Table_1_Psychodynamic profiles of major depressive disorder and generalized anxiety disorder in China.docx

Traditional clinical diagnoses relying on symptoms may overlook latent factors that illuminate mechanisms and potentially guide treatment. The Operationalized Psychodynamic Diagnosis (OPD) system may compensate for symptom-based diagnosis by measuring psychodynamic profiles underlying mental disorders through conflicts and structure axes. However, OPD has not been widely adopted in China, and it remains unclear whether OPD can be used as an effective approach to distinguish between depression and anxiety. The current study aims to adopt the OPD system to investigate the psychodynamic profiles of major depressive disorder (MDD) and generalized anxiety disorder (GAD) in China, targeting patients with “pure” symptoms without comorbidity. We recruited 42 MDD patients, 32 GAD patients, and 31 healthy controls (HC), and assessed their self-report depression and anxiety symptoms, along with their underlying psychodynamic profiles through OPD interviews. Overall, both MDD and GAD patients showed more prominent conflict issues and lower levels of structure than HC. The MDD and GAD groups yielded different conflict profiles and conflict processing modes when processing their second conflicts. Importantly, the multi-dimensional psychodynamic profiles achieved machine learning classification of clinical groups with an accuracy of 0.84, supporting successful distinction of MDD and GAD patients. In conclusion, the OPD demonstrated sensitivity in revealing distinct psychodynamic profiles underlying “pure” depression and anxiety clinical populations in China. This work calls for future incorporation of OPD as a tool to investigate psychodynamic formulations underlying mental disorders, compensating for traditional symptom-based diagnostic approaches to guide precise individualized interventions.</p

Dominating Role of Aligned MoS₂/Ni₃S₂ Nanoarrays Supported on Three-Dimensional Ni Foam with Hydrophilic Interface for Highly Enhanced Hydrogen Evolution Reaction

When using water splitting to achieve sustainable hydrogen production, low-cost, stable, and naturally abundant electrocatalysts are required to replace Pt-based ones for the hydrogen evolution reaction (HER). Herein, for the first time, a novel nanostructure with one-dimensional (1D) MoS₂/Ni₃S₂ nanoarrays directly grow on a three-dimensional (3D) Ni foam is developed for this purpose, showing excellent catalytic activity and stability. The as-prepared 3D MoS₂/Ni₃S₂/Ni composite has an onset overpotential as low as 13 mV in 1 M KOH, which is comparable to Pt-based electrocatalyst for HER. According to the classical theory, the Tafel slope of the new composite is relatively low, as it goes through a combined Volmer–Heyrovsky mechanism during hydrogen evolution. All the results attribute the excellent electrocatalytic activity of the nanostructure to the electrical coupling among Ni, Ni₃S₂, and MoS₂, the super hydrophilic interface, the synergistic catalytic effects produced by the MoS₂/Ni₃S₂ nanoarrays, and abundant exposed active edge sites. These unique and previously undeveloped characteristics of the 3D MoS₂/Ni₃S₂/Ni composite make it a very promising earth-abundant electrocatalyst for HER

Growth of Copper Nanocubes on Graphene Paper as Free-Standing Electrodes for Direct Hydrazine Fuel Cells

We have developed a new type of flexible electrodes based on Cu nanocube-decorated free-standing graphene paper (GP) using a facile electrodeposition method. The Cu nanocubes–graphene paper (Cu–GP) hybrid electrode processes remarkable electrocatalytic activity with an onset potential of −0.10 V toward hydrazine oxidation in alkaline solutions and can serve as the catalyst layer for direct hydrazine fuel cells. One interesting finding is that a copper hydroxide/oxide layer in situ formed on Cu nanocube surfaces plays an important role in enhancing the electrocatalytic activity and durability of the electrocatalyst. A totally irreversible and diffusion-controlled oxidation of hydrazine occurs on the electrocatalyst, eventually leading to environmentally friendly products such as nitrogen and water

Table_1_Residue determination and dietary risk assessment of mesotrione, nicosulfuron, atrazine and its four metabolites in maize in China.DOCX

IntroductionTo improve maize yield in China, multiple herbicides have been simultaneously applied to control more weeds. However, this combined application raises concerns about potential residues and their subsequent risks to human health. Therefore, evaluating the residues and dietary risk of new herbicide formulations is critical for the sustainability of maize production.MethodsUsing UHPLC-MS/MS, we developed quick methods for the determination of residues of mesotrione, nicosulfuron, atrazine and its four metabolites with acceptable accuracy and precision. The limits of quantification (LOQs) were 0.01 mg/kg for mesotrione and atrazine-desethyl-desopropyl, and 0.005 mg/kg for nicosulfuron, atrazine, 6-deisopropyl atrazine, 2 hydroxyatrazine, and deethylatrazine. Field trials were conducted at 12 different locations in China. And the risk quotient (RQ) model was used to evaluate the chronic risk of residues of these herbicides.ResultsThe residues of straw samples were in the ranges of ConclusionBased on the RQ model, the dietary risk of exposure to three herbicides through maize was acceptable by consumers. This study helps guide the rational use of mesotrione, nicosulfuron and atrazine to ensure the safe production of maize and our human health.</p

