Tethered peptide activation mechanism of the adhesion GPCRs ADGRG2 and ADGRG4.

Adhesion G protein-coupled receptors (aGPCRs) constitute an evolutionarily ancient family of receptors that often undergo autoproteolysis to produce α and ß subunits1-3. A tethered agonism mediated by the 'Stachel sequence' of the ß subunit has been proposed to have central roles in aGPCR activation4-6. Here we present three cryo-electron microscopy structures of aGPCRs coupled to the Gs heterotrimer. Two of these aGPCRs are activated by tethered Stachel sequences-the ADGRG2-ß-Gs complex and the ADGRG4-ß-Gs complex (in which ß indicates the ß subunit of the aGPCR)-and the other is the full-length ADGRG2 in complex with the exogenous ADGRG2 Stachel-sequence-derived peptide agonist IP15 (ADGRG2(FL)-IP15-Gs). The Stachel sequences of both ADGRG2-ß and ADGRG4-ß assume a U shape and insert deeply into the seven-transmembrane bundles. Constituting the FXφφφXφ motif (in which φ represents a hydrophobic residue), five residues of ADGRG2-ß or ADGRG4-ß extend like fingers to mediate binding to the seven-transmembrane domain and activation of the receptor. The structure of the ADGRG2(FL)-IP15-Gs complex reveals the structural basis for the improved binding affinity of IP15 compared with VPM-p15 and indicates that rational design of peptidic agonists could be achieved by exploiting aGPCR-ß structures. By converting the 'finger residues' to acidic residues, we develop a method to generate peptidic antagonists towards several aGPCRs. Collectively, our study provides structural and biochemical insights into the tethered activation mechanism of aGPCRs.

Uncovering global-scale risks from commercial chemicals in air.

Commercial chemicals are used extensively across urban centres worldwide1, posing a potential exposure risk to 4.2 billion people2. Harmful chemicals are often assessed on the basis of their environmental persistence, accumulation in biological organisms and toxic properties, under international and national initiatives such as the Stockholm Convention3. However, existing regulatory frameworks rely largely upon knowledge of the properties of the parent chemicals, with minimal consideration given to the products of their transformation in the atmosphere. This is mainly due to a dearth of experimental data, as identifying transformation products in complex mixtures of airborne chemicals is an immense analytical challenge4. Here we develop a new framework-combining laboratory and field experiments, advanced techniques for screening suspect chemicals, and in silico modelling-to assess the risks of airborne chemicals, while accounting for atmospheric chemical reactions. By applying this framework to organophosphate flame retardants, as representative chemicals of emerging concern5, we find that their transformation products are globally distributed across 18 megacities, representing a previously unrecognized exposure risk for the world's urban populations. More importantly, individual transformation products can be more toxic and up to an order-of-magnitude more persistent than the parent chemicals, such that the overall risks associated with the mixture of transformation products are also higher than those of the parent flame retardants. Together our results highlight the need to consider atmospheric transformations when assessing the risks of commercial chemicals.

Proton-assisted growth of ultra-flat graphene films.

Graphene films grown by chemical vapour deposition have unusual physical and chemical properties that offer promise for applications such as flexible electronics and high-frequency transistors1-10. However, wrinkles invariably form during growth because of the strong coupling to the substrate, and these limit the large-scale homogeneity of the film1-4,11,12. Here we develop a proton-assisted method of chemical vapour deposition to grow ultra-flat graphene films that are wrinkle-free. Our method of proton penetration13-17 and recombination to form hydrogen can also reduce the wrinkles formed during traditional chemical vapour deposition of graphene. Some of the wrinkles disappear entirely, owing to the decoupling of van der Waals interactions and possibly an increase in distance from the growth surface. The electronic band structure of the as-grown graphene films shows a V-shaped Dirac cone and a linear dispersion relation within the atomic plane or across an atomic step, confirming the decoupling from the substrate. The ultra-flat nature of the graphene films ensures that their surfaces are easy to clean after a wet transfer process. A robust quantum Hall effect appears even at room temperature in a device with a linewidth of 100 micrometres. Graphene films grown by proton-assisted chemical vapour deposition should largely retain their intrinsic performance, and our method should be easily generalizable to other nanomaterials for strain and doping engineering.

