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Lymphocyte subsets are the most intuitive expression of the body's immune ability, and the lymphocyte-to-monocyte ratio (LMR) also clearly reflect the degree of chronic inflammation activity. The purpose of this study is to investigate their predictive value of lymphocyte subsets and LMR to neoadjuvant therapy (NAT) efficacy in breast cancer patients. In this study, lymphocyte subsets and LMR were compared between breast cancer patients (n = 70) and benign breast tumor female populations (n = 48). Breast cancer patients were treated with NAT, and the chemotherapy response of the breast was evaluated using established criteria. The differences in lymphocyte subsets and LMR were also compared between pathological complete response (pCR) and non-pCR patients before and after NAT. Finally, data were analyzed using SPSS. The analytical results demonstrated that breast cancer patients showed significantly lower levels of CD3 + T cells, CD4 + T cells, CD4 + /CD8 + ratio, NK cells, and LMR compared to benign breast tumor women (P < 0.05). Among breast cancer patients, those who achieved pCR had higher levels of CD4 + T cells, NK cells, and LMR before NAT (P < 0.05). NAT increased CD4 + /CD8 + ratio and decreased CD8 + T cells in pCR patients (P < 0.05). Additionally, both pCR and non-pCR patients exhibited an increase in CD3 + T cells and CD4 + T cells after treatment, but the increase was significantly higher in pCR patients (P < 0.05). Conversely, both pCR and non-pCR patients experienced a decrease in LMR after treatment. However, this decrease was significantly lower in pCR patients (P < 0.05). These indicators demonstrated their predictive value for therapeutic efficacy. In conclusion, breast cancer patients experience tumor-related immunosuppression and high chronic inflammation response. But this phenomenon can be reversed to varying degrees by NAT. It has been found that lymphocyte subsets and LMR have good predictive value for pCR. Therefore, these markers can be utilized to identify individuals who are insensitive to NAT early on, enabling the adjustment of treatment plans and achieving precise breast cancer treatment. [ABSTRACT FROM AUTHOR]

Different from the Qaidam basin with about 320 billion m3 microbial gas, only limited microbial gases were found from the Junggar basin with similarly abundant type III kerogen. To determine whether microbial gases have not yet identified, natural gas samples from the Carboniferous to Cretaceous in the Junggar basin have been analyzed for chemical and stable isotope compositions. The results reveal some of the gases from the Mahu sag, Zhongguai, Luliang and Wu-Xia areas in the basin may have mixed with microbial gas leading to straight ethane to butane trends with a "dogleg" light methane in the Chung's plot. Primary microbial gas from degradation of immature sedimentary organic matter is found to occur in the Mahu sag and secondary microbial gas from biodegradation of oils and propane occurred in the Zhongguai, Luliang and Beisantai areas where the associated oils were biodegraded to produce calcites with δ13C values from + 22.10‰ to + 22.16‰ or propane was biodegraded leading to its 13C enrichment. Microbial CH4 in the Mahu sag is most likely to have migrated up from the Lower Wuerhe Formation coal-bearing strata by the end of the Triassic, and secondary microbial gas in Zhongguai and Beisantan uplifts may have generated after the reservoirs were uplifted during the period of the Middle and Late Jurassic. This study suggests widespread distribution of microbial gas and shows the potential to find large microbial gas accumulation in the basin. [ABSTRACT FROM AUTHOR]

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species. [ABSTRACT FROM AUTHOR]

Research into new composites utilizing cubic boron nitride (PcBN) shows promise for enhancing cutting tool performance. The unique properties of these materials stem from the addition of microfibers made of refractory compounds to their structure. This study looks at developing two-layer composites based on cBN group BL, reinforced with SiCw and Al2O3w microfibers. The goal is to improve tool stability when cutting hardened steels with impact loads. PcBN composite samples were made by sintering a mixture of cBN powder with bundles and microfibers under 7.7 GPa pressure. Bond material selection was based on analyzing the relationship between Poisson's ratio (η) and plasticity parameter (G/B). The density, Young's modulus, Poisson's ratio, and hardness of the composites were determined, and the microstructure of samples with TiCN bond was studied. Tool-life tests were conducted on two-layer cutting inserts made of PcBN reinforced with SiCw and Al2O3w microfibers during the machining of hardened KhVG steel (HRC 55) under impact loads at cutting speeds of 100 and 200 m/min. [ABSTRACT FROM AUTHOR]

