Systematic metabolic profiling of mice with caerulein-induced acute pancreatitis

Introduction

Acute pancreatitis (AP) is a prevalent inflammatory condition affecting the pancreas. Well-known causes of AP include gallstones, alcohol, hypertriglyceridemia, hypercalcaemia, drugs, autoimmune diseases, genetic factors, infection, surgical trauma and endoscopic retrograde cholangiopancreatography (ERCP) (1,2). Worldwide, the approximate incidence rate of AP stands at 34 cases per 100,000 person-years (3). With the increasing prevalence of hypertriglyceridemia and metabolic syndrome worldwide, the incidence of AP is on the rise, making the leading cause of gastrointestinal disease-related hospital admissions (4). It is important to note that AP is a complex disease with a variable course, often challenging to predict its stages. Around 80% of patients present with mild to moderately severe AP, which typically resolves within 1 week (5,6). However, about 20% of patients experience severe AP, resulting in a mortality rate ranging from 20% to 40% (5,7). Hence, it is imperative to promptly and precisely identify and diagnose AP.

Although there are several biomarkers available for diagnosing AP, such as serum amylase and lipase, they still have limitations. For instance, serum amylase levels can be within the normal range in patients with alcoholic or hypertriglyceridemia pancreatitis (8,9). Additionally, serum amylase levels or lipase levels can also be elevated in many non-pancreatitis diseases such as bowel perforation, bowel obstruction, bowel infarction, abdominal aortic aneurysm, cholecystitis, biliary obstruction, peptic ulcer disease and acute intestinal pathologies (9). Serum amylase levels in patients with AP typically rise within 6 to 24 hours after onset, while serum lipase levels usually increase within 4 to 8 hours after onset (10). The abdominal computed tomography (CT) manifestations of AP may not be obvious in the early stage, and may even be completely normal (1,10). Therefore, there are no definitive diagnostic biomarkers for early diagnosis of AP, such as within 4 hours. However, due to ethical reasons, early events of AP and studies on new targeted therapies cannot be conducted directly in humans (11). Moreover, the pathophysiology of the AP remains unclear.

The recent development of “omics” technologies has facilitated high-throughput and high-content screening of disease-related biomarkers in biological samples. Metabolomics, in particular, is often employed in biomarker discovery due to its high sensitivity, capable of detecting subtle changes in biological pathways and providing insights into the underlying mechanisms of diseases. Furthermore, metabolomics can provide a more comprehensive understanding of the disease network, as it offers information that is closely related to the pathological or physiological phenotype. Therefore, it has emerged as one of the most promising approaches in precision medicine (12-14).

AP can affect other tissues and organs in addition to pancreas, highlighting the need for metabolomic research in other biological samples. However, previous metabolomics studies on AP have primarily focused on analyzing serum, urine, and pancreatic samples from AP patients or animal models, which limits the ability to obtain a complete metabolic profile of the disease. Therefore, a comprehensive and systematic metabolomics analysis of AP is therefore essential. Hence, we adopted a widely used protocol to induce AP in mice (eight intraperitoneal injections of cerulein, each separated by 1 hour, at a dose of 50 µg/kg body weight). Mice were euthanized 1 hour after the last injection, and serum, pancreas, liver, spleen, colon, and kidney were collected for gas chromatography-mass spectrometry (GC-MS) analysis, equivalent to metabolomic analysis 1 hour after onset of AP, in search of differential metabolites that could serve as early diagnostic markers. By detecting alterations in metabolite levels and metabolic pathways, we can gain valuable clues to explore the pathophysiological mechanisms of AP. We present this article in accordance with the ARRIVE reporting checklist (available at https://tgh.amegroups.com/article/view/10.21037/tgh-24-14/rc).

Methods

Animal treatment

Twelve male C57BL/6J mice, each 8 weeks old, sourced from Jinan Pengyue (Jinan, China) were acclimated for a week in a climate-controlled chamber (24±2 ℃, 40% humidity). During this time, they had unrestricted access to both water and food. Subsequently, the mice were randomly divided into two groups: the AP group and the control (CON) group, with each group comprising six mice. All mice underwent a 12-hour fasting period. The AP group received intraperitoneal caerulein injections (50 µg/kg body weight, eight injections at 1-hour intervals) (15), while the mice in the CON group received equivalent injections of normal saline. Euthanasia was performed on all mice 1 hour after the final injection. All experimental procedures followed the Regulations of Experimental Animal Administration set by the State Committee of Science and Technology of the People’s Republic of China and received approval from the Ethics Committee of Jining First People’s Hospital, Jining, China (No. JNRM-2023-DW-098).

