Diagnostic support in pediatric craniopharyngioma using deep learning.

PURPOSE: We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort. METHODS: T1w and T2w preoperative magnetic resonance images of 226 Chilean patients were collected at the Institute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68 subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects). RESULTS: The PPV among classes was 0.828±0.039, and the NPV was 0.919±0.063. Also explainable artificial intelligence (XAI) was used, finding that structures that are relevant during diagnosis and radiological evaluation highly influence the decision-making process of the machine. CONCLUSION: This is the first experience of this kind of study in our institution, and it led to promising results on the task of radiological diagnostic support based on explainable artificial intelligence (AI) and deep learning models.

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.

First Detection of Algal Caribbean Ciguatoxin in Amberjack Causing Ciguatera Poisoning in the Canary Islands (Spain).

Ciguatera Poisoning (CP) is an illness associated with the consumption of fish contaminated with potent natural toxins found in the marine environment, commonly known as ciguatoxins (CTXs). The risk characterization of CP has become a worldwide concern due to the widespread expansion of these natural toxins. The identification of CTXs is hindered by the lack of commercially available reference materials. This limitation impedes progress in developing analytical tools and conducting toxicological studies essential for establishing regulatory levels for control. This study focuses on characterizing the CTX profile of an amberjack responsible for a recent CP case in the Canary Islands (Spain), located on the east Atlantic coast. The exceptional sensitivity offered by Capillary Liquid Chromatography coupled with High-Resolution Mass Spectrometry (cLC-HRMS) enabled the detection, for the first time in fish contaminated in the Canary Islands, of traces of an algal ciguatoxin recently identified in G. silvae and G. caribeaus from the Caribbean Sea. This algal toxin was structurally characterized by cLC-HRMS being initially identified as C-CTX5. The total toxin concentration of CTXs was eight times higher than the guidance level proposed by the Food and Drug Administration (0.1 ng C-CTX1/g fish tissue), with C-CTX1 and 17-hydroxy-C-CTX1 as major CTXs.

Inequality in economic shock exposures across the global firm-level supply network.

For centuries, national economies have been engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also allow for economic shocks to propagate across borders. Using global, firm-level supply network data, we estimate a country's exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of international systemic risk-flows. We find that rich countries expose poor countries stronger to systemic risk than vice-versa. The risk is highly concentrated, however, higher risk levels are not compensated with a risk premium in GDP levels, nor higher GDP growth. Our findings put the often praised benefits for developing countries from globalized production in a new light, by relating them to risks involved in the production processes. Exposure risks present a new dimension of global inequality that most affects the poor in supply shock crises.