Objective To analyze Chinese and English publications pertaining to Oncomelania hupensis control from 2005 to 2024, so as to decipher the research status and hotspots of O. hupensis control. Methods Chinese and English publications pertaining to O. hupensis control from 2005 to 2024 were retrieved in the Web of Science Core Collection Database and China National Knowledge Infrastructure. The annual number of publications was analyzed from 2005 to 2024, and the author and institution cooperation networks were mapped using the software CiteSpace 6.3.1. Keywords were extracted from publications to map the co-occurrence, burst and timeline of keywords to identify the research hotspots of O. hupensis control. Results A total of 158 English publications and 771 Chinese publications were included for bibliometric analyses. The overall output of English publications was relatively small from 2005 to 2024, the annual average publication was 7.90 publications. Parasites & Vectors was the most productive journal by the number of publications (21 publications). The three most productive authors included Li Shizhu (24 publications), Zhou Xiaonong (13 publications), and Yang Kun (12 publications), and the three most productive institutions included Chinese Center for Disease Control and Prevention (49 publications), the WHO (27 publications), and Fudan University (25 publications). The annual average number of Chinese publications was high from 2005 to 2015 (57.73 publications), and reduced to 15.11 publications during the period from 2016 to 2024, with Chinese Journal of Schistosomiasis Control as the most productive journal (241 publications). The three most productive authors included Wang Wanxian (18 publications), Sun Qixiang (16 publications), and Dai Jianrong (16 publications), and the three most productive institutions included Jiangsu Institute of Parasitic Diseases (55 publications), Chinese Center for Disease Control and Prevention (47 publications), and Hubei Uni-versity (38 publications). Among the 158 English publications, molluscicidal effect, climate change, geographic information, biological control, machine learning were current research hotspots, and the Yangtze River and elimination were emerging research hotspots. Among the 771 Chinese publications, molluscicidal effect, niclosamide, comprehensive management, molluscicide, effectiveness evaluation, marshland, and endophyte were current research hotspots, and the future research hotspots shifted to molluscicidal effect and pyriclobenzuron. Conclusions Limited attention is paid to the research on O. hupensis control across the world. The Yangtze River, elimination, molluscicidal effect, and pyriclobenzuron may be future research hotspots. High attention is recommended to be paid to the research on O. hupensis control, and development of diverse approaches for O. hupensis control is of urgent needs. We should continue to attach importance to the control research of O. hupensis and strengthen the exploration of diverse snail extermination and control methods.

Chronic pain is a complex condition shaped by long-standing alterations in both physiological and psychological processes. Rather than representing a simple continuation of acute nociceptive signaling, chronic pain is increasingly understood as the outcome of progressive dysregulation within distributed neural systems that govern sensation, affect, motivation, and cognitive control. Neuroimaging and electrophysiological studies indicate that this state is accompanied by extensive plastic changes in deep brain structures and large-scale networks. Beyond well-described central sensitization processes, chronic pain is characterized by disrupted oscillatory rhythms and altered connectivity within large-scale brain networks, including thalamo-cortical circuits and prefrontal-limbic-reward networks. These findings support a conceptual shift from viewing chronic pain as a focal, lesion-driven phenomenon toward recognizing it as a disorder of distributed network pathology. Pharmacological treatments remain central to clinical practice, yet their long-term efficacy is often limited and frequently accompanied by substantial side effects. The ongoing concerns about opioid-related risks and the inadequate therapeutic response in a subset of patients highlight the need for safe, non-pharmacological approaches that can address not only pain but also comorbid disturbances in mood, sleep, and social functioning. Neuromodulation provides a promising path toward mechanism-based and non-pharmacological management of chronic pain by employing physical or chemical stimulation to alter the excitability and synchrony of specific neural populations within central, peripheral, and autonomic systems. While invasive deep brain stimulation demonstrates that targeting deep brain structures can be effective, its clinical application is restricted by surgical risks and cost, highlighting the importance of non-invasive techniques capable of reaching deep targets. Current non-invasive approaches, such as transcranial electric stimulation, are constrained by limited penetration depth and insufficient spatial precision. These limitations hinder reliable engagement of deep regions implicated in pain, including the thalamus and nucleus accumbens, and tend to produce broad, non-specific modulation of cross-network oscillatory activity. Temporal interference (TI) stimulation has emerged as a means of overcoming these obstacles. By delivering interacting high-frequency currents that generate a low-frequency envelope within the head, TI enables focal stimulation of deep targets while minimizing superficial current delivery. Recent multiscale modeling and animal studies indicate that TI exploits the nonlinear rectification properties of neuronal membranes in response to high-frequency carriers, as well as their phase-locked responses to low-frequency envelopes, to generate “peak-focused” electric fields in deep regions under relatively low superficial current loads. Moreover, TI appears to exhibit potential advantages in terms of cell-type selectivity and rhythm-specific engagement, including differential responses across neuronal subtypes and distinct coupling to θ-, β-, and γ-band oscillations. These features suggest a promising avenue for correcting abnormal rhythms and network dynamics that contribute to chronic pain. This review summarizes current knowledge of the neural mechanisms underlying chronic pain and recent advances in TI research. It examines functional disturbances across key pain-related regions and networks, outlines the principles and technical characteristics of TI, and discusses potential deep-brain targets and stimulation strategies relevant to chronic pain. Evidence to date indicates that TI, with its non-invasiveness, tolerability, and capacity for precise deep brain modulation, holds great promise for the management of treatment-resistant chronic pain and may evolve into a new generation of precise and efficient non-pharmacological analgesic strategies.

