Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To systematically evaluate the current status of quality of life in infertile patients and its influencing factors.Methods Computer search of PubMed,Cochrane Library,Web of Science,Embase,CBM,Knowledge Network,Wanfang,and Wipu,with a search timeframe from the establishment of the library to December 2023,and meta-analysis using CMA 3.3 software.Results A total of 33 literatures were included in the current status of quality of life,which showed that the level of quality of life was 60.841[95％CI(58.029,63.654),P＜0.001]for the FertiQoL scale and 67.630[95％CI(59.541,75.718),P＜0.001]for the SF-36 scale.A total of 45 papers were included in the literature on factors influencing quality of life,and the results showed that personal trait factors of young age(r=0.181,P=0.006)and history of pregnancy(r=0.258,P＜0.001)were lowly positively correlated with quality of life,and that long years of infertility(r=-0.267,P＜0.001),a history of treatment failure(r=-0.292,P＜0.001),and long duration of treatment(r=-0.213,P＜0.001)had a low negative association with quality of life.Behavioural factors such as anxiety(r=-0.547,P＜0.001),depression(r=-0.556,P＜0.001),and stigma(r=-0.530,P＜0.001)were highly negatively correlated with the quality of life;high fertility stress(r=-0.333,P=0.010)was moderately negatively correlated with the quality of life;and good psychological resilience(r=0.383,P＜0.001)and high self-esteem(r=0.343,P=0.001)were moderately positively associated with quality of life.Interpersonal network factors of good relationship with husband(r=0.337,P＜0.001)were moderately positively correlated with quality of life,and having a reproductive history(r=0.124,P＜0.001)was lowly positively correlated with quality of life.Good occupation(r=0.149,P=0.002)was low positively associated with quality of life among the factors of work and living conditions;high annual household income(r=0.307,P＜0.001)was moderately positively associated with quality of life.Having health insurance in the policy environment(r=0.131,P＜0.001)was low positively associated with quality of life.Conclusion The level of quality of life of infertile patients is impaired to some extent and there are many influencing factors,so healthcare professionals should comprehensively assess the quality of life of patients and adopt targeted and personalised interventions to improve the quality of life of patients.

Objective To explore the value of time-dependent diffusion MRI(td-dMRI)parameters for differentiating invasive breast cancer(IBC)with ductal carcinoma in situ(DCIS)(IBC-DCIS)from simple IBC.Methods A total of 19 patients with IBC-DCIS(IBC-DCIS group)and 53 patients with simple IBC(IBC group)confirmed by surgery and postoperation pathology were retrospectively enrolled.Breast td-dMRI acquired with oscillating gradient spin-echo(OGSE)and pulsed gradient spin-echo(PGSE)sequences before operation were interpreted,and apparent diffusion coefficient(ADC)and microstructure parameters,including OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were obtained and compared between groups.Receiver operating characteristic(ROC)curves of parameters being significantly different between groups were plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of these parameters for differentiating IBC-DCIS from IBC.Results Significant differences of OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were found between groups(all P＜0.05).The AUC of the above parameters for differentiating IBC-DCIS from IBC was 0.81,0.79,0.78,0.68,0.77 and 0.81,respectively.Conclusion td-dMRI parameters could be used to noninvasively and effectively differentiate IBC-DCIS from simple IBC.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To systematically evaluate the current status of quality of life in infertile patients and its influencing factors.Methods Computer search of PubMed,Cochrane Library,Web of Science,Embase,CBM,Knowledge Network,Wanfang,and Wipu,with a search timeframe from the establishment of the library to December 2023,and meta-analysis using CMA 3.3 software.Results A total of 33 literatures were included in the current status of quality of life,which showed that the level of quality of life was 60.841[95％CI(58.029,63.654),P＜0.001]for the FertiQoL scale and 67.630[95％CI(59.541,75.718),P＜0.001]for the SF-36 scale.A total of 45 papers were included in the literature on factors influencing quality of life,and the results showed that personal trait factors of young age(r=0.181,P=0.006)and history of pregnancy(r=0.258,P＜0.001)were lowly positively correlated with quality of life,and that long years of infertility(r=-0.267,P＜0.001),a history of treatment failure(r=-0.292,P＜0.001),and long duration of treatment(r=-0.213,P＜0.001)had a low negative association with quality of life.Behavioural factors such as anxiety(r=-0.547,P＜0.001),depression(r=-0.556,P＜0.001),and stigma(r=-0.530,P＜0.001)were highly negatively correlated with the quality of life;high fertility stress(r=-0.333,P=0.010)was moderately negatively correlated with the quality of life;and good psychological resilience(r=0.383,P＜0.001)and high self-esteem(r=0.343,P=0.001)were moderately positively associated with quality of life.Interpersonal network factors of good relationship with husband(r=0.337,P＜0.001)were moderately positively correlated with quality of life,and having a reproductive history(r=0.124,P＜0.001)was lowly positively correlated with quality of life.Good occupation(r=0.149,P=0.002)was low positively associated with quality of life among the factors of work and living conditions;high annual household income(r=0.307,P＜0.001)was moderately positively associated with quality of life.Having health insurance in the policy environment(r=0.131,P＜0.001)was low positively associated with quality of life.Conclusion The level of quality of life of infertile patients is impaired to some extent and there are many influencing factors,so healthcare professionals should comprehensively assess the quality of life of patients and adopt targeted and personalised interventions to improve the quality of life of patients.

