Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data.

BACKGROUND: Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing. AIM: This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques. METHOD: We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted. RESULTS: A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively. CONCLUSION: We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.

Trefoil factor 3 can stimulate Th17 cell response in the development of type 2 diabetes mellitus.

This study aims to evaluate the role of trefoil factor 3 (TFF3) peptides in type 2 diabetes mellitus (T2DM) from an inflammatory perspective. The focus was on exploring how TFF3 affects the function of T cells. TFF3 overexpression model was constructed using lentivirus in Jurkat cell lines. We evaluated the impact of TFF3 on the proliferation, apoptosis, and IL-17A levels of Jurkat cells cultured in high glucose. The T2DM model was induced in TFF3 knockout (KO) mice through streptozotocin combined with high-fat diet. The measurements included glucose tolerance, insulin tolerance, inflammation markers, Th17 cell proportion, and pancreatic pathological changes. The T2DM modeling led to splenomegaly in mice, and increased expression of TFF3 in their spleens. Overexpression of TFF3 increased the proportion of IL-17+ T cells and the levels of Th17-related cytokines in Jurkat cells. There was no difference in body weight and blood glucose levels between wild-type and TFF3 KO mice. However, T2DM mice lacking the TFF3 gene showed improved glucose utilization, ameliorated pancreatic pathology, decreased inflammation levels, and reduced Th17 cell ratio. TFF3 may be involved in the chronic inflammatory immune response in T2DM. Its mechanism may be related to the regulation of the RORγt/IL-17 signaling pathway and its impact on T cell proliferation and apoptosis.

MyoD1 promotes the transcription of BIK and plays an apoptosis-promoting role in the development of gastric cancer.

Myogenic differentiation (MyoD) 1, which is known as a pivotal transcription factor during myogenesis, has been proven dysregulated in several cancers. However, litter is known about the precise role and downstream genes of MyoD1 in gastric cancer (GC) cells. Here, we report that MyoD1 is lowly expressed in primary GC tissues and cells. In our experiments, overexpression of MyoD1 inhibited cell proliferation. Downstream genes of MyoD1 regulation were investigated using RNA-Seq. As a result, 138 up-regulated genes and 20 down-regulated genes and 27 up-regulated lncRNAs and 20 down-regulated lncRNAs were identified in MyoD1 overexpressed MKN-45 cells, which participated in epithelial cell signaling in Helicobacter pylori infection, glycosaminoglycan biosynthesis (keratan sulfate), notch signaling pathway, and others. Among these genes, BIK was directly regulated by MyoD1 in GC cells and inhibited cancer cell proliferation. The BIK knockdown rescued the effects of MyoD1 overexpression on GC cells. In conclusion, MyoD1 inhibited cell proliferation via 158 genes and 47 lncRNAs downstream directly or indirectly that participated in multiple signaling pathways in GC, and among these, MyoD1 promotes BIK transcription by binding to its promoter, then promotes BIK-Bcl2-caspase 3 axis and regulates GC cell apoptosis.

Transition of intracranial aneurysmal wall enhancement from high to low wall shear stress mediation with size increase: A hemodynamic study based on 7T magnetic resonance imaging.

