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This study is a practical exploration of the application of machine learning for the mechanical analysis of filament-wound thin-composite hydrogen storage tanks under internal pressure. Our innovative approach seamlessly integrates classical laminate theory, comprehensive parametric analysis, and machine learning to advance the state-of-the-art in composite tank design. This versatile framework holds promise for addressing challenges in other structurally complex systems. A comprehensive parametric study was then conducted to explore the influence of key design parameters, such as internal pressure, tank radius, order of layer, and layer orientation. Pearson's correlation analysis was used to determine the parameters that had the most significant impact on the mechanical behavior of the tank. In addition, five machine learning models, namely, Extreme Gradient Boosting, Adaptive Boosting, Artificial Neural Network, Support Vector Machine, and Random Forest, were examined to determine their predictive capability for the mechanical performance of these composite tanks. Key findings demonstrate XGBoost's superior predictive capabilities, achieving a remarkable 99% accuracy in predicting stress in the x-direction compared to Random Forest's 96%. XGBoost also outperformed Random Forest in terms of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) with values of 26 and 0.02, respectively, versus 207 and 0.16. These results underscore XGBoost's potential to revolutionize hydrogen storage tank design. The development of a user-friendly GUI application further enhances the practicality of this research, allowing engineers and composite designers to efficiently simulate the mechanical behavior of these tanks in real time by adjusting the cursors for variables such as the pressure, radius, layer order, and layer orientation. This tool, with its intuitive interface and elimination of manual calculations, promises significant enhancements in the design process, providing a platform for the rapid assessment and optimization of tank architectures, thereby improving the efficiency and reliability of the design process. [Display omitted] • Developed a hybrid ML approach with laminate theory for accurate composite hydrogen tank predictions. • Key design parameters affecting tank performance were identified through advanced statistical analysis. • Achieved 99% accuracy in stress prediction with XGBoost, outperforming traditional methods. • Enhanced the design process with a user-friendly GUI for real-time tank optimization, significantly reducing development time. [ABSTRACT FROM AUTHOR]

Background: Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies. The outcomes of interest include the selected key variables that contribute to adverse events and the performance and validation measures of the predictive models. Methods: Three databases, Ovid MEDLINE, PsycINFO, and Embase, were searched between February 2023 and April 2023 to identify all relevant studies that included a combination of (and were not limited to) the following search terms: "Early warning," "Alerting tool," and "Psychiatry". Peer-reviewed primary research publications were included without imposing any date restrictions. A total of 1,193 studies were screened. A total of 9 studies met the inclusion and exclusion criteria and were included in this review. The PICOS model, the Joanna Briggs Institute (JBI) Reviewer's Manual, and PRISMA guidelines were applied. Results: This review identified nine studies that developed predictive models for adverse events in psychiatric settings. Encompassing 41,566 participants across studies that used both ML and non-ML algorithmic approaches, performance metrics, primarily AUC ROC, varied among studies between 0.62 and 0.95. The best performing model that had also been validated was the random forest (RF) ML model, with a score of 0.87 and a high sensitivity of 74% and a specificity of 88%. Conclusion: Currently, few predictive models have been developed for adverse events and patient deterioration in psychiatric settings. The findings of this review suggest that the use of ML and non-ML algorithms show moderate to good performance in predicting adverse events at the hospitals/units where the tool was developed. Understanding these models and the methodology of the studies is crucial for enhancing patient care as well as staff and patient safety research. Further research on the development and implementation of predictive tools in psychiatry should be carried out to assess the feasibility and efficacy of the tool in psychiatric patients. [ABSTRACT FROM AUTHOR]

The diverse composition of milk throughout the year can potentially impact subsequent processing in the dairy industry and the quality of the final products. This fluctuation in milk composition is influenced by direct and indirect factors like seasonal variability. Milk powders are produced through a complex process and are intended for various end uses and require consistency of composition along with desirable physical and functional properties. This review aims to report technical challenges and limitations related to the functional and quality characteristics of milk powder and highlight recent approaches used to mitigate seasonally related differences in milk composition. [ABSTRACT FROM AUTHOR]

