Patient-Adaptive Automated Segmentation in Daily kVCT Images for Radiotherapy of Head and Neck and Prostate Cancer

PURPOSE/OBJECTIVE(S)

The purpose of this study was to examine the use of transfer learning in deep learning-based auto-segmentation of daily kilovoltage computed tomography (kVCT) images for patient-specific adaptive radiotherapy. Using data from the first cohort of patients treated with the innovative BgRT system, the objective of this study was to evaluate the potential benefits of this approach in facilitating efficient and effective adaptive radiotherapy.

MATERIALS/METHODS

For the head and neck (HaN) site and pelvic site, we first trained a deep convolutional segmentation network using a population dataset, consisting of 67 and 56 patient cases, respectively. This population network was then fine-tuned for a specific patient using a transfer learning approach, adapting the network weights. The auto-segmentation network utilized in this study was a 23-layer U-Net with batch normalization, a dropout rate of 0.5, and four skip connections between the encoder and decoder at different levels. We used initial planning CT and 5-26 sets of daily kVCT scans with a total of 8,039 images for patient-specific learning in the 6 HaN cases and 4 pelvic cases, particularly analyzing the relationship between the number of sequential patient-specific training data and the performance of the auto-segmentation. We compared the performance of the patient-specific network with the population network and the clinical rigid registration method, using the Dice similarity coefficient (DSC) as the evaluation metric. Additionally, we investigated the corresponding dosimetric impacts of the different auto-segmentation and registration methods.

RESULTS

The patient-specific network showed improved mean DSC scores of 0.88 and 0.90 for three HaN organs at risk (OARs) and eight pelvic targets and OARs, respectively, compared to the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network steadily improved as the number of longitudinal training cases increased, reaching near saturation after 6 training cases. The use of the patient-specific auto-segmentation resulted in a reduction of the mean discrepancy in target and OAR doses between delivery and planning from 5.5% with the clinical rigid registration to 1.1%.

CONCLUSION

The use of patient-specific transfer learning in auto-segmenting kVCT images showed higher accuracy compared to a conventional population network and clinical registration-based method. This approach holds promise for enhancing dose evaluation accuracy in adaptive radiotherapy.

Real Time Volumetric MRI for MR-Guided 3D Motion Tracking via Sparse Prior-Augmented Neural Representation Learning

PURPOSE/OBJECTIVE(S)

To reconstruct volumetric MRI from orthogonal cine acquisition aided by sparse priors of 2 static 3D MRI through implicit neural representation (NeRP) learning, with the goal of eliminating large-scale training datasets for data-driven sparse MRI reconstruction and supporting clinical workflow of real time 3D motion tracking during MR-guided radiotherapy.

MATERIALS/METHODS

A multi-layer perceptron network was trained to learn the NeRP of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first learning the NeRP of 2 static 3D MRI with different breathing motion states, prior knowledge of patient breathing motion was embedded into network weights through optimization. The prior knowledge was then augmented from 2 to 31 motion states by querying the optimized network at interpolated/extrapolated motion state coordinates. Starting from the prior-augmented network as an initialization point, the network was further trained using sparse samples of 2 orthogonal cine slices. The final volumetric reconstruction was obtained by querying the trained network at desired 3D spatial locations. We evaluated the proposed method using 5-minute volumetric MRI time series with 340 ms temporal resolution collected from 7 liver carcinoma patients. The time series was acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two MRI with inhale and exhale states respectively were selected from the first 30 sec of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to 2 orthogonal slices and compared network-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.

RESULTS

Across the 7 patients evaluated, the peak signal to noise ratio between model reconstruction and ground truth was 54.66 ± 6.16 dB and the structural similarity index measure was 0.99 ± 0.01. Gross tumor volume (GTV) contours estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 1.89 ± 1.13 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all 3 orthogonal directions.

CONCLUSION

Volumetric MRI from orthogonal cine acquisition with sparse priors is feasible by modeling prior knowledge through implicit neural representation learning. The model-reconstructed images showed sufficient accuracy in supporting 3D motion tracking of abdominal targets. By eliminating the need for large scale training datasets, the method promises to enable clinical implementation of 3D motion tracking for precision radiation therapy.

Analyzing the Predictive Effects of PD-L1 Expression, Early Changes of bTMB and Circulated CD8+T Cells during Treatment for Responses of RT Combined with ICI in NSCLC

PURPOSE/OBJECTIVE(S)

The beneficial role of immunotherapy and the clinical relevance of current biomarkers remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. A rational combination of biomarkers is needed. Here, we estimated potential predictive factors for responses of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) in a phase II trial to determine the efficacy and safety of combination of moderate hypofractionated RT with ICI in patients with oligometastatic NSCLC (NCT03557411).

