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IntroductionAcute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions.MethodsThis study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions.ResultsThe proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance.DiscussionOur system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

Purpose – The purpose of this research is to identify and determine reputation risk mitigation strategies that may arise in the business processes of wedding activities at Lifetime Organizer Bogor. A poor reputation for a wedding organizer can drive consumers to switch to competitors with a better reputation. Therefore, effective reputation risk management is crucial for wedding organizers. Design/methodology/approach – This study employs the Risk Assessment Godfrey method and Flanagan & Norman Risk Response Analysis. Risks are identified through direct observation at multiple weddings organized by Lifetime Organizer, in-depth interviews with the owner and field coordinators, and the distribution of questionnaires to partner vendors and consumers of Lifetime Organizer. The risk assessment is calculated using a comprehensive approach that combines qualitative and quantitative elements, facilitated by questionnaires, and processed through geometric mean calculations. Findings – The findings in this study contain mitigation action strategies for risks with high and extreme risk levels. These mitigation strategies are organized based on implementation that aligns with the company's needs and resources, thereby enabling the company to proactively prevent the occurrence of such risks. Research limitations/implications – In this study, the identified risks pertain to factors that may influence the company's reputation. Since reputation is a subjective perception held by individuals, it introduces subjectivity in the process of identifying and evaluating risks. It is anticipated that in future research endeavors, this aspect will be duly considered and addressed. Practical implications – The research holds significant practical implications for wedding organizers, providing a tailored approach to reputation risk management. The proposed strategies, informed by the risk assessment framework, empower companies to navigate the dynamic event planning industry effectively, fostering long-term success and resilience. Originality/value – This study contributes novelty by adapting and applying established risk assessment methods to the specific context of the wedding planning industry in Indonesia. The findings offer a unique perspective on reputation risk management, providing valuable insights for both industry practitioners and researchers in developing countries.

Patients with localised, high‐risk gastrointestinal stromal tumours (GIST) benefit from adjuvant imatinib treatment. Still, approximately 40% of patients relapse within 3 years after adjuvant therapy and the clinical and histopathological features currently used for risk classification cannot precisely predict poor outcomes after standard treatment. This study aimed to identify genomic and transcriptomic profiles that could be associated with disease relapse and thus a more aggressive phenotype. Using a multi‐omics approach, we analysed a cohort of primary tumours from patients with untreated, resectable high‐risk GISTs. We compared patients who developed metastatic disease within 3 years after finishing adjuvant imatinib treatment and patients without disease relapse after more than 5 years of follow‐up. Combining genomics and transcriptomics data, we identified somatic mutations and deregulated mRNA and miRNA genes intrinsic to each group. Our study shows that increased chromosomal instability (CIN), including chromothripsis and deregulated kinetochore and cell cycle signalling, separates high‐risk samples according to metastatic potential. The increased CIN seems to be an intrinsic feature for tumours that metastasise and should be further validated as a novel prognostic biomarker for high‐risk GIST.

“Metro economy” has led to intensive land development along the metro lines. However, engineering activities associated with land development inevitably disturb the service environment, causing severe settlement of adjacent metro tunnel structures in soft soil areas. As a reference point for pre-treating this type of tunnel settlement, a method is proposed to classify settlement risk sections along metro lines based on a land-use development simulation and corresponding uncertainty analysis. First, the land-use development along the metro line was simulated by Artificial Neural Network–Cellular Automata (ANN-CA). Second, the land-use development process was considered a random event rather than a deterministic prediction as in a typical ANN-CA, with its probability quantified based on the cells’ conversion probability. The classification of the surrounding land-use development probability was used to allocate the settlement risk sections of metro lines. This method was applied to the Han–You section of the Nanjing Metro Line 2. The predicted settlement risk sections corresponded suitably with the actual settlement troughs, demonstrating the effectiveness of this method. Thus, this method provides a novel consideration for the pre-treatment of metro tunnel settlement from the perspective of interactions between the metro line and surrounding land development.

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed.

Abstract Background Aim of the current study was the assessment of risk classes according to a previously established system to classify the anamnesis-related risk of complications and of oral diseases in a cohort of patients attending the dental student course for dental preventive measures. Methods Patients attending the student course between April 2020 and December 2021 were included. To assess the medical history, a specific anamnesis tool was used, which included the classification of different potential anamneis-related risks originating from general diseases, medications or lifestyle factors into a low, moderate or high risk of complications and/or risk of oral diseases. Thereby, the risk of complications was defined as the increaeed probability of harm during dental measures (professional tooth cleaning, invasive dental treatment or any kind of manipulation, which may cause a bacteremia), e.g. infectious endocarditis. On the other hand, the risk of oral diseases was an increaeed probability of development and/or progression of oral diseases like caries, periodontitis or mucosal diseases. Those risk classes were subsequently analyzed under consideration of age and gender. Results 858 patients, with a mean age of 50.48 ± 20.72 [median: 52.0] years, and a nearly balanced gender distribution (50.8% female and 49.2% male) were included. In the overall cohort, the risk of complications related with dental measures was low in 38.3%, moderate in 42.4% and high in 19.3% of participants. The risk of oral diseases was low in 33.1%, moderate in 37.2% and high in 29.7% of participants. Both, the risk of complications and the risk of oral diseases increased with patients’ age (p

