Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To investigate the prevalence and associated factors of diabetic retinopathy (DR) among diabetic residents in Longyan of Fujian Province.Methods:A investigative research. From January 2022 to December 2023, a total of 10 061 diabetic patients enrolled in the chronic disease follow-up management system from 112 towns and sub-districts in 7 counties and districts of Longyan of Fujian Province were selected as the target population. A questionnaire survey, routine physical examination, vision test, and non-mydriatic fundus photography were conducted. A total of 762 cases with missing height, weight, blood pressure, fasting plasma glucose (FPG), and diabetes duration, and 507 cases with unclear fundus photography were excluded, resulting in 8 792 cases included in the final statistical analysis. DR diagnosis and classification were based on the 2019 International Clinical Classification of DR. The prevalence of DR was calculated for single-eye or double-eye DR cases as 1 case; the more severe eye was used for DR grading in double-eye DR cases. Statistical analysis was performed by grouping based on the presence or absence of DR and dividing into age groups ≤67 years and >67 years. χ2 test was used to analyze factors associated with prevalence; binary multivariate logistic regression analysis was employed to identify influencing factors of DR. Results:Among the 8 792 cases, 888 (10.1%, 888/8 792) were diagnosed with DR (DR group), and 7 904 (89.9%, 7 904/8 792) had no DR (non-DR group). Compared to the non-DR group, the DR group showed significant increases in FPG ( Z=-12.448), diabetes duration ( Z=-18.936), systolic blood pressure ( Z=-4.237), diastolic blood pressure ( Z=-2.881), and body mass index (BMI) ≥24 kg/m 2 ( P<0.001). Significant differences were also found between the two groups in hypertension ( χ2=11.450), hyperlipidemia ( χ2=5.100), kidney disease ( χ2=7.039), family history of diabetes ( χ2=5.025), and regular medication use ( χ2=66.034) ( P<0.05). There were 4 688 cases in the ≤67 years group and 4 104 in the >67 years group. In the ≤67 years group, significant differences in DR prevalence were found for FPG levels ( χ2=111.754), diabetes duration ( χ2=231.658), BMI ( χ2=12.404), systolic blood pressure ( χ2=17.912), regular medication use ( χ2=40.727), hyperlipidemia ( χ2=6.816), and hypertension history ( χ2=6.775) ( P<0.05). In the >67 years group, significant differences in DR prevalence were found for FPG levels ( χ2=59.916), diabetes duration ( χ2=128.362), systolic blood pressure ( χ2=5.183), regular medication use ( χ2=22.097), kidney disease ( χ2=6.251), and family history of diabetes duration ( χ2=4.967) ( P<0.05). No significant differences in DR prevalence were found based on sex, education level, smoking history, alcohol consumption, exercise habits, heart disease history, or other family disease history ( P>0.05). Logistics regression analysis results show that patients aged >67 years, FPG [odds ratio (OR)=1.074, 95%confidence interval ( CI) 1.046-1.102], diabetes duration ( OR=1.088, 95% CI 1.071-1.106), systolic blood pressure ( OR=1.007, 95% CI 1.001-1.013), and kidney disease ( OR=3.617, 95% CI 1.268-10.320) were identified as risk factors for DR ( P<0.05). In patients aged ≤67 years, FPG ( OR=1.088, 95% CI 1.067-1.110), diabetes duration ( OR=1.108, 95% CI 1.091-1.125), and systolic blood pressure ( OR=1.008, 95% CI 1.003-1.013) were identified as independent risk factors for DR ( P<0.05), while BMI ≥24 kg/m 2 ( OR=0.934, 95% CI 0.908-0.965) was a protective factor for DR ( P<0.05). Age, regular medication use, hypertension, and hyperlipidemia were identified as potential confounding factors for DR occurrence. Conclusions:The prevalence of DR among diabetes patients in Longyan of Fujian Province, is 10.1%. FPG, diabete duration, and systolic blood pressure are independent risk factors for DR, while age, regular medication use, hypertension, and hyperlipidemia are potential confounding factors for DR occurrence.