MATERIALS/METHODS

Pretreatment tumor tissue samples and longitudinal blood were collected for immune and tumor biomarker analysis. We examined pre-treatment (pre-ICI) PD-L1 expression in tumor cells. Circulating tumor cell (CTC), PD-L1+CTC, blood tumor mutation burden (bTMB), CD8+T cells, CD4+T cells, NK cells, B cells in circulation were acquired pre-ICI and 1 month after ICI starting (1-mth). In addition, early changes of CTC (CTC), PD-L1+CTC (PD-L1+CTC), bTMB (bTMB), CD8+T cells (CD8+T cells), CD4+T cells (CD4+T cells), NK cells (NK cells), B cells (B cells) were also analyzed to estimate the predictive effects for treatment.

RESULTS

High pre-ICI bTMB and increased CD8+T cells at 1 month was associated with better PFS (p = 0.016; p = 0.006). Interaction analyses revealed that each combination of two markers in the 5 markers including PD-L1, pre-ICI bTMB, 1-mth bTMB, 1-mth CD8+T cells and CD8+T cells was significantly associated with PFS, except for CTC, PD-L1+CTC, CD4+T cells, NK cells and B cells in circulation due to low power. Unsupervised cluster analysis based on these markers revealed three sub-cohorts. Cohort-1 was overrepresented by patients with progressive disease (81%) of whom were negative for 3-4 of the 5 biomarkers. Cohort-3 was overrepresented by patients with partial response (70%) of whom were positive for 3-4 of the 5 biomarkers. Survival analyses of the 3 cohorts indicated a significant association with PFS (p = 0.017).

CONCLUSION

This study suggests that a combination of PD-L1 expression, early changes of bTMB and circulated CD8+T cells as a better predictive biomarker for response to RT combined with ICI. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Does Epigallocatechin Gallate as a Radiation Protective Agent Reduce the Anti-Tumor Effect of Radiotherapy in Postoperative Breast Cancer Radiotherapy?

PURPOSE/OBJECTIVE(S)

Based on the previous encouraging results, we further explored whether EGCG would have a protective effect on potential tumor lesions, that is, reduce the efficacy of radiotherapy. We selected patients with stage III breast cancer with or without EGCG. The local control rate, distant metastasis rate, DFS and OS were compared between the two groups.

MATERIALS/METHODS

Patients with stage III breast cancer who were treated with EGCG and radiotherapy was selected from a phase II clinical study (ClinicalTrials.gov, No. NCT02580279). Each patient was matched with one control patient without EGCG From the medical database of our hospital matching for age and stage. The control group of stage-and age-matched patients was selected at random from the medical database of our hospital RESULTS: We identified 43 EGCG patients and 43 matched controls. The median age was 45 years (range: 26-67). Between the two groups, there were no obvious differences in the baseline demographic or clinical features. When compared to the placebo group, the mean radiation-induced dermatitis index (RIDI) in the EGCG group was substantially lower (2.56±1.14 vs 3.36±1.16 T = -3.232, P = 0.002). Repeated measures ANOVA indicated the significant differences in the RTOG score during the course of radiotherapy between the two groups (F = 9.611 P = 0.003). The patients mostly experienced RID two or three weeks after starting radiotherapy, although in the EGCG group, it appeared later (3.19±0.91 weeks) than it did in the placebo group (2.67±0.84 weeks), P = 0.008. The median follow-up for patients in the EGCG group at the time of data collection was 50.6 months with 95% confidence interval (CI) from 43.9 to 57.3. While it was 48.6 months (95% CI: 43.4-53.9) for patients in the control group. There was no significant difference in overall survival (OS), disease free-survival (DFS) and freedom from locoregional (LRF) and distant failure (DMF) (P > 0.05). At the data cut-off (December 2021), the 4-year DFS with EGCG was 71.4% compared to 65.4% with conventional therapy, and the 4-year OS was 77.0% compared to 80.3%.

CONCLUSION

The prophylactic use of EGCG solution reduced the RID score of stage III breast cancer patients without negatively impacting the therapeutic effect of radiotherapy on the tumor. EGCG is safe and feasible choice for RID for breast cancer during radiotherapy.

Prospective Clinical Adoption of Artificial Intelligence for Organ Contouring in Head and Neck Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

Patients that undergo head and neck (H&N) radiation therapy (RT) require laborious delineation of organs-at-risk (OARs) on computed tomography (CT) scans in a treatment planning system (TPS) to minimize radiation to normal tissue. This task can be completed rapidly and accurately with recently developed artificial intelligence-based semantic segmentation models. The current study aims to deploy and evaluate a strategy for improving clinical practice with this technology.

