Explaining the improving effect of dough crumb-sheet composite rolling on fresh noodle quality: From microstructure and moisture distribution perspective.

A new technique known as dough crumb-sheet composite rolling (DC-SCR) was used to improve the quality of fresh noodles. However, there is a dearth of theoretical investigations into the optimal selection of specific parameters for this technology, and the underlying mechanisms are not fully understood. Therefore, the effects of dough crumb addition times in DC-SCR on the texture, cooking, and eating quality of fresh noodles were first studied. Then, the underlying regulation mechanism of DC-SCR technology on fresh noodles was analyzed in terms of moisture distribution and microstructure. The study demonstrated that the most significant enhancement in the quality of fresh noodles was achieved by adding dough crumbs six times. Compared with fresh noodles made without the addition of dough crumbs, the initial hardness and chewiness of fresh noodles made by adding six times of dough crumbs increased by 25.32% and 46.82%, respectively. In contrast, the cooking time and cooking loss were reduced by 28.45% and 29.69%, respectively. This quality improvement in fresh noodles made by DC-SCR came from the microstructural differences of the gluten network between the inner and outer layers of the dough sheet. A dense structure on the outside and a loose structure on the inside could endow the fresh noodles made by DC-SCR with higher hardness, a shortened cooking time, and less cooking loss. This study would provide a theoretical and experimental basis for creating high-quality fresh noodles.

Highly Reversible Zn-Air Batteries Enabled by Tuned Valence Electron and Steric Hindrance on Atomic Fe-N4-C Sites.

The bifunctional oxygen electrocatalyst is the Achilles' heel of achieving robust reversible Zn-air batteries (ZABs). Herein, durable bifunctional oxygen electrocatalysis in alkaline media is realized on atomic Fe-N4-C sites reinforced by NixCo3-xO4 (NixCo3-xO4@Fe1/NC). Compared with that of pristine Fe1/NC, the stability of the oxygen evolution reaction (OER) is increased 10 times and the oxygen reduction reaction (ORR) performance is also improved. The steric hindrance alters the valence electron at the Fe-N4-C sites, resulting in a shorter Fe-N bond and enhanced stability of the Fe-N4-C sites. The corresponding solid-state ZABs exhibit an ultralong lifespan (>460 h at 5 mA cm-2) and high rate performance (from 2 to 50 mA cm-2). Furthermore, the structural evolution of NixCo3-xO4@Fe1/NC before and after the OER and ORR as well as charge-discharge cycling is explored. This work develops an efficient strategy for improving bifunctional oxygen electrocatalysis and possibly other processes.

Anomaly Detection of Permanent Magnet Synchronous Motor Based on Improved DWT-CNN Multi-Current Fusion.

The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method's strong capability in fusing current features and detecting anomalies.

Folding during sheeting improved qualities of dried noodles through gluten network proteins.

The texture properties after cooking for 12 min were selected to optimize the sheeting parameters, and the results were verified using the comprehensive quality of dried noodles. The distribution of water, characteristics of gluten protein, and interaction between gluten network and starch were analyzed to clarify the mechanism of the quality of dried noodles. Results showed that the optimal folding angle was 45°, under this condition, the largest anti-extension displacement perpendicular to the rolling direction and the smallest cooking loss were obtained. The hardness and smoothness of cooked noodles increased by about 14% to 17%. Further, the transverse relaxation time of strongly bound water significantly decreased, while the relative content and binding strength increased. The hydrogen bonds and α-helix contents increased by about 68.8% and 53.1%, respectively. Folding and sheeting enhanced the combination of starch granules and gluten network causing, decreased in the average length and porosity of the gluten network. It is depicted from the results that the method of optimizing the sheeting process based on the texture of dried noodles cooked for 12 min was feasible. And the 45° folding and sheeting could help to improve the quality of dried noodles.

Dark-field imaging and fluorescence dual-mode detection of microRNA-21 in living cells by core-satellite plasmonic nanoprobes.

