Predictive Value of Fidgety Movement Assessment and Magnetic Resonance Imaging for Cerebral Palsy in Infants.

BACKGROUND: The early prediction of cerebral palsy (CP) could enable the follow-up of high-risk infants during the neuroplasticity period. This study aimed to explore the predictive value of fidgety movement assessment (FMA) and brain magnetic resonance imaging (MRI) for the development of CP in clinic rehabilitation setting. METHODS: This retrospective observational study included infants who underwent FMA and brain MRI at age nine to 20 weeks at Children's Hospital, Zhejiang University School of Medicine, between March 2018 and September 2019. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of FMA and MRI for predicting the development of CP were assessed. RESULTS: A total of 258 infants (169 males, gestational age 37.4 ± 3.0 weeks, birth weight 2987.9 ± 757.1 g) were included. Fifteen children had CP after age two years. The diagnostic value of FMA and brain MRI combination showed 86.7% sensitivity (95% confidence interval [CI]: 58.4% to 97.7%), 98.4% specificity (95% CI: 95.6% to 99.5%), and 97.7% accuracy (95% CI: 95.0% to 99.1%); the combination diagnostic value also showed a significantly higher AUC for predicting CP after age two years than FMA alone (AUC: 0.981 vs 0.893, P = 0.013). CONCLUSIONS: The diagnostic value of FMA and brain MRI combination during infancy showed a high predictive value for CP development in clinical rehabilitation setting.

Targeted amplicon sequencing facilitated a novel risk assessment framework for assessing the prevalence of broad spectrum bacterial and coronavirus diseases.

How to effectively leverage wastewater data to estimate the risk of various infectious diseases remains a great challenge. To address this issue, we conducted continuous wastewater surveillance in Dalian city during the summer-autumn seasons of 2022, targeting coronavirus and bacterial diseases. The surveillance included daily sampling at a wastewater treatment plant (WWTP) and weekly sampling in three sewersheds. Targeting the bacteria's 16S rRNA gene and the coronavirus's RNA-dependent RNA polymerase (RdRp) gene, we first employed RT-PCR and amplicon sequencing techniques to analyze the presence and phylogenetic relationship of detected coronavirus and bacterial pathogens. Next, qPCR was used to quantify the abundances of detected coronavirus and bacterial species. Based on the daily shedding dynamics of SARS-CoV-2, a novel model was developed to predict daily new cases. Based on the medium shedding density of 12 pathogens, two thresholds of sewage pathogen load (indicating 0.1 % and 1 % infection rates) were proposed. Our PanCoV RT-PCR detected coronavirus on 12th August and from 26th August to 12th September 2022. Targeted amplicon sequencing further identified human coronavirus OC43 (hCoV-OC43) on 12th August and the SARS-CoV-2 Omicron variant since 26th August in samples from WWTPs and sewersheds. Phylogenetic analysis revealed that hCoV-OC43 from this study belonged to genotype K and suggested a close relationship between the amplified coronavirus sequences from wastewater and clinical samples in a local COVID-19 outbreak on 26th August. Amplicon sequencing targeting the bacterial 16S rRNA gene also revealed the presence of several bacterial pathogens. Finally, we assessed the microbial risk of specific pathogens in sewersheds and identified a number of pathogens that reached high (>1 % prevalence) and medium risk levels (>0.1 % prevalence) at sewershed B. Our findings underline wastewater surveillance as a valuable early warning system for coronavirus and other waterborne bacterial diseases, complementing public health response measures.

Saliva sampling strategies affecting the salivary glucose measurement.

Characterized by sustained elevated blood glucose levels, diabetes mellitus has become one of the largest global public health concerns by imposing a heavy global burden on socio-economic development. To date, regular blood glucose level check by performing a finger-prick test has been a routine strategy to monitor diabetes. However, the intrusive nature of finger blood prick tests makes it challenging for individuals to maintain consistent testing routines. Recently, salivary glucose measurement (SGM) has increasingly become a non-invasive alternative to traditional blood glucose testing for diabetes. Despite that, further research is needed to standardize the collection methods and address the issues of variability to ensure accurate and reliable SGM. To resolve possible remaining issues in SGM, we here thoroughly explored saliva sampling strategies that could impact the measurement results. Additionally, the effects of supplements taken, mouth washing, gum chewing, and smoking were collectively analyzed, followed by a continuous SGM over a long period, forming the stepping stone for the practical transitional development of SGM in non-invasive diabetes monitoring.

Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model.

Introduction: Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD. Methods: In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC. Results: The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD. Discussion: In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.

A comparative study of the disease burden attributable to asbestos in Brazil, China, Kazakhstan, and Russia between 1990 and 2019.

