Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Objective:To analyze the effects of the disease and injury spectrum on health-adjusted life expectancy (HALE) among permanent residents aged 55 and above in Shenzhen from 2016 to 2030.Methods:Based on the mortality surveillance data and the permanent resident population data in Shenzhen from 2016 to 2022, the Sullivan method was used to calculate the HALE during 2016—2022. The Bayesian age-period-cohort model and the grey system model were used to predict the HALE during 2023—2030. The HALE changes in the two periods were decomposed into the contributions of 20 categories of diseases and injuries, respectively.Results:From 2016 to 2022, the HALE increased from 31.41 years (95% CI: 30.50-32.32) to 33.57 years (95% CI: 32.47-34.67). During this period, the mortality effect of neurological disorders slowed the increase of HALE, with a reduction of 0.27 years. By 2030, it is anticipated that the HALE will reach 36.40 years (95% CI: 34.78-38.01). This is expected to be influenced by the mortality effects of nutritional deficiencies (-0.40 years) and mental disorders (-0.29 years), as well as the disability effects of musculoskeletal disorders (-0.66 years), skin and subcutaneous diseases (-0.21 years) and nutritional deficiencies (-0.13 years). Conclusion:The HALE of permanent residents aged 55 years and above in Shenzhen demonstrated an increasing trend over time. Greater attention should be paid to the adverse effects of neurological disorders, nutritional deficiencies, mental disorders, musculoskeletal disorders, and skin and subcutaneous diseases on the continuous increase of HALE in this population.

Objective:To analyze the genetic characteristics of the prevalent strains of Varicella-Zoster virus (VZV) in the population of Dali, Yunnan, and to understand its evolutionary status in the population of Dali.Methods:Herpes fluid and 163 sera were collected from 249 patients clinically suspected to have varicella or herpes zoster in the Department of Dermatology of the Second People′s Hospital of Dali city, Yunnan province, China, from 2023 to 2024. The levels of VZV-specific IgG and IgM antibodies in serum were detected using enzyme-linked immunoassay. Viral DNA was extracted from the herpes fluid, and the cycle threshold ( Ct) of the samples was detected using quantitative real-time polymerase chain reaction (qPCR), and some samples with Ct ≦ 22 were selected for sequencing by next-generation sequencing technology (next-generation sequencing). Next-generation sequencing (NGS) was used to obtain 90 whole genome sequences of VZV, and the sequencing result were compared with the sequences of reference strains for multiple sequence comparison and evolutionary analysis. Snapgene was used to translate the nucleotides into amino acids, and the result were compared with the amino acid sequences of the reference strain. Results:Of the 90 VZV whole-genome sequences, one whole-genome sequence was from an adult varicella patient, and the remaining 89 whole-genome sequences were from herpes zoster patients. The serum-specific IgG antibody positivity rate was 99.4%, and the IgM antibody positivity rate was 52.8%. The result of both single nucleotide polymorphism (SNPs) site typing and genome-wide phylogenetic tree analysis showed that 83 of the 90 VZV whole-genome sequences in this study were on the same branch as Clade 2, and 7 VZV whole-genome sequences were on the branch of Clade 9.Conclusions:The main endemic branch in Dali region in 2023-2024 was Clade 2, with the emergence of Clade 9 branch; there were amino acid mutations in the proteins encoded by ORF22 and ORF68 in 83 VZV whole genome sequences of Clade 2 branch, and the mutations did not cause significant changes to the protein structure.