The in situ precise quantification and simultaneous imaging of low abundance microRNAs (miRNAs) within living cells is critical for cancer diagnosis, yet it remains a significant challenge. Leveraging the excellent sensitivity and spatiotemporal resolution of dark-field microscopy (DFM) and fluorescence imaging, we have successfully devised a novel detection approach using dual-signal reporter probes (DSRPs). These probes allow for highly sensitive detection of miRNA-21 in living cells via toehold-mediated strand displacement cascades. The DSRPs were constructed by Au nanoparticles and Ag nanoclusters core-satellite nanostructures. After the recognition of miRNA-21, the strand displacement cascades were triggered, inducing the disassembly of the Au/Ag core-satellite nanostructure with apparent scattering intensity decrease and peak wavelength shifts. Additionally, the fluorescence of Ag clusters could be recovered and further enhanced when in close proximity to specific guanine-rich strands. The dual-signal response capability enables the accurate detection of miRNA-21 from 1 fM to 1 nM, with a limit of detection reached 0.75 fM. DFM and fluorescent imaging of living cells efficiently confirms the applicable detection of miRNA-21 in complex detection media. The biosensor based on DSRPs represents a promising nanoplatform for visual monitoring and imaging of biomolecules in living cells, even at the single particle level.

Tanshinone IIA alleviates bleomycin-induced pulmonary fibrosis by inhibiting Zbtb16.

Pulmonary fibrosis is a complex disease that can occur in a variety of clinical settings. The Zinc Finger and BTB Domain Containing 16 (Zbtb16) is a transcription factor and has not been studied in pulmonary fibrosis. Lung tissues from rats which were treated with bleomycin and Tanshinone IIA (Tan IIA) were collected for mRNA sequencing. Zbtb16, a differentially expressed gene, was screened. Using adeno-associated virus to knock down Zbtb16 in rats, it was found that the lung index and the content of hydroxyproline in lung tissue were decreased. HE and Masson staining revealed that pathological symptoms of lung histopathology were relieved after Zbtb16 knockdown. Protein expressions of α-SMA, Collagen I and Fibronectin were significantly decreased after Zbtb16 knockdown in vivo and in vitro. Meanwhile, the protein content of TGF-ß1 and the phosphorylation of Smad2/3 were inhibited by Zbtb16 knockdown. Conversely, under the treatment of Tan IIA and TGF-ß1, overexpression of Zbtb16 improved cell viability, increased the expression of fibrosis-related proteins, and promoted the phosphorylation of Smad 2/3. All above demonstrates that Zbtb16 inhibition ameliorates pulmonary fibrosis and suppresses the TGF-ß/Smad pathway. Furthermore, Zbtb16 mediates the inhibitory process of Tan IIA on pulmonary fibrosis. This study provides a novel candidate therapeutic target for pulmonary fibrosis.

US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES: Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS: The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS: The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS: The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT: Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS: • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. MATERIALS AND METHODS: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system. RESULTS: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively. CONCLUSION: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Gasless Single-Incision Endoscopic Surgery via Subclavicular Approach for Lateral Neck Dissection in Patients with Papillary Thyroid Cancer.

