Immediate implant placement in the aesthetic zone has become increasingly widespread and has gradually evolved into a conventional techniques for implant procedures in the aesthetic region. To achieve favorable aesthetic and long-term outcomes, clinicians must possess extensive clinical experience as well as proficient surgical and restorative skills. This study summarizes the key factors influencing the long-term success of immediate implants in the aesthetic zone: strict adherence to the indications for immediate implant placement; thorough preoperative assessment of the patient’s systemic and local conditions, along with comprehensive evaluation of aesthetic risks; minimally invasive tooth extraction while preserving the integrity of the labial bone plate; selection of appropriately designed implants and their placement in an ideal three-dimensional position based on the implant’s characteristics; utilization of suitable bone and soft tissue augmentation techniques according to the patient’s specific hard and soft tissue anatomy, extent of bone defects, and periodontal phenotype; dynamic shaping of soft tissues through continuous adjustments in the emergence profile of provisional restorations; design of definitive restorations from the perspectives of health, function, and aesthetics; and implementation of regular follow-up and maintenance protocols after implant treatment, with increased emphasis on peri-implant care for patients who smoke, have diabetes, or undergo anti-osteoporosis therapy. This study proposes a decision-making framework to achieve long-term stable clinical outcomes with immediate implants in the aesthetic zone, providing a reference for clinicians in their clinical decision-making and treatment planning: ① for patients assessed as low aesthetic risk (e.g., thick gingival biotype, absence of hard and soft tissue defects, intact labial bone plate with thickness >1 mm, no acute infection), immediate implant placement after minimally invasive extraction is recommended, with the implant positioned in an ideal three-dimensional location, along with bone grafting in the gap between the implant and the labial bone plate and consideration of connective tissue grafting when required; ② for patients assessed as moderate aesthetic risk (e.g., thin gingival biotype, absence of soft tissue defects, intact labial bone plate but with thickness <1 mm or mild to moderate bone defects involving less than 50% height loss, chronic infection present), immediate implant placement with optimal three-dimensional positioning is feasible, accompanied by bone grafting in the implant-labial bone gap or external bone grafting on the labial aspect, with simultaneous or staged connective tissue grafting, or alternatively, use of the socket shield technique for immediate implant placement; ③ for patients assessed as high aesthetic risk (e.g., thin gingival biotype, presence of soft tissue defects, vertical bone deficiency, severe labial bone loss involving >50% height loss, acute infection present), ridge preservation followed by delayed implant placement is advised. By adhering to these treatment principles, immediate implant placement in the aesthetic zone can achieve reliable success rates and excellent aesthetic outcomes.

OBJECTIVE: To investigate the potential mechanisms of sepsis pathogenesis in intensive care unit (ICU), with a specific focus on the role of skin microbiota, and to evaluate the causal relationships between skin microbiota and ICU sepsis using Mendelian randomization (MR). METHODS: A two-sample MR analysis was performed using skin microbiota genome-wide association study (GWAS) summary data from German population cohorts as exposures, combined with ICU sepsis susceptibility and 28-day mortality GWAS summary data from the IEU OpenGWAS database as outcomes. The primary causal effect estimates were generated using the inverse variance weighted (IVW) method, supplemented by validation through MR-Egger and weighted median approaches. Heterogeneity and pleiotropy tests, along with sensitivity analyses, were conducted to evaluate the robustness of the results. RESULTS: Regarding