Bicyclol attenuates pulmonary fibrosis with silicosis via both canonical and non-canonical TGF-ß1 signaling pathways.

BACKGROUND: Silicosis is an irreversible fibrotic disease of the lung caused by chronic exposure to silica dust, which manifests as infiltration of inflammatory cells, excessive secretion of pro-inflammatory cytokines, and pulmonary diffuse fibrosis. As the disease progresses, lung function further deteriorates, leading to poorer quality of life of patients. Currently, few effective drugs are available for the treatment of silicosis. Bicyclol (BIC) is a compound widely employed to treat chronic viral hepatitis and drug-induced liver injury. While recent studies have demonstrated anti-fibrosis effects of BIC on multiple organs, including liver, lung, and kidney, its therapeutic benefit against silicosis remains unclear. In this study, we established a rat model of silicosis, with the aim of evaluating the potential therapeutic effects of BIC. METHODS: We constructed a silicotic rat model and administered BIC after injury. The FlexiVent instrument with a forced oscillation system was used to detect the pulmonary function of rats. HE and Masson staining were used to assess the effect of BIC on silica-induced rats. Macrophages-inflammatory model of RAW264.7 cells, fibroblast-myofibroblast transition (FMT) model of NIH-3T3 cells, and epithelial-mesenchymal transition (EMT) model of TC-1 cells were established in vitro. And the levels of inflammatory mediators and fibrosis-related proteins were evaluated in vivo and in vitro after BIC treatment by Western Blot analysis, RT-PCR, ELISA, and flow cytometry experiments. RESULTS: BIC significantly improved static compliance of lung and expiratory and inspiratory capacity of silica-induced rats. Moreover, BIC reduced number of inflammatory cells and cytokines as well as collagen deposition in lungs, leading to delayed fibrosis progression in the silicosis rat model. Further exploration of the underlying molecular mechanisms revealed that BIC suppressed the activation, polarization, and apoptosis of RAW264.7 macrophages induced by SiO2. Additionally, BIC inhibited SiO2-mediated secretion of the inflammatory cytokines IL-1ß, IL-6, TNF-α, and TGF-ß1 in macrophages. BIC inhibited FMT of NIH-3T3 as well as EMT of TC-1 in the in vitro silicosis model, resulting in reduced proliferation and migration capability of NIH-3T3 cells. Further investigation of the cytokines secreted by macrophages revealed suppression of both FMT and EMT by BIC through targeting of TGF-ß1. Notably, BIC blocked the activation of JAK2/STAT3 in NIH-3T3 cells required for FMT while preventing both phosphorylation and nuclear translocation of SMAD2/3 in TC-1 cells necessary for the EMT process. CONCLUSION: The collective data suggest that BIC prevents both FMT and EMT processes, in turn, reducing aberrant collagen deposition. Our findings demonstrate for the first time that BIC ameliorates inflammatory cytokine secretion, in particular, TGF-ß1, and consequently inhibits FMT and EMT via TGF-ß1 canonical and non-canonical pathways, ultimately resulting in reduction of aberrant collagen deposition and slower progression of silicosis, supporting its potential as a novel therapeutic agent.

Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.

Semisynthesis and antitumor activity of endertiin B and related triterpenoids from Ganoderma lucidum.

Ganoderma lucidum, a fungus used in traditional Chinese medicine, is known for its medicinal value attributed to its active components called Ganoderma triterpenoids (GTs). However, the limited isolation rate of these GTs has hindered their potential as promising drug candidates. Therefore, it is imperative to achieve large-scale preparation of GTs. In this study, four GTs were effectively synthesised from lanosterol. The antitumor activity of these GTs was evaluated in vivo. Endertiin B exhibited potent inhibitory activity against breast cancer cells (9.85 ± 0.91 µM and 12.12 ± 0.95 µM). Further investigations demonstrated that endertiin B significantly upregulated p21 and p27 and downregulated cyclinD1 expression, arresting the cell cycle at the G0/G1 phase and inducing apoptosis by decreasing BCL-2 and increasing BAX and BAK levels. Additionally, endertiin B was found to reduce the expression of proteins associated with the PI3K-AKT signaling pathway. To summarize, endertiin B effectively inhibited cell proliferation by blocking the cell cycle and inducing apoptosis through the PI3K-AKT pathway.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 µm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

4.4 kW Nd:YAG slab amplifier with a high repetition frequency and high beam quality.

