Atherosclerosis （AS） is the main pathological basis of cardiovascular diseases and seriously threatens human quality of life. Its prevention and treatment urgently need breakthroughs. The inflammatory response， which runs through the physiological and pathological evolution process of AS， is one of the important mechanisms for AS occurrence. Currently， the treatment methods for AS in Western medicine are relatively mature. However， they have adverse reactions such as abnormal liver and kidney function， drug tolerance， target vessel restenosis， and stent thrombosis， which remain the key bottleneck restricting clinical efficacy. Traditional Chinese medicine （TCM）， characterized by multiple components， multiple targets， and multi-pathway synergy， shows unique clinical application potential and efficacy advantages in the intervention of AS. This article reviewed the research progress of TCM in intervening in AS by regulating inflammatory-related signaling pathways， such as nuclear factor-κB （NF-κB）， Toll-like receptors （TLRs）， mitogen-activated protein kinase （MAPK）， and Janus kinase/signal transducer and activator of transcription （JAK/STAT）， in the past five years. It summarized the combined mechanism of action of TCM monomers， TCM pairs， and compound preparations in inhibiting the inflammatory cascade reaction through multiple targets， regulating lipid metabolism disorders， and improving vascular endothelial dysfunction and the imbalance of the microenvironment. It deepened the research on the molecular mechanism of TCM in anti-AS， so as to provide a scientific basis for the clinical transformation application and related theoretical research of TCM in anti-AS.

Atherosclerosis （AS） is the main pathological basis of cardiovascular diseases and seriously threatens human quality of life. Its prevention and treatment urgently need breakthroughs. The inflammatory response， which runs through the physiological and pathological evolution process of AS， is one of the important mechanisms for AS occurrence. Currently， the treatment methods for AS in Western medicine are relatively mature. However， they have adverse reactions such as abnormal liver and kidney function， drug tolerance， target vessel restenosis， and stent thrombosis， which remain the key bottleneck restricting clinical efficacy. Traditional Chinese medicine （TCM）， characterized by multiple components， multiple targets， and multi-pathway synergy， shows unique clinical application potential and efficacy advantages in the intervention of AS. This article reviewed the research progress of TCM in intervening in AS by regulating inflammatory-related signaling pathways， such as nuclear factor-κB （NF-κB）， Toll-like receptors （TLRs）， mitogen-activated protein kinase （MAPK）， and Janus kinase/signal transducer and activator of transcription （JAK/STAT）， in the past five years. It summarized the combined mechanism of action of TCM monomers， TCM pairs， and compound preparations in inhibiting the inflammatory cascade reaction through multiple targets， regulating lipid metabolism disorders， and improving vascular endothelial dysfunction and the imbalance of the microenvironment. It deepened the research on the molecular mechanism of TCM in anti-AS， so as to provide a scientific basis for the clinical transformation application and related theoretical research of TCM in anti-AS.

Objective To compare the efficacy of five Blastocystis staining method for staining cells with different morphologies and in different developmental stages to select appropriate staining techniques for specific scenarios and experimental purposes. Methods Blastocystis was stained using Wright-Giemsa, periodic acid-Schiff (PAS), Oil Red O, Nile Red, and Lugol's iodine solution respectively. The staining effects of the five method on Blastocystis were compared. Results Wright-Giemsa staining colored the cytoplasm and nucleus of Blastocystis bluish-purple, with other parts lightly stained; PAS staining rendered Blastocystis cells red; Oil Red O staining showed different structures within Blastocystis in varying intensities of orange-red; Nile Red enabled Blastocystis to fluoresce orange-yellow under a fluorescence microscope; Lugol's iodine solution stained the cytoplasm and nucleus a dark brownish-yellow. Conclusions Wright-Giemsa staining is recommended for clinical diagnosis. For metabolic studies, a combination of periodic acid-Schiff (PAS) staining and Oil Red O staining is suggested; PAS + Oil Red O staining can provide comprehensive morphological evidence for cell metabolism research. Nile Red staining is recommended for dynamic observation of live Blastocystis. Lugol's iodine staining is suitable for preliminary screening of large-scale samples.

