Objective:This study aimed to analyze the genetic variation of the human papillomavirus (HPV) type 39 genomes and to predict and screen the dominant T-cell and B-cell epitopes of the viral early proteins (E1, E2, E6, E7) and late proteins (L1, L2).Methods:A total of 70 full-length sequences of HPV39 variants were retrieved from the clinical samples and the National Center for Biotechnology Information (NCBI) to construct a phylogenetic tree, analyze genetic polymorphisms, and predict the physicochemical properties of the viral proteins. Next, T-cell and B-cell epitopes were predicted using IEDB and ABCpred, and potential dominant epitopes were further selected based on parameters such as the secondary structure of the epitope region, peptide flexibility, hydrophilicity, surface accessibility and antigenicity. Finally, a homology analysis of the potential dominant epitopes was performed with 12 high-risk HPV types.Results:HPV39 variants from different sources can be clustered into two lineages (A and B), each exhibiting distinct mutation patterns. The mutation rate was the highest in E7 and the lowest in E1 among the different viral genes. However, these nucleotide/amino acid mutations did not significantly impact the physicochemical properties of the viral proteins. After prediction and screening, 5 and 6 potential dominant B-cell epitopes were identified in both L1 and L2, respectively. E1, E2, E6, and E7 yielded 18, 10, 4, and 1 potential dominant HLA-I restricted T-cell epitopes, respectively. Additionally, E1, E2, and E6 yielded 7, 3, and 2 potential dominant HLA-II restricted T-cell epitopes, respectively. Homology analysis indicated that T-cell dominant epitopes in E1, E2, and E6, as well as B-cell epitopes in L2, showed high homology (93%-100%) with HPV68, HPV33, HPV45, and HPV59.Conclusions:Bioinformatics analysis and prediction revealed that HPV39 variants can be clustered into two main evolutionary branches, A and B, each exhibiting a specific mutation pattern. The viral proteins contain potential dominant T-cell and B-cell epitopes that can be further investigated, providing valuable theoretical support for the development of HPV39-related peptide-based vaccines and therapeutics.

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Objective:To investigate the relationship between thyroid hormone sensitivity indicators and the risk of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) who have normal thyroid function.Methods:A retrospective study design was used to gather data from 100 patients with T2DM who had normal thyroid function and were treated at Shangluo Central Hospital between June 2022 and January 2024. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were used for evaluation. Patients with an MMSE score of ≤ 26 or a MoCA score of ≤ 26 were included in the MCI group ( n = 22), whereas those with an MMSE score of > 26 and a MoCA score of > 26 were included in the non-MCI group ( n = 78). Multivariate logistic regression analysis was performed to identify factors influencing MCI in patients with T2DM. Additionally, receiver operating characteristic curve analysis was conducted to assess the predictive value of thyroid hormone sensitivity indicators for MCI in this patient population. Results:In the MCI group, 68.18% of patients had an education level of high school or below. The level of glycosylated hemoglobin in this group was (8.45 ± 1.95)%, insulin resistance index was (3.34 ± 0.25), the level of triglycerides was (2.59 ± 0.19) mmol/L, the level of uric acid was (354.76 ± 46.16) μmol/L, thyroid-stimulating hormone index was (3.09 ± 0.26), and thyroid feedback quantile-based index was [0.2 (0.0, 0.5)]. All of these values were significantly higher than those in the non-MCI group: [43.59%, (7.63 ± 1.61)%, (3.05 ± 0.17), (2.09 ± 0.15) mmol/L, (311.74 ± 67.15) μmol/L, (2.87 ± 0.13), 0.2 (-0.1, 0.4), χ2 = 4.15, t = 2.01, 6.32, 12.99, 3.47, 5.47, Z = -2.54, all P < 0.05]. In the MCI group, the level of free triiodothyronine was (4.32 ± 0.21) pmol/L, and thyrotroph T4 resistance index (TT4RI) was (34.54 ± 4.30), both of which were significantly lower than those in the non-MCI group [(4.61 ± 0.36) pmol/L, (37.15 ± 5.55), t = 2.58, 2.04, both P < 0.05]. There were no significant differences in gender, age, body mass index, alcohol consumption, smoking, history of cerebral infarction, hypertension, duration of diabetes, use of only metformin, fasting blood glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, alanine aminotransferase, aspartate aminotransferase, free thyroxine, and thyroid-stimulating hormone between the two groups (all P > 0.05). Logistic regression analysis indicated that education level, triglyceride, uric acid, free triiodothyronine, thyroid-stimulating hormone index, and TT4RI were risk factors for MCI (all P < 0.05). The predictive value of the combined thyroid hormone sensitivity indicators for MCI in patients with T2DM was high, with an area under the receiver operating characteristic curve of 0.778 [95% CI(0.676, 0.880), P < 0.001], a sensitivity of 0.538, a specificity of 0.909, and a maximum Youden index of 0.447. Conclusions:In patients with T2DM who have normal thyroid function, the thyroid hormone sensitivity indicators (TT4RI and thyroid feedback quantile-based index) exhibited a negative correlation with MCI, whereas TSHI demonstrated a positive correlation with MCI. The combined use of these thyroid hormone sensitivity indicators is valuable for predicting MCI and offers significant guidance for the early intervention of cognitive impairment in patients with T2DM.

Objective:To investigate the correlation between glycated hemoglobin (HbA1c) and 25-hydroxyvitamin D [25(OH)D] levels with peripheral vascular disease in patients with type 2 diabetes mellitus (T2DM).Methods:A total of 165 patients with T2DM who were treated at Shangluo Central Hospital from April 2021 to April 2023 were included in this retrospective study. Based on the presence or absence of peripheral vascular disease, the patients were divided into two groups: the positive group (115 patients with T2DM and peripheral vascular disease) and the negative group (50 patients with T2DM only). Data on general clinical characteristics including age, gender, duration of diabetes, family history of the disease, and the presence of hypertension were collected from both groups. Laboratory data, including 25(OH)D, serum calcium, phosphorus, apolipoprotein B, lipoprotein(a), uric acid, and apolipoprotein AⅠ, were also gathered. Logistic regression analysis was performed to identify high-risk factors for peripheral vascular disease, while Pearson or Spearman correlation analyses were used to evaluate the correlation between HbA1c, 25(OH)D, and peripheral vascular disease. The diagnostic efficiency of the high-risk factors for peripheral vascular disease was assessed using receiver operating characteristic analysis.Results:Long disease duration ( t = 2.74, P = 0.007), a history of hypertension ( χ2 = 6.60, P = 0.010), poor control of HbA1c ( χ2 = 12.14, P < 0.001), vitamin D deficiency ( χ2 = 10.20, P < 0.001), high levels of low-density lipoprotein cholesterol ( t = 2.58, P = 0.024), low levels of high-density lipoprotein cholesterol ( t = 3.65, P = 0.008), high HbA1c levels ( t = 6.68, P < 0.001), and low levels of 25(OH)D ( t = 6.60, P < 0.001) are the independent risk factors for peripheral vascular disease in patients with T2DM. Logistic regression analysis revealed that long disease duration ( OR = 2.327, P = 0.008), vitamin D deficiency ( OR = 1.255, P = 0.034), high HbA1c levels ( OR = 3.124, P < 0.001), and low 25(OH)D levels ( OR = 4.256, P = 0.002) are significant risk factors for peripheral vascular disease. Pearson correlation analysis indicated a negative correlation between HbA1c levels and 25(OH)D levels ( r = -0.810, P < 0.001). Spearman correlation analysis showed a positive correlation between HbA1c levels and peripheral vascular disease ( r = 0.472, P < 0.001), while 25(OH)D levels were negatively correlated with peripheral vascular disease ( r = -0.443, P < 0.001). Receiver operating characteristic curve analysis revealed that the area under the curve (AUC) for HbA1c predicting peripheral vascular disease was 0.797 (95% CI: 0.719, 0.874), with a sensitivity of 0.817 and specificity of 0.760 at a cutoff value of 8.27%. The AUC for 25(OH)D predicting peripheral vascular disease was 0.778 (95% CI: 0.695, 0.861), with a sensitivity of 0.748 and specificity of 0.780 at a cutoff value of 63.49 nmol/L. The prediction of peripheral vascular disease using both HbA1c and 25(OH)D yielded an AUC of 0.805 (95% CI: 0.730, 0.876), with a sensitivity of 0.815 and specificity of 0.800. Conclusions:The duration of the disease, HbA1c levels, and 25(OH)D levels are high risk factors for peripheral vascular disease in patients with T2DM. When patients with T2DM exhibit vitamin D deficiency and poor control of HbA1c levels, prompt intervention should be implemented to reduce the risk of developing peripheral vascular disease.

