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.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

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.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate the mechanism by which Tianxiang pills （TXP） alleviates myocardial cell pyroptosis in the rat model of myocardial ischemia/reperfusion （I/R） injury through modulating the reactive oxygen species （ROS）/cysteinyl aspartate-specific proteinase-1 （Caspase-1）/gasdermin D （GSDMD） signaling pathway. MethodsA total of 54 SPF-grade adult male Sprague-Dawley rats were randomized into six groups （n=9）： sham （thoracotomy only without ligation）， I/R （left anterior descending coronary artery ligation for 30 min followed by reperfusion）， NAC （positive control， N-acetylcysteine， 150 mg·kg^-1， by gavage）， low-dose Tianxiang pills （TXP-L， 2.5 g·kg^-1， by gavage）］， medium-dose Tianxiang pills （TXP-M， 5 g·kg^-1， by gavage）， and high-dose Tianxiang pills （TXP-H， 10 g·kg^-1， by gavage）. The fluorescent probe method was employed to measure the ROS level. Biochemical assay kits were employed to determine the Caspase-1 activity， superoxide dismutase （SOD） activity， malondialdehyde （MDA） content， and glutathione peroxidase （GSH-Px） activity. Myocardial infarct size was assessed by 2，3，5-triphenyltetrazolium chloride （TTC） staining. Western blot analysis was performed to evaluate the expression levels of proteins in the ROS/Caspase-1/GSDMD pathway， including GSDMD， cleaved Caspase-1/Caspase-1 ratio， the N-terminal domain of GSDMD （GSDMD-N）， and the p22-phox subunit of the reduced nicotinamide adenine dinucleotide phosphate （NADPH） oxidase complex. Enzyme-linked immunosorbent assay （ELISA） was utilized to determine the concentration of interleukin-1β （IL-1β）. Transmission electron microscopy （TEM） was employed to observe mitochondrial structure and to score the degree of mitochondrial swelling. ResultsCompared with the Sham group， the I/R group exhibited elevated the levels of ROS， Caspase-1 activity， GSDMD expression， IL-1β concentration， MDA content， infarct area， mitochondrial swelling score， cleaved Caspase-1/Caspase-1， GSDMD-N， and p22phox and decreased activities of SOD and GSH-Px （P<0.05）. Compared with the I/R group， NAC and different doses of TXP significantly reduced the ROS level， Caspase-1 activity， GSDMD expression， IL-1β concentration， MDA content， infarct area， and mitochondrial swelling score. Moreover， they downregulated the expression of cleaved Caspase-1/Caspase-1， GSDMD-N， and p22phox and increased the activities of SOD and GSH-Px （P<0.05）. Furthermore， TXP exerted effects on ROS， MDA， SOD， GSH-Px， Caspase-1 activity， GSDMD expression， IL-1β concentration， cleaved Caspase-1/Caspase-1， GSDMD-N， p22phox， infarct area， and mitochondrial swelling score in a dose-dependent manner （P<0.05）. ConclusionTianxiang pills alleviates myocardial I/R injury by mitigating oxidative stress and cell pyroptosis through the inhibition of the ROS/Caspase-1/GSDMD signaling pathway.

ObjectiveTo investigate the effects of Shenxiong Huanglian Jiedu decoction on the clearance of amyloid β-protein （Aβ） and neuronal damage in the mouse model of Alzheimer's disease （AD）. MethodsA total of 36 SPF-grade 2-month-old C57BL/6J mice were used in this study， and the modeling was performed by bilateral hippocampal injection of Aβ oligomers in C57BL/6J mice. The experiment was conducted with a blank group， a sham operation group， a model group， low- and high-dose （3.27，6.54 g·kg^-1， respectively） Shenxiong Huanglian Jiedu decoction groups， and a positive control （donepezil hydrochloride， 0.65 mg·kg^-1） group. At the end of the drug intervention， the learning and memory abilities and the activities of mice were evaluated by the Morris water maze and open field tests. Brain histopathology was examined by hematoxylin-eosin and Nissl staining. Additionally， in vivo imaging was employed to measure the metabolism of fluorescent Aβ in the cerebrospinal fluid， and staining of ionized calcium-binding adapter molecule-1 （Iba-1） was employed to assess microglial activation in the hippocampal tissue. Additionally， neurotrophin-3 （NT-3） and brain-derived neurotrophic factor （BDNF） levels in the brain tissue and serum were determined by the immunofluorescence assay and enzyme-linked immunosorbent assay. Western blot was conducted to determine the expression of inflammation and pathway-related proteins in the hippocampal tissue. ResultsCompared with the blank group and the sham operation group， the escape latency of the mice in the model group was prolonged， the platform residence time was shortened， the hippocampal tissue showed pathological manifestations such as neuronal pyknosis， Nissl body dissolution， and microglia activation. The metabolic rate of fluorescent Aβ through cerebrospinal fluid was slowed down， and the expression levels of BDNF， NT-3， and interleukin-10 （IL-10） in the hippocampus were significantly decreased （P<0.01）. The expression levels of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， interleukin-1β （IL-1β）， Toll-like receptor 4 （TLR4）， myeloid differentiation factor 88 （MyD88） and phosphorylated nuclear transcription factor-κB （p-NF-κB p65） in hippocampus were significantly increased （P<0.05， P<0.01）. Compared with the model group， the escape latency of mice in the low and high dose groups of Chinese medicine and donepezil group was shortened， and the platform residence time was prolonged. Neuronal karyopyknosis， Nissl body dissolution and microglia activation in hippocampus were improved. Fluorescence Aβ was metabolized faster by cerebrospinal fluid. The expression of BDNF and NT-3 in hippocampus was increased （P<0.01）， and the expression of TLR4， MyD88 and p-NF-κB p65 was significantly decreased （P<0.05， P<0.01）. The expression of TNF-α in the hippocampus of the high-dose group was significantly decreased （P<0.05）， and the expression of IL-10 was significantly increased （P<0.05）. The expression of TNF-α， IL-6 and IL-1β in the hippocampus of the donepezil group was significantly decreased （P<0.05， P<0.01）. ConclusionShenxiong Huanglian Jiedu decoction may mitigate neuronal damage and enhance cerebrospinal fluid flow in the mouse model of AD， thereby promoting the clearance of Aβ and improving the learning and memory abilities. These beneficial effects are likely mediated through the inhibition of microglial activation， reduction of inflammation， and modulation of the TLR4/MyD88/NF-κB signaling pathway.

