“Runner’s high” refers to a momentary sense of pleasure that suddenly appears during running or other exercise activities, characterized by anti-anxiety, pain relief, and other symptoms. The neurobiological mechanism of “runner’s high” is unclear. This review summarizes human and animal models for studying “runner’s high”, analyzes the neurotransmitters and neural circuits involved in runner’s high, and elucidates the evidence and shortcomings of researches related to “runner’s high”. This review also provides prospects for future research. Research has found that exercise lasting more than 30 min and with an intensity exceeding 70% of the maximum heart rate can reach a “runner’s high”. Human experiments on “runner’s high” mostly use treadmill exercise intervention, and evaluate it through questionnaire surveys, measurement of plasma AEA, miRNA and other indicators. Animal experiments often use voluntary wheel running intervention, and evaluate it through behavioral experiments such as conditional place preference, light dark box experiments (anxiety), hot plate experiments (pain sensitivity), and measurement of plasma AEA and other indicators. Dopamine, endogenous opioid peptides, endogenous cannabinoids, brain-derived neurotrophic factor, and other substances increase after exercise, which may be related to the “runner’s high”. However, attention should be paid to the functional differences of these substances in the central and peripheral regions, as well as in different brain regions. Moreover, current studies have not identified the targets of the neurotransmitters or neural factors mentioned above, and further in-depth researches are needed. The mesolimbic dopamine system, prefrontal cortex-nucleus accumbens projection, ventral hippocampus-nucleus accumbens projection, red nucleus-ventral tegmental area projection, cerebellar-ventral tegmental area projection, and brain-gut axis may be involved in the regulation of runner’s high, but there is a lack of direct evidence to prove their involvement. There are still many issues that need to be addressed in the research on the neurobiological mechanisms of “runner’s high”. (1) Most studies on “runner’s high” involve one-time exercise, and the characteristics of changes in “runner’s high” during long-term exercise still need to be explored. (2) The using of scales to evaluate subjects lead to the lacking of objective indicators. However, some potential biomarkers (such as endocannabinoids) have inconsistent characteristics of changes after one-time and long-term exercise. (3) The neurotransmitters involved in the formation of the “runner’s high” all increase in the peripheral and/or central nervous system after exercise. Attention should be paid to whether peripheral substances can enter the blood-brain barrier and the binding effects of neurotransmitters to different receptors are completely different in different brain regions. (4) Most of the current evidence show that some brain regions are activated after exercise. Is there a functional circuit mediating “runner’s high” between these brain regions? (5) Although training at a specific exercise intensity can lead to “runner’s high”, most runners have not experienced “runner’s high”. Can more scientific training methods or technological means be used to make it easier for people to experience the “runner’s high” and thus be more willing to engage in exercise? (6) The “runner’s high” and “addiction” behaviors are extremely similar, and there are evidences that exercise can reverse addictive behaviors. However, why is there still a considerable number of people in the sports population and even athletes who smoke or use addictive drugs instead of pursuing the “pleasure” brought by exercise? Solving the problems above is of great significance for enhancing the desire of exercise, improving the clinical application of neurological and psychiatric diseases through exercise, and enhancing the overall physical fitness of the population.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

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