Objective:To investigate the relationship between thyroid hormone sensitivity indicators and the risk of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) who have normal thyroid function.Methods:A retrospective study design was used to gather data from 100 patients with T2DM who had normal thyroid function and were treated at Shangluo Central Hospital between June 2022 and January 2024. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were used for evaluation. Patients with an MMSE score of ≤ 26 or a MoCA score of ≤ 26 were included in the MCI group ( n = 22), whereas those with an MMSE score of > 26 and a MoCA score of > 26 were included in the non-MCI group ( n = 78). Multivariate logistic regression analysis was performed to identify factors influencing MCI in patients with T2DM. Additionally, receiver operating characteristic curve analysis was conducted to assess the predictive value of thyroid hormone sensitivity indicators for MCI in this patient population. Results:In the MCI group, 68.18% of patients had an education level of high school or below. The level of glycosylated hemoglobin in this group was (8.45 ± 1.95)%, insulin resistance index was (3.34 ± 0.25), the level of triglycerides was (2.59 ± 0.19) mmol/L, the level of uric acid was (354.76 ± 46.16) μmol/L, thyroid-stimulating hormone index was (3.09 ± 0.26), and thyroid feedback quantile-based index was [0.2 (0.0, 0.5)]. All of these values were significantly higher than those in the non-MCI group: [43.59%, (7.63 ± 1.61)%, (3.05 ± 0.17), (2.09 ± 0.15) mmol/L, (311.74 ± 67.15) μmol/L, (2.87 ± 0.13), 0.2 (-0.1, 0.4), χ2 = 4.15, t = 2.01, 6.32, 12.99, 3.47, 5.47, Z = -2.54, all P < 0.05]. In the MCI group, the level of free triiodothyronine was (4.32 ± 0.21) pmol/L, and thyrotroph T4 resistance index (TT4RI) was (34.54 ± 4.30), both of which were significantly lower than those in the non-MCI group [(4.61 ± 0.36) pmol/L, (37.15 ± 5.55), t = 2.58, 2.04, both P < 0.05]. There were no significant differences in gender, age, body mass index, alcohol consumption, smoking, history of cerebral infarction, hypertension, duration of diabetes, use of only metformin, fasting blood glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, alanine aminotransferase, aspartate aminotransferase, free thyroxine, and thyroid-stimulating hormone between the two groups (all P > 0.05). Logistic regression analysis indicated that education level, triglyceride, uric acid, free triiodothyronine, thyroid-stimulating hormone index, and TT4RI were risk factors for MCI (all P < 0.05). The predictive value of the combined thyroid hormone sensitivity indicators for MCI in patients with T2DM was high, with an area under the receiver operating characteristic curve of 0.778 [95% CI(0.676, 0.880), P < 0.001], a sensitivity of 0.538, a specificity of 0.909, and a maximum Youden index of 0.447. Conclusions:In patients with T2DM who have normal thyroid function, the thyroid hormone sensitivity indicators (TT4RI and thyroid feedback quantile-based index) exhibited a negative correlation with MCI, whereas TSHI demonstrated a positive correlation with MCI. The combined use of these thyroid hormone sensitivity indicators is valuable for predicting MCI and offers significant guidance for the early intervention of cognitive impairment in patients with T2DM.

