ABSTRACT To analyze the relationship between the composition of urinary stones and various influencing factors in the Enshi region. We used FT-IR to examine the composition of 1092 stone

samples. Combined with the relevant clinical materials, the data were analyzed using both one-dimensional statistical methods and multivariate statistical methods. The study included 1092

stone samples, classified as follows: 457 (41.8%) with a single component, 453 (41.5%) with two components, 149 (13.6%) with three components, and 33 (3.0%) with four components. Stones were

categorized into five types: Calcium Oxalate (CaOx) (76.4%), carbapatite (CaP) (9.3%), Struvite (ST) (8.3%), Uric Acid (UA) (4.9%), and Others (1.0%). Age, gender, urinary tract infection

the Enshi region is consistent with that of the entire China. Additionally, the predisposing factors for stone formation vary in terms of gender, age, FH, UTI, hyperuricemia HUA, and stone

location. SIMILAR CONTENT BEING VIEWED BY OTHERS URINARY STONE COMPOSITION ANALYSIS AND CLINICAL CHARACTERIZATION OF 1520 PATIENTS IN CENTRAL CHINA Article Open access 19 March 2021 ANALYSIS

OF COMPONENTS AND RELATED RISK FACTORS OF URINARY STONES: A RETROSPECTIVE STUDY OF 1055 PATIENTS IN SOUTHERN CHINA Article Open access 16 November 2024 URINARY STONE ANALYSIS AND CLINICAL

CHARACTERISTICS OF 496 PATIENTS IN TAIWAN Article Open access 19 June 2024 INTRODUCTION In the past few decades, urolithiasis has become an increasingly common disease, with a rising global

incidence, prevalence, and recurrence rates1. The causes of urolithiasis are complex and closely related to various factors, such as race, gender, age, genetics, environment, and diet2.

Despite the common features of urolithiasis worldwide, unique variants exist in certain countries and regions. For instance, the incidence of urologic stones globally ranges from 1 to 5%3,

while in the southern region of China, it is notably higher at 5–10%, with recurrence rates of 50% within five years and 75% within twenty years4. In 2019, the Urolithiasis Group of the

Chinese Urological Association (CUA) conducted a prospective nationwide multicenter survey, investigating the impact of gender, age, body mass index (BMI), stone location, and geographical

region on the diversity of urinary stone composition5. In Enshi Prefecture, Southwest China, urinary stone disease poses a significant health concern. Due to its status as a

minority-inhabited area, the region's unique geographical environment, lifestyle habits, and climatic conditions may lead to distinct characteristics in the development of urinary

stones. Accurate analysis of urinary stone composition is crucial for identifying stone etiology, prevention, and treatment6. However, there is currently no research conducted to determine

the specific features of urinary stone composition in this region. Fourier transform infrared spectroscopy (FT-IR) has emerged as a sensitive, reliable, and accurate method for detecting

urinary stones, and it has become the standard diagnosis process7,8. FT-IR utilizes distinctive infrared absorption patterns exhibited by different chemical compounds to precisely identify

and characterize stone components7,9. Its high sensitivity allows for the detection of trace elements, and its rapid analysis capabilities require minimal sample preparation10. By comparing

the spectra of unknown stones to reference spectra, FT-IR enables accurate determination of stone composition and type11. The application of FT-IR in urinary stone analysis has significantly

advanced our understanding of stone formation, composition, and recurrence patterns. To better comprehend the composition of urinary tract stones and their association with various

influencing factors in the Enshi region, we conducted a retrospective analysis of 1092 stone specimens using FT-IR. These stone specimens were collected from the Department of Urology at the