Using the self-developed fused indium wetting technology and planar waveguide, the uniform heat dissipation of the slab crystal and uniform pumping of the pump light were achieved, respectively. Based on the master oscillator power amplification (MOPA) scheme, the power was then amplified when the seed light source passed through the Nd:YAG slab crystal three times. Additionally, the image transfer system that we added to the amplified optical path achieved high beam quality. Finally, we obtained a rectangular pulsed laser with an output average power of 4461 W, a repetition frequency of 20 kHz, a pulse width of 62 ns, an optical-to-optical conversion efficiency of 26.8%, and a beam quality of ß x=7.0 and ß y=7.7.

Small water body significantly contributes to nitrous oxide emissions in China's aquaculture.

Aquaculture systems are expected to act as potential hotspots for nitrous oxide (N2O) emissions, largely attributed to substantial nutrient loading from aquafeed applications. However, the specific patterns and contributions of N2O fluxes from these systems to the global emissions inventory are not well characterized due to limited data. This study investigates the patterns of N2O flux across 127 freshwater systems in China to elucidate the role of aquaculture ponds and lakes/reservoirs in landscape N2O emission. Our findings show that the average N2O flux from aquaculture ponds was 3.63 times higher (28.73 µg N2O m-2 h-1) than that from non-aquaculture ponds. Additionally, the average N2O flux from aquaculture lakes/reservoirs (15.65 µg N2O m-2 h-1) increased 3.05 times compared to non-aquaculture lakes/reservoirs. The transition from non-aquaculture to aquaculture practices has resulted in a net annual increase of 7589 ± 2409 Mg N2O emissions in China's freshwater systems from 2003 to 2022, equivalent to 20% of total N2O emissions from China's inland water. Particularly, the robust negative regression relationship between N2O emission intensity and water area suggests that small ponds are hotspots of N2O emissions, a result of both elevated nutrient concentrations and more vigorous biogeochemical cycles. This indicates that N2O emissions from smaller aquaculture ponds are larger per unit area compared to equivalent larger water bodies. Our findings highlight that N2O emissions from aquaculture systems can not be proxied by those from natural water bodies, especially small water bodies exhibiting significant but largely unquantified N2O emissions. In the context of the rapid global expansion of aquaculture, this underscores the critical need to integrate aquaculture into global assessments of inland water N2O emissions to advance towards a low-carbon future.

Improving denitrification estimation by joint inclusion of suspended particles and chlorophyll a in aquaculture ponds.

The denitrification process in aquaculture systems plays a crucial role in nitrogen (N) cycle and N budget estimation. Reliable models are needed to rapidly quantify denitrification rates and assess nitrogen losses. This study conducted a comparative analysis of denitrification rates in fish, crabs, and natural ponds in the Taihu region from March to November 2021, covering a complete aquaculture cycle. The results revealed that aquaculture ponds exhibited higher denitrification rates compared to natural ponds. Key variables influencing denitrification rates were Nitrate nitrogen (NO3--N), Suspended particles (SPS), and chlorophyll a (Chla). There was a significant positive correlation between SPS concentration and denitrification rates. However, we observed that the denitrification rate initially rose with increasing Chla concentration, followed by a subsequent decline. To develop parsimonious models for denitrification rates in aquaculture ponds, we constructed five different statistical models to predict denitrification rates, among which the improved quadratic polynomial regression model (SQPR) that incorporated the three key parameters accounted for 80.7% of the variability in denitrification rates. Additionally, a remote sensing model (RSM) utilizing SPS and Chla explained 43.8% of the variability. The RSM model is particularly valuable for rapid estimation in large regions where remote sensing data are the only available source. This study enhances the understanding of denitrification processes in aquaculture systems, introduces a new model for estimating denitrification in aquaculture ponds, and offers valuable insights for environmental management.

Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China.

As surface ozone (O3) gains increasing attention, there is an urgent need for high temporal resolution and accurate O3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O3 retrieval. However, the underlying physical mechanisms that influence surface O3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O3. Using the developed model, we performed hourly inversions of surface O3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) µg/m3, indicating the accuracy of the models. The average surface O3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 µg/m3, respectively. There was a diurnal pattern in surface O3 in China, with levels rising significantly from 55 µg/m3 at 9:00 a.m. to 96 µg/m3 at 15:00. Between 15:00 and 16:00, the O3 concentration remained stable at 95 µg/m3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone.

Methionine deficiency facilitates antitumour immunity by altering m6A methylation of immune checkpoint transcripts.

OBJECTIVE: Methionine metabolism is involved in a myriad of cellular functions, including methylation reactions and redox maintenance. Nevertheless, it remains unclear whether methionine metabolism, RNA methylation and antitumour immunity are molecularly intertwined. DESIGN: The antitumour immunity effect of methionine-restricted diet (MRD) feeding was assessed in murine models. The mechanisms of methionine and YTH domain-containing family protein 1 (YTHDF1) in tumour immune escape were determined in vitro and in vivo. The synergistic effects of MRD or YTHDF1 depletion with PD-1 blockade were also investigated. RESULTS: We found that dietary methionine restriction reduced tumour growth and enhanced antitumour immunity by increasing the number and cytotoxicity of tumour-infiltrating CD8+ T cells in different mouse models. Mechanistically, the S-adenosylmethionine derived from methionine metabolism promoted the N6-methyladenosine (m6A) methylation and translation of immune checkpoints, including PD-L1 and V-domain Ig suppressor of T cell activation (VISTA), in tumour cells. Furthermore, MRD or m6A-specific binding protein YTHDF1 depletion inhibited tumour growth by restoring the infiltration of CD8+ T cells, and synergised with PD-1 blockade for better tumour control. Clinically, YTHDF1 expression correlated with poor prognosis and immunotherapy outcomes for cancer patients. CONCLUSIONS: Methionine and YTHDF1 play a critical role in anticancer immunity through regulating the functions of T cells. Targeting methionine metabolism or YTHDF1 could be a potential new strategy for cancer immunotherapy.

KLF14/miR-1283/TFAP2C axis inhibits HER2-positive breast cancer progression via declining tumor cell proliferation.

MiR-1283 has been identified as a tumor suppressor in some malignancies. Whereas, the role of miR-1283 in HER2-positive (HER2+) breast cancer, particularly its role in regulating cell proliferation, one of the most significant features of tumor progression, is unclear. The related microRNA screened by the breast cancer sample GSE131599 dataset were detected in HER2+ breast cancer tissues and cell lines. Then, the obtained miR-1283 was overexpressed in SKBR3 and BT-474 cells followed by relevant functional assays concerning cell proliferation and apoptosis. The xenograft mouse model was induced and the effect of miR-1283 on tumor growth and cell proliferation was examined. The target of miR-1283 and the transcription factor regulating miR-1283 were predicted and identified. Finally, the influence of transcription factor KLF14 on cell proliferation and apoptosis was investigated. An integrated analysis confirmed that miR-1283 expression was significantly decreased in HER2+ breast cancer tissues. Also, by q-RT-PCR detection, miR-1283 expression was markedly reduced in HER2+ breast cancer tissues and cell lines. The miR-1283 overexpression prevented the proliferation and enhanced apoptosis of HER2+ breast cancer cells, as well as inhibited tumor growth. Mechanistically, miR-1283 inhibited TFAP2C expression by targeting the 3'-untranslated regions of TFAP2C messenger RNA, and the KLF14 enhanced miR-1283 level via binding to its promoter. The result subsequently confirmed the KLF14/miR-1283 signaling suppressed cell proliferation in HER2+ breast cancer. Our results suggested that the KLF14/miR-1283/TFAP2C axis inhibited HER2+ breast cancer progression, which might provide novel insight into mechanical exploration for this disease.