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

iSMOD: an integrative browser for image-based single-cell multi-omics data.

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

Temporal dynamics of Grime's CSR strategies in plant communities during 60 years of succession.

Grime's competitive, stress-tolerant, ruderal (CSR) theory predicts a shift in plant communities from ruderal to stress-tolerant strategies during secondary succession. However, this fundamental tenet lacks empirical validation using long-term continuous successional data. Utilizing a 60-year longitudinal data of old-field succession, we investigated the community-level dynamics of plant strategies over time. Our findings reveal that while plant communities generally transitioned from ruderal to stress-tolerant strategies during succession, initial abandonment conditions crucially shaped early successional strategies, leading to varied strategy trajectories across different fields. Furthermore, we found a notable divergence in the CSR strategies of alien and native species over succession. Initially, alien and native species exhibited similar ruderal strategies, but in later stages, alien species exhibited higher ruderal and lower stress tolerance compared to native species. Overall, our findings underscore the applicability of Grime's predictions regarding temporal shifts in CSR strategies depending on both initial community conditions and species origin.

Observation on Microenvironment Changes of Dynamic Catalysts in Acidic CO2 Reduction.

Electrochemical CO2 reduction reaction (CO2RR) in acid can solve alkalinity issues while highly corrosive and reductive acidic electrolytes usually cause catalyst degradation. Inhibiting catalyst degradation is crucial for the stability of acidic CO2RR. Here, we reveal the microenvironment changes of dynamic Bi-based catalysts and develop a pulse chronoamperometry (CA) strategy to improve the stability of acidic CO2RR. In situ fluorescence mappings show that the local pH changes from neutral to acid, and the in situ Raman spectra reveal the dynamic evolution of interfacial water structures in the microenvironment. We propose that the surface charge properties of dynamic catalysts affect the competitive adsorption of K+ and protons, thereby causing the differences in local pH and CO2RR intermediate adsorption. We also develop a pulse CA strategy to reactivate catalysts, and the stability of acidic CO2RR is improved by 2 orders of magnitude for 100 h operation, which is higher than most reports on the stability of acidic CO2RR. This work gives insights on how microenvironment changes affecting the stability of acidic CO2RR, and provides guidance for designing stable catalysts in acidic electrolytes.

SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment.

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM's ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization.

Decoding multilevel relationships with the human tissue-cell-molecule network.

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Development of an online calculator for the prediction of seizure freedom following pediatric hemispherectomy using the Hemispherectomy Outcome Prediction Scale (HOPS).

OBJECTIVES: Although hemispheric surgeries are among the most effective procedures for drug-resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1-, 2-, and 5-years post-hemispherectomy. METHODS: Retrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time-to-seizure recurrence. A multivariate Cox proportional-hazards regression model was developed to predict the likelihood of post-hemispheric surgery seizure freedom at three time points (1-, 2- and 5- years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops-calculator.com/). RESULTS: The selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non-hemispheric resective surgery, and contralateral fluorodeoxyglucose-positron emission tomography [FDG-PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time-to-seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG-PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non-lesional MRI, non-stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%. SIGNIFICANCE: Online calculators are useful, cost-free tools that can assist physicians in risk estimation and inform joint decision-making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator.

A New Methodology for High Spatiotemporal Resolution Measurements of Air Volatile Organic Compounds: From Sampling to Data Deconvolution.

Monitoring of volatile organic compounds (VOCs) in air is crucial for understanding their atmospheric impacts and advancing their emission reduction plans. This study presents an innovative integrated methodology suitable for achieving semireal-time high spatiotemporal resolution three-dimensional measurements of VOCs from ground to hundreds of meters above ground. The methodology integrates an active AirCore sampler, custom-designed for deployment from unmanned aerial vehicles (UAV), a proton-transfer-reaction mass spectrometry (PTR-MS) for sample analysis, and a data deconvolution algorithm for improved time resolution for measurements of multiple VOCs in air. The application of the deconvolution technique significantly improves the signal strength of data from PTR-MS analysis of AirCore samples and enhances their temporal resolution by 4 to 8 times to 4-11 s. A case study demonstrates that the methodology can achieve sample collection and analysis of VOCs within 45 min, resulting in >120-360 spatially resolved data points for each VOC measured and achieving a horizontal resolution of 20-55 m at a UAV flight speed of 5 m/s and a vertical resolution of 5 m. This methodology presents new possibilities for acquiring 3-dimensional spatial distributions of VOC concentrations, effectively tackling the longstanding challenge of characterizing three-dimensional VOC distributions in the lowest portion of the atmospheric boundary layer.

