Targeting the Glutamine-Arginine-Proline Metabolism Axis in Cancer

Metabolic abnormalities are an important feature of tumours. The glutamine-arginine-proline axis is an important node of cancer metabolism and plays a major role in amino acid metabolism. This axis also acts as a scaffold for the synthesis of other nonessential amino acids and essential metabolites. In this paper, we briefly review (1) the glutamine addiction exhibited by tumour cells with accelerated glutamine transport and metabolism; (2) the methods regulating extracellular glutamine entry, intracellular glutamine synthesis and the fate of intracellular glutamine; (3) the glutamine, proline and arginine metabolic pathways and their interaction; and (4) the research progress in tumour therapy targeting the glutamine-arginine-proline metabolic system, with a focus on summarising the therapeutic research progress of strategies targeting of one of the key enzymes of this metabolic system, P5CS (ALDH18A1). This review provides a new basis for treatments targeting the metabolic characteristics of tumours

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors

Genome-wide identification, characterization, and evolutionary analysis of the barley TALE gene family and its expression profiles in response to exogenous hormones

Three-amino-loop-extension (TALE) family belongs to the homeobox gene superfamily and occurs widely in plants, playing a crucial role in regulating their growth and development. Currently, genome-wide analysis of the TALE family has been completed in many plants. However, the systematic identification and hormone response analysis of the TALE gene family in barley are still lacking. In this study, 21 TALE candidate genes were identified in barley, which can be divided into KNOX and BELL subfamilies. Barley TALE members in the same subfamily of the phylogenetic tree have analogically conserved motifs and gene structures, and segmental duplications are largely responsible for the expansion of the HvTALE family. Analysis of TALE orthologous and homologous gene pairs indicated that the HvTALE family has mainly undergone purifying selective pressure. Through spatial structure simulation, HvKNOX5–HvKNOX6 and HvKNOX5–HvBELL11 complexes are all formed through hydrogen bonding sites on both the KNOX2 and homeodomain (HD) domains of HvKNOX5, which may be essential for protein interactions among the HvTALE family members. Expression pattern analyses reveal the potential involvement of most HvTALE genes in responses to exogenous hormones. These results will lay the foundation for regulation and function analyses of the barley TALE gene family in plant growth and development by hormone regulation

ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU

Transformer-based large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly when fine-turned for specific domains. Recent studies suggest that the resources required for fine-tuning LLMs can be economized through parameter-efficient methods such as Low-Rank Adaptation (LoRA). While LoRA effectively reduces computational burdens and resource demands, it currently supports only a single-job fine-tuning setup. In this paper, we present ASPEN, a high-throughput framework for fine-tuning LLMs. ASPEN efficiently trains multiple jobs on a single GPU using the LoRA method, leveraging shared pre-trained model and adaptive scheduling. ASPEN is compatible with transformer-based language models like LLaMA and ChatGLM, etc. Experiments show that ASPEN saves 53% of GPU memory when training multiple LLaMA-7B models on NVIDIA A100 80GB GPU and boosts training throughput by about 17% compared to existing methods when training with various pre-trained models on different GPUs. The adaptive scheduling algorithm reduces turnaround time by 24%, end-to-end training latency by 12%, prioritizing jobs and preventing out-of-memory issues.Comment: 14 pages, 14 figure

Characterizing the Blood Oxygen Level-Dependent Fluctuations in Musculoskeletal Tumours Using Functional Magnetic Resonance Imaging

This study characterized the blood oxygen level-dependent (BOLD) fluctuations in benign and malignant musculoskeletal tumours via power spectrum analyses in pre-established low-frequency bands. BOLD MRI and T1-weighted imaging (T1WI) were collected for 52 patients with musculoskeletal tumours. Three ROIs were drawn on the T1WI image in the tumours’ central regions, peripheral regions and neighbouring tissue. The power spectrum of the BOLD within each ROI was calculated and divided into the following four frequency bands: 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, and 0.198–0.25 Hz. ANOVA was conducted for each frequency band with the following two factors: the location of the region of interest (LoR, three levels: tumour “centre”, “peripheral” and “healthy tissue”) and tumour characteristic (TC, two levels: “malignant” and “benign”). There was a significant main effect of LoR in the frequencies of 0.073–0.198 Hz and 0.198–0.25 Hz. These data were further processed with post-hoc pair-wise comparisons. BOLD fluctuations at 0.073–0.198 Hz were stronger in the peripheral than central regions of the malignant tumours; however, no such difference was observed for the benign tumours. Our findings provide evidence that the BOLD signal fluctuates with spatial heterogeneity in malignant musculoskeletal tumours at the frequency band of 0.073–0.198 Hz

Preoperative inflammatory markers predict postoperative clinical outcomes in patients undergoing heart valve surgery: A large-sample retrospective study

IntroductionPreoperative inflammation affects the postoperative outcomes of patients undergoing heart valve surgery. This study aimed to explore the role and predictive effects of preoperative inflammation on the primary outcomes after valvular cardiac surgery.MethodsThis retrospective study utilized a medical recording system to screen 5075 patients who underwent heart valve surgery. Data on the C-reactive protein (CRP) levels, erythrocyte sedimentation rate (ESR), and neutrophil-to-lymphocyte ratio (NLR) before heart valve surgery were collected from the hospital’s medical system. Postoperative hepatic insufficiency, acute kidney injury, heart failure, and myocardial damage were assessed using blood indicators. Patients with and without prolonged mechanical ventilation, extended intensive care unit stays, prolonged hospital stays, and death within 30 days after surgery (considered the primary outcome in this study) were compared. Group comparisons, receiver operating characteristic (ROC) curve analyses, and logistic analyses were performed to determine the associations between preoperative inflammation and outcomes after heart valve surgery.ResultsA total of 3249 patients were included in the analysis. Significant differences in CRP level, ESR, and NLR were found between patients with and without postoperative adverse outcomes. ROC analysis showed that CRP levels >5 mg/L effectively predicted postoperative heart failure, and NLR >3.5 had a good predictive effect on all-cause mortality within 30 days after surgery. Patients with CRP levels >5 mg/L had a higher incidence of postoperative heart failure than other patients (20.7% vs. 12.6%, P<0.001), with a relative risk of 1.447 (95% confidence interval: 1.155–1.814). Patients with NLR >3.5 had a higher incidence of death within 30 days after surgery (5.3% vs. 1.2%, P<0.001), with a relative risk of 3.236 (95% confidence interval: 1.773–5.906).ConclusionPreoperative inflammation can affect postoperative outcomes in patients undergoing heart valve surgery. CRP level >5 mg/L and NLR >3.5 can effectively predict postoperative heart failure and death within 30 days after surgery, respectively