Objective:To analyze the effects of the disease and injury spectrum on health-adjusted life expectancy (HALE) among permanent residents aged 55 and above in Shenzhen from 2016 to 2030.Methods:Based on the mortality surveillance data and the permanent resident population data in Shenzhen from 2016 to 2022, the Sullivan method was used to calculate the HALE during 2016—2022. The Bayesian age-period-cohort model and the grey system model were used to predict the HALE during 2023—2030. The HALE changes in the two periods were decomposed into the contributions of 20 categories of diseases and injuries, respectively.Results:From 2016 to 2022, the HALE increased from 31.41 years (95% CI: 30.50-32.32) to 33.57 years (95% CI: 32.47-34.67). During this period, the mortality effect of neurological disorders slowed the increase of HALE, with a reduction of 0.27 years. By 2030, it is anticipated that the HALE will reach 36.40 years (95% CI: 34.78-38.01). This is expected to be influenced by the mortality effects of nutritional deficiencies (-0.40 years) and mental disorders (-0.29 years), as well as the disability effects of musculoskeletal disorders (-0.66 years), skin and subcutaneous diseases (-0.21 years) and nutritional deficiencies (-0.13 years). Conclusion:The HALE of permanent residents aged 55 years and above in Shenzhen demonstrated an increasing trend over time. Greater attention should be paid to the adverse effects of neurological disorders, nutritional deficiencies, mental disorders, musculoskeletal disorders, and skin and subcutaneous diseases on the continuous increase of HALE in this population.

Objective:To analyze the genetic characteristics of the prevalent strains of Varicella-Zoster virus (VZV) in the population of Dali, Yunnan, and to understand its evolutionary status in the population of Dali.Methods:Herpes fluid and 163 sera were collected from 249 patients clinically suspected to have varicella or herpes zoster in the Department of Dermatology of the Second People′s Hospital of Dali city, Yunnan province, China, from 2023 to 2024. The levels of VZV-specific IgG and IgM antibodies in serum were detected using enzyme-linked immunoassay. Viral DNA was extracted from the herpes fluid, and the cycle threshold ( Ct) of the samples was detected using quantitative real-time polymerase chain reaction (qPCR), and some samples with Ct ≦ 22 were selected for sequencing by next-generation sequencing technology (next-generation sequencing). Next-generation sequencing (NGS) was used to obtain 90 whole genome sequences of VZV, and the sequencing result were compared with the sequences of reference strains for multiple sequence comparison and evolutionary analysis. Snapgene was used to translate the nucleotides into amino acids, and the result were compared with the amino acid sequences of the reference strain. Results:Of the 90 VZV whole-genome sequences, one whole-genome sequence was from an adult varicella patient, and the remaining 89 whole-genome sequences were from herpes zoster patients. The serum-specific IgG antibody positivity rate was 99.4%, and the IgM antibody positivity rate was 52.8%. The result of both single nucleotide polymorphism (SNPs) site typing and genome-wide phylogenetic tree analysis showed that 83 of the 90 VZV whole-genome sequences in this study were on the same branch as Clade 2, and 7 VZV whole-genome sequences were on the branch of Clade 9.Conclusions:The main endemic branch in Dali region in 2023-2024 was Clade 2, with the emergence of Clade 9 branch; there were amino acid mutations in the proteins encoded by ORF22 and ORF68 in 83 VZV whole genome sequences of Clade 2 branch, and the mutations did not cause significant changes to the protein structure.

