To investigate the changes in health risk behaviors among high-risk groups in the lung cancer screening cohort in Ma’anshan City, and to explore the association between lung cancer screening and health risk behavior changes.

Follow-up survey was conducted among the high-risk population participating in screening.Basic data and health risk behavior information were collected at baseline and during follow-up. Univariate analysis and multivariate logistic regression analysis were used to explore the association between lung cancer screening and changes in three kinds of health risk behaviors.

The results of multivariate logistic regression analysis showed that women(OR=4.24, 95% CI: 1.80-9.96,P=0.001), those with positive pulmonary nodules(OR=1.98, 95% CI: 1.23-3.20, P=0.005) and those who consulted the doctor after screening(OR=1.65, 95% CI: 1.08-2.52, P=0.020) were more likely to quit smoking. Persistent smokers who were 60 to 69 years old(OR=1.60, 95% CI: 1.14-2.24, P=0.007), had a history of lung-related diseases(OR=1.95, 95% CI: 1.13-3.39, P=0.017), and discussed their medical condition with relatives and friends after screening(OR=1.65，95% CI: 1.16-2.33，P=0.005) were more likely to smoke less. Women(OR=2.19, 95% CI: 1.20-4.02,P=0.011), patients with a history of lung related diseases(OR=1.91,95% CI: 1.13-3.22,P=0.015), patients with a history of malignant tumors(OR=3.07,95% CI: 1.18-8.01,P=0.022), and patients receiving medical advice(OR=1.69, 95% CI: 1.04-2.75, P=0.035) were more likely to abstain from alcohol. Women(OR=1.41, 95% CI: 1.02-1.94, P=0.037), older than 70 years(OR=1.70, 95% CI: 1.07-2.68, P=0.024) and those who had annual physical examination(OR=2.03, 95% CI: 1.44-2.86, P<0.001) were more likely to start physical exercise.

Lung cancer screening is closely related to changes in health risk behaviors of screening population such as smoking, drinking, physical exercise, etc. Health education should be strengthened to guide high-risk population to make changes in health risk behaviors.

To understand the impact and pathways of lifestyle behaviors on physiological sub-health of medical students, and to provide a basis for health management and health education in universities.

Under the use of stratified whole cluster random sampling method, 3 100 medical students from a medical university were selected as the study subjects, general situation questionnaire, lifestyle factor questionnaire and Sub-Health Measurement Scale Version 1.0 (SHMS V1.0) were used for questionnaire survey. Chi-square test, exploratory factor analysis and structural equation model were used for statistical analysis and path construction.

The total detection rate of medical students’ sub-health status was 62.2%. There were significant differences in the distribution of drinking (χ²=12.245,P<0.001), activity(Frequency of exercise(χ²=46.115, P<0.001);Frequency of activities between classes(χ²=7.179,P=0.028);Leisure promotes health(χ²=60.789,P<0.001)), dietary habits (Frequency of takeout orders(χ²=8.912, P=0.012);Frequency of consumption of sugar-sweetened beverages(χ²=23.437,P<0.001);Bad eating habits(χ²=82.863,P<0.001))and mobile phone and computer usage(Time spent on mobile phones and computers(χ²=12.350, P=0.002); Short video viewing time(χ²=9.291,P=0.026)) among medical students with different physiological health states. The direct effect of activity on physiological sub-health of medical students was 0.26(95% CI:0.137-0.366). The direct effect of dietary habits on physiological sub-health of medical students was -0.171 (95% CI:-0.233--0.035), and the indirect effect was -0.04(95% CI:-0.085--0.015). The indirect effect of mobile phone and computer usage on physiological sub-health of medical students was -0.043 (95% CI: -0.087--0.017).

The overall detection rate of sub-health status among medical students is relatively high. Physical activity is a significant influencing factor in the physiological sub-health status of medical students and serves as a mediator between dietary habits and mobile phone and computer usage with respect to the physiological sub-health status of medical students.

To measure China’s medical resources mismatch index, to analyze the differences in the spatial distribution of medical resources mismatch, and to clarify the specific path to improve the mismatch of medical resources.

Based on the 2009-2021 data, China’s medical capital and labor mismatch indices were measured, the spatial and temporal evolution characteristics and sources of differences were analyzed using Dagum’s Gini coefficient and its decomposition method, and the fsQCA method was used to analyze the conditional groupings of high or low medical resource mismatches.

