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.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

Purpose: To compare the anatomical and clinical outcomes based on the distance from the corneal limbus to the scleral tunnel in flanged intrascleral intraocular lens (IOL) fixation. Methods: We retrospectively analyzed the medical records of patients who underwent scleral fixation of flanged IOLs. Group 1 (54 eyes) had a distance of 2.1 mm from the corneal limbus to the scleral tunnel, and Group 2 (48 eyes) had a distance of 2.8 mm. We evaluated the best corrected visual acuity (BCVA), postoperative complications, IOL tilt and decentration, refractive prediction error (RPE), effective lens position, and iris-IOL distance. Results: The BCVA, postoperative complications, IOL tilt, and IOL decentration did not differ between the two groups (p > 0.05). The RPE showed a hyperopic shift in Group 1 and a myopic shift in Group 2 (Group 1: +0.24 ± 0.68 D, Group 2: -0.03 ± 0.43 D, p = 0.03). The iris-IOL distance was statistically longer in Group 1 compared to Group 2 (Group 1: 1.02 ± 0.40 mm, Group 2: 0.57 ± 0.32 mm, p = 0.02). The incidence of pupillary optic capture was significantly higher in Group 2 compared to Group 1 (Group 1; 0%, Group 2; 8.3%, p = 0.03). Conclusions It should be considered that a shorter distance from the corneal limbus to the scleral tunnel results in a postoperative hyperopic shift and reduces the incidence of pupillary optic capture when performing flanged intrascleral IOLs fixation.

Purpose: The male predominance in the incidence of gastric cancer (GC) is established; however, sex differences in the prognosis of GC remain controversial. As such, this study analyzed the prognosis of patients with GC based on age and sex. Materials and Methods: Data from 14,739 patients diagnosed with GC at Seoul National University Bundang Hospital between 2003 and 2023 were analyzed. Baseline characteristics, histological types of GC, overall and GC-specific survival rates (age and stage stratification), and associated risk factors were analyzed. Results: Females were significantly younger (p < 0.001) and exhibited more gastric body cancers (p < 0.001) and tumors with diffuse-type or poorly differentiated histology (p < 0.001) than males. Females exhibited an advantage over males in terms of overall survival (p=0.004), but not in GC-specific survival. However, age stratification revealed significant sex differences, that females < 50 years of age exhibited survival disadvantages (p < 0.001); however, this trend was reversed with age, and females > 60 years exhibited survival advantages (p < 0.001) for both overall and GC-specific survival. This may be explained by the lower ratio of diffuse-type GC as females age. Furthermore, in the analysis according to stage, females with stage IV disease exhibited significant survival disadvantages, with significantly younger age and a higher proportion of diffuse-type GC which exhibits aggressive features, resulting in poorer survival than in males. Conclusion Age and stage stratification revealed significant differences in survival between the sexes, which can be helpful for public health strategies.

Background: With the aging population and advancements in medical care worldwide, the number of older patients with end-stage kidney disease continues to rise. This study aimed to identify factors associated with osteoporosis and osteopenia in older patients undergoing incident hemodialysis and assess their impact on mortality. Methods: We analyzed a large multicenter retrospective cohort of patients aged ≥70 years undergoing incident hemodialysis to identify factors associated with osteoporosis using logistic regression analysis and to assess the association of death with osteoporosis and osteopenia using Cox multivariable analysis. Results: Among 710 patients, 39.0% and 19.6% had osteoporosis and osteopenia, respectively. Osteoporosis was significantly associated with female sex, a history of fractures, and the absence of phosphate binder use. During a median follow-up of 36.8 months, 348 participants (58.8%) died. Mortality rates were the highest in the osteoporosis group (79.8%), followed by the osteopenia (77.2%) and normal bone mineral density (BMD) groups (35.2%). Cox regression analysis revealed that even after adjusting for covariates, the osteoporosis group was significantly associated with a higher mortality risk than the normal BMD group. Osteoporosis at the start of hemodialysis was significantly associated with higher mortality. Conclusion We should consider the importance of bone health in patients undergoing incident hemodialysis and pay attention to the use of phosphate binders and fracture prevention.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

