Nvidia (NVDA) remains the dominant force in AI computing — becoming the first company to reach a market capitalization of $5 trillion — as it transforms from a chipmaker into the essential platform powering the global shift toward accelerated computing and generative AI. The company’s new Blackwell architecture is driving a fresh product cycle, supported by expanding partnerships with major hyperscalers and rapid adoption of Spectrum-X networking, which extends NVDA’s influence across the entire data center stack. With management guiding to another record quarter and visibility into a $2T+ AI infrastructure opportunity, NVDA continues to combine exceptional growth, profitability and execution at a valuation that remains attractive relative to its earnings trajectory. Trade timing & outlook NVDA’s breakout above its prior $200 ceiling completes a three-month consolidation pattern, signaling continuation of its multi-quarter uptrend. Relative strength remains a key tailwind, with NVDA leading both the semiconductor sector and S & P 500, suggesting upside towards our $235 target. Fundamentals At roughly 31x forward earnings, NVDA trades modestly above the industry average of 26x but with far superior growth — consensus expects 37% EPS growth and 36% revenue growth, more than triple sector peers. With net margins exceeding 52%, NVDA’s profitability is unmatched among large-cap technology firms and reflects its scale advantage and vertical integration across hardware, networking, and software. Bullish thesis Record growth cycle: Q2 FY26 revenue grew 56% YoY to $46.7B, and guidance for Q3 at $54B signals sustained acceleration. Blackwell ramp: Strong demand for GB200 systems, up 17% sequentially, positions the new architecture as the key driver of 2026 growth. Ecosystem expansion: Spectrum-X networking wins with Meta and Oracle broaden NVDA’s reach beyond GPUs into AI infrastructure. Operational agility: The company effectively reallocated supply amid China export restrictions, preserving top-line momentum. Secular AI demand: With hyperscalers, governments, and enterprises all investing in AI compute, NVDA remains the defining beneficiary of the multi-year AI buildout. Options trade With an IV Rank of 40%, options premiums are moderately priced, offering attractive risk/reward through defined-risk debit spreads. I’m buying the Dec 19, 2025 $200/$235 Call Vertical @ $11 Debit. This entails: Buying the Dec 19, 2025 $200 call @ $14.90 Selling the Dec 19, 2025 $235 call @ $3.90 The maximum reward is $2,400 per contract if NVDA is above $235 at expiration. The maximum risk is $1,100 per contract if NVDA is below $200 at expiration. The breakeven point for this trade is $211. View this Trade with Updated Prices at OptionsPlay Summary NVDA’s breakout confirms renewed leadership in both AI infrastructure and networking, margin expansion, and visibility into another record-setting quarter. With the Blackwell cycle ramping, networking attach broadening, and trade headwinds proving manageable, NVDA remains one of the market’s clearest high-conviction plays on the continued acceleration of AI-driven compute demand. DISCLOSURES: Zhang has a position in NVDA. All opinions expressed by the CNBC Pro contributors are solely their opinions and do not reflect the opinions of CNBC, NBC UNIVERSAL, their parent company or affiliates, and may have been previously disseminated by them on television, radio, internet or another medium. THE ABOVE CONTENT IS SUBJECT TO OUR TERMS AND CONDITIONS AND PRIVACY POLICY . THIS CONTENT IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY AND DOES NOT CONSITUTE FINANCIAL, INVESTMENT, TAX OR LEGAL ADVICE OR A RECOMMENDATION TO BUY ANY SECURITY OR OTHER FINANCIAL ASSET. THE CONTENT IS GENERAL IN NATURE AND DOES NOT REFLECT ANY INDIVIDUAL’S UNIQUE PERSONAL CIRCUMSTANCES. THE ABOVE CONTENT MIGHT NOT BE SUITABLE FOR YOUR PARTICULAR CIRCUMSTANCES. BEFORE MAKING ANY FINANCIAL DECISIONS, YOU SHOULD STRONGLY CONSIDER SEEKING ADVICE FROM YOUR OWN FINANCIAL OR INVESTMENT ADVISOR. Click here for the full disclaimer.
The Japanese auto giant Toyota Motor has denied Donald Trump’s suggestion that it is poised to invest more than $10bn in the United States over the coming years.
On a visit to Japan earlier this week, the US president claimed he had been told that the carmaker was going to be setting up factories “all over” the US “to the tune of over $10bn”.
“Go out and buy a Toyota,” added Trump.
But a senior executive at Toyota – the world’s largest automaker – said that no such explicit promise of investment at that level had been made, although Toyota plans to invest and create new jobs in the US.
The firm held talks with Japanese and American officials ahead of Trump’s visit.
“During the first Trump administration, I think the figure was roughly around $10bn, so while we didn’t say the same scale, we did explain that we’ll keep investing and providing employment as before,” Hiroyuki Ueda told reporters, on the sidelines of the Japan Mobility Show in Tokyo. “So, probably because of that context, the figure of about $10bn came up.”
Toyota “didn’t specifically say that we’ll invest $10bn over the next few years”, Ueda said, adding that the topic of investment did not come up when Akio Toyoda, the firm’s chairman, spoke with Trump at a US Embassy event on Tuesday.
Trump met with Japan’s new prime minister and first female premier, Sanae Takaichi, on Tuesday. He welcomed Takaichi’s pledge to accelerate a military buildup, while also signing deals on trade and rare earths.
skip past newsletter promotion
after newsletter promotion
During the visit, Takaichi pledged to realise a “golden age” in relations with the US and to “fundamentally reinforce” her country’s defense posture. The two leaders signed an agreement laying out a framework to secure the mining and processing of rare earths and other minerals.
Today’s investors have a lot of options for where to invest their money. Between private markets, cryptocurrencies, and other financial instruments, more traditional stocks may look a little old-fashioned.
“If you dial the clock back [to] two decades ago, if you had money and wanted to invest, you would call up your brokers and talk about what stocks there are available,” Bonnie Chan, CEO of Hong Kong Exchanges and Clearing (HKEX), said Monday at the Fortune Global Forum in Riyadh.
“Now, people can get exposure to all sorts of investment opportunities. We’re entering a stage where exchanges are not really competing with one another, but working together.”
Since the first Bitcoin boom in the early 2010s, investors have increasingly explored new investment instruments, such as cryptocurrencies and other digital assets.
Meanwhile, stock markets are performing well this year, with indices reaching all-time highs, in part due to retail investors piling into buzzy companies and investment fads. On Monday, Chan’s fellow panelists, Saudi Tadawul Group CEO Eng. Khalid Abdullah Al Hussan and Nasdaq vice chairman Bob McCooey, noted that investor appetite was returning globally.
“The U.S. went through, from the end of 2021, two or three years of tough markets where people couldn’t get public. In 2025, we’re getting some momentum here,” McCooey said, referring to U.S. markets. He added that a growing number of companies want to go public (i.e. list shares for sale on the stock exchange), including private equity firms and government-backed companies.
Al Hussan also pointed to burgeoning investor appetite in Saudi Arabia’s market, noting that in the last three years, the country went from having eight to nine IPOs a year, to around 40 to 45 annually.
Chan, from HKEX, pointed out that Hong Kong’s exchanges have in recent times completed close to 80 IPOs. “We went through a phase in the last few years where there were questions as to the invest-ability of Chinese stocks. But I think we have made a lot of progress,” she said.
She attributed the global rise in IPOs to investors’ desire to diversify their investment and trading strategies, in order to hedge against market volatility from geopolitical uncertainty and new protectionist policies.
“They want to put their eggs in more than one basket,” she said, adding that Hong Kong has recently seen a return of international investors. “This year, we’ve seen a strong appetite from investors. They want AI, semiconductors, and names in the green technology space.”
Aside from tech, Chan noted a new investment trend, which she called “new consumption.” She cited the latest consumer craze for Labubu dolls, collectible plush toys designed by Hong Kong illustrator Kasing Lung. Pop Mart, which sells Labubu dolls in blind boxes, currently has a market value of over $40 billion.
Bank of Canada trims key interest rate, hints at end to cuts Reuters
Will the Bank of Canada pause its easing cycle after delivering another rate cut? FXStreet
BoC preview: a rate cut is expected but the focus will centre on forward guidance investingLive
In the news today: Is an interest rate cut coming? Blue Jays win Game 4 over Dodgers iNFOnews.ca
Canada Economics Brief: Bank of Canada Governing Council Sees Current Policy Rate at “About the Right Level” to keep Inflation Close to 2% MarketScreener
Carmakers in the EU are “days away” from closing production lines, the industry has warned as a crisis over computer chip supplies from China escalates.
The European Automobile Manufacturers’ Association (ACEA) issued an urgent warning on Wednesday saying its members, which include Volkswagen, Fiat, Peugeot and BMW, were now working on “reserve stocks but supplies are dwindling”.
