Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion.

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes.

MOTIVATION: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations.

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.

A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling.

The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

Bioluminescence assay for rapid detection of live Staphylococcus aureus based on the enrichment of egg yolk antibody modified magnetic metal organic framework immunobeads.

Specific and rapid detection of live Staphylococcus aureus (S.A) in environmental and food samples is critically important for protecting human health. In order to fulfill this purpose, two kinds of novel egg yolk antibody (IgY) immobilized immunomagnetic beads (IMBs; mSiO2-IgY and mMOF-IgY), with core-shell mSiO2 and mMOF as substrate, were prepared for selectively enriching S.A from samples. Furthermore, the IMBs with captured S.A were collected and re-dissolved in 0.5 mL PBS. After that, a cotton swab coated with sodium dodecylsulfate (SDS) was put in the solution to lyse S.A cells and emit ATP bioluminescence of the luciferin/luciferase system. Finally, a portable bioluminescence detector was used for quantification of ATP corresponding to S.A concentration. The results demonstrated that mMOF-IgY can enrich more S.A than mSiO2-IgY and emit a stronger signal. The reasons may be due to the higher immobilization amount of IgY on the IMBs. Under optimal conditions, the calibration line of S.A concentration was 10-105 CFU mL-1 by mMOF-IgY within 30 min. The low detection limit of S.A was 3 CFU mL-1. The results demonstrated that the assay takes much shorter time than plate counting. Its portability and excellent detection capability are suitable for rapid monitoring of specific pathogens in foods.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

An effective multi-task learning framework for drug repurposing based on graph representation learning.

Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.

Overall signature of acquired KRAS gene changes in advanced non-small cell lung cancer patient with EGFR-TKI resistance.

OBJECTIVE: Numerous scattered case studies continue to demonstrate a strong correlation between acquired KRAS mutations and epidermal growth factor receptor-tyrosine kinase inhibitor resistance in non-small cell lung cancer. However, the comprehensive understanding of the KRAS pathway following the failure of epidermal growth factor receptor-tyrosine kinase inhibitor therapy remains limited. METHODS: We conducted a retrospective evaluation of the next generation sequencing data from 323 patients with advanced non-small cell lung cancer and EGFR-activating mutations after experiencing progression with epidermal growth factor receptor-tyrosine kinase inhibitor therapy. Our analysis specifically focused on the acquired changes to the KRAS gene. RESULTS: Among the 323 patients with advanced non-small cell lung cancer and EGFR-activating mutations who experienced resistance to epidermal growth factor receptor-tyrosine kinase inhibitor therapy, 14 individuals (4.3%) developed resistance due to acquired KRAS alterations. Of these 14 patients, 10 cases (71.4%) were due to KRAS missense mutations, 1 case (7.2%) was due to KRAS gene fusion and 3 cases (21.4%) were due to KRAS amplification. Notably, we identified one newly demonstrated KRAS gene fusion (KRAS and LMNTD1), one KRAS G13D and one KRAS K117N. The emergence of acquired KRAS alterations was often accompanied by novel mutations and high tumor mutation burden, with TP53, CNKN2A, PIK3CA, MYC, STK11, CDK4, BRCA2 and ERBB2 being the most frequently observed concurrent mutations. The median progression-free survival and overall survival for the 14 patients were 5.2 and 7.3 months, respectively. Acquired KRAS missense variants were associated with significantly worse progression-free survival compared with other KRAS variant subtypes (P < 0.028). CONCLUSIONS: This study provides significant evidence of the role of acquired KRAS variants in the development of resistance to epidermal growth factor receptor-tyrosine kinase inhibitor therapy. Our results contribute to the growing body of knowledge on the mutational profiles associated with resistance to epidermal growth factor receptor-tyrosine kinase inhibitor treatment. Furthermore, our study highlights the KRAS gene change as a significant mechanism of resistance to epidermal growth factor receptor-tyrosine kinase inhibitor therapy.

