Image Quality Assessment of a Deep Learning-Based Automatic Bone Removal Algorithm for Cervical CTA.

BACKGROUND: The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed. RESULTS: Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity (r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm. CONCLUSIONS: The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.

Allogeneic CD19-targeted CAR-T therapy in patients with severe myositis and systemic sclerosis.

Allogeneic chimeric antigen receptor (CAR)-T cells hold great promise for expanding the accessibility of CAR-T therapy, whereas the risks of allograft rejection have hampered its application. Here, we genetically engineered healthy-donor-derived, CD19-targeting CAR-T cells using CRISPR-Cas9 to address the issue of immune rejection and treated one patient with refractory immune-mediated necrotizing myopathy and two patients with diffuse cutaneous systemic sclerosis with these cells. This study was registered at ClinicalTrials.gov (NCT05859997). The infused cells persisted for over 3 months, achieving complete B cell depletion within 2 weeks of treatment. During the 6-month follow-up, we observed deep remission without cytokine release syndrome or other serious adverse events in all three patients, primarily shown by the significant improvement in the clinical response index scores for the two diseases, respectively, and supported by the observations of reversal of inflammation and fibrosis. Our results demonstrate the high safety and promising immune modulatory effect of the off-the-shelf CAR-T cells in treating severe refractory autoimmune diseases.

TRPC6 Knockout Alleviates Renal Fibrosis through PI3K/AKT/GSK3B Pathway.

OBJECTIVE: Renal fibrosis is the ultimate pathway of various forms of acute and chronic kidney damage. Notably, the knockout of transient receptor potential channel 6 (TRPC6) has shown promise in alleviating renal fibrosis. However, the regulatory impact of TRPC6 on renal fibrosis remains unclear. METHODS: In vivo, TRPC6 knockout (TRPC6-/-) mice and age-matched 129 SvEv (WT) mice underwent unilateral renal ischemia-reperfusion (uIR) injury surgery on the left renal pedicle or sham operation. Kidneys and serum were collected on days 7, 14, 21, and 28 after euthanasia. In vitro, primary tubular epithelial cells (PTECs) were isolated from TRPC6-/- and WT mice, followed by treatment with transforming growth factor ß1 (TGFß1) for 72 h. The anti-fibrotic effect of TRPC6-/- and the underlying mechanisms were assessed through hematoxylin-eosin staining, Masson staining, immunostaining, qRT-PCR, and Western blotting. RESULTS: Increased TRPC6 expression was observed in uIR mice and PTECs treated with TGFß1. TRPC6-/- alleviated renal fibrosis by reducing the expression of fibrotic markers (Col-1, α-SMA, and vimentin), as well as decreasing the apoptosis and inflammation of PTECs during fibrotic progression both in vivo and in vitro. Additionally, we found that the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/glycogen synthase kinase 3 beta (GSK3ß) signaling pathway, a pivotal player in renal fibrosis, was down-regulated following TRPC6 deletion. CONCLUSION: These results suggest that the ablation of TRPC6 may mitigate renal fibrosis by inhibiting the apoptosis and inflammation of PTECs through down-regulation of the PI3K/AKT/GSK3ß pathway. Targeting TRPC6 could be a novel therapeutic strategy for preventing chronic kidney disease.

Collision Oxidation Behavior of Silver Nanoparticles in Alkaline Solution.

In recent years, silver nanoparticles (Ag NPs) have been used as positive electrode material for zinc/silver batteries, and the silver oxides formed during the charging process determine the discharge performance of batteries. Therefore, it is important to study the oxidation behavior of Ag NPs in alkaline solution. Single-nanoparticle collision is an important tool for analyzing oxidation behavior of individual nanoparticles. Based on thermodynamic information from collision events, it is known that oxidation products are potential-dependent and size-dependent. Based on dynamic information, including collisional peak shapes and duration time, it was observed that the Ag NP collision oxidation process changed from stepwise oxidation to direct oxidation as the potential increased or size decreased. This work provides guidance for application of Ag NPs in zinc/silver batteries and proposed a strategy for oxidation behavior of individual NP that could be tracked in situ through an all-encompassing view of thermodynamic and dynamic information.