There were different degrees of capital mismatch or labor mismatch in each region. The overall Gini coefficient showed an "inverted V" evolution trend, the overall Gini coefficient within the region showed a fluctuating downward trend, and the overall Gini coefficient between regions was more complicated. Inter-region differences were the most important factor in the differences in the spatial allocation of medical resources. There were two types of pathways that lead to a high medical resource mismatch, i.e., labor-labor mismatch index and labor-labor mismatch index. There were 2 types of paths, i.e., labor force-hospital structure dual-driven (H1) and service level auxiliary-driven (H2), and 2 types of paths leading to low medical resource mismatch, i.e., urbanization-hospital structure-service level linkage-restricted (L1, L2) and internal and external linkage-restricted (L3, L4).

It’s supposed to play themacro-controlrole for the government to promote the rational allocation of regional medical resources, take the medical association as an important hand to play the leading role of tertiary hospitals, and fully implement the development strategy of new type of urbanization and lower the threshold of population settlement.

To analyze the changes in lung cancer mortality and DALY attributed to tobacco in China from 1990 to 2019, providing scientific basis for formulating lung cancer prevention and control strategies attributed to tobacco.

Extract lung cancer mortality and DALY rates attributed to tobacco in China from the 2019 Global Burden of Disease database from 1990 to 2019, use Joinpoint software to analyze the trends in mortality and DALY rates, and use an age period cohort (APC) model to analyze age, period, and cohort effects.

The number of lung cancer deaths attributed to tobacco in China increased from 16 500 in 1990 to 51 800 in 2019, and the standardized mortality rate increased from 20.22/100 000 in 1990 to 26.27/100 000 in 2019, with an average annual increase of 0.92% (95% CI: 0.72%-1.11%). The DALY caused by lung cancer attributed to tobacco in China increased from 42.40 million person-years in 1990 to 113.90 million person-years in 2019. The standardized DALY rate increased from 474.81/100 000 in 1990 to 547.54/100 000 in 2019, with an average annual increase of 0.51% (95% CI: 0.27%-0.74%). Compared with the global and different SDI regions, China’s lung cancer standardized mortality rate and standardized DALY rate attributed to tobacco showed the largest increase from 1990 to 2019, reaching 29.93% and 15.32% respectively. The lung cancer mortality rate attributed to tobacco in China from 1990 to 2019 showed an increasing trend with age, with a period effect showing a first decreasing, then increasing, and then decreasing trend. The cohort effect showed a first increasing, then decreasing trend. The prediction model shows that from 2020 to 2030, the standardized mortality rate and standardized DALY rate caused by lung cancer attributed to tobacco in China are both showing a slow upward trend, and may reach 26.96/100 000 and 554.20/100 000 respectively by 2030.

The burden of lung cancer attributed to tobacco in China remained on the rise from 1990 to 2019. Further attention should be paid to the elderly male population, health education, popularization of cancer core knowledge, and early screening, diagnosis, and treatment of lung cancer.

To investigate the effects of different high-fat diets with various types of oils on serum lipid levels and gut microbiota in mice.

Thirty SPF male C57BL/6J mice were randomly divided into three groups based on their body weight (n = 10 per group). The normal diet group (ND) was fed a basal diet while the palm oil high-fat group (PHFD) and soybean oil high-fat group (SHFD) were fed their respective high-fat diets for 17 weeks. Blood samples were collected to evaluate serum biochemical indexes. Bacterial RNA was extracted from the feces of mice, followed by 16S rRNA sequencing.

Compared with the ND group, the body weights of mice were significantly increased in the PHFD and SHFD groups (P<0.05). Additionally, body weights of the PHFD group were significantly higher than that of the SHFD group (P<0.05). Compared with the ND group, the levels of serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) were significantly increased in the PHFD group (P<0.05), the levels of serum TC and triglyceride (TG) were significantly increased in the SHFD group (P<0.05), the levels of serum TC and LDL-C were significantly higher in the PHFD group compared to the SHFD group (P<0.05). The results of gut microbiota showed that compared with ND, the PCA diagrams of the β diversity presented clear distinction, the Firmicutes/Bacteroidota ratio (F/B ratio) of the PHFD group was significantly increased (P<0.05), with the PHFD group having a significantly higher F/B ratio than the SHFD group (P<0.05). Moreover, the relative abundance of Muribaculaceae was significantly decreased (P<0.05). The PHFD group had significant increases in the relative abundance of characteristic bacteria, including Ileibacterium, Pseudomonas, and Bifidobacterium (P<0.05), whereas the SHFD group had significant increases in the relative abundance of characteristic bacteria, such as Mucispirillum, Allobaculum, and Colidextribacter (P<0.05). Additionally, the function of glucose metabolism-related pathways was significantly increased in the PHFD group (P<0.05), while function of lipid metabolism-related pathway was significantly decreased in the SHFD group (P<0.05).