Objective:To investigate the correlation between glycated hemoglobin (HbA1c) and 25-hydroxyvitamin D [25(OH)D] levels with peripheral vascular disease in patients with type 2 diabetes mellitus (T2DM).Methods:A total of 165 patients with T2DM who were treated at Shangluo Central Hospital from April 2021 to April 2023 were included in this retrospective study. Based on the presence or absence of peripheral vascular disease, the patients were divided into two groups: the positive group (115 patients with T2DM and peripheral vascular disease) and the negative group (50 patients with T2DM only). Data on general clinical characteristics including age, gender, duration of diabetes, family history of the disease, and the presence of hypertension were collected from both groups. Laboratory data, including 25(OH)D, serum calcium, phosphorus, apolipoprotein B, lipoprotein(a), uric acid, and apolipoprotein AⅠ, were also gathered. Logistic regression analysis was performed to identify high-risk factors for peripheral vascular disease, while Pearson or Spearman correlation analyses were used to evaluate the correlation between HbA1c, 25(OH)D, and peripheral vascular disease. The diagnostic efficiency of the high-risk factors for peripheral vascular disease was assessed using receiver operating characteristic analysis.Results:Long disease duration ( t = 2.74, P = 0.007), a history of hypertension ( χ2 = 6.60, P = 0.010), poor control of HbA1c ( χ2 = 12.14, P < 0.001), vitamin D deficiency ( χ2 = 10.20, P < 0.001), high levels of low-density lipoprotein cholesterol ( t = 2.58, P = 0.024), low levels of high-density lipoprotein cholesterol ( t = 3.65, P = 0.008), high HbA1c levels ( t = 6.68, P < 0.001), and low levels of 25(OH)D ( t = 6.60, P < 0.001) are the independent risk factors for peripheral vascular disease in patients with T2DM. Logistic regression analysis revealed that long disease duration ( OR = 2.327, P = 0.008), vitamin D deficiency ( OR = 1.255, P = 0.034), high HbA1c levels ( OR = 3.124, P < 0.001), and low 25(OH)D levels ( OR = 4.256, P = 0.002) are significant risk factors for peripheral vascular disease. Pearson correlation analysis indicated a negative correlation between HbA1c levels and 25(OH)D levels ( r = -0.810, P < 0.001). Spearman correlation analysis showed a positive correlation between HbA1c levels and peripheral vascular disease ( r = 0.472, P < 0.001), while 25(OH)D levels were negatively correlated with peripheral vascular disease ( r = -0.443, P < 0.001). Receiver operating characteristic curve analysis revealed that the area under the curve (AUC) for HbA1c predicting peripheral vascular disease was 0.797 (95% CI: 0.719, 0.874), with a sensitivity of 0.817 and specificity of 0.760 at a cutoff value of 8.27%. The AUC for 25(OH)D predicting peripheral vascular disease was 0.778 (95% CI: 0.695, 0.861), with a sensitivity of 0.748 and specificity of 0.780 at a cutoff value of 63.49 nmol/L. The prediction of peripheral vascular disease using both HbA1c and 25(OH)D yielded an AUC of 0.805 (95% CI: 0.730, 0.876), with a sensitivity of 0.815 and specificity of 0.800. Conclusions:The duration of the disease, HbA1c levels, and 25(OH)D levels are high risk factors for peripheral vascular disease in patients with T2DM. When patients with T2DM exhibit vitamin D deficiency and poor control of HbA1c levels, prompt intervention should be implemented to reduce the risk of developing peripheral vascular disease.

Objective This study aims to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022 using the gray correlation analysis method.Methods Based on the"SHA2011"accounting results of current health expenditure in Guizhou Province,as well as data from the"Guizhou Statistical Yearbook"and"Guizhou Health Statistical Yearbook",the gray correlation analysis method was used to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022.Results The factors with the highest correlation to per capita current health expenditure in Guizhou Province were health expenditure(0.829),followed by the number of health technical personnel per thousand people(0.715),the number of practicing(assistant)physicians per thousand people(0.705),and per capita GDP(0.704).The factor with the lowest correlation was the proportion of the tertiary industry to GDP(0.543).Conclusion Health expenditure investment has the strongest correlation with per capita current health expenditure in Guizhou Province.Health re-source investment and health service capacity are the main influencing factors of per capita current health expenditure in Guizhou Province.At the same time,the impact of economic and social factors on current health expenditure should be fully recognized.

