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Abstract Background This study aimed to investigate the safety and efficacy of suture fixation of the levonorgestrel-releasing intrauterine device (LNG-IUD) by hysteroscope for the treatment of adenomyosis in patients at high risk of device expulsion, to provide a viable treatment option for these patients. Methods Twenty-one patients with large symptomatic adenomyosis were sewed and fixed with the LNG-IUD in the uterus by hysteroscopy to prevent the device expulsion. Results In this prospective case series, all 21 patients were successfully sewed and fixed with the LNG-IUD in the uterus by hysteroscopy. The amount of menstruation was significantly decreased and dysmenorrhea was significantly relieved in 3, 6, and 12 months after surgery compared with before surgery. Only one patient underwent a laparoscopic hysterectomy due to dysmenorrhea which was not alleviated one month later. Serious IUD complications including expulsion and perforation were not observed. There was no development of new side effects than LNG-IUD insertion. Conclusion Our findings indicate that suture fixation of the LNG-IUD by hysteroscope is a safe, effective, and minimally invasive surgical procedure to prevent the device expulsion. It is a good solution for the treatment of large symptomatic adenomyosis and has shown to be effective in managing symptoms related to adenomyosis.

Abstract We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree graph structure, we can learn all parameters with root mean square error ϵ using $${\mathcal{O}}(1/\epsilon )$$ O ( 1 / ϵ ) total evolution time, which is independent of the system size, in a way that is robust against state-preparation and measurement error. In the protocol, we only use bosonic coherent states, beam splitters, phase shifters, and homodyne measurements, which are easy to implement on many experimental platforms. A key technique we develop is to apply random unitaries to enforce symmetry in the effective Hamiltonian, which may be of independent interest.

Abstract China’s higher education system is shifting from quantitative expansion to connotative development to advance its quality. Since 2015, Chinese governments have been implementing a strategic policy for higher education called “double first-class”, which aims to promote a number of Chinese top universities to construct world-class universities or to establish world-class disciplines. “Double first-class” universities have received a large amount of educational resources through this policy. Taking advantage of resources efficiently is an important element in promoting the development of higher quality higher education. However, research on resource allocation in China’s “double first-class” universities is incomplete. Current research has not clarified the level of resource allocation efficiency or the factors affecting China’s “double first-class” universities. With the help of the superefficient data envelopment analysis (DEA)-Malmquist–Tobit model, this study actively explores the current status of the resource allocation efficiency of China’s “double first-class” universities to fill this gap in the field. Specifically, the development level and change trend of the resource allocation efficiency of 13 “double first-class” universities in China from 2015 to 2019 were measured with the help of the superefficient DEA-Malmquist model. The internal and external factors affecting the resource allocation efficiency of “double first-class” universities are also analysed with the help of the Tobit model. The overall level of resource allocation efficiency of “double first-class” universities is high, but the internal variability is large. From the perspective of efficiency decomposition, it is found that both technical efficiency change (EFch) and technical progress efficiency (TEch) play important roles in improving the total factor productivity (TFP) of resource allocation. Compared with TEch, EFch plays a more significant pulling role. This study confirms that the factors affecting resource allocation efficiency are complex. Among them, the regional economic environment, faculty title structure, and degree of international exchange have significant roles in promoting the resource allocation efficiency of “double first-class” universities, but local financial support and the time of policy implementation have certain negative effects.

C-terminus of HSP70 interacting protein (CHIP) is an E3 ubiquitin ligase and HSP70 cochaperone. Mutations in the CHIP encoding gene are the cause of two neurodegenerative conditions: spinocerebellar ataxia autosomal dominant type 48 (SCA48) and autosomal recessive type 16 (SCAR16). The mechanisms underlying CHIP-associated diseases are currently unknown. Mitochondrial dysfunction, specifically dysfunction in mitochondrial autophagy (mitophagy), is increasingly implicated in neurodegenerative diseases and loss of CHIP has been demonstrated to result in mitochondrial dysfunction in multiple animal models, although how CHIP is involved in mitophagy regulation has been previously unknown. Here, we demonstrate that CHIP acts as a negative regulator of the PTEN-induced kinase 1 (PINK1)/Parkin-mediated mitophagy pathway, promoting the degradation of PINK1, impairing Parkin translocation to the mitochondria, and suppressing mitophagy in response to mitochondrial stress. We also show that loss of CHIP enhances neuronal mitophagy in a PINK1 and Parkin dependent manner in Caenorhabditis elegans. Furthermore, we find that multiple disease-associated mutations in CHIP dysregulate mitophagy both in vitro and in vivo in C. elegans neurons, a finding which could implicate mitophagy dysregulation in CHIP-associated diseases.

Myocardial infarction (MI) is associated with higher morbidity and mortality in the world, especially in cold weather. YBX1 is an RNA-binding protein that is required for pathological growth of cardiomyocyte by regulating cell growth and protein synthesis. But YBX1, as an individual RNA-binding protein, regulates cardiomyocytes through signaling cascades during myocardial infarction remain largely unexplored.

