BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive...BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive measures.This retrospective study aimed to explore the incidence and risk factors for postpartum PTSD in women who give birth after emergency admission.METHODS:Medical records of women who gave birth after emergency admission were collected between March 2021 and April 2023.The patients’general conditions and perinatal clinical indicators were recorded.The puerperae were divided into PTSD group and control group based on symptom occurrence at six weeks postpartum.Multivariate logistic regression analysis was performed to identify risk factors.RESULTS:A total of 276 puerperae were included,with a PTSD incidence of 20.3% at six weeks postpartum.Multivariate logistic regression analysis identified emergency cesarean section(odds ratio[OR]=2.102;95%confidence interval[CI]:1.114-3.966,P=0.022),admission to the emergency department after midnight(12:00 AM)(OR=2.245;95%CI:1.170-4.305,P<0.001),and cervical dilation(OR=3.203;95%CI:1.670–6.141,P=0.039)as independent risk factors for postpartum PTSD.Analgesia pump use(OR=0.500;95%CI:0.259–0.966,P=0.015)was found to be a protective factor against postpartum PTSD.CONCLUSION:Emergency cesarean section,admission to the emergency department after midnight,and cervical dilation were identified as independent risk factors for postpartum PTSD,while analgesic pump use was a protective factor.These findings provide insights for developing more effective preventive measures for women who give birth after emergency admission.展开更多
Severe trauma is one of the main causes of premature death,posing a significant challenge to public health systems.[1]At present,there is a lack of universally accepted guidelines for rapid detection of life-threateni...Severe trauma is one of the main causes of premature death,posing a significant challenge to public health systems.[1]At present,there is a lack of universally accepted guidelines for rapid detection of life-threatening severe trauma,[2]and the accuracy of existing prognostic models in predicting early death is limited.[3,4]Severe non-brain-injured trauma(SNT)patients account for approximately 70%of all trauma-related deaths.Moreover,there is a lack of studies on early death in SNT patients.[5]This study aims to identify risk factors associated with early death(≤72 h post-admission)in SNT patients.展开更多
DNA methylation plays a significant role in various biological events, and its precise determination is vital for the prognosis and treatment of cancer. Here, we proposed an ultrasensitive electrochemical biosensor fo...DNA methylation plays a significant role in various biological events, and its precise determination is vital for the prognosis and treatment of cancer. Here, we proposed an ultrasensitive electrochemical biosensor for the quantitative analysis of multiple methylation-locus in DNA sequence via DNA anchoring the gold nanoparticles (DNA-AuNPs) and bienzyme dual signal amplifications. After the target DNA captured by the DNA-AuNPs of the biosensor, the methyl-CpG binding protein MeCP2 could specifically conjugate to the methylation-loci in the double-stranded DNA. Successively, the glucose oxidase (GOD) and horseradish (HRP) co-labeled antibody captured the His tagged MeCP2, which leads to a cascade enzymatic catalysis of the substrates to yield a detectable electrochemical signal. Both the two strategies, including the high content of DNA-AuNPs and the associated catalysis of bienzyme, dramatically enhanced the sensitivity of the biosensor. The response current elevated with the increasing numbers of methylation-locus, thus the multiple methylated DNA was identified by detecting the corresponding current signals. This method could detect the methylated target as low as 0.1 fM, and showed a wide linear range from 10 - 15 M to 10 - 7 M. Besides, the long-term stability and repeatability of the biosensor were also validated. The prepared electrochemical immunosensor exhibits ultrasensitivity through the bienzyme labeling process, which can be applied for the detection of DNA methylation with low concentration.展开更多
In additive manufacturing of metal parts,the ability to accurately predict the extremely variable temperature field in detail,and relate it quantitatively to structure and properties,is a key step in predicting part p...In additive manufacturing of metal parts,the ability to accurately predict the extremely variable temperature field in detail,and relate it quantitatively to structure and properties,is a key step in predicting part performance and optimizing process design.In this work,a finite element simulation of the directed energy deposition(DED)process is used to predict the space-and time-dependent temperature field during the multi-layer build process for Inconel 718 walls.The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds.The relationship between predicted cooling rate,microstructural features,and mechanical properties is examined,and cooling rate alone is found to be insufficient in giving quantitative property predictions.Because machine learning offers an efficient way to identify important features from series data,we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history.Very good predictions of material properties,especially ultimate tensile strength,are obtained using simulated thermal history data.To further interpret the convolutional neural network predictions,we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases.A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.展开更多
基金Science and Technology Development Plan Project of Suzhou(SKJYD2021035)Science and Technology Development Plan Project of Suzhou(SKJYD2022078)The Key Project Research Fund of the Second Affiliated Hospital of Wannan Medical College(YK2023Z04)。
文摘BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive measures.This retrospective study aimed to explore the incidence and risk factors for postpartum PTSD in women who give birth after emergency admission.METHODS:Medical records of women who gave birth after emergency admission were collected between March 2021 and April 2023.The patients’general conditions and perinatal clinical indicators were recorded.The puerperae were divided into PTSD group and control group based on symptom occurrence at six weeks postpartum.Multivariate logistic regression analysis was performed to identify risk factors.RESULTS:A total of 276 puerperae were included,with a PTSD incidence of 20.3% at six weeks postpartum.Multivariate logistic regression analysis identified emergency cesarean section(odds ratio[OR]=2.102;95%confidence interval[CI]:1.114-3.966,P=0.022),admission to the emergency department after midnight(12:00 AM)(OR=2.245;95%CI:1.170-4.305,P<0.001),and cervical dilation(OR=3.203;95%CI:1.670–6.141,P=0.039)as independent risk factors for postpartum PTSD.Analgesia pump use(OR=0.500;95%CI:0.259–0.966,P=0.015)was found to be a protective factor against postpartum PTSD.CONCLUSION:Emergency cesarean section,admission to the emergency department after midnight,and cervical dilation were identified as independent risk factors for postpartum PTSD,while analgesic pump use was a protective factor.These findings provide insights for developing more effective preventive measures for women who give birth after emergency admission.
