Classification of lung cancer subtypes on CT images with synthetic pathological priors.

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.

Parental Migration Trajectories From Childhood Through Adolescence: Longitudinal Impact on Emotional and Behavioral Problems Among Chinese Adolescents.

It is estimated that there are about 23% of all children in China experiencing parental migration and being left behind at hometown. Existing research indicated a significant association between parental migration and children development but overlooked the dynamic changes in family structure caused by parental migration. In this study, data was derived from a nationally representative longitudinal survey-the China Family Panel Studies. The main analyses employed four waves of data (2012, 2014, 2016, and 2018) and included 1401 adolescents aged 10-15 years (Mean:12.35, SD:1.67; 54.2% female). Six typical trajectories of parental migration capturing both migration status at each timepoint and changes in the status across six years were created. Children's depression and internalizing problems and externalizing problems were concerned outcomes. The mediating roles of the caregiver-child interaction and caregiver's depression were examined. Adolescents in the trajectory group described as experiencing transitions between being left behind by both parents and non had a higher risk of depression and internalizing and externalizing problems. Caregivers' depression was a significant mediator between parental migration and adolescent depression.

Automatic Lung Cancer Subtypes Classification on CT Images with Self-generated Multi-modality Hybrid Features.

Lung cancer is a malignant tumor with rapid progression and high fatality rate. According to histological morphology and cell behaviours of cancerous tissues, lung cancer can be classified into a variety of subtypes. Since different cancer subtype corresponds to distinct therapies, the early and accurate diagnosis is critical for following treatments and prognostic managements. In clinical practice, the pathological examination is regarded as the gold standard for cancer subtypes diagnosis, while the disadvantage of invasiveness limits its extensive use, leading the non-invasive and fast-imaging computed tomography (CT) test a more commonly used modality in early cancer diagnosis. However, the diagnostic results of CT test are less accurate due to the relatively low image resolution and the atypical manifestations of cancer subtypes. In this work, we propose a novel automatic classification model to offer the assistance in accurately diagnosing the lung cancer subtypes on CT images. Inspired by the findings of cross-modality associations between CT images and their corresponding pathological images, our proposed model is developed to incorporate general histopathological information into CT imagery-based lung cancer subtypes diagnostic by omitting the invasive tissue sample collection or biopsy, and thereby augmenting the diagnostic accuracy. Experimental results on both internal evaluation datasets and external evaluation datasets demonstrate that our proposed model outputs more accurate lung cancer subtypes diagnostic predictions compared to existing CT-based state-of-the-art (SOTA) classification models, by achieving significant improvements in both accuracy (ACC) and area under the receiver operating characteristic curve (AUC).Clinical Relevance- This work provides a method for automatically classifying the lung cancer subtypes on CT images.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires.

Objective.Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used.Approach.To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain.Main results.The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method.Significance.The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

Early stimulation and responsive care: A mediator of caregivers' depression on the suspected developmental delay of left-behind children in China.

BACKGROUND: Due to multiple factors, left-behind children in rural areas suffer from neurodevelopment delay and their caregivers suffer from depressive symptoms. This study aimed to analyze the effect of caregivers' depressive symptoms on left-behind children's neurodevelopment, with early stimulation and responsive care mediating. METHODS: We conducted a cross-sectional survey in five counties in China. A total of 904 left-behind children aged 0-3 and their primary caregivers were enrolled. The Zung Self-rating Depression Scale (ZSDS) was used to measure caregivers' depressive symptoms. The Ages and Stages questionnaires-third edition (ASQ-3), which contains five domains: communication (CM), gross motor (GM), fine motor (FM), problem-solving (CG), and personal social (PS), was used to screen children for suspected developmental delay (SDD). RESULTS: 31.4 % of left-behind children suffered from SDD, while 39.7 % of left-behind children's caregivers experienced depressive symptoms. Caregivers' ZSDS scores were positively correlated with the SDD on four domains (FM, GM, CG, and PS), while Early stimulation and responsive care was negatively correlated with the SDD on four domains (CM, FM, CG, and PS). LIMITATIONS: The cross-sectional design limited the ability to ascertain causal relations. Besides, the findings may not be generalized to all regions of China due to the heterogeneity of the study population. CONCLUSIONS: Left-behind children under three years old in rural China were at high risk of SDD, while a substantial proportion of their caregivers had depressive symptoms. Caregivers' depressive symptoms may negatively affect the SDD of left-behind children through caregivers providing less early stimulation and responsive care.

Magnolol-loaded cholesteryl biguanide conjugate hydrochloride nanoparticles for triple-negative breast cancer therapy.

