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Lanthanide‐doped upconversion nanoparticles (UCNPs) have attractive emission properties but suffer from weak light‐absorbing capacities and thereby relatively low brightnesses. This motivates using strongly absorbing dye molecules as antennas and sensitizers. However, despite much effort, understanding of this dye‐UCNP interplay is still limited. Major sensitization mechanisms are still under discussion, largely because there is a lack of effective means to observe key factors such as dark state transitions within the dyes. Here, a combined spectroscopic procedure is established to systematically investigate the photophysics behind the dye‐UCNP interaction, embracing fluorescence‐based transient‐state excitation‐modulation, lifetime and correlation spectroscopy, and spectrofluorometry/spectrophotometry. With this procedure the heptamethine cyanine dye IR806, a typical UCNP sensitizer is studied, its photophysical model is established, its photophysics in UCL‐sensitization‐related environments is deciphered, and the energy transfer from the IR806 singlet excited state to Yb3+ (UCNP sensitizer ion) can be identified as the dominant sensitization mechanism. These studies suggest that IR806 can form non‐emissive H‐aggregates at the nanoparticle surfaces, which can be dissociated after certain light excitation duration (typically>100 µs). Moreover, buildup of a non‐fluorescent, photo‐redox state of IR806 after longer irradiation times (10–100 ms) can deleteriously affect its UCL sensitization effect, inferring an optimal excitation duration for dye‐sensitized UCNPs, relevant for, e.g., optical imaging applications. [ABSTRACT FROM AUTHOR]

The assessment of compound blood‐brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor‐intensive, cost‐ineffective, and time‐consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease. [ABSTRACT FROM AUTHOR]

Aim: Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high‐risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR. Methods: We enrolled 53 individuals with CHR who completed a 5‐year follow‐up with a confirmed conversion to psychosis. Granulocyte macrophage‐colony stimulating factor (GM‐CSF), interleukin (IL)‐1β, 2, 6, 8, 10, tumor necrosis factor‐α (TNF‐α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1‐year. Correlation and quantile regression analyses were performed. Results: The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF‐α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF‐α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM‐CSF levels below the 0.5 quantile, IL‐10 levels below the 0.3 quantile, IL‐2 levels below the 0.25 quantile, IL‐6 levels between the 0.65 and 0.75 quantiles, TNF‐α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL‐10 and IL‐2, and significant group effects for changes in IL‐2 and TNF‐α. Conclusions: Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles. [ABSTRACT FROM AUTHOR]

Fragment‐based drug design (FBDD) has emerged as a captivating subject in the realm of computer‐aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library. The fragment library comprises seed fragments and collected fragments. The extraction of seed fragments is guided by receptor information, serving as a prerequisite for establishing a focused libraries. Conversely, collected fragments are obtained using the feature graph method, which offers a simplified representation of fragments and strikes a balance between diversity and similarity when categorizing different fragments. The utilization of feature graph facilitates the rational partitioning of chemical space at fragment level, enabling the exploration of desired chemical space and enhancing the efficiency of screening compound library. Analysis demonstrates that our workflow enables the enumeration of a greater number of entirely new potential compounds, thereby aiding in the rational design of drugs. [ABSTRACT FROM AUTHOR]

Objectives: The efficacy of electroconvulsive therapy (ECT) in treating mood disorders (MDs) is hypothesized to be mediated by the induction of neurotrophic factors (denoted "angioneurins") that trigger neuronal plasticity. This study aimed to assess the effects of ECT on serum angioneurin levels in patients with MD. Methods: A total of 110 patients with MDs including 30 with unipolar depression, 25 with bipolar depression (BD), 55 with bipolar mania (BM), and 50 healthy controls were included in the study. Patients were subdivided into two groups: those who received ECT + medication (12 ECT sessions) and those who received only medication (no‐ECT). Depressive and manic symptom assessments and measurements of vascular endothelial growth factor (VEGF), fibroblast growth factor‐2, nerve growth factor (NGF), and insulin‐like growth factor‐1 levels in blood samples were performed at baseline and week 8. Results: Patients in the ECT group, specifically those with BD and BM, had significantly increased levels of VEGF compared to their baseline VEGF levels (p = 0.002). No significant changes in angioneurin levels were observed in the no‐ECT group. Serum NGF levels were significantly associated with a reduction in depressive symptoms. Angioneurin levels were not associated with manic symptom reduction. Conclusions: This study hints that ECT may increase VEGF levels with angiogenic mechanisms that amplify NGF signaling to promote neurogenesis. It may also contribute to changes in brain function and emotional regulation. However, further animal experiments and clinical validation are needed. [ABSTRACT FROM AUTHOR]