MATERIALS/METHODS

Deep learning models were trained and tested with CT scans and OAR contours from previous H&N RT cases at our clinic. Two medical physicists vetted the models and selected a 2.5D U-Net for further implementation. The model was embedded in a dedicated server at the hospital, programmed to read H&N CT scans staged for import into the TPS, generate auto-contours, and write them into a TPS-compatible format made available alongside the scan. In the pilot implementation, the auto-contouring service was utilized for more than 60 cases, prospectively. The auto-contours were quantitatively evaluated against the treatment-approved contours to determine how much modification was performed by the clinical team.

RESULTS

The 2.5D U-Net selected for clinical integration segments 21 OARs in less than 3 minutes per scan. Across all the prospective cases, the mean Dice score and mean 95th percentile Hausdorff distance (mm) between the auto-contour and treatment-approved contour for each of the 21 OARs were as follows, respectively: brainstem (0.93, 1.94), optic chiasm (0.70, 2.96), left cochlea (0.69, 2.37), right cochlea (0.68, 2.44), esophagus (0.88, 2.46), left globe (0.93, 1.50), right globe (0.93, 1.63), glottis (0.91, 2.13), larynx (0.93, 2.76), mandible (0.90, 4.86), left optic nerve (0.78, 1.64), right optic nerve (0.82, 1.65), oral cavity (0.86, 8.46), left parotid gland (0.91, 2.78), right parotid gland (0.91, 2.39), pharynx (0.85, 2.39), spinal cord (0.87, 2.27), left submandibular gland (0.85, 3.46), right submandibular gland (0.83, 3.69), left temporal lobe (0.94, 2.20), and right temporal lobe (0.95, 2.09). The auto-contours for the optic chiasm, optic nerves, cochleas, and submandibular glands differed substantially from the final contours, a finding corroborated by the clinical team; the rest were clinically acceptable with minor or no edits necessary.

CONCLUSION

The proposed strategy provides a sophisticated starting point for treatment planning that has garnered overall favorable feedback from the participating radiation oncologists and dosimetrists. Consequently, the technique is being extended to other treatment sites.

XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population

INTRODUCTION

Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far.

METHODS

A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods.

RESULTS

Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit.

CONCLUSIONS

The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.

Clinical correlation research of 3D reconstruction of retrolabyrinthine space based on HRCT of temporal bone

Background: The retrolabyrinthine approach helps clinicians perform complex surgeries such as vestibular neurectomy, resection of petrous apex cholesteatoma, or use this space to complete endoscopic combined with microscope surgical operations in a relatively safe buffer space. Some of our current studies using 3D reconstruction in the clinic have also helped us perform some complex surgical procedures.Objective: This study aims to reveal the relationship between important structures in retrolabyrinthine space through objective parameters. These measurement data help clinicians locate intraoperatively and provide a reference for clinical surgery. Also, we are intended to help improve surgical techniques and expand the operating space to increase reachable anatomic structure.Material and Methods: The inner structures of the temporal bone from HRCT (High-resolution computed tomography) images which were taken at the Eye & ENT Hospital of Fudan University were reconstructed. Precise measurement of the structures was accomplished by using the software 3D-Slicer (3D Slicer, https://www.slicer.org/; version 4.8.0, Massachusetts, USA).Results: 3D model of temporal bone structures, including the cochlea, semicircular canals (SCCs), the internal auditory canal (IAC), facial nerve (FN), jugular bulb(JB), and carotid artery was reconstructed. The combination of HRCT and 3D models is utilized to analyze the Quantitative data of the retrolabyrinthine space and its adjacent structures.Conclusions and Significance: 3D reconstruction of CT images clearly displayed the detailed structures of the temporal bone. Surgical adaptability of the retrolabyrinthine approach can be assessed preoperatively by image and other methods, and anatomical parameters play an important role in the retrolabyrinthine space. Therefore, this study helps to skeleton the bone as much as possible to expand the surgical space, so that the surgeon can contact the anatomical structure more diversified to expand the surgical indications.

Mapping of major QTL and candidate gene analysis for hull colour in foxtail millet (Setaria italica (L.) P. Beauv.)

BACKGROUND

Hull colour is an important morphological marker for selection in seed production of foxtail millet. However, the molecular mechanisms underlying hull colour variation remain unknown.

RESULTS

An F7 recombinant inbred line (RIL) population containing 215 lines derived from Hongjiugu × Yugu18 was used to analyze inheritance and detect the quantitative trait loci (QTL) for four hull colour traits using major gene plus polygene mixed inheritance analysis and composite interval mapping (CIM) in four environments. Genetic analysis revealed that the hull colour L* value (HCL*) was controlled by two major genes plus additive polygenes, the hull colour a* value (HCa*) was controlled by three major genes, the hull colour b* value (HCb*) was controlled by two major genes plus polygenes, and the hull colour C* value (HCC*) was controlled by four major genes. A high-density genetic linkage map covering 1227.383 cM of the foxtail millet genome, with an average interval of 0.879 cM between adjacent bin markers, was constructed using 1420 bin markers. Based on the genetic linkage map and the phenotypic data, a total of 39 QTL were detected for these four hull colour traits across four environments, each explaining 1.50%-49.20% of the phenotypic variation. Of these, six environmentally stable major QTL were co-localized to regions on chromosomes 1 and 9, playing a major role in hull colour. There were 556 annotated genes within the two QTL regions. Based on the functions of homologous genes in Arabidopsis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) gene annotations, five genes were predicted as candidate genes for further studies.

