Predictability Research Articles

Climate Change Perception, Climate Change Anxiety, & Pro-environmental Behaviors of Tertiary Students

The Philippines is one of the most climate change-vulnerable countries in the world. As the climate crisis pose physical and mental threats, it is crucial to understand the factors affecting environmental behavior. However, local studies on the role of Psychology in encouraging pro-environmental behaviors (PEB) appear to be few in the Philippines. This study was designed to determine the level and the predictive ability of climate change perception (CCP) and climate change anxiety (CCA) on PEB. A stepwise regression analysis was used on data collected from 430 stratified randomly sampled tertiary students in Bacolod city, Philippines who completed the Climate Change Perception Scale, Climate Change Anxiety Scale, and Pro-environmental Behavior Scale. Results showed that the parsimonious model that include climate change anxiety (t=13.585, p=.000) and climate change perception (t=6.409, p=.000) significantly predicted pro-environmental behaviors F(1, 426) = 115.284, p < .001, adj. R2 = .349. Further, findings indicate that 34.9% of the variance in the pro-environmental behaviors can be explained by the model. In conclusion, when tertiary students experience higher levels of climate change anxiety and perceive climate change as a significant threat, they are more likely to engage in pro-environmental behaviors. Results suggest a need for further research in determining other factors that predict PEB. This study contributes to the scarce literature on Environmental Psychology in the country.

Polygenic Risk Assessment for Schizophrenia in Patients with Nonpsychotic Disorders and Attenuated Symptoms of Psychosis: A Pilot Study

Background: significant contribution of genetic factors in the development of schizophrenia is a generally recognized fact. Polygenic risk index for schizophrenia turned out to be an effective tool allowing to draw a dividing line between schizophrenia and mentally healthy control in terms of genetics. Objective: to assess the predictive ability of the polygenic risk score for schizophrenia (SZ-PRS) in adolescent patients with a first depressive episode and attenuated psychotic symptoms (APS). Patients and Methods: sixty adolescent inpatient with a first depressive episode were examined. Based on the presence of APS at admission, patients were divided into two groups: a group with APS and a group without APS. Subgroups of patients in the first group were identified through follow-up observations: those with psychosis manifestation and/or low social functioning and those without manifestation and with high social functioning. Whole-genome genotyping was performed for all participants, and SZ-PRS were calculated. For comparison, a group of patients diagnosed with schizophrenia (n = 879) and a group of mentally healthy individuals (n = 759), who had previously undergone whole-genome genotyping and had their SZ-PRS calculated, were used. Results: SZ-PRS of the APS group occupy an intermediate position between the healthy control and schizophrenia patients, significantly differing from each of them. The group without APS did not differ from the control group, but compared to the group of schizophrenia patients, the SZ-PRS in this group was significantly lower. Comparing subgroups of patients showed that the SZ-PRS in the APS group without psychosis manifestation and social functioning impairment was significantly lower than in the group with schizophrenia manifestation. The APS subgroups with psychosis manifestation and with functioning impairment did not differ significantly from each other or from the schizophrenia group. Conclusion: the results obtained for the first time for the russian population showed that SZ-PRS can be considered as a tool for assessing the risk of developing psychosis or reduced social functioning in patients with APS.

Mean platelet volume/platelet count ratio can predict the recurrence-free survival rate of patients after complete resection of gastrointestinal stromal tumors

PurposeThe aim of this study is to compare mean platelet volume/platelet count ratio (PVPR) and other indicators’ predictive abilities. Simultaneously, a new nomogram for predicting recurrence-free survival (RFS) after gastrointestinal stromal tumors (GISTs) R0 resection was developed.MethodsFrom January 2010 to July 2019, 295 patients with GIST who were operated on at Harbin Medical University Cancer Hospital were retrospectively reviewed. With a 4-year RFS as the end point, using the Kaplan–Meier methods and log rank test, and then conducting Cox regression analysis, we compared and identified meaningful indicators for predicting prognosis. Finally, a nomogram was developed and validated using calibration curves.ResultsThe receiver operating characteristic curve indicated that a cutoff point of 0.044 was the ideal threshold for PVPR, and patients were divided into a high-PVPR group (≤0.044) and a low-PVPR group (>0.044). Kaplan–Meier curves suggested that PVPR>0.044 had obvious associations with better RFS (p < 0.001). In accordance with multivariate analysis, PVPR (>0.044 vs. ≤0.044) (p = 0.005), National Institutes of Health (NIH) risk category (p < 0.001), and Ki-67 (p = 0.005) were the independent prognostic indicators of RFS. Tumor size, gastrointestinal bleeding, mitotic index, NIH risk category, CD34, and Ki-67 all exhibited an obvious correlation with PVPR (all p < 0.05). The nomogram’s probability of concordance was 0.823, indicating that the nomogram predictions were well calibrated.ConclusionIn GISTs, RFS can be independently predicted by PVPR. Patients with higher PVPR have better RFS. The nomogram including PVPR could be used to assist clinical treatment decision-making.