Chronic pain is a complex condition shaped by long-standing alterations in both physiological and psychological processes. Rather than representing a simple continuation of acute nociceptive signaling, chronic pain is increasingly understood as the outcome of progressive dysregulation within distributed neural systems that govern sensation, affect, motivation, and cognitive control. Neuroimaging and electrophysiological studies indicate that this state is accompanied by extensive plastic changes in deep brain structures and large-scale networks. Beyond well-described central sensitization processes, chronic pain is characterized by disrupted oscillatory rhythms and altered connectivity within large-scale brain networks, including thalamo-cortical circuits and prefrontal-limbic-reward networks. These findings support a conceptual shift from viewing chronic pain as a focal, lesion-driven phenomenon toward recognizing it as a disorder of distributed network pathology. Pharmacological treatments remain central to clinical practice, yet their long-term efficacy is often limited and frequently accompanied by substantial side effects. The ongoing concerns about opioid-related risks and the inadequate therapeutic response in a subset of patients highlight the need for safe, non-pharmacological approaches that can address not only pain but also comorbid disturbances in mood, sleep, and social functioning. Neuromodulation provides a promising path toward mechanism-based and non-pharmacological management of chronic pain by employing physical or chemical stimulation to alter the excitability and synchrony of specific neural populations within central, peripheral, and autonomic systems. While invasive deep brain stimulation demonstrates that targeting deep brain structures can be effective, its clinical application is restricted by surgical risks and cost, highlighting the importance of non-invasive techniques capable of reaching deep targets. Current non-invasive approaches, such as transcranial electric stimulation, are constrained by limited penetration depth and insufficient spatial precision. These limitations hinder reliable engagement of deep regions implicated in pain, including the thalamus and nucleus accumbens, and tend to produce broad, non-specific modulation of cross-network oscillatory activity. Temporal interference (TI) stimulation has emerged as a means of overcoming these obstacles. By delivering interacting high-frequency currents that generate a low-frequency envelope within the head, TI enables focal stimulation of deep targets while minimizing superficial current delivery. Recent multiscale modeling and animal studies indicate that TI exploits the nonlinear rectification properties of neuronal membranes in response to high-frequency carriers, as well as their phase-locked responses to low-frequency envelopes, to generate “peak-focused” electric fields in deep regions under relatively low superficial current loads. Moreover, TI appears to exhibit potential advantages in terms of cell-type selectivity and rhythm-specific engagement, including differential responses across neuronal subtypes and distinct coupling to θ-, β-, and γ-band oscillations. These features suggest a promising avenue for correcting abnormal rhythms and network dynamics that contribute to chronic pain. This review summarizes current knowledge of the neural mechanisms underlying chronic pain and recent advances in TI research. It examines functional disturbances across key pain-related regions and networks, outlines the principles and technical characteristics of TI, and discusses potential deep-brain targets and stimulation strategies relevant to chronic pain. Evidence to date indicates that TI, with its non-invasiveness, tolerability, and capacity for precise deep brain modulation, holds great promise for the management of treatment-resistant chronic pain and may evolve into a new generation of precise and efficient non-pharmacological analgesic strategies.

