A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Diffusion-Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High-Grade Serous Ovarian Carcinoma.

BACKGROUND: Preoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded. PURPOSE: To investigate the value of diffusion-weighted MRI (DW-MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients. STUDY TYPE: Prospective. SUBJECTS: A total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years). FIELDS STRENGTH/SEQUENCE: A 3.0 T, readout-segmented echo-planar DWI. ASSESSMENT: The MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI-PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI-PCI alone; and a fusion model combining the clinical-morphological information and DWI-PCI. STATISTICAL TESTS: Multivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant. RESULTS: Sixty-seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI-PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA-125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI-PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI. DATA CONCLUSIONS: The combination of two clinical factors, MRI morphological characteristics and DWI-PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors.

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management. PURPOSE: To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists. STUDY TYPE: Retrospective study of eight clinical centers. SUBJECTS: Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159). FIELD STRENGTH/SEQUENCE: Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2-weighted imaging with fat saturation (T2WI FS), echo planar diffusion-weighted imaging, and 2D volumetric interpolated breath-hold examination of contrast-enhanced T1-weighted imaging (CE-T1WI) with FS. ASSESSMENT: Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE-T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience. STATISTICAL TESTS: We used DeLong test, chi-square test, Mann-Whitney U-test, and t-test, with significance level of 0.05. RESULTS: Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795-0.915) and 0.884 (95% CI, 0.831-0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797). DATA CONCLUSION: The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Latitudinal Distribution of Gaseous Elemental Mercury in Tropical Western Pacific: The Role of the Doldrums and the ITCZ.

The role of the tropical western Pacific in the latitudinal distribution of atmospheric mercury is still unclear. In this study, we conducted continuous measurements of gaseous elemental mercury (GEM) in the marine boundary layer (MBL) along a large latitudinal transect (∼60° S to ∼30° N) of the western Pacific, accompanied by measurements of dissolved gaseous mercury (DGM) in the surface seawater. We found that the GEM latitudinal gradient is the most significant in the tropical western Pacific, which to some extent might be attributed to the impact of the doldrums and the Intertropical Convergence Zone (ITCZ) in this area. For the doldrums, calm weather may delay the transport of GEM, facilitating its accumulation in the tropical western Pacific. Furthermore, the regional transport, and low O3 and sea-salt aerosol levels in this area which would not favor the oxidation of GEM in the MBL, would intensify the accumulation of GEM in the tropical western Pacific. For the ITCZ, the vast wet deposition of Hg would drive elevated DGM in the surface seawater, which can increase the evasion flux and may further influence the spatial distribution of GEM. This study provides insight into the role of the tropical western Pacific in the regional atmospheric mercury cycle.

Growth differentiation factor-15 promotes immune escape of ovarian cancer via targeting CD44 in dendritic cells.

Immune escape is the main cause of the low response rate to immunotherapy for cancer, including ovarian cancer. Growth differentiation factor-15 (GDF-15) inhibits immune cell function. However, only few reports described the mechanism. Therefore, the aim of this study was to investigate the mechanism of immune escape regulated by GDF-15 in ovarian cancer. Ovarian cancer patients and healthy women were enrolled in this study. Immunohistochemistry and ELISA were performed to measure GDF-15 expression. Immunoprecipitation combined with mass spectrometry, surface plasmon resonance, and co-immunoprecipitation assay were used to evaluate the interaction between GDF-15 and the surface molecules of DCs. Immunofluorescence analysis, flow cytometry and transwell assay were used to evaluate additional effects of GDF-15 on DCs. The results showed that GDF-15 expression was higher in the ovarian cancer patients compared to that in the healthy women. The TIMER algorithm revealed that highly GDF-15 expression is associated with immune DC infiltration in immunoreactive high-grade serous carcinoma. A further study showed that GDF-15 suppressed DCs maturation, as well as IL-12p40 and TNF-α secretion, the length and number of protrusions and the migration. More importantly, CD44 in the surface of DCs interacted with GDF-15. The overexpression of CD44 in DCs resulted in the suppression of the inhibitory effect of GDF-15 on the length and number of DC synapses. In DCs overexpressing CD44 the inhibition of GDF-15 on the expression of CD11c, CD83 and CD86 was decreased, while in DCs with a knockdown of CD44 the inhibition was further enhanced. Knockdown of CD44 in DCs enhanced the inhibitory effect of GDF-15 on DC migration, while the overexpression of CD44 inhibited the inhibitory effect of GDF-15 on DC migration. In conclusion, the present study suggested that GDF-15 might facilitate ovarian cancer immune escape by interacting with CD44 in DCs to inhibit their function.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

