AIM:To describe the characteristics of peripapillary hyperreflective ovoid mass-like structure(PHOMS)in myopic children and to investigate factors associated with PHOMS.METHODS:This retrospective observational study i...AIM:To describe the characteristics of peripapillary hyperreflective ovoid mass-like structure(PHOMS)in myopic children and to investigate factors associated with PHOMS.METHODS:This retrospective observational study included 101 eyes of 101 children(age≤17y)with myopia.All included patients underwent comprehensive clinical examination.Optic nerve canal parameters,including disc diameter,optic nerve head(ONH)tilt angle,and border tissue angle were measured using serial enhanced-depth imaging spectral-domain optical coherence tomography(EDI-OCT).Based on the optic disc drusen consortium’s definition of PHOMS,eyes were classified as PHOMS group and non-PHOMS group.PHOMS was categorized according to height.RESULTS:Sixty-seven(66.3%)eyes were found with PHOMS.Small PHOMS could only be detected by optical coherence tomography(OCT).Medium PHOMS could be seen with blurred optic disc borders corresponding to OCT.The most frequent location of PHOMS was at the nasosuperior(91%,61 of 67 eyes)to ONH disc.The axial length and spherical equivalent were more myopic in the PHOMS group than in the non-PHOMS group(both P<0.001).ONH tilt angle was also significantly greater in PHOMS group than in non-PHOMS group[8.90(7.16-10.54)vs 3.93(3.09-5.25),P<0.001].Border tissue angle was significantly smaller in PHOMS group than in non-PHOMS group[29.70(20.90-43.81)vs 45.62(35.18-60.45),P<0.001].In the multivariable analysis,spherical equivalent(OR=3.246,95%CI=1.209-8.718,P=0.019)and ONH tilt angle(OR=3.275,95%CI=1.422-7.542,P=0.005)were significantly correlated with PHOMS.There was no disc diameter associated with PHOMS.In the linear regression analysis,border tissue angle was negatively associated with PHOMS height(β=-2.227,P<0.001).CONCLUSION:PHOMS is associated with optic disc tilt and optic disc nasal shift in myopia.Disc diameter is not a risk factor for PHOMS.The changes in ONH caused by axial elongation facilitated an understanding of the mechanism of PHOMS.展开更多
BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in a...BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.展开更多
Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored ...Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.展开更多
基金Supported by Wuhan Central Hospital Discipline Fund(No.2021XK017).
文摘AIM:To describe the characteristics of peripapillary hyperreflective ovoid mass-like structure(PHOMS)in myopic children and to investigate factors associated with PHOMS.METHODS:This retrospective observational study included 101 eyes of 101 children(age≤17y)with myopia.All included patients underwent comprehensive clinical examination.Optic nerve canal parameters,including disc diameter,optic nerve head(ONH)tilt angle,and border tissue angle were measured using serial enhanced-depth imaging spectral-domain optical coherence tomography(EDI-OCT).Based on the optic disc drusen consortium’s definition of PHOMS,eyes were classified as PHOMS group and non-PHOMS group.PHOMS was categorized according to height.RESULTS:Sixty-seven(66.3%)eyes were found with PHOMS.Small PHOMS could only be detected by optical coherence tomography(OCT).Medium PHOMS could be seen with blurred optic disc borders corresponding to OCT.The most frequent location of PHOMS was at the nasosuperior(91%,61 of 67 eyes)to ONH disc.The axial length and spherical equivalent were more myopic in the PHOMS group than in the non-PHOMS group(both P<0.001).ONH tilt angle was also significantly greater in PHOMS group than in non-PHOMS group[8.90(7.16-10.54)vs 3.93(3.09-5.25),P<0.001].Border tissue angle was significantly smaller in PHOMS group than in non-PHOMS group[29.70(20.90-43.81)vs 45.62(35.18-60.45),P<0.001].In the multivariable analysis,spherical equivalent(OR=3.246,95%CI=1.209-8.718,P=0.019)and ONH tilt angle(OR=3.275,95%CI=1.422-7.542,P=0.005)were significantly correlated with PHOMS.There was no disc diameter associated with PHOMS.In the linear regression analysis,border tissue angle was negatively associated with PHOMS height(β=-2.227,P<0.001).CONCLUSION:PHOMS is associated with optic disc tilt and optic disc nasal shift in myopia.Disc diameter is not a risk factor for PHOMS.The changes in ONH caused by axial elongation facilitated an understanding of the mechanism of PHOMS.
基金The study was reviewed and approved by the Second Hospital of Shandong University Institutional Review Board,IRB No.KYLL-2023LW044.
文摘BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.
基金the China Academy of Railway Sciences Corporation Limited(2023YJ257).
文摘Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.