Sample collection and preparation

Anesthesia was initiated by administering sodium pentobarbital via intraperitoneal injection at a dosage of 50 mg/kg. Once anesthetized, blood samples were gathered by removing the eyeballs, followed by centrifugation (3,500 rpm, 8 minutes) to extract serum. Euthanasia was performed on all mice using cervical dislocation. Following this, each mouse underwent necropsy on an ice-cooled surface to retrieve the pancreas, liver, spleen, colon, and kidney. All gathered tissue samples were thoroughly washed with phosphate-buffered saline and promptly frozen in liquid nitrogen. Subsequently, these samples were preserved at −80 ℃ for later use.

For serum sample preparation, a volume of 100 µL of serum was mixed with 350 µL of methanol, incorporating 100 µg/mL of heptadecanoic acid. To collect the supernatant, the resulting mixture was subjected to vortexing and centrifugation (14,000 rpm, 10 minutes, 4 ℃). The collected supernatant was moved to a tube (2 mL) and then dehydrated at 37 ℃ using a nitrogen gas flow. Then, the dried sample was mixed thoroughly with 80 µL of o-methylhydroxylamine hydrochloride, which was dissolved in pyridine at a concentration of 15 mg/mL, and subsequently incubated at 70 ℃ for 90 minutes. Following this step, each sample received an addition of 100 µL of N,O-bis(trimethylsilyl)trifluoroacetamide, including 1% trimethylchlorosilane (Sigma-Aldrich, Saint Louis, MO, USA), followed by an incubation at 70 ℃ lasting 60 minutes. The resulting solution obtained underwent vortexing, followed by centrifugation (14,000 rpm, 2 minutes, 4 ℃), and was subsequently filtered using a 0.22-µm filter before GC-MS analysis. The levels of serum amylase and lipase were assessed through enzymatic kinetic chemistry employing commercial kits within the Roche/Hitachi modular analysis system, following the guidelines outlined by the manufacturer (Roche, Berlin, Germany).

Tissue samples from the pancreas, liver, spleen, colon, and kidney, each weighing 50 mg, underwent homogenization in 1 mL of methanol with 1 mg/mL heptadecanoic acid. Following this, centrifugation (14,000 rpm, 15 minutes, 4 ℃) was performed to obtain the supernatant. More detailed steps regarding further preparation can be referenced in the serum sample preparation description. Quality control samples (QCs) were generated by combining equal amounts of samples from both the CON group and the AP group.

Histopathological staining

The pancreatic tissue underwent fixation in 10% phosphate-buffered paraformaldehyde for 48 hours, followed by embedding in paraffin for histological examination. Sections measuring five microns were sliced utilizing a microtome capable of delivering powerful blows, and subsequently stained employing the hematoxylin-eosin (H&E) technique.

GC-MS analysis

The 7000C mass spectrometer, manufactured by Agilent Technologies (CA, USA), was used to perform all sample analyses in conjunction with a 7890 B gas chromatograph system. The chromatographic analysis employed an HP-5MS capillary column with high-purity helium as the carrier gas, flowing at 1 mL/min with a split ratio of 50:1. Operating temperatures were maintained at 280, 250, and 230 ℃ for injection, transfer line, and ion source, respectively. The gas chromatograph temperature sequence initiated at 60 ℃ for 4 minutes, gradually increasing to 300 ℃ at an 8 ℃/min rate, where it held steady for 5 minutes. The ionization electron impact voltage stood at −70 eV, with a spectra acquisition rate of 20 per second. A full scan mode was employed, covering a scanning range from 50 to 800 m/z.

