In March 2024, three male and two female specimens of the genus Lispe were intercepted on cargo ships inbound for Wan Zai Port under Gongbei Customs. Based on morphological characteristics and molecular analysis, the specimens were identified as Lispe pumila. In this study, we describe the morphological features, diagnostic characteristics, and geographical distribution of L. pumila, with the aim of providing a reference for the identification of this species when intercepted at ports.

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.

The 2024 National Education Work Conference pointed out that at the current juncture of the critical period for achieving the goals and tasks of the 14th Five-Year Plan,the implementation of the Education Powerhouse Construction Plan Outline should be taken as the main line of work,and building first-class disciplines is an crucial task for a higher education powerhouse.In 2022,forensic medicine was officially listed as a first-level discipline under the medical category,presenting an un-precedented historical opportunity for the development of forensic medicine.The forensic medicine dis-cipline of Southern Medical University comprehensively improves the quality of talent cultivation and facilitates the construction of first-class disciplines as its main direction.It aims to initiate and imple-ment a high-level faculty team building plan featuring"combining recruitment and cultivation,inter-disciplinary integration";make vigorous efforts to establish a first-level doctoral program,refine advan-tageous second-level disciplines and research directions;and establish an innovative research platform from a high starting point with deep integration.The discipline adheres to moral cultivation and the Five Domains of Education simultaneous development,to build a high-quality talent joint training model.Guided by the construction of the national legal system and industry needs,the discipline will enhance social service capabilities.The forensic medicine construction in our university will continue to contribute to the rule of law in China and educational power.

Objective To explore the gender differences and the influence of varus and valgus deformities on distal femur rotational alignment in patients with knee osteoarthritis so as to provide reference for the distal femur rotational osteotomy in total knee arthroplasty(TKA).Methods CT data of 125 knees with osteoarthritis suitable for TKA(including 56 males and 69 females;78 varus knees and 47 valgus knees)were collected and received three-dimensional imaging reconstruction with Mimics software.We measured the posterior condylar angle(PCA),the angle between the anteroposterior axis and the surgical epicondylar axis(ASA),and the angle between the anteroposterior axis and the posterior condylar axis(APA).We also compared gender differences and the influences of varus-valgus deformities on distal femur rotational alignments.Results The PCA and ASA values of distal femur rotational alignments were(3.5±1.1)° vs.(2.8±1.2)°(P=0.001)and(90.6±3.4)° vs.(91.9±3.5)°(P=0.028)for males and females,with statistically differences between the genders.There was no statistically difference in APA between males and females(P=0.482).The PCA values of distal femur rotational alignment in varus and valgus knees were(3.6±1.1)° and(2.9±1.2)°,with a statistically difference between the two groups(P=0.001).There were no statistically differences in ASA or APA between varus and valgus knees(P＞0.05).Conclusion Gender and varus-valgus deformities of knee joint affect the distal femur rotational alignment.Distal femur rotational osteotomy angle should be determined according to the patient's CT imaging before TKA to reduce the errors caused by individual differences and improve the accuracy of the distal femur rotational alignment during TKA.

