Mediation of association between benzo[a]pyrene exposure and lung cancer risk by plasma microRNAs: A Chinese case-control study.

Epidemiologic studies have reported the positive relationship of benzo[a]pyrene (BaP) exposure with the risk of lung cancer. However, the mechanisms underlying the relationship is still unclear. Plasma microRNA (miRNA) is a typical epigenetic biomarker that was linked to environment exposure and lung cancer development. We aimed to reveal the mediation effect of plasma miRNAs on BaP-related lung cancer. We designed a lung cancer case-control study including 136 lung cancer patients and 136 controls, and measured the adducts of benzo[a]pyrene diol epoxide-albumin (BPDE-Alb) and sequenced miRNA profiles in plasma. The relationships between BPDE-Alb adducts, normalized miRNA levels and the risk of lung cancer were assessed by linear regression models. The mediation effects of miRNAs on BaP-related lung cancer were investigated. A total of 190 plasma miRNAs were significantly related to lung cancer status at Bonferroni adjusted P < 0.05, among which 57 miRNAs showed different levels with |fold change| > 2 between plasma samples before and after tumor resection surgery at Bonferroni adjusted P < 0.05. Especially, among the 57 lung cancer-associated miRNAs, BPDE-Alb adducts were significantly related to miR-17-3p, miR-20a-3p, miR-135a-5p, miR-374a-5p, miR-374b-5p, miR-423-5p and miR-664a-5p, which could in turn mediate a separate 42.2%, 33.0%, 57.5%, 36.4%, 48.8%, 32.5% and 38.2% of the relationship of BPDE-Alb adducts with the risk of lung cancer. Our results provide non-invasion biomarker candidates for lung cancer, and highlight miRNAs dysregulation as a potential intermediate mechanism by which BaP exposure lead to lung tumorigenesis.

Small object detection algorithm incorporating swin transformer for tea buds.

Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

Health-related quality of life in children with childhood acute myeloid leukemia in China: A five-year prospective study.

Purpose: This study aims to identify the key factors influencing health-related quality of life (HRQoL) of pediatric acute myeloid leukemia (AML) patients following their initial diagnosis and examine their impact on the five-year survival prognosis. Methods: A chart review and follow-up were conducted for children with AML who participated in a prospective cohort study between 2017 and 2020. We identified factors influencing HRQoL through Pediatric Quality of Life Inventory™ (PedsQL™ 4.0), PedsQL™ Cancer Module 3.0 (CM 3.0) and PedsQL™ Family Impact Module 2.0 (FIM 2.0), as well as assessed the impact of impaired HRQoL on the overall outcomes of patients. Results: Sixty-four subjects enrolled in the study had complete HRQoL outcome data, and 61 of them completed the 5-year follow-up. In CM 3.0, age was positively associated with parental proxy reports (p = 0.040), whereas divorced families were negatively associated with child self-reports (p = 0.045). A positive medical history correlates with FIM 2.0 (p = 0.025). Residence (p = 0.046), the occupation of caregivers (p = 0.014), disease severity (p = 0.024), and the only child (p = 0.029) exhibited statistically significant associations with the impairment of HRQoL. Impaired HRQoL scores shown by the PedsQL™4.0 parent proxy report (p = 0.013) and FIM 2.0 (p = 0.011) were associated with a reduced 5-year survival rate. Conclusions: This study demonstrated that early impairment of HRQoL in pediatric acute myeloid leukemia patients has predictive value for long-term prognosis. Once validated, these findings may provide some guidance to clinicians treating children with AML.

Real-Time Wireless Sensing of Cardiomyocyte Contractility by Integrating Magnetic Microbeam and Oriented Nanofibers.

In vitro cardiomyocyte mechano-sensing platform is crucial for evaluating the mechanical performance of cardiac tissues and will be an indispensable tool for application in drug discovery and disease mechanism study. Magnetic sensing offers significant advantages in real-time, in situ wireless monitoring and resistance to ion interference. However, due to the mismatch between the stiffness of traditional rigid magnetic material and myocardial tissue, sensitivity is insufficient and it is difficult to achieve cell structure induction and three-dimensional cultivation. Herein, a magnetic sensing platform that integrates a neodymium-iron-boron/polydimethylsiloxane (NdFeB/PDMS) flexible microbeam with suspended and ordered polycaprolactone (PCL) nanofiber membranes was developed, providing a three-dimensional anisotropic culture environment for cardiomyocyte growth and simultaneously realizing in situ wireless contractility monitoring. The as-prepared sensor presented an ultrahigh sensitivity of 442.2 µV/µm and a deflection resolution of 2 µm. By continuously monitoring the cardiomyocyte growth status and drug stimulation feedback, we verified the capability of the platform to capture dynamic changes in cardiomyocyte contractility. This platform provides a perspective tool for evaluating cardiomyocyte maturity and drug performance.