Immune checkpoint inhibitor for different age patients with NSCLC in efficacy: A systematic review and meta-analysis.

OBJECTIVE: This article is a Meta-analysis aiming to systematically evaluate the difference in efficacy of immune checkpoint inhibitor in patients with non-small cell lung cancer (NSCLC) by age. METHODS: We performed a Meta-analysis of published randomized controlled trials concerning for patients with NSCLC by age. We compared overall survival among three groups (age <65 years, age 65-75 years, age ≥75 years). Hazard ratios (HRs) and 95% confidence intervals (CIs) were collected and pooled. RESULTS: A total of 10,291 patients from 17 RCTs were included. In the group under age 65 years, immune checkpoint inhibitor can significantly prolong the overall survival of patients with NSCLC (HR = 0.73, 95% CI: 0.66∼0.81, P < 0.00001). In the age 65-75 years group, immune checkpoint inhibitors prolonged overall survival in patients with NSCLC (HR = 0.78, 95% CI:0.71∼0.84, P < 0.00001). However, it has no significant effect on the overall survival of NSCLC patients (HR = 0.88, 95% CI:0.72∼1.08, P > 0.05) in the group older than 75 years. CONCLUSIONS: Immune checkpoint inhibitors prolonged the overall survival of NSCLC patients in the age <65 years group and the age 65-75 years group, but in the age ≥75 years group, there was no significant effect on overall survival. This may be related to innate immune and adaptive immune dysregulation due to "immunosenescence" in older patients.

A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging.

PURPOSE: To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians. METHODS: A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance. RESULTS: A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise. CONCLUSIONS: This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses. LEVEL OF EVIDENCE: Level III, retrospective comparative case series.

Clinical Feasibility of a Markerless Gait Analysis System

Background@#The gait analysis method that has been used in clinical practice to date is an optical tracking system (OTS) using a marker, but a markerless gait analysis (MGA) system is being developed because of the expensive cost and complicated examination of the OTS. To apply this MGA clinically, a comparative study of the MGA and OTS methods is necessary. The purpose of this study was to evaluate the compatibility between the OTS and the MGA methods and to evaluate the usefulness of the MGA system in actual clinical settings. @*Methods@#From March 2021 to August 2021, 14 patients underwent gait analysis using the OTS and MGA system, and the spatiotemporal parameters and kinematic results obtained by the 2 methods were compared. To evaluate the practicality of the MGA system in an actual clinical setting, MGA was performed on 14 symptomatic children with idiopathic toe walking, who had been treated with a corrective cast, and the pre-cast and post-cast results were compared. For the OTS, the Motion Analysis Eagle system was used, and for MGA, DH Walk was used. @*Results@#The spatiotemporal parameters showed no significant difference between the OTS and MGA system. The joint angle graphs of the kinematics along the sagittal plane showed similar shapes as a whole, with particularly high correlations in the hip and knee (pelvis: 29.4%, hip joint: 96.7%, knee joint: 94.9%, and ankle joint: 68.5%). A quantified comparison using the CORrelation and Analysis (CORA) score also showed high similarity between the 2 methods. The MGA results of pre-cast application and post-cast removal for children with idiopathic toe walking showed a statistically significant improvement in ankle dorsiflexion after treatment (p < 0.001). @*Conclusions@#MGA showed a good correlation with the conventional OTS in terms of spatiotemporal parameters and kinematics. We demonstrated that ankle sagittal kinematics improved after treatment by corrective cast in children with idiopathic toe walking using the MGA method. Thus, after the improvement of a few limitations, the MGA system may soon be able to be clinically applied.

Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias.