“Assembly line stoppages might only be days away. We urge all involved to redouble their efforts to find a diplomatic way out of this critical situation,” said its director general, Sigrid de Vries.
Another ACEA member, Mercedes, is now searching globally for alternative sources of the crucial semiconductors, according to its chief executive, Ola Källenius.
The chip shortage is also causing problems in Japan, where Nissan’s chief performance officer, Guillaume Cartier, told reporters at a car show in Tokyo that the company was only “OK to the first week of November” in terms of supply.
Beijing banned exports of Nexperia chips near the start of the month in response to the Dutch government’s decision to take over the Netherlands-headquartered company on 30 September and suspend its Chinese chief executive after the US flagged security concerns.
Last week car companies in the UK, EU and Japan, including brands such as Volvo, Volkswagen, Honda and Nissan, said the ban on exports from Nexperia factories in China could halt production lines.
“The industry is currently working through reserve stocks but supplies are rapidly dwindling. From a survey of our members this week, some are already expecting imminent assembly line stoppages,” de Vries said.
The Nexperia chip ban was a blow to Europe’s car sector, which has already been hit by President Xi Jinping’s decision to reintroduce controls on exports of rare earth exports as part of the escalating trade tensions with the US.
Xi and Donald Trump are expected to sign off on a trade agreement when they meet on the sidelines of a summit in South Korea on Thursday. The proposed deal would pause the export ban on the crucial minerals for a year, but it is unclear if this will also cover deliveries to the EU.
Rare earths, in particular magnets, are used across the car industry for window, door and boot openings, while chips are critical to all electronics in vehicles, ranging from dashboard functions to ignition and transmission systems.
De Vries said while alternative suppliers for chips existed, it could take “months to build up additional capacity”. She said the “industry does not have that long before the worst effects of this shortage are felt”.
A high-level delegation from Beijing will arrive in Brussels on Friday for talks but there are fears the diplomatic tools deployed by the EU in the past months are not as effective as the hardballing used by the US and China.
“We know that all parties to this dispute are working very hard to find a diplomatic solution. At the same time, our members are telling us that part supplies are already being stopped due to the shortage,” de Vries said.
The Dutch government seized control of Nexperia on 30 September, citing lapses in governance. On 4 October, the Chinese ministry of commerce blocked exports of the chipmaker’s products out of China. While most of Nexperia’s semiconductors are produced in Europe, about 70% are packaged in China before distribution.
The company’s Chinese arm has taken steps toward independence and has resumed selling products to domestic Chinese customers.
The sources said the Dutch government believes it can negotiate a resolution with China that will restore the company to a unified Dutch-Chinese structure.
Ladha JK, Jat ML, Stirling CM, Chakraborty D, Pradhan P, Krupnik TJ et al. Achieving the sustainable development goals in agriculture: The crucial role of nitrogen in cereal-based systems. In: Sparks DL, editor. Advances in Agronomy, Vol 163. Advances in Agronomy. 1632020. pp. 39–116.
Menegat S, Ledo A, Tirado R. Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture. Sci Rep. 2022;12(1):14490.
Chauhan BSJ, Khawar J, Mahajan G. Rice production worldwide. Springer. 2017;247.
Xu JZ, Peng SZ, Yang SH, Wang WG. Ammonia volatilization losses from a rice paddy with different irrigation and nitrogen managements. Agric Water Manage. 2012;104:184–92.
Article
Google Scholar
Hu B, Wang W, Chen JJ, Liu YQ, Chu CC. Genetic improvement toward nitrogen-use efficiency in rice: lessons and perspectives. Mol Plant. 2023;16(1):64–74.
Article
CAS
PubMed
Google Scholar
Neeraja CN, Barbadikar KM, Mangrauthia SK, Rao PR, Subrahmanayam D, Sundaram RM. Genes for NUE in rice: a way forward for molecular breeding and genome editing. Plant Physiol Rep. 2021;26(4):587–99.
Article
CAS
Google Scholar
Cassman KG, Peng S, Olk DC, Ladha JK, Reichardt W, Dobermann A, et al. Opportunities for increased nitrogen-use efficiency from improved resource management in irrigated rice systems. Field Crops Res. 1998;56(1–2):7–39.
Article
Google Scholar
Li H, Hu B, Chu CC. Nitrogen use efficiency in crops: lessons from Arabidopsis and rice. J Exp Bot. 2017;68(10):2477–88.
Article
CAS
PubMed
Google Scholar
Reig-Valiente J, Viruel J, Sales E, Marqués L, Terol J, Gut M et al. Genetic diversity and population structure of rice varieties cultivated in temperate regions. Rice. 2016;9:58.
Andaya VC, Tai TH. Fine mapping of the qCTS12 locus, a major QTL for seedling cold tolerance in rice. Theor Appl Genet. 2006;113(3):467–75.
Article
CAS
PubMed
Google Scholar
Hour AL, Hsieh WH, Chang SH, Wu YP, Chin HS, Lin YR. Genetic diversity of landraces and improved varieties of rice (Oryza sativa L.) in Taiwan. Rice. 2020;13:82.
Kumar V, Rithesh L, Raghuvanshi N, Kumar A, Parmar K. Advancing nitrogen use efficiency in cereal crops: A comprehensive exploration of genetic manipulation, nitrogen dynamics, and plant nitrogen assimilation. South Afr J Bot. 2024;169:486–98.
Article
CAS
Google Scholar
Nazish T, Arshad M, Jan SU, Javaid A, Khan MH, Naeem MA, et al. Transporters and transcription factors gene families involved in improving nitrogen use efficiency (NUE) and assimilation in rice (Oryza sativa L). Transgenic Res. 2022;31(1):23–42.
Article
CAS
PubMed
Google Scholar
Hou MM, Yu M, Li ZQ, Ai ZY, Chen JG. Molecular regulatory networks for improving nitrogen use efficiency in rice. Int J Mol Sci. 2021;22(16):9040.
Chaudhary S, Kalkal M. Rice transcriptome analysis reveals nitrogen starvation modulates differential alternative splicing and transcript usage in various Metabolism-Related genes. Life-Basel. 2021;11(4):285.
Feng Y, Lu Q, Zhai RR, Zhang MC, Xu Q, Yang YL, et al. Genome wide association mapping for grain shape traits in indica rice. Planta. 2016;244(4):819–30.
Article
CAS
PubMed
PubMed Central
Google Scholar
Qiu XJ, Zhu SB, Hu H, Wang CC, Lv WK, He LP, et al. Genome-Wide association mapping for grain shape in rice accessions. Int J Agric Biology. 2020;23(3):582–8.
CAS
Google Scholar
Liu YZ, Xin W, Chen LQ, Liu YQ, Wang X, Ma C et al. Genome-Wide association analysis of effective tillers in rice under different nitrogen gradients. Int J Mol Sci. 2024;25(5):2969.
Cheng XP, Chang YP, Sun JH, Du MY, Liang LP, Zhang MY et al. Identification of candidate genes and favourable haplotypes for yield traits in rice based on a genome-wide association study. Euphytica. 2023;219(12):125.
Peng SS, Liu YC, Xu YC, Zhao JH, Gao P, Liu Q et al. Genome-Wide association study identifies a Plant-Height-Associated Gene OsPG3 in a population of commercial rice varieties. Int J Mol Sci. 2023;24(14):11454.
Rakotoson T, Dusserre J, Letourmy P, Frouin J, Ratsimiala IR, Rakotoarisoa NV, et al. Genome-Wide association study of nitrogen use efficiency and agronomic traits in upland rice. Rice Sci. 2021;28(4):379–90.
Article
Google Scholar
Wang YY, Cheng YH, Chen KE, Tsay YF. Nitrate Transport, Signaling, and Use Efficiency. In: Merchant SS, editor. Annual Review of Plant Biology, Vol 69. Annual Review of Plant Biology. 692018. pp. 85–122.
Wei D, Cui KH, Ye GY, Pan JF, Xiang J, Huang JL, et al. QTL mapping for nitrogen-use efficiency and nitrogen-deficiency tolerance traits in rice. Plant Soil. 2012;359(1–2):281–95.
Article
CAS
Google Scholar
Li Q, Lu XL, Wang CJ, Shen L, Dai LP, He JL, et al. Genome-wide association study and transcriptome analysis reveal new QTL and candidate genes for nitrogen-deficiency tolerance in rice. Crop J. 2022;10(4):942–51.
Article
CAS
Google Scholar
Ding CQ, You J, Chen L, Wang SH, Ding YF. Nitrogen fertilizer increases spikelet number per panicle by enhancing cytokinin synthesis in rice. Plant Cell Rep. 2014;33(2):363–71.
Article
CAS
PubMed
Google Scholar
Das K, Panda BB, Shaw BP, Das SR, Dash SK, Kariali E et al. Grain density and its impact on grain filling characteristic of rice: mechanistic testing of the concept in genetically related cultivars. Sci Rep. 2018;8:4149.