Background Risk stratification is important in patients with post–cardiac arrest syndrome. The Post‐Cardiac Arrest Syndrome for Therapeutic Hypothermia (CAST) and revised CAST (rCAST) scores have been well validated for predicting neurological outcomes, particularly for conventionally resuscitated patients with post–cardiac arrest syndrome. However, no studies have evaluated patients undergoing extracorporeal cardiopulmonary resuscitation. Methods and Results Adult patients with out‐of‐hospital cardiac arrest who underwent extracorporeal cardiopulmonary resuscitation were analyzed in this retrospective observational multicenter cohort study. We validated the accuracy of the CAST/rCAST scores for predicting neurological outcomes at 30 days. Moreover, we compared the predictive performance of these scores with the TiPS65 risk score derived from patients with out‐of‐hospital cardiac arrest who were resuscitated using extracorporeal cardiopulmonary resuscitation. A total of 1135 patients were analyzed. The proportion of patients with favorable neurological outcomes was 16.6%. In the external validation, the area under the receiver operating characteristic curve of the CAST score was significantly higher than that of the rCAST score (area under the receiver operating characteristic curve 0.677 versus 0.603; P

Peruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative efforts and employ diverse methodologies for forest monitoring. In this study, we propose an alternative approach to decision-making for forest preservation, aiming to classify the risk of forest loss in districts within the Peruvian Amazon rainforest. This classification enables sustainable forest management. To accomplish this, we utilized unsupervised learning artificial intelligence through Kohonen's neural network. The network was trained using a historical database spanning from 2001 to 2021, which includes variables such as forest cover and loss, climate, topography, hydrographic networks, and timber forest concessions. Through this approach, the network successfully established five clusters. Following preliminary analysis, we designated these clusters as: low, medium, high, very high, and extremely high risk of forest loss. Kohonen networks demonstrated their effectiveness in clustering forest loss and forest cover. The results indicate a shifting trend among the classes over time, with an increase in the categories exhibiting high and very high risk of forest cover loss. This study provides valuable information for decision-making in the prevention and conservation of Peruvian forests. We strongly recommend maintaining vigilance, particularly in districts classified as a very high or extremely high risk of losing forest cover.

Patients with localised, high‐risk gastrointestinal stromal tumours (GIST) benefit from adjuvant imatinib treatment. Still, approximately 40% of patients relapse within 3 years after adjuvant therapy and the clinical and histopathological features currently used for risk classification cannot precisely predict poor outcomes after standard treatment. This study aimed to identify genomic and transcriptomic profiles that could be associated with disease relapse and thus a more aggressive phenotype. Using a multi‐omics approach, we analysed a cohort of primary tumours from patients with untreated, resectable high‐risk GISTs. We compared patients who developed metastatic disease within 3 years after finishing adjuvant imatinib treatment and patients without disease relapse after more than 5 years of follow‐up. Combining genomics and transcriptomics data, we identified somatic mutations and deregulated mRNA and miRNA genes intrinsic to each group. Our study shows that increased chromosomal instability (CIN), including chromothripsis and deregulated kinetochore and cell cycle signalling, separates high‐risk samples according to metastatic potential. The increased CIN seems to be an intrinsic feature for tumours that metastasise and should be further validated as a novel prognostic biomarker for high‐risk GIST. [ABSTRACT FROM AUTHOR]

Background: The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively. Methods: The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package. Results: We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872–0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759–0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively. Conclusions: The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to identify preoperative risk factors and create a risk classification for intravesical recurrence of upper urinary tract urothelial carcinoma only after laparoscopic radical nephroureterectomy in a multi‐institutional cohort. Methods: We retrospectively analyzed 283 patients who had undergone laparoscopic radical nephroureterectomy for nonmetastatic upper tract urothelial cancer between March 2002 and March 2020. The cumulative incidence of intravesical recurrence for 224 patients without previous or concomitant bladder cancer was examined using multivariate Fine‐Gray competing risks proportional hazards models. A risk stratification model was created to predict subsequent patient outcomes based on the results. Results: The median follow‐up duration was 33.3 months, and 71 (31.7%) patients experienced intravesical recurrence. The estimated cumulative incidence of intravesical recurrence at one and 5 years was 23.5% and 36.4%, respectively. In multivariate analysis, the presence of ureter tumors and multiple tumors were shown to be independently significant predictive factors for intravesical recurrence. Based on the results, we classified patients into three risk groups. The cumulative incidence rates of intravesical recurrence within 5 years after surgery were 24.4%, 42.5%, and 66.7% in the low‐, intermediate‐, and high‐risk groups, respectively. Conclusions: We identified risk factors and created a risk classification model for intravesical recurrence of upper urinary tract urothelial carcinoma only after laparoscopic radical nephroureterectomy. Based on this model, an individualized surveillance protocol or adjuvant therapy could be provided. [ABSTRACT FROM AUTHOR]

Decentralized finance (DeFi) promises a revolution in financial accessibility, transparency, and automation. Yet, its very novelty exposes participants to a number of additional risks and challenges. This study aims to address the risks associated with DeFi, while also conducting a comparative analysis to those of classical/traditional finance (TradFi). After introducing DeFi and its defining characteristics, such as the use of smart contracts, blockchain technology, and decentralized governance, the paper outlines the principal risks associated with DeFi. Drawing insights from an extensive literature review of 200 recent articles, of which 50 were thoroughly analyzed, the study compares risks of DeFi and TradFi, categorizing these into systematic and unsystematic risks. Furthermore, we introduce the 'risk wheel', an innovative tool tailored to understand and navigate the subtleties of DeFi risks, finding potential applications in risk assessment, management, and even education. This paper's primary objective is to provide a detailed and impartial examination of the risks associated with DeFi and their comparison to traditional finance in order to assist stakeholders in making informed decisions and mitigating possible losses. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC. [ABSTRACT FROM AUTHOR]