Multivariate statistical analysis

The GC-MS data underwent initial processing using Agilent Mass Hunter Quantitative Analysis and Unknowns Analysis software, allowing identification of the compounds associated with the detected peaks. Statistical analysis of normalized peak area percentages utilized SIMCA 14.1 software (Umetrics, Sweden). The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model was used to differentiate between the AP group and the CON group. For validation, 200 permutation tests were conducted to assess the validity of the OPLS-DA models.

Two-tailed Student’s t-tests were performed using SPSS 19.0. Metabolites were considered as potential discriminants if they demonstrated Variable Importance in Projection (VIP) values greater than 1.0 in the OPLS-DA analysis and P values less than 0.05 in the two-tailed Student’s t-tests. The identified differential metabolites were brought into MetaboAnalyst 5.0 (available at http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes (KEGG, accessible at http://www.kegg.jp) to perform metabolic pathway analysis. The metabolic pathways that exhibited impact values greater than 0 and P values less than 0.05 were regarded as significantly affected.

Results

Evaluation of caerulein-induced AP in mice

One of the most widely employed models for inducing AP involves administering elevated concentrations of caerulein, an analogue of cholecystokinin (16). Accordingly, our research employed the classical technique of intraperitoneal caerulein injection to induce AP. Figure 1A,1B illustrated a notable increase in serum amylase and lipase levels within the AP groups. Figure 1C depicted representative H&E-stained pancreas images from the AP groups, revealing the infiltration of lymphocytes and neutrophils in the interstitium, accompanied by interstitial edema and a disorganized arrangement of connective tissue. This confirms the successful establishment of caerulein-induced AP mouse models in our study.

Figure 1 Evaluation of caerulein-induced acute pancreatitis in mice. (A) Serum amylase. (B) Serum lipase. (C) Representative images of pancreatic H&E staining. Values displayed are the mean ± SE, ***, P<0.001 indicates differences between CON and AP groups. CON, control; AP, acute pancreatitis; H&E, hematoxylin and eosin; SE, standard error.

GC-MS total ion chromatograms (TICs) of samples

Figure 2 displayed the TICs of QCs. These chromatograms demonstrated robust signal responses across all samples, signifying the identification of a wide range of metabolites with a substantial peak capacity during the analysis. Consistent retention times were noted, and the chromatograms exhibited excellent reproducibility in every tissue.

Figure 2 Representative GC-MS total ion current chromatograms from QCs. (A) Serum; (B) pancreas; (C) liver; (D) spleen; (E) colon; (F) kidney (x-axis represents retention time and y-axis represents intensity). GC-MS, gas chromatography-mass spectrometry; QCs, quality control samples.

Multivariate statistical analysis

The OPLS-DA model was utilized to assess the data disparities between the two groups. As evident from the score chart, distinct separation exists between the CON group and the AP group, signifying substantial differences. In order to validate the model, permutation tests were conducted. These tests revealed that the intersections of the regression line for all Q2-points with the left vertical axis were situated below zero, as illustrated in Figure 3, suggesting the effectiveness of the model. Cluster analysis focused on the metabolite expression in samples between the two groups, revealing that the majority of samples could be segregated into two separate clusters. There was only a minor overlap observed among the sample clusters (illustrated in Figure 4). These findings aligned consistently with the outcomes obtained from the OPLS-DA analysis.

Figure 3 OPLS-DA scores and 200 permutation tests for OPLS-DA models. (A) Serum; (B) pancreas; (C) liver; (D) spleen; (E) colon; (F) kidney. Statistical validation of the significant OPLS-DA models by permutation tests revealed no over-fitting (note that the blue regression line of the Q2 points intersect the vertical axis at values <0). OPLS-DA, Orthogonal Projections to Latent Structures Discriminant Analysis; CON, control; AP, acute pancreatitis.

Figure 4 Heatmap illustrating variance in metabolites between AP and CON groups. (A) Serum; (B) pancreas; (C) liver; (D) spleen; (E) colon; (F) kidney. Red and blue colors denote upregulation and downregulation, respectively. Rows and columns correspond to metabolites and samples, respectively. AP, acute pancreatitis; CON, control.

