Identification of Near-Pan-neutralizing Antibodies against HIV-1 by Deconvolution of Plasma Humoral Responses.

Anti-HIV-1 envelope broadly neutralizing monoclonal antibodies (bNAbs) isolated from memory B cells may not fully represent HIV-1-neutralizing profiles measured in plasma. Accordingly, we characterized near-pan-neutralizing antibodies extracted directly from the plasma of two "elite neutralizers." Circulating anti-gp120 polyclonal antibodies were deconvoluted using proteomics to guide lineage analysis of bone marrow plasma cells. In both subjects, a single lineage of anti-CD4-binding site (CD4bs) antibodies explained the plasma-neutralizing activity. Importantly, members of these lineages potently neutralized 89%-100% of a multi-tier 117 pseudovirus panel, closely matching the specificity and breadth of the circulating antibodies. X-ray crystallographic analysis of one monoclonal, N49P7, suggested a unique ability to bypass the CD4bs Phe43 cavity, while reaching deep into highly conserved residues of Layer 3 of the gp120 inner domain, likely explaining its extreme potency and breadth. Further direct analyses of plasma anti-HIV-1 bNAbs should provide new insights for developing antibody-based antiviral agents and vaccines.

Uncovering Availability of the Secondary Iron Resources in China: Integrating Material Flow Analysis and Secondary Resources Reserve Assessment.

As the largest iron and steel producer, China still cannot meet its demand of iron and steel only through domestic primary supply in the last few decades. Hence, secondary iron resources are increasingly significant in meeting China's iron supply and demand balance. However, the secondary iron resource availability in China and how it impacts the future supply demand balance were still insufficiently discussed. In this work, we developed a material flow analysis and secondary resources reserve assessment (MFA-SRRA) integrated model, assessed secondary iron resources availability, and conducted a supply demand analysis through nine scenarios for irons in China. The results showed that China's secondary iron reserves will increase from 8.9 Gt in 2021 to 14.04 to 19.01 Gt in 2050. With the increasing secondary iron supply, more than 60% of iron ore as a source of steelmaking can be replaced by 2050. Landfills, as a significant reserve of iron but always ignored, will accumulate 1.42-1.51 Gt secondary iron resources by 2050 and should be noticed to be mined and utilized in the future. Last, we suggest that promoting innovation in landfill mining technology and making sustainable material management policies are urgent to prevent these secondary iron resources from becoming real waste.

Spatiotemporal and Multilayer Trade Network Patterns of the Global Cobalt Cycle.

Recent years have witnessed increasing attempts to track trade flows of critical materials across world regions and along the life cycle for renewable energy and the low carbon transition. Previous studies often had limited spatiotemporal coverage, excluded end-use products, and modeled different life cycle stages as single-layer networks. Here, we integrated material flow analysis and complex network analysis into a multilayer framework to characterize the spatiotemporal and multilayer trade network patterns of the global cobalt cycle from 1988 to 2020. We found substantial growth and notable structural changes in global cobalt trade over the past 30 years. China, Germany, and the United States play pivotal roles in different layers and stages of the global cobalt cycle. The interlayer relationships among alloys, batteries, and materials are robust and continually strengthening, indicating a trend toward synergistic trade. However, cobalt ore-exporting countries are highly concentrated and rarely involved in later life cycle stages, resulting in the weakest relationship between the ore layer and other layers. This causes fluctuations and uncertainty in the global cobalt trade. Our model, linking industrial ecology, supply chain analysis, and network analysis, can be extended to other materials that are critical for the future green transition.

A protein encoded by circular ZNF609 RNA induces acute kidney injury by activating the AKT/mTOR-autophagy pathway.

Autophagy plays a crucial role in the development and progression of ischemic acute kidney injury (AKI). However, the function and mechanism of circular RNAs (circRNAs) in the regulation of autophagy in ischemic AKI remain unexplored. Herein, we find that circ-ZNF609, originating from the ZNF609 locus, is highly expressed in the kidney after ischemia/reperfusion injury, and urinary circ-ZNF609 is a moderate predictor for AKI in heart disease patients. Overexpression of circ-ZNF609 can activate AKT3/mTOR signaling and induce autophagy flux impairment and cell apoptosis while inhibiting proliferation in HK-2 cells, which is blocked by silencing circ-ZNF609. Mechanistically, circ-ZNF609 encodes a functional protein consisting of 250 amino acids (aa), termed ZNF609-250aa, the overexpression of which can activate AKT3/mTOR signaling and induce autophagy flux impairment and cell apoptosis in HK-2 cells in vitro and in AKI kidneys in vivo. The blockade of AKT and mTOR signaling with pharmacological inhibitors is capable of reversing ZNF609-250aa-induced autophagy flux impairment and cell apoptosis in HK-2 cells. The present study demonstrates that highly expressed circ-ZNF609-encoded ZNF609-250aa induces cell apoptosis and AKI by impairing the autophagy flux via an AKT/mTOR-dependent mechanism. These findings imply that targeting circ-ZNF609 may be a novel therapy for ischemic AKI.