Organic solid-electrolyte interface layers for Zn metal anodes.

Zinc ion batteries (ZIBs) have emerged as promising candidates for renewable energy storage owing to their affordability, safety, and sustainability. However, issues with Zn metal anodes, such as dendrite growth, hydrogen evolution reaction (HER), and corrosion, significantly hinder the practical application of ZIBs. To address these issues, organic solid electrolyte interface (SEI) layers have gained traction in the ZIB community as they can, for instance, help achieve uniform Zn plating/stripping and suppress side reactions. This article summarizes recent advances in organic artificial SEI layers for ZIB anodes, including their fabrication methods, electrochemical performance, and degradation suppression mechanisms.

Hand skeletal features of children and adolescents with different growth statuses and periods.

Background: The hand skeletal features of children and adolescents at different growth statuses and development periods, and the correlation between these skeletal features and hand asymmetric force are currently unclear. Thus, this study sought to investigate the hand skeletal features of children and adolescents at different growth statuses and at different periods of development, and the correlation between these skeletal features and asymmetric force in hands. Methods: A retrospective study was performed on subjects aged 4-20 years with good growth status (group A) or short stature (group B). Additional subjects aged 4-20, 21-40, and >40 years were enrolled in groups C, D, and E, respectively. All the subjects underwent left-hand posteroanterior X-ray radiography. Brachymesophalangia-V (BMP-V), conical epiphysis, epiphysis/metaphysis symmetry of the proximal phalanx (ESP), and the angle of the metacarpal-phalangeal axis were analyzed. Results: Of the 654 children and teenagers aged 4-20 years (median: 11 years) enrolled in the study, 432 were allocated to group A, of whom 237 (54.9%) were male and 195 (45.1%) were female, and 222 matched cases were allocated to group B, of whom 112 (50.5%) were male and 110 (49.5%) were female. The first to third ESPs were significantly (P<0.05) greater in group A than in group B, while the first to third angles of the metacarpal-phalangeal axis were significantly (P<0.05) smaller in group A than in group B. The correlation analysis revealed a highly significant (P<0.01) negative correlation between the ESP and angle of the metacarpal-phalangeal axis (r=-0.948, -0.926, -0.940, -0.885, and -0.848, respectively). The incidence of BMP-V was 15.4% in all patients, while that of conical epiphysis was 19.5%. The incidence of BMP-V and conical epiphysis was significantly (P<0.05) smaller in group A than in group B (11.1% vs. 23.8% for BMP-V and 16.6% vs. 25.2% for conical epiphysis, respectively). Additionally, 216 subjects were enrolled in group C (108 male and 108 female), 185 subjects were enrolled in in group D (93 male and 92 female), and 176 subjects were enrolled in in group E (104 male and 72 female). The second to fifth ESPs in group C were significantly (P<0.05) smaller than those in both groups D and E, while the second to fifth angles of the metacarpal-phalangeal axis were significantly (P<0.05) larger in group C than in both groups D and E. A BMP-V was present in 35 (16.2%) patients in group C, 8 (4.3%) in group D, and 2 (1.1%) in group E, and the difference among the three groups was statistically significant (P<0.05). Conclusions: The epiphyseal symmetry of the proximal phalanges is poor in short stature children and adolescents, and the angle between the metacarpal and phalangeal axes is larger in children and adolescents with short stature than those with normal height and good growth status. A negative correlation was found between the epiphyseal symmetry of the proximal phalanges and asymmetrical stress.

Dependence of evolution of Cyanobacteria superiority on temperature and nutrient use efficiency in a meso-eutrophic plateau lake.