BACKGROUND: Brazil, China, Kazakhstan, and Russia are the main asbestos-producing countries, and all forms of asbestos are carcinogenic to humans. The objective of this study was to estimate the disease burden attributable to asbestos between 1990 and 2019 in major producing countries, including Brazil, China, Kazakhstan, and Russia. METHODS: Age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life year (DALY) rates (ASDR) of disease burden attributable to asbestos by country, age, and sex were extracted from the Global Burden of Disease 2019. Percentage change and estimated annual percentage change (EAPC) were used to assess the trends of ASDR and ASMR of disease burden attributable to asbestos between 1990 and 2019. RESULTS: Asbestos-related diseases were highly heterogeneous across Global, Brazil, China, Kazakhstan, and Russia. There was a downward trend in ASMR and ASDR of diseases burden related to asbestos globally. The age-specific mortality rate of disease attributable to asbestos increased in men and women, although it decreased in women aged 85-89, the highest age-specific mortality rate were observed in age 95 + group in men [162.14 (95% UI: 103.76-215.45)] and women [30.58 (95% UI: 14.83-44.33)] per 100 000 population, respectively. Tracheal, bronchus, and lung (TBL) cancer was the leading cause of death and DALYS attributable to asbestos between 1990 and 2019 globally and in Brazil, China, Kazakhstan, and Russia. China had the highest percentage change (73.31%) and EAPC [3.41 (95% CI: 2.75-4.08)] in ASMR related to exposure to asbestos in men, with the highest percentage change (73.31%) and EAPC [3.41 (95% CI: 2.75-4.08)] in ASDR in men. CONCLUSIONS: The ASMR and ASDR of disease burden attributable to asbestos decreased between 1990 and 2019 globally. TBL cancer was the leading cause of death and DALYs attributable to asbestos between 1990 and 2019. There has been an increasing trend in mortality and DALYs globally, especially in older men. The burden of disease attributable to asbestos is increasing in China, especially in men.

Health Inequality in the Global Burden of Chronic Obstructive Pulmonary Disease: Findings from the Global Burden of Disease Study 2019.

Background: This study assessed health inequality in the global burden of chronic obstructive pulmonary disease (COPD) between 1990 and 2019 using data extracted from the Global Burden of Diseases (GBD 2019) study. Methods: Data were extracted from the GBD 2019 study. A series of comparative and descriptive analyses of the disease burden between women and men in countries with different socioeconomic development (SDI) status were performed. The slope index of inequality (SII), relative index of inequality (RII), and concentration index (CI) were calculated to measure the socioeconomic-related cross-national health inequity between 1990 and 2019. Results: The global health burden caused by COPD increased by 25.7% in terms of disability-adjusted life years (DALY) from 59.2 million years in 1990 to 74.4 million years in 2019. Global age-standardized DALY rate (ASDR) associated with COPD decreased by 40.0%, from 1537.7 per 100,000 population in 1990 to 926.1 per 100,000 population in 2019. The highest sex-specific DALY number was at age 70-74 in male and female, and female is lower than male. However, after controlling for population size, the burden of COPD is more concentrated in the population living in low SDI countries, relative health inequality indicators (RII and CI) supported this conclusion. Conclusion: The health inequalities caused by the disparity of socioeconomic status are increasing, and the increasing concentration of wealth worldwide is likely to aggravate health inequalities associated with COPD.

The Burden of Trachea, Bronchus, and Lung Cancer Attributable to Occupational Exposure From 1990 to 2019.

Objectives: Occupational exposure to carcinogens is associated with trachea, bronchus, and lung (TBL) cancer. The objective of this study was to provide global and regional estimates of the burden of TBL cancer associated with occupational carcinogens (OCs) between 1990 and 2019. Methods: Age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life years (DALYs) rates (ASDR) of TBL cancer related to exposure to OCs at the global and regional levels were extracted for 1990-2019 from the Global Burden of Disease 2019. Joinpoint regression was used to analyze trends in the ASMR and ASDR of TBL cancer burden related to OCs, and the annual percent change and the average annual percent change (AAPC) were recorded. Results: The mortality from TBL cancer related to exposure to OCs increased globally. The ASMR and ASDR decreased in both sexes and in men between 1990 and 2019. The AAPC of ASMR and ASDR decreased in men between 1990 and 2019, but increased in women. Asbestos accounted for the highest death number and beryllium accounted for the lowest; diesel engine exhaust caused the largest percentage change in death number (145.3%), in ASDR (14.9%), and in all ages DALY rates (57.6%). Asbestos accounted for the largest death number in high social development index (SDI) countries, whereas low-middle SDI countries had the largest percent change (321.4%). Asbestos was associated with decreased ASDR in high SDI countries and increased ASDR in low-middle SDI countries, and similar changes were observed for other OCs. Conclusions: The overall mortality and DALYs of TBL cancer burden related to OCs showed a decreasing trend between 1990 and 2019, whereas death number increased. Asbestos accounted for the highest death number. TBL cancer burden related to OCs decreased to different degrees in high, low, low-middle, and middle SDI countries, which showed variable levels of TBL cancer burden related to exposure to OCs (except asbestos).