Background: Wall shear stress (WSS) has been proved to be related to the formation, development and rupture of intracranial aneurysms. Aneurysm wall enhancement (AWE) on magnetic resonance imaging (MRI) can be caused by inflammation and have confirmed its relationship with low WSS. High WSS can also result in inflammation but the research of its correlation with AWE is lack because of the focus on large aneurysms limited by 3T MRI in most previous studies.This study aimed to assess the potential association between high or low WSS and AWE in different aneuryms. Especially the relationship between high WSS and AWE in small aneurysm. Methods: Forty-three unruptured intracranial aneurysms in 42 patients were prospectively included for analysis. 7.0 T MRI was used for imaging. Aneurysm size was measured on three-dimensional time-of-flight (TOF) images. Aneurysm-to-pituitary stalk contrast ratio (CRstalk) was calculated on post-contrast black-blood T1-weighted fast spin echo sequence images. Hemodynamics were assessed by four-dimensional flow MRI. Results: The small aneurysms group had more positive WSS-CRstalk correlation coefficient distribution (dome: 78.6 %, p = 0.009; body: 50.0 %, p = 0.025), and large group had more negative coefficient distribution (dome: 44.8 %, p = 0.001; body: 69.0 %, p = 0.002). Aneurysm size was positively correlated with the significant OSI-CRstalk correlation coefficient at the dome (p = 0.012) and body (p = 0.010) but negatively correlated with the significant WSS-CRstalk correlation coefficient at the dome (p < 0.001) and body (p = 0.017). Conclusion: AWE can be mediated by both high and low WSS, and translate from high WSS- to low WSS-mediated pathways as size increase. Additionally, AWE may serve as an indicator of the stage of aneurysm development via different correlations with hemodynamic factors.

Intravenous immunoglobulin alleviates Japanese encephalitis virus-induced peripheral neuropathy by inhibiting the ASM/ceramide pathway.

Japanese encephalitis virus (JEV) infection is considered a global public health emergency. Severe peripheral neuropathy caused by JEV infection has increased disability and mortality rates in recent years. Because there are very few therapeutic options for JEV infection, prompt investigations of the ability of clinically safe, efficacious and globally available drugs to inhibit JEV infection and ameliorate peripheral neuropathy are urgently needed. In this study, we found that high doses of intravenous immunoglobulin, a function inhibitor of acid sphingomyelinase (FIASMA), inhibited acid sphingomyelinase (ASM) and ceramide activity in the serum and sciatic nerve of JEV-infected rats, reduced disease severity, reversed electrophysiological and histological abnormalities, significantly reduced circulating proinflammatory cytokine levels, inhibited Th1 and Th17 cell proliferation, and suppressed the infiltration of inflammatory CD4 + cells into the sciatic nerve. It also maintained the peripheral nerve-blood barrier without causing severe clinical side effects. In terms of the potential mechanisms, ASM was found to participate in immune cell differentiation and to activate immune cells, thereby exerting proinflammatory effects. Therefore, immunoglobulin is a FIASMA that reduces abnormal immune responses and thus targets the ASM/ceramide system to treat peripheral neuropathy caused by JEV infection.

Fecal Microbiota Transplantation Improves Clinical Symptoms of Fibromyalgia: An open-label, Randomized, Nonplacebo-Controlled Study.

Fibromyalgia (FM) is a complex and poorly understood disorder characterized by chronic and widespread musculoskeletal pain, of which the etiology remains unknown. Now, the disorder of the gut microbiome is considered as one of the main causes of FM. This study was aimed to investigate the potential benefits of fecal microbiota transplantation (FMT) in patients with FM. A total of 45 patients completed this open-label randomized, nonplacebo-controlled clinical study. The Numerical Rating Scale (NRS) scores in the FMT group were slightly lower than the control group at 1 month (P> 0.05), and they decreased significantly at 2, 3, 6, and 12 months after treatment (P < 0.001). Besides, compared with the control group, the Widespread Pain Index (WPI), Symptom Severity (SS), Hospital Anxiety and Depression Scale (HADS) and Pittsburgh Sleep Quality Index (PSQI) scores were significantly lower in the FMT group at different time points (P < 0.001). After 6 months of treatment, there was a significant increase in serotonin (5-HT) and gamma-aminobutyric acid (GABA) levels (P < 0.001), while glutamate levels significantly decreased in the FMT group (P < 0.001). The total effective rate was higher in the FMT group (90.9%) compared to the control group (56.5%) after 6 months of treatment (P < 0.05). FMT can effectively improve the clinical symptoms of FM. With the close relations between the changes of neurotransmitters and FM, certain neurotransmitters may serve as a diagnostic marker or potential target for FM patients. PERSPECTIVE: Fecal microbiota transplantation (FMT) is a novel therapy that aims to restore the gut microbial balance and modulate the gut-brain axis. It is valuable to further explore the therapeutic effect of FMT on FM. Furthermore, certain neurotransmitters may become a diagnostic marker or a new therapeutic target for FM patients.

Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.

Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.

Type I/II Immune Balance Contributes to the Protective Effect of AIF-1 on Hepatic Immunopathology Induced by Schistosoma japonicum in a Transgenic Mouse Model.

Schistosomiasis is the second most debilitating neglected tropical disease in the world. Liver egg granuloma and fibrosis are the main damage of schistosomiasis. In this study, the role of allograft inflammatory factor-1 (AIF-1) in liver pathology and its regulation in immune responses were investigated in a transgenic mouse infected with Schistosoma japonicum. We found that AIF-1 overexpression reduced worm burden and decreased egg granuloma sizes and serum alanine aminotransferase levels, along with inhibited hepatic collagen deposition and serum hydroxyproline levels during S. japonicum infection. Moreover, AIF-1 overexpression resulted in an increased ratio of Th1/Th2, increased levels of IFN-γ and T-bet, and lower levels of GATA-3 in the spleen, accompanied by increased M1 percentages, decreased M2 percentages, and thus a higher ratio of M1/M2 in the peritoneal cavity and liver. AIF-1 induced CD68 and iNOS mRNA expression and protein levels of cytoplasmic p-P38 and nuclear NF-κB, along with enhanced levels of TNF-α and TGF-ß in macrophages in vitro. Moreover, the hepatic pathology had a negative correlation with Th1/Th2 and M1/M2 ratios in the infected mice. The findings reveal that the beneficial role of AIF-1 in alleviating hepatic damage is related to restoring type I/II immune balance in S. japonicum infection.

The mechanism of anthracene degradation by tryptophan -2,3-dioxygenase (T23D) in Comamonas testosteroni.

It is well known that anthracene is a persistent organic pollutant. Among the four natural polycyclic aromatic hydrocarbons (PAHs) degrading strains, Comamonas testosterone (CT1) was selected as the strain with the highest degradation efficiency. In the present study, prokaryotic transcriptome analysis of CT1 revealed an increase in a gene that encodes tryptophane-2,3-dioxygenase (T23D) in the anthracene and erythromycin groups compared to CK. Compared to the wild-type CT1 strain, anthracene degradation by the CtT23D knockout mutant (CT-M1) was significantly reduced. Compared to Escherichia coli (DH5α), CtT23D transformed DH5α (EC-M1) had a higher degradation efficiency for anthracene. The recombinant protein rT23D oxidized tryptophan at pH 7.0 and 37 °C with an enzyme activity of 2.42 ± 0.06 µmol min-1·mg-1 protein. In addition, gas chromatography-mass (GC-MS) analysis of anthracene degradation by EC-M1 and the purified rT23D revealed that 2-methyl-1-benzofuran-3-carbaldehyde is an anthracene metabolite, suggesting that it is a new pathway.

A sharp and selective fluorescence paper-based sensor based on inner filter effect for ratiometric detection of four Sudan dyes in food matrix.

The addition of Sudan dyes with carcinogenic effects to food threatens human health. Herein, a ratiometric fluorescence strip consisting of core-shell upconversion particles (NaYF4:Yb,Tm@NaYF4:Yb,Er), metal-organic frameworks and dual-template molecularly imprinted polymers was developed to selectively and sensitively detect four Sudan dyes based on inner filter effect (detection time only takes 8 min). The high adsorption capacity of metal-organic frameworks and the greater overlap between the emission of NaYF4:Yb,Tm@NaYF4:Yb,Er and the absorbance of four Sudan dyes enable the signal responses to be more sensitive. The limits of detection in chilli powder samples are as low as 29.87 ng/g, 37.55 ng/g, 47.89 ng/g and 51.02 ng/g, with satisfactory recovery (93.32-103.4%) and minor relative standard deviations (≤4.3%). This method broadens the idea for low-cost and portable detection of multiple illegal additives in complex substrates with high selectivity and sensitivity based on one kind of fluorescent strip.