In order to investigate the strategy choice of each player in capacity sharing, the article constructs a tripartite game model based on capacity provider-capacity demander-government, introduces the prospect theory and conducts numerical simulation analysis using MATLAB. The results show that capacity sharing in the manufacturing industry is related to three parties: capacity providers, capacity demanders and the government, and their strategies in the game process influence each other; the sensitivity of capacity providers and capacity demanders is higher than that of the government; the increase of risk-return coefficient and loss-avoidance coefficient is conducive to the evolution of subjects to the ideal state. [ABSTRACT FROM AUTHOR]

To elevate the accuracy of diagnostic results, CNV-seq is usually performed simultaneously with karyotyping or QF-PCR. Although several studies have investigated the performance of the combined use of CNV-seq with karyotyping or QF-PCR, there have been no reports focusing on the comparison of these 2 diagnostic strategies. In our study, 2507 pregnant women were included to investigate these 2 strategies. The detection rates of foetal genetic abnormalities and turnaround time were compared between these 2 groups. Moreover, the detection rates of foetal genetic abnormalities in different indications were analyzed. Our results unveiled that the detection rates of numerical chromosomal abnormalities were nearly the same in these 2 groups. In addition to numerical chromosomal abnormalities, 39 balanced karyotypic changes and chromosome polymorphisms were detected via the combined use of karyotyping and CNV-seq. Further investigation revealed that the vast majority of these karyotypic changes were inherited from parents. Compared with the karyotyping group, the combination of QF-PCR and CNV-seq reduced the reporting time from 31.593 ± 4.944 days to 11.460 ± 4.894 days. Meanwhile, NIPT, maternal serum screening and ultrasound scan significantly improved the detection of foetal genetic abnormalities. In conclusion, our results revealed that parental karyotyping is a useful supplementary method for CNV-seq and systematic prenatal examinations improved the detection of foetal genetic defects. [ABSTRACT FROM AUTHOR]

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes—homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria. [ABSTRACT FROM AUTHOR]

Exploring a modified stage (mStage) for pN0 colon cancer patients. 39,637 pN0 colon cancer patients were collected from the SEER database (2010–2015) (development cohort) and 455 pN0 colon cancer patients from the Second Affiliated Hospital of Harbin Medical University (2011–2015) (validation cohort). The optimal lymph nodes examined (LNE) stratification for cancer-specific survival (CSS) was obtained by X-tile software in the development cohort. LNE is combined with conventional T stage to form the mStage. The novel N stage was built based on the LNE (N0a: LNE ≥ 26, N0b: LNE = 11–25 and N0c: LNE ≤ 10). The mStage include mStageA (T1N0a, T1N0b, T1N0c and T2N0a), mStageB (T2N0b, T2N0c and T3N0a), mStageC (T3N0b), mStageD (T3N0c, T4aN0a and T4bN0a), mStageE (T4aN0b and T4bN0b) and mStageF (T4aN0c and T4bN0c). Cox regression model showed that mStage was an independent prognostic factor. AUC showed that the predictive accuracy of mStage was better than the conventional T stage for 5-year CSS in the development (0.700 vs. 0.678, P < 0.001) and validation cohort (0.649 vs. 0.603, P = 0.018). The C-index also showed that mStage had a superior model-fitting. Besides, calibration curves for 3-year and 5-year CSS revealed good consistencies between observed and predicted survival rates. For pN0 colon cancer patients, mStage might be superior to conventional T stage in predicting the prognosis. [ABSTRACT FROM AUTHOR]