Simple Summary: Sarcomas are rare and diverse tumors that develop in various tissues like bone, muscle, and fat. This study examines the outcomes of two types of surgical removal of these tumors: planned resections (PEs), where the tumor is diagnosed a-priori and surgically removed with a plan, and unplanned resections (UEs), where the tumor is removed without prior diagnosis. We found that planned surgeries, while better at completely removing the tumor initially, are associated with more advanced tumors that have a higher chance of spreading and leading to cancer-related deaths. On the other hand, unplanned surgeries tend to have higher chances of the tumor coming back locally but do not significantly affect long-term survival. These findings highlight the importance of early detection and timely treatment at specialized centers to improve patient outcomes. Our research aims to help develop better tools for predicting and managing sarcomas to enhance patient care. Background: Sarcomas, a diverse group of malignant tumors arising from mesenchymal tissues, pose significant diagnostic and therapeutic challenges. This study compares the outcomes of planned resections (PEs) and unplanned resections (UEs) to inform better clinical practices. Methods: Data were analyzed from the Swiss Sarcoma Network (SSN), including patients with soft tissue and bone sarcomas treated at two major hospitals. This study utilized logistic regression and Cox regression models to examine the odds of UEs and their impact on local recurrence-free survival. Results: Among 429 patients registered by SSN members, 323 (75%) underwent PEs and 106 (25%) experienced UEs. PEs were associated with significantly larger tumors (94 mm vs. 47 mm, p < 0.001) and higher-grade tumors (Grade 3: 50.5% vs. 37.4%, p = 0.03). Despite achieving superior resection margins (R0: 78.8% vs. 12.6%, p < 0.001), PEs showed higher metastasis rates at follow-up (31.0% vs. 10.4%, p < 0.001) and greater cancer-specific mortality (16.7% vs. 6.6%, p = 0.01). UEs, while linked to higher local recurrence, did not significantly affect metastasis-free survival (MFS) or overall survival (OS). Conclusions: PEs achieve superior immediate surgical outcomes but are linked to higher metastasis and cancer-specific mortality due to the advanced stage of tumors. UEs, while associated with higher local recurrence rates, do not significantly impact MFS or OS. Early detection, comprehensive diagnostics, and timely referrals to specialized sarcoma hubs are essential to avoid UEs and reduce metastatic risk. Future research should focus on developing diagnostic tools using individual tumor and patient characteristics to improve sarcoma management. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methods: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. Results: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Conclusions: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. [ABSTRACT FROM AUTHOR]

Abstract Background Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies. The outcomes of interest include the selected key variables that contribute to adverse events and the performance and validation measures of the predictive models. Methods Three databases, Ovid MEDLINE, PsycINFO, and Embase, were searched between February 2023 and April 2023 to identify all relevant studies that included a combination of (and were not limited to) the following search terms: "Early warning," "Alerting tool," and "Psychiatry". Peer-reviewed primary research publications were included without imposing any date restrictions. A total of 1,193 studies were screened. A total of 9 studies met the inclusion and exclusion criteria and were included in this review. The PICOS model, the Joanna Briggs Institute (JBI) Reviewer's Manual, and PRISMA guidelines were applied. Results This review identified nine studies that developed predictive models for adverse events in psychiatric settings. Encompassing 41,566 participants across studies that used both ML and non-ML algorithmic approaches, performance metrics, primarily AUC ROC, varied among studies between 0.62 and 0.95. The best performing model that had also been validated was the random forest (RF) ML model, with a score of 0.87 and a high sensitivity of 74% and a specificity of 88%. Conclusion Currently, few predictive models have been developed for adverse events and patient deterioration in psychiatric settings. The findings of this review suggest that the use of ML and non-ML algorithms show moderate to good performance in predicting adverse events at the hospitals/units where the tool was developed. Understanding these models and the methodology of the studies is crucial for enhancing patient care as well as staff and patient safety research. Further research on the development and implementation of predictive tools in psychiatry should be carried out to assess the feasibility and efficacy of the tool in psychiatric patients.

The purpose of this study was to analyze the association and predictive capacity between the acute:chronic workload ratio (ACWR) and non-contact injuries in a semiprofessional football team. Seventeen football or soccer players from a Spanish Third Division football team participated voluntarily in this study. A prospective longitudinal study was developed during the 2020/2021 season. Twenty-four weeks were analyzed from October to March, including a regenerative microcycle due to the absence of competition during Christmas. Rate of perceived exertion (RPE) and session-rate of perceived exertion (sRPE) were registered for every training and game session. Afterward, acute and chronic workloads were calculated, and ACWR was subsequently derived from them. Furthermore, non-contact injuries were registered during the period mentioned. The main findings were that there is a poor correlation between the ACWR and non-contact injuries (r=0.069 (p<0.05)), and the use of the ACWR by itself is insufficient to predict the occurrence of non-contact injuries in a semiprofessional football team. Consequently, the ACWR is not an useful predictive tool for injuries in semiprofessional football teams. [ABSTRACT FROM AUTHOR]

Tomato grey leaf spot is a fungal disease that provokes losses in tomato yield. The aim of this work is to analyse genomic differences among three isolates of Stemphylium lycopersici that differ in virulence and sporulation. The bioinformatics analysis led us to predict the identity of putative effectors, pathogenesis and virulence factors. Like the genome of other necrotrophic pathogens, Stemphylium encodes a wide spectrum of effectors, including an ample and diverse array of carbohydrate‐degrading enzymes. Interestingly, the number of predicted effectors was unrelated to virulence. Low virulence appeared to be associated with the presence of several double‐stranded RNAs from viruses as well as cellular processes related to protein degradation, redox and detoxifying processes and monoterpenes production. This is the first identification of the potential effectors of tomato–S. lycopersici interaction; nonetheless functional studies should be done. [ABSTRACT FROM AUTHOR]