Background and aims The immunosuppressive tumor microenvironment (TME) plays an essential role in cancer progression and immunotherapy response. Despite the considerable advancements in cancer immunotherapy, the limited response to immune checkpoint blockade (ICB) therapies in patients with hepatocellular carcinoma (HCC) remains a major challenge for its clinical implications. Here, we investigated the molecular basis of the protein O-fucosyltransferase 1 (POFUT1) that drives HCC immune evasion and explored a potential therapeutic strategy for enhancing ICB efficacy.Methods De novo MYC/Trp53−/− liver tumor and the xenograft tumor models were used to evaluate the function of POFUT1 in immune evasion. Biochemical assays were performed to elucidate the underlying mechanism of POFUT1-mediated immune evasion.Results We identified POFUT1 as a crucial promoter of immune evasion in liver cancer. Notably, POFUT1 promoted HCC progression and inhibited T-cell infiltration in the xenograft tumor and de novo MYC/Trp53−/− mouse liver tumor models. Mechanistically, we demonstrated that POFUT1 stabilized programmed death ligand 1 (PD-L1) protein by preventing tripartite motif containing 21-mediated PD-L1 ubiquitination and degradation independently of its protein-O-fucosyltransferase activity. In addition, we further demonstrated that PD-L1 was required for the tumor-promoting and immune evasion effects of POFUT1 in HCC. Importantly, inhibition of POFUT1 could synergize with anti-programmed death receptor 1 therapy by remodeling TME in the xenograft tumor mouse model. Clinically, POFUT1 high expression displayed a lower response rate and worse clinical outcome to ICB therapies.Conclusions Our findings demonstrate that POFUT1 functions as a novel regulator of tumor immune evasion and inhibition of POFUT1 may be a potential therapeutic strategy to enhance the efficacy of immune therapy in HCC.

PurposeConventional cephalomedullary nails (CMNs) are commonly employed for internal fixation in the treatment of reverse obliquity intertrochanteric (ROI) fractures. However, the limited effectiveness of conventional CMNs in addressing ROI fractures results in significant implant-related complications. To address challenges associated with internal fixation, a novel Proximal Femoral Bionic Nail (PFBN) has been developed.MethodsIn this study, a finite element model was constructed using a normal femoral specimen, and biomechanical verification was conducted using the GOM non-contact optical strain measurement system. Four intramedullary fixation approaches—PFBN, Proximal Femoral Nail Antirotation InterTan nail (ITN), and Gamma nail (Gamma nail)—were employed to address three variations of ROI fractures (AO/OTA 31-A3). The biomechanical stability of the implant models was evaluated through the calculation of the von Mises stress contact pressure and displacement.ResultsCompared to conventional CMNs, the PFBN group demonstrated a 9.36%–59.32% reduction in the maximum VMS at the implant. The A3.3 ROI fracture (75% bone density) was the most unstable type of fracture. In comparison to conventional CMNs, PFBN demonstrated more stable data, including VMS values (implant: 506.33 MPa, proximal fracture fragment: 34.41 MPa), contact pressure (13.28 MPa), and displacement (17.59 mm).ConclusionCompared to the PFNA, ITN, and GN, the PFBN exhibits improvements in stress concentration, stress conduction, and overall model stability in ROI fractures. The double triangle structure aligns better with the tissue structure and biomechanical properties of the proximal femur. Consequently, the PFBN has significant potential as a new fixation strategy for the clinical treatment of ROI fractures.

Abstract Studying complex relaxation behaviors is of critical importance for understanding the nature of glasses. Here we report a Kovacs-like memory effect in glasses, manifested by non-monotonic stress relaxation during two-step high-to-low strains stimulations. During the stress relaxation process, if the strain jumps from a higher state to a lower state, the stress does not continue to decrease, but increases first and then decreases. The memory effect becomes stronger when the atomic motions become highly collective with a large activation energy, e.g. the strain in the first stage is larger, the temperature is higher, and the stimulation is longer. The physical origin of the stress memory effect is studied based on the relaxation kinetics and the in-situ synchrotron X-ray experiments. The stress memory effect is probably a universal phenomenon in different types of glasses.

In this extensive analysis, we delve into the utilization of advanced hybrid model for molecular separation description via mesoporous materials. The process considered here is adsorption which is utilized for molecular separation in liquid solution. Data for adsorption are obtained in terms of solute concentration by solving mass transfer equations numerically. Then, the data is used for training and validation of several machine learning techniques to establish the hybrid model. For machine learning model, several methods are utilized including three robust tree-based ensemble models, namely Gradient Boosting (GB), Random Forest (RF), and Extra Trees (ET). The methos were utilized to tackle the challenging task of predicting chemical concentrations in a dataset comprising an array of over 19,000 data. These data points are characterized by input variables representing coordinates (x and y), while the output variable encapsulates chemical concentration (C). By subjecting these models to a meticulous optimization process, we achieved substantial enhancements in their predictive capabilities. RF emerged as the standout performer, boasting a remarkable R2 score of 0.99902. ET and GB also demonstrated commendable performance improvements, underscoring the versatility of tree-based ensemble models in delivering precise and reliable predictions for chemical concentrations within intricate and multifaceted datasets.

IntroductionGroup 2 innate lymphoid cells (ILC2s) play a crucial role in house dust mite (HDM)-induced allergic inflammation, and allergen immunotherapy (AIT) holds promise for treating the disease by reducing the frequency of ILC2s. Despite significant progress in AIT for allergic diseases, there remains a need to improve the control of allergic symptoms.MethodsWe investigated the synergistic effect of the Notch signaling pathway and subcutaneous immunotherapy (SCIT) in treating allergic airway inflammation in mice and their impact on the ratio of ILC2s in lung tissues. This was achieved by establishing the HDM-induced airway allergic disorders (HAAD) model and SCIT model. Additionally, we conducted in vitro investigations into the effect of the Notch signaling pathway on the secretory function of activated ILC2s using fluorescence-activated cell sorting. Furthermore, we explored the coactivation of the Notch signaling pathway with SCIT in vitro by sorting ILC2s from the lung tissues of mice after SCIT modeling.ResultsPreviously, our group demonstrated that Notch signaling pathway inhibitors can reduce allergic airway inflammation in mice. Notch signaling induces lineage plasticity of mature ILC2s. In this study, we showed that AIT alleviates allergic airway inflammation and suppresses the frequency of ILC2s induced by HDM. Interestingly, AIT combined with a γ-secretase inhibitor (GSI), an inhibitor of the Notch signaling pathway, significantly inhibited the frequency of ILC2s, reduced airway inflammation, and suppressed Th2-type responses in a mouse model. Furthermore, lung ILC2s from HDM-challenged mice with or without AIT were treated with GSI in vitro, and we found that GSI dramatically reduced the secretion of type 2 inflammatory factors in ILC2s.DiscussionThese findings suggest that Notch signaling pathway inhibitors can be used as adjuvant therapy for AIT and may hold potential treatment value in the cooperative control of allergic airway inflammation during early AIT.

