Objectives: . Head and neck squamous cell carcinoma (HNSCC) exhibits high recurrence rates, particularly in cases of radioresistant HNSCC (RR-HNSCC). Non-thermal plasma (NTP) therapy effectively suppresses the progression of HNSCC. However, the therapeutic mechanisms of NTP therapy in treating RR-HNSCC are not well understood. In this study, we explored the regulatory role of NTP in the RR-HNSCC signaling pathway and identified its signature genes. Methods: . After constructing two RR-HNSCC cell lines, we prepared cell lysates from cells treated or not treated with NTP-activated media (NTPAM) and performed RNA sequencing to determine their mRNA expression profiles. Based on the RNA sequencing results, we identified differentially expressed genes (DEGs), followed by a bioinformatics analysis to identify candidate molecules potentially associated with NTPAM therapy for RR-HNSCC. Results: . NTPAM reduced RR-HNSCC cell viability in vitro. RNA sequencing results indicated that NTPAM treatment activated the reactive oxygen species (ROS) pathway and induced ferroptosis in RR-HNSCC cell lines. Among the 1,924 genes correlated with radiation treatment, eight showed statistical significance in both the cell lines and The Cancer Genome Atlas (TCGA) cohort. Only five genes—ABCC3, DUSP16, PDGFB, RAF1, and THBS1—showed consistent results between the NTPAM data sequencing and TCGA data. LASSO regression analysis revealed that five genes were associated with cancer prognosis, with a hazard ratio of 2.26. In RR-HNSCC cells, NTPAM affected DUSP16, PDGFB, and THBS1 as activated markers within 6 hours, and this effect persisted for 12 hours. Furthermore, enrichment analysis indicated that these three DEGs were associated with the extracellular matrix, transforming growth factor-beta, phosphoinositide 3-kinase/protein kinase B, and mesenchymal-epithelial transition factor pathways. Conclusion . NTPAM therapy exerts cytotoxic effects in RR-HNSCC cell lines by inducing specific ROS-mediated ferroptosis. DUSP16, PDGFB, and THBS1 were identified as crucial targets for reversing the radiation resistance induced by NTPAM therapy, providing insights into the mechanisms and clinical applications of NTPAM treatment in RR-HNSCC.

Objectives: . Head and neck squamous cell carcinoma (HNSCC) exhibits high recurrence rates, particularly in cases of radioresistant HNSCC (RR-HNSCC). Non-thermal plasma (NTP) therapy effectively suppresses the progression of HNSCC. However, the therapeutic mechanisms of NTP therapy in treating RR-HNSCC are not well understood. In this study, we explored the regulatory role of NTP in the RR-HNSCC signaling pathway and identified its signature genes. Methods: . After constructing two RR-HNSCC cell lines, we prepared cell lysates from cells treated or not treated with NTP-activated media (NTPAM) and performed RNA sequencing to determine their mRNA expression profiles. Based on the RNA sequencing results, we identified differentially expressed genes (DEGs), followed by a bioinformatics analysis to identify candidate molecules potentially associated with NTPAM therapy for RR-HNSCC. Results: . NTPAM reduced RR-HNSCC cell viability in vitro. RNA sequencing results indicated that NTPAM treatment activated the reactive oxygen species (ROS) pathway and induced ferroptosis in RR-HNSCC cell lines. Among the 1,924 genes correlated with radiation treatment, eight showed statistical significance in both the cell lines and The Cancer Genome Atlas (TCGA) cohort. Only five genes—ABCC3, DUSP16, PDGFB, RAF1, and THBS1—showed consistent results between the NTPAM data sequencing and TCGA data. LASSO regression analysis revealed that five genes were associated with cancer prognosis, with a hazard ratio of 2.26. In RR-HNSCC cells, NTPAM affected DUSP16, PDGFB, and THBS1 as activated markers within 6 hours, and this effect persisted for 12 hours. Furthermore, enrichment analysis indicated that these three DEGs were associated with the extracellular matrix, transforming growth factor-beta, phosphoinositide 3-kinase/protein kinase B, and mesenchymal-epithelial transition factor pathways. Conclusion . NTPAM therapy exerts cytotoxic effects in RR-HNSCC cell lines by inducing specific ROS-mediated ferroptosis. DUSP16, PDGFB, and THBS1 were identified as crucial targets for reversing the radiation resistance induced by NTPAM therapy, providing insights into the mechanisms and clinical applications of NTPAM treatment in RR-HNSCC.

