Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective: To analyze the serological phenotypes and molecular genetic characteristics of a proband with ABO forward and reverse typing discrepancy and their family members, and to investigate the molecular genetic mechanism underlying the Ael/AelB phenotype caused by the c. 575T>C variant on an ABO A1.02 background. Methods: Serological testing and adsorption-elution assays were performed for blood group identification of the proband and family members. PCR-SBT, cloning sequencing, and Nanopore third-generation sequencing were used to analyze ABO gene variants and haplotypes. Phyre2 and PyMOL were used to predict the effect of the p. Ile192Thr variant on the structure of glycosyltransferase A (GTA), and plasma GTA activity was evaluated using an in vitro transferase assay. Results: Among thirteen family members, four members showed a serological phenotype consistent with the Ael subtype and one with the AelB subtype. Cloning sequencing and third-generation sequencing confirmed that five family members carried the ABO A1.02 (c. 575T>C) variant, which co-segregated with the weak A phenotype in the family. Structural modeling suggested that p. Ile192Thr may alter the local spatial conformation and hydrogen-bond interactions of GTA. Plasma GTA activity testing showed markedly reduced GTA activity in the tested individuals. Conclusion: The ABO A1.02 (c. 575T>C) variant is associated with the Ael/AelB phenotype. The p. Ile192Thr substitution may weaken A antigen expression by altering GTA conformation and reducing enzymatic activity. This study provides additional evidence for the role of this variant in the Ael/AelB phenotype from the perspectives of familial inheritance, full-length haplotype analysis, and functional assessment, which is important for accurate ABO subtype identification and individualized transfusion strategies.

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels (pLGICs) that mediate rapid cholinergic synaptic transmission throughout the central and peripheral nervous systems. These receptors are integral to neuromuscular junction signaling, autonomic regulation, cognitive learning, reward-related behavior, and neuroplasticity. The nAChR family exhibits remarkable functional diversity through combinatorial assembly of seventeen identified subunits (α1-α10, β1-β4, γ, δ, and ε). Among these, the α9 subunit occupies a distinctive phylogenetic position, exhibiting greater sequence divergence from other neuronal α subunits and possessing unique biochemical properties that distinguish it from all other family members. α9 can form homopentamers and also co-assemble with the α10 subunit to form functional heteropentamers, primarily in (α9)₂(α10)₃ and (α9)₃(α10)₂ stoichiometries. This dual assembly strategy generates functional heterogeneity, as distinct subunit compositions confer differential ion permeation, ligand sensitivity, and desensitization kinetics. The structural determinants governing this stoichiometric variability remain incompletely resolved, representing a critical gap in our mechanistic understanding. Unlike most other nAChR subtypes, traditional agonists of nAChRs (such as nicotine) elicit virtually no agonistic effect on α9* nAChRs (α9-containing receptors, including α9 homopentamers and α9α10 heteropentamers). This pharmacological divergence reflects structural differences within the orthosteric site, particularly in regions at complementary subunit interfaces. As cation-selective channels, α9* nAChRs exhibit high permeability to Ca²⁺. In cochlear outer hair cells, α9α10 nAChRs mediate cholinergic efferent modulation by the medial olivocochlear (MOC) bundle. Acetylcholine-evoked Ca²⁺ influx activates functionally coupled SK2 potassium channels, generating net hyperpolarization that dampens electromechanical amplification through a tightly constrained signaling microdomain. This Ca²⁺-dependent excitatory-to-inhibitory conversion exemplifies sophisticated sensory gain control, and its disruption contributes to noise-induced hearing loss, age-related hearing loss, and tinnitus. Beyond canonical ionotropic signaling, α9* nAChR engages metabotropic transduction pathways. In macrophages, receptor activation modulates cytokine production and inflammatory responses. In keratinocytes, it regulates wound healing by modulating cell migration and differentiation. In dorsal root ganglion sensory neurons, α9* signaling contributes to nociceptive processing and inflammatory hyperalgesia. This functional pleiotropy, spanning both ionotropic signaling and metabotropic transduction, positions α9* nAChR as an exemplary model for investigating signal polymorphism within the pLGIC superfamily. Pathophysiologically, α9* nAChR dysfunction is implicated across multiple organ systems. In the auditory system, disruption of α9α10 nAChR-mediated MOC efferent feedback impairs cochlear gain control, predisposing to noise-induced synaptopathy and age-related hearing loss. In immune cells, dysfunctional α9* nAChR signaling disrupts cholinergic anti-inflammatory pathway activity, exacerbating pro-inflammatory responses. In the peripheral sensory system, aberrant α9* nAChR signaling has been implicated in neuropathic and inflammatory pain states. In skin, impaired receptor function compromises keratinocyte migration and re-epithelialization through disrupted signaling pathways, leading to chronic wound healing defects and inflammation. These pathophysiological associations have catalyzed pharmacological interest, yielding promising chemical entities including α‑conotoxins, small-molecule antagonists/agonists, and allosteric modulators.α-Conotoxin peptides, in particular, demonstrate remarkable subunit selectivity and potent antinociceptive effects in preclinical pain models. However, therapeutic translation faces substantial challenges: the broad tissue distribution of α9* nAChR risks on-target adverse effects in non-target organs; pronounced species differences between rodent and human receptors complicate preclinical validation; and the complexity of ionotropic-metabotropic signaling crosstalk demands pharmacological strategies that extend beyond conventional orthosteric agonism or antagonism. Looking forward, integrating cryo-electron microscopy of full-length receptors in distinct conformational states with single-channel electrophysiology and systems-level circuit analysis promises to illuminate the molecular mechanisms governing α9* nAChR function and regulation. The development of signal pathway-biased ligands and tissue-selective delivery strategies may ultimately harness the therapeutic potential of this receptor while mitigating safety liabilities. As a paradigm for understanding pLGIC signaling diversification, α9* nAChR research will continue to inform broader questions regarding ion channel evolution, allosteric regulation, and pathophysiological mechanisms of chronic pain and autoimmune inflammatory diseases.