Central Hospital of Enshi Tujia and Miao Autonomous Prefecture from January 2019 to August 2022. In this study, we explore whether differences in the stone composition may be explained by

differences in gender, age, diabetes mellitus (DM), hypertension (HTN), urinary tract infections (UTI), family history of urinary stones (FH), hyperuricemia (HUA), and anatomical locations

using univariate statistical analysis (The abbreviation table at the end of the document presents common abbreviations and their full forms). Additionally, we established an unordered

multi-categorical logistic statistical model to discern the relative risk factors associated with less common stone types. Ultimately, this study seeks to offer more scientifically accurate

urinary stone prevention strategies tailored to the specific population in the Enshi region. PATIENTS AND METHODS STONE COMPOSITION ANALYSES AND CLASSIFICATION The study received approval

from the Ethics Committee of the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture (NO. 2022-013-01). Between January 2019 and August 2022, a total of 1092 urinary stone samples

and associated clinical information were included in the study. The patients were long-term residents (≥ 5 years) in the Enshi area, experiencing urinary stones symptoms for the first time

and undergoing surgical treatment. Stone fragments were collected intraoperatively during percutaneous nephrolithotomy (PCNL), ureteroscopic lithotripsy, and cystolithotripsy. Clinical and

demographic data, including age, gender, stone anatomical localization, geographical region, and underlying disease, were obtained. For stone sample analysis, FT-IR (Dingshun, SUN-3G, Jinan,

China) was employed, following standard operating procedures established by manufacturers and hospitals. Stone samples were split, washed, and dried in a 100 °C oven for 2–3 min. The stone

powder (1.5 mg) was then mixed with potassium bromide (200 mg) and ground to the micrometer level using an agate mortar and pestle. The mixed powder was pressed into a uniform and

translucent sheet using a tablet press mold and tablet press (16 Mpa, stop 5 s) as per the manufacturer's instructions. In addition, a potassium bromide tablet (without stones) was

prepared as a control. The assay parameters were set at a sampling resolution of 2 cm−1, a spectral range of 4000–400 cm−1, and a total of 32 scans. Before sample analysis, the instrument

background was measured using potassium bromide tablets (without stones). Subsequently, the stone samples were inserted into the sample compartment, and the FT-IR instrument recorded the

transmittance spectrum, generated the infrared spectrum, automatically analyzed the composition of the stones, and generated a corresponding test report. The stone composition was classified

following to the European Association of Urology stone classification practices6. The most common form of stone is calcium oxalate (CaOx), with two types: monohydrate (COM) and dihydrate

(COD)12. Stones containing over 50% calcium oxalate were classified as CaOx stones. Uric acid stones (UA) included stones with more than 50% uric acid and uric acid dihydrate. Stones with

over 50% carbapatite were classified as carbapatite (CaP) stones, while those with more than 50% struvite were labeled as struvite uroliths (ST). Cystine stones encompassed stones containing

cystine. For statistical analysis, rare stones such as cystine stones and drug-related stones were grouped into the "Other" category. ETHICS STATEMENT The experiments and

procedures were conducted following the relevant guidelines and regulations. Ethical approval was obtained from The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture (Approval

No. 2022-013-01). All patients provided informed consent to participate in the study. CONSENT FOR PUBLICATION Informed consent was obtained from all participants involved in the study. For

participants under the age of 16 years, informed consent was obtained from their parents and/or legal guardians. STATISTICAL ANALYSES Quantitative data were presented as mean ± standard

deviation. Qualitative data were expressed as n (%). The chi-squared test was employed to evaluate the impact of gender, DM, HTN, and UTI on different types of urinary stones. In cases where

the chi-squared test was not applicable, Fisher's exact test was utilized for variables FH and HUA. Cramer's V coefficient was employed to assess the strength of correlation

between categorical variables, with values between 0.1 and 0.3 indicating weak correlation, between 0.3 and 0.5 indicating moderate correlation, and greater than 0.5 indicating strong

correlation. Adjusted standardized residuals (Adj.R) were used to assess the association between two categorical variables, with values greater than 3 indicating significant deviations

between observed and expected values, signifying a notable correlation between the two variables. The Kruskal–Wallis’s test was conducted for unidirectional ordered data (age groups),

followed by Z-tests for multiple comparisons, with p-values adjusted using Bonferroni correction. Unordered multi-categorical logistic regression was performed to evaluate the influence of

gender, age, DM, HTN, UTI, FH, HUA, and stone anatomical location on stone composition. Statistical analyses were conducted using SPSS software (version 23, IBM Corp., Armonk, NY, USA), and

a p-value (two-tailed) < 0.05 was considered statistically significant. Data visualization and graph plotting were performed using ORIGINE software (version 2022 Pro, OriginLab

