Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer

BACKGROUND

Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

PURPOSE

To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy.

STUDY TYPE

Retrospective.

POPULATION

After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52).

FIELD STRENGTH/SEQUENCE

3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI).

ASSESSMENT

The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used.

STATISTICAL TESTS

The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant.

RESULTS

The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789).

DATA CONCLUSION

In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.

Changes in brain susceptibility in Wilson's disease patients: a quantitative susceptibility mapping study

AIM

To assess changes in the susceptibility of the caudate nucleus (CN), putamen, and globus pallidus (GP) in patients with neurological and hepatic Wilson's disease (WD) by quantitative susceptibility mapping (QSM).

MATERIAL AND METHODS

The brain MRI images of 33 patients diagnosed with WD and 20 age-matched controls were analysed retrospectively. All participants underwent brain T1-weighted, T2-weighted, and QSM imaging using a 1.5 T magnetic resonance imaging (MRI) machine. QSM maps were evaluated with the STISuite toolbox. The quantitative susceptibility levels of the CN, putamen, and GP were analysed using region of interest analysis on QSM maps. Differences among neurological WD patients, hepatic patients, and controls were determined.

RESULTS

Susceptibility levels were significantly higher for all examined structures (CN, putamen and GP) in patients with neurological WD compared with controls (all p<0.05) and hepatic WD patients (all p<0.05). No statistically significant differences were found in susceptibility levels between patients with hepatic WD and controls (all p>0.05).

CONCLUSION

The QSM technique is a valuable tool for detecting changes in brain susceptibility in WD patients, indicating abnormal metal deposition. Notably, the current findings suggest that neurological WD patients exhibit more severe susceptibility changes compared with hepatic WD patients. Therefore, QSM can be utilised as a complementary method to detect brain injury in WD patients.

Mechanism of balloon burst during transcatheter aortic valve replacement pre-dilatation: Image observation and validation by finite element analysis

BACKGROUND

Balloon burst during transcatheter aortic valve replacement (TAVR) is serious complication. This study pioneers a novel approach by combining image observation and computer simulation validation to unravel the mechanism of balloon burst in a patient with bicuspid aortic valve (BAV) stenosis.

METHOD

A new computational model for balloon pre-dilatation was developed by incorporating the element failure criteria according to the Law of Laplace. The effects of calcification and aortic tissue material parameters, friction coefficients, balloon types and aortic anatomy classification were performed to validate and compare the expansion behavior and rupture mode of actual balloon.

RESULTS

Balloon burst was dissected into three distinct stages based on observable morphological changes. The mechanism leading to the complete transverse burst of the non-compliant balloon initiated at the folding edges, where contacted with heavily calcified masses at the right coronary sinus, resulting in high maximum principal stress. Local sharp spiked calcifications facilitated rapid crack propagation. The elastic moduli of calcification significantly influenced balloon expansion behavior and crack morphology. The simulation case of the calcific elastic modulus was set at 12.6 MPa could closely mirror clinical appearance of expansion behavior and crack pattern. Furthermore, the case of semi-compliant balloons introduced an alternative rupture mechanism as pinhole rupture, driven by local sharp spiked calcifications.

CONCLUSIONS

The computational model of virtual balloons could effectively simulate balloon dilation behavior and burst mode during TAVR pre-dilation. Further research with a larger cohort is needed to investigate the balloon morphology during pre-dilation by using this method to guide prosthesis sizing for potential favorable outcomes.

Cell division cycle 42 effector protein 4 inhibits prostate cancer progression by suppressing ERK signaling pathway

Prostate cancer (PCa) is the most common malignancy among men worldwide. The cell division cycle 42 effector protein 4 (CDC42EP4) functions downstream of CDC42, yet its role and molecular mechanisms in PCa remain unexplored. This study aimed to elucidate the role of CDC42EP4 in the progression of PCa and its underlying mechanisms. Bioinformatical analysis indicated that CDC42EP4 expression was significantly lower in PCa tissue compared to normal prostate tissue. Cellular phenotyping analysis suggested that CDC42EP4 markedly inhibited the proliferation, migration, and invasion of PCa cells. Xenograft tumor assays further demonstrated that CDC42EP4 suppressed the growth of PCa cells in vivo. Mechanistically, the study established that CDC42EP4 inhibited the ERK pathway in PCa cells. Additionally, the ERK pathway inhibitor PD0325901 was employed, revealing that PD0325901 significantly nullified the effects of CDC42EP4 on PCa cell proliferation, migration, and invasion. Collectively, our findings demonstrate that CDC42EP4 acts as a critical tumor suppressor gene, inhibiting PCa cell proliferation, migration, and invasion through the ERK pathway, thereby presenting potential targets for PCa therapy.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Background

Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.

Methods

In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.

Results

The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.

