BACKGROUND AND OBJECTIVE: Patients with glioma experience a high symptom burden and have diverse palliative care needs. However, the assessment scales used in palliative care remain non-standardized and highly heterogeneous. To evaluate the application patterns of the current scales used in palliative care for glioma, we aim to identify gaps and assess the need for disease-specific scales in glioma palliative care. METHODS: We conducted a systematic search of five databases including PubMed, Web of Science, Medline, EMBASE, and CINAHL for quantitative studies that reported scale-based assessments in glioma palliative care. We extracted data on scale characteristics, domains, frequency, and psychometric properties. Quality assessments were performed using the Cochrane ROB 2.0 and ROBINS-I tools. RESULTS: Of the 3,405 records initially identified, 72 studies were included. These studies contained 75 distinct scales that were used 193 times. Mood (21.7%), quality of life (24.4%), and supportive care needs (5.2%) assessments were the most frequently assessed items, exceeding half of all scale applications. Among the various assessment dimensions, the Distress Thermometer (DT) was the most frequently used tool for assessing mood, while the Short Form-36 Health Survey Questionnaire (SF-36) was the most frequently used tool for assessing quality of life. The Mini Mental Status Examination (MMSE) was the most common tool for cognitive assessment. Performance status (5.2%) and social support (6.8%) were underrepresented. Only three brain tumor-specific scales were identified. Caregiver-focused scales were limited and predominantly burden-oriented. CONCLUSIONS: There are significant heterogeneity, domain imbalances, and validation gaps in the current use of assessment scales for patients with glioma receiving palliative care. The scale selected for use should be comprehensive and user-friendly.
Humans ; Glioma/psychology* ; Palliative Care/methods* ; Quality of Life ; Psychometrics ; Brain Neoplasms/psychology*

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To analyze the risk factors for prolonged length of hospital stay in patients with peritoneal dialysis associated peritonitis(PDAP)and construct a nomogram based on Logistic regression model.Methods A retrospective study was conducted on patients with PDAP who were hospitalized at the Affiliated Hospital of Jiangsu University from January 2013 to December 2023.Using the 75th percentile of hospitalization time as the cutoff(>21 days),the patients were divided into prolonged length of hospital stay group and normal length of hospital stay group.Clinical data were compared between the two groups.Logistic regression analysis was used to analyze the risk factors for prolonged hospital stay in PDAP patients and to construct a nomogram.Results A total of 131 PDAP patients were included in this study,including 40 cases in prolonged length of hospital stay group and 91 cases in normal length of hospital stay group.Multivariate Logistic regression analysis showed that Gram-negative bacteria detected in ascites(OR=6.012,95% CI=1.878-19.248,P=0.003)and elevated platelet count(OR=1.010,95% CI=1.005-1.015,P<0.001)were independent risk factors for prolonged length of hospital stay,while elevated serum chloride(OR=0.885,95% CI=0.802-0.978,P=0.016)was a protective factor.Based on the above three indicators,a nomogram was constructed.The multivariate Logistic regression model showed an area under the receiver operating characteristic curve(AUC)of 0.755,with an internal validation AUC of 0.727 using the Bootstrap method.The calibration curve indicated that the predicted probability was consistent with the actual probability.The decision curve showed that the model was clinically applicable when the threshold probabilities were 9%-10%,13% and 18%-92%.Conclusion A nomogram,based on the detection of gram-negative bacteria in ascites,platelet count and serum chloride,was helpful for clinical screening PADP patients at risk for prolonged length of hospital stay,and can provide a basis for optimizing clinical decision-making.
Humans ; Nomograms ; Risk Factors ; Peritoneal Dialysis/adverse effects* ; Retrospective Studies ; Length of Stay ; Peritonitis/etiology* ; Logistic Models ; Male ; Female ; Middle Aged ; Aged

Objective To analyze the correlations of the expressions of miR-4500 and nerve growth factor(NGF)in breast cancer patients with preoperative magnetic resonance(MR)signs.Methods A total of 105 patients with breast cancer admitted to our hospital were selected,the mRNA expression levels of miR-4500 and NGF in breast cancer tissues and adjacent tissues of patients were detected by qRT-PCR,and the positive expression rates of NGF in breast cancer tissues and adjacent tissues were determined by immunohistochemistry method.The correlations of the expressions of miR-4500 and NGF in breast cancer tissues with clinicopathological features and MR signs of patients were analyzed.The targeting relationship between miR-4500 and NGF was predicted by the bioinformatics website,and the correla-tion between the expressions of the two was analyzed.Results Compared with the adjacent tissues,the expression level of miR-4500 in breast cancer tissue decreased(P<0.05),while the level of NGF mRNA and the positive expression rate of NGF increased(P<0.05).There was no significant difference in age,pathological type,tumor classification,parenchymal background,apparent diffusion coefficient or enhancement curve among different expressions of miR-4500 and NGF(P>0.05).The histological grade,lymph node metastasis,tumor diameter,tumor morphology,ring-like enhancement,and peritu-moral brain edema were related to the expressions of miR-4500 and NGF(P<0.05).The bioinformatics website prediction showed that miR-4500 and NGF had binding sites,and the expressions of miR-4500 and NGF mRNA in breast cancer tissues were negatively correlated(r=-0.576,P<0.05).Conclusion The expression of miR-4500 in breast cancer tissue is low,and the expression of NGF is high,which are correlated with the preoperative MR signs in patients,providing a molecu-lar basis for MR signs.