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

To investigate the effects and mechanism of Vitamin D receptor (VDR) signaling on arteriovenous fistula (AVF) endothelial cell injury. Venous tissues of AVF stenosis patients were collected and analyzed, vascular morphology, reactive oxygen species (ROS), and the expression of VDR, P66Shc, fibronectin (FN), collagen-1 (Col-1) were detected. In addition, human umbilical vein endothelial cells (HUVECs) was used in in vitro studies. HUVECs was incubated with transforming growth factor-beta (TGF-β, 50 ng/ml). Aditionally, paricalcitol, VDR overexpression plasmid and Pin1 inhibitor Juglone were used to investigate the regulatory mechanism of VDR in mitochondrial ROS. The parameters of ROS (e.g. MitoSox) and the expression of FN, Col-1 were tested. Moreover, the mitochondrial translocation of P66Shc was analyzed. The expression of VDR was obviously decreased in the venous tissues of AVF stenosis patients. On the contrary, the expression of P66Shc, P-P66Shc, FN, Col-1 and 8-OHdG were increased significantly in the venous tissues of AVF stenosis patients (P < 0.05). In line with this, the level of mitochondrial ROS and the expression of P66Shc, P-P66Shc, FN, Col-1 increased obviously in HUVECs cells under TGF-β condition. Both VDR over-expression plasmid and Pin1 inhibitor Juglone could alleviate TGF-β induced endothelial injury. Mechanistically, VDR overexpression plasmid and Juglone could inhibit the expression of Pin1, and then restrain P66Shc mitochondrial translocation, eventually reduce the level of mitochondrial ROS. Our research indicated that activation of VDR could alleviate venous endothelial cell dysfunction through inhibiting Pin1-mediated mitochondrial translocation of P66Shc and consequently reducing mitochondrial ROS. It suggested that VDR signaling might be an effective target for AVF stenosis treatment. [ABSTRACT FROM AUTHOR]

Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3−-N, PO43− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3−-N, and PO43−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture. [ABSTRACT FROM AUTHOR]

Describing the material flow stress and the associated uncertainty is essential for the plastic stochastic structural analysis. In this context, a data-driven approach-heteroscedastic sparse Gaussian process regression (HSGPR) with enhanced efficiency is introduced to model the material flow stress. Different from other machine learning approaches, e.g. artificial neural network (ANN), which only estimate the deterministic flow stress, the HSGPR model can capture the flow stress and its uncertainty simultaneously from the dataset. For validating the proposed model, the experimental data of the Al 6061 alloy is used here. Without setting a priori assumption on the mathematical expression, the proposed HSGPR-based flow stress model can produce a better prediction of the experimental stress data than the ANN model, the conventional GPR model, and Johnson Cook model at elevated temperatures. After the HSGPR-based flow stress model is implemented into finite element analysis, two numerical examples with synthetic material properties are performed to demonstrate the model's capability in stochastic plastic structural analysis. The results have shown that with sufficient data, the distribution of the structural load carrying capacity at elevated temperatures and the variation of load–displacement curves during the loading and unloading processes can be accurately predicted by the HSGPR-based flow stress model. [ABSTRACT FROM AUTHOR]

CircRNAs are a class of highly stable noncoding RNAs that play an important role in the progression of many diseases, especially cancer. In this study, high-throughput sequencing was used to screen for abnormally expressed circRNAs, and we found that circGPC3 was overexpressed in HCC tissues. However, the underlying mechanism of circGPC3 in the development and metastasis of hepatocellular carcinoma (HCC) remains unknown. In our study, we found that circGPC3 was significantly upregulated in HCC tissues and cells and that its overexpression was positively correlated with overall survival, TNM stage and lymph node metastasis. In vivo and in vitro experiments showed that circGPC3 knockdown repressed HCC cell migration, invasion and proliferation and promoted apoptosis. Mechanistically, circGPC3 promoted HCC proliferation and metastasis through the miR-578/RAB7A/PSME3 axis. Our results demonstrate that circGPC3 contributes to the progression of HCC and provides an intervention target for HCC. [ABSTRACT FROM AUTHOR]

Congenital adrenal hyperplasia (CAH) is a rare X-linked recessive inherited disease that is considered a major cause of steroidogenesis disorder and is associated with variants or complete deletion of the NR0B1 gene. The DAX-1 protein (encoded by NR0B1) is a vertebrate-specific orphan nuclear receptor and is also a transcriptional factor for adrenal and reproductive development. CAH usually causes adrenal insufficiency in infancy and early childhood, leading to hypogonadotropic hypogonadism in adulthood; however, few adult cases have been reported to date. In this study, we examined a Chinese family with one adult patient with CAH, and identified a putative variant of NR0B1 gene via next-generation sequencing (NGS), which was confirmed with Sanger sequencing. A novel nonsense variant (c.265C>T) was identified in the NR0B1 gene, which caused the premature termination of DAX-1 at residue 89 (p.G89*). Furthermore, mutant NR0B1 gene displayed a partial DAX-1 function, which may explain the late pathogenesis in our case. Additionally, qPCR revealed the abnormal expression of four important genes identified from ChIP-seq, which were associated with energy homeostasis and steroidogenesis, and were influenced by the DAX-1 mutant. In addition, hormone disorders can be caused by DAX-1 mutant and partially recovered by siRNA of PPARGC1A. Herein, we identified a novel nonsense variant (c.265C>T) of NR0B1 in a 24-year-old Chinese male who was suffering from CAH. This mutant DAX-1 protein was found to have disordered energy homeostasis and steroidogenesis based on in vitro studies, which was clinically consistent with the patient's phenotypic features. [ABSTRACT FROM AUTHOR]

Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is associated with osteosarcopenia, and because a physical decline in patients correlates with an increased risk of morbidity, an improvement of the musculoskeletal system is expected to improve morbi-mortality. We recently uncovered that the intestinal hormone Fibroblast Growth Factor 19 (FGF19) is able to promote skeletal muscle mass and strength in rodent models, in addition to its capacity to improve glucose homeostasis. Here, we tested the effects of a treatment with recombinant human FGF19 in a CKD mouse model, which associates sarcopenia and metabolic disorders. In 5/6 nephrectomized (5/6Nx) mice, subcutaneous FGF19 injection (0.1 mg/kg) during 18 days increased skeletal muscle fiber size independently of food intake and weight gain, associated with decreased gene expression of myostatin. Furthermore, FGF19 treatment attenuated glucose intolerance and reduced hepatic expression of gluconeogenic genes in uremic mice. Importantly, the treatment also decreased gene expression of liver inflammatory markers in CKD mice. Therefore, our results suggest that FGF19 may represent a novel interesting therapeutic strategy for a global improvement of sarcopenia and metabolic complications in CKD. [ABSTRACT FROM AUTHOR]

Although the smoking rate of human immunodeficiency virus (HIV), hepatitis B virus (HBV) or hepatitis C virus (HCV) infected people was much higher than that of the general population, smoking cessation interventions have long been ineffective. We aimed to examine the estimates of prevalence, time-trend, and association of smoking among people living with HIV, HBV, or HCV. This cohort was composed of 32,115 individuals from the NHANES database (1999–2018) and they were collected in the US. The time trend analysis of smoking and quitting rates was conducted using different years of survey follow-up and different infected groups. Multivariable logistic regression analysis was used to identify the risk factors related to smoking behavior of these infected people. Compared to non-infected smokers, infected smokers were more likely to be older (aged 30–39, OR = 9.92, CI 6.07–16.21; aged 40–49,OR = 3.51, CI 2.49–4.94), males (1.99, 1.54–2.55), lower education and economic level (1.78, 1.39–2.29; 2.05, 1.59–2.65), unemployed (1.63, 1.21–2.20), suffering depression (1.35, 1.05–1.72), and drug users (7.65, 5.04–11.59). Taken together, our study showed that these complex psychosocial characteristics and unhealthy behavioral factors might be major independent risk factors for increasing smoking rate and decreasing smoking cessation rate among these infected people. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients has been observed and trial populations may not be representative of clinical practice. Predictive models can help understand heterogeneity of treatment effects, identify predictors of meaningful clinical outcomes, and may inform treatment decisions. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database. Model performance was evaluated using metrics including concordance index (c-index), and we used explainability techniques to identify significant predictors of overall survival (OS) and progression-free survival (PFS). The ML model achieved c-indices of 0.672 and 0.612 for OS and PFS, respectively, and Kaplan–Meier survival curves showed significant differences between low- and high-risk groups for OS and PFS (both log-rank test p < 0.0001). Identified predictors were mostly consistent with the published literature and/or clinical expectations and largely overlapped for OS and PFS; Eastern Cooperative Oncology Group performance status, programmed cell death-ligand 1 expression levels, and serum albumin were among the top 5 predictors for both outcomes. Prospective and independent data set evaluation is required to confirm these results. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients has been observed and trial populations may not be representative of clinical practice. Predictive models can help understand heterogeneity of treatment effects, identify predictors of meaningful clinical outcomes, and may inform treatment decisions. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database. Model performance was evaluated using metrics including concordance index (c-index), and we used explainability techniques to identify significant predictors of overall survival (OS) and progression-free survival (PFS). The ML model achieved c-indices of 0.672 and 0.612 for OS and PFS, respectively, and Kaplan–Meier survival curves showed significant differences between low- and high-risk groups for OS and PFS (both log-rank test p < 0.0001). Identified predictors were mostly consistent with the published literature and/or clinical expectations and largely overlapped for OS and PFS; Eastern Cooperative Oncology Group performance status, programmed cell death-ligand 1 expression levels, and serum albumin were among the top 5 predictors for both outcomes. Prospective and independent data set evaluation is required to confirm these results. [ABSTRACT FROM AUTHOR]

