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Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine. [ABSTRACT FROM AUTHOR]

Background: Indirect sampling methods are not only inexpensive but also efficient for establishing reference intervals (RIs) using clinical data. This study was conducted to select fully normal records to establish age- and gender-specific RIs for common biochemical analytes by laboratory data mining. Methods: In total, 280,206 records from 2014 to 2018 were obtained from Peking Union Medical College Hospital. Common biochemical analytes total protein, albumin, total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), potassium, sodium, chlorine, calcium, urea, glucose, uric acid (UA), inorganic phosphorus, creatinine (Cr), total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol] were measured using an automatic analyzer. Sources of variation were identified by multiple regression analysis. The 2.5th and 97.5th percentiles were calculated as the lower and upper limits of the RIs, respectively Results: Gender was the major source of variation among the 13 common biochemical analytes with an rp > 0.15. In contrast to the value listed in the WS/T 404, nearly all RIs established in this study were significantly narrower. Furthermore, age-specific RIs should be determined for DBil, LDH, and urea, whereas gender-specific RIs are suggested for GGT, LDH, and urea. Conclusions: We recommend that gender-specific RIs should be established for ALT, AST, GGT, DBil, TBil, UA, and Cr as well as gender- and age-specific RIs for urea and ALP. Through indirect sampling, age- and gender-specific RIs for common biochemical analytes were established and analyzed.

Considering the aerodynamic loads arising during the process of two high-speed trains passing each other (hereinafter referred to as TPEO) as well as different track excitations on different railway lines, this study examined the combined effect of TPEO and different track excitations on wheel wear. A vehicle system dynamics model was built using the actual parameters of a high-speed train. The wheel/rail contact problems were analyzed using a combination of semi-Hertzian contact theory and Kalker's simplified theory. Elkins wear index was used as a criteria for evaluation of wheel wear. The additional excitation arising from the pressure wave during TPEO at a constant speed of 300 km/h was applied to the model. Then the effect of the aerodynamic excitation due to TPEO on wheel wear under four track excitation conditions was investigated for the whole train, different cars, and different axles. The results show that under different track excitations, the train's wheel wear increased in the case where one TPEO event occurred compared to that in the case of no TPEO event. Under the four track conditions (no track excitation, the track excitations on the Wuhan–Guangzhou high-speed railway, German track excitation spectra, and track excitation on the Beijing–Tianjin intercity railway), the total wheel wear increased by 17.36%, 0.69%, 0.82%, and 1% respectively over the entire trip, and increased by 1600%, 65.77%, 78.62%, and 79.73% at the moment of TPEO. A comparison between different cars shows that under different track excitation conditions, the tail car's wheel wear was the most affected by TPEO, while the head car's wheel wear was the least affected. A similar pattern is observed in the variation in transverse displacement of wheels under TPEO between different cars. For each bogie, TPEO under different track excitations had a greater effect on the front-axle wheel wear than on the rear-axle wheel wear. [ABSTRACT FROM AUTHOR]

Objectives: Diagnosis of thyroid disease among individuals dwelling at high altitude remains a challenge. Reference intervals (RIs) for thyroid-associated hormones among Tibetans living at various high altitudes were established to improve diagnosis. Methods: One thousand two hundred eighty-one subjects were randomly recruited from Nyingchi, Shigatse/Lhasa, and Ali of Tibet. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were measured by the Cobas e601 electrochemiluminescence analyzer. We used multiple linear regression and variance component analysis to assess the effect of sex, age, and altitude on hormones. RIs were established by refineR algorithm and compared with those provided by the manufacturer. Results: Serum TSH was significantly lower in males than in females, while FT3 and FT4 were higher in males. Both FT3 and FT4 decreased with increasing age. FT3 increased with altitude, while TSH and FT4 were less influenced by altitude. The RI for TSH was 0.764–5.784 μIU/ml, while for FT4, the RIs were 12.36–19.38 pmol/L in females and 14.84–20.18 pmol/L in males. The RIs for FT3 at Nyingchi, Shigatse/Lhasa, and Ali in females were 4.09–4.98, 4.31–5.45, and 4.82–5.58 pmol/L, while in males, the values were 4.82–5.41, 4.88–5.95, and 5.26–6.06 pmol/L, respectively. The obtained RIs for TSH and FT4 were generally higher, while that for FT3 was narrower than the RIs provided by Cobas. Conclusions: Specific RIs were established for thyroid-associated hormones among Tibetans, which were significantly different from those provided by the manufacturer. [ABSTRACT FROM AUTHOR]