AIM: To compare the efficacy of small-incision lenticule extraction(SMILE)and femtosecond assisted laser in situ keratomileusis(FS-LASIK)in the treatment of patients with myopia and astigmatism.METHODS: Retrospective analysis. A total of 100 cases(200 eyes)of patients with myopia and astigmatism treated in our hospital from December 2021 to December 2022 were collected. Among them, 50 cases(100 eyes)were divided into SMILE group and 50 cases(100 eyes)were divided into FS-LASIK group according to the treatment plans. The visual acuity and astigmatism, corneal morphology parameters, subjective visual quality scores, ocular surface indicators, postoperative complications, and quality of life were compared between the two groups before and after surgery.RESULTS: There was no significant difference in uncorrected visual acuity(UCVA), best corrected visual acuity(BCVA), astigmatism, corneal asphericity Q value, corneal surface regularity index(SRI), corneal thickness, and corneal curvature between the two groups before surgery and at 1 d, 1, and 6 mo after surgery(all P>0.05). At 1 and 6 mo after surgery, the subjective visual quality score, the quality of life score, Schirmer I test(SⅠt)and tear film break-up time(BUT)in the SMILE group were better than that in the FS-LASIK group(all P<0.05). The incidence of complications in the SMILE group was lower than that in the FS-LASIK group at 6 mo after surgery(P=0.005).CONCLUSION: Both SMILE and FS-LASIK have good clinical effects in the treatment of myopia with astigmatism, but the SMILE could alleviate ocular surface injury, reduce the risk of complications and improve the quality of lifes for patients.

Overactive bladder (OAB) and detrusor underactivity (DUA) are representative voiding dysfunctions with a chronic nature and limited treatment modalities, and are ideal targets for stem cell therapy. In the present study, we investigated the therapeutic efficacy of human mesenchymal stem cells (MSCs) with a high antioxidant capacity generated by the Primed Fresh OCT4 (PFO) procedure in chronic bladder ischemia (CBI)-induced OAB and DUA rat models. Sixteen-week-old male Sprague-Dawley rats were divided into three groups (sham, OAB or DUA, and stem cell groups; n=10, respectively).CBI was induced by bilateral iliac arterial injury (OAB, 10 times; DUA, 30 times) followed by a 1.25% cholesterol diet for 8 weeks. Seven weeks after injury, rats in the stem cell and other groups were injected with 1×10 6 PFO-MSCs and phosphate buffer, respectively. One week later, bladder function was analyzed by awake cystometry and bladders were harvested for histological analysis. CBI with a high-fat diet resulted in atrophy of smooth muscle and increased collagen deposits correlating with reduced detrusor contractility in both rat models. Arterial injury 10 and 30 times induced OAB (increased number of non-voiding contractions and shortened micturition interval) and DUA (prolonged micturition interval and increased residual volume), respectively. Injection of PFO-MSCs with the enhanced glutathione dynamics reversed both functional and histological changes; it restored the contractility, micturition interval, residual volume, and muscle layer, with reduced fibrosis. CBI followed by a high-fat diet with varying degrees of arterial injury induced OAB and DUA in rats. In addition, PFO-MSCs alleviated functional and histological changes in both rat models.

Purpose: Breast cancer gene (BRCA) mutation is a well-known risk factor for breast cancer, and clinical interest in prophylactic mastectomy has increased in recent years.We investigated patients who were BRCA mutation carriers and underwent contralateral risk-reducing mastectomy (RRM), focusing on the incidence of occult malignancy after contralateral RRM. Methods: Prospectively collected data of patients with breast cancer treated at a single institution were retrospectively reviewed. Patients who underwent RRM with BRCA mutation who underwent RRM between January 2010 and November 2023 were included in this study.Among patients who underwent contralateral RRM, those with a primary cancer diagnosis were included, and those with occult malignancy on the contralateral RRM side were reviewed additionally. The demographics and pathologies of both primary breast cancer and occult malignancies were evaluated. Results: In our institution, 925 patients were identified as BRCA mutation carriers, and 320 patients underwent contralateral RRM along with primary breast cancer surgery. BRCA2 mutation occurred more frequently (54.8%) in the overall BRCA mutation cohort. Furthermore, we reviewed 320 patients diagnosed with breast cancer and detected as BRCA mutation carriers who underwent contralateral RRM; high proportion of them were BRCA1 mutation carriers.Interestingly, we found a low incidence of only seven patients (2.2%) with occult malignancy on contralateral RRM side, which is different from that reported in other nations. Conclusion The incidence of occult malignancy in the contralateral breast of breast cancer patients with breast cancer with BRCA mutation is significantly low, and may be influenced by several factors. Increased utilization of screening and advancements in diagnostic technologies in South Korea have reduced the chance of occult malignancy in RRM, and a variety of pathologic examination methods may affect the rate of incidence.