With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective To analyze the serum of patients with pancreatic cancer by fourier transform infrared spectroscopy(FTIR),explore the characteristic spectral parameters related to pancreatic cancer,and evaluate its potential clinical diagnostic value.Methods Serum samples were collected from 100 patients diagnosed with pancreatic cancer and 92 healthy volunteers at Chongming Hospital Affiliated to Shanghai Health Medical College from August 2022 to July 2023.These samples underwent FTIR and principal component analysis(PCA)to assess spectral differences between the two cohorts.The diagnostic potential of the serum spectra in distinguishing pancreatic cancer patients from healthy individuals was further evaluated using machine learning techniques,specifically support vector machine(SVM),k-nearest neighbor(kNN),and linear discriminant analysis(LDA)as classification methods.The diagnostic efficacy across various thresholds was evaluated using the receiver operating characteristic(ROC)curve.Results The findings indicate that the peak positions within the 1 090～1 070cm-1(1 076.537±15.183cm-1 vs 1 081.061±4.043cm-1),1 420～1 380 cm-1(1 399.958±1.508cm-1 vs 1 400.500±1.782cm-1),2 990～2 950cm-1(2 940.167±15.287cm-1 vs 2 945.124±7.498cm-1)and 3 500～3 000 cm-1(3 293.155±3.096cm-1 vs 3 294.893±2.582cm-1)range in the serum of individuals with pancreatic cancer exhibited a significant blue-shift compared to the healthy group and was statistically significant(t=2.265～4.236,all P<0.05),suggesting alterations in the structures of proteins,lipids and nucleic acids.Furthermore,a statistically significant disparity in peak absorption was observed between the group of patients with pancreatic cancer and the healthy group within the spectral ranges of 1 700～1 600cm-1(0.918±0.012cm-1 vs 0.858±0.021cm-1)and 3 500～3 000 cm-1(0.766±0.096cm-1 vs 0.804±0.090cm-1)(t=-24.031,2.830,all P<0.05),indicating that the protein concentration changes.PCA results showed that the PC2 axis was clearly separated,which could distinguish serum samples from patients with pancreatic cancer.Utilizing a machine learning model to differentiate the serum spectra of patients with pancreatic cancer from those of healthy controls,the sensitivity,the spesitivity,the specificity and accuracy of linear discriminant analysis(LDA)classification method were 93.2%,97.3%and 95.8%,respectively.The area under curve(AUC)as determined by ROC analysis was 0.982.Conclusion Serum spectroscopy using FTIR combined with PCA and machine learning model can be a simple,minimally invasive and reliable diagnostic test for pancreatic cancer detection.

This study employed 16S r RNA gene high-throughput sequencing and metabolomics to explore the mechanism of Angelicae Dahuricae Radix polysaccharides(RP) in the treatment of ulcerative colitis(UC). A mouse model of UC was induced with 2. 5% dextran sulfate sodium. The therapeutic effects of RP on UC in mice were evaluated based on changes in body weight, disease activity index( DAI), and colon length, as well as pathological changes. RT-qPCR was performed to assess the m RNA levels of interleukin(IL)-6, IL-1β, tumor necrosis factor(TNF)-α, myeloperoxidase(MPO), mucin 2(Muc2), Occludin, Claudin2, and ZO-1 in the mouse colon tissue. ELISA was employed to measure the expression of IL-1β and TNF-α in the colon tissue. The intestinal permeability of mice was evaluated by the fluorescent dye permeability assay. Immunohistochemistry was employed to detect the expression of Muc2 and occludin in the colon tissue. Changes in gut microbiota and metabolites were analyzed by 16S r RNA sequencing and ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry( UPLC-Q-Exactive Plus Orbitrap MS), respectively. The results indicated that low-dose RP alleviated general symptoms, reduced colonic inflammation and intestinal permeability, and promoted Muc2 secretion and tight junction protein expression in UC mice. In addition, low-dose RP increased gut microbiota diversity in UC mice and decreased the relative abundance of harmful bacteria such as Ochrobactrum and Streptococcus. Twenty-seven differential metabolites were identified in feces, and low-dose RP restored the levels of disturbed metabolites. Notably, arginine and proline metabolism were the most significantly altered amino acid metabolic pathways following lowdose RP intervention. In conclusion, RP can ameliorate general symptoms, inhibit colonic inflammation, and maintain intestinal mucosal barrier integrity in UC mice by modulating gut microbiota composition and arginine and proline metabolism.
Animals ; Gastrointestinal Microbiome/drug effects* ; Colitis, Ulcerative/genetics* ; Mice ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Polysaccharides/administration & dosage* ; Angelica/chemistry* ; Humans ; Colon/metabolism* ; Disease Models, Animal ; Mucin-2/metabolism* ; Tumor Necrosis Factor-alpha/metabolism*