Freeze-drying technique,also known as lyophilization,is a process of removing moisture from a solution or suspension through freezing and vacuum dehydration to maintain the stability of the samples and prolong their shelf life.Freeze-drying technology has been widely used in food,pharmaceutical,clinical testing,chemical and other fields but its application in the field of forensic medicine has just started.Polymerase chain reaction(PCR)and its derivative detection technologies are widely used in forensic DNA detection,but PCR reagents need to be stored and transported at low temperature.In recent years,forensic scientists have begun applying freeze-drying technology to PCR amplification reagents to solve the transportation and storage problems of PCR reagents.In order to promote the application of PCR freeze-drying technology in forensic genetics,this paper mainly expounds the research course,system and process of PCR freeze-drying technology,compares the advantages and disadvantages of PCR reagents with traditional PCR reagents,and introduces the advantages and challenges of PCR freeze-drying reagents in forensic medicine.

Objective To explore the correlation between serum hypoxia-inducible factor-1α(HIF-1α)levels and cerebral microbleeds(CMBs)and cognitive impairment and to assess the predictive value of HIF-1α for CSVD-related cognitive impairment.Methods A total of 104 patients with CSVD who attended the Department of Neurology,First Affiliated Hospital of Xinxiang Medical University from June 2022 to November 2023 were enrolled.All enrolled patients were subjected to basic statistics,cranial nuclear magnetic resonance examination,cognitive function assessment,and serum HIF-1α test,and the number and location of CMBs were counted.Based on the above data the enrolled patients were grouped.The correlation between HIF-1α and cognitive function and CMBs was studied the influencing factors of CMBs and cognitive impairment were analyzed,and the predictive value of HIF-1α on the occurrence of cognitive impairment was evaluated.Results There were statistically significant differences in HIF-1α levels and cognitive function among different CMBs groups.Serum HIF-1α levels were significantly negatively correlated with overall cognitive function,visuospatial and executive function,attention,and delayed recall,and serum HIF-1α was positively correlated with the number of CMBs.HIF-1α may be a risk factor for CMBs and cognitive impairment associated with CSVD,and serum HIF-1α has potential in predict the cognitive impairment caused by CSVD.Conclusion Serum levels of HIF-1α were associated with the number of CMB and CSVD-related cognitive impairment,and serum levels of HIF-1α may have a predictive value for CSVD-related cognitive impairment.

Occupational chronic n-hexane poisoning incidents have been effectively curtailed in traditional printing and footwear industries, but its hazards are emerging in new industries. In recent years, two cluster incidents involving eight patients with occupational chronic n-hexane poisoning had occurred in Longgang District, Shenzhen City. Unlike the cleaning processes of electronic components in the electronics industry, these two incidents occurred during cleaning operations of non-electronic products. The rapid on-site detection tubes indicated the presence of n-hexane in the organic solvents used at the work site, and subsequent analysis of volatile components of the organic solvents further confirmed the involvement of n-hexane. Although the n-hexane exposure concentration of short term in the workplace air samples were below its occupational exposure limit, all eight cases were diagnosed as occupational chronic n-hexane poisoning, based on occupational exposure history, clinical manifestations, field investigations, and laboratory test results. These two poisoning incidents highlight that in air-conditioned or enclosed workshops with substandard occupational disease prevention facilities, the use of n-hexane containing organic solvents may result in occupational chronic n-hexane poisoning, even when the air monitoring results do not exceed the occupational exposure limits.