Bridges are essential elements in the built environment since they underpin the functioning of transportation systems. Nevertheless, they are vulnerable due to aggressive environment, demand beyond the design level, and other contingencies such as extreme events. The management of bridges represents a significant challenge for improving transport performance and ensure the safety of users. In Italy, bridge management is addressed by the recent "Guidelines for risk classification and management, safety evaluation and monitoring of existing bridges" which were issued by the Italian Ministry for Public Work in 2020. The guidelines propose an operational approach with distinctive characteristics: (i) risk‐based, i.e., based on the typical parameters of hazard, vulnerability, and exposure; (ii) multi‐level, i.e., they include six assessment levels (0‐5) of increasing complexity; and (iii) multi‐risk, i.e., they assess structural/foundation, seismic, hydrological and landslide risk. This paper presents a general overview of the 2020 Italian Guidelines and their application to a selected case study. Lessons from the application to the case study are drafted and possible criticalities highlighted. [ABSTRACT FROM AUTHOR]

In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit‐weighted Reduced Mixture‐of‐Experts, which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness‐of‐fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Background: Aim of the current study was the assessment of risk classes according to a previously established system to classify the anamnesis-related risk of complications and of oral diseases in a cohort of patients attending the dental student course for dental preventive measures. Methods: Patients attending the student course between April 2020 and December 2021 were included. To assess the medical history, a specific anamnesis tool was used, which included the classification of different potential anamneis-related risks originating from general diseases, medications or lifestyle factors into a low, moderate or high risk of complications and/or risk of oral diseases. Thereby, the risk of complications was defined as the increaeed probability of harm during dental measures (professional tooth cleaning, invasive dental treatment or any kind of manipulation, which may cause a bacteremia), e.g. infectious endocarditis. On the other hand, the risk of oral diseases was an increaeed probability of development and/or progression of oral diseases like caries, periodontitis or mucosal diseases. Those risk classes were subsequently analyzed under consideration of age and gender. Results: 858 patients, with a mean age of 50.48 ± 20.72 [median: 52.0] years, and a nearly balanced gender distribution (50.8% female and 49.2% male) were included. In the overall cohort, the risk of complications related with dental measures was low in 38.3%, moderate in 42.4% and high in 19.3% of participants. The risk of oral diseases was low in 33.1%, moderate in 37.2% and high in 29.7% of participants. Both, the risk of complications and the risk of oral diseases increased with patients' age (p < 0.01). Thereby, the risk of complications as well as the risk of oral diseases related to general diseases and medication significantly increased with age (p < 0.01). Conclusion: Nearly one fifth of patients in dental student couse show a high risk of complications related with dental measures. Morever, nearly one third of those individuals have a high anamnesis-related risk of oral diseases. With increasing age, the amount of patients in high risk classes becomes higher. Future dental education and research should address the high relevance of anamnesis-related risk factors. [ABSTRACT FROM AUTHOR]

Background: Over the past few years, significant advancements have been achieved in the front-line treatment of metastatic renal cell carcinomas (mRCCs). However, most patients will eventually encounter disease progression during this front-line treatment and require further therapeutic options. While treatment choices for mRCCs patients are determined by established risk classification models, knowledge of prognostic factors in subsequent line therapy is essential in patient care. Methods: In this retrospective, single-center study, patients diagnosed with mRCCs who experienced progression after first-line therapy were enrolled. Fifteen factors were analyzed for their prognostic impact on survival using the Kaplan–Meier method and the Cox proportional hazards model. Results: Poor International Metastatic RCCs Database Consortium (IMDC) and Memorial Sloan-Kettering Cancer Center (MSKCC) risk scores, NLR value > 3, clinical benefit < 3 months from a therapeutic line, and the presence of sarcomatoid differentiation were found to be poor independent prognostic factors for shortened overall survival. Conclusions: This study provided new insights into the identification of potential prognostic parameters for late-line treatment in mRCCs. The results indicated that good IMDC and MSKCC prognostic scores are effective in second-line therapy. Moreover, patients with NLR < 3, no sarcomatoid differentiation, and clinical benefit > 3 months experienced significantly longer overall survival. [ABSTRACT FROM AUTHOR]

Modern forest management requires a well-founded knowledge and understanding of all the risks involved in forest management. It requires a wealth of information not only on natural hazards, but also related to the financial aspect of running a business. The most important activity in the risk management phase is to identify all the known risk areas, and on this basis, to determine the appropriate classification of risks accompanying the activities of the entity in question, taking into account the various categories of risk division. In forest management, the global risk of activities should be considered in terms of two main risks: operational risk (including natural and anthropogenic factors), and financial risk, which, in simple terms, is the consequence of decisions and economic processes in an entity's area of activity. Considering the impact of the production factors that affect the results of the activities carried out, it should be emphasized that forest management has a specific distribution of standard production factors. The forest stand plays the role of both a production resource and an object of production, and in the final analysis, represents a production effect. Any consideration of financial risk management in forest management should be carried out based on long-term analytical data series. Another factor for forests under state management is the various functions that forest management performs. Taking into account the ownership criterion cited earlier, it needs to be emphasized that the weighting of individual financial risks may vary depending on the ownership structure of forests in a given country and taking into account the ownership share of forests in the timber sales market. In conclusion, it was pointed out that, unlike other industries, financial risk management in forest holdings can be disrupted by the function of forests, as well as the strong influence in the long term of natural phenomena that have a significant impact on determining the types and ordering the degree of significance of individual risks in the process of identifying them. [ABSTRACT FROM AUTHOR]