基金supported by Suzhou Gusu Health Talents Scientifi c Research Project(GSWS2021017)Scientific Pre-research Fund of the Second Affiliated Hospital of Soochow University(SDFEYQN2007).
文摘Severe trauma is one of the main causes of premature death,posing a significant challenge to public health systems.[1]At present,there is a lack of universally accepted guidelines for rapid detection of life-threatening severe trauma,[2]and the accuracy of existing prognostic models in predicting early death is limited.[3,4]Severe non-brain-injured trauma(SNT)patients account for approximately 70%of all trauma-related deaths.Moreover,there is a lack of studies on early death in SNT patients.[5]This study aims to identify risk factors associated with early death(≤72 h post-admission)in SNT patients.
文摘DNA methylation plays a significant role in various biological events, and its precise determination is vital for the prognosis and treatment of cancer. Here, we proposed an ultrasensitive electrochemical biosensor for the quantitative analysis of multiple methylation-locus in DNA sequence via DNA anchoring the gold nanoparticles (DNA-AuNPs) and bienzyme dual signal amplifications. After the target DNA captured by the DNA-AuNPs of the biosensor, the methyl-CpG binding protein MeCP2 could specifically conjugate to the methylation-loci in the double-stranded DNA. Successively, the glucose oxidase (GOD) and horseradish (HRP) co-labeled antibody captured the His tagged MeCP2, which leads to a cascade enzymatic catalysis of the substrates to yield a detectable electrochemical signal. Both the two strategies, including the high content of DNA-AuNPs and the associated catalysis of bienzyme, dramatically enhanced the sensitivity of the biosensor. The response current elevated with the increasing numbers of methylation-locus, thus the multiple methylated DNA was identified by detecting the corresponding current signals. This method could detect the methylated target as low as 0.1 fM, and showed a wide linear range from 10 - 15 M to 10 - 7 M. Besides, the long-term stability and repeatability of the biosensor were also validated. The prepared electrochemical immunosensor exhibits ultrasensitivity through the bienzyme labeling process, which can be applied for the detection of DNA methylation with low concentration.
基金This work was supported by the National Science Foundation(NSF)under Grant No.CMMI-1934367the Beijing Institute of Collaborative Innovation under Award No.20183405+1 种基金J.A.G.and J.B.acknowledge support by the US Army Research Laboratory under Grant No.W911NF-19-2-0092The SEM analysis work made use of the EPIC facility of NUANCE Center and the MatCI Facility of the Materials Research Center at Northwestern University,which was supported by NSF under Grant No.ECCS-1542205 and DMR-1720139,the International Institute for Nanotechnology(IIN),the Keck Foundation,and the State of Illinois through the IIN.
文摘In additive manufacturing of metal parts,the ability to accurately predict the extremely variable temperature field in detail,and relate it quantitatively to structure and properties,is a key step in predicting part performance and optimizing process design.In this work,a finite element simulation of the directed energy deposition(DED)process is used to predict the space-and time-dependent temperature field during the multi-layer build process for Inconel 718 walls.The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds.The relationship between predicted cooling rate,microstructural features,and mechanical properties is examined,and cooling rate alone is found to be insufficient in giving quantitative property predictions.Because machine learning offers an efficient way to identify important features from series data,we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history.Very good predictions of material properties,especially ultimate tensile strength,are obtained using simulated thermal history data.To further interpret the convolutional neural network predictions,we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases.A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.