BACKGROUND: The technical difficulties and trauma of remote access methods in endoscopic surgery (ES) for lateral neck dissection (LND) can be daunting for most patients with papillary thyroid cancer (PTC) and surgeons. The purpose of study was to introduce gasless single-incision ES via a subclavicular approach (ESSA) and to explore its safety and efficacy for LND. METHODS: Between January 2022 and February 2023, we retrospectively reviewed 17 patients with PTC who underwent ESSA for LND. In addition, 22 patients who received video-assisted ES (VAES) and 48 patients who underwent open surgery (OP) for LND during the same period were included. Clinicopathological characteristics, complications, and efficacy of the lymph node yield (LNY) were compared between the ESSA and the other two groups (VAES and OP). RESULTS: The LNY from central and lateral neck dissection by ESSA was comparable to that by VAES (9.2 ± 8.1 vs. 9.5 ± 4.2, P = 0.986, and 33.5 ± 11.6 vs. 30.6 ± 9.2, P = 0.382, respectively) and OP (9.2 ± 8.1 vs. 11.0 ± 5.4, P = 0.420, and 33.5 ± 11.6 vs. 31.5 ± 7.9, P = 0.383, respectively). Swallowing impairment scores at 1 and 3 months were significantly lower after ESSA than those after VAES (1.8 ± 1.0 vs. 3.0 ± 1.2, P = 0.003, and 0.9 ± 0.8 vs. 1.7 ± 0.8, P = 0.006, respectively). The cosmetic satisfaction rate 1 month after surgery was significantly higher in the ESSA group than that in the VAES group (100 vs. 31.8%, P < 0.001). CONCLUSIONS: ESSA is a safe and minimally invasive procedure that provides a scarless cervical appearance and has good efficacy for LND. Therefore, ESSA may be a feasible choice for selected patients with N1b PTC with cervical cosmetic needs.

A novel cuproptosis-related lncRNA prognostic signature in thyroid cancer.

Aims: We aimed to investigate the value of cuproptosis-related lncRNA in screening out high-risk thyroid cancer patients. Materials & methods: RNA sequencing data of thyroid cancer were obtained from The Cancer Genome Atlas. A cuproptosis-related lncRNA signature was constructed by using Cox regression. Results: Four cuproptosis-related lncRNAs were used to construct a survival prognosis model for thyroid cancer. The receiver operating characteristic curve showed that the area under the curve reached 0.830 at 1 year, 0.790 at 3 years and 0.824 at 5 years. Conclusion: The model may help to screen out thyroid cancer patients at high risk, and thus develop more appropriate treatment strategies.

The incidence of thyroid cancer (TC) is increasing and in most patients the standard treatment is effective. However, a regional recurrence or distant metastasis is observed in some patients with aggressive TC. Hence, finding reliable biomarkers to predict patients at risk of recurrence or distant metastasis and formulating effective treatment strategies have important clinical significance. In this study, we proposed a novel prediction model for TC patients. The model may help to screen out TC patients at risk of local recurrence or distant metastasis, and thus develop more appropriate treatment strategies.

Targeting hnRNPC suppresses thyroid follicular epithelial cell apoptosis and necroptosis through m6A-modified ATF4 in autoimmune thyroid disease.

Both environmental and genetic factors contribute to the etiology of autoimmune thyroid disease (AITD) including Graves' disease (GD) and Hashimoto's thyroiditis (HT). However, the exact pathogenesis and interactions that occur between environmental factors and genes remain unclear, and therapeutic targets require further investigation due to limited therapeutic options. To solve such problems, this study utilized single-cell transcriptome, whole transcriptome, full-length transcriptome (Oxford nanopore technology), and metabolome sequencing to examine thyroid lesion tissues from 2 HT patients and 2 GD patients as well as healthy thyroid tissue from 1 control subject. HT patients had increased ATF4-positive thyroid follicular epithelial (ThyFoEp) cells, which significantly increased endoplasmic reticulum stress. The enhanced sustained stress resulted in cell death mainly including apoptosis and necroptosis. The ATF4-based global gene regulatory network and experimental validation revealed that N6-methyladenosine (m6A) reader hnRNPC promoted the transcriptional activity, synthesis, and translation of ATF4 through mediating m6A modification of ATF4. Increased ATF4 expression initiated endoplasmic reticulum stress signaling, which when sustained, caused apoptosis and necroptosis in ThyFoEp cells, and mediated HT development. Targeting hnRNPC and ATF4 notably decreased ThyFoEp cell death, thus ameliorating disease progression. Collectively, this study reveals the mechanisms by which microenvironmental cells in HT and GD patients trigger and amplify the thyroid autoimmune cascade response. Furthermore, we identify new therapeutic targets for the treatment of autoimmune thyroid disease, hoping to provide a potential way for targeted therapy.

