Home Correlation analysis between semen routine parameters and sperm DNA fragmentation index in patients with semen non-liquefaction: A retrospective study
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Correlation analysis between semen routine parameters and sperm DNA fragmentation index in patients with semen non-liquefaction: A retrospective study

  • Fengqing Ji EMAIL logo , Junying Chen and Liyun Lin
Published/Copyright: May 20, 2025

Abstract

This research investigated the correlation between routine semen parameters and the sperm DNA fragmentation index (DFI) in infertile males with non-liquefying semen, aiming to provide insights for infertility management. We conducted a retrospective analysis of 152 infertile males at our Andrology Department from March 2023 to March 2024, assessing differences in semen parameters and DFI based on liquefaction times and identifying influential factors on DFI. Participants were divided into two groups based on their semen liquefaction times: less than 60 min (111 patients) and 60 min or more (41 patients). The group with normal liquefaction times demonstrated significantly better forward and total sperm motility, and lower sperm morphology indices and DFI values. A positive correlation was found between DFI and liquefaction time, while a negative correlation was observed with motility and normal sperm ratio. Specifically, each additional minute of liquefaction time increased DFI by 0.13, and a 1% decrease in normal sperm ratio increased DFI by 0.73. The findings indicate that delayed semen liquefaction correlates with poorer sperm motility and morphology and higher DFI, underscoring the importance of comprehensive semen assessment in evaluating male fertility.

1 Introduction

Approximately 15% of couples in their reproductive age experience infertility, defined as the inability to conceive after 1 year of unprotected intercourse. Male factors contribute to about half of these case, with semen quality being a critical determinant of male fertility, affected by various environmental, lifestyle, and age-related factors [1]. These influences can lead to abnormal sperm morphology, reduced semen volume, and decreased sperm motility. Notably, recent studies have documented a decline in semen quality among Chinese men [2].

Globally, conventional semen analysis is considered the gold standard for diagnosing male infertility. The diagnostic method assesses various parameters, including semen appearance, volume, pH, liquefaction time, sperm concentration, and total sperm count [3]. However, it has limitations in accurately evaluating sperm functionality. Notably, studies have indicated that approximately 15% of infertile men exhibit semen parameters that fall within the normal ranges established by the World Health Organization [4], suggesting defects of the current diagnosis.

The sperm DNA fragmentation index (DFI) has emerged as a novel marker for assessing sperm function, indicating the integrity of sperm DNA and the extent of DNA damage during spermatogenesis and sperm maturation. DFI is crucial for evaluating male fertility potential and is strongly associated with adverse pregnancy outcomes [5]. Although there are ongoing debates about its routine use, the diagnostic value of DFI is recognized by both the American Urological Association and the European Association of Urology [5]. A comprehensive assessment that incorporates both conventional semen parameters and DFI provides a more complete evaluation of a patient’s semen status, including sperm vitality, morphology, genetic integrity, and accessory gland function, which are essential for accurately diagnosing and managing infertility [6,7].

Semen liquefaction time – the period needed for semen to transition from a gelatinous to a liquid state – typically ranges from 20–30 min. Semen that does not liquefy within 60 min is considered to exhibit non-liquefaction, which may indicate issues with sperm quality or function [8]. In such instances, sperm can become trapped in the coagulum, impeding motility and passage through the cervix, and increasing the likelihood of sperm death due to prolonged exposure in the reproductive tract [9].

We hypothesize that semen non-liquefaction is associated with increased sperm DNA fragmentation, contributing to reduced male fertility. This study aims to evaluate the correlation between routine semen parameters and the sperm DFI in infertile males with non-liquefying semen, to provide insights into the implications of non-liquefaction on male fertility and to develop more nuanced diagnostic approaches.

2 Materials and methods

2.1 Study design

A retrospective analysis was conducted on a cohort of infertile males admitted to our Andrology Department between March 2023 and March 2024, focusing on their clinical data.

