Novel quantitative immunohistochemistry method using histone H3, family 3B as the internal reference standard for measuring human epidermal growth factor receptor 2 expression in breast cancer.

BACKGROUND: Immunohistochemistry (IHC) is an essential technique in surgical and clinical pathology for detecting diagnostic, prognostic, and predictive biomarkers for personalized cancer therapy. However, the lack of standardization and reference controls results in poor reproducibility, and a reliable tool for IHC quantification is urgently required. The objective of this study was to describe a novel approach in which H3F3B (histone H3, family 3B) can be used as an internal reference standard to quantify protein expression levels using IHC. METHODS: The authors enrolled 89 patients who had human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). They used a novel IHC-based assay to measure protein expression using H3F3B as the internal reference standard. H3F3B was uniformly expressed at the protein level in all tumor regions in cancer tissues. HER2 expression levels were measured with the H-score using HALO software. RESULTS: Kaplan-Meier analysis indicated that, among patients who had HER2-positive BC in The Cancer Genome Atlas data set and the authors' data set, the subgroup with low HER2 expression had a significantly better prognosis than the subgroup with high HER2 expression. Furthermore, the authors observed that HER2 expression levels were precisely evaluated using the proposed method, which can classify patients who are at higher risk of HER2-positive BC to receive trastuzumab-based adjuvant therapy. Dual-color IHC with H3F3B is an excellent tool for internal and external quality control of HER2 expression assays. CONCLUSIONS: The proposed IHC-based quantification method accurately assesses HER2 expression levels and provides insights for predicting clinical prognosis in patients with HER2-positive BC who receive trastuzumab-based adjuvant therapy.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

Enhanced food system efficiency is the key to China's 2060 carbon neutrality target.

Bioenergy with carbon capture and storage, among other negative-emission technologies, is required for China to achieve carbon neutrality-yet it may hinder land-based Sustainable Development Goals. Using modelling and scenario analysis, we investigate how to mitigate the potential adverse impacts on the food system of ambitious bioenergy deployment in China and its trading partners. We find that producing bioenergy domestically while sticking to the food self-sufficiency ratio redlines would lower China's daily per capita calorie intake by 8% and increase domestic food prices by 23% by 2060. Removing China's food self-sufficiency ratio restrictions could halve the domestic food dilemma but risks transferring environmental burdens to other countries, whereas halving food loss and waste, shifting to healthier diets and narrowing crop yield gaps could effectively mitigate these external effects. Our results show that simultaneously achieving carbon neutrality, food security and global sustainability requires a careful combination of these measures.

Adjuvant TACE may not improve recurrence-free or overall survival in HCC patients with low risk of recurrence after hepatectomy.

Background: To identify whether adjuvant transarterial chemoembolization (TACE) can improve prognosis in HCC patients with a low risk of recurrence (tumor size ≤ 5 cm, single nodule, no satellites, and no microvascular or macrovascular invasions) after hepatectomy. Methods: The data of 489 HCC patients with a low risk of recurrence after hepatectomy from Shanghai Cancer Center (SHCC) and Eastern Hepatobiliary Surgery Hospital (EHBH) were retrospectively reviewed. Recurrence-free survival (RFS) and overall survival (OS) were analyzed with Kaplan-Meier curves and Cox proportional hazards regression models. The effects of selection bias and confounding factors were balanced using propensity score matching (PSM). Results: In the SHCC cohort, 40 patients (19.9%, 40/201) received adjuvant TACE, and in the EHBH cohort, 113 patients (46.2%, 133/288) received adjuvant TACE. Compared to the patients without adjuvant TACE after hepatectomy, patients receiving adjuvant TACE had significantly shorter RFS (P=0.022; P=0.014) in both cohorts before PSM. However, no significant difference existed in OS (P=0.568; P=0.082). Multivariate analysis revealed that serum alkaline phosphatase and adjuvant TACE were independent prognostic factors for recurrence in both cohorts. Furthermore, significant differences existed in tumor size between the adjuvant TACE and non-adjuvant TACE groups in the SHCC cohort. There were differences in transfusion, Barcelona Clinic Liver Cancer stage and tumor-node-metastasis stage in the EHBH cohort. These factors were balanced by PSM. After PSM, patients with adjuvant TACE after hepatectomy still had significantly shorter RFS than those without (P=0.035; P=0.035) in both cohorts, but there was no difference in OS (P=0.638; P=0.159). Adjuvant TACE was the only independent prognostic factor for recurrence in multivariate analysis, with hazard ratios of 1.95 and 1.57. Conclusions: Adjuvant TACE may not improve long-term survival and might promote postoperative recurrence in HCC patients with a low risk of recurrence after hepatectomy.