Different types of high-fat diets with various types of oils lead to lipid metabolism disorder and significant differences in the composition and structural changes of gut microbiota in mice. The PHFD group shows increases in the relative abundances of genuses associated with glycolipid metabolism, while the SHFD group shows increases in the relative abundances of genuses associated with lipid-metabolism and decrease in the relative abundance of antioxidant capacity.

To investigate the epidemiological characteristics and pathogen distribution of burn patients at the hospital.

The medical records of 2 434 burn patients who met the inclusion criteria in the First Affiliated Hospital of Anhui Medical University from 2019 to 2022 were analyzed, including sex, age, injury factors, burn site, burn severity, and seasonal distribution of burns, as well as the type of pathogen of infection, the source of tissue/body fluid and its drug resistance. The species of pathogens were identified by Matter-assisted Laser Desorption Time-of-flight Mass Spectrometry (VITEK-MS), and the drug sensitivity of the top three pathogens was determined by VITEK2 Compact. SPSS27.0 and WHONET5.6 statistical software were used to analyze the data.

A total of 2 434 burn patients were enrolled in this study, with a male-to-female ratio of 2.2915. The average age was 29.8 ±25.95 years old. Adults and the elderly were most likely to have burns in summer, while children were more likely to suffer from burns in spring. Hot water scald was the main cause of disease. A total of 952 strains of pathogenic bacteria were collected, all of which were isolated for the first time. The top five pathogens were Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, Klebsiella pneumoniae, and Enterobacter cloacae. The number of pathogens isolated from wound secretions was the largest, accounting for 83.93% (799/952). The resistance rate of Pseudomonas aeruginosa to the first and second generation cephalosporins was 100%, but the resistance rate to aztreonam, tobramycin,and piperacillin decreased. The resistance rates of Acinetobacter baumannii to polymyxin B, minocycline, and tigecyclines were all 0, and the resistance rates to other antibiotics were all ≥ 50.0%. The resistance rate to penicillin and Staphylococcus aureus was more than 90%, but it was completely sensitive to vancomycin, linezolid, tigecyclines, and ampicillin/sulbactam.

Based on the age range division, there are significant differences in gender, cause of injury, prehospital treatment, burn severity, and burn site. Scald is the main cause of injury, limbs are the most prone to burn, and cases occur in summer most commonly. The main pathogen of burn infection is Pseudomonas aeruginosa, which mainly comes from wound secretions.

To explore the mutual predictive relationship between mobile social media Fear of Missing Out (FoMO) and Mobile Phone Addiction (MPA) among vocational college students, to provide scientific reference for the prevention and education of college students’ mobile phone addiction behavior.

With the use of cluster random sampling, 1 000 college students from a vocational college in Shenzhen were investigated by mobile social media FoMO scale and MPA scale in September 2021 (T1) and December 2021 (T2). The data were analyzed by cross-lag regression analysis.

There was a significant positive correlation including the synchronous correlation and the consecutive correlation between mobile social media FoMO and MPA (P<0.05). Mobile social media FoMO T1 positively predicted MPA T2 (β=0.25, P<0.001), whereas MPA T1 did not predict mobile social media FoMO T2 (β=-0.01, P=0.76). Three dimensions of mobile social media FoMO could predict MPA synchronously (P<0.05) and behavioral performance T1 could predict MPA T2 consecutively (P<0.05). Two dimensions of MPA,loss of control and avoidance, could predict mobile social media FoMO synchronously (P<0.05), and avoidance T1 could predict mobile social media FoMO T2 consecutively (P<0.05).

Vocational college students with higher FoMO are more likely to have MPA. There is a one-way predictive relationship between mobile social media FoMO and MPA.

To investigate the utilization of outpatient and inpatient services in patients with pneumoconiosis and its influencing factors.

By using the convenient sampling method, 199 pneumoconiosis patients were selected from the West China Fourth Hospital, and their population characteristics, medical treatment-seeking behavior, and influencing factors were investigated by using the questionnaire on pneumoconiosis patients and its influencing factors compiled by CDC of China.Multivariate logistic regression model and multivariate poisons regression model were used to analyze the influencing factors of outpatient and inpatient service utilization.

