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Prodrugs are bioreversible, inactive drug derivatives, which have the ability to convert into a parent drug in the body. In the past, prodrugs were used as a last option; however, nowadays, prodrugs are considered already in the early stages of drug development. Optimal prodrug needs to have effective absorption, distribution, metabolism, and elimination (ADME) features to be chemically stable, to be selective towards the particular site in the body, and to have appropriate safety. Traditional prodrug approach aims to improve physicochemical/biopharmaceutical drug properties; modern prodrugs also include cellular and molecular parameters to accomplish desired drug effect and site-specificity. Here, we present recently investigated prodrugs, their pharmaceutical and clinical advantages, and challenges facing the overall prodrug development. Given examples illustrate that prodrugs can accomplish appropriate solubility, increase permeability, provide site-specific targeting (i.e., to organs, tissues, enzymes, or transporters), overcome rapid drug metabolism, decrease toxicity, or provide better patient compliance, all with the aim to provide optimal drug therapy and outcome. Overall, the prodrug approach is a powerful tool to decrease the time/costs of developing new drug entities and improve overall drug therapy.

Infectious diseases still remain one of the major causes of death worldwide, despite the fact that various treatments, such as antibiotics, antiviral drugs, and vaccines for some diseases, are more available to people. Factors such as drug resistance, lack of access to health care, and environmental changes contribute to their persistence and spread. Motivated by this fact, in this study, the stochastic susceptible-infectious-recovered (SIR) epidemiological model with treatment and non-linear incidence rate is extended, by introducing coloured noise, to model which takes into account the seasonal nature of the disease, as well as the fact that the disease is constantly changing through mutations, which leads to the appearance of new disease strains. For the model formulated in this way, we first prove the existence and uniqueness of the global positive solution. Then, we provide conditions under which the disease persists in the population, as well as sufficient conditions for the disease to die out. The theoretical results of the current study are validated by numerical simulations. For that purpose, we use data on the spread of the Ebola epidemic in Sierra Leone and the coronavirus disease 2019 (COVID-19) pandemic in Pakistan. Both theoretical and numerical results can lead us to conclusion that our model represents the solid research base for further investigation in the field of epidemiological modelling.

Competitive circuits in the amygdala of mice drive either freezing or flight behaviour in response to threat, and involve distinct neuronal subtypes. Freeze or flee — choosing the best response to danger The appropriate selection of either a passive or an active fear response when faced with a threat is critical to an animal's survival, but how that decision is made remains poorly understood. Here, Andreas Lüthi and colleagues describe competitive circuits in the amygdala that involve distinct neuronal subtypes and drive either the freezing or the flight behaviour. When faced with threat, the survival of an organism is contingent upon the selection of appropriate active or passive behavioural responses 1 , 2 , 3 . Freezing is an evolutionarily conserved passive fear response that has been used extensively to study the neuronal mechanisms of fear and fear conditioning in rodents 4 . However, rodents also exhibit active responses such as flight under natural conditions 2 . The central amygdala (CEA) is a forebrain structure vital for the acquisition and expression of conditioned fear responses, and the role of specific neuronal sub-populations of the CEA in freezing behaviour is well-established 1 , 5 , 6 , 7 . Whether the CEA is also involved in flight behaviour, and how neuronal circuits for active and passive fear behaviour interact within the CEA, are not yet understood. Here, using in vivo optogenetics and extracellular recordings of identified cell types in a behavioural model in which mice switch between conditioned freezing and flight, we show that active and passive fear responses are mediated by distinct and mutually inhibitory CEA neurons. Cells expressing corticotropin-releasing factor (CRF + ) mediate conditioned flight, and activation of somatostatin-positive (SOM + ) neurons initiates passive freezing behaviour. Moreover, we find that the balance between conditioned flight and freezing behaviour is regulated by means of local inhibitory connections between CRF + and SOM + neurons, indicating that the selection of appropriate behavioural responses to threat is based on competitive interactions between two defined populations of inhibitory neurons, a circuit motif allowing for rapid and flexible action selection.