The chemical composition of Paeoniae Radix Alba(PRA) is complex, with primary secondary metabolites including monoterpenoids, tannins, triterpenoids, and flavonoids. In previous studies on the material basis of PRA, it was found that, in addition to the widely studied characteristic monoterpene glycosides, tannic acid components also play an important role in the efficacy of PRA. However, their pharmacological effects have not been thoroughly investigated. This paper reviews the tannic acid components in PRA, including pentagaloyl glucose(PGG), tetragaloyl glucose(TGG), trigaloyl glucose(TriGG), and gallic acid, along with their structures, properties, and characteristics to provide a detailed discussion of their pharmacological activities and related mechanisms, aiming to offer a theoretical basis for the material basis research and clinical application of PRA.
Paeonia/chemistry* ; Tannins/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Plant Extracts

ObjectiveTo construct a group-based trajectory model (GBTM) for early medication adherence check-in, and to analyze the relationship between different trajectories and treatment outcomes in tuberculosis patients using data that were generated from smart tools for monitoring their medication adherence and check-in. MethodsFrom October 1, 2022 to September 30, 2023, a total of 163 pulmonary tuberculosis patients diagnosed in Fengxian District were selected as the study subjects. The GBTM was utilized to analyze the weekly active check-in trajectories of the subjects during the first 4 weeks and establish different trajectory groups. The χ² tests were employed to compare the differences between groups and logistic regression analysis was conducted to explore the relationship between different trajectory groups and treatment outcomes. ResultsA total of four groups were generated by GBTM analyses, of which a low level of punch card was maintained in group A, 6% of the drug users increased rapidly from a low level in group B, 17% of drug users increased gradually from a low level in group C, and 18% of drug users maintained a high level of punch card in group D. The trajectory group was divided into two groups according to homogeneity, namely the low level medication punch card group (group A) and the high level medication punch card group (group B, group C, and group D). The results of multivariate logistic regression analyses revealed that low-level medication check-in (OR=3.250, 95%CI: 1.089‒9.696), increasing age (OR=1.030, 95%CI: 1.004‒1.056), and not undergoing sputum examination at the end of the fifth month (OR=2.746, 95%CI: 1.090‒7.009) were significantly associated with poor treatment outcomes. ConclusionThe medication check-in trajectory of pulmonary tuberculosis patients within the first 4 weeks is correlated with adverse outcomes, or namely consistent low-level medication adherence check-ins are associated with poor treatment outcomes, while high-level medication adherence check-ins are associated with a lower incidence of adverse outcomes.

Objective To analyze the serum of patients with pancreatic cancer by fourier transform infrared spectroscopy(FTIR),explore the characteristic spectral parameters related to pancreatic cancer,and evaluate its potential clinical diagnostic value.Methods Serum samples were collected from 100 patients diagnosed with pancreatic cancer and 92 healthy volunteers at Chongming Hospital Affiliated to Shanghai Health Medical College from August 2022 to July 2023.These samples underwent FTIR and principal component analysis(PCA)to assess spectral differences between the two cohorts.The diagnostic potential of the serum spectra in distinguishing pancreatic cancer patients from healthy individuals was further evaluated using machine learning techniques,specifically support vector machine(SVM),k-nearest neighbor(kNN),and linear discriminant analysis(LDA)as classification methods.The diagnostic efficacy across various thresholds was evaluated using the receiver operating characteristic(ROC)curve.Results The findings indicate that the peak positions within the 1 090～1 070cm-1(1 076.537±15.183cm-1 vs 1 081.061±4.043cm-1),1 420～1 380 cm-1(1 399.958±1.508cm-1 vs 1 400.500±1.782cm-1),2 990～2 950cm-1(2 940.167±15.287cm-1 vs 2 945.124±7.498cm-1)and 3 500～3 000 cm-1(3 293.155±3.096cm-1 vs 3 294.893±2.582cm-1)range in the serum of individuals with pancreatic cancer exhibited a significant blue-shift compared to the healthy group and was statistically significant(t=2.265～4.236,all P<0.05),suggesting alterations in the structures of proteins,lipids and nucleic acids.Furthermore,a statistically significant disparity in peak absorption was observed between the group of patients with pancreatic cancer and the healthy group within the spectral ranges of 1 700～1 600cm-1(0.918±0.012cm-1 vs 0.858±0.021cm-1)and 3 500～3 000 cm-1(0.766±0.096cm-1 vs 0.804±0.090cm-1)(t=-24.031,2.830,all P<0.05),indicating that the protein concentration changes.PCA results showed that the PC2 axis was clearly separated,which could distinguish serum samples from patients with pancreatic cancer.Utilizing a machine learning model to differentiate the serum spectra of patients with pancreatic cancer from those of healthy controls,the sensitivity,the spesitivity,the specificity and accuracy of linear discriminant analysis(LDA)classification method were 93.2%,97.3%and 95.8%,respectively.The area under curve(AUC)as determined by ROC analysis was 0.982.Conclusion Serum spectroscopy using FTIR combined with PCA and machine learning model can be a simple,minimally invasive and reliable diagnostic test for pancreatic cancer detection.

