Neutrophil extracellular traps promote macrophage inflammation in psoriasis.

Psoriasis is a chronic inflammatory skin disease connected with immune dysregulation. Macrophages are key inflammatory cells in psoriasis but the specific mechanism of their activation is not fully understood. Neutrophil extracellular traps (NETs) have been shown to regulate macrophage function. Here, we found that NET deposition was increased in psoriasis lesions. Peptidylarginine deaminase 4 (PAD4, a key enzyme for NET formation) deficiency attenuated skin lesions and inflammation in an imiquimod-induced psoriatic mouse model. Furthermore, the STING signaling pathway was markedly activated in psoriasis and abolished by PAD4 deficiency. PAD4-deficient mice treated with the STING agonist DMXAA exhibited more severe symptoms and inflammation than control mice. Mechanistically, the STING inhibitor C-176 inhibited NET-induced macrophage inflammation and further inhibited the proliferation of HaCaT cells. Our findings suggest an important role of NETs in the pathogenesis of psoriasis, and activation of macrophage STING/NF-κB signaling pathway might involve in NETs related psoriasis.

Highly extensile approach for comminuted ulna coronoid process fractures with mini-plate fixation: a case series of 31 patients.

BACKGROUND: For the treatment of coronoid process fractures, medial, lateral, anterior, anteromedial, and posterior approaches have been increasingly reported; however, there is no general consensus on the method of fixation of coronal fractures. Here, we present a highly-extensile minimally invasive approach to treat coronoid process fractures using a mini-plate that can achieve anatomic reduction, stable fixation, and anterior capsular repair. Further, the study aimed to determine the complication rate of the anterior minimally invasive approach and to evaluate functional and clinical patient-reported outcomes during follow-up. METHODS: Thirty-one patients diagnosed with coronoid fractures accompanied with a "terrible triad" or posteromedial rotational instability between April 2012 and October 2018 were included in the analysis. Anatomical reduction and mini-plate fixation of coronoid fractures were performed using an anterior minimally invasive approach. Patient-reported outcomes were evaluated using the Mayo Elbow Performance Index (MEPI) score, range of motion (ROM), and the visual analog score (VAS). The time of fracture healing and complications were recorded. RESULTS: The mean follow-up time was 26.7 months (range, 14-60 months). The average time to radiological union was 3.6 ± 1.3 months. During the follow-up period, the average elbow extension was 6.8 ± 2.9° while the average flexion was 129.6 ± 4.6°. According to Morrey's criteria, 26 (81%) elbows achieved a normal desired ROM. At the last follow-up, the mean MEPI score was 98 ± 3.3 points. There were no instances of elbow instability, elbow joint stiffness, subluxation or dislocation, infection, blood vessel complications, or nerve palsy. Overall, 10 elbows (31%) experienced heterotopic ossification. CONCLUSION: An anterior minimally invasive approach allows satisfactory fixation of coronoid fractures while reducing incision complications due to over-dissection of soft tissue injuries. In addition, this incision does not compromise the soft tissue stability of the elbow joint and allows the patient a more rapid return to rehabilitation exercises.

Temperature-Switch-Controlled Second Harmonic Mode Sensor for Brain-Tissue Detection.

Identifying brain-tissue types holds significant research value in the biomedical field of non-contact brain-tissue measurement applications. In this paper, a layered metastructure is proposed, and the second harmonic generation (SHG) in a multilayer metastructure is derived using the transfer matrix method. With the SHG conversion efficiency (CE) as the measurement signal, the refractive index ranges that can be distinguished are 1.23~1.31 refractive index unit (RIU) and 1.38~1.44 RIU, with sensitivities of 0.8597 RIU-1 and 1.2967 RIU-1, respectively. It can distinguish various brain tissues, including gray matter, white matter, and low-grade glioma, achieving the function of a second harmonic mode sensor (SHMS). Furthermore, temperature has a significant impact on the SHG CE, which can be used to define the switch signal indicating whether the SHMS is functioning properly. When the temperature range is 291.4~307.9 Kelvin (K), the temperature switch is in the "open" state, and the optimal SHG CE is higher than 0.298%, indicating that the SHMS is in the working state. For other temperature ranges, the SHG CE will decrease significantly, indicating that the temperature switch is in the "off" state, and the SHMS is not working. By stimulating temperature and using the response of SHG CE, the temperature-switch function is achieved, providing a new approach for temperature-controlled second harmonic detection.

Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules.