Objective: The study was to investigate the potential mechanisms and preliminarily pharmacological effects of ginseng-licorice in the treatment of non-small cell lung cancer (NSCLC) by network pharmacology and in vitro experiments. Methods: Firstly, the active components and potential targets of ginseng-licorice were screened by using the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP), and the drug-components-target map was constructed using Cytoscape 3.7.1 software. TDD (Therapeutic Target Database), CTD (Comparative Toxicogenomics Database) and Gene Cards (The Human Gene Database) were used to obtain targets for NSCLC, and disease-drug intersection targets were obtained. Protein Interaction Network (PPI) was established by database STRING and Cytoscape software. Combined with PPI, GO and KEGG, the overall analysis was conducted to determine the main pathway and key targets of ginseng-licorice in the treatment of NSCLC. Finally, the anti-tumor effect of ginseng-licorice on NSCLC was verified by MTT colorimetric assay and flow cytometry. Results: The AKT1, TP53, CASP3 and VEGFA should be the potential targets for the treatment of NSCLC 122 targets of "ginseng-licorice" -NSCLC intersection was obtained from the screening of TTD, CTD and gene cards databases. Combined with the comprehensive analysis of PPI, GO and KEGG, the key targets of ginseng and licorice on NSCLC were identified as AKT1, TP53, IL, AMPK and TNF. The results of in vitro experiments showed that the drug to ginseng-licorice could significantly inhibit the proliferation of human non-small cell lung cancer cells, and could induce tumor cell apoptosis, thereby exerting an anti-tumor effect. Conclusion: This study initially explored the potential mechanisms and preliminarily pharmacological effects of the active ingredients of ginseng-licorice on NSCLC, providing a new theoretical basis for the clinical application of drug for the treatment of ginseng-licorice in the treatment of non-small cell lung cancer.

Background Low shearing force can induce vascular inflammatory response, which leads to the occurrence and development of hypertension. Estrogen can regulate the immune system and inflammatory response through estrogen receptor mediated pathway, with a protective effect on hypertension. Cardiovascular diseases have become the leading cause of death in China. Objective To investigate the current prevalence and influencing factors of hypertension in women, explore the correlation of shearing force and estradiol with hypertension as well as the effect of estradiol on hypertension under various shearing force, so as to further explore the effect of the interaction between estradiol and shearing force on hypertension. Methods Female patients admitted to the Department of Cardiology of Hunan People's Hospital from January to August 2021 were selected as the study subjects, 288 of whom with essential hypertension were included as the case group, and 296 female non-hypertensive medical examiners in the physical examination department of Hunan People's Hospital were randomly selected as the control group. The self-designed questionnaire was used to investigate the general information of the study subjects, and the fasting venous blood samples of the subjects were taken in the early morning for the examination of laboratory indicators. The color doppler ultrasound was used for ultrasonography on vessels to calculate the shearing force. Multivariate Logistic regression was used to construct the hypertension prediction model and draw the nomogram. Receiver operating characteristic curve (ROC curve) was plotted to evaluate the hypertension prediction model. Stratification analysis was conducted on estradiol in four groups to explore the effect of each estradiol group on the occurrence of hypertension under different shearing force dichotomous groups. The multiplicative interaction model was used to evaluate the interaction between estradiol and shearing force. Results There were significant differences in age, education level, body mass index (BMI), menopause, family history of hypertension, triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), estradiol, uric acid (UA), creatinine (Scr), urea nitrogen (BUN), shear force, and blood viscosity between the case group and control group (P60 years old〔OR=2.172, 95%CI (1.361, 3.468) 〕, menopause〔OR=14.940, 95%CI (1.938, 115.176) 〕, family history of hypertension〔OR=1.614, 95%CI (1.014, 2.570) 〕, BMI of 24-28 kg/m2〔OR=3.454, 95%CI (2.072, 5.758) 〕, BMI>28 kg/m2〔OR=6.750, 95%CI (2.380, 19.146) 〕, TG of 1.30-1.83 mmol/L〔OR=1.980, 95%CI (1.114, 3.520) 〕, TG>1.83 mmol/L〔OR=2.311, 95%CI (1.308, 4.082) 〕, TC of 4.29-5.12 mmol/L〔OR=2.222, 95%CI (1.250, 3.950) 〕, TC>5.12 mmol/L〔OR=1.884, 95%CI (1.062, 3.341) 〕, LDL-C>2.81 mmol/L〔OR=1.546, 95%CI (1.150, 2.078) 〕, HDL-C>1.17 mmol/L〔OR=0.140, 95%CI (0.065, 0.301) 〕, BUN>5.47 mmol/L〔OR=3.249, 95%CI (2.380, 4.434) 〕, Scr>69 μmol/L〔OR=1.166, 95%CI (1.126, 1.207) 〕, UA>324 μmol/L〔OR=1.040, 95%CI (1.029, 1.050) 〕, WBV4.41 mPa·s〔OR=3.456, 95%CI (2.557, 4.671) 〕, estradiol of 10.60-23.99 ng/L〔OR=0.005, 95%CI (0.001, 0.020) 〕, shearing force >8.21 dyne/cm2〔OR=0.033, 95%CI (0.017, 0.062) 〕were influncing factors for hypertension (P8.21 dyne/cm2 group, estradiol≥15.39 ng/L was significantly associated with reduced risk of hypertension〔OR=0.012, 95%CI (0.005, 0.019), P

