Predictive Tool Research Articles

Predictive analysis of terrorist activities in Thailand's Southern provinces: a deep learning approach

Terrorist activities have been on the rise globally, with Thailand experiencing significant challenges, particularly in its three southern border provinces. This study offers a comprehensive analysis aiming to predict forthcoming terrorist events in these provinces. We employed historical data, categorized into nine groups based on military expert recommendations, to train our prediction model. This research tested the prediction capabilities of various methodologies, including decision trees, naïve Bayesian learning techniques, and deep learning artificial neural networks. Notably, the deep neural network emerged as the superior predictive tool, achieving an impressive accuracy of 98.21% and a root mean square error (RMSE) of 0.59%. The primary anticipated events include bombings, shootings, assaults, and acts of vandalism. Our findings also revealed that Pattani Province was the most affected, accounting for 45% of incidents. Specific districts, such as Panare and Yarang, exhibited high crime rates of 40% and 36.84%, respectively. Yala Province, particularly Bannang Sata District, was identified as the hotspot for shooting incidents, with a rate of 34%.

Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications

AbstractThe growing demand for applications based on Generative Adversarial Networks (GANs) has prompted substantial study and analysis in a variety of fields. GAN models have applications in NLP, architectural design, text-to-image, image-to-image, 3D object production, audio-to-image, and prediction. This technique is an important tool for both production and prediction, notably in identifying falsely created pictures, particularly in the context of face forgeries, to ensure visual integrity and security. GANs are critical in determining visual credibility in social media by identifying and assessing forgeries. As the field progresses, a variety of GAN variations arise, along with the development of diverse assessment techniques for assessing model efficacy and scope. The article provides a complete and exhaustive overview of the most recent advances in GAN model designs, the efficacy and breadth of GAN variations, GAN limits and potential solutions, and the blooming ecosystem of upcoming GAN tool domains. Additionally, it investigates key measures like as Inception Score (IS) and Fréchet Inception Distance (FID) as critical benchmarks for improving GAN performance in contrast to existing approaches.

Prediction of nitrate leaching from soil amended with biosolids by machine learning algorithms

This study focused on employing machine learning algorithms to forecast nitrate leaching from soils treated with biochar and vermicompost derived from sugarcane bagasse. input variables including bulk density, porosity, organic carbon, nitrogen, phosphorus, anion exchange capacity, cation exchange capacity, pH, and electrical conductivity, while nitrate leaching was the target variable for prediction. A comparative analysis of machine learning models indicated that Random Forest Regression outperformed linear regression in the prediction of nitrate leaching. Additionally, among the input variables, anion exchange capacity, cation exchange capacity, bulk density, and EC showed the most significant influence in utilizing these models as predictive tools for nitrate leaching from soils treated with slow-release fertilizers.

Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation

RNA-binding proteins are central for many biological processes and their characterization has demonstrated a broad range of functions as well as a wide spectrum of target structures. RNA G-quadruplexes are important regulatory elements occurring in both coding and non-coding transcripts, yet our knowledge of their structure-based interactions is at present limited. Here, using theoretical predictions and experimental approaches, we show that many chromatin-binding proteins bind to RNA G-quadruplexes, and we classify them based on their RNA G-quadruplex-binding potential. Combining experimental identification of nuclear RNA G-quadruplex-binding proteins with computational approaches, we build a prediction tool that assigns probability score for a nuclear protein to bind RNA G-quadruplexes. We show that predicted G-quadruplex RNA-binding proteins exhibit a high degree of protein disorder and hydrophilicity and suggest involvement in both transcription and phase-separation into membrane-less organelles. Finally, we present the G4-Folded/UNfolded Nuclear Interaction Explorer System (G4-FUNNIES) for estimating RNA G4-binding propensities at http://service.tartaglialab.com/new_submission/G4FUNNIES.

