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Abstract Background The objective was to evaluate the level of neutrophil extracellular traps (NETs) in patients with chronic kidney disease (CKD) and periodontitis, and to explore the relationship between NETs and both diseases. Methods 63 CKD and 40 non-CKD participants were recruited and underwent periodontal examination, among which 35 early CKD patients underwent periodontal therapy. The concentrations of NETs were determined by dsDNA assay in gingival crevicular fluid (GCF) and plasma, and by flow cytometry or immunofluorescence assay in blood and gingival tissues. The correlations between NETs and clinical parameters were analyzed. The influence of periodontal therapy on periodontitis, CKD and NETs concentrations was also evaluated. Results CKD patients had higher concentrations of NETs in plasma than non-CKD patients, and NETs concentrations were also increased in both GCF and plasma of patients with periodontitis than that of periodontally healthy patients. NETs concentrations were positively correlated with increased clinical parameters of CKD and periodontitis. The positive correlation between CKD and periodontitis was demonstrated. Moreover, periodontal therapy ameliorated periodontitis and CKD, and reduced NETs concentrations in GCF of patients. Conclusions This study revealed that NETs might be a possible bridge between periodontitis and CKD, and suggested the potential target for therapy.

Abstract Background Novel therapeutic targets are urgently needed for treating drug-resistant non-small cell lung cancer (NSCLC) and overcoming drug resistance to molecular-targeted therapies. Regulator of G protein signaling 20 (RGS20) is identified as an upregulated factor in many cancers, yet its specific role and the mechanism through which RGS20 functions in NSCLC remain unclear. Our study aimed to identify the role of RGS20 in NSCLC prognosis and delineate associated cellular and molecular pathways. Methods Immunohistochemistry and lung cancer tissue microarray were used to verify the expression of RGS20 between NSCLC patients. CCK8 and cell cloning were conducted to determine the proliferation ability of H1299 and Anip973 cells in vitro. Furthermore, Transcriptome sequencing was performed to show enrichment genes and pathways. Immunofluorescence was used to detect the translocation changes of YAP to nucleus. Western blotting demonstrated different expressions of autophagy and the Hippo-PKA signal pathway. In vitro and in vivo experiments verified whether overexpression of RGS20 affect the proliferation and autophagy of NSCLC through regulating the Hippo pathway. Results The higher RGS20 expression was found to be significantly correlated with a poorer five-year survival rate. Further, RGS20 accelerated cell proliferation by increasing autophagy. Transcriptomic sequencing suggested the involvement of the Hippo signaling pathway in the action of RGS20 in NSCLC. RGS20 activation reduced YAP phosphorylation and facilitated its nuclear translocation. Remarkably, inhibiting Hippo signaling with GA-017 promoted cell proliferation and activated autophagy in RGS20 knock-down cells. However, forskolin, a GPCR activator, increased YAP phosphorylation and reversed the promoting effect of RGS20 in RGS20-overexpressing cells. Lastly, in vivo experiments further confirmed role of RGS20 in aggravating tumorigenicity, as its overexpression increased NSCLC cell proliferation. Conclusion Our findings indicate that RGS20 drives NSCLC cell proliferation by triggering autophagy via the inhibition of PKA-Hippo signaling. These insights support the role of RGS20 as a promising novel molecular marker and a target for future targeted therapies in lung cancer treatment.

Abstract Background The reduced treatment time of dental implants with immediate loading protocol is an appealing solution for dentists and patients. However, there remains a significant risk of early peri-implant bone response following the placement of immediately loaded implants, and limited information is available regarding loading directions and the associated in vivo characteristics of peri-implant bone during the early stages. This study aimed to investigate the effects of immediate loading directionality on the expression of mechanical sensing protein PIEZO1 and the healing process of peri-implant bone in the early stage. Methods Thirty-two implants were inserted into the goat iliac crest models with 10 N static lateral immediate loading applied, followed by histological, histomorphological, immunohistochemical, X-ray microscopy and energy dispersive X-ray spectroscopy evaluations conducted after 10 days. Results From evaluations at the cellular, tissue, and organ levels, it was observed that the expression of mechanical sensing protein PIEZO1 in peri-implant bone was significantly higher in the compressive side compared to the tensile side. This finding coincided with trends observed in interfacial bone extracellular matrix (ECM) contact percentage, bone mass, and new bone formation. Conclusions This study provides a novel insight into the immediate loading directionality as a potential influence factor for dental implant treatments by demonstrating differential effects on the mechanical sensing protein PIEZO1 expression and related early-stage healing processes of peri-implant bone. Immediate loading directions serve as potential therapeutic influence factors for peri-implant bone during its early healing stage.