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks. [ABSTRACT FROM AUTHOR]

Abstract The availability of many microplastic analysis methods is challenging for researchers and policy makers when tasked with choosing optimal methods for their research question and a given budget. In this study, a cost-effectiveness analysis of methods for microplastic analysis in seawater was performed using survey data acquired from experts. Total analysis cost per method was determined accounting for labour and equipment costs, while method effectiveness was scored based on their ability to confirm the plastic nature of particles, their minimum detectable particle size, and other parameters. Results were validated and discussed during two workshops with scientists and policy makers. The resulting predictive tools allow to identify the most cost-effective methods for specific scenarios, and their associated cost. They mark an important step towards a more effective and informed approach to monitoring and managing microplastic pollution in the marine environment, ultimately contributing to the protection of marine ecosystems and human health.

The availability of many microplastic analysis methods is challenging for researchers and policy makers when tasked with choosing optimal methods for their research question and a given budget. In this study, a cost-effectiveness analysis of methods for microplastic analysis in seawater was performed using survey data acquired from experts. Total analysis cost per method was determined accounting for labour and equipment costs, while method effectiveness was scored based on their ability to confirm the plastic nature of particles, their minimum detectable particle size, and other parameters. Results were validated and discussed during two workshops with scientists and policy makers. The resulting predictive tools allow to identify the most cost-effective methods for specific scenarios, and their associated cost. They mark an important step towards a more effective and informed approach to monitoring and managing microplastic pollution in the marine environment, ultimately contributing to the protection of marine ecosystems and human health. [ABSTRACT FROM AUTHOR]

We assessed the efficacy of oversampling techniques to enhance machine learning model performance in predicting Escherichia coli MG1655 presence in spinach wash water. Three oversampling methods were applied to balance two datasets, forming the basis for training random forest (RF), support vector machines (SVMs), and binomial logistic regression (BLR) models. Data underwent method‐specific centering and standardization, with outliers replaced by feature‐specific means in training datasets. Testing occurred without these preprocessing steps. Model hyperparameters were optimized using a subset of testing data via 10‐fold cross‐validation. Models were trained on full datasets and tested on newly acquired spinach wash water samples. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling approach (ADASYN) achieved strong results, with SMOTE RF reaching an accuracy of 90.0%, sensitivity of 93.8%, specificity of 87.5%, and an area under the curve (AUC) of 98.2% (without data preprocessing) and ADASYN achieving 86.55% accuracy, 87.5% sensitivity, 83.3% specificity, and a 92.4% AUC. SMOTE and ADASYN significantly improved (p < 0.05) SVM and RF models, compared to their non‐oversampled counterparts without preprocessing. Data preprocessing had a mixed impact, improving (p < 0.05) the accuracy and specificity of the BLR model but decreasing the accuracy and specificity (p < 0.05) of the SVM and RF models. The most influential physiochemical feature for E. coli detection in wash water was water conductivity, ranging from 7.9 to 196.2 µS. Following closely was water turbidity, ranging from 2.97 to 72.35 NTU within this study. [ABSTRACT FROM AUTHOR]

Abstract Background There is increasing research interest in the development of preventive treatment for individuals at risk of rheumatoid arthritis (RA). Previous studies have explored the perceptions of at-risk groups and patients about predictive and preventive strategies for RA, but little is known about health care professionals’ (HCPs) perspectives. Methods One-to-one semi-structured qualitative interviews were conducted (face-to-face or by telephone) with HCPs. Audio recordings of the interviews were transcribed, and the data were analysed by thematic analysis. Results Nineteen HCPs (11 female) were interviewed, including ten GPs, six rheumatologists and three rheumatology nurse specialists. The thematic analysis identified four organising themes: 1) Attributes of predictive and preventive approaches; 2) Ethical and psychological concerns; 3) Implementation issues and 4) Learning from management of other conditions. Theme 1 described necessary attributes of predictive and preventive approaches, including the type and performance of predictive tools, the need for a sound evidence base and consideration of risks and benefits associated with preventive treatment. Theme 2 described the ethical and psycho-social concerns that interviewees raised, including the potential negative economic, financial and psychological effects of risk disclosure for ‘at-risk’ individuals, uncertainty around the development of RA and the potential for benefit associated with the treatments being considered. Theme 3 describes the implementation issues considered, including knowledge and training needs, costs and resource implications of implementing predictive and preventive approaches, the role of different types of HCPs, guidelines and tools needed, and patient characteristics relating to the appropriateness of preventive treatments. Theme 4 describes lessons that could be learned from interviewees’ experiences of prediction and prevention in other disease areas, including how preventive treatment is prescribed, existing guidelines and tools for other diseases and issues relating to risk communication. Conclusions For successful implementation of predictive and preventative approaches in RA, HCPs need appropriate training about use and interpretation of predictive tools, communication of results to at-risk individuals, and options for intervention. Evidence of cost-efficiency, appropriate resource allocation, adaptation of official guidelines and careful consideration of the at-risk individuals’ psycho-social needs are also needed.