Corporation, Northampton, MA, USA). RESULTS INDIVIDUAL AND CLINICAL CHARACTERISTICS OF THE PATIENTS A total of 1092 patients were included in the study, with 745 (68.2%) men and 347 (31.8%)

women, resulting in a male-to-female ratio of 2.1:1. The age of the patients ranged from 3 to 85 years, with a mean age of 50.9 ± 12.8 years. There were no significant differences in age

between males (50.6 ± 12.9 years) and females (51.4 ± 12.8 years) (t = − 1.027, v = 1090, p > 0.05). The distribution of urinary stone locations among the patients was as follows: 536

(49.1%) cases of renal calculi, 502 (46.0%) cases of ureteral calculi, 47 (4.3%) cases of bladder calculi, and 7 (0.6%) cases of urethral calculi. Moreover, 74 (6.8%) patients had diabetes

mellitus (DM), 217 (19.9%) patients had hypertension (HTN), 205 (19.8%) patients had a family history (FH) of urinary stones, 230 (21.0%) patients had urinary infections (UTI), and 82 (7.5%)

patients had hyperuricemia (HUA) (Fig. 1). GENERAL INFORMATION ABOUT THE STONE COMPOSITION Infrared spectra of substances are unique, akin to human fingerprints13,14,15. Consequently,

distinct components of urinary tract stones exhibit characteristic infrared spectra, indicating their specific compositions. By utilizing these characteristic spectra (Fig. 2), we conducted

qualitative and quantitative analyses of different stone compositions. A total of 1092 stone samples underwent FT-IR analysis for stone composition. Quantitative analysis revealed that

single-component samples constituted 457 (41.8%) cases, including 367 (33.6%) cases of COM, 66 (6.0%) cases of COD, 13(1.2%) cases of UA, and 11(1.0%) cases of Others (rare components:

L-cystine, drug-induced stones). Stones composed of two components accounted for 453 (41.5%) cases, comprising 278 (25.4%) cases of COM + CaP, 47 (4.3%) cases of COM + COD, 54 (4.9%) cases

of Struvite + CaP, 41(3.8%) cases of UA + COM, and 33(3.0%) cases of COD + CaP. Stones with three components were observed in 149 (13.6%) cases, including 92 (8.4%) cases of COM + COD + CaP,

46 (4.2%) cases of CaP + COM + COD, 7(0.6%) cases of CaP + COM + COD, and 4 (0.4%) cases of Struvite + CaP + COM. Additionally, stones with four components were found in 33(3.0%) cases,

comprising 24 (2.2%) cases of Struvite + CaP + COM + COD and 9 (0.8%) cases of Struvite + COM + COD + CaP (Fig. 3). Based on the stone classification principle described above, we

categorized all stones into five types: CaOx, CaP, ST, UA, and Others (Fig. 4A). CaOx was the most prevalent stone type, with a total of 834(76.4%) cases, including 623(57.1%) in males and

211 (19.3%) in females. It was followed by 102 (9.3%) cases of CaP, including 61(5.6%) cases in males and 41(3.7%) cases in females. A total of 91(8.3%) cases of ST were detected, comprising

27 (5.8%) cases in males and 64(2.5%) cases in females. Among the 54 (4.9%) cases of UA, 46 (4.2%) were male and 8 (0.7%) were female. Additionally, there were 11(1.0%) other rare stone

types, including 8(0.07%) in males and 3(0.03%) in females, notably 3 of which were drug-induced stones in children. THE RELATIONSHIP BETWEEN GENDER AND FOUR TYPES OF URINARY STONES Because

the “Other” stone types are rare and inclusion in statistical models can produce extreme values that affect the accuracy of statistical results, we do not include this type in some

statistical models for analysis. A total of 1081 cases were included in the statistical model, which analyzed the relationship between sex and four main types of stones (CaOx, CaP, ST, UA).

