Research progress and prospects of benefit-risk assessment methods for umbilical cord mesenchymal stem cell transplantation in the clinical treatment of spinal cord injury.

Over the past decade, we have witnessed the development of cell transplantation as a new strategy for repairing spinal cord injury (SCI). However, due to the complexity of the central nervous system (CNS), achieving successful clinical translation remains a significant challenge. Human umbilical cord mesenchymal stem cells (hUMSCs) possess distinct advantages, such as easy collection, lack of ethical concerns, high self-renewal ability, multilineage differentiation potential, and immunomodulatory properties. hUMSCs are promising for regenerating the injured spinal cord to a significant extent. At the same time, for advancing SCI treatment, the appropriate benefit and risk evaluation methods play a pivotal role in determining the clinical applicability of treatment plans. Hence, this study discusses the advantages and risks of hUMSCs in SCI treatment across four dimensions-comprehensive evaluation of motor and sensory function, imaging, electrophysiology, and autonomic nervous system (ANS) function-aiming to improve the rationality of relevant clinical research and the feasibility of clinical translation.

Kinetic Monte Carlo simulations on electroforming in nanomanipulated conductive bridge random access memory devices.

Conductive bridge random access memory (CBRAM) devices exhibit great potential as the next-generation nonvolatile memory devices. However, they suffer from two major disadvantages, namely relatively high power consumption and large cycle-to-cycle and device-to-device variations, which hinder their more extensive commercialization. To learn how to enhance their device performance, kinetic Monte Carlo (KMC) simulations were employed to illustrate the variation of electroforming processes in nanomanipulated CBRAM devices by introducing an ion-blocking layer with scalable nanopores and tuning the microstructures of dielectric layers. Both the size of nanopores and the inhomogeneity of dielectric layers have significant impacts on the forming processes of conductive filaments. The dielectric layer with a high-content loose texture plus the scalable nanopore-containing ion-blocking layer leads to the formation of size-controlled and uniform filaments, which remarkably contributes to miniaturizable and stable CBRAM devices. Our study provides insights into nanomanipulation strategies to realize high-performance CBRAM devices, still awaiting future experimental confirmation.

The impact of China's value-added tax credit refunds on enterprise labor demand: Ex-ante analysis and ex-post test.

This paper aims to explore the impact of China's value-added tax (VAT) credit refunds policy on the enterprises' labor demand through a paradigm combining ex-ante analysis and ex-post test. By introducing the VAT credit refunds into the production-decision model of the enterprise, calibrating the parameters and conducting the dynamic effects tests using the data of Chinese A-share listed enterprises, this paper finds that the labor employment of the pilot enterprises exhibits a V-shaped fluctuation trend. In the initial implementation of the policy, due to the existence of layoff costs, iso-cost line of the enterprise bends, which results in that the enterprise with a capital-labor substitution elasticity greater than 1 will not reduce labor hiring, as it has already deployed labor force before the implementation of the policy. When the enterprise enter the next production cycle where the labor force can be freely allocated, the labor employment of the enterprise with a capital-labor elasticity of substitution greater than 1 will decline compared to that without the policy. In the long run, as output increases, the labor demand will recover. The results of ex-post test are consistent with that of the ex-ante analysis. Additionally, heterogeneity test reveals that the greater the elasticity of capital-labor substitution of the sub-industry is, the more severe the degree of the V-shaped fluctuation is. Following the implementation of the policy, the continuous increase in enterprise output and capital stock verifies the relevant transmission mechanism. This study provides a more detailed perspective for comprehensively understanding the impact of VAT credit refunds policy on employment.

Effectiveness of dance interventions for falls prevention in older adults: systematic review and meta-analysis.