Aiming at the problem of subway equivalent in-phase wheel diameter difference during the operation of subway wheels, the vehicle system dynamics model was established to) calculate and analyze the influence off the equivalent m-phase wheel diameter difference on the curve passing capacity off subway vehicles. The results show that when there is an equivalent in-phase wheel di¬ameter difference between the front and rear bogies off the same carriage, the dynamic response off the outer wheels through the curved line is stronger. When the outer wheel diameter is larger than the inner wheel diameter, it is beneficial for the subway vehicle to pass through the small radius curve, on the contrary, it increases the force between the wheel,/rail and the risk off derail¬ment off the subway vehicle. When the curve radius is 300 m and the wheel diameter difference is 1.0 mm, the derailment coefficient and wheel unloading rate off the outer wheel exceedes the safety limit. Therefore, the derailment coefficient and wheel load reduction rate are used as the safety limit for judging the working; condition. At the same time, in order to ensure the safe opera- ion of the vehicle, the operating speed should be controlled beloww 65 km/h, or 0. 5 mm should be used as the index for wheel re¬pair on the 3300 m curve. [ABSTRACT FROM AUTHOR]

Introduction: Long-term exposure to ambient air pollution is related to major cardiovascular risk factors including diabetes, hypertension, hyperlipidemia and overweight, but with few studies in high-concentration nations like China so far. We aimed to investigate the association between long-term exposure to ambient fine particulate matter (particles with an aerodynamic diameter ≤ 2.5 μm, PM2.5) and major cardiovascular risk factors in China.Methods: Adult participants with selected biochemical tests were recruited from the Chinese Physiological Constant and Health Condition (CPCHC) survey conducted from 2007 to 2011. Gridded PM2.5 data used were derived from satellite-observed data with adjustment of ground-observed data. District-level PM2.5 data were generated to estimate the association using multivariate logistic regression model and generalized additive model.Results: A total of 19,236 participants from the CPCHC survey were included with an average age of 42.8 ± 16.1 years, of which nearly half were male (47.0%). The annual average PM2.5 exposure before the CPCHC survey was 33.4 (14.8-53.4) μg/m3, ranging from 8.0 μg/m3 (Xiwuqi) to 94.7 μg/m3 (Chengdu). Elevated PM2.5 was associated with increased prevalence of hypertension (odds ratio (OR) =1.022, 95% confidence interval (95%CI): 1.001, 1.043) and decreased prevalence of overweight (OR = 0.926, 95%CI: 0.910, 0.942). Education significantly interacted with PM2.5 in association with all the interesting risk factors. Each 10 μg/m3 increment of PM2.5 was associated with increased prevalence of diabetes (OR = 1.118, 95%CI: 1.037, 1.206), hypertension (OR = 1.101, 95%CI: 1.056, 1.147), overweight (OR = 1.071, 95%CI: 1.030, 1.114) in participants with poor education, but not in well-educated population. PM2.5 exposure was negatively associated with hyperlipidemia in all participants (OR = 0.939, 95%CI: 0.921, 0.957). The results were robust in all the sensitivity analyses.Conclusion: Association between long-term PM2.5 exposure and cardiovascular risk factors might be modified by education. PM2.5 was associated with a higher prevalence of diabetes, hypertension, and overweight in a less-educated population with time-expose dependency. Long-term exposure to PM2.5 might be associated with a lower prevalence of hyperlipidemia. [ABSTRACT FROM AUTHOR]