Objective To explore the value of time-dependent diffusion MRI(td-dMRI)parameters for differentiating invasive breast cancer(IBC)with ductal carcinoma in situ(DCIS)(IBC-DCIS)from simple IBC.Methods A total of 19 patients with IBC-DCIS(IBC-DCIS group)and 53 patients with simple IBC(IBC group)confirmed by surgery and postoperation pathology were retrospectively enrolled.Breast td-dMRI acquired with oscillating gradient spin-echo(OGSE)and pulsed gradient spin-echo(PGSE)sequences before operation were interpreted,and apparent diffusion coefficient(ADC)and microstructure parameters,including OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were obtained and compared between groups.Receiver operating characteristic(ROC)curves of parameters being significantly different between groups were plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of these parameters for differentiating IBC-DCIS from IBC.Results Significant differences of OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were found between groups(all P＜0.05).The AUC of the above parameters for differentiating IBC-DCIS from IBC was 0.81,0.79,0.78,0.68,0.77 and 0.81,respectively.Conclusion td-dMRI parameters could be used to noninvasively and effectively differentiate IBC-DCIS from simple IBC.

Phytoremediation plays an important role in the treatment of heavy metal pollution in soil. In order to elucidate the mechanism of salicylic acid (SA) on copper absorption, seedlings from Xuzhou (with strong Cu-tolerance) and Weifang Helianthus tuberosus cultivars (with weak Cu-tolerance) were selected for pot culture experiments. 1 mmol/L SA was sprayed upon 300 mg/kg soil copper stress, and the photosynthesis, leaf antioxidant system, several essential mineral nutrients and the changes of root upon copper stress were analyzed to explore the mechanism of copper resistance. The results showed that Pn, Tr, Gs and Ci upon copper stress decreased significantly compared to the control group. Meanwhile, chlorophyll a, chlorophyll b and carotenoid decreased with significant increase in initial fluorescence (F₀), maximum photochemical quantum yield of PSⅡ (Fv/Fm), electron transfer rate (ETR) and photochemical quenching coefficient (qP) content all decreased. The ascorbic acid (AsA) content was decreased, the glutathione (GSH) value was increased, the superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) activity in the leaves were decreased, and the peroxidase (POD) activity was significantly increased. SA increased the Cu content in the ground and root system, and weakened the nutrient uptake capacity of K, Ca, Mg, and Zn in the root stem and leaves. Spray of exogenous SA can maintain the opening of leaf stomata, improve the adverse effect of copper on photosynthetic pigment and PSⅡ reaction center. Mediating the SOD and APX activity started the AsA-GSH cycle process, effectively regulated the antioxidant enzyme system in chrysanthemum taro, significantly reduced the copper content of all parts of the plant, and improved the ion exchange capacity in the body. External SA increased the content of the negative electric group on the root by changing the proportion of components in the root, promoted the absorption of mineral nutrient elements and the accumulation of osmoregulatory substances, strengthened the fixation effect of the root on metal copper, and avoided its massive accumulation in the H. tuberosus body, so as to alleviate the inhibitory effect of copper on plant growth. The study revealed the physiological regulation of SA upon copper stress, and provided a theoretical basis for planting H. tuberosus to repair soil copper pollution.
Antioxidants ; Copper ; Helianthus/metabolism* ; Salicylic Acid/pharmacology* ; Chlorophyll A/pharmacology* ; Spectroscopy, Fourier Transform Infrared ; Chlorophyll/pharmacology* ; Ascorbic Acid ; Superoxide Dismutase/metabolism* ; Photosynthesis ; Glutathione ; Plant Leaves ; Stress, Physiological ; Seedlings

