Development Of Predictive Models Research Articles

A Predictive Model for Graft Failure in Femtosecond Laser-Assisted Penetrating Keratoplasty Among Chinese Patients: A 2-Year Study.

Graft failure is a major challenge in femtosecond laser-assisted penetrating keratoplasty (Fs-PKP). This study focuses on the development and validation of a clinical predictive model aimed at identifying the risk of graft failure in individuals undergoing Fs-PKP in China, offering a tailored approach to improve surgical outcomes. This retrospective cohort study at Nanjing First Hospital involved 238 patients and followed the TRIPOD statement. The cohort was divided into a training set (n = 166) and a validation set (n = 72) in a 7:3 ratio. It analyzed 23 predictor variables related to recipient, donor, and surgical factors, defining graft failure as "visually significant and irreversible corneal stromal edema, haze, or scarring." A comprehensive nomogram was created using univariate and multivariate Cox regression analyses and assessed by concordance index (C-index), time-dependent receiver operating characteristics (ROC) curve, calibration plots, and decision curve analysis (DCA). Five critical risk factors were identified: recipients' history of systemic autoimmune disorders, ocular trauma, prior penetrating keratoplasty (PKP) history, donors' diabetes history, and the endothelial cell density of the donor cornea. The nomogram showed a C-index of 0.72 (95% CI 0.65-0.79) in the training group and 0.66 (95% CI 0.55-0.76) in the validation group, indicating robust predictive accuracy. Time-dependent ROC curves, calibration plots, and DCA consistently validated the model's reliability, predictive power, and clinical utility across both training and validation cohorts. Our study developed and validated a model incorporating five key factors, enhancing preoperative prediction and management for Chinese patients with Fs-PKP graft failure.

Pancreatic and cardiometabolic complications of severe hypertriglyceridaemia.

This review endeavours to explore the aetiopathogenesis and impact of severe hypertriglyceridemia (SHTG) and chylomicronaemia on cardiovascular, and pancreatic complications and summarizes the novel pharmacological options for management. SHTG, although rare, presents significant diagnostic and therapeutic challenges. Familial chylomicronaemia syndrome (FCS), is the rare monogenic form of SHTG, associated with increased acute pancreatitis (AP) risk, whereas relatively common multifactorial chylomicronaemia syndrome (MCS) leans more towards cardiovascular complications. Despite the introduction and validation of the FCS Score, FCS continues to be underdiagnosed and diagnosis is often delayed. Longitudinal data on disease progression remains scant. SHTG-induced AP remains a life-threatening concern, with conservative treatment as the cornerstone while blood purification techniques offer limited additional benefit. Conventional lipid-lowering medications exhibit minimal efficacy, underscoring the growing interest in novel therapeutic avenues, that is, antisense oligonucleotides (ASO) and short interfering RNA (siRNA) targeting apolipoprotein C3 (ApoC3) and angiopoietin-like protein 3 and/or 8 (ANGPTL3/8). Despite advancements in understanding the genetic basis and pathogenesis of SHTG, diagnostic and therapeutic challenges persist. The rarity of FCS and the heterogenous phenotype of MCS underscore the need for the development of predictive models for complications and tailored personalized treatment strategies. The establishment of national and international registries is advocated to augment disease comprehension and identify high-risk individuals.

Development and validation of a social alienation predictive model for older maintenance hemodialysis patients based on latent profile analysis—a cross-sectional study

