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 effects of the first dorsal metatarsal artery terminal branch flaps in repairing skin and soft tissue defects of fingers.Methods:The study was a retrospective observational study. From October 2021 to December 2022, 44 patients with skin and soft tissue defects in 55 fingers who met the inclusion criteria were admitted to Suzhou Ruihua Orthopedic Hospital. There were 39 males (48 fingers) and 5 females (7 fingers), aged 18 to 54 years. The single wound area after debridement ranged from 1.5 cm×1.0 cm to 3.0 cm×2.0 cm. The color Doppler ultrasonography was performed before operation to locate the first dorsal metatarsal artery and its terminal branches, and a first dorsal metatarsal artery terminal branch flap was designed according to the wound condition, with the area of harvested single flap ranged from 1.7 cm×1.2 cm to 3.2 cm×2.2 cm. The wounds in the flap donor areas were transplanted with full-thickness skin grafts from ipsilateral inner calf. The type of flap was recorded, and the diameter of the terminal branch of the first dorsal metatarsal artery was measured during operation. The survival of the flap was observed one week after operation. The wound healing in the flap donor and recipient areas was observed two weeks after operation. At the last follow-up, the functional recovery of the affected fingers was evaluated by the trial standards for evaluation of partial function of upper extremity by the Hand Surgery Society of Chinese Medical Association, the sensory function of the flap was evaluated using the sensory function evaluation standard of British Medical Research Council, the scar in the donor and recipient areas of the flap was evaluated using the Vancouver scar scale (VSS), and the Allen test was conducted in the toe of flap donor area to evaluate the blood flow.Results:The monoblock type flaps in 31 patients and flow-through type flaps in 2 patients were used to repair wounds in single finger, 2 monoblock type flaps in 8 patients were used to repair wounds in 2 fingers at the same time, and the single-pedicle and two-flap type flaps in 3 patients were used to repair wounds in 2 fingers at the same time. The diameter of the fibular terminal branch of the first dorsal metatarsal artery ranged from 0.40 to 1.10 mm, and the diameter of the tibial terminal branch of the first dorsal metatarsal artery ranged from 0.70 to 0.75 mm. All the flaps survived at one week after operation, and all the wounds demonstrated optimal healing in the flap donor and recipient areas at two weeks after operation. All patients were followed up for 6 to 18 months. At the last follow-up, the functional recovery of 48 fingers was evaluated as excellent, and the functional recovery of 7 fingers was evaluated as good; the sensory function of 8 flaps was rated as S2, and the sensory function of 47 flaps was rated as S3, and the two-point discrimination distance of the flaps was 8-14 mm; the VSS scores in the flap recipient areas ranged from 3 to 6, and the VSS scores in the flap donor areas ranged from 4 to 7; the Allen test result of the toes in the donor areas were all negative with normal blood flow.Conclusions:The first dorsal metatarsal artery terminal branch flaps have several advantages, including relatively hidden donor area, shallow anatomical level, simple intraoperative operation, and flexible flap design. The flap is incised without damaging the main artery of the toe, which can repair skin and soft tissue defects of the fingers and ensure the utmost protection of the toes in donor areas. The fingers exhibit improved appearance, texture, sensation, and function after operation.

Intraperitoneal administration of timosaponin A-III (TA-III) has therapeutic effects on high-fat diet-induced metabolic dysfunction-associated steatotic liver disease (MASLD), but oral administration has no effect. This suggests that gut microbiota may affect the oral bioavailability of TA-III. Metabolic dysfunction-associated steatohepatitis (MASH) is an inflammatory subtype of MASLD. To investigate the therapeutic effect of different administration modes of TA-III on MASH and its relationship with gut microbiota metabolism. In this study, a MASH mouse model was induced by choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD). Comparing the therapeutic effect of intraperitoneal injection (10 mg·kg^-1, ip) and intragastric administration (100 mg·kg^-1, ig) of TA-III. The concentration of TA-III in serum of rats under the two administration modes was analyzed. On this basis, the metabolic effect of gut microbiota on TA-III in mice was verified by the experiment of metabolism of gut microbiota in vitro. The pharmacokinetic experiment of combined antibiotic intervention in mice further verified the metabolism of TA-III by gut microbiota in mice. Finally, the concentration of TA-III in serum of mice after the administration of TA-III by intragastric administration under different antibiotic intervention conditions was compared, and 16S rRNA sequencing analysis was combined to find the key bacteria that may participate in the metabolism of TA-III. The animal welfare and experimental procedures in this paper were in accordance with the provisions of the Animal Ethics Committee of Shanghai University of Traditional Chinese Medicine. The ethics approval number is PZSHUTCM2307030004 and PZSHUTCM2310200003. The results showed that TA-III (10 mg·kg^-1, ip) had definite therapeutic effect on MASH mice, but TA-III (100 mg·kg^-1, ig) was ineffective. The analysis showed that the prototype concentration of TA-III in serum and liver of mice in TA-III (100 mg·kg^-1, ig) was significantly lower than TA-III (10 mg·kg^-1, ip), suggesting that the oral administration of TA-III may be metabolized by gut microbiota. The concentration of TA-III in serum of streptomycin (Str) treated mice was higher than normal mice. Combined with 16S rRNA gene sequencing analysis, it was found that the abundance of Akkermansia_muciniphila (A. muciniphila) was significantly reduced in the Str group. In vitro experiments showed that A. muciniphila could metabolize TA-III. In conclusion, gut microbiota is an important factor affecting the efficacy of TA-III administration through the gastrointestinal tract, in which A. muciniphila may play an important role.