INTRODUCTION: Fall prevention is a global health priority. Strength and balance exercise programmes are effective at reducing falls. Emerging literature suggests dance is an enjoyable and sociable form of exercise. However, there is little evidence that dance reduces fall incidence. METHODS: Systematic review and meta-analysis examining effectiveness and cost-effectiveness of dance for falls prevention in older adults. Five databases were searched with no restrictions on publication date or intervention settings. Risk of bias was assessed using variants of Cochrane Risk of bias tools, Mixed-Methods Appraisal and Drummond checklist as appropriate. Certainty of evidence was assessed using GRADE. RESULTS: Forty-one studies were included (19 RCTs, 13 quasi-experimental, two mixed-method, seven observational studies, 2,451 participants). Five types of dance interventions were identified: ballroom and Latin dance, dance exercise, cultural dance, dance therapy, and low-impact dance. Meta-analysis was only possible for functional outcome measures: Timed-Up-and-Go (dance versus usual care, mean difference (MD) = 1.36; 95% CI -3.57 to 0.85), Sit-to-Stand (dance versus exercise MD = -0.85; 95% CI -2.64 to 0.93: dance versus education MD = -1.64; 95% CI -4.12 to 0.85), Berg Balance Scale (dance versus usual care MD = 0.61; 95% CI -4.26 to 5.47). There was unexplained variance in effects and no significant differences between intervention and control groups. Overall, certainty of evidence was very low; we are uncertain about the effect of dance interventions in reducing falls. CONCLUSIONS: There is very low certainty evidence for dance as an alternative to strength and balance training if the aim is to prevent falls. No robust evidence on the cost-effectiveness of dance interventions for the prevention of falls was found. PROSPERO REGISTRATION: CRD42022382908.

Establishment and validation of a risk score model based on EUS: assessment of lymph node metastasis in early gastric cancer.

BACKGROUND AND AIMS: Lymph node metastasis significantly affects the prognosis of early gastric cancer patients. EUS plays a crucial role in the preoperative assessment of early gastric cancer. This study evaluated the efficacy of EUS in identifying lymph node metastasis in early gastric cancer patients and developed a risk score model to aid in choosing the best treatment options. METHODS: We retrospectively analyzed the effectiveness of EUS for detecting lymph node metastasis in early gastric cancer patients. A risk score model for predicting lymph node metastasis preoperatively was created using independent risk factors identified through binary logistic regression analysis and subsequently validated. Receiver operating characteristic curves were generated for both the development and validation cohorts. RESULTS: The overall accuracy of EUS in identifying lymph node metastasis was 85.3%, although its sensitivity (29.2%) and positive predictive value (38.7%) were relatively low. Patients were categorized based on preoperative risk factors for lymph node metastasis, including tumor size of ≥20 mm, lymph nodes of ≥10 mm, body mass index of ≥24 kg/m2, and lymph node metastasis on CT scans. A 7-point risk score model was developed to assess the likelihood of lymph node metastasis. The areas under the receiver operating characteristic curve for the development and validation sets were 0.842 and 0.837, respectively, with sensitivities of 64% and 79%, respectively. CONCLUSIONS: We developed a practical risk score model based on preoperative factors to help EUS predict lymph node metastasis in early gastric cancer patients, guiding the selection of optimal treatment approaches for these patients.

Assessment of Pulmonary Arterial Vascular Impedance in Fetuses With Tetralogy of Fallot.

OBJECTIVE: To evaluate the vascular impedance of the pulmonary arteries in fetuses with tetralogy of Fallot (TOF) by Doppler echocardiography. METHODS: A total of 42 fetuses with TOF (TOF group) and 84 gestational age-matched normal fetuses (control group) were prospectively collected from the Second Xiangya Hospital of Central South University from August 2022 to January 2023. The severity of TOF was classified into mild TOF (z score ≥-2), moderate TOF (-4 < z score < -2), or severe TOF (z score ≤-4) according to the z score value of the pulmonary annulus diameter. The pulsatility index (PI) of the main pulmonary artery (MPA), distal left pulmonary artery (DLPA), and distal right pulmonary artery (DRPA) were measured by pulsed-wave Doppler. The differences in clinical data and echocardiographic parameters between TOF group, control group, and TOF subgroups were compared. RESULTS: Compared with the control group, MPA-PI increased significantly, whereas DLPA-PI and DRPA-PI decreased in TOF group (all P < .001). There were no significant differences in MPA-PI and DRPA-PI among mild TOF, moderate TOF, and severe TOF (all P > .05). However, DLPA-PI decreased significantly in severe TOF compared with mild TOF (P < .05). CONCLUSION: Fetuses with TOF presented increased vascular impedance in the pulmonary trunk and decreased impedance in distal pulmonary artery branches. Further large and follow-up studies are needed to demonstrate the associations between those changed vascular impedances and the development of PA in patients with TOF.