PLIC11 drives lung cancer progression through regulating the YY1/PIWIL4 axis.

Lung cancer is the leading cause of cancer related deaths worldwide. Nonsmall cell lung cancers (NSCLC), the most common histological type of lung cancer, are known to be less well characterized. Long noncoding RNAs are a new class of cancer regulators. Here, we aimed to investigate the effect of lncRNA PLIC11 in NSCLC progression. In our study, we found that PLIC11 was upregulated in lung cancer, particularly in metastatic lung cancer tissues. Overexpression of PLIC11 enhanced cell proliferation, migration, and metastasis in vitro and in vivo. Mechanically, PLIC11 could interact with YY1 and promote PIWIL4 expression by transcription activation. Therefore, PLIC11 upregulation is a potential indicator of aggressive lung cancer, Silencing of PLIC11 has great potential therapeutic strategy in NSCLC.

A Practical Risk Classification of Early Recurrence in Hepatocellular Carcinoma Patients with Microvascular invasion after Hepatectomy: A Decision Tree Analysis.

PURPOSE: This study was designed to establish risk classifications for early recurrence in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) after hepatectomy. METHODS: The data of 563 HCC patients with MVI after hepatectomy from two hospitals were retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyse early recurrence. The risk classification for early recurrence was established by using classification and regression tree (CART) analysis and validated by using two independent validation cohorts from two hospitals. RESULTS: Multivariate analysis revealed that four indices, namely, infection of chronic viral hepatitis, MVI classification, tumour size, and serum alpha-fetoprotein (AFP), were independent prognostic factors for early recurrence in HCC patients with MVI. By CART analysis, MVI classification and serum AFP became the nodes of a decision tree and 3-stratification classifications that satisfactorily determined the risk of early recurrence were established. The area under the time-dependent receiver operating characteristic curve (AUC) values of the classification for early recurrence at 0.5, 1.0, and 2.0 years were 0.75, 0.73, and 0.71, respectively, which were all significantly higher than three common classic HCC stages (BCLC stage, Chinese stage, and TNM stage). The calibration curves showed good agreement between predictions by classification for early recurrence and actual survival outcomes. These prediction results also were confirmed in the independent internal and external validation cohorts. CONCLUSIONS: The 3 stratification classifications enabled satisfactory risk evaluation of early recurrence in HCC patients with MVI after hepatectomy.

ACTB may serve as a predictive marker for the efficacy of lenvatinib in patients with HBV-related early-stage hepatocellular carcinoma following partial hepatectomy: a retrospective cohort study.

Background: The lack of effective biomarkers for the treatment of postoperative recurrence in hepatocellular carcinoma (HCC) persists despite lenvatinib therapy. This study aims to identify beta-actin (ACTB) as a predictive biomarker for lenvatinib that can facilitate individualized treatment for HCC. Methods: This retrospective study included a subset of patients with HCC who underwent partial hepatectomy, with some receiving postoperative lenvatinib treatment and others not receiving lenvatinib treatment. A propensity score matching (PSM) analysis of patients who underwent treatment with or without lenvatinib following HCC partial hepatectomy was performed. Immunohistochemistry was employed to determine the levels of ACTB expression in HCC samples obtained from matched patients (n=225) enrolled in this study. The X-Tile was employed to determine the optimal cut-off point of ACTB levels for predicting time to recurrence (TTR). To assess the correlation between ACTB levels and lenvatinib efficacy, a subgroup analysis of TTR was conducted. A Cox regression model with an interaction term was utilized to assess the predictive significance of the model. Subsequently, a nomogram was developed and its discriminative ability and predictive accuracy were assessed using the concordance index (C-index) and calibration curve. For the investigation of the ACTB expression, HCC and para-tumoral normal tissues were employed. The patient-derived xenograft (PDX) model was utilized to validate the correlation between ACTB levels and lenvatinib responsiveness. Results: After PSM, a total of 76 patients who underwent postoperative lenvatinib treatment were included in the analysis, with a median TTR of 24.35 months. Early-stage HCC patients with lower levels of ACTB exhibited a more favorable response to lenvatinib therapy compared to those with higher levels. The reduced expression of ACTB was indicative of the benefits of lenvatinib, as opposed to higher levels {hazard ratio (HR) =0.243 [95% confidence interval (CI): 0.096-0.619], P<0.001, P value for interaction =0.014}. In approximately 81.8% of cases involving HCC patients, there was an observed increase in the expression of ACTB. Multivariate analysis of the lenvatinib cohort revealed Child-Pugh [HR =5.416 (95% CI: 1.390-21.104), P=0.015], Barcelona Clinic Liver Cancer (BCLC) stage [HR =2.508 (95% CI: 1.116-5.639), P=0.026], and ACTB [HR =5.879 (95% CI: 2.424-14.259), P<0.001] score as independent factors for TTR, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.76 (95% CI: 0.71-0.84). Moreover, the PDXs derived from tumors exhibiting low levels of ACTB expression demonstrated a heightened sensitivity to lenvatinib treatment. Conclusions: In patients with tumors treated with lenvatinib, low ACTB expression can predict a lower risk of recurrence. The validation of this potential biomarker in independent cohorts is necessary prior to its implementation for precision treatment stratification in patients undergoing partial hepatectomy for early-stage HCC.