Enhanced light extraction efficiency of GaN-based green micro-LED modulating by a thickness-tunable SiO2 passivation structure.

Green micro-light emitting diodes (micro-LEDs) is one of the three primary color light sources as full-color display, which serves as a key research object in the field of micro-LED display. As the micro-LED size decreases, the surface-area-to-volume ratio of the device increases, leading to more serious damage on the sidewall by inductively coupled plasma (ICP) etching. The passivation process of SiO2 provides an effective method to reduce sidewall damage caused by ICP etching. In this work, green rectangular micro-LEDs with passivation layer thickness of 0∼600 nm was designed using the finite-difference time-domain (FDTD) simulation. In order to verify the simulation results, the micro-LED array was fabricated by parallel laser micro-lens array (MLA) lithography in high speed and large area. The effect of the SiO2 passivation layer thickness on the performance of the green micro-LED was analyzed, which shows that the passivation layer thickness-light extraction efficiency curve fluctuates periodically. For the sample with 90 nm thickness of SiO2 passivation layer, there exists a small leakage current and higher operating current density, and the maximum external quantum efficiency (EQE) is 2.8 times higher than micro-LED without SiO2 passivation layer.

High-precision angular rate detection based on an optomechanical micro hemispherical shell resonator gyroscope.

Cavity optomechanics with picometer displacement measurement resolution has shown vital applications in high-precision sensing areas. In this paper, an optomechanical micro hemispherical shell resonator gyroscope (MHSRG) is proposed, for the first time. The MHSRG is driven by the strong opto-mechanical coupling effect based on the established whispering gallery mode (WGM). And the angular rate is characterized by measuring the transmission amplitude changing of laser coupled in and out from the optomechanical MHSRG based on the dispersive resonance wavelength shift and/or dissipative losses varying. The detailed operating principle of high-precision angular rate detection is theoretically explored and the fully characteristic parameters are numerically investigated. Simulation results show that the optomechanical MHSRG can achieve scale factor of 414.8 mV/ (°/ s) and angular random walk of 0.0555 °/ h1/2 when the input laser power is 3 mW and resonator mass is just 98 ng. Such proposed optomechanical MHSRG can be widely used for chip-scale inertial navigation, attitude measurement, and stabilization.

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video).

BACKGROUND AND AIMS: Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. METHODS: Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. RESULTS: The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. CONCLUSIONS: The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

Comparative effectiveness of prophylactic strategies for preeclampsia: a network meta-analysis of randomized controlled trials.

OBJECTIVE: Preeclampsia is a common disease during pregnancy that leads to fetal and maternal adverse events. Few head-to-head clinical trials are currently comparing the effectiveness of prophylactic strategies for preeclampsia. In this network meta-analysis, we aimed to compare the efficacy of prophylactic strategies for preventing preeclampsia in pregnant women at risk. DATA SOURCES: Articles published in or before September 2021 from PubMed, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov, references of key articles, and previous meta-analyses were manually searched. STUDY ELIGIBILITY CRITERIA: Randomized controlled trials comparing prophylactic strategies preventing preeclampsia with each other or with negative controls were included. METHODS: Two reviewers independently extracted data, assessed the risk of bias, and assessed evidence certainty. The efficacy of prophylactic strategies was estimated by frequentist and Bayesian network meta-analysis models. The primary composite outcome was preeclampsia/ pregnancy-induced hypertension. RESULTS: In total, 130 trials with a total of 112,916 patients were included to assess 13 prophylactic strategies. Low-molecular-weight heparin (0.60; 95% confidence interval, 0.42-0.87), vitamin D supplementation (0.65; 95% confidence interval, 0.45-0.95), and exercise (0.68; 95% confidence interval, 0.50-0.92) were as efficacious as calcium supplementation (0.71; 95% confidence interval, 0.62-0.82) and aspirin (0.79; 95% confidence interval, 0.72-0.86) in preventing preeclampsia/pregnancy-induced hypertension, with a P score ranking of 85%, 79%, 76%, 74%, and 61%, respectively. In the head-to-head comparison, no differences were found between these effective prophylactic strategies for preventing preeclampsia and pregnancy-induced hypertension, except with regard to exercise, which tended to be superior to aspirin and calcium supplementation in preventing pregnancy-induced hypertension. Furthermore, the prophylactic effects of aspirin and calcium supplementation were robust across subgroups. However, the prophylactic effects of low-molecular-weight heparin, exercise, and vitamin D supplementation on preeclampsia and pregnancy-induced hypertension varied with different risk populations, dosages, areas, etc. The certainty of the evidence was moderate to very low. CONCLUSION: Low-molecular-weight heparin, vitamin D supplementation, exercise, calcium supplementation, and aspirin reduce the risk of preeclampsia/pregnancy-induced hypertension. No significant differences between effective prophylactic strategies were found in preventing preeclampsia. These findings raise the necessity to reevaluate the prophylactic effects of low-molecular-weight heparin, vitamin D supplementation, and exercise on preeclampsia.

Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.

Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001-2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth's reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001-2021 (>3 × 10-3/year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.

Aiphanol, a multi-targeting stilbenolignan, potently suppresses mouse lymphangiogenesis and lymphatic metastasis.

The high incidence of lymphatic metastasis is closely related to poor prognosis and mortality in cancers. Potent inhibitors to prevent pathological lymphangiogenesis and lymphatic spread are urgently needed. The VEGF-C-VEGFR3 pathway plays a vital role in driving lymphangiogenesis and lymph node metastasis. In addition, COX2 in tumor cells and tumor-associated macrophages (TAMs) facilitates lymphangiogenesis. We recently reported that aiphanol, a natural stilbenolignan, attenuates tumor angiogenesis by repressing VEGFR2 and COX2. In this study, we evaluated the antilymphangiogenic and antimetastatic potency of aiphanol using in vitro, ex vivo and in vivo systems. We first demonstrated that aiphanol directly bound to VEGFR3 and blocked its kinase activity with an half-maximal inhibitory concentration (IC50) value of 0.29 µM in an in vitro ADP-GloTM kinase assay. Furthermore, we showed that aiphanol (7.5-30 µM) dose-dependently counteracted VEGF-C-induced proliferation, migration and tubular formation of lymphatic endothelial cells (LECs), which was further verified in vivo. VEGFR3 knockdown markedly mitigated the inhibitory potency of aiphanol on lymphangiogenesis. In 4T1-luc breast tumor-bearing mice, oral administration of aiphanol (5 and 30 mg· kg-1 ·d-1) dose-dependently decreased lymphatic metastasis and prolonged survival time, which was associated with impaired lymphangiogenesis, angiogenesis and, interestingly, macrophage infiltration. In addition, we found that aiphanol decreased the COX2-dependent secretion of PGE2 and VEGF-C from tumor cells and macrophages. These results demonstrate that aiphanol is an appealing agent for preventing lymphangiogenesis and lymphatic dissemination by synergistically targeting VEGFR3 and inhibiting the COX2-PGE2-VEGF-C signaling axis.

A comprehensive review of the metabolism of citrus flavonoids and their binding to bitter taste receptors.

Citrus is an important source of flavonoids in our daily diet. Citrus flavonoids have antioxidant, anticancer, anti-inflammatory, and cardiovascular disease prevention functions. Studies have shown that some pharmaceutical values of flavonoids may be related to their binding to bitter taste receptors, thus activating downstream signal transduction pathways; however, the underlying mechanism has not been systematically elucidated. In this paper, the biosynthesis pathway and the absorption and metabolism of citrus flavonoids were briefly reviewed, and the relationship between flavonoid structure and bitter taste intensity was investigated. In addition, the pharmacological effects of bitter flavonoids and the activation of bitter taste receptors in combating various diseases were discussed. This review provides an important basis for the targeted design of citrus flavonoid structures to make them more biologically active and more attractive as powerful drugs for the effective treatment of chronic diseases such as obesity, asthma, and neurological diseases.