Conclusion

The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Deep Learning for Automated Contouring of Primary Gross Tumor Volumes by MRI for Radiation Therapy of Brain Metastasis

PURPOSE/OBJECTIVE(S)

Radiotherapy is one of the most effective methods for the treatment of brain metastases (BMs). Traditional manual delineation of primary gross tumor volumes (GTV) of multiple BMs (especially small metastases) in radiotherapy practice is extremely labor intensive and highly dependent on oncologists' experience, achieving the precise and efficient automatic delineation of BMs is of great significance for efficient and homogeneous one-stop adaptive radiotherapy.

MATERIALS/METHODS

We retrospectively collected 62 MRI (non-enhanced T1-weighted sequences) sequences of 50 patients with BMs from January 2020 to July 2021. An automatic model (BUC-Net) for automatic delineation BMs was proposed in this work, which was based on deep learning by combining 3D bottler layer module and the cascade architecture to improve the accuracy and efficient of BMs' automatic delineation, especially for small metastases with tiny size and relatively low contrast. The prosed method was compared with the existing 3D U-Net (U-Net) and 3D U-Net Cascade (U-Net Cascade). The performance of our proposed method was evaluated by Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) with human experts.

RESULTS

The automatic segmentation results of BUC-Net evaluated with 310 BMs in 13 test patients was summarized in Table 1. These BMs in each test patient were automatically delineated by two types of contours: as a whole tumor contour (Whole-delineation) and the multiple tumor contours (Multiple-delineation). BUC-Net performed the best mean DSC and HD95, which is significantly outperformed U-Net (Whole-delineation: 0.911 & 0.894 of DSC, Multiple-delineation: 0.794 & 0.754 of DSC, P < 0.05 for both) and U-Net cascade (Whole-delineation: 0.947 & 7.141 of HD95, Multiple-delineation: 0.902 & 1.171 of HD95, P < 0.05 for both); Additionally, BUC-Net achieved the best mean ASD for Whole-delineation and comparable ASD (0.290 & 0.277, P > 0) for Multiple-delineation with U-Net Cascade.

CONCLUSION

Our results showed that the proposed approach is promising for the automatic delineation of BMs in MRI, which can be integrated into a radiotherapy workflow to significantly shorten segmentation time.

[Experimental study on the molluscicidal activity of surfactin against Oncomelania hupensis]

OBJECTIVE

To evaluate the molluscicidal activity of surfactin against Oncomelania hupensis, so as to provide the experimental basis for use of Bacillus for killing O. hupensis.

METHODS

O. hupensis snails were collected from schistosomiasisendemic foci of Wuhu City on September 2022, and Schistosoma japonicum-infected snails were removed. Then, 60 snails were immersed in surfactin at concentrations of 2, 1, 0.5, 0.25, 0.125 mg/mL and 0.062 5 mg/mL for 24, 48, 72 hours at 26 °C, while ultrapure water-treated snails served as controls. The median lethal concentration (LC50) of surfactin against O. hupensis snails was estimated. O. hupensis snails were immersed in surfactin at a concentration of 24 h LC50 and ultrapure water, and then stained with propidium iodide (PI). The PI uptake in haemocyte was observed in O. hupensis snails using fluorescence microscopy.

RESULTS

The mortality of O. hupensis was 5.0% following immersion in surfactin at a concentration of 0.062 5 mg/mL for 24 h, and the mortality was 100.0% following immersion in surfactin at a concentration of 2 mg/mL for 72 h, while no snail mortality was observed in the control group. There were significant differences in the mortality of O. hupensis in each surfactin treatment groups at 24 (χ2 = 180.150, P < 0.05), 48 h (χ2 = 176.786, P < 0.05) and 72 h (χ2 = 216.487, P < 0.05), respectively. The average mortality rates of O. hupensis were 38.9% (140/360), 62.2% (224/360) and 83.3% (300/360) 24, 48 h and 72 h post-immersion in surfactin, respectively (χ2 = 150.264, P < 0.05), and the 24, 48 h and 72 h LC50 values of surfactin were 0.591, 0.191 mg/mL and 0.054 mg/mL against O. hupensis snails. Fluorescence microscopy showed more numbers of haemocytes with PI uptake in 0.5 mg/mL surfactintreated O. hupensis snails than in ultrapure water-treated snails for 24 h, and there was a significant difference in the proportion of PI uptake in haemocytes between surfactin-and ultrapure water-treated snails (χ2 = 6.690, P < 0.05).

CONCLUSIONS

Surfactin is active against O. hupensis snails, which may be associated with the alteration in the integrity of haemocyte membrane.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

A versatile control program for positioning and shooting targets in laser-plasma experiments

We introduce a LabVIEW-based control program that significantly improves the efficiency and flexibility in positioning and shooting solid targets in laser-plasma experiments. The hardware driven by this program incorporates a target positioning subsystem and an imaging subsystem, which enables us to install up to 400 targets for one experimental campaign and precisely adjust them in six freedom degrees. The overall architecture and the working modes of the control program are demonstrated in detail. In addition, we characterized the distributions of target positions of every target holder and simultaneously saved the target images, resulting in a large dataset that can be used to train machine learning models and develop image recognition algorithms. This versatile control system has become an indispensable platform when preparing and conducting laser-plasma experiments.