Background::T-cell-mediated immunity is crucial for the effective clearance of viral infection, but the T-cell-mediated immune responses that are induced by booster doses of inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in people living with human immunodeficiency virus (PLWH) remain unclear.Methods::Forty-five PLWH who had received antiretroviral therapy (ART) for more than two years and 29 healthy controls (HCs) at Beijing Youan Hospital were enrolled to assess the dynamic changes in T-cell responses between the day before the third vaccine dose (week 0) and 4 or 12 weeks (week 4 or week 12) after receiving the third dose of inactivated SARS-CoV-2 vaccine. Flow cytometry, enzyme-linked immunospot (ELISpot), and multiplex cytokines profiling were used to assess T-cell responses at the three timepoints in this study.Results::The results of the ELISpot and activation-induced marker (AIM) assays showed that SARS-CoV-2-specific T-cell responses were increased in both PLWH and HCs after the third dose of the inactivated SARS-CoV-2 vaccine, and a similar magnitude of immune response was induced against the Omicron (B.1.1.529) variant compared to the wild-type strain. In detail, spike-specific T-cell responses (measured by the ELISpot assay for interferon γ [IFN-γ] release) in both PLWH and HCs significantly increased in week 4, and the spike-specific T-cell responses in HCs were significantly stronger than those in PLWH 4 weeks after the third vaccination. In the AIM assay, spike-specific CD4 + T-cell responses peaked in both PLWH and HCs in week 12. Additionally, significantly higher spike-specific CD8 + T-cell responses were induced in PLWH than in HCs in week 12. In PLWH, the release of the cytokines interleukin-2 (IL-2), tumour necrosis factor-alpha (TNF-α), and IL-22 by peripheral blood mononuclear cells (PBMCs) that were stimulated with spike peptides increased in week 12. In addition, the levels of IL-4 and IL-5 were higher in PLWH than in HCs in week 12. Interestingly, the magnitude of SARS-CoV-2-specific T-cell responses in PLWH was negatively associated with the extent of CD8 + T-cell activation and exhaustion. In addition, positive correlations were observed between the magnitude of spike-specific T-cell responses (determined by measuring IFN-γ release by ELISpot) and the amounts of IL-4, IL-5, IL-2 and IL-17F. Conclusions::Our findings suggested that SARS-CoV-2-specific T-cell responses could be enhanced by the booster dose of inactivated COVID-19 vaccines and further illustrate the importance of additional vaccination for PLWH.

Objective:To analyze the disease burden in middle-aged and elderly population aged ≥55 in Shenzhen from 2016 to 2030 and provide evidence for the development of healthy aging strategies.Methods:The years of life lost (YLL), years lost due to disability (YLD), and the disability-adjusted life year (DALY) in this population from 2016 to 2022 were calculated. Joinpoint log-linear regression model was used to analyze the time trend. Bayesian age-period-cohort model and grey system model were used to predict YLL, YLD, and DALY in this population in 2030.Results:From 2016 to 2022, the crude DALY rate showed a transient fluctuation in age group 55-74 years, but a pronounced increase in age group ≥85 years. The proportions of YLL and YLD due to non-communicable diseases in all age groups was considerably higher than those due to communicable and nutritional diseases and injuries. In 2022, in all age groups, the YLL due to neoplasms (55-74 years old) and cardiovascular disease (≥75 years old) ranked first, and the YLD due to musculoskeletal disorder ranked first. By 2030, the causes of YLL and YLD ranking first in each age group would be remained, while the ranks of some causes would increase.Conclusions:The age specific characteristics of current and predicted disease burden differed in individuals aged ≥55 years. Therefore, it is necessary to allocate social and medical resources according to the disease burden pattern.

Background::Cluster of differentiation 8 (CD8 T) cells play critical roles in eradicating human immunodeficiency virus (HIV)-1 infection, but little is known about the effects of T cells expressing CD8 at low levels (CD8 low) or high levels (CD8 high) on HIV-1 replication inhibition after HIV-1 invasion into individual. Methods::Nineteen patients who had been acutely infected with HIV-1 (AHI) and 20 patients with chronic infection (CHI) for ≥2 years were enrolled in this study to investigate the dynamics of the quantity, activation, and immune responses of CD3 +CD8 low T cells and their counterpart CD3 +CD8 high T cells at different stages of HIV-1 infection. Results::Compared with healthy donors, CD3 +CD8 low T cells expanded in HIV-1-infected individuals at different stages of infection. As HIV-1 infection progressed, CD3 +CD8 low T cells gradually decreased. Simultaneously, CD3 +CD8 high T cells was significantly reduced in the first month of AHI and then increased gradually as HIV-1 infection progressed. The classical activation of CD3 +CD8 low T cells was highest in the first month of AHI and then reduced as HIV-1 infection progressed and entered the chronic stage. Meanwhile, activated CD38 -HLA-DR +CD8 low T cells did not increase in the first month of AHI, and the number of these cells was inversely associated with viral load ( r = -0.664, P = 0.004) but positively associated with the CD4 T-cell count ( r = 0.586, P = 0.014). Increased programmed cell death protein 1 (PD-1) abundance on CD3 +CD8 low T cells was observed from the 1st month of AHI but did not continue to be enhanced, while a significant T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif (ITIM) domains (TIGIT) abundance increase was observed in the 12th month of infection. Furthermore, increased PD-1 and TIGIT abundance on CD3 +CD8 low T cells was associated with a low CD4 T-cell count (PD-1: r = -0.456, P = 0.043; TIGIT: r = -0.488, P = 0.029) in CHI. Nonetheless, the nonincrease in PD-1 expression on classically activated CD3 +CD8 low T cells was inversely associated with HIV-1 viremia in the first month of AHI ( r = -0.578, P = 0.015). Notably, in the first month of AHI, few CD3 +CD8 low T cells, but comparable amounts of CD3 +CD8 high T cells, responded to Gag peptides. Then, weaker HIV-1-specific T-cell responses were induced in CD3 +CD8 low T cells than CD3 +CD8 high T cells at the 3rd and 12th months of AHI and in CHI. Conclusions::Our findings suggest that CD3 +CD8 low T cells play an anti-HIV role in the first month of infection due to their abundance but induce a weak HIV-1-specific immune response. Subsequently, CD3 +CD8 low T-cell number decreased gradually as infection persisted, and their anti-HIV functions were inferior to those of CD3 +CD8 high T cells.