Accurate multi-horizon forecasting of reservoir water levels is essential for effective water resource management and flood risk mitigation. While deep learning models have demonstrated strong predictive capabilities, they often struggle to adequately represent uncertainty and extreme hydrological events, particularly at longer forecast horizons. This study proposes four variants of a Conv1D–LSTM–Temporal Attention (CLTA) architecture for probabilistic multi-horizon forecasting, differing exclusively in loss function design. The models incorporate non-crossing constraints, horizon-aware weighting, and event-aware weighting to address key limitations of standard quantile regression approaches. All models are trained on hourly water level data from May 2021 to October 2022 and evaluated on a fully unseen dataset spanning December 2022 to May 2023. The results demonstrate that horizon-aware weighting achieves the lowest average RMSE (0.0149) and the most stable performance across forecast horizons on unseen data, while event-aware weighting improves representation of extreme hydrological events and achieves the highest coefficient of determination (R2=0.9961). However, a controlled experiment further reveals that model performance is strongly influenced by the data partitioning strategy, even when architecture and loss formulation are held constant. Overall, the findings indicate that loss function design, in interaction with data partitioning strategy, is a key contributing factor to model performance in deep learning-based hydrological forecasting. A Multi-Criteria Decision Analysis (MCDA) framework identifies the horizon-weighted model as the most robust general-purpose solution, while the event-aware model is preferable for applications focused on extreme event representation. These results highlight the importance of integrating domain knowledge into both model design and evaluation strategy, offering a scalable and computationally efficient alternative to increasing architectural complexity.

Learning drives behavioral adaptations necessary for survival. While plasticity of excitatory projection neurons during associative learning has been extensively studied, little is known about the contributions of local interneurons. Using fear conditioning as a model for associative learning, we found that behaviorally relevant, salient stimuli cause learning by tapping into a local microcircuit consisting of precisely connected subtypes of inhibitory interneurons. By employing deep-brain calcium imaging and optogenetics, we demonstrate that vasoactive intestinal peptide (VIP)-expressing interneurons in the basolateral amygdala are activated by aversive events and provide a mandatory disinhibitory signal for associative learning. Notably, VIP interneuron responses during learning are strongly modulated by expectations. Our findings indicate that VIP interneurons are a central component of a dynamic circuit motif that mediates adaptive disinhibitory gating to specifically learn about unexpected, salient events, thereby ensuring appropriate behavioral adaptations.

Benzodiazepines are frequently encountered in crime scenes, often mixed with adulterants and diluents, complicating their analysis. This study investigates the interactions between two benzodiazepines, lorazepam (LOR) and alprazolam (ALP), with common adulterants/diluents (paracetamol, caffeine, glucose, and lactose) using infrared (IR) spectroscopy and quantum chemical methods. The crystallographic structures of LOR and ALP were optimized using several functionals (B3LYP, B3LYP-D3BJ, B3PW91, CAM-B3LYP, M05-2X, and M06-2X) combined with the 6-311++G(d,p) basis set. M05-2X was the most accurate when comparing experimental and theoretical bond lengths and angles. Vibrational and 13C NMR spectra were calculated to validate the functional’s applicability. The differences between LOR’s experimental and theoretical IR spectra were attributed to intramolecular interactions between LOR monomers, examined through density functional theory (DFT) optimization and quantum theory of atoms in molecules (QTAIM) analysis. Molecular dynamics simulations modeled benzodiazepine–adulterant/diluent systems, predicting the most stable structures, which were further analyzed using QTAIM. The strongest interactions and their effects on IR spectra were identified. Comparisons between experimental and theoretical spectra confirmed spectral changes due to interactions. This study demonstrates the potential of quantum chemical methods in analyzing complex mixtures, elucidating spectral changes, and assessing the structural stability of benzodiazepines in forensic samples.

Accurate representation of short-term reservoir water-level dynamics is essential for operational analysis and scenario-based assessment under prescribed inflow–outflow conditions. In many practical applications, physically based modelling is limited by incomplete process knowledge, unavailable boundary conditions, or insufficient temporal resolution of input data. This study presents a data-driven framework for hourly conditional simulation of reservoir water level based on a hybrid Conv1D–LSTM architecture. The model learns nonlinear relationships among hydraulic forcing, operational control, and system state from historical observations, and is evaluated in a recursive multi-step simulation (rollout) mode to reflect its intended use and capture error accumulation over time. A systematic analysis of input sequence length and activation function is performed to identify a robust model configuration. On the test set, the selected configuration (L = 24, GELU) achieved RMSE = 0.1057 m, MAE = 0.0881 m, and R2 = 0.972 in rollout evaluation. The proposed framework is designed for scenario-based simulation rather than one-step deterministic forecasting, enabling rapid operational screening of alternative inflow–outflow regimes. Unlike many previous studies that emphasize one-step predictive accuracy, this work explicitly assesses model stability in recursive multi-step simulation, which is more relevant for reservoir scenario analysis.

