Predictive Tool Research Articles

Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.

Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study

BackgroundVentilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked. MethodsThis study aimed to establish a weighted risk scoring system to examine VAP risk among a two-stage VAP case-control study, and to evaluate the diagnostic performance of risk factor score (RFS) for VAP. We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. Finally, the diagnostic performance of the RFS was testified and further validated in another 177 pairs of VAP case-control study. ResultsLASSO, RF and XGBoost consistently revealed significant associations of length of stay before MV, MV time, surgery, tracheotomy, multiple drug resistant organism infection, C-reactive protein, PaO2, and APACHE II score with VAP. RFS was significantly linearly associated with VAP risk [odds ratio and 95 % confidence interval = 2.699 (2.347, 3.135)], and showed good discriminations for VAP both in discovery stage [area under the curve (AUC) = 0.857] and validation stage (AUC = 0.879). ConclusionsResults of this study revealed co-occurrence of multiple predictors for VAP risk. The risk factor scoring system proposed is a potentially useful predictive tool for clinical targets for VAP prevention.

Systematic review and meta-analysis of non-SCORTEN predictors of mortality in Stevens-Johnson syndrome and toxic epidermal necrolysis.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare, severe cutaneous adverse reactions that result in in-hospital death in 12-49% of cases. The severity-of-illness score for toxic epidermal necrolysis (SCORTEN) is the most widely used mortality prognosis score; however, it has been shown to have critical limitations. Other mortality predictors not incorporated in SCORTEN or other predictor tools are being increasingly reported. This systematic review and meta-analysis aimed to synthesize and evaluate the predictors of mortality in adults with Stevens-Johnson syndrome and toxic epidermal necrolysis not included in SCORTEN. It was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Potential bias was assessed using the National Institutes of Health (NIH) criteria. Forty articles describing results from 52,398 cases were included. Sixteen predictors were reported in five or more articles, and thirty-three were reported in two to four articles. Meta-analysis showed preexisting renal disease (odds ratio (OR): 3.14, 95% confidence interval (CI): 1.99-4.97, P < 0.0001, I2 = 21%), renal involvement (OR: 5.62, 95% CI: 2.29-13.77, P = 0.0002, I2 = 36%), respiratory involvement (OR: 3.14, 95% CI: 1.25-7.92, P = 0.015, I2 = 66%), diabetes mellitus (OR: 1.87, 95% CI: 1.21-2.89, P = 0.005, I2 = 19%), sepsis (OR: 5.64, 95% CI: 2.81-11.29, P < 0.0001, I2 = 63%), comorbidity (OR: 9.13, 95% CI: 4.60-18.12, P < 0.0001, I2 = 0%), and time to hospitalization (OR: 2.56, 95% CI: 1.15-5.65, P = 0.021, I2 = 93) increased risk of mortality. This systematic review and meta-analysis support several clinical and laboratory parameters not included in SCORTEN (preexisting renal disease, renal involvement, respiratory involvement, diabetes mellitus, sepsis, comorbidities, and time to hospitalization) as predictors of mortality in adults with SJS/TEN. The future utilization of these factors may improve mortality prognostication in adults with SJS/TEN.

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study.

