In audiovisual emotion recognition, representational learning is a research direction receiving considerable attention, and the key lies in constructing effective affective representations with both consistency and variability. However, there are still many challenges to accurately realize affective representations. For this reason, in this paper we proposed a cross-modal audiovisual recognition model based on a multi-head cross-attention mechanism. The model achieved fused feature and modality alignment through a multi-head cross-attention architecture, and adopted a segmented training strategy to cope with the modality missing problem. In addition, a unimodal auxiliary loss task was designed and shared parameters were used in order to preserve the independent information of each modality. Ultimately, the model achieved macro and micro F1 scores of 84.5% and 88.2%, respectively, on the crowdsourced annotated multimodal emotion dataset of actor performances (CREMA-D). The model in this paper can effectively capture intra- and inter-modal feature representations of audio and video modalities, and successfully solves the unity problem of the unimodal and multimodal emotion recognition frameworks, which provides a brand-new solution to the audiovisual emotion recognition.
Emotions ; Humans ; Attention ; Algorithms

Automatic classification of medical questions is of great significance in improving the quality and efficiency of online medical services, and belongs to the task of intent recognition. Joint entity recognition and intent recognition perform better than single task models. Currently, most publicly available medical text intent recognition datasets lack entity annotation, and manual annotation of these entities requires a lot of time and manpower. To solve this problem, this paper proposes a medical text classification model, bidirectional encoder representation based on transformer-recurrent convolutional neural network-entity-label-semantics (BRELS), which integrates medical entity label semantics. This model firstly utilizes an adaptive fusion mechanism to absorb prior knowledge of medical entity labels, achieving local feature enhancement. Then in global feature extraction, a lightweight recurrent convolutional neural network (LRCNN) is used to suppress parameter growth while preserving the original semantics of the text. The ablation and comparison experiments are conducted on three public medical text intent recognition datasets to validate the performance of the model. The results show that F1 score reaches 87.34%, 81.71%, and 77.74% on each dataset, respectively. The results show that the BRELS model can effectively identify and understand medical terminology, thereby effectively identifying users' intentions, which can improve the quality and efficiency of online medical services.
Semantics ; Neural Networks, Computer ; Humans ; Natural Language Processing

Trifluridine-tipiracil(TAS-102),as a novel fluorouracil-based chemotherapeutic agent,effectively overcomes fluorouracil resistance in colorectal cancer(CRC)through its unique pharmacological mechanisms,providing a critical treatment option for advanced CRC patients.Probiotics have shown unique value in CRC prevention and adjuvant therapy by regulating intestinal flora homeostasis and modulating host immune response.It is worth noting that the combination of chemotherapeutic drugs and probiotics not only enhances the anti-tumor activity through synergistic effects,but also reduces the adverse effects caused by chemotherapeutic drugs.Based on this,this paper systematically reviews the pharmacological properties of TAS-102 and describes its synergistic effects in combination with other antitumor drugs.It also summarizes the synergistic effect of fluorouracil drugs combined with probiotics to enhance the effectiveness and reduce the toxicity,and discusses the potential value of the combination of TAS-102 and probiotics,which provides a new research direction for optimizing the precision treatment of CRC and a scientific basis for improving the quality of life of patients.

Spinal cord injury represents a severe central nervous system trauma characterized by prolonged treatment duration, limited neural regeneration, and delayed functional recovery, greatly affecting patients′ quality of life. The impaired neural tissue struggles to recover effectively due to both the hostile microenvironment and its own compromised state. Current clinical interventions, including early reduction, laminectomy decompression, and intravenous or intrathecal methylprednisolone administration, fail to simultaneously modulate the microenvironment and improve the neural status. Zinc, a trace element abundant in the central nervous system, plays a critical role in gene expression regulation, synaptic plasticity, and neuronal activity. Clinical evidences have indicated that lower serum zinc concentration in patients with spinal cord injury correlates with poorer outcomes and animal experiments have also demonstrated the neuroprotective effects of zinc. In fact, zinc supplementation therapy has not yet been developed into a mature clinical protocol. Besides, related animal studies still lack comprehensive understanding. To this end, the authors reviewed the biological characteristics of zinc, its administration routes, neuroprotective effects and mechanisms in the management of spinal cord injury, aiming to provide references for future basic research and clinical practice.