Removal, transformation and ecological risk assessment of pesticide in rural wastewater by field-scale horizontal flow constructed wetlands of treated effluent.

Constructed wetlands (CWs) are widely used in sewage treatment in rural areas, but there are only a few studies on field-scale CWs in treating wastewater-borne pesticides. In this study, the treatment and metabolic transformation of 29 pesticides in rural domestic sewage by 10 field-scale horizontal flow CWs (HF-CWs), each with a treatment scale of 36‒5000 m3/d and operated for 2‒10 years, in Guangzhou, Southern China was investigated. The risk of pesticides in treated effluent and main factors influencing such risk were evaluated. Results demonstrated that HF-CWs could remove pesticides in sewage and reduce their ecological risk in effluent, but the degree varied among types of pesticides. Herbicides had the highest mean removal rate (67.35 %) followed by insecticides (60.13 %), and the least was fungicides (53.22 %). In terms of single pesticide compounds, the mean removal rate of butachlor was the highest (73.32 %), then acetochlor (69.41 %), atrazine (68.28 %), metolachlor (58.40 %), and oxadixyl (53.28 %). The overall removal rates of targeted pesticides in each HF-CWs ranged from 11 %‒57 %, excluding two HF-CWs showing increases in pesticides in treated effluent. Residues of malathion, phorate, and endosulfan in effluent had high-risks (RQ > 5). The pesticide concentration in effluent was mainly affected by that in influent (P = 0.042), and source control was the key to reducing risk. The main metabolic pathways of pesticide in HF-CWs were oxidation, with hydroxyl group to carbonyl group or to form sulfones, the second pathways by hydrolysis, aerobic condition was conducive to the transformation of pesticides. Sulfones were generally more toxic than the metabolites produced by hydrolytic pathways. The present study provides a reference on pesticides for the purification performance improvement, long-term maintenance, and practical sustainable application of field-scale HF-CWs.

Remedial Dosing Recommendations for Sirolimus Delayed or Missed Dosages Caused by Poor Medication Compliance in Pediatric Tuberous Sclerosis Complex Patients.

BACKGROUND: Delayed or missed dosages caused by poor medication compliance significantly affected the treatment of diseases in children. AIMS: The present study aimed to investigate the influence of delayed or missed dosages on sirolimus pharmacokinetics (PK) in pediatric tuberous sclerosis complex (TSC) patients and to recommend remedial dosages for nonadherent patients. METHODS: A published sirolimus population PK model in pediatric TSC patients was used to assess the influence of different nonadherence scenarios and recommend optimally remedial dosages based on Monte Carlo simulation. Thirteen nonadherent scenarios were simulated in this study, including delayed 2h, 4 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, 18 h, 20 h, 22 h, 23.5 h, and missed one dosage. Remedial dosing strategies contained 10-200% of scheduled dosages. The optimal remedial dosage was that with the maximum probability of returning the individual therapeutic range. RESULTS: For delayed or missed sirolimus dosages in pediatric TSC patients, when the delayed time was 0-8 h, 8-10 h, 10-18 h, 18-22.7 h, 22.7-24 h, 70%, 60%, 40%, 30%, 20% scheduled dosages were recommended to take immediately. When one dosage was missed, 120% of scheduled dosages were recommended at the next dose. CONCLUSION: It was the first time to recommend remedial dosages for delayed or missed sirolimus therapy caused by poor medication compliance in pediatric TSC patients based on Monte Carlo simulation. Meanwhile, the present study provided a potential solution for delayed or missed dosages in clinical practice.

Assessment of sulfamethoxazole removal by three wetland plant species under hydroponic conditions: uptake, accumulation, and physiological responses.

