A nomogram and online calculator for predicting depression risk in obese Americans.

Background: Obese patients with depression face higher risks of adverse events. However, depression is often misdiagnosed and undertreated in this group. This study aimed to identify predictors of depression and create a nomogram and calculator to assess depression risk in obese Americans. Methods: This cross-sectional study included 2674 patients from the National Health and Nutrition Examination Survey database (NHANES). These participants were randomly classified into the training and validation groups in a 7:3 ratio. Predictors were selected by LASSO and multivariate logistic regression analysis to create the nomogram. C-statistics, calibration plots, and decision curve analysis (DCA) were used to test the nomogram's discriminative ability, calibration quality, and clinical value. Internal validation with bootstrap resampling and external validation with the validation group were also conducted. Results: The training and validation group consists of 1871 and 803 participants. Depression was presented in 11.4 % (203/2674) of these participants. Seven predictors were found, including gender, hypertension, weekday sleep duration, poverty to income ratio, history of seeing mental health doctor, diabetes, and feeling sleepy during the day. The nomogram showed good discrimination, with the area under the receiver operating characteristic curve (AUC) of 0.817 (95 % CI: 0.786-0.848) (0.806 through internal validation and 0.772 through external validation) and good calibration (P = 0.536). The DCA further confirmed the nomogram's clinical usefulness. Conclusion: The nomogram and calculator effectively predict depression risk in obese Americans and can be used as auxiliary tools for early screening in primary care.

Mid-Long-Term Effect of Metabolic Surgery on Type 2 Diabetes in Nonobese Patients: a Meta-analysis.

BACKGROUND: This study aimed to perform a meta-analysis regarding the mid-long-term effect (≥ 2-year follow-up) of metabolic surgery on T2DM in non-obese patients. METHODS: PubMed, EMBASE and CENTRAL databases were searched for clinical studies from inception to March 2023. Stata 12.0 was used for data aggregation. Sensitivity, subgroup, and meta-regression analyses were performed when feasible. RESULTS: This meta-analysis included 18 articles involving 548 patients. A pooled rate of 47.5% of T2DM remission was found after metabolic surgery. To be more specific, 83.5% was obtained for hemoglobin A1c (HbA1c) < 7.0%, 45.1% for HbA1c < 6.5%, and 40.4% for HbA1c < 6.0%. Subgroup analysis showed that one-anastomosis gastric bypass (OAGB) had a higher remission rate (93.9%) than other surgeries. Studies conducted in America had a higher remission rate (61.4%) than in Asia (43.6%). Meta-regression analysis displayed that publication year, number of patients, study design, preoperative age, BMI, and quality assessment score were not significantly associated with T2DM remission rate. Additionally, metabolic surgery could result in significant reductions in BMI (-4.133 kg/m2), weight (-9.874 kg), HbA1c (-1.939%), fasting blood glucose, fasting C-peptide, and fasting insulin. However, metabolic surgery seemed to have poorer glycemic control in non-obese than obese T2DM patients. CONCLUSION: A moderate mid-long-term effect of T2DM remission was observed after metabolic surgery in non-obese patients. However, we still need more prospective multi-institutional studies using the same definitions for diabetes and the same surgical technique for the surgery. Without this, the exact role of bariatric surgery in non-obese patients is unanswered.

Nursing Students' Perceptions of Using Virtual Patients to Prepare for Clinical Practice.

BACKGROUND: Educators are called on to provide opportunities for students to practice and integrate skills and knowledge to ensure preparation for the complexities of today's health care environment. This study explored nursing students' perceptions of using virtual patients to prepare for clinical practice. METHOD: This study used an exploratory qualitative design. Four focus group interviews were conducted with 25 third-year nursing students. Data were collected and analyzed using thematic content analysis. RESULTS: Four main themes and eight subthemes emerged. Main themes included personal engagement, learning environment, organizational factors, and improvement needed. Subthemes included being a computer game player, becoming confident in clinical practice, convenient to practice, an authentic stressful learning environment, organization, better to be combined with mannequin-based simulation, technology difficulties, and aural factors. CONCLUSION: Although some perceived disadvantages were identified, the use of virtual patients could be an effective strategy to improve nursing students' preparation for clinical preparation. [J Nurs Educ. 2022;61(7):398-402.].