risk of ICU sepsis, IVW analysis showed that order Pseudomonadales [odds ratio (OR) = 0.93, 95% confidence interval (95%CI) was 0.88-0.98], family Flavobacteriaceae (OR = 0.93, 95%CI was 0.90-0.96), and genus Acinetobacter (OR = 0.96, 95%CI was 0.93-0.99) were significantly negatively correlated with the risk of ICU sepsis (all P < 0.05). There was a significant positive correlation between the risk of ICU sepsis and the presence of β-Proteobacteria (OR = 1.05, 95%CI was 1.00-1.11) and Actinobacteria (OR = 1.05, 95%CI was 1.00-1.11), both P < 0.05. Regarding 28-day mortality of ICU sepsis, IVW analysis showed that phylum Bacteroidetes (OR = 0.92, 95%CI was 0.86-0.99), family Streptococcaceae (OR = 0.92, 95%CI was 0.85-0.98), family Flavobacteriaceae (OR = 0.90, 95%CI was 0.83-0.97), genus Streptococcus (OR = 0.92, 95%CI was 0.86-0.99), ASV016 [Enhydrobacter] (OR = 0.92, 95%CI was 0.87-0.98), and ASV042 [Acinetobacter] (OR = 0.92, 95%CI was 0.88-0.97) were significantly negatively correlated with the 28-day mortality of ICU sepsis (all P < 0.05); family Moraxellaceae (OR = 1.09, 95%CI was 1.00-1.18) and ASV008 [Staphylococcus] (OR = 1.08, 95%CI was 1.03-1.14) was significantly positively correlated with the 28-day mortality of ICU sepsis (both P < 0.05). Sensitivity analysis and MR-PRESSO showed no heterogeneity, pleiotropy, or horizontal pleiotropy between skin microbiota and ICU sepsis risk and 28-day mortality rate. Analysis of confounding factors showed that single nucleotide polymorphisms (SNPs) associated with relevant skin bacteria could independently and causally affect the risk of ICU sepsis or ICU sepsis related mortality rate, independent of other confounding factors. The Steiger test results indicated that the established causal relationship was not due to reverse causality. CONCLUSIONS Skin microbiota composition may influence both sepsis susceptibility and 28-day mortality in ICU settings. Family Flavobacteriaceae demonstrated protective effects against sepsis onset and mortality. These findings provide new perspectives for early detection and management strategies.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Mendelian Randomization Analysis ; Microbiota ; Skin/microbiology* ; Genome-Wide Association Study ; Risk Factors ; Skin Microbiome

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective: To review the current development of artificial intelligence (AI) technology in the field of transfusion medicine. Methods: A systematic search was conducted in the Clarivate Web of Science Database from inception to December 2024 for literature related to AI and transfusion. A total of 4 775 publications were identified. Based on inclusion and exclusion criteria, 133 original studies were ultimately included and analyzed using a narrative synthesis approach. Results: Research on AI in transfusion has surged since 2020 (accounting for 77% of all publications), with China ranking second globally in publication volume. Among the included studies, 69.2% focused on predicting individual transfusion needs, followed by inventory management (8.3%), diagnosis and prediction of adverse transfusion reactions (6.0%), factors influencing transfusion outcomes (5.3%), blood group identification (5.3%), blood quality testing (4.5%), and precise blood volume measurement (1.5%). Additionally, 4.5% of the studies were published in journals with an impact factor greater than 10; 19.5% developed software or applications; 31.5% were multi-center studies; 48.1% utilized decision tree methods, while 31.5% employed neural network approaches; and 14.2% conducted external validation of the algorithms. Conclusion: AI demonstrates significant potential in transfusion risk prediction, decision support, and blood management. However, challenges remain, including limited model generalizability, insufficient algorithm interpretability, and barriers to clinical translation. The deep integration of AI with transfusion medicine will accelerate the advent of precision transfusion era, maximizing blood resource utilization, reducing waste, and ensuring transfusion safety.