One-pot synthesis and hydrogen peroxide electrochemical sensing of 3D TiO2/MnO2 nanorods assembled microspheres.

Nanorods assembled 3D microspheres of TiO2/MnO2 were prepared via a simple one-pot hydrothermal approach. The resultant composite material exhibited remarkable electrocatalytic activity for hydrogen peroxide (H2O2) in comparison to each single component. The electrochemical sensor constructed with TiO2/MnO2 exhibited a linear relationship within the range 0.0001-5.6 mmol·L-1 for H2O2. The limit of detection (LOD) and sensitivity for H2O2 were 0.03 µmol·L-1 (S/N = 3) and 316.6 µA (mmol·L-1)-1 cm-2. Moreover, this sensor can be employed to detect trace amount of H2O2 in serum and urine samples successfully, supporting an insight and strategy for a more sensitive electrochemical sensor.

Psychometric testing of the Chinese version of the Perceived Maternal Parenting Self-Efficacy Scale among postpartum women.

BACKGROUND: Maternal parenting self-efficacy plays a critical role in facilitating positive parenting practices and successful adaption to motherhood. The Perceived Maternal Parenting Self-Efficacy Scale (PMPS-E), as a task-specific measure, confirms its psychometric properties in cultural contexts. Compared with other tools, the advantages of the PMPS-E are as follows: (i) specific context or time period during the lifespan of a child, (ii) explicitly assess parenting self-efficacy across a diverse enough range of parenting tasks or activities during the perinatal/postnatal period and (iii) having robust psychometric properties. The aim of this study was to translate and determine the psychometric properties of the PMPS-E among Chinese postpartum women (C-PMPS-E). METHOD: The cross-cultural adaptation process followed Beaton et al.'s intercultural debugging guidelines. A total of 471 women were included to establish the psychometric properties of the C-PMPS-E. Mothers were asked to complete the C-PMPS-E, Edinburgh Postnatal Depression Scale (EPDS), the Generalized Anxiety Disorder-7 (GAD-7) and several demographic questions. The psychometric testing of the C-PMPS-E was established through item analysis, construct validity and internal consistency reliability. RESULTS: Item analysis showed that the critical ratios of all items were greater than 3 between the low-score group and high-score group, and all item-total correlation coefficients were greater than 0.4. The fit indices showed that the original correlated four-factor model of C-PMPS-E was observed to be an excellent fit to the data. The PMPS-E was negatively correlated with the EPDS and GAD-7 demonstrating its discriminant validity. As expected, no significant correlation was found between PMPS-E total or subscale scores and mothers' age. In addition, statistically significant differences for parity were detected for C-PMPS-E total and subscale scores with multipara having higher scores. This was taken as further evidence of the scale known-groups discriminant validity. In terms of internal consistency, the Cronbach's alpha of the C-PMPS-E total scale was 0.950, and subscales ranged from 0.76 to 0.89. Furthermore, a ROC curve analysis was conducted to establish the ability of the C-PMPS-E to distinguish between symptoms of depression and symptoms of anxiety. A cut-off value of 55 was identified that resulted in good specificity and fair sensitivity. CONCLUSION: The C-PMPS-E is a reliable and valid tool to assess maternal parenting self-efficacy in a Chinese context.

Small and Micro-Water Quality Monitoring Based on the Integration of a Full-Space Real 3D Model and IoT.

In order to address the challenges of small and micro-water pollution in parks and the low level of 3D visualization of water quality monitoring systems, this research paper proposes a novel wireless remote water quality monitoring system that combines the Internet of Things (IoT) and a 3D model of reality. To begin with, the construction of a comprehensive 3D model relies on various technologies, including unmanned aerial vehicle (UAV) tilt photography, 3D laser scanning, unmanned ship measurement, and close-range photogrammetry. These techniques are utilized to capture the park's geographical terrain, natural resources, and ecological environment, which are then integrated into the three-dimensional model. Secondly, GNSS positioning, multi-source water quality sensors, NB-IoT wireless communication, and video surveillance are combined with IoT technologies to enable wireless remote real-time monitoring of small and micro-water bodies. Finally, a high-precision underwater, indoor, and outdoor full-space real-scene three-dimensional visual water quality monitoring system integrated with IoT is constructed. The integrated system significantly reduces water pollution in small and micro-water bodies and optimizes the water quality monitoring system.