2.2 Inclusion and exclusion criteria

2.2.1 Inclusion criteria

  1. Married males aged 20–45 years.

  2. Diagnosed with infertility, defined as the inability to conceive after 1 year of unprotected intercourse with no female factor infertility identified.

  3. Negative for Mycoplasma, Chlamydia, and Neisseria gonorrhoeae.

  4. Semen liquefaction time exceeding 60 min.

2.2.2 Exclusion criteria

  1. Presence of urological disorders such as varicocele, cryptorchidism, prostatitis, epididymitis, hematospermia, or azoospermia.

  2. History of reproductive tract injury.

  3. Recent use of medications known to affect semen quality (within the last month).

  4. Presence of concurrent malignancy or hematological disorders.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

2.3 Semen analysis

Semen samples were collected via masturbation after 2–7 days of sexual abstinence, as per WHO guidelines. The samples were then liquefied at 37°C, and a 5 μL aliquot was used for microscopic enumeration on a disposable hemocytometer slide, assessing parameters including semen volume, total sperm count, sperm concentration, sperm motility (progressive, non-progressive, immotile), liquefaction time, and pH. These parameters were quantitatively assessed using an SCA Sperm Analyzer (Spain), with established normal reference ranges: semen volume ≥1.5 mL, sperm concentration ≥15 × 106/mL, progressive motile sperm (PR) ≥32%, and total motility ≥40%.

2.4 Sperm morphology assessment

Sperm morphology was evaluated using the Papanicolaou staining method, where a technician examined at least 200 sperm per slide under a 100× oil immersion lens and a 10× ocular lens, identifying defects in the head, midpiece, and tail. The sperm deformity index (SDI) was calculated according to the WHO 5th edition guidelines.

2.5 Sperm DFI detection and grouping

Semen samples were sent to a third-party laboratory for sperm DFI testing using a Sparrow flow cytometer and Selena reagent kit. The analysis adjusted sperm concentration to 1 × 106–2 × 106 cells/mL. A 100 μL aliquot was analyzed for at least 5,000 sperm using flow cytometry and the sperm chromatin structure assay, exploiting acridine orange’s metachromatic properties to differentiate intact double-stranded DNA (green fluorescence) from fragmented single-stranded DNA (red fluorescence). Patients were categorized into three groups based on DFI: ≤15.00% (good sperm DNA integrity), 15.00 to <30.00% (moderate sperm DNA integrity), and ≥30.00% (poor sperm DNA integrity).

2.6 Sample size justification

The target sample size was calculated to detect a medium effect size (Cohen’s d = 0.5) with a power of 80% and a significance level of 0.05, which initially suggested a minimum of 64 participants per group. However, due to the lower than expected prevalence of non-liquefaction among our patient population during the study period, only 41 cases were included in the Delayed Liquefaction group. To mitigate this limitation, we increased the overall sample size to 152 participants to enhance the study’s statistical power. Additionally, we employed robust statistical techniques to maximize the use of the available data and ensure that our findings remained statistically significant despite the smaller subgroup size. The difference in group sizes was further accounted for in our statistical analysis, adjusting for potential biases that could arise from the uneven distribution.

2.7 Statistical analysis

Statistical analyses were conducted using SPSS 25.0 and the R programming language. Continuous data were expressed as mean ± standard deviation and compared using t-tests, while categorical data were reported as percentages and analyzed using chi-square tests. The relationships between DFI and routine semen parameters were assessed via Pearson correlation analysis, defining the strength of correlation as follows: |r| > 0.9 (very strong), 0.7 < |r| < 0.9 (strong), 0.4 < |r| < 0.7 (moderate), 0.2 < |r| < 0.4 (weak), and 0 < |r| < 0.2 (very weak or none). Statistical significance was established at P < 0.05, using a two-tailed approach.

3 Results

3.1 Semen parameters and DFI based on different semen liquefaction times

In this study, we examined 152 infertile males who met the inclusion criteria. These participants were divided into two groups according to their semen liquefaction times: a normal liquefaction group (liquefaction time <60 min, 111 cases) and a delayed liquefaction group (liquefaction time ≥60 min, 41 cases). Table 1 displays a comparative analysis of routine semen parameters and the sperm DFI between these groups.