Hydraulic jump is a common physical phenomenon in the field of hydraulic engineering. The essence of hydraulic jump is the conversion and dissipation of a large amount of energy due to the interaction between vortex structures, mainly released in the form of turbulence and water waves. This process significantly reduces the kinetic energy of water flow, thereby mitigating downstream erosion and protecting hydraulic structures, which in turn extends their service life. As a crucial factor in the energy dissipation design of discharge structures, the length of the hydraulic jump is influenced by various factors, including flow velocity, upstream and downstream water depths, riverbed roughness height, and Froude number. In this study, we applied dimensional analysis to identify the key parameters influencing hydraulic jumps on the dataset provided by literature. We utilized a multi-task learning strategy, incorporating a shared feature extraction layer for characteristic modeling of hydraulic jumps within Physics-Informed Neural Networks (PINNs). Furthermore, we compared the performance of PINNs with other data-driven models such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Transformers. The results demonstrated that these models are effective in estimating the length of hydraulic transitions and distinguishing between steady and unsteady hydraulic jump processes. Notably, the PINNs model exhibited better performance than other models, achieving an $\mathbf {R^{2}}$ score of $\mathbf {0.8818}$ , RMSE of 4.4627(cm), MAE of 3.3784(cm), precision of $\mathbf {0.9677}$ and recall of 0.9677 on the test set. These findings are significant for elucidating the characteristics and effects of hydraulic jumps in hydraulic structures, providing a scientific basis for the safe operation and design of practical hydraulic engineering projects.

Objectives: Environmental exposure may affect the telomere length (TL) of newborns, which is considered as an early biomarker indicating susceptibility for later life diseases. However, the effects of prenatal organochlorine pesticide (OCP) exposure on newborn TL remain unclear. This study aimed to investigate the association between prenatal exposure levels of OCPs during pregnancy and TL in neonatal cord blood. Methods: A total of 168 mother–infant pairs from a birth cohort in Wuhan, China, were included this study. The concentrations of hexachlorocyclohexanes (HCHs, including β-HCH, α-HCH, and γ-HCH), p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT) and its metabolites (p,p’-dichlorodiphenyldichloroethane, p,p’-DDD; p,p’-dichlorodiphenyldichloroethylene, p,p’-DDE) were measured in cord blood. The associations between the OCPs and the TL in newborns were analyzed by a generalized linear regression model. Stratified analyses by newborn sex, maternal gestational weight gain, and pregnancy body mass index (BMI) were performed to evaluate if the associations were modified by these factors. Results: The detection rates of various OCPs ranged from 50.9% to 100.0%. The median concentration of p,p’-DDE was the highest (33.90 ng/g lipid), followed by β-HCH (8.67 ng/g lipid), and the median concentrations of the other OCPs were between 0.12 and 0.33 ng/g lipid. Among the all newborns, a two-fold increase in the γ-HCH concentration in the cord blood was significantly associated with a 0.024 (95% CI: −0.041, −0.007) decrease in the TL. After stratification by newborn sex, the inverse association between γ-HCH and the TL was only statistically significant in boys, but not in girls (P for interaction Conclusions: Prenatal exposure to OCPs during pregnancy was associated with a decreased neonatal telomere length, which may be affected by the newborn sex and pre-pregnancy BMI. These findings may provide new insights into the mechanisms underlying OCP-induced adverse health effects.