Study of Bitespiramycin Distribution in Rats and Cerebrospinal Fluid of Patients by a Sensitive LC-MS/MS Method with Rapid Sample Preparation.

Bitespiramycin, has been shown to have a therapeutic effect against respiratory tract inflammation, including a potential effect against COVID-19. A current clinical trial in China showed that bitespiramycin was an effective treatment for severe pneumonia and intracranial infection. However, there is lack of an analytical method to elucidate the distribution of bitespiramycin. In this study, a highly sensitive, rapid and reliable UPLC-MS/MS method was developed to comprehensively characterize the bitespiramycin distribution in various bio-samples, which is significantly improved upon the published work. A rapid sample preparation method was developed by using n-butanol as the solvent to extract bitespiramycin from different bio-samples. The extract was then directly analyzed by UPLC-MS/MS coupled with an alkaline-resistant column after centrifugation which avoids the time-consuming concentration process under nitrogen and redissolution. The method was employed to accurately quantify bitespiramycin and its metabolites in rat plasma, tissues, and human cerebrospinal fluid. Notably, the presence of bitespiramycin and its metabolites was identified for the first time in various rat organs including brain, testis, bladder and prostate as well as in human cerebrospinal fluid. This newly developed approach shows great promise for drug distribution assays including other antibiotics and can help elucidate the ADME of bitespiramycin.

Prognostic value of Interleukin-36s in cancers: A systematic review and meta-analysis.

BACKGROUND: Interleukin-36s (IL-36s) are a category of inflammatory cytokines and an increasing number of studies over the past decade have found that different kinds of IL-36s play different roles in cancers. This systematic review and meta-analysis aimed to evaluate the prognostic value of IL-36s in different cancer types. METHOD: Two reviewers independently searched in PubMed, Cochrane Library and EMBASE up to December 13, 2022. We extracted the hazard ratio (HR) and the confidence intervals (CIs) of the related prognostic outcomes and analyzed the pooled HR. RESULTS: We included 12 studies including 1925 patients. In all, six studies including IL-36α, five including IL-36γ and one including IL-36ß. A high expression of IL-36α was associated with better overall survival (OS) (HR = 0.48, 95 %CI: 0.37-0.62, P < 0.001) of cancer patients. The expression of IL-36γ was not related with cancers. Further, subgroup analysis showed that the expression of IL-36γ had no correlation with the OS of colorectal cancer (CRC) and non­small cell lung cancer (NSCLC) patients. Interestingly, a high expression of IL-36γ played contrasting prognostic roles in hepatocellular carcinoma (HCC) (HR = 0.43, 95 %CI: 0.27-0.69, P < 0.001) patients and gastric cancer (GC) (HR = 1.58, 95 %CI: 1.33-1.87, P < 0.001) patients. The only IL-36ß related study showed the expression of IL-36ß was not correlated with the prognosis of CRC patients (P > 0.05). CONCLUSION: IL-36α, IL-36ß and IL-36γ possibly play different roles in different cancers. High expression of IL-36α may be associated with good prognostic value in cancer patients, especially in CRC patients. The association between cancers prognosis and expression of IL-36ß or IL-36γ needs further evaluation. These conclusions need more clinical prognostic data for confirmation.

Circular RNA hsa_circ_0005239 contributes to hepatocellular carcinoma cell migration, invasion, and angiogenesis by targeting the miR-34a-5p/PD-L1 axis.

