Objective To investigate the semen quality and the influence of urogenital Ureaplasma urealyticum infection in pre‐conception males in Shanghai .Methods From Jan ,2014 to Nov ,2015 ,5 306 preconception males in our andrology department were received semen analysis .The difference of semen index between ages and urogenital Ureaplasma urealyticum infected or not were analyzed .Results The average pH value was(7 .27 ± 0 .14) ,the average volume was(3 .15 ± 1 .42)mL ,the average sperm concen‐trationwas(57.51±40.22)×106/mL,theaverageoftotalspermcountwas(172.83±134.90)×106 ,theaveragevitalityratioof grade(A+B) sperm was(38 .50 ± 17 .54)% ,the average motile sperm ratio was(46 .36 ± 20 .08)% ,the average of total motile sperm count was(89 .86 ± 92 .82) × 106 .Motile sperm ratio of preconception males from twenty to twenty nine was(49 .60 ± 20 .70)% ,total motile sperm count was(93 .40 ± 95 .83) × 106 ,which was greater than other ages .Ureaplasma urealyticum turned to positive in 2 311 cases was (43 .55% ) .There were statistically significant differences in total sperm count ,total motile sperm count ,sperm concentration ,vitality ratio of grade A sperm ,vitality ratio of grade(A+B) sperm ,motile sperm ratio ,seminal acid phosphatase ,seminalα‐glucosidase and seminal plasma fructose between positive and negative samples(P<0 .01) .There was differ‐ence in pH and seminal plasma zinc between positive and negative samples(P<0 .05) .Conclusion The sperm quality of preconcep‐tion males in Shanghai is not encouraging ,urogenital Ureaplasma urealyticum infection in preconception men is very important .

Objective To study the effects of different temporal resolution of spiral computed tomography (CT)on perfusion parameters and perfusion curve of cervical cervical cancer.Methods Ten cases of cervical cancer were clinically confirmed with CT perfusion scanning.The original data were acquired using temporal resolution of 0.75 s.Then the original data were grouped according to different temporal resolution,namely,1.5 s group,2.25 s group,3 s group,3.75 s group,4.5 s group,5.25 s group,and 6 s group (experiment group). According to the same mathematical model and ROI of the same part,perfusion parameters (BF,BV,MTT,and PS)in each group were calculated respectively and compared with the original data.Results BF and MTT were relatively sensitive to the change of temporal resolution.When the temporal resolution was 3 s,it had a significant impact.PS and BV were not so sensitive to the change of temporal resolution.Temporal resolution of 4.5 s had a significant effect on PS. There was a significant effect on BV until the temporal resolution was 5.25 s. Conclusion Changing the temporal resolution will lead to corresponding changes of perfusion curve and perfusion parameters.Under the premise that it does not affect the diagnosis,properly decreasing temporal resolution (circu-lar scanning temporal ≤2.25 s)of CT perfusion scanning of cervical cancer can reduce the radiation dose effectively.

OBJECTIVETo investigate the effects in vitro of 17 beta-estradiol (E2) on the proliferation and metabolism of rabbit mandibular condylar cartilage cells.

METHODSChondrocytes were derived from neonatal rabbit mandibular condylar cartilage using a modified enzyme method. 17 beta-estradiol was added to the culture medium in a variety of concentrations. Cell growth and DNA, collagen, and proteoglycan synthesis were used as indicators of proliferation and differentiation of condylar chondrocytes. These were measured by cell number, 3H-proline and 35S-incorporation, respectively.

RESULTSE2 increased cell number and 3H-thymidine incorporation at 10(-8) to 10(-10) mol/L, and 10(-8) to 10(-11) mol/L in a dose-dependent manner, peaking at 10(-8) mol/L and 10(-9) mol/L, respectively. However, further increase in the concentration of estradiol caused inhibition of both cell number and 3H-thymidine incorporation, and this was significant at 10(-6) mol/L. The effect of E2 on proteoglycan synthesis was similar; the maximum stimulating effect was at 10(-8) mol/L, and inhibition was significant at 10(-6) mol/L. There was no obvious stimulatory effect of E2 on 3H-thymidine incorporation observed.

CONCLUSIONSEstradiol affects condylar chondrocyte cell growth, DNA, and proteoglycan synthesis in a biphasic manner depending on its concentration. This indicates that estrogen may be important in the proliferation and differentiation of mandibular condylar chondrocytes, and could be relevant to some aspects of certain temporomandibular joint diseases by modulating the function of the chondrocytes.