Gao HB, Wang WG, Wang YH, Liang Y. Molecular mechanisms underlying plant architecture and its environmental plasticity in rice. Mol Breeding. 2019;39(12):167.
Dou Z, Zhou YC, Zhang YY, Guo W, Xu Q, Gao H. Influence of nitrogen applications during grain-Filling stage on rice (Oryza sativa L.) yield and grain quality under high temperature. Agronomy-Basel. 2024;14(1):216.
Li RH, Li MJ, Ashraf U, Liu SW, Zhang JE. Exploring the relationships between yield and yield-Related traits for rice varieties released in China from 1978 to 2017. Front Plant Sci. 2019;10:543.
Zhang QG, Sun J, Wang LP, Chen J, Ke J, Wu LQ. Effects of nitrogen application at different panicle development stages on the panicle structure and grain yield in hybrid indica rice cultivars. Agronomy-Basel. 2025;15(3):595.
Xin W, Wang JG, Li J, Zhao HW, Liu HL, Zheng HL et al. Candidate gene analysis for nitrogen absorption and utilization in Japonica rice at the seedling stage based on a Genome-Wide association study. Front Plant Sci. 2021;12:670861.
García-Romeral J, Castanera R, Casacuberta J, Domingo C. Deciphering the genetic basis of allelopathy in japonica rice cultivated in temperate regions using a Genome-Wide association study. Rice. 2024;17(1):22.
Walkley A, Black IA. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 1934;37(1):29–38.
Article
CAS
Google Scholar
Bouyoucos GJ. The hydrometer as a new method for the mechanical analysis of soils. Soil Sci. 1927;23(5):343–54.
Article
CAS
Google Scholar
Lancashire PD, Bleiholder H, Vandenboom T, Langeluddeke P, Stauss R, Weber E, et al. A uniform decimal code for growth-stages of crops and weeds. Ann Appl Biol. 1991;119(3):561–601.
Article
Google Scholar
Inc. SI. JMP®. 16 ed. Cary, NC: SAS Institute Inc.; 1989–2023.
Team RC. R: A Language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023.
Google Scholar
Kowarik A, Templ M. Imputation with the R package VIM. J Stat Softw. 2016;74(7):1–16.
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.
Article
CAS
PubMed
Google Scholar
Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8.
Article
CAS
PubMed
Google Scholar
Patil I. Visualizations with statistical details: the ‘ggstatsplot’ approach. J Open Source Softw. 2021;6:3167.
Article
Google Scholar
Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, et al. Rice annotation project database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Physiol. 2013;54(2):e6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kawahara Y, de la Bastide M, Hamilton JP, Kanamori H, McCombie WR, Ouyang S, et al. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Volume 6. New York, NY: Rice; 2013. p. 4. 1.
Google Scholar
McCouch SR, Rice Genetics C. Gene nomenclature system for rice. Rice. 2008;1(1):72–84.
Article
Google Scholar
Kurata N, Yamazaki Y, Oryzabase. An integrated biological and genome information database for rice. Plant Physiol. 2006;140(1):12–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mansueto L, Fuentes RR, Borja FN, Detras J, Abriol-Santos JM, Chebotarov D, et al. Rice SNP-seek database update: new snps, indels, and queries. Nucleic Acids Res. 2017;45(D1):D1075–81.
Article
CAS
PubMed
Google Scholar
Alexandrov N, Tai SS, Wang WS, Mansueto L, Palis K, Fuentes RR, et al. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 2015;43(D1):D1023–7.
Article
CAS
PubMed
Google Scholar
Yang WZ, Yoon J, Choi H, Fan YL, Chen RM, An G. Transcriptome analysis of nitrogen-starvation-responsive genes in rice. BMC Plant Biol. 2015;15:31.
Yan M, Fan XR, Feng HM, Miller AJ, Shen QR, Xu GH. Rice OsNAR2.1 interacts with OsNRT2.1, OsNRT2.2 and OsNRT2.3a nitrate transporters to provide uptake over high and low concentration ranges. Plant Cell Environ. 2011;34(8):1360–72.
Article
CAS
PubMed
Google Scholar
Liu XQ, Huang DM, Tao JY, Miller AJ, Fan XR, Xu GH. Identification and functional assay of the interaction motifs in the partner protein OsNAR2.1 of the two-component system for high-affinity nitrate transport. New Phytol. 2014;204(1):74–80.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jiang HZ, Wang YM, Lai LR, Liu XT, Miao CJ, Liu RF, et al. OsAMT1.1 Expression by Nitrate-Inducible promoter of OsNAR2.1 Increases nitrogen use efficiency and rice yield. Rice Sci. 2023;30(3):222–34.
Article
Google Scholar
Chen HL, Xu N, Wu Q, Yu B, Chu YL, Li XX, et al. OsMADS27 regulates the root development in a NO3-dependent manner and modulates the salt tolerance in rice (Oryza sativa L.). Plant Sci. 2018;277:20–32.
Article
CAS
PubMed
Google Scholar
Pachamuthu K, Sundar VH, Narjala A, Singh RR, Das S, Pal H, et al. Nitrate-dependent regulation of miR444-OsMADS27 signalling cascade controls root development in rice. J Exp Bot. 2022;73(11):3511–30.
Article
CAS
PubMed
Google Scholar
Singh RK, Khan W, editors. Effect of nitrogen levels on growth and yield of basmati rice varieties2021.
Li G, Zhang Y, Zhou C, Xu J, Cj Z, Ni C, et al. Agronomic and physiological characteristics of high yield and nitrogen use efficient varieties of rice: comparison between two near-isogenic lines. Food Energy Secur. 2024;13(2):e539.
Article
CAS
Google Scholar
Tayefe M, Gerayzade A, Amiri E, Zade AN. Effect of nitrogen on rice yield, yield components and quality parameters. 2014.
Haghshenas H, Ghanbari Malidarreh A. Response of yield and yield components of released rice cultivars from 1990–2010 to nitrogen rates. Cent Asian J Plant Sci Innov. 2021;1(1):23–31.
Google Scholar
Zhang SY, Huang YZ, Ji Z, Fang YZ, Tian YN, Shen CB, et al. Discovery of SOD5 as a novel regulator of nitrogen-use efficiency and grain yield via altering auxin level. New Phytol. 2025;246(3):1084–95.
Article
CAS
PubMed
Google Scholar
Wu J, Sun LQ, Song Y, Bai Y, Wan GY, Wang JX, et al. The OsNLP3/4-OsRFL module regulates nitrogen-promoted panicle architecture in rice. New Phytol. 2023;240(6):2404–18.
Article
CAS
PubMed
Google Scholar
Duan YH, Zhang YL, Ye LT, Fan XR, Xu GH, Shen QR. Responses of rice cultivars with different nitrogen use efficiency to partial nitrate nutrition. Ann Botany. 2007;99(6):1153–60.
Article
CAS
Google Scholar
Tsednee M. Linking timing to nitrogen use efficiency: rice OsEC-Ghd7-ARE1 module works on it. Plant Physiol. 2024;196(3):1720–1.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhang L, Shen C, Zhu S, Ren N, Chen K, Xu J. Effects of sowing date and nitrogen (N) application rate on grain yield, nitrogen use efficiency and 2-acetyl-1-pyrroline formation in fragrant rice. Agronomy. 2022;12(12):3035.
Article
CAS
Google Scholar
Zhang SN, Zhang YY, Li KN, Yan M, Zhang JF, Yu M, et al. Nitrogen mediates flowering time and nitrogen use efficiency via floral regulators in rice. Curr Biol. 2021;31(4):671–83.
Article
CAS
PubMed
Google Scholar
Mo Y, Jeong JM, Ha SK, Kim J, Lee C, Lee GP et al. Characterization of QTLs and candidate genes for days to heading in rice Recombinant inbred lines. Genes. 2020;11(9):957.
Song J, Tang LQ, Cui YT, Fan HH, Zhen XQ, Wang JJ. Research progress on photoperiod gene regulation of heading date in rice. Curr Issues Mol Biol. 2024;46(9):10299–311.
Article
CAS
PubMed
PubMed Central
Google Scholar
Reig-Valiente JL, Marqués L, Talón M, Domingo C. Genome-wide association study of agronomic traits in rice cultivated in temperate regions. BMC Genomics. 2018;19:706.
Song YL, Gao ZC, Luan WJ. Interaction between temperature and photoperiod in regulation of flowering time in rice. Sci China-Life Sci. 2012;55(3):241–9.