Objective:To explore the predictive value of platelet aggregation function for acute kidney injury (AKI) in sepsis patients based on decision curve.Methods:A retrospective study was conducted to collect and analyze the clinical data of 120 sepsis patients admitted to the Affiliated Hospital of Jiangnan University from January 2021 to December 2023. According to the incidence of AKI during hospitalization, they were divided into AKI group (37 cases) and non-AKI group (83 cases). The general data, platelet aggregation function index (platelet aggregation rate) and other laboratory indexes of the two groups were collected and compared. Logistic regression model was used to analyze the relationship between AKI and main indexes of platelet aggregation function in patients with sepsis. The area under the curve (AUC) was obtained by drawing the receiver operating characteristic (ROC) curve, and the predictive value of platelet aggregation function on AKI in patients with sepsis was analyzed. R language software was used to construct a nomogram model of platelet aggregation function combined with other main indicators to predict AKI in patients with sepsis. Based on the decision curve, the predictive efficacy of the model on AKI in patients with sepsis was analyzed.Results:The platelet aggregation rate in the AKI group was lower than that in the non-AKI group: (56.23 ± 7.86)% vs. (68.79 ± 8.54)%, and the thrombin time was longer than that in the non-AKI group: 17.00 (16.50, 18.00) s vs. 16.00 (15.00, 17.00) s. The levels of D-dimer, C-reactive protein and procalcitonin were higher than those in the non-AKI group: (1.55 ± 0.45) mg/L vs. (1.32 ± 0.41) mg/L, (107.53 ± 18.41) mg/L vs. (99.86 ± 17.25) mg/L, (3.10 ± 0.46) μg/L vs. (2.88 ± 0.42) μg/L, and the differences were statistically significant ( P<0.05). The results of constructing a Logistic regression model showed that AKI in sepsis patients may be related to abnormal levels of platelet aggregation rate, thrombin time, C-reactive protein and procalcitonin ( P<0.05). The ROC curve was drawn to obtain the corresponding AUC: the AUC of platelet aggregation rate predicting sepsis complicated with AKI was 0.860 (95% CI 0.789 to 0.931), which had certain predictive value. When the platelet aggregation rate was set to 62.84%, the best predictive value can be obtained, with sensitivity, specificity, and Jorden index of 83.80%, 80.70%, and 0.645, respectively. The nomogram model of platelet aggregation function assisting other major indicators in predicting AKI in sepsis patients had a C-index of 0.904 (95% CI 0.851 to 0.957), indicating good discrimination of the model. Through decision curve analysis of the clinical net benefit of the model, the results showed that the clinical net benefit of the model was higher than that of platelet aggregation rate and other major indicators when applied alone. When the risk threshold was within the range of 0 to 0.81 and 0.97 to 1.00, the model could provide a significant increase in clinical net benefit rate. Conclusions:Platelet aggregation function (platelet aggregation rate) can serve as an early auxiliary predictive indicator for the risk of AKI in sepsis patients, and can assist other major indicators to improve the predictive value of AKI in sepsis patients.

Objective To evaluate the efficacy and safety of polyethylene glycol electrolyte powder(PEG)combined with linaclotide(Lin)for bowel preparation in elderly constipated patients before colonoscopy.Methods In this prospective,randomized controlled trial,90 elderly patients with constipation undergoing colonoscopy were recruited at our hospital from June 2022 to December 2023.Participants were randomly assigned to three groups(n=30 each):PEG-3L alone,PEG-3L+Lin,and PEG-2L+Lin.Primary outcome was Boston Bowel Preparation Scale(BBPS)score and secondary outcomes included adverse event rates,colonoscopy completion rate,withdrawal time,and polyp detection rate.Statistical analysis was performed using independent t-tests and chi-square tests.Results The PEG-3L+Lin group achieved significantly higher BBPS scores than both PEG-3L alone and PEG-2L+Lin groups did(both P<0.001).The PEG-2L+Lin group also outperformed the PEG-3L alone group in cleansing efficacy(90.0%vs.76.7%,P=0.008).The PEG-2L+Lin group demonstrated the best tolerability and lowest adverse event rate,the PEG-3L group had the longest withdrawal time(P<0.05),but the three groups showed no significant difference in polyp detection rates.Conclusion PEG combined with linaclotide significantly improves bowel cleansing in elderly constipated patients.PEG-2L+Lin regimen provides optimal balance between efficacy,safety,and tolerability,making it a preferable choice for this population.

Isoflavones which mainly distributed in leguminous plants have plenty of health benefits.Isoflavone synthase(IFS)is a membrane-associated cytochrome P450 enzyme(CYP450)which carries out the unique aryl-ring migration and hydroxylation.So far,few crystal structures of plant P450s have been obtained.We determined the crystal structure of IFS from Medicago truncatula at 1.9 ? by MAD method using a selenomethionine substituted crystal and conducted molecular docking and mutagenesis study.The structure of IFS complexed with imidazole exhibits the helix Ⅰa-loop-helix Ⅰβ motif which cor-responds to helix Ⅰ of other P450s.Compared with structures of common P450s,IFS/imidazole structure contains an extra domain,i.e.,the γ-domain.The structure reveals a homodimer in which the γ-domain of one molecule interacts with the β-domain of another.The plane of heme group makes an angle of ap-proximately 40° with the helix Ⅰa-loop-helix Ⅰβ motif.Molecular docking combined with mutagenesis study suggested that Trp-128 and Asp-300 might play important roles in substrate binding and recogni-tion.Phe-301,Ser-303 and Gly-305 from the helix Ⅰa-loop-helix Ⅰβ motif may play important roles in the aryl-ring migration.These novel structural features reveal insights into the unique reaction mechanism of IFS and provide a basis for engineering IFS in leguminous crops for health purpose.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.