The European GO-VIKING project on flow-induced vibrations: overview and current status

Abstract The interaction between cooling fluid and solid structures (rods, tubes) in nuclear power plants may lead to flow-induced vibrations (FIV), causing material fatigue, fretting wear, and eventually loss of component integrity. This can cause further safety issues as well as substantial standstill costs due to longer or unplanned outages. With the growing computational power, the application of modern 3D numerical simulation tools for the accurate prediction of FIV phenomena is rapidly increasing. In 2022, the GO-VIKING (Gathering expertise On Vibration ImpaKt In Nuclear power Generation) project received a grant within the Horizon Europe research and innovation funding program. Sixteen European and two US partners started their collaboration in the field of FIV experiments and analysis. Over four years, the GO-VIKING project investigates FIV phenomena occurring in nuclear reactor cores and steam generators under single- and two-phase flow conditions. The project’s main objectives are to expand the expertise in the field of FIV through generation of new experimental and high-resolution numerical data; development, improvement, and validation of fluid-structure interaction (FSI) methods for FIV evaluation; training stakeholders in the application of these methods; and synthesizing guidelines for the prediction and assessment of FIV phenomena in nuclear reactors. This paper provides an overview of the GO-VIKING objectives, scientific program, as well as of the main scientific achievements in the first project year.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD

Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Assessing the toxicity of pesticides exposure on hepatic miRNA-target gene alterations in rat liver tissues via molecular and integrated network bioinformatics analysis

The prevalent use of pesticides, including pirimiphos-methyl (PPM) and bifenthrin (BF), poses a serious health risk, particularly to workers who encounter these chemicals daily. Despite the recognized hepatotoxic effects, the specific molecular mechanisms, especially those involving miRNAs in liver damage caused by PPM and BF, are not fully elucidated. Prior studies have not exhaustively analyzed the hepatic miRNA-target gene dynamics following exposure to these pesticides; thus, this research aims to fill that gap through an extensive miRNA analysis to discern their regulation in PPM or BF-induced hepatic toxicity. In this study, male Sprague-Dawley rats were exposed to BF or PPM for 28 days through oral gavage, simulating the chronic exposure faced by humans. We conducted a thorough assessment of the hepatotoxicity induced by PPM and BF, employing multiple evaluation levels, including histological analysis, liver enzyme measurements, and real-time PCR to detect changes in hepatic miRNA-target gene expressions. Additionally, we utilized DIANA-miRPath prediction tools to delineate the functional implications of these hepatic miRNA target genes. Our findings reveal a significant modulation in the expression of rno-miR-155–5p and rno-miR-122–5p, along with their target genes, following PPM and BF treatment. In contrast, rno-miR-21–5p levels remained unaltered. These observations suggest potential utility of these specific hepatic miRNAs as biomarkers for liver injury resulting from pesticide exposure. Subsequent GO enrichment analysis linked target genes to functions like molecular activity, protein binding, and cellular processes. Additionally, KEGG pathway analysis showed these genes, influenced by varied miRNA expressions, play significant roles in metabolic and signaling pathways In conclusion, this study enhances our comprehension of the biological roles of miRNAs in hepatic toxicity induced by PPM and BF. The insights gained here not only shed light on molecular mechanisms but also open avenues for considering these miRNAs as potential diagnostic biomarkers in conditions of pesticide-induced hepatotoxicity, thereby guiding future therapeutic strategies.

Situating the phosphonated calixarene-cytochrome C association by molecular dynamics simulations.

Protein-calixarenes binding plays an increasingly central role in many applications, spanning from molecular recognition to drug delivery strategies and protein inhibition. These ligands obey a specific bio-supramolecular chemistry, which can be revealed by computational approaches, such as molecular dynamics simulations. In this paper, we rely on all-atom, explicit-solvent molecular dynamics simulations to capture the electrostatically driven association of a phosphonated calix-[4]-arene with cytochome-C, which critically relies on surface-exposed paired lysines. Beyond two binding sites identified in direct agreement with the x-ray structure, the association has a larger structural impact on the protein dynamics. Then, our simulations allow a direct comparison to analogous calixarenes, namely, sulfonato, similarly reported as "molecular glue." Our work can contribute to a robust in silico predictive tool to assess binding sites for any given protein of interest for crystallization, with the specificity of a macromolecular cage whose endo/exo orientation plays a role in the binding.