Algal blooms in lakes have been a challenging environmental issue globally under the dual influence of human activity and climate change. Considerable progress has been made in the study of phytoplankton dynamics in lakes; The long-term in situ evolution of dominant bloom-forming cyanobacteria in meso-eutrophic plateau lakes, however, lacks systematic research. Here, the monthly parameters from 12 sampling sites during the period of 1997-2022 were utilized to investigate the underlying mechanisms driving the superiority of bloom-forming cyanobacteria in Erhai, a representative meso-eutrophic plateau lake. The findings indicate that global warming will intensify the risk of cynaobacteria blooms, prolong Microcystis blooms in autumn to winter or even into the following year, and increase the superiority of filamentous Planktothrix and Cylindrospermum in summer and autumn. High RUETN (1.52 Biomass/TN, 0.95-3.04 times higher than other species) under N limitation (TN < 0.5 mg/L, TN/TP < 22.6) in the meso-eutrophic Lake Erhai facilitates the superiority of Dolichospermum. High RUETP (43.8 Biomass/TP, 2.1-10.2 times higher than others) in TP of 0.03-0.05 mg/L promotes the superiority of Planktothrix and Cylindrospermum. We provided a novel insight into the formation of Planktothrix and Cylindrospermum superiority in meso-eutrophic plateau lake with low TP (0.005-0.07 mg/L), which is mainly influenced by warming, high RUETP and their vertical migration characteristics. Therefore, we posit that although the obvious improvement of lake water quality is not directly proportional to the control efficacy of cyanobacterial blooms, the evolutionary shift in cyanobacteria population structure from Microcystis, which thrives under high nitrogen and phosphorus conditions, to filamentous cyanobacteria adapted to low nitrogen and phosphorus levels may serve as a significant indicator of water quality amelioration. Therefore, we suggest that the risk of filamentous cyanobacteria blooms in the meso-eutrophic plateau lake should be given attention, particularly in light of improving water quality and global warming, to ensure drinking water safety.

Associations of ambient air pollution exposure and lifestyle factors with incident dementia in the elderly: A prospective study in the UK Biobank.

OBJECTIVE: Dementia is an important disease burden among the elderly, and its occurrence may be profoundly affected by environmental factors. Evidence of the relationship between air pollution and dementia is emerging, but the extent to which this can be offset by lifestyle factors remains ambiguous. METHODS: This study comprised 155,828 elder adults aged 60 years and above in the UK Biobank who were dementia-free at baseline. Cox proportional hazard models were conducted to examine the associations of annual average levels of air pollutants in 2010, including nitrogen dioxide (NO2), nitrogen oxides (NOX), particulate matter (PM2.5, PM10, and PMcoarse) and lifestyle factors recorded at baseline [physical activity (PA), sleep patterns, or smoking status] with incident risk of dementia, and their interactions on both multiplicative and additive scales. RESULTS: During a 12-year period of follow-up, 4,389 incidents of all-cause dementia were identified. For each standarddeviationincrease in ambient NO2, NOX or PM2.5, all-cause dementia risk increases by 1.07-fold [hazard ratio (HR) and 95 % confidence interval (CI) = 1.07 (1.04, 1.10)], 1.05-fold (95 % CI: 1.02, 1.08) and 1.07-fold (95 % CI: 1.04, 1.10), whereas low levels of PA, poor sleep patterns, and smoking are associated with an elevated risk of dementia [HR (95 % CI) = 1.17 (1.09, 1.26), 1.13 (1.00, 1.27), and 1.14 (1.07, 1.21), respectively]. Furthermore, these air pollutants show joint effects with low PA, poor sleep patterns, and smoking on the onset of dementia. The moderate to high levels of PA could significantly or marginally significantly modify the associations between NO2, NOX or PM2.5 (P-int = 0.067, 0.036, and 0.067, respectively) and Alzheimer's disease (AD), but no significant modification effects are found for sleep patterns or smoking status. CONCLUSION: The increased exposures of NO2, NOX, or PM2.5 are associated with elevated risk of dementia among elderly UK Biobank population. These air pollutants take joint effects with low PA, poor sleep patterns, and smoking on the development of dementia. In addition, moderate to high levels of PA could attenuate the incident risk of AD caused by air pollution. Further prospective researches among other cohort populations are warranted to validate these findings.

Force-induced Caspase-1-dependent pyroptosis regulates orthodontic tooth movement.

Pyroptosis, an inflammatory caspase-dependent programmed cell death, plays a vital role in maintaining tissue homeostasis and activating inflammatory responses. Orthodontic tooth movement (OTM) is an aseptic force-induced inflammatory bone remodeling process mediated by the activation of periodontal ligament (PDL) progenitor cells. However, whether and how force induces PDL progenitor cell pyroptosis, thereby influencing OTM and alveolar bone remodeling remains unknown. In this study, we found that mechanical force induced the expression of pyroptosis-related markers in rat OTM and alveolar bone remodeling process. Blocking or enhancing pyroptosis level could suppress or promote OTM and alveolar bone remodeling respectively. Using Caspase-1-/- mice, we further demonstrated that the functional role of the force-induced pyroptosis in PDL progenitor cells depended on Caspase-1. Moreover, mechanical force could also induce pyroptosis in human ex-vivo force-treated PDL progenitor cells and in compressive force-loaded PDL progenitor cells in vitro, which influenced osteoclastogenesis. Mechanistically, transient receptor potential subfamily V member 4 signaling was involved in force-induced Caspase-1-dependent pyroptosis in PDL progenitor cells. Overall, this study suggested a novel mechanism contributing to the modulation of osteoclastogenesis and alveolar bone remodeling under mechanical stimuli, indicating a promising approach to accelerate OTM by targeting Caspase-1.

