Objective: To investigate the status and influencing factors of influenza vaccination among the elderly, so as to provide insights into improving the strategies for influenza vaccination among the elderly. Methods: Elderly people aged 60 years and above were recruited from one community each in five sub-districts of Shihezi City, Xinjiang Uygur Autonomous Region using a random sampling method. Demographic information, knowledge about influenza and influenza vaccines, vaccine literacy and influenza vaccination status in the past year were collected through questionnaire surveys. Factors affecting influenza vaccination among the elderly were analyzed using a multivariable logistic regression model. Results: Totally 1 121 valid questionnaires were recovered, with an effective recovery rate of 95.08%. There were 417 males (37.20%) and 704 females (62.80%). The majority were aged 60-<81 years, accounting for 80.37% (901 individuals). The awareness of knowledge about influenza and influenza vaccines was 78.86%. Low vaccine literacy was observed in 786 individuals, representing 70.12%. The influenza vaccination rate was 20.96%. Multivariable logistic regression analysis showed that age (71-<81 years, OR=1.607, 95%CI: 1.041-2.479; ≥81 years, OR=1.719, 95%CI: 1.040-2.842), educational level (middle school/technical secondary school, OR=0.616, 95%CI: 0.416-0.911), medical expense payment (employee medical insurance, OR=6.531, 95%CI: 2.030-21.010; resident medical insurance, OR=3.385, 95%CI: 1.095-10.466; public expense, OR=4.828, 95%CI: 1.700-13.712), vaccination willingness (yes, OR=6.237, 95%CI: 3.277-11.871), influenza vaccination history (yes, OR=14.600, 95%CI: 8.733-24.408) and vaccine literacy (medium and above, OR=2.412, 95%CI: 1.636-3.555) were associated with influenza vaccination among the elderly. Conclusion The influenza vaccination rate among the elderly was relatively low, and was mainly affected by age, educational level, medical expense payment, vaccination willingness, influenza vaccination history and vaccine literacy.

Objective:To investigate the electrophysiological characteristics of medium spiny neurons(MSNs)expressing D1 or D2 dopamine receptors in the striatum of mice,providing a theoretical basis for exploring their roles in motor disorders,reward,and anxiety-depression.Methods:Six male adult D1-tdTomato and D2-tdTomato mice were used.After anesthesia and decapitation,the brains were quickly removed,and coronal slices were prepared.Whole-cell patch recording were employed to record spontaneous firing,resting membrane potential,and action potential parameters of D1 and D2 MSNs in the striatum.Comparative analyses were conducted to identify differences between the two types of neurons.Results:The frequency of spontaneous excitatory postsynaptic currents(sEPSCs)in D1-MSNs was signifi-cantly lower than that in D2-MSNs(P＜0.001),but there was no difference in amplitude;whereas no significant differences were observed in the frequency or amplitude of spontaneous inhibitory postsynaptic currents(sIPSCs)between D1-MSNs and D2-MSNs.Compared with D1-MSNs,D2-MSNs had a lower resting membrane potential(P＜0.01),a significantly lower rheobase for action potential generation(P＜0.01),and the frequency of action potentials induced by depolarizing current injection was significantly higher in D2-MSNs(P＜0.001).Following perfusion with dopamine(60 μmol/L),the frequency of action potentials in D1-MSNs significantly increased(P＜0.001),while the frequency of action potentials in D2-MSNs significantly decreased(P＜0.001).Conclusion:There are significant differences in the electrophysiological properties between D1-MSNs and D2-MSNs in the striatum of mice.

Objective:To investigate the electrophysiological characteristics of medium spiny neurons(MSNs)expressing D1 or D2 dopamine receptors in the striatum of mice,providing a theoretical basis for exploring their roles in motor disorders,reward,and anxiety-depression.Methods:Six male adult D1-tdTomato and D2-tdTomato mice were used.After anesthesia and decapitation,the brains were quickly removed,and coronal slices were prepared.Whole-cell patch recording were employed to record spontaneous firing,resting membrane potential,and action potential parameters of D1 and D2 MSNs in the striatum.Comparative analyses were conducted to identify differences between the two types of neurons.Results:The frequency of spontaneous excitatory postsynaptic currents(sEPSCs)in D1-MSNs was signifi-cantly lower than that in D2-MSNs(P＜0.001),but there was no difference in amplitude;whereas no significant differences were observed in the frequency or amplitude of spontaneous inhibitory postsynaptic currents(sIPSCs)between D1-MSNs and D2-MSNs.Compared with D1-MSNs,D2-MSNs had a lower resting membrane potential(P＜0.01),a significantly lower rheobase for action potential generation(P＜0.01),and the frequency of action potentials induced by depolarizing current injection was significantly higher in D2-MSNs(P＜0.001).Following perfusion with dopamine(60 μmol/L),the frequency of action potentials in D1-MSNs significantly increased(P＜0.001),while the frequency of action potentials in D2-MSNs significantly decreased(P＜0.001).Conclusion:There are significant differences in the electrophysiological properties between D1-MSNs and D2-MSNs in the striatum of mice.

