Objective To develop an automatic segmentation network for thyroid nodules by integrating wavelet transform and CNN-Transformer in order to improve the efficiency and accuracy of ultrasound image segmentation.Methods A total of 1 371 sets of ultrasound images of thyroid nodules were collected from Department of Ultrasonography of Second Affiliated Hospital of Army Medical University between May 2023 and February 2024.After preprocessing and normalization,the data were divided into training,validation,and testing sets in a ratio of 8∶1∶1.Based on UNet,CNN and Swin-Transformer were used in parallel as the encoder,with a wavelet transform module inserted between the encoder and decoder to construct a thyroid nodule segmentation network.The performance of the segmentation model was evaluated on the collected internal dataset using accuracy,IoU,and Dice coefficient metrics.Results The finally verified 1 371 sets of ultrasonic thyroid nodules had an average Dice coefficient of 79.63%and an IoU of 67.30%.Compared with UNet,the segmentation accuracy was improved by 1.02%.The segmentation model obtained accurate location and smooth edges of thyroid nodules,and the segmentation was more consistent in thyroid nodule edge and morphology with those marked by doctors manually when compared with other segmentations.Compared with UNet,this segmentation method can learn the texture of nodules more fully and avoid the situation that nodules had been incorrectly divided into surrounding tissues.Conclusion Our developed segmentation model based on wavelet transform and CNN-Transformer demonstrates better segmentation accuracy in comparison to conventional UNet variants,such as UNet,Attention-UNet,and UNetv2,and medical segment anything models like SAM Med2D.This segmentation method enables accurate segmentation of ultrasound thyroid nodules,thereby enhancing clinical workflow efficiency through automated precise delineation.

Food safety problems caused by foodborne pathogenic bacteria pose a serious threat to public health,creating an urgent need to develop testing methods and techniques with excellent performance and are simple to use and of affordable cost.Traditional testing methods,such as isolation and culture,morphological observation,biochemical identification,and serological tests,have many limitations,including complex procedures,reliance on specialized technical equipment and personnel,and long turnaround time,rendering them inadequate for meeting current and future testing demands.Therefore,it is particularly important to develop simple,rapid,and highly sensitive methods for analyzing pathogenic bacteria.The fusion of nucleic acid aptamers and nanozymes brings new ideas for the rapid testing of pathogenic bacteria.On one hand,aptamers offer specific recognition capability for target bacteria and can be combined with various nucleic acid signal amplification techniques.On the other hand,the enzyme-like catalytic activity and signal amplification effect of many nanomaterials provide a basis for highly sensitive testing.This review highlights the application potential of nanozyme?aptamer coupling systems in the field of microbial analysis by briefly summarizing the latest research progress in the use of nanozymes combined with aptamers for the detection of foodborne pathogenic bacteria.First of all,two main approaches to conjugating nanozymes with aptamers are introduced.Then,the testing mechanisms and typical applications of nanozyme?aptamer coupling systems for foodborne pathogenic bacteria are discussed.Finally,future development trends and existing challenges are disucssed from four perspectives,including specificity,high sensitivity,high throughput,and intelligent detection.This review aims to provide a useful reference for the fusion of nanozymes and aptamers and for the development of on-site rapid testing techniques for foodborne pathogens,and to encourage broader academic interest to further advance this promising research field.

Objective:To investigate the sleep status of conscious patients in the ICU, their perception of ICU environmental stressors, and the correlation between the two, providing a basis for nursing interventions.Methods:A convenience sampling method was used to recruit 120 conscious patients admitted to Zhongshan Hospital, Fudan University for the first time to the ICU from August 2022 to January 2023 for the study. Data were collected using a general information questionnaire, the Intensive Care Unit Environmental Stressor Scale (ICUESS), and the Richards-Campbell Sleep Questionnaire (RCSQ). Multiple linear regression analysis was performed to identify factors influencing sleep quality in conscious ICU patients.Results:The total RCSQ and ICUESS scores of 120 conscious ICU patients were 36.0 (12.3, 60.8) and 76.0 (69.0±84.0), respectively. The primary stressors identified by patients were "inability to fall asleep" "thirst" and "restraint by medical tubing". Multiple linear regression analysis showed that the treatment environment, one's own experience and noise were significant factors affecting sleep quality in conscious ICU patients ( P<0.05) . Conclusions:The overall sleep quality of conscious ICU patients was poor and closely related to their perceived level of ICU environmental stressors. ICU medical staff should focus on optimizing the treatment environment, noise and patients' own experience to improve their sleep quality.