ObjectiveTo observe the effect of modified Huanglian Wendantang on oxidative stress in metabolic syndrome. MethodsA total of 180 patients with metabolic syndrome treated in the outpatient clinics and wards of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine from October 2023 to May 2025 were enrolled in this study. According to the randomized numerical table method， the patients were assigned into two groups （90 cases）. The basic treatment of the two groups was the same， and the observation group was additionally treated with modified Huanglian Wendantang. The course of treatment was 3 months. The two groups were compared in terms of traditional Chinese medicine （TCM） efficacy， obesity degree ［waist circumference （WC）， body mass index （BMI）， waist-to-hip ratio （WHR）， waist-to-height ratio （WHtR）］， blood pressure indicators ［systolic blood pressure （SBP） and diastolic blood pressure （DBP）］， lipids and glucose indicators ［total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， fasting insulin （FINS）， fasting glucose （FPG）， 2-hour postprandial glucose （2 h PG）， and glycosylated hemoglobin （HbA1c）］， insulin resistance indicators ［homeostatic model assessment for insulin resistance （HOMA-IR）， triglyceride-glucose index （TYG index）， triacylglycerol-to-high-density lipoprotein cholesterol （TG/HDL） ratio］， oxidative stress indicators ［malondialdehyde （MDA）， superoxide dismutase （SOD）， glutathione （GSH）， glutathione peroxidase （GSH-Px）， catalase （CAT）， and reactive oxygen species （ROS）］， and the safety of treatment. ResultsIn terms of TCM efficacy， the treatment in the observation group was significantly effective in 46 cases， effective in 30 cases， and ineffective in 10 cases， with the total effective rate of 88.4%. The treatment in the control group was significantly effective in 33 cases， effective in 28 cases， and ineffective in 23 cases， with the total effective rate of 72.6%. The observation group had higher total effective rate than the control group （χ²=11.26，P<0.05）. After treatment， both groups showed declined levels of WC， BMI， WHR， WHtR， SBP， DBP， TC， TG， LDL-C， FPG， FINS， 2 h PG， HbA1c， HOMA-IR， TG/HDL ratio， TYG index， MDA， and ROS and elevated levels of HDL-C， SOD， GSH， GSH-Px， and CAT （P<0.05）. After treatment， the observation group showed lower levels of WC， BMI， WHR， WHtR， TC， TG， LDL-C， FPG， FINS， 2 h PG， HbA1c， HOMA-IR， TG/HDL ratio， TYG index， MDA， and ROS and higher levels of HDL-C， SOD， GSH， GSH-Px， and CAT than the control group （P<0.05）. However， after treatment， there was no significant difference in SBP or DBP between the observation group and the control group. No significant adverse reactions were observed in either group. ConclusionModified Huanglian Wendantang can significantly alleviate metabolic syndrome symptoms and reduce oxidative stress， exhibiting antioxidant effects. Moreover， it does not induce adverse reactions， demonstrating safety.

