Serum IL-18 Is Closely Associated with Renal Tubulointerstitial Injury and Predicts Renal Prognosis in IgA Nephropathy

Background. IgA nephropathy (IgAN) was thought to be benign but recently found it slowly progresses and leads to ESRD eventually. The aim of this research is to investigate the value of serum IL-18 level, a sensitive biomarker for proximal tubule injury, for assessing the histopathological severity and disease progression in IgAN. Methods. Serum IL-18 levels in 76 IgAN patients and 36 healthy blood donors were measured by ELISA. We evaluated percentage of global and segmental sclerosis (GSS) and extent of tubulointerstitial damage (TID). The correlations between serum IL-18 levels with clinical, histopathological features and renal prognosis were evaluated. Results. The patients were 38.85 ± 10.95 years old, presented with 2.61 (1.43∼4.08) g/day proteinuria. Serum IL-18 levels were significantly elevated in IgAN patients. Baseline serum IL-18 levels were significantly correlated with urinary protein excretion (r = 0.494, P = 0.002), Scr (r = 0.61, P < 0.001), and eGFR (r = −0.598, P < 0.001). TID scores showed a borderline significance with serum IL-18 levels (r = 0.355, P = 0.05). During follow-up, 26 patients (34.21%) had a declined renal function. Kaplan-Meier analysis found those patients with elevated IL-18 had a significant poor renal outcome (P = 0.03), and Cox analysis further confirmed that serum IL-18 levels were an independent predictor of renal prognosis (β = 1.98, P = 0.003)

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

High-performance Data Management for Whole Slide Image Analysis in Digital Pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we've developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario"). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios

Substituted N-(Biphenyl-4′-yl)methyl (R)-2-Acetamido-3-methoxypropionamides: Potent Anticonvulsants That Affect Frequency (Use) Dependence and Slow Inactivation of Sodium Channels

, We prepared 13 derivatives of N-(biphenyl-4′-yl)methyl (R)-2-acetamido-3-methoxypropionamide that differed in type and placement of a R-substituent in the terminal aryl unit. We demonstrated that the R-substituent impacted the compound’s whole animal and cellular pharmacological activities. In rodents, select compounds exhibited excellent anticonvulsant activities and protective indices (PI = TD50/ED50) that compared favorably with clinical antiseizure drugs. Compounds with a polar, aprotic R-substituent potently promoted Na+ channel slow inactivation and displayed frequency (use) inhibition of Na+ currents at low micromolar concentrations. The possible advantage of affecting these two pathways to decrease neurological hyperexcitability is discussed

Updated SARS-CoV-2 Single Nucleotide Variants and Mortality Association

Since its outbreak in December 2019, COVID-19 has caused 100,5844,555 cases and 2,167,313 deaths as of Jan 27, 2021. Comparing our previous study of SARS-CoV-2 single nucleotide variants (SNVs) before June 2020, we found out that the SNV clustering had changed considerably since June 2020. Apart from that the group SNVs represented by two non-synonymous mutations A23403G (S: D614G) and C14408T (ORF1ab: P4715L) became dominant and carried by over 95% genomes, a few emerging groups of SNVs were recognized with sharply increased monthly occurrence ratios up to 70% in November 2020. Further investigation revealed that several SNVs were strongly associated with the mortality, but they presented distinct distribution in specific countries, e.g., Brazil, USA, Saudi Arabia, India, and Italy. SNVs including G25088T, T25A, G29861T and G29864A were adopted in a regularized logistic regression model to predict the mortality status in Brazil with the AUC of 0.84. Protein structure analysis showed that the emerging subgroups of non-synonymous SNVs and those mortality-related ones in Brazil were located on protein surface area. The clashes in protein structure introduced by these mutations might in turn affect virus pathogenesis through conformation changes, leading to the difference in transmission and virulence. Particularly, we found that SNVs tended to occur in intrinsic disordered regions (IDRs) of Spike (S) and ORF1ab, suggesting a critical role of SNVs in protein IDRs to determine protein folding and immune evasion

Progress and perspectives of perioperative immunotherapy in non-small cell lung cancer

