Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a rare autosomal dominant hereditary disorder characterized by hyperuricemia, gout, impaired urinary concentration, and progressive renal failure. It is primarily caused by mutations in uromodulin (UMOD) gene. This study reports a family with ADTKD in which whole-exome sequencing and Sanger sequencing identified a missense mutation in the UMOD gene, c.761A>C (p.H254P), present in both the proband and affected relatives. According to American College of Medical Genetics and Genomics (ACMG) guidelines, this variant is classified as likely pathogenic. The mutation results in an amino acid substitution that may impair UMOD protein folding and intracellular trafficking. UMOD gene mutations are associated with ADTKD, and genetic testing plays a vital role in the early diagnosis and treatment of this condition, highlighting its importance in the diagnosis of rare kidney diseases.
Adult ; Humans ; Male ; Exome Sequencing ; Mutation ; Mutation, Missense ; Nephritis, Interstitial/genetics* ; Pedigree ; Uromodulin/genetics*

Objective:To establish a method for determination of fluoride in water using a fully automated biochemical analyzer (abbreviated as this method).Methods:Based on the parameters of the fully automatic biochemical analyzer, appropriate reagent volumes and reaction time were optimized to determine fluoride ions in water within the standard curve range (0.0 - 2.0 mg/L). The method was validated through evaluation of linear range, detection limit, precision, accuracy, and comparative analysis with the manual measurement results of fluoride reagent spectrophotometric method specified in the "Standard Examination Methods for Drinking Water - Part 5: Inorganic Nonmetallic indices" (GB/T 5750.5-2023).Results:Within the range of 0.0 - 2.0 mg/L fluoride mass concentration, the absolute values of the correlation coefficients were > 0.999 0 and the detection limit of this method was 0.082 mg/L. The relative standard deviation for the determination of fluoride level in water samples with low, medium and high fluoride mass concentrations was 4.03%, 2.16% and 1.68%, respectively. The spiked recovery rates were 98.1%, 99.5%, and 100.2%, respectively. There was no statistically significant difference between the results obtained by this method and manual measurement ( t = 1.07, P = 0.295). Conclusion:This method exhibits high sensitivity, accuracy, and efficiency in detecting fluoride, enabling rapid sample detection through instrumental automation instead of manual operation.

Objective:To establish a method for determination of fluoride in water using a fully automated biochemical analyzer (abbreviated as this method).Methods:Based on the parameters of the fully automatic biochemical analyzer, appropriate reagent volumes and reaction time were optimized to determine fluoride ions in water within the standard curve range (0.0 - 2.0 mg/L). The method was validated through evaluation of linear range, detection limit, precision, accuracy, and comparative analysis with the manual measurement results of fluoride reagent spectrophotometric method specified in the "Standard Examination Methods for Drinking Water - Part 5: Inorganic Nonmetallic indices" (GB/T 5750.5-2023).Results:Within the range of 0.0 - 2.0 mg/L fluoride mass concentration, the absolute values of the correlation coefficients were > 0.999 0 and the detection limit of this method was 0.082 mg/L. The relative standard deviation for the determination of fluoride level in water samples with low, medium and high fluoride mass concentrations was 4.03%, 2.16% and 1.68%, respectively. The spiked recovery rates were 98.1%, 99.5%, and 100.2%, respectively. There was no statistically significant difference between the results obtained by this method and manual measurement ( t = 1.07, P = 0.295). Conclusion:This method exhibits high sensitivity, accuracy, and efficiency in detecting fluoride, enabling rapid sample detection through instrumental automation instead of manual operation.

Objective:By analyzing factors associated with lower urinary tract symptoms in middle-aged and elderly women aged 55-65 years old, a nomogram model for lower urinary tract symptoms was constructed to assist the clinical development of targeted interventions to reduce the incidence of lower urinary tract symptoms in this population.Methods:In the cross-sectional study, 798 middle-aged and elderly women aged 55-65 years receiving physical examination in the Health Management Center of Third Xiangya Hospital from November 2013 to December 2020 were selected as research participants.Univariate regression analysis was used to compare differences in the basic data related to lower urinary tract symptoms in the population.Multivariate Logistic regression analysis was used to identify related influencing factors, and a nomogram model for lower urinary tract symptoms in the population was established.Results:Among 798 middle-aged and elderly women surveyed, the prevalence of lower urinary tract symptoms was 81.08 %(647 cases), and the rate of urinary tract infections was 21.48 %(139 cases). Variables with statistical significance in univariate regression analysis were included in multivariate Logistic regression analysis.The results showed that body mass index, vaginal delivery, strength of pelvic floor type Ⅱ fiber muscle, pelvic and abdominal coordination and mental health were independent influencing factors for lower urinary tract symptoms in middle-aged and elderly women aged 55-65 years( OR=1.099, 2.681, 0.895, 0.658, 1.057, P=0.010, 0.030, 0.040, 0.010, 0.038). The monogram model based on the five risk prediction indexes produced a consistency index(C-index)of 0.651 and a diagnostic sensitivity and specificity of 66.9% and 58.9%, respectively.The correction curve showed that the predicted results of the model were essentially the same as the actual probability of condition. Conclusions:In this study, the independent influencing factors for lower urinary tract symptoms in middle-aged and elderly women aged 55-65 years were screened by univariate regression and multivariate Logistic regression analysis and a nomogram model for risk prediction of the population was constructed, was proved to have a good ability for accurately and effectively predicting the risk of lower urinary tract symptoms in middle-aged and elderly women aged 55-65 years, and will assist clinicians to screen for high-risk patients, formulate targeted interventions, and reduce the incidence of lower urinary tract symptoms in middle-aged and elderly women aged 55-65 years.

In order to understand the situation of schistosomiasis in Fujian Province, the surveillance and field survey data of 16 counties (cities or districts) were statistically analyzed from 2003 to 2008 according to the national and provincial surveillance protocols. The snail areas were 69 400 m~2 in 2003, and 506 700 m~2 in 2004, then decreased to 57 700 m~2 in 2008 after the snail control in 2005 and 2006. Serological examinations were conducted in 18 000 people, among which 5 412 people were the mobile population from schistosome-endemic areas, and 110 persons were positive, among which 8 persons had eggs and miracidia in their stools, among 7 885 appendix samples, Schistosoma japonicum eggs were found in 8 samples of the patients who came from the o-riginal endemic areas. By the stool examination, 2 709 head of farm cattle in the region with snails were not found infected with Schistosoma japonicum. It is suggested that the areas of residual snails are large and widespread, and the imported patients are found frequently, so the danger of schistosomisis transmission still remains.