Plants play a crucial role as a removal pathway in constructed wetlands, demonstrating the ability to absorb and tolerate antibiotics from wastewater. However, the specific contribution of plants in this regard has not yet to be sufficiently established. To gain a more comprehensive insight into the associated processes, we selected three common wetland plant species, Canna indica L. (C. indica), Cyperus alternifolius L. (C. alternifolius), and Thalia dealbata Fraser (T. dealbata), to evaluate their capacity for uptake, accumulation, and physiological response in the removal of sulfamethoxazole (SMX) at varying initial concentrations (10, 30, 100, and 300 µg/L) under hydroponic conditions. The results showed that SMX removal was more efficient at lower concentrations (10 and 30 µg/L) than at higher concentrations (100 and 300 µg/L). Moreover, plant systems were found to consistently outperform unplanted systems in SMX removal. Among the assessed species, C. indica was identified as being relatively effective in the removal of SMX, whereas the performance of C. alternifolius was notably less pronounced. A positive correlation was observed between the concentration of SMX in the plant tissues and that in the external aqueous medium. However, plant tissue residues contributed only a minor fraction to the overall removal of SMX. Wetland plants absorb SMX through their roots, and we accordingly detected significantly higher concentrations in submerged plant tissues. Furthermore, we also detected reductions in net photosynthetic rates indicative of potential phytotoxicity, which is associated with the accumulation of antibiotic in the shoot tissues. Accumulation of SMX in the roots and rhizomes was also found to be associated with the development of shorter roots, with this effect becoming more pronounced with an increase in the concentration of exogenous SMX. However, despite these adverse effects, plants can detoxify antibiotics via the glutathione pathway. Of the assessed plant species, C. indica was identified as the most SMX tolerant, as indicated by Km and Vmax values, with C. alternifolius being the least tolerant. Our findings in this study reveal the potential value of wetland plants in the sequestration of antibiotics and provide evidence for the underlying mechanisms of action. These findings could make an important contribution to the implementation of phytoremediation in antibiotic-contaminated water.

Three wetland plants with fibrous root systems, namely Canna indica, Cyperus alterniflius, and Thalia dealbata, were selected to investigate the removal efficiencies of sulfamethoxazole in the hydroponic system by different emergent plants, quantify the contribution of uptake and accumulation for sulfamethoxazole in plant tissues, and assess the physiological responses of plants and their effect on the removal of sulfamethoxazole. The knowledge obtained from this study shows the potential use of wetland plants for removing antibiotics and the inherent mechanisms, which will be useful for the application of phytoremediation in antibiotic contaminated water.

Risk assessment and pathogen profile of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy: A retrospective study.

Emergency craniotomy in patients with traumatic brain injury poses a significant risk for surgical site infections (SSIs). Understanding the risk factors and pathogenic characteristics of SSIs in this context is crucial for improving outcomes. This comprehensive retrospective analysis spanned from February 2020 to February 2023 at our institution. We included 25 patients with SSIs post-emergency craniotomy and a control group of 50 patients without SSIs. Data on various potential risk factors were collected, including demographic information, preoperative conditions, and intraoperative details. The BACT/ALERT3D Automated Bacterial Culture and Detection System was utilized for rapid bacterial pathogen identification. Statistical analyses included univariate and multivariate logistic regression to identify significant risk factors for SSIs. The study identified Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus as the most prevalent pathogens in SSIs. Significant risk factors for SSIs included the lack of preoperative antibiotic use, postoperative drainage tube placement, diabetes mellitus, and the incorporation of invasive procedures, all of which showed a significant association with SSIs in the univariate analysis. The multivariate analysis further highlighted the protective effect of preoperative antibiotics and the increased risks associated with anaemia, diabetes mellitus, postoperative drainage tube placement, and the incorporation of invasive procedures. Our research underscores the critical role of factors like insufficient preoperative antibiotics, postoperative drainage, invasive techniques, anaemia, and diabetes mellitus in elevating the risk of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy. Enhanced focus on these areas is essential for improving surgical outcomes.

Ultrahigh-Spatial-Resolution Photon-counting Detector CT Angiography of Coronary Artery Disease for Stenosis Assessment.