Use of the Griffiths mental development scale-Chinese in the assessment of children with autism spectrum disorder and global developmental delay/intellectual disability.

ABSTRACT: The Griffiths Mental Development Scale-Chinese (GDS-C) is used in China to assess the development of children from birth to 8 years of age. Language disorders are a common symptom of autism spectrum disorder (ASD) and global developmental delay (GDD)/intellectual disability (ID). There is a need to identify distinct clinical characteristics in children suspected of having these 2 disorders, mainly presenting as language disorders. Here, we aimed to use the GDS-C to evaluate children presenting with language problems to identify characteristics that distinguish ASD and GDD/ID. Children with language problems were recruited between August 2018 and December 2019. A total of 150 children aged 25 to 95.2 months were enrolled (50 in the ASD group, 50 in the GDD/ID group, and 50 in the typical group). Each group was subdivided by age as follows: 24-36 months, >36-60 months, and >60-96 months. Developmental characteristics assessed using the GDS-C were analyzed and compared. Both, children with ASD and GDD/ID presented with a lower developmental level than typical children in all six subscales of the GDS-C. No significant differences were observed in the six subscale scores between the ASD and GDD/ID groups, except for the practical reasoning subscale score in the >36 to 60 months subgroups, which was significantly lower in the GDD/ID group than in the ASD group. The developmental imbalance of subscales within the ASD and GDD/ID groups identified troughs in the personal-social, language, and practical reasoning areas in children with ASD and in the language and practical reasoning areas in children with GDD/ID relative to typical children. The GDS-C is a useful, comprehensive tool for the assessment of the developmental state of children with ASD and GDD/ID. Characteristics of practical reasoning subscale help diagnose autism in >36 to 60 months old children.

Environmental damage cost assessment from municipal solid waste treatment based on LIME3 model.

Environmental damage cost can measure the impact of pollution caused by human activity on final safeguard subjects from endpoint perspective. Application of environmental damage cost to municipal solid waste (MSW) management is still rare. To fill such a research gap, this study established a MSW environmental damage cost assessment method using Life Cycle Impact Assessment Method based on Endpoint (LIME) model. Four types of environmental damage cost, namely, primary productivity, social assets, biodiversity and human health for waste treatment were investigated in the city of Shanghai. Results show that the environmental damage cost was 113.7, 116.9, 140.0, 144.0 and 170.8 million dollars in Shanghai from 2014 to 2018, respectively. Damage cost of social assets was the dominant component, accounting for about 55%. Landfill mainly caused social assets damage cost, while incineration mainly caused human health damage cost. Scenarios analysis further revealed that after implementing MSW separation, the total environmental damage cost could be reduced by about 1/3. The best MSW treatment method is fermentation, followed by compost, incineration and landfill, with unit environmental damage cost being 0.22, 4.51, 16.04 and 23.47 USD/ton, respectively.

Understanding the learning mechanism of convolutional neural networks in spectral analysis.

Deep learning approaches, especially convolutional neural network (CNN) models, have achieved excellent performances in vibrational spectral analysis. The critical drawback of the CNN approach is the lack of interpretation, and it is regarded as a black box. Interpreting the learning mechanism of chemometric models is critical for intuitive understanding and further application. In this study, an interpretable CNN model with a global average pooling layer is presented for Raman and mid-infrared spectral data analysis. A class activation mapping (CAM)-based approach is leveraged to visualize the active variables in the whole spectrum. The visualization of active variables shows a discriminative pattern in which the most contributed variables peaked around theoretical chemical characteristic bands. The visualization of the feature maps by three convolutional layers demonstrates the data transformation pipeline and how the CNN model hierarchically extracts informative spectral features. The first layer acts as a Savitzky-Golay filter and learns spectral shape characteristics, while the second layer learns enhanced patterns from typical spectral peaks on a few correlated variables. The third layer shows stable activations on critical spectral peaks. A partial least squares - linear discriminant analysis (PLS-LDA) model is presented for comparison on classification accuracy and model interpretation. The CNN model yields mean classification accuracies of 99.01 and 100% for E. coli and meat datasets on the test set, while the PLS-LDA models obtain accuracies of 98.83 and 100%. Both the CNN and PLS-LDA models demonstrate stable patterns on active variables while CNN models are more stable than PLS-LDA models on classification performances for various dataset partitions with Monte-Carlo cross-validation.

Distribution and assessment of heavy metals in the surface sediment of Yellow River, China.