Immunotherapy for non-small cell lung cancer (NSCLC) has advanced considerably over the past two decades. In particular, immune checkpoint inhibitors are widely used for treating NSCLC. However, the overall cure and survival rates of patients with NSCLC remain low. Therefore, continuous investigation into complementary treatments is necessary to expand the clinical advantages of immunotherapy to a larger cohort of patients with NSCLC. Recently, the distinctive role of the gut microbiota (GM) in the initiation, progression, and dissemination of cancer has attracted increasing attention. Emerging evidence indicates a close relationship between the gut and lungs, known as the gut–lung axis (GLA). In this review, we aim to provide a comprehensive summary of the current knowledge regarding the connection between the GM and the outcomes of immunotherapy in NSCLC, with particular focus on the recent understanding of GLA. Overall, promising GM-based therapeutic strategies have been observed to improve the effectiveness or reduce the toxicity of immunotherapy in patients with NSCLC, thus advancing the utilization of microbiota precision medicine.

BackgroundThe role of certain biomarkers in the development of single cardiometabolic disease (CMD) has been intensively investigated. Less is known about the association of biomarkers with multiple CMDs (cardiometabolic multimorbidity, CMM), which is essential for the exploration of molecular targets for the prevention and treatment of CMM. We aimed to systematically synthesize the current evidence on CMM-related biomarkers.MethodsWe searched PubMed, Embase, Web of Science, and Ebsco for relevant studies from inception until August 31st, 2022. Studies reported the association of serum/plasma biomarkers with CMM, and relevant effect sizes were included. The outcomes were five progression patterns of CMM: (1) no CMD to CMM; (2) type 2 diabetes mellitus (T2DM) followed by stroke; (3) T2DM followed by coronary heart disease (CHD); (4) T2DM followed by stroke or CHD; and (5) CHD followed by T2DM. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies. A meta-analysis was conducted to quantify the association of biomarkers and CMM.ResultsA total of 68 biomarkers were identified from 42 studies, which could be categorized into five groups: lipid metabolism, glycometabolism, liver function, immunity, and others. Lipid metabolism biomarkers were most reported to associate with CMM, including TC, TGs, HDL-C, LDL-C, and Lp(a). Fasting plasma glucose was also reported by several studies, and it was particularly associated with coexisting T2DM with vascular diseases. According to the quantitative meta-analysis, HDL-C was negatively associated with CHD risk among patients with T2DM (pooled OR for per 1 mmol/L increase = 0.79, 95% CI = 0.77–0.82), whereas a higher TGs level (pooled OR for higher than 150 mg/dL = 1.39, 95% CI = 1.10–1.75) was positively associated with CHD risk among female patients with T2DM.ConclusionCertain serum/plasma biomarkers were associated with the progression of CMM, in particular for those related to lipid metabolism, but heterogeneity and inconsistent findings still existed among included studies. There is a need for future research to explore more relevant biomarkers associated with the occurrence and progression of CMM, targeted at which is important for the early identification and prevention of CMM.

The supercritical carbon dioxide (S–CO2) Brayton cycle is considered a promising power generation system because of its high efficiency, simple layout, and compact configuration. Furthermore, it is applicable to thermal and nuclear power generation. Because the key end equipment of this system is exposed to a high-temperature and high-pressure S–CO2 environment for a long duration, the high-temperature corrosion resistance of this equipment has been investigated extensively. This paper provides a review of recent studies pertaining to the corrosion behavior of candidate materials for high-temperature components in the S–CO2 Brayton cycle system. Additionally, the effects of internal microstructure, metal element content and external environment (temperature, pressure, impurities, etc.) on the corrosion behavior of alloys, including oxidation and carburizing corrosion are analyzed. Problems pertaining to the corrosion behavior of candidate materials are highlighted, and possible areas for future research are proposed.