BackgroundSocial alienation refers to the state of feeling isolated, helpless, and unsatisfied due to maintaining distance from others or avoiding social interaction and activities. This phenomenon is caused by a lack of social skills, social anxiety, physical health problems, and other reasons. Older maintenance hemodialysis patients are exposed to a higher risk of social alienation. However, previous studies have been performed using the total score of the scale, which does not allow the identification of the characteristics of various patient groups with different levels of social alienation. In contrast, latent profile analysis can classify individuals into different categories based on continuous observational indicators, which improves accuracy and provides a more objective assessment by accounting for the uncertainty of variables. Given the concealed nature of social alienation and the differences in characteristics and treatment measures between different profiles, developing a predictive model for social alienation in older maintenance hemodialysis patients holds significance.ObjectiveTo explore the latent profile analysis of social alienation in older maintenance hemodialysis patients and to develop and validate a predictive model for social alienation in this population.MethodsA total of 350 older maintenance hemodialysis patients were selected as the study subjects using convenience sampling. A cross-sectional survey was conducted using a general information questionnaire, the Generalized Alienation Scale, and the Self-Perceived Burden Scale. Based on the results of the Generalized Alienation Scale, a latent profile analysis was performed, followed by univariate analysis and multinomial logistic regression to develop a predictive model. The effectiveness of the predictive model was evaluated in terms of its authenticity, reliability, and predictive ability.ResultsThree hundred nineteen valid questionnaires were collected. The social alienation of older maintenance hemodialysis patients based on latent profile analysis were divided into three profiles, which were named the low/medium/high-symptom groups, comprising 21%, 38.9%, and 40.1% of participants, respectively. Based on male, monthly social activity hours, Age-Adjusted Charlson Comorbidity Index, dialysis age, and Self-Perceived Burden Scale, a predictive model of social alienation for older maintenance hemodialysis patients was developed, and the Hosmer–Lemeshow tests showed no statistical significance (P > 0.05). The model has high predictive efficiency in authenticity, reliability and predictability.ConclusionOlder maintenance hemodialysis patients exhibited moderate to high levels of social alienation. The latent profile analysis based method was used to divide patients into low/medium/high-symptom profiles, and the predictive model demonstrates excellent authenticity, reliability, and predictability.

Satellite Tracking Reveals the Speed Up of the Lacustrine Algal Bloom Drift in Response to Climate Change.

Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young

BackgroundClinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges.MethodsWe compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment (“offset”), shrinkage recalibration in the population-level dataset (“recalibration”), and a refitting of the model to the population-level dataset (“re-estimation”). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations.ResultsThe offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model’s predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities.ConclusionWe have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

Fire4CAST – a new integrative epidemiological forecasting model for the accurate prediction of infection risk and effective control of fire blight in Pyrus orchards

AbstractFire blight disease, caused by Erwinia amylovora is present worldwide and affects over 40 countries in Europe where it is listed as a quarantine or regulated pest often due to ineffective control strategies maladapted to the respective production systems. In Portugal, the disease was confirmed in 2010 and the occurrence of disease outbreaks in new production areas has risen over the years. The disease affects the national production of apple and pear fruits, with greater impact on the national pear variety ‘Rocha’, widely exported to European countries and Brazil. The mild temperatures and high relative humidity promote the progression of the disease during winter, revealing the potential activity of the bacterium in the latency period (LP) of the host. Infection alert risk using the established predictive models Maryblight TM, Cougarblight and BIS98 was put in place in 2013 by Centro Operativo e Tecnológico Hortofrutícola Nacional (COTHN). These attempts to control the spread of this disease, showed low accuracy for the Portuguese epidemiological reality. Within the framework of project Fire4CAST we developed a new epidemiological model to predict fire blight disease using a systems biology approach integrating microbiological, cytological and genomic pathogen data with phenological host development and climatic variables. The presence of E. amylovora was monitored in orchards with fire blight history using standard laboratory tests. Simultaneously, the implementation of immune-flow cytometry (IFCM) highlighted the viability of E. amylovora populations prevailing during winter and early spring, long before bloom risk period. The integration of the whole data set allowed the development of the Fire4CAST predictive model, able to monitor the expected infection date (EID) and to define accurate outbreak alarms. Fire4CAST model enabled the detection of outbreak risk during winter based on rules that consider climatic data variables, which were validated by effective presence of live and active E. amylovora populations and real-time quantitative PCR (qPCR) data, accomplishing a precision rate of 83%. Field application of Fire4CAST can hopefully guide the implementation of successful control strategies, leading to more sustainable pome chain production areas.

Predictive models as a clinical decision support tool for genetic cancer risk assessment among Mexican patients at risk of hereditary colorectal cancer.

10617 Background: Hereditary cancer syndromes, including Lynch syndrome (LS) with an increased risk of colorectal cancer (CRC), have prompted the development of clinical criteria and predictive models (PM) to identify at-risk individuals. However, these criteria have been validated with limited representation from the Latino population. Several factors may influence CRC penetrance, potentially leading to variations in PM performance across populations. Given that CRC ranks as the third most prevalent cancer worldwide, the increasing incidence of early-onset disease highlights the need for expanding Genetic Cancer Risk Assessment (GCRA) services. Methods: This prospective study included Mexican patients who were diagnosed with CRC and met NCCN criteria for GCRA. Participants were enrolled at two referral centers in Mexico, the Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City and at the Hospital Zambrano Hellion TecSalud, Nuevo Leon. Participants' carrier risk was estimated using the PREMM5 model. Amsterdam II and revised Bethesda criteria, immunohistochemistry (IHC) for DNA mismatch repair (MMR) proteins in tumor tissue and multigene panel testing results were reviewed. For this analysis, only pathogenic and likely pathogenic (P/LP) variants in probands were reported. Results: From April 2016 to October 2023, 194 patients were included, with a median age at diagnosis of 45.0 (range 17-82) years, and 49.5% were women. P/LP variants were identified in 37.6% (n=73). 27.5% (n=50) were carriers of P/LP variants associated with LS (29 MLH1, 13 MSH2, 6 MSH6, and 2 PMS2). 11.8% (n=23) were carriers of P/LP variants in other genes (7 APC, 4 ATM, 3 CHEK2, 2 BRCA1, 2 TP53, 2 STK11, 1 PALB2, 1 BRIP1, and 1 MUTYH). Overall, carriers of PVs were diagnosed with CRC at a younger age than non-carriers (42 vs 46 years, p=0.034). The Amsterdam II criteria proved to be accurate (Sen 55%, Spe 91%, PPV 0.69, NPV 0.85), while the Bethesda criteria (Sen 98%, Spe 18%, PPV 0.29, NPV 0.96) and IHC for MMR proteins (Sen 94%, Spe 67%, PPV 0.61, NPV 0.95) were found to be highly sensitive. The area under the ROC curve for PREMM5 was 0.82 with a mean score 28.5 (2.1 – 50) in patients with LS and 6.0 (0.9 – 50) in non-carriers. Carriers of PVs had a higher rate of multiple primary malignancies than non-carriers (41.0% vs 19.0%, p=0.0014), and self-reported family history of CRC (65.7% vs 24.8%, p<0.001). Conclusions: Our results showed the PREMM5 model and IHC for MMR proteins effectively prioritized patients eligible for germline genetic testing in resource-limited settings. The performance of PREMM5 in this Mexican cohort was similar to that reported in the validation study of this model in other populations. Considering the spectrum of identified P/LP variants, the multigene panel test should be used in the comprehensive evaluation of Mexican patients with CRC.

Enhancing oncology research through data sharing and re-use: A five-year review of Vivli's global data platform.

e13670 Background: The last decade has seen a paradigm shift in clinical research with the accessibility of trial data, facilitating secondary analyses and the development of novel predictive models. Vivli a non-profit entity established in 2018, epitomizes this movement with its global platform (vivli.org) dedicated to the facilitating access to and re-use of individual participant data from completed clinical trials for use by researchers worldwide. Methods: Commencing with approximately 2,500 trials from 15 members, Vivli has expanded over five years to encompass more than 7,000 trials involving 3.6 million participants across 50 members who contribute trial data from various sectors (industry, academic, government and non-profit foundation). Results: Oncology trials constitute 11% of the Vivli data pool of 7,000 trials, constituting 36% of Vivli’s research proposals due to the therapeutic area's high-impact potential. Researchers accessing data in the Vivli platform have produced a significant body of scientific work, with 74 oncology-focused publications emerging from their analyses. Notably, Roche's data has been a key resource, denoted by the 28% of all Vivli proposals requesting access to Roche data. Fifty-eight publications have resulted from secondary analysis of Roche oncology data. The motivation for secondary analyses in oncology have been predominantly for new research into treatment effectiveness or safety (39%). Other reasons include confirmation of existing research (15%), individual participant data (IPD) meta-analysis (14%), statistical method (10%), clinical trial design (5%), and summary-level meta-analysis (4%), training/testing (7%), other (5%). Conclusions: The Vivli platform has proven to be a key resource in recent years for the oncology community enabling the integration and publication of new insights. The sharing of IPD is in part motivated to ensure reproducibility of findings; our experience demonstrates that a minority of researchers are interested in reproducibility analysis but the majority of research is focused on new insight generation. The platform's success in facilitating the findability, accessibility and reuse of patient level data is signified by ever increasing numbers of scientific publications emerging from analyses on the Vivli platform and this underscores its role in advancing scientific discovery and development within the field of oncology.

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

Development of Prediction Model for 1-year Mortality after Hip Fracture Surgery.

Hip fractures are associated with increased mortality. The identification of risk factors of mortality could improve patient care. The aim of the study was to identify risk factors of mortality after surgery for a hip fracture and construct a mortality model. A cohort study was conducted on patients with hip fractures at two institutions. Five hundred and ninety-seven patients with hip fractures that were treated in the tertiary hospital, and another 147 patients that were treated in a secondary hospital. The perioperative data were collected from medical charts and interviews. Functional Assessment Measure score, Short Form-12 and mortality were recorded at 12 months. Patients and surgery variables that were associated with increased mortality were used to develop a mortality model. Mortality for the whole cohort was 19.4% at one year. From the variables tested only age >80 years, American Society of Anesthesiologists category, time to surgery (>48 hours), Charlson comorbidity index, sex, use of anti-coagulants, and body mass index <25 kg/m2 were associated with increased mortality and used to construct the mortality model. The area under the curve for the prediction model was 0.814. Functional outcome at one year was similar to preoperative status, even though their level of physical function dropped after the hip surgery and slowly recovered. The mortality prediction model that was developed in this study calculates the risk of death at one year for patients with hip fractures, is simple, and could detect high risk patients that need special management.

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia

Background and purposeIntracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term mortality risk based on the non-contrast computed tomography (NCCT) image features. MethodsThe NCCT image features of ICH in 135 leukemia patients between 2007–2023 were retrospectively extracted using manual assessment and radiomics methods. After multiple imputation of missing laboratory data, univariate logistic regression and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Random forest models were built with comprehensive evaluation and ranking of feature importance. Result135 and 129 patients were included in the studies for 7-day and short-term prognostic models, respectively. The median age of all enrolled patients was 35 years, and there were 86 male patients (63.7 %). Clinical models (validation: AUC [area under the curve] = 0.78, AUPRC [area under the precision-recall curve] = 0.73; AUC = 0.84, AUPRC = 0.86), radiomics models (validation: AUC = 0.82, AUPRC = 0.78; AUC = 0.75, AUPRC = 0.77), and the combined models (validation: AUC = 0.84, AUPRC = 0.83; AUC = 0.87, AUPRC = 0.89) predicted 7-day and short-term mortality with good predictive efficacy. Clinical decision curve analysis showed that the combined models predicted 7-day and 30-day risk of death would be more beneficial than other models. Shape features contributed significantly more than semantic features in both radiomics models and combined models (93.3 %, 52.1 %, as well as 85.2 %,37.4 %, respectively) for 7-day and 30-day mortality. ConclusionsCombined models constructed based on NCCT perform well in predicting the risk of 7-day and short-term mortality in ICH patients with leukemia. Shape features extracted by radiomics are important markers for modeling the prognosis.

LASSO-derived model for the prediction of bleeding in aspirin users

Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857–0.874) in the training dataset and 0.861 (95% CI 0.848–0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population. RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy. The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.

Unveiling optimal half-cell potentials in RCC slabs through cutting-edge ANFIS, ANN and genetic algorithm integration

Purpose The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms. Design/methodology/approach In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model. Findings In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model. Originality/value Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.

