ObjectiveTo explore the effect of individualized intermittent catheterization (IC) guided by 24-hour dynamic urine volume monitoring on bladder function and urinary tract infection complications in patients with neurogenic bladder (NB). MethodsA total of 50 NB patients admitted to the First Affiliated Hospital of Nanjing Medical University from August, 2025 to June, 2026 were enrolled. The patients were randomly divided into control group (n = 25) and experimental group (n = 25). The control group received routine timed/empirical IC, and the experimental group adopted dynamic urine volume-guided individualized IC, for a week. Urine leukocyte positive rate, urine bacterial positive rate, frequencies of single catheterized volume > 400 mL, 24-hour catheterization and 24-hour urine leakage were recorded before and after intervention. ResultsAfter intervention, the positive rates of urine leukocytes and urine bacteria were lower in the experimental group than in the control group (χ² > 5.094, P < 0.05), while the frequencies of single catheterized volume > 400 mL, 24-hour catheterization and 24-hour urine leakage were less (t > 2.078, P < 0.05). ConclusionThe individualized IC guided by 24-hour dynamic urine volume monitoring can accurately match individual urine secretion rhythm, reduce over-distension of bladder and unnecessary catheterization, and lower the incidence of urine leakage and urinary tract infection.

Nanoparticle delivery systems have good application prospects in the field of precision therapy, but the preparation process of nanomaterial has problems such as short in vivo circulation time, easy recognition, and clearance by the immune system in the body. In recent years, biomimetic nanoparticle delivery systems mediated by natural cell membranes have become a research hotspot to address these issues. The natural membrane biomimetic nanoparticle delivery system cleverly integrates the advantages of natural biofilm "autologous" and "artificial" functional carriers by using endogenous cell membranes to modify the surface of nanocarriers, endowing them with characteristics such as tumor targeting, low immunogenicity, and long blood circulation. Currently, biomimetic nanoparticle delivery systems have been used in the treatment of malignant tumors, cardiovascular diseases, bacterial infections, and other diseases. This paper analyzes the development status and current research hotspots of natural cell membrane camouflaged biomimetic nanoparticle delivery systems mainly reviews the latest research progress of red blood cell membrane camouflaged biomimetic nanoparticle delivery systems in the field of disease treatment in recent years. It focuses on exploring the advantages, future development prospects, and limitations of biomimetic nanoparticle delivery systems based on red blood cell membrane camouflage in improving drug delivery, to provide a reference for the in-depth research and development of this system.

Objective: There were associations between emotional abuse and neglect (EAN) and depression, but few studies had tested potential mechanisms underlying these relationships. We aimed to provide insights on how (the mediation role of neuroticism), and under what conditions (the moderator role of psychological resilience), led to a higher level of depression. Methods: This study was a cross-sectional study that used a random cluster sampling method. We randomly selected 3,993 participants from four junior middle schools in northern city of China. Participants were asked to complete four self-reported questionnaires, including the Childhood Trauma Questionnaire, Children Depression Inventory-Short Form, Chinese Big Five Personality Inventory Brief Version, and Chinese Resilience Scale. Results: The results showed that neuroticism mediated the associations between EAN and depression. In addition, the mediating effect of neuroticism was moderated by psychological resilience (p<0.05). Conclusion EAN and neuroticism could have an adverse impact on depression, and psychological resilience could alleviate these negative effects as a moderator. Our model suggested psychological resilience could be a particularly effective intervention point for victims of EAN.

Objective: There were associations between emotional abuse and neglect (EAN) and depression, but few studies had tested potential mechanisms underlying these relationships. We aimed to provide insights on how (the mediation role of neuroticism), and under what conditions (the moderator role of psychological resilience), led to a higher level of depression. Methods: This study was a cross-sectional study that used a random cluster sampling method. We randomly selected 3,993 participants from four junior middle schools in northern city of China. Participants were asked to complete four self-reported questionnaires, including the Childhood Trauma Questionnaire, Children Depression Inventory-Short Form, Chinese Big Five Personality Inventory Brief Version, and Chinese Resilience Scale. Results: The results showed that neuroticism mediated the associations between EAN and depression. In addition, the mediating effect of neuroticism was moderated by psychological resilience (p<0.05). Conclusion EAN and neuroticism could have an adverse impact on depression, and psychological resilience could alleviate these negative effects as a moderator. Our model suggested psychological resilience could be a particularly effective intervention point for victims of EAN.