Table 1

Semen parameters and DFI based on different semen liquefaction times

Normal liquefaction group (n = 111) Delayed liquefaction group (n = 41) t/χ 2 P
Age (years) 34.26 ± 6.33 36.21 ± 7.15 1.669 0.097
Semen volume (mL) 3.57 ± 1.43 3.36 ± 1.14 0.846 0.399
Sperm concentration (×106/mL) 57.36 ± 20.96 52.39 ± 17.36 1.355 0.177
PR (%) 41.36 ± 11.75 29.17 ± 5.33 6.394 <0.001
Sperm vitality (A + B) (%) 61.36 ± 19.36 40.11 ± 11.93 6.574 <0.001
Teratozoospermia index 1.42 ± 0.07 1.97 ± 0.12 34.91 <0.001
Normal morphology (%) 4.37 ± 1.74 3.93 ± 1.61 1.411 0.160
DFI 21.16 ± 5.36 32.31 ± 8.31 9.710 <0.001

The age distribution was similar between both groups, indicating that age was not a confounding factor in the analysis. Notably, the normal liquefaction group exhibited significantly higher levels of progressive motility and overall sperm motility compared to the delayed liquefaction group. Furthermore, this group also showed a lower SDI and decreased levels of DFI, with these differences being statistically significant (P < 0.05).

3.2 Comparison of abnormal semen parameters rates at different DFI levels

Within this study, semen parameters were categorized as normal if they met the following criteria: semen volume ≥1.5 mL, sperm concentration ≥15 × 106/mL, progressive motility (PR) ≥32%, and total sperm motility ≥40%. The 152 infertile patients were further stratified based on their sperm DFI levels into three groups: DFI ≤ 15%, 15% < DFI < 30%, and DFI ≥ 30%. The prevalence of abnormal semen parameters among these groups is detailed in Table 2.

Table 2

Comparison of abnormal semen parameters rates at different DFI levels

Total Normal semen parameters Abnormal semen parameters
Total 152 121 31
DFI ≤ 15% 59 17 42
15% < DFI < 30% 52 12 40
DFI ≥ 30% 41 2 39

Out of the total participants, 31 exhibited normal semen parameters. Distribution within the DFI categories was as follows: 17 patients in the DFI ≤ 15% group, 12 in the 15% < DFI < 30% group, and only 2 in the DFI ≥ 30% group. Significant differences were observed in the rates of abnormal semen parameters across these DFI levels. The groups with DFI ≤ 15% and 15% < DFI < 30% had considerably lower rates of abnormal parameters compared to the DFI ≥ 30% group. However, no significant difference was noted between the DFI ≤ 15% and 15% < DFI < 30% groups.

3.3 Correlation analysis between DFI and semen parameters

To further elucidate the relationship between sperm DFI and key semen characteristics, correlation analyses were conducted. In this analysis, DFI was treated as the dependent variable, while the following semen parameters served as independent variables: semen liquefaction time, sperm volume, sperm concentration, progressive motility (PR), total sperm motility, sperm morphology index, and the ratio of normal sperm (illustrated in Figure 1).

Figure 1 
                  Correlation analysis between DFI and semen parameters.
Figure 1

Correlation analysis between DFI and semen parameters.

The results indicated a positive correlation between DFI and semen liquefaction time, implying that longer liquefaction times are associated with increased DNA fragmentation in sperm. Conversely, negative correlations were observed between DFI and several other parameters: progressive motility (PR), overall sperm motility, and the ratio of morphologically normal sperm. These findings suggest that higher levels of DFI are linked to reduced sperm motility and a lower proportion of morphologically normal sperm, highlighting the detrimental impact of compromised DNA integrity on sperm function and quality.

3.4 Univariate linear regression analysis of DFI

To further explore the relationship between sperm DFI and semen parameters, both univariate and multivariate linear regression analyses were performed. In these analyses, DFI was established as the dependent variable, with predictors including age and various semen parameters.