Prospectively predicting 6-month risk for non-suicidal self-injury among adolescents after psychiatric hospitalization based on a predictive model

BackgroundIt is challenging to predict the occurrence of non-suicidal self-injury (NSSI) among adolescents over short periods. Moreover, the predictive value of indices for NSSI remains elusive. Thus, this study aimed to identify predictors of NSSI within 6 months among adolescents after psychiatric hospitalization by establishing a risk assessment model.MethodsA total of 632 high-risk participants were included in this study. The distribution characteristics of adolescent NSSI were initially assessed through a cross-sectional survey, following which risk factors were identified using logistic regression analysis. The risk score method was then used to construct a 6-month risk assessment model for NSSI. Lastly, the predictive effect of the model was evaluated by indicators such as the area under the receiver operating characteristic (ROC) curve and the positive predictive value.ResultsAfter 6 months, 412 cases of NSSI were identified. According to the logistic regression model, the frequency of relapses, medication status, and NSSI history were identified as influencing factors. Higher scores on the Impulsive Behavior Scale and Pittsburgh Sleep Quality Index were associated with a higher risk of NSSI. Conversely, higher scores on the Pain and Belief Perception Scale were correlated with a lower risk of NSSI. Moreover, the area under the ROC curve for the predictive model was 0.9989, with a 95% confidence interval of (0.9979, 0.9999), highlighting its high predictive ability and accuracy. The predictive model was validated using 78 patients, yielding an area under the ROC curve of 0.9703 and a 95% confidence interval of (0.9167, 0.9999), demonstrating outstanding predictability.ConclusionThese results collectively showed that the predictive model could accurately predict adolescent NSSI. Thus, the model’s primary variables may be applied to predict the risk of NSSI in the clinical setting.

Soluble and EV-bound CD27 act as antagonistic biomarkers in patients with solid tumors undergoing immunotherapy

BackgroundThe major breakthrough in cancer therapy with immune checkpoint inhibitors (ICIs) has highlighted the important role of immune checkpoints in antitumoral immunity. However, most patients do not achieve durable responses, making biomarker research in this setting essential. CD27 is a well known costimulatory molecule, however the impact of its soluble form in ICI is poorly investigated. Therefore, we aimed at testing circulating concentrations of soluble CD27 (sCD27) and CD27 bound to extracellular vesicles (EVs) as potential biomarkers to predict response and overall survival (OS) in patients undergoing ICI.MethodsSerum and plasma levels of sCD27 were assessed by immunoassay in three patient cohorts (n = 187) with advanced solid malignancies including longitudinal samples (n = 126): a training (n = 84, 210 specimens, Aachen ICI) and validation cohort (n = 70, 70 specimens, Hamburg ICI), both treated with ICI therapy, and a second independent validation cohort (n = 33, 33 specimens, Hamburg non-ICI) undergoing systemic therapy without any ICI. In a subset (n = 36, 36 baseline and 108 longitudinal specimens), EV-bound CD27 from serum was measured, while EV characterization studies were conducted on a fourth cohort (n = 45).ResultsIn the Aachen and Hamburg ICI cohorts, patients with lower circulating sCD27 levels before and during ICI therapy had a significantly longer progression-free survival (PFS) and OS compared to patients with higher levels, a finding that was confirmed by multivariate analysis (MVA) (Aachen ICI: pPFS = 0.012, pOS = 0.001; Hamburg ICI: pPFS = 0.040, pOS = 0.004) and after randomly splitting both cohorts into training and validation. This phenomenon was not observed in the Hamburg non-ICI cohort, providing a rationale for the predictive biomarker role of sCD27 in immune checkpoint blockade. Remarkably, EV-bound CD27 baseline levels and dynamics during ICI therapy also emerged as potent predictive biomarkers, acting however antagonistically to soluble sCD27, i.e. higher levels were associated with PFS and OS benefit. Combining both molecules (“multi-CD27” score) enhanced the predictive ability (HRPFS: 17.21 with p < 0.001, HROS: 6.47 with p = 0.011).ConclusionSoluble and EV-bound CD27 appear to have opposing immunomodulatory functions and may represent easily measurable, non-invasive prognostic markers to predict response and survival in patients undergoing ICI therapy.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data

AimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.ResultsFollowing the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.ConclusionWe developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

Prognostic value of serum complement cleavage factor Bb in idiopathic membranous nephropathy and establishment of nomogram model

Complement activation is involved in idiopathic membranous nephropathy (IMN). We aimed to investigate the relationship of serum complement cleavage factor Bb with IMN progression, and to establish a model for early prediction of kidney outcomes. We measured serum factor Bb in a retrospective cohort of 449 IMN patients at the time of kidney biopsy. Cox regression analysis showed that higher levels of serum factor Bb were independently associated with IMN progression event defined as end-stage renal disease or ≥ 40% decline in estimated glomerular filtration rate. Patients in the middle and highest tertiles of serum factor Bb had respectively 2.1-, and 2.6-fold higher risk for disease progression compared with those in the lowest tertile. We developed an optimized prognostic nomogram model incorporating age, log serum anti-PLA2R antibody, log serum Bb, proteinuria and tubular atrophy/interstitial fibrosis. The model demonstrated good predictive ability with a concordance index of 0.77 (bootstrap-corrected of 0.76) for predicting 3-, and 5-year kidney survival. Calibration curves and decision curve analysis confirmed the model’s good calibration and clinical utility. Our findings suggest that serum factor Bb may serve as an essential prognostic indicator of IMN. The novel nomogram model may offer important guidance on the management of this patient population.

Development and validation of a predictive scoring model for complications following endoscopic endonasal skull base surgery.

The endoscopic endonasal approach (EEA) has evolved into an established technique in skull base surgery. The authors previously examined 1002 EEA procedures and reported factors associated with postoperative complications. Here they report the development and validation of a scoring model based on risk factors to better predict complications following EEA. The authors developed an optimized EEA scoring model for predicting postoperative complications as evidenced by the area under the receiver operating characteristic (AUROC) curve using their previously published data in addition to data collected from the subsequent 292 EEA procedures from years 2010-2020. The model was built systematically by evaluating the contributions that different variables had on the overall predictive ability of the model. The aim was to design a model containing as few variables as possible for practicality and to facilitate calculation and use at the bedside. The Clavien-Dindo grading system was used to classify complications into grades I-V based on the level of intervention that was required to manage the complication, with grades III-V considered to be higher-grade (i.e., those requiring reoperation or ICU-level care or death). The authors identified 1294 EEA operations performed between July 2010 and July 2020 that met their inclusion criteria. Higher-grade complications were identified following 135 EEA operations. The variables that were ultimately included in the model were age, BMI, operative time, meningioma, chordoma, expanded intradural approach, and nasoseptal flap use. The final model yielded an acceptable AUROC curve of 0.72 and predicted a stepwise increase in the rate of higher-grade complications as the score increased. A score of 0-2 (low) on the grading system was associated with an average complication rate of 5.1%. A score of 3-5 (medium) was associated with an average complication rate of 12.6%. A score of 6 or above (high) was associated with an average complication rate of 26%. This EEA complications scoring model accurately categorizes patients into low-, medium-, and high-risk groups with readily obtained variables. A high score in this complications model does not suggest that a patient is ineligible for surgery, but rather highlights the importance of thorough case selection, operating with caution, and appropriate preoperative counseling.

The MangLE score: A novel simple tool to identify patients who are unlikely to require amputation following severe lower extremity injury