With the rising incidence of breast cancer and the widespread use of chemotherapy,the efficacy and adverse effects of chemotherapeutic agents have become a focus of attention.There is a potential association between microorganisms and breast cancer development and therapeutic response,and the potential value of probiotics with the role of regulating intestinal flora and other effects in breast cancer chemotherapy has been increasingly valued.In this paper,we review the regulatory role of intestinal flora in the efficacy of breast cancer chemotherapy and post-chemotherapy adverse reactions,predict its impact on chemotherapeutic effects and adverse reactions to chemotherapy,and focus on the positive role of probiotics in improving post-chemotherapeutic adverse reactions to breast cancer chemotherapy,to provide the scientific basis for the improvement of the quality of life of breast cancer patients,optimization of the chemotherapy regimen for breast cancer,and enhancement of the comprehensive effect of chemotherapy for breast cancer.

Objective:To evaluate the accuracy of the oral healthcare information provided by different large language models (LLM) to explore their feasibility and limitations in the application of oral auxiliary, treatment and health consultation.Methods:This study designed eight items comprising 47 questions in total related to the diagnosis and treatment of oral diseases [to assess the performance of LLM as an artificial intelligence (AI) medical assistant], and five items comprising 35 questions in total about oral health consultations (to assess the performance of LLM as a simulated doctor). These questions were answered individually by the five LLM models (Erine Bot, HuatuoGPT, Tongyi Qianwen, iFlytek Spark, ChatGPT). Two attending physicians with more than 5 years of experience independently rated the responses using the 3C criteria (correct, clear, concise), and the consistency between the raters was assessed using the Spearman rank correlation coefficient, and the Kruskal-Wallis test and Dunn post hoc test were used to assess the statistical differences between the models. Additionally, this study used 600 questions from the 2023 dental licensing examination to evaluate the time taken to answer, scores, and accuracy of each model.Results:As an AI medical assistant, LLM can assist doctors in diagnosis and treatment decision-making, with an inter-evaluator Spearman coefficient of 0.505 ( P<0.01). As a simulated doctor, LLM can carry out patient popularization, with an inter-evaluator Spearman coefficient of 0.533 ( P<0.01). The 3C scores of each model as an AI medical assistant and a simulated doctor were respectively: 2.00 (1.00, 3.00) and 2.00 (2.00, 3.00) points of Erine Bot, 1.00 (1.00, 2.00) and 2.00 (1.00, 2.00) points of HuatuoGPT, 2.00 (1.00, 2.00) and 2.00 (1.00, 3.00) points of Tongyi Qianwen, 2.00 (1.00, 2.00) and 2.00 (1.75, 2.25) points of iFlytek Spark, 3.00 (2.00, 3.00) and 3.00 (2.00, 3.00) points of ChatGPT (full score of 4 points). The Kruskal-Wallis test results showed that, as an AI medical assistant or a simulated doctor, there were statistically differences in the 3C scores among the five large language models (all P<0.001). The average score of the 5 LLMs on the dental licensing examination was 370.2, with an accuracy rate of 61.7% (370.2/600) and a time consumption of 94.6 min. Specifically, Erine Bot took 115 min, scored 363 points with an accuracy rate of 60.5% (363/600), HuatuoGPT took 224 min and scored 305 points with an accuracy rate of 50.8% (305/600), Tongyi Qianwen took 43 min, scored 438 points with an accuracy rate of 73.0% (438/600), iFlytek Spark took 32 min, scored 364 points with an accuracy rate of 60.7% (364/600), and ChatGPT took 59 min, scored 381 points with an accuracy rate of 63.5% (381/600). Conclusions:Based on the evaluation of LLM′s dual roles as an AI medical assistant and a simulated doctor, ChatGPT performes the best, with basically correct, clear and concise answers, followed by Erine Bot, Tongyi Qianwen and iFlytek Spark, with HuatuoGPT lagging behind significantly. In the dental licensing examination, all the 4 LLM, except for HuatuoGPT, reach the passing level, and the time consumpution for answering is significantly reduced compared to the 8 h required for the exam regulations in all of the five models. LLM has the feasibility of application in oral auxiliary, treatment and health consultation, and it can help both doctors and patients obtain medical information quickly. Howere, their outputs carry a risk of errors (since the 3C scoring results do not reach the full marks), so prudent judgment should be exercised when using them.

Objective:To explore the anal function and postoperative complications of 2-stage Turnbull-Cutait pull-through coloanal anastomosis (TCA) for low rectal cancer.Methods:Patients undergoing radical rectal cancer resection from Feb 2023 to Nov 2024 in the First Hospital of Jilin University were divided into the TCA surgery group and the low anterior resection combined with prophylactic stoma (LAR) surgery group.Results:Among the 102 patients, there were 50 cases in the TCA group and 52 cases in the LAR group. In the single-arm analysis of the TCA group, the overall complication rate was 44%. The incidence rates of severe LARS at 1 month, 3 months, and 6 months after surgery were 97%, 77%, and 64% respectively. There was no significant difference in the complication rate within 30 days after surgery between the two groups,(44% vs. 38%, χ2=0.135, P>0.05). There was no significant difference in the incidence rate of severe LARS between the TCA group and the LAR group (77% vs. 69%, χ2=0.202, P>0.05), and there was not significant difference in the incidence rate of severe LARS between the two groups at the 6th month after surgery,(64% vs. 48%, χ2=1.132, P>0.05). Conclusion:In patients who underwent TCA surgery, the LARS symptoms gradually decreased over time. Compared with patients undergoing low anterior resection and stoma reversal, there were no significant differences in complications within 30 days after surgery and LARS symptoms within half a year.