Lung cancer is one of the leading causes of cancer-related death. Lung cancer mortality has decreased over the past decade, which is partly attributed to improved treatments. Curative surgery for patients with early-stage lung cancer is the standard of care, but not all surgical treatments have a good prognosis. Adjuvant and neoadjuvant chemotherapy are used to improve the prognosis of patients with resectable lung cancer. Immunotherapy, an epoch-defining treatment, has improved curative effects, prognosis, and tolerability compared with traditional and ordinary cytotoxic chemotherapy, providing new hope for patients with non-small cell lung cancer (NSCLC). Immunotherapy-related clinical trials have reported encouraging clinical outcomes in their exploration of different types of perioperative immunotherapy, from neoadjuvant immune checkpoint inhibitor (ICI) monotherapy, neoadjuvant immune-combination therapy (chemoimmunotherapy, immunotherapy plus antiangiogenic therapy, immunotherapy plus radiotherapy, or concurrent chemoradiotherapy), adjuvant immunotherapy, and neoadjuvant combined adjuvant immunotherapy. Phase 3 studies such as IMpower 010 and CheckMate 816 reported survival benefits of perioperative immunotherapy for operable patients. This review summarizes up-to-date clinical studies and analyzes the efficiency and feasibility of different neoadjuvant therapies and biomarkers to identify optimal types of perioperative immunotherapy for NSCLC

Development and verification of a combined immune- and cancer-associated fibroblast related prognostic signature for colon adenocarcinoma

IntroductionTo better understand the role of immune escape and cancer-associated fibroblasts (CAFs) in colon adenocarcinoma (COAD), an integrative analysis of the tumor microenvironment was performed using a set of 12 immune- and CAF-related genes (ICRGs).MethodsUnivariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a prognostic signature based on the expression of these 12 genes (S1PR5, AEN, IL20RB, FGF9, OSBPL1A, HSF4, PCAT6, FABP4, KIF15, ZNF792, CD1B and GLP2R). This signature was validated in both internal and external cohorts and was found to have a higher C-index than previous COAD signatures, confirming its robustness and reliability. To make use of this signature in clinical settings, a nomogram incorporating ICRG signatures and key clinical parameters, such as age and T stage, was developed. Finally, the role of S1PR5 in the immune response of COAD was validated through in vitro cytotoxicity experiments.ResultsThe developed nomogram exhibited slightly improved predictive accuracy compared to the ICRG signature alone, as indicated by the areas under the receiver operating characteristic curves (AUC, nomogram:0.838; ICRGs:0.807). The study also evaluated the relationships between risk scores (RS) based on the expression of the ICRGs and other key immunotherapy variables, including immune checkpoint expression, immunophenoscore (IPS), and microsatellite instability (MSI). Integration of these variables led to more precise prediction of treatment efficacy, enabling personalized immunotherapy for COAD patients. Knocking down S1PR5 can enhance the efficacy of PD-1 monoclonal antibody, promoting the cytotoxicity of T cells against HCT116 cells ((p&lt;0.05).DiscussionThese findings indicate that the ICRG signature may be a valuable tool for predicting prognostic risk, evaluating the efficacy of immunotherapy, and tailoring personalized treatment options for patients with COAD

Sedimentary ancient DNA reveals past ecosystem and biodiversity changes on the Tibetan Plateau: Overview and prospects

Alpine ecosystems on the Tibetan Plateau are being threatened by ongoing climate warming and intensified human activities. Ecological time-series obtained from sedimentary ancient DNA (sedaDNA) are essential for understanding past ecosystem and biodiversity dynamics on the Tibetan Plateau and their responses to climate change at a high taxonomic resolution. Hitherto only few but promising studies have been published on this topic. The potential and limitations of using sedaDNA on the Tibetan Plateau are not fully understood. Here, we (i) provide updated knowledge of and a brief introduction to the suitable archives, region-specific taphonomy, state-of-the-art methodologies, and research questions of sedaDNA on the Tibetan Plateau; (ii) review published and ongoing sedaDNA studies from the Tibetan Plateau; and (iii) give some recommendations for future sedaDNA study designs. Based on the current knowledge of taphonomy, we infer that deep glacial lakes with freshwater and high clay sediment input, such as those from the southern and southeastern Tibetan Plateau, may have a high potential for sedaDNA studies. Metabarcoding (for microorganisms and plants), metagenomics (for ecosystems), and hybridization capture (for prehistoric humans) are three primary sedaDNA approaches which have been successfully applied on the Tibetan Plateau, but their power is still limited by several technical issues, such as PCR bias and incompleteness of taxonomic reference databases. Setting up high-quality and open-access regional taxonomic reference databases for the Tibetan Plateau should be given priority in the future. To conclude, the archival, taphonomic, and methodological conditions of the Tibetan Plateau are favorable for performing sedaDNA studies. More research should be encouraged to address questions about long-term ecological dynamics at ecosystem scale and to bring the paleoecology of the Tibetan Plateau into a new era

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science