The results show that any of the expected frequencies are greater than 5, and the chi-square test can be used, χ2 = 122.5, _p_ < 0.001, suggesting that the four main types of stones

differ significantly from gender type. There is a moderately strong correlation between four different stone types and gender (Cramer's V = 0.337, _p_ < 0.05) (Fig. 4B). We conducted

a more in-depth analysis of the above results using post hoc testing and assessed the relationship between the two categorical variables based on the adjusted standardized residuals

(Adj.R). In the CaOx group, 623 (57.6%) were males and the Adj. R value of 8.5 indicated a significant association between CaOx stones and male gender, suggesting a higher likelihood of CaOx

stones in male patients compared to females. Similarly, in the CaP group, there were 61 (5.6%) females, and the Adj.R value of 6.4 revealed a significant association between CaP stones and

the female gender. Likewise, in the ST group, there were 64 (5.9%) females, and the Adj.R value of 8.2 also indicated a significant association between ST stones and female gender. These

results consistently suggest a higher likelihood of CaP and ST stones in female patients compared to males (Fig. 5A). THE RELATIONSHIP BETWEEN DM, HTN, FH, UTI, HUA AND URINARY STONE TYPES A

total of 1081 cases were included in the statistical model, which examined the difference between DM, HTN, FH, UTI, HUA, and four main types of stones (CaOx, CaP, ST, UA). The results

showed that for DM, HTN, and UTI, all expected frequencies were greater than 5, allowing the use of the chi-square test. From the perspective of DM (χ2 = 2.8, _p_ = 0.418; Cramer's V = 

0.051, _p_ > 0.05) and HTN (χ2 = 4.6, _p_ = 0.202; Cramer's V = 0.065,_ p_ > 0.05), there was no statistically significant difference, indicating that the presence or absence of

DM/HTN did not significantly affect the type of stone (Fig. 5B,C). However, among the 1081 study participants, 230 had UTI, and the data (χ2 = 161.7, _p_ < 0.001) indicated a significant

difference in the distribution of the four different stone types based on the presence or absence of UTI. There was a moderately strong correlation between four different stone types and

UTI, with Cramer's V = 0.387, _P_ < 0.001 (Fig. 5D). Further post hoc testing revealed that patients with UTI were more likely to develop CaP (Adj.R = 9.0) and ST (Adj.R = 8.0)

stones (Fig. 5A). Among the 1081 study participants, 205 had FH, and 82 had HUA. Due to expected frequencies of less than 5, Fisher's exact test was chosen. The results indicated a

significant difference between FH and the four different stone types (_p_ < 0.05), but there was no strong correlation between them (Cramer's V = 0.092, _p_ > 0.05) (Fig. 5E).

Furthermore, the test results showed a clear difference between HUA and different types of stones (_p_ < 0.001), and there was a weak correlation between four different stone types and

HUA, with Cramer's V = 0.26, _p_ < 0.001 (Fig. 5F). Post hoc testing revealed that patients with Hyperuricemia were more likely to develop UA stones (Adj.R = 8.4) (Fig. 5A). THE

RELATIONSHIP BETWEEN AGE AND URINARY STONE TYPES The relationship between age and urinary stone types was examined by dividing all study participants into 9 age strata, each representing a

10-year age group. This division allowed for a comprehensive analysis of the association between age and different stone compositions. Using Kruskal–Wallis tests, we found a statistically

significant difference between the various age groups and stone types (H = 78.388, p < 0.001) (Fig. 6A). Further multiple comparisons were performed using the Z test with