BACKGROUND There are no validated and sensitive models that can guide the decision regarding amputation in patients with mangled lower extremities. We sought to describe a simple scoring model, the Mangled Lower Extremity (MangLE) score, which can predict those who are highly unlikely to need an amputation as a means to direct resources to this cohort. METHODS This is a retrospective study using the 2013–2021 American College of Surgeons Trauma Quality Improvement Program data set. Adult patients with a mangled lower extremity, defined as a crush injury or a fracture of the femur or tibia combined with severe soft tissue injury, arterial injury, or nerve injury, were included. Patients who suffered a traumatic lower extremity amputation, underwent amputation within 24 hours of admission, or who died within 24 hours of admission were excluded. Patients were divided into those who did/did not undergo amputation during their hospital stay. Demographics, injury mechanism, Injury Severity Score, and Abbreviated Injury Scale score, initial vital signs, and comorbid conditions were abstracted. A logistic regression model was constructed and the top five most important variables were used to create the score. RESULTS The study includes 107,620 patients, of whom 2,711 (2.5%) underwent amputation. The five variables with the highest predictive value for amputation were arterial injury, lower-extremity Abbreviated Injury Scale score of ≥3, crush injury, blunt mechanism, and shock index. The lowest possible MangLE score was 0, and the highest was 15. The model demonstrated an excellent predictive ability for lower extremity amputation in both the development and validation data set with an area under the receiver operating characteristic curve of 0.81 (95% confidence interval, 0.80–0.82) and 0.82 (95% confidence interval, 0.81–0.84), respectively. The negative predictive value for a score of &lt;8 is 99%. CONCLUSION The MangLE score is able to identify patients who are unlikely to require amputation. Resources for limb salvage can be directed to this cohort. LEVEL OF EVIDENCE Prospective and Epidemiologic; Level IV.

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.

Prognostic and Immunotherapeutic Role of TACC3 in Pancancer, and its Impact on Proliferation and Docetaxel Resistance in Lung Adenocarcinoma

Introduction:Transforming acidic coiled-coil containing protein 3 (TACC3) exerts a vital role in cancer progression by modulating cell division and facilitating tumor growth. Given the lack of comprehensive research on the pan-cancer implications of TACC3, our study aimed to analyze the functional role of TACC3 in pan-cancer and validate it through experimental investigations in lung adenocarcinoma. Methods: We first employed various bioinformatics techniques to investigate the expression and prognostic significance of TACC3 in pancancer. Subsequently, we analyzed the correlation between TACC3 and immune infiltration, immune checkpoints, drug sensitivity, as well as the prediction of immune therapy response. Finally, we validated the association between TACC3 and the proliferation of lung adenocarcinoma, as well as its resistance to docetaxel, through in vitro experiments. Results: Here, TACC3 exhibited high expression in human cancers and was associated with poor prognosis in various cancer types. It was also involved in immune infiltration and demonstrated a strong predictive ability for immune therapy response. Through drug sensitivity prediction, we further identified a potential association between TACC3 and docetaxel resistance, which was subsequently validated in lung adenocarcinoma. Conclusion: Our investigation of TACC3 revealed its potential as a promising target both for immunosuppression and docetaxel resistance in pan-cancer, especially in lung adenocarcinoma.

Triglyceride-glucose index (TyG index) is associated with a higher risk of colorectal adenoma and multiple adenomas in asymptomatic subjects

Hypothesis The objective of this study is to evaluate the predictive ability of the TyG index for the presence of adenoma and multiple adenomas in an asymptomatic population. Design A secondary analysis was conducted on a prospective cohort of asymptomatic subjects aged between 50 and 75 who underwent CRC screening. Fasting blood glucose (FBG) and lipid profiles were measured within three months prior colonoscopy. TyG index was estimated as ln [fasting triglycerides (mg/dL) × FBG (mg/dL)/2]. Multivariate logistic regression was performed to assess the association between the TyG index and the risk of adenoma. Its association with multiple adenomas (≥5) and the continuous number of adenomas were assessed by multinomial regression and log-normal linear regression, respectively. Results A total of 1,538 subjects were recruited among which 876 subjects (57%) had at least one adenoma detected. Elevated TyG index was positively associated with the incidence of adenoma (adjusted odds ratio [aOR]: 1.26, 95% confidence interval [CI]: 1.04–1.54). Compared with the lowest TyG index (≤ 8) group, the risk of adenoma was the highest among subjects in the highest TyG index (&gt; 10) group (aOR: 3.36, 95% CI: 1.44–7.73). As compared to the non-adenoma group, the TyG index was also positively associated with multiple adenomas (aOR: 1.74, 95% CI: 1.17–2.57), and the estimate was also the highest in the highest TyG group (aOR: 14.49, 95% CI: 3.12–67.20). As for the number of adenomas, the positive association was maintained (Estimates: 1.06, 95% CI: 1.01–1.12) while the number of adenomas increase the most in the highest TyG index group (Estimates: 1.35, 95% CI: 1.10–1.65). Conclusions Elevated TyG index is associated with an increased risk of colorectal adenoma and an increased number of adenomas for asymptomatic subjects aged ≥50. Trial registration This study was registered on clinicaltrials.gov (NCT03597204 and NCT04034953).