Genomic temporal heterogeneity of circulating tumour DNA in unresectable metastatic colorectal cancer under first-line treatment.

OBJECTIVE: Circulating tumour DNA (ctDNA) sequencing is increasingly used in the clinical management of patients with colorectal cancer. However, the genomic heterogeneity in ctDNA during treatments and its impact on clinical outcomes remain largely unknown. DESIGN: We conducted a prospective cohort study (NCT04228614) of 171 patients with unresectable metastatic colorectal cancer (mCRC) who underwent first-line treatment and prospectively collected blood samples with or without tumour samples from patients at baseline and sequentially until disease progression or last follow-up. RESULTS: The RAS/BRAF alterations in paired baseline tissue and plasma samples from 63 patients displayed a favourable concordance (81.0%, 51/63). After a period of first-line treatment (median time between baseline and last liquid biopsy, 4.67 months), 42.6% (26/61) of RAS-mutant patients showed RAS clearance and 50.0% (5/10) of BRAF-mutant patients showed BRAF clearance, while 3.6% (3/84) and 0.7% (1/135) of patients showed new RAS or BRAF mutations in ctDNA. Patients with plasma RAS/BRAF clearance showed similar progression-free survival (PFS) and overall survival (OS) with patients who remained RAS/BRAF wild-type, while much better outcomes than those who remained RAS/BRAF mutant. Patients who gained new RAS/BRAF mutations showed similar prognosis as those who maintained RAS/BRAF mutations, and shorter PFS and OS than those who remained RAS/BRAF wild-type. CONCLUSION: This prospective, serial and large-scale ctDNA profiling study reveals the temporal heterogeneity of mCRC-related somatic variants, which should be given special attention in clinical practice, as evidenced by the finding that the shift in plasma RAS/BRAF mutational status can yield a drastic change in survival outcomes.

A Novel Convolutional Neural Network Model as an Alternative Approach to Bowel Preparation Evaluation Before Colonoscopy in the COVID-19 Era: A Multicenter, Single-Blinded, Randomized Study.

INTRODUCTION: Adequate bowel preparation is key to a successful colonoscopy, which is necessary for detecting adenomas and preventing colorectal cancer. We developed an artificial intelligence (AI) platform using a convolutional neural network (CNN) model (AI-CNN model) to evaluate the quality of bowel preparation before colonoscopy. METHODS: This was a colonoscopist-blinded, randomized study. Enrolled patients were randomized into an experimental group, in which our AI-CNN model was used to evaluate the quality of bowel preparation (AI-CNN group), or a control group, which performed self-evaluation per routine practice (control group). The primary outcome was the consistency (homogeneity) between the results of the 2 methods. The secondary outcomes included the quality of bowel preparation according to the Boston Bowel Preparation Scale (BBPS), polyp detection rate, and adenoma detection rate. RESULTS: A total of 1,434 patients were enrolled (AI-CNN, n = 730; control, n = 704). No significant difference was observed between the evaluation results ("pass" or "not pass") of the groups in the adequacy of bowel preparation as represented by BBPS scores. The mean BBPS scores, polyp detection rate, and adenoma detection rate were similar between the groups. These results indicated that the AI-CNN model and routine practice were generally consistent in the evaluation of bowel preparation quality. However, the mean BBPS score of patients with "pass" results were significantly higher in the AI-CNN group than in the control group, indicating that the AI-CNN model may further improve the quality of bowel preparation in patients exhibiting adequate bowel preparation. DISCUSSION: The novel AI-CNN model, which demonstrated comparable outcomes to the routine practice, may serve as an alternative approach for evaluating bowel preparation quality before colonoscopy.

Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study.

BACKGROUND: Intrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC. METHODS: Images of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists. RESULTS: A total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated. CONCLUSIONS: The proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.