A binary pulsar in a 53-minute orbit

Spider pulsars are neutron stars that have a companion star in a close orbit. The companion star sheds material to the neutron star, spinning it up to millisecond rotation periods, while the orbit shortens to hours. The companion is eventually ablated and destroyed by the pulsar wind and radiation1,2. Spider pulsars are key for studying the evolutionary link between accreting X-ray pulsars and isolated millisecond pulsars, pulsar irradiation effects and the birth of massive neutron stars3-6. Black widow pulsars in extremely compact orbits (as short as 62 minutes7) have companions with masses much smaller than 0.1 M⊙. They may have evolved from redback pulsars with companion masses of about 0.1-0.4 M⊙ and orbital periods of less than 1 day8. If this is true, then there should be a population of millisecond pulsars with moderate-mass companions and very short orbital periods9, but, hitherto, no such system was known. Here we report radio observations of the binary millisecond pulsar PSR J1953+1844 (M71E) that show it to have an orbital period of 53.3 minutes and a companion with a mass of around 0.07 M⊙. It is a faint X-ray source and located 2.5 arcminutes from the centre of the globular cluster M71.

[Tumor cell lysate with low content of HMGB1 enhances immune response of dendritic cells against lung cancer in mice]

OBJECTIVE

To assess the effect of tumor cell lysate (TCL) with low high-mobility group B1 (HMGB1) content for enhancing immune responses of dendritic cells (DCs) against lung cancer.

METHODS

TCLs with low HMGB1 content (LH-TCL) and normal HMGB1 content (NH-TCL) were prepared using Lewis lung cancer (LLC) cells in which HMGB1 was inhibited with 30 nmol/L glycyrrhizic acid (GA) and using LLC cells without GA treatment, respectively. Cultured mouse DCs were exposed to different doses of NH-TCL and LH-TCL, using PBS as the control. Flow cytometry was used to detect the expressions of CD11b, CD11c and CD86 and apoptosis of the stimulated DCs, and IL-12 levels in the cell cultures were detected by ELISA. Mouse spleen cells were co-cultured with the stimulated DCs, and the activation of the spleen cells was assessed by detecting CD69 expression using flow cytometry; TNF-β production in the spleen cells was detected with ELISA. The spleen cells were then co-cultured with LLC cells at the effector: target ratios of 5:1, 10:1 and 20:1 to observe the tumor cell killing. In the animal experiment, C57/BL6 mouse models bearing subcutaneous LLC xenograft received multiple injections with the stimulated DCs, and the tumor growth was observed.

RESULTS

The content of HMGB1 in the TCL prepared using GA-treated LLC cells was significantly reduced (P < 0.01). Compared with NH-TCL, LH-TCL showed a stronger ability to reduce apoptosis (P < 0.001) and promote activation and IL- 12 production in the DCs. Compared with those with NH-TCL stimulation, the DCs stimulated with LH-TCL more effectively induced activation of splenic lymphocytes and enhanced their anti-tumor immunity (P < 0.05). In the cell co-cultures, the spleen lymphocytes activated by LH-TCL-stimulated DCs showed significantly enhanced LLC cell killing activity (P < 0.01). In the tumor-bearing mice, injections of LH-TCL-stimulated DCs effectively activated host anti-tumor immunity and inhibited the growth of the tumor xenografts (P < 0.05).

CONCLUSION

Stimulation of the DCs with LH-TCL enhances the anti-tumor immune activity of the DCs and improve the efficacy of DCbased immunotherapy for LLC in mice.

Attention guided neural ODE network for breast tumor segmentation in medical images

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis

BACKGROUND AND AIM

Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP).

METHODS

In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP.

RESULTS

In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively.

CONCLUSIONS

Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Ribosomal protein L22-like1 promotes prostate cancer progression by activating PI3K/Akt/mTOR signalling pathway

Prostate cancer (PCa) is one of the most common malignancies in men. Ribosomal protein L22-like1 (RPL22L1), a component of the ribosomal 60 S subunit, is associated with cancer progression, but the role and potential mechanism of RPL22L1 in PCa remain unclear. The aim of this study was to investigate the role of RPL22L1 in PCa progression and the mechanisms involved. Bioinformatics and immunohistochemistry analysis showed that the expression of RPL22L1 was significantly higher in PCa tissues than in normal prostate tissues. The cell function analysis revealed that RPL22L1 significantly promoted the proliferation, migration and invasion of PCa cells. The data of xenograft tumour assay suggested that the low expression of RPL22L1 inhibited the growth and invasion of PCa cells in vivo. Mechanistically, the results of Western blot proved that RPL22L1 activated PI3K/Akt/mTOR pathway in PCa cells. Additionally, LY294002, an inhibitor of PI3K/Akt pathway, was used to block this pathway. The results showed that LY294002 remarkably abrogated the oncogenic effect of RPL22L1 on PCa cell proliferation and invasion. Taken together, our study demonstrated that RPL22L1 is a key gene in PCa progression and promotes PCa cell proliferation and invasion via PI3K/Akt/mTOR pathway, thus potentially providing a new target for PCa therapy.