Background Coronary CT angiography is a first-line test in coronary artery disease but is limited by severe calcifications. Photon-counting-detector (PCD) CT improves spatial resolution. Purpose To investigate the effect of improved spatial resolution on coronary stenosis assessment and reclassification. Materials and Methods Coronary stenoses were evaluated prospectively in a vessel phantom (in vitro) containing two stenoses (25%, 50%), and retrospectively in patients (in vivo) who underwent ultrahigh-spatial-resolution cardiac PCD CT (from July 2022 to April 2023). Images were reconstructed at standard resolution (section thickness, 0.6 mm; increment, 0.4 mm; Bv44 kernel), high spatial resolution (section thickness, 0.4 mm; increment, 0.2 mm; Bv44 kernel), and ultrahigh spatial resolution (section thickness, 0.2; increment, 0.1 mm; Bv64 kernel). Percentages of diameter stenosis (DS) were compared between reconstructions. In vitro values were compared with the manufacturer specifications of the phantom and patient results were assessed regarding effects on Coronary Artery Disease Reporting and Data System (CAD-RADS) reclassification. Results The in vivo sample included 114 patients (mean age, 68 years ± 9 [SD]; 71 male patients). In vitro percentage DS measurements were more accurate with increasing spatial resolution for both 25% and 50% stenoses (mean bias for standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 10.1%, 8.0%, and 2.3%; P < .001). In vivo results confirmed decreasing median percentage DS with increasing spatial resolution for calcified stenoses (n = 161) (standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 41.5% [IQR, 27.3%-58.2%], 34.8% [IQR, 23.7%-55.1%], and 26.7% [IQR, 18.6%-44.3%]; P < .001), whereas noncalcified (n = 13) and mixed plaques (n = 19) did not show evidence of a difference (P ≥ .88). Ultrahigh-spatial-resolution reconstructions led to reclassification of 62 of 114 (54.4%) patients to lower CAD-RADS category than that assigned using standard resolution. Conclusion In vivo and in vitro coronary stenosis assessment improved for calcified stenoses by using ultrahigh-spatial-resolution PCD CT reconstructions, leading to lower percentage DS compared with standard resolution and clinically relevant rates of reclassification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by McCollough in this issue.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

A postpartum functional assessment tool for women based on the international classification of functioning, disability and health.

BACKGROUND: Postpartum dysfunctions and complications can occur in women. However, functional assessment should be conducted to make treatment plans before any intervention is implemented. In this context, the International Classification of Functioning, Disability and Health (ICF) may be a useful tool for women postpartum to document functional data and set rehabilitation goals. The purpose of this study was to determine the corresponding domains that should be considered in the evaluation of women's postpartum functioning based on the International Classification of Functioning, Disability and Health (ICF) model using the Delphi method. METHODS: Fifteen domestic experts were invited to conduct two rounds of expert consensus survey on the ICF-based postpartum functional assessment category pool obtained through literature retrieval, clinical investigation, and reference to relevant literature. The sample was medical staff with professional knowledge of women's health. The opinions of experts were summarized, and the positive coefficient, authority coefficient and coordination degree of experts were calculated. RESULTS: A total of 15 domestic experts participated in this expert consensus. Through two rounds of a questionnaire survey, 69 items were finally selected to form the ICF-based postpartum functional assessment tool for women. The items included 32 items of body function, 12 items of body structure, 17 items of activity and participation, and 8 items of environmental factors. In addition, we identified 8 items of personal factors. The expert positive coefficients of the two rounds of expert consensus were both 100%, the authority coefficient was 0.789, and the coefficient of variation was between 0.09 to 0.31. CONCLUSION: A postpartum functional assessment tool for women based on the ICF model was constructed based on the Delphi method, which can provide more comprehensive health management and life intervention for postpartum women. TRIAL REGISTRATION: The Registration number of the Chinese Clinical Trial Registry is ChiCTR2200066163, 25/11/2022.

Development of a Multifaceted Perspective for Systematic Analysis, Assessment, and Performance for Environmental Standards of Contaminated Sites.

Contaminated soil and groundwater can pose significant risks to human health and ecological environments, making the remediation of contaminated sites a pressing and sustained challenge. It is significant to identify key performance indicators and advance environmental management standards of contaminated sites. The traditional study currently focuses on the inflexible collection of related files and displays configurable limitations regarding integrated assessment and in-depth analysis of published standards. In addition, there is a relative lack of research focusing on the analysis of different types of standard documents. Herein, we introduce a cross-systematic retrospective and review for the development of standards of the contaminated sites, including the comprehensive framework, multifaceted analysis, and improved suggestion of soil and groundwater standards related to the environment. The classification and structural characteristics of different types of files are systematically analyzed of over 300 national, trade, local, and group standards for the contaminated sites. It exhibits that trade standards are the main types and testing methods are the important format within numerical considerations of soil standards. The guide standard serves as a crucial component in environmental management for investigating, assessing, and remediating of contaminated sites. Future improvement plans and development directions are proposed for advancing robust technical support for effective soil contamination prevention and control. This multidimensional analysis and the accompanying suggestions can provide improved guidance for Chinese environmental management of contaminated sites and sparkle the application of standards in a wide range of countries.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

The association between wealth and sleep medication use in a nationally-representative sample of older Medicare beneficiaries.

