Objective:To clarify the effect of methotrexate on blood uric acid levels and the incidence of hyperuricemia in patients with rheumatic and musculoskeletal diseases (RMDs).Methods:The clinical data were collected from 349 patients with RMDs who took methotrexate for more than 52 weeks and 429 patients with RMDs who did not take methotrexate, who were treated at Anqing Medical Center of Auhui Medical University from June 1, 2022 to June 30, 2024, to compare the differences in serum uric acid concentration and the incidence of hyperuricemia before and after 24 weeks of methotrexate administration in the two groups of patients with RMDs. The changes in serum uric acid concentration and serum creatinine value in the MTX na?ve patients who had taking MTX for 0, 24 and 52 weeks were compared. The relationship between serum uric acid concentration and methotrexate dosage was analyzed. Measurement data were compared using t-test or ANOVA, repeated measures analysis of variance, and count data were compared using χ2 test. Results:①At week 0, there was no significant difference in serum uric acid concentration [(300±63)μmol/L vs. (306±64)μmol/L, t=-1.416, P=0.157] and the incidence of hyperuricemia [9.3%(40/429) vs. 10.3%(36/349) , χ2=0.215, P=0.643] between the two groups. At week24, the serum uric acid concentration (307±70)μmol/L vs. (246±89)μmol/L was statistically significantly ( t=10.909, P<0.001) different. The incidence of hyperuricemia (11.0%, 47/429) vs. (4.6%, 16/349), was statistically significantly different ( χ2=10.497, P<0.001). There was a statistically significant difference in serum uric acid concentration between week 0 and week 24 in the methotrexate group ( t=10.237, P<0.001), and there was a statistically significant difference in the incidence of hyperuricemia ( χ2=8.312, P=0.004). ②The overall serum uric acid concentrations at week 0, weeks 24, and weeks 52 were (306±64)μmol/L, (246±89)μmol/L, and (247±66)μmol/L, respectively. The difference in overall serum uric acid concentration was statistically significant ( F= 29.506, P<0.001). There was no significant difference in serum uric acid concentration between weeks 24 and 52 ( P=1.000). There were significant differences in serum creatinine levels between weeks 0, 24 and 52 ( P<0.001). There was no significant difference in serum creatinine levels between weeks 0 ,52, weeks 24 and 52 ( P=0.077, P=1.000). There were statistically significant differences in the overall serum uric acid concentration and serum creatinine value at weeks 0, 24 and 52 of medication ( P<0.001).③ There was no significant difference in serum uric acid concentration before and after taking hydroxychloroquine, cyclosporine, tripterygium wilfordii, mycophenolate mofetil, tofacitinib, etanercept and adalimumab alone for weeks 0 and 24(all P>0.05). ④There was no significant difference in serum uric acid concentration between patients taking different doses of methotrexate (7.5 mg once weekly, 10 mg once weekly, 12.5 mg once weekly, 15 mg once weekly) at weeks 0 and 24 weeks(all P>0.05). Conclusion:MTX, as an anti-rheumatic drug, reduces the serum uric acid level and the incidence of hyperuricemia in patients with RMDs during the treatment.

Objective:To clarify the effect of methotrexate on blood uric acid levels and the incidence of hyperuricemia in patients with rheumatic and musculoskeletal diseases (RMDs).Methods:The clinical data were collected from 349 patients with RMDs who took methotrexate for more than 52 weeks and 429 patients with RMDs who did not take methotrexate, who were treated at Anqing Medical Center of Auhui Medical University from June 1, 2022 to June 30, 2024, to compare the differences in serum uric acid concentration and the incidence of hyperuricemia before and after 24 weeks of methotrexate administration in the two groups of patients with RMDs. The changes in serum uric acid concentration and serum creatinine value in the MTX na?ve patients who had taking MTX for 0, 24 and 52 weeks were compared. The relationship between serum uric acid concentration and methotrexate dosage was analyzed. Measurement data were compared using t-test or ANOVA, repeated measures analysis of variance, and count data were compared using χ2 test. Results:①At week 0, there was no significant difference in serum uric acid concentration [(300±63)μmol/L vs. (306±64)μmol/L, t=-1.416, P=0.157] and the incidence of hyperuricemia [9.3%(40/429) vs. 10.3%(36/349) , χ2=0.215, P=0.643] between the two groups. At week24, the serum uric acid concentration (307±70)μmol/L vs. (246±89)μmol/L was statistically significantly ( t=10.909, P<0.001) different. The incidence of hyperuricemia (11.0%, 47/429) vs. (4.6%, 16/349), was statistically significantly different ( χ2=10.497, P<0.001). There was a statistically significant difference in serum uric acid concentration between week 0 and week 24 in the methotrexate group ( t=10.237, P<0.001), and there was a statistically significant difference in the incidence of hyperuricemia ( χ2=8.312, P=0.004). ②The overall serum uric acid concentrations at week 0, weeks 24, and weeks 52 were (306±64)μmol/L, (246±89)μmol/L, and (247±66)μmol/L, respectively. The difference in overall serum uric acid concentration was statistically significant ( F= 29.506, P<0.001). There was no significant difference in serum uric acid concentration between weeks 24 and 52 ( P=1.000). There were significant differences in serum creatinine levels between weeks 0, 24 and 52 ( P<0.001). There was no significant difference in serum creatinine levels between weeks 0 ,52, weeks 24 and 52 ( P=0.077, P=1.000). There were statistically significant differences in the overall serum uric acid concentration and serum creatinine value at weeks 0, 24 and 52 of medication ( P<0.001).③ There was no significant difference in serum uric acid concentration before and after taking hydroxychloroquine, cyclosporine, tripterygium wilfordii, mycophenolate mofetil, tofacitinib, etanercept and adalimumab alone for weeks 0 and 24(all P>0.05). ④There was no significant difference in serum uric acid concentration between patients taking different doses of methotrexate (7.5 mg once weekly, 10 mg once weekly, 12.5 mg once weekly, 15 mg once weekly) at weeks 0 and 24 weeks(all P>0.05). Conclusion:MTX, as an anti-rheumatic drug, reduces the serum uric acid level and the incidence of hyperuricemia in patients with RMDs during the treatment.