BACKGROUND: Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation bias. METHODS: This was a laboratory study with a randomized cross-over design. The diagnosis of anterior cruciate ligament (ACL) rupture, a common injury, on magnetic resonance imaging (MRI) was used as an example. Forty clinicians were invited to diagnose 200 ACLs with and without AI assistance. The AI's correcting and misleading (automation bias) effects on the clinicians' decision-making processes were analyzed. An ordinal logistic regression model was employed to predict the correcting and misleading probabilities of the AI. We further proposed an AI suppression strategy that retracted AI diagnoses with a higher misleading probability and provided AI diagnoses with a higher correcting probability. RESULTS: The AI significantly increased clinicians' accuracy from 87.2%±13.1% to 96.4%±1.9% (P < .001). However, the clinicians' errors in the AI-assisted round were associated with automation bias, accounting for 45.5% of the total mistakes. The automation bias was found to affect clinicians of all levels of expertise. Using a logistic regression model, we identified an AI output zone with higher probability to generate misleading diagnoses. The proposed AI suppression strategy was estimated to decrease clinicians' automation bias by 41.7%. CONCLUSION: Although AI improved clinicians' diagnostic performance, automation bias was a serious problem that should be addressed in clinical practice. The proposed AI suppression strategy is a practical method for decreasing automation bias.

Flooding drives the temporal turnover of antibiotic resistance gene in manure-amended soil-water continuum.

Rice paddy soil is a hotspot of antibiotic resistance genes (ARGs) due to the application of organic fertilizers. However, the temporal dynamics of ARGs in rice paddy soil and its flooded water during the growing season remain underexplored. In this study, a microcosm experiment was conducted to explore the ARG profiles in a long term (130 days) flooded two-phase manure-amended soil-water system. By using high-throughput quantitative PCR array, a total of 23-98 and 34-85 ARGs were detected in the soil and overlying water, respectively. Regression analysis exhibited significant negative correlations between ARG profile similarities and flooding duration, indicating that flooding significantly altered the resistome (P < 0.001). This finding was validated by the increased ARG abundance in the soil and the overlying water, for example, after 130 days flooding, the abundance of ARGs in CK soil was increased from 0.03 to 1.20 copies per 16S rRNA. The PCoA analysis further suggested pig manure application resulted in distinct ARG profiles in the soil-water continuum compared with those of the non-amended control (Adonis, P < 0.05). The Venn diagram showed that all ARGs detected in the pig manure were present in the treated soil. Twelve ARGs (e.g., sul1) were shared among the pig manure, manure-amended soil, and overlying water, indicating that certain manure- or soil-borne ARGs were readily dispersed from the soil to the overlying water. Moreover, the enhanced relationships between the ARGs and mobile genetic elements in pig manure applied soil-water continuum indicate that the application of organic matter could accelerate the emergence and dissemination of ARGs. These findings suggested that flooding represents a crucial pathway for dispersal of ARGs from the soil to the overlying water. Identification of highly mobile ARGs in the soil-water continuum is essential for assessing their potential risk to human health and promoting the development of sustainable agricultural practices to mitigate their spread.

Elucidation of Geniposide and Crocin Accumulation and Their Biosysnthsis-Related Key Enzymes during Gardenia jasminoides Fruit Growth.

Gardenia jasminoides fruits are extensively grown worldwide, with a large harvest, and its major medicinal ingredients are geniposide and crocins. Research on their accumulation and biosynthsis-related enzymes is rare. In this study, the accumulation of geniposide and crocin of G. jasminoides fruits at different developmental stages were clarified by HPLC. The highest cumulative amount of geniposide was 2.035% during the unripe-fruit period, and the highest content of crocin was 1.098% during the mature-fruit period. Furthermore, transcriptome sequencing was performed. A total of 50 unigenes encoding 4 key enzymes related in geniposide biosynthsis pathways were screened, and 41 unigenes encoding 7 key enzymes in the pathways of crocin were elucidated. It was found that the expression levels of differentially expressed genes of DN67890_c0_g1_i2-encoding GGPS, which is highly related to geniposide biosynthesis, and DN81253_c0_g1_i1-encoding lcyB, DN79477_c0_g1_i2-encoding lcyE, and DN84975_c1_g7_i11-encoding CCD, which are highly related to crocin biosynthesis, were consistent with the accumulation of geniposide and crocin content, respectively. The qRT-PCR results showed that the trends of relative expression were consistent with transcribed genes. This study provides insights for understanding the geniposide and crocin accumulation and biosynthsis during fruit development in G. jasminoides.

Deciphering anti-benign prostatic hyperplasia potential of liangwanoside II based on metabolite profile characterization combined with targeted network pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: Metapanax delavayi (Franch.) J.Wen & Frodin (Araliaceae), known as "liang wang cha" in China, has been used to treat prostatitis as herbal tea in folk. Recent research suggested that aqueous extract of Metapanax delavayi leaves showed an advantage in anti-benign prostate hyperplasia (BPH) activity, and liangwanoside II was the main component of the active fraction. However, the anti-BPH effect of liangwanosdie II remains to be revealed. AIM OF THE STUDY: This study aims to decipher anti-BPH potential of liangwanoside II. MATERIALS AND METHODS: The anti-BPH effect was evaluated by testosterone propionate-induced BPH rats after oral administration of liangwanoside II at the doses of 30, 60 and 120 mg/kg in vivo. Then, the metabolites of liangwanoside II in rats were identified using ultra-performance liquid chromatography coupled with quadrupole tandem time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Finally, the targeted network pharmacology combined with experimental verification were explored for the mechanism elucidation in vivo. RESULTS: Liangwanoside II exhibited an anti-BPH effect through reducing the weight of the prostate, prostate index and serum prostatic acid phosphatase level, and improving the prostate tissue morphology in BPH rats. Further, 16 metabolites of liangwanoside II in vivo were identified by UPLC-Q-TOF-MS analysis, in which the prototype compound and 4 metabolites, such as liangwanoside I and serratagenic acid could be absorbed in the plasma and then penetrate the blood-prostate barrier. Then, followed by the targeted network pharmacology and experimental verification, we found that liangwanoside II and its metabolites could jointly involve in the inhibition of the inflammation reaction and hormone imbalance, thus reducing oxidative stress damage, and restoring the balance between cell proliferation and apoptosis, which contributed to the anti-BPH effect of liangwanoside II. CONCLUSION: The anti-BPH potential of liangwanoside II was revealed using metabolite profile characterization combined with targeted network pharmacology, providing new insight into the development and utilization of liangwanoside II.

An immune-related prognostic signature associated with immune landscape and therapeutic responses in gastric cancer.

Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets in various tumors. However, the role of IRGs in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features characterizing the IRGs in GC. Data were acquired from the TCGA and GEO databases. The Cox regression analyses were performed to develop a prognostic risk signature. The genetic variants, immune infiltration, and drug responses associated with the risk signature were explored using bioinformatics methods. Lastly, the expression of the IRS was verified by qRT-PCR in cell lines. In this manner, an immune-related signature (IRS) was established based on 8 IRGs. According to the IRS, patients were divided into the low-risk group (LRG) and high-risk group (HRG). Compared with the HRG, the LRG was characterized by a better prognosis, high genomic instability, more CD8+ T cell infiltration, greater sensitivity to chemotherapeutic drugs, and greater likelihood of benefiting from the immunotherapy. Moreover, the expression result showed good consistency between the qRT-PCR and TCGA cohort. Our findings provide insights into the specific clinical and immune features underlying the IRS, which may be important for patient treatment.

Assessing transfer of aluminum during tea brewing and associated population health risks.

Tea is consumed widely around the world owing to its refreshing taste and potential health benefits. However, drinking tea is considered a major route for dietary aluminum exposure in areas where tea consumption is relatively large. To assess the health risk associated with drinking tea, the contamination level of aluminum was determined in 81 tea samples. The transfer rate of aluminum during tea brewing was investigated. Then based on the site-specific exposure parameters such as consumption data and body weight for six different subpopulations in Fujian, the exposure risks were estimated using a probabilistic approach. Results demonstrate that the contents of aluminum in green tea, white tea, oolong tea, and black tea were significantly different according to the one-way ANOVA analysis (p < 0.05). The transfer rate of aluminum were 32.6%, 31.6%, 26.3%, and 14% for white tea, black tea, oolong tea, and green tea, respectively. With respect to the oral reference dose, the exposure of inhabitants in Fujian to aluminum through drinking tea is under control (even at the 99th percentile).

Lactiplantibacillus plantarum Regulated Intestinal Microbial Community and Cytokines to Inhibit Salmonella typhimurium Infection.