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools. [ABSTRACT FROM AUTHOR]

• Predictive tools to identify best responders to modulators appear cost-effective. • Predictive tools may save $931 K to $1.1 M per quality adjusted life year lost. • Predictive tools could optimize health benefits and reduce high healthcare costs. Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulator therapies show variable efficacy for patients with CF. Patient-derived predictive tools may identify individuals likely to respond to CFTRs, but are not in routine use. We aimed to determine the cost-utility of predictive tool-guided treatment with CFTRs as add-on to standard of care (SoC) for individuals with CF. This economic evaluation compared two strategies using an individual level simulation: (i) Treat All , where all patients received CFTRs plus SoC and (ii) Test→Treat , where patients who tested positive on predictive tools received CFTRs plus SoC and those who tested negative received SoC only. We simulated 50,000 individuals over their lifetime, and estimated costs (2020 CAD) per quality-adjusted life year (QALY) from the healthcare payer's perspective, discounted at 1.5% annually. The model was populated using Canadian CF registry data and published literature. Probabilistic and deterministic sensitivity were conducted. The Treat All and Test→Treat and strategies yielded 22.41 and 21.36 QALYs, and cost $4.21 M and $3.15 M respectively. Results of probabilistic sensitivity analysis showed that Test→Treat was highly cost-effective compared to Treat All in 100% of simulations at cost-effectiveness thresholds as high as $500,000 per QALY. Test→Treat may save between $931 K to $1.1 M per QALY lost, depending on sensitivity and specificity of predictive tools. The use of predictive tools could optimize the health benefits of CFTR modulators while reducing costs. Our findings support the use of pre-treatment predictive testing and may help inform coverage and reimbursement policies for individuals with CF. [ABSTRACT FROM AUTHOR]

• The difficulties and obstacles of current data-driven toxicity prediction are analyzed. • The evolution of modeling methods and the achievements in each stage are reviewed. • Available software and tools for toxicity prediction are collated and discussed. • Several new directions and suggestions for computational toxicology are put forward. Early toxicity assessment plays a vital role in the drug discovery process on account of its significant influence on the attrition rate of candidates. Recently, constant upgrading of information technology has greatly promoted the continuous development of toxicity prediction. To give an overview of the current state of data-driven toxicity prediction, we reviewed relevant studies and summarized them in three main respects: the features and difficulties of toxicity prediction, the evolution of modeling approaches, and the available tools for toxicity prediction. For each part, we expound the research status, existing challenges, and feasible solutions. Finally, several new directions and suggestions for toxicity prediction are also put forward. [ABSTRACT FROM AUTHOR]

Ovarian cancer represents worldwide the second most frequent and the most fatal gynecological malignancy, with approximately two thirds of the patients presenting with advanced disease. Cytoreductive surgery, primary or after neoadjuvant chemotherapy, in combination with platinum-based chemotherapy is the standard of care for these patients. Despite the improvement in quality of cytoreductive surgery as well as development of novel drugs and chemotherapy regimens, still most women with ovarian cancer will ultimately develop recurrent disease and die of their disease. In contrast to the management of primary disease, the standard treatment of patients with recurrent ovarian cancer remains a topic of debate. While platinum-based or second line systemic chemotherapy, depending on the time after last platinum treatment, is standard of care, the role of secondary cytoreductive surgery has been a controversial issue for the last decades. Potential outcome benefit must be also weighed against the risk of severe surgical morbidity, impairment of quality of life and costs. In platinum-resistant recurrent disease, i.e., relapse after less than 6 months from the last platinumbased chemotherapy for primary disease, secondary cytoreduction seems generally not to be indicated due to its aggressive biological behavior and the absence of effective systemic treatment. In this comprehensive review, the current role of cytoreductive surgery in platinum-sensitive recurrent ovarian cancer is discussed thoroughly in view of the results of most recent randomized trials and a meta-analysis. There seems to be definitely a role for secondary cytoreductive surgery in selected patients with ovarian cancer recurrence in whom complete resection of macroscopic disease is feasible. However, its role should be continuously reviewed due to the changing systemic treatment of patients with ovarian cancer recurrence over time. [ABSTRACT FROM AUTHOR]

Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance.