The potential of combination therapy using nanoparticle delivery systems in improving triple-negative breast cancer treatment efficacy remains to be explored. Here, we report a novel nanoparticle system using a cholesterol biguanide conjugate hydrochloride (CBH) as both a drug and carrier to load magnolol (MAG). Poly(ethylene glycol)-poly(lactic-co-glycolic acid) (mPEG-PLGA) and aminoethyl anisamide-poly(ethylene glycol)-poly(lactic-co-glycolic acid) (AEAA-PEG-PLGA) were added to form nanoparticles. Nanoparticles accumulated most in tumor tissues when the weight ratio of AEAA-PEG-PLGA to mPEG-PLGA was 4:1. MAG and CBH exerted a synergistic inhibitory effect on 4 T1 cells. An in vitro study showed that nanoparticles displayed the highest tumor cell uptake rate, highest apoptosis rate, and strongest inhibitory effect on tumor cell migration and monoclonal formation. CBH might promote nanoparticle uptake by cells and lysosomal escape. After intravenous administration to mice with 4 T1 breast tumors in situ, the nanoparticles inhibited tumor growth without obvious toxicity. Western blot results showed that nanoparticles altered the levels of p53, p-AKT, and p-AMPK in the tumor tissue. Moreover, cell apoptosis was found in the same area of H&E-stained and TUNEL-stained tumors treated with the nanoparticles. Collectively, this nanoparticle system provides a novel combination drug delivery strategy for treating triple-negative breast cancer.

Roles of drinking and diet in the U-shaped relationship between smoking and BMI in middle-aged and elderly Chinese rural adults.

The study aimed to investigate the relationship between smoking and BMI, from the perspective of the roles of alcohol drinking and dietary factors in a rural population. We analysed cross-sectional data from 10,837 middle-aged and elderly Chinese rural adults who completed a questionnaire that included questions on demographic characteristics, dietary intake, and detailed smoking and drinking status. Results showed that current smokers had lower BMI and consumed foods less frequently (except coriander, onion, garlic, hawthorn and fermented bean curd) than non-smokers. The relationship between smoking amount and the risk of overweight or obesity was U-shaped, and the trends were also similar by stratum of baseline age groups (all p for interaction < 0.001). Heavy smokers tended to have drinking habits, which was associated with increased BMI (all p for trend < 0.001). Additionally, despite the lower risk of overweight or obesity for current smokers, normal weight individuals were found to have the minimum smoking amount. In conclusion, smoking may cause suppression of appetite but smokers tend to have other unhealthy habits relating to increased BMI. Dietary factors and alcohol use play important roles in the U-shaped relationship between smoking behaviours and BMI in the middle-aged and elderly Chinese rural population.

A versatile 4D capacitive imaging array: a touchless skin and an obstacle-avoidance sensor for robotic applications.

There are growing interests in the use of robots in collaborative environments with humans or other intelligent machines. Sensing the environment for which the robot is operating can be done in many ways, generally guided by skin-like sensors. Some of the skins are inspired by natural sensing in humans or other species. As humans, we use many of our senses, such as version, hearing, smell, and touch, to move around by avoiding colliding with other humans or objects. Different from humans, many other mammals also use whiskers as an additional sensor to help navigate around. In this paper, we demonstrate a touchless capacitive imaging-based sensor in the situation where the obstacles are in close vicinity to the robot. The proposed imaging system can sense the changes in areas near to the skin-like sensors by measuring the capacitances between the array of electrodes. A 4D sensing approach has been developed with the spatiotemporal Total Variation algorithm. The 4D operational mode gives sensors the time awareness that allows for dynamical responses and hence the better control of the robots. Several experiments are conducted to show the skin-like behaviour of this sensor by simulating various scenarios. The sensor shows the excellent ability to detect an object in its vicinity, where the depth is close to half of the planar sensor array size.

A New Label-Free and Contactless Bio-Tomographic Imaging with Miniaturized Capacitively-Coupled Spectroscopy Measurements.

A new bio-imaging method has been developed by introducing an experimental verification of capacitively coupled resistivity imaging in a small scale. This paper focuses on the 2D circular array imaging sensor as well as a 3D planar array imaging sensor with spectroscopic measurements in a wide range from low frequency to radiofrequency. Both these two setups are well suited for standard containers used in cell and culture biological studies, allowing for fully non-invasive testing. This is true as the capacitive based imaging sensor can extract dielectric spectroscopic images from the sample without direct contact with the medium. The paper shows the concept by deriving a wide range of spectroscopic information from biological test samples. We drive both spectra of electrical conductivity and the change rate of electrical conductivity with frequency as a piece of fundamentally important information. The high-frequency excitation allows the interrogation of critical properties that arise from the cell nucleus.