Lactic acid bacteria (LAB) are recognized as food-grade safe microorganisms and have many beneficial effects. LAB could maintain the host intestinal homeostasis and regulate intestinal microbial community to exert antibacterial effects. In this study, Lactiplantibacillus plantarum (L. plantarum, Lp01) strain isolated from pig intestine was orally administered to C57BL/6 mice, and mice were then infected with Salmonella typhimurium (ATCC14028). The protective effects of L. plantarum were evaluated by monitoring body weight loss, survival rates, bacterial loads in tissue, colon histopathology analysis, and cytokine secretion. 16S rRNA gene sequencing was also utilized to detect the dynamics of the blind gut microbial community in mice. We found that L. plantarum could significantly reduce the body weight loss and improve the survival rates. The survival rate in the L. P-Sty group was up to 67.5%, which was much higher than that in the STY group (25%). Counting of bacterial loads displayed that the colony-forming unit (CFU) of S. typhimurium in the spleen (p < 0.05) and the liver (p < 0.05) from L. P-Sty group both decreased, compared with STY group. Intestinal histopathology showed that it alleviated the intestinal injury caused by Salmonella, inhibited the secretion of pro-inflammatory cytokines, and promoted anti-inflammatory cytokines (p < 0. 01). In addition, L. plantarum also significantly ameliorated the intestinal gut microbiome disturbance caused by Salmonella. It displayed an obvious increase of beneficial bacteria including Lactobacillus and Bacteroidetes and reduction of pathogenic bacteria like Proteobacteria. In conclusion, L. plantarum could regulate microbial community to inhibit Salmonella typhimurium infection.

DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation.

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.

Endophytic nitrogen-fixing bacteria DX120E inoculation altered the carbon and nitrogen metabolism in sugarcane.

Endophytic nitrogen-fixing bacteria are versatile and widely distributed in plants. Numerous strains of endophytic nitrogen-fixing bacteria are used as biofertilizers to minimize the utilization of chemical fertilizers, improve nutrient use efficiency, increase crop productivity, and reduce environmental pollution. However, the mechanism underlying the interaction between nitrogen-fixing bacteria and plants is still unclear. So, the present study was planned to assess the effects of endophytic nitrogen-fixing bacteria on sugarcane by analyzing the changes in physiological and biochemical activities. In the current study, Klebsiella variicola DX120E, an endophytic nitrogen-fixing bacterium, was inoculated on sugarcane varieties B8 and ROC22 to evaluate the effects on nitrogen and carbon metabolism-related enzymatic activity and biomass. Results showed that DX120E inoculation improved the enzymatic activities related to gluconeogenesis and nitrogen metabolism increased the sugarcane plant's height, cane juice Brix, biomass, chlorophyll, and soluble sugar content in sugarcane. Metabolomics analysis revealed that the metabolome modules were highly enriched in carbon and nitrogen metabolic pathways of strain-affected sugarcane than uninoculated control. The identified carbohydrates were associated with the glycolysis or gluconeogenesis and tricarboxylic acid (TCA) cycle in plants. Metabolomic profiling in the present investigation showed that carbohydrate metabolism is coordinated with nitrogen metabolism to provide carbon skeletons and energy to amino acid synthesis, and amino acid degradation results in several metabolites used by the citric acid cycle as an energy source. Moreover, differentially expressed metabolites of non-proteinogenic amino acids have a further complementary role to the action of endophytic nitrogen-fixing bacteria. Meanwhile, a significant difference in metabolites and metabolic pathways present in stems and leaves of B8 and ROC22 varieties was found. This study discovered the potential benefits of DX120E in sugarcane and suggested candidate regulatory elements to enhance interactions between nitrogen-fixing microbes and sugarcane.

Fisetin Ameliorates Alcohol-Induced Liver Injury through Regulating SIRT1 and SphK1 Pathway.