Optimization of lanthanide‐doped luminescent nanoparticles for use in nanomedicine has encountered some difficulties due to the specific properties of water as a solvent. In this review, the current challenges for the adaptation of lanthanide‐doped luminescent nanoparticles to aqueous environments, and promising strategies to optimize their colloidal dispersibility and stability in water and physiological buffers, are summarized. Moreover, the possible luminescence de‐excitation paths caused by water molecule vibrations and how they can be prevented under different measurement conditions are discussed. This review also deals with the latest developments in lanthanide‐doped luminescent nanoparticle design for nanomedicine, to increase the depth at which they can be monitored, which is mainly limited by the absorption bands of water. Furthermore, the anomalous temperature dependence of water and the different effects it has on lanthanide‐doped luminescent nanoparticles in the physiological temperature range are commented on. Finally, a critical opinion on the possible next steps in this field is provided. [ABSTRACT FROM AUTHOR]

The toxicity of compounds is closely related to the effectiveness and safety of drug development, and accurately predicting the toxicity of compounds is one of the most challenging tasks in medicinal chemistry and pharmacology. In this paper, we construct three types of models for single and multi‐tasking based on 2D and 3D descriptors, fingerprints and molecular graphs, and then validate the models with benchmark tests on the Tox21 data challenge. We found that due to the information sharing mechanism of multi‐task learning, it could address the imbalance problem of the Tox21 data sets to some extent, and the prediction performance of the multi‐task was significantly improved compared with the single task in general. Given the complement of the different molecular representations and modeling algorithms, we attempted to integrate them into a robust Co‐Model. Our Co‐Model performs well in various evaluation metrics on the test set and also achieves significant performance improvement compared to other models in the literature, which clearly demonstrates its superior predictive power and robustness. [ABSTRACT FROM AUTHOR]

Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation. [ABSTRACT FROM AUTHOR]

Background: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high‐grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods: The images were collected from 6002 colposcopy examinations of normal control, low‐grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic‐acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet‐b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results: The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion: Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer. [ABSTRACT FROM AUTHOR]

Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target‐focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R‐groups of known ligands. This criterion is based on both ligand‐ and structure‐based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand‐ and structure‐based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine‐protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability. [ABSTRACT FROM AUTHOR]

Individuals at clinical high risk (CHR) for psychosis exhibit a compromised mismatch negativity (MMN) response, which indicates dysfunction of pre‐attentive deviance processing. Event‐related potential and time‐frequency (TF) information, in combination with clinical and cognitive profiles, may provide insight into the pathophysiology and psychopathology of the CHR stage and predict the prognosis of CHR individuals. A total of 92 individuals with CHR were recruited and followed up regularly for up to 3 years. Individuals with CHR were classified into three clinical subtypes demonstrated previously, specifically 28 from Cluster 1 (characterized by extensive negative symptoms and cognitive deficits), 31 from Cluster 2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 33 from Cluster 3 (characterized by the mildest symptoms and cognitive deficits). Auditory MMN to frequency and duration deviants was assessed. The event‐related spectral perturbation (ERSP) and inter‐trial coherence (ITC) were acquired using TF analysis. Predictive indices for remission were identified using logistic regression analyses. As expected, reduced frequency MMN (fMMN) and duration MMN (dMMN) responses were noted in Cluster 1 relative to the other two clusters. In the TF analysis, Cluster 1 showed decreased theta and alpha ITC in response to deviant stimuli. The regression analyses revealed that dMMN latency and alpha ERSP to duration deviants, theta ITC to frequency deviants and alpha ERSP to frequency deviants, and fMMN latency were significant MMN predictors of remission for the three clusters. MMN variables outperformed behavioral variables in predicting remission of Clusters 1 and 2. Our findings indicate relatively disrupted automatic auditory processing in a certain CHR subtype and a close affinity between these electrophysiological indexes and clinical profiles within different clusters. Furthermore, MMN indexes may serve as predictors of subsequent remission from the CHR state. These findings suggest that the auditory MMN response is a potential neurophysiological marker for distinct clinical subtypes of CHR. [ABSTRACT FROM AUTHOR]