Microcystin‑leucine arginine promotes colorectal cancer cell proliferation by activating the PI3K/Akt/Wnt/β‑catenin pathway

Microcystin‑leucine arginine (MC‑LR) is an environmental toxin produced by cyanobacteria and is considered to be a potent carcinogen. However, to the best of our knowledge, the effect of MC‑LR on colorectal cancer (CRC) cell proliferation has never been studied. The aim of the present study was to investigate the effect of MC‑LR on CRC cell proliferation and the underlying mechanisms. Firstly, a Cell Counting Kit‑8 (CCK‑8) assay was conducted to determine cell viability at different concentrations, and 50 nM MC‑LR was chosen for further study. Subsequently, a longer CCK‑8 assay and a cell colony formation assay showed that MC‑LR promoted SW620 and HT29 cell proliferation. Furthermore, western blotting analysis showed that MC‑LR significantly upregulated protein expression of PI3K, p‑Akt (Ser473), p‑GSK3β (Ser9), β‑catenin, c‑myc and cyclin D1, suggesting that MC‑LR activated the PI3K/Akt and Wnt/β‑catenin pathways in SW620 and HT29 cells. Finally, the pathway inhibitors LY294002 and ICG001 were used to validate the role of the PI3K/Akt and Wnt/β‑catenin pathways in MC‑LR‑accelerated cell proliferation. The results revealed that MC‑LR activated Wnt/β‑catenin through the PI3K/Akt pathway to promote cell proliferation. Taken together, these data showed that MC‑LR promoted CRC cell proliferation by activating the PI3K/Akt/Wnt/β‑catenin pathway. The present study provided a novel insight into the toxicological mechanism of MC‑LR.

Enhanced Moth-Flame Optimizer with Quasi-Reflection and Refraction Learning with Application to Image Segmentation and Medical Diagnosis

Background:

Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks.

Objective:

To overcome the above shortcomings, this paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields.

Method:

In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase; refraction learning improves the accuracy of the potential optimal solution.

Results:

Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper; first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms; afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case.

Conclusion:

Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy.

Moderate-Severe White Matter Lesion Predicts Delayed Intraventricular Hemorrhage in Intracerebral Hemorrhage

BACKGROUND

Most existing studies have focused on the correlation between white matter lesion (WML) and baseline intraventricular hemorrhage (IVH) in patients with intracerebral hemorrhage (ICH), whereas few studies have investigated the relationship between WML severity and delayed IVH after admission. This study aimed to investigate the correlation between WML severity and delayed IVH and to verify the association between WML and baseline IVH.

METHODS

A total of 480 patients with spontaneous ICH from February 2018 to October 2020 were selected. WML was scored using the Van Swieten Scale, with scores of 0-2 representing nonslight WML and scores of 3-4 representing moderate-severe WML. We determined the presence of IVH on baseline (< 6 h) and follow-up computed tomography (< 72 h) images. Univariate analysis and multiple logistic regression were used to analyze the influencing factors of baseline and delayed IVH.

RESULTS

Among 480 patients with ICH, 172 (35.8%) had baseline IVH, and there was a higher proportion of moderate-severe WML in patients with baseline IVH (20.3%) than in those without baseline IVH (12.7%) (P = 0.025). Among 308 patients without baseline IVH, delayed IVH was found in 40 patients (12.9%), whose proportion of moderate-severe WML (25.0%) was higher than that in patients without delayed IVH (10.8%) (P = 0.012). Multiple logistic regression results showed that moderate-severe WML was independently correlated with baseline IVH (P = 0.006, odds ratio = 2.266, 95% confidence interval = 1.270-4.042) and delayed IVH (P = 0.002, odds ratio = 7.009, 95% confidence interval = 12.086-23.552).

CONCLUSIONS

Moderate-severe WML was an independent risk factor for delayed IVH as well as baseline IVH.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

The protective effect of mangiferin on osteoarthritis: An in vitro and in vivo study