BACKGROUND: Sleep disorders are common among older adults, leading to high prevalence of over-the-counter and prescription sleep medication use. Socioeconomically disadvantaged individuals have higher prevalence of sleep disorders. Frequent use of sleep medications can increase the risk of falls. Little is known about the association between wealth and sleep medication use in older adults. METHODS: We conducted a cross-sectional analysis using a nationwide sample of 7603 Medicare beneficiaries (65+ years) from Round 1 (2011) of the National Health and Aging Trends Study. We measured self-reported wealth as the sum of assets (retirement savings, stocks/bonds, checking/savings accounts, business assets, and home value) minus liabilities (mortgage, credit card, and medical debt). Self-reported sleep medication use in the past month was categorized as frequent (5-7 nights/week), sometimes (1-4 nights/week), or never (0 night/week). We estimated differences in the prevalence of sleep medication use by quintiles of wealth using crude and adjusted binomial regression models. Individuals missing sleep medication information were excluded. RESULTS: Median wealth was $152 582 (IQR: $24 023-412 992). Sixteen percent reported frequent sleep medication use, 15% reported some use, and 70% reported no use. Frequent sleep medication use was more common in lower wealth quintiles (lowest: 20%, highest: 12%). Alternatively, some use was more common in higher wealth quintiles (lowest: 11%, highest: 18%). Results were similar after adjustment for demographic factors, anxiety, depression, and sleep disorders. CONCLUSIONS: In this study, less wealthy older adults had higher prevalence of frequent sleep medication use. This may lead to dependency or increased fall risk in this vulnerable population.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

An Early and Unequal Decline: Life Course Trajectories of Cognitive Aging in the United States.

Objectives: Cognitive aging is a lifelong process with implications for Alzheimer's disease and dementia. This study aims to fill major gaps in research on the natural history of and social disparities in aging-related cognitive decline over the life span. Methods: We conducted integrative data analysis of four large U.S. population-based longitudinal studies of individuals aged 12 to 105 followed over two decades and modeled age trajectories of cognitive function in multiple domains. Results: We found evidence for the onset of cognitive decline in the 4th decade of life, varying gender differences with age, and persistent disadvantage among non-Hispanic Blacks, Hispanics, and those without college education. We further found improvement in cognitive function across 20th century birth cohorts but widening social inequalities in more recent cohorts. Discussion: These findings advance an understanding of early life origins of dementia risk and invite future research on strategies for promoting cognitive health for all Americans.

Fine-Grained Spatio-Temporal Parsing Network for Action Quality Assessment.

Action Quality Assessment (AQA) plays an important role in video analysis, which is applied to evaluate the quality of specific actions, i.e., sports activities. However, it is still challenging because there are lots of small action discrepancies with similar backgrounds, but current approaches mostly adopt holistic video representations. So that fine-grained intra-class variations are unable to be captured. To address the aforementioned challenge, we propose a Fine-grained Spatio-temporal Parsing Network (FSPN) which is composed of the intra-sequence action parsing module and spatiotemporal multiscale transformer module to learn fine-grained spatiotemporal sub-action representations for more reliable AQA. The intra-sequence action parsing module performs semantical sub-action parsing by mining sub-actions at fine-grained levels. It enables a correct description of the subtle differences between action sequences. The spatiotemporal multiscale transformer module learns motion-oriented action features and obtains their long-range dependencies among sub-actions at different scales. Furthermore, we design a group contrastive loss to train the model and learn more discriminative feature representations for sub-actions without explicit supervision. We exhaustively evaluate our proposed approach in the FineDiving, AQA-7, and MTL-AQA datasets. Extensive experiment results demonstrate the effectiveness and feasibility of our proposed approach, which outperforms the state-of-the-art methods by a significant margin.