Tracking Three Decades of Global Neodymium Stocks and Flows with a Trade-Linked Multiregional Material Flow Analysis.

Neodymium (Nd), an essential type of rare earth element, has attracted increasing attention in recent years due to its significant role in emerging technologies and its globally imbalanced demand and supply. Understanding the global and regional Nd stocks and flows would thus be important for understanding and mitigating potential supply risks. In this work, we applied a trade-linked multiregional material flow analysis to map the global and regional neodymium cycles from 1990 to 2020. We reveal increasingly complex trade patterns of Nd-containing products and a clearly dominant but slightly weakening role of China in the global Nd trade (for both raw materials and semi- and final products) along the life cycle in the last 30 years. A total of 880 kt Nd was mined accumulatively and flowed into the global socioeconomic system, mainly as NdFeB permanent magnets (79%) in semi-products and conventional vehicles and home appliances (together 48%) in final products. Approximately 64% (i.e., 563 kt Nd) of all the mined Nd globally were not recycled, indicating a largely untapped potential of recycling in securing Nd supply and an urgency to overcome the present technological and non-technical challenges. The global Nd cycle in the past three decades is characterized by different but complementary roles of different regions along the global Nd value chain: China dominates in the provision of raw materials and semi- and final products, Japan focuses on the manufacturing of magnets and electronics, and the United States and European Union show advantages in the vehicle industry. Anticipating increasing demand of Nd in emerging energy and transport technologies in the future, more coordinated efforts among different regions and increased recycling are urgently needed for ensuring both regional and global Nd supply and demand balance and a common green future.

Diaporisoindole B Reduces Lipid Accumulation in THP-1 Macrophage Cells via MAPKs and PPARγ-LXRα Pathways and Promotes the Reverse Cholesterol Transport by Upregulating SR-B1 and LDLR in HepG2 Cells.

Diaporisoindole B (DPB), an isoprenylisoindole alkaloid isolated from the mangrove endophytic fungus Diaporthe sp. SYSU-HQ3, has been proved to have a good anti-inflammatory activity in macrophage cells. In this study, we found that DPB was able to reduce lipid accumulation in THP-1 macrophage-derived foam cells. DPB could inhibit the lipid influx-related gene CD36 and increase the expression of lipid efflux-related genes ATP binding cassette transporter A1 (ABCA1), ATP binding cassette transporter G1 (ABCG1), and scavenger receptor B1 (SR-B1). Moreover, DPB elevated low-density lipoprotein receptor (LDLR) protein expression in HepG2 cells, which can increase the transport of LDL. Meanwhile, DPB could downregulate the expression levels of proteins related to cholesterol and fatty acid synthesis. Further study showed that DPB could activate peroxisome proliferator-activated receptor gamma (PPARγ) and inhibit mitogen-activated protein kinase (MAPK) phosphorylation. Taken together, our findings demonstrated that DPB could reduce lipid accumulation in THP-1 macrophage cells by reducing the intake of lipids and promoting the efflux of lipids and also could promote the reverse cholesterol transport (RCT) mechanism by upregulating SR-B1 and LDLR in HepG2 cells.

Hsa_circ_0010729 is Involved in Oxygen-Glucose Deprivation/Reoxygenation-Induced Human Microvascular Endothelial Cell Deprivation by Targeting miR-665/ING5.