Objective To evaluate the pharmacokinetics(PK)of tenofovir alafenamide Fumarate tablets(25 mg)in healthy Chinese subjects after single oral administration to provide a basis for bioequivalence evaluation.Methods Using a single-dose,randomized,open-lable,two-period,two-way crossover design under fasting condition,while three-way crossover design under fed condition,42 healthy subjects respectively for fasting and fed study were enrolled,and randomized into two groups to receive a single dose of test product(T)or reference product(R)25 mg.Plasma concentration of tenofovir alafenamide and tenofovir were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS)method.The pharmacokinetic parameters were calculated by WinNonlin software(8.1 version)using non-compartmental model,and bioequivalence evaluation was performed for the two preparations.Relevant safety evaluations were performed during the trial.Results The test product and the reference product under fasting study,the main PK parameters of tenofovir alafenamide were as follows:Cmax were(215.17±94.24)and(199.30±71.11)ng·mL-1;AUC0-t were(135.44±71.60)and(123.91±53.82)h·ng·mL-1;the main PK parameters of tenofovir were as follows:Cmax were(7.30±2.27)and(7.12±1.74)ng·mL-1,AUC0-t of tenofovir were(237.16±47.09)and(230.06±43.41)h·ng·mL-1,respectively.The test product and the reference product under fed study,the main PK parameters of tenofovir were as follows:Cmax were(197.69±82.19)and(197.10±110.54)ng·mL-1;AUC0-t were(197.69±82.19)and(197.10±110.54)h·ng·mL-1;the main PK parameters of tenofovir were as follows:CMax were(2.57±1.37)and(2.58±1.31)ng·mL-1;AUC0-t were(227.08±74.33)and(238.51±128.30)h·ng·mL-1,respectively.The 90％confidence interval for geometric mean ratio of Cmax,AUC0-tof T and R under fed condition were between 80.00％-125.00％,respectively.The incidence of adverse events in fasting and fed tests was 21.43％and 30.95％,respectively,and no serious adverse event was reported.Conclusion The test formulation and reference formulation of tenofovir alafenamide fumarate tablets were equivalent and was safe.

Objective: To analyze the impact of the sinonasal anatomic changes after endonasal endoscopic anterior skull base surgery on the nasal airflow and heating and humidification by computational fluid dynamics (CFD), and to explore the correlation between the postoperative CFD parameters and the subjective symptoms of the patients. Methods: The clinical data in the Rhinology Department of the First Affiliated Hospital of Zhengzhou University from 2016 to 2021 were retrospectively analyzed. The patients received the endoscopic resection of the anterior skull base tumor were selected as the case group, and the adults whose CT scans had no sinonasal abnormalities were chosen as the control group. The CFD simulation was performed on the sinonasal models after reconstructed from the patients' sinus CT images during the post-surgical follow-up. All the patients were asked to complete the Empty Nose Syndrome 6-Item Questionnaire (ENS6Q) to assess the subjective symptoms. The comparison between two independent groups and the correlation analysis were carried out by using the Mann-Whitney U test and the Spearman correlation test in the SPSS 26.0 software. Results: Nineteen patients (including 8 males and 11 females, from 22 to 67 years old) in the case group and 2 patients (a male of 38 years old and a female of 45 years old) in the control group were enrolled in this study. After the anterior skull base surgery, the high-speed airflow moved to the upper part of the nasal cavity, and the lowest temperature shifted upwards on the choana. Comparing with the control group, the ratio of nasal mucosal surface area to nasal ventilation volume in the case group decreased [0.41 (0.40, 0.41) mm^-1 vs 0.32 (0.30, 0.38) mm^-1; Z=-2.04, P=0.041], the air flow in the upper and middle part of the nasal cavity increased [61.14 (59.78, 62.51)% vs 78.07 (76.22, 94.43)%; Z=-2.28, P=0.023], the nasal resistance decreased [0.024 (0.022, 0.026) Pa·s/ml vs 0.016 (0.009, 0.018) Pa·s/ml; Z=-2.29, P=0.022], the lowest temperature in the middle of the nasal cavity decreased [28.29 (27.23, 29.35)℃ vs 25.06 (24.07, 25.50)℃; Z=-2.28, P=0.023], the nasal heating efficiency decreased [98.74 (97.95, 99.52)% vs 82.16 (80.24, 86.91)%; Z=-2.28, P=0.023], the lowest relative humidity decreased [(79.62 (76.55, 82.69)% vs 73.28 (71.27, 75.05)%; Z=-2.28, P=0.023], and the nasal humidification efficiency decreased [99.50 (97.69, 101.30)% vs 86.09 (79.33, 87.16)%; Z=-2.28, P=0.023]. The ENS6Q total scores of all patients in the case group were less than 11 points. There was a moderate negative correlation between the proportion of the inferior airflow in the post-surgical nasal cavity negatively and the ENS6Q total scores (r_s=-0.50, P=0.029). Conclusions: The sinonasal anatomic changes after the endoscopic anterior skull base surgery alter the nasal airflow patterns, reducing the efficiency of nasal heating and humidification. However, the post-surgical occurrence tendency of the empty nose syndrome is weak.
Adult ; Humans ; Male ; Female ; Young Adult ; Middle Aged ; Aged ; Retrospective Studies ; Hydrodynamics ; Air Conditioning ; Nose ; Nasal Cavity ; Skull Base/surgery*