Objectives: . Head and neck squamous cell carcinoma (HNSCC) exhibits high recurrence rates, particularly in cases of radioresistant HNSCC (RR-HNSCC). Non-thermal plasma (NTP) therapy effectively suppresses the progression of HNSCC. However, the therapeutic mechanisms of NTP therapy in treating RR-HNSCC are not well understood. In this study, we explored the regulatory role of NTP in the RR-HNSCC signaling pathway and identified its signature genes. Methods: . After constructing two RR-HNSCC cell lines, we prepared cell lysates from cells treated or not treated with NTP-activated media (NTPAM) and performed RNA sequencing to determine their mRNA expression profiles. Based on the RNA sequencing results, we identified differentially expressed genes (DEGs), followed by a bioinformatics analysis to identify candidate molecules potentially associated with NTPAM therapy for RR-HNSCC. Results: . NTPAM reduced RR-HNSCC cell viability in vitro. RNA sequencing results indicated that NTPAM treatment activated the reactive oxygen species (ROS) pathway and induced ferroptosis in RR-HNSCC cell lines. Among the 1,924 genes correlated with radiation treatment, eight showed statistical significance in both the cell lines and The Cancer Genome Atlas (TCGA) cohort. Only five genes—ABCC3, DUSP16, PDGFB, RAF1, and THBS1—showed consistent results between the NTPAM data sequencing and TCGA data. LASSO regression analysis revealed that five genes were associated with cancer prognosis, with a hazard ratio of 2.26. In RR-HNSCC cells, NTPAM affected DUSP16, PDGFB, and THBS1 as activated markers within 6 hours, and this effect persisted for 12 hours. Furthermore, enrichment analysis indicated that these three DEGs were associated with the extracellular matrix, transforming growth factor-beta, phosphoinositide 3-kinase/protein kinase B, and mesenchymal-epithelial transition factor pathways. Conclusion . NTPAM therapy exerts cytotoxic effects in RR-HNSCC cell lines by inducing specific ROS-mediated ferroptosis. DUSP16, PDGFB, and THBS1 were identified as crucial targets for reversing the radiation resistance induced by NTPAM therapy, providing insights into the mechanisms and clinical applications of NTPAM treatment in RR-HNSCC.

Objective:To investigate the influencing factors of enlarged perivascular space (PVS) in relapsing-remitting multiple sclerosis (RRMS) patients and their relationship with cognitive function.Methods:Twenty-seven individuals with RRMS (RRMS group) and 27 healthy controls (healthy control group) who presented to the Department of Neurology, the Sixth Medical Center of People′s Liberation Army General Hospital from July 2022 to November 2024 underwent cognitive function assessments. PVS volume fractions, lesion volumes, and brain volumes were calculated using FreeSurfer, FSL, and other relevant softwares. Group differences in PVS volume fractions, lesion volumes, brain volumes, and cognitive function assessments were compared. Furthermore, correlations between PVS volume fractions and lesion volumes, brain volumes, and cognitive function assessments were analyzed within the RRMS group.Results:Compared with the healthy control group, the RRMS group exhibited significantly higher PVS volume fractions in white matter (PVS_w) (3.14‰±0.29‰ vs 2.91‰±0.30‰, t=2.877, P=0.006) and PVS volume fractions in deep gray matter (PVS_d) (2.25‰±0.10‰ vs 2.17‰±0.09‰, t=2.681, P=0.010), indicating an enlargement of the PVS. Compared with the healthy control group, the RRMS group showed a significant decrease in both white matter volumes [297.3 (274.3, 340.2) ml vs (324.2 (311.0, 350.0) ml, U=-2.085, P=0.037] and deep grey matter volumes [40.2 (34.9, 43.6) ml vs 42.7 (40.2, 44.8) ml, U=-2.292, P=0.022]. Compared