Integrating Intellectual Consciousness AI based on Ensemble Machine Learning for Price Negotiation in E-commerce using Text and Voice-Based Chatbot

Online shopping has experienced an enormous boost in recent years. With this evolution, the majority of internet shopping's capabilities have been developed, but some functions, such as negotiating with store owners, are still not available. This paper suggests employing a chatbot with a voice assistant to negotiate product prices. Customers can communicate with the chatbot to get assistance in finding a reasonable price for a product. In online purchasing, there is a possibility that the consumers or the product seller's budget may be compromised. In order to assist in purchasing, algorithm has been created in machine learning that uses the forecasting of historical data to avoid compromising situations. However, improper dataset or when irrelevant aspects or at- tributes of the data are used, price prediction might become less accurate. Ecommerce companies do not merely depend on price prediction tools due to the significant financial losses brought on even by a single inaccurate price prediction. Additionally, few models fail to perform well when the data saturates or when an attribute becomes inaccessible after the period for which the model's prediction was reliant. By controlling these alterations, the accuracy and dependability are preserved in the model pro- posed in this study.

Prodromal symptoms of rheumatoid arthritis in a primary care database: variation by ethnicity and socioeconomic status.

To assess whether prodromal symptoms of rheumatoid arthritis (RA), as recorded in the Clinical Practice Research Datalink Aurum (CPRD) database of English primary care records, differ by ethnicity and socioeconomic status. A cross-sectional study to determine the coding of common symptoms (≥0.1% in the sample) in the 24 months preceding RA diagnosis in CPRD Aurum, recorded between January 1st 2004 to May 1st 2022. Eligible cases were adults with a code for RA diagnosis. For each symptom, a logistic regression was performed with the symptom as dependent variable, and ethnicity and socioeconomic status as independent variables. Results were adjusted for sex, age, BMI, and smoking status. White ethnicity and the highest socioeconomic quintile were comparators. In total, 70115 cases were eligible for inclusion, of which 66.4% female. Twenty-one symptoms were coded in > 0.1% of cases so were included in the analysis. Patients of South Asian ethnicity had higher frequency of codes for several symptoms, with the largest difference by odds ratio being muscle cramps (OR 1.71, 1.44-2.57) and shoulder pain (1.44, 1.25-1.66). Patients of Black ethnicity had higher prevalence of several codes including unintended weight loss (2.02, 1.25-3.28) and ankle pain (1.51, 1.02-2.23). Low socioeconomic status was associated with morning stiffness (1.74, 1.08-2.80) and falls (1.37, 2.03-1.82). There are significant differences in coded symptoms between demographic groups, which must be considered in clinical practice in diverse populations and to avoid algorithmic bias in prediction tools derived from routinely collected healthcare data.

Untargeted NMR-based metabolomics analysis of kidney allograft perfusates identifies a signature of delayed graft function.

Kidney transplantation (KTx) necessarily conveys an ischemia/reperfusion (I/R) process, which impacts on allograft outcomes. Delayed graft function (DGF) is defined as a non-decrease of serum creatinine by at least 10% daily on 3 consecutive days during the first 7days post-KTx. DGF significantly conditions both short- and long-term graft outcomes. Still there is a lack of DGF predictive biomarkers. This study aimed to explore the potential of kidney graft perfusate metabolomics to predict DGF occurrence. 49 human perfusates from grafts categorized upon donor type [donation after brain death (DBD)/donation after circulatory death (DCD)] and DGF occurrence and 19 perfusates from a murine model classified upon death type (DBD/DCD) were collected and analyzed by NMR-based metabolomics. The multivariate analysis of the murine data highlighted significant differences between perfusate metabolomes of DBD versus DCD. These differences were similarly observed in the human perfusates. After correcting for the type of donor, multivariate analysis of human data demonstrated a metabolomics signature that could be correlated with DGF occurrence. The metabolome of kidney grafts is influenced by the donor's type in both human and pre-clinical studies and could be correlated with DGF in the human DBD cohort. Thus, metabolomic analysis of perfusate applied prior to KTx may represent a new predictive tool for clinicians in a more personalized management of DGF. Moreover, our data paves the way to better understand the impact of donor's types on the biochemical events occurring between death and the hypothermic storage.

Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis

Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain. To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA. This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications. The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool. The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument. Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64). Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA. ANZCTR.org.au Identifier: ACTRN12622000072718.