Inflammatory cytokine-regulated LNCPTCTS suppresses thyroid cancer progression via enhancing Snail nuclear export.

Lymph node metastases are commonly observed in diverse malignancies where they promote cancer progression and poor outcomes, although the molecular basis is incompletely understood. Thyroid cancer is the most prevalent endocrine neoplasm characterized by high frequency of lymph node metastases. Here, we uncover an inflammatory cytokines-controlled epigenetic program during thyroid cancer progression. LNCPTCTS acts as a novel tumor suppressive lncRNA with remarkably decreased expression in thyroid cancer specimens, especially in metastatic lymph nodes. Inflammatory cytokines TNFα or CXCL10, which are released from tumor microenvironment (TME), impair binding capabilities of the transcription factor (TF) EGR1 to the LNCPTCTS promoter and reduce the lncRNA expression in cells. Notably, LNCPTCTS binds to eEF1A2 protein and facilitates the interaction between eEF1A2 and Snail, which promotes Snail nucleus export via the RanGTP-Exp5-aa-tRNA-eEF1A2 complex. Loss of LNCPTCTS in tumors leads to accumulation of Snail in the nucleus, suppressed transcription of E-cadherin and PEBP1, reduced E-cadherin and PEBP1 protein levels, and activated epithelial-mesenchymal transition and MAPK signaling. Our results reveal what we believe to be a novel paradigm between TME and epigenetic reprogram in cancer cells which drives lymph node metastases, therefore illuminating the suitability of LNCPTCTS as a targetable vulnerability in thyroid cancer.

Performance of CT-based deep learning in diagnostic assessment of suspicious lateral lymph nodes in papillary thyroid cancer: a prospective diagnostic study.

BACKGROUND: Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer is challenging. Strategies for using deep learning to diagnosis of lateral LN metastasis require additional development and testing. This study aimed to build a deep learning-based model to distinguish benign lateral LNs from metastatic lateral LNs in papillary thyroid cancer and test the model's diagnostic performance in a real-world clinical setting. METHODS: This was a prospective diagnostic study. An ensemble model integrating a three-dimensional residual network algorithm with clinical risk factors available before surgery was developed based on computed tomography images of lateral LNs in an internal dataset and validated in two external datasets. The diagnostic performance of the ensemble model was tested and compared with the results of fine-needle aspiration (FNA) (used as the standard reference method) and the diagnoses made by two senior radiologists in 113 suspicious lateral LNs in patients enrolled prospectively. RESULTS: The area under the receiver operating characteristic curve of the ensemble model for diagnosing suspicious lateral LNs was 0.829 (95% CI: 0.732-0.927). The sensitivity and specificity of the ensemble model were 0.839 (95% CI: 0.762-0.916) and 0.769 (95% CI: 0.607-0.931), respectively. The diagnostic accuracy of the ensemble model was 82.3%. With FNA results as the criterion standard, the ensemble model had excellent diagnostic performance ( P =0.115), similar to that of the two senior radiologists ( P =1.000 and P =0.392, respectively). CONCLUSION: A three-dimensional residual network-based ensemble model was successfully developed for the diagnostic assessment of suspicious lateral LNs and achieved diagnostic performance similar to that of FNA and senior radiologists. The model appears promising for clinical application.

Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT.