with the healthy control group, the RRMS group showed significantly lower scores in cognitive function assessments ( P<0.05). Univariate analysis showed that PVS_w in the RRMS group was significantly positively correlated with age ( r=0.486), white matter lesion volumes ( r=0.437) and deep gray matter lesion volumes ( r=0.394;all P<0.05); PVS_d was also significantly positively correlated with white matter lesion volumes ( r=0.418) and deep gray matter lesion volumes ( r=0.480; both P<0.05). Multiple linear regression analysis showed that age ( B=0.011,95% CI 0.004-0.017), white matter lesion volumes ( B=0.026,95% CI 0.011-0.040) and deep gray matter lesion volumes ( B=0.401,95% CI 0.032-0.771) in the RRMS group were significantly positively correlated with PVS_w, while white matter lesion volumes ( B=0.007,95% CI 0.001-0.014) and deep gray matter lesion volumes ( B=0.204,95% CI 0.029-0.380) were significantly positively correlated with PVS_d (both P<0.05). Univariate analysis showed that immediate memory score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.428), and was significantly positively correlated with education level ( r=0.471), deep gray matter volumes ( r=0.530) and total brain volumes ( r=0.389; all P<0.05); short-term delayed memory score in the RRMS group was significantly negatively correlated with age ( r=-0.390), PVS_w ( r=-0.417) and white matter lesion volumes ( r=-0.438), and was significantly positively correlated with gender ( r=0.393), white matter volumes ( r=0.478), deep gray matter volumes ( r=0.579) and total brain volumes ( r=0.602;all P<0.05); verbal fluency test score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.409) and was significantly positively correlated with education level ( r=0.419) and total brain volumes ( r=0.400;all P<0.05). Multiple linear regression analysis revealed that PVS_d ( B=-5.572, 95% CI -11.513--0.368) and brain volumes ( B=0.012, 95% CI 0.001-0.023) in the RRMS group were both significant predictors of immediate recall score, while PVS_d ( B=-14.203,95% CI -27.514--0.891) was an independent predictor of verbal fluency test score (all P<0.05). Conclusions:The PVS is enlarged in individuals with RRMS compared with the healthy controls, and increased lesion volumes may be a significant predictor. Furthermore, enlarged PVS in the deep gray matter may be a significant predictor of impairment of verbal memory and verbal function in individuals with RRMS.

Objective To explore the influencing factors of olfactory dysfunction in patients with Parkinson's disease(PD)and conduct a systematic review and Meta-analysis.Methods Articles on factors influencing olfactory dysfunction in PD were retrieved from databases including SinoMed,VIP,China National Knowledge Infrastructure,Wanfang Data Knowledge Service Platform,Web of Science,PubMed,Cochrane,Embase,and MEDLINE.The search period spanned from the inception of each database to November 30,2024.Results A total of 13 articles(with a total sample size of 2465)were included,with a total of 18 influencing factors summarized as two themes:core features and progression factors of PD,and individual background and environmental interaction factors.Meta-analysis showed that age(MD=1.01,95％CI:-0.46-2.49,P=0.18),smoking(OR=0.88,95％CI:0.57-1.37,P=0.57),and constipation(OR=1.22,95％CI:0.38-3.93,P=0.74)were not factors affecting olfactory dysfunction in PD patients.Conclusion Factors influencing olfactory dysfunction in PD are predominantly associated with non-motor symptoms.Intervention strategies targeting non-motor symptoms(such as improving sleep quality,vitamin D supplementation,and early cognitive training)may provide novel approaches for delaying the progression of olfactory dysfunction.

