Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

BackgroundEarly identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.MethodsThis retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.ResultsTen features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.ConclusionsThe CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations

Heart failure and cognitive impairment: A narrative review of neuroimaging mechanism from the perspective of brain MRI

Both heart failure (HF) and cognitive impairment (CI) have a significant negative impact on the health of the elderly individuals. Magnetic resonance imaging (MRI) can non-invasively detect functional and structural variations in the heart and brain, making it easier to explore the connection between the heart and brain. According to neuroimaging studies, HF patients have a higher chance of developing CI because they have a variety of different types of brain injuries. To examine how HF and CI are influenced by one another, English-language literature was searched in the Web of Science, PubMed EMBASE (OVID), PsycInfo, and Scopus databases. The search terms included “high-frequency,” “brain function,” “brain injury,” “cognition,” “cognitive impairment,” and “magnetic resonance imaging.” Normal brain function is typically impaired by HF in the form of decreased cerebral perfusion pressure, inflammation, oxidative stress, and damage to the BBB, resulting in CI and subsequent HF. Early pathophysiological alterations in patients’ brains have been widely detected using a range of novel MRI techniques, opening up new avenues for investigating the connection between HF and CI. This review aims to describe the pathogenesis of HF with CI and the early diagnostic role of MRI in the heart-brain domain

Experimental Study of Foam Flooding in Low Permeability Sandstones: Effects of Rock Permeability and Microscopic Heterogeneity

Foam flooding (or injection of foam) is a common technology to enhance oil recovery. Although the effects of permeability on foam flooding were well studied in many laboratory experiments, little research has been focused on the specificity of low permeability. In this paper, a series of constant-quality nitrogen foam flow experiments were conducted to investigate the effects of permeability on the foam performance and oil displacement efficiency. Moreover, the results indicated that foam can be generated in low permeability porous media. With uniform experimental conditions, the higher permeability core has a bigger recovery amplification and greater decreasing range of water cut decline. Furthermore, the effect of microscopic heterogeneities of low permeability reservoir on foam displacement is considered. Moreover, experimental comparative analysis with different microscopic heterogeneity cores showed that, in low permeability condition, homogeneous porous media has a better prospects of oil-displacement. Finally, in this work, the results of the permeability effects on the foam performance and oil displacement efficiency exemplify a potential to apply the technology to low permeability reservoir

FXYD3: A Promising Biomarker for Urothelial Carcinoma

Objective Urothelial carcinoma (UC) of the kidney is a relatively rare but aggressive form of kidney cancer. Differential diagnosis of renal UC from renal cell carcinoma (RCC) can be difficult, but is critical for correct patient management. We aimed to use global gene expression profiling to identify genes specifically expressed in urothelial carcinoma (UC) of the kidney, with purpose of finding new biomarkers for differential diagnosis of UC of both upper and lower tract from normal tissues. Materials and Methods Microarray gene expression profiling was performed on a variety of human kidney tumor samples, including clear cell, papillary, chromophobe, oncocytoma, renal UC and normal kidney controls. Differentially expressed mRNAs in various kidney tumor subtypes were thus identified. Protein expression in human UC tumor samples from both upper and lower urinary tract was evaluated by immunohistochemistry. Results FXYD3 (MAT-8) mRNA was specifically expressed in UC of the kidney and not in normal kidney tissue or in any RCC tumor subtypes. FXYD3 mRNA levels displayed equal or better prediction rate for the detection of renal UC than the mRNA levels of selected known UC markers as p63, vimentin, S100P, KRT20 and KRT7. Finally, immunohistochemical staining of clinical UC samples showed that FXYD3 protein is overexpressed in majority of UC of the upper genitourinary tract (encompassing the kidney, ~90%) and in majority of high grade bladder UC (~84%, it's < 40% in low grade tumors, P < 0.001) compared to normal kidney and bladder tissues. Conclusion FXYD3 may be a promising novel biomarker for the differential diagnosis of renal UC and a promising prognosis marker of UC from bladder. Because it was identified genome-widely, FXYD3 may have important biological ramifications for the genetic study of UC

Faith and Fate: Limits of Transformers on Compositionality

Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question: Are these errors incidental, or do they signal more substantial limitations? In an attempt to demystify Transformers, we investigate the limits of these models across three representative compositional tasks -- multi-digit multiplication, logic grid puzzles, and a classic dynamic programming problem. These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures. Our empirical findings suggest that Transformers solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching, without necessarily developing systematic problem-solving skills. To round off our empirical study, we provide theoretical arguments on abstract multi-step reasoning problems that highlight how Transformers' performance will rapidly decay with increased task complexity.Comment: 10 pages + appendix (21 pages