Ovarian cancer represents worldwide the second most frequent and the most fatal gynecological malignancy, with approximately two thirds of the patients presenting with advanced disease. Cytoreductive surgery, primary or after neoadjuvant chemotherapy, in combination with platinum-based chemotherapy is the standard of care for these patients. Despite the improvement in quality of cytoreductive surgery as well as development of novel drugs and chemotherapy regimens, still most women with ovarian cancer will ultimately develop recurrent disease and die of their disease. In contrast to the management of primary disease, the standard treatment of patients with recurrent ovarian cancer remains a topic of debate. While platinum-based or second line systemic chemotherapy, depending on the time after last platinum treatment, is standard of care, the role of secondary cytoreductive surgery has been a controversial issue for the last decades. Potential outcome benefit must be also weighed against the risk of severe surgical morbidity, impairment of quality of life and costs. In platinum-resistant recurrent disease, i.e., relapse after less than 6 months from the last platinum-based chemotherapy for primary disease, secondary cytoreduction seems generally not to be indicated due to its aggressive biological behavior and the absence of effective systemic treatment. In this comprehensive review, the current role of cytoreductive surgery in platinum-sensitive recurrent ovarian cancer is discussed thoroughly in view of the results of most recent randomized trials and a meta-analysis. There seems to be definitely a role for secondary cytoreductive surgery in selected patients with ovarian cancer recurrence in whom complete resection of macroscopic disease is feasible. However, its role should be continuously reviewed due to the changing systemic treatment of patients with ovarian cancer recurrence over time.

Background/Aim: Triple-negative breast cancers represent 15% of all mammary malignancies and encompass several entities with different genomic characteristics. Among these, luminal androgen receptor (LAR) tumors express the androgen receptor (AR) and are characterized by a genomic profile which resembles luminal breast cancers. Moreover, LAR malignancies are usually enriched in PIK3CA, KMTC, CDH, NF1, and AKT1 alterations. Still, molecular features, clinical behavior and prognosis of this variant remain controversial, while identification of effective treatments represents an unmet medical need. Additionally, the predictive role of the AR is unclear. Materials and Methods: We performed an extensive next generation sequencing analysis using a commercially available panel in a cohort of patients with LAR breast cancer followed at two local Institutions. We next employed bioinformatic tools to identify signaling pathways involved in LAR pathogenesis and looked for potentially targetable alterations. Results: Eight patients were included in the study. In our cohort we found 26 known genetic alterations (KGAs) in 15 genes and 64 variants of unknown significance (VUS) in 59 genes. The most frequent KGAs were single nucleotide variants in PIK3CA, HER2, PTEN and TP53. Among VUS, CBFB, EP300, GRP124, MAP3K1, RANBP2 and TSC2 represented recurrently altered genes. We identified five signaling pathways (MAPK, PI3K/AKT, TP53, apoptosis and angiogenesis) involved in the pathogenesis of LAR breast cancer. Several alterations, including those in PIK3CA, ERBB2 and PI3K/AKT/mTOR signaling, were potentially targetable. Conclusion: Our findings confirm a role for PI3K/AKT/mTOR signaling in the pathogenesis of LAR breast cancers and indicate that targeting this pathway, along with ERBB2 mutations, may represent an additional therapeutic strategy which deserves further exploration in larger studies. [ABSTRACT FROM AUTHOR]

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director’s role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda’s and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today.

Introduction: Preeclampsia, characterized by hypertensive disorders and systemic inflammatory response, remains a leading cause of maternal morbidity and mortality globally. Effective risk assessment tools are crucial for predicting adverse maternal outcomes., Objective: This study evaluates the performance of the fullPIERS (Preeclampsia Integrated Estimate of Risk) model in predicting adverse maternal outcomes within 24 hours of admission for preeclampsia., Methods: A cross-sectional study was conducted over one year, involving 100 preeclamptic patients admitted to Nil Ratan Sircar Medical College & Hospital (NRSMCH). Predictor variables were collected within 24 hours of admission and analyzed using the fullPIERS model., Results: The fullPIERS model effectively stratified maternal risk. Adverse outcomes were significantly associated with systolic blood pressure (BP) ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, oxygen saturation ≤ 95%, frontal headache, visual disturbances, chest pain/dyspnea, and abnormal random blood sugar, albumin, alanine aminotransferase, platelet count, and creatinine levels. A fullPIERS score ≥ 30 was strongly predictive of adverse maternal outcomes., Conclusion: The fullPIERS model is a valuable tool for predicting adverse maternal outcomes in preeclampsia, aiding in timely and effective clinical decision-making., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. NRS Medical College & Hospital, Institutional Ethics Committee issued approval NMC/450. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Burman et al.)

Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in‐depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short‐ or longer‐term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance. [ABSTRACT FROM AUTHOR]

Childhood asthma develops in 30–40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011–2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools' AUCs (95%CI) ranged from 0.57 (95%CI 0.54–0.59) to 0.68 (95%CI 0.65–0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC 0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis.