Article
CAS
PubMed
Google Scholar
Matsubara K, Yamanouchi U, Wang ZX, Minobe Y, Izawa T, Yano M. Ehd2, a rice ortholog of the maize INDETERMINATE1 gene, promotes flowering by up-regulating Ehd1. Plant Physiol. 2008;148(3):1425–35.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ohkubo Y, Kuwata K, Matsubayashi Y. A type 2 C protein phosphatase activates high-affinity nitrate uptake by dephosphorylating NRT2.1. Nat Plants. 2021;7(3):310.
Article
CAS
PubMed
Google Scholar
Wu JY, Yang SQ, Chen NA, Jiang QN, Huang LL, Qi JX et al. Nuclear translocation of OsMADS25 facilitated by OsNAR2.1 in reponse to nitrate signals promotes rice root growth by targeting OsMADS27 and OsARF7. Plant Commun. 2023;4(6):100642.
Alfatih A, Zhang J, Song Y, Jan SU, Zhang ZS, Xia JQ et al. Nitrate-responsive OsMADS27 promotes salt tolerance in rice. Plant Commun. 2023;4(2):100458.
Song X, Xiong Y, Kong X, Huang G. Roles of auxin response factors in rice development and stress responses. Plant Cell Environ. 2023;46(4):1075–86.
Article
CAS
PubMed
Google Scholar
Zhang S, Wu T, Liu S, Liu X, Jiang L, Wan J. Disruption of OsARF19 is critical for floral organ development and plant architecture in rice (Oryza sativa L). Plant Mol Biology Report. 2016;34:748–60.
Article
CAS
Google Scholar
Gho YS, Song MY, Bae DY, Choi H, Jung KH. Rice PIN auxin efflux carriers modulate the nitrogen response in a changing nitrogen growth environment. Int J Mol Sci. 2021;22(6):3243.
Zhang S, Zhu L, Shen C, Ji Z, Zhang H, Zhang T, et al. Natural allelic variation in a modulator of auxin homeostasis improves grain yield and nitrogen use efficiency in rice. Plant Cell. 2020;33(3):566–80.
Article
PubMed Central
Google Scholar
Zhang S, Wang S, Xu Y, Yu C, Shen C, Qian Q, et al. The auxin response factor, OsARF19, controls rice leaf angles through positively regulating OsGH3-5 and OsBRI1. Plant Cell Environ. 2015;38(4):638–54.
Article
PubMed
Google Scholar
Wu D, Cao Y, Wang D, Zong G, Han K, Zhang W, et al. Auxin receptor OsTIR1 mediates auxin signaling during seed filling in rice. Plant Physiol. 2024;194(4):2434–48.
Article
CAS
PubMed
Google Scholar
Cortes LT, Zhang ZW, Yu JM. Status and prospects of genome-wide association studies in plants. Plant Genome. 2021;14(1):e20077.
Curtin SJ, Tiffin P, Guhlin J, Trujillo DI, Burghardt LT, Atkins P, et al. Validating genome-wide association candidates controlling quantitative variation in nodulation. Plant Physiol. 2017;173(2):921–31.
Immunotherapy, particularly immune checkpoint inhibition, has revolutionized modern oncology by achieving durable remissions across a wide range of tumor types. Despite these transformative outcomes, only 20–30% of patientsexperience sustained benefit, underscoring a critical challenge in accurately predicting who will respond to treatment. The clinical and economic implications are substantial—given that the global ICI market is projected to surpass $189 billion by 2032, the urgency for precise, clinically actionable predictive tools has never been greater.
In this comprehensive and forward-looking review, Oisakede et al. explore the rapidly advancing field of predictive modeling for immunotherapy response, encompassing biomarker-driven strategies, artificial intelligence and machine learning algorithms, mechanistic modeling, and multi-modal integration frameworks. Drawing from more than 200 studies, the authors deliver one of the most extensive comparative analyses to date, evaluating predictive accuracy, clinical applicability, and translational readiness across emerging methodologies.
Key Findings and Conceptual Advances
Limitations of single biomarkers
Traditional biomarkers—such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI)—have long served as the foundation for selecting patients who might benefit from immune checkpoint inhibitors. However, their predictive accuracy remains limited.
PD-L1 expression, for instance, is predictive in only about 29% of FDA-approved indications, and both TMB and MSI show highly variable reliability across cancer types. These inconsistencies stem from biological heterogeneity, differences in testing platforms, and the complex interplay between tumor and immune factors.
The authors highlight that predicting immunotherapy response cannot rely on any single molecular marker. Instead, multi-parametric models—those integrating molecular, immunologic, and spatial data—are needed to capture the full biological context of the tumor microenvironment (TME).
The rise of metabolic biomarkers
Beyond genomics, metabolic reprogramming has emerged as a critical determinant of immune evasion and treatment resistance. Tumors with elevated expression of glucose transporters GLUT1 and GLUT3 exhibit enhanced glycolysis, creating an acidic microenvironment that suppresses T-cell activity. This glucose competition between tumor and immune cells not only limits immune effector function but also promotes immune exhaustion.
Incorporating these metabolic biomarkers into predictive models could refine patient stratification—particularly for tumors that are both metabolically active and immunologically “cold.” Such integrated models could help identify patients who may benefit from therapies that target both metabolism and immune regulation.
Artificial intelligence outperforms traditional biomarkers
Artificial intelligence (AI) and machine learning (ML) now represent the fastest-growing frontiers in predictive oncology. These approaches are capable of integrating high-dimensional clinical, molecular, and imaging data to uncover complex patterns not visible to human observers.
The SCORPIO model, developed at Memorial Sloan Kettering Cancer Center, analyzed data from nearly 10,000 patients across 21 cancer types and achieved an AUC of 0.76 for predicting overall survival—significantly outperforming PD-L1 and TMB.
The LORIS model, based on six routine clinical and genomic parameters (age, albumin, neutrophil-to-lymphocyte ratio, TMB, prior therapy, and cancer type), achieved 81% predictive accuracy and showed strong external validation across multiple international cohorts.
Deep learning approaches applied to histopathology images have further advanced predictive precision, enabling automated assessment of PD-L1 expression and tumor-infiltrating lymphocytes (TILs) with AUC values exceeding 0.9 in controlled research settings.
Despite these advances, one major challenge persists: external validation. Many AI models perform exceptionally well within the institution where they were developed but fail to maintain accuracy when tested on independent patient populations—a problem the authors refer to as the “validation gap.”
Integrating multi-modal data for precision prediction
Combining multiple types of data—genomic, spatial, clinical, and metabolic—has proven far more effective than using single-modality biomarkers. These multi-modal frameworks have achieved AUC values above 0.85 in several cancers, outperforming traditional metrics. For example, integrating PD-L1 expression, TMB, and immune cell infiltration patterns improved predictive power in non–small cell lung cancer and melanoma.
Modern spatial profiling technologies, such as multiplex immunofluorescence and digital spatial transcriptomics, now reveal how immune and tumor cells are organized within the TME. This spatial information often correlates more strongly with treatment response than bulk biomarker measurements, underscoring the importance of tumor architecture in predicting therapeutic outcomes.
Dynamic and mechanistic modeling approaches
A new generation of mathematical and systems biology models aims to simulate tumor–immune interactions in real time. These models capture key parameters—such as tumor growth kinetics, immune infiltration, and checkpoint blockade dynamics—to forecast treatment outcomes and understand resistance mechanisms.
Early studies show promising results: some of these mechanistic models can classify responders versus non-responders with up to 81% accuracy in pilot validation cohorts. Although still in early stages, these models complement data-driven AI approaches by offering a mechanistic understanding of immune dynamics, which could ultimately guide personalized dosing, combination strategies, and treatment adaptation.
Natural Compounds and Metabolic–Immune Crosstalk
A novel section of the review explores the integration of natural bioactive compounds—such as thymoquinone (Nigella sativa), cucurbitacins (Cucurbitaceae), and organosulfur compounds (garlic derivatives)—which have demonstrated immunomodulatory and metabolic reprogramming effects in preclinical studies. These agents may augment ICI efficacy by:
Reducing tumor glycolysis and acidosis;
Enhancing T-cell function and cytokine production;
Such multi-target actions highlight a potential role for metabolic–immune combinatorial therapy, though clinical validation remains limited.
Implementation Challenges
The review identifies three persistent barriers that hinder translation from research to practice:
Validation and reproducibility: promising models rarely replicate performance outside their development cohort.
Data standardisation: inconsistencies in biomarker assays, imaging platforms, and sequencing pipelines undermine generalisability.
Healthcare integration: lack of interoperability between predictive algorithms and clinical information systems delays implementation in real-world oncology workflows.
The authors call for international standardisation frameworks, similar to those of the Global Alliance for Genomics and Health (GA4GH), to harmonise data collection, model validation, and AI governance in oncology.