The two-week outpatient rate of pneumoconiosis patients surveyed was 27.6%, the median number of outpatients used in one year was twice, and the annual hospitalization rate was 61.8%. For hospitalized patients, the median number of hospitalizations per year was 1, and the median cumulative hospitalization days was 30 days. The average visual health scale (VAS) score of patients was 48.3±17.4. Multivariate logistic regression and multivariate poisons regression analysis showed that pneumoconiosis patients with basic health insurance (OR=0.067, 95%CI: 0.006-0.716),higher frequency of medication (OR=0.100, 95%CI: 0.037-0.269), and lower VAS score (OR=0.992, 95%CI: 0.988-0.997) had less use of outpatient services. Pneumoconiosis patients with recent exacerbation of cough (OR=5.530, 95%CI: 2.446-12.501), work injury insurance (OR=1.183, 95%CI:1.015-1.379), and lower income (OR=0.980, 95%CI: 0.965-0.997) had more use of outpatient services. Pneumoconiosis patients who were married (OR=0.063, 95%CI: 0.005-0.771) and had lower VAS scores (OR=8.463, 95%CI: 3.090-23.248) had less use of inpatient services. Pneumoconiosis patients with higher frequency of medication (OR=8.463, 95%CI: 3.090-23.248), complicated with core pulmonale (OR=1.855, 95%CI: 1.307-2.634), pneumoconiosis compensation (OR=3.358, 95%CI: 1.183-9.529), social relief (OR=1.402, 95%CI: 1.047-1.877) and more active communication with patients (OR=2.158, 95%CI: 1.061-4.390) were likely to have more use of inpatient services.

The utilization rates of outpatients and inpatients with pneumoconiosis are higher than the national average. The health status, economic level, and social support level of patients with pneumoconiosis are important factors affecting their medical treatment behavior.

To evaluate the causal relationship between tea drinking and cholelithiasis by two-way Mendelian randomization.

The instrumental variables for tea drinking were determined from 447 485 participants in the UK Biobank genome-wide association study (GWAS). A total of 32 single nucleotide polymorphisms (SNPs) associated with tea drinking were used for two-sample Mendelian randomized analysis to evaluate the causal relationship between tea drinking and cholelithiasis. Cholelithiasis data were obtained from GWAS data of 35 712 cholelithiasis patients and 273 442 controls publicly available from the Finnegan Alliance. In this study, inverse variance weighted (IVW) was used as the main method to estimate causality, and sensitivity analysis was carried out to ensure the robustness of the results. Finally, reverse Mendelian randomization analysis was used to verify whether there was a reverse correlation.

There was a causal relationship between tea drinking and cholelithiasis. In the IVW method, each additional standard deviation (2.85 cups/day) of tea consumption reduced the risk of cholelithiasis by 26.1% (OR=0.739, 95%CI: 0.536-0.990). Similar results were obtained in weighted mode (OR=0.652, 95%CI: 0.458-0.928), but there was no evidence to prove reverse correlation (IVW: P > 0.05).

The genetic evidence provided by two-way Mendelian randomized analysis shows that the increase of tea drinking reduces the risk of cholelithiasis, but there is no evidence of reverse association.

To explore and compare the application value of post-stroke fatigue (PSF) risk prediction nomogram and Web calculator program in post-stroke fatigue risk prediction in convalescent stroke patients.

The patients with acute stroke admitted to the Department of Encephalopathy of a tertiary hospital from February 2023 to August 2023 were prospectively selected, and the nomogram and Web calculator program were used to predict the risk probability of PSF in convalescent stage. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical utility of the two models, respectively.

A total of 282 patients were included, of which 128 had PSF, and the incidence of PSF was 46.89%. The AUC of the nomogram and Web calculator were 0.687 (95%CI:0.624-0.749) and 0.743 (95%CI: 0.683-0.803), respectively. The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram (χ²=8.357, P=0.213) and Web calculator program (χ²=4.467, P=0.614) were well fitted in stroke patients. The clinical decision curve analysis showed that the nomogram and Web calculator program had clinical benefits in the range of threshold probability of 0.35-0.81 and 0.33-0.88, respectively.

The nomogram and Web calculator program can effectively predict the PSF risk of stroke convalescent patients, and the Web calculator program is superior to the nomogram. However, the future Web calculator program model still needs to be further updated to balance simplicity and accuracy, thereby improving its clinical applicability.