Bacterial expression systems remain a widely used host for recombinant protein production. However, overexpression of recombinant target proteins in bacterial systems such as Escherichia coli can result in poor solubility and the formation of insoluble aggregates. As a consequence, numerous strategies or alternative engineering approaches have been employed to increase recombinant protein production. In this case study, we present the strategies used to increase the recombinant production and solubility of 'difficult-to-express' bacterial antigens, termed Ant2 and Ant3, from Absynth Biologics Ltd.'s Clostridium difficile vaccine programme. Single recombinant antigens (Ant2 and Ant3) and fusion proteins (Ant2-3 and Ant3-2) formed insoluble aggregates (inclusion bodies) when overexpressed in bacterial cells. Further, proteolytic cleavage of Ant2-3 was observed. Optimisation of culture conditions and changes to the construct design to include N-terminal solubility tags did not improve antigen solubility. However, screening of different buffer/additives showed that the addition of 1–15 mM dithiothreitol alone decreased the formation of insoluble aggregates and improved the stability of both Ant2 and Ant3. Structural models were generated for Ant2 and Ant3, and solubility-based prediction tools were employed to determine the role of hydrophobicity and charge on protein production. The results showed that a large non-polar region (containing hydrophobic amino acids) was detected on the surface of Ant2 structures, whereas positively charged regions (containing lysine and arginine amino acids) were observed for Ant3, both of which were associated with poor protein solubility. We present a guide of strategies and predictive approaches that aim to guide the construct design, prior to expression studies, to define and engineer sequences/structures that could lead to increased expression and stability of single and potentially multi-domain (or fusion) antigens in bacterial expression systems. [ABSTRACT FROM AUTHOR]

Study Design: Clinical case series.Objective: The aim of this study was to determine the effectiveness of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator in the prediction of complications after anterior lumbar interbody fusion (ALIF).Summary Of Background Data: Identifying at-risk patients may aid in the prevention of complications after spine procedures. The ACS NSQIP surgical risk calculator was developed to predict 30-day postoperative complications for a variety of operative procedures.Methods: Medical records of patients undergoing ALIF at our institution from 2009 to 2019 were retrospectively reviewed. Demographic and comorbidity variables were entered into the ACS NSQIP surgical risk calculator to generate percentage predictions for complication incidence within 30 days postoperatively. The observed incidences of these complications were also abstracted from the medical record. The predictive ability of the ACS NSQIP surgical risk calculator was assessed in comparison to the observed incidence of complications using area under the curve (AUC) analyses.Results: Two hundred fifty-three (253) patients were analyzed. The ACS NSQIP surgical risk calculator was a fair predictor of discharge to non-home facility (AUC 0.71) and surgical site infection (AUC 0.70). The ACS NSQIP surgical risk calculator was a good predictor of acute kidney injury/progressive renal insufficiency (AUC 0.81). The ACS NSQIP surgical risk calculator was not an adequate predictive tool for any other category, including: pneumonia, urinary tract infections, venous thromboembolism, readmission, reoperations, and aggregate complications (AUC < 0.70).Conclusion: The ACS NSQIP surgical risk calculator is an adequate predictive tool for a subset of complications after ALIF including acute kidney injury/progressive renal insufficiency, surgical site infections, and discharge to non-home facilities. However, it is a poor predictor for all other complication groups. The reliability of the ACS NSQIP surgical risk calculator is limited, and further identification of models for risk stratification is necessary for patients undergoing ALIF.Level of Evidence: 3. [ABSTRACT FROM AUTHOR]

Chao Lu,1,* Lina Yu,2,* Jinfeng Wei,1 Jimei Chen,3 Jian Zhuang,3 Sheng Wang11Department of Anesthesiology, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People’s Republic of China; 2Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People’s Republic of China*These authors contributed equally to this workBackground: Despite improvements in neonatal cardiac surgery and postoperative care, hospitalized death for infants with low birth weight remains high.Objective: This study sought to identify predictors of postoperative outcomes in low-birth-weight infants undergoing congenital heart surgery and establish nomograms to predict postoperative intensive-care unit (ICU) stay.Methods: From June 2009 to June 2018, a retrospective review of 114 infants with low birth weight (≤2.5 kg) undergoing congenital heart surgery was conducted at Guangdong Provincial People’s Hospital. Purely surgical ligation of patent ductus arteriosus was excluded from this study. A total of 26 clinical variables were chosen for univariate, multivariate, and Cox regression analysis, and 14 variables were analyzed as predictors of postoperative outcomes. Nomograms were established to predict risk of postoperative cardiac ICU (CICU) stay, postoperative neonatal ICU (NICU) stay, and total ICU length of stay in infants with cardiac diseases.Results: Two variables were independent predictors in multiple logistic regression analysis of hospitalized death: operation weight and Society of Thoracic Surgeons–European Association for Cardio-Thoracic Surgery (STAT) risk categories. Six variables were independent predictors in the Cox model of postoperative ICU length of stay, including sex, prematurity, birth weight, preoperative stay time in NICU, diagnostic classification, and STAT risk categories. We calculated concordance-index values to estimate the discriminative ability of models of risk of postoperative CICU stay, postoperative NICU stay, and total ICU length of stay, with values of 0.758 (95% CI 0.696–0.820), 0.604 (95% CI 0.525–0.682), and 0.716 (95% CI 0.657–0.776), which indicated the possibility of true-positive results.Conclusion: Our findings might help clinicians predict postoperative outcomes and optimize therapeutic strategies.Keywords: low birth weight, congenital heart disease, neonatal cardiac surgery, outcome prognosis, predictive tools, nomogram