OBJECTIVES: To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with papillary thyroid cancer (PTC) based on CT images. METHODS: This multicenter retrospective study included the preoperative CT of PTC patients who were divided into the development, internal, and external test sets. The region of interest of the primary tumor was outlined manually on the CT images by a radiologist who has eight years of experience. With the use of the CT images and lesions masks, the deep learning (DL) signature was developed by the DenseNet combined with convolutional block attention module. One-way analysis of variance and least absolute shrinkage and selection operator were used to select features, and a support vector machine was used to construct the radiomics signature. Random forest was used to combine the DL, radiomics, and clinical signature to perform the final prediction. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used by two radiologists (R1 and R2) to evaluate and compare the AI system. RESULTS: For the internal and external test set, the AI system achieved excellent performance with AUCs of 0.84 and 0.81, higher than the DL (p = .03, .82), radiomics (p < .001, .04), and clinical model (p < .001, .006). With the aid of the AI system, the specificities of radiologists were improved by 9% and 15% for R1 and 13% and 9% for R2, respectively. CONCLUSIONS: The AI system can help predict CLNM in patients with PTC, and the radiologists' performance improved with AI assistance. CLINICAL RELEVANCE STATEMENT: This study developed an AI system for preoperative prediction of CLNM in PTC patients based on CT images, and the radiologists' performance improved with AI assistance, which could improve the effectiveness of individual clinical decision-making. KEY POINTS: • This multicenter retrospective study showed that the preoperative CT image-based AI system has the potential for predicting the CLNM of PTC. • The AI system was superior to the radiomics and clinical model in predicting the CLNM of PTC. • The radiologists' diagnostic performance improved when they received the AI system assistance.

Pediatric hypereosinophilic syndrome associated with liver damage, portal vein, splenic vein and superior mesenteric vein thromboses: a case report.

BACKGROUND: The hypereosinophilic syndrome (HES) is a group of rare blood disorders characterized by persistent eosinophilia and damage to multiple organs. HES can be either primary, secondary or idiopathic. Secondary HES are commonly caused by parasitic infections, allergic reactions or cancer. We described a pediatric case of HES associated with liver damage and multiple thrombi. A 12-year-old boy with eosinophilia was complicated with severe thrombocytopenia, liver damage, portal vein, splenic vein, and superior mesenteric vein thromboses. The thrombi recanalized after treatment with methylprednisolone succinate and low molecular weight heparin. No side effects appeared after 1-month. CONCLUSIONS: Corticosteroids should be used at an early stage of HES to prevent further damage to vital organs. Anticoagulants should be recommended only in cases with thrombosis which should be actively screened as a part of evaluation of end organ damage.

The ambulatory transoral endoscopic thyroidectomy vestibular approach is safe and economical for patients with thyroid nodules.

Background: Ambulatory thyroid surgery has been increasingly performed in recent years. However, the feasibility of the ambulatory transoral endoscopic thyroidectomy vestibular approach (TOETVA) has not been evaluated. We aimed to evaluate the safety, economy, and mental health outcomes of ambulatory TOETVA. Methods: We retrospectively reviewed the data of patients who underwent TOETVA between March 2019 and August 2022. The procedure was performed by a skilled surgical team from the Department of Thyroid Surgery of the affiliated Yantai Yuhuangding Hospital of Qingdao University. Patients were enrolled in the ambulatory (n=166) and conventional (n=290) groups, based on their chosen procedure. We analyzed patients' clinical characteristics, surgical outcomes, Hamilton Anxiety Rating Scale (HAM-A) scores, and hospitalization costs. Results: Of 456 patients, 166 underwent ambulatory TOETVA and 290 underwent conventional TOETVA. No significant differences were found in clinical and surgical characteristics between the groups, including sex (P=0.363), age (P=0.077), body mass index (P=0.351), presence of internal diseases (P=0.613), presence of Hashimoto's thyroiditis (P=0.429), pathology (P=0.362), maximum tumor diameter (P=0.520), scope of surgery (P=0.850), or operative time (P=0.351). There were no significant differences in maximum tumor diameter (P=0.349), extrathyroidal tissue invasion (P=0.516), number of retrieved central lymph nodes (P=0.069), or metastatic central lymph nodes (P=0.897) between the groups. No significant differences were found in complications, including transient hypoparathyroidism (P=0.438), transient vocal cord palsy (P=0.876), transient mental nerve injury (P=0.749), permanent mental nerve injury (P=0.926), and other complications (P=1.000). Ambulatory patients had shorter hospital stays (P<0.001) and reduced hospitalization costs (P<0.001). There was no significant difference in HAM-A scores between the groups (P=0.056). Conclusions: Ambulatory TOETVA is a safe, feasible, and cost-effective procedure for selected patients. This procedure resulted in shorter hospital stays, decreased medical costs, and did not increase patient anxiety. To ensure patient safety, surgical teams must inform patients of the indications, when to seek help, and how to receive the fastest medical attention.

