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When and how Earth's earliest continents—the cratons—first emerged above the oceans (i.e., emersion) remain uncertain. Here, we analyze a craton-wide record of Paleo-to-Mesoarchean granitoid magmatism and terrestrial to shallow-marine sedimentation preserved in the Singhbhum Craton (India) and combine the results with isostatic modeling to examine the timing and mechanism of one of the earliest episodes of large-scale continental emersion on Earth. Detrital zircon U-Pb(-Hf) data constrain the timing of terrestrial to shallow-marine sedimentation on the Singhbhum Craton, which resolves the timing of craton-wide emersion. Time-integrated petrogenetic modeling of the granitoids quantifies the progressive changes in the cratonic crustal thickness and composition and the pressure–temperature conditions of granitoid magmatism, which elucidates the underlying mechanism and tectonic setting of emersion. The results show that the entire Singhbhum Craton became subaerial ∼3.3 to 3.2 billion years ago (Ga) due to progressive crustal maturation and thickening driven by voluminous granitoid magmatism within a plateau-like setting. A similar sedimentary–magmatic evolution also accompanied the early (>3 Ga) emersion of other cratons (e.g., Kaapvaal Craton). Therefore, we propose that the emersion of Earth’s earliest continents began during the late Paleoarchean to early Mesoarchean and was driven by the isostatic rise of their magmatically thickened (∼50 km thick), buoyant, silica-rich crust. The inferred plateau-like tectonic settings suggest that subduction collision–driven compressional orogenesis was not essential in driving continental emersion, at least before the Neoarchean. We further surmise that this early emersion of cratons could be responsible for the transient and localized episodes of atmospheric–oceanic oxygenation (O₂-whiffs) and glaciation on Archean Earth.

Estimates of the volume of the earliest crust based on zircon ages and radiogenic isotopes remain equivocal. Stable isotope systems, such as molybdenum, have the potential to provide further constraints but remain underused due to the lack of complementarity between mantle and crustal reservoirs. Here we present molybdenum isotope data for Archaean komatiites and Phanerozoic komatiites and picrites and demonstrate that their mantle sources all possess subchondritic signatures complementary to the superchondritic continental crust. These results confirm that the present-day degree of mantle depletion was achieved by 3.5 billion years ago and that Earth has been in a steady state with respect to molybdenum recycling. Mass balance modelling shows that this early mantle depletion requires the extraction of a far greater volume of mafic-dominated protocrust than previously thought, more than twice the volume of the continental crust today, implying rapid crustal growth and destruction in the first billion years of Earth’s history.

The rock record and geochemical evidence indicate that continental recycling has been occurring since the early history of the Earth. The stabilization of felsic continents in place of Earth’s early mafic crust about 3.0 to 2.0 billion years ago, perhaps due to the initiation of plate tectonics, implies widespread destruction of mafic crust during this time interval. However, the physical mechanisms of such intense recycling on a hotter, (late) Archaean and presumably plate-tectonic Earth remain largely unknown. Here we use thermomechanical modelling to show that extensive recycling via lower crustal peeling-off (delamination but not eclogitic dripping) during continent–continent convergence was near ubiquitous during the late Archaean to early Proterozoic. We propose that such destruction of the early mafic crust, together with felsic magmatism, may have caused both the emergence of silicic continents and their subsequent isostatic rise, possibly above the sea level. Such changes in the continental character have been proposed to influence the Great Oxidation Event and, therefore, peeling-off plate tectonics could be the geodynamic trigger for this event. A transition to the slab break-off controlled syn-orogenic recycling occurred as the Earth aged and cooled, leading to reduced recycling and enhanced preservation of the continental crust of present-day composition. The processes for crustal recycling during the Archaean are unclear. Numerical simulations suggest that dense lower crust would have peeled off into the mantle, leading to a rapid concentration of buoyant silicic rocks that formed the continents.