Objective:The objective of this longitudinal study is to longitudinally monitor the lexical development and language proficiency of Mandarin-speaking children with cochlear implants (CI) over a three-year post-implantation period while also investigating whether early receptive and expressive vocabulary skills can serve as predictors for later language abilities in CI recipients.Methods:In this study, 42 children with CIs were selected as participants, including 19 males and 23 females, and with a mean age at CI activation of 16.6±4.9 months. Receptive and expressive vocabulary skills were assessed using the Infant Checklist of the Early Vocabulary Inventory for Mandarin Chinese (EVI) at one-year post-activation (T1). Additionally, expressive vocabulary sizes were evaluated using the EVI-Toddler Checklist, while syntactic ability was measured by the Grammatical Complexity test of the Mandarin-Chinese Communicative Developmental Inventory for Infants and Toddlers at two years post-activation (T2). The comprehensive language development level of preschool children, including language comprehension, expression, and grammar ability, was examined during the third year post-activation (T3) using the Revised Scale for Assessment of Language Disorders in Preschool Children. Data analysis was conducted using SPSS 22.0.Results:One year after CI activation, children exhibited a mean receptive vocabulary size of 155.7±52.8 and an expressive vocabulary size of 85.1±63.9. T2′s expressive vocabulary size was 455.7±167.7, while the Grammatical Complexity score was 36.5±13.0. The original language development score was determined to be 53.6±14.2 at T3. Correlation analysis revealed significant positive associations between T1′s receptive and expressive vocabulary with tests conducted at T2 and subsequent language development measured at T3 ( P<0.01). Furthermore, there was a significant correlation between expressive vocabulary at T1 and both tests conducted at T2 ( P<0.01), but no significant correlation with subsequent language development measured at T3. Regression analysis showed that T1 receptive vocabulary significantly predicted T2 expressive vocabulary and Grammatical Complexity scores and T3 language development scores. Moreover, the syntactic ability assessed during T2 independently predicted subsequent language proficiency measured at T3. Conclusions:Early receptive vocabulary and grammar ability two years post CI activation significantly predict later language proficiency in children with cochlear implants. Thus, during rehabilitation, emphasis should be placed on enhancing vocabulary and grammar comprehension.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

Objective:To investigate the factors influencing the persistence of prostate specific antigen(PSA) following radical prostatectomy, and to develop and validate a predictive model for PSA persistence.Methods:Clinical data from 1 828 patients who underwent radical prostatectomy at Shanghai Changhai Hospital between January 2015 and December 2023 were retrospectively analyzed. Of these, 1 295 patients from January 2015 to April 2021 comprised the modeling group, while 533 patients from May 2021 to December 2023 formed the validation group. Additionally, 109 patients who underwent radical surgery at the Third Affiliated Hospital of Naval Medical University between March and December 2023 were included as an external validation group. Patients with incomplete clinical information, serum PSA levels exceeding 100 ng/ml, or those who received preoperative neoadjuvant therapy were excluded. Ultimately, 1 003, 369, and 86 patients were included in the modeling, validation, and external validation groups, respectively. The modeling group had serum PSA of 19.29 (8.43, 23.73) ng/ml; the clinical stages were distributed as T 1, T 2, T 3, and T 4 in 191, 673, 123, and 16 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 460, 466, and 77 patients, respectively; and the secondary Gleason scores were 3, 4, and 5 in 363, 486, and 154 patients, respectively. The validation group had serum PSA of 12.80 (6.82, 14.40) ng/ml; the clinical stages were distributed as T 1, T 2, T 3, and T 4 in 40, 289, 37, and 3 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 218, 145, and 6 patients, respectively; and the secondary Gleason scores were 3, 4, and 5 in 140, 184, and 45 patients, respectively. The external validation group had serum PSA of 12.84 (7.11, 12.97) ng/ml; the clinical stages were distributed as T 1, T 2 and T 3 in 9, 68, and 9 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 58, 27, and 1 patient, respectively; and the secondary Gleason scores were 3, 4, and 5 in 28, 50, and 8 patients, respectively. Logistic regression analysis was used to identify independent risk factors for PSA persistence after radical prostatectomy in the modeling group and a prediction model was constructed. The predictive performance of the model was analyzed using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve, the calibration curve, and the clinical decision curve. The predictive performance of the model was verified by the ROC curve in the validation group and the external validation group. Results:The incidence of persistent PSA after surgery in the modeling group, validation group, and external validation group was 8.97% (90/1 003), 7.32% (27/369), and 17.4% (15/86), respectively. In the modeling group, univariate and multivariate logistic regression analysis revealed that serum PSA, percentage of positive needle cores, primary Gleason score on biopsy, and secondary Gleason score on biopsy were independent risk factors for PSA persistence ( P<0.05), and a prediction model was constructed based on these factors. The AUC value of this model was 0.790 (95% CI 0.745-0.835). Calibration curve and clinical decision curve analyses showed that the model's predicted probabilities aligned well with actual risks within the 0-40% prediction interval, providing clinical benefit. The AUC values of the ROC curves in the validation group and external validation group were 0.808 (95% CI 0.719-0.897) and 0.822 (95% CI 0.714-0.929), respectively, indicating that the model had good predictive performance. Conclusions:The predictive model for PSA persistence, constructed based on serum PSA, percentage of positive needle cores, primary and secondary Gleason score on biopsy, demonstrated good clinical predictive performance, exhibiting high accuracy in both internal and cross-center validation.

