Association between metabolic syndrome and the risk of colorectal cancer: a prospective study in China.

OBJECTIVE: To evaluate the correlation between metabolic syndrome (MetS) and its components on the incidence of colorectal cancer (CRC) based on data from Jinchang Cohort. METHODS: This is a large prospective cohort study. Between 2011 and 2020, a total of 43 516 individuals from Jinchang Cohort were included for this study. Hazard ratios (HRs) with 95% confidence intervals (CIs) for CRC according to MetS were calculated with the Cox proportional hazard models. The restricted cubic spine models with four knots were conducted to fit the dose-response relationships. RESULTS: MetS was associated with increased risk of CRC (n = 141; HR: 1.64, 95% CI: 1.15-2.33) after adjusting for confounding factors (age, sex, education level, family history of CRC, smoking index and alcohol index). Participants with hyperglycemia had a significantly higher risk of developing incident CRC (HR: 1.70; 95% CI: 1.19-2.43). The positive association between MetS and CRC was observed in males (HR: 1.76; 95% CI: 1.17-2.63), but not in females (HR: 1.24; 95% CI: 0.59-2.64). Furthermore, linear dose-response relationship was found between fasting plasma glucose (FPG) and CRC risk in males ( Poverall < 0.05, Pnon-linear = 0.35). When stratified by smoke and drink, MetS was found to increase the incidence of CRC only in the smoke (HR: 2.07, 95% CI: 1.35-3.18) and drink (HR: 2.93, 95% CI: 1.51-5.69) groups. CONCLUSION: MetS was associated with a higher risk of CRC incidence. Hyperglycemia lended strong support to the role of MetS in new-onset CRC, especially in males. Other components of MetS were not found to be associated with increased risk of CRC.

The Dresden in vivo OCT dataset for automatic middle ear segmentation.

Endoscopic optical coherence tomography (OCT) offers a non-invasive approach to perform the morphological and functional assessment of the middle ear in vivo. However, interpreting such OCT images is challenging and time-consuming due to the shadowing of preceding structures. Deep neural networks have emerged as a promising tool to enhance this process in multiple aspects, including segmentation, classification, and registration. Nevertheless, the scarcity of annotated datasets of OCT middle ear images poses a significant hurdle to the performance of neural networks. We introduce the Dresden in vivo OCT Dataset of the Middle Ear (DIOME) featuring 43 OCT volumes from both healthy and pathological middle ears of 29 subjects. DIOME provides semantic segmentations of five crucial anatomical structures (tympanic membrane, malleus, incus, stapes and promontory), and sparse landmarks delineating the salient features of the structures. The availability of these data facilitates the training and evaluation of algorithms regarding various analysis tasks with middle ear OCT images, e.g. diagnostics.

Quantifying social roles in multi-animal videos using subject-aware deep-learning.

Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited social interactions under specific experimental conditions, automated identification of the social roles of freely moving individuals in a multi-animal group remains unresolved. Here we describe a deep-learning-based system - named LabGym2 - for identifying and quantifying social roles in multi-animal groups. This system uses a subject-aware approach: it evaluates the behavioral state of every individual in a group of two or more animals while factoring in its social and environmental surroundings. We demonstrate the performance of subject-aware deep-learning in different species and assays, from partner preference in freely-moving insects to primate social interactions in the field. Our subject-aware deep learning approach provides a controllable, interpretable, and efficient framework to enable new experimental paradigms and systematic evaluation of interactive behavior in individuals identified within a group.