Objective:To investigate the sleep status of conscious patients in the ICU, their perception of ICU environmental stressors, and the correlation between the two, providing a basis for nursing interventions.Methods:A convenience sampling method was used to recruit 120 conscious patients admitted to Zhongshan Hospital, Fudan University for the first time to the ICU from August 2022 to January 2023 for the study. Data were collected using a general information questionnaire, the Intensive Care Unit Environmental Stressor Scale (ICUESS), and the Richards-Campbell Sleep Questionnaire (RCSQ). Multiple linear regression analysis was performed to identify factors influencing sleep quality in conscious ICU patients.Results:The total RCSQ and ICUESS scores of 120 conscious ICU patients were 36.0 (12.3, 60.8) and 76.0 (69.0±84.0), respectively. The primary stressors identified by patients were "inability to fall asleep" "thirst" and "restraint by medical tubing". Multiple linear regression analysis showed that the treatment environment, one's own experience and noise were significant factors affecting sleep quality in conscious ICU patients ( P<0.05) . Conclusions:The overall sleep quality of conscious ICU patients was poor and closely related to their perceived level of ICU environmental stressors. ICU medical staff should focus on optimizing the treatment environment, noise and patients' own experience to improve their sleep quality.

Objective:To investigate the effects of multidisciplinary small-class teaching on expertise and skill acquisition and learning experience in standardized residency training in general surgery.Methods:Sixty residents of grade 2021 rotating in general surgery from January to August 2023 were divided into multidisciplinary teaching group ( n=30) and traditional teaching group ( n=30, to receive tradition one-on-one teaching). All the residents underwent a theoretical examination and Mini-Clinical Evaluation Exercise (Mini-CEX) skill assessment before admission, and the scores were compared between the two groups. At the end of training, the two groups were compared in terms of theoretical and Mini-CEX skill assessment scores and the degree of satisfaction with teaching. Statistical analysis was conducted using SPSS 26.0. Results:There were no significant differences in the theoretical assessment and Mini-CEX skill assessment scores before admission between the two groups ( P>0.05). At the end of training, the multidisciplinary teaching group had a significantly higher theoretical examination score [(88.15±3.45) vs. (72.25±4.36), P<0.05] and a significantly higher Mini-CEX score [(86.35±2.24) vs. (76.28±3.92), P<0.05] compared with the traditional teaching group. According to the survey, the residents in the multidisciplinary teaching group were more satisfied with teaching and more likely to recognize the teaching effects. Conclusions:Multidisciplinary small-class teaching can help improve the quality of standardized general surgery residency training on gastrointestinal tumor treatment, which is a highly accepted and effective attempt at standardized residency training.

Objective:To explore the association between changes in brain structural network during the early stage of stroke recovery and the onset of post-stroke depression (PSD).Methods:A total of 87 acute ischemic stroke patients scheduled for discharge, who were admitted to the Yixing Hospital Affiliated to Jiangsu University from March 2020 to May 2021, were prospectively collected. During the same period, 34 healthy control subjects matched with the stroke patients were also collected. All participants underwent systematic magnetic resonance imaging scans and scale assessments, and were followed up longitudinally for 2 years. Based on the occurrence of depression during follow-up, the stroke patients were divided into PSD group and post-stroke non-depression (PSND) group. Graph theoretical analysis was used to analyze the topological characteristics of brain structural network. Analysis of variance was used to explore the differences in brain structural network attributes among groups. Logistic regression model was used to analyze the predictive power of differential brain network attributes for PSD. Linear regression analysis was conducted to investigate the relationship between the synergism of non-rich-club regions and changes in rich-club connectivity.Results:The rich-club connectivity and synergism of the non-rich-club regions were significantly lower in the PSD group than in the PSND group (rich-club connectivity, P<0.01; synergism of feeder/local, P<0.001). The regression model demonstrated that the synergism of non-rich-club regions had a good predictive power for the occurrence of PSD ( OR=1.195, 95%CI 1.073-1.471, P<0.001). Furthermore, linear regression analysis revealed a significant correlation between the synergism of non-rich-club regions and Δrich-club connectivity ( r=-0.691, P<0.001). Conclusion:The good synergism of non-rich-club regions during the early stage of stroke recovery promotes the repair of rich-club connectivity and inhibits the onset of PSD.

