Effects of train speed and passenger capacity on ground vibration of underground suburban railways.

This study aims to explore the optimal driving speed for ground vibration in suburban railway underground sections. We focused on the ground surface of suburban railway underground sections and developed a 3D finite element dynamic coupling model for the tunnel-soil system. Subsequently, considering factors such as train speed and passenger load, we analyzed the propagation characteristics of ground vibration responses in urban railway underground sections. The research results indicate a significant amplification phenomenon in the peak power spectrum of measurement points near the tunnels in underground sections. The high-frequency components of the power spectrum between measurement points are noticeably higher between the two tunnels. Furthermore, as the train speed increases, this amplification phenomenon becomes more pronounced, and the power spectrum of each measurement point mainly concentrates on several frequency bands, with the amplitude of the power spectrum near the prominent frequencies also increasing. However, when the train speed is between 100 and 120 km/h, the impact on the amplitude of the power spectrum at measurement points above the running tunnel is minimal. Additionally, the amplitude of the middle-to-high frequency components in the power spectrum increases with the increase in passenger numbers. The impact on the peak acceleration amplitude at each measurement point is minimal when the train speed is 80 km/h or below. However, once the train speed exceeds 80 km/h, the peak acceleration amplitude above the running tunnel rapidly increases, reaching its maximum value at 113 km/h, and then gradually decreasing.

Asymmetric Three-Component Radical Alkene Carboazidation by Direct Activation of Aliphatic C-H Bonds.

Azide compounds are widely present in natural products and drug molecules, and their easy-to-transform characteristics make them widely used in the field of organic synthesis. The merging of transition-metal catalysis with radical chemistry offers a versatile platform for radical carboazidation of alkenes, allowing the rapid assembly of highly functionalized organic azides. However, the direct use of readily available hydrocarbon feedstocks as sp3-hybridized carbon radical precursors to participate in catalytic enantioselective carboazidation of alkenes remains a significant challenge that has yet to be addressed. Herein, we describe an iron-catalyzed asymmetric three-component radical carboazidation of electron-deficient alkenes by direct activation of aliphatic C-H bonds. This approach involves intermolecular hydrogen atom transfer between a hydrocarbon and an alkoxy/aryl carboxyl radical, leading to the formation of a carbon-centered radical. The resulting radical then reacts with electron-deficient alkenes to generate a new radical species that undergoes chiral iron-complex-mediated C-N3 bond coupling. An array of valuable chiral azides bearing a quaternary stereocenter were directly accessed from widely available chemical feedstocks, and their synthetic potential is further demonstrated through more facile transformations to give other valuable enantioenriched building blocks.

Implications of bacteria‒bacteria interactions within the plant microbiota for plant health and productivity. / 植物微生物群落中细菌-细菌互作对植物健康和生产力的影响.

Crop production currently relies on the widespread use of agrochemicals to ensure food security. This practice is considered unsustainable, yet has no viable alternative at present. The plant microbiota can fulfil various functions for its host, some of which could be the basis for developing sustainable protection and fertilization strategies for plants without relying on chemicals. To harness such functions, a detailed understanding of plant‒microbe and microbe‒microbe interactions is necessary. Among interactions within the plant microbiota, those between bacteria are the most common ones; they are not only of ecological importance but also essential for maintaining the health and productivity of the host plants. This review focuses on recent literature in this field and highlights various consequences of bacteria‒bacteria interactions under different agricultural settings. In addition, the molecular and genetic backgrounds of bacteria that facilitate such interactions are emphasized. Representative examples of commonly found bacterial metabolites with bioactive properties, as well as their modes of action, are given. Integrating our understanding of various binary interactions into complex models that encompass the entire microbiota will benefit future developments in agriculture and beyond, which could be further facilitated by artificial intelligence-based technologies.

Roles of CcDFR and CcOMT9 in the cyanidin biosynthesis and development of Cordyceps cicadae.