Circular RNAs (circRNAs) may be involved in tumorigenesis. Recently, the role of circRNAs in hepatocellular carcinoma (HCC) has drawn wide attention. Herein, we aimed to explore the regulation and function of hsa_circ_0005239 in the malignant biological behavior and angiogenesis of HCC, as well as the link between hsa_circ_0005239 and programmed cell death ligand 1 (PD-L1) in HCC. Quantitative real-time polymerase chain reaction (qRT-PCR) assays revealed that hsa_circ_0005239 was upregulated in HCC tumor samples and cell lines. Furthermore, a series of in vitro and in vivo assays explored the effects of hsa_circ_0005239 on biological processes involved in the development of HCC. Knockdown of hsa_circ_0005239 significantly inhibited cell migration, cell invasion, and angiogenesis in HCC, while overexpression showed the opposite effect. In the in vivo assays, hsa_circ_0005239 downregulation suppressed the growth of xenograft tumors in nude mice, which supported that hsa_circ_0005239 is a tumor promoter in HCC. Mechanistically, hsa_circ_0005239 binds to miR-34a-5p and functions as a competing endogenous RNA to modulate the expression of PD-L1. Further experiments revealed that the hsa_circ_0005239/PD-L1 axis regulates the malignant phenotypes of HCC cells through the phosphoinositide-3 kinase/protein kinase B (PI3K/Akt) signaling pathway. These results revealed the role of hsa_circ_0005239 and the hsa_circ_0005239/miR-34a-5p/PD-L1 axis in HCC, providing a potential diagnostic biomarker and therapeutic target for HCC.

A novel imaging index for predicting adverse progression in acute-on-chronic liver failure related to hepatitis B virus: the low erector spine index.

BACKGROUND: It is widely known that muscle mass influences the outcomes of many chronic diseases. Erector spine mass is a convenient parameter obtained from routine abdominal computed tomography (CT). The clinical application value of erector spine mass, and whether erector spine mass could predict the outcome of disease has not been studied. AIM: To evaluate the role of the erector spine index (ESI) calculated based on abdominal CT imaging in the progression of acute-on-chronic liver failure related to the hepatitis B virus (HBV-ACLF). METHODS: We performed a retrospective study of 118 HBV-ACLF patients and calculated the ESI (the total erector spine area normalized for height2 in meters) for each patient through abdominal CT. The findings were analyzed regarding the progression of HBV-ACLF and the ESI at baseline, including mortality and the development of complications. RESULTS: The ESI level was associated with mortality and the development of complications. During the 90-day follow-up period, patients with a low ESI (<12.05 cm2/m2) had higher mortality than those with a high ESI (≥ 12.05 cm2/m2) (51.7% vs. 26.7%), and the cumulative survival rates were 71.0%±4.6 and 85.8%±3.9, respectively (log-rank P = 0.003). The hazard ratios (HRs) calculated using univariable and multivariable analyses were 2.23(95% confidence interval (CI): 1.25-4.21, P = 0.005) and 2.52 (95% CI: 1.34-9.24, P = 0.011), respectively. Patients with a low ESI (<12.05 cm2/m2) had higher incidences of kidney dysfunction (43.5% vs. 23.2%, P = 0.029; log-rank P = 0.017) and hepatic encephalopathy (39.6% vs. 14.0%, P = 0.003; log-rank P = 0.010) than those with a high ESI. A low ESI was an independent risk factor for kidney dysfunction (adjusted HR = 1.36, 95% CI: 1.05-2.93, P = 0.043) and the development of hepatic encephalopathy (adjusted HR = 2.26; 95% CI: 2.05-3.13, P = 0.036). In addition, the presence of hepatic encephalopathy (the odds ratio (OR) = 2.26, 95% CI: 2.05-3.18, P = 0.006), spontaneous bacterial peritonitis (OR = 3.95, 95% CI: 1.01-5.46, P = 0.037), and kidney dysfunction (OR = 4.47, 95% CI: 1.02-9.64, P = 0.032) was independently associated with a low ESI in patients. CONCLUSION: A low ESI is an independent risk factor for mortality in patients with HBV-ACLF, as well as the development of kidney dysfunction and hepatic encephalopathy.