Objective:To explore the efficacy of doctor-nurse co-led care involving education and engagement of patients on improving compliance of patients, and a treat-to-target urate-lowering rate for gout.Methods:Nurses were trained in practice management of gout. Patients diagnosed with gout in the departments of rheumatology and immunology of Anqing Municipal Hospital in Anhui Province were prospectively enrolled from January 1 to June 30, 2019. The patients were divided into the continuous-care group and the conventional management group by random number table method. The patients of continuous-care group received telephone follow-up, WeChat tracking and regular face-to-face communication. The patients of conventional management group were told to follow up regularly in the outpatient department, and the nurses did not follow up. Patients were evaluated before intervention and 12 months after intervention. The treat-to-target rate of blood uric acid and the frequency of gout flares were observed.Results:After 12 months of intervention, the patients of serum uric acid concentrations below 360 μmol/L were 92.39% (85/92) in the continuous-care group, and 26.74% (23/86) in the conventional management group. There was significant difference ( χ2 value was 80.282, P<0.001). After 12 months of intervention, the average serum uric acid concentration of patients in the continuous-care group was (301.6±61.4) μmol/L, and that in the conventional management group was (419.0±98.0) μmol/L, both of which were significantly lower than before intervention, continuous-care group (466.1±119.7) μmol/L, conventional management group (477.8±113.1) μmol/L. But the average serum uric acid concentration of patients in the continuous-care group was significantly lower than that in the conventional management group. There was significant difference between them ( t value was 96.678, P<0.001). At the end of 12 months, the patients of uric-acid-lowering therapy increased in both groups. The proportion of patients was 94.56% (87/92) in the continuous-care group, which was significantly higher than that in the conventional management group (58.14%, 50/86), there was significant difference ( χ2 value was 33.260, P<0.001). Conclusions:The mode of continuing nursing combined with specialized physician-led treatment can significantly improve the compliance and the control rate of treat-to-target for gout, and this management method is simple and feasible which provides a new management concept for clinical treatment of gout.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective: To compare a new agar thickener with xanthan gum as a thickener in treating dysphagia patients with nasopharyngeal carcinoma after radiotherapy. Methods: Twenty nasopharyngeal carcinoma patients with dysphagia after radiotherapy were asked to swallow moderately and extremely thick liquids thickened with the agar and xanthan gum, and their swallowing was recorded with a videofluoroscope. Results: The average pharyngeal constriction ratio when swallowing agar thickener was significantly lower than when swallowing the traditional thickener. The average oral transit time, the initiation of pharyngeal swallowing were both significantly quicker. There was no significant difference in the average penetration aspiration scale scores between the two thickeners. In the subjective evaluation, the agar thickener was adjudged smoother and with better residual mouthfeel than the xanthan gum, but the scent of the xanthan gum was preferred. Conclusion The new agar thickener is smooth and not sticky. It produces faster transport with less oropharyngeal residue. It can be widely used among nasopharyngeal carcinoma patients with dysphagia after radiotherapy.

Objective To evaluate the effect of radiotherapy on the swallowing ability of persons with nasopharyngeal carcinoma (NPC) when swallowing food with different consistencies.Methods Twenty NPC patients were monitored fluoroscopically while swallowing materials with three different consistencies after radiotherapy.The oral transit time,oral residue,pharyngeal residue,penetration-aspiration and cricopharyngeal muscle function were observed.Results There were significant differences in all of the measurements when swallowing the three different foods.There were significant differences in all of the measurements between swallowing paste and liquids,but only in the oral transit time,oral residue and pharyngeal residue between swallowing thin and thick liquids.Conclusions The severity of swallowing dysfunction varies in NPC patients after radiotherapy.Foods with different consistencies have different effects on swallowing ability.Videofluoroscopy can evaluate swallowing objectively and provide an objective basis for food preparation.

Objective To evaluate the characteristics of dysphagia after brain stem infarction,and to determine the mechanism of aspiration.Methods The fluoroscopic videos of 12 dysphagia patients who had suffered brain stem infarction and 10 healthy counterparts were analyzed quantitatively using a digital analysis system.Each participant was requested to twice swallow 5ml of thick liquid.The observations included the oral transit time (OTT),the swallow response time (SRT),the hyoid movement time (HMT),the upper esophageal sphincter opening time (UOT) and the laryngeal closure time (LCT).An 8-point penetration-aspiration scale (PAS) was used to evaluate the severity of aspiration,and the results were correlated with the other 5 quantitative observations.Results The average OTT [(3.091±1.803)s],HMT [(1.498±0.550)s] and LCT [(0.651±0.186)s] of the brain stem infarction patients were all significantly longer than those of the healthy controls.However,no significant differences were found between the patients and the healthy volunteers in terms of SRT or UOT.Aspiration severity was significantly correlated with SRT but not with LCT.Conclusion Dysphagia after brain stem infarction involves both the oral and pharyngeal phases.OTT,HMT and LCT can be used to quantify dysphagia after brain stem infarction,while SRT is a predictor of aspiration.