Objective:To evaluate the dose-response relationship of alfentanil inhibiting gag reflex when combined with propofol in elderly patients undergoing painless gastroscopy.Methods:Patients of American Society of Anesthesiologists physical statusⅠor Ⅱ, aged ≥60 yr, scheduled for elective painless gastroscopy, were selected. Propofol 1.5 mg/kg combined with alfentanil was given intravenously in all the patients. The dose of alfentanil was determined by the Dixon up-and-down method. The initial dose of alfentanil was set at 5 μg/kg. The dose of alfentanil in the next patient was determined according to the development of gag reflex, and the ratio between the two successive doses was 1.1. The median effective dose (ED 50) and 95% confidence interval of alfentanil-induced inhibition of gag reflex when combined with propofol in elderly patients undergoing painless gastroscopy were calculated using the by up-and-down sequential allocation. Results:The ED 50 (95% confidence interval) of alfentanil-induced inhibition of gag reflex when combined with propofol 1.5 mg/kg was 2.8 (2.4-3.2) μg/kg in elderly patients undergoing painless gastroscopy. Conclusion:When combined with propofol 1.5 mg/kg, the ED 50 of alfentanil inhibiting gag reflex is 2.8 μg/kg in elderly patients undergoing painless gastroscopy.

Objective To investigate the effect of Zhutan Tongluo Tang on the articular cartilage degeneration in a rat osteoar-thritis model ,and explore the potential mechanism .Methods Thirty-healthy SD rats were equally divided into the normal control group ,the model control group and Zhutan Tongluo Tang group .Rats were induced osteoarthritis models with 4% mass fraction of papain .After replication of osteoarthritis models ,Zhutan Tongluo Tang group were given Zhutan Tongluo Tang with 2 mL/(kg · d) ,the model control group were given lavage with normal saline ,the normal control group with rovtine feeding .After treat-ments for 3 months ,the cartilage morphology were observed using HE stains ,and the expression of IL-1β,TNF-α and MMP-3 in synovial fluid were measured by ELISA .Results The degeneration of cartilage in Zhutan Tongluo Tang group was better than the model control group .Compared with the model control group ,the expression levels of IL-1β (12 .13 pg/mL vs .16 .62 pg/mL ) , TNF-α (13 .76 pg/mL vs .18 .26 pg/mL) and MMP-3 (14 .21 ng/mL vs .21 .57 ng/mL) in synovial fluid were significantly de-creased in Zhutan Tongluo Tang group(P<0 .05) .Conclusion Zhutan Tongluo Tang has the function of treating delaying osteoar-thritis cartilage degeneration and decreasing inflammation ,whose mechanism is related to inhibiting the expression of inflammatory cytokines .

Objective:To explore the calvarial critical size defect (CSD)in rats with type 2 diabetes mellitus(T2DM).Methods:T2DM model of SD rats(weighted 300-320 g)was induced by high fat and high sugar diet and low dose intraperitoneal streptozotocin (STZ)injection.The rats with T2DMand the normal controls were divided into 4 groups(n=3)respectively.Defects with the diame-ter(mm)of 2,3,4 and 5 were made on the central calvaria of each rat.General observation,X-ray examination and histological study were performed 8 weeks postoperatively.Results:In the T2DM group,only the defects of 2 mm diameter were healed completely,X-ray resistance and new bone formation were observed;the defects of 3,4 and 5 mm diameter were unhealed,X-ray transmission was observed and newly formed bone was insufficient.In the control group,the defects of 2,3 and 4 mm diameter were healed completely, X-ray resistance and new bone formation were observed;the defects of 5 mm diameter were unhealed,X-ray transmission was ob-served,newly formed bone was insufficient.Conclusion:The calvarial CSD of T2DM rat model can be defined as the defect with the diameter of 3 mm.