Animals ; Cartilage, Articular ; cytology ; metabolism ; Cell Differentiation ; Cell Division ; drug effects ; Cells, Cultured ; Estradiol ; pharmacology ; Mandibular Condyle ; cytology ; metabolism ; Rabbits

Purpose To explore the value of three-dimensions densely connected convolutional networks(3D-DenseNet)in the differential diagnosis of high-grade gliomas(HGGs)and single brain metastases(BMs)via MRI,and to compare the diagnostic performance of models built with different sequences.Materials and Methods T2WI and T1WI contra-enhanced(T1C)imaging data of 230 cases of HGGs and 111 cases of BMs confirmed by surgical pathology in Lanzhou University Second Hospital from June 2016 to June 2021 were retrospectively collected,and the volume of interest under the 3D model was delineated in advance as the input data.All data were randomly divided into a training set(n=254)and a validation set(n=87)in a ratio of 7∶3.Based on the 3D-DenseNet,T2WI,T1C and two sequence fusion prediction models(T2-net,T1C-net and TS-net)were constructed respectively.The predictive efficiency of each model was evaluated and compared by the receiver operating characteristic curve,and the predictive performance of models built with different sequences were compared.Results The area under curve(AUC)of T1C-net,T2-net and TS-net in the training and validation sets were 0.852,0.853,0.802,0.721,0.856 and 0.745,respectively.The AUC and accuracy of the validation set of T1C-net were significantly higher than those of T2-net and TS-net,respectively,and the AUC and accuracy of the validation set of TS-net were significantly higher than those of T2-net.There was a significant difference between T1C-net and T2-net models(P<0.05),while there were no statistical differences between the models of TS-net and T2-net,T1C-net and TS-net(P>0.05).The T1C-net model based on 3D-DenseNet had the best performance,the accuracy of the validation set was 80.5%,the sensitivity was 90.9%,the specificity was 62.5%.Conclusion The 3D-DenseNet model based on MRI conventional sequence has better diagnostic performance,and the model built by T1C-net sequence has better performance in differentiating HGGs and BMs.Deep learning models can be a potential tool to identify HGGs and BMs and to guide the clinical formulation of precise treatment plans.

OBJECTIVE: To explore the risk factors for poor prognosis in sepsis-associated acute kidney injury (SA-AKI) and establish a nomogram predictive model. METHODS: The clinical data of patients with SA-AKI admitted to the department of critical care medicine of Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2019 to September 2022 were retrospectively analyzed, including demographic information, worst values of blood cell counts and biochemical indicators within 24 hours of SA-AKI diagnosis, whether the patient received renal replacement therapy (RRT), mechanical ventilation, vasopressor therapy during hospitalization, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), fibrinogen-to-albumin ratio (FAR) within 24 hours of diagnosis, acute kidney injury (AKI) staging, total length of hospital stay, length of intensive care unit (ICU) stay, and others. According to the 28-day outcome, the patients were divided into survival group and death group, and the indicators between the two groups were compared. Univariate and multivariate Logistic regression analyses were used to screen for risk factors associated with mortality in SA-AKI patients. A nomogram predictive model for SA-AKI prognosis was constructed based on the identified risk factors. Receiver operator characteristic curve (ROC curve) and calibration plots were generated to evaluate the predictive value of the nomogram model for SA-AKI prognosis. RESULTS: A total of 113 SA-AKI patients were included, with 67 in the survival group and 46 in the death group. The 28-day mortality among SA-AKI patients was 40.7%. The comparison between the two groups showed that there were statistically significant differences in age ≥ 65 years, AKI stage, mechanical ventilation, vasopressors, RRT, length of ICU stay, and laboratory indicators cystatin C (Cys C), fibrinogen (Fib), and FAR. Multivariate Logistic regression analysis showed that age ≥ 65 years [odds ratio (OR) = 7.967, 95% confidence interval (95%CI) was 1.803-35.203, P = 0.006], cystatin C (OR = 7.202, 95%CI was 1.756-29.534, P = 0.006), FAR (OR = 2.444, 95%CI was 1.506-3.968, P < 0.001), and RRT (OR = 7.639, 95%CI was 1.391-41.951, P = 0.019) were independent risk factors for mortality in SA-AKI patients. ROC curve analysis showed that the area under the ROC curve (AUC) for age ≥ 65 years, cystatin C, FAR, and RRT in predicting SA-AKI patient mortality were 0.713, 0.856, 0.911, and 0.701, respectively. A nomogram predictive model for SA-AKI patient prognosis was constructed based on age ≥ 65 years, cystatin C, FAR, and RRT, with an AUC of 0.967 (95%CI was 0.932-1.000) according to ROC curve analysis. The calibration plot indicated good consistency between predicted and actual probabilities. CONCLUSIONS Age ≥ 65 years, cystatin C, FAR, and RRT are independent risk factors for mortality in SA-AKI patients. The nomogram predictive model based on these four factors can accurately predict SA-AKI patient prognosis, helping physicians adjust treatment strategies in a timely manner and improve patient outcomes.
Humans ; Aged ; Cystatin C ; Retrospective Studies ; Intensive Care Units ; Sepsis/diagnosis* ; Acute Kidney Injury/therapy* ; Prognosis ; ROC Curve ; Fibrinogen