OBJECTIVES: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

OBJECTIVES: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

Non-ignorable contribution of anthropogenic source to aerosols in Arctic Ocean.

Arctic Ocean (AO) atmospheric aerosols, which are a factor influencing regional and global climate, have been greatly influenced by an increase in anthropogenic sources. To identify the impact of anthropogenic sources on regional aerosols in the AO and middle and low latitudes (MLO), a single-particle aerosol mass spectrometer was used to count and size aerosols with diameters less than 2.5 µm (PM2.5) and determine their chemical composition. The mean hourly count of PM2.5 aerosols was 1639/h in the AO, which was 57.1% lower than that in the MLO. Na_MSA, sulfate, and Na_rich were three major components, which accounted for 74.3% of PM2.5 aerosols in the AO. The size distribution of PM2.5 aerosols was unimodal, peaking between 0.42 µm and 1.64 µm. A source apportionment method for single aerosol particles in the Arctic was established using positive matrix factorization (PMF) combined with backward air mass trajectory and principal component analysis (PCA). Three potential sources of aerosols were identified: marine sources; anthropogenic sources; and secondary formation. The largest contribution to aerosols in the AO was from marine sources, accounting for 50.6%. This source was 20.4% higher in the AO than that in the MLO. Secondary formation contributed 19.8% and 36.5% to aerosols in the AO and MLO, respectively. However, the contribution of anthropogenic sources to aerosols was 29.6% in the AO, and this was 3.7% lower than that in the MLO. Our study provides a useful method for identifying sources of aerosols in the Arctic, and the results showed that although marine sources were the largest contributors to aerosols in the AO, the contribution of anthropogenic sources could not be ignored.

Perfusion-based functional magnetic resonance imaging for differentiating serous borderline ovarian tumors from early serous ovarian cancers in a rat model.

BACKGROUND: Differentiation of borderline tumors from early ovarian cancer has recently received increasing attention, since borderline tumors often affect young women of childbearing age who desire to preserve fertility. However, previous studies have demonstrated that non-enhanced magnetic resonance imaging (MRI) sequences cannot sufficiently differentiate these tumors. PURPOSE: To investigate the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating serous borderline ovarian tumors (SBOT) from early serous ovarian cancers (eSOCA). MATERIAL AND METHODS: Twenty SBOT and 20 eSOCA rat models were performed with DCE-MRI and IVIM-DWI at 3.0-T MR scanner. Qualitative and quantitative parameters of DCE-MRI were acquired and compared between two groups and correlated with the microvessel density (MVD). The receiver operating characteristic (ROC) curve analyses were conducted to determine their differentiating performances. RESULTS: SBOTs presented significantly lower values of the initial area under the enhancement curve (iAUC), volume transfer constant (Ktrans), and extracellular extravascular volume fraction (ve) (P < 0.05) and a significantly higher value of true diffusion (D) (P = 0.001) compared with eSOCAs. The diagnostic effectiveness of ve combined with D was significantly better than that of ve or Ktrans alone (P ≤ 0.039). CONCLUSION: DCE-MRI may represent a promising tool for differentiating SBOTs from eSOCAs and may not be replaced by IVIM-DWI. Combining DCE-MRI with DWI may improve the diagnostic performance of ovarian tumors.

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.

Molecular imaging-monitored radiofrequency hyperthermia-enhanced intratumoral herpes simplex virus-thymidine kinase gene therapy for rat orthotopic ovarian cancer.