ObjectiveTo observe the effect of modified Huanglian Wendantang on oxidative stress in metabolic syndrome. MethodsA total of 180 patients with metabolic syndrome treated in the outpatient clinics and wards of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine from October 2023 to May 2025 were enrolled in this study. According to the randomized numerical table method， the patients were assigned into two groups （90 cases）. The basic treatment of the two groups was the same， and the observation group was additionally treated with modified Huanglian Wendantang. The course of treatment was 3 months. The two groups were compared in terms of traditional Chinese medicine （TCM） efficacy， obesity degree ［waist circumference （WC）， body mass index （BMI）， waist-to-hip ratio （WHR）， waist-to-height ratio （WHtR）］， blood pressure indicators ［systolic blood pressure （SBP） and diastolic blood pressure （DBP）］， lipids and glucose indicators ［total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， fasting insulin （FINS）， fasting glucose （FPG）， 2-hour postprandial glucose （2 h PG）， and glycosylated hemoglobin （HbA1c）］， insulin resistance indicators ［homeostatic model assessment for insulin resistance （HOMA-IR）， triglyceride-glucose index （TYG index）， triacylglycerol-to-high-density lipoprotein cholesterol （TG/HDL） ratio］， oxidative stress indicators ［malondialdehyde （MDA）， superoxide dismutase （SOD）， glutathione （GSH）， glutathione peroxidase （GSH-Px）， catalase （CAT）， and reactive oxygen species （ROS）］， and the safety of treatment. ResultsIn terms of TCM efficacy， the treatment in the observation group was significantly effective in 46 cases， effective in 30 cases， and ineffective in 10 cases， with the total effective rate of 88.4%. The treatment in the control group was significantly effective in 33 cases， effective in 28 cases， and ineffective in 23 cases， with the total effective rate of 72.6%. The observation group had higher total effective rate than the control group （χ²=11.26，P<0.05）. After treatment， both groups showed declined levels of WC， BMI， WHR， WHtR， SBP， DBP， TC， TG， LDL-C， FPG， FINS， 2 h PG， HbA1c， HOMA-IR， TG/HDL ratio， TYG index， MDA， and ROS and elevated levels of HDL-C， SOD， GSH， GSH-Px， and CAT （P<0.05）. After treatment， the observation group showed lower levels of WC， BMI， WHR， WHtR， TC， TG， LDL-C， FPG， FINS， 2 h PG， HbA1c， HOMA-IR， TG/HDL ratio， TYG index， MDA， and ROS and higher levels of HDL-C， SOD， GSH， GSH-Px， and CAT than the control group （P<0.05）. However， after treatment， there was no significant difference in SBP or DBP between the observation group and the control group. No significant adverse reactions were observed in either group. ConclusionModified Huanglian Wendantang can significantly alleviate metabolic syndrome symptoms and reduce oxidative stress， exhibiting antioxidant effects. Moreover， it does not induce adverse reactions， demonstrating safety.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveTo observe the clinical effect of modified Huanglian Wendantang on cardiovascular risk factors in patients with metabolic syndrome under the guidance of treating disease before its onset. MethodsA total of 82 patients with metabolic syndrome treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from July 2023 to July 2024 were selected and allocated into an observation group （41 cases） and a control group （41 cases） by the random number table method. The control group received routine treatment， and the observation group was treated with modified Huanglian Wendantang on the basis of routine treatment. Both groups were treated for 8 weeks. The therapeutic effects on TCM symptoms after treatment in the two groups were evaluated. The levels of obesity degree indicators， blood pressure indicators， glucose and lipid metabolism indicators， inflammatory factors， and vascular endothelial function indicators before and after treatment in the two groups were measured， and the treatment safety was evaluated. ResultsAfter treatment， the total response rate of TCM symptoms in the observation group was 97.56% （40/41）， which was higher than that （87.80%， 36/41） in the control group （χ²=5.205， P<0.05）. After treatment， both groups showed declines （P<0.05） in systolic blood pressure （SBD）， diastolic blood pressure （DBP）， triglyceride （TG）， total cholesterol （TC）， low density lipoprotein cholesterol （LDL-C）， fasting blood glucose， 2-hour postprandial blood glucose （2 h PG）， glycosylated hemoglobin （HbA1c）， fasting insulin （FINS）， Homeostatic Model Assessment for Insulin Resistance （HOMA-IR）， body mass index （BMI）， waist circumference （WC）， waist-to-hip ratio （WHR）， leptin （LEP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， endothelin-1 （ET-1）， and inducible nitric oxide synthase （iNOS）. Moreover， the declines in the observation group were more obvious than those in the control group （P<0.05， P<0.01）. After treatment， both groups showed elevated levels of high density lipoprotein cholesterol （HDL-C）， adiponectin （ADP）， nitric oxide （NO）， and endothelial nitric oxide synthase （eNOS） （P<0.05）， and the above indexes in the observation group were higher than those in the control group （P<0.01）. ConclusionUnder the guidance of the thought of treating disease before its onset， modified Huanglian Wendantang was used to treat patients with metabolic syndrome. The decoction improved the clinical efficacy by ameliorating IR to improve insulin sensitivity， reducing inflammation， and protecting the vascular endothelial function. It inhibits cardiovascular risk factors without inducing adverse reactions， being worthy of clinical application and promotion.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.