Design, synthesis, and biological evaluation of 4-(1H-1,2,3-triazol-1-yl)benzamides as HSP90 inhibitors.

Heat shock protein 90 (HSP90) is a promising anticancer drug target, which could be employed to construct HSP90 inhibitors-based drug conjugates for selective tumor therapy. Herein, a series of 4-(1H-1,2,3-triazol-1-yl)benzamides were rationally designed, synthesized as HSP90 inhibitors, and their structures were characterized by 1H NMR, 13C NMR, and HR-MS. Preliminary HSP90 binding assay showed that compounds 6b, 6l, 6m, 6n, 6t, and 6u exhibited significant HSP90α binding affinity. Among these selected compounds, 6u displayed the most potent anti-proliferative activities and particularly in Capan-1 cell line. Molecular modeling studies also confirmed possible mode of interaction between 6u and the binding sites of HSP90 by hydrogen bond and hydrophobic interactions. Above all, these encouraging data indicated that 6u could be used as a HSP90 inhibitor for further study and helped the recognition of the 4-(1H-1,2,3-triazol-1-yl)benzamide motif as a new scaffold for HSP90 inhibitors.

Cross-cultural adaptation and psychometric properties of the Chinese version of the Person-Centered Maternity Care Scale.

BACKGROUND: Increasing evidence show that women across the world face unacceptable mistreatment during childbirth. Person-centered maternity care is fundamental and essential to quality of healthcare services. The aim of this study was to translate and determine the psychometric properties of the Person-Centered Maternity Care (PCMC) Scale among Chinese postpartum women. METHODS: A cross-sectional study was conducted among 1235 post-partum women in China. The cross-cultural adaptation process followed the Beaton intercultural debugging guidelines. A total of 1235 women were included to establish the psychometric properties of the PCMC. A demographic characteristics form and the PCMC were used for data collection. The psychometric properties of the PCMC were evaluated by examining item analysis, exploratory factor analysis, known-groups discriminant validity, and internal consistency. RESULTS: The number of extracted common factors was limited to three (dignity & respect, communication & autonomy, supportive care), explaining a total variance of 40.8%. Regarding internal consistency, the Cronbach's alpha coefficient and split-half reliability of the full PCMC score were 0.989 and 0.852, respectively. CONCLUSIONS: The Chinese version of the PCMC is a reliable and valid tool to assess person-centered care during childbirth in China.

Using POI and time series Landsat data to identify and rebuilt surface mining, vegetation disturbance and land reclamation process based on Google Earth Engine.

The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006-2011. By 2020, the total disturbed area is 627.8 km2, with an average annual disturbance of 18.5 km2, and the annual maximum disturbance to the ground reached 64.6 km2 in 2008. With the total reclaimed area being 236.3 km2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines' location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making.

Rutaecarpine Ameliorates Murine N-Methyl-N'-Nitro-N-Nitrosoguanidine-Induced Chronic Atrophic Gastritis by Sonic Hedgehog Pathway.

CAG is a burdensome and progressive disease. Numerous studies have shown the effectiveness of RUT in digestive system diseases. The therapeutic effects of RUT on MNNG-induced CAG and the potential mechanisms were probed. MNNG administration was employed to establish a CAG model. The HE and ELISA methods were applied to detect the treatment effects. WB, qRT-PCR, immunohistochemistry, TUNEL, and GES-1 cell flow cytometry approaches were employed to probe the mechanisms. The CAG model was successfully established. The ELISA and HE staining data showed that the RUT treatment effects on CAG rats were reflected by the amelioration of histological damage. The qRT-PCR and WB analyses indicated that the protective effect of RUT is related to the upregulation of the SHH pathway and downregulation of the downstream of apoptosis to improve gastric cellular survival. Our data suggest that RUT induces a gastroprotective effect by upregulating the SHH signaling pathway and stimulating anti-apoptosis downstream.