The calorie and taste choices of food have been shown to be related to the external environment, including music. Previous studies have mostly focused on manipulating basic auditory parameters, with few scholars exploring the impact of complex musical parameters on food selection. This study explored the effects of different kinds of music (classical, rock, jazz, and hip-hop) on food liking based on the calories (high and low) and taste (sweet and salty) using event-related potentials (ERPs). Twenty-four participants (8 males, 16 females) were recruited from Southwest University, China to participate in the food liking task using a Likert seven-point rating and simultaneously recording EEG signals (N2, P2, N3, and LPC). This study used repeated-measures analyses of covariances and found that the score of the high-calorie foods was greater than that of the low-calorie foods. Additionally, results revealed that the score in classical music was greatest for sweet foods, while there was no difference among music kinds in the salty foods. The ERP results showed that P2 amplitudes were greater for sweet foods than those for the salty foods. N2 amplitudes for the salty foods were greater than those for the sweet foods during rock music; in addition, N2 amplitudes during hip-hop music were greatest for sweet foods. However, N2 amplitudes during rock music were the greatest for salty foods. The results also revealed that N2 amplitudes during hip-hop music were greater than those during jazz music. This study provides unique operational insights for businesses.

Stratospheric airships with a solar array have demonstrated overwhelming superiority in many aspects, such as earth observation, meteorological survey, and communication relay. The solar array supplies sufficient power for the airship to be in flight for months, but excessive heat is also transferred to the airship, causing high overpressure of inner gas. However, the optimal arrangement of the insulation layer on the airship has not been investigated. The theoretical method, including the geometry, thermal, and energy models, is developed and validated. The distribution of the temperatures and power of the solar cells, with different installation angles on the curved surface, is investigated. The thickness of insulation layer has a significant effect on the solar output power and internal pressure of the airship. An optimized configuration of the insulation structure is proposed, in order to improve the total output energy of solar array. The optimized configuration of insulations helps to reduce the structural mass by 24.9% and increase the payload mass by 9%. Moreover, the optimized arrangement improves the output energy of solar array in a year, and the maximum improvement is 8.2% on the winter solstice. The work displays the optimization of the thermal insulation layer for the stratospheric airship with a solar array, in order to improve the everyday energy acquirement during flight in a year.

Quaternary biogenic gas fields in the Sanhu area of the Qaidam Basin are characterized by gentle structures, shallow burial, weak compaction, high porosity and great bulk thickness, which determine their unique geophysical anomalies, such as the locally low gravity, micro-magnetic high frequency anomaly, seismic event push-down and attribute anomaly. An integrated geophysical processing and interpretation scheme is put forward for shallow biogenic gas prediction in this area. Amplitude preservation is performed for 1:100000 gravity data and the processing results are correlated with known gas distributions and aeromagnetic results, remote sensing and seismic hydrocarbon detection. This study shows that the residual gravity low anomaly has a good correlation with the gas field and, as an important feature of the biogenic gas field, the amplitude of residual gravity low anomaly is directly proportional to the measure of gas reservoir. Based on the residual gravity low anomaly and other exploration data, the distribution of gas reservoirs in the Sanhu area is characterized by vertical overlapping, lateral zoning and local enrichment. Residual gravity anomaly, in combination with regional geologic data, reveals three favorable zones, one fairly favorable zone (low gas production zone), five possibly favorable zones and four favorable targets. Key words: Qaidam Basin, Sanhu area, Quaternary, biogenic gas, gravity, magnetic, seismic, geophysical integration, exploration direction

Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a raster-based DWT (discrete wavelet transform) method for thunderstorm identification, this method uses DWT, CFSFD (clustering algorithm for fast search and finding density peaks) algorithm and ADTD (active divectory topology diagrammer) lightning location data for thunderstorm identification. The advantage of this method is that it supports different spatial resolutions and can identify any shape and number of thunderstorms at the same time and in the same area. It is effective in eliminating some of cluttered, scattered lightning data and extracting dense areas of thunderstorms. Furthermore, the method has a time complexity of O(n), and the computational efficiency is significantly better than the current TITAN (thunderstorm identification, tracking, analysis, and nowcasting) algorithm, which provides a good basis for subsequent extrapolation studies of thunderstorms. [ABSTRACT FROM AUTHOR]

In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model can effectively capture the atypical behavioral approach and operation of the author. As such, it is able to detect various types of misconduct, thereby improving the accuracy of detection when combined with a text content analysis. The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct. First, the effect of scholars’ behavioral features on the model are considered and analyzed. Second, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars. Finally, the text features of the papers are combined with the scholars’ behavioral data to improve recognition precision. Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall. [ABSTRACT FROM AUTHOR]