Toward Better and Healthier Air Quality: Global PM2.5 and O3 Pollution Status and Risk Assessment Based on the New WHO Air Quality Guidelines for 2021.

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m-3) and O3 (≤ 100 µg m-3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m-3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m-3) and O3 (> 100 µg m-3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS: Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS: DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS: DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Prediction of Lymphovascular invasion status in breast cancer based on magnetic resonance imaging radiomics features.

OBJECTIVE: This study intended to investigate the feasibility and effectiveness of using clinical magnetic resonance imaging (MRI) radiomics features to predict lymphovascular invasion (LVI) status in breast cancer (BC) patients. METHODS: A total of 182 BC patients were retrospectively collected and randomly divided into a training set (n = 127) and a validation set (n = 55) in a 7:3 ratio. Based on pathological examination results, the training set was further divided into LVI group (n = 60) and non-LVI group (n = 67), and the validation set was divided into LVI group (n = 24) and non-LVI group (n = 31). General data and MRI examination indicators were compared. Multivariate logistic regression was utilized to analyze MRI radiomics features and clinically relevant indicators that were significant in the baseline information of patients in training set, independent risk factors were identified, and a logistic regression model was built. The accuracy of logistic model was validated using ROC curves in training and validation sets. RESULTS: Age, pathohistological classification, tumor length, tumor width, presence or absence of Magnetic Resonance Spectroscopy (MRS) cho peak, presence or absence of spicule sign, peritumoral enhancement, and peritumoral edema were statistically significant (P < 0.05) between the two groups. Multivariate logistic regression analysis presented that spicule and peritumoral edema were independent risk factors for LVI in BC patients (P < 0.05). The ROC curve illustrated that AUC of the logistic regression model in the training set was 0.807 (95%CI: 0.730-0.885) and that in the validation set was 0.837 (95%CI: 0.731-0.944). CONCLUSION: Radiomics features of spicule sign and peritumoral edema were independent risk factors for LVI in BC patients. A logistic regression model based on these factors, along with age, could accurately predict LVI occurrence in BC patients, providing data support for diagnosis and modeling of LVI in BC patients.

Prognostic role of C-reactive protein to albumin ratio in lung cancer: An updated systematic review and meta-analysis.

Background: C-reactive protein to albumin ratio (CRP/Alb ratio, CAR) has been suggested as a potential prognostic biomarker in lung cancer. This updated systematic review and meta-analysis aimed to assess the association between CAR and lung cancer prognosis in current literature. Methods: A systematic search of databases was conducted to identify relevant studies published up to April 2023. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the association between CAR and overall survival (OS) and progression-free survival (PFS) and recurrence-free survival (RF) in lung cancer patients. Results: This meta-analysis includes 16 studies with a total of 5337 patients, indicating a significant association between higher CAR and poorer OS, PFS, and RFS in lung cancer patients, with a pooled HR of 1.78 (95% CI = 1.60-1.99), 1.57 (95% CI = 1.36-1.80), and 1.97 (95% CI = 1.40-2.77), respectively. Conclusions: This updated meta-analysis provides evidence for the potential prognostic role of CAR in lung cancer, suggesting its utility as an effective and noninvasive biomarker for identifying high-risk patients and informing treatment decisions in a cost-effective manner. However, further large-scale studies will be necessary to establish the optimal cut-off value for CAR in lung cancer and confirm the present findings.

The expanding Pandora's toolbox of CD8+T cell: from transcriptional control to metabolic firing.

CD8+ T cells are the executor in adaptive immune response, especially in anti-tumor immunity. They are the subset immune cells that are of high plasticity and multifunction. Their development, differentiation, activation and metabolism are delicately regulated by multiple factors. Stimuli from the internal and external environment could remodel CD8+ T cells, and correspondingly they will also make adjustments to the microenvironmental changes. Here we describe the most updated progresses in CD8+ T biology from transcriptional regulation to metabolism mechanisms, and also their interactions with the microenvironment, especially in cancer and immunotherapy. The expanding landscape of CD8+ T cell biology and discovery of potential targets to regulate CD8+ T cells will provide new viewpoints for clinical immunotherapy.

Multi-Wavelength Selective and Broadband Near-Infrared Plasmonic Switches in Anisotropic Plasmonic Metasurfaces.

Anisotropic plasmonic metasurfaces have attracted broad research interest since they possess novel optical properties superior to natural materials and their tremendous design flexibility. However, the realization of multi-wavelength selective plasmonic metasurfaces that have emerged as promising candidates to uncover multichannel optical devices remains a challenge associated with weak modulation depths and narrow operation bandwidth. Herein, we propose and numerically demonstrate near-infrared multi-wavelength selective passive plasmonic switching (PPS) that encompasses high ON/OFF ratios and strong modulation depths via multiple Fano resonances (FRs) in anisotropic plasmonic metasurfaces. Specifically, the double FRs can be fulfilled and dedicated to establishing tailorable near-infrared dual-wavelength PPS. The multiple FRs mediated by in-plane mirror asymmetries cause the emergence of triple-wavelength PPS, whereas the multiple FRs governed by in-plane rotational asymmetries avail the implementation of the quasi-bound states in the continuum-endowed multi-wavelength PPS with the ability to unfold a tunable broad bandwidth. In addition, the strong polarization effects with in-plane anisotropic properties further validate the existence of the polarization-resolved multi-wavelength PPS. Our results provide an alternative approach to foster the achievement of multifunctional meta-devices in optical communication and information processing.

Maternal melatonin supplementation shapes gut microbiota and protects against inflammation in early life.

BACKGROUND: Gut microbiota colonization is critical for immune education and nutrient metabolism. Research shows that melatonin has beneficial effects as a therapy for many diseases via modulating gut dysbiosis. However, it is unclear whether melatonin alters gut microbiota colonization in early life. METHODS: In the experimental group (Mel), mice were intraperitoneally injected with melatonin at 10 mg/kg body weight for embryonic days 14-16 and received drinking water containing 0.4 mg/mL melatonin until 28 days postpartum. In the control group (Ctrl), mice were injected with the same volume of 2.5% ethanol in saline and provided with standard water. Two more groups were created by treating neonatal mice with 20 mg/kg lipopolysaccharide (LPS) to induce inflammation, resulting in the groups Ctrl + LPS and Mel + LPS, respectively. We examined the gut microbiota of the neonatal mice in the Ctrl and Mel group on Days 7, 14, 21, and 28 post-birth. On Day 14, melatonin and short-chain fatty acids (SCFAs) concentrations were measured in the Ctrl and Mel group and the mice were treated with LPS to be evaluated for intestinal injury and inflammatory response 15 h post treatment. According to the result of the SCFAs concentrations, some neonatal mice were intraperitoneally injected with 500 mg/kg sodium butyrate (SB) from Days 11-13, intraperitoneally injected with 20 mg/kg LPS on Day 14, and then euthanized by carbon dioxide inhalation the next morning. Intestinal injury and inflammatory responses were evaluated in the Ctrl + LPS and SB + LPS groups, respectively. RESULTS: By Day 14, it was evident that maternal melatonin supplementation significantly increased the relative abundance of Firmicutes in the ileal [61.03 (35.35 - 76.18) % vs. 98.02 (86.61 - 99.01) %, P = 0.003] and colonic [73.88 (69.77 - 85.99) % vs. 96.16 (94.57 - 96.34) %, P = 0.04] microbiota, the concentration of melatonin (0.79 ± 0.49 ng/ml vs. 6.11 ± 3.48 ng/ml, P = 0.008) in the gut lumen, and the fecal butyric acid (12.91 ± 5.74 µg/g vs. 23.58 ± 10.71 µg/g, P = 0.026) concentration of neonatal mice. Melatonin supplementation, and sodium butyrate treatment markedly alleviated intestinal injury and decreased inflammatory factors in neonatal mice. CONCLUSION: This study suggests that maternal melatonin supplementation can shape the gut microbiota and metabolism of offspring under normal physiological conditions and protect them against LPS-induced inflammation in early life.

Adipose tissue macrophage-derived exosomal miR-210-5p in modulating insulin sensitivity in rats born small for gestational age with catch-up growth.

Background: Insulin resistance has been implicated in the pathogenesis of children born small for gestational age (SGA) with catch-up growth (CUG). Adipose tissue macrophages (ATMs) regulate insulin resistance by secreting exosomes containing microRNA (miRNA) cargo; however, their pathogenic roles and molecular mechanism are not fully understood. This study aimed to investigate the role of miR-210-5p in rats born SGA with CUG and insulin resistance. Methods: The dietary needs of pregnant rats were restricted to ensure the birth of SGA rats. Transmission electron microscopy (TEM) and Western blot analysis were used to identify the exosomes from ATMs of CUG-SGA and adequate-for-gestational-age (AGA) rats. PKH-67 staining was performed to confirm the uptake of exosomes. miR-210-5p expression was measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Glucose uptake and output were detected with glucose uptake and output assays, respectively. Insulin resistance was detected with glucose and insulin tolerance tests in vivo. The interaction between miR-210-5p and SID1 transmembrane family member 2 (SIDT2) was validated with dual-luciferase reporter assay. Results: miR-210-5p was observed to be highly expressed in the exosomes derived from the ATMs of CUG-SGA rats. ATM-derived exosomes can serve as vehicles to deliver miR-210-5p into adipocytes, myocytes, and hepatocytes, where it can enhance cellular insulin resistance. SIDT2 was identified as a direct target gene of miR-210-5p. The miR-210-5p-induced insulin resistance was reversed by the restored SIDT2 expression. However, overexpression of SIDT2 abolished the inhibitory effect of CUG-SGA-ATM-exosomal miR-210-5p on insulin sensitivity in vivo. Conclusions: ATM-derived exosomal miR-210-5p promoted insulin resistance in CUG-SGA rats by targeting SIDT2, which may act as a new potential therapeutic target for children born SGA with CUG.

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network.

Background: Remarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge. Materials and methods: A group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients' objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later. Results: In the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset. Conclusions: This immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well.

Evaluation of the antioxidant characteristics of craft beer with green tea.

The addition of green tea as antioxidants to beer can improve the beer's flavor stability by protecting against staling during storage. To analyze the effect of different green teas on the flavor stability of beer, we developed an approach to rapidly evaluate their antioxidant activity. Ten types of craft beer were produced by adding different kinds of green tea during brewing, and their antioxidant activity and phenolic profiles were evaluated. The results showed remarkable variations in antioxidant activity and antioxidative compound contents, which were considerably higher in green tea beers than in non-tea beer (p < 0.05). A comprehensive evaluation function was developed to evaluate the total antioxidant activity of beers using principal component analysis. The highest total antioxidant activity was observed in Taiping Houkui beer, with a comprehensive evaluation score of 2.53. Pearson correlation analysis suggested that (-)-epigallocatechin gallate, (-)-epicatechin gallate, and (-)-epigallocatechin were strongly correlated with the total antioxidant activity of green tea beers (p < 0.01). The summation of their contents represented more than 60% of the total phenolic content of the teas, which can be used to predict the total antioxidant activity of green tea beers. PRACTICAL APPLICATION: Flavor stability is of prime concern for brewers, and flavor aging is increasingly becoming the limiting factor in beer shelf life. The application of green tea as antioxidants in beer can improve the flavor stability by protecting against beer staling during storage. The analytical method developed in this study will contribute to the rapid comparison of the effect of different green teas on the flavor stability of beer. Furthermore, the research findings demonstrate the potential benefits of green teas to beer flavor stability, which is of considerable importance in promoting the development and consumption of green teas.

Isolation and identification of the bitter compound from Huangjiu.

The strategy of taste-guided assisted by solvent extraction, solid-phase extraction and semipreparative HPLC were applied to isolate the main nonvolatile bitter components from mechanized Huangjiu. The potential fraction was identified by amino acid analysis and ultra-performance liquid chromatography-quadrupole-time-of-flight-MS/MS. Bitter pyroglutamate peptide Pyr-LFNPSTNPWHSP (PGP) was successfully identified from Huangjiu for the first time. Quantitative analysis showed that PGP contents ranged from below the limit of quantitation to 32.97 mg/L, among mechanized Huangjiu had higher contents than manual and commercial Huangjiu. The formation of PGP mainly occurred in the primary fermentation and it was stable in Huangjiu. Moreover, the PGP content of the Huangjiu brewed using raw wheat Qu was 112.6% higher than that using cooked wheat Qu, but presented subtle change with the increase of raw wheat Qu. The results revealed that PGP contributed the bitterness to Huangjiu, which may offer a possibility to reduce the bitterness of Huangjiu.

OSluca: An Interactive Web Server to Evaluate Prognostic Biomarkers for Lung Cancer.

Lung cancer is the principal cause of leading cancer-related incidence and mortality in the world. Various studies have excavated the potential prognostic biomarkers for cancer patients based on gene expression profiles. However, most of these reported biomarkers lack independent validation in multiple cohorts. Herein, we collected 35 datasets with long-term follow-up clinical information from TCGA (2 cohorts), GEO (32 cohorts), and Roepman study (1 cohort), and developed a web server named OSluca (Online consensus Survival for Lung Cancer) to assess the prognostic value of genes in lung cancer. The input of OSluca is an official gene symbol, and the output web page of OSluca displays the survival analysis summary with a forest plot and a survival table from Cox proportional regression in each cohort and combined cohorts. To test the performance of OSluca, 104 previously reported prognostic biomarkers in lung carcinoma were evaluated in OSluca. In conclusion, OSluca is a highly valuable and interactive prognostic web server for lung cancer. It can be accessed at http:// bioinfo.henu.edu.cn/LUCA/LUCAList.jsp.

OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts.

Potential prognostic mRNA biomarkers are exploited to assist in the clinical management and treatment of breast cancer, which is the first life-threatening tumor in women worldwide. However, it is technically challenging for untrained researchers to process high dimensional profiling data to screen and validate the potential prognostic values of genes of interests in multiple cohorts. Our aim is to develop an easy-to-use web server to facilitate the screening, developing, and evaluating of prognostic biomarkers in breast cancers. Herein, we collected more than 7,400 cases of breast cancer with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas and Gene Expression Omnibus data, and built an Online consensus Survival analysis web server for Breast Cancers, abbreviated OSbrca, to generate the Kaplan-Meier survival plot with a hazard ratio and log rank P-value for given genes in an interactive way. To examine the performance of OSbrca, the prognostic potency of 128 previously published biomarkers of breast cancer was reassessed in OSbrca. In conclusion, it is highly valuable for biologists and clinicians to perform the preliminary assessment and validation of novel or putative prognostic biomarkers for breast cancers. OSbrca could be accessed at http://bioinfo.henu.edu.cn/BRCA/BRCAList.jsp.