Bonferroni-adjusted p-values, revealing specific patterns. The proportion of CaOx cases was higher in the age groups of 21–30 years (compared to age groups 1–10, 51–60, 61–70, 71–80, 81–90 

years, p < 0.05), 31–40 years (compared to age groups 1–10, 51–60, 61–70 years, p < 0.05), and 41–50 years (compared to age groups 1–10, 51–60, 61–70 years, p < 0.05) compared to

other types of stones. This suggests that individuals in these age groups are more likely to develop CaOx stones. In the 51–60 and 61–70 age groups, the proportion of ST cases (compared to

age group 41–50 years, p < 0.05; 61–70 years compared to age group 41–50 years, p < 0.05) and UA cases (compared to age groups 31–40, 41–50 years, p < 0.05; 61–70 years compared to

age group 41–50 years) was higher than other stone types. This indicates a higher likelihood of developing ST and UA stones in these age groups. Furthermore, we observed that rare stones

were more likely to affect children aged 1–10 (compared to age groups 31–40, 41–50, 51–60, p < 0.05). Additionally, individuals aged 81–90 (compared to age group 31–40, p < 0.05) were

most likely to develop CaP stones (Table 1).In summary, age appears to play a significant role in the distribution of different stone types, with distinct patterns observed in various age

groups. THE RELATIONSHIP BETWEEN STONE LOCATION AND URINARY STONE TYPES In the analysis of 1092 stone samples, due to the expected frequencies being less than 5, we opted for Fisher's

exact test. The results revealed a significant difference (_p_ < 0.001) in the distribution of stone types based on their locations. Furthermore, there was a weak correlation

(Cramer's V = 0.108, _p_ < 0.001) between different stone types and their locations (Fig. 6B). Subsequent post hoc testing indicated that ST tended to occur more frequently in the

kidneys (Adj.R = 3.1), UA tended to occur more in the bladder (Adj.R = 3.2), and rare stones tended to occur in the urethra (Adj.R = 3.5) (Fig. 6C). RELATIVE RISK ANALYSIS FOR NON-CAOX STONE

TYPES USING UNORDERED MULTI-CLASSIFICATION LOGISTIC REGRESSION In this study, we employed an unordered multi-classification Logistic regression model with four different stone types (CaOx,

CaP, ST, UA) as the unordered multi-class response variables. Simultaneously, we considered six statistically significant factors (gender, age, family history, urinary tract infection,

hyperuricemia, and stone location) from the previous research as independent variables. As CaOx had the highest number of cases, it served as the reference category. The Logistic regression

model was utilized to predict and assess the relative risk factors for stone types other than CaOx in specific populations. Compared to CaOx, we observed that female gender (RR= 3.087, 95%

CI 1.944–4.903) and urinary tract infection (RR= 5.272, 95% CI 3.219–8.635) were relative risk factors for predicting CaP. Similarly, female gender (RR= 4.871, 95% CI 2.944–8.060) and

urinary tract infection (RR= 4.921, 95% CI 2.910–8.323) were relative risk factors for predicting ST. Additionally, family history (RR= 2.539, 95% CI 1.286–5.012) and hyperuricemia (RR=

7.729, 95% CI 4.010–14.898) were relative risk factors for predicting UA (Tables 2, 3).This model enables us to understand the relative risk factors associated with specific factors and

different stone types, thus providing better support for predicting and implementing personalized treatment approaches for individuals with specific urinary stone types. DISCUSSION In this

study, we aimed to investigate the relationship between disease-related factors and urinary stone composition or types in the Enshi region. Urinary stones' composition and types vary in

different countries and regions due to multiple etiological factors16,17. Currently, FT-IR is the most widely used method for detecting urinary stone components18,19. Therefore, FT-IR was

employed to analyze 1092 urinary stone samples, revealing 457 single-component stones and the remainder being mixed stones (Fig. 3). The most frequent component was CaOx, accounting for

