Human Health Indicator Prediction from Gait Video

Body Mass Index (BMI), age, height and weight are important indicators of human health conditions, which can provide useful information for plenty of practical purposes, such as health care, monitoring and re-identification. Most existing methods of health indicator prediction mainly use front-view body or face images. These inputs are hard to be obtained in daily life and often lead to the lack of robustness for the models, considering their strict requirements on view and pose. In this paper, we propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. However, the study of health indicator prediction from gait videos using deep learning was hindered due to the small amount of open-sourced data. To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task. Furthermore, to better suit the health indicator prediction task, we bring forward Global-Local Aware aNd Centrosymmetric Encoder (GLANCE) module. It first extracts local and global features by progressive convolutions and then fuses multi-level features by a centrosymmetric double-path hourglass structure in two different ways. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi, and that the GLANCE module is also beneficial for pose estimation on 3DPW

Outdoor particulate matter exposure affects metabolome in chronic obstructive pulmonary disease: Preliminary study

IntroductionThe metabolomic changes caused by airborne fine particulate matter (PM2.5) exposure in patients with chronic obstructive pulmonary disease (COPD) remain unclear. The aim of this study was to determine whether it is possible to predict PM2.5-induced acute exacerbation of COPD (AECOPD) using metabolic markers.MethodsThirty-eight patients with COPD diagnosed by the 2018 Global Initiative for Obstructive Lung Disease were selected and divided into high exposure and low exposure groups. Questionnaire data, clinical data, and peripheral blood data were collected from the patients. Targeted metabolomics using liquid chromatography-tandem mass spectrometry was performed on the plasma samples to investigate the metabolic differences between the two groups and its correlation with the risk of acute exacerbation.ResultsMetabolomic analysis identified 311 metabolites in the plasma of patients with COPD, among which 21 metabolites showed significant changes between the two groups, involving seven pathways, including glycerophospholipid, alanine, aspartate, and glutamate metabolism. Among the 21 metabolites, arginine and glycochenodeoxycholic acid were positively associated with AECOPD during the three months of follow-up, with an area under the curve of 72.50% and 67.14%, respectively.DiscussionPM2.5 exposure can lead to changes in multiple metabolic pathways that contribute to the development of AECOPD, and arginine is a bridge between PM2.5 exposure and AECOPD

Short-term exposure to fine particulate matter and genome-wide DNA methylation in chronic obstructive pulmonary disease: A panel study conducted in Beijing, China

BackgroundFine particulate matter (PM2.5) is a crucial risk factor for chronic obstructive pulmonary disease (COPD). However, the mechanisms whereby PM2.5 contribute to COPD risk have not been fully elucidated. Accumulating evidence suggests that epigenetics, including DNA methylation, play an important role in this process; however, the association between PM2.5 exposure and genome-wide DNA methylation in patients with COPD has not been studied.ObjectiveTo evaluate the association of personal exposure to PM2.5 and genome-wide DNA methylation changes in the peripheral blood of patients with COPD.MethodsA panel study was conducted in Beijing, China. We repeatedly measured and collected personal PM2.5 data for 72 h. Genome-wide DNA-methylation of peripheral blood was analyzed using the Illumina Infinium Human Methylation BeadChip (850 k). A linear-mixed effect model was used to identify the differentially methylated probe (DMP) associated with PM2.5. Finally, we performed a functional enrichment analysis of the DMPs that were significantly associated with PM2.5.ResultsA total of 24 COPD patients were enrolled and 48 repeated DNA methylation measurements were associated in this study. When the false discovery rate was < 0.05, 19 DMPs were significantly associated with PM2.5 and were annotated to corresponding genes. Functional enrichment analysis of these genes showed that they were related to the response to toxic substances, regulation of tumor necrosis factor superfamily cytokine production, regulation of photosensitivity 3-kinase signaling, and other pathways.ConclusionThis study provided evidence for a significant relationship between personal PM2.5 exposure and DNA methylation in patients with COPD. Our research also revealed a new biological pathway explaining the adverse effects of PM2.5 exposure on COPD risk

The versatile application of cervicofacial and cervicothoracic rotation flaps in head and neck surgery

<p>Abstract</p> <p>Background</p> <p>The large defects resulting from head and neck tumour surgeries present a reconstructive challenge to surgeons. Although numerous methods can be used, they all have their own limitations. In this paper, we present our experience with cervicofacial and cervicothoracic rotation flaps to help expand the awareness and application of this useful system of flaps.</p> <p>Methods</p> <p>Twenty-one consecutive patients who underwent repair of a variety of defects of the head and neck with cervicofacial or cervicothoracic flaps in our hospital from 2006 to 2009 were retrospectively analysed. Statistics pertaining to the patients' clinical factors were gathered.</p> <p>Results</p> <p>Cheek neoplasms are the most common indication for cervicofacial and cervicothoracic rotation flaps, followed by parotid tumours. Among the 12 patients with medical comorbidities, the most common was hypertension. Defects ranging from 1.5 cm × 1.5 cm to 7 cm × 6 cm were reconstructed by cervicofacial flap, and defects from 3 cm × 2 cm to 16 cm × 7 cm were reconstructed by cervicothoracic flap. The two flaps also exhibited versatility in these reconstructions. When combined with the pectoralis major myocutaneous flap, the cervicothoracic flap could repair through-and-through cheek defects, and in combination with a temporalis myofacial flap, the cervicofacial flap was able to cover orbital defects. Additionally, 95% patients were satisfied with their resulting contour results.</p> <p>Conclusions</p> <p>Cervicofacial and cervicothoracic flaps provide a technically simple, reliable, safe, efficient and cosmetic means to reconstruct defects of the head and neck.</p