BACKGROUND: T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domains (TIGIT), an inhibitory receptor expressed on T cells, plays a dysfunctional role in antiviral infection and antitumor activity. However, it is unknown whether TIGIT expression on T cells influences the immunological effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inactivated vaccines. METHODS: Forty-five people living with HIV (PLWH) on antiretroviral therapy (ART) for more than two years and 31 healthy controls (HCs), all received a third dose of a SARS-CoV-2 inactivated vaccine, were enrolled in this study. The amounts, activation, proportion of cell subsets, and magnitude of the SARS-CoV-2-specific immune response of TIGIT + CD4 + and TIGIT + CD8 + T cells were investigated before the third dose but 6 months after the second vaccine dose (0W), 4 weeks (4W) and 12 weeks (12W) after the third dose. RESULTS: Compared to that in HCs, the frequency of TIGIT + CD8 + T cells in the peripheral blood of PLWH increased at 12W after the third dose of the inactivated vaccine, and the immune activation of TIGIT + CD8 + T cells also increased. A decrease in the ratio of both T naïve (T N ) and central memory (T CM ) cells among TIGIT + CD8 + T cells and an increase in the ratio of the effector memory (T EM ) subpopulation were observed at 12W in PLWH. Interestingly, particularly at 12W, a higher proportion of TIGIT + CD8 + T cells expressing CD137 and CD69 simultaneously was observed in HCs than in PLWH based on the activation-induced marker assay. Compared with 0W, SARS-CoV-2-specific TIGIT + CD8 + T-cell responses in PLWH were not enhanced at 12W but were enhanced in HCs. Additionally, at all time points, the SARS-CoV-2-specific responses of TIGIT + CD8 + T cells in PLWH were significantly weaker than those of TIGIT - CD8 + T cells. However, in HCs, the difference in the SARS-CoV-2-specific responses induced between TIGIT + CD8 + T cells and TIGIT - CD8 + T cells was insignificant at 4W and 12W, except at 0W. CONCLUSIONS TIGIT expression on CD8 + T cells may hinder the T-cell immune response to a booster dose of an inactivated SARS-CoV-2 vaccine, suggesting weakened resistance to SARS-CoV-2 infection, especially in PLWH. Furthermore, TIGIT may be used as a potential target to increase the production of SARS-CoV-2-specific CD8 + T cells, thereby enhancing the effectiveness of vaccination.
Humans ; Antibodies, Viral ; CD8-Positive T-Lymphocytes ; COVID-19/complications* ; COVID-19 Vaccines/immunology* ; HIV Infections/complications* ; Receptors, Immunologic ; SARS-CoV-2