Background Ethnobotanical research in Southeast Europe—one of the most important European hotspots for biocultural diversity—is significant for the acquisition of Traditional Ecological Knowledge related to plants as well as for encouraging the development of local environments. The current ethnobotanical research was conducted in the region of Mt Stara Planina (south-eastern Serbia), which is characterised by rich phytodiversity with a large number of endemic and relict plant species. The aim of the study was to document the diversity of uses of medicinal plants and of traditional knowledge on their therapeutic uses. Methods Ethnobotanical data was collected through both open and semi-structured interviews with locals. Fifty-one inhabitants were interviewed (26 men and 25 women), aged 30–91, and data was analysed by means of use reports, citation frequency, use values (UV), and the informant consensus factor (ICF). Results The study identified 136 vascular medicinal plant taxa and one lichen species belonging to 53 families and 116 genera. Lamiaceae (19), Rosaceae (18), and Asteraceae (17) had the highest species diversity. The plant parts most commonly used to make a variety of herbal preparations were the aerial parts (54 citations), leaves (35 citations), fruits (20 citations), flowers (18 citations), and roots (16 citations), while the most common forms of preparation were teas (60.78%), consumption of fresh tubers, leaves, roots, and fructus (6.86%), compresses (5.88%), juices (5.39%), decoctions (3.92%), ‘travarica’ brandy (3.92%), and syrups (2.45%). Of the recorded species, 102 were administered orally, 17 topically, and 18 both orally and topically. The plants with a maximum use value (UV = 1) were Allium sativum, Allium ursinum, Gentiana asclepiadea, Gentiana cruciata, Gentiana lutea, Hypericum perforatum, Thymus serpyllum and Urtica dioica . The highest ICF value (ICF = 0.95) was recorded in the categories of Skin and Blood, Blood Forming Organs, and Immune Mechanism. Conclusions This study shows that medicinal plants in the research area are an extremely important natural resource for the local population as they are an important component of their health culture and provide a better standard of living.

New Ni (II) and Cu (II) complexes with pyridoxal-semicarbazone were synthesized and their structures were solved by X-ray crystallography. This analysis showed the bis-ligand octahedral structure of [Ni(PLSC-H)2]·H2O and the dimer octahedral structure of [Cu(PLSC)(SO4)(H2O)]2·2H2O. Hirshfeld surface analysis was employed to determine the most important intermolecular interactions in the crystallographic structures. The structures of both complexes were further examined using density functional theory and natural bond orbital analysis. The photocatalytic decomposition of methylene blue in the presence of both compounds was investigated. Both compounds were active toward E. coli and S. aureus, with a minimum inhibition concentration similar to that of chloramphenicol. The obtained complexes led to the formation of free radical species, as was demonstrated in an experiment with dichlorofluorescein-diacetate. It is postulated that this is the mechanistic pathway of the antibacterial and photocatalytic activities. Cyclic voltammograms of the compounds showed the peaks of the reduction of metal ions. A molecular docking study showed that the Ni(II) complex exhibited promising activity towards Janus kinase (JAK), as a potential therapy for inflammatory diseases, cancers, and immunologic disorders.

Lysergic acid diethylamide (LSD) and its analogs are commonly encountered substances at crime scenes due to their misuse as hallucinogenic compounds. Modern methods have led to synthesizing different LSD analogs with pronounced physiological effects. Theoretical methods can be a valuable tool for predicting the spectra and stability of novel substances, especially when experimental data are partially available. The current work describes the application of theoretical methods in predicting IR, NMR, UV–VIS, and MS spectra of LSD based on the optimized structure at the M05-2X/6-311++G(d,p) level of theory. A suitable functional has been determined by comparison of the theoretically obtained geometrical parameters with the experimental ones based on the crystallographic structure. The MAE values for the structure optimized at M05-2X/6-311++G(d,p) level of theory were 0.0436 Å (bond lengths) and 2.70° (bond angles). The IR spectra of LSD and LSD tartrate have been described in detail, with the prominent bands being well reproduced (the difference between experimental and theoretical C=O stretching vibration wavenumbers was lower than 11 cm−1). Detailed assignment of 13C NMR spectra led to a high correlation factor (0.999) and low mean absolute error (2.0 ppm) between experimental and theoretical chemical shifts. Optimizing the ground and excited states allowed for the calculation of the energy difference of 330 nm, which reproduced the observed band position in the UV–VIS spectrum of LSD. The most abundant fragments in the experimental mass spectrum (at 323, 221, 207, 181, and 72 m/z) have been optimized, and their stability has been discussed from the structural point of view. This methodology has been validated by comparison with the experimental GC-MS spectra of sample seized at the crime screen and by structure optimization and computation of NMR spectra of common LSD analogs. The theoretical methods for the structure determination and prediction of spectra show great potential in the fast-developing world of new psychedelics.