BackgroundAddictive non-suicidal self-injury （NSSI） behaviors among adolescents have become increasingly prominent， although previous studies have identified multiple related risk factors and have examined the association between screen time and NSSI behaviors， the impact of screen time on NSSI behaviors addiction， as well as the mediating role of family dysfunction in this relationship， remain to be further clarified. ObjectiveTo investigate the mediating role of family dysfunction in the relationship between screen time and NSSI behaviors addiction among adolescent female patients with depression disorder， with the aim of providing references for reducing NSSI behaviors addiction. MethodsFrom September 2024 to November 2025， a total of 652 adolescent female patients with depression disorder were enrolled from both outpatient and inpatient departments of Xiamen Xian-yue Hospital， all of whom met the diagnostic criteria for depressive episode （F32） or recurrent depressive disorder （F33） according to the International Classification of Diseases， tenth edition （ICD-10）. Assessments included a self-developed demographic questionnaire， screen use questionnaire， Chinese Family Assessment Instrument （C-FAI）， and Ottawa Self-injury Inventory Chinese Revised version （OSIC）. Among participants with NSSI behaviors， Spearman correlation analysis was used to examine the correlation between screen time and scale scores. Model 4 of the Process 4.1 for SPSS 26.0 was then applied to test the mediating role， and Bootstrapping procedure involving 5 000 replicates was employed to confirm the statistical significance. ResultsAmong the 652 patients， 569 （87.27%） exhibited NSSI behaviors. Among them， 398 cases （69.95%） belonged to the addictive NSSI group， and 171 cases （30.05%） belonged to the non-addictive NSSI group. The OSIC addiction dimension score was positively correlated with screen time and C-FAI scores （r_s=0.114， 0.224， P<0.01）. Family dysfunction mediated the relationship between screen time and NSSI addiction， with an indirect effect value of 0.036 （95% CI： 0.016–0.062）， accounting for 35.88% of the total effect. ConclusionScreen time may affect the NSSI behaviors addiction in adolescent female patients with depression disorder through family dysfunction. ［Funded by Joint Funds for the Innovation of Science and Technology， Fujian Province （number， 2025Y9762）］

Tumors exhibit abnormal glucose metabolism, consuming excessive glucose and excreting lactate, which constructs a tumor microenvironment that facilitates cancer progression and disrupts immunotherapeutic efficacy. Currently, tumor glucose metabolic dysregulation to reshape the immunosuppressive microenvironment and enhance immunotherapy efficacy is emerging as an innovative therapeutic strategy. However, glucose metabolism modulators lack specificity and still face significant challenges in overcoming tumor delivery barriers, microenvironmental complexity, and metabolic heterogeneity, resulting in poor clinical benefit. Nanomedicines, with their ability to selectively target tumors or immune cells, respond to the tumor microenvironment, co-deliver multiple drugs, and facilitate combinatorial therapies, hold significant promise for enhancing immunotherapy through tumor glucose metabolic reprogramming. This review explores the complex interactions between tumor glucose metabolism-specifically metabolite transport, glycolysis processes, and lactate-and the immune microenvironment. We summarize how nanomedicine-mediated reprogramming of tumor glucose metabolism can enhance immunotherapy efficacy and outline the prospects and challenges in this field.

Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK _a values. The high reliability and interpretability of GraFpK _a ensure accurate pK _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK _a values, making it a valuable tool in the field of pK _a prediction.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

The biological nitrogen removal technology utilizing heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria has shown effectiveness in wastewater treatment. However, the nitrogen removal efficiency of HN-AD bacteria significantly decreases as the salinity increases. To tackle the challenge of treating high-salt and high-nitrogen wastewater, we isolated a moderately halophilic HN-AD strain 5505 from a salt lake in Xinjiang. The strain was identified based on morphological, physiological, and biochemical characteristics and the 16S rRNA gene sequence. Single-factor experiments were carried out with NH₄⁺-N, NO₃^--N, and NO₂^--N as sole or mixed nitrogen sources to study the nitrifying effect, denitrifying effect, and nitrogen metabolism pathway of the strain. The strain was identified as Halomonas sp.. It can grow in the presence of 1%-25% (W/V) NaCl and exhibited efficient nitrogen removal ability in the presence of 3%-8% NaCl. At the optimal NaCl concentration (8%), the strain showed the NH₄⁺-N, NO₃^--N and NO₂^--N removal rates of 100.0%, 94.11% and 74.43%, respectively. Strain 5505 removed inorganic nitrogen mainly by assimilation, which accounted for over 62.68% of total nitrogen removal. In the presence of mixed nitrogen sources, strain 5505 showed a preference for utilizing ammonia, with a potential HN-AD pathway of NH₄⁺→NH₂OH→NO₂^-→NO₃^-→NO₂^-→NO/N₂O/N₂. The findings provide efficient salt-tolerant bacterial resources, enhance our understanding of biological nitrogen removal, and contribute to the nitrogen removal efficiency improvement in the treatment of high-salt and high-nitrogen wastewater.
Halomonas/classification* ; Nitrogen/isolation & purification* ; Denitrification ; Nitrification ; Wastewater/microbiology* ; Aerobiosis ; Biodegradation, Environmental ; Salinity

Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans ; Diabetes Insipidus/etiology* ; Neurosurgical Procedures/adverse effects* ; Child ; Postoperative Complications/therapy* ; Healthcare Failure Mode and Effect Analysis ; Intensive Care Units, Pediatric ; Risk Factors

Houpo Sanwutang， included in the Catalogue of Ancient Classical Prescriptions （Second Batch）， was first recorded in the Synopsis of Golden Chamber written by ZHANG Zhongjing from the Eastern Han dynasty and was modified by successive generations of medical experts. A total of 37 pieces of effective data involving 37 ancient Chinese medical books were retrieved from different databases. Through literature mining， statistical analysis， and data processing， combined with modern articles， this study employed bibliometrics to investigate the historical origin， composition， decoction methods， clinical application， and other key information. The results showed that the medicinal origin of Houpo Sanwutang was clearly documented in classic books. Based on the conversion of the measurements from the Han Dynasty， it is recommended that 110.4 g Magnolia Officinalis Cortex， 55.2 g Rhei Radix et Rhizoma， and 72 g Aurantii Fructus Immaturus should be taken. Magnolia Officinalis Cortex and Aurantii Fructus Immaturus should be decocted with 2 400 mL water first， and 1 000 mL should be taken from the decocted liquid. Following this， Rhei Radix et Rhizoma should be added for further decoction， and then 600 mL should be taken from the decocted liquid. A single dose of administration is 200 mL， and the medication can be stopped when patients restore smooth bowel movement. Houpo Sanwutang has the effect of moving Qi， relieving stuffiness and fullness， removing food stagnation， and regulating bowels. It can be used in treating abdominal distending pain， guarding， constipation， and other diseases with the pathogenesis of stagnated heat and stagnated Qi in the stomach. The above results provide reference for the future development and research of Houpo Sanwutang.

Objective: To explore the long term trend of forced vital capacity to weight index (FWI) among Chinese Han students aged 7-18 from 2000 to 2019, and to predict its changes over the next decade, so as to provide scientific evidences for targeted health interventions and school health policies. Methods: Based on the data of the five Chinese National Surveys on Students Constitution and Health conducted from 2000 to 2019, a total of 216 500, 233 565, 215 267, 214 256 and 212 632 Han students aged 7-18 were included, respectively. The long term trend of FWI among students was analyzed, and the GM (1,1) grey model was used to predict FWI changes over the next decade. Subgroup analyses were conducted by sex, age, and urban-rural residence. Results: The FWI levels of Chinese Han students aged 7-18 were (55.30±11.47)(47.43±11.92)(48.11±12.46)(48.75±12.81)(50.93±13.11)mL/kg in 2000, 2005, 2010, 2014, and 2019, respectively. The FWI of Chinese Han students showed a decreasing then increasing trend from 2000 to 2019, reaching the lowest point of approximately 47.03 mL/kg around 2006, and was projected to recover to 52.88 mL/kg by 2029. Boys had higher FWI for each year and the total level than girls from 2000 to 2019( t =72.58-304.66), and the decline between 2000 and 2005 was smaller in boys (13.1%) than in girls (15.4%). However, the gender gap gradually narrowed and was projected to reduce to 5.36 mL/kg by 2029. FWI increased with age, with the largest difference observed in 2014 between the 7-9 and 16-18 age groups (8.62 mL/kg). Before 2014, urban boys had slightly lower FWI than rural boys; the gap narrowed thereafter, and their FWI levels were expected to become similar by 2029. Urban girls generally had higher FWI than rural girls, and the urban-rural gap showed an increasing trend. By 2029, the largest difference was projected to occur in the 13-15 age group, reaching 7.74 mL/kg. Conclusions The FWI of Chinese Han students showed a trend of initial decline followed by a gradual increase from 2000 to 2019, with notable differences across sex, age, and urban-rural residence. Greater attention should be paid to the respiratory health of rural girls, and effective measures should be taken to reduce urban-rural disparities.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β‑protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF‑κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 toco-regulate antioxidant activity. NF‑κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such asα-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.