The univariate linear regression analysis revealed significant linear correlations between DFI and key semen parameters such as semen liquefaction time (p < 0.001), progressive motility (PR) (p = 0.02), total sperm motility, (p = 0.06) and the ratio of normal sperm (p = 0.010) (results detailed in Table 3). These findings indicate that certain semen parameters can serve as predictors of the extent of DNA fragmentation in sperm. This analysis provides valuable insights into the potential mechanisms influencing sperm quality and fertility outcomes, suggesting that alterations in these parameters could be indicative of underlying reproductive health issues.

Table 3

Univariate linear regression analysis for DFI

Variables b S.E t P β (95% CI)
Age −0.09 0.14 −0.64 0.523 −0.09 (−0.35 to 0.18)
Liquefaction time (min) 0.15 0.04 3.37 <0.001 0.15 (0.06 to 0.24)
Semen volume (mL) 0.10 0.29 0.35 0.727 0.10 (−0.47 to 0.68)
Sperm concentration (×106/mL) 0.04 0.03 1.47 0.142 0.04 (−0.01 to 0.09)
PR (%) −0.13 0.04 −3.19 0.002 −0.13 (−0.21 to −0.05)
Sperm motility (%) −0.08 0.03 −2.81 0.006 −0.08 (−0.14 to −0.02)
SDI −0.70 2.33 −0.30 0.763 −0.70 (−5.27 to 3.86)
Normal morphology −0.87 0.34 −2.60 0.010 −0.87 (−1.53 to −0.21)

CI: confidence interval.

3.5 Multivariate linear regression analysis

A multivariate linear regression analysis was employed to develop a predictive model for the sperm DFI, with DFI considered as the dependent variable (Y). The predictors included in the model were semen liquefaction time (X1), progressive motility (PR) (X2), sperm motility (X3), and the ratio of normal sperm (X4). The resulting model equation is as follows:

DFI (Y) = 0.13(X1) − 0.73(X4) + 23.85.

This model elucidates the relationships between DFI and the chosen predictors. Specifically, the analysis reveals that each 1 min increase in semen liquefaction time is associated with a 0.13 unit increase in DFI (p = 0.004). Additionally, a 1% decrease in the normal sperm ratio results in a 0.73 unit increase in DFI (p = 0.025). These findings, summarized in Table 4, underscore the significant influence of semen liquefaction time and the quality of sperm on the level of DNA fragmentation. This information provides critical insights for clinical evaluations and developing treatment strategies for male infertility.

Table 4

Multivariate linear regression analysis for DFI

Variables b SE t P β (95% CI)
Intercept 23.85 3.12 7.65 <0.001 23.85 (17.74 to 29.97)
Liquefaction time (min) 0.13 0.04 2.90 0.004 0.13 (0.04 to 0.22)
PR (%) −0.08 0.10 −0.81 0.422 −0.08 (−0.29 to 0.12)
Sperm motility (%) −0.01 0.07 −0.21 0.834 −0.01 (−0.15 to 0.12)
Normal morphology −0.73 0.32 −2.26 0.025 −0.73 (−1.37 to −0.10)

CI: confidence interval.

4 Discussion

This study highlights a significant association between delayed semen liquefaction and impaired sperm quality in infertile men, with delayed liquefaction observed in approximately 12% of infertility cases [9]. Semen liquefaction, facilitated by SEMG1 and SEMG2 proteins from the seminal vesicles, is crucial for sperm release and fertilization. These proteins form a gel-like barrier that encapsulates sperm post-ejaculation, which is then broken down by prostate-specific antigen or KLK3, enhancing sperm motility for successful fertilization [1012]. Our findings suggest that optimizing liquefaction times could markedly improve fertility outcomes, underscoring the importance of further research into the mechanisms and treatment of delayed liquefaction to boost reproductive success in affected individuals.

Genetic variations, biochemical disruptions, and pathological conditions affecting the male accessory organs are significant contributors to defects in semen liquefaction processes [13]. Under microscopic examination, non-liquefied semen exhibits a fibrous net structure that impedes sperm movement, thus reducing motility and increasing reproductive challenges [8].