The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review. [ABSTRACT FROM AUTHOR]

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director's role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda's and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today. [ABSTRACT FROM AUTHOR]

Background: In primary ciliary dyskinesia (PCD) there is no single diagnostic test. Different predictive tools have been proposed to guide referral of high-risk patients for further diagnostic workup. We aimed to test clinical index (CI) on a large unselected cohort and compare its characteristics with other widely used tools—PICADAR and NA-CDCF. Methods: CI, PICADAR, and NA-CDCF scores were calculated in 1401 patients with suspected PCD referred to our center. Their predictive characteristics were analyzed using receiver operating characteristics (ROC) curves and compared to each other. Nasal nitric oxide (nNO) was measured in 569 patients older than 3 years. Results: PCD was diagnosed in 67 (4.8%) patients. CI, PICADAR, and NA-CDCF scores were higher in PCD than in nonPCD group (all p < 0.001). The area under the ROC curve (AUC) for CI was larger than for NA-CDCF (p = 0.005); AUCPICADAR and AUCNA-CDCF did not differ (p = 0.093). An overlap in signs and symptoms among tools was identified. PICADAR could not be assessed in 86 (6.1%) patients without chronic wet cough. For CI laterality or congenital heart defects assessment was not necessary. nNO further improved predictive power of all three tools. Conclusion: CI is a feasible predictive tool for PCD that may outperform PICADAR and NA-CFCD.

New tools are needed to evaluate and predict the efficacy and safety of medical products. However, the development of such tools requires collaboration and effective data sharing. In this issue of Therapeutic Innovation & Regulatory Science, scientists from the Critical Path Institute (C-Path) discuss their experiences using patient-level data to facilitate innovation in drug development. We share our perspective on the issues discussed in the C-Path paper and offer suggestions on future efforts. [ABSTRACT FROM AUTHOR]

Background: Sinonasal squamous cell carcinoma (SNSCC) is a rare malignancy with varied outcomes. The aim of this study was to develop a nomogram for predicting survival of patients with SNSCC. Methods: From the Surveillance, Epidemiology, and End Results database, we identified 1766 patients diagnosed with SNSCC between 2004 and 2015. Patients were randomly separated into a training set and a validation set in 4:1 ratio. An external validation was also performed by a set of 74 SNSCC patients who had been treated in our department. We used the training set to build a nomogram based on stratified multivariable Cox proportional hazard models for predicting overall survival. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index and calibration curve. Results: Based on 1412 cases of the training cohort, our Cox regression analysis revealed that age, marital status, primary site, differentiation, T stage, N classification, M stage, and treatment modalities were associated with overall survival. A nomogram was established based on the results of multivariate analysis. The C‐index values of the nomogram for predicting survival were superior to those of the tumor‐node‐metastasis staging system (0.745 vs 0.679 in the training cohort, 0.752 vs 0.656 in the validation set, and 0.678 vs 0.596 in the external validation set). The calibration plots demonstrated good consistency between the predicted and observed results. Conclusion: We have developed a nomogram to accurately predict the clinical outcomes of SNSCC patients. This model was effective and can help clinicians to improve patient counseling. [ABSTRACT FROM AUTHOR]

No antimicrobial peptide has been identified in cephalopods to date. Annotation of transcriptomes or genomes using basic local alignment Search Tool failed to yield any from sequence identities. Therefore, we searched for antimicrobial sequences in the cuttlefish (Sepia officinalis) database by in silico analysis of a transcriptomic database. Using an original approach based on the analysis of cysteine-free antimicrobial peptides selected from our Antimicrobial Peptide Database (APD3), the online prediction tool of the Collection of Anti-Microbial Peptides (CAMPR3), and a homemade software program, we identified potential antibacterial sequences. Nine peptides less than 25 amino acids long were synthesized. The hydrophobic content of all nine of them ranged from 30 to 70%, and they could form alpha-helices. Three peptides possessed similarities with piscidins, one with BMAP-27, and five were totally new. Their antibacterial activity was evaluated on eight bacteria including the aquatic pathogens Vibrio alginolyticus, Aeromonas salmonicida, or human pathogens such as Salmonella typhimurium, Listeria monocytogenes, or Staphylococcus aureus. Despite the prediction of an antimicrobial potential for eight of the peptides, only two—GR21 and KT19—inhibited more than one bacterial strain with minimal inhibitory concentrations below 25 µM. Some sequences like VA20 and FK19 were hemolytic, while GR21 induced less than 10% of hemolysis on human blood cells at a concentration of 200 µM. GR21 was the only peptide derived from a precursor with a signal peptide, suggesting a real role in cuttlefish immune defense. [ABSTRACT FROM AUTHOR]