76.4% of cases, followed by CaP (9.3%), ST (8.3%), UA (4.9%), and Others (1.0%) (Fig. 4A). The study included 745 males and 347 females, with an approximate male to female ratio of 2.1:1,

which is similar to previous studies in other regions of Korea20, India21, the United Kingdom16, and Brazil17. Our results showed statistically significant differences (_p_ < 0.001) and

moderate correlations (Cramer's V = 0.337, _p_ < 0.05) between gender and the four main stone types. CaOx stones are more prevalent in men, whereas ST and CaP are more common in

women (Figs. 4B and 5A). Similar gender differences in stone types were observed in a multicenter study across China5. The male predisposition to CaOx may be attributed to the following

factors: the influence of testosterone and dihydrotestosterone on CaOx crystal formation in rat models22, the promotion of CaOx crystal formation by enhanced androgen receptor (AR) signaling

in hepatocytes23,24, as well as factors such as heavy physical work, excessive sweating, inadequate fluid intake, and concentrated urine volume25. Furthermore, the incidence of UA was

higher in men compared to women, with a male-to-female ratio of 5.75:1, which is consistent with previous studies26,27. Additionally, our study aligns with previous research, indicating a

higher incidence of ST and CaP in women17. The highest prevalence of urinary stones was observed in the age group of 13–60 years, with a distinct peak distribution (Fig. 6A). Our analysis

revealed significant differences in the distribution of five stone types across nine age groups (p < 0.001). Individuals aged 31–60, who constitute the main productive force in society,

showed higher susceptibility to CaOx stones, likely due to factors such as heavy physical labor leading to increased sweating and inadequate water intake, particularly in economically

disadvantaged rural areas5,28. Additionally, the risk of UTI tends to increase in women during menopause after the age of 50, when estrogen levels significantly decline, which may contribute

to the development of infectious stones in older women29. Our study observed similar trends to previous research conducted in China5,18 and other countries26,30, we further employed the

Kruskal–Wallis test and multiple comparison tests. This approach allowed us to offer a more detailed and comprehensive statistical analysis of the variations in stone occurrence among

different age groups (Fig. 6A and Table 1). Indeed, urolithiasis has been associated with metabolic syndrome, suggesting it may result from interactions between various metabolic risk

factors31. We observed that patients with DM and HTN did not exhibit a specific predisposition to any particular type of stone (_p_ > 0.05), while HUA was significantly associated with an

increased risk of UA formation (_p_ < 0.05) (Fig. 5). UA stones are strongly associated with causative factors such as dietary habits (red meat, seafood, beer), hyperuricemia, urine pH,

hypercholesterolemia, obesity and insulin resistance34,35. Studies in China have demonstrated that patients with metabolic syndrome have a significantly increased risk of developing

urolithiasis32,33. The complex interplay between metabolic factors and stone formation requires further exploration to better understand the underlying mechanisms. Furthermore, our study

showed significant variability (p < 0.001) and correlation (Cramer's V = 0.108) between stone locations and types. ST stones were more likely to occur in the kidney (Adj. R = 3.1),

UA stones in the bladder (Adj. R = 3.2), and rare stones in the urethra (Adj. R = 3.5) compared to the other anatomical locations (Fig. 6B,C). Consistent with previous studies in

China5,18,34, the most common stones in the kidney (36.0%) and ureter (36.9%) from a composition ratio standpoint were CaOx stones. The relationship between stone location and stone

composition can reflect part of the causes of stone formation: bladder stones are significantly associated with urinary tract obstruction, nutritional deficiencies, and uric acid excretion,

and therefore uric acid stones tend to form there35. It is widely known that CaOx stones constitute the largest proportion of patients and that patients have the highest probability of

getting CaOx stones30,36. However, predicting the type of stone with a lower composition ratio or probability of occurrence in patients with urinary stones has greater challenges and

implications. We performed a multifactorial logistic regression analysis with CaOx as the reference group, identifying female gender and urinary tract infection as relative risk factors for

CaP and ST, and FH and HUA as relative risk factors for UA stones (Tables 2, 3). Our study has some limitations. First, we only focused on the disease characteristics in the local area of