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective:To explore the strategy of prostate biopsy in patients with prostate specific antigen(PSA)gray zone based on prostate imaging reporting and data system (PI-RADS).Methods:The clinical data of 427 patients who underwent transperineal prostate biopsy in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2022 were retrospectively analyzed. The median age was 66 (61, 72) years old. The median PSA was 6.62 (5.46, 8.19) ng/ml. The median PSA density (PSAD) was 0.15 (0.11, 0.21) ng/ml 2. The median prostate volume (PV) was 43.68 (31.12, 56.82) ml. PSA velocity (PSAV) data were available in 65 patients with negative MRI examination(PI-RADS <3), and the median PSAV was 1.40 (0.69, 2.89) ng/(ml· year). Among the patients with positive MRI(PI-RADS≥3), there were 174 patients with only 1 lesion and 83 patients with ≥2 lesions. A total of 170 patients with negative MRI underwent systematic biopsy, and 257 patients with positive MRI underwent systematic combined targeted biopsy. The PI-RADS score, regions of interest(ROI), PSAD, f/tPSA and PSAV were analyzed to explore the biopsy strategy for patients with PSA gray area based on bpMRI imaging. Results:Of the 427 patients included in the study, 194 were positive and 233 were negative. Among the patients with positive biopsy pathology, 140 cases were clinically significant prostate cancer (CsPCa). Among the MRI-negative patients, there were 33 cases with PSAV ≥1.4 ng/(ml·year), and 10 cases of prostate cancer and 6 cases of CsPCa were detected by systematic biopsy.In 32 cases with PSAV <1.4 ng/(ml·year), 3 cases of prostate cancer and 0 case of CsPCa were detected by systematic biopsy. The sensitivity of systematic biopsy for the diagnosis of prostate cancer and CsPCa in patients with PSAV≥1.4 ng/(ml·year) were 76.9% (10/13) and 100.0% (6/6) respectively, the specificity were 55.8% (29/52) and 54.2% (32/59) respectively, the negative predictive value were 90.6% (29/32) and 100.0% (32/32) respectively, and the positive predictive value were 30.3% (10/33) and 18.2% (6/33) respectively. In MRI-positive patients with PI-RADS 3, the prostate cancer detection rates of targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 41.7% (45/108), 32.4% (35/108) and 35.2% (38/108), respectively ( P=0.349). The detection rates of CsPCa were 27.8% (30/108), 21.3% (23/108) and 25.0% (27/108), respectively ( P=0.541). In patients with PI-RADS 4-5 and PSAD > 0.15 ng/ml 2, the detection rates of CsPCa in targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 67.8% (61/90), 58.9% (53/90) and 67.8% (61/90), respectively ( P=0.354). Conclusions:For MRI-negative patients, all CsPCa could be detected by perineal systematic biopsy when PSAV ≥1.4 ng/(ml·year), and active observation could be performed when PSAV <1.4 ng/(ml·year). For MRI-positive patients, targeted combined systemic biopsy was required when PI-RADS score was 3, and targeted biopsy only could be performed when PI-RADS score ≥4 and PSAD >0.15 ng/ml 2, otherwise targeted combined systemic biopsy was required.

Objective:To investigate the value of bi-parameter magnetic resonance imaging (bpMRI) in diagnosis and treatment of hematospermia.Methods:The clinical data and bpMRI of 182 patients with hematospermia (hematospermia group) and 51 patients without urinary system diseases (control group) were retrospectively analyzed. Both the control group and the hematospermia group underwent semen quality analysis, blood routine, urine routine, coagulation function, serum PSA test, and bpMRI examination before treatment. There were no significant differences in age [40(33, 50)years vs. 39(31, 53) years, Z=-0.77, P=0.43], body mass index [23.9(22.0, 25.7)kg/m2 vs. 24.5(22.3, 26.1) kg/m 2, Z=-0.50, P=0.62], smoking rate [24.7%(45/182) vs. 27.5%(14/51), χ2=0.16, P=0.69], alcohol consumption rate [29.1%(53/182) vs. 29.4%(15/51), χ2=0.002, P=0.97], and comorbid hypertension [20.9%(38/182) vs. 17.6%(9/51), χ2=0.26, P=0.61] between the hematospermia group and the control group. There was a statistically significant difference in PSA levels between the hematospermia group and the control group [2.82(2.08, 