Cysteine-altering missense variants (NOTCH3 cys) in one of the 34 epidermal growth-factor-like repeat (EGFr) domains of the NOTCH3 protein are the cause of NOTCH3-associated small vessel disease (NOTCH3-SVD). NOTCH3-SVD is highly variable, ranging from cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) at the severe end of the spectrum to non-penetrance. The strongest known NOTCH3-SVD modifier is NOTCH3 cys variant position: NOTCH3 cys variants located in EGFr domains 1–6 are associated with a more severe phenotype than NOTCH3 cys variants located in EGFr domains 7–34. The objective of this study was to further improve NOTCH3-SVD genotype-based risk prediction by using relative differences in NOTCH3 cys variant frequencies between large CADASIL and population cohorts as a starting point. Scientific CADASIL literature, cohorts and population databases were queried for NOTCH3 cys variants. For each EGFr domain, the relative difference in NOTCH3 cys variant frequency (NVFOR) was calculated using genotypes of 2574 CADASIL patients and 1647 individuals from population databases. Based on NVFOR cut-off values, EGFr domains were classified as either low (LR-EGFr), medium (MR-EGFr) or high risk (HR-EGFr). The clinical relevance of this new three-tiered EGFr risk classification was cross-sectionally validated by comparing SVD imaging markers and clinical outcomes between EGFr risk categories using a genotype-phenotype data set of 434 CADASIL patients and 1003 NOTCH3 cys positive community-dwelling individuals. CADASIL patients and community-dwelling individuals harboured 379 unique NOTCH3 cys variants. Nine EGFr domains were classified as an HR-EGFr, which included EGFr domains 1–6, but additionally also EGFr domains 8, 11 and 26. Ten EGFr domains were classified as MR-EGFr and 11 as LR-EGFr. In the population genotype–phenotype data set, HR-EGFr individuals had the highest risk of stroke [odds ratio (OR) = 10.81, 95% confidence interval (CI): 5.46–21.37], followed by MR-EGFr individuals (OR = 1.81, 95% CI: 0.84–3.88) and LR-EGFr individuals (OR = 1 [reference]). MR-EGFr individuals had a significantly higher normalized white matter hyperintensity volume (nWMHv; P = 0.005) and peak width of skeletonized mean diffusivity (PSMD; P = 0.035) than LR-EGFr individuals. In the CADASIL genotype–phenotype data set, HR-EGFr domains 8, 11 and 26 patients had a significantly higher risk of stroke (P = 0.002), disability (P = 0.041), nWMHv (P = 1.8 × 10−8), PSMD (P = 2.6 × 10−8) and lacune volume (P = 0.006) than MR-EGFr patients. SVD imaging marker load and clinical outcomes were similar between HR-EGFr 1–6 patients and HR-EGFr 8, 11 and 26 patients. NVFOR was significantly associated with vascular NOTCH3 aggregation load (P = 0.006), but not with NOTCH3 signalling activity (P = 0.88). In conclusion, we identified three clinically distinct NOTCH3-SVD EGFr risk categories based on NFVOR cut-off values, and identified three additional HR-EGFr domains located outside of EGFr domains 1–6. This EGFr risk classification will provide an important key to individualized NOTCH3-SVD disease prediction. [ABSTRACT FROM AUTHOR]

Objectives: There is limited prospective evidence to guide the management of late‐onset fetal growth restriction (FGR) and its differentiation from small‐for‐gestational age. The aim of this study was to assess prospectively a novel protocol in which ultrasound criteria were used to classify women with suspected late FGR into two groups: those at low risk, who were managed expectantly until the anticipated date of delivery, and those at high risk, who were delivered soon after 37 weeks of gestation. We also compared the outcome of this prospective cohort with that of a historical cohort of women presenting similarly with suspected late FGR, in order to evaluate the impact of the new protocol. Methods: This was a prospective study of women with a non‐anomalous singleton pregnancy at ≥ 32 weeks' gestation attending a tertiary hospital in London, UK, between February 2018 and September 2019, with estimated fetal weight (EFW) ≤ 10th centile, or EFW > 10th centile in addition to a decrease in fetal abdominal circumference of ≥ 50 centiles compared with a previous scan, umbilical artery Doppler pulsatility index > 95th centile or cerebroplacental ratio < 5th centile. Women were classified as low or high risk based on ultrasound and Doppler criteria. Women in the low‐risk group were delivered by 41 weeks of gestation, unless they subsequently met high‐risk criteria, whereas women in the high‐risk group (EFW < 3rd centile, umbilical artery Doppler pulsatility index > 95th centile or EFW between 3rd and 10th centiles (inclusive) with abdominal circumference drop or abnormal Dopplers) were delivered at or soon after 37 weeks. The primary outcome was adverse neonatal outcome and included hypothermia, hypoglycemia, neonatal unit admission, jaundice requiring treatment, suspected infection, feeding difficulties, 1‐min Apgar score < 7, hospital readmission and any severe adverse neonatal outcome (perinatal death, resuscitation using inotropes or mechanical ventilation, 5‐min Apgar score < 7, metabolic acidosis, sepsis, and cerebral, cardiac or respiratory morbidity). Secondary outcomes were adverse maternal outcome (operative delivery for abnormal fetal heart rate) and severe adverse neonatal outcome. Women managed according to the new protocol were compared with a historical cohort of 323 women delivered prior to the implementation of the new protocol, for whom management was guided by individual clinician expertise. Results: Over 18 months, 321 women were recruited to the prospective cohort, of whom 156 were classified as low risk and 165 were high risk. Adverse neonatal outcome was significantly less common in the low‐risk compared with the high‐risk group (45% vs 58%; adjusted odds ratio (aOR), 0.6 (95% CI, 0.4–0.9); P = 0.022). There was no significant difference in the rate of adverse maternal outcome (18% vs 24%; aOR, 0.7 (95% CI, 0.4–1.2); P = 0.142) or severe adverse neonatal outcome (3.8% vs 8.5%; aOR, 0.5 (95% CI, 0.2–1.3); P = 0.153) between the low‐ and high‐risk groups. Compared with women in the historical cohort classified retrospectively as low risk, low‐risk women managed under the new protocol had a lower rate of adverse neonatal outcome (45% vs 58%; aOR, 0.6 (95% CI, 0.4–0.9); P = 0.026). Conclusions: Appropriate risk stratification to guide management of late FGR was associated with a reduced rate of adverse neonatal outcome in low‐risk pregnancies. In clinical practice, a policy of expectantly managing women with a low‐risk late‐onset FGR pregnancy at term could improve neonatal and long‐term development. Randomized controlled trials are needed to assess the effect of an evidence‐based conservative management protocol for late FGR on perinatal morbidity and mortality and long‐term neurodevelopment. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Thyroid nodules are common and the prevalence varies between 4 and 7% by palpation and 19-68% by high-resolution USG. Most thyroid nodules are benign, and the malignancy rate varies between 7 and 15% of patients. Thyroid nodules are detected incidentally during clinical examination or, more often, during imaging studies performed for another reason. All detected thyroid nodules should be evaluated clinically. The main test in evaluating thyroid function is thyroid stimulating hormone (TSH). If the serum TSH level is below the normal reference range, a radionuclide thyroid scan should be performed to determine whether the nodule is hyperfunctioning. If the serum TSH level is normal or high, ultrasonography (US) should be performed to evaluate the nodule. US is the most sensitive imaging method in the evaluation of thyroid nodules. Computed tomography (CT) and magnetic resonance imaging are not routinely used in the initial evaluation of thyroid nodules. There are many risk classification systems according to the USG characteristics of thyroid nodules, and the most widely used in clinical practice are the American Thyroid Association guideline and the American College of Radiology Thyroid Imaging Reporting and Data System. Fine needle aspiration biopsy (FNAB) is the gold standard method in the evaluation of nodules with indication according to USG risk class. In the cytological evaluation of FNAB, the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) is the most frequently applied cytological classification. TBSRTC is a simplified, 6-category reporting system and was updated in 2023. The application of molecular tests to FNAB specimens, especially those diagnosed with Bethesda III and IV, is increasing to reduce the need for diagnostic surgery. Especially in Bethesda III and IV nodules, different methods are applied in the treatment of nodules according to the malignancy risk of each category, these are follow-up, surgical treatment, radioactive iodine treatment, and non-surgical ablation methods. [ABSTRACT FROM AUTHOR]