Our findings reveal clear disparities in semen parameter abnormalities across different DFI levels. Groups with DFI ≤ 15% and 15% < DFI < 30% demonstrated significantly lower rates of abnormal parameters compared to the DFI ≥ 30% group. Correlation analyses showed a positive association between DFI and semen liquefaction time, and negative correlations with progressive motility (PR), total sperm motility, and the ratio of normal sperm. Our multivariate regression model further highlighted these relationships, indicating that an increase in DFI is associated with prolonged liquefaction time and a reduced ratio of normal sperm. Such sperm damage is often due to factors like improper chromatin packing, abnormal sperm apoptosis, and oxidative stress, which also influence seminal vesicle secretions – such as coagulation factors and fibronectin – that affect liquefaction time [14,15].

Our findings are consistent with previous research. For example, L-carnitine treatments have been shown to reduce DFI and improve progressive motility (PR) [16]. Similarly, prior studies confirmed negative correlations between DFI and critical semen parameters such as sperm concentration and motility [17,18]. Additionally, the presence of leukocytes in semen, which release reactive oxygen species during phagocytosis, contributes to DNA damage and is associated with increased DFI, illustrating the role of oxidative stress in affecting semen quality [19]. Moreover, viscous semen, which induces oxidative stress, correlates with poor sperm quality and increased DFI, leading to semen non-liquefaction [20].

The study’s limitations include its small sample size and cross-sectional design, which may limit the generalizability of the findings and prevent the establishment of causality. Future research would benefit from a larger and more diverse sample to improve representativeness and from using longitudinal designs to better explore temporal relationships and causality. This study focused primarily on the relationship between delayed liquefaction and basic semen parameters; however, further research should also consider other influencing factors such as genetic variations, biochemical disruptions, and pathological conditions of male accessory organs. Moreover, controlling for confounding variables like age, lifestyle, comorbidities, and medication use will be crucial for enhancing the accuracy and reliability of the results. Expanding these research areas will provide deeper insights into the complex dynamics influencing male fertility and semen quality.

Our findings highlight a significant correlation between prolonged semen liquefaction times and increased sperm DFI, suggesting that DFI should be considered in routine infertility assessments. This could lead to more targeted and effective treatments for infertile males, particularly those with non-liquefying semen. Integrating DFI assessments into clinical practice could also aid clinicians in identifying cases where genetic integrity of sperm is compromised, potentially guiding more personalized fertility treatments. The variability in semen quality and DFI observed globally suggests that regional healthcare systems might need to adapt our findings differently. Developing countries, where access to specialized reproductive health services may be limited, could benefit from basic interventions aimed at improving general health and lifestyle factors that contribute to better semen quality. Meanwhile, in more developed regions, advanced diagnostic and treatment options could be integrated into existing healthcare frameworks to address infertility more effectively.

In conclusion, this study has established significant associations between delayed semen liquefaction in infertile males and several adverse semen quality parameters, including decreased progressive motility (PR), reduced total sperm motility, increased abnormal sperm morphology, and elevated DFI. These findings underline the importance of a comprehensive assessment of male fertility potential, integrating both physical semen characteristics and molecular markers of sperm integrity. Understanding these interrelationships is crucial for the accurate diagnosis and effective treatment of male infertility, highlighting the need for multifaceted approaches in clinical settings.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: Guarantor of integrity of the entire study: Fengqing Ji, Junying Chen, and Liyun Lin; study concepts: Fengqing Ji, Junying Chen, and Liyun Lin; study design: Fengqing Ji; definition of intellectual content: Fengqing Ji, Junying Chen, and Liyun Lin; literature research: Fengqing Ji; clinical studies: Fengqing Ji; experimental studies: Fengqing Ji, Junying Chen, and Liyun Lin; data acquisition: Fengqing Ji; data analysis: Fengqing Ji; statistical analysis: Fengqing Ji; manuscript preparation: Fengqing Ji; manuscript editing: Fengqing Ji; manuscript review: Fengqing Ji.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2024-08-12
Revised: 2024-11-15
Accepted: 2024-11-20
Published Online: 2025-05-20

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  52. The effect of the PKR inhibitor, 2-aminopurine, on the replication of influenza A virus, and segment 8 mRNA splicing
  53. Effects of Ire1 gene on virulence and pathogenicity of Candida albicans
  54. Small cell lung cancer with small intestinal metastasis: Case report and literature review
  55. GRB14: A prognostic biomarker driving tumor progression in gastric cancer through the PI3K/AKT signaling pathway by interacting with COBLL1
  56. 15-Lipoxygenase-2 deficiency induces foam cell formation that can be restored by salidroside through the inhibition of arachidonic acid effects
  57. FTO alleviated the diabetic nephropathy progression by regulating the N6-methyladenosine levels of DACT1
  58. Clinical relevance of inflammatory markers in the evaluation of severity of ulcerative colitis: A retrospective study
  59. Zinc valproic acid complex promotes osteoblast differentiation and exhibits anti-osteoporotic potential
  60. Primary pulmonary synovial sarcoma in the bronchial cavity: A case report
  61. Metagenomic next-generation sequencing of alveolar lavage fluid improves the detection of pulmonary infection
  62. Uterine tumor resembling ovarian sex cord tumor with extensive rhabdoid differentiation: A case report
  63. Genomic analysis of a novel ST11(PR34365) Clostridioides difficile strain isolated from the human fecal of a CDI patient in Guizhou, China
  64. Effects of tiered cardiac rehabilitation on CRP, TNF-α, and physical endurance in older adults with coronary heart disease
  65. Changes in T-lymphocyte subpopulations in patients with colorectal cancer before and after acupoint catgut embedding acupuncture observation
  66. Modulating the tumor microenvironment: The role of traditional Chinese medicine in improving lung cancer treatment
  67. Alterations of metabolites related to microbiota–gut–brain axis in plasma of colon cancer, esophageal cancer, stomach cancer, and lung cancer patients
  68. Research on individualized drug sensitivity detection technology based on bio-3D printing technology for precision treatment of gastrointestinal stromal tumors
  69. CEBPB promotes ulcerative colitis-associated colorectal cancer by stimulating tumor growth and activating the NF-κB/STAT3 signaling pathway
  70. Oncolytic bacteria: A revolutionary approach to cancer therapy
  71. A de novo meningioma with rapid growth: A possible malignancy imposter?
  72. Diagnosis of secondary tuberculosis infection in an asymptomatic elderly with cancer using next-generation sequencing: Case report
  73. Hesperidin and its zinc(ii) complex enhance osteoblast differentiation and bone formation: In vitro and in vivo evaluations
  74. Research progress on the regulation of autophagy in cardiovascular diseases by chemokines
  75. Anti-arthritic, immunomodulatory, and inflammatory regulation by the benzimidazole derivative BMZ-AD: Insights from an FCA-induced rat model
  76. Immunoassay for pyruvate kinase M1/2 as an Alzheimer’s biomarker in CSF
  77. The role of HDAC11 in age-related hearing loss: Mechanisms and therapeutic implications
  78. Evaluation and application analysis of animal models of PIPNP based on data mining
  79. Therapeutic approaches for liver fibrosis/cirrhosis by targeting pyroptosis
  80. Fabrication of zinc oxide nanoparticles using Ruellia tuberosa leaf extract induces apoptosis through P53 and STAT3 signalling pathways in prostate cancer cells
  81. Haplo-hematopoietic stem cell transplantation and immunoradiotherapy for severe aplastic anemia complicated with nasopharyngeal carcinoma: A case report
  82. Modulation of the KEAP1-NRF2 pathway by Erianin: A novel approach to reduce psoriasiform inflammation and inflammatory signaling
  83. The expression of epidermal growth factor receptor 2 and its relationship with tumor-infiltrating lymphocytes and clinical pathological features in breast cancer patients
  84. Innovations in MALDI-TOF Mass Spectrometry: Bridging modern diagnostics and historical insights
  85. BAP1 complexes with YY1 and RBBP7 and its downstream targets in ccRCC cells
  86. Hypereosinophilic syndrome with elevated IgG4 and T-cell clonality: A report of two cases
  87. Electroacupuncture alleviates sciatic nerve injury in sciatica rats by regulating BDNF and NGF levels, myelin sheath degradation, and autophagy
  88. Polydatin prevents cholesterol gallstone formation by regulating cholesterol metabolism via PPAR-γ signaling
  89. RNF144A and RNF144B: Important molecules for health
  90. Analysis of the detection rate and related factors of thyroid nodules in the healthy population
  91. Artesunate inhibits hepatocellular carcinoma cell migration and invasion through OGA-mediated O-GlcNAcylation of ZEB1
  92. Endovascular management of post-pancreatectomy hemorrhage caused by a hepatic artery pseudoaneurysm: Case report and review of the literature
  93. Efficacy and safety of anti-PD-1/PD-L1 antibodies in patients with relapsed refractory diffuse large B-cell lymphoma: A meta-analysis
  94. SATB2 promotes humeral fracture healing in rats by activating the PI3K/AKT pathway
  95. Overexpression of the ferroptosis-related gene, NFS1, corresponds to gastric cancer growth and tumor immune infiltration
  96. Understanding risk factors and prognosis in diabetic foot ulcers
  97. Atractylenolide I alleviates the experimental allergic response in mice by suppressing TLR4/NF-kB/NLRP3 signalling
  98. FBXO31 inhibits the stemness characteristics of CD147 (+) melanoma stem cells
  99. Immune molecule diagnostics in colorectal cancer: CCL2 and CXCL11
  100. Inhibiting CXCR6 promotes senescence of activated hepatic stellate cells with limited proinflammatory SASP to attenuate hepatic fibrosis
  101. Cadmium toxicity, health risk and its remediation using low-cost biochar adsorbents
  102. Pulmonary cryptococcosis with headache as the first presentation: A case report
  103. Solitary pulmonary metastasis with cystic airspaces in colon cancer: A rare case report
  104. RUNX1 promotes denervation-induced muscle atrophy by activating the JUNB/NF-κB pathway and driving M1 macrophage polarization
  105. Morphometric analysis and immunobiological investigation of Indigofera oblongifolia on the infected lung with Plasmodium chabaudi
  106. The NuA4/TIP60 histone-modifying complex and Hr78 modulate the Lobe2 mutant eye phenotype
  107. Experimental study on salmon demineralized bone matrix loaded with recombinant human bone morphogenetic protein-2: In vitro and in vivo study
  108. A case of IgA nephropathy treated with a combination of telitacicept and half-dose glucocorticoids
  109. Analgesic and toxicological evaluation of cannabidiol-rich Moroccan Cannabis sativa L. (Khardala variety) extract: Evidence from an in vivo and in silico study
  110. Wound healing and signaling pathways
  111. Combination of immunotherapy and whole-brain radiotherapy on prognosis of patients with multiple brain metastases: A retrospective cohort study
  112. To explore the relationship between endometrial hyperemia and polycystic ovary syndrome
  113. Research progress on the impact of curcumin on immune responses in breast cancer
  114. Biogenic Cu/Ni nanotherapeutics from Descurainia sophia (L.) Webb ex Prantl seeds for the treatment of lung cancer
  115. Dapagliflozin attenuates atrial fibrosis via the HMGB1/RAGE pathway in atrial fibrillation rats
  116. Ecology and Environmental Science
  117. Optimization and comparative study of Bacillus consortia for cellulolytic potential and cellulase enzyme activity
  118. The complete mitochondrial genome analysis of Haemaphysalis hystricis Supino, 1897 (Ixodida: Ixodidae) and its phylogenetic implications
  119. Epidemiological characteristics and risk factors analysis of multidrug-resistant tuberculosis among tuberculosis population in Huzhou City, Eastern China
  120. Indices of human impacts on landscapes: How do they reflect the proportions of natural habitats?
  121. Genetic analysis of the Siberian flying squirrel population in the northern Changbai Mountains, Northeast China: Insights into population status and conservation