Objective:To explore the predictive value of admission serum homocysteine levels and quantitative electroencephalogram (qEEG) indicators for adverse outcomes in patients with cerebral hemorrhage.Methods:A retrospective study was conducted on 89 patients, who were collected as the study objects with hemorrhagic stroke treated in the neurology intensive care unit at Kailuan General Hospital from January 2017 to December 2022. Patients were categorized into two groups based on modified Rankin Scale (mRS) scores at discharge: a good prognosis group (mRS≤2) and a poor prognosis group (mRS 3-6). Clinical data and qEEG monitoring of various brain regions were collected. The impact factors of hemorrhagic prognosis were analyzed using multifactorial logistic regression. ROC curve analysis was performed to assess the predictive value of qEEG and admission homocysteine levels for adverse outcomes in hemorrhagic stroke patients.Results:(1) The age of the poor prognosis group was higher than that of the good prognosis group((66.51+13.64) to (60.53+11.69), t=2.15, P=0.034) and admission serum homocysteine levels were significantly higher in the poor prognosis group than in the good prognosis group (17.28(15.52,24.72)mmol/L to 14.50(10.28,16.00)mmol/L, Z=4.14, P<0.001). (2) In the poor prognosis group, power values of δ brain waves in leads Fp1-2, F4, C4, P4, F8, and T4 were higher than those in the good prognosis group (87.99(41.57,196.69) to 50.67(26.64,54.75), Z=2.76, P=0.006); (79.17(40.71,200.00) to 45.06(20.22,61.00), Z=2.10, P=0.036); (72.64(34.97,219.78) to 34.42(19.81,63.4), Z=2.03, P=0.043); (65.06(33.36,177.45) to 28.12(15.88,63.36), Z=2.08, P=0.038); (52.92(25.64,187.91) to 23.61(11.67,43.26), Z=2.21, P=0.027); (66.67(32.56,180.76) to 36.31(17.2,53.78), Z=2.46, P=0.014); (57.30(25.24,127.04) to 29.57(11.91,41.89), Z=2.26, P=0.024). Power values of θ brain waves in leads Fp1-2, F3, F4, C3, C4, P3-4, O1, F7-8, and T3-4 were higher in the poor prognosis group(77.45(47.63,138.72)比35.88(20.92,44.81), Z=3.50, P<0.001); (77.05(35.16,120.22) to 38.74(19.86,58.09), Z=2.27, P=0.023); (85.24(52.53,147.90) to 35.42(14.7,52.59), Z=2.61, P=0.009); (75.81(37.90,124.97) to 36.85(17.92,55.43), Z=2.30, P=0.021); (72.00(43.92,123.54) to 28.37(14.02,51.9), Z=2.22, P=0.027); (67.08(32.01,104.05) to 31.32(17.98,45.28), Z=2.10, P=0.035); (55.33(32.29,94.30) to 25.64(11.87,34.01), Z=2.24, P=0.025); (48.84(20.64,96.28) to 19.85(9.83,28.58), Z=2.30, P=0.022);(48.46(25.06,81.78) to 23.95(8.80,29.16), Z=2.51, P=0.012); (64.46(39.38,112.44) to 26.85(15.74,39.58), Z=2.80, P=0.005); (65.68(31.78,102.00) to 31.09(15.98,46.96), Z=2.38, P=0.017); (45.26(28.34,73.14) to 21.45(10.57,36.59), Z=2.04, P=0.042); (43.50(22.58,78.67) to 25.45(11.91,32.26), Z=2.22, P=0.027). Power values of slow-wave index in leads Fp1-2, F3-4, C3-4, P4, F7-8, and T4, as well as the overall brain average, were higher in the poor prognosis group (6.64(2.98,10.42) to 3.65(2.31,4.30), Z=2.65, P=0.01); (6.53(3.96,11.65) to 3.53(2.56,4.51), Z=2.30, P=0.022); (7.38(4.62,13.12) to 3.83(1.70,4.71), Z=2.38, P=0.017); (5.88(4.02,12.15) to 3.18(2.21,4.46), Z=2.29, P=0.022); (6.13(3.83,11.22) to 2.97(1.53,4.58), Z=2.01, P=0.044); (6.07(3.53,9.39) to 2.74(2.00,3.81), Z=2.40, P=0.016);(4.11(2.51,9.23) to 2.18(1.37,2.82), Z=2.25, P=0.024); (5.71(3.81,10.44) to 3.22(1.86,4.04), Z=2.28, P=0.023); (6.00(3.65,10.37) to 3.04(2.00,4.00), Z=2.39, P=0.017); (4.08(2.56,8.33) to 2.08(1.60,3.14), Z=2.50, P=0.013), with significant statistical differences noted (5.45(3.31,10.08) to 3.17(2.02,4.88), Z=3.62, P=0.005). (3) Logistic regression results showed that admission homocysteine levels ( OR 1.311,95% CI 1.008-1.705, P=0.044), admission NIHSS scores ( OR 1.588,95% CI 1.074-2.349, P=0.020), and overall brain average slow-wave index were influencing factors for poor prognosis in cerebral hemorrhage ( OR 8.596,95% CI 1.088-67.889, P=0.041). (4) ROC curve analysis revealed that the AUC for predicting adverse outcomes in cerebral hemorrhage was 0.768 (95% CI (0.665, 0.872)) for admission homocysteine levels, 0.743 (95% CI (0.634, 0.852)) for the overall brain average slow-wave index, and 0.896 (95% CI (0.827, 0.965)) for admission NIHSS. The cutoff values were 15.67, 3.62, and 8.5, respectively. Sensitivity was 77.8%, 71.1%, and 68.9%, and specificity was 59.4%, 68.7%, and 100%, respectively. The Youden indices were 0.372, 0.398, and 0.689. Conclusion:In the acute phase of cerebral hemorrhage, electroencephalographic physiological changes manifest shows an increase in the δ, θ, and slow-wave index throughout the entire brain. Higher admission homocysteine levels suggest a worse prognosis in patients with cerebral hemorrhage. Admission homocysteine levels and overall brain average slow-wave index have certain predictive value for adverse outcomes in acute cerebral hemorrhage.