High-throughput color determination of red raspberry puree and correlation of color parameters with total anthocyanins

BackgroundRed raspberry fruit color is a key driver of consumer preference and a major target of breeding programs worldwide. Screening for fruit color typically involves the determination of anthocyanin content and/or the assessment of color through a colorimeter. However, both procedures are time-consuming when the analyses involve hundreds or thousands of samples. The objectives of this study were to develop a high-throughput method for red raspberry puree color measurement and to test the correlations between color parameters and total anthocyanin content. Color coordinates were collected with a colorimeter on 126 puree samples contained in Petri dishes and with the Tomato Analyzer Color Test (TACT) module to assess the same samples prepared in Petri dishes and in 96-well plates. An additional 425 samples were analyzed using only 96-well plates. Total anthocyanins were extracted from all 551 samples.ResultsRegression models for L*, a*, b* measured with the colorimeter and TACT using Petri dishes were all significant (p < 0.001), but very consistent only for L* (R2 = 0.94). Significant (p < 0.001) and very consistent regressions (R2 = 0.94 for L* and b*, R2 = 0.93 for a*) were obtained for color parameters measured with TACT using Petri dishes and TACT using plates. Of the color parameters measured with the colorimeter, only L*, a*/b*, and hue significantly correlated with total anthocyanins (p < 0.05), but, except for L* (R = − 0.79), the correlations were weak (R = − 0.23 for a*/b* and R = 0.22 for hue). Conversely, all correlations with total anthocyanins and color parameters measured with TACT were significant (p < 0.001) and moderately strong (e.g., R = − 0.69 for L* and R = 0.55 for a*/b*). These values were indicative of darker colors as total anthocyanin content increased.ConclusionsWhile the colorimeter and TACT-based methods were not fully interchangeable, TACT better captured color differences among raspberry genotypes than the colorimeter. The correlations between color parameters measured with TACT and total anthocyanins were not strong enough to develop prediction models, yet the use of TACT with 96-well plates instead of Petri dishes would enable the high-throughput measurement of red raspberry puree color.

PREDICTING DRUG-DRUG INTERACTION USING MACHINE LEARNING

The ever-expanding landscape of pharmaceutical compounds and medical treatments has underscored the critical need for an advanced system to predict drug-drug interactions (DDIs) using machine learning. This paper delves into the development of a novel predictive model that leverages diverse data sources, including chemical structures, pharmacological properties, and scientific literature, to comprehensively analyse and foresee potential DDIs. The current state of DDIs is rife with limitations in scope and accuracy, making the development of an advanced system not only a necessity for enhancing patient safety but also a boon to pharmaceutical research and regulatory oversight. The relationship between two medications in which one drug’s pharmacological actions are altered by another is known as a drug-drug interaction (DDI). Favourable drug-drug interactions (DDIs) generally contribute to improved therapeutic results for patients. On the contrary, adverse DDIs pose a significant risk, often resulting in undesirable drug reactions that can potentially lead to the withdrawal of a drug from the market and even patient fatalities. Consequently, the identification of DDIs is essential for advancing new drug development and effectively managing diseases. Keywords: Drug, Drug-drug interaction, Machine learning, Bioinformatics, Drug development.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Prediction of compressive strength of cross‐shaped stub CFSTs under axial loading: Numerical and analytical study

AbstractSpecial‐shaped concrete‐filled steel tubes (CFSTs) have been used in modern structures like high‐rise commercial and residential buildings due to their superior structural performance compared to steel and reinforced concrete members. Various shapes of special‐shaped CFSTs might be necessary to meet architectural and aesthetic needs. Cross‐shaped CFSTs could be used where two orthogonal walls cross in high‐rise buildings. However, at present, the research on the compressive performance of cross‐shaped CFSTs is limited, consequently, the unavailability of design guidelines and design‐oriented strength prediction models. Therefore, in this study, a finite element (FE) model of cross‐shaped CFSTs was developed following the past experimental data, and the model's accuracy was verified by the failure modes and load–strain curves of specimens. Sensitivity analysis was performed for some parameters of the concrete damaged plasticity model besides imperfections and residual stress. The parametric analysis was conducted considering various study parameters such as the width‐to‐thickness ratio, width‐to‐depth ratio, and steel and concrete strengths. The compressive strength of cross‐shaped CFSTs was predicted by different design codes and available design formulas, which gave unsatisfactory results necessitating the development of new strength prediction models. Finally, a new design formula was developed by performing a linear regression of FE and test results. The proposed formula predicted the strength of cross‐shaped CFSTs with great accuracy and can be used for design purposes.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis.

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS. Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%). The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain. A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.