Quantification of Methane Emissions from Cold Heavy Oil Production with Sand Extraction in Alberta and Saskatchewan, Canada.

Cold heavy oil production with sand (CHOPS) is an extraction process for heavy oil in Canada, with the potential to lead to higher CH4 venting than conventional oil sites, that have not been adequately characterized. In order to quantify CH4 emissions from CHOPS activities, a focused aerial measurement campaign was conducted in the Canadian provinces of Alberta and Saskatchewan in June 2018. Total CH4 emissions from each of 10 clusters of CHOPS wells (containing 22-167 well sites per cluster) were derived using a mass balance computation algorithm that uses in situ wind data measurement on board aircraft. Results show that there is no statistically significant difference in CH4 emissions from CHOPS wells between the two provinces. Cluster-aggregated emission factors (EF) were determined using correspondingly aggregated production volumes. The average CH4 EF was 70.4 ± 36.9 kg/m3 produced oil for the Alberta wells and 55.1 ± 13.7 kg/m3 produced oil for the Saskatchewan wells. Using these EF and heavy oil production volumes reported to provincial regulators, the annual CH4 emissions from CHOPS were estimated to be 121% larger than CHOPS emissions extracted from Canada's National Inventory Report (NIR) for Saskatchewan. The EF were found to be positively correlated with the percentage of nonpiped production volumes in each cluster, indicating higher emissions for nonpiped wells while suggesting an avenue for methane emission reductions. A comparison with recent measurements indicates relatively limited effectiveness of regulations for Saskatchewan compared to those in Alberta. The results of this study indicate the substantial contribution of CHOPS operations to the underreporting observed in the NIR and provide measurement-based EF that can be used to develop improved emissions inventories for this sector and mitigate CH4 emissions from CHOPS operations.

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high- and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low- and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

A Prospective Self-controlled Clinical Trial of Nonactivated Low Leukocyte PRP in Female Pattern Hair Loss Patients of Childbearing Age.

BACKGROUND: Alopecia significantly affects the mental health and social relationship of women since childbearing age, highlighting the need for a safe, effective, and convenient treatment. METHODS: The authors have conducted a prospective self-controlled trial involving 15 female patients at childbearing age with alopecia. These patients received a subcutaneous scalp injection of platelet-rich plasma once every 4 weeks for 3 treatments in total. Outcome measurements were included below: changes in hair density (hair/cm2), hair follicle density (hair follicle/cm2), and overall photographic assessment (improved or not) at 4, 12, and 24 weeks right after the first treatment. RESULTS: Comparing the photographs taken before and after the intervention, 67% of patients' hair density increased from 151 ± 39.82 hairs/cm2 (preintervention) to 170.96 ± 37.14 hairs/cm2 (at 24-week follow-up), representing an approximate increase of 19 hairs/cm2. Meanwhile, hair follicle density increased by approximately 15 follicles/cm2 after 24 weeks since the first treatment, rising from 151.04 ± 41.99 follicles/cm2 to 166.72 ± 37.13 follicles/cm2. The primary adverse reactions observed were local swelling and pain due to injections. CONCLUSION: Local injection of nonactivated platelet-rich plasma with low leukocytes concentration could be an effective strategy to alleviate alopecia symptoms in female patients.

A qualitative study on inner experience of self-management behavior among elderly patients with type 2 diabetes in rural areas.