Objective:To assess the agreement of manual measurement, semi-automatic measurement based on computer-aided diagnosis (CAD), and automatic measurement based on artificial intelligence (AI) in measuring diameters and volumes of solid pulmonary nodules.Methods:The clinical and low dose CT (LDCT) data of 165 participants in lung cancer screening of Sichuan Cancer Hospital from July 2018 to April 2020 were retrospectively analyzed. The largest nodule of each participant was selected to analyze, and its diameter and volume were measured by one junior and one senior radiologist using manual measurement, semi-automatic measurement based on CAD, and automatic measurement based on AI. Referring to Lung CT imaging reporting and data system (Lung-RADS, version 1.1), all nodules were classified into Lung-RADS 2, 3, 4A, 4B, 4X categories and low and high risk groups according to the diameter and volume measured by different measurement methods. Repeated-measures analysis of variance and paired t-test were used to compare the differences in the diameter and volume of lung nodules measured by different methods. The consistency of the three methods in measuring nodule diameter and volume was assessed by the correlation coefficient (ICC). Linear weighted Kappa coefficient was applied to assess the consistency of different measurement methods in Lung-RADS classification results; simple Kappa coefficient was applied to assess the consistency of different methods in high and low risk grouping results. Results:Difference in the diameters of pulmonary nodules measured by manual measurement, semi-automatic measurement based on CAD, and automatic measurement based on AI was statistically significant [(14.9±6.3) mm, (17.0±6.7) mm, (15.0±5.7) mm, F=88.39, P<0.001], and the pairwise comparisons showed that there was significant difference between semi-automatic measurement based on CAD and manual measurement method ( t=10.97, P<0.001), semi-automatic measurement based on CAD and automatic measurement based on AI ( t=10.07, P<0.001), but no significant difference between manual measurement method and automatic measurement based on AI method ( t=1.04, P=0.301). There was no significant difference in the measurement of pulmonary nodule volumes between the semi-automatic measurement and the automatic measurement method based on AI ( Z=0.70, P=0.482). The consistency of pulmonary nodules diameter measured by different measurement methods was high (ICC>0.75), and the consistency of semi-automatic and automatic measurement of lung nodule volume was high (ICC=0.927). The consistency of three methods for lung-RADS classification and high-and low-risk grouping according to nodule diameter was good (Kappa>0.80). The agreements between the semi-automatic measurement and the automatic measurement method for Lung-RADS classification and high-and low-risk grouping according to nodule volume were good (Kappa>0.80). Conclusion:In terms of diameter measurement of solid pulmonary nodules, automatic measurement based on AI is more consistent with manual measurement than semi-automatic measurement based on CAD. The agreement between automatic measurement and semi-automatic measurement is high in terms of volume measurement.

Asthma is a serious health problem that involves not only the respiratory system but also the central nervous system. Previous studies identified either regional or network alterations in patients with asthma, but inconsistent results were obtained. A key question remains unclear: are the regional and neural network deficits related or are they two independent characteristics in asthma? Answering this question is the aim of this study. By collecting resting-state functional magnetic resonance imaging from 39 patients with asthma and 40 matched health controls, brain functional measures including regional activity (amplitude of low-frequency fluctuations) and neural network function (degree centrality (DC) and functional connectivity) were calculated to systematically characterize the functional alterations. Patients exhibited regional abnormities in the left angular gyrus, right precuneus, and inferior temporal gyrus within the default mode network. Network abnormalities involved both the sensorimotor network and visual network with key regions including the superior frontal gyrus and occipital lobes. Altered DC in the lingual gyrus was correlated with the degree of airway obstruction. This study elucidated different patterns of regional and network changes, thereby suggesting that the two parameters reflect different brain characteristics of asthma. These findings provide evidence for further understanding the potential cerebral alterations in the pathophysiology of asthma.
Asthma/diagnostic imaging* ; Brain/diagnostic imaging* ; Brain Mapping ; Humans ; Magnetic Resonance Imaging

Objective Toexploretherelationshipbetweenthemicrostructuralintegrityofwhitematter(WM)andperipheralinterleukin10 (IL-10)inschizophrenia.Methods Diffusion MRIdataandvenousbloodsampleswereacquiredfrom47schizophreniapatients(SZ) and49healthycontrols (HC).Tract-basedspatialstatisticswasconductedtoexaminethedifferencesinFAandradialdiffusivity (RD)betweentwogroups.QuantitativechemiluminescenceassaywasperformedtomeasureperipheralIL-10levels.Regressionanalysiswas conductedtoinvestigatetherelationshipbetweenperipheralIL-10levelsanddiffusion measures (FAandRD).Results Compared withHC,therewerewidespreadreductionsinFAandincreaseinRDinSZ.Additionally,comparedwith HC,peripheralIL-10levels werehigherinSZ.PeripheralIL-10wasnegativelycorrelatedwithFAintherightposteriorthalamicradiationandleftinferiorfronto-occipitalfasciculusamongthepatients(β=-0.51,P=0.01andβ=-0.47,P=0.02,respectively)butnotcontrols(β=-0.01,P=0.95 andβ=-00.03,P=09.8,respectively).Andthecorrelationcoefficientsofthetwogroupsweredifferent(z=25.0,P=00.1andz=23.7,P=00.2, respectively).Conclusion TheperipheralIL-10maycontributetothedisruptionsofmicrostructuralWMintegrityinschizophrenia, supportingthenotionforanimportantroleofinflammationinthepathogenesisofschizophrenia.