Jinshui Huanxian granules (JSHX) is a clinical Chinese medicine formula used for treating pulmonary fibrosis (PF). However, the effective components and molecular mechanisms of JSHX are still unclear. In this study, a combination approach using ultra-high performance liquid chromatography-Orbitrap Fusion mass spectrometry (UPLC-Orbitrap Fusion MS) integrated with network pharmacology was followed to identify the components of JSHX and the underlying molecular mechanisms against PF. UPLC-Orbitrap Fusion MS was used to identify the components present in JSHX. On the basis of the identified components, we performed target prediction using the SwissTargetPrediction database, protein–protein interaction (PPI) analysis using STRING database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using Metascape and constructed a component-target-pathway network using Cytoscape 3.7.2. Molecular docking technology was used to verify the affinity between the core components and targets. Finally, the pharmacological activities of three potentially bioactive components were validated in transforming growth factor β1 (TGF-β1)-induced A549 cell fibrosis model. As a result, we identified 266 components, including 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components. Of these, 90 validated components were predicted to act on 172 PF-related targets and they exhibited therapeutic effects against PF via regulation of cell migration, regulation of the mitogen-activated protein kinase (MAPK) cascade, reduction of oxidative stress, and anti-inflammatory activity. Molecular docking showed that the core components could spontaneously bind to receptor proteins with a strong binding force. In vitro, compared to model group, hesperetin, ruscogenin and liquiritin significantly inhibited the increase of α-smooth muscle actin (α-SMA) and fibronectin (FN) and the decrease of e-cadherin (E-cad) in TGF-β1-induced A549 cells. This study is the first to show, using UPLC-Orbitrap Fusion MS combined with network pharmacology and experimental validation, that JSHX might exert therapeutic actions against PF by suppressing the expression of key factors in PF. The findings provide a deeper understanding of the chemical profiling and pharmacological activities of JSHX and a reference for further scientific research and clinical use of JSHX in PF treatment. [ABSTRACT FROM AUTHOR]

Jinshui Huanxian granules (JSHX) is a clinical Chinese medicine formula used for treating pulmonary fibrosis (PF). However, the effective components and molecular mechanisms of JSHX are still unclear. In this study, a combination approach using ultra-high performance liquid chromatography-Orbitrap Fusion mass spectrometry (UPLC-Orbitrap Fusion MS) integrated with network pharmacology was followed to identify the components of JSHX and the underlying molecular mechanisms against PF. UPLC-Orbitrap Fusion MS was used to identify the components present in JSHX. On the basis of the identified components, we performed target prediction using the SwissTargetPrediction database, protein–protein interaction (PPI) analysis using STRING database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using Metascape and constructed a component-target-pathway network using Cytoscape 3.7.2. Molecular docking technology was used to verify the affinity between the core components and targets. Finally, the pharmacological activities of three potentially bioactive components were validated in transforming growth factor β1 (TGF-β1)-induced A549 cell fibrosis model. As a result, we identified 266 components, including 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components. Of these, 90 validated components were predicted to act on 172 PF-related targets and they exhibited therapeutic effects against PF via regulation of cell migration, regulation of the mitogen-activated protein kinase (MAPK) cascade, reduction of oxidative stress, and anti-inflammatory activity. Molecular docking showed that the core components could spontaneously bind to receptor proteins with a strong binding force. In vitro, compared to model group, hesperetin, ruscogenin and liquiritin significantly inhibited the increase of α-smooth muscle actin (α-SMA) and fibronectin (FN) and the decrease of e-cadherin (E-cad) in TGF-β1-induced A549 cells. This study is the first to show, using UPLC-Orbitrap Fusion MS combined with network pharmacology and experimental validation, that JSHX might exert therapeutic actions against PF by suppressing the expression of key factors in PF. The findings provide a deeper understanding of the chemical profiling and pharmacological activities of JSHX and a reference for further scientific research and clinical use of JSHX in PF treatment. [ABSTRACT FROM AUTHOR]

