Objective:To explore the predictive value of the combined application of total prostate-specific antigen (TPSA), vascular endothelial growth factor A (VEGF-A), and interleukin (IL) in predicting postoperative biochemical recurrence in patients with prostate cancer.Methods:This study adopted a retrospective cohort research method. 202 male prostate cancer patients who visited Xi′an Gaoxin Hospital from April 2021 to January 2024 were selected as the research subjects. The age of the patients was 68(64, 71) years, and their postoperative conditions were classified into the non-recurrence group ( n=144) and the biochemical recurrence group ( n=58). The general clinical data and serumological test indicators SA, free prostate-specific antigen (FPSA), VEGF-A, IL-6, IL-17] were detected and compared between the two groups. Quantitative data with normal distribution were expressed as mean±standard deviation, and the comparison between groups was performed using the independent sample t-test; non-normal distribution quantitative data were expressed as M( Q1, Q3), and the comparison between groups was performed using the Mann-Whitney U test. The comparison between groups of count data was performed using the chi-square test. Through Spearman correlation analysis and multivariate Logistic regression analysis, the risk factors for biochemical recurrence after surgery in prostate cancer patients were screened out, and the efficacy of the combined prediction model based on TPSA, VEGF-A, and IL-17 was evaluated by receiver operating characteristic (ROC) curve, decision curve (DCA), and calibration curve. Results:The average tumor diameter, proportion of positive surgical margins, proportion of seminal vesicle invasion, and proportion of patients with Gleason score 3-5 in the biochemical recurrence group were significantly higher than those in the non-recurrence group ( P<0.05). The serumological indicators TPSA, VEGF-A, IL-6, IL-17 in the biochemical recurrence group were 44.28 (42.37, 48.57) ng/mL, (28.24±3.99) ng/mL, (39.14±2.95) ng/L and (66.64±6.04) pg/mL; those in the non-recurrence group were 41.25 (36.61, 43.56) ng/mL, (23.52±3.75) ng/mL, (37.19±4.19) ng/L, and (57.31±6.63) pg/mL. The biochemical recurrence group was higher than the non-recurrence group, and the difference was statistically significant ( P<0.05). Spearman correlation analysis and Logistic regression analysis found that TPSA, VEGF-A, and IL-17 were risk factors for biochemical recurrence after surgery in prostate cancer patients ( P<0.05); the DCA curve and calibration curve indicated that the combined prediction model based on TPSA, VEGF-A, and IL-17 had good accuracy (Hosmer-Lemeshow P=0.421), and the ROC curve suggested that the efficacy of the above indicators combined for predicting biochemical recurrence after surgery in prostate cancer patients was higher [AUC (95% CI)=0.899 (0.832-0.966)], and higher than the independent predictive efficacy of each indicator. Conclusion:Continuous monitoring of serum TPSA, VEGF-A, and IL-17 levels can effectively predict the risk of postoperative recurrence in prostate cancer patients and also provide biological markers for preventing disease recurrence.

Acute lateral ankle sprain (ALAS) is one of the most common sport injuries, with high incidence, recurrence and disability rates. Currently, exercise rehabilitation-based non-surgical treatment is the primary management approach for ALAS. However, there remain improper practices such as excessive immobilization or uncontrolled activity, which contribute to recurrent sprains and chronic ankle instability, significantly impairing patients′ athletic function and quality of life. To standardize the non-surgical management of ALAS, improve the cure rates, and reduce the recurrence and disability rates, Chinese Sports Rehabilitation Medicine Training Project of Chinese Medical Association, Foot and Ankle Basics and Orthopedics Group, Orthopedic Branch of Chinese Medical Doctor Association, and Sports Medicine Branch of Jiangsu Medical Association organized relevant experts to formulate Expert consensus on non-surgical treatment for acute lateral ankle sprain ( version 2025), following the principles of scientific vigor, practicality, and innovation. Thirteen recommendations were proposed for standardized treatment protocols across different healing phases, aiming to provide references for standard management of ALAS and improve the therapeutic outcomes.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Acute lateral ankle sprain (ALAS) is one of the most common sport injuries, with high incidence, recurrence and disability rates. Currently, exercise rehabilitation-based non-surgical treatment is the primary management approach for ALAS. However, there remain improper practices such as excessive immobilization or uncontrolled activity, which contribute to recurrent sprains and chronic ankle instability, significantly impairing patients′ athletic function and quality of life. To standardize the non-surgical management of ALAS, improve the cure rates, and reduce the recurrence and disability rates, Chinese Sports Rehabilitation Medicine Training Project of Chinese Medical Association, Foot and Ankle Basics and Orthopedics Group, Orthopedic Branch of Chinese Medical Doctor Association, and Sports Medicine Branch of Jiangsu Medical Association organized relevant experts to formulate Expert consensus on non-surgical treatment for acute lateral ankle sprain ( version 2025), following the principles of scientific vigor, practicality, and innovation. Thirteen recommendations were proposed for standardized treatment protocols across different healing phases, aiming to provide references for standard management of ALAS and improve the therapeutic outcomes.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

BACKGROUND:Bioactive glass is a multifunctional synthetic composite material that releases active ions slowly and exhibits certain biological activities after affinity with tissues.Their versatility stems from the versatility of their preparation processes and components,allowing them to be applied in different clinical scenarios. OBJECTIVE:To review the main application forms,application fields of bioactive glass,as well as the influence of doping different elements on its function. METHODS:A literature search was conducted across WanFang Medical Database,CNKI Database,PubMed Database,and Web of Science Database,using the search terms"bioactive glass,slow-release ions,bone tissue engineering,composite scaffold,tissue regeneration and repair,biomedical engineering"in Chinese and English.The timeframe was limited from 2000 to 2023.Finally,88 articles were included for review. RESULTS AND CONCLUSION:(1)In terms of application forms,bioactive glass can be fabricated as coatings,particles,bone cements,and scaffolds according to needs.Coatings have the potential to enhance the biological activity of implants,yet they are susceptible to instability as a result of degradation.Particles offer a viable solution for the repair of irregular bone defects;however,particles produced through traditional methods often possess limited functionality.Bone cement provides the benefits of minimal invasiveness and injectability,yet its application is restricted to smaller bone defects.Scaffolds exhibit excellent mechanical properties and are commonly used for larger-sized bone defects,yet they have limited toughness.(2)In terms of applications,bioactive glass can be used in a variety of tissue regeneration and repair and disease treatment fields,including dentistry,orthopedics,soft tissue engineering,and cancer.(3)In terms of element doping,the addition of specific elements to bioactive glass not only improves its mechanical properties but also endows it with special biological functions such as bioactivity,degradability,and antibacterial properties.(4)Biologically active glass is a versatile material that can be used in different forms and functions by adjusting the preparation process and element doping to meet various clinical needs in bone tissue engineering and is widely used in the field of biomedical engineering.