Mangiferin is a kind of polyphenol chemical compound separated from these herbal medicines of Mangifera indica L., Anemarrhena asphodeloides Bge. and Belamcanda chinensis L., which has anti-inflammatory, anti-virus, and other physiological activities without toxic effects. Osteoarthritis (OA) is a chronic disease that is also a kind of arthritis disease in which articular cartilage or bones under the joint is damaged. In addition, artificial replacements are required in severe cases. At present, there are not too much researches on the potential biological activities of mangiferin that plays a protective role in the treatment of OA. In this study, we evaluated the protective effect of mangiferin on osteoarthritis (OA) in vitro and in vivo. First, the effect of different concentrations of mangiferin on rat chondrocytes was determined by MTT assay. Second, the effects of mangiferin on the expression levels of matrix metalloproteinase (MMP)-13, TNF alpha, Collagen II, Caspase-3, and cystatin-C in interleukin-1beta (IL-1beta)-induced rat chondrocytes were examined by the real-time polymerase chain reaction in vitro, meanwhile the effects of mangiferin on the nuclear factor kappa-B (NF-kappaB) signaling pathway were also investigated by Western Blot. Finally, the anti-osteoarthritic protective effect of mangiferin was evaluated in the rat model by anterior cruciate ligament transection (ACLT) combined with bilateral ovariectomy-induced OA in vivo. The results showed that the mangiferin was found to inhibit the expression of MMP-13, TNF-alpha, and Caspase-3 which also increased the expression of Collagen II and cystatin-C in IL 1beta induced rat chondrocytes. In addition, IL-1beta-induced activation of nuclear factor kappa-B (NF-kappaB) and the degradation of inhibitor of kappaB (IkappaB)-alpha were suppressed by mangiferin. For the in vivo study in a rat model of OA, 100 microl of mangiferin was administered by intra-articular injections for rats, the results showed that the cartilage degradation was suppressed by mangiferin through Micro CT and Histological Examination. According to both in vitro and in vivo results, mangiferin has a protective effect in the treatment of OA which may be a promising therapeutic agent for OA.

Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.

Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm

Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

Background

A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.

Materials and Methods

A total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.

Results

The diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.

Conclusion

Diagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

Enzyme-induced morphological transformation of drug carriers: Implications for cytotoxicity and the retention time of antitumor agents

Nanocarriers have been widely employed to deliver chemotherapeutic drugs for cancer treatment. However, the insufficient accumulation of nanoparticles in tumors is an important reason for the poor efficacy of nanodrugs. In this study, a novel drug delivery system with a self-assembled amphiphilic peptide was designed to respond specifically to alkaline phosphatase (ALP), a protease overexpressed in cancer cells. The amphiphilic peptide self-assembled into spherical and fibrous nanostructures, and it easily assembled into spherical drug-loaded peptide nanoparticles after loading of a hydrophobic chemotherapeutic drug. The cytotoxicity of the drug carriers was enhanced against tumor cells over time. These spherical nanoparticles transformed into nanofibers under the induction of ALP, leading to efficient release of the encapsulated drug. This drug delivery strategy relying on responsiveness to an enzyme present in the tumor microenvironment can enhance local drug accumulation at the tumor site. The results of live animal imaging showed that the residence time of the morphologically transformable drug-loaded peptide nanoparticles at the tumor site was prolonged in vivo, confirming their potential use in antitumor therapy. These findings can contribute to a better understanding of the influence of drug carrier morphology on intracellular retention.

Reactive-oxygen-species-scavenging nanomaterials for resolving inflammation

Reactive oxygen species (ROS) mediate multiple physiological functions; however, the over-accumulation of ROS causes premature aging and/or death and is associated with various inflammatory conditions. Nevertheless, there are limited clinical treatment options that are currently available. The good news is that owing to the considerable advances in nanoscience, multiple types of nanomaterials with unique ROS-scavenging abilities that influence the temporospatial dynamic behaviors of ROS in biological systems have been developed. This has led to the emergence of next-generation nanomaterial-controlled strategies aimed at ameliorating ROS-related inflammatory conditions. Accordingly, herein we reviewed recent progress in research on nanotherapy based on ROS scavenging. The underlying mechanisms of the employed nanomaterials are emphasized. Furthermore, important issues in developing cross-disciplinary nanomedicine-based strategies for ROS-based inflammatory conditions are discussed. Our review of this increasing interdisciplinary field will benefit ongoing studies and clinical applications of nanomedicine based on ROS scavenging.

Doppler-based predictive model for methotrexate resistance in low-risk gestational trophoblastic neoplasia with myometrial invasion: prospective study of 147 patients

OBJECTIVES

This prospective clinical study aimed to evaluate the vascularization characteristics of low-risk gestational trophoblastic neoplasia (GTN) using Doppler imaging and to develop a predictive model for resistance to methotrexate (MTX).

METHODS

Patients with low-risk GTN receiving primary MTX treatment were enrolled from the Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China, from September 2012 to August 2018. The primary endpoint was to develop and internally validate a predictive model for resistance to MTX therapy in these patients. In the training set, clinical features and Doppler hemodynamic parameters before MTX therapy were analyzed using logistic regression to identify independent predictors of MTX resistance, which were integrated into the model. The predictive performance of the model was evaluated by leave-one-out cross-validation in the training dataset and internal validation in an independent-sample test dataset.