Neurodegenerative diseases (NDs), particularly Alzheimer’s disease (AD) and Parkinson’s disease (PD), and cardiovascular diseases (CVDs), including heart failure (HF) and coronary artery disease (CAD), are major chronic disorders associated with population aging. Their frequent coexistence in clinical practice suggests that they may not progress independently, but may instead be linked through bidirectional pathological communication within the brain-heart axis. Epidemiological evidence underscores this clinical burden: systematic reviews and meta-analyses have reported pooled relative risks for dementia of approximately 1.27 among individuals with a history of CAD and 1.60 among those with HF, while the pooled prevalence of cognitive impairment and dementia in patients with HF has been estimated at approximately 41.42% and 19.79%, respectively. This review synthesizes current evidence on the mechanistic links, disease-state manifestations, biomarkers, and potential therapeutic strategies involved in the comorbidity between NDs and CVDs from the perspective of the brain-heart axis. The brain and heart communicate through autonomic, neuroendocrine, neuroimmune, vascular, and humoral pathways. Sympathetic activation, parasympathetic withdrawal, vagal dysfunction, cerebral hypoperfusion, endothelial injury, blood-brain barrier (BBB) alterations, peripheral immune activation, and circulating inflammatory mediators may jointly contribute to cross-organ pathological signaling. Oxidative stress and inflammatory response are not specific to the brain-heart axis; rather, they may act as candidate amplifying processes within this network. Mitochondrial dysfunction and NADPH oxidase activation can increase the generation of reactive oxygen species and reactive nitrogen species, which may activate nuclear factor-κB, the NLRP3 inflammasome, and mitogen-activated protein kinase/c-Jun N-terminal kinase signaling. These pathways can promote the release of interleukin-1β, interleukin-6, tumor necrosis factor‑α, and other inflammatory mediators, which may in turn aggravate mitochondrial injury, endothelial dysfunction, immune cell activation, and further oxidant generation. Cytokines, chemokines, oxidized lipids, mitochondrial DNA, damage-associated molecular patterns, and extracellular vesicles may transmit these signals between the cardiovascular and central nervous systems, whereas autonomic reflexes provide an additional route for bidirectional amplification. Distinct disease states illustrate different manifestations of this network. The association between AD and CVDs may involve vascular risk factors, impaired amyloid-β clearance, abnormal cholesterol homeostasis, cerebral microvascular injury, neuroinflammation, and endothelial dysfunction. HF-related cognitive impairment may be linked to reduced cardiac output, recurrent cerebral hypoperfusion, hypoxia-related signaling, BBB dysfunction, circulating inflammation, and impaired autonomic feedback. In PD, cardiovascular autonomic dysfunction and abnormalities in heart rate and blood pressure regulation may interact with vascular injury, reduced cerebral perfusion, inflammation, and oxidative stress. However, the relationship between PD and CAD remains heterogeneous and may be modified by age, ethnicity, medication use, metabolic factors, and coexisting diseases. Biomarker evaluation should therefore move beyond isolated indicators. Traditional markers, including malondialdehyde, superoxide dismutase, glutathione, C-reactive protein, and interleukin-6, reflect systemic oxidative or inflammatory burden but lack sufficient specificity to distinguish primary neurodegeneration, cardiovascular injury, or a comorbid state involving both. Greater clinical value may be obtained from cross-system panels that combine oxidative stress and inflammatory markers with cardiac stress or injury biomarkers, such as N-terminal pro-B-type natriuretic peptide; glial or neuronal biomarkers, such as glial fibrillary acidic protein and neurofilament light chain; BBB- or endothelial injury-related biomarkers; and omics-derived candidates, including microRNAs, long non-coding RNAs, DNA methylation signatures, metabolites, and proteins. Multi-omics integration and artificial intelligence-assisted analysis may support risk stratification, disease monitoring, and prediction of treatment response. However, these approaches require standardized analytical platforms, validation in independent cohorts, and careful distinction between associative findings and causal mechanisms. Potential interventions include antioxidant, anti-inflammatory, mitochondria-targeted, and multi-target combination strategies, together with emerging approaches such as nanodelivery, CRISPR/Cas9- or CRISPR/dCas9-based regulation, mesenchymal stem cell-mediated mitochondrial transfer, and ergothioneine supplementation. Nevertheless, most available evidence is indirect, preclinical, or derived from other disease indications, and no single drug or technology can currently be regarded as specific to brain-heart axis comorbidity. The clinical relevance of these strategies will depend on whether they can be matched to dominant pathological phenotypes and improve both neurological and cardiovascular outcomes. Overall, comorbidity between NDs and CVDs is more appropriately understood as a heterogeneous cross-organ regulatory network than as a disorder driven by a single pathway. Future research should prioritize multicenter longitudinal cohorts, standardized clinical phenotyping, validation of cross-system biomarker panels, and mechanism-guided interventions in clearly defined patient subgroups, with integrated assessment of cognitive function, cardiovascular events, quality of life, long-term safety, and treatment response.