Significance
This landmark review represents one of the most comprehensive syntheses of predictive model development in immuno-oncology. It bridges traditional pathology and next-generation computational science, highlighting the need for multidisciplinary collaboration among pathologists, oncologists, data scientists, and regulatory bodies. By systematically evaluating every major class of predictive approach—from PD-L1 scoring to AI integration—this work outlines a roadmap for clinically implementable, validated, and interpretable models capable of guiding patient selection and optimizing immune checkpoint inhibitor therapy.
Key Takeaway Messages
ICIs benefit only a minority of patients; precision prediction is critical for therapeutic success.
AI and multi-modal models outperform traditional biomarkers, but external validation remains the main translational bottleneck.
Integration of metabolic and spatial biomarkers provides new biological dimensions for prediction.
Natural bioactive compounds may enhance checkpoint inhibitor efficacy via metabolic and immune modulation.
Future success depends on global standardisation, real-time adaptive modeling, and clinically interpretable AI integration.
Supermicro Announces U.S Federal Entity to Expand Further into the Federal Market — Extensive US-Based Manufacturing of AI Server Portfolio Targets the Federal Ecosystem Supermicro
Supermicro Expands Collaboration with NVIDIA and Strengthens Compliance, Data Integrity, and Quality of U.S.-Based Manufacturing of AI Infrastructure Solutions Optimized for Government Applications Supermicro
Super Micro announces creation of Super Micro Federal LLC TipRanks
DDN Launches Enterprise AI HyperPOD, the DDN AI Data Platform Built on Supermicro, Accelerated by NVIDIA at GTC-DC Yahoo Finance
Supermicro Announces US Federal Entity to Expand Further into Federal Market HPCwire
Children and adults face challenges with focus and attention. This not only makes learning difficult but can seriously impact a person’s ability to plan tasks, complete work and finish assignments. With children, the effects of limited focus can be particularly pronounced, inhibiting their educational, social and emotional development.
Samsung Electronics, a world leader in consumer electronics, and Pearson (FTSE: PSON.L), the world’s lifelong learning company, are collaborating to help children and adults overcome these challenges.
Pearson recently introduced Revibe, an AI-enabled wearable solution delivered via the Samsung Galaxy Watch7, to help individuals build skills in focus, attention and self-regulation.
Revibe tracks on-task behavior, fidgeting, work completion and exercise while providing reminders to stay focused, remember tasks and complete work, which, as part of a healthy lifestyle, may help individuals living with conditions such as ADHD.
By leveraging AI to translate real-time behavioral data into actionable insights, Revibe equips professionals and individual users with data-informed pathways to improve focus in the classroom and beyond.
Combining Pearson’s proprietary, attention-enhancing software with the Galaxy Watch7, including Samsung’s Knox mobile security platform, Revibe is a discreet, real-time tool designed to help users improve concentration and develop stronger self-regulation skills throughout the day. This collaboration reflects a shared commitment to advancing innovation and creating inclusive, accessible solutions that empower individuals of all ages who are navigating focus and attention-related challenges.
Using AI and advanced algorithms to understand Galaxy Watch sensor data, Revibe learns each user’s behavior patterns, including attention span, fidgeting, steps, calories burned and more. Revibe’s software then addresses individuals with focus and attention challenges from multiple angles. Vibrating alerts bring the user back on task and bolster executive function, while on-screen “light bulb moments” provide guidance that won’t disturb others.
Leveraging Samsung’s Freestanding Mode on the Galaxy Watch, Revibe also eliminates smartphone distractions by enabling the Galaxy Watch to operate independently, without a smartphone, for a streamlined user experience. Freestanding Mode is especially important when Revibe is used by children, since most smartwatches are simply an extension of a smartphone, and many schools don’t allow children to carry phones.
The Revibe app offers users, families, educators, and clinicians a user-friendly dashboard that visualizes progress in near real time, which can lead to more customized support in the classroom and elsewhere to help individuals succeed.
“With Revibe, Pearson empowers individuals who experience focus and attention barriers, along with their families and support networks, by helping them build the self-regulation skills they need for success,” said Rich Brancaccio, Senior Director, Pearson, and the Founder of Revibe. “After evaluating multiple wearable solutions, we determined that the Samsung Galaxy Watch was the right device for Revibe, offering the ideal balance of a low-distraction interface, extended battery life1 and secure data collection capabilities to serve the needs of these individuals and help them reach their fullest potential.”
“Samsung Galaxy Watch perfectly fits Revibe’s needs thanks to capabilities such as Samsung’s Knox mobile security management platform, Freestanding Mode, and Kiosk Mode,” said Cherry Drulis, MBA, BSN, RN, Senior Director, Regulated Industry Samsung. “With Knox, Revibe can apply policies to the Galaxy Watch, including software updates to ensure continued compatibility, then detach it from its phone dependency as a freestanding device. Freestanding Mode maintains location tracking so lost devices can be recovered2, while Kiosk Mode keeps Galaxy Watch focused on Revibe’s application, ensuring individuals with focus and attention challenges enjoy easier access with fewer distractions.”
The Samsung and Revibe collaboration will begin with the Samsung Galaxy Watch7 series and is expected to expand to additional Samsung devices. Revibe will offer the solution to clinical professionals across education and healthcare, as well as individual users, parents, and other care teams.
To learn more about Samsung and Pearson’s collaboration, please visit: https://insights.samsung.com/2025/10/29/the-power-of-collaboration or watch the video: https://www.youtube.com/watch?v=ILiCdec7rp4
For more information on the Revibe wearable, please visit: https://www.pearsonassessments.com/campaign/revibe.html
For more information on Samsung Galaxy Watch7, please visit: https://www.samsung.com/us/watches/galaxy-watch7/
Sung JH, et al. An unusual degenerative disorder of neurons associated with a novel intranuclear hyaline inclusion (neuronal intranuclear hyaline inclusion disease). A clinicopathological study of a case. J Neuropathol Exp Neurol. 1980;39(2):107–30.
Article
CAS
PubMed
Google Scholar
Sone J, et al. Clinicopathological features of adult-onset neuronal intranuclear inclusion disease. Brain. 2016;139(Pt 12):3170–86.
Article
PubMed
PubMed Central
Google Scholar
Sone J, et al. Long-read sequencing identifies GGC repeat expansions in NOTCH2NLC associated with neuronal intranuclear inclusion disease. Nat Genet. 2019;51(8):1215–21.
Article
CAS
PubMed
Google Scholar
Tian Y, et al. Expansion of human-specific GGC repeat in neuronal intranuclear inclusion disease-related disorders. Am J Hum Genet. 2019;105(1):166–76.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sun QY, et al. Expansion of GGC repeat in the human-specific NOTCH2NLC gene is associated with essential tremor. Brain. 2020;143(1):222–33.
Article
PubMed
Google Scholar
Ehrlich ME, Ellerby LM. Neuronal intranuclear inclusion disease: polyglycine protein is the culprit. Neuron. 2021;109(11):1757–60.
Article
CAS
PubMed
PubMed Central
Google Scholar
Yu J, et al. CGG repeat expansion in NOTCH2NLC causes mitochondrial dysfunction and progressive neurodegeneration in drosophila model. Proc Natl Acad Sci U S A. 2022;119(41):e2208649119.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu Q, et al. Expression of expanded GGC repeats within NOTCH2NLC causes behavioral deficits and neurodegeneration in a mouse model of neuronal intranuclear inclusion disease. Sci Adv. 2022;8(47):eadd6391.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhong S, et al. Upstream open reading frame with NOTCH2NLC GGC expansion generates polyglycine aggregates and disrupts nucleocytoplasmic transport: implications for polyglycine diseases. Acta Neuropathol. 2021;142(6):1003–23.
Article
CAS
PubMed
Google Scholar
Fan Y, et al. GGC repeat expansion in NOTCH2NLC induces dysfunction in ribosome biogenesis and translation. Brain. 2023;146(8):3373–91.
Article
PubMed
Google Scholar
Tian Y, et al. Clinical features of NOTCH2NLC-related neuronal intranuclear inclusion disease. J Neurol Neurosurg Psychiatry. 2022;93(12):1289–98.
Article
PubMed
Google Scholar
Chen H, et al. Re-defining the clinicopathological spectrum of neuronal intranuclear inclusion disease. Ann Clin Transl Neurol. 2020;7(10):1930–41.
Article
CAS
PubMed
PubMed Central
Google Scholar
Tai H, et al. Clinical features and classification of neuronal intranuclear inclusion disease. Neurol Genet. 2023;9(2):e200057.
Article
PubMed
PubMed Central
Google Scholar
Jiang S, et al. Generic diagramming platform (GDP): a comprehensive database of high-quality biomedical graphics. Nucleic Acids Res. 2025;53(D1):D1670–6.
Article
PubMed
Google Scholar
Pang J, et al. The value of NOTCH2NLC gene detection and skin biopsy in the diagnosis of neuronal intranuclear inclusion disease. Front Neurol. 2021;12:624321.