Molecular characterization of a novel endornavirus isolated from Ophiostoma bicolor associated with bark beetles.

Ophiostoma bicolor is a pathogenic fungus associated with bark beetles that can cause serious damage to host plants. In this study, a novel fungal virus, "Ophiostoma bicolor endornavirus 1" (ObEV1), was obtained from O. bicolor, and its complete genome sequence was determined. ObEV1 has a single-stranded positive-sense (+ ss) RNA genome of 10,119 nucleotides. Sequence annotation and comparison showed that the viral genome has a single large open reading frame (ORF) encoding a polyprotein of 362.48 kDa. The polyprotein contains seven conserved domains: RNA-dependent RNA polymerase (RdRp), viral RNA helicase 1 (VHel1), viral methyltransferase (VMet), DEAD-like helicase (DEXDc), gliding-GltJ (G1), large tegument protein UL36 (PHA), and YlqF-related-GTPase (Y). Sequence comparisons and phylogenetic analysis showed that ObEV1 is a novel mycovirus belonging to the genus Betaendornavirus of the family Endornaviridae. This is the first report of a mycovirus in the ophiostomatoid fungus O. bicolor.

ECERIFERUM1-6A is required for the synthesis of cuticular wax alkanes and promotes drought tolerance in wheat.

Cuticular waxes cover the aerial surfaces of land plants and protect them from various environmental stresses. Alkanes are major wax components and contribute to plant drought tolerance, but the biosynthesis and regulation of alkanes remain largely unknown in wheat (Triticum aestivum L.). Here, we identified and functionally characterized a key alkane biosynthesis gene ECERIFERUM1-6A (TaCER1-6A) from wheat. The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9)-mediated knockout mutation in TaCER1-6A greatly reduced the contents of C27, C29, C31, and C33 alkanes in wheat leaves, while TaCER1-6A overexpression significantly increased the contents of these alkanes in wheat leaves, suggesting that TaCER1-6A is specifically involved in the biosynthesis of C27, C29, C31, and C33 alkanes on wheat leaf surfaces. TaCER1-6A knockout lines exhibited increased cuticle permeability and reduced drought tolerance, whereas TaCER1-6A overexpression lines displayed reduced cuticle permeability and enhanced drought tolerance. TaCER1-6A was highly expressed in flag leaf blades and seedling leaf blades and could respond to abiotic stresses and abscisic acid. TaCER1-6A was located in the endoplasmic reticulum, which is the subcellular compartment responsible for wax biosynthesis. A total of three haplotypes (HapI/II/III) of TaCER1-6A were identified in 43 wheat accessions, and HapI was the dominant haplotype (95%) in these wheat varieties. Additionally, we identified two R2R3-MYB transcription factors TaMYB96-2D and TaMYB96-5D that bound directly to the conserved motif CAACCA in promoters of the cuticular wax biosynthesis genes TaCER1-6A, TaCER1-1A, and fatty acyl-CoA reductase4. Collectively, these results suggest that TaCER1-6A is required for C27, C29, C31, and C33 alkanes biosynthesis and improves drought tolerance in wheat.

A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy.

INTRODUCTION: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new factor of MVI area to the other independent risk factors. METHODS: Consecutive patients with HCC who underwent R0 liver resection from January to December 2016 at the Eastern Hepatobiliary Surgery Hospital were included in this retrospective study. For patients with MVI detected on resected specimens, they were divided into two groups according to the size of the maximal MVI area: the small-MVI group and the large-MVI group. RESULTS: Of 193 patients who had MVI in the 337 HCC patients, 130 patients formed the training cohort and 63 patients formed the validation cohort. The large-MVI group of patients had worse overall survival (OS) when compared with the small-MVI group (p = 0.009). A deep learning model was developed based on the following independent risk factors found in this study: MVI stage, maximal MVI area, presence/absence of cirrhosis, and maximal tumor diameter. The areas under the receiver operating characteristic of the deep learning model for the 1-, 3-, and 5-year predictions of OS were 80.65, 74.04, and 79.44, respectively, which outperformed the traditional COX proportional hazards model. CONCLUSION: The deep learning model, by incorporating the maximal MVI area as an additional prognostic factor to the other previously known independent risk factors, predicted more accurately postoperative long-term OS for HCC patients with MVI after R0 liver resection.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021.