IL-8 from CD248-expressing cancer-associated fibroblasts generates cisplatin resistance in non-small cell lung cancer.

Chemotherapy-resistant non-small cell lung cancer (NSCLC) presents a substantial barrier to effective care. It is still unclear how cancer-associated fibroblasts (CAFs) contribute to NSCLC resistance to chemotherapy. Here, we found that CD248+ CAFs released IL-8 in NSCLC, which, in turn, enhanced the cisplatin (CDDP) IC50 in A549 and NCI-H460 while decreasing the apoptotic percentage of A549 and NCI-H460 in vitro. The CD248+ CAFs-based IL-8 secretion induced NSCLC chemoresistance by stimulating nuclear factor kappa B (NF-κB) and elevating ATP-binding cassette transporter B1 (ABCB1). We also revealed that the CD248+ CAFs-based IL-8 release enhanced cisplatin chemoresistance in NSCLC mouse models in vivo. Relative to wild-type control mice, the CD248 conditional knockout mice exhibited significant reduction of IL-8 secretion, which, in turn, enhanced the therapeutic efficacy of cisplatin in vivo. In summary, our study identified CD248 activates the NF-κB axis, which, consecutively induces the CAFs-based secretion of IL-8, which promotes NSCLC chemoresistance. This report highlights a potential new approach to enhancing the chemotherapeutic potential of NSCLC-treating cisplatin.

Localized Oxidation Embellishing Strategy Enables High-Performance Perovskite Solar Cells.

Perovskite solar cell (pero-SC) has attracted extensive studies as a promising photovoltaic technology, wherein the electron extraction and transfer exhibit pivotal effect to the device performance. The planar SnO2 electron transport layer (ETL) has contributed the recent record power conversion efficiency (PCE) of the pero-SCs, yet still suffers from surface defects of SnO2 nanoparticles which brings energy loss and phase instability. Herein, we report a localized oxidation embellishing (LOE) strategy by applying (NH4 )2 CrO4 on the SnO2 ETL. The LOE strategy builds up plentiful nano-heterojunctions of p-Cr2 O3 /n-SnO2 and the nano-heterojunctions compensate the surface defects and realize benign energy alignment, which reduces surface non-radiative recombination and voltage loss of the pero-SCs. Meanwhile, the decrease of lattice mismatch released the lattice distortion and eliminated tensile stress, contributing to better stability of the devices. The pero-SCs based on α-FAPbI3 with the SnO2 ETL treated by the LOE strategy realized a PCE of 25.72 % (certified as 25.41 %), along with eminent stability performance of T90 >700 h. This work provides a brand-new view for defect modification of SnO2 electron transport layer.

Tailoring the Position of Ester Group on N-Alkyl Chains of Benzotriazole-based Small Molecule Acceptors for High-Performance Organic Solar Cells.

Side chain engineering plays a vital role in exploring high-performance small molecule acceptors (SMAs) for organic solar cells (OSCs). In this work, we designed and synthesized a series of A-DA'D-A type SMAs by introducing different N-substituted alkyl and ester alkyl side chains on benzotriazole (BZ) central unit and aimed to investigate the effect of different ester substitution positions on photovoltaic performances. All the new SMAs with ester groups exhibit lower the lowest unoccupied molecular orbital (LUMO) energy levels and more blue-shifted absorption, but relatively higher absorption coefficients than alkyl chain counterpart. After blending with the donor PM6, the ester side chain-based devices demonstrate enhanced charge mobility, reduced amorphous intermixing domain size and long-lived charge transfer state compared to the alkyl chain counterpart, which are beneficial to achieve higher short-circuit current density (Jsc ) and fill factor (FF), simultaneously. Thereinto, the PM6 : BZ-E31 based device achieves a higher power conversion efficiency (PCE) of 18.33 %, which is the highest PCE among the OSCs based on the SMAs with BZ-core. Our work demonstrated the strategy of ester substituted side chain is a feasible and effective approach to develop more efficient SMAs for OSCs.

Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence.

BACKGROUND: Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. METHODS: The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. RESULTS: Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. CONCLUSIONS: In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study.

BACKGROUND: Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques. METHODS: Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed. RESULTS: CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively). CONCLUSION: The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

Control of excitation selectivity in pulse EPR on spin-correlated radical pairs with shaped pulses.

Spin-correlated radical pairs generated by photoinduced electron transfer are characterised by a distinctive spin polarisation and a unique behaviour in pulse electron paramagnetic resonance (EPR) spectroscopy. Under non-selective excitation, an out-of-phase echo signal modulated by the dipolar and exchange coupling interactions characterising the radical pair is observed and allows extraction of geometric information in the two-pulse out-of-phase electron spin echo envelope modulation (ESEEM) experiment. The investigation of the role of spin-correlated radical pairs in a variety of biological processes and in the fundamental mechanisms underlying device function in optoelectronics, as well as their potential use in quantum information science, relies on the ability to precisely address and manipulate the spins using microwave pulses. Here, we explore the use of shaped pulses for controlled narrowband selective and broadband non-selective excitation of spin-correlated radical pairs in two model donor-bridge-acceptor triads, characterised by different spectral widths, at X- and Q-band frequencies. We demonstrate selective excitation with close to rectangular excitation profiles using BURP (band-selective, uniform response, pure-phase) pulses and complete non-selective excitation of both spins of the radical pair using frequency-swept chirp pulses. The use of frequency-swept pulses in out-of-phase ESEEM experiments enables increased modulation depths and, combined with echo transient detection and Fourier transformation, correlation of the dipolar frequencies with the EPR spectrum and therefore the potential to extract additional information on the donor-acceptor pair geometry.

Whether the watch-and-wait strategy has application value for rectal cancer with clinical complete response after neoadjuvant chemoradiotherapy? A network meta-analysis.

The aim was to evaluate the efficacy and safety between the watch-and-wait strategy (WW), radical surgery (RS), and local excision (LE) for rectal cancer with clinical complete response (cCR) after neoadjuvant radiotherapy (nCRT). We searched MEDLINE, EMBASE, the Cochrane Library, and clinical trials to compare WW with RS and LE for patients with cCR until March 2023 and collected the following data: local recurrence (LR), distant metastasis (DM), cancer-related death (CRD), overall survival (OS), and disease-free survival (DFS). In total, 2240 patients from 21 studies were included. Pairwise meta-analysis revealed no statistically significant differences between the three groups in terms of CRD and 2-, 3-, and 5-year OS (P < 0.05). The RS group was significantly better than the WW group in terms of the LR rate (odds ratio [OR] = 0.12, 95 % confidence interval [CI]: 0.06-0.21, P < 0.001, I2 = 0 %], 3-year DFS (OR = 1.56, 95 % CI: 1.10-2.21, P = 0.01, I2 = 38 %), and 5-year DFS (OR = 2.30, 95 % CI: 1.53-3.46, P < 0.001, I2 = 34 %). The results of network meta-analysis were also similar. After sensitivity analysis, the 5-year OS of the RS group was significantly better than that of the WW group (OR = 2.77, 95 % CI: 1.28-6.00, P = 0.009, I2 = 33 %). Nevertheless, neither regression analysis nor subgroup analysis provided meaningful results. However, the cumulative meta-analysis of LR, DM, and 3- and 5-year DFS revealed significant turning points (P < 0.05). Our meta-analysis recommends using the WW strategy for patients with cCR having poor underlying conditions and high surgical risk; however, there is a risk of higher LR and worse survival after 3 years.