Leveraging genomic prediction to surpass current yield gains in spring barley

Key messageGenetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines.Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22–60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019–2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

American Foregut Classification is Superior to Hill Classification in Evaluation of the Gastroesophageal Junction

Background: The American Foregut Society (AFS) classification is intended to be more comprehensive and accurate than the Hill grade in assessing the gastroesophageal junction. However, real-world data regarding predictive ability and interobserver variability are limited. Methods: We performed a multi-reader validation study on images collected from patients who underwent endoscopic evaluation of reflux with hernia provocation using both AFS and Hill classification. The primary outcome was inter-reader agreement, assessed using Fleiss K. Secondary outcomes were percentage of patients with pathologic reflux at each Hill grade or AFS grade, correlation between acid exposure time (AET) and individual components of the AFS grade, and difference in predictive ability based on expert versus non-expert graders. Results: Sixty-four eligible patients were identified. Inter-rater reliability was substantial for AFS classification ( K = 0.65) and fair for Hill classification ( K= 0.28). The AFS grade demonstrated moderate overall correlation with AET (ρ = .36) and the Hill grade demonstrated overall weak correlation (ρ = .28). No single component of the AFS score performed significantly better in predicting pathologic reflux than the AFS grade (AFS F = 9.93, P = .0025) and there was no significant difference in overall grading between expert- and non-expert readers (ρ = .38 vs ρ = .29). Conclusion: The AFS score can classify the GEJ with moderate interobserver agreement and is superior to the Hill grade in predicting acid exposure time. AFS grade has better predictive power than the individual components used in scoring and is robust to differences in reader experience.

Genome‐Wide Association Mapping and Genomic Prediction of Septoria nodorum Blotch Resistance in Central European Winter Wheat (Triticum aestivum L.)

ABSTRACTSeptoria nodorum blotch (SNB) is a fungal disease of wheat caused by the necrotrophic fungus Parastagonospora nodorum (Berk.), considered as one of the most devastating fungal diseases affecting winter wheat (Triticum aestivum L.). The complex inheritance of resistance to SNB poses significant challenges to breeding programmes. Improving selection precision and identifying novel resistance QTLs are crucial for enhancing SNB resistance. This study investigated strategies for crop genetic improvement, including genome‐wide association mapping (GWAS) and genomic prediction (GP), within a practical breeding programme. A population of 1500 winter wheat breeding lines was phenotyped for SNB resistance over 5 years across 19 geographical locations under natural infection conditions. Despite highly unbalanced breeder's data, medium to high heritabilities for SNB resistance were achieved. GWAS identified 11 significant marker‐trait associations for SNB resistance across Chromosomes 2A, 2B, 4B, 4D, 5D and 7B. GP fivefold cross‐validation analysis revealed a predictive ability of 0.52 for SNB resistance. The resistant wheat genotypes and SNP markers identified in this study will be valuable assets for future breeding efforts to enhance SNB resistance in wheat.

An automated computational framework to construct printability maps for additively manufactured metal alloys

In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map classifies regions in the processing space where an alloy can be printed with or without porosity defects. However, the creation of these printability maps is resource-intensive. Previous efforts to generate printability maps have required single-track experiments on pre-alloyed powder, limiting the utilization of these printability maps for the high-throughput design of printable alloys. We address these challenges in the case of Laser Powder Bed Fusion AM (L-PBF-AM) by introducing a fully computational, predictive approach to create printability maps for arbitrary alloys. Our framework uses physics-based thermal models and a variety of defect formation criteria. We benchmark the predictive ability of the proposed framework against literature data for the following commonly printed alloys: 316 Stainless Steel, Inconel 718, Ti-6Al-4V, AF96, and Ni-5Nb. Furthermore, we deploy the framework on NiTi-based Shape Memory Alloys (SMAs) as a case study. We scrutinize the accuracy of various sets of defect criteria and use these accuracy measurements to create an uncertainty-aware probabilistic framework capable of predicting the printability maps of arbitrary alloys. This framework has the potential to guide alloy designers to potentially easy-to-print alloys, enabling the co-design of high-performing printable alloys.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study.