  122. Diversity and environmental drivers of Suillus communities in Pinus sylvestris var. mongolica forests of Inner Mongolia
  123. Agriculture
  124. Integrated analysis of transcriptome, sRNAome, and degradome involved in the drought-response of maize Zhengdan958
  125. Variation in flower frost tolerance among seven apple cultivars and transcriptome response patterns in two contrastingly frost-tolerant selected cultivars
  126. Heritability of durable resistance to stripe rust in bread wheat (Triticum aestivum L.)
  127. Animal Science
  128. Effect of sex ratio on the life history traits of an important invasive species, Spodoptera frugiperda
  129. Plant Sciences
  130. Hairpin in a haystack: In silico identification and characterization of plant-conserved microRNA in Rafflesiaceae
  131. Widely targeted metabolomics of different tissues in Rubus corchorifolius
  132. The complete chloroplast genome of Gerbera piloselloides (L.) Cass., 1820 (Carduoideae, Asteraceae) and its phylogenetic analysis
  133. Field trial to correlate mineral solubilization activity of Pseudomonas aeruginosa and biochemical content of groundnut plants
  134. Correlation analysis between semen routine parameters and sperm DNA fragmentation index in patients with semen non-liquefaction: A retrospective study
  135. Plasticity of the anatomical traits of Rhododendron L. (Ericaceae) leaves and its implications in adaptation to the plateau environment
  136. Effects of Piriformospora indica and arbuscular mycorrhizal fungus on growth and physiology of Moringa oleifera under low-temperature stress
  137. Effects of different sources of potassium fertiliser on yield, fruit quality and nutrient absorption in “Harward” kiwifruit (Actinidia deliciosa)
  138. Comparative efficiency and residue levels of spraying programs against powdery mildew in grape varieties
  139. The DREB7 transcription factor enhances salt tolerance in soybean plants under salt stress
  140. Food Science
  141. Phytochemical analysis of Stachys iva: Discovering the optimal extract conditions and its bioactive compounds
  142. Review on role of honey in disease prevention and treatment through modulation of biological activities
  143. Computational analysis of polymorphic residues in maltose and maltotriose transporters of a wild Saccharomyces cerevisiae strain
  144. Optimization of phenolic compound extraction from Tunisian squash by-products: A sustainable approach for antioxidant and antibacterial applications
  145. Liupao tea aqueous extract alleviates dextran sulfate sodium-induced ulcerative colitis in rats by modulating the gut microbiota
  146. Toxicological qualities and detoxification trends of fruit by-products for valorization: A review
  147. Polyphenolic spectrum of cornelian cherry fruits and their health-promoting effect
  148. Optimizing the encapsulation of the refined extract of squash peels for functional food applications: A sustainable approach to reduce food waste
  149. Advancements in curcuminoid formulations: An update on bioavailability enhancement strategies curcuminoid bioavailability and formulations
  150. Impact of saline sprouting on antioxidant properties and bioactive compounds in chia seeds
  151. The dilemma of food genetics and improvement
  152. Bioengineering and Biotechnology
  153. Impact of hyaluronic acid-modified hafnium metalorganic frameworks containing rhynchophylline on Alzheimer’s disease
  154. Emerging patterns in nanoparticle-based therapeutic approaches for rheumatoid arthritis: A comprehensive bibliometric and visual analysis spanning two decades
  155. Application of CRISPR/Cas gene editing for infectious disease control in poultry
  156. Preparation of hafnium nitride-coated titanium implants by magnetron sputtering technology and evaluation of their antibacterial properties and biocompatibility
  157. Preparation and characterization of lemongrass oil nanoemulsion: Antimicrobial, antibiofilm, antioxidant, and anticancer activities
  158. Corrigendum
  159. Corrigendum to “Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells”
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