Purpose: The objective of the current study was to assess whether and how preoperative risk group distribution and pathological outcomes have changed in men treated with radical prostatectomy (RP) over the past 25 years. Methods: 11,071 patients from a large contemporary registry-based nationwide cohort with RP as primary treatment between 1995 and 2019 were included. Preoperative risk stratification, postoperative outcomes, and 10 years other-cause mortality (OCM) were analyzed. Results: After 2005, the proportion of low-risk prostate cancer (PCa) decreased from 39.6% to 25.5% in 2010 and decreased further to 15.5% in 2015, and 9.4% in 2019 (p < 0.001). The proportion of high-risk cases increased from 13.1% in 2005 to 23.1% in 2010 and 36.7% in 2015, and 40.4% in 2019 (p < 0.001). After 2005, the proportion of cases with favorable localized PCa decreased from 37.3% to 24.9% in 2010 and decreased further to 13.9% in 2015, and 1.6% in 2019 (p < 0.001). The overall 10 years OCM was 7.7%. Conclusion: The current analysis documents a clear shift in utilization of RP toward higher-risk PCa in men with long life expectancy. Patients with low-risk PCa or favorable localized PCa are rarely operated. This suggests a shift in applying surgery only to patients who may really benefit from RP and the long-standing discussion of overtreatment might become outdated. [ABSTRACT FROM AUTHOR]

Lyme disease risk areas have increased across Canada in recent decades with the ongoing range expansion of Ixodes scapularis and Borrelia burgdorferi. Different methodologies are used by federal and provincial governments to determine local Lyme disease risk, which can make comparisons between regions challenging. In this study, seven Canadian Lyme disease risk classification methodologies were compared with each other to highlight the strengths and limitations of how each definition measured I. scapularis and B. burgdorferi risk. Each methodology was applied to active surveillance data from Ontario, and per cent agreement and kappa statistics were calculated. The methodologies varied in their measurements of the risk of exposure to I. scapularis and B. burgdorferi based on their use of active surveillance techniques, multiple types of collected surveillance data and laboratory confirmation of B. burgdorferi. Most initial Lyme disease risk site classifications were maintained over time. Kappa and per cent agreement statistics highlighted large differences between 8 of the 15 methodology pairings, indicating the presence of inconsistencies between most methodologies. Accurate, consistent surveillance and assessment of the spread of I. scapularis and its pathogens will aid with communicating Lyme disease risk to the public and preventing tick‐borne pathogen transmission. [ABSTRACT FROM AUTHOR]

Autocalibration is a desirable property since it ensures that the information contained in a candidate premium is used without any bias. It turns out to be intimately related to the method of marginal totals that predates modern risk classification methods. The present note aims to assess the impact of autocalibration on the goodness of lift. It is shown on a case study that autocalibration does not only restore global and local balances but also improve lift. [ABSTRACT FROM AUTHOR]

Abstract China’s economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control.

Introduction: Computer-aided diagnostic (CAD) programs for malignancy risk stratification from ultrasound (US) imaging of thyroid nodules are being validated both experimentally and in real-world practice. However, they have not been tested for reliability in analyzing difficult or unclear images. Methods: US images with indeterminate characteristics were evaluated by five observers with different experience in US examination and by a commercial CAD program. The nodules, on which the observers widely agreed, were considered concordant and, if there was little agreement, not concordant or difficult to ass ess. The diagnostic performance of the readers and the CAD program was calculated and compared in both groups of nodule images. Results: In the group of concordant thyroid nodules (n = 37), the clinicians and the CAD system obtained similar levels of accuracy (77.0% vs 74.2%, res pectively; P = 0.7) and no differences were found in sensitivity (SEN) (95.0% vs 87.5%, P = 0.2), specificity (SPE) (45.5 vs 49.4, respectively; P = 0.7), positive predictive value (PPV) (75.2% vs 77.7%, respectively; P = 0.8), nor negative predictive value (NPV) (85.6 vs 77.7, respectively; P = 0.3). When analyzing the non-concordant nodules (n = 43), the CAD system presented a decrease in accuracy of 4.2%, which was significantly lower th an that observed by the experts (19.9%, P = 0.02). Conclusions: Clinical observers are similar to the CAD system in the US assessment of the risk of thyroid nodules. However, the AI system for thyroid nodules AmCAD-UT® showed more reliability in the analysis of unclear or misleading images.