Background: Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government. Method: In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis. Results: We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter. Conclusion: Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

BRET-Based Self-Cleaving Biosensors for SARS-CoV-2 3CLpro Inhibitor Discovery.

The 3C-like protease (3CLpro) of SARS-CoV-2 is an attractive drug target for developing antivirals against SARS-CoV-2. A few small molecule inhibitors of 3CLpro are in clinical trials for COVID-19 treatments, and more inhibitors are under development. One limiting factor for 3CLpro inhibitors development is that the cellular activities of such inhibitors should be evaluated in Biosafety Level 3 (BSL-3) laboratories. Here, we design DNA-coded biosensors that can be used in BSL-2 laboratories to set up cell-based assays for 3CLpro inhibitor discovery. The biosensors were constructed by linking a green fluorescent protein (GFP2) to the N-terminus and a Renilla luciferase (RLuc8) to the C-terminus of SARS-CoV-2 3CLpro, with the linkers derived from the cleavage sequences of 3CLpro. After overexpression of the biosensors in human embryonic kidney (HEK) 293T cells, 3CLpro can be released from GFP2 and RLuc by self-cleavage, resulting in a decrease of the bioluminescence resonance energy transfer (BRET) signal. Using one of these biosensors, pBRET-10, we evaluated the cellular activities of several 3CLpro inhibitors. These inhibitors restored the BRET signal by blocking the proteolysis of pBRET-10, and their relative activities measured using pBRET-10 were consistent with their previously reported anti-SARS-CoV-2 activities. We conclude that the biosensor pBRET-10 is a useful tool for SARS-CoV-2 3CLpro inhibitor discovery. IMPORTANCE The virus proteases 3CLpro are validated drug targets for developing antivirals to treat coronavirus diseases, such as COVID-19. However, the development of 3CLpro inhibitors relies heavily on BSL-3 laboratories. Here, we report a series of BRET-based self-cleaving biosensors that can be used to set up cell-based assays to evaluate the cell permeability and cellular activity of SARS-CoV-2 3CLpro inhibitors in BSL-2 laboratories. The cell-based assay is suitable for high-throughput screening for 3CLpro inhibitors because of the simplicity and good reproducibility of our biosensors. The design strategy can also be used to design biosensors for other viral proteases for which the activation processes involve the self-cleavage of polyproteins.

Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study.

BACKGROUND: COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. OBJECTIVE: Nonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. METHODS: Time series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People's Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. RESULTS: We found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. CONCLUSIONS: Our results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.

MetaLnc9-Antisense RNA Contributes to Lung Cancer Metastasis via Modulating RNA-RNA Duplex with MetaLnc9.

Lung cancer is a common life-threatening tumor with high malignancy and high invasiveness. Long non-coding RNAs (lncRNAs) are involved in almost every stage of tumor initiation and progression. Here, we identified an antisense lncRNA, MetaLnc9 antisense (Metalnc9-AS), which arises from the antisense strand of Metalnc9, located on chr9q34.11, while its biological function and mechanism are not clear in lung cancer. In this study, we demonstrated that the expression of Metalnc9-AS was upregulated in non-small cell lung cancer (NSCLC) tissues compared with corresponding non-tumorous tissues. The gain of MetaLnc9-AS was highly associated with the malignant features of NSCLC. Overexpression of MetaLnc9-AS enhanced tumor metastasis in vitro and in vivo. Mechanically, MetaLnc9-AS could form an RNA-RNA hybrid with its cognate sense counterpart, MetaLnc9, to regulate its expression in NSCLC cells, and that such complexes were protected from ribonuclease degradation. Thus, Metalnc9-AS might be a potential and effective treatment for NSCLC.

Associations of CXCL12 polymorphisms with clinicopathological features in breast cancer: a case-control study.