Olfactory receptors (ORs) form the largest superfamily of G protein-coupled receptors (GPCRs). Traditionally recognized for their role in the nasal olfactory epithelium, where they mediate the sense of smell, accumulating evidence has firmly established their ectopic expression in non-olfactory tissues, including the intestine, lungs, and kidneys. The intestine, as the primary site for nutrient digestion and absorption, harbors a highly complex chemical environment. To adapt to this environment, the gut employs a sophisticated network of “chemosensors” to monitor luminal contents and maintain homeostasis. Among these sensors, intestinal ORs have emerged as crucial functional components, serving as a molecular bridge that connects environmental chemical signals—such as food-derived odorants—to specific physiological responses. This discovery has significantly deepened our understanding of how dietary flavors and compounds influence intestinal physiology at the molecular level. This review systematically summarizes the expression profiles, ligand classification, and biological functions of ORs within the gastrointestinal tract. Studies indicate that intestinal ORs exhibit distinct spatial distribution patterns across different gut segments and display cell-type specificity, particularly within enterocytes and enteroendocrine cells. These receptors function as versatile sensors capable of recognizing a wide variety of ligands, including exogenous dietary components, gut microbiota metabolites such as short-chain fatty acids, and endogenous small molecules like azelaic acid. Upon activation by specific ligands, intestinal ORs trigger intracellular signaling cascades, primarily involving the AC-cAMP-PKA pathway or calcium influx channels. A major focus of this review is to elucidate the molecular mechanisms by which these receptors regulate the secretion of gut hormones. Activation of specific ORs in enteroendocrine cells has been shown to stimulate the release of hormones such as glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and serotonin (5-HT), thereby modulating systemic energy metabolism, glucose homeostasis, and gastrointestinal motility. Furthermore, the review addresses the critical roles of ORs in immune regulation and pathology. Evidence suggests that specific ORs contribute to the maintenance of intestinal immune homeostasis and may offer protection against inflammation. Beyond their involvement in inflammatory responses, ORs such as Olfr78 have been shown to regulate the differentiation and function of intestinal endocrine cells. Similarly, Olfr544 has been demonstrated to alleviate intestinal inflammation by remodeling the gut microbiome and metabolome. These findings collectively suggest that specific ORs hold promise as therapeutic targets for mitigating intestinal inflammation and maintaining gut homeostasis. Additionally, the review explores the emerging role of ORs in cancer. Although OR expression is often downregulated in tumor tissues compared to normal mucosa, activation of specific ORs by certain ligands can inhibit tumor cell proliferation and migration and induce apoptosis via pathways such as MEK/ERK and p38 MAPK. Conversely, other receptors, such as OR7C1, may serve as biomarkers for cancer-initiating cells. In conclusion, intestinal ORs represent a vital component of the gut’s sensory network. The review also discusses the translational potential of these findings. By elucidating the precise pairing relationships between dietary components and specific ORs, novel therapeutic strategies could be developed. Intestinal ORs may thus emerge as promising targets for nutritional and pharmacological interventions in metabolic diseases, inflammatory bowel diseases, and malignancies.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.

Abstract To promote the high quality development of school meal services, and to shape civilized and healthy eating habits for children and adolescents, the study analyzes Finnish school meal services. Finnish school meal services are not only free of charge, nutritionally balanced, safe and hygienic, but also carry the educational function of edutainment in meals. In order to ensure the high quality and efficient operation of the school meal service system, Finland has built a horizontal collaborative governance model, that is, the departments of education, finance, health and quality inspection have a clear division of labor and respective responsibilities. China can learn from the experience of Finnish schools in meal services, strengthen financial and legal guarantees, develop the educational functions of school meal services, and make full use of information technology such as the Internet, the Internet of things, and big data to promote the high quality development of school meal services.