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

The study presents a comprehensive examination of the recent advancements in the field of wine production using the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain Technology (BCT). The paper aims to provide insights into the implementation of these technologies in the wine supply chain and to identify the potential benefits associated with their use. The study highlights the various applications of IoT, AI, and BCT in wine production, including vineyard management, wine quality control, and supply chain management. It also discusses the potential benefits of these technologies, such as improved efficiency, increased transparency, and reduced costs. The study concludes by presenting the framework proposed by the authors in order to overcome the challenges associated with the implementation of these technologies in the wine supply chain and suggests areas for future research. The proposed framework meets the challenges of lack of transparency, lack of ecosystem management in the wine industry and irresponsible spending associated with the lack of monitoring and prediction tools. Overall, the study provides valuable insights into the potential of IoT, AI, and BCT in optimizing the wine supply chain and offers a comprehensive review of the existing literature on the study subject.

Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Prostate MRI followed by prostate biopsy. The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

Analyzing Microsoft stock price trends, factors, and market efficiency for optimal investment strategies

Microsoft, one of the tech giants, has seen significant fluctuations in its stock prices in recent years. This article delves into an analytical study of these changes using data sourced from Yahoo Finance. The background for this research stems from the desire to understand the intricacies of stock price movements and the factors influencing them, especially for prominent companies like Microsoft that have a vast impact on global markets. The methodology adopted for this study is the technical approach, where the moving average is calculated to determine the stocks trend. Moving averages are a cornerstone of technical analysis and can provide insights into potential buy and sell signals. By employing this method, the research aims to validate the appropriateness of certain investment strategies based on historical data. The primary objective of the study is not just to chart the course of the stocks price but to delve deeper into the causative factors behind its movement. Various external and internal factors can influence stock prices, from global economic trends to company-specific news. By analyzing these factors, the research also seeks to test the efficiency of the market, a long-debated topic among economists and investors. In conclusion, while past data can offer insights, the stock markets volatile nature demands continuous adaptation of investment strategies. The findings from this study can serve as a foundation for developing more refined prediction tools and investment tactics. Future studies might consider integrating more sophisticated models or incorporating other predictive variables to enhance accuracy and investment outcomes.

Genetic and in silico analysis of Indian sporadic young onset patient with amyotrophic lateral sclerosis

Background Amyotrophic lateral sclerosis (ALS) is an old onset devastating neurodegenerative disorder. Young-onset ALS cases especially sporadic ones who are between 25 and 45 years are rarely affected by the disease. Despite the identification of numerous candidate genes associated with ALS, the etiology of the disease remains elusive due to extreme genetic and phenotypic variability. The advent of affordable whole exome sequencing (WES) has opened new avenues for unraveling the disease’s pathophysiology better. Methods and results We aimed to determine the genetic basis of an Indian-origin, young onset sporadic ALS patient with very rapid deterioration of the disease course without any cognitive decline who was screened for mutations in major ALS candidate genes by WES. Variants detected were reconfirmed by Sanger sequencing. The clinicopathological features were investigated and two heterozygous missense variants were identified: R452W, not previously associated with ALS, present in one of the four conserved C terminal domains in ANXA11 and R208W in SIGMAR1, respectively. Both of these variants were predicted to be damaging by pathogenicity prediction tools and various in silico methods. Conclusion Our study revealed two potentially pathogenic variants in two ALS candidate genes. The genetic makeup of ALS patients from India has been the subject of a few prior studies, but none of them examined ANXA11 and SIGMAR1 genes so far. These results establish the framework for additional research into the pathogenic processes behind these variations that result in sporadic ALS disease and further our understanding of the genetic makeup of Indian ALS patients.

Platelet indices and neutrophil:lymphocyte ratio as a predictive tool in acute sickle cell vaso-occlusive crisis: A study protocol

Platelet indices and the neutrophil:lymphocyte ratio are newly emerging indicators of systemic inflammation in chronic diseases, such as hemoglobinopathies. The objectives of this study include estimation of platelet indices and neutrophil:lymphocyte ratio in sickle cell disease (SCD) patients with and without vaso-occlusive crisis. This cross-sectional study will be conducted in the Department of Medicine at a tertiary care teaching hospital situated in the rural area of Wardha district. The participants will include patients of SCD older than 18 years of age attending the sickle cell clinic. We hypothesize the use of neutrophil:lymphocyte ratio (NLR) and platelet indices as clinically significant predictive markers in acute sickle cell vaso-occlusive crisis.