Article
PubMed
PubMed Central
Google Scholar
Lee HG, et al. Neuroinflammation: an astrocyte perspective. Sci Transl Med. 2023;15(721):eadi7828.
Article
CAS
PubMed
Google Scholar
Bao L et al. Immune system involvement in neuronal intranuclear inclusion disease. Neuropathol Appl Neurobiol, 2024;50(2):e12976.
Mori K, et al. Imaging findings and pathological correlations of subacute encephalopathy with neuronal intranuclear inclusion disease-case report. Radiol Case Rep. 2022;17(12):4481–6.
Article
PubMed
PubMed Central
Google Scholar
Prinz M, Priller J. The role of peripheral immune cells in the CNS in steady state and disease. Nat Neurosci. 2017;20(2):136–44.
Article
CAS
PubMed
Google Scholar
Yoshii D, et al. An autopsy case of adult-onset neuronal intranuclear inclusion disease with perivascular preservation in cerebral white matter. Neuropathology. 2021;42(1):66–73.
Article
PubMed
Google Scholar
Tracey KJ, Cerami A. Tumor necrosis factor: a pleiotropic cytokine and therapeutic target. Annu Rev Med. 1994;45:491–503.
Article
CAS
PubMed
Google Scholar
Dinarello CA. Interleukin-1 in the pathogenesis and treatment of inflammatory diseases. Blood. 2011;117(14):3720–32.
Article
CAS
PubMed
PubMed Central
Google Scholar
Tanaka T, Narazaki M, Kishimoto T. IL-6 in inflammation, immunity, and disease. Cold Spring Harb Perspect Biol. 2014;6(10):a016295.
Article
PubMed
PubMed Central
Google Scholar
Lu X, Hong D. Neuronal intranuclear inclusion disease: recognition and update. J Neural Transm. 2021;128(3):295–303.
Article
PubMed
Google Scholar
Bao L, et al. Utility of labial salivary gland biopsy in the histological diagnosis of neuronal intranuclear inclusion disease. Eur J Neurol. 2024;31(1):e16102.
Article
PubMed
Google Scholar
Zhong S, et al. Spatial and Temporal distribution of white matter lesions in NOTCH2NLC-Related neuronal intranuclear inclusion disease. Neurology. 2025;104(4):e213360.
Article
CAS
PubMed
Google Scholar
Du N et al. Value of neutrophil-to-lymphocyte ratio in neuronal intranuclear inclusion disease. Heliyon, 2024;10(6):e27953.
Yan Y, et al. The clinical characteristics of neuronal intranuclear inclusion disease and its relation with inflammation. Neurol Sci. 2023;44(9):3189–97.
Article
PubMed
Google Scholar
Shen Y et al. uN2CpolyG-mediated p65 nuclear sequestration suppresses the NF-κB-NLRP3 pathway in neuronal intranuclear inclusion disease. Cell Communication Signal, 2025;23(1):68.
Tateishi J, et al. Intranuclear inclusions in muscle, nervous tissue, and adrenal gland. Acta Neuropathol. 1984;63(1):24–32.
Article
CAS
PubMed
Google Scholar
Platero JL et al. The impact of coconut oil and Epigallocatechin gallate on the levels of IL-6, anxiety and disability in multiple sclerosis patients. Nutrients, 2020;12(2):305.
Casali BT, et al. Omega-3 fatty acids augment the actions of nuclear receptor agonists in a mouse model of alzheimer’s disease. J Neurosci. 2015;35(24):9173–81.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chauhan A, et al. Phytochemicals targeting NF-κB signaling: potential anti-cancer interventions. J Pharm Anal. 2022;12(3):394–405.
Article
CAS
PubMed
Google Scholar
Shabab T, et al. Neuroinflammation pathways: a general review. Int J Neurosci. 2017;127(7):624–33.
Article
CAS
PubMed
Google Scholar
Lawrence T. The nuclear factor NF-kappaB pathway in inflammation. Cold Spring Harb Perspect Biol. 2009;1(6):a001651.
Article
PubMed
PubMed Central
Google Scholar
Gleeson M, et al. The anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease. Nat Rev Immunol. 2011;11(9):607–15.
Article
CAS
PubMed
Google Scholar
Pedersen BK. Adolph distinguished lecture: muscle as an endocrine organ: IL-6 and other myokines. J Appl Physiol (1985). 2009;107(4):1006–14. Edward F.
Article
CAS
PubMed
Google Scholar
Steensberg A, et al. IL-6 enhances plasma IL-1ra, IL-10, and cortisol in humans. Am J Physiol Endocrinol Metab. 2003;285(2):E433–7.
Article
CAS
PubMed
Google Scholar
Ma Q. Beneficial effects of moderate voluntary physical exercise and its biological mechanisms on brain health. Neurosci Bull. 2008;24(4):265–70.
Article
CAS
PubMed
PubMed Central
Google Scholar
Adlard PA, et al. Voluntary exercise decreases amyloid load in a transgenic model of Alzheimer’s disease. J Neurosci. 2005;25(17):4217–21.
Article
CAS
PubMed
PubMed Central
Google Scholar
Periasamy S, et al. Sleep deprivation-induced multi-organ injury: role of oxidative stress and inflammation. Excli J. 2015;14:672–83.
PubMed
PubMed Central
Google Scholar
Shearer WT, et al. Soluble TNF-alpha receptor 1 and IL-6 plasma levels in humans subjected to the sleep deprivation model of spaceflight. J Allergy Clin Immunol. 2001;107(1):165–70.
Article
CAS
PubMed
Google Scholar
Ooms S, et al. Effect of 1 night of total sleep deprivation on cerebrospinal fluid β-amyloid 42 in healthy middle-aged men: a randomized clinical trial. JAMA Neurol. 2014;71(8):971–7.
Article
PubMed
Google Scholar
Reiter RJ, et al. Brain washing and neural health: role of age, sleep, and the cerebrospinal fluid melatonin rhythm. Cell Mol Life Sci. 2023;80(4):88.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhao HY, et al. Chronic sleep restriction induces cognitive deficits and cortical Beta-Amyloid deposition in mice via BACE1-Antisense activation. CNS Neurosci Ther. 2017;23(3):233–40.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhang Y, et al. Quercetin ameliorates memory impairment by inhibiting abnormal microglial activation in a mouse model of Paradoxical sleep deprivation. Biochem Biophys Res Commun. 2022;632:10–6.
Article
CAS
PubMed
Google Scholar
Irwin MR, et al. Tai Chi compared with cognitive behavioral therapy and the reversal of systemic, cellular and genomic markers of inflammation in breast cancer survivors with insomnia: A randomized clinical trial. Brain Behav Immun. 2024;120:159–66.
Article
CAS
PubMed
Google Scholar
Dutcher JM, et al. Smartphone mindfulness meditation training reduces Pro-inflammatory gene expression in stressed adults: A randomized controlled trial. Brain Behav Immun. 2022;103:171–7.
Article
CAS
PubMed
Google Scholar
Walsh E, Eisenlohr-Moul T, Baer R. Brief mindfulness training reduces salivary IL-6 and TNF-α in young women with depressive symptomatology. J Consult Clin Psychol. 2016;84(10):887–97.
Article
PubMed
PubMed Central
Google Scholar
Patel DI, et al. Therapeutic yoga reduces pro-tumorigenic cytokines in cancer survivors. Support Care Cancer. 2022;31(1):33.
PubMed
PubMed Central
Google Scholar
Streeter CC, et al. Effects of yoga on the autonomic nervous system, gamma-aminobutyric-acid, and allostasis in epilepsy, depression, and post-traumatic stress disorder. Med Hypotheses. 2012;78(5):571–9.
Article
CAS
PubMed
Google Scholar
Ozturk FO, Tezel A. Effect of laughter yoga on mental symptoms and salivary cortisol levels in first-year nursing students: A randomized controlled trial. Int J Nurs Pract. 2021;27(2):e12924.
Article
PubMed
Google Scholar
Nair RG, Vasudev MM, Mavathur R. Role of yoga and its plausible mechanism in the mitigation of DNA damage in Type-2 diabetes: A randomized clinical trial. Ann Behav Med. 2022;56(3):235–44.
Article
PubMed
Google Scholar
Chen SJ, et al. Association of fecal and plasma levels of Short-Chain fatty acids with gut microbiota and clinical severity in patients with Parkinson disease. Neurology. 2022;98(8):e848–58.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chakraborty P, Gamage H, Laird AS. Butyrate as a potential therapeutic agent for neurodegenerative disorders. Neurochem Int. 2024;176:105745.
Article
CAS
PubMed
Google Scholar
Li N, et al. Prebiotic inulin controls Th17 cells mediated central nervous system autoimmunity through modulating the gut microbiota and short chain fatty acids. Gut Microbes. 2024;16(1):2402547.