Large amounts of heavy metals discharged by industrial cities that are located along the middle reach of Yellow River, China have detrimental impacts on both the ecological environment and human health. In this study, fourteen surface sediment samples were taken in the middle reach of the Yellow River. Contents of Zn, Pb, Ni, Cu, Cr, Cd, As were measured, and the pollution status was assessed using three widely used pollution assessment methods, including the single factor index method, Nemerow pollution index method and potential ecological risk index. The concentrations of the studied heavy metals followed the order: Zn>Cr>Cu>Ni>Pb>As>Cd. Nearly 50% of sites had Cu and Cr accumulation. The concentration of Cu at the Yiluo River exceeded the secondary standard value of the Environmental quality standard for soils. Comparison of heavy metal concentrations between this study and other selected rivers indicated that Cu and Cr may be the major pollutants in our case. The single factor index indicated that many samples were at high levels of pollution for Cu and Cd; the Nemerow pollution index indicated that the Yihe River, Luohe River, Yiluo River and Huayuankou were polluted. According to the results of potential ecological risk assessment, Cd in the tributaries of Luo River, Yihe River, and Yiluo River showed high risk toward the ecosystem and human health, Cd in Huanyuankou and Cu in Yiluo River showed a middle level of risk and other samples were at a low level of risk.

The Historical Speciation of Mauremys Sensu Lato: Ancestral Area Reconstruction and Interspecific Gene Flow Level Assessment Provide New Insights.

Mauremys sensu lato was divided into Mauremys, Chinemys, Ocadia, and Annamemys based on earlier research on morphology. Phylogenetic research on this group has been controversial because of disagreements regarding taxonomy, and the historical speciation is still poorly understood. In this study, 32 individuals of eight species that are widely distributed in Eurasia were collected. The complete mitochondrial (mt) sequences of 14 individuals of eight species were sequenced. Phylogenetic relationships, interspecific divergence times, and ancestral area reconstructions were explored using mt genome data (10,854 bp). Subsequent interspecific gene flow level assessment was performed using five unlinked polymorphic microsatellite loci. The Bayesian and maximum likelihood analyses revealed a paraphyletic relationship among four old genera (Mauremys, Annamemys, Chinemys, and Ocadia) and suggested the four old genera should be merged into the genus (Mauremys). Ancestral area reconstruction and divergence time estimation suggested Southeast Asia may be the area of origin for the common ancestral species of this genus and genetic drift may have played a decisive role in species divergence due to the isolated event of a glacial age. However, M. japonica may have been speciated due to the creation of the island of Japan. The detection of extensive gene flow suggested no vicariance occurred between Asia and Southeast Asia. Inconsistent results between gene flow assessment and phylogenetic analysis revealed the hybrid origin of M. mutica (Southeast Asian). Here ancestral area reconstruction and interspecific gene flow level assessment were first used to explore species origins and evolution of Mauremys sensu lato, which provided new insights on this genus.

Full-solution processed flexible organic solar cells using low-cost printable copper electrodes.

Full-solution-processed flexible organic solar cells (OSCs) are fabricated using low-cost and high-quality printable Cu electrodes, which achieve a power conversion efficiency as high as 2.77% and show remarkable stability upon 1000 bending cycles. This device performance is thought to be the best among all full-solution-processed OSCs reported in the literature using the same active materials. This printed Cu electrode is promising for application in roll-to-roll fabrication of flexible OSCs.

[Distribution and assessment of mercury in the ambient soil of a municipal solid waste incinerator].

The emission of mercury (Hg) from the municipal solid waste incineration has inspired widespread attention, especially regarding to the deposition of Hg in the surrounding soil, which is issued to be the potential negative factor of ambient environment and human health. This study mainly focused on the distributions of Hg in the ambient soil of a municipal solid waste incinerator located in North China. The pollution of the mercury and its risks to the local environment and human health were assessed. Results showed that Hg levels were in the range of 0.015-0.25 mg x kg(-1), with an average (0.088 +/- 0.064) mg x kg(-1). The concentrations of Hg in the soil were obviously influenced by wind direction and they were relatively higher in the northwest (downwind) comparing with that in the southeast (upwind). The Kriging interpolation method was adopted to create a contour map, which intuitively displayed a spatial mercury distribution in the soil. The regions with a higher Hg concentration are mainly distributed in the north northwest, the north northeast and the west southwest of the municipal solid waste incinerator. According to the evaluation results of single factor pollution index and geoaccumulation Index, some ambient soil samples were polluted by the mercury emission from the municipal solid waste incinerator; however, the results of the health risk assessment showed that the mercury in the soil had not pose a health hazard to the local population.

Integrative missing value estimation for microarray data.

BACKGROUND: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. RESULTS: We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. CONCLUSION: We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.