Objective:To investigate the factors influencing the persistence of prostate specific antigen(PSA) following radical prostatectomy, and to develop and validate a predictive model for PSA persistence.Methods:Clinical data from 1 828 patients who underwent radical prostatectomy at Shanghai Changhai Hospital between January 2015 and December 2023 were retrospectively analyzed. Of these, 1 295 patients from January 2015 to April 2021 comprised the modeling group, while 533 patients from May 2021 to December 2023 formed the validation group. Additionally, 109 patients who underwent radical surgery at the Third Affiliated Hospital of Naval Medical University between March and December 2023 were included as an external validation group. Patients with incomplete clinical information, serum PSA levels exceeding 100 ng/ml, or those who received preoperative neoadjuvant therapy were excluded. Ultimately, 1 003, 369, and 86 patients were included in the modeling, validation, and external validation groups, respectively. The modeling group had serum PSA of 19.29 (8.43, 23.73) ng/ml; the clinical stages were distributed as T 1, T 2, T 3, and T 4 in 191, 673, 123, and 16 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 460, 466, and 77 patients, respectively; and the secondary Gleason scores were 3, 4, and 5 in 363, 486, and 154 patients, respectively. The validation group had serum PSA of 12.80 (6.82, 14.40) ng/ml; the clinical stages were distributed as T 1, T 2, T 3, and T 4 in 40, 289, 37, and 3 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 218, 145, and 6 patients, respectively; and the secondary Gleason scores were 3, 4, and 5 in 140, 184, and 45 patients, respectively. The external validation group had serum PSA of 12.84 (7.11, 12.97) ng/ml; the clinical stages were distributed as T 1, T 2 and T 3 in 9, 68, and 9 patients, respectively; the primary Gleason scores of biopsy were 3, 4, and 5 in 58, 27, and 1 patient, respectively; and the secondary Gleason scores were 3, 4, and 5 in 28, 50, and 8 patients, respectively. Logistic regression analysis was used to identify independent risk factors for PSA persistence after radical prostatectomy in the modeling group and a prediction model was constructed. The predictive performance of the model was analyzed using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve, the calibration curve, and the clinical decision curve. The predictive performance of the model was verified by the ROC curve in the validation group and the external validation group. Results:The incidence of persistent PSA after surgery in the modeling group, validation group, and external validation group was 8.97% (90/1 003), 7.32% (27/369), and 17.4% (15/86), respectively. In the modeling group, univariate and multivariate logistic regression analysis revealed that serum PSA, percentage of positive needle cores, primary Gleason score on biopsy, and secondary Gleason score on biopsy were independent risk factors for PSA persistence ( P<0.05), and a prediction model was constructed based on these factors. The AUC value of this model was 0.790 (95% CI 0.745-0.835). Calibration curve and clinical decision curve analyses showed that the model's predicted probabilities aligned well with actual risks within the 0-40% prediction interval, providing clinical benefit. The AUC values of the ROC curves in the validation group and external validation group were 0.808 (95% CI 0.719-0.897) and 0.822 (95% CI 0.714-0.929), respectively, indicating that the model had good predictive performance. Conclusions:The predictive model for PSA persistence, constructed based on serum PSA, percentage of positive needle cores, primary and secondary Gleason score on biopsy, demonstrated good clinical predictive performance, exhibiting high accuracy in both internal and cross-center validation.