Light Steel Frame (LSF) system is mainly used for construction of short and intermediate-height buildings in developed countries whereas considerable heed is not given to it in developing countries. Unfamiliarity to LSF risks is one of the main reasons for this averseness so risk management can remedy this challenge and develop its application. Hence, this paper investigates the risk management of LSF considering design, construction and operation phase. Three steps entailing risk identification, assessment and responding using fuzzy Failure Mode and Effect Analysis (FMEA) technique are suggested for risk management implementation and validation of responses, a novel index with respect to weighted combination of project quality, time and cost is calculated. The methodology is demonstrated on a pilot study in a developing country. By using interview, 29 significant risks are extracted in design, construction and operation and then evaluated by proposed fuzzy method. Results showed that the share of the risks in these steps are 21%, 31% and 48% respectively. The results revealed that the risks in the construction and operation phases are higher than those in the design phase. The results also show that involving safety as a project object in the risk management process could eventuate acceptable results.

The article examines the most significant risks and threats to energy security in the conditions of integration of the Belarusian NPP into the energy system of the Republic of Belarus. The article also presents an analysis of existing methodological approaches to the assessment of energy security. The risk typology was carried out according to two criteria: the stages of the technological process and the sources of risks. As a result, production, financial and information risks (caused by information technologies) are highlighted. On the basis of an expert survey of specialists of republican unitary energy enterprises and of “Belenergo” State Production Association, a ranking of risks was carried out and the most significant of them were identified. It was determined that the most significant production risks are: depreciation of fixed assets; reduction in the utilization of own generating capacities and reliability of the generating equipment and network; outages that lead to disruption of the functioning of facilities for ensuring the vital activity of the population. The most important financial risks include: the increase in the cost of basic fuel and energy resources; shortage of working capital and investments; lack of sources of financing for the reconstruction of heating networks; reduction of consumer solvency; provision of discounts from tariffs to a significant number of business entities. According to respondents, among information risks the most important ones are the following: failures in the operation of informatization facilities that directly control the generating equipment of power plants, equipment of switchgears of power plants, equipment of substations; hacker cyberattacks; insufficient level of qualification of personnel, lack of interest of employees in the introduction of information technologies. The proposed threat identification and risk ranking at all stages of energy production can be used to develop measures to strengthen the country's energy security and assess their impact on socio-economic development of the Republic of Belarus.

Abstract Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. Results To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. Conclusion PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

Background: The growing numbers of early esophageal cancer (EEC) have increased the demand for endoscopic therapy. Objectives: To clarify the influential factors for the prognosis of patients with EEC receiving endoscopic surgery, and to construct a nomogram to evaluate the prognostic value of endoscopic therapy. Design: Prognostic analysis study. Methods: Clinical data of EEC patients who received endoscopic therapy between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database and used to construct the nomogram. The prognosis was analyzed by R language; the nomogram was constructed by Cox survival analysis; and the accuracy of the nomogram was verified by C index and the receiver operating characteristic (ROC) and calibration curves. X-Tile software was used to stratify the risk of patients. Results: Our study constructed the nomogram of the prognosis of patients with EEC treated by endoscopic surgery, including 1118 patients and 5 independent prognostic factors of esophageal cancer-specific survival. The C index and the area under the ROC curve (AUC) of the training and verification cohorts were all >0.75. The calibration curve also reflected the good consistency of the model in predicting survival. Significant difference in the risk of patients from different stratifications with the same T staging existed, and the model had a better C index than that of the T staging. Conclusion: Our study reports potential influential factors affecting the prognosis of EEC patients who received endoscopic therapy and establishes a reliable nomogram to predict the risk and prognosis, which has certain advantages compared with traditional TNM staging system. [ABSTRACT FROM AUTHOR]

The evaluation of seismic risk in structures requires information on both site hazard and structural vulnerability. Hazard can be quantified via probabilistic seismic hazard analysis, while seismic vulnerability can be quantified using structural analysis; the former is typically specified via predefined national hazard models, whereas the latter is more structure-specific and can be computationally expensive depending on the numerical modelling and analysis procedure complexity. As a compromise, various simplified seismic response evaluation methods have emerged and been implemented in assessment codes and guidelines (e.g., N2 method in Eurocode 8, SPO2IDA in FEMA P695). Furthermore, the seismic assessment and rehabilitation of existing structures have seen the introduction of risk classification guidelines. These are tools to primarily assist practitioners and decision-makers in understanding and managing seismic risk and the possible implications of different retrofitting schemes. Risk classification guidelines typically use loading-based quantities such as the seismic capacity-to-demand ratio. However, several drawbacks are associated with adopting loading-based quantities, namely the lack of uniformity of the actual seismic risk, expressed as a mean annual frequency of limit state exceedance. To address these drawbacks, this paper outlines a relatively simple pushover-based methodology for the direct evaluation of seismic risk (PB-Risk) for non-ductile infilled frame structures. The article describes a step-by-step framework for the simplified characterisation of hazard, vulnerability and subsequently seismic risk. It is quick and easy to implement within a practical and code-based setting and can be adopted within risk classification guidelines. The application of the proposed PB-Risk methodology is demonstrated via several case study applications, and its robustness in characterising seismic risk with respect to other simplified non-linear static formulations for infilled frame buildings is shown. [ABSTRACT FROM AUTHOR]