RESULTS

The entire imaging protocol was completed by 147 eligible patients, of which 110 comprised the training set and 37 the test set. In the training set, cases with myometrial invasion (81.8%; 90/110) showed vascular-enriched areas in the myometrium and high velocity and low impedance ratios of the uterine artery (UtA) compared to cases without myometrial invasion (18.2%; 20/110). On multivariate logistic regression analysis, time-averaged mean velocity in UtA (UtA-TAmean) and the International Federation of Gynecology and Obstetrics (FIGO) score were identified as independent predictors (P = 0.009 and P = 0.043, respectively) of MTX resistance. The Doppler-based predictive model, developed based on the 90 cases with myometrial invasion, was y = -2.95332 + 0.41696 × FIGO score + 0.03551 × UtA-TAmean. The model showed an area under the curve of 0.757 (95% CI, 0.653-0.862) and the optimal cut-off value was 0.50622, which had 45.2% sensitivity and 96.6% specificity. The model stratified patients with low-risk GTN into low (< 10%), intermediate (10-90%) and high (> 90%) probability of MTX resistance, based on the threshold values of -1.59544 and 0.10046. The model had an accuracy of 74.4% (95% CI, 64.5-82.3%) in the cross-validation and 72.7% (95% CI, 55.8-84.9%) in the internal validation.

CONCLUSIONS

The Doppler-based predictive model, combining a non-invasive marker of tumor vascularity with the FIGO scoring system, can differentiate cases with low from those with high probability of developing MTX resistance and therefore has the potential to guide treatment options in patients with low-risk GTN and myometrial invasion. © 2020 Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Upregulation of miR-146b promotes porcine ovarian granulosa cell apoptosis by attenuating CYP19A1

MicroRNAs (miRNAs) are 21- to 24-nucleotide long small noncoding RNAs, which play an important role in follicular atresia and granulosa cell (GC) apoptosis in the mammalian ovary. Here, we report that miR-146b, a conserved and ovary-enriched miRNA, modulates estradiol (E2) secretion, GC apoptosis, and follicular atresia in pigs. Genome-wide analysis and quantitative real-time PCR revealed that miR-146b was significantly upregulated during follicular atresia, and fluorescence-activated cell sorting showed that miR-146b functioned as a proapoptotic factor to induce GC apoptosis. MicroRNA-mRNA network analysis and luciferase reporter assays showed that CYP19A1, the pivotal enzyme for E2 synthesis signaling, was directly targeted by miR-146b. Furthermore, miR-146b interacted with the 3'untranslated region of CYP19A1 to prevent translation, thereby regulating CYP19A1-mediated E2 secretion and GC apoptosis. However, miR-146b was not regulated by the transcription factor SMAD4 or oxidative stress, both of which are critical regulators of CYP19A1. We, thus, conclude that miR-146b is a novel epigenetic factor regulating GC functions, follicular development, and female reproduction.

[Value of ALBI grade on precised estimation liver reserve function of patients with hepatocellular carcinoma]

Précised liver reserve function estimation is of great significance for predicting the survival time, post-hepatectomy liver failure and individualized comprehensive treatment strategies in hepatocellular carcinoma (HCC) patients. Currently, the widely used Child-Pugh (CP) classification and indocyanine green 15-minute retention rate (ICGR 15) have certain flaws and limitations. The albumin-bilirubin (ALBI) grading especially makes up for the deficiency of CP classification, and can provide an objective, simple, accurate and evidence-based method to estimate and guide the liver reserve function of HCC patients. This paper follows up and summarizes the research progress of ALBI grading estimation at home and abroad on liver reserve function of HCC patients.

The association between napping and the risk of cardiovascular disease and all-cause mortality: a systematic review and dose-response meta-analysis

Background

Napping is a habit prevalent worldwide and occurs from an early age. Some sleep specialists have suggested it as a potential public health tool due to the prevalence of sleep disorder. However, the association between napping and the risk of cardiovascular disease (CVD) and all-cause mortality remains unclear.

Purpose

To assess the association between napping and the risk of CVD and all-cause mortality.

Methods

We conducted a systematic search of Medline, Embase and Cochrane databases from inception through December 2019 for prospective cohort studies investigating the association between napping and the risk of CVD and/or all-cause mortality. Overall estimates were calculated using random effect models with inverse variance weighting. Dose-response meta-analysis was performed using restricted cubic spline models. The results were reported as hazard ratio (HR) and 95% confidence interval (CI).

Results

A total of 313651 participants (57.8% female, 38.9% took naps) from 20 cohort studies were included in the analysis. Overall, pooled analysis detected no association between daytime nap and CVD (HR 1.13, 95% CI 0.99–1.28). However, in subgroup analysis including only participants who were female (HR 1.31, 95% CI 1.09–1.58), older (age&gt;65 years) (HR 1.36, 95% CI 1.07–1.72), or took a longer nap (nap time&gt;60 minutes) (HR 1.34, 95% CI 1.05–1.63), napping was significantly associated with a higher risk of CVD comparing to not napping. All-cause mortality was associated with napping overall (HR 1.19, 95% CI 1.12–1.26), and effect sizes were even more pronounced in females (HR 1.22, 95% CI 1.13–1.31), older participants (HR 1.27, 95% CI 1.11–1.45) and those who took a long nap (HR 1.30, 95% CI 1.12–1.47). Furthermore, after stratifying participants by night sleep time (&lt;6 and &gt;6h/day), no significant association was detected except those who slept &gt;6h/day at night and took a long nap (HR 1.13, 95% CI 1.03–1.24). Dose-response analysis showed a J-curve relation between nap time and CVD (Figure 1). The HR decreased from 0 to 25 min/day, followed by a sharp increase in the risk at longer times. A positive linear relationship between nap time and all-cause mortality was also observed.

Conclusion

Long napping over 60 minutes per day is associated with increased risks of CVD and all-cause mortality. Night sleep duration may play a role in the relation between napping and all-cause mortality. Further, large-scale prospective cohort studies need to confirm our conclusion and investigate the underlying mechanisms driving these associations.

Funding Acknowledgement

Type of funding source: None

Assessing the impact of drinking water iodine concentrations on the iodine intake of Chinese pregnant women living in areas with restricted iodized salt supply

PURPOSE

The supply of non-iodized salt and the water improvement project have been conducted to reduce the iodine concentration in drinking water in areas with elevated water iodine. We aimed to assess the impact of water iodine concentration (WIC) on the iodine intake of pregnant women in areas with restricted iodized salt supply, and determine the cutoff values of WIC in areas with non-iodized salt supply.

METHODS

Overall, 534 pregnant women who attended routine antenatal outpatient visits in Zibo Maternal and Child Health Hospital in Gaoqing County were recruited. The 24-h urine iodine excretion (UIE) in 534 samples and the iodine concentration in 534 drinking water samples were estimated. Urinary iodine excretion, daily iodine intake, and daily iodine intake from drinking water (WII) were calculated. The relationship between WIC and daily iodine take was analyzed.

RESULTS

The median WIC, spot urine iodine concentration (UIC), and 24-h UIE were 17 (6, 226) μg/L, 145 (88, 267) μg/L, and 190 (110, 390) μg/day, respectively. A significant positive correlation was found between WIC and UIE (R² = 0.265, p < 0.001) and UIC (R² = 0.261, p < 0.001). The contribution rate of WII to total iodine intake increased from 3.0% in the group with WIC of < 10 μg/L to 45.7% in the group with WIC of 50-99 μg/L.

CONCLUSION

The iodine content in drinking water is the major iodine source in pregnant women living in high-water iodine areas where iodized salt supply is restricted. The contribution rate of daily iodine intake from drinking water increases with the increase in water iodine concentration.

Elevated microRNA-125b inhibits cytotrophoblast invasion and impairs endothelial cell function in preeclampsia

Preeclampsia (PE) is a life-threatening disorder of human pregnancy affecting 5-8% of all pregnancies. Currently, PE remains an elusive complicated and heterogenous medical condition with no early marker or symptoms is recognized for this serious pregnancy complications. Here, we profiled the plasma miRNA expression patterns associated with preeclampsia and found 16 miRNAs were deregulated (p < 0.01) in patients who later developed PE. Circulating hsa-miR-125b was aberrantly upregulated in early pregnancy and significantly reduced after delivery in preeclampsia. We then investigated the underlying molecular mechanisms between miR-125b and PE in vitro. We found that upregulated miR-125b can target KCNA1 to inhibit trophoblast invasion in human trophoblast cells. Moreover, overexpression of miR-125b in HUVECs impaired endothelial cell function through GPC1. The findings indicated that upregulated miR-125b leads to impaired placentation, and an increased risk of preeclampsia, Our studies provide novel insights into the underlying mechanisms on the association of miR-125b in early pregnancy and risk of PE, miR-125b might be a more specific predictive marker and a safe therapeutic target for treating patients with PE.

Broadband, millimeter-wave antireflection coatings for large-format, cryogenic aluminum oxide optics

We present two prescriptions for broadband ($ {\sim} 77 - 252\;{\rm GHz} $), millimeter-wave antireflection coatings for cryogenic, sintered polycrystalline aluminum oxide optics: one for large-format (700 mm diameter) planar and plano-convex elements, the other for densely packed arrays of quasi-optical elements-in our case, 5 mm diameter half-spheres (called "lenslets"). The coatings comprise three layers of commercially available, polytetrafluoroethylene-based, dielectric sheet material. The lenslet coating is molded to fit the 150 mm diameter arrays directly, while the large-diameter lenses are coated using a tiled approach. We review the fabrication processes for both prescriptions, then discuss laboratory measurements of their transmittance and reflectance. In addition, we present the inferred refractive indices and loss tangents for the coating materials and the aluminum oxide substrate. We find that at 150 GHz and 300 K the large-format coating sample achieves $ (97 \pm 2)\% $ transmittance, and the lenslet coating sample achieves $ (94 \pm 3)\% $ transmittance.