OBJECTIVE To evaluate the quality of Lufu Pill （LFP）.METHODS The high-performance liquid chromatography （HPLC） fingerprint of LFP was established using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 Edition）， and similarity evaluation was performed. Hierarchical cluster analysis （HCA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed using SIMCA 14.1 software. The contents of strychnine， brucine， liquiritin， notoginsenoside R 1 ， ginsenoside Rg 1 ， ginsenoside Rb 1 ， licoricesaponin G 2 ， and ginsenoside Rd in 12 batches of samples were determined by the HPLC method.RESULTS A total of 26 common peaks were identified in the HPLC fingerprint of 12 batches of LFP samples， and eight common peaks were identified as strychnine （peak 3）， brucine （peak 4）， liquiritin （peak 6）， notoginsenoside R 1 （peak 7）， ginsenoside Rg 1 （peak 8）， ginsenoside Rb 1 （peak 15）， licoricesaponin G 2 （peak 17）， and ginsenoside Rd （peak 18）. The similarities of 12 batches of LFP samples were all greater than 0.90. The results of HCA and PCA both showed that the 12 batches of samples were divided into two categories： S2， S4-S6， and S12 were grouped into one category， while S1， S3， S7-S11 were grouped into another category. The upper control limits for Hotelling’s T 2 and DMod X were 16.631 and 1.578， respectively. The variable importance in projection values of ginsenoside Rg 1 （peak 8）， ginsenoside Rb 1 （peak 15）， peak 20， strychnine （peak 3）， peak 11， notoginsenoside R 1 （peak 7）， peak 2， peak 17， peak 13， peak 10， ginsenoside Rd （peak 18）， peak 23， peak 26， peak 21， and peak 24 were greater than 1.The content determination results showed that strychnine， brucine， liquiritin， notoginsenoside R 1 ， ginsenoside Rg 1 ， ginsenoside Rb 1 ， licoricesaponin G 2 ， and ginsenoside Rd were 0.005%-0.018%， 0.005%-0.010%， 0.050%-0.118%， 0.033%-0.073%， 0.132%-0.248%， 0.122%-0.242%， 0.021%-0.043%， and 0.016%-0.048%， respectively.CONCLUSIONS The established HPLC fingerprint and content determination method are accurate and reproducible， and can be used for the quality evaluation of LFP. Ginsenoside Rg 1 ， ginsenoside Rb 1 ， strychnine， notoginsenoside R 1 ， and ginsenoside Rd may be the differential components affecting its overall quality.

Objective:To establish a standardized code database of adverse drug reactions (ADRs) terms related to linezolid and analyze the common ADRs of linezolid.Methods:Linezolid drug labels, websites (including Side Effect Resource, and the official websites of US Food and Drug Administration, European Medicines Agency and National Medical Products Administration) and scientific literature database (including CNKI, Wanfang, VIP, PubMed, Embase and Web of Science databases) were systematically searched, and ADR terms about linezolid were collected. ADR terms were mapped to the international classification of diseases-10 (ICD-10) code to establish a linezolid adverse reaction database.Results:A total of 117 ADR terms about linezolid were collected and 91 ICD-10 codes were obtained after being mapped to ICD-10. A standardized database was constructed and successfully embedded into the ADR spontaneous reporting system as a specific drug submodule. The gastrointestinal system, skin and subcutaneous tissue system, various nervous systems, blood and lymphatic systems were the most common system organs involved in linezolid-related ADRs under the 91 ICD-10 codes. Among them, ADRs under the gastrointestinal system codes K14.302 (black hairy tongue) and K52.104 (drug-induced gastroenteritis and colitis), the skin and subcutaneous tissue system code L27.005 (drug-induced dermatitis), various nervous system codes G90.800 (other disorders of autonomic nervous system), G62.001 (drug-induced polyneuropathy) and G44.400 (drug-induced headache, not elsewhere classified), the blood and lymphatic system codes D69.502 (drug- induced thrombocytopenia) and D70.x02 (drug-induced granulocytopenia), the metabolic and nutritional codes E87.204 (lactic acidosis), as well as the endocrine system code E16.000 (drug-induced hypoglycaemia without coma) had been reported frequently in the scientific literature. In addition, there were 14 ADR terms related to linezolid under 13 ICD-10 codes not recorded in the drug instructions.Conclusions:It is feasible to use ICD-10 code to standardize ADR terms related to linezolid and establish a database. Common ADRs of linezolid include thrombocytopenia, lactic acidosis, neutropenia, black hairy tongue, gastroenteritis/colitis, hypoglycemia, rash, serotonin syndrome, peripheral neuropathy and headache, which should be paid attention to and researched furtherly.