Objective: There were associations between emotional abuse and neglect (EAN) and depression, but few studies had tested potential mechanisms underlying these relationships. We aimed to provide insights on how (the mediation role of neuroticism), and under what conditions (the moderator role of psychological resilience), led to a higher level of depression. Methods: This study was a cross-sectional study that used a random cluster sampling method. We randomly selected 3,993 participants from four junior middle schools in northern city of China. Participants were asked to complete four self-reported questionnaires, including the Childhood Trauma Questionnaire, Children Depression Inventory-Short Form, Chinese Big Five Personality Inventory Brief Version, and Chinese Resilience Scale. Results: The results showed that neuroticism mediated the associations between EAN and depression. In addition, the mediating effect of neuroticism was moderated by psychological resilience (p<0.05). Conclusion EAN and neuroticism could have an adverse impact on depression, and psychological resilience could alleviate these negative effects as a moderator. Our model suggested psychological resilience could be a particularly effective intervention point for victims of EAN.

As a new generation of time-of-flight mass spectrometry,multiple-reflection time-of-flight mass spectrometry(MR-TOF-MS)has been increasingly applied in the fields such as nuclear physics,chemistry,and biology due to its ultra-high resolution and rapid analysis capabilities.However,the analytical performance of MR-TOF-MS largely depends on the ion bunch state entering the mass analyzer.In this study,a rectangular ion trap(RIT)was developed,designed and processed using printed circuit board technology,as an ion accumulating and focusing device for MR-TOF mass analyzer.Compared to traditional ion traps composed of two sets of planar electrodes,this RIT had higher voltage utilization efficiency,resulting in more efficient ion collection and focusing.The ions were cooled to a sufficiently small bunch for precise mass measurement with MR-TOF-MS mass spectrometry in only 1 ms of cooling time in the RIT,then orthogonally ejected to the MR-TOF mass spectrometer for mass analysis.Experimental results indicated that the working cycle,ion flux,and ion focusing state of the RIT fully met the requirements of the MR-TOF mass analyzer.When coupled with the MR-TOF mass analyzer,the RIT enabled MR-TOF-MS to achieve a mass resolution of 1.5×105.

A method for determining volatile organic compounds(VOCs)in environmental air samples using thermal desorption-gas chromatography/mass spectrometry(TD-GC/MS)was developed.The qualitative and quantitative analyses of 103 kinds of VOC species were achieved under the optimal conditions such as cold trap desorption temperature and time during the thermal desorption process,combined with full-scan data acquisition using an electron impact ionization source.With a sampling volume of 3.0 L,the method exhibited detection limits of 0.1-0.5 μg/m3 and quantitation limits of 0.4-2.0 μg/m3.At spiked concentrations of 1.0 μg/m3 and 10.0 μg/m3,the recoveries ranged from 60.5%to 118.0%,with relative standard deviations varying from 2.37%to 18.70%.Furthermore,all target compounds showed correlation coefficients(R2)exceeding 0.997 across their respective concentration ranges,demonstrating that the method had high accuracy and reliability.Using this method,a study was conducted to investigate the spatial variations and source apportionment of VOCs across urban,suburban,and rural sites in Anyang,a representative industrial city,during the summer season.The results revealed significant differences in VOC concentrations among the three regions.The urban site recorded the highest concentration at 40.1 μg/m3,followed by the rural site at 23.5 μg/m3,while the suburban site had the lowest concentration of 9.74 μg/m3.With regard to compositional characteristics,alkanes were the dominant components in the urban and rural areas,whereas oxygenated VOCs were predominant in the suburban site.The ozone formation potential(OFP)also varied significantly across regions:96.0 μg/m3 in urban areas,72.0 μg/m3 in rural areas,and only 27.6 μg/m3 in suburban areas.Alkenes were identified as the primary contributors to the total ozone formation potential(TOFP)in all regions,highlighting their critical role in atmospheric oxidation processes.Source apportionment analysis using the positive matrix factorization(PMF)model identified combustion sources,natural sources,chemical industry emissions,industrial emissions,solvent use,and vehicle emissions as the major sources of VOCs in Anyang during summer.Notably,chemical industry emissions and combustion sources were dominant in urban and rural areas,whereas combustion sources and natural sources were more prominent in the suburban area,reflecting distinct emission patterns and anthropogenic activities across the regions.

ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.