Distribution and assessment of heavy metals in suspended particles in the Sundarban mangrove river, Bangladesh.

The suspended particulate matter (SPM) is an important carrier of heavy metals transportation from land to sea, so it is significant to study the heavy metal pollution in SPM. The distribution and assessment of five heavy metals (Mn, Cr, Ni, Cu, and Pb) in SPM collected from Passur River and its estuary in Sundarban were studied in combination with water temperature, salinity, and turbidity. The results show that the heavy metal content and distribution in SPM are mainly controlled by runoff input, hydrodynamic process and the interaction process of salt and fresh water in estuaries. The quality evaluation results of heavy metals in SPM show that pollution degree is light. Studies on the heavy metals in SPM are of great significance to comprehensively evaluate regional pollution status and carry out early warning.

Autologous reference types can confound the detection of somatic mutation in solid cancers.

Vast number of somatic mutations has been proved to be affected by the factors of sequencing methods, analysis pipelines and validation methods. We here showed the effect of autologous reference types on the detection of cancer-associated somatic mutations with the somatic single nucleotide variations (SNVs) and clinical data of solid tumors from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The distribution of somatic SNVs was significantly different among groups of autologous references in 6 cancers detected by whole genome sequencing (WGS) and 5 cancers detected by the random sequencing of exonic regions selected from the genome (WXS), especially in protein coding region of 5 cancers with age, gender and TNM adjusted. In addition, only 60.24% (95% CI: 49.65%-70.83%) of the somatic SNVs called from normal blood by WXS were found in those called from normal solid tissue tested by WXS / WGS, while 31.78% (95%CI: 4.14%-59.42%) of the somatic SNVs called from normal tissue adjacent to primary by WXS were found in those from normal blood tested by WXS / WGS. These findings suggested that more representative types of normal tissues should be included in detection of cancer-associated somatic mutations.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Learning to Model Task-Oriented Attention.

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene with a particular task. Models of saliency can be used to predict fixation locations, but a large body of previous saliency models focused on free-viewing task. They are based on bottom-up computation that does not consider task-oriented image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 11 subjects when they performed some particular search task in 1307 images and annotation data of 2,511 segmented objects with fine contours and 8 semantic attributes. Using this database as training and testing examples, we learn a model of saliency based on bottom-up image features and target position feature. Experimental results demonstrate the importance of the target information in the prediction of task-oriented visual attention.

Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image.

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist's image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden's J statistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

Multistimuli-responsive supramolecular vesicles based on water-soluble pillar[6]arene and SAINT complexation for controllable drug release.

Supramolecular binary vesicles based on the host-guest complexation of water-soluble pillar[6]arene (WP6) and SAINT molecule have been successfully constructed, which showed pH-, Ca(2+)-, and thermal-responsiveness. These supramolecular vesicles can efficiently encapsulate model substrate calcein, which then can be efficiently released either by adjusting the solution pH to acidic condition due to the complete disruption of vesicular structure, or particularly, by adding a certain amount of Ca(2+) due to the Ca(2+)-induced vesicle fusion and accompanied by the structure disruption. More importantly, drug loading and releasing experiments demonstrate that an anticancer drug, DOX, can be successfully encapsulated by the supramolecular vesicles, and the resulting DOX-loaded vesicles exhibit efficient release of the encapsulated DOX with the pH adjustment or the introduction of Ca(2+). Cytotoxicity experiments suggest that the resulting DOX-loaded supramolecular vesicles exhibit comparable therapeutic effect for cancer cells as free DOX and the remarkably reduced damage for normal cells as well. The present multistimuli-responsive supramolecular vesicles have great potential applications in the field of controlled drug delivery. In addition, giant supramolecular vesicles (~3 µm) with large internal volume and good stability can be achieved by increasing the temperature of WP6 ⊃ SAINT vesicular solution, and they might have potential applications for bioimaging.