Introduction: Impaired sensitivity of the skin flush response to niacin is found in approximately 30% of patients with schizophrenia. Although the niacin response abnormality (NRA) may serve as a useful endophenotype for schizophrenia, few studies have directly replicated NRA in patients with first‐episode psychosis (FEP). Methods: In total, 204 patients with FEP, 16 with psychotic mood disorder (PMD), and 68 healthy controls (HC) were included. The log10(EC50) values represent the concentration of methyl nicotinate required to elicit a half‐maximal blood flow (MBF) response, and the MBF value was calculated. The NRA was defined as having log10(EC50) molar value above the 90% and an MBF value below the 60% of those in the HC group. Results: In total, 13.7% of the FEP, 12.5% of the PMD, and 7.4% of the HC group met the definition of NRA. Significant differences were found in the log10(EC50) values between the FEP and HC groups (p =.014) and in the MBF between the FEP and PMD groups (p =.011). Patients with FEP and NRA had more severe negative symptoms than those with a normal niacin response. Discussion: These data represent the NRA in patients with FEP, defining a small subgroup of patients with early‐phase psychosis possessing a clinically significant phospholipid‐signaling defect. [ABSTRACT FROM AUTHOR]

This study aimed to probe into the effect of TRIM33 on oxidative stress‐induced apoptosis of osteoblasts in osteoporosis and to probe into the underlying mechanism. The apoptosis of osteoblasts was induced by H2O2 treatment and tested by flow cytometry. A mouse osteoporosis model was conducted by ovariectomy (OVX). The function of TRIM33 was assessed by in vitro and in vivo experiments. The mechanism of TRIM33 was determined by immunoprecipitation, immunofluorescent staining and co‐transfection experiments. Here, we found that TRIM33 expression was lessened in the osteoblasts of patients with osteoporosis and was positively correlated with the bone mineral density of these patients. FOXO3a and TRIM33 were co‐localized in the osteoblasts nuclei. TRIM33 silence boosted FOXO3a degradation in normal osteoblasts, while TRIM33 overexpression restrained FOXO3a degradation in H2O2‐treated osteoblasts. The binding of TRIM33 to CBP and its overexpression restrained CBP‐mediated FOXO3a acetylation, thereby attenuating FOXO3a ubiquitylation. The H2O2‐induced apoptosis of osteoblasts was restrained by TRIM33 overexpression, while the FOXO3a knockdown reversed this trend. The in vivo experiments corroborated that TRIM33 overexpression attenuated the OVX‐driven impacts in mice. In general, our findings expounded that TRIM33 protected osteoblasts against oxidative stress‐induced apoptosis in osteoporosis and that the underlying mechanism was the restraint of FOXO3a ubiquitylation and degradation. [ABSTRACT FROM AUTHOR]

Multidrug resistance is recognized as one of the main reasons leading to the failure of chemotherapy. Studies have shown that glutathione S‐transferase inhibitors could be regarded as multidrug resistance reversal agents. Herein, a method of applying enzyme immobilization, molecular docking, and high‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was employed to screen glutathione S‐transferase inhibitors from natural products. Magnetic mesoporous silica microspheres were synthesized and modified with a poly(dopamine) layer, which has a large quantity of amino, enabling further non‐covalent binding with glutathione S‐transferase. Moreover, the immobilization conditions, namely, potential of hydrogen, catalase concentration, reaction temperature and reaction time, were optimized. In total, six potential compounds were isolated and identified from Perilla frutescens (L.) Britt leaves and green tea and molecular docking was applied to identify the binding site. Rosmarinic acid, (−)‐epigallocatechin‐3‐O‐gallate and (−)‐epicatechin‐3‐gallate showed higher binding affinity than the compounds, and their half maximal inhibitory concentration values were further determined. The results suggested that this proposed method was effective and convenient for identifying glutathione S‐transferase inhibitors from natural products. [ABSTRACT FROM AUTHOR]