The South China Sea suffers strongly from the typhoon storm surge disasters in China, and its northern coastal areas are facing severe risks. Therefore, it is necessary and urgent to establish an assessment system for rating typhoon storm surge disaster. We constructed an effective and reliable rating assessment system for typhoon storm surge disaster based on the theories of over-threshold, distribution function family, and composite extreme value. The over-threshold sample was used as the basis of data analysis, the composite extreme value expansion model was used to derive the design water increment, and then the disaster level was delineated based on the return period level. The results of the extreme value model comparison show that the Weibull-Pareto distribution is more suitable than the classical extreme value distribution for fitting the over-threshold samples. The results of the return period projection are relatively stable based on different analysis samples. Taking the 10 typhoon storm surges as examples, they caused landfall in the Guangdong area in the past 10 years. The results of the assessment ranking indicate that the risk levels based on the return period levels obtained from different distributions are generally consistent. When classifying low-risk areas, the classification criteria of the State Oceanic Administration, China (SOA, 2012) are more conservative. In the high-risk areas, the results of the assessment ranking based on return period are more consistent with those of the SOA. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, the gap between urban and rural areas is decreasing. The traditional approach of constructing fire stations based on urban built-up areas is no longer suitable for the needs of modern fire rescue. Therefore, a comprehensive fire station location model is proposed based on fire risk assessment. This method divides the protected area units based on the urban road network. By evaluating different regions based on spatial position, land attributes, population density, floor area ratio, and fire incident indicators, the fire rescue risk levels and categorize regions into four risk levels are assessed. Corresponding response times were determined, and an objective model was developed to maximize the coverage area for fire response. The Baidu API was utilized to accurately calculate driving distances and times, and Gurobi optimization software was used to solve the model. Taking Shanghai as an example, the fire station location and layout from two perspectives—re-planning based on overall station placement and re-planning based on existing stations—were analyzed. The results suggest that constructing around 150 fire stations in Shanghai would effectively meet the fire rescue needs, which aligns with the actual situation in Shanghai and demonstrates the strong applicability of this model. This approach enables the meeting of new demand for fire station construction due to the significant increase in the coverage area while effectively utilizing firefighting resources.

Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions in a functional form for modeling insurance claim count data. It is shown that these models are useful in addressing the problem of excess zeros and over-dispersion of the claim count variable. In addition, we show that including the exposure as a covariate in both the zero and the count part of the model is an effective approach to incorporating exposure information in zero-inflated and hurdle models. We illustrate the effectiveness and versatility of the introduced models using three real datasets. The results suggest their promising applications in insurance risk classification and beyond.

Abstract Background A comprehensive medical history is needed to establish and ensure a high standard in dental care; however, it is challenging to draw clinical consequences on the variety of potential diseases and medications, especially for dental students. Aim of this observational study was to investigate, whether undergraduate dental students using an analog anamnesis tool for risk classification would be more confident and have more knowledge in risk classification than other students in the same year of study. Methods A cohort of 48 fifth year dental students was included and allocated into two groups based on their curriculum-related division (group A: n = 25, group B: n = 23). Group A received a teaching event and provision of an analog anamnesis tool for risk classification; group B received neither a teaching event nor the anamnesis tool. At baseline and after two weeks (follow-up), questionnaires regarding self-perceived confidence with risk classification, questions on different disease, medications and lifestyle factors and a task with 15 medical histories of prepared patient cases were applied. The data was statistically analyzed using Mann–Whitney or Wilcoxon test. Results In group comparison of the differences between baseline and follow-up regarding self-perceived confidence, significantly higher improvement was noted in group A compared to group B for all questions (p

The coal-seam fire is one of the most significant disasters in the coal mining industry in China, affecting the safety of coal production in China. The working-position risk in coal mining has an important impact on the risk of fire occurrence, and thus it would be worthwhile to analyze working-position risks so as to effectively prevent and control coal-seam fires. Based on the kernel density estimation (KDE), this research puts forward an innovative calculation-model and assessment method of the superposition risk of the working position on coal-seam fire accidents. This research aims to evaluate the priority of risk management of working positions in coal-seam fire accidents. In order to achieve this research aim and objectives, this research carried out a statistical analysis of 100 classic cases of coal-seam fire accidents from 2000 to 2022, using the accident-tree-structure importance analysis method. This research contributed to the evaluation of the frequency and severity of various risk factors leading to fire accidents, and the development of the value at risk (VaR) of various risk factors in the coal-seam fire accidents. Integrating all the risk factors involved in each position and their risk values, and building a position-risk calculation model was carried out. In addition, in accordance with the kernel density estimation (KDE), a post-superposition risk model was established. Moreover, ArcGIS software was used to obtain the superimposed risk of posts and build a risk-distribution map. Based on the possibility of post-risk occurrence and the severity of the consequences, a risk-assessment matrix was developed, a post-risk grading standard was established, and risk levels of the working position were divided up in this research. Results indicated that (1) before risk superposition, working-position risks and risk levels are densely distributed, and nearly 80% of risk levels of the working position are focused on Level II and III, without Level I. (2) After risk superposition, the post-risk is affected by the surrounding post-risk, and the risk- and level-distribution is more hierarchical; the number of Level I risks in working positions increased to 12, which were mainly distributed among the comprehensive mining team, comprehensive excavation team and ventilation team, which accords more with the objective and actual production-conditions. The risk-distribution map directly showed that the post-fire risk at the mining face and shaft is higher, a result which will take on a significant guiding role in the effective control and prevention of risk in coal-seam fires in the future. [ABSTRACT FROM AUTHOR]