Objective:To investigate the features of the brain structural network in patients with postpartum depression (PPD).Methods:This cross-sectional study included PPD patients who visited the mental health counseling clinic after delivery at the Jiangsu University Affiliated Yixing Hospital from June 2013 to September 2022 (PPD group). Matched non-PPD postpartum women based on age, years of education, and body mass index who came for postpartum follow-up (non-PPD postpartum group), and non-pregnant women who visited the hospital or underwent physical examinations during the same period (non-pregnant group) were also included. Demographic data and diffusion tensor imaging (DTI) data were collected for all three groups. The brain was partitioned into 90 regions using an anatomical template to construct the brain structural network. Network-based statistics (NBS) were applied to further screen and construct subnetworks. The efficacy of the subnetworks in identifying PPD was evaluated through multivariable logistics regression models and receiver operating characteristic curves. A comparison of the connectivity strength of white matter tracts and topological attributes of brain structural network parameters was conducted using independent samples t-tests, and the results were corrected using the false discovery rate (FDR) method. Results:(1) A total of 116 subjects were included, with 40 in the non-pregnant group, 40 in the non-PPD postpartum group, and 36 in the PPD group. PPD group had higher Edinburgh Postnatal Depression Scale (EPDS) scores than the non-pregnant and non-PPD postpartum groups [(18.0±4.1) scores vs. (2.5±1.2) and (6.1±2.1) scores, F=340.40; t=24.65,10.60 and 16.16 in pairwise comparison; all P<0.001]. (2) Compared to the non-pregnant group, there was a decrease in the connectivity strength of nine white matter tracts within the brain structural network of the postpartum group (including left dorsolateral superior frontal gyrus-left anterior cingulate and paracingulate gyrus, left dorsolateral superior frontal gyrus-right amygdala, left dorsolateral superior frontal gyrus-left insula, left insula-left lentiform nucleus, left insula-left hippocampus, left hippocampus-right amygdala, left hippocampus-left precuneus, left anterior cingulate and paracingulate gyrus-right amygdala, and right amygdala-right hippocampus) (all P<0.05, FDR corrected). No increased connection strengths were observed. There were no significant differences in the connection strengths of these nine tracts between the non-PPD and PPD groups. (3) A characteristic subnetwork for the maternal group was successfully constructed based on the nine tracts, which exhibited typical small-world properties (σ>1). Compared to the non-PPD maternal group, the characteristic path length in the PPD group was increased [(3.904±0.328) vs. (4.130±0.433), t=－2.58], and global efficiency was decreased [(0.361±0.036) vs. (0.331±0.053), t=2.91] (both P<0.05). Local property comparisons showed that the node efficiency values for the left dorsolateral superior frontal gyrus, left insula, left anterior cingulate and paracingulate gyrus, left hippocampus, right hippocampus, right amygdala, left precuneus and left putamen in the PPD group were significantly reduced [(0.273±0.023) vs. (0.267±0.030), t=0.98; (0.299±0.035) vs. (0.276±0.041), t=2.64; (0.265±0.019) vs. (0.258±0.025), t=1.38; (0.318±0.028) vs. (0.305±0.031), t=1.92; (0.312±0.027) vs. (0.302±0.031), t=1.50; (0.322±0.030) vs. (0.298±0.026), t=3.71; (0.356±0.040) vs. (0.338±0.056), t=1.62; (0.346±0.028) vs. (0.331±0.036), t=1.74; all P<0.05]. However, only the differences in node efficiency values for the left insula and right amygdala remained significant after FDR correction (corrected P=0.041 and 0.003). (4) Global efficiency, as well as node efficiency for the left insula and right amygdala, demonstrated good value for identifying PPD [areas under the curve (AUC) and their 95% CI were 0.827 (0.732-0.922), 0.741 (0.628-0.854), and 0.761 (0.653-0.867), respectively], with even better performance when combined [0.897 (0.828-0.969)]. (5) In the PPD group, global efficiency ( r=－0.43, P=0.008), node efficiency for the left insula ( r=－0.39, P=0.019), and node efficiency for the right amygdala ( r=－0.42, P=0.011) were all negatively correlated with EPDS scores. Conclusion:Aberrations in global efficiency, node efficiency for the left insula, and node efficiency for the right amygdala may serve as characteristic neuroimaging biomarkers for PPD.