Freeze-thaw processes can influence hydrology, soil erosion, and morphological development by altering the connectivity between active pathways of water and sediment transport. Concentrated flow experiments were conducted involving frozen, shallow thawed, and unfrozen soil slopes under 1, 2, and 4 L/min runoff rates at a temperature of approximately 5 °C. In this study, hydrological connectivity was analysed via the simplified hydrological curve and relative surface connection function. Sediment connectivity was analysed via the sediment structure connectivity and sediment functional connectivity. Results indicated that hydrological connectivity was greatest on frozen slopes (FS), followed by shallow thawed slopes (STS), and unfrozen slopes (UFS) given a constant flow rate. Hydrological connectivity increased with increasing runoff rate for each freeze-thaw condition. Freezing condition and runoff rate exhibited a positive response to the hydrological connectivity. Sediment structure connectivity increased with increasing runoff rate for each slope condition. The ordering of sediment structure connectivity across freeze-thaw condition was that FS was greater than STS while STS was greater than UFS independent of flow rate. Sediment functional connectivity included longitudinal, lateral, and vertical connectivity components. Sediment longitudinal and vertical connectivity indicated a trend of first increasing and then decreasing under the different runoff rates and freeze–thaw conditions. For a given runoff rate, the ordering of sediment longitudinal and vertical connectivity across freeze-thaw condition was that FS was greater than STS while STS was greater than UFS. Sediment lateral connectivity exhibited a trend of first decreasing and then stabilizing. The ordering of sediment lateral connectivity across freeze-thaw condition was that UFS was greater than STS while STS was greater than FS. FS could more easily reach longitudinal and vertical penetration. Sediment longitudinal and vertical connectivity rates demonstrated increasing trends with increasing runoff rate after runoff generation stabilization and gradually approached unity. This research further improves our understanding of the hydrological and erosional mechanisms of meltwater and the generation of flooding in frozen soil conditions.

Interleukin-17A (IL-17A) levels are elevated in patients with asthma. Ferroptosis has been identified as the non-apoptotic cell death type associated with asthma. Data regarding the relation of ferroptosis with asthma and the effect of IL-17A on modulating ferroptosis in asthma remain largely unclear. The present work focused on investigating the role of IL-17A in allergic asthma-related ferroptosis and its associated molecular mechanisms using public datasets, clinical samples, human bronchial epithelial cells, and an allergic asthma mouse model. We found that IL-17A was significantly upregulated within serum in asthma cases. Adding IL-17A significantly increased ferroptosis within human bronchial epithelial cells (BEAS-2B). In ovalbumin (OVA)-induced allergic asthmatic mice, IL-17A regulated and activated lipid peroxidation induced ferroptosis, whereas IL-17A knockdown effectively inhibited ferroptosis in vivo by protection of airway epithelial cells via the xCT-GSH-GPX4 antioxidant system and reduced airway inflammation. Mouse mRNA sequencing results indicated that the tumor necrosis factor (TNF) pathway was the differential KEGG pathway in the OVA group compared to healthy controls and the OVA group compared to the IL-17A knockout OVA group. We further used N-acetylcysteine (TNF inhibitor) to inhibit the TNF signaling pathway, which was found to protect BEAS-2B cells from IL-17A induced lipid peroxidation and ferroptosis damage. Our findings reveal a novel mechanism for the suppression of ferroptosis in airway epithelial cells, which may represent a new strategy for the use of IL-17A inhibitors against allergic asthma.

It is unclear whether the health equity of autism spectrum disorders (ASDs) has changed in different years, regions, and gender. The aims of this study were to provide a comprehensive description of the ASDs burden and provide evidence for improvement in health policies regarding ASDs inequality. This study is a population-based cross-sectional study based on the Global Burden of Disease datasets 1990–2019. We collected detailed information on ASDs between 1990 and 2019 in 204 countries worldwide, derived from the Global Burden of Disease study in 2019. Burden was calculated in terms of the incidence, prevalence and years lived with disability (YLDs). Concentration curves and concentration indices were used to summarize the degree of income-related inequality in the burden of ASDs. The overall age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR) and age-standardized YLDs rate (ASYR) of ASDs was 9.3 (95 %UI 7.7–11.1), 369.4 (95 %UI 305.9–441.2), 56.3 (95 %UI 36.8–81.5) per 100,000 people, respectively. The ASIR, ASPR and ASYR of ASDs affected three times as many males as females. The changing trends of age-standardized rates of ASDs showed that the ASIR of ASDs a slow growing trend globally. However, the ASPR and ASYR of ASDs showed a slow decreasing trend globally. All the concentration curves were below the line of equality and statistically significant. There was no significant difference in the age-standardized rate for different years in socio-demographic index-related inequality happened over 29 years (p > 0.05). The global burden of ASDs has remained higher in males and pro-rich, the income-related inequality tended not to change between 1990 and 2019.

The extent of problems in quantum chemistry for which quantum algorithms could provide a speedup is still unclear, as well as the kind of speedup one should expect. Here, the authors look at the problem of ground state energy estimation, and gather theoretical and numerical evidence for the fact that an exponential quantum advantage is unlikely for generic problems of interest.