Objective Accurate differentiation within the LI-RADS category M (LR-M) between hepatocellular carcinoma (HCC) and non-HCC malignancies (mainly intrahepatic cholangiocarcinoma [CCA] and combined hepatocellular and cholangiocarcinoma [cHCC-CCA]) is an area of active investigation. We aimed to use radiomics-based machine learning classification strategy for differentiating HCC from CCA and cHCC-CCA on contrast-enhanced ultrasound (CEUS) images in high-risk patients with LR-M nodules. Methods A total of 159 high-risk patients with LR-M nodules (69 HCC and 90 CCA/cHCC-CCA) who underwent CEUS within 1 month before pathologic confirmation from January 2006 to December 2019 were retrospectively included (111 patients for training set and 48 for test set). The training set was used to build models, while the test set was used to compare models. For each observation, six CEUS images captured at predetermined time points (T1, peak enhancement after contrast injection; T2, 30 seconds; T3, 45 seconds; T4, 60 seconds; T5, 1-2 minutes; and T6, 2-3 minutes) were collected for tumor segmentation and selection of radiomics features, which included seven types of features: first-order statistics, shape (2D), gray-level co-occurrence matrix, gray-level size zone matrix, gray-level run length matrix, neighboring gray tone difference matrix, and gray-level dependence matrix. Clinical data and key radiomics features were employed to develop the clinical model, radiomics signature (RS), and combined RS-clinical (RS-C) model. The RS and RS-C model were built using the machine learning framework. The diagnostic performance of these three models was calculated and compared. Results Alpha-fetoprotein (AFP), CA19-9, enhancement pattern, and time of washout were included as independent factors for clinical model (all p < 0.05). Both the RS and RS-C model performed better than the clinical model in the test set (area under the curve [AUC] of 0.698 [0.571-0.812] for clinical model, 0.903 [0.830-0.970] for RS, and 0.912 [0.838-0.977] for the RS-C model; both p < 0.05). Conclusions Radiomics-based machine learning classifiers may be competent for differentiating HCC from CCA and cHCC-CCA in high-risk patients with LR-M nodules.

Role of Histogram Features on Arterial Spin Labeling Perfusion Magnetic Resonance Imaging in Identifying Isocitrate Dehydrogenase Genotypes and Glioma Malignancies.

AIM: To explore the use of histogram features on noninvasive arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) in differentiating isocitrate dehydrogenase mutant-type (IDH-mut) from isocitrate dehydrogenase wild-type (IDH-wt) gliomas, and lower-grade gliomas (LGGs) from glioblastomas. MATERIAL AND METHODS: This retrospective study included 131 patients who underwent ASL MRI and anatomic MRI. Cerebral blood flow (CBF) maps were calculated, from which 10 histogram features describing the CBF distribution were extracted within the tumor region. Correlation analysis was performed to determine the correlations between histogram features as well as tumor grades and IDH genotypes. The independent t-test and Fisher's exact test were used to determine differences in the extracted histogram features, age at diagnosis, and sex in different glioma subtypes. Multivariate binary logistic regression analysis was performed, and diagnostic performances were evaluated with the receiver operating characteristic curves. RESULTS: CBF histogram features were significantly correlated with tumor grades and IDH genotypes. These features can effectively differentiate LGGs from glioblastomas, and IDH-mut from IDH-wt gliomas. The area under the receiving operating characteristic curve of the model calculated using combined CBF 30th percentile and age at diagnosis in differentiating LGGs from glioblastomas was 0.73. Integrating age at diagnosis and CBF 10th percentile could be more effective in differentiating IDH-mut from IDH-wt gliomas. Furthermore, the combined model had a better area under the receiving operating characteristic curve at 0.856 (sensitivity: 84.4%, specificity: 82.9%). CONCLUSION: The histogram features on ASL were significantly correlated with tumor grade and IDH genotypes. Moreover, the use of these features could effectively differentiate glioma subtypes. The combined application of age at diagnosis and perfusion histogram features resulted in a more comprehensive identification of tumor subtypes. Therefore, ASL can be a noninvasive tool for the pre-surgical evaluation of gliomas.

Fe-Catalyzed Fluorosulfonylation of Alkenes via Sulfur Dioxide Insertion: Synthesis of Lactam-Functionalized Alkyl Sulfonyl Fluorides.

A novel Fe-catalyzed fluorosulfonylation of alkenes with Na2S2O4 and N-fluorobenzenesulfonimide (NFSI) for assembling various lactam-functionalized alkyl sulfonyl fluorides is disclosed. In this reaction, Na2S2O4 acts as both an SO2 source and a reductant. Furthermore, the resulting products can be efficiently transformed into valuable chemicals, including sulfonyl esters and sulfonamides, via the sulfur(VI) fluoride exchange (SuFEx) click reaction. Preliminary mechanistic studies suggest that the transformation proceeds through intramolecular radical cyclization, SO2 insertion, sulfite anion formation, and fluorination.

A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients.

Objective: BRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methods: In this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. Results: In the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647-0.776) vs 0.768 (95% CI, 0.704-0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597-0.808) vs 0.723 (95% CI, 0.625-0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748-0.861) in the training dataset and 0.811 (95% CI, 0.724-0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong's test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. Conclusion: This machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.