Lithospheric yield stress is a key parameter in controlling tectonic processes. We calculate yield stress for a range of conditions appropriate to the Archean Earth, including hotter mantle potential temperatures and a range of Moho temperatures using 2D high resolution numerical geodynamic modelling techniques. This range of conditions are evaluated for generating felsic, tonalite-trondhjemite-granodiorite (TTG), crust with the results bench marked against the preserved rock record. The model results indicate that lithospheric yield stress slightly lower than the present-day Earth values (i.e. < 100 MPa) generates TTG melt volumes similar to those preserved in the rock record. In particular, large volumes of TTG melts form in the tails of lithospheric drips. Melting occurs profusely within the thinner portions of the drips as these regions are more ef ciently heated by the enclosing hotter mantle. In contrast, only limited melting occurs in regions of thickened crust, in part because the weaker lithosphere cannot sustain crustal thickening for long time periods, resulting in its removal through drips. Our models highlight the dominance of non-plate tectonic mechanisms in producing TTGs under the conditions that operated on the hotter Archean Earth.

Cardio‐ankle vascular index (CAVI) is an innovative indicator of large‐artery stiffness, which is evaluated by the pulse wave velocity (PWV) measurement. Mortality and morbidity due to cardiovascular diseases among the general public with high‐risk conditions such as hypertension are usually associated with arterial stiffness. CAVI modelizes the hazard of future cardiovascular events with standard risk factors. Additionally, the “European Society of Hypertension and Cardiology” included the aortic PWV assessment in managing hypertension in their updated guidelines in 2007. We conducted this systematic review to collect, summarize, and evaluate the evidence from relevant reported studies. A literature search of four databases was conducted comprehensively until February 2024. Cardiovascular events are the primary outcome of interest in this study, cardiovascular events that have been defined as major adverse cardiac events include “heart failure”, “stroke”, “myocardial infarction”, “cardiovascular deaths”, “stable angina pectoris”, “coronary revascularization”, and “unstable angina pectoris”. We included five studies with a 11 698 sample size in this systematic review. All five prospective studies investigated composite cardiovascular events as an outcome. Three of them revealed a statistically significant prediction ability of CAVI to assess Cardiovascular disease (CVD) risk. Further analysis is required. Current evidence is insufficient to confirm the predictive power of CAVI in the assessment of cardiovascular risk in hypertensive patients. CAVI is modestly associated with incidents of CVD risk. It is necessary to conduct further studies to assess CAVI concerning CVD predictor measures in the masses and nations other than Asia.

Quantifying the biological processes that drive cancer progression remains a key challenge in oncology. Although the hallmarks of cancer provide a foundational framework for understanding tumor behavior, existing diagnostic tools rarely measure these hallmarks directly. Here we present a neural multi-task learning-based framework that estimates hallmark activity using gene expression data from tumor biopsies. The model was trained on transcriptomic profiles from 941 tumors spanning 14 tissue types and tested on five independent datasets. It predicts the activity of ten cancer hallmarks simultaneously and with high accuracy. Additional validation on large-scale datasets including normal and cancer samples confirmed its sensitivity and specificity. Predicted hallmark activity was associated with clinical staging, suggesting biological relevance. A web-based tool was developed to facilitate integration into research and clinical workflows. This approach enables efficient analysis of transcriptomic data to inform understanding of tumor biology and support individualized treatment strategies. Hallmark-based modeling of tumors using neural multi-task learning enables accurate, simultaneous quantification of all ten cancer hallmarks from transcriptomic data, offering insights into tumor biology and advancing precision oncology.

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial–mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment. Pan-cancer analysis of kindlin genes on patient genomic data reveals a distinct pattern of kindlin activity. Our analysis suggests kindlins as mechanochemical connector of major cancer hallmark pathways.