Abstract Little is currently known about the effect of smoking on osteoarthritis (OA). This study aimed to investigate the relationship between smoking and OA in the United States (US) general population. Cross-sectional study. Level of evidence, 3. 40,201 eligible participants from the National Health and Nutrition Examination Survey 1999–2018 were included and divided into OA and non-arthritis groups. Participants demographics and characteristics were compared between the two groups. Then the participants were divided into non-smokers, former smokers, and current smokers based on their smoking status, also demographics and characteristics among the three groups were compared. Multivariable logistic regression was used to determine the relationship between smoking and OA. The current and former smoking rate in the OA group (53.0%) was significantly higher than that in the non-arthritis group (42.5%; p

Cancer cells frequently change their metabolism from aerobic glycolysis to lipid metabolism and amino acid metabolism to adapt to the malignant biological behaviours of infinite proliferation and distant metastasis. The significance of metabolic substances and patterns in tumour cell metastasis is becoming increasingly prominent. Tumour metastasis involves a series of significant steps such as the shedding of cancer cells from a primary tumour, resistance to apoptosis, and colonisation of metastatic sites. However, the role of glutamine in these processes remains unclear. This review summarises the key enzymes and transporters involved in glutamine metabolism that are related to the pathogenesis of malignant tumour metastasis. We also list the roles of glutamine in resisting oxidative stress and promoting immune escape. Finally, the significance of targeting glutamine metabolism in inhibiting tumour metastasis was proposed, research in this field improving our understanding of amino acid metabolism rewiring and simultaneously bringing about new and exciting therapeutic prospects.

Abstract Background Hepatocellular carcinoma (HCC) is one of the major causes of death from cancer and has a very poor prognosis with few effective therapeutic options. Despite the approval of lenvatinib for the treatment of patients suffering from advanced HCC, only a small number of patients can benefit from this targeted therapy. Methods Diethylnitrosamine (DEN)‐CCL4 mouse liver tumour and the xenograft tumour models were used to evaluate the function of KDM6A in HCC progression. The xenograft tumour model and HCC cell lines were used to evaluate the role of KDM6A in HCC drug sensitivity to lenvatinib. RNA‐seq and ChIP assays were conducted for mechanical investigation. Results We revealed that KDM6A exhibited a significant upregulation in HCC tissues and was associated with an unfavourable prognosis. We further demonstrated that KDM6A knockdown remarkably suppressed HCC cell proliferation and migration in vitro. Moreover, hepatic Kdm6a loss also inhibited liver tumourigenesis in a mouse liver tumour model. Mechanistically, KDM6A loss downregulated the FGFR4 expression to suppress the PI3K–AKT–mTOR signalling pathway, leading to a glucose and lipid metabolism re‐programming in HCC. KDM6A and FGFR4 levels were positively correlated in HCC specimens and mouse liver tumour tissues. Notably, KDM6A knockdown significantly inhibited the efficacy of lenvatinib therapy in HCC cells in vitro and in vivo. Conclusions Our findings revealed that KDM6A promoted HCC progression by activating FGFR4 expression and may be an essential molecule for influencing the efficacy of lenvatinib in HCC therapy.

Osthole is widely used in medicine because of its anti-oxidative, anti-allergenic and anti-inflammatory. In this study, we explored the protective effects of osthole on food allergenic mice and investigated its potential mechanism of action. We first established a food allergic mouse model by intraperitoneal sensitization and OVA (ovalbumin) oral challenge. Osthole were orally administered before OVA challenge. Diarrhea and allergy scores were used to assess food allergy symptoms. The pathological changes of the intestine were examined by H&E staining and electron microscopy. OVA-IgE, mucosal mast cell protease-1 (mMCP-1), IL-33, ST2, group 2 innate lymphoid cell (ILC2), and regulatory T (Treg) related-cytokine levels were assessed.We found that the oral administration of osthole after OVA sensitization could improve food allergy symptoms and induce beneficial intestinal pathological changes in mice. Osthole reduce the levels of IgE and mMCP-1 and inhibited the IL33/ST2 signaling pathway, thereby blocking ILC2 response and improving Treg response.

The purpose of the systematic review was to evaluate the efficacy and safety of interleukin-6 receptor (IL-6) antagonists (tocilizumab, sarilumab) in adult patients with severe or critical COVID-19. A systematic review of the literature was conducted in Medline, Cochrane and Embase databases, and World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) and ClinicalTrials.gov from the inception dates to10 January 2023. Randomized clinical trials comparing IL-6 receptor antagonists (tocilizumab, sarilumab) with a placebo or usual care treatment for adult patients with severe or critical COVID-19 were identified. Two independent reviewers performed the assessment and selection of eligible studies, assessed study quality and extracted data. Relative risk (RR), mean difference (MD), and 95% confidence interval (CI) with random-effects models was performed in meta-analysis. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess the quality of the evidence. The search retrieved a total of 11 RCTs involving 5028 participants were eligible for meta-analysis. Our findings suggest that as the new drug used in adult patients with severe or critical COVID-19, IL-6 antagonists (tocilizumab, sarilumab) may reduce the length of ICU stay and hospital stay. However, they did not significantly increase the risks of serious adverse events and did not reduce all-cause mortality (28-day, 14-day, and 7-day).

Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temporal dependencies more effectively. Previous architectures are effective at capturing either local and global information, but not both. We propose to employ a U-Net like structure to model both types of dependencies in a unified structure where the encoder learns global dependencies hierarchically on top of local ones; then the decoder propagates this global information back to the segment level for classification. Second, overfitting is a non-trivial issue for video anomaly detection due to limited training data. We propose weakly supervised contrastive regularization which adopts a feature-based approach to regularize the network. Contrastive regularization learns more generalizable features by enforcing inter-class separability and intra-class compactness. Extensive experiments on the UCF-Crime dataset shows that our approach outperforms several state-of-the-art methods.