Objective: To establish the technique of intratumoral combination therapy of radiofrequency hyperthermia (RFH) with herpes simplex virus-thymidine kinase/ganciclovir (HSV-TK/GCV) gene therapy for rat ovarian cancers.Material and methods: This study consisted of three parts: (1) in vitro experiments to establish the 'proof of principal' that combination of RFH and HSV-TK gene therapy has the synergistic effect on human ovarian cancer cells; (2) creation of bioluminescence imaging-detectable rat ovarian cancer model; and (3) in vivo experiments using this rat model to validate the technical feasibility of the combination therapy. Cells and nude rats were divided into four groups: (i) combination therapy (HSV-TK/GCV + RFH); (ii) RFH; (iii) HSV-TK/GCV; and (iv) phosphate-buffered saline (PBS). Data were analyzed using Dunnett t-test or Kruskal-Wallis test.Results: Cell proliferation assay demonstrated significantly greater reduction in viable cells with the combination therapy [0.52 (0.43, 0.61)] compared to other treatments [RFH 0.90 (0.84, 0.96), HSV-TK/GCV 0.71 (0.53, 0.88), PBS 1 (1, 1); p < .05]. For 24 rat models with bioluminescence imaging-detectable orthotopic ovarian cancer (n = 6 per group), optical imaging demonstrated significantly decreased relative bioluminescence signal with the combination therapy [0.81 (0.52, 1.08)] compared to other treatments [RFH 3.60 (2.34, 4.86), HSV-TK/GCV 2.21 (1.71, 2.71), PBS 3.74 (3.19, 4.29); p < .001]. Ultrasound imaging demonstrated the smallest relative tumor volume with the combination therapy [0.78 (0.45, 1.11) versus 3.50 (2.67, 4.33), 2.10 (0.83, 3.37), 3.70 (1.79, 5.61); p < .05].Conclusion: The feasibility of intratumoral RFH-enhanced HSV-TK/GCV gene therapy was established on a unique rat model with molecular imaging-detectable orthotopic ovarian cancer.

Significant Underestimation of Gaseous Methanesulfonic Acid (MSA) over Southern Ocean.

Methanesulfonic acid (MSA), derived from the oxidation of dimethylsulfide (DMS), has a significant impact on biogenic sulfur cycle and climate. Gaseous MSA (MSAg) has been often ignored in previous studies due to its quick conversion to particulate MSA (MSAp) and low concentrations. MSAg, MSAp, and nss-SO42- were observed simultaneously for the first time with high-time-resolution (1 h) in the Southern Ocean (SO). The mean MSAg level reached up to 3.3 ± 1.6 pptv, ranging from ∼24.5 pptv in the SO, contributing to 31% ± 3% to the total MSA (MSAT). A reduction of the MSA to nss-SO42- ratios by about 30% was obtained when MSAg was not accounted for in the calculation, indicating that MSAg was very important in the assessment of the biogenic sulfur contributions in the atmosphere. Mass ratios of MSA to nss-SO42- increased first and then decreased with the temperature from -10 to 5 °C, with a maximum value at the temperature of -3 °C. Positive correlations between MSAg to MSAT ratios and temperature were presented, when the temperature was higher than 5 °C. This study highlights the importance of MSAg for understanding the atmospheric DMS oxidation mechanism and extends the knowledge of MSA formation in the marine atmosphere.

Combining Clinical Characteristics and Specific Magnetic Resonance Imaging Features to Predict Placenta Accreta.

OBJECTIVE: The aim of this study was to explore the independent clinical and magnetic resonance imaging (MRI) performance risk factors for predicting placenta accreta. METHODS: From January 2012 to December 2015, we retrospectively reviewed the clinical characteristics and MRI features of 97 patients. Of these, 42 were confirmed to be placenta accreta by pathological results or cesarean delivery findings. We tried to identify the independent risk factors by multivariate logistic regression model for significant differences in variables determined by univariate analysis. RESULTS: The multivariate logistic regression model indicated that 2 or more instances of previous cesarean deliveries and/or abortions, placenta previa, and placenta-myometrial interface interruption were independent risk factors for placenta accreta. The odd ratios were 3.79 for patients who had 2 or more instances of previous cesarean deliveries and/or abortions, 0.04 for marginal/partial placenta previa, 0.024 for complete placenta previa, and 6.56 for placenta-myometrial interface interruption. The values of accuracy and positive prediction by combination of a single clinical risk factor and placenta-myometrial interface interruption and of positive prediction by a combination of all 3 risk factors for predicting placenta accreta were raised to 83.5%, 75%, and 92.9%, respectively. We obtained 3 different risk groups by different combinations of all 3 risk factors. CONCLUSIONS: The study suggested that 2 or more instances of previous cesarean deliveries and/or abortion, placenta previa, and placenta-myometrial interface interruption were independent risk factors for placenta accreta. A combination of a single clinical risk factor and an MRI risk factor can improve the diagnosis of placenta accreta, and a combination of all 3 risk factors could help recognize patients with placenta accreta.

Effect of air masses motion on the rapid change of aerosols in marine atmosphere.

The impact of air masses motion on marine aerosol properties was investigated using an on-board single particle mass spectrometer (SPAMS) deployed for the determination of single particle size resolved chemical composition over Southeast China Sea. Two aerosol blooms (E1 and E2) were observed during the cruise. High average particle number count occurred in E1 (7320), followed by E2 (5850), which was more than 100-150 times of the average particle number count during normal periods. Particles were classified as four major sources, including continental source, shipping source, marine source, and transport source based on the mass spectral similarity. Transport source was identified as those particles with high particle number count occurred only during aerosol bloom period. Three sub-types of EC-Ca, OC-Ca, and Al-rich were classified as transport source. EC-Ca was the dominant particles of the transport source, accounting for more than 70% of the total particles in aerosol bloom events. A uni-modal size distribution in the size range of 0.1-2.0 µm was observed during normal period, while a bimodal distribution with a tiny mode (<0.3 µm) and a coarse mode between 0.4 and 0.6 µm was present during aerosol bloom. The variation of aerosol source is consistent with air masses back trajectories, for the reason that most of the long-range air trajectories are from the ocean, while short air trajectories originate in the continental regions, which means that air masses have a significant impact on the aerosol physical-chemical properties along their tracks.

Pollutants identification of ambient aerosols by two types of aerosol mass spectrometers over southeast coastal area, China.

Two different aerosol mass spectrometers, Aerodyne Aerosol Mass Spectrometer (AMS) and Single Particle Aerosol Mass Spectrometer (SPAMS) were deployed to identify the aerosol pollutants over Xiamen, representing the coastal urban area. Five obvious processes were classified during the whole observation period. Organics and sulfate were the dominant components in ambient aerosols over Xiamen. Most of the particles were in the size range of 0.2-1.0µm, accounting for over 97% of the total particles measured by both instruments. Organics, as well as sulfate, measured by AMS were in good correlation with measured by SPAMS. However, high concentration of NH4+ was obtained by AMS, while extremely low value of NH4+ was detected by SPAMS. Contrarily, high particle number counts of NO3- and Cl- were given by SPAMS while low concentrations of NO3- and Cl- were measured by AMS. The variations of POA and SOA obtained from SPAMS during event 1 and event 2 were in accordance with the analysis of HOA and OOA given by AMS, suggesting that both of AMS and SPAMS can well identify the organic clusters of aerosol particles. Overestimate or underestimate of the aerosol sources and acidity would be present in some circumstances when the measurement results were used to analyze the aerosol properties, because of the detection loss of some species for both instruments.

Magnetic resonance imaging findings of intravascular leiomyomatosis of the uterus: a case report.

Background: Intravascular leiomyomatosis (IVL) is often a non-malignant condition that grows inside the blood vessels and does not invade surrounding tissues. However, its presence within the blood vessels can lead to obstructions. The majority of IVL cases manifest symptoms related to blockage. Case presentation: We present a case of a 50-year-old female patient who was referred to our institution due to the presence of a common non-cancerous tumor in the uterus, known as a benign uterine leiomyoma. The tumor was identified during an ultrasound examination during a routine physical examination. Postoperative pathology established the existence of intrapelvic IVL. Conclusion: Intrapelvic IVL often not present with complications when it is confined to the pelvic cavity. Furthermore, the imaging features of intrapelvic IVL resemble those of typical benign uterine leiomyomas. This can often result in the clinical misdiagnosis of the tumor as a typical benign uterine leiomyoma.

Development, characterization and functional properties of sodium alginate-based films incorporated with Schisandra chinensis extract-natamycin complex.

Because of the impact of petroleum-based polymers on environmental deterioration and the need for safe, efficient, and functional packaging films, a sodium alginate (SA)-based film incorporating a Schisandra chinensis extract (SCE)-natamycin (NA) complex was developed for the desired physical and functional properties. The incorporation of SCE-NA into SA-based films decreased the water vapor transmission rate (WVTR), moisture content (MC), and hydrophilicity of the films and improved their opacity, elongation at break (EAB), and thermal stability. Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and X-ray Diffraction (XRD) analyses showed that SA, SCE, and NA had positive interactions and compatibility. In addition, the antimicrobial activity analysis indicated that the SA-SCE-NA film-forming solutions had satisfactory antimicrobial activity against Staphylococcus aureus, Escherichia coli, Saccharomyces cerevisiae, and Aspergillus niger. SA-based composite films have been used to coat cucumbers and blueberries to extend their shelf life. Compared to the neat SA film, the shelf life of cucumbers treated with the SA-SCE-NA film increased by 6 days compared to that in the untreated group at 28 °C, and the shelf life of blueberries increased by 5 days at 4 °C, revealing its potential utilization in food packaging.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma.

PURPOSE: To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). METHODS: Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. RESULTS: A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. CONCLUSION: Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Double-wing switch nanodevice-mediated primer exchange reaction for the activity analysis of cancer biomarker FEN1.

DNA damage repair is one of the foremost factors leading to changes in tumor drug resistance. The analysis of Flap endonuclease 1 (FEN1), a kind of pivotal enzyme in various DNA metabolic pathways, has been of great support to tumor research and the development of chemotherapeutics. Nevertheless, few analytical techniques can achieve quantitative and simplified FEN1 measurement. Here, we constructed a double-wing switch nanodevice (DWSN)-mediated primer exchange technique for rapid and label-free quantification of FEN1 activity. Target FEN1 triggered the generation of numerous telomeric repeat fragments in different lengths through recognizing the three-base mismatched sites on the DWSN to release the 5'-Flaps. Further binding to the fluorescent dye ThT resulted in significantly enhanced fluorescence. This study broke the limitation of traditional single-site identification and demonstrated good sensitivity and specificity with detection limits up to 0.55 mU. Besides, the extraordinary analytical performance allowed the method to be utilized to monitor FEN1 extracted from cells and clinical serum samples and to compare the effect of targeted FEN1 inhibitors.

The fluorescence amplification strategy based on 3D DNA walker and CRISPR/Cas12a for the rapid detection of BRAF V600E.

Circulating tumor DNA (ctDNA) is a fragment of single- or double-stranded DNA originating from tumor or circulating tumor cells and provides accurate information regarding the molecular characteristics of tumors. Therefore, sensitive detection of ctDNA is of great significance to mutation analysis and clinical diagnosis. Among various ctDNAs, the BRAF V600E is related to aggressive behavior, disease recurrence, and disease-specific mortality in papillary thyroid carcinoma. Herein, we selected the BRAF V600E gene sequence as an in vitro biomarker, and established a fluorescence detection strategy combined 3D DNA walker with CRISPR/Cas12a. In the presence of the target ctDNA, 3D DNA walker could identify and bind it, and thus released a large amount of output DNAs through cyclic cleavage with the assistance of specific endonuclease (Nb.BbvCI). The output DNAs were specifically bound to crRNA and activated the non-specific trans-cleavage activity of Cas12a. Finally, the fluorescence signal was significantly enhanced. Notably, this method can detect the BRAF V600E in a range of 1 fM ~ 20 nM with a detection limit of 0.37 fM without DNA polymerase. Due to the powerful amplification capability of 3D DNA walker and high specificity and programmability of CRISPR/Cas12a, the entire process took only a maximum of 70 min. Furthermore, it can be potentially used for the detection of ctDNA in human serum. In summary, this method not only provides a platform for the rapid detection of ctDNA, but also shows good potential for early clinical diagnosis and biomedical research.