A multifaceted picture of the dynamic concepts of time and temporality is demonstrated in medieval and Renaissance art, as adopted in speculative, ecclesiastical, socio-political, propagandist, ...moralistic, and poetic contexts. Questions regarding perception of time are investigated through innovative aspects of Renaissance iconography.
This study examines the multiplicity of styles and heterogeneity of the arts created on the southern coasts of India during the period of colonial rule. Diverging from the trajectory of numerous ...studies that underline biased and distorted conceptions of India promoted in European and Indian literary sources, I examine ways in which Indian cultural traditions and religious beliefs found substantial expression in visual arts that were ostensibly geared to reinforce Christian worship and colonial ideology. This investigation is divided into two parts. Following a brief overview, my initial focus will be on Indo-Portuguese polychrome woodcarvings executed by local artisans for churches in the areas of Goa and Kerala on the Malabar coast. I will then relate to Portuguese religious strategies reflected in south Indian churches, involving the destruction of Hindu temples and images and their replacement with Catholic equivalents, inadvertently contributing to the survival of indigenous beliefs and recuperation of the Hindu monuments they replaced.
The tenacity of medieval animal iconography in the Renaissance, disguised under the veil of genre, narrative and allegory, is demonstrated in this book. A comprehensive introduction to sources ...precedes case studies illustrating traditional animal symbolism in Renaissance masterpieces.
A mass-casualty natural disaster such as an earthquake is a rare, surprising event that is usually characterized by chaos and a lack of information, resulting in an overload of casualties in ...hospitals. Thus, it is very important to refer minor and moderately-injured casualties, that are the majority of casualties and whose injuries are usually not life threatening, to ad hoc care facilities such as Emergency Treatment Sites (ETSs). These facilities support the efficient use of health resources and reduce the burden on permanent healthcare facilities. In our study, a hybrid simulation model, based on a combination of discrete events and an agent-based simulation, provides a solution to the uncertainty of positioning temporary treatment sites. The simulation methodology used compares between "rigid" and "flexible" operating concepts of ETSs (main vs. main+minor ETSs) and found the "flexible" concept to be more efficient in terms of the average walking distance and number of casualties treated in the disaster area.
Problem
We aimed to investigate the main causes of recurrent miscarriage (RM) in patients with losses after spontaneous gestation (SG) and after in vitro fertilization (IVF).
Method of Study
A ...prospective case‐control study was conducted. The eligible patients were women who had experienced two or more consecutive abortions after <12 weeks’ gestation, two consecutive losses after SG, or two consecutive losses after IVF. All patients were subjected to the following evaluations: karyotyping of the aborted material, alloimmune and autoimmune marker testing, and acquired and hereditary thrombophilia marker testing.
Results
In total, 58 patients were eligible: 32 patients with RM after SG and 26 patients with RM after IVF. The factors associated with RM were genetic (29%), immune (14%), thrombophilic (21%), and thrombophilic and immune (24%), and only 12% of the cases were idiopathic. Comparing the two study groups (SG and IVF), all studied factors were similar, except for a higher ANA positivity observed in the SG group (SG 30.4% vs IVF 5.3%, OR 8.6 (CI 1.1‐21.1, P .048).
Conclusion
Our study identified the possibly factors associated with recurrent miscarriage in 86% of the cases, and these factors appear to be similar in patients with recurrent miscarriage after spontaneous gestation and IVF. This study demonstrates that IVF with PGT‐A with euploid embryo transfer could reduce abortions by up to 29%, but other factors need to be investigated even in patients undergoing in vitro fertilization.
Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate ...the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.
The retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems.
The median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.
Our AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.
Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very ...poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient's condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.
In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the ...cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
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