Objective This study aims to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022 using the gray correlation analysis method.Methods Based on the"SHA2011"accounting results of current health expenditure in Guizhou Province,as well as data from the"Guizhou Statistical Yearbook"and"Guizhou Health Statistical Yearbook",the gray correlation analysis method was used to analyze the factors influencing per capita current health expenditure in Guizhou Province from 2016 to 2022.Results The factors with the highest correlation to per capita current health expenditure in Guizhou Province were health expenditure(0.829),followed by the number of health technical personnel per thousand people(0.715),the number of practicing(assistant)physicians per thousand people(0.705),and per capita GDP(0.704).The factor with the lowest correlation was the proportion of the tertiary industry to GDP(0.543).Conclusion Health expenditure investment has the strongest correlation with per capita current health expenditure in Guizhou Province.Health re-source investment and health service capacity are the main influencing factors of per capita current health expenditure in Guizhou Province.At the same time,the impact of economic and social factors on current health expenditure should be fully recognized.

Objective:This study aimed to analyze the genetic variation of the human papillomavirus (HPV) type 39 genomes and to predict and screen the dominant T-cell and B-cell epitopes of the viral early proteins (E1, E2, E6, E7) and late proteins (L1, L2).Methods:A total of 70 full-length sequences of HPV39 variants were retrieved from the clinical samples and the National Center for Biotechnology Information (NCBI) to construct a phylogenetic tree, analyze genetic polymorphisms, and predict the physicochemical properties of the viral proteins. Next, T-cell and B-cell epitopes were predicted using IEDB and ABCpred, and potential dominant epitopes were further selected based on parameters such as the secondary structure of the epitope region, peptide flexibility, hydrophilicity, surface accessibility and antigenicity. Finally, a homology analysis of the potential dominant epitopes was performed with 12 high-risk HPV types.Results:HPV39 variants from different sources can be clustered into two lineages (A and B), each exhibiting distinct mutation patterns. The mutation rate was the highest in E7 and the lowest in E1 among the different viral genes. However, these nucleotide/amino acid mutations did not significantly impact the physicochemical properties of the viral proteins. After prediction and screening, 5 and 6 potential dominant B-cell epitopes were identified in both L1 and L2, respectively. E1, E2, E6, and E7 yielded 18, 10, 4, and 1 potential dominant HLA-I restricted T-cell epitopes, respectively. Additionally, E1, E2, and E6 yielded 7, 3, and 2 potential dominant HLA-II restricted T-cell epitopes, respectively. Homology analysis indicated that T-cell dominant epitopes in E1, E2, and E6, as well as B-cell epitopes in L2, showed high homology (93%-100%) with HPV68, HPV33, HPV45, and HPV59.Conclusions:Bioinformatics analysis and prediction revealed that HPV39 variants can be clustered into two main evolutionary branches, A and B, each exhibiting a specific mutation pattern. The viral proteins contain potential dominant T-cell and B-cell epitopes that can be further investigated, providing valuable theoretical support for the development of HPV39-related peptide-based vaccines and therapeutics.

Essential oils (EOs) are natural, volatile substances derived from aromatic plants. They exhibit multiple pharmacological effects, including antibacterial, anticancer, anti-inflammatory, and antioxidant properties, with broad application prospects in health care, food, and agriculture. However, the instability of volatile components, which are susceptible to deterioration under light, heat, and oxygen exposure, as well as limited water solubility, have significantly impeded the development and application of EOs. Porous nanoclays are natural clay minerals with a layered structure. They possess unique structural characteristics such as large pore size, regular distribution, and tunable particle size, which are extensively utilized in drug delivery, adsorption separation, reaction catalysis, and other fields. Natural-derived porous nanoclays have garnered considerable attention for the encapsulation and delivery of EOs. This review comprehensively summarizes the structure, types, and properties of natural-derived porous nanoclays, focusing on the structural characteristics of porous nanoclays such as montmorillonite, palygorskite, halloysite, kaolinite, vermiculite, and natural zeolite. It also examines research advances in their delivery of EOs and explores engineering strategies to enhance the delivery of EOs by natural-derived porous nanoclays. Finally, various applications of natural-derived porous nanoclays for EOs in antibacterial, food preservation, repellent, and insecticide aspects are presented, providing a reference for the development and application of EOs.
Humans ; Nanoparticles/chemistry* ; Oils, Volatile/administration & dosage* ; Porosity ; Nanoparticle Drug Delivery System/chemistry*