Identification of differential metabolites

Table 1 presented a thorough overview of the distinct metabolites found in specific tissues, namely serum, pancreas, liver, spleen, colon, and kidney. In order to identify significant differences between the CON and AP groups, a threshold was established using VIP values greater than 1.0 and P values lower than 0.05. Metabolites meeting the threshold were determined to be significantly distinct. Moreover, metabolites with a fold change (FC) greater than 1 were classified as displaying an increase, while those with an FC less than 1 were categorized as exhibiting a decrease. It was found that there were 11, 17, 11, 15, 3, and 4 differential metabolites in serum, pancreas, liver, spleen, colon, and kidney, respectively.

Analysis of metabolic pathways

We used MetaboAnalyst 5.0 to explore the metabolic pathways linked to discriminant metabolites. Our analysis revealed 13 impacted metabolic pathways with notable differences (P values <0.05 and impact values >0). Specifically, in serum, the affected pathways involved arginine biosynthesis, glutathione metabolism, and the biosynthesis of phenylalanine, tyrosine, and tryptophan. Moreover, arginine and proline metabolism, as well as D-glutamine and D-glutamate metabolism, were also affected. Within the pancreas, the impacted pathways involved the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as phenylalanine metabolism. In the liver, the pathways comprised the pentose phosphate pathway, the metabolism of pyrimidine, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. In the spleen, the affected pathways involved the metabolism of taurine and hypotaurine, the metabolism of glycerolipids, the metabolism of glutathione, the metabolism of cysteine and methionine, the metabolism of glycine, serine, and threonine, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. Meanwhile, the colon included glycerolipid metabolism and sphingolipid metabolism. These affected metabolic pathways were detailed in Table 2 and visually represented in Figure 5. Comprehensive information regarding the impacted metabolic pathways were presented in Figure 6.

Figure 5 Summary of differential metabolic pathways using MetaboAnalyst 5.0. The node color is based on the P values (y axis) and the node radius represents the pathway impact values (x axis). (A) Serum; (B) pancreas; (C) liver; (D) spleen; (E) colon. (a) Arginine biosynthesis; (b) glutathione metabolism; (c) phenylalanine, tyrosine and tryptophan biosynthesis; (d) arginine and proline metabolism; (e) D-glutamine and D-glutamate metabolism; (f) phenylalanine metabolism; (g) pentose phosphate pathway; (h) pyrimidine metabolism; (i) taurine and hypotaurine metabolism; (j) glycerolipid metabolism; (k) cysteine and methionine metabolism; (l) glycine, serine and threonine metabolism; (m) sphingolipid metabolism.

Figure 6 Schematic diagram of related differential metabolic pathways affected by AP. Black arrows indicate energy metabolism pathways, including the tricarboxylic acid cycle. Deep red arrows indicate phenylalanine, tyrosine, and tryptophan biosynthesis. Red arrows indicate arginine biosynthesis. Golden arrows indicate arginine and proline metabolism. Light green arrows indicate L-glutamine and L-glutamate metabolism. Blue arrows indicate taurine and hypotaurine metabolism. Green arrows indicate glycerolipid metabolism. Purple arrows indicate glutathione metabolism. Dark orange arrows indicate cysteine and methionine metabolism. Light blue arrows indicate glycine, serine and threonine metabolism. Metabolites marked in red represent potential biomarkers identified in the study. TCA, tricarboxylic acid; AP, acute pancreatitis.

Discussion

AP is an inflammatory condition affecting the exocrine pancreas, a vital organ involved in both digestion and metabolism. Metabolic diseases such as obesity and hypertriglyceridemia can trigger AP. Of note, AP can also lead to metabolic disorders such as new-onset diabetes, exocrine pancreatic insufficiency (17). This intricate connection underscores the close association between AP and metabolism. Employing untargeted metabolomics, we systematically investigated the alterations in metabolic profiles across serum, pancreas, liver, spleen, colon, and kidney in mice with AP induced by caerulein. Within these tissues, we detected 11 differential metabolites in serum, 17 in the pancreas, 11 in the liver, 15 in the spleen, 3 in the colon, and 4 in the kidney. Among these, significant changes were observed in ketone bodies, amino acids, citric acid, and lipids, indicating their noteworthy alterations.