Metformin and 2-Deoxy-d-glucose synergistically inhibit the viability of thyroid papillary carcinoma with BRAF mutation by ROS-dependent PI3K/AKT signaling pathway.

The aim of this study was to explore the molecular mechanism through which metformin combined with 2-deoxy-d-glucose (2-DG) decreases the viability of BCPAP thyroid papillary carcinoma cells. BCPAP cells were treated with only metformin, only 2-DG, and both metformin and 2-DG. We used the CCK-8 assay to assess cell viability, dichlorofluorescein staining to detect reactive oxygen species (ROS), and western blot analysis to quantify protein expression. We found that metformin and 2-DG alone decreased cell viability in a time- and dose-dependent manner. The IC50 values of metformin and 2-DG were 5.329 mM and 1.154 mM, respectively. Coadministration of metformin and 2-DG significantly inhibited BCPAP cell proliferation and increased cellular ROS levels and AKT phosphorylation at Ser437. These effects were reversed following the treatment of the cells with N-acetyl-L-cysteine (NAC). Our findings suggest that metformin and 2-DG synergistically suppress BCPAP cell proliferation, potentially via inhibition of the PI3K/AKT signaling pathway by increasing cellular ROS levels.

Knowledge-Based Reasoning Network for Relation Detection.

With the rapid growth of large-scale knowledge bases (KBs), knowledge base question answering (KBQA) has attracted increasing attention recently. Relation detection plays an important role in the KBQA system, which finds a compatible answer by analyzing the semantics of questions and querying and reasoning with multiple KB triples. Significant progress has been made by deep neural networks. However, existing methods often concern on detecting single-hop relation without path reasoning, and a few of these methods exploit the multihop relation reasoning, which involves the answer reasoning from the noisy and abundant relational paths in the KB. Meanwhile, the relatedness between question and answer candidates has received little attention and remains unsolved. This article proposes a novel knowledge-based reasoning network (KRN) for relation detection, including both single-hop relation and multihop relation. To address the semantic gap problem in question-answer interaction, we first learn attentive question representations according to the influence of answer aspects. Then, we learn the single-hop relation sequence through different levels of abstraction and adopt the KB entity and structure information to denoise the multihop relation detection task. Finally, we adopt a Siamese network to measure the similarity between question representation and relation representation for both single-hop and multihop relation KBQA tasks. We conduct experiments on two well-known benchmarks, SimpleQuestions and WebQSP, and the results show the superiority of our approach over the state-of-the-art models for both single-hop and multihop relation detection. Our model also achieves a significant improvement over existing methods on KBQA end task. Further analysis demonstrates the robustness and the applicability of the proposed approach.

Advances in coastal ocean boundary layer detection technology and equipment in China.

Accurate and comprehensive knowledge of the atmospheric environment and its evolution within the coastal ocean boundary layer are necessary for understanding the sources, chemical mechanisms, and transport processes of air pollution in land, sea, and atmosphere. We present an overview of coastal ocean boundary layer detection technology and equipment in China and summarize the progress and main achievements in recent years. China has developed a series of coastal ocean boundary layer detection technologies, including Light Detection and Ranging (LIDAR), turbulent exchange analyzer, air-sea flux analyzer, stereoscopic remote sensing of air pollutants, and oceanic aerosol detection equipment to address the technical bottleneck caused by harsh environmental conditions in coastal ocean regions. Advances in these technologies and equipment have provided scientific assistance for addressing air pollution issues and understanding land-sea-atmosphere interactions over coastal ocean regions in China. In the future, routine atmospheric observations should cover the coastal ocean boundary layer of China.