Beatriz Nistal-Nuño designed a machine learning system type of ensemble learning for patients undergoing cardiac surgery and intensive care unit cardiology patients, based on sequences of cardiovascular physiological measurements and other intensive care unit physiological measurements in addition to static features, which generates a score for prediction of mortality of cardiac intensive care unit patients. ■ Gradient Boosting Machine and Random Forest models were built for prediction of mortality at cardiac intensive care units. ■ A total of 9,761 intensive care unit stays of patients admitted under a Cardiac Surgery and Cardiac Medical services were studied. ■ The AUROC and AUPRC values were significantly superior to seven conventional systems compared. ■ The machine learning models' calibration curves were substantially closer to the ideal line. Logistic Regression has been used traditionally for the development of most predictor tools of intensive care unit mortality. The purpose of this study is to combine shared risk factors between patients undergoing cardiac surgery and intensive care unit cardiology patients to develop a risk score for prediction of mortality in cardiac intensive care unit patients, using machine learning. Gradient Boosting Machine and Distributed Random Forest models were developed based on 9,761 intensive care unit-stays from the MIMIC-III database. Sequential and static features were collected. The primary endpoint was intensive care unit mortality prediction. Discrimination, calibration, and accuracy statistics were evaluated. The predictive performance of traditional scoring systems was compared. Machine learning models' AUROC and AUPRC were significantly superior to all conventional systems for the primary endpoint (p<0.05), with AUROC of 0.9413 for Gradient Boosting Machine and 0.9311 for Distributed Random Forest. Sensitivity was 0.6421 for Gradient Boosting Machine, 0.6 for Distributed Random Forest, and <0.3 for all conventional systems except for serial SOFA (0.6316). Precision was 0.574 for Gradient Boosting Machine, 0.566 for Distributed Random Forest, and <0.5 for all conventional systems. Diagnostic odds ratio was 58.8144 for Gradient Boosting Machine, 51.2926 for Distributed Random Forest and <34 for all conventional systems. Brier score was 0.025 for Gradient Boosting Machine and 0.028 for Distributed Random Forest, being worse for the traditional systems. Calibration curves of Gradient Boosting Machine and Distributed Random Forest were substantially closer to the ideal line. The machine learning models showed superiority over the traditional scoring systems compared, with Gradient Boosting Machine having the best performance. Discrimination and calibration were excellent for Gradient Boosting Machine, followed by Distributed Random Forest. The machine learning methods exhibited better capacity for most accuracy statistics.

DNA methylation of serotonin genes as predictive biomarkers of antidepressant treatment response

Selective serotonin reuptake inhibitors (SSRI) are frequently ineffective in treating depressive episodes and biomarkers are needed to optimize antidepressant treatment outcomes. DNA methylation levels of serotonin transporter (SLC6A4) and tryptophan hydroxylase 2 genes (TPH2) have been suggested to predict antidepressant clinical outcomes but their applicability remains uncertain. In this study, we: 1) evaluated SLC6A4/TPH2 methylation biomarker potential for predicting clinical outcomes after escitalopram treatment; 2) evaluated whether changes in SLC6A4/TPH2 methylation are informative of treatment mechanisms. We used a cohort of 90 unmedicated patients with major depressive disorder that were part of a 12-week open-label longitudinal trial and compared our observations with previous findings. Depressive symptoms were measured at baseline and after 8 and 12 weeks of treatment using the Hamilton Depression Rating Scale (HAMD6/17). We found an association between baseline TPH2 methylation and both clinical response (β:3.43; p = 0.01; 95 % CI:[0.80; 6.06]) and change in depressive symptoms after 8 weeks (β:−45.44; p = 0.01; 95 %CI:[− −78.58; −12.30]). However, we found no evidence for predictive value of any gene (TPH2 AUC: 0.74 95 % CI:[0.42;0.79]; SLC6A4: AUC: 0.61; 95 % CI: [0.48–0.78]). Methylation levels changed at the trend level for CpG sites of SLC6A4 and TPH2 over the course of 12 weeks of treatment. In addition, similar to previous observations, we found a trend for an association between methylation of SLC6A4 CpG2 (chr17:30,236,083) and HAMD17 change after 12 weeks. Our findings suggest that although TPH2 and SLC6A4 methylation may be informative of antidepressant treatment outcome, they are unlikely to prove useful as clinical predictor tools.

Base Deficit, International Normalized Ratio, and Glasgow Coma Scale (BIG) is a Predictor Tool for Survival and Mortality of Pediatric Trauma Patients.