The serum metabolome represents the final output of all organs and is a detailed systematic characterization of physiological state or pathological condition. In our study, 11 differential metabolites were detected in serum, these differential metabolites may be used as potential biomarkers, may be helpful for the early diagnosis of AP. The differential metabolites most likely to be potential biomarkers are discussed in detail in the following article.

3-Hydroxybutyric acid (3-HBA), categorized among ketone bodies, is an important metabolite of AP. We discovered serum 3-HBA levels were significantly elevated in AP mice, consistent with previous studies (18-20). Elevated levels of 3-HBA in serum due to AP are caused and maintained by significantly high levels of pancreatic lipase in the bloodstream (21). In the course of AP, the improper release of pancreatic lipase can lead to both self-digestion of the pancreas and excessive breakdown of adipose tissues (22-24). If uncontrolled, this process will generate an overwhelming quantity of nonesterified fatty acids (NEFAs), which further damage pancreatic acinar cells and potentially lead to organ failure (22,23,25). It is well known that fatty acid β-oxidation (FAO), a principal catabolic process, breaks down NEFAs into acetyl-CoA (26), which can then be used for energy production through the tricarboxylic acid (TCA) cycle or ketogenesis. The liver, highly active in FAO, also serves as a primary site for synthesizing ketone bodies. Of note, both human and experimental animal models of AP have shown activation of hepatic FAO and ketogenesis processes (27). These analyses might explain the elevated levels of 3-HBA. Initially, there was a belief that excessive production of ketone bodies might result in harmful conditions like ketoacidosis or ketonemia. Significantly, a recent study discovered that 3-HBA significantly diminished the activation of pancreatic and systemic proinflammatory macrophages, assuming an inherent protective role during AP (27). Furthermore, our study identified increased levels of 3-HBA in the pancreas, liver, and spleen. Consequently, 3-HBA shows promise as a potential biomarker for AP (18).

Besides the rise in 3-HBA, heightened levels of branched-chain amino acids (BCAAs) were noted in both serum and pancreatic tissue of mice with caerulein-induced AP, consistent with findings from previous studies involving AP patients or murine models (28,29). BCAAs, a group of three essential amino acids: leucine, isoleucine, and valine, are crucial for maintaining regular pancreatic enzyme functions (30). Earlier proteomic investigations of AP revealed a decrease in the breakdown of BCAAs during the condition (31,32). Additionally, AP has been observed to induce alterations in gut microbiota (33,34). Specifically, AP presents a distinct gut microbial profile characterized by reduced microbial diversity, increased presence of pathogenic bacteria, and diminished levels of beneficial bacteria (35-37). BCAAs are synthesized by various sources such as plants, fungi, and bacteria, notably by members of the gut microbiota (38). We propose that shifts in gut microbiota in AP patients or animal models could influence BCAA synthesis, warranting further studies to confirm this hypothesis. These analyses might explain the increased levels of BCAAs detected in serum and pancreatic tissues during AP. Now, the question arises: what is the significance of these elevated BCAAs? BCAAs play a critical role in maintaining mammalian homeostasis by regulating immunity (39). An intriguing recent study suggested that a high BCAA diet could reduce inflammatory levels in mice with Parkinson’s disease (40). Therefore, whether the increased levels of BCAAs might exhibit an anti-inflammatory role in AP. To validate this hypothesis, further research is essential. Consequently, BCAAs shows promise as potential biomarkers for AP.

Consistent with prior research (20,41), the levels of phenylalanine in serum were also elevated in AP mice. AP typically commences as localized sterile inflammation and can potentially advance to systemic inflammatory response syndrome (SIRS). Infections or states of inflammation often lead to considerable rises in serum phenylalanine (42). Therefore, phenylalanine can be considered as a biomarker for early diagnosis of AP.

In our study, we observed substantial elevations in tyrosine levels in the pancreas and spleen. Interestingly, a recent study highlighted that the microbial metabolism of L-tyrosine provided protection against allergic airway inflammation and was also effective against neutrophilic inflammation (43). Therefore, we propose that the heightened tyrosine levels might indicate the body’s compensatory anti-inflammatory responses. Nevertheless, further research is necessary to validate this hypothesis.