A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma.

γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

Identification of GB3 as a Novel Biomarker of Tumor-Derived Vasculature in Neuroblastoma Using a Stiffness-Based Model.

Neuroblastoma (NB) is a childhood cancer in sympathetic nervous system cells. NB exhibits cellular heterogeneity, with adrenergic and mesenchymal states displaying distinct tumorigenic potentials. NB is highly vascularized, and blood vessels can form through various mechanisms, including endothelial transdifferentiation, leading to the development of tumor-derived endothelial cells (TECs) associated with chemoresistance. We lack specific biomarkers for TECs. Therefore, identifying new TEC biomarkers is vital for effective NB therapies. A stiffness-based platform simulating human arterial and venous stiffness was developed to study NB TECs in vitro. Adrenergic cells cultured on arterial-like stiffness transdifferentiated into TECs, while mesenchymal state cells did not. The TECs derived from adrenergic cells served as a model to explore new biomarkers, with a particular focus on GB3, a glycosphingolipid receptor implicated in angiogenesis, metastasis, and drug resistance. Notably, the TECs unequivocally expressed GB3, validating its novelty as a marker. To explore targeted therapeutic interventions, nanoparticles functionalized with the non-toxic subunit B of the Shiga toxin were generated, because they demonstrated a robust affinity for GB3-positive cells. Our results demonstrate the value of the stiffness-based platform as a predictive tool for assessing NB aggressiveness, the discovery of new biomarkers, and the evaluation of the effectiveness of targeted therapeutic strategies.

Physicians' perceptions of intensive care patients' 1-year prognoses compared to realistic prognoses.

It is unknown whether physicians treating critically ill patients have realistic perceptions of their patients' prognoses. We sent a survey by email to Finnish anesthesiologists to investigate their ability to estimate the probability of 1-year survival of intensive care unit (ICU) patients based on data available at the beginning of intensive care. We presented 12 fictional but real-life-based patient cases and asked the respondent to estimate the probability of 1-year survival in each case by choosing one of the alternatives 5%, 10%-90% in 10% intervals and 95%. We compared the physicians' estimates to registry data-based realistic prognoses of comparable patients treated in the ICU. Based on the difference between the estimate and the realistic prognosis, we categorized the estimates into three groups: (1) difference less than 10 percentage points, (2) difference between 10 and 20 percentage points, and (3) difference over 20 percentage points. We received 210 responses (totally 2520 estimates). Of the respondents, 43 (20.5%) were specialists working mainly in the ICU, 81 (38.6%) were specialists working occasionally in the ICU, 47 (22.4%) were specialists not working in the ICU, and 39 (18.6%) were doctors in training. The difference between the estimate and the realistic prognosis was less than 10 percentage points for 1083 (43.0%) estimates, between 10 and 20 percentage points for 645 (25.6%) estimates, and over 20 percentage points for 792 (31.4%) estimates, out of which 612 (24.3% of all estimates) underestimated and 180 (7.1%) overestimated the likelihood of survival. The median error (the median of the differences between the estimate and the realistic prognosis) for all estimates was -8.8 [interquartile range (IQR), -20.0 to -0.2], which means that the most typical response underestimated the likelihood of survival by 9 percentage points. Based on the 12 estimates, we calculated the median error for each respondent. The median (IQR) of these median errors was -8.6 (-12.6 to -5.0) for specialists working mainly in the ICU, -8.1 (-13.0 to -5.2) for specialists working occasionally in the ICU, -9.7 (-17.7 to -6.3) for specialists not working in the ICU, and -9.1 (-14.5 to -5.1) for doctors in training (p = .29). Finnish anesthesiologists commonly misestimate the long-term prognoses of ICU patients, more often underestimating than overestimating the likelihood of 1-year survival. More education about critically ill patients' prognoses and better prediction tools are needed.