3.68)ng/ml vs 1.59(0.88, 2.28) ng/ml, Z=6.08, P=0.03].The median duration of illness in the hematospermia group was 10(5, 15) months, the median number of red blood cells reported in semen analysis was 17(10, 23)/HP, 59(32.4%) cases had infections in urine routine results, 15(8.2%) cases had infections in blood routine results, and 19(10.4%) cases had coagulation abnormalities. Hematospermia patients can be divided into five categories based on their causes: 105 cases of infection and inflammation, 42 cases of obstruction, 19 cases of tumors, 8 cases of systemic diseases, and 8 cases of iatrogenic factors and trauma. The treatment option was based on etiology: ①Infections, Inflammation, Systemic Diseases, Iatrogenic Factors, and Trauma: Remove the underlying cause and observe or watchful waiting. ②Recurrence of Systemic Diseases, Infections, and Inflammation: Treat the underlying cause with appropriate medication, including nonsteroidal anti-inflammatory drugs (NSAIDs), α-receptor blockers, etc. If there is an infection, administer oral antibiotics for 1-2 weeks. ③Obstruction and Tumors: Perform seminal vesiculoscopy surgery or radical prostatectomy. The efficacy evaluation was porfeomed after 12 months of treatment. Cure: Hematospermia symptoms disappear, with no recurrence. Effective: Symptoms significantly improve, no visible hematospermia, semen analysis shows marked improvement in red blood cells, and neither clinical symptoms nor semen analysis worsen. Not Cured: Visible hematospermia persists, and semen analysis shows no change in red blood cells compared to before treatment. Recurrence: Clinical symptoms improve but significant visible hematospermia reappears, and semen analysis shows red blood cell count >5/HP. Results:The proportion of patients with PI-RADS scores ≥ 3 in the hematospermia group was higher than that in the control group [29.1%(53/182)vs. 13.7%(7/51), χ2=4.94, P=0.03], and the difference was statistically significant. Comparing the imaging characteristics and related parameters of two groups of bpMRI, the results showed that the length and width of the left and right seminal vesicles in the hematospermia group were greater than those in the control group. The length of the left seminal vesicle was [29.9(25.9, 33.4)mm vs. 23.0(21.2, 25.4)mm, Z=7.30, P<0.01], the width of the left seminal vesicle was[20.4(17.8, 23.5)mm vs. 17.2(15.1, 18.5)mm, Z=5.85, P<0.01], the length of the right seminal vesicle was [28.9(24.8, 32.4)mm vs. 23.4(21.5, 28.1)mm, Z=4.68, P<0.01], and the width of the right seminal vesicle was[19.8(17.7, 23.1)mm vs. 17.2(15.1, 18.6)mm, Z=5.45, P<0.01]. The differences were statistically significant. After 12 months of follow-up, 152(83.5%) cases were cured, 21(11.5%) cases were defined as effective, 4(2.2%) cases were not cured, and 5(2.7%) cases had recurrence. Conclusions:The bpMRI examination can clearly identify the location of the hematospermia lesion and the timing of the bleeding. Based on the results of bpMRI, determining the cause and selecting the appropriate treatment strategy is reliable, convenient, and effective.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

A retrospective analysis was conducted on 5 516 patients diagnosed with prostate cancer(PCa) at our hospital. Among these, 52 patients aged ≤ 50 years were defined as the early-onset group.For the control group, 228 patients aged >50 years were randomly selected at a ratio of 1∶4.4. The early-onset group predominantly presented with elevated PSA levels at diagnosis and had a lower positive rate of digital rectal examination. There were no significant differences in clinical and pathological characteristics between the early-onset group and the control group. Young PCa patients in the low to intermediate risk categories had similar survival prognosis to older patients. However, young patients with high-risk prostate cancer had 5-year progression-free survival rate of 38.4% compared to 55.6% for older patients, and 5-year cancer-specific survival rate of 70.1% compared to 84.1% for older patients, indicating that high-risk young patients exhibited poorer oncological outcomes.