Objective:To investigate the influencing factors of enlarged perivascular space (PVS) in relapsing-remitting multiple sclerosis (RRMS) patients and their relationship with cognitive function.Methods:Twenty-seven individuals with RRMS (RRMS group) and 27 healthy controls (healthy control group) who presented to the Department of Neurology, the Sixth Medical Center of People′s Liberation Army General Hospital from July 2022 to November 2024 underwent cognitive function assessments. PVS volume fractions, lesion volumes, and brain volumes were calculated using FreeSurfer, FSL, and other relevant softwares. Group differences in PVS volume fractions, lesion volumes, brain volumes, and cognitive function assessments were compared. Furthermore, correlations between PVS volume fractions and lesion volumes, brain volumes, and cognitive function assessments were analyzed within the RRMS group.Results:Compared with the healthy control group, the RRMS group exhibited significantly higher PVS volume fractions in white matter (PVS_w) (3.14‰±0.29‰ vs 2.91‰±0.30‰, t=2.877, P=0.006) and PVS volume fractions in deep gray matter (PVS_d) (2.25‰±0.10‰ vs 2.17‰±0.09‰, t=2.681, P=0.010), indicating an enlargement of the PVS. Compared with the healthy control group, the RRMS group showed a significant decrease in both white matter volumes [297.3 (274.3, 340.2) ml vs (324.2 (311.0, 350.0) ml, U=-2.085, P=0.037] and deep grey matter volumes [40.2 (34.9, 43.6) ml vs 42.7 (40.2, 44.8) ml, U=-2.292, P=0.022]. Compared with the healthy control group, the RRMS group showed significantly lower scores in cognitive function assessments ( P<0.05). Univariate analysis showed that PVS_w in the RRMS group was significantly positively correlated with age ( r=0.486), white matter lesion volumes ( r=0.437) and deep gray matter lesion volumes ( r=0.394;all P<0.05); PVS_d was also significantly positively correlated with white matter lesion volumes ( r=0.418) and deep gray matter lesion volumes ( r=0.480; both P<0.05). Multiple linear regression analysis showed that age ( B=0.011,95% CI 0.004-0.017), white matter lesion volumes ( B=0.026,95% CI 0.011-0.040) and deep gray matter lesion volumes ( B=0.401,95% CI 0.032-0.771) in the RRMS group were significantly positively correlated with PVS_w, while white matter lesion volumes ( B=0.007,95% CI 0.001-0.014) and deep gray matter lesion volumes ( B=0.204,95% CI 0.029-0.380) were significantly positively correlated with PVS_d (both P<0.05). Univariate analysis showed that immediate memory score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.428), and was significantly positively correlated with education level ( r=0.471), deep gray matter volumes ( r=0.530) and total brain volumes ( r=0.389; all P<0.05); short-term delayed memory score in the RRMS group was significantly negatively correlated with age ( r=-0.390), PVS_w ( r=-0.417) and white matter lesion volumes ( r=-0.438), and was significantly positively correlated with gender ( r=0.393), white matter volumes ( r=0.478), deep gray matter volumes ( r=0.579) and total brain volumes ( r=0.602;all P<0.05); verbal fluency test score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.409) and was significantly positively correlated with education level ( r=0.419) and total brain volumes ( r=0.400;all P<0.05). Multiple linear regression analysis revealed that PVS_d ( B=-5.572, 95% CI -11.513--0.368) and brain volumes ( B=0.012, 95% CI 0.001-0.023) in the RRMS group were both significant predictors of immediate recall score, while PVS_d ( B=-14.203,95% CI -27.514--0.891) was an independent predictor of verbal fluency test score (all P<0.05). Conclusions:The PVS is enlarged in individuals with RRMS compared with the healthy controls, and increased lesion volumes may be a significant predictor. Furthermore, enlarged PVS in the deep gray matter may be a significant predictor of impairment of verbal memory and verbal function in individuals with RRMS.

Objective To explore the influencing factors of olfactory dysfunction in patients with Parkinson's disease(PD)and conduct a systematic review and Meta-analysis.Methods Articles on factors influencing olfactory dysfunction in PD were retrieved from databases including SinoMed,VIP,China National Knowledge Infrastructure,Wanfang Data Knowledge Service Platform,Web of Science,PubMed,Cochrane,Embase,and MEDLINE.The search period spanned from the inception of each database to November 30,2024.Results A total of 13 articles(with a total sample size of 2465)were included,with a total of 18 influencing factors summarized as two themes:core features and progression factors of PD,and individual background and environmental interaction factors.Meta-analysis showed that age(MD=1.01,95％CI:-0.46-2.49,P=0.18),smoking(OR=0.88,95％CI:0.57-1.37,P=0.57),and constipation(OR=1.22,95％CI:0.38-3.93,P=0.74)were not factors affecting olfactory dysfunction in PD patients.Conclusion Factors influencing olfactory dysfunction in PD are predominantly associated with non-motor symptoms.Intervention strategies targeting non-motor symptoms(such as improving sleep quality,vitamin D supplementation,and early cognitive training)may provide novel approaches for delaying the progression of olfactory dysfunction.