Article
PubMed
PubMed Central
Google Scholar
Zhang Y, et al. Transmission of alzheimer’s disease-associated microbiota dysbiosis and its impact on cognitive function: evidence from mice and patients. Mol Psychiatry. 2023;28(10):4421–37.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kim MS, et al. Transfer of a healthy microbiota reduces amyloid and Tau pathology in an alzheimer’s disease animal model. Gut. 2020;69(2):283–94.
Article
CAS
PubMed
Google Scholar
Beydoun MA, et al. Clinical and bacterial markers of periodontitis and their association with incident All-Cause and alzheimer’s disease dementia in a large National survey. J Alzheimers Dis. 2020;75(1):157–72.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ilievski V, et al. Chronic oral application of a periodontal pathogen results in brain inflammation, neurodegeneration and amyloid beta production in wild type mice. PLoS ONE. 2018;13(10):e0204941.
Article
PubMed
PubMed Central
Google Scholar
Wang RP, et al. IL-1β and TNF-α play an important role in modulating the risk of periodontitis and alzheimer’s disease. J Neuroinflammation. 2023;20(1):71.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dominy SS, et al. Porphyromonas gingivalis in alzheimer’s disease brains: evidence for disease causation and treatment with small-molecule inhibitors. Sci Adv. 2019;5(1):eaau3333.
Article
PubMed
PubMed Central
Google Scholar
Brettschneider J, et al. Microglial activation correlates with disease progression and upper motor neuron clinical symptoms in amyotrophic lateral sclerosis. PLoS ONE. 2012;7(6):e39216.
Article
CAS
PubMed
PubMed Central
Google Scholar
Graves MC, et al. Inflammation in amyotrophic lateral sclerosis spinal cord and brain is mediated by activated macrophages, mast cells and T cells. Amyotroph Lateral Scler Other Motor Neuron Disord. 2004;5(4):213–9.
Article
CAS
PubMed
Google Scholar
Henkel JS, et al. Presence of dendritic cells, MCP-1, and activated microglia/macrophages in amyotrophic lateral sclerosis spinal cord tissue. Ann Neurol. 2004;55(2):221–35.
Article
CAS
PubMed
Google Scholar
Liu YH, et al. Neuronal intranuclear inclusion disease in patients with adult-onset non-vascular leukoencephalopathy. Brain. 2022;145(9):3010–21.
Article
PubMed
Google Scholar
Chen L, et al. Teaching neuroimages: the zigzag edging sign of adult-onset neuronal intranuclear inclusion disease. Neurology. 2019;92(19):e2295-6.
Article
PubMed
Google Scholar
Fragoso DC, et al. Imaging of Creutzfeldt-Jakob disease: imaging patterns and their differential diagnosis. Radiographics. 2017;37(1):234–57.
Article
PubMed
Google Scholar
Muttikkal TJ, Wintermark M. MRI patterns of global hypoxic-ischemic injury in adults. J Neuroradiol. 2013;40(3):164–71.
Article
PubMed
Google Scholar
Hasebe M, et al. Hypoglycemic encephalopathy. QJM. 2022;115(7):478–9.
Article
CAS
PubMed
Google Scholar
Zhang Z, et al. MRI features of neuronal intranuclear inclusion disease, combining visual and quantitative imaging investigations. J Neuroradiol. 2024;51(3):274–80.
Article
PubMed
Google Scholar
Okamura S, et al. A case of neuronal intranuclear inclusion disease with recurrent vomiting and without apparent DWI abnormality for the first seven years. Heliyon. 2020;6(8):e04675.
Article
PubMed
PubMed Central
Google Scholar
Liu Y, et al. Clinical and mechanism advances of neuronal intranuclear inclusion disease. Front Aging Neurosci. 2022;14:934725.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liang H, et al. Clinical and pathological features in adult-onset NIID patients with cortical enhancement. J Neurol. 2020;267(11):3187–98.
Article
PubMed
Google Scholar
Shen Y, et al. Encephalitis-like episodes with cortical edema and enhancement in patients with neuronal intranuclear inclusion disease. Neurol Sci. 2024;45(9):4501–11.
Article
PubMed
Google Scholar
Jacobs AH, Tavitian B. Noninvasive molecular imaging of neuroinflammation. J Cereb Blood Flow Metab. 2012;32(7):1393–415.
Article
CAS
PubMed
PubMed Central
Google Scholar
Corica F et al. PET imaging of Neuro-Inflammation with tracers targeting the translocator protein (TSPO), a systematic review: from bench to bedside. Diagnostics (Basel), 2023;13(6):1029.
Banati RB. Visualising microglial activation in vivo. Glia. 2002;40(2):206–17.
Article
PubMed
Google Scholar
Turner MR, et al. Evidence of widespread cerebral microglial activation in amyotrophic lateral sclerosis: an [11 C](R)-PK11195 positron emission tomography study. Neurobiol Dis. 2004;15(3):601–9.
Article
CAS
PubMed
Google Scholar
Tondo G, et al. (11) C-PK11195 PET-based molecular study of microglia activation in SOD1 amyotrophic lateral sclerosis. Ann Clin Transl Neurol. 2020;7(9):1513–23.
Article
CAS
PubMed
PubMed Central
Google Scholar
Malpetti M, et al. Microglial activation in the frontal cortex predicts cognitive decline in frontotemporal dementia. Brain. 2023;146(8):3221–31.
Article
PubMed
PubMed Central
Google Scholar
Zhong S, et al. Microglia contribute to polyG-dependent neurodegeneration in neuronal intranuclear inclusion disease. Acta Neuropathol. 2024;148(1):21.
Article
CAS
PubMed
Google Scholar
Hutton BF. The origins of SPECT and SPECT/CT. Eur J Nucl Med Mol Imaging. 2014;41(Suppl 1):S3-16.
Article
PubMed
Google Scholar
Benadiba M, et al. New molecular targets for PET and SPECT imaging in neurodegenerative diseases. Braz J Psychiatry. 2012;34(Suppl 2):S125–36.
Article
PubMed
Google Scholar
Kimachi T, et al. Reversible encephalopathy with focal brain edema in patients with neuronal intranuclear inclusion disease. Neurology and Clinical Neuroscience. 2017;5(6):198–200.
Article
Google Scholar
Fujita K, et al. Neurologic attack and dynamic perfusion abnormality in neuronal intranuclear inclusion disease. Neurol Clin Pract. 2017;7(6):e39-42.
Article
PubMed
PubMed Central
Google Scholar
Pan Y, et al. Expression of expanded GGC repeats within NOTCH2NLC causes cardiac dysfunction in mouse models. Cell Biosci. 2023;13(1):157.
Article
CAS
PubMed
PubMed Central
Google Scholar
Heslegrave A, et al. Increased cerebrospinal fluid soluble TREM2 concentration in Alzheimer’s disease. Mol Neurodegener. 2016;11:3.
Article
PubMed
PubMed Central
Google Scholar
Craig-Schapiro R, et al. YKL-40: a novel prognostic fluid biomarker for preclinical alzheimer’s disease. Biol Psychiatry. 2010;68(10):903–12.
Article
CAS
PubMed
PubMed Central
Google Scholar
Crols R, et al. Increased GFAP levels in CSF as a marker of organicity in patients with Alzheimer’s disease and other types of irreversible chronic organic brain syndrome. J Neurol. 1986;233(3):157–60.
Article
CAS
PubMed
Google Scholar
Andrés-Benito P, et al. Differential astrocyte and oligodendrocyte vulnerability in murine Creutzfeldt-Jakob disease. Prion. 2021;15(1):112–20.
Article
PubMed
PubMed Central
Google Scholar
Mussbacher M, et al. NF-κB in monocytes and macrophages – an inflammatory master regulator in multitalented immune cells. Front Immunol. 2023;14:1134661.
Article
CAS
PubMed
PubMed Central
Google Scholar
Biasizzo M, Kopitar-Jerala N. Interplay between NLRP3 inflammasome and autophagy. Front Immunol. 2020;11:591803.
Article
CAS
PubMed
PubMed Central
Google Scholar
Deng Q, et al. Pseudomonas aeruginosa triggers macrophage autophagy to escape intracellular killing by activation of the NLRP3 inflammasome. Infect Immun. 2016;84(1):56–66.
Article
CAS
PubMed
Google Scholar
Geissmann F, et al. Development of monocytes, macrophages, and dendritic cells. Science. 2010;327(5966):656–61.
Article
CAS
PubMed
PubMed Central
Google Scholar
Guilliams M, et al. Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny. Nat Rev Immunol. 2014;14(8):571–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Prinz M, Jung S, Priller J. Microglia Biology: One Century Evol Concepts Cell. 2019;179(2):292–311.
CAS
Google Scholar
Li Q, Barres BA. Microglia and macrophages in brain homeostasis and disease. Nat Rev Immunol. 2018;18(4):225–42.