Introduction: Cordyceps cicadae is a traditional Chinese medicinal fungus known for its rich production of bioactive substances, particularly cyanidin, an anthocyanin commonly found in plants with notable anti-inflammatory, anti-tumor, antiviral, and antibacterial properties. This study revealed two key genes, CcDFR and CcOMT9, affecting cyanidin biosynthesis in C. cicadae. Methods: The roles of these genes in cyanidin production, growth, and development were elucidated through the gene knockout method, phenotypic analysis, transcriptomics, and metabolomics. Results: CcDFR deletion led to reduced cyanidin-3-O-glucoside (C3G), suppressed expression of cyanidin biosynthesis genes, impaired synnemata formation, decreased polysaccharide and adenosine content, and diminished chitinase activity. Meanwhile, the ΔCcOMT9 mutant exhibited an increase in C3G production, promoted expression of cyanidin biosynthesis genes and rising bioactive compounds, suppressed RNA methylation, and led to phenylalanine accumulation with no effect on fruiting body formation. Discussion: We revealed a distinct anthocyanin biosynthesis pathway in C. cicadae and identified two genes with opposite functions, laying the foundation for future genetic modification of cyanidin-producing strains using modern biological techniques. This will shorten the production period of this valuable compound, facilitating the industrial-scale production of cyanidin.

IL-17A deficiency inhibits lung cancer-induced osteoclastogenesis by promoting apoptosis of osteoclast precursor cells.

Osteoclasts are crucial in the events leading to bone metastasis of lung cancer. Interleukin-17A (IL-17A) affects osteogenesis by regulating the survival of osteoclast precursors (OCPs) and is enriched in lung cancer cells. However, how factors derived from tumor cells that metastasize to bone affect osteoclastogenesis remains poorly understood. We examined whether IL-17A derived from lung cancer cells affects osteoclast differentiation by regulating OCP apoptosis. IL-17A expression was inhibited in A549 non-small cell lung cancer cells using RNA interference. Compared with conditioned medium (CM) from A549 cells (A549-CM), CM from IL-17A-deficient A549 cells (A549-si-CM) suppressed osteoclastogenesis. The mRNA expression of osteoclast-specific genes was downregulated following A549-si-CM treatment. Furthermore, A549-si-CM promoted osteoclast precursor apoptosis at an early stage of osteoclastogenesis, which was related to the promotion of caspase-3 expression by A549-si-CM during osteoclast differentiation. In vivo experiments also showed that inhibition of IL-17A expression in A549 cells reduced osteoclast activation and bone tissue destruction. Collectively, our results indicate that IL-17A deficiency inhibits lung cancer-induced osteoclast differentiation by promoting apoptosis of osteoclast precursors in the early stage of osteoclast formation and that IL-17A is a potential therapeutic target for cancer-associated bone resorption in patients with lung cancer.

Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas.

PURPOSE: The training of deep medical image segmentation networks usually requires a large amount of human-annotated data. To alleviate the burden of human labor, many semi- or non-supervised methods have been developed. However, due to the complexity of clinical scenario, insufficient training labels still causes inaccurate segmentation in some difficult local areas such as heterogeneous tumors and fuzzy boundaries. METHODS: We propose an annotation-efficient training approach, which only requires scribble guidance in the difficult areas. A segmentation network is initially trained with a small amount of fully annotated data and then used to produce pseudo labels for more training data. Human supervisors draw scribbles in the areas of incorrect pseudo labels (i.e., difficult areas), and the scribbles are converted into pseudo label maps using a probability-modulated geodesic transform. To reduce the influence of the potential errors in the pseudo labels, a confidence map of the pseudo labels is generated by jointly considering the pixel-to-scribble geodesic distance and the network output probability. The pseudo labels and confidence maps are iteratively optimized with the update of the network, and the network training is promoted by the pseudo labels and the confidence maps in turn. RESULTS: Cross-validation based on two data sets (brain tumor MRI and liver tumor CT) showed that our method significantly reduces the annotation time while maintains the segmentation accuracy of difficult areas (e.g., tumors). Using 90 scribble-annotated training images (annotated time: ~ 9 h), our method achieved the same performance as using 45 fully annotated images (annotation time: > 100 h) but required much shorter annotation time. CONCLUSION: Compared to the conventional full annotation approaches, the proposed method significantly saves the annotation efforts by focusing the human supervisions on the most difficult regions. It provides an annotation-efficient way for training medical image segmentation networks in complex clinical scenario.

Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.

PURPOSE: Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications. METHODS: This work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary. RESULTS: This method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24 mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10 s, which was only half of the U-Net run time. CONCLUSION: Using the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.

Automated tooth crown design with optimized shape and biomechanics properties.

Despite the large demand for dental restoration each year, the design of crown restorations is mainly performed via manual software operation, which is tedious and subjective. Moreover, the current design process lacks biomechanics optimization, leading to localized stress concentration and reduced working life. To tackle these challenges, we develop a fully automated algorithm for crown restoration based on deformable model fitting and biomechanical optimization. From a library of dental oral scans, a conditional shape model (CSM) is constructed to represent the inter-teeth shape correlation. By matching the CSM to the patient's oral scan, the optimal crown shape is estimated to coincide with the surrounding teeth. Next, the crown is seamlessly integrated into the finish line of preparation via a surface warping step. Finally, porous internal supporting structures of the crown are generated to avoid excessive localized stresses. This algorithm is validated on clinical oral scan data and achieved less than 2 mm mean surface distance as compared to the manual designs of experienced human operators. The mechanical simulation was conducted to prove that the internal supporting structures lead to uniform stress distribution all over the model.

Environmentally relevant concentrations of tris (2-chloroethyl) phosphate (TCEP) induce hepatotoxicity in zebrafish (Danio rerio): a whole life-cycle assessment.

Tris (2-chloroethyl) phosphate (TCEP), a typical organophosphate flame retardant, is of increasingly great concern considering their ubiquitous presence in aquatic environments and potential ecotoxicity. The present work was aimed to investigate the potential growth inhibition and hepatic stress induced by whole life-cycle exposure to TCEP (0.8, 4, 20 and 100 µg/L) in zebrafish. The results revealed that the body length, body mass and hepatic-somatic index (HSI) of zebrafish were significantly declined after exposure to TCEP for 120 days. GPx activity and GSH content were increased in the liver of zebrafish treated with low concentrations (0.8 and 4 µg/L) of TCEP, while exposure to high concentrations (20 and 100 µg/L) of TCEP reduced antioxidative capacity and elevated lipid peroxidation (LPO) levels. Gene transcription analysis demonstrated that the mRNA levels of nrf2 were altered in a similar manner to the transcription of the downstream genes nqo1 and hmox1, suggesting that Nrf2-Keap1 pathway mediated TCEP-induced oxidative stress in zebrafish liver. In addition, TCEP exposure might alleviate inflammatory response through down-regulating transcription of inflammatory cytokines (il-1ß, il-6 and inos), and induce apoptosis via activating the p53-Bax pathway. Moreover, whole life-cycle exposure to TCEP caused a series of histopathological anomalies in zebrafish liver. Overall, our results revealed that lifetime exposure to environmentally relevant concentrations of TCEP could result in growth retardation and induce significant hepatotoxicity in zebrafish.

Deep learning-based workflow for hip joint morphometric parameter measurement from CT images.

Objective.Precise hip joint morphometry measurement from CT images is crucial for successful preoperative arthroplasty planning and biomechanical simulations. Although deep learning approaches have been applied to clinical bone surgery planning, there is still a lack of relevant research on quantifying hip joint morphometric parameters from CT images.Approach.This paper proposes a deep learning workflow for CT-based hip morphometry measurement. For the first step, a coarse-to-fine deep learning model is designed for accurate reconstruction of the hip geometry (3D bone models and key landmark points). Based on the geometric models, a robust measurement method is developed to calculate a full set of morphometric parameters, including the acetabular anteversion and inclination, the femoral neck shaft angle and the inclination, etc. Our methods were validated on two datasets with different imaging protocol parameters and further compared with the conventional 2D x-ray-based measurement method.Main results. The proposed method yields high bone segmentation accuracies (Dice coefficients of 98.18% and 97.85%, respectively) and low landmark prediction errors (1.55 mm and 1.65 mm) on both datasets. The automated measurements agree well with the radiologists' manual measurements (Pearson correlation coefficients between 0.47 and 0.99 and intraclass correlation coefficients between 0.46 and 0.98). This method provides more accurate measurements than the conventional 2D x-ray-based measurement method, reducing the error of acetabular cup size from over 2 mm to less than 1 mm. Moreover, our morphometry measurement method is robust against the error of the previous bone segmentation step. As we tested different deep learning methods for the prerequisite bone segmentation, our method produced consistent final measurement results, with only a 0.37 mm maximum inter-method difference in the cup size.Significance. This study proposes a deep learning approach with improved robustness and accuracy for pelvis arthroplasty planning.

Life-cycle exposure to tris (2-chloroethyl) phosphate (TCEP) causes alterations in antioxidative status, ion regulation and histology of zebrafish gills.

Tris (2-chloroethyl) phosphate (TCEP) has been receiving great concerns owing to its ubiquitous occurrence in various environmental compartments and potential risks to wildlife and humans. Gill is structural basis for ion regulation and homeostasis in fish and susceptible to xenobiotics. However, current knowledge on the impacts of long-term exposure to TCEP on the structure and physiological function of fish gills are insufficient. In this work, zebrafish were exposed to environmental realistic concentrations (0.8, 4, 20 and 100 µg/L) of TCEP from 3 h post ferterlization (hpf) till 120 days post ferterlization (dpf). Our results demonstrated that life-cycle exposure to TCEP significantly decreased the activity of glutathione S-transferase (GST), but elevated the activities of antioxidative enzymes including superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and increased malondialdehyde (MDA) content in zebrafish gills. Gene transcription analysis implied that the mRNA expressions of antioxidant-related genes (nrf2, cat and nqo1) were induced, while the transcription of gstα1, hmox1, keap1 were down-regulated, indicating that Nrf2-Keap1 pathway might be activated to defend the oxidative stress induced by TCEP. Additionally, the ion homeostasis was disrupted by TCEP exposure, evidenced by reduced activities of Na+/K+-ATPase (NKA), Ca2+-ATPase and Mg2+-ATPase and downregulated transcription levels of ncc, nkcc, cftr and clc-3. Besides, whole-life exposure to TCEP resulted in a series of structural damages to gills, including epithelial lifting, epithelial rupture, telangiectasis, vacuolation, edema and shortened gill lamellae. Overall, our results demonstrated that long-term TCEP exposure could induce oxidative stress, affect ion regulation and cause histological changes in zebrafish gills.

Study of the Relationship between Pulmonary Artery Pressure and Heart Valve Vibration Sound Based on Mock Loop.

The vibration of the heart valves' closure is an important component of the heart sound and contains important information about the mechanical activity of a heart. Stenosis of the distal pulmonary artery can lead to pulmonary hypertension (PH). Therefore, in this paper, the relationship between the vibration sound of heart valves and the pulmonary artery blood pressure was investigated to contribute to the noninvasive detection of PH. In this paper, a lumped parameter circuit platform of pulmonary circulation was first set to guide the establishment of a mock loop of circulation. By adjusting the distal vascular resistance of the pulmonary artery, six different pulmonary arterial pressure states were achieved. In the experiment, pulmonary artery blood pressure, right ventricular blood pressure, and the vibration sound of the pulmonary valve and tricuspid valve were measured synchronously. Features of the time domain and frequency domain of two valves' vibration sound were extracted. By conducting a significance analysis of the inter-group features, it was found that the amplitude, energy and frequency features of vibration sounds changed significantly. Finally, the continuously varied pulmonary arterial blood pressure and valves' vibration sound were obtained by continuously adjusting the resistance of the distal pulmonary artery. A backward propagation neural network and deep learning model were used, respectively, to estimate the features of pulmonary arterial blood pressure, pulmonary artery systolic blood pressure, the maximum rising rate of pulmonary artery blood pressure and the maximum falling rate of pulmonary artery blood pressure by the vibration sound of the pulmonary and tricuspid valves. The results showed that the pulmonary artery pressure parameters can be well estimated by valve vibration sounds.

High urea induces anxiety disorders associated with chronic kidney disease by promoting abnormal proliferation of OPC in amygdala.

Chronic kidney disease (CKD) with anxiety disorder is of a great concern due to its high morbidity and mortality. Urea, as an important toxin in CKD, is not only a pathological factor for complications in patients with CKD, but also is accumulated in the brain of aging and neurodegenerative diseases. However, the pathological role and underlying regulatory mechanism of urea in CKD related mood disorders have not been well established. We previously reported a depression phenotype in mice with abnormal urea metabolism. Since patients with depression are more likely to suffer from anxiety, we speculate that high urea may be an important factor causing anxiety in CKD patients. In adenine-induced CKD mouse model and UT-B-/- mouse model, multiple behavioral studies confirmed that high urea induces anxiety-like behavior. Single-cell transcriptome revealed that down-regulation of Egr1 induced compensatory proliferation of oligodendrocyte progenitor cells (OPC). Myelin-related signaling pathways of oligodendrocytes (OL) were change significant in the urea accumulation amygdala. The study showed that high urea downregulated Egr1 with subsequent upregulation of ERK pathways in OPCs. These data indicate that the pathological role and molecular mechanism of high urea in CKD-related anxiety, and provide objective serological indicator and a potential new drug target for the prevention and treatment of anxiety in CKD patients.

Noncoplanar Volumetric Modulated Arc Therapy for Hepatocellular Carcinoma Based on a Cage-Like Radiotherapy System: A Simulation Study.

BACKGROUND: The incorporation of noncoplanar beam arrangements has been proposed in liver radiotherapy modalities, which can reduce the dose in normal tissues compared to coplanar techniques. Noncoplanar radiotherapy techniques for hepatocellular carcinoma treatment based on the Linac design have a limited effective arc angle to avoid collisions. PURPOSE: To propose a novel noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system and investigate its performance in hepatocellular carcinoma patients. METHODS: The computed tomography was deflected 90° to meet the structure of a cage-like radiotherapy system and design the noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan in the Pinnacle3 planning system. An noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan was customized for each of 10 included hepatocellular carcinoma patients, with 6 dual arcs ranging from -30° to 30°. Six couch angles were set with an interval of 36° and distributed along with the longest diameter of planning target volume. The dosimetric parameters of noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan were compared with the noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy plan. RESULTS: The 3 radiotherapy techniques regarding planning target volume were statistically different for D98%, D2%, conformity index, and homogeneity index with χ2 = 9.692, 14.600, 8.600, and 12.600, and P = .008, .001, .014, and .002, respectively. Further multiple comparisons revealed that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .005) of the stomach, and V30 (P = .028) of the lung compared to noncoplanar volumetric modulated arc therapy. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .017) of the spinal cord, V50 (P = .043) of the duodenum, the maximum dose (P = .007) of the esophagus, and V30 (P = .047) of the whole lung compared to volumetric modulated arc therapy. The results indicate that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system protects the normal liver, stomach, and lung better than noncoplanar volumetric modulated arc therapy and protects the normal liver, spinal cord, duodenum, esophagus, and lung better than volumetric modulated arc therapy. CONCLUSIONS: The noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system technique with the arrangement of noncoplanar arcs provided optimal dosimetric gains compared with noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy, except for the heart. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system should be considered in more clinically challenging cases.

Erratum: Bioluminescence tomography reconstruction in conjunction with an organ probability map as an anatomical reference: erratum.

[This corrects the article on p. 1275 in vol. 13, PMID: 35414991.].

Enhancement of herbicolin A production by integrated fermentation optimization and strain engineering in Pantoea agglomerans ZJU23.