To ensure the ecological operation of the proposed C hydropower stations of the Upper Yellow River, the suitable minimum ecological flow of the study reach after the completion of hydropower station is studied. The native plateau fish was considered as an indicator species of the reach downstream of the dam for ecological conservation. The study is based on a 2D shallow water model with high-precision solution methods and GPU-accelerated performance, combined with Tennant, hydraulics method and habitat suitability models to obtain habitat conditions of river for fish survival during non-spawning periods and effective habitat areas during spawning period under different discharges. The results indicated that the suitable minimum ecological flow downstream of the C hydropower station was 87.5 m³/s to protect fish downstream. This achievement not only provides basic data for the optimal operation of C hydropower station based on the ecological response, but also some reference value for the actual operation management of water conservancy projects. Besides, it is of great ecological significance for the protection of native fish habitat in the Yellow River Basin.

Dysfunction of neuronal nitric oxide synthase contributes to neurotoxicity, which triggers cell death in various neuropathological diseases, including epilepsy. Studies have shown that inhibition of neuronal nitric oxide synthase activity increases the epilepsy threshold, that is, has an anticonvulsant effect. However, the exact role and potential mechanism of neuronal nitric oxide synthase in seizures are still unclear. In this study, we performed RNA sequencing, functional enrichment analysis, and weighted gene coexpression network analysis of the hippocampus of tremor rats, a rat model of genetic epilepsy. We found damaged hippocampal mitochondria and abnormal succinate dehydrogenase level and Na+-K+-ATPase activity. In addition, we used a pilocarpine-induced N2a cell model to mimic epileptic injury. After application of neuronal nitric oxide synthase inhibitor 7-nitroindazole, changes in malondialdehyde, lactate dehydrogenase and superoxide dismutase, which are associated with oxidative stress, were reversed, and the increase in reactive oxygen species level was reversed by 7-nitroindazole or reactive oxygen species inhibitor N-acetylcysteine. Application of 7-nitroindazole or N-acetylcysteine downregulated the expression of caspase-3 and cytochrome c and reversed the apoptosis of epileptic cells. Furthermore, 7-nitroindazole or N-acetylcysteine downregulated the abnormally high expression of NLRP3, gasdermin-D, interleukin-1β and interleukin-18. This indicated that 7-nitroindazole and N-acetylcysteine each reversed epileptic cell death. Taken together, our findings suggest that the neuronal nitric oxide synthase/reactive oxygen species pathway is involved in pyroptosis of epileptic cells, and inhibiting neuronal nitric oxide synthase activity or its induced oxidative stress may play a neuroprotective role in epilepsy.

Energy stress activates AMPK leading to metabolic plasticity and therapy resistance in cancer. Here, the authors show that AMPK activation decreases Prospero-related homeobox 1 (PROX1) levels impairing branched amino acid metabolism and tumourigenesis in liver and lung cancer models.

BackgroundOvercrowding in public hospitals, a common issue in many countries, leads to a range of negative outcomes, such as insufficient access to medical services and patient dissatisfaction. Prior literature regarding solutions to reducing hospital overcrowding primarily focuses on organizational-level operational efficiency. However, few studies have investigated the strategies from the individual patient perspective. Specifically, we considered using mobile health and fitness apps to promote users’ health behaviors and produce health benefits, thereby reducing hospital visits. ObjectiveThis study estimated the causal effect of health and fitness app adoption on hospital visits by exploiting the staggered timing of adoption. We also investigated how the effect varied with users’ socioeconomic status and digital literacy. This study provides causal evidence for the effects of health apps, extends the digital health literature, and sheds light on mobile health policies. MethodsThis study used a data set containing health and fitness app use and hospital-related geolocation data of 267,651 Chinese mobile phone users from January to December 2019. We used the difference-in-differences and difference-in-difference-in-differences designs to estimate the causal effect. We performed a sensitivity analysis to establish the robustness of the findings. We also conducted heterogeneity analyses based on the interactions of postadoption indicators with users’ consumption levels, city tiers, and digital literacy. ResultsThe preferred model (difference-in-difference-in-differences) showed a significant decrease in hospital visits after the adoption of health and fitness apps. App adoption led to a 5.8% (P

ObjectiveThis study aimed to investigate the correlation between vaginal microbiota and pregnancy outcomes of women who achieved pregnancy via in vitro fertilization (IVF) in Northern China, and to determine a biomarker for evaluation of the risk of preterm births in these women.MethodsIn total, 19 women from Northern China women who conceived after IVF and 6 women who conceived naturally were recruited in this study. The vaginal samples of the healthy participants were collected throughout pregnancy, that is, during the first, second, and third trimesters. The V3–V4 region of 16S rRNA was used to analyze the vaginal microbiome, and the bioinformatic analysis was performed using QIIME Alpha and Beta diversity analysis.ResultsEither IVF group or Natural conception group, bacterial community diversities and total species number of vagnal samples from who delivered at term were significantly higher than those who delivered before term. Low abundance of vaginal bacteria indicates an increased risk of preterm delivery. Further, more abundant vaginal bacteria was found in first trimesters instead of the next two trimesters. Vignal samples collected during first trimester showed richer differences and more predictive value for pregnancy outcoes. In addition, the diversity of the vaginal bacterial community decreased as the gestational age increased, in all samples. Alloscardovia was only found in participants who conceived after IVF, and the percentage of Alloscardovia in viginal samples of normal delivery group is much higher than the samples from preterm delivery group.Vobrio specifically colonized in vagina of pregnant woman in AFT group (those who conceived after IVF (A), first trimester (F), and delivered at term (T)) and Sporosarcina was detected only in women with AFT and AST (those who conceived after IVF (A), second trimester (S), and delivered at term (T)). These data indicates that Alloscardovia, Vobrio and Sporosarcina have great potential in predicting pregnancy outcomes who pregnanted by vitro fertilizationConclusionsVaginal microbiota were more stable in women who conceived naturally and those who carried pregnancy to term. Oceanobacillus might act as a positive biomarker, whereas Sulfurospirillum and Propionispira may act as negative biomarkers for the risk of preterm birth.