Subcutaneous fat and intramuscular fat (IMF) deposition are closely related to meat production and pork quality. Dingyuan pig, as a native pig breed in China, low selection leads to obvious genetic and phenotypic differences in the population. Individuals with extreme fat content in the population are ideal models for studying the mechanism of fat deposition. In this study, we used RNA-Seq and tandem mass tags-based (TMT) proteomics to analyze the key pathways and genes that specifically regulate subcutaneous fat and IMF deposition in Dingyuan pigs. We identified 191 differentially expressed genes (DEGs) and 61 differentially abundant proteins (DAPs) in the high and low back fat thickness (HBF, LBF) groups, 85 DEGs and 12 DAPs were obtained in the high and low intramuscular fat (HIMF, LIMF) groups. The functional analysis showed that the DEGs and DAPs in the backfat groups were mainly involved in carbohydrates, amino acids, and fatty acids metabolism, whereas the IMF groups were involved in the insulin pathway, longevity, and some disease-related pathways. We found 40 candidate genes that might tissue-specifically lipids deposition for subcutaneous and intramuscular fat. Our research provides theoretical reference materials for the improvement of fat deposition traits of local pig breeds in my country. [ABSTRACT FROM AUTHOR]

Here, a Selenium-enriched Bacillus subtilis (SEBS) strain was generated and supplemented to broiler chickens' diet, and the impact in ileum bacterial microbiome, immunity and body weight were assessed. In a nutshell, five hundred 1-old old chicken were randomly divided into five groups: control, inorganic Se, Bacillus subtilis (B. subtilis), SEBS, and antibiotic, and colonization with B. subtilis and SEBS in the gastrointestinal tract (GIT) were measured by fluorescence in situ hybridization (FISH) assay and quantitative real-time polymerase chain reaction (qPCR). In summary, Chicks fed SEBS or B. subtilis had higher body weight than the control chicks or those given inorganic Se. SEBS colonized in distal segments of the ileum improved bacterial diversity, reduced the endogenous pathogen burden and increased the number of Lactobacillus sp. in the ileal mucous membrane. Species of unclassified Lachnospiraceae, uncultured Anaerosporobacter, Peptococcus, Lactobacillus salivarius, and Ruminococcaceae_UCG-014, and unclassified Butyricicoccus in the ileal mucous membrane played a key role in promoting immunity. Inorganic Se supplementation also improved bacterial composition of ileal mucous membranes, but to a less extent. In conclusion, SEBS improved performance and immunity of broiler chickens through colonization and modulation of the ileal mucous membrane microbiome. [ABSTRACT FROM AUTHOR]

The rotation of organic cations is considered to be an important reason for the dynamic changes in stability and photoelectric properties of organic perovskites. However, the specific effect of organic cations rotation on formamidine based perovskite is still unknown. In our work, first-principles calculations based on density functional theory are used to examine the effect of the rotation of formamidine cations in FAPbI3 and FA0.875Cs0.125PbI3. We have comprehensively calculated the structure, electronic and optical properties of them. We found a coupling effect between formamidine cations rotation and cesium atom. This coupling effect changes the inclination angle of octahedron to regulate electron distribution, band gaps, and optical absorption. Hence, changing the cation orientation and substitution atom is a feasible way to dynamically adjust the energy band, dielectric constant and absorption edge of perovskite. Preparing perovskite with tunable properties is just around the corner through this way. [ABSTRACT FROM AUTHOR]