Human ether‐a‐go‐go‐related gene (hERG) K+ channel blockage may cause severe cardiac side‐effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates. [ABSTRACT FROM AUTHOR]

Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k‐Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross‐validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure‐based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure‐based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target. [ABSTRACT FROM AUTHOR]

Abstract: Lanthanide‐doped photon upconversion nanoparticles (UCNPs) are capable of converting low‐intensity near‐infrared light to UV and visible emission through the synergistic effects of light excitation and mutual interactions between doped ions. UCNPs have attracted strong interest as unique spectrum converters and found a multitude of applications in areas like biomedical imaging, energy harvesting and information technology. UCNPs are distinct from many other types of luminescent materials in terms of the involvement of a host lattice and multiple optical centers, i.e., trivalent lanthanide ions with manyfolds of accessible long‐lived energy states, in individual nanoparticles. The mutual interactions between these optical centers, i.e., sequential energy transfers, make them operate as an integrated unit and co‐determine the luminescence kinetics and other optical properties of the individual nanoparticle. Thus, each nanoparticle consititutes a kinetic optical system. In this work, we explore UCNPs from the outset of being such kinetic optical systems and review their physical formation, the underlying photophysics, macroscopic statistical description, and their response to various optical stimuli in the spectral, polarization, intensity, temporal and frequency domains, and demonstrate ways that their optical output can be optimized by manipulating the excitation schemes. Our review highlights upconversion nanotechnology as an interdisciplinary field across chemistry, physics and biomedical engineering, with great future possibilities, flexibility and ramifications. We outline some of the potential directions of upconversion nanoparticle research. [ABSTRACT FROM AUTHOR]

Sodium-dependent glucose cotransporters (SGLTs) play an important role in glucose reabsorption in the kidney and have been identified as promising targets to treat diabetes. Because of the side effects like glucose and galactose malabsorption by targeting SGLT1, highly selective SGLT2 inhibitors are more promising in the treatment of diabetes. To understand the mechanism of selectivity, we conducted selectivity-based three-dimensional quantitative structure-activity relationship studies to highlight the structure requirements for highly selective SGLT2 inhibitors. The best comparative molecular field analysis and comparative molecular similarity indices analysis models showed the noncross-validated coefficient ( r2) of 0.967 and 0.943, respectively. The predicted correlation coefficients ( r2pred) of 0.974 and 0.938 validated the reliability and predictability of these models. Besides, homology models of SGLT2 and SGLT1 were also constructed to investigate the selective mechanism from structure-based perspective. Molecular dynamics simulation and binding free energy calculation were performed on the systems of a potent and selective compound interacting with SGLT2 and SGLT1 to compare the different binding modes. The simulation results showed that the stretch of the methylthio group on Met241 had an essential effect on the different binding modes between SGLT1 and SGLT2, which was consistent with the three-dimensional quantitative structure-activity relationship analysis. Hydrogen bond analysis and binding free energy calculation revealed that SGLT2 binding complex was more stable and favorable than SGLT1 complex, which was highly correlated with the experimental results. Our obtained results give useful information for the investigation of the inhibitors' selectivity between SGLT2 and SGLT1 and will help for further development of highly selective SGLT2 inhibitors. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Upconverting nanoparticles (UCNPs) are a class of recently developed luminescent biomarkers that - in several aspects - are superior to organic dyes and quantum dots. UCNPs can emit spectrally narrow anti-Stokes shifted light with quantum yields which greatly exceed those of two-photon dyes for fluence rates relevant for deep tissue imaging. Compared with conventionally used Stokes-shifting fluorophores, UCNP-based imaging systems can acquire completely autofluorescence-free data with superb contrast. For diffuse optical imaging, the multi-photon process involved in the upconversion process can be used to obtain images with unprecedented resolution. These unique properties make UCNPs extremely attractive in the field of biophotonics. UCNPs have already been applied in microscopy, small-animal imaging, multi-modal imaging, highly sensitive bioassays, temperature sensing and photodynamic therapy. In this review, the current state-of-the-art UCNPs and their applications for diffuse imaging, microscopy and sensing targeted towards solving essential biological issues are discussed. [ABSTRACT FROM AUTHOR]