Introduction: The underground drainage-pipe network is one of the vital components of a modern city, and it plays an important role in preventing or mitigating urban flooding. Thus, pipe network data are necessary for simulation of the entire urban rainfall–runoff process. However, pipe network data are sparse or unavailable in most urban areas.Methods: To solve this problem, we developed a novel approximation method that can be used calculate the drainage capacity of the pipe system. This method is named the road-drainage method, and it works under the assumption that the pipe network functions by subtracting the corresponding mass from the water conservation equation only from areas of road. The mass is determined from weir flow formulas together with the road properties and correction for this mass is applied to the rainfall source term.Results: Two test cases were used to compare the performance of the new method with an existing method, under which mass is subtracted from the entire area during the rainfall–runoff process. The results showed that the new method considerably improves the accuracy of simulated peak volume, with an improvement of 2.62%–58.75% compared with the existing method across various scenarios. Moreover, the proposed new method reduces the time shift of the rise and peak of surface inundation by 10–45 min in various scenarios, which reflects a more realistic model of the rainfall–runoff process.Discussion: These results demonstrate that the proposed new method can represent the drainage capability more accurately and is more consistent with reality. The road-drainage method has promising potential for application in urban flood simulation in areas without drainage system data and for the support of large-scale urban hydrologic modeling.

The subcutaneous administration of therapeutic peptides would provide significant benefits to patients. However, subcutaneous injections are limited in dosing volume, potentially resulting in high peptide concentrations that can incur significant challenges with solubility limitations, high viscosity, and stability liabilities. Herein, we report on the discovery that low-shear resonant acoustic mixing can be used as a general method to prepare stable nanoparticles of a number of peptides of diverse molecular weights and structures in water without the need for extensive amounts of organic solvents or lipid excipients. This approach avoids the stability issues observed with typical high-shear, high-intensity milling methods. The resultant peptide nanosuspensions exhibit low viscosity even at high concentrations of >100 mg/mL while remaining chemically and physically stable. An example nanosuspension of cyclosporine nanoparticles was dosed in rats via a subcutaneous injection and exhibited sustained release behavior. This suggests that peptide nanosuspension formulations can be one approach to overcome the challenges with high-concentration peptide formulations.

Background To assess the safety and efficacy of preoperative simulation and three-dimensional (3D) models in the treatment of ulnoradial diaphyses fracture. It was hypothesized that preoperative simulation and 3D printing might significantly shorten the mean operative time, intraoperative bleeding, and intraoperative fluoroscopy. Material and Methods Forty patients with forearm double fracture were divided into 3D printing group and conventional surgery group. Preoperative simulation and 3D printing were performed on patients in the 3D printing group to examine implant reduction and placement as well as preoperative plate/screw size. The operation time, intraoperative bleeding, and frequency of fluoroscopies were recorded. Results In the conventional surgery group, the operative time, intraoperative bleeding, and the frequency of fluoroscopy were 106.2 ± 15.92 min, 61.45 ± 11.33 ml and 5.65 ± 1.23 times, whereas in the 3D printing group, values of all the three parameters were better than those of the conventional surgery group (91.3 ± 14.85 min, 48.6 ± 10.39 ml and 3.85 ± 1.04 times, respectively). The forearm pronation and supination of the 3D printing group improved to 79.55 ± 5.12° and 76.80 ± 3.96°, respectively. In the conventional surgery group, patients also had significant improvement in these indicators, which improved to 78.60 ± 5.18° and 75.4 ± 5.30°. Conclusions The results showed that preoperative simulation and 3D printing can enhance the safety as well as personalization of the surgical process during the treatment of forearm double fracture and therefore holds potential for future application in clinical practice. Trial registry Name of the registry: This study was registered in the Chinese Clinical Trial Registry; Trial registration number: ChiCTR2100045790.

ABSTRACTA magnetorheological fluid (MRF) is a smart composite suspension composed of nonmagnetic liquid and soft magnetic particles. Carrier fluids can considerably influence the performance of MRFs; therefore, to investigate the effect of carrier fluids on MRFs, an SO/IL-MRF was prepared by mixing an ionic liquid (IL) with silicone oil (SO) in this study. Three types of MRF samples were prepared for experiments (pure SO, pure IL, and SO/IL). According to the experimental results, the SO/IL-MRF has better sedimentation stability than those based on pure SO and pure IL. Further, three methods were used to determine the shear yield stresses of the MRFs. The SO/IL-MRF achieved a higher shear yield stress than those of the other two because a network structure is formed between the ionic fragments and the molecular chains of the SO in the SO/IL-MRF. This increases the movement resistance of the particles in the carrier fluid, and it is unlike the mechanism of the IL-enhanced MRF. This work provides new ideas for improving the MRF performance.