DNA methylation aging signatures of multiple metals exposure and their mediation effects in metal-associated mortality: Evidence from the Dongfeng-Tongji cohort study.

Humans were exposed to multiple metals, but the impact of metals on DNA methylation-age (DNAm-age), a well-recognized aging measure, remains inconclusive. This study included 2942 participants from the Dongfeng-Tongji cohort. We detected their plasma concentrations of 23 metals and determined their genome-wide DNA methylation using the Illumina Human-MethylationEPIC BeadChip. Five DNAm-age acceleration indexes (DAIs), including HannumAge-Accel, HorvathAge-Accel, PhenoAge-Accel, GrimAge-Accel (residual from regressing corresponding DNAm-age on chronological age) and DNAm-mortality score (DNAm-MS), were separately calculated. We found that each 1-unit increase in ln-transformed copper (Cu) was associated with a separate 1.02-, 0.83- and 0.07-unit increase in PhenoAge-Accel, GrimAge-Accel, and DNAm-MS (all FDR<0.05). Each 1-unit increase in ln-transformed nickel (Ni) was associated with a 0.34-year increase in PhenoAge-Accel, while each 1-unit increase in ln-transformed strontium (Sr) was associated with a 0.05-unit increase in DNAm-MS. The Cu, Ni and Sr showed joint positive effects on above three DAIs. PhenoAge-Accel, GrimAge-Accel, and DNAm-MS mediated a separate 6.5%, 12.3%, 6.0% of the positive association between Cu and all-cause mortality; GrimAge-Accel mediated 14.3% of the inverse association of selenium with all-cause mortality. Our findings revealed the effects of Cu, Ni, Sr and their co-exposure on accelerated aging and highlighted mediation roles of DNAm-age on metal-associated mortality.

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE: To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS: CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS: The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION: T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.

Relationship between digital media use and anxiety in college students during the COVID-19 outbreak / 中国学校卫生

Objective@#To describe digital media use in college students during the coronavirus disease 2019(COVID-19) outbreak.@*Methods@#A structured self-administered electronic questionnaire was designed and admin istered during February 4 to 12, 2020, collecting information on the use time of digital media and anxiety symptoms of college students in China during the COVID-19 outbreak. Data was analyzed by Chi-square test, univariate and multivariate Logistic regression models.@*Results@#A total of 11 787 university students were recruited. The detection rate of anxiety symptoms was 17.8%. The percentages of screen time ≤2, >2-4 and >4 h/d were 21.3%, 31.4% and 47.3%. The proportions of mobile phone use time ≤2, >2-4 and >4 h/d were 14.6%, 33.2% and 52.3%. The proportion of browsing information regarding COVID-19 ≤1, 1-2 and >2 h/d were 66.6%, 19.4% and 13.9%. The results of Logistic regression analysis revealed that the detection rate of anxiety symptoms was lower among the students having 2-4 h/d of screen time(OR=0.70, 95%CI=0.61-0.81), but higher among the students having more than 4 h/d of screen time(OR=1.13, 95%CI=1.00-1.28) compared with those having ≤2 h/d of video usage time. Compared with the mobile phone usage ≤2 h/d group, the detection rate was lower among >2-4 h/d group(OR=0.78, 95%CI=0.66-0.91). However, anxiety symptoms were more likely to occur in the group of > 4 h/d(OR=1.20, 95%CI=1.04-1.39). The detection of anxiety symptoms was higher in the group with more browsing time. The OR for anxiety symptoms detection rate was 1.55(1.38-1.75) among the students with >1-2 h/d of browsing information regarding COVID-19 and 2.15(1.89-2.44) among the students with >2 h/d of browsing information regarding COVID-19 compared with those with ≤1 h/d of browsing information regarding COVID-19.@*Conclusion@#During the epidemic period, the use of >2-4 h/d digital media helps to reduce the occurrence of anxiety symptoms. But the use of computers and mobile phones for more than 4 h/d is associated with higher level of anxiety symptoms among college students. Less screen time spent on fact-checking on COVID-19 might help reduce anxiety sympotoms among college students.