Article
CAS
PubMed
Google Scholar
Liddelow SA, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541(7638):481–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Keren-Shaul H, et al. A unique microglia type associated with restricting development of alzheimer’s disease. Cell. 2017;169(7):1276–e129017.
Article
CAS
PubMed
Google Scholar
Mathys H, et al. Temporal tracking of microglia activation in neurodegeneration at single-cell resolution. Cell Rep. 2017;21(2):366–80.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhou Y, et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat Med. 2020;26(1):131–42.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ginhoux F, Guilliams M. Tissue-resident macrophage ontogeny and homeostasis. Immunity. 2016;44(3):439–49.
Article
CAS
PubMed
Google Scholar
Italiani P, Boraschi D. From monocytes to M1/M2 macrophages: phenotypical vs. functional differentiation. Front Immunol. 2014;5:514.
Article
PubMed
PubMed Central
Google Scholar
Merad M, et al. The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting. Annu Rev Immunol. 2013;31:563–604.
Article
CAS
PubMed
Google Scholar
Sun R, Jiang H. Border-associated macrophages in the central nervous system. J Neuroinflammation. 2024;21(1):67.
Article
PubMed
PubMed Central
Google Scholar
Goldmann T, et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat Immunol. 2016;17(7):797–805.
Article
CAS
PubMed
PubMed Central
Google Scholar
Van Hove H, et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat Neurosci. 2019;22(6):1021–35.
Article
CAS
PubMed
Google Scholar
Bell RD, Zlokovic BV. Neurovascular mechanisms and blood-brain barrier disorder in Alzheimer’s disease. Acta Neuropathol. 2009;118(1):103–13.
Article
CAS
PubMed
PubMed Central
Google Scholar
Faraco G, et al. Perivascular macrophages mediate the neurovascular and cognitive dysfunction associated with hypertension. J Clin Invest. 2016;126(12):4674–89.
Article
PubMed
PubMed Central
Google Scholar
Rua R, et al. Infection drives meningeal engraftment by inflammatory monocytes that impairs CNS immunity. Nat Immunol. 2019;20(4):407–19.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dani N, et al. A cellular and Spatial map of the choroid plexus across brain ventricles and ages. Cell. 2021;184(11):3056–e307421.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mrdjen D, et al. High-Dimensional Single-Cell mapping of central nervous system immune cells reveals distinct myeloid subsets in Health, Aging, and disease. Immunity. 2018;48(2):380–95. .e6.
Article
CAS
PubMed
Google Scholar
Park L, et al. Brain perivascular macrophages initiate the neurovascular dysfunction of Alzheimer Aβ peptides. Circ Res. 2017;121(3):258–69.
Article
CAS
PubMed
PubMed Central
Google Scholar
Schläger C, et al. Effector T-cell trafficking between the leptomeninges and the cerebrospinal fluid. Nature. 2016;530(7590):349–53.
Article
PubMed
Google Scholar
Herz J, et al. Role of neutrophils in exacerbation of brain injury after focal cerebral ischemia in hyperlipidemic mice. Stroke. 2015;46(10):2916–25.
Article
CAS
PubMed
PubMed Central
Google Scholar
Carmeliet P. Angiogenesis in health and disease. Nat Med. 2003;9(6):653–60.
Article
CAS
PubMed
Google Scholar
Greenberg SM, et al. Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurol. 2009;8(2):165–74.
Article
PubMed
PubMed Central
Google Scholar
Charidimou A, Gang Q, Werring DJ. Sporadic cerebral amyloid angiopathy revisited: recent insights into pathophysiology and clinical spectrum. J Neurol Neurosurg Psychiatry. 2012;83(2):124–37.
Article
PubMed
Google Scholar
Liao YC, et al. NOTCH2NLC GGC repeat expansion in patients with vascular leukoencephalopathy. Stroke. 2023;54(5):1236–45.
Article
CAS
PubMed
Google Scholar
Bao L, et al. GGC repeat expansions in NOTCH2NLC cause uN2CpolyG cerebral amyloid angiopathy. Brain. 2025;148(2):467–79.
Article
PubMed
Google Scholar
Ryu JK, McLarnon JG. A leaky blood-brain barrier, fibrinogen infiltration and microglial reactivity in inflamed Alzheimer’s disease brain. J Cell Mol Med. 2009;13(9a):2911–25.
Article
CAS
PubMed
Google Scholar
Shan Y, et al. The glucagon-like peptide-1 receptor agonist reduces inflammation and blood-brain barrier breakdown in an astrocyte-dependent manner in experimental stroke. J Neuroinflammation. 2019;16(1):242.
Article
CAS
PubMed
PubMed Central
Google Scholar
Orihara A, et al. Acute reversible encephalopathy with neuronal intranuclear inclusion disease diagnosed by a brain biopsy: inferring the mechanism of encephalopathy from radiological and histological findings. Intern Med. 2023;62(12):1821–5.
Article
PubMed
Google Scholar
Li YY, et al. Interactions between beta-Amyloid and pericytes in Alzheimer’s disease. Front Biosci (Landmark Ed). 2024;29(4):136.
Article
CAS
PubMed
Google Scholar
Huang X, Hussain B, Chang J. Peripheral inflammation and blood-brain barrier disruption: effects and mechanisms. CNS Neurosci Ther. 2021;27(1):36–47.
Article
CAS
PubMed
Google Scholar
Rahimifard M, et al. Targeting the TLR4 signaling pathway by polyphenols: a novel therapeutic strategy for neuroinflammation. Ageing Res Rev. 2017;36:11–9.
Article
CAS
PubMed
Google Scholar
Zhang M, et al. Blockage of VEGF function by bevacizumab alleviates early-stage cerebrovascular dysfunction and improves cognitive function in a mouse model of Alzheimer’s disease. Transl Neurodegener. 2024;13(1):1.
Article
PubMed
PubMed Central
Google Scholar
Wild CP. Complementing the genome with an exposome: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005;14(8):1847–50.
Article
CAS
PubMed
Google Scholar
Doroszkiewicz J et al. Common and trace metals in alzheimer’s and parkinson’s diseases. Int J Mol Sci, 2023;24(21):15721.
Gu Y, et al. Mediterranean diet, inflammatory and metabolic biomarkers, and risk of Alzheimer’s disease. J Alzheimers Dis. 2010;22(2):483–92.
Article
CAS
PubMed
PubMed Central
Google Scholar
Flanagan E, et al. Nutrition and the ageing brain: moving towards clinical applications. Ageing Res Rev. 2020;62:101079.
Article
CAS
PubMed
Google Scholar
Gelpi E, et al. Neuronal intranuclear (hyaline) inclusion disease and fragile X-associated tremor/ataxia syndrome: a morphological and molecular dilemma. Brain. 2017;140(8):e51.
Article
PubMed
Google Scholar
Tolar M et al. Neurotoxic soluble amyloid oligomers drive alzheimer’s pathogenesis and represent a clinically validated target for slowing disease progression. Int J Mol Sci, 2021;22(12):6355.
Habashi M, et al. Early diagnosis and treatment of alzheimer’s disease by targeting toxic soluble Aβ oligomers. Proc Natl Acad Sci U S A. 2022;119(49):e2210766119.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wang ZX, et al. The essential role of soluble Aβ oligomers in alzheimer’s disease. Mol Neurobiol. 2016;53(3):1905–24.
Article
CAS
PubMed
Google Scholar
Besedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99(3):1325–80.
Article
PubMed
PubMed Central
Google Scholar
Su WJ, et al. Antidiabetic drug glyburide modulates depressive-like behavior comorbid with insulin resistance. J Neuroinflammation. 2017;14(1):210.
Zhang J, et al. Porphyromonas gingivalis lipopolysaccharide induces cognitive dysfunction, mediated by neuronal inflammation via activation of the TLR4 signaling pathway in C57BL/6 mice. J Neuroinflammation. 2018;15(1):37.
Article
PubMed
PubMed Central
Google Scholar
Nie R, et al. Porphyromonas gingivalis infection induces Amyloid-β accumulation in Monocytes/Macrophages. J Alzheimers Dis. 2019;72(2):479–94.
Article
CAS
PubMed
Google Scholar
Kemaladewi DU, et al. Correction of a splicing defect in a mouse model of congenital muscular dystrophy type 1A using a homology-directed-repair-independent mechanism. Nat Med. 2017;23(8):984–9.
Article
CAS
PubMed
Google Scholar
Sarkar S, et al. Rapamycin and mTOR-independent autophagy inducers ameliorate toxicity of polyglutamine-expanded Huntingtin and related proteinopathies. Cell Death Differ. 2009;16(1):46–56.
Article
CAS
PubMed
Google Scholar
Grima JC, et al. Mutant Huntingtin disrupts the nuclear pore complex. Neuron. 2017;94(1):93–e1076.