Mechanical Changes and Microfilament Reorganization Involved in Microcystin-LR-Promoted Cell Invasion in DU145 and WPMY Cells

Microcystin-leucine arginine (MC-LR) is a potent tumor initiator that can induce malignant cell transformation. Cellular mechanical characteristics are pivotal parameters that are closely related to cell invasion. The aim of this study is to determine the effect of MC-LR on mechanical parameters, microfilament, and cell invasion in DU145 and WPMY cells. Firstly, 10 μM MC-LR was selected as the appropriate concentration via cell viability assay. Subsequently, after MC-LR treatment, the cellular deformability and viscoelastic parameters were tested using the micropipette aspiration technique. The results showed that MC-LR increased the cellular deformability, reduced the cellular viscoelastic parameter values, and caused the cells to become softer. Furthermore, microfilament and microfilament-associated proteins were examined by immunofluorescence and Western blot, respectively. Our results showed that MC-LR induced microfilament reorganization and increased the expression of p-VASP and p-ezrin. Finally, the impact of MC-LR on cell invasion was evaluated. The results revealed that MC-LR promoted cell invasion. Taken together, our results suggested that mechanical changes and microfilament reorganization were involved in MC-LR-promoted cell invasion in DU145 and WPMY cells. Our data provide novel information to explain the toxicological mechanism of MC-LR.

Rice momilactone gene cluster: transcriptional response to barnyard grass (Echinochloa crus-galli)

Expression of genes involved in diterpene biosynthesis, especially momilactone and gibberellins (GAs), in rice plants (Oryza sativa L.) in response to barnyard grass (Echinochloa crus-galli) stress was examined. The three analyzed class II diterpene synthases had the highest fold change expression. Transcription patterns of genes for two homologs of momilactone synthases, OsMAS and OsMAS2, suggests their distinct roles in response to the presence of barnyard grass.

Dual roles of misshapen/NIK-related kinase (MINK1) in osteoarthritis subtypes through the activation of TGFβ signaling

OBJECTIVE

To identify the role of misshapen/NIK-related kinase (MINK1) in age-related Osteoarthritis (OA) and injury-induced OA, and the effects of enhanced TGFβ signaling in these progresses.

DESIGN

The effect of MINK1 was analyzed with MINK1 knock out (Mink1-/-) mice and C57BL/6J mice. OA progress was studied in age-related OA and instability-associated OA (destabilization of the medial meniscus, DMM) models. The murine knee joint was evaluated through histological staining, Osteoarthritis Research Society International (OARSI) scores, immunohistochemistry, and μCT analysis. Primary chondrocytes were isolated from wild type and Mink1-/- mice and subjected to osteogenic induction and Western blot analysis.

RESULTS

MINK1 is highly expressed during cartilage development and in normal cartilage. Mink1-/- mice displayed markedly lower OARSI scores, aggrecan degradation neoepitope positive cells and increased Safranin O and pSMAD2 staining in aging-related OA model. However, in injury-induced OA, loss of MINK1 accelerates extracellular matrix (ECM) destruction, osteophyte formation, and subchondral bone sclerosis. Accelerated subchondral bone remodeling in Mink1-/- mice was accompanied with increased numbers of nestin-positive mesenchymal stem cells (MSCs) and osterix-positive osteoprogenitors. pSMAD2 staining was increased in the subchondral bone marrow of Mink1-/- mice and overexpression of MINK1 inhibited SMAD2 phosphorylation in vitro.

CONCLUSIONS

This study shows for the first time that activation of TGFβ/SMAD2 by MINK1 deficiency plays opposite roles in aging-related and injury-induced OA. MINK1 deficiency protects cartilage from degeneration in aging joints through increased SMAD2 activation in chondrocytes, while accelerating OA progress in injury-induced model through enhanced osteogenesis of MSCs in the subchondral bone. These findings provide insights for developing precision OA therapeutics targeting TGFβ/SMAD2 signaling.

Functional expression of the Spodoptera exigua chitinase to examine the virtually screened inhibitor candidates

Chitinase is responsible for insect chitin hydrolyzation, which is a key process in insect molting and pupation. However, little is known about the chitinase of Spodoptera exigua (SeChi). In this study, based on the SeChi gene (ADI24346) identified in our laboratory, we constructed the recombinant baculovirus P-Chi for the expression of recombinant SeChi (rSeChi) in Hi5 cells. The rSeChi was purified by chelate affinity chromatography, and the purified protein showed activity comparable with that of a commercial SgChi, suggesting that we harvested active SeChi for the first time. The purified protein was subsequently tested for enzymatic properties and revealed to exhibit its highest activity at pH 8 and 40 C. Using homology modeling and molecular docking techniques, the three-dimensional model of SeChi was constructed and screened for inhibitors. In two rounds of screening, twenty compounds were selected. With the purified rSeChi, we tested each of the twenty compounds for inhibitor activity against rSeChi, and seven compounds showed obvious activity. This study provided new information for the chitinase of beet armyworm and for chitinase inhibitor development.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

BACKGROUND

Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.

PURPOSE

To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration.

STUDY TYPE

Retrospective.

POPULATION

In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing).

FIELD STRENGTH/SEQUENCE

3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence.

ASSESSMENT

3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs.

STATISTICAL TESTS

The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis.

RESULTS

In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%.

DATA CONCLUSION

Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2.