Background: The ideal cardiovascular health score (ICHS) is recommended for use in primary prevention. Simpler tools not requiring laboratory tests, such as the Fuster-BEWAT (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) score (FBS), are also available.Objectives: The purpose of this study was to compare the effectiveness of ICHS and FBS in predicting the presence and extent of subclinical atherosclerosis.Methods: A total of 3,983 participants 40 to 54 years of age were enrolled in the PESA (Progression of Early Subclinical Atherosclerosis) cohort. Subclinical atherosclerosis was measured in right and left carotids, abdominal aorta, right and left iliofemoral arteries, and coronary arteries. Subjects were classified as having poor, intermediate, or ideal cardiovascular health based on the number of favorable ICHS or FBS.Results: With poor ICHS and FBS as references, individuals with ideal ICHS and FBS showed lower adjusted odds of having atherosclerotic plaques (ICHS odds ratio [OR]: 0.41; 95% confidence interval [CI]: 0.31 to 0.55 vs.Fbs or: 0.49; 95% CI: 0.36 to 0.66), coronary artery calcium (CACS) ≥1 (CACS OR: 0.41; 95% CI: 0.28 to 0.60 vs.Cacs or: 0.53; 95% CI: 0.38 to 0.74), higher number of affected territories (OR: 0.32; 95% CI: 0.26 to 0.41 vs. OR: 0.39; 95% CI: 0.31 to 0.50), and higher CACS level (OR: 0.40; 95% CI: 0.28 to 0.58 vs. OR: 0.52; 95% CI: 0.38 to 0.72). Similar levels of significantly discriminating accuracy were found for ICHS and FBS with respect to the presence of plaques (C-statistic: 0.694; 95% CI: 0.678 to 0.711 vs. 0.692; 95% CI: 0.676 to 0.709, respectively) and for CACS ≥1 (C-statistic: 0.782; 95% CI: 0.765 to 0.800 vs. 0.780; 95% CI: 0.762 to 0.798, respectively).Conclusions: Both scores predict the presence and extent of subclinical atherosclerosis with similar accuracy, highlighting the value of the FBS as a simpler and more affordable score for evaluating the risk of subclinical disease. [ABSTRACT FROM AUTHOR]

Background Maxillary sinus carcinoma is an uncommon malignancy. Most reports on prognosis of this disease are from single institutions and include few patients. We used data from the United States National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program to construct models and nomograms for predicting outcomes of patients with maxillary sinus carcinomas. Methods We used records from 668 patients with primary maxillary sinus carcinomas reported to the SEER program from 2004 to 2013 to build nomograms based on stratified multivariable Cox proportional hazard models for predicting 5-year overall survival (OS) and cause-specific survival (CSS). Model building was internally validated with the bootstrap approach. Results Five-year survival was 39.7% (95% confidence interval [CI], 35.5% to 44.5%) and 46.8% (42.3% to 51.8%) for OS and CSS, respectively. The final Cox model included the variables of age at diagnosis, tumor size, histologic type, TNM stage, and surgery. Radiotherapy was a stratification factor in the models. The models demonstrated good accuracy for predicting survival with a bootstrap-corrected Somers Dxy of 0.44 for both OS and CSS models. Calibration curves indicated acceptable model calibration. Conclusion We developed tools for predicting prognosis that incorporate TNM stage and other readily available variables for patients with maxillary sinus carcinomas. The model performance was validated as good. These models can help clinicians to offer improved patient counseling in terms of clinical outcomes and make optimal treatment plans. [ABSTRACT FROM AUTHOR]

Purpose: Upper-tract urothelial carcinoma (UTUC) is a relatively uncommon disease with limited available evidence on specific topics. The purpose of this article was to review the previous literature to summarize the current knowledge about UTUC epidemiology, diagnosis, preoperative evaluation and prognostic assessment. Methods: Using MEDLINE, a non-systematic review was performed including articles between January 2000 and February 2016. English language original articles, reviews and editorials were selected based on their clinical relevance. Results: UTUC accounts for 5-10 % of all urothelial cancers, with an increasing incidence. UTUC and bladder cancer share some common risk factors, even if they are two different entities regarding practical, biological and clinical characteristics. Aristolochic acid plays an important role in UTUC pathogenesis in certain regions. It is further estimated that approximately 10 % of UTUC are part of the hereditary non-polyposis colorectal cancer spectrum disease. UTUC diagnosis remains mainly based on imaging and endoscopy, but development of new technologies is rapidly changing the diagnosis algorithm. To help the decision-making process regarding surgical treatment, extent of lymphadenectomy and selection of neoadjuvant systemic therapies, predictive tools based on preoperative patient and tumor characteristics have been developed. Conclusions: Awareness regarding epidemiology, diagnosis, preoperative evaluation and prognostic assessment changes is essential to correctly diagnose and manage UTUC patients, thereby potentially improving their outcomes. [ABSTRACT FROM AUTHOR]