Objective:To investigate the value of bi-parameter magnetic resonance imaging (bpMRI) in diagnosis and treatment of hematospermia.Methods:The clinical data and bpMRI of 182 patients with hematospermia (hematospermia group) and 51 patients without urinary system diseases (control group) were retrospectively analyzed. Both the control group and the hematospermia group underwent semen quality analysis, blood routine, urine routine, coagulation function, serum PSA test, and bpMRI examination before treatment. There were no significant differences in age [40(33, 50)years vs. 39(31, 53) years, Z=-0.77, P=0.43], body mass index [23.9(22.0, 25.7)kg/m2 vs. 24.5(22.3, 26.1) kg/m 2, Z=-0.50, P=0.62], smoking rate [24.7%(45/182) vs. 27.5%(14/51), χ2=0.16, P=0.69], alcohol consumption rate [29.1%(53/182) vs. 29.4%(15/51), χ2=0.002, P=0.97], and comorbid hypertension [20.9%(38/182) vs. 17.6%(9/51), χ2=0.26, P=0.61] between the hematospermia group and the control group. There was a statistically significant difference in PSA levels between the hematospermia group and the control group [2.82(2.08, 3.68)ng/ml vs 1.59(0.88, 2.28) ng/ml, Z=6.08, P=0.03].The median duration of illness in the hematospermia group was 10(5, 15) months, the median number of red blood cells reported in semen analysis was 17(10, 23)/HP, 59(32.4%) cases had infections in urine routine results, 15(8.2%) cases had infections in blood routine results, and 19(10.4%) cases had coagulation abnormalities. Hematospermia patients can be divided into five categories based on their causes: 105 cases of infection and inflammation, 42 cases of obstruction, 19 cases of tumors, 8 cases of systemic diseases, and 8 cases of iatrogenic factors and trauma. The treatment option was based on etiology: ①Infections, Inflammation, Systemic Diseases, Iatrogenic Factors, and Trauma: Remove the underlying cause and observe or watchful waiting. ②Recurrence of Systemic Diseases, Infections, and Inflammation: Treat the underlying cause with appropriate medication, including nonsteroidal anti-inflammatory drugs (NSAIDs), α-receptor blockers, etc. If there is an infection, administer oral antibiotics for 1-2 weeks. ③Obstruction and Tumors: Perform seminal vesiculoscopy surgery or radical prostatectomy. The efficacy evaluation was porfeomed after 12 months of treatment. Cure: Hematospermia symptoms disappear, with no recurrence. Effective: Symptoms significantly improve, no visible hematospermia, semen analysis shows marked improvement in red blood cells, and neither clinical symptoms nor semen analysis worsen. Not Cured: Visible hematospermia persists, and semen analysis shows no change in red blood cells compared to before treatment. Recurrence: Clinical symptoms improve but significant visible hematospermia reappears, and semen analysis shows red blood cell count >5/HP. Results:The proportion of patients with PI-RADS scores ≥ 3 in the hematospermia group was higher than that in the control group [29.1%(53/182)vs. 13.7%(7/51), χ2=4.94, P=0.03], and the difference was statistically significant. Comparing the imaging characteristics and related parameters of two groups of bpMRI, the results showed that the length and width of the left and right seminal vesicles in the hematospermia group were greater than those in the control group. The length of the left seminal vesicle was [29.9(25.9, 33.4)mm vs. 23.0(21.2, 25.4)mm, Z=7.30, P<0.01], the width of the left seminal vesicle was[20.4(17.8, 23.5)mm vs. 17.2(15.1, 18.5)mm, Z=5.85, P<0.01], the length of the right seminal vesicle was [28.9(24.8, 32.4)mm vs. 23.4(21.5, 28.1)mm, Z=4.68, P<0.01], and the width of the right seminal vesicle was[19.8(17.7, 23.1)mm vs. 17.2(15.1, 18.6)mm, Z=5.45, P<0.01]. The differences were statistically significant. After 12 months of follow-up, 152(83.5%) cases were cured, 21(11.5%) cases were defined as effective, 4(2.2%) cases were not cured, and 5(2.7%) cases had recurrence. Conclusions:The bpMRI examination can clearly identify the location of the hematospermia lesion and the timing of the bleeding. Based on the results of bpMRI, determining the cause and selecting the appropriate treatment strategy is reliable, convenient, and effective.