C–H···O Interaction in Methanol–Water Solution Revealed from Raman Spectroscopy and Theoretical Calculations

A combination of temperature-dependent Raman spectroscopy and quantum chemistry calculation was employed to investigate the blue shift of CH₃ stretching vibration in methanol–water mixtures. It shows that the conventional O–H···O hydrogen bonds do not fully dominate the origin of the C–H blue shift and the weak C–H···O interactions also contribute to it. This is consistent with the temperature-dependent results, which reveal that the C–H···O interaction is enhanced upon increasing the temperature, leading to further C–H blue shift in observed spectra at high temperature. This behavior is in contrast with the general trend that the conventional O–H···O hydrogen bond is destroyed by the temperature. The results will shed new light onto the nature of the C–H···O interaction and be helpful to understand hydrophilic and hydrophobic interactions of amphiphilic molecules in different environments

Thermosensitive Liposomal Codelivery of HSA–Paclitaxel and HSA–Ellagic Acid Complexes for Enhanced Drug Perfusion and Efficacy Against Pancreatic Cancer

Fibrotic stroma and tumor-promoting pancreatic stellate cells (PSCs), critical characters in the pancreatic ductal adenocarcinoma (PDA) microenvironment, promote a tumor-facilitating environment that simultaneously prevents drug penetration into tumor foci and stimulates tumor growth. Nab-PTX, a human serum albumin (HSA) nanoparticle of paclitaxel (PTX), indicates enhanced matrix penetration in PDA probably due to its small size <i>in vivo</i> and high affinity of HSA with secreted protein acidic and rich in cysteine (SPARC), overexpressed in the PDA stroma. However, this HSA nanoparticle shows poor drug blood retention because of its weak colloidal stability <i>in vivo</i>, thus resulting in insufficient drug accumulation within tumor. Encapsulating HSA nanoparticles into the internal aqueous phase of ordinary liposomes improves their blood retention and the following tumor accumulation, but the large 200 nm size and shielding of HSA in the interior might make it difficult for this hybrid nanomedicine to penetrate the fibrotic PDA matrix and promote bioavailability of the payload. In our current work, we prepared ∼9 nm HSA complexes with an antitumor drug (PTX) and an anti-PSC drug (ellagic acid, EA), and these two HSA–drug complexes were further coencapsulated into thermosensitive liposomes (TSLs). This nanomedicine was named TSL/HSA-PE. The use of TSL/HSA-PE could improve drug blood retention, and upon reaching locally heated tumors, these TSLs can rapidly release their payloads (HSA–drug complexes) to facilitate their further tumor accumulation and matrix penetration. With superior tumor accumulation, impressive matrix penetration, and simultaneous action upon tumor cells and PSCs to disrupt PSCs–PDA interaction, TSL/HSA-PE treatment combined with heat exhibited strong tumor growth inhibition and apoptosis <i>in vivo</i>

Cation Dynamics Governed Thermal Properties of Lead Halide Perovskite Nanowires

Metal halide perovskite (MHP) nanowires such as hybrid organic–inorganic CH₃NH₃PbX₃ (X = Cl, Br, I) have drawn significant attention as promising building blocks for high-performance solar cells, light-emitting devices, and semiconductor lasers. However, the physics of thermal transport in MHP nanowires is still elusive even though it is highly relevant to the device thermal stability and optoelectronic performance. Through combined experimental measurements and theoretical analyses, here we disclose the underlying mechanisms governing thermal transport in three different kinds of lead halide perovskite nanowires (CH₃NH₃PbI₃, CH₃NH₃PbBr₃ and CsPbBr₃). It is shown that the thermal conductivity of CH₃NH₃PbBr₃ nanowires is significantly suppressed as compared to that of CsPbBr₃ nanowires, which is attributed to the cation dynamic disorder. Furthermore, we observed different temperature-dependent thermal conductivities of hybrid perovskites CH₃NH₃PbBr₃ and CH₃NH₃PbI₃, which can be attributed to accelerated cation dynamics in CH₃NH₃PbBr₃ at low temperature and the combined effects of lower phonon group velocity and higher Umklapp scattering rate in CH₃NH₃PbI₃ at high temperature. These data and understanding should shed light on the design of high-performance MHP based thermal and optoelectronic devices

Image1_Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis.JPEG

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.</p

Strategic Approach to 8‑Azacoumarins

8-Azacoumarins have emerged as a promising class of compounds but are rarely explored due to challenging access. A novel, general, and practical method is provided for this class of compounds. The key lactonization step employs <i>trans</i>-acrylic acid attached pyridine <i>N</i>-oxides as the starting material, with acetic anhydride as both the activation agent and the solvent. Multiple transformations were involved in this reaction, including conjugate addition, nucleophilic aromatic substitution, and elimination. These studies provide the basis for access to 8-azacoumarins, enabling future work including the discovery and development of novel coumarin-type drugs, fluorescent probes, photolabile protecting groups, and other active molecules

Table1_Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis.DOCX

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.</p