Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.

Background and objective: Several studies have shown that not only mothers, but also fathers can suffer from peripartum depression. This phenomenon has not been researched in Chile; therefore, the aim of present study is to explore the presence of depressive symptoms in fathers and mothers during the postpartum period and describe their interaction. Material and Methods: users of the Western Metropolitan Health Service Unit were assessed two months after childbirth with a sociodemographic questionnaire, the Beck Depression Inventory (BDI-I), and the Edinburgh Postnatal Depression Scale (EPDS). Results: even though mothers score significantly higher in both scales, 18.5% of men surpass the cutoff score in the EPDS and 10.5% in the BDI-I. Conclusion: these results stress the need to continue researching this phenomenon and incorporate father assessment in perinatal checkups. Keywords: depression, peripartum, fathers, Edinburgh Postnatal Depression Scale

Anthropogenic pollution has a huge impact on the water quality of marine ecosystems. Heavy metals and antibiotics are anthropogenic stressors that have a major effect on the health of the marine organisms. Although heavy metals are also associate with volcanic eruptions, wind erosion or evaporation, most of them come from industrial and urban waste. Such contamination, coupled to the use and subsequent misuse of antimicrobials in aquatic environments, is an important stress factor capable of affecting the marine communities in the ecosystem. Bivalves are important ecological components of the oceanic environments and can bioaccumulate pollutants during their feeding through water filtration, acting as environmental sentinels. However, heavy metals and antibiotics pollution can affect several of their physiologic and immunological processes, including their microbiome. In fact, heavy metals and antibiotics have the potential to select resistance genes in bacteria, including those that are part of the microbiota of bivalves, such as Vibrio spp. Worryingly, antibiotic-resistant phenotypes have been shown to be more tolerant to heavy metals, and vice versa, which probably occurs through co- and cross-resistance pathways. In this regard, a crucial role of heavy metal resistance genes in the spread of mobile element-mediated antibiotic resistance has been suggested. Thus, it might be expected that antibiotic resistance of Vibrio spp. associated with bivalves would be higher in contaminated environments. In this review, we focused on co-occurrence of heavy metal and antibiotic resistance in Vibrio spp. In addition, we explore the Chilean situation with respect to the contaminants described above, focusing on the main bivalves-producing region for human consumption, considering bivalves as potential vehicles of antibiotic resistance genes to humans through the ingestion of contaminated seafood.

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