BACKGROUND: The lipopeptide herbicolin A (HA) secreted by the biocontrol agent Pantoea agglomerans ZJU23 is a promising antifungal drug to combat fungal pathogens by targeting lipid rafts, both in agricultural and clinical settings. Improvement of HA production would be of great significance in promoting its commercialization. This study aims to enhance the HA production in ZJU23 by combining fermentation optimization and strain engineering. RESULTS: Based on the results in the single-factor experiments, corn steep liquor, temperature and initial pH were identified as the significant affecting factors by the Plackett-Burman design. The fermentation medium and conditions were further optimized using the Box-Behnken response surface method, and the HA production of the wild type strain ZJU23 was improved from ~ 87 mg/mL in King's B medium to ~ 211 mg/mL in HA induction (HAI) medium. A transposon library was constructed in ZJU23 to screen for mutants with higher HA production, and two transcriptional repressors for HA biosynthesis, LrhA and PurR, were identified. Disruption of the LrhA gene led to increased mRNA expression of HA biosynthetic genes, and subsequently improved about twofold HA production. Finally, the HA production reached ~ 471 mg/mL in the ΔLrhA mutant under optimized fermentation conditions, which is about 5.4 times higher than before (~ 87 mg/mL). The bacterial suspension of the ΔLrhA mutant fermented in HAI medium significantly enhanced its biocontrol efficacy against gray mold disease and Fusarium crown rot of wheat, showing equivalent control efficacies as the chemical fungicides used in this study. Furthermore, HA was effective against fungicide resistant Botrytis cinerea. Increased HA production substantially improved the control efficacy against gray mold disease caused by a pyrimethanil resistant strain. CONCLUSIONS: This study reveals that the transcriptional repressor LrhA negatively regulates HA biosynthesis and the defined HAI medium is suitable for HA production. These findings provide an extended basis for large-scale production of HA and promote biofungicide development based on ZJU23 and HA in the future.

[Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.

IL-17A regulates autophagy and promotes osteoclast differentiation through the ERK/mTOR/Beclin1 pathway.

Bone is a frequent target of tumor metastasis, with high incidence rate and poor prognosis. Osteoclasts play a key role in the process of tumor bone metastasis. Interleukin-17A (IL-17A) is an inflammatory cytokine, highly expressed in a variety of tumor cells, that can alter the autophagic activity of other cells, thereby causing corresponding lesions. Previous studies have shown that low concentration IL-17A can promote osteoclastogenesis. The aim of this study was to clarify the mechanism of low concentration IL-17A promoting osteoclastogenesis by regulating autophagic activity. The results of our study showed that IL-17A could promote the differentiation of osteoclast precursors (OCPs) into osteoclasts in the presence of RANKL, and increase the mRNA levels of osteoclast-specific genes. Moreover, IL-17A increased the expression of Beclin1 by inhibiting the phosphorylation of ERK and mTOR, leading to enhanced autophagy of OCPs, accompanied by decreased OCP apoptosis. Furthermore, knockdown of Beclin1 and suppression of autophagy by 3-methyladenine (3-MA) significantly attenuated the enhanced osteoclastogenesis induced by IL-17A. In summary, these results indicate that low concentration IL-17A enhances the autophagic activity of OCPs through the ERK/mTOR/Beclin1 pathway during osteoclastogenesis, and further promotes osteoclast differentiation, suggesting that IL-17A may serve as a potential therapeutic target for cancer-related bone resorption in cancer patients.

Evaluation of Chinese populational exposure to environmental electromagnetic field based on stochastic dosimetry and parametric human modelling.

This study aimed to estimate the distribution of the whole-body averaged specific absorption rate (WBSAR) using several measurable physique parameters for Chinese adult population exposed to environmental electromagnetic fields (EMFs) of current wireless communication frequencies, and to discuss the effects of these physique parameters in the frequency-dependent dosimetric results. The physique distribution of Chinese adults was obtained from the National Physical Fitness and Health Database comprising 81,490 adult samples. The number of physique parameters used to construct the surrogate model was reduced to three via mutual information analysis. A stochastic method with 40 deterministic simulations was used to generate frequency-dependent and gender-specific surrogate models for WBSAR via polynomial chaos expansion. In the simulations, we constructed anatomically correct models conforming to the targeted physique parameters via deformable human modelling technique, which was based on deep learning from the image database including 767 Chinese adults. Thereafter, we analysed the sensitivity of the physique parameters to WBSAR by covariance-based Sobol decomposition. The results indicated that the generated models were consistent with the targeted physique parameters. The estimated dosimetric results were validated using finite-difference time-domain simulations (the error was < 6% across all the investigated frequencies for WBSAR). The novelty of the study included that it demonstrated the feasibility of estimating the individual WBSAR using a limited number of physique parameters with the aid of surrogate modelling. In addition, the population-based distribution of the WBSAR in Chinese adults was firstly presented in the manuscript. The results also indicated that the different combinations of physique parameter, dependent on genders and frequencies, significantly influenced the WBSAR, although the general conservativeness of the guidelines of the International Commission on Non-Ionizing Radiation and Protection can be confirmed in the surveyed population.