Precise synthesis and photovoltaic properties of giant molecule acceptors.

Series of giant molecule acceptors DY, TY and QY with two, three and four small molecule acceptor subunits are synthesized by a stepwise synthetic method and used for systematically investigating the influence of subunit numbers on the structure-property relationship from small molecule acceptor YDT to giant molecule acceptors and to polymerized small molecule acceptor PY-IT. Among these acceptors-based devices, the TY-based film shows proper donor/acceptor phase separation, higher charge transfer state yield and longer charge transfer state lifetime. Combining with the highest electron mobility, more efficient exciton dissociation and lower charge carrier recombination properties, the TY-based device exhibits the highest power conversion efficiency of 16.32%. These results indicate that the subunit number in these acceptors has significant influence on their photovoltaic properties. This stepwise synthetic method of giant molecule acceptors will be beneficial to diversify their structures and promote their applications in high-efficiency and stable organic solar cells.

The coupling relationship and driving mechanism between urbanization and ecosystem services in the Yellow River Basin from a multi-spatial scale perspective.

Rapid urbanization has led to ecological destruction and associated issues. Macro policies wield substantial influence over urbanization and its relationship with the environment. Without considering the differences in scale, macro policies may be ineffective at addressing urbanization's adverse impacts on the environment, and even worsen this relationship. We used data on 622 counties, 76 prefectures, and 7 urban agglomerations in the Yellow River Basin to examine the development level, coupling coordination degree, and spatial patterns of urbanization and ecosystem services at three scales during 2000-2020. Further, we explored the driving mechanisms in the relationship between urbanization and ecosystem services. We found that: First, the coupling coordination was relatively low but showed an upward trend. A sizeable spatiotemporal difference existed, with higher (lower) coordination in the east (west). Second, the coupling coordination of each scale exhibited significant spatial positive correlations. The low-value heterogeneous region was embedded around the agglomeration region, and polarization was significant. The larger the scale, the stronger the agglomeration effect. Further, the coupling coordination spatial agglomeration effect of each scale gradually weakened over time. Third, the spatial and temporal distributions of coupling coordination and its agglomeration characteristics at different scales differed. The urban agglomeration scale showed significant overall coordination or agglomeration characteristics, and prefecture and county regions showed local and unique characteristics within urban agglomerations. Fourth, the dominant factors influencing the spatial patterns of the coupling coordination at the county, prefecture, and urban agglomeration scales differed. The interaction and factor detection showed linear and double-factor enhancements. We find that economic development, government policies, environmental protection, and natural factors are the combined effects of urbanization and ecosystem services. Our research method can provide a reference for other river basins, and the results can help governments in formulating policies for sustainable development at different spatial scales.

UHPLC-MS/MS metabolomics analysis of sea cucumber (Apostichopus japonicus) processed using different methods.

The effects of different processing methods on the nutritional components of sea cucumber (Apostichopus japonicus) are of concern to consumers who select sea cucumber products. This study employed liquid chromatography tandem mass spectrometry to examine the metabolites in fresh, unsoaked salted, soaked salted, and instant sea cucumber body wall samples sourced from Dalian, China. Metabolites were evaluated utilizing partial least squares discriminant analysis (PLS-DA) and subsequently subjected to KEGG metabolic pathway analysis for further investigation. PLS-DA effectively discriminated the body wall metabolites of sea cucumbers obtained via various processing techniques. The differential metabolites identified predominantly encompassed amino acids, lipids, and carbohydrates. Subsequent KEGG metabolic pathway analysis demonstrated a significant association between lipid, carbohydrate, and amino acid metabolism and the specific processing methods employed. The assessment of nutritional differences corresponding to the various A. japonicus processing methods was conducted. The findings of this study can assist in the choice of sea cucumber products and the selection of suitable processing methods.