An accurate estimate of the survival and mortality at the initial trauma evaluation is essential to ensure appropriate triage and stratification of the patients for progressive care. One of the recognized tools for predicting mortality is the BIG Score, composed of admission base deficit, international normalized ratio (INR), and Glasgow Coma Scale (GCS). This study evaluates the BIG scale in predicting survival and mortality rates among pediatric trauma patients. Pediatric trauma patients, aged <18 years, visiting the emergency department of a tertiary hospital in the North of Jordan from 2014 to 2019 were included. Demographic data, trauma details, and lab results were collected. The BIG score for each patient was calculated. A receiver operator characteristic (ROC) curve was generated to determine the best suitable BIG cutoff point and its probabilities. A total of 424 patients were included in this study. About two-thirds of the patients were males (n=298). The mean±SD of pH and GCS values were significantly lower among dead patients (6.0±2.5, 3.7±3.9, respectively) in comparison to alive ones (7.3±2.7, 11.5±5.5, respectively) (p=0.026, p<0.001, respectively). On the other hand, base deficit and INR values were significantly higher among dead patients (6.5±6.2, 1.9±2.5, respectively) than alive ones (1.9±3.6, 0.8±0.5, respectively), (p<0.001). The BIG score with a cutoff point of ≥10.0 has a high sensitivity (88.5%) and specificity (76.3%) for mortality prediction. The survival rate was correctly predicted in 100% of patients with a BIG score between 2.1 and 6. Also, the best survival predictions were seen in intubated patients (100%), followed by RTA-related trauma and ICU admission with decreasing frequency. The current study has shown the added value of the BIG score as a simple and rapid tool to predict prognosis in pediatric trauma settings. The BIG score with a cutoff point of ≥10.0 is highly efficient in predicting pediatric trauma patients' mortality rates. However, the BIG score demonstrates greater accuracy in predicting survival outcomes compared to its ability to predict mortality among pediatric trauma patients.

Impact of frailty on outcomes of patients undergoing elective endovascular thoraco-abdominal aortic aneurysm repair.

Frailty and it score assessment by the Clinical Frailty Scale (CFS) have been recently proposed in surgery to overcome chronological age and major comorbidities as predictor tools of the surgical risks. We aim to evaluate the impact of frailty on outcomes of patients undergoing TAAA endovascular repair and whether CFS may be used as screening tool in the preoperative work-up and peri-operative risk stratification. REtrospective analysis of 76 patients (61 male, 74.9±6.9 years) undergoing elective branched-EVAR. Patients were divided in Group A (CFS<5) and Group B (CFS≥5). Post-operative morbidity, access-site related-complications, ICU- and in-hospital length-of-stay, reintervention rate, surgery- and all-causes related mortality were evaluated. Fifty-four patients (71.1%) were classified as CFS<5, whereas twenty-two as CFS≥5. Demographics and comorbidities were homogeneous regardless of CFS class. No differences in term of MAE and of access-site related-complication but a greater perioperative and early mortality rate in the group of frail patients was noted (P=0.009, OR 11.8, 95% CI 1.35-3.58; P=0.019, respectively), as a longer hospitalization (P=0.007) and more frequent non-home discharge. Mid-term aneurysm- and all-causes related mortality was similar in both groups. Frailty seems to be associated with worse perioperative outcomes. CFS is a reliable tool to quantify the degree of disability due to frailty and to better assess the risks and benefits of endovascular TAAA repair. Frailty is not equated with inoperability but indicate the need for a tailored approach for the more vulnerable patients. Larger studies and a widespread use of frailty screening methods are needed to confirm its efficacy in the prediction of outcomes after endovascular interventions.

Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.

STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer

Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches. Methods: We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs. Results: Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations. Conclusion: Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.

Minor role of TP53 and TERT promoter mutations in medullary thyroid carcinoma: report of new cases and revision of the literature.

Aims of this study were to investigate the prevalence of TP53 and TERT mutations in Medullary Thyroid carcinoma (MTC) and their role in inducing aggressiveness in positive cases. We performed a literature search in PubMed to identify studies investigating the prevalence of TERT and TP53 mutations in MTC. We also included data on MTC cases (n = 193) obtained at our center and unpublished. The in-silico pathogenicity of the TP53 mutations has been evaluated by predictor tools. We identified a total of 25 and 11 published papers: all together 1280 cases have been investigated for the presence of TP53 mutations and 974 for TERT promoter mutation. Twenty-five out of 1280 (2%) cases had a TP53 mutation while only 3/974 MTC cases (0.3%) have been found to be positive for TERT promoter mutations. Among all, we identified 19 different TP53 mutations that in 12 cases were demonstrated to have an in silico predicted high pathogenic role and a high impact on protein function. Three non-sense and 4 probably not damaging mutations were also reported. The pathogenic role of the TERT promoter mutations has been previously in vitro determined. No correlation between TP53 and/or TERT mutations and aggressiveness of MTC has been demonstrated. The prevalence of TP53 and TERT promoter mutations is very low in MTC. The reported mutations are pathogenic in the majority of cases. Because of their rarity it is not possible to clarify if they play or not a role in the pathogenesis and/or aggressiveness of this specific thyroid tumor.

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

A Rare Combination of Compound Heterozygous Mutations in the PAH Gene in Three Unrelated Consanguineous Iranian Families with Classical Phenylketonuria.

The PAH gene mutations have been linked to the development of phenylketonuria (PKU), which is recognized as the most common inborn metabolic disorder, and is caused by a deficiency in the phenylalanine hydroxylase (PAH) enzyme. The Iranian population, known for its diversity and high consanguinity, offers a valuable sample for studying autosomal recessive disorders. Our study investigated three unrelated families with PKU from Iran, utilizing clinical, laboratory, and computational approaches. We performed direct PCR sequencing for 13 exons of the PAH gene on three Iranian patients who were diagnosed with PKU. Then, Sanger sequencing confirmed the segregation of the mutations from parents to probands. Pathogenicity predictor tools, including ACMG, CADD, SIFT, Polyphen-2, and Mutation Taster, were utilized to analyze the identified genetic variants. The three-dimensional structure of the mutant forms of the protein was predicted. We also analyzed the protein-protein interactions of PAH using the STRING database. All three patients exhibited rare compound heterozygosity rearrangements in the PAH gene (NM_000277.3). These included three missense variants: [c.533A>G/c.1222C>T], [c.526C>T/c.1222C>T], and [c.533A> / c.526C>T]. This study adds to the body of evidence establishing the association between PAH mutations and the development of PKU. We speculated that the conjunction of a high consanguinity rate in populations such as Iran, coupled with the founder effect, can give rise to atypical genetic profiles, as observed in the rearrangement of compound heterozygosity in this study Moreover, our research underscores the significance of genetic testing in the precise diagnosis of individuals affected by inborn errors of metabolism.

Development of clinical prediction model to guide the use of CT head scans for non-traumatic Thai patient with seizure: A cross-sectional study.

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient's presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model's predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

O-255 Ovarian Response Prediction Index (ORPI) as a predictor tool for ovarian response and clinical pregnancy in IVF/ICSI cycle : A Prospective Cohort Study

Abstract Study question Can incorporation of the Ovarian Response Prediction Index (ORPI), integrating AMH, AFC and age, offer a reliable prognostication of ovarian stimulation response in ART cycles? Summary answer The study affirms that ORPI, a three-variable index, emerges as a robust predictor for both ovarian response and clinical pregnancy rates within IVF/ICSI cycles. What is known already In the realm of individualized Controlled Ovarian Stimulation, precision in forecasting ovarian response proves pivotal. Ovarian reserve, influenced by age, AMH, and AFC, exhibits considerable heterogeneity among women of analogous age. While AMH and AFC function as pivotal markers, a holistic approach, exemplified by ORPI, stands out as a more comprehensive tool for predicting ovarian response. Study design, size, duration This prospective cohort study enrolled 302 subjects undergoing IVF cycles at a tertiary infertility center spanning from 1st January 2022 to 31st December 2023. Rigorous inclusion criteria mandated age ≤38 years, a BMI between 20–25 kg/m2, regular menstrual cycles, intact bilateral ovaries, no history of ovarian surgeries, absence of severe endometriosis, and no evidence of endocrine disorders. Participants/materials, setting, methods Subjects adhering to the inclusion criteria underwent venous blood sampling for AMH measurement and transvaginal ultrasound for AFC assessment. The ORPI was meticulously computed as (AMH × AFC) / patient age. Key endpoints encompassed the total oocytes retrieved, mature MII oocytes, and occurrences of clinical pregnancy. Rigorous statistical methodologies were employed for robust analysis. Main results and the role of chance The mean values for age (years), AMH (ng/mL), ORPI, AFC, and fertilized oocytes were 31.75 ± 4, 3.75 ± 3.39, 1.06 ± 1.22, 7.1 ± 2.94, and 6.11 ± 3.26, respectively. A highly significant positive correlation (p-value &amp;lt; 0.0001) was observed between ORPI and the numbers of oocytes and MII oocytes, with correlation coefficients of 0.714 and 0.746, respectively. Interpretation of the area under the ROC curve revealed that ORPI was a significant predictor of obtaining ≥4 oocytes at a cutoff point of &amp;gt; 0.2, with an area under the curve of 0.907. Similarly, for MII oocytes ≥4, ORPI was a significant predictor at a cutoff point of &amp;gt; 0.2, with an area under the curve of 0.937. In the case of positive clinical pregnancy, ORPI emerged as a significant predictor at a cutoff point of &amp;gt; 0.75, with an area under the curve of 0.822. The study underscored a substantial positive correlation between ORPI and both oocyte and MII oocyte counts, affirming ORPI’s pivotal role as a predictor for clinical pregnancy. Limitations, reasons for caution Limitations due to the constraint of sample size temper the generalizability of findings. The study did not account for live birth rates, and a comparative analysis with alternative markers was omitted. A more expansive, meticulously designed study is imperative. Wider implications of the findings ORPI, an innovative ovarian response biomarker, showcases considerable potential for predicting both ovarian response and clinical pregnancy rates. Its integration into individualized Controlled Ovarian Stimulation regimens holds promise for augmenting the counselling and prognostication process for patients embarking on IVF/ICSI cycles. Trial registration number MCDH/2023/28

Intelligent Reconfiguration-Promoted Cellular Internalization of Core-Shell DNA Nanoprobe Equipped with Successive Dual Stimuli-Responsive Protective Satellites for Amplification Fluorescence Imaging of Tumor Cells.

Although DNA probes have attracted increasing interest for precise tumor cell identification by imaging intracellular biomarkers, the requirement of commercial transfection reagents, limited targeting ligands, and/or non-biocompatible inorganic nanostructures has hampered the clinic translation. To circumvent these shortcomings, a reconfigurable ES-NC (Na+-dependent DNAzyme (E)-based substrate (S) cleavage core/shell DNA nanocluster (NC)) entirely from DNA strands is assembled for precise imaging of cancerous cells in a successive dual-stimuli-responsive manner. This nanoprobe is composed of a strung DNA tetrahedral satellites-based protective (DTP) shell, parallelly aligned target-responsive sensing (PTS) interlayer, and hydrophobic cholesterol-packed innermost layer (HCI core). Tetrahedral axial rotation-activated reconfiguration of DTP shell promotes the exposure of interior hydrophobic moieties, enabling cholesterol-mediated cellular internalization without auxiliary elements. Within cells, over-expressed glutathione triggers the disassembly of the DTP protective shell (first stimulus), facilitating target-stimulated signal transduction/amplification process (second stimuli). Target miRNA-21 is detected down to 10.6 fM without interference from coexisting miRNAs. Compared with transfection reagent-mediated counterpart, ES-NC displays a higher imaging ability, resists nuclease degradation, and has no detectable damage to healthy cells. The blindtest demonstrates that the ES-NC is suitable for the identification of cancerous cells from healthy cells, indicating a promising tool for early diagnosis and prediction of cancer.

Identifying early language predictors: A replication of Gasparini etal. (2023) confirming applicability in a general population cohort.

Identifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it. To validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome. A total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11-12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent-reported variables from ages 2-3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11-12 years. Random forests were used to identify any additional variables predictive of language outcome. Complete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors. We identified an ultra-short set of variables that predicts 11-12-year language outcome with 'fair' accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population-based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context. What is already known on the subject There are no robust predictor sets of child language disorder despite its prevalence and far-reaching impacts. A previous study identified six variables collected at age 2-3 years that predicted 11-12-year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort. What this study adds to the existing knowledge We used machine learning methods to identify a set of six questions asked at age 2-3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11-12 years, now showing consistent results across two large-scale population-based cohort studies. What are the potential or clinical implications of this work? This predictor set is more accurate than existing feasible methods and can be translated into a low-resource and time-efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties.

Subset of retinoblastoma tumours is associated with BRCA1/2 mutations

BackgroundWe investigated the potential association between pathogenic BRCA1/2 variants and retinoblastoma pathogenicity.MethodsIn this single-centre, retrospective case series, we performed hereditary cancer panel tests using blood samples for patients with retinoblastoma...

Development and validation of a clinical prediction model for glioma grade using machine learning.

Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

Five-Axis Finish Milling Machining for an Inconel 718 Alloy Monolithic Blisk

Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters at effective cutting speeds of 46.53 m/min, feed per tooth of 0.1 mm/tooth, and depth of cut of 0.05 mm for marginal tool wear and fast cutting speeds. Then the corresponding tool life was estimated by using the derived parameters.

Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications.

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.