Imaging macular carotenoids and their related proteins in the human retina with confocal resonance Raman and fluorescence microscopy.

Lutein and zeaxanthin are highly concentrated at the central region of the human retina, forming a distinct yellow spot known as the macula lutea. The delivery and retention of the macular pigment carotenoids in the macula lutea involves many proteins, but their exact roles remain incompletely understood. In our study, we examined the distribution of the twelve known macular carotenoid-related proteins within the human macula and the underlying retinal pigment epithelium (RPE) using both fluorescence and Raman modes on our confocal resonance Raman microscope. Additionally, we assessed protein and gene expression through Western blot analysis and a single-cell RNA sequencing database. Our findings revealed that GSTP1, BCO2, and Aster-B exhibited distribution patterns similar to the macular carotenoids, with higher expression levels within the macular region compared to the periphery, while SR-BI and ABCA1 did not exhibit specific distribution patterns within the macula or RPE. Interestingly, LIPC, SR-BI's partner, accumulated specifically in the sub-foveal RPE. All three of these carotenoid transport proteins were found to be highly expressed in the RPE. These results offer valuable insights into the roles these proteins play in the formation of the macula lutea.

Assessing genetic and environmental components for pterygium: a nationwide study in Taiwan.

This study aims to estimate the familial risks of pterygium and assess its relative contributions to environmental and genetic factors using the 2000-2017 Taiwan National Health Insurance Research Database. The marginal Cox's model and the polygenic liability model were made. In Taiwan, the prevalence rate of pterygium in 2017 was 1.64% for individuals with affected first-degree relatives, higher than the general population (1.34%). The adjusted relative risk (RR) for pterygium was highest for twins of the same sex (15.54), followed by siblings of the same sex (4.69), offsprings (3.39), siblings of the different sex (2.88), spouse (2.12), parents (1.86), twins of the different sex (1.57), respectively. The phenotypic variance of pterygium was 21.6% from additive genetic variance, 24.3% from common environmental factors shared by family members, and 54.1% from non-shared environmental factors, respectively. Sensitivity analysis by restricting those with surgical pterygium reveals that aRRs and the three components were similar to those of the overall pterygium. In summary, the prevalence rate of pterygium was higher for individuals with affected first-degree relatives than for the general population. The non-shared environmental factors account for half of the phenotypic variance of pterygium; genetic and shared environmental factors explain the rest.

Temporal variation of mercury and methyl mercury in water and accumulation by phytoplankton in the eutrophic estuary, northern Taiwan.

Three surveys were carried out to study the phytoplankton role in influencing the Hg distribution in a poorly eutrophic estuary by measuring the total Hg (THg) and methylHg (MeHg) concentrations in waters and four-size fractions of phytoplankton. The THg and MeHg concentrations in waters and phytoplankton varied markedly temporal during the three surveys. The total concentrations of THg and MeHg in the four-size fractions of phytoplankton ranged between 0.62 and 28.15 mg/kg and 0.022-4.411 mg/kg, respectively. The dominance of THg and MeHg phytoplankton concentrations differed from different size fractions and varied with the various surveys. The huge uptake of Hg by abundant phytoplankton decreased both Hg concentrations in waters and phytoplankton, which was attributed to the biomass dilution effect during the July survey. The Hg partition between water and phytoplankton provided substantial evidence to illustrate the huge uptake of Hg by the abundant phytoplankton.

Assessment of the impact of resting heart rate on the risk of major adverse cardiovascular events after ischemic stroke: a retrospective observational study.

BACKGROUND: Although elevated heart rate is a risk factor for cardiovascular morbidity and mortality in healthy people, the association between resting heart rate and major cardiovascular risk in patients after acute ischemic stroke remains debated. This study evaluated the association between heart rate and major adverse cardiovascular events after ischemic stroke. METHODS: We conducted a retrospective cohort study analyzing data from the Chang Gung Research Database for 21,655 patients with recent ischemic stroke enrolled between January 1, 2010, and September 30, 2018. Initial in-hospital heart rates were averaged and categorized into 10-beats per minute (bpm) increments. The primary outcome was the composite of hospitalization for recurrent ischemic stroke, myocardial infarction, or all-cause mortality. Secondary outcomes were hospitalization for recurrent ischemic stroke, myocardial infarction, and heart failure. Hazard ratios and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models, using the heart rate < 60 bpm subgroup as the reference. RESULTS: After a median follow-up of 3.2 years, the adjusted hazard ratios for the primary outcome were 1.13 (95% CI: 1.01 to 1.26) for heart rate 60-69 bpm, 1.35 (95% CI: 1.22 to 1.50) for heart rate 70-79 bpm, 1.64 (95% CI: 1.47 to 1.83) for heart rate 80-89 bpm, and 2.08 (95% CI: 1.85 to 2.34) for heart rate ≥ 90 bpm compared with the reference group. Heart rate ≥ 70 bpm was associated with increased risk of all secondary outcomes compared with the reference group except heart failure.  CONCLUSIONS: Heart rate is a simple measurement with important prognostic implications. In patients with ischemic stroke, initial in-hospital heart rate was associated with major adverse cardiovascular events.

Development of DNA-Based Lateral Flow Assay for Detection of LDLR Gene Mutation for Familial Hypercholesterolemia.

Background: The techniques for detecting single nucleotide polymorphisms (SNP) require lengthy and complex experimental procedures and expensive instruments that may only be available in some laboratories. Thus, a deoxyribonucleic acid (DNA)-based lateral flow assay (LFA) was developed as a point-of-care test (POCT) diagnostic tool for genotyping. In this study, single nucleotide variation (E101K) in the low-density lipoprotein receptor (LDLR) gene leading to familial hypercholesterolemia (FH) was chosen as a model. Methods: Hypercholesterolemic individuals (n = 103) were selected from the Malaysian Cohort project (UKM Medical Molecular Biology Institute) while the control samples were selected from the Biobank (UKM Medical Molecular Biology Institute). The DNA samples were isolated from whole blood. Polymerase chain reaction (PCR) amplification process was performed using bifunctional labelled primers specifically designed to correspond to the variant that differentiates wild-type and mutant DNA for visual detection on LFA. The variant was confirmed using Sanger sequencing, and the sensitivity and specificity of the LFA detection method were validated using the Agena MassARRAY® technique. Results: Out of 103 hypercholesterolemic individuals, 5 individuals (4.8%) tested positive for E101K, LDLR mutation and the rest, including healthy control individuals, tested negative. This result was concordant with Sanger sequencing and Agena MassARRAY®. These five individuals could be classified as Definite FH, as the DNA diagnosis was confirmed. The sensitivity and specificity of the variant detection by LFA is 100% compared to results using the genotyping method using Agena MassARRAY®. Conclusion: The developed LFA can potentially be used in the POC setting for detecting the E101K variant in the LDLR gene. This LFA can also be used to screen family members with E101K variant in the LDLR gene and is applicable for other SNP's detection.

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome.

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function, while accessory genes are dispensable, particularly in nutrient-rich environments. However, this assumption is seldom tested genetically within the pangenome context. In this study, we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome. To evaluate the robustness of fitness category assignment, Tn-seq data for three independent mutant libraries per strain were analyzed by three methods, which indicates that the Hidden Markov Model (HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size, outperforming the Bayesian and Monte Carlo simulation-based methods. Consequently, the HMM method was used to classify the fitness category. Fitness genes, categorized as essential (ES), advantage (GA), and disadvantage (GD) genes for growth, are enriched in core genes, while nonessential genes (NE) are over-represented in accessory genes. Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes. Connectivity degrees in the cofitness network decrease in the order of ES, GD, and GA/NE. In addition to accessory genes, 1599 out of 3284 core genes display differential essentiality across test strains. Within the pangenome core, both shared quasi-essential (ES and GA) and strain-dependent fitness genes are enriched in similar functional categories. Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.).

Assessing the Suitability of CHA2DS2-VASc for Predicting Adverse Limb Events and Cardiovascular Outcomes in Peripheral Artery Disease Patients with Percutaneous Transluminal Angioplasty: A Retrospective Cohort Study.

Patients with peripheral artery disease (PAD) are at high risk of major adverse limb events (MALEs) and major adverse cardiovascular events (MACEs). CHA2DS2-VASc is a prognostic score for atrial fibrillation stroke risk; however, no study has evaluated its predictive ability for MALEs and MACEs in PAD patients who underwent percutaneous transluminal angioplasty. We conducted a retrospective cohort study on patients from Taiwan with PAD. The patients were stratified into four risk groups based on their modified CHA2DS2-VASc score. Cox proportional hazard models, 10-fold cross-validation, and receiver operating characteristic (ROC) analyses were utilized to evaluate the predictive ability of CHA2DS2-VASc for MALEs, MACEs, and MALEs + MACEs. Kaplan-Meier analysis estimated the survival probability of the risk groups. CHA2DS2-VASc was found to be a significant predictor of MACEs (hazard ratio (HR) 3.52 (95% confidence interval (95% CI) 1.00-12.12; p = 0.048), HR 4.18 (95% CI 1.19-14.36; p = 0.023), and HR 5.08 (95% CI 1.49-17.36; p = 0.009), for moderate-, high-, and very high-risk groups, respectively), while for MALEs and MALEs + MACEs, significance was achieved only for the high-risk group using a univariate model. For the multivariate adjusted model, the score was found to be a significant predictor of MACEs for only the very high-risk group, with an HR of 4.67 (95% CI 1.03-21.09; p = 0.045). The score demonstrated an AUC > 0.8, good discrimination (c-index > 0.8), and good calibration for predicting MACEs. However, it failed to achieve good performance for predicting MALEs and MALEs + MACEs. Based on all of the findings, CHA2DS2-VASc could potentially serve as a risk stratification score for predicting MACEs in patients with PAD, but it failed to qualify as a good predictor for MALEs.

Changes in Combined Lifestyle Risks and the Transition of Activities of Daily Living in the Elderly Population of Taiwan: Evidence from the Taiwan Longitudinal Study on Aging.

Functional ability decline occurs with age. This study aims to investigate the associations between the lifestyle factors-exercising, food consumption, and smoking-and the functional ability of the activities of daily living (ADL) by gender. The data were obtained from the Taiwan longitudinal study on aging, a national cohort study. The cross-sectional results demonstrated that the frequency of exercising was negatively associated with ADL in both men and women. Dairy products were positively associated with ADL in men. The longitudinal results illustrated that current and consistent exercising were negatively associated with changes in ADL scores over 4- and 8-year periods in both men and women. Seafood consumption was negatively associated with changes in the subsequent 4-year ADL scores. Past smoking was positively associated with changes in subsequent 4-year ADL scores in men, while current smoking was positively associated with changes in subsequent 8-year ADL scores in women. Therefore, consistent exercising, food consumption, and smoking were associated with ADL functional ability in elderly people, and the associations differed by gender. Elders exercising consistently had good ADL performance and maintained their ADL ability better, especially women. Seafood consumption decreased the risk of ADL decline, while smoking increased the risk of ADL decline.

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms.

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. METHOD: Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. RESULT: Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. CONCLUSION: This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Interstitial fibrosis increases the risk of end-stage kidney disease in patients with lupus nephritis.

OBJECTIVE: To evaluate the risk of end-stage kidney disease (ESKD) in lupus nephritis (LN) patients using tubulointerstitial lesion scores. METHODS: Clinical profiles and histopathological presentations of 151 biopsy-proven LN patients were retrospectively examined. Risk factors of ESKD based on characteristics and scoring of their tubulointerstitial lesions (e.g. interstitial inflammation [II], tubular atrophy [TA], and interstitial fibrosis [IF]) were analyzed. RESULTS: The mean age of 151 LN patients was 36 years old, and 136 (90.1%) were female. The LN cases examined included: class I/II (n = 3, 2%), class III/IV (n = 119, 78.8%), class V (n = 23, 15.2%), and class VI (n = 6, 4.0%). The mean serum creatinine level was 1.4 mg/dl. Tubulointerstitial lesions were recorded in 120 (79.5%) patients. Prior to receiving renal biopsy, 9 (6.0%) patients developed ESKD. During the follow-up period (mean, 58 months), an additional 47 patients (31.1%) progressed to ESKD. Multivariate analyses identified serum creatinine (hazard ratio [HR]: 1.7, 95% confidence interval [CI]: 1.42-2.03, p < 0.001) and IF (HR: 3.2, 95% CI: 1.58-6.49, p = 0.001) as independent risk factors of ESKD. Kaplan-Meier analysis further confirmed a heightened risk of ESKD associated with IF. CONCLUSION: Tubulointerstitial involvement is commonly observed in histopathological presentation of LN. However, IF, rather than II, or TA, was found to increase the risk of ESKD in our cohort. Therefore, to predict renal outcome in LN patients prior to adjusting immunosuppressive treatment, degree of IF should be reviewed.

Artificial Intelligence Model for a Distinction between Early-Stage Gastric Cancer Invasive Depth T1a and T1b.

Background: Endoscopic submucosal dissection (ESD) is a widely accepted treatment for patients with mucosa (T1a) disease without lymph node metastasis. However, the inconsistency of inspection quality of tumor staging under the standard tool combining endoscopic ultrasound (EUS) with computed tomography (CT) scanning makes it restrictive. Methods: We conducted a study using data augmentation and artificial intelligence (AI) to address the early gastric cancer (EGC) staging problem. The proposed AI model simplifies early cancer treatment by eliminating the need for ultrasound or other staging methods. We developed an AI model utilizing data augmentation and the You-Only-Look-Once (YOLO) approach. We collected a white-light image dataset of 351 stage T1a and 542 T1b images to build, test, and validate the model. An external white-light images dataset that consists of 47 T1a and 9 T1b images was then collected to validate our AI model. The result of the external dataset validation indicated that our model also applies to other peer health institutes. Results: The results of k-fold cross-validation using the original dataset demonstrated that the proposed model had a sensitivity of 85.08% and an average specificity of 87.17%. Additionally, the k-fold cross-validation model had an average accuracy rate of 86.18%; the external data set demonstrated similar validation results with a sensitivity of 82.98%, a specificity of 77.78%, and an overall accuracy of 82.14%. Conclusions: Our findings suggest that the AI model can effectively replace EUS and CT in early GC staging, with an average validation accuracy rate of 86.18% for the original dataset from Linkou Cheng Gun Memorial Hospital and 82.14% for the external validation dataset from Kaohsiung Cheng Gun Memorial Hospital. Moreover, our AI model's accuracy rate outperformed the average EUS and CT rates in previous literature (around 70%).

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

Phylogenomic analyses and reclassification of the Mesorhizobium complex: proposal for 9 novel genera and reclassification of 15 species.

BACKGROUD: The genus Mesorhizobium is shown by phylogenomics to be paraphyletic and forms part of a complex that includes the genera Aminobacter, Aquamicrobium, Pseudaminobacter and Tianweitania. The relationships for type strains belong to these genera need to be carefully re-evaluated. RESULTS: The relationships of Mesorhizobium complex are evaluated based on phylogenomic analyses and overall genome relatedness indices (OGRIs) of 61 type strains. According to the maximum likelihood phylogenetic tree based on concatenated sequences of 539 core proteins and the tree constructed using the bac120 bacterial marker set from Genome Taxonomy Database, 65 type strains were grouped into 9 clusters. Moreover, 10 subclusters were identified based on the OGRIs including average nucleotide identity (ANI), average amino acid identity (AAI) and core-proteome average amino acid identity (cAAI), with AAI and cAAI showing a clear intra- and inter-(sub)cluster gaps of 77.40-80.91% and 83.98-86.16%, respectively. Combined with the phylogenetic trees and OGRIs, the type strains were reclassified into 15 genera. This list includes five defined genera Mesorhizobium, Aquamicrobium, Pseudaminobacter, Aminobacterand Tianweitania, among which 40/41 Mesorhizobium species and one Aminobacter species are canonical legume microsymbionts. The other nine (sub)clusters are classified as novel genera. Cluster III, comprising symbiotic M. alhagi and M. camelthorni, is classified as Allomesorhizobium gen. nov. Cluster VI harbored a single symbiotic species M. albiziae and is classified as Neomesorhizobium gen. nov. The remaining seven non-symbiotic members were proposed as: Neoaquamicrobium gen. nov., Manganibacter gen. nov., Ollibium gen. nov., Terribium gen. nov., Kumtagia gen. nov., Borborobacter gen. nov., Aerobium gen. nov.. Furthermore, the genus Corticibacterium is restored and two species in Subcluster IX-1 are reclassified as the member of this genus. CONCLUSION: The Mesorhizobium complex are classified into 15 genera based on phylogenomic analyses and OGRIs of 65 type strains. This study resolved previously non-monophyletic genera in the Mesorhizobium complex.

Wheat flour-derived amyloid fibrils for efficient removal of organic dyes from contaminated water.

Amyloid fibrils derived from different proteins have been proved as a promising material for adsorption of various pollutants from wastewater, which showed advantages of low cost and eco-friendliness. However, most of the amyloid fibrils derived from animal-based proteins with high environmental footprint, while more sustainable amyloid fibrils derived from plant materials are desirable. In this study, a plant-derived amyloid fibril was extracted from the commonly used wheat flour with a simple and scalable protein purification and fibrillization process. Interestingly, the amyloid fibrils showed good adsorption capacity towards typical organic dyes (Eosin Y (EY) and Congo red (CR)) from contaminated water. Adsorption kinetic analysis indicated the adsorption process to EY or CR by wheat flour amyloid well fitted with a pseudo-second-order model. The adsorption also followed a Langmuir isothermal model with adsorption capacities of 333 mg/g and 138 mg/g towards CR and EY, respectively. This work demonstrated the feasibility to utilize the plant-based amyloid fibril for organic dyes removal from contaminated water, which provided an affordable, sustainable and scalable tool for organic dyes removal from wastewater.

Strictosamide alleviates acute lung injury via regulating T helper 17 cells, regulatory T cells, and gut microbiota.

BACKGROUND: Nauclea officinalis (Pierre ex Pit.) Merr. & Chun (Rubiaceae) is widely used to treat respiratory diseases in China. Strictosamide is its main active component and has significant anti-inflammatory activity. However, the effects and molecular mechanisms of strictosamide in the treatment of acute lung injury (ALI) remain largely unknown. PURPOSE: This study aimed to examine the regulatory effects of strictosamide on T helper 17 cells (Th17 cells)/Regulatory T cells (Treg cells) and gut microbiota in ALI-affected mice. MATERIALS AND METHODS: The ALI model was induced using lipopolysaccharide (LPS) intraperitoneal injection. Hematoxylin-eosin (H&E) staining, the number of inflammatory cells in broncho-alveolar lavage fluid (BALF), the Wet/Dry (W/D) ratio, and myeloperoxidase (MPO) activity were utilized as evaluation indices for the therapeutic efficacy of strictosamide on ALI. Flow cytometry (FCM), enzyme-linked immune sorbent assay (ELISA), quantitative reverse transcription polymerase chain reaction (qRT-PCR), and western blotting were used to determine the regulation of strictosamide on the Th17/Treg cells and the STAT3/STAT5 signaling pathway. The analysis of gut microbiota was conducted using 16S rDNA sequencing. The verification of the relationship between the gut microbiome and immune function was conducted using Spearman analysis. RESULTS: Strictosamide attenuated inflammation on ALI induced by LPS, which reduced the levels of Th17-related factors interleukin (IL)-6 and IL-17 and increased Treg-related factors IL-10 and transforming growth factor (TGF)-ß. In the spleens and whole blood, strictosamide reduced the proportion of Th17 cells and increased the proportion of Treg cells. Furthermore, strictosamide increased Forkhead/winged helix transcription factor 3 (Foxp3) and p-STAT5 protein expression while inhibiting Retinoid-related orphan nuclear receptors-γt (RORγt) and p-STAT3 expression. Moreover, strictosamide reshaped the diversity and structure of the gut microbiota, and influence the associations between immune parameters and gut microbiota in ALI mice. CONCLUSIONS: In summary, the results of the current investigation showed that strictosamide has a therapeutic impact on LPS-induced ALI. The mechanism of action of this effect may be associated with the modulation of Th17 and Treg cells differentiation via the SATA signaling pathway, as well as the impact of the gut microbiota.