Genetic susceptibility is an important modifier of clinical outcome and natural history of progression in Alcoholic liver disease (ALD). While the significance of ethnicity in this evolution is very clear, subtle inter-individual genetic variant(s) might be important and thus we investigated those in an Indian population. Fourteen markers were genotyped within two alcohol metabolism genes [Alcohol dehydrogenase (ADH) gene clusters (ADH1B and ADH1C) and Aldehyde dehydrogenase (ALDH2)], one microsomal ethanol oxidizing enzyme cytochrome p450 (CYP2E1) and three oxidative stress response (OSR) genes (MnSOD, GSTT1 and GSTM1) among 490 Bengali individuals (322 ALD and 168 control) from Eastern and North-Eastern India and validation was performed in a new cohort of 150 Bengali patients including 100 ALD and 50 advanced non-alcoholic steatohepatitis (NASH). Out of 14 genetic variants, carriage of 5 genotypes (rs2066701CC in ADH1B, rs1693425TT in ADH1C, rs4880TT in MnSOD and GSTT1/GSTM1 null, p-value <0.05) were noted significantly higher among ALD patients while inter or intra group gene-gene interaction analysis revealed that addition of risk genotype of any OSR gene enhanced the possibility of ALD synergistically. Multiple logistic regression analysis showed independent association of rs2066701CC, rs4880TT and GSTM1 null genotype with ALD while lower frequencies of those genotypes in advanced NASH patients further confirmed their causal relation to ALD. Thus these findings suggest that the three variants of ADH1C, MnSOD and GSTM1 can be used to identify individuals who are at high risk to develop ALD and may be helpful in proper management of Indian alcoholics.

Nonalcoholic fatty liver disease (NAFLD) is a distinct pathologic condition characterized by a disease spectrum ranging from simple steatosis to steato-hepatitis, cirrhosis and hepatocellular carcinoma. Prevalence of NAFLD varies in different ethnic groups, ranging from 12% in Chinese to 45% in Hispanics. Among Indian populations, the diversity in prevalence is high, ranging from 9% in rural populations to 32% in urban populations, with geographic differences as well. Here, we wished to find out if this difference is reflected in their genetic makeup. To date, several candidate genes and a few genomewide association studies (GWAS) have been carried out, and many associations between single nucleotide polymorphisms (SNPs) and NAFLD have been observed. In this study, the risk allele frequencies (RAFs) of NAFLD-associated SNPs in 20 Indian ethnic populations (376 individuals) were analysed. We used two different measures for calculating genetic risk scores and compared their performance. The correlation of additive risk scores of NAFLD for three Hapmap populations with their weighted mean prevalence was found to be high ( R 2 =0.93). Later we used this method to compare NAFLD risk among ethnic Indian populations. Based on our observation, the Indian caste populations have high risk scores compared to Caucasians, who are often used as surrogate and similar to Indian caste population in disease gene association studies, and is significantly higher than the Indian tribal populations.

Cancer stem cells (CSCs), a distinct subpopulation within tumors, are pivotal in driving treatment resistance and tumor recurrence, posing substantial challenges to conventional therapeutic strategies. Precise quantification and profiling of these cells are essential for improving cancer treatment outcomes. We present ACSCeND, an advanced deep neural network model accompanied by a robust workflow, specifically developed to quantify cellular compositions from bulk RNA-seq data, enabling accurate CSC profiling. By integrating bulk RNA-seq data with insights derived from single-cell RNA-seq datasets, ACSCeND effectively captures the diversity and hierarchical organization of tumor-resident cell states, alongside cell-specific gene expression profiles (GEPs). Compared to current tissue deconvolution models, ACSCeND exhibits superior performance, achieving significantly higher Concordance Correlation Coefficient (CCC) values and lower Root Mean Square Error (RMSE) across various pseudobulk and real-world bulk tissue samples. Application of ACSCeND to TCGA and PRECOG datasets reveals a strong association between CSC abundance and poorer disease-free survival outcomes, underscoring the clinical relevance of CSCs in cancer progression. Furthermore, cell-specific GEPs for distinct CSC states unveil novel molecular signatures and illuminate the origins of CSC-driven tumor heterogeneity. In summary, ACSCeND provides a powerful, scalable platform for high-throughput quantification of cellular compositions and distinct potency states within normal tissues as well as highly heterogeneous tissues, such as tumors.Competing Interest StatementThe authors have declared no competing interest.