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Physician distribution across China’s cities: regional variations

Abstract Background Distribution of physicians is a key component of access to health care. Although there is extensive research on urban-rural disparities in physician distribution, limited attention has been directed to the heterogeneity across urban areas. This research depicts variations in physician density across over 600 cities in the context of China’s rapid urbanization. Methods Data came from National Census Surveys and China statistical yearbooks, 2000–2003, and 2010–2013. Cities were characterized in terms of not only administrative level but also geographic regions and urban agglomerations. We analyzed variations in physician supply by applying generalized estimating equations with an ordinal logistic linking function. Results Although overall physician density increased between 2003 and 2013, with population and socioeconomic attributes adjusted, physician density declined in urban China. On average, urban districts had a higher physician density than county-level cities, but there were regional variations. Cities in urban agglomerations and those outsides did not differ in physician density. Conclusion Despite the reduced inequality between 2003 and 2013, the growth in physician density did not appear to be commensurate with the changes in population health demand. Assessment in physician distribution needs to take into account heterogeneity in population and socioeconomic characteristics.http://deepblue.lib.umich.edu/bitstream/2027.42/173717/1/12939_2021_Article_1503.pd

Cumulative loneliness and subsequent memory function and rate of decline among adults aged ≥50 in the United States, 1996 to 2016

IntroductionThe study objective was to investigate the association between loneliness duration and memory function over a 20-year period.MethodsData were from 9032 adults aged ≥50 in the Health and Retirement Study. Loneliness status (yes vs. no) was assessed biennially from 1996 to 2004 and its duration was categorized as never, 1 time point, 2 time points, and ≥3 time points. Episodic memory was assessed from 2004 to 2016 as a composite of immediate and delayed recall trials combined with proxy-reported memory. Mixed-effects linear regression models were fitted.ResultsA longer duration of loneliness was associated with lower memory scores (P < 0.001) and a faster rate of decline (P < 0.001). The association was stronger among adults aged ≥65 than those aged <65 (three-way interaction P = 0.013) and was stronger among women than men (three-way interaction P = 0.002).DiscussionCumulative loneliness may be a salient risk factor for accelerated memory aging, especially among women aged ≥65.HighlightA longer duration of loneliness was associated with accelerated memory aging.The association was stronger among women than men and among older adults than the younger.Reducing loneliness in mid- to late life may help maintain memory function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175885/1/alz12734_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175885/2/alz12734.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175885/3/alz12734-sup-0002-SuppMatS2.pd

Cumulative loneliness and subsequent memory function and rate of decline among adults aged ≥50 in the United States, 1996-2016

BackgroundTo investigate the association between the cumulative duration of loneliness and subsequent memory function and rate of decline over a 20-year period in middle aged and older adults.MethodData were from biennial interviews with 9,032 adults aged ≥50 in the population-based US Health and Retirement Study from 1996-2016. Loneliness status (yes vs. no) was assessed biennially from 1996-2004 and its duration over this period was categorized as never, 1 time point, 2 time points, and ≥3 time points. Episodic memory was assessed biennially from 2004-2016 as a composite of immediate and delayed recall trials combined with proxy-reported memory. We used multivariable-adjusted mixed effects regression to estimate the association between loneliness duration and subsequent memory function and decline. Three-way interaction terms were included to test effect modification by sex and age.ResultMean (SD) age at baseline was 64 (9) years, 63% of the sample was female (5,646/9,032), and the median follow-up time was 10 years, contributing 46,890 observations from 2004-2016. A total of 1,624 adults (18%) experienced loneliness at one time point, 825 (9%) experienced loneliness at two time points, and 1,069 (12%) experienced loneliness at ≥3 time points. Annual memory decline among non-lonely adults was 0.08 standard deviation (SD) units (95% CI: -0.08, -0.07) per year. A longer duration of loneliness was associated with a faster rate of memory decline (1 time point: 0.01 SD additional annual decline, 95% CI: -0.01, -0.01; 2 time points: 0.02 SD additional annual decline, 95% CI: -0.02, -0.01; ≥3 time points: 0.03 SD additional annual decline, 95% CI: -0.03, -0.02; P trend <0.001). The association was stronger among women than men (three-way interaction P trend <0.001) and adults aged ≥65 years than those aged <65 years (three-way interaction P trend = 0.01).ConclusionLong-term loneliness may be a salient risk factor for accelerated memory aging, especially among women aged 65 and over. Our findings could help rule out reverse causation between loneliness and memory outcomes, suggesting that loneliness could be part of a preclinical syndrome of Alzheimer’s disease and related dementias.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175462/1/alz064679.pd

Long-term trends and regional variations of hypertension incidence in China: a prospective cohort study from the China Health and Nutrition Survey, 1991–2015

Objective The aim is to explore the trends of hypertension incidence and regional variations in China from 1991 to 2015.Design A dynamic prospective cohort study.Setting China Health and Nutrition Survey 1991–2015.Participants 12 952 Chinese adults aged 18+ years.Primary outcome measures Incident hypertension from 1993 to 2015.Results Age-standardised hypertension incidence increased from 40.8 per 1000 person-years (95% CI 38.3 to 43.4) between 1993 and 1997 to 48.6 (95% CI 46.1 to 51.0) between 2011 and 2015. The increasing trends were further supported by results from subsequent extended Cox proportional hazard model. In addition, results from the modelling analysis showed that individuals in eastern, central and northeastern China had greater risks of hypertension occurrence in comparison with their counterparts in western China.Conclusion Hypertension incidence increased during the study period. The growth called for more attention on the health education and health promotion of individuals with great risks