BACKGROUND: As a chronic metabolic disease, diabetes poses a serious threat to human health and has become a major public health problem in China and worldwide. In 2020, 30% of Chinese people (aged ≥ 60 years) reported having diabetes mellitus. Moreover, individuals with diabetes living in rural areas face a significantly higher mortality risk compared to those in urban areas. In this study, we explored the inner experience of self-management behaviors in elderly patients with type 2 diabetes in rural areas to inform targeted interventions. METHODS: A phenomenological research design was used to explore the inner experience of self-management in rural elderly diabetes. Ten elderly diabetic patients were sampled from December 2022 to March 2023 in rural areas of Yangcheng County, Jincheng City, ShanXi Province, China. The seven-step Colaizzi phenomenological was used to analyze the interview data and generate themes. RESULTS: Four themes emerged: "Insufficient self-management cognition", "Negative self-management attitude", "Slack self-management behavior", and "No time for self-management". CONCLUSION: The level of self-management among elderly patients with type 2 diabetes in rural areas is low. Healthcare professionals should develop targeted interventions aimed at enhancing their cognitive levels, modifying their coping styles, and improving their self-management abilities to improve their quality of life.

Differential impacts of nonsteroidal anti-inflammatory drugs on lifespan and healthspan in aged Caenorhabditis elegans.

Aging and age-related diseases are intricately associated with oxidative stress and inflammation. Nonsteroidal anti-inflammatory drugs (NSAIDs) have shown their promise in mitigating age-related conditions and potentially extending lifespan in various model organisms. However, the efficacy of NSAIDs in older individuals may be influenced by age-related changes in drug metabolism and tolerance, which could result in age-dependent toxicities. This study aimed to evaluate the potential risks of toxicities associated with commonly used NSAIDs (aspirin, ibuprofen, acetaminophen, and indomethacin) on lifespan, healthspan, and oxidative stress levels in both young and old Caenorhabditis elegans. The results revealed that aspirin and ibuprofen were able to extend lifespan in both young and old worms by suppressing ROS generation and enhancing the expression of antioxidant SOD genes. In contrast, acetaminophen and indomeacin accelerated aging process in old worms, leading to oxidative stress damage and reduced resistance to heat stress through the pmk-1/skn-1 pathway. Notably, the harmful effects of acetaminophen and indomeacin were mitigated when pmk-1 was knocked out in the pmk-1(km25) strain. These results underscore the potential lack of benefit from acetaminophen and indomeacin in elderly individuals due to their increased susceptibility to toxicity. Further research is essential to elucidate the underlying mechanisms driving these age-dependent responses and to evaluate the potential risks associated with NSAID use in the elderly population.

Sex different effect of antibiotic and probiotic treatment on intestinal microbiota composition in chemically induced liver injury rats.

Differences in the gut microbiota and metabolic processes between males and females may explain differences in the risk of liver injury; however, the sex-specific effects of antibiotics and probiotics on these relationships are not clear. We evaluated differences in the gut microbiota and the risk of liver injury between male and female rats after the oral administration of antibiotics or probiotics followed by a period of diethylnitrosamine treatment to chemically induce liver injuryusing high-throughput sequencing of fecal microbiota combined with histological analyses of liver and colon tissues. Our results suggest that the ratio of gram-positive to gram-negative bacteria in kanamycin-treated rats was significantly higher than that of other groups, and this difference persisted for the duration of the experiment. Antibiotics significantly changed the composition of the gut microbiota of experimental rats. Clindamycin caused more diethylnitrosamine-induced damage to livers of male rats. Probiotics did not influencethe gut microbiota; however, they hadprotective effects against liver injury induced by diethylnitrosamine, especially in female rats. These results strengthen our understanding of sex differences in the indirect effects of antibiotics or probiotics on metabolism and liver injury in hosts via the gut microbiota.

Qualitative study on the perception of good death in patients with end-stage cancer in oncology nurses.

OBJECTIVE: To explore the perception of good death of patients with end-stage cancer by nurses in the oncology department. METHOD: In the study we used a phenomenological approach and semi-structured interviews. A total of 11 nurses from the oncology department of a Grade A hospital in Taizhou were interviewed on the cognition of good death from July 1 to September 30, 2022. Colaizzi's analysis method was used to analyse the interview data. This study followed the consolidated criteria for reporting qualitative research (COREQ). RESULT: Four themes were identified: a strong sense of responsibility and mission; To sustain hope and faith; The important role of family members; Improve patients' quality of life. CONCLUSION: The nurses in the department of oncology have a low level of knowledge about the "good death", and the correct understanding and view of the "good death" is the premise of the realization of " good death". The ability of nursing staff to improve the "good death", attention, and meet the needs and wishes of individuals and families, is the guarantee of the realization of "good death".