Abstract Patients with pancreatic cancer (PC) show dismal prognosis and high mortality. The development of PC is associated with the overactivation of STAT3. Here, we have determined that the non-peptide small molecule Stattic inhibits PC development by targeting STAT3. In vitro, Stattic treatment time- and dose-dependently inhibited proliferation of pancreatic cancer cells (PCCs) by reducing c-Myc expression and enhancing p53 activity. Consequently, p-Rb, cyclin D1, Chk1, and p21 (cell cycle proteins) were downregulated, and PCCs were arrested at the G1 phase, which was also confirmed by decreased Ki67 expression and unaltered PCNA expression. In addition, Stattic-induced mitochondrial-dependent apoptosis by elevating cleaved caspase-3, and Bax, cytochrome C levels, while reducing expression of Bcl-2, which may be regulated by reduced survivin expression. Further studies showed that Stattic exerts its anti-tumor effect via inhibition of STAT3Y705 phosphorylation and nuclear localization in PCCs. In a nude mouse tumorigenesis model, Stattic inhibited PC growth by antagonizing STAT3Y705 phosphorylation. Interleukin-6 used as a molecule agonist to activate STAT3, as well as overexpression of STAT3, could partially reverse Stattic-mediated anti-proliferation and pro-apoptotic effects of PCCs. Thus, these findings indicate that inhibition of STAT3Y705 phosphorylation by Stattic suppresses PCC proliferation and promotes mitochondrial-mediated apoptosis.

Apelin, an endogenous ligand of G-protein coupled receptor (GPCR), is a secreted peptide involved in the development of various tumors. However, the relationship between apelin and non-small cell lung cancer (NSCLC) is not quite clear. This study was designed to investigate the effect and mechanism of apelin on cell proliferation, migration and invasion of NSCLC cells.

To migrate Loss of Control In-flight, the number one cause of aviation fatalities, pilots need to undergo upset prevention and recovery training with flight simulators. The fidelity of a moving base flight simulator is greatly dependent on the washout algorithm of the Stewart platform, which may reach the workspace limits when simulating the aircraft recovery from upset conditions. In this paper, a washout algorithm optimal design method based on the model predictive control technique is proposed for flight simulator upset prevention and recovery training. The parameters of the washout algorithm are calculated directly based on the platform model, and the system limits are explicitly taken into account. The human perception model is incorporated into the optimization problem, for which the objective is to minimize the pilot’s perceived motion mismatch between the real flight and the simulator training. Simulations are conducted and compared with the classical filter-based washout algorithm. Responses of the flight simulator model show that the proposed method can improve the motion cueing effect when the aircraft is in upset conditions.

Androgens and chronic inflammation, which play essential roles in the development of benign prostatic hyperplasia (BPH), are considered to be important factors in disorders of prostate homeostasis. These two factors may lead to pathological hyperplasia in the prostate transition zone of patients with BPH. However, few studies have examined the mechanism of how dihydrotestosterone (DHT) affects chronic inflammation in prostate tissue during the progression of BPH. This study examined the performance of DHT in lipopolysaccharide-treated M1 macrophages and the subsequent effects on the proliferation of prostate stromal and epithelial cells. We found that DHT increased secretion of the pro-inflammatory factor tumor necrosis factor (TNF)-α from M1 macrophages differentiated from THP-1 cells. The supernatant of M1 macrophages promoted the proliferation of WPMY-1 prostate stromal cells by upregulating B-cell lymphoma-extra large (Bcl-xL) and cellular Myc (c-Myc) levels by activating TNF-α-mediated nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways. Moreover, this supernatant increased the expression of androgen receptor in WPMY-1 cells, which was TNF-α-independent. Additionally, TNF-α protein expression was significantly higher in patients with BPH and a large prostate volume than that in those with a small prostate volume. Further analysis showed that higher serum testosterone combined with prostate-specific androgen concentrations was related to TNF-α expression. This study suggests that DHT modulates the inflammatory environment of BPH by increasing TNF-α expression from lipopolysaccharide-treated M1 macrophages and promotes the proliferation of prostate stromal cells. Targeting TNF-α, but not DHT, may be a promising strategy for patients with BPH.

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (EGFR-TKIs) provide dramatic response to patients with advanced EGFR-mutant non-small cell lung cancer (NSCLC). However, the use of neoadjuvant therapy with EGFR-TKIs in EGFR-mutant NSCLC remains controversial, especially in pulmonary sarcomatoid carcinoma (PSC). One patient with initially unresectable stage III (cT4N0M0) PSC was found to carry EGFR mutation by the next generation sequencing. After neoadjuvant therapy with osimertinib plus chemotherapy, radical resection of the right upper lung lesion was achieved, and the pathological results reached pathological complete response (pCR). To the best of our knowledge, this is the first report of an EGFR-mutant patient with initially unresectable stage III PSC achieved pCR by neoadjuvant therapy with osimertinib plus chemotherapy. Therefore, neoadjuvant therapy with EGFR-TKIs may be a viable option for EGFR-mutant PSC patients.

BackgroundBone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.MethodsThe study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.ResultsOur prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083–0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979–0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.ConclusionOur developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

The rapid development of tissue engineering makes it an effective strategy for repairing cartilage defects. The significant advantages of injectable hydrogels for cartilage injury include the properties of natural extracellular matrix (ECM), good biocompatibility, and strong plasticity to adapt to irregular cartilage defect surfaces. These inherent properties make injectable hydrogels a promising tool for cartilage tissue engineering. This paper reviews the research progress on advanced injectable hydrogels. The cross-linking method and structure of injectable hydrogels are thoroughly discussed. Furthermore, polymers, cells, and stimulators commonly used in the preparation of injectable hydrogels are thoroughly reviewed. Finally, we summarize the research progress of the latest advanced hydrogels for cartilage repair and the future challenges for injectable hydrogels.

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.