Objective: To systematically evaluate the influencing factors for medication compliance in patients with comorbidities of chronic diseases, so as to provide the evidence for improving medication compliance. Methods: Literature on influencing factors for medication compliance in patients with comorbidities of chronic diseases were retrived from CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, Cochrane Library and Embase from inception to January 20, 2024. After independent literature screening, data extraction, and quality assessment by two researchers, a meta-analysis was performed using RevMan 5.4 and Stata 16.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was assessed using Egger's test. Results: Initially, 7 365 relevant articles were retrieved, and 35 of them were finally included, with a total sample size of about 150 000 individuals. There were 30 cross-sectional studies and 5 cohort studies; and 11 high-quality studies and 24 medium-quality studies. The meta-analysis showed that the demographic factors of lower level of education (OR=2.148, 95%CI: 1.711-2.696), lower economic income (OR=1.897, 95%CI: 1.589-2.264), male (OR=0.877, 95%CI: 0.782-0.985), living alone (OR=2.833, 95%CI: 1.756-4.569) and unmarried (OR=2.784, 95%CI: 1.251-6.196); the medication treatment factors of polypharmacy (OR=1.794, 95%CI: 1.190-2.706), potentially inappropriate medication (OR=2.988, 95%CI: 1.527-5.847), low frequency of daily medication (OR=0.533, 95%CI: 0.376-0.754) and adverse drug reactions (OR=3.319, 95%CI: 1.967-5.602); the disease factors of long course of disease (OR=2.118, 95%CI: 1.643-2.730), more comorbidities (OR=1.667, 95%CI: 1.143-2.431) and cognitive impairment (OR=2.007, 95%CI: 1.401-2.874); and the psychosocial factors of poor belief in taking medication (OR=1.251, 95%CI: 1.011-1.547), poor self-rated health (OR=1.990, 95%CI: 1.571-2.522) and being guided by healthcare professionals (OR=0.151, 95%CI: 0.062-0.368) were the influencing factors for medication compliance in patients with chronic comorbidities. Conclusion The medication compliance in patients with comorbidities of chronic diseases is associated with demographic factors, pharmacological factors, disease factors and psychosocial factors, mainly including living alone, adverse drug reactions, course of disease, number of comorbidities and medication beliefs.

Objective: To conduct a scoping review on prognostic prediction models for patients with comorbidity of chronic diseases, and understand modeling methods, predictive factors and predictive effect of the models, so as to provide the reference for prognostic evaluation on patients with comorbidity of chronic diseases. Methods: Literature on prognostic prediction models for patients with comorbidity of chronic diseases was collected through SinoMed, CNKI, Wanfang Data, VIP, PubMed, Embase, Cochrane Library and Web of Science published from the time of their establishment to November 1, 2023. The quality of literature was assessed using prediction model risk of bias assessment tool (PROBAST), then modeling methods, predictive factors and predictive effects were reviewed. Results: Totally 2 130 publications were retrieved, and nine publications were finally enrolled, with an overall high risk of bias. Thirteen models were involved, with three established using machine learning methods and ten established using logistic regression. The prediction results of four models were death, with main predictive factors being age, gender, body mass index (BMI), Barthel index and pressure ulcers; the prediction results of nine models were rehospitalization, with main predictive factors being age, BMI, hospitalization frequency, duration of hospital stay and hospitalization costs. Eleven models reported the area under the receiver operating characteristic curve (AUC), ranging from 0.663 to 0.991 6; two models reported the C-index, ranging from 0.64 to 0.70. Eight models performed internal validation, one model performed external validation, and four models did not reported verification methods. Conclusions The prognostic prediction models for patients with comorbidity of chronic diseases are established by logistic regression and machine learning methods with common nursing evaluation indicators, and perform well. Laboratory indicators should be considered to add in the models to further improve the predictive effects.