Objective: To understand the depression status and its influencing factors in elderly patients with MS in China and to explore the correlation between various components of elderly MS and depression. Methods: This study is based on the "Prevention and Intervention of Key Diseases in Elderly" project. We used a multi-stage stratified cluster random sampling method to complete 16 199 elderly aged 60 years and above in 16 counties (districts) in Liaoning, Henan, and Guangdong Provinces in 2019, excluding 1 001 missing variables. Finally, 15 198 valid samples were included for analysis. The respondents' MS disease was obtained through questionnaires and physical examinations, and the respondents' depression status within the past half month was assessed using the PHQ-9 Depression Screening Scale. The correlation between elderly MS and its components and depression and its influencing factors were analyzed by logistic regression. Results: A total of 15 198 elderly aged 60 years and above were included in this study, with the prevalence of MS at 10.84% and the detection rate of depressive symptoms in MS patients at 25.49%. The detection rates of depressive symptoms in patients with 0, 1, 2, 3, and 4 MS abnormal group scores were 14.56%, 15.17%, 18.01%, 25.21%, and 26.65%, respectively. The number of abnormal components of MS was positively correlated with the detection rate of depressive symptoms, and the difference between groups was statistically significant (P<0.05). The risk of depression symptoms in patients with MS, overweight/obesity, hypertension, diabetes, and dyslipidemia was 1.73 times (OR=1.73, 95%CI:1.51-1.97), 1.13 times (OR=1.13, 95%CI:1.03-1.24), 1.25 times (OR=1.25, 95%CI:1.14-1.38), 1.41 times (OR=1.41, 95%CI:1.24-1.60), 1.81 times (OR=1.81,95%CI:1.61-2.04), respectively, more than those without the disease. Multivariate logistic regression analysis showed that the detection rate of depressive symptoms in patients with sleep disorders was higher than that with normal sleep (OR=4.89, 95%CI: 3.79-6.32). The detection rate of depressive symptoms in patients with cognitive dysfunction was 2.12 times higher than that in the average population (OR=2.12, 95%CI: 1.56-2.89). The detection rate of depressive symptoms in patients with impaired instrumental activities of daily living (IADL) was 2.31 times (OR=2.31, 95%CI: 1.64-3.26) higher than that in the average population. Tea drinking (OR=0.73, 95%CI: 0.54-0.98) and physical exercise (OR=0.67, 95%CI: 0.49-0.90) seemed to be protective factors for depression in elderly MS patients (P<0.05). Conclusions: Older patients with MS and its component abnormalities have a higher risk of depression than the average population. Sleep disorders, cognitive impairment, and IADL impairment are important influencing factors for depression in elderly MS patients, while tea drinking and physical exercise may help to reduce the risk of the disease.
Aged ; Humans ; Metabolic Syndrome/epidemiology* ; Activities of Daily Living/psychology* ; Depression/epidemiology* ; China/epidemiology* ; Tea ; Risk Factors

Non-syndromic oral cleft (NSOC), a common birth defect, remains to be a critical public health problem in China. In the context of adjustment of childbearing policy for two times in China and the increase of pregnancy at older childbearing age, NSOC risk prediction will provide evidence for high-risk population identification and prenatal counseling. Genome-wide association study and second generation sequencing have identified multiple loci associated with NSOC, facilitating the development of genetic risk prediction of NSOC. Despite the marked progress, risk prediction models of NSOC still faces multiple challenges. This paper summarizes the recent progress in research of NSOC risk prediction models based on the results of extensive literature retrieval to provide some insights for the model development regarding research design, variable selection, model-build strategy and evaluation methods.
Humans ; Cleft Palate/genetics* ; Cleft Lip/genetics* ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Risk Factors ; Polymorphism, Single Nucleotide