Objective: To explore the types of medication adherence and their influencing factors among inpatients with chronic diseases based on latent profile analysis, so as to provide the basis for improving medication adherence among patients with chronic diseases. Methods: The inpatients with chronic diseases admitted to the Second Affiliated Hospital of Hainan Medical University were selected as the study subjects. Demographic information, chronic disease status, and health education were collected through questionnaire surveys. Medication adherence was assessed using the Medication Adherence Scale and categorized based on the scores of its eight items through latent profile analysis. Factors affecting medication adherence among inpatients with chronic diseases were analyzed using a multinomial logistic regression model. Results: Totally 290 valid questionnaires were recovered, with an effective recovery rate of 97.64%. There were 157 males (54.14%) and 133 females (45.86%), with a median age of 61 (interquartile range, 21) years. The median score of medication adherence was 4.75 (interquartile range, 4.50). Based on latent profile analysis, medication adherence was categorized into three types: subjective neglect with poor adherence (38.97%), subjective confidence with fluctuating adherence (28.28%), and self-reflective with good adherence (32.76%). Multinomial logistic regression analysis showed that compared to the subjective confidence with fluctuating adherence, family monthly income (5 000-10 000 yuan, OR=2.981, 95%CI: 1.055-8.429), comorbidity of chronic diseases (OR=3.478, 95%CI: 1.579-7.661), number of health education sessions received in the past year (≤1 session, OR=0.329, 95%CI: 0.120-0.907; 2 sessions, OR=0.363, 95%CI: 0.138-0.950), and health information literacy scores (<60 points, OR=2.596, 95%CI: 1.209-5.573) were statistically associated with subjective neglect with poor adherence (all P<0.05). Conclusion Subjective neglect with poor medication adherence among inpatients with chronic diseases is associated with family monthly income, comorbidity of chronic diseases, the number of health education sessions received, and health information literacy.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Objective: This study aimed to elucidate the hand function recovery capacity of degenerative cervical myelopathy (DCM) patients with different severities of hand dexterity impairment. Methods: Hand functional outcome measures such as the 10-second grip and release (10s-G&R) test, modified Japanese Orthopaedic Association (mJOA) upper extremity score and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire (JOACMEQ) upper extremity function were collected before surgery and at the 1-year follow-up. A total of 102 DCM patients were categorized into mild, moderate and severe group based on the preoperative 10s-G&R test result. Hand functional parameters were compared across the 3 groups. Multivariate linear regression was conducted to explore predictive factors. Receiver operating characteristic curve analysis was performed to assess the predictive efficacy of the preoperative 10s-G&R test and establish the cutoff value for incomplete recovery of hand dexterity. Results: At the 1-year follow-up, significant improvements were observed in all hand functional parameters across all 3 groups. However, the incomplete recovery rates of the mild, moderate, severe groups were 26.67%, 46.88%, and 57.50%, respectively (p < 0.05). Multivariate regression revealed that preoperative 10s-G&R test result, age, Hoffmann sign, duration of symptom, and mJOA Upper score serve as significant predictors for postoperative 10s-G&R test outcomes. Patients with a preoperative 10s-G&R test < 15 cycles have a 1.9 times higher risk of incomplete recovery of hand function (p = 0.005). Conclusion Most patients, regardless of their preoperative hand function, exhibit potential for improvement in hand dexterity. However, worse initial hand dexterity correlates with poorer outcomes. Surgical treatment is recommended before the 10s-G&R test drops below 15 cycles.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.