Abstract The health literacy level among the elderly in China remains at a low level. The 14th Five-Year Plan for Healthy Aging clearly points out that health literacy promotion projects should be implemented to improve the health literacy level among the elderly. The health literacy promotion strategies for the elderly require individual, social, policy and environmental supports. This article reviewed four types of health literacy promotion strategies for the elderly, including social strategies, lecture-based health education strategies, new media-based health communication strategies and environmental strategies. It also proposed that health education institutions, communities and other parties should work together, take advantage of digital technology and internet, and take various measures simultaneously to improve the health literacy of the elderly.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

ObjectiveTo investigate the noise level and influencing factors in metro platforms and station halls， thereby providing the scientific basis for the establishment of hygienic standards. MethodsDuring the morning peak（7：00‒9：30）and off-peak （9：30‒17：00） on weekdays， the noise levels were measured with noise meters at 39 monitoring points of 13 station platforms and 31 monitoring points of 6 station halls. The monitoring points arrangement and detection methods referred to the Examination methods for public places—Part 1： physical parameters（GB/T 18204.1‒2013）. ResultsThe measured noise level in the station ranged from 69.25 to 86.17 dB（A）， accounting for 44.74% below 75 dB（A）， 89.47% below 80 dB（A） and 97.37% below 85 dB（A）.The noise level of the platform ［（76.38±4.19） dB（A）］ was higher than that of the station hall ［（74.24±4.50） dB（A）］（P<0.01）. The noise level of the elevated platforms ［（80.01±2.25） dB（A）］ was higher than that of the underground platforms ［（75.73±4.13） dB（A）］（P<0.01）， and the noise level of the platforms without platform screen doors（PSD） ［（80.21±5.08） dB（A）］ was higher than that of platforms with PSD［（74.73±3.16） dB（A）］ （P<0.01）. No statistical significant differences were observed among the different areas of the platforms， monitoring periods， platform depth， exit mode and operation years （P>0.05）. ConclusionThe noise level in metro stations in the city does not fully meet the requirements of current relevant standards. It is suggested to take noise reduction measures to reduce the noise of metro stations.

Objective To investigate the imaging features of intestinal schwannoma(IS)in order to improve the diagnostic ability of the disease.Methods The clinical and imaging data of 14 patients with surgically and pathologically confirmed IS were retrospectively analyzed,including the location,size,morphology,nature,growth pattern,CT density,MRI signal,PET/CT metabolism and other characteristics of the tumors.Results Of the 14 IS cases,the lesions of 3 cases were located in the duodenum,2 cases in the cecum,8 cases in the colon and 1 case in the rectum.The lesions were all round or oval,with an average maximum diameter of(2.4±1.1)cm.The lesions were solid in 13 cases,extraluminal growth in 10 cases,cystic degeneration in 1 case and myxoid degeneration in 1 case.Chronic inflammatory lymph nodes were seen around the diseased intestines in 9 cases,and the short diameter of lymph nodes was greater than 5 mm in 6 cases.All 14 cases of IS showed low attenuation on plain CT scan,and progressive enhancement after contrast injection,including 1 case of mild enhancement,2 cases of moderate enhancement,and 11 cases of obvious enhancement.Two cases of IS showed low signal intensity on T1WI,slightly high signal intensity on T2WI,significantly high signal intensity on DWI,and obvious progressive enhancement after contrast injection on MRI.Two cases of IS showed high metabolism on 18F-FDG-PET/CT,and the SUVmax was 9.4 and 8.8,respectively.Conclusion The imaging findings of IS were characteristic to a certain extent.They mainly manifested as solid nodules or masses derived from the intestinal submucosa,with uniform attenuation or signal intensity,obvious progressive enhancement after contrast injection,obvious hypermetabolism on 18F-FDG-PET/CT,and slightly larger homogeneous lymph nodes were common around the lesions.

Objective To explore the application value of single energy metal artifact reduction technology (SEMAR) in the CTA follow-up of complex abdominal aortic aneurysm endovascular repair (EVAR) combined with coil embolization using 320-slice CT spiral scanning mode. Methods A retrospective analysis was conducted on the images of 14 patients with abdominal aortic aneurysm and 2 patients with internal iliac artery aneurysm who underwent abdominal CTA reexamination 30 days after EVAR combined with coil embolization at Zhongshan Hospital, Fudan University from August 2023 to February 2024. The original data were reconstructed using a hybrid iterative reconstruction algorithm (non-SEMAR group) and a combined reconstruction and SEMAR algorithm (SEMAR group), and the artifact index (AI), contrast-to-noise ratio (CNR), and subjective scores between the two groups were compared. Results The AI values of up, down, left, right around the spring coil and adjacent aortic cavity in the SEMAR group were lower than those in the non-SEMAR group (38.16±19.20 vs 89.29±30.93, 30.75±16.28 vs 82.62±28.01, 33.61±16.18 vs 74.90±26.28, 44.99±15.91 vs 87.72±33.70, and 24.49±12.58 vs 47.29±13.55; P＜0.001), and the CNRs in the SEMAR group were higher than those in the non-SEMAR group (2.47±2.15 vs 1.01±0.74, 2.32±2.01 vs 0.72±0.50, 4.93±4.15 vs 1.38±0.79, 4.10±4.14 vs 1.56±1.18, and 19.91±11.01 vs 11.01±7.77; P＜0.05). Compared with the non-SEMAR group, the subjective scores of image in the SEMAR group were higher (P＜0.001). Conclusions SEMAR technology can significantly reduce the artifact of spring coil, improve the clarity of tumors, visceral arteries, stents, and internal leaks images, and has important clinical significance for follow-up after EVAR surgery.

Glioma is the most common malignancy of the central nervous system, originating mainly from glial cells. Because of its highly aggressive nature, glioma has one of the highest rates of death among all types of cancer. Therefore, it is very important to develop new therapeutic approaches and drugs for glioma treatment. Instead of activate the telomerase, approximately 30% of glioma use alternative lenthening of telomere (ALT) to maintain telomere length. The mechanism of ALT development is poorly understood, however, some genetic mutations have been reported to induce the development of ALT glioma, such as ATRX, IDH1, p53, etc. The lack of ALT glioma cell lines and preclinical ALT glioma models has limited the mechanistic studies of ALT glioma. Therefore, this review listed ALT glioma cell lines that derived from primary culture or gene editing in the last decade, as well as the xenografted animal models established by ALT glioma cell lines, and discussed the role and significance these cell and animal models play in preclinical studies.