Background: The growing numbers of early esophageal cancer (EEC) have increased the demand for endoscopic therapy. Objectives: To clarify the influential factors for the prognosis of patients with EEC receiving endoscopic surgery, and to construct a nomogram to evaluate the prognostic value of endoscopic therapy. Design: Prognostic analysis study. Methods: Clinical data of EEC patients who received endoscopic therapy between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database and used to construct the nomogram. The prognosis was analyzed by R language; the nomogram was constructed by Cox survival analysis; and the accuracy of the nomogram was verified by C index and the receiver operating characteristic (ROC) and calibration curves. X-Tile software was used to stratify the risk of patients. Results: Our study constructed the nomogram of the prognosis of patients with EEC treated by endoscopic surgery, including 1118 patients and 5 independent prognostic factors of esophageal cancer-specific survival. The C index and the area under the ROC curve (AUC) of the training and verification cohorts were all >0.75. The calibration curve also reflected the good consistency of the model in predicting survival. Significant difference in the risk of patients from different stratifications with the same T staging existed, and the model had a better C index than that of the T staging. Conclusion: Our study reports potential influential factors affecting the prognosis of EEC patients who received endoscopic therapy and establishes a reliable nomogram to predict the risk and prognosis, which has certain advantages compared with traditional TNM staging system. [ABSTRACT FROM AUTHOR]

Objective: Assessment of treatment outcome in current de‐escalation for differentiated thyroid cancer (DTC) according to the 2015 Dutch thyroid cancer guidelines (NL‐15) and American Thyroid Association guidelines (ATA‐15). Design: Retrospectively, the recommendations of the NL‐15 and ATA‐15 guidelines were evaluated to estimate potentially adequate, under‐ and overtreatment of DTC in patients treated in the University Medical Center Groningen between 2007 and 2017. Patients: A total of 240 patients with a cT1‐T3aN0‐1aM0 DTC fulfilled the inclusion criteria. Measurements: After actual treatment was given, patients were again categorized according to both guidelines into low, intermediate, or high‐risk based on tumour status. Next, they were categorized into a congruent low‐risk (n = 60), congruent high‐risk (n = 73), or incongruent risk group (n = 107). Follow‐up data were used to estimate the proportion of potentially adequate, under‐, and overtreatment according to both guidelines. Results: Comparing treatment recommended by NL‐15 and ATA‐15 showed significantly more over‐ and adequate treatment when following NL‐15 recommendations, and more undertreatment following ATA‐15 (all: p <.001). Subanalysis of the congruent low‐risk group showed overtreatment in 64% when following NL‐15 guidelines (p <.001). No treatment differences were found in the congruent high‐risk group. Undertreatment was most often seen in the incongruent risk group when following ATA‐15 (p <.001). Conclusions: Low‐risk patients were treated too aggressively when following NL‐15 recommendations, where the less aggressive ATA‐15 approach seemed more adequate. Treatment of intermediate risk DTC patients varies greatly, with a relative higher rate of undertreatment according to the recommendations of the ATA‐15, advocating further refining of the risk classification in this patient group. [ABSTRACT FROM AUTHOR]

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Simple Summary: In the modern era, characterized by parenchymal-sparing procedures, in some cases pneumonectomy remains the only therapeutic approach to achieving oncological radicality. One of the most feared complications is undoubtedly respiratory failure and ARDS. Its cause after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. In this paper, we evaluate the main risk factors for ARDS of a large cohort of patients and we classify them in four classes of growing risk in order to quantify their postoperative risk of ARDS and facilitate their global management. (1) Background: The cause of ARDS after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. (2) Methods: We reviewed a large panel of pre-, peri- and postoperative data of 211 patients who underwent pneumonectomy during the period 2014–2021. Univariable and multivariable logistic regression was used to quantify the association between preoperative parameters and the risk of developing ARDS, in addition to odds ratios and their respective 95% confidence intervals. A backward stepwise selection approach was used to limit the number of variables in the final multivariable model to significant independent predictors of ARDS. A nomogram was constructed based on the results of the final multivariable model, making it possible to estimate the probability of developing ARDS. Statistical significance was defined by a two-tailed p-value < 0.05. (3) Results: Out of 211 patients (13.3%), 28 developed ARDS. In the univariate analysis, increasing age, Charlson Comorbidity Index and ASA scores, DLCO < 75% predicted, preoperative C-reactive protein (CRP), lung perfusion and duration of surgery were associated with ARDS; a significant increase in ARDS was also observed with decreasing VO2max level. Multivariable analysis confirmed the role of ASA score, DLCO < 75% predicted, preoperative C-reactive protein and lung perfusion. Using the nomogram, we classified patients into four classes with rates of ARDS ranking from 2.0% to 34.0%. (4) Conclusions: Classification in four classes of growing risk allows a correct preoperative stratification of these patients in order to quantify the postoperative risk of ARDS and facilitate their global management. [ABSTRACT FROM AUTHOR]

Thanks to its outstanding performances, boosting has rapidly gained wide acceptance among actuaries. To speed up calculations, boosting is often applied to gradients of the loss function, not to responses (hence the name gradient boosting). When the model is trained by minimizing Poisson deviance, this amounts to apply the least-squares principle to raw residuals. This exposes gradient boosting to the same problems that lead to replace least-squares with Poisson Generalized Linear Models (GLM) to analyze low counts (typically, the number of reported claims at policy level in personal lines). This paper shows that boosting can be conducted directly on the response under Tweedie loss function and log-link, by adapting the weights at each step. Numerical illustrations demonstrate similar or better performances compared to gradient boosting when trees are used as weak learners, with a higher level of transparency since responses are used instead of gradients. [ABSTRACT FROM AUTHOR]

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products. • Research Significance : Risk classification strategies for cellular products issued by drug regulatory authorities are compared using real-world data. • Research Innovation : Bayesian networks are innovatively applied in analyzing the risk classification strategies of cellular products. • Main Results : The risk of allogeneic products is overemphasized; minimally manipulated and homologous uses are not always low-risk factors. • Future Outlook : Machine learning methods can streamline the review process, foster risk consensus, and boost regulatory efficiency. [ABSTRACT FROM AUTHOR]