Objective: To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

Objective: To analyze the drug resistance of multidrug-resistant organism (MDRO) among hospitalized children in a children's hospital in Hebei Province from 2019 to 2023, so as to provide the basis for the rational clinical application of antibacterial drugs. Methods: Specimens including sputum, blood, urine, pus, bronchoalveolar lavage fluid, secretions, pleural fluid, and peritoneal fluid of hospitalized children from January 2019 to December 2023 were collected. Pathogen identification and drug susceptibility tests were performed on methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum β-lactamase-producing Escherichia coli (ESBLs-EC), extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBLs-KP), carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Pseudomonas aeruginosa (CRPA) and carbapenem-resistant Escherichia coli (CREC). The department distribution, specimen distribution, and drug resistance of MDROs were analyzed. Results: A total of 279 086 samples were submitted for testing, with 3 512 MDROs detected. Among these, MRSA and ESBLs-EC had relatively high detection rates of 35.76% and 41.50%, respectively. In the internal medicine pediatric patients, 1 869 MDROs were detected, accounting for 53.22%. The main departments were respiratory medicine, neonatology, and intensive care. In the surgical department, 1 643 MDROs were detected, accounting for 46.78%, with the main sources being general surgery and cardiac surgery. The highest numbers of MDROs were detected in sputum, pus, and urine samples, with 1 372, 527, and 494 isolates, representing 39.07%, 15.01%, and 14.07%, respectively. The resistance rates of MRSA to penicillin, oxacillin, and erythromycin were between 81.76% and 100.00%. ESBLs-EC and ESBLs-KP had a resistance rate of 100.00% to ceftriaxone. CRKP had a resistance rate of 100.00% to ampicillin/sulbactam and imipenem. CRAB had a resistance rate of 100.00% to cefoxitin, imipenem, and meropenem. CRPA had a resistance rate of 100.00% to ampicillin/sulbactam, ceftriaxone, cefoxitin, and imipenem. CREC had a resistance rate of 100.00% to imipenem. Conclusions In a children's hospital in Hebei Province, infections with MDROs among hospitalized pediatric patients are primarily caused by MRSA and ESBLs-EC. These infections are mainly distributed in the departments of respiratory medicine, neonatology, intensive care, general surgery, and cardiac surgery, with the highest detection rates in sputum, pus, and urine samples. Additionally, MRSA, ESBLs-EC, ESBLs-KP, CRKP, CRAB, CRPA, and CREC show high resistance rate to most antimicrobial agents.

Objective To explore the value of CT signs and quantitative parameters of artificial intelligence (AI) in predicting the efficacy of neoadjuvant chemotherapy for colorectal cancer. Methods A total of 349 colorectal cancer patients who received neoadjuvant chemotherapy at Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine in Hebei Province from January 2022 to January 2025 were selected and and divided into the effective group (n = 267) and the ineffective group (n = 82) according to the evaluation criteria for the efficacy of solid tumors. Conduct a CT examination and extract AI quantitative parameters from the CT images based on the lesion. The data were analyzed using SPSS21.0 software, Logistic regression was used to screen the influencing factors of ineffective neoadjuvant chemotherapy in patients with colorectal cancer, and separate and combined models of CT signs and AI quantitative parameters were established. The predictive effect of the model was verified by using the ROC curve, calibration curve and decision curve. Results Compared with the effective group, the proportion of regular tumor morphology and the proportion of non-enlarged lymph nodesin the ineffective group were smaller. The tumor volume, peak value and entropy value were larger (P < 0.05). Multivariable analysis showed that irregular shape (OR= 4.216), presence of lymph node enlargement (OR = 8.998), larger tumor volume (OR = 1.109), higher average CT value (OR = 1.120), elevated peak value (OR = 2.528), and increased entropy value (OR = 1.390) were independent risk factors for ineffective neoadjuvant chemotherapy in colorectal cancer (P < 0.05). The areas under the ROC curves of the individual and combined models of CT signs and AI quantitative parameters were 0.777, 0.818, and 0.877, respectively(P < 0.05). The calibration curve showed a Brier score of 0.091. The decision curve showed that the threshold was between 0.10 and 0.85, and the combined model achieved a relatively high net clinical benefit. Conclusion CT signs combined with AI quantitative parameters has a predictive value for the efficacy of neoadjuvant chemotherapy in colorectal cancer. To provide evidence-based basis for clinical screening of the population benefiting from chemotherapy and optimization of treatment strategies.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve