Palladium-Catalyzed Hydrocyanation of Ynoates: En Route to the Stereodivergent Synthesis of ß-Cyanated α,ß-Unsaturated Esters via Ligand Controlled Regio- and Stereoselectivity.

A Pd-catalyzed highly regio- and stereoselective hydrocyanation was developed, providing a novel approach to the stereodivergent synthesis of ß-cyano-substituted acrylates in good yields with a wide substrate scope. The judicious selection of ligands was crucial for elegant control over the stereodivergence. Furthermore, the success of the E-hydrocyanation hinges on the right matching of Pd and L1, which not only ensured the catalytic activity but also prevented the formation of α-cyanation products.

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 [Formula: see text] 0.25, 0.76 [Formula: see text] 0.18, and 0.60 [Formula: see text] 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists' assessment.

Histogram Analysis of Apparent Diffusion Coefficient on Diffusion Weighted Magnetic Resonance Imaging in Differentiation between Low and High Grade Serous Ovarian Cancer.

BACKGROUND: Ovarian cancer is a leading cause of death in gynecological malignancies. Being the most common subtype in OEC, ovarian serous cancer also includes two subtypes: low grade serous ovarian cancer (LGSC) and high grade serous ovarian cancer (HGSC) (1). PURPOSE: The study aims to assess the capability of apparent diffusion coefficient (ADC) histogram analysis and conventional measurements on magnetic resonance imaging (MRI) in differentiating between LGSC and HGSC. METHODS: We retrospectively recruited 38 patients with pathologically proven ovarian serous epithelial cancer. The mean ADC value was measured by one technician using two methods on post-processed workstation. The ADC value and histogram parameter difference between LGSC and HGSC groups were compared. The correlation between the ADC value and the Ki-67 expression was calculated across both groups. RESULTS: The repeatability of ADC measurements across two methods was good; the ROI method (ADC-roi) had better performance repeatability than the area method (ADC-area). The value of ADC-mean , ADC-min, ADC-max, and ADC-area significantly differed between both groups (p < 0.001). The value of ADC-area correlated inversely with ki-67 expression in the whole group (Pearson coefficient = -0.382, p = 0.02). The 3D computerized-diagnostic model had the best discriminative performance in determining HGSC than 2D and conventional ADC measurements. The 3D model yielded a sensitivity of 100%, a specificity of 95.45%, and an accuracy of 97.73%. CONCLUSION: In the present study, the 3D ADC histogram model help differentiate HGSC from LGSC with a better performance than conventional ADC measurements.

A computerized diagnostic model for automatically evaluating placenta accrete spectrum disorders based on the combined MR radiomics-clinical signatures.

We aimed to establish a computerized diagnostic model to predict placenta accrete spectrum (PAS) disorders based on T2-weighted MR imaging. We recruited pregnant women with clinically suspected PAS disorders between January 2015 and December 2018 in our institution. All preoperative T2-weighted imaging (T2WI) MR images were manually outlined on the picture archive communication system terminal server. A nnU-Net network for automatic segmentation and the corresponding radiomics features extracted from the segmented region were applied to build a radiomics-clinical model for PAS disorders identification. Taking the surgical or pathological findings as the reference standard, we compared this computerized model's diagnostic performance in detecting PAS disorders. In the training cohort, our model combining both radiomics and clinical characteristics yielded an accuracy of 0.771, a sensitivity of 0.854, and a specificity of 0.750 in identifying PAS disorders. In the testing cohort, this model achieved a segmentation mean Dice coefficient of 0.890 and yielded an accuracy of 0.825, a sensitivity of 0.830 and a specificity of 0.822. In the external validation cohort, this computer-aided diagnostic model yielded an accuracy of 0.690, a sensitivity of 0.929 and a specificity of 0.467 in identifying placenta increta. In the present study, a machine learning model based on preoperative T2WI-based imaging had high accuracy in identifying PAS disorders in respect of surgical and histological findings.

SIB-IMRT combined with apatinib for unresectable hepatocellular carcinoma in patients with poor response to transarterial chemoembolization.

BACKGROUND: Radiotherapy combined with apatinib exhibits synergistic anti-tumor effect, while the application of simultaneous integrated boost intensity modulated radiotherapy (SIB-IMRT) combined with apatinib in HCC patients is scarce. Hence, this study aimed to explore the treatment response, survival, and safety profile of the SIB-IMRT combined with apatinib in unresectable HCC (uHCC) patients. METHODS: A total of 19 uHCC patients with deficient response to transarterial chemoembolization (TACE), who scheduled for SIB-IMRT combined with apatinib treatment were enrolled. The SIB-IMRT was applied at the following dose: 95% planning target volume (PTV) at 30-50 Gy/2-2.5 Gy/15-20f and 90% Boost of 45-72 Gy/3-4.5 Gy/15-20f at 5 times per week with cone beam computerized tomography validation. During and after radiotherapy, the apatinib was administrated orally with the initial dose of 500 mg per day. RESULTS: The complete response, partial response, stable disease, and progressive disease rates were 31.6%, 36.8%, 21.1% and 10.5%, respectively. Consequently, the objective response rate and disease control rate were 68.4% and 89.5%, respectively. During a median follow-up duration of 9.0 months, the median progression-free survival (PFS) was 6.0 (95% confidential interval (CI): 4.9-7.1) months with 1-year PFS rate of 42.1%; the median overall survival (OS) was not reached with 1-year OS rate of 54.6%. The safety profile was acceptable with the most common adverse events including myelosuppression (42.1%), skin reaction (36.8%), and albuminuria (26.3%). CONCLUSION: SIB-IMRT combined with apatinib exhibits a good efficacy and tolerable safety profile, which could be considered as a potential treatment choice for uHCC patients who have deficient response to TACE.

Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors.

BACKGROUND: Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. PURPOSE: To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. METHODS: A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. RESULTS: The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. CONCLUSIONS: Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.

MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols.

BACKGROUND: Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. METHODS: A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. RESULTS: The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. CONCLUSION: MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

Tailoring the Pore Surface of 3D Covalent Organic Frameworks via Post-Synthetic Click Chemistry.

Three-dimensional covalent organic frameworks (3D COFs) have gained increasing attention for their attractive features. However, the development of 3D COFs is strongly restricted, mainly due to their synthetic difficulty and complicated structure determination. Post-synthetic modification, which can avoid these problems by incorporating functional moieties into a predetermined framework, provides an alternative way to construct 3D COFs with specific functions. Herein, we report the designed synthesis and characterization of a series of highly crystalline 3D COFs with different loadings of ethynyl groups. Notably, these alkyne-tagged 3D COFs provide a platform for targeted anchoring various specific groups onto the pore walls via click reactions. Moreover, the pore surface engineering can accordingly change their properties, for example, the obtained click products exhibited higher CO2 /N2 selectivity. We describe a simple but powerful strategy to build functional 3D COFs, which will certainly advance them for a ranging of interesting applications in the future.

Overexpression of lncRNA SLC26A4-AS1 inhibits papillary thyroid carcinoma progression through recruiting ETS1 to promote ITPR1-mediated autophagy.

Papillary thyroid carcinoma (PTC), accounting for approximately 85% cases of thyroid cancer, is a common endocrine tumour with a relatively low mortality but an alarmingly high rate of recurrence or persistence. Long non-coding RNAs (lncRNAs) is emerging as a critical player modulating diverse cellular mechanisms correlated with the progression of various cancers, including PTC. Herein, we aimed to investigate the role of lncRNA SLC26A4-AS1 in regulating autophagy and tumour growth during PTC progression. Initially, ITPR1 was identified by bioinformatics analysis as a differentially expressed gene. Then, Western blot and RT-qPCR were conducted to determine the expression of ITPR1 and SLC26A4-AS1 in PTC tissues and cells, both of which were found to be poorly expressed in PTC tissues and cells. Then, we constructed ITPR1-overexpressing cells and revealed that ITPR1 overexpression could trigger the autophagy of PTC cells. Further, we performed a series of gain- and loss-of function experiments. The results suggested that silencing of SLC26A4-AS1 led to declined ITPR1 level, up-regulation of ETS1 promoted ITPR1 expression, and either ETS1 knockdown or autophagy inhibitor Bafilomycin A1 could mitigate the promoting effects of SLC26A4-AS1 overexpression on PTC cell autophagy. In vivo experiments also revealed that SLC26A4-AS1 overexpression suppressed PTC tumour growth. In conclusion, our study elucidated that SLC26A4-AS1 overexpression promoted ITPR1 expression through recruiting ETS1 and thereby promotes autophagy, alleviating PTC progression. These finding provides insight into novel target therapy for the clinical treatment of PTC.

A Crystalline Three-Dimensional Covalent Organic Framework with Flexible Building Blocks.

The construction of three-dimensional covalent organic frameworks (3D COFs) has proven to be very challenging, as their synthetic driving force mainly comes from the formation of covalent bonds. To facilitate the synthesis, rigid building blocks are always the first choice for designing 3D COFs. In principle, it should be very appealing to construct 3D COFs from flexible building blocks, but there are some obstacles blocking the development of such systems, especially for the designed synthesis and structure determination. Herein, we reported a novel highly crystalline 3D COF (FCOF-5) with flexible C-O single bonds in the building block backbone. By merging 17 continuous rotation electron diffraction data sets, we successfully determined the crystal structure of FCOF-5 to be a 6-fold interpenetrated pts topology. Interestingly, FCOF-5 is flexible and can undergo reversible expansion/contraction upon vapor adsorption/desorption, indicating a breathing motion. Moreover, a smart soft polymer composite film with FCOF-5 was fabricated, which can show a reversible vapor-triggered shape transformation. Therefore, 3D COFs constructed from flexible building blocks can exhibit interesting breathing behavior, and finally, a totally new type of soft porous crystals made of pure organic framework was announced.

Percolation Characteristics and Fluid Movability Analysis in Tight Sandstone Oil Reservoirs.

The development of tight oil has started relatively late, and the flow mechanisms and fluid movability are still research spotlights. The goal of this paper is to investigate the percolation characteristics and fluid movability of the Chang 6 tight sandstone oil layer in the Upper Triassic Yanchang Formation, Ordos Basin, China. Results show that (1) at low flow velocity, the percolation curve of flow velocity vs pressure gradient is a concave-up nonlinear curve and does not pass through the origin. It is more difficult for oil flow than water flow in cores with similar permeability due to rock wettability and fluid apparent mobility. The application of back pressure makes the nonlinear stage eliminated and the percolation character improved. (2) Two-phase flow tests reveal that oil-phase permeability decreases faster in samples with lower permeability, and the coexistent flow region of oil and water is relatively narrow. The contribution of oil recovery mainly happens at the early stage. The permeability at the isotonic point reduces with the decrease of sample permeability. (3) Flow during water flooding can be roughly divided into four stages according to the injection pressure and flow change. The injection pressure experiences stages of increasing to a peak, then decreasing, and finally becoming stable, accompanied by an increase of oil production until water breaks through. (4) The pore throats of the target reservoir mainly range from 0.001 to 10 µm, and the bound water mainly distributes in pores less than 0.2 µm. The irreducible water saturation is 30-35%, and the movable fluid saturation is 65-70%, mainly distributed in pores at 0.2-10.0 µm with a maximum of 2.0 µm. The results will supplement the existing knowledge of percolation characters and fluid movability in tight sandstone oil reservoirs.

Donor-acceptor fluorophores as efficient energy transfer photocatalysts for [2 + 2] photodimerization.

Mild [2 + 2] photodimerization of enone substrates was induced by donor-acceptor fluorophores. Enone substrates were activated efficiently for anti-head to head dimerizations with a high yield (up to 83%) and high selectivity. The adjustable excited state potential also allows donor-acceptor fluorophores to be used for isomerization of the above substrates, confirming the potential of donor-acceptor fluorophores as energy transfer photocatalysts.

Validation of spectral energy for the quantitative analysis of ventricular fibrillation waveform to guide defibrillation in a porcine model of cardiac arrest and resuscitation.

BACKGROUND: The amplitude spectrum area (AMSA), a frequency-domain ventricular fibrillation (VF) waveform metric, can predict successful defibrillation and the return of spontaneous circulation (ROSC) after defibrillation attempts. We aimed to investigate the validation of Spectral Energy for the quantitative analysis of the VF waveform to guide defibrillation in a porcine model of cardiac arrest and compare it with the AMSA metric. In addition, we sought to determine the effects of epinephrine and cardiopulmonary resuscitation (CPR) on AMSA and Spectral Energy. METHODS: Sixty male domestic pigs weighing 35 to 45 kg were involved in this study. VF was initially untreated for 10 min followed by 6 min of CPR. Epinephrine was administered to the animals after 2 min of CPR. After the CPR, a single 120-J biphasic shock was applied to the animals. AMSA and Spectral Energy values were measured every minute from the electrocardiogram (ECG) to defibrillation. Receiver operating characteristic (ROC) curves were calculated for both the Spectral Energy and AMSA methods. RESULTS: Spectral Energy and AMSA values gradually decayed during untreated VF in all the animals. However, after the application of CPR and epinephrine, Spectral Energy and AMSA values were significantly increased in animals which were later successfully defibrillated, but did not increase in animals in which defibrillation was unsuccessful. The ROC curves showed that the Spectral Energy and AMSA methods possessed similar levels of sensitivity and specificity in predicting defibrillation success (P<0.001). CONCLUSIONS: Both the Spectral Energy and AMSA methods accurately predict successful defibrillation. Moreover, increases in the value of either Spectral Energy or AMSA after application of CPR and epinephrine may also predict successful defibrillation.

Endovascular hypothermia improves post-resuscitation myocardial dysfunction by increasing mitochondrial biogenesis in a pig model of cardiac arrest.

The aim of the study was to investigate the effects of endovascular hypothermia on mitochondrial biogenesis in a pig model of prolonged cardiac arrest (CA). Ventricular fibrillation was electrically induced, and animals were left untreated for 10 min; then after 6min of cardiopulmonary resuscitation (CPR), defibrillation was attempted. 25 animals that were successfully resuscitated were randomized into three groups: Sham group (SG, 5, no CA), normal temperature group (NTG, 5 for 12 h observation and 5 for 24 h observation), and endovascular hypothermia group (EHG, 5 for 12 h observation and 5 for 24 h observation). The core temperatures (Tc) in the EHG were maintained at 34 ±â€¯0.5 °C for 6 h by an endovascular hypothermia device (Coolgard 3000), then actively increased at the speed of 0.5 °C per hour during the next 6 h to achieve a normal body temperature, while Tc were maintained at 37.5 ±â€¯0.5 °C in the NTG. Cardiac and mitochondrial functions, the quantification of myocardial mitochondrial DNA (mtDNA), peroxisome proliferator-activated receptor coactivator-1α (PGC-1α), nuclear respiratory factor (NRF)-1, and NRF-2 were examined. Results showed that myocardial and mitochondrial injury and dysfunction increased significantly at 12 h and 24 h after CA. Endovascular hypothermia offered a method to rapidly achieve the target temperature and provide stable target temperature management (TTM). Cardiac outcomes were improved and myocardial injuries were alleviated with endovascular hypothermia. Compared with NTG, endovascular hypothermia significantly increased mitochondrial activity and biogenesis by amplifying mitochondrial biogenesis factors' expressions, including PGC-1α, NRF-1, and NRF-2. In conclusions, endovascular hypothermia after CA alleviated myocardial and mitochondrial dysfunction, and was associated with increasing mitochondrial biogenesis.

Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level.

BACKGROUND: To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression). METHODS AND MATERIALS: MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival. RESULTS: Both clinical and MRI features differed significantly between Type I and Type II cancer groups (p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer (ρ = - 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01). CONCLUSIONS: MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients.

Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

PURPOSE: To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients. METHOD: A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis. RESULT: For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001). CONCLUSION: Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy. KEY POINTS: • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis.

LDK378 improves micro- and macro-circulation via alleviating STING-mediated inflammatory injury in a Sepsis rat model induced by Cecal ligation and puncture.

BACKGROUND: Sepsis is a systemic inflammatory response syndrome caused by severe infections. LDK378, a second-generation ALK inhibitor, exhibits a potential anti-inflammatory function against sepsis. Micro- and macro-circulatory dysfunctions are pivotal elements of the pathogenesis of severe sepsis and septic shock. We hypothesized that LDK378 can improve micro- and macro-circulation of septic rats, therefore improving the outcome of survival via blocking the ALK-STING pathway to attenuate inflammatory injuries. METHODS: A septic rat model was established by the cecal ligation and puncture (CLP) method. A total of 60 rats were randomized into three groups: a sham group, CLP group, and CLP + LDK378 group (n = 20 in each group). Five rats were randomly selected from each group for the mechanism study; the remaining 15 rats in each group were involved in a survival curve examination. A sidestream dark field video microscope was used to record sublingual microcirculation and mean arterial pressure (MAP) and levels of inflammatory cytokine secretion were examined at 6 h, 30 h, and 54 h after CLP surgery. Expressions of TANK binding kinase 1 (TBK1) and its downstream targets were determined, and histological alterations to the heart, lungs, and kidneys were examined at 54 h after CLP surgery. RESULTS: We found the group that received LDK378 treatment showed increased MAP levels compared to the CLP group at 30 h and 54 h. Meanwhile, LDK378 ameliorated the perfused small vessel density and microvascular flow index, decreased the expression of TNF-a and IL-6, and upregulated the expression of IL-10 in comparison with the CLP group. LDK378 injections also downregulated the expression of TBK1 and its downstream targets. Furthermore, LDK378 treatment significantly reduced sepsis-induced organ injuries, therefore improving survival rates. CONCLUSIONS: These findings demonstrate that LDK378 treatment can improve microcirculation and reduce organ injuries in CLP-induced septic rats via the regulation of inflammatory cytokine secretion and the downstream signaling components of the ALK-STING pathway.

MR findings of primary ovarian granulosa cell tumor with focus on the differentiation with other ovarian sex cord-stromal tumors.

BACKGROUND: To describe magnetic resonance imaging (MRI) features of ovarian granulosa cell tumors (OGCTs) and compare with other sex cord-stromal tumors (OSCs) in ovary. METHODS: MR findings of 18 patients with surgically confirmed ovarian granulosa cell tumor were retrospectively reviewed by two radiologists with consensus reading. All MR examinations were prospectively performed within one month. Clinical and imaging characteristics of OGCTs were evaluated and compared with OSCs (control group). RESULTS: In 18 patients, 20 ovarian granulosa cell tumors were detected on MRI. Sixteen tumors appeared as solid or mostly solid mass (16/20), while 4 tumors as cystic mass. Pathological pelvic fluid was detected in 1 OGCT (1/18) and 11 OSCs (11/34) (p = 0.031).On T2 weighted imaging (T2WI), most of OGCTs displayed hyperintense signal and mixed signal (19/20); on T1 weighted imaging (T1WI), 11 OGCTs (11/20) displayed similar signal as on T2WI imaging. The lesion signal between OGCT and OSC differed significantly on both T1WI (p = 0.017) and T2WI (p = 0.002). Tumoral bleeding was detected in 6 OGCTs on MRI. On diffusion weighted imaging (DWI) images, OGCTs mostly appeared as high signal (16/20). Average apparent diffusion coefficient (ADC) value derived from DWI images in the OGCT group (0.84 ± 0.26× 10- 3 mm2/s was less than the control group (1.22 ± 0.47 × 10- 3 mm2/s) with statistical difference (p = 0.002). CONCLUSIONS: MRI could provide important information in OGCT diagnosis. ADC value might be useful in differentiating OGCT from OSC.