Primers and probes were designed based on the conserved regions of Senecavirus A(SVA),foot-and-mouth disease virus(FMDV),and porcine teschovirus(PTV)and used to devel-op a TaqMan fluorescent quantitative PCR method for detecting the above-mentioned three viru-ses.The triplex fluorescent quantitative PCR system was developed using recombinant positive plasmids containing conserved sequences of the three viruses as templates.After optimizing the conditions,the specificity,sensitivity,repeatability,standard curve,and mixed infection model were evaluated,and the constructed triplex fluorescent quantitative PCR was used for clinical detection.The results showed that this method could specifically detect SVA,FMDV and PTV without cross-reactivity with other pathogens with the minimal detection concentrations for SVA,FMDV,and PTV as low as 1X101 copies/μL,respectively.The coefficients of variation within and between groups were less than 5％.Furthermore,none of the three viruses were detected in 126 samples.The above results indicate that this method is highly specific,sensitive,and stable,making it suit-able for clinical detection.

ObjectiveTo investigate the possible mechanism of Shenfuhuang Formula （参附黄配方） in prevention and treatment of epsis-associated encephalopathy from the perspective of brain genomics. MethodsC57BL/6 mice were randomly divided into sham surgery group， sepsis group， and Shenfuhuang group， with 20 mice in each group. The sepsis group and Shenfuhuang group were induced to develop sepsis by cecal ligation and puncture （CLP） procedure. At 4 hours after modelling， Shenfuhuang group were gavaged with 2.5 g/（kg·d） of Shenfuhuang Formula， 0.5 ml each time， at 12 hours intervals， for a total of 4 times after modelling. Sepsis group and sham surgery group were given 0.5 ml of purified water orally. At 48 hours after modeling， the transcriptome sequencing was used to explore the differential gene expression in the effects of Shenfuhuang Formula on the brain regions of septic mice， and real-time PCR and ELISA were later used to further validate the differential gene and proteins expression. ResultsA total of 4605 genes were differentially expressed in Shenfuhuang group compared with sepsis group， of which 2353 genes were up-regulated and 2252 genes were down-regulated. According to the results of previous publications， six key genes were screened， including serine/threonine-protein kinase （Nek1）， myelin-associated glycoprotein （Mag）， endothelial cell-specific tyrosine kinase receptor （Tek）， a disintegrin and metalloproteinase with thrombospondin motifs 20 （Adamts20）， lymphocyte antigen 86 （Ly86）， and E3 ubiquitin-protein ligase （Traip）. Further genetic and protein validation revealed that， compared to the sham surgery group， the mRNA levels and corresponding protein levels of Nek1， Mag， Tek， Adamts20， Ly86， and Traip in the brain tissue of septic mice significantly reduced （P＜0.05）. In comparison to the sepsis group， Shenfuhuang group showed significantly increased mRNA levels and corresponding protein levels of Nek1， Mag， Tek， Adamts20， Ly86， and Traip （P＜0.05）. ConclusionThe potential therapeutic targets of Shenfuhuang Formula for treating sepsis-associated encephalopathy may be related to the Nek1， Mag， Tek， Adamts20， Ly86， and Traip genes and their encoded proteins.

Middle compartment defects,a common subtype of pelvic floor dysfunction(PFD),are primarily characterized by the prolapse of the uterus or vaginal vault.Magnetic resonance imaging(MRI)has emerged as a valuable diagnostic tool for PFD,offering superior soft tissue resolution while eliminating exposure to ionizing radiation.This review comprehensive summarizes current applications of MRI in the diagnosis and treatment of PFD,covering measurement methods,manifestations of three-level structural defects,postoperative efficacy evaluation,vaginal axial assessment,and evaluation of the mesh status.The authors suggest that MRI enables precise preoperative evaluation of three-level defects,thereby facilitating the development of personalized treatment plans.Additionally,MRI provides an accurate postoperative assessment of surgical outcomes and mesh status,offering a new basis for postoperative assessment.MRI demonstrates unique value in the diagnosis and treatment of middle compartment defects.