To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC). This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring. K-means clustering (K = 4-6) was applied to obtain multiple sub-regions (n = 3-5), and corresponding histogram features (including mean, standard deviation, minimum, maximum, and 10th, 25th, 50th, 75th, and 90th quantile) of APT and apparent diffusion coefficient (ADC) maps were extracted and filtered using stepwise regression. When K = 5, the K-means clustering is four sub-regions, showing the best prediction for TB grade compared to K = 4 or 6. When K = 5, there were significantly higher histogram features of the APT map in sub-regions 3 and 4 in the high TB grade group compared to the low-intermediate TB grade group. Receiver operating characteristic (ROC) curve and internal validation suggested that the predictive efficiency of the model was highest when K = 5, with AUC, sensitivity, specificity, accuracy, and kappa values of 0.92, 93%, 71%, 87%, and 0.65, respectively. There were no significant differences in the histogram features of each sub-region in the ADC map (p > 0.05). The sub-regional histogram features of APTw images can help to distinguish the heterogeneous regions of RC, which can be used to predict the TB grade of RC. Question Can the sub-regional histogram features of APTw MRI predict the tumor budding (TB) grade of rectal cancer (RC)? Findings Differences exist in histogram features of APT map subregions between high and low-intermediate TB grade groups; subregions of the APT map have different predictive abilities. Clinical relevance APT-weighted imaging might outperform DWI in predicting TB grade in RC.

Caecal metabolomics of two divergently selected rabbit lines revealed microbial mechanisms correlated to intramuscular fat deposition.

The gastrointestinal microbiota plays a key role in the host physiology and health through a complex host-microbiota co-metabolism. Metabolites produced by microbial metabolism can travel through the bloodstream to reach distal organs and affect their function, ultimately influencing the development of relevant production traits such as meat quality. Meat quality is a complex trait made up of a number of characteristics and intramuscular fat content (IMF) is considered to be one of the most important parameters. In this study, 52 rabbits from two lines divergently selected for IMF (high-IMF (H) and low-IMF (L) lines) were used to perform an untargeted metabolomic analysis of their caecal content, with the aim to obtain information on genetically determined microbial metabolism related to IMF. A large, correlated response to selection was found in their caecal metabolome composition. Partial least squares discriminant analysis was used to identify the pathways differentiating the lines, which showed a classification accuracy of 99%. On the other hand, two linear partial least squares analyses were performed, one for each line, to extract evidence on the specific pathways associated with IMF deposition within each line, which showed predictive abilities (estimated using the Q2) of approximately 60%. The most relevant pathways differentiating the lines were those related to amino acids (aromatic, branched-chain and gamma-glutamyl), secondary bile acids, and purines. The higher content of secondary bile acids in the L-line was related to greater lipid absorption, while the differences found in purines suggested different fermentation activities, which could be related to greater nitrogen utilisation and energy efficiency in the L-line. The linear analyses showed that lipid metabolism had a greater relative importance for IMF deposition in the L-line, whereas a more complex microbial metabolism was associated in the H-line. The lysophospholipids and gamma-glutamyl amino acids were associated with IMF in both lines; the nucleotide and secondary bile acid metabolisms were mostly associated in the H-line; and the long-chain and branched-chain fatty acids were mostly associated in the L-line. A metabolic signature consisting of two secondary bile acids and two protein metabolites was found with 88% classification accuracy, pointing to the interaction between lipid absorption and protein metabolism as a relevant driver of the microbiome activity influencing IMF.

Dynamic Multiblock Regression for Process Modelling

ABSTRACTThe study introduces three novel strategies for incorporating capabilities for dynamic modelling into multiblock regression methods by integrating sequentially orthogonalised partial least squares (SO‐PLS) with different dynamic modelling techniques. The study evaluates these strategies using synthetic datasets and an industrial example, comparing their performance in predictive ability, identification of process dynamics, and quantification of block contributions. Results suggest that these approaches can effectively model the dynamics with performance comparable to state‐of‐the‐art methods, providing, at the same time, insight into the dynamic order and block contributions. One of the strategies, sequentially orthogonalised dynamic augmented (SODA)–PLS, shows promise by ensuring that redundant information in the time dimension is not included, resulting in simpler and more easily interpretable dynamic models. These multiblock dynamic regression strategies have potential applications for improved process understanding in industrial settings, especially where multiple data sources and inherent time dynamics are present.