Alcoholic liver disease (ALD) often leads to hepatitis, hepatic cirrhosis, and even hepatocellular carcinoma. Fisetin has been shown to confer protection against liver injury. Herein, we investigated whether fisetin could prevent ethanol-induced hepatotoxicity. Mice were fed on 5% (v/v) Lieber-DeCarli ethanol diet. Human primary hepatic stellate cells (HSCs) co-cultured with ethanol were used to verify the therapeutic effect of fisetin. The results of alanine/aspartate aminotransferase (ALT/AST), Triglyceride (TG), total cholesterol (TC) in serum, Oil O Red and Masson staining revealed that fisetin (80[Formula: see text]mg/kg) ameliorated ethanol-induced mice liver injury and fibrosis. Besides, immunofluorescence results of [Formula: see text]-SMA revealed that fisetin suppressed HSCs activation. The suppression was dose-dependent. Furthermore, fisetin promoted SIRT1-mediated autophagy and inhibited Sphk1-mediated endoplasmic reticulum stress (ER stress) both in vitro and in vivo. Molecular docking results indicated potential interaction of fisetin with SIRT1 and SphK1. The inhibitory effect of fisetin on HSCs activation was reversed on co-culturing with EX-527, a specific inhibitor against STIR1 overexpression. Thus, fisetin has the potential to ameliorate alcohol-induced liver injury through suppression of HSCs activation, SIRT1-mediated autophagy and Sphk1-mediated ER stress.

Aridity differentially alters the stability of soil bacterial and fungal networks in coastal and inland areas of Australia.

Despite the importance of soil bacterial and fungal communities for ecosystem services and human welfare, how their ecological networks respond to climatic aridity have yet been evaluated. Here, we collected soil samples from 47 sites across 2500 km in coastal and inland areas of eastern Australia with contrasting status of aridity. We found that the diversity of both bacteria and fungi significantly differed between inland and coastal soils. Despite the significant differences in soil nutrient availability and stoichiometry between the inland and coastal regions, aridity was the most important predictor of bacterial and fungal community compositions. Aridity has altered the potential microbial migration rates and further impacted the microbial assembly processes by increasing the importance of stochasticity in bacterial and fungal communities. More importantly, ecological network analysis indicated that aridity enhanced the complexity and stability of the bacterial network but reduced that of the fungal network, possibly due to the contrasting impacts of aridity on the community-level habitat niche breadth and overlaps. Our work paves the way towards a more comprehensive understanding of how climate changes will alter soil microbial communities, which is integral to predicting their long-term consequences for ecosystem sustainability and resilience to future disturbances.

Lung cancer coexisting with Papiliotrema flavescens infection diagnosed by next-generation sequencing: a case report.

BACKGROUND: Papiliotrema flavescens is a rare environmental yeast, which has been isolated from air, trees, kernels of wheat and corn, fermenting soya sauce, and cerebrospinal fluid of patient with AIDS. Additionally, it is also reported to cause subcutaneous infection in a dog. In this case, we describe primary lung adenocarcinoma coexisting with Papiliotrema flavescens infection in a female patient diagnosed by next-generation sequencing (NGS) technique, which is the first such reported case. CASE PRESENTATION: The patient was a 52-year-old female with recurrent cough for 3 months. Chest CT examination revealed a ground glass nodule of 17 * 23 * 18 mm in the right upper lung, and 3 new pulmonary nodules appeared around it 2 months later. The patient underwent right upper lobe lobectomy and pathology confirmed that the primary 2-cm-lesion in the right upper lobe was invasive lung adenocarcinoma, and two of the three surrounding lung nodules were pathologically suggestive of pulmonary fungal infection (not known in specific fungal types). Hence, the patient received empirical anti-fungal treatments with fluconazole 400 mg/day for a week and follow-up CT scanning showed no tumor progression and no relapse of fungal infection. The specific pathogen was eventually identified as Papiliotrema flavescens by the next-generation sequencing of pathogen. DISCUSSION AND CONCLUSION: We first reported that lung cancer coexisting with Papiliotrema flavescens infection in a female patient. The diagnosis of lung cancer with typical CT imaging features is relatively simple, while the diagnosis of lung cancer coexisting with rare fungal infection is challenging. NGS technique is an effective supplementary technique for clinical diagnosis of bacterial or fungal infectious diseases, enabling precise clinical decision-making and appropriate treatment. In this case, the lung cancer may result in a degree of immune suppression, at least locally, resulting in the formation of pulmonary fungal nodular lesions around the tumor.