Ischemic stroke is a disease with high mortality. Circular RNA_0010729 (hsa_circ_0010729) has been reported to be involved in ischemic heart disease. However, it is not clear whether hsa_circ_0010729 is involved in the regulation of ischemic stroke. In this study, we used oxygen-glucose deprivation/reoxygenation (OGD/R) to stimulate human brain microvascular endothelial cells (HBMECs) model to investigate the potential role of hsa_circ_0010729 in stroke in vitro. The expression levels of hsa_circ_0010729, miR-665, and ING5 in ischemic stroke were detected by quantitative real-time polymerase chain reaction (qRT-PCR). HBMECs proliferation was detected by CCK-8. Cell apoptosis was detected by flow cytometry. The levels of inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA). Western blot was used to detect the related protein expression. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) were used to examine the target relationship between miR-665 and hsa_circ_0010729 or ING5. Compared with the control group, hsa_circ_0010729 and ING5 were highly expressed in OGD/R-induced HBMECs, while miR-665 was lowly expressed. Hsa_circ_0010729 silencing promoted OGD/R-induced cell proliferation and inhibited apoptosis. However, the effect of hsa_circ_0010729 down-regulation on OGD/R-induced cell was partially restored after co-transfection with miR-665 inhibitor. Overexpression of miR-665 can promote the proliferation and inhibit apoptosis of OGD/R-induced HBMECs by inhibiting ING5 expression. In OGD/R-induced HBMECs, hsa_circ_0010729 silencing decreased ING5 expression by upregulating miR-665. Hsa_circ_0010729 regulated miR-665/ING5 axis in OGD/R-induced HBMECs. Therefore, hsa_circ_0010729 may be a new therapeutic target for ischemic stroke.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients.

BACKGROUND: Combining tubular damage and functional biomarkers may improve prediction precision of acute kidney injury (AKI). Serum cystatin C (sCysC) represents functional damage of kidney, while urinary N-acetyl-ß-D-glucosaminidase (uNAG) is considered as a tubular damage biomarker. So far, there is no nomogram containing this combination to predict AKI in septic cohort. We aimed to compare the performance of AKI prediction models with or without incorporating these two biomarkers and develop an effective nomogram for septic patients in intensive care unit (ICU). METHODS: This was a prospective study conducted in the mixed medical-surgical ICU of a tertiary care hospital. Adults with sepsis were enrolled. The patients were divided into development and validation cohorts in chronological order of ICU admission. A logistic regression model for AKI prediction was first constructed in the development cohort. The contribution of the biomarkers (sCysC, uNAG) to this model for AKI prediction was assessed with the area under the receiver operator characteristic curve (AUC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). Then nomogram was established based on the model with the best performance. This nomogram was validated in the validation cohort in terms of discrimination and calibration. The decision curve analysis (DCA) was performed to evaluate the nomogram's clinical utility. RESULTS: Of 358 enrolled patients, 232 were in the development cohort (69 AKI), while 126 in the validation cohort (52 AKI). The first clinical model included the APACHE II score, serum creatinine, and vasopressor used at ICU admission. Adding sCysC and uNAG to this model improved the AUC to 0.831. Furthermore, incorporating them significantly improved risk reclassification over the predictive model alone, with cNRI (0.575) and IDI (0.085). A nomogram was then established based on the new model including sCysC and uNAG. Application of this nomogram in the validation cohort yielded fair discrimination with an AUC of 0.784 and good calibration. The DCA revealed good clinical utility of this nomogram. CONCLUSIONS: A nomogram that incorporates functional marker (sCysC) and tubular damage marker (uNAG), together with routine clinical factors may be a useful prognostic tool for individualized prediction of AKI in septic patients.

Development and validation of a Chinese-language instrument measuring empowerment needs of patients after a percutaneous coronary intervention.

Patient empowerment has been shown to have some positive impacts on self-efficacy, self-esteem, and recovery. However, information about the empowerment needs of patients after a percutaneous coronary intervention is scarce. The aim of this study was to develop a Chinese-language instrument to measure empowerment needs of such patients. The initial instrument was generated based on a literature review and interviews with patients after a percutaneous coronary intervention procedure. Content validity was tested with a panel of experts using the Delphi method. In total, 226 patients were recruited for psychometric tests using the revised instrument. Expert authority coefficient was 0.92, and content validity index was 0.95. The internal consistency reliability was demonstrated by Cronbach's α coefficients (0.86 for the total score, 0.66-0.74 for the dimensions). The newly developed 19-item, five-dimension instrument has shown satisfactory validity (face/content validity and construct validity) and internal consistency reliability. The instrument could help clinical nurses who have close contact with patients after a percutaneous coronary intervention to gain a better understanding of their empowerment needs and could help develop appropriate health education to address such needs.