The levels of L-lysine in serum and pancreas of AP mice were elevated significantly compared with CON group. Previous research has confirmed that L-lysine administration caused early pancreatic mitochondrial damage, may lead to the development of AP (44). Therefore, L-lysine can be considered as a biomarker for early diagnosis of AP.

Consistent with a preceding study (45), there was a notable elevation in the level of citric acid observed within the pancreas of caerulein-induced AP mice models. Pancreatitis disrupts the equilibrium of Ca²⁺, leading to a notable rise in Ca²⁺ concentration within pancreatic acinar cells (46). The excessive increase in cellular concentration of Ca²⁺ induces the activation of the mitochondrial permeability transition pore (MPTP), causing the depletion of crucial mitochondrial membrane potential necessary for adenosine triphosphate (ATP) synthesis (47,48). Taken together, pancreatic acinar cells exhibit mitochondrial dysfunction and decreased ATP production during AP (47,49,50). Our hypothesis suggests that the TCA cycle, responsible for generating energy within the mitochondria of pancreatic acinar cells during pancreatitis, is highly likely to be affected. This may elucidate the rise in citric acid, an essential intermediate of the TCA cycle, observed in the pancreas during pancreatitis. It is worth noting that citric acid accumulation in macrophages serves distinct nonmetabolic signaling roles influencing inflammatory gene expression during the activation of proinflammatory macrophages (51). However, it remains uncertain whether citric acid within pancreatic acinar cells also participates in signaling processes within the inflammatory pathway, necessitating further studies to explore this potential aspect.

Although our study yielded valuable insights, it is important to recognize certain limitations that persist. Metabolic profiling of caerulein-induced AP mice in only one time point may be accidental. In the further, we will design this study at several time points, instead of one time point, which help to understand the changes in metabolites. Also, additional clinical studies are needed to determine whether these biomarkers are applicable to humans. Metabolomics is the only omics approach we have used to study AP. To ensure the credibility of our findings, it is crucial to incorporate various methodologies comprising genomics, transcriptomics, proteomics, and metabolomics. Many metabolites exhibit short lifespans, and our study involves gathering samples from multiple tissues, a task that is time-consuming and challenging to entirely avoid. This circumstance may potentially impact the accuracy of the final measurement of metabolites. Additionally, it is crucial to acknowledge that anesthesia used during sampling procedures can trigger metabolic shifts in tissues, potentially influencing the outcomes (52).

Conclusions

Utilizing GC-MS and multivariate statistical analysis, we discerned systematic metabolic alterations induced by AP. Many differential metabolites in serum, pancreas, liver, spleen, colon, and kidney were identified, primarily comprising ketone bodies, amino acids, lipids, and intermediates of the TCA cycle. These metabolites were chiefly associated with amino acid and glycerolipid metabolism. Notably, 3-HBA, BCAAs, phenylalanine and L-lysine emerge as promising biomarkers for early diagnosis of AP. Recent research has unveiled non-metabolic functions for specific metabolites (e.g., 3-HBA, BCAAs, L-tyrosine, citric acid), indicating their roles in regulating inflammatory processes. Furthermore, gut microbiota significantly influences metabolite synthesis and contributes to the progression of AP. Further investigations are imperative to unravel the intricate connections between metabolites, gut microbiota, and AP, potentially offering a new perspective on its pathogenesis.

Acknowledgments

Funding: This research was funded by the National Natural Science Foundation of China (Nos. 81602846 and 82272253), Natural Science Foundation of Shandong Province (No. ZR2021MH145), Taishan Scholar Project of Shandong Province (No. tsqn201812159), Science and Technology Program of Traditional Chinese Medicine of Shandong Province (No. M-2022066), and China International Medical Foundation (No. Z-2018-35-2002).

Footnote

Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://tgh.amegroups.com/article/view/10.21037/tgh-24-14/rc

Data Sharing Statement: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-24-14/dss

Peer Review File: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-24-14/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tgh.amegroups.com/article/view/10.21037/tgh-24-14/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All experimental procedures followed the Regulations of Experimental Animal Administration set by the State Committee of Science and Technology of the People’s Republic of China and received approval from the Ethics Committee of Jining First People’s Hospital, Jining, China (No. JNRM-2023-DW-098).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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