BACKGROUND: Previous studies suggested that CXCL12 was involved in the development, metastasis, and invasion of breast cancer, and genetic variants were associated with the diagnosis and prognosis of patients with breast cancer. The present study was aimed to assess the relationships between CXCL12 polymorphisms (rs1801157, rs2297630, and rs2839693) and susceptibility and clinicopathological features of breast cancer. METHODS: A case-control study was conducted in 434 breast cancer patients and 450 health controls. Student t-test and chi-square test were used to analyze the differences of age distribution and genotype frequencies between the two groups. Correlations between polymorphisms and clinical parameters were also assessed by chi-square test. The potential effects of the three polymorphisms on CXCL12 were investigated by the public database. RESULTS: A statistical association was found between CXCL12 rs1801157 polymorphism and breast cancer risk, possibility of metastasis, and estrogen receptor status. Patients with rs2839693 C/T or C/T-T/T genotypes were more likely to be progesterone receptor-negative. However, no associations of rs2297630 polymorphism with breast cancer risk or any clinicopathological characteristics were observed. In addition, rs2297630 affected the splicing quantitative trait loci of CXCL12 in the subcutaneous fat, rs2839693 polymorphism affected the splicing quantitative trait loci of CXCL12 in the human breast mammary tissues. CONCLUSIONS: Those results indicated that CXCL12 polymorphisms might be potential diagnostic indicators, and more investigation is needed in the future.

Evaluation of nuclear PGAM2 value in hepatocellular carcinoma prognosis.

Phosphoglycerate mutase (PGAM) is a critical enzyme in glycolysis. PGAM2 is abundant in several types of tissues and malignant tumours. However, there is limited information regarding their clinicopathological significance in dysplastic nodules and hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of PGAM2 as a new biomarker for HCC. The PGAM2 expression level was evaluated by immunohistochemistry in liver cirrhosis (n = 10), low-grade dysplastic nodules (n = 15), high-grade dysplastic nodules (n = 15) and HCCs (n = 20) and 178 pairs of HCC and adjacent peritumoral liver tissues. We selected X-tile software for counting cut-point based on the outcomes for prognosis analysis, and used Kaplan-Meier analysis and Cox regression analysis can assess the prognosis of clinicopathologic parameters. Nuclear PGAM2 was significantly overexpressed in peritumoral liver tissues compared with HCC tissues (P = 0.0010). Kaplan-Meier analyses of 178 HCC samples revealed that nuclear PGAM2's high expression level, but not cytoplasmic PGAM2, was significantly related to good overall survival rate (OS). In addition, univariate and multivariate Cox analyses indicated nuclear PGAM2 expression could be regarded as valuable predictors for OS in HCC. PGAM2 was highly expressed in HCC tissues than liver cirrhosis tissues, and nuclear PGAM2's high expression might demonstrate HCC patients have poor postoperative results. Thus, nuclear PGAM2 can be regarded as valuable predictors for OS in HCC patients after surgery.

Global Burden of Respiratory Diseases Attributable to Ambient Particulate Matter Pollution: Findings From the Global Burden of Disease Study 2019.

Background: Exposure to ambient particulate matter pollution (APMP) is a global health issue that directly affects the human respiratory system. Thus, we estimated the spatiotemporal trends in the burden of APMP-related respiratory diseases from 1990 to 2019. Methods: Based on the Global Burden of Disease Study 2019, data on the burden of APMP-related respiratory diseases were analyzed by age, sex, cause, and location. Joinpoint regression analysis was used to analyze the temporal trends in the burden of different respiratory diseases over the 30 years. Results: Globally, in 2019, APMP contributed the most to chronic obstructive pulmonary disease (COPD), with 695.1 thousand deaths and 15.4 million disability-adjusted life years (DALYs); however, the corresponding age-standardized death and DALY rates declined from 1990 to 2019. Similarly, although age-standardized death and DALY rates since 1990 decreased by 24% and 40%, respectively, lower respiratory infections (LRIs) still had the second highest number of deaths and DALYs attributable to APMP. This was followed by tracheal, bronchus, and lung (TBL) cancer, which showed increased age-standardized death and DALY rates during the past 30 years and reached 3.78 deaths per 100,000 persons and 84.22 DALYs per 100,000 persons in 2019. Among children aged < 5 years, LRIs had a huge burden attributable to APMP, whereas for older people, COPD was the leading cause of death and DALYs attributable to APMP. The APMP-related burdens of LRIs and COPD were relatively higher among countries with low and low-middle socio-demographic index (SDI), while countries with high-middle SDI showed the highest burden of TBL cancer attributable to APMP. Conclusions: APMP contributed substantially to the global burden of respiratory diseases, posing a significant threat to human health. Effective actions aimed at air pollution can potentially avoid an increase in the PM2.5-associated disease burden, especially in highly polluted areas.