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning

Large language models excel at a variety of language tasks when prompted with examples or instructions. Yet controlling these models through prompting alone is limited. Tailoring language models through fine-tuning (e.g., via reinforcement learning) can be effective, but it is expensive and requires model access. We propose Inference-time Policy Adapters (IPA), which efficiently tailors a language model such as GPT-3 without fine-tuning it. IPA guides a large base model during decoding time through a lightweight policy adaptor trained to optimize an arbitrary user objective with reinforcement learning. On five challenging text generation tasks, such as toxicity reduction and open-domain generation, IPA consistently brings significant improvements over off-the-shelf language models. It outperforms competitive baseline methods, sometimes even including expensive fine-tuning. In particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major performance boost over GPT-3 (and sometimes even over GPT-4). Our promising results highlight the potential of IPA as a lightweight alternative to tailoring extreme-scale language models

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis

BackgroundLung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction.ObjectiveThis study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots.ResultsA total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations).ConclusionsResearch related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities

TGF-β Regulates DNA Methyltransferase Expression in Prostate Cancer, Correlates with Aggressive Capabilities, and Predicts Disease Recurrence

DNA methyltransferase (DNMT) is one of the major factors mediating the methylation of cancer related genes such as TGF-β receptors (TβRs). This in turn may result in a loss of sensitivity to physiologic levels of TGF-β in aggressive prostate cancer (CaP). The specific mechanisms of DNMT's role in CaP remain undetermined. In this study, we describe the mechanism of TGF-β-mediated DNMT in CaP and its association with clinical outcomes following radical prostatectomy.We used human CaP cell lines with varying degrees of invasive capability to describe how TGF-β mediates the expression of DNMT in CaP, and its effects on methylation status of TGF-β receptors and the invasive capability of CaP in vitro and in vivo. Furthermore, we determined the association between DNMT expression and clinical outcome after radical prostatectomy. We found that more aggressive CaP cells had significantly higher TGF-β levels, increased expression of DNMT, but reduced TβRs when compared to benign prostate cells and less aggressive prostate cancer cells. Blockade of TGF-β signaling or ERK activation (p-ERK) was associated with a dramatic decrease in the expression of DNMT, which results in a coincident increase in the expression of TβRs. Blockade of either TGF-β signaling or DNMT dramatically decreased the invasive capabilities of CaP. Inhibition of TGF-β in an TRAMP-C2 CaP model in C57BL/6 mice using 1D11 was associated with downregulation of DNMTs and p-ERK and impairment in tumor growth. Finally, independent of Gleason grade, increased DNMT1 expression was associated with biochemical recurrence following surgical treatment for prostate cancer.Our findings demonstrate that CaP derived TGF-β may induce the expression of DNMTs in CaP which is associated with methylation of its receptors and the aggressive potential of CaP. In addition, DNMTs is an independent predictor for disease recurrence after prostatectomy, and may have clinical implications for CaP prognostication and therapy

Pre-anesthetic use of butorphanol for the prevention of emergence agitation in thoracic surgery: A multicenter, randomized controlled trial

BackgroundEmergence agitation (EA) is common in patients after general anesthesia (GA) and is associated with poor outcomes. Patients with thoracic surgery have a higher incidence of EA compared with other surgery. This study aimed to investigate the impact of pre-anesthetic butorphanol infusion on the incidence of EA in patients undergoing thoracic surgery with GA.Materials and methodsThis prospective randomized controlled trial (RCT) was conducted in 20 tertiary hospitals in China. A total of 668 patients undergoing elective video-assisted thoracoscopic lobectomy/segmentectomy for lung cancer were assessed for eligibility, and 620 patients were enrolled. In total, 296 patients who received butorphanol and 306 control patients were included in the intention-to-treat analysis. Patients in the intervention group received butorphanol 0.02 mg/kg 15 min before induction of anesthesia. Patients in the control group received volume-matched normal saline in the same schedule. The primary outcome was the incidence of EA after 5 min of extubation, and EA was evaluated using the Riker Sedation-Agitation Scale (RSAS). The incidence of EA was determined by the chi-square test, with a significance of P &lt; 0.05.ResultsIn total, 296 patients who received butorphanol and 306 control patients were included in the intention-to-treat analysis. The incidence of EA 5 min after extubation was lower with butorphanol treatment: 9.8% (29 of 296) vs. 24.5% (75 of 306) in the control group (P = 0.0001). Patients who received butorphanol had a lower incidence of drug-related complications (including injecting propofol pain and coughing with sufentanil): 112 of 296 vs. 199 of 306 in the control group (P = 0.001) and 3 of 296 vs. 35 of 306 in the control group (P = 0.0001).ConclusionThe pre-anesthetic administration of butorphanol reduced the incidence of EA after thoracic surgery under GA.Clinical trial registration[http://www.chictr.org.cn/showproj.aspx?proj=42684], identifier [ChiCTR1900025705]