Secondary organic aerosol(SOA)produced by photooxidation of styrene and other aromatic compounds is a major part of fine particles in urban atmosphere.In this study,the measurement of component and content of SOA formed from photooxidation of styrene in smog chamber using synchronous radiation vacuum-ultraviolet photoionization aerosol mass spectrometer(VUV-PIAMS)was conducted.Photoionization mass spectra of styrene SOA was detected by synchrotron radiation photon with 10.5 eV,and the proportion of main components was quantified based on the peak area of each ion peak.The photoionization efficiency curve of ion peak was obtained under synchrotron radiation photons in the range from 7.5 to 11.5 eV,and then the ionization potential was acquired for qualitative analysis of the component structure.The results showed that the photoionization mass spectra of styrene SOA mainly contained ion peaks at m/z 106,108,120 and 122,and the ionization potentials of each peak were(9.41±0.03)eV,(8.93±0.03)eV,(9.24±0.03)eV and(9.25±0.03)eV,respectively.Combined with quantum chemistry calculation and off-line measurement verification of infrared absorption spectra and electrospray ionization mass spectra,it was determined that benzaldehyde,benzyl alcohol,4-vinylphenol and benzoic acid were main components of styrene SOA,accounting for 32.5%,17.5%,25%and 15%of the measured components,respectively,and the generated quantity ratio was 13∶7∶10∶6.VUV-PIAMS could overcome the disadvantages of off-line method,and could on-line detect component and content of SOA,proving a useful tool to measure the chemical components and reveal the formation process of SOA particles.

OBJECTIVE: To determine the role of Tripterygium wilfordii multiglycoside (TGW) in the treatment of psoriatic dermatitis from a cellular immunological perspective. METHODS: Mouse models of psoriatic dermatitis were established by imiquimod (IMQ). Twelve male BALB/c mice were assigned to IMQ or IMQ₊TGW groups according to a random number table. Histopathological changes in vivo were assessed by hematoxylin and eosin staining. Ratios of immune cells and cytokines in mice, as well as PAM212 cell proliferation in vitro were assessed by flow cytometry. Pro-inflammatory cytokine expression was determined using reverse transcription quantitative polymerase chain reaction. RESULTS: TGW significantly ameliorated the severity of IMQ-induced psoriasis-like mouse skin lesions and restrained the activation of CD45⁺ cells, neutrophils and T lymphocytes (all P<0.01). Moreover, TGW significantly attenuated keratinocytes (KCs) proliferation and downregulated the mRNA levels of inflammatory cytokines including interleukin (IL)-17A, IL-23, tumor necrosis factor α, and chemokine (C-X-C motif) ligand 1 (P<0.01 or P<0.05). Furthermore, it reduced the number of γ δ T17 cells in skin lesion of mice and draining lymph nodes (P<0.01). CONCLUSIONS TGW improved psoriasis-like inflammation by inhibiting KCs proliferation, as well as the associated immune cells and cytokine expression. It inhibited IL-17 secretion from γ δ T cells, which improved the immune-inflammatory microenvironment of psoriasis.
Male ; Animals ; Mice ; Tripterygium ; Psoriasis/drug therapy* ; Keratinocytes ; Skin Diseases/metabolism* ; Cytokines/metabolism* ; Imiquimod/metabolism* ; Dermatitis/pathology* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism*