULATION OF GBM CYCLING STATE IS DEPENDENT ON RTK/PI3K/AKT SIGNALING [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr LT05.

DIRECTIONS FOR THE DEVELOPMENT OF THE INNOVATION POTENTIAL OF ENTERPRISES IN THE CONTEXT OF DIGITAL TRANSFORMATIONS

The article is aimed at reviewing and clarifying the theoretical and methodological aspects of forming the concept of "innovation potential of enterprise". To assess the state of development of the innovative potential of Ukrainian enterprises in the context of digital transformation. To substantiate the feasibility of using a digital transformation strategy to achieve the set goals in the current competitive environment. The level of development of the innovation potential of Ukrainian enterprises was measured over a number of years (2010-2021). Methodology. The article uses economic and abstract and logical research methods in combination with methods of analysis, synthesis and graphical representation. This made it possible to assess the level of development of the innovative potential of Ukrainian enterprises according to the main international rating and macroeconomic indicators. Results. The results of the assessment of the development of Ukraine's innovation potential revealed that the cost of implementing innovative developments in the country's GDP is decreasing every year. It was found that Ukrainian enterprises are slowly diversifying their production towards knowledge-intensive goods and services. This slow state of innovation is reflected in global indices, where Ukraine ranks among lower-middle-income countries. The main reason for this situation is the limited state policy in the field of innovation. It is established that at the state level, the development of innovation potential in the context of digital transformations is formed within the framework of scientific research and high-tech start-ups. Practical implications. In order to increase the innovation activity of enterprises and grow their innovation potential, it is advisable to change the values and guidelines of strategic management. In these conditions, the digital transformation strategy, which is considered as part of the overall communication strategy of the enterprise, becomes relevant for application. In the course of implementing the digital transformation strategy, effective communication interaction between the enterprise and its customers is ensured, and a qualitatively new model of communication processes management is formed and applied, taking into account digital technologies. Value/originality. Research on the development of innovation potential in Ukraine proves the need to use a digital transformation strategy as an effective modern tool for managing an enterprise in the context of digital transformation.

Switching Dynamics in Anti‐Ferroelectric Transistor for Multimodal Reservoir Computing

AbstractSpatial‐temporal time series analysis and forecasting are crucial for understanding dynamic systems and making informed decisions. Recurrent neural networks (RNNs) have paved the way for reservoir computing (RC), a method enabling effective temporal information processing at low training costs. While software‐based RC performs well, physical RC systems face challenges like slow processing speed and limited state richness, leading to high hardware costs. This study introduces an innovative approach, i.e., the antiferroelectric field effect transistor‐based RC (AFeFET‐based RC) system for efficient temporal data processing. By exploiting the fading memory property inherent in hafnium oxide‐based antiferroelectric material, this system demonstrates promise for physical RC implementation. Moreover, it leverages the light sensitivity of 2D molybdenum disulfide (MoS2) channels for controllable temporal dynamics under electrical and optical stimuli. This dual‐mode modulation significantly enriches the reservoir state, boosting overall system performance. Experimental tests on standard benchmarking tasks using the AFeFET‐based RC system yielded impressive accuracy results (95.4%) in spoken‐digit recognition and a remarkable normalized root mean square error (NRMSE) of 0.015 in Mackey–Glass time series prediction.

Abstract 535: Resistant PDO models: Unraveling drug resistance mechanisms for discovering new drug targets

Abstract Resistance to cancer treatment remains a significant obstacle in curing patients with cancer. Despite recent advancements in novel drug targets for cancer therapeutics, patients often develop treatment resistance even after initial disease remission, leading to tumor relapse. To investigate the mechanisms responsible for developing resistance to standard-of-care therapies, it is essential to gain insights into the stepwise progression leading to complete resistance. Recent research has revealed that cancer cells can evade the effects of chemotherapy and targeted therapy by entering a reversible and slow proliferation state known as the drug-tolerant persister (DTP) state. This DTP state allows cancer cells to survive drug therapy long enough for additional mechanisms of acquired drug resistance to develop. HUB Organoids® or patient-derived organoid (PDO) models are derived from patient tissues, making them more physiologically relevant than traditional cell lines. Thus, studying drug persistence and resistance in PDO models is more likely to reflect in vivo conditions. This study demonstrates the successful development of persistent and resistant CRC PDO models to standard-of-care therapies. All models were characterized using whole exome sequencing and RNA sequencing to confirm their persistent or resistant state and to identify possible pathways involved in developing therapy resistance, as well as potential new drug targets. In conclusion, developing persistent and resistant PDO models is crucial for understanding drug resistance mechanisms, enabling the development of more effective and personalized cancer treatments, and advancing our overall knowledge of how resistance develops in cancer patients. Citation Format: Yasmine Abouleila, Mayke Doorn, Roel Verkerk, Joris Maas, Carla Verissimo, Sylvia Boj. Resistant PDO models: Unraveling drug resistance mechanisms for discovering new drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 535.