Objective:To detect adverse reaction risk signals of triazole antifungal agents and provide evidences for their safe use in clinic.Methods:Adverse reaction/event reports with fluconazole, itraconazole, voriconazole, posaconazole, or isavuconazonium as the primary suspect drug were collected from the data in National Adverse Drug Reaction Monitoring System of China reported by Shandong Province from January 2004 to June 2024 and the US Food and Drug Administration Adverse Event Reporting System (FAERS) database from the first quarter of 2004 to the second quarter of 2023. Adverse reaction/event terms were standardized using the preferred term (PT) and system organ class in Medical Dictionary for Regulatory Activities 24.0. Risk signals were detected using the reporting odds ratio (ROR) method and the Bayesian confidence propagation neural network (BCPNN) algorithm. A PT was defined as an adverse reaction risk signal if the number of reports was ≥3, the lower limit of the 95% confidence interval ( CI) for ROR was >2, and the lower limit of the 95% CI for the information component ( IC) was >0. Descriptive statistical analysis was performed. Results:A total of 3 988 reports with the above 5 antifungal drugs as the primary suspect drug were collected from data in National Adverse Drug Reaction Monitoring System of China reported by Shandong Province, 822 (20.6%) of which were serious cases. Voriconazole, fluconazole, itraconazole, posaconazole, and isavuconazonium was the primary suspect drug in 1 852, 1 395, 703, 27, and 11 cases among the 3 988 reports, and in 591 (31.9%), 149 (10.7%), 59 (8.4%), 18 (66.7%), and 5 (5/11) serious cases among the 822 serious case reports, respectively. A total of 20 066 reports with the above 5 drugs as the primary suspect drug were collected in FAERS database, 9 635 (48.0%) of which were serious cases. Voriconazole, fluconazole, itraconazole, posaconazole, and isavuconazonium was the primary suspect drug in 7 758, 6 180, 2 869, 1 796, and 1 463 cases among the 20 066 reports, and in 4 295 (55.4%), 2 806 (45.4%), 1 191 (41.5%), 828 (46.1%), and 515 (35.2%) serious cases among the 9 635 serious case reports, respectively. Based on the data reported by Shandong Province and in FAERS database, 18 and 207 risk signals of adverse reaction not mentioned in the labels were identified, respectively, and 5 of them were identified in both databases, including fluconazole-induced renal impairment and voriconazole-induced oliguria, delirium, psychiatric disorders, and rhabdomyolysis. In the data reported by Shandong Province and in FAERS database, 13 and 189 reports of muscle-related disorders (rhabdomyolysis, myopathy, and myositis) were identified respectively, involving voriconazole (in 8 and 62 cases), itraconazole (in 4 and 74 cases), and fluconazole (in 1 and 53 cases).Conclusions:Renal impairment induced by fluconazole and oliguria, delirium, psychiatric disorders, and rhabdomyolysis induced by voriconazole are risk signals of adverse reaction not mentioned in the labels for triazole antifungal agents. Voriconazole, itraconazole, and fluconazole may also cause muscle-related disorders, warranting vigilance in clinical practice.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Currently, transcatheter intervention is the preferred treatment for patients with anatomically suitable atrial septal defects. However, the use of nickel-titanium alloy occluders in interventional procedures results in lifelong presence of the implant in the body, leading to complications such as metal allergies and arrhythmias in some patients. To overcome the short-term and long-term complications associated with the presence of metal, and to avoid radiation exposure and metal toxicity, this paper reports a case of successful transcatheter closure of atrial septal defect in a pediatric patient with metal allergies using fully biodegradable occluder under ultrasound guidance, achieving excellent results by interventional therapy.