Enshi, which is not widely representative. Second, our data did not study the urine biochemical characteristics and some serum characteristics of patients. Third, information bias inevitably

causes some errors, such as possible recall of information provided by patients when collecting relevant disease information. We provide valuable insights into the characteristics of

urinary stones in the Enshi region. The consistency of our findings with studies from other regions in China highlights the universality of certain patterns of urinary stone prevalence

nationwide. However, it is important to recognize that regional differences and local risk factors may still influence the patterns of stone formation. Regional variations and local risk

factors require longer-term and larger cohort observations for further investigation. In summary, our study comprehensively investigated the relationships between individual characteristics,

disease history, stone location, and stone composition. Based on our findings, targeted management and prevention strategies can be developed for specific populations in the region. In

future research, exploring stone composition differences among different regions and populations to understand regional and individual influences on stone formation is crucial. Additionally,

delving into the association between metabolic factors and stone formation, considering lifestyle, dietary habits, and genetic factors, is important. Improved study designs and methods,

such as larger cohort observations, multicenter studies, and molecular biology techniques, will further enhance stone research and provide more effective prevention and treatment strategies

for patients. DATA AVAILABILITY On reasonable request, the corresponding author will provide the datasets used and analyzed in this study. ABBREVIATIONS * FT-IR: Fourier transform infrared

spectroscopy * CaOx: Calcium oxalate stone * COM: Calcium oxalate monohydrate * COD: Calcium oxalate dihydrate * CaP: Carbapatite * ST: Struvite * UA: Uric acid stones * Other: Rare stones

such as cystine * DM: Diabetes mellitus * HTN: Hypertension * UTI: Urinary tract infection * HUA: Hyperuricemia * FH: Family history of urinary stones * Adj.R: Adjusted standardized

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Download references FUNDING The National Natural Science Foundation of China funded this study (No. 82260791). AUTHOR INFORMATION Author notes * These authors contributed equally: Junfeng

Zhang and Kailing Li. AUTHORS AND AFFILIATIONS * Department of Urology, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China Junfeng Zhang & Peng Chen * Department of

Urology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, No. 158 Wuyang Avenue, Enshi City, 445000, Hubei, China Junfeng Zhang, Kailing Li, Hongbo Chen, Xiaohui Hu, 

Zicheng Guo, Su Chen, Fu Zheng, Wusong Cheng, Qian Mu & Yong Lan Authors * Junfeng Zhang View author publications You can also search for this author inPubMed Google Scholar * Kailing Li

View author publications You can also search for this author inPubMed Google Scholar * Hongbo Chen View author publications You can also search for this author inPubMed Google Scholar *

Xiaohui Hu View author publications You can also search for this author inPubMed Google Scholar * Zicheng Guo View author publications You can also search for this author inPubMed Google

Scholar * Su Chen View author publications You can also search for this author inPubMed Google Scholar * Fu Zheng View author publications You can also search for this author inPubMed Google

Scholar * Wusong Cheng View author publications You can also search for this author inPubMed Google Scholar * Qian Mu View author publications You can also search for this author inPubMed 

Google Scholar * Yong Lan View author publications You can also search for this author inPubMed Google Scholar * Peng Chen View author publications You can also search for this author

inPubMed Google Scholar CONTRIBUTIONS J.Z. and K.L. designed the research and wrote the paper; H.C., X.H., Z.G., S.C. and F. Z. collected data on stone composition; W.C. and Q.M. performed

the test for stone composition; Y.L. and P.C. guided the statistical analysis and the design of the research. All authors read and approved the final manuscript. CORRESPONDING AUTHORS

Correspondence to Yong Lan or Peng Chen. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature

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visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Zhang, J., Li, K., Chen, H. _et al._ Retrospective analysis of urinary tract

stone composition in a Chinese ethnic minority colony based on Fourier transform infrared spectroscopy. _Sci Rep_ 13, 13453 (2023). https://doi.org/10.1038/s41598-023-40603-w Download

citation * Received: 05 April 2023 * Accepted: 14 August 2023 * Published: 18 August 2023 * DOI: https://doi.org/10.1038/s41598-023-40603-w SHARE THIS ARTICLE Anyone you share the following

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