Objective:To examine the structural network changes in participants with amnestic mild cognitive impairment(aMCI)and investigate the correlation between these changes and the onset of Alzheimer's disease(AD).Methods:In this prospective study, a total of 100 individuals with amnestic mild cognitive impairment(aMCI)were enrolled as the research group.Additionally, 25 healthy individuals who were matched in terms of age and sex were enrolled as healthy controls.Upon enrollment, all participants underwent MRI scans, neuropsychological assessments, and clinical evaluations.The participants were then followed every 6 months for a period of 36 months or until they withdrew from the study.Based on the outcome of the follow-up(whether Alzheimer's disease occurred), the aMCI participants were divided into two groups: stable aMCI group and progressive aMCI group.The Chinese version of the Brief Mental State Examination(MMSE), the Montreal Cognitive Assessment(MoCA), the Clinical Dementia Rating Scale(CDR), and the Auditory Word Learning Test(AVLT)were utilized to evaluate the overall mental and cognitive status of the subjects.Pearson correlation analysis was employed to investigate the relationship between structural network changes and cognitive decline.Logistic regression was performed to analyze the predictive ability of structural network changes in determining the onset of AD.Results:Compared to the stable aMCI group, the progressive aMCI group exhibited lower levels of global efficiency( P=0.002), local efficiency( P=0.007), feeder connections( P=0.003), local connections( P=0.008), and right precuneus nodal efficiency( P=0.010).Correlation analysis revealed that global efficiency( r=0.604, P=0.002), feeder connections( r=0.513, P=0.012), and right precuneus nodal efficiency( r=0.504, P=0.014)were correlated with AVLT-delay scores(baseline)in the progressive aMCI group.A logistic regression model demonstrated that global efficiency, feeder connections, and right precuneus nodal efficiency could significantly predict the onset of AD(all P<0.05, AUCunited=0.797, 95% CI: 0.684-0.884, sensitivity=73.91, 95% CI: 51.6-89.8, specificity=76.60, 95% CI: 62.0-87.7). Conclusions:Among participants with aMCI, individuals who exhibit lower global efficiency, feeder connections, or right precuneus nodal efficiency are at a higher risk of developing AD.These indicators are anticipated to serve as new targets for clinical intervention.

Minimal residual disease (MRD), a crucial biomarker for assessing efficacy and predicting recurrence, refers to residual tumor cells remaining in the body of patients with hematological malignancies who achieved complete remission after treatment. This study aimed to conduct a retrospective analysis of the clinical diagnosis, treatment, and MRD monitoring of a pediatric patient with multiple acute B-lymphocytic leukemia relapses, alongside a review of relevant literature. In this case, Ig rearrangement based on next-generation sequencing (NGS) was more accurate in assessing the MRD level, compared with the traditional method of MRD detection, indicating the risk of earlier relapse and guided interventions in time. Additionally, NGS-MRD detected clonal evolution, providing new ideas to further investigate the intrinsic factors of disease development.

Minimal residual disease (MRD), a crucial biomarker for assessing efficacy and predicting recurrence, refers to residual tumor cells remaining in the body of patients with hematological malignancies who achieved complete remission after treatment. This study aimed to conduct a retrospective analysis of the clinical diagnosis, treatment, and MRD monitoring of a pediatric patient with multiple acute B-lymphocytic leukemia relapses, alongside a review of relevant literature. In this case, Ig rearrangement based on next-generation sequencing (NGS) was more accurate in assessing the MRD level, compared with the traditional method of MRD detection, indicating the risk of earlier relapse and guided interventions in time. Additionally, NGS-MRD detected clonal evolution, providing new ideas to further investigate the intrinsic factors of disease development.