Objective:The objective of this longitudinal study is to longitudinally monitor the lexical development and language proficiency of Mandarin-speaking children with cochlear implants (CI) over a three-year post-implantation period while also investigating whether early receptive and expressive vocabulary skills can serve as predictors for later language abilities in CI recipients.Methods:In this study, 42 children with CIs were selected as participants, including 19 males and 23 females, and with a mean age at CI activation of 16.6±4.9 months. Receptive and expressive vocabulary skills were assessed using the Infant Checklist of the Early Vocabulary Inventory for Mandarin Chinese (EVI) at one-year post-activation (T1). Additionally, expressive vocabulary sizes were evaluated using the EVI-Toddler Checklist, while syntactic ability was measured by the Grammatical Complexity test of the Mandarin-Chinese Communicative Developmental Inventory for Infants and Toddlers at two years post-activation (T2). The comprehensive language development level of preschool children, including language comprehension, expression, and grammar ability, was examined during the third year post-activation (T3) using the Revised Scale for Assessment of Language Disorders in Preschool Children. Data analysis was conducted using SPSS 22.0.Results:One year after CI activation, children exhibited a mean receptive vocabulary size of 155.7±52.8 and an expressive vocabulary size of 85.1±63.9. T2′s expressive vocabulary size was 455.7±167.7, while the Grammatical Complexity score was 36.5±13.0. The original language development score was determined to be 53.6±14.2 at T3. Correlation analysis revealed significant positive associations between T1′s receptive and expressive vocabulary with tests conducted at T2 and subsequent language development measured at T3 ( P<0.01). Furthermore, there was a significant correlation between expressive vocabulary at T1 and both tests conducted at T2 ( P<0.01), but no significant correlation with subsequent language development measured at T3. Regression analysis showed that T1 receptive vocabulary significantly predicted T2 expressive vocabulary and Grammatical Complexity scores and T3 language development scores. Moreover, the syntactic ability assessed during T2 independently predicted subsequent language proficiency measured at T3. Conclusions:Early receptive vocabulary and grammar ability two years post CI activation significantly predict later language proficiency in children with cochlear implants. Thus, during rehabilitation, emphasis should be placed on enhancing vocabulary and grammar comprehension.

Objective:To investigate the efficacy and safety of daratumumab (Dara) - combination regimens for newly diagnosed multiple myeloma (NDMM).Methods:A retrospective case series study was conducted. The clinical data of 34 patients with NDMM receiving treatment regimen including Dara from Qilu Hospital of Shandong University, Yantai Yuhuangding Hospital, Huangdao Branch of Affiliated Hospital of Qingdao University and Taian City Central Hospital between April 2020 and March 2022 were retrospectively collected. The efficacy, survival and adverse reactions of patients were analyzed. Cox proportional risk model was used to analyze the factors affecting overall survival (OS) and minimal residual disease (MRD) turning negative.Results:Among 34 patients with NDMM, there were 19 males and 15 females, with 21 cases aged < 65 years and 13 cases aged ≥65 years. The median follow-up duration [ M ( Q1, Q3)] was 22 months (19 months, 26 months), the median of Dara treatment cycles was 7 (5, 11), and the overall response rate (ORR) reached 97.1% (33/34). There were statistically significant differences in the optimal efficacy of patients stratified by receiving hematopoietic stem cell transplantation or not and receiving different treatment cycles (all P ≤ 0.05), while there were no statistically significant differences in patients stratified by other clinical features (all P > 0.05). The 1-year progression-free survival rate was 79.4% and the 1-year OS rate was 94.1%. Multivariate Cox regression analysis showed that the cycle number of treatment regimens containing Dara was an independent influencing factor of MRD turning negative (6 cycles vs. 2 cycles, HR = 0.267, 95% CI: 0.076-0.935, P = 0.039); age ≥ 65 years was an independent risk factor for OS ( HR = 35.313, 95% CI: 1.709-729.669, P = 0.021). The incidence of hematological adverse reactions grade 3 or above was 20.6% (7/34), and the non-hematological adverse reactions primarily included infection [44.1% (15/34)] and edema of extremity and trunk [41.2% (14/34)]. Conclusions:The Dara-based regimens for NDMM exhibit a high ORR. The remission depth accelerated with the increasing number of treatment cycle, and the adverse reactions are mild.