Abstract 5882: Targeting vulnerabilities of drug-tolerant persister cells in gastric cancer

Abstract Cytotoxic drugs often fail to eradicate cancers due to the presence of treatment-persistent tumor cells that represent a reservoir for relapse. These “residual” cancer cells escape from chemotherapy- induced death by entering a reversible slow proliferation state, known as drug tolerant persister (DTP) state. Although improvement of treatment options achieved a survival benefit, Gastric Cancer (GC) is still endowed with poor prognosis. Surgery and neo/adjuvant chemotherapy remain the keystone of treatment. However, most patients with advanced cancer relapse even after complete surgical resection, suggesting a critical role for DTP cells. We obtained GC DTP cells from primary cells upon first- and second-line chemotherapy regimens (FLOT and FOLFIRI). Cells were treated with doses corresponding to the drug maximal plasmatic concentration. At the end of the treatment, cells were left without drugs (washout). During this window of time, when cells had regrown, they were re-challenged with chemotherapy and resulted still sensitive to the treatments, thus fulfilling the criteria for bona fide DTP cells. GC DTPs showed increased expression of markers associated with gastric stem cells (i.e. LGR5), downregulation of proliferative markers (i.e. Ki67) and decreased levels of PS6, evaluated as a readout of mTOR activation. Interestingly, DTPs resulted positive to X-gal-based β-galactosidase staining, suggesting a chemotherapy-induced senescence-like status. RNA-seq analysis and gene expression arrays allowed the identification of genes specifically down/upregulated in DTP cells. Using the intuitive enrichment analysis web-based tool Enrichr, we discovered that 54/173 of the downregulated genes characterizing the persister state were targets of a single miRNA. By quantitative real-time PCR, we validated the expression of this miR in all the models of FOLFIRI- and FLOT-obtained persister cells and we found it strongly upregulated (up to 8-fold), highlighting its possible contribute in the establishment of the persister state. Since among the target genes of this miR there are genes involved in the Homolougus Recombination (HR) system, we performed TaqMan™ Array assay for Human DNA repair gene expression on FOLFIRI-obtained DTPs. We identified a significant downregulation of genes strictly correlated to the HR machinery, suggesting that the FOLFIRI-induced persister cells can temporarily enter in a “BRCAness” status that may be used for a synthetic lethality approach using PARP inhibitors. Targeting the drug tolerant persister state is an essential approach to diminish cancer cells when they are potentially in their most vulnerable state. Citation Format: Elisabetta Puliga, Lisa Negro, Martina Olivero, Claudia Orrù, Fabrizio Maina, Daniela Conticelli, Simona Corso, Silvia Giordano. Targeting vulnerabilities of drug-tolerant persister cells in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5882.

Research Status, Synthesis and Clinical Application of Antiepileptic Drugs.

According to the 2017 ILAE's official definition, epilepsy is a slow brain disease state characterized by recurrent episodes. Due to information released by ILAE in 2017, it can be divided into four types, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and unknown epilepsy. Since 1989, 24 new antiepileptic drugs have been approved to treat different types of epilepsy. Besides, there are a variety of antiepileptic medications under clinical monitoring. These novel antiepileptic drugs have plenty of advantages. Over the past 33 years, there have been many antiepileptic drugs on the mearket, but no one has been found that can completely cure epilepsy. In this paper, the mentioned drugs were classified according to their targets, and the essential information, and clinical studies of each drug were described. The structure-activity relationship of different chemical structures was summarized. This paper provides help for the follow-up research on epilepsy drugs.