CONCLUSIONS

This is the first study to use an inheritance model and QTL mapping to determine the genetic mechanisms of hull colour trait in foxtail millet. We identified six major environmentally stable QTL and predicted five potential candidate genes to be associated with hull colour. These results advance the current understanding of the genetic mechanisms underlying hull colour traits in foxtail millet and provide additional resources for application in genomics-assisted breeding and potential isolation and functional characterization of the candidate genes.

Roles of hydrogen bonding interactions and hydrophobic effects on enhanced water structure in aqueous solutions of amphiphilic organic molecules

Insights into the solute-induced water structural transformations are essential to understand the role of water in biological and chemical reaction processes. Herein, the structural changes in water induced by amphiphilic organic molecules were investigated using concentration-dependent derivative Raman spectroscopy (DRS) combined with two-dimensional Raman correlation spectroscopy (2D Raman-COS). We shall restrict our attention in this work to binary mixtures of water with dimethyl sulfoxide (DMSO), acetone, and isopropanol (IPA), all of which have similar chemical structures. The spectral changes in O:H and OH stretching modes illustrate that the solute molecules induce an enhancement of the water structure in dilute solutions, where the enhanced degree of water structure is closely related to the size of the dipole moment of organic molecules. In addition, the transformations of solute-induced water-specific structures were evaluated by 2D Raman-COS, which shows that the strong hydrogen bond (H-bond) structure of water is more sensitive to organic molecules and induces a transition to the weak H-bond structure of water.

A Nitroreductase-MOF Biocatalyst for the Degradation of Nitroaromatic Contaminants and Fluorescent Labelling of Biomolecules

NfsB (nitroreductase from Escherichia coli) can catalyze nitroaromatic compounds to aromatic amines under mild conditions. Compared with the purified enzyme NfsB, we found that the crude enzyme demonstrated better thermal stability and tolerance against a wide pH range, rendering it convenient to use and cost-effective as it did not require any downstream processing. In addition, we introduced metal-organic frameworks (MOFs) to immobilize the crude-NfsB. The resulting composite, crude-NfsB@ZIF-90, showed excellent catalytic performance and reusability, and it also demonstrated good catalytic activity in organic solvents, rendering it more efficient for the removal of nitroaromatic contaminants in complex environments. The nitroreductase-ZIF-90 biocatalyst can be used for fluorescent labeling of carbohydrates, which is favorable for the study of the function of carbohydrates.

Exploring the knowledge, attitudes, behaviors and training needs of obstetric and gynecological nurses regarding COVID-19 during the peak period of the pandemic in middle-risk areas of China: A cross-sectional study

Background

COVID-19 has become a global pandemic, which necessitates that health workers be capable of protecting themselves and their patients. This article aimed to describe knowledge levels, attitudes, behaviors and training needs regarding COVID-19 among obstetric and gynecological nurses in medium-risk areas during the pandemic.

Method

A cross-sectional survey of obstetric and gynecological nurses in medium-risk areas was performed during the peak period of the pandemic in China. The main survey tool was the self-designed COVID-19 Knowledge, Attitude, Behavior and Training Needs Questionnaire. Pearson correlation analysis was conducted to analyze the relationships among knowledge, attitudes, behaviors and training needs.

Results

A total of 599 nurses were recruited, of whom 27.7% failed the knowledge part of the questionnaire. Positive correlations were found between knowledge and attitudes (r = 0.100, P = 0.015) and between attitudes and behaviors (r = 0.352, P = 0.000) regarding occupational protection against COVID-19. A total of 88.5% of nurses preferred online training to traditional training, and more than 70% believed that demonstration of and training in operations by their own department were effective ways to learn about how to protect themselves against COVID-19.

Conclusion

The higher the level of knowledge about the disease was, the more positive the attitude toward occupational protection, and consequently, the more actively protective behaviors were adopted. Training affected nurses' knowledge of COVID-19 occupational protection and promoted positive attitudes, which further facilitated the effective prevention and control of the disease. Online training with demonstrations are recommended for COVID-19 training of nurses.

Intermittent and Mild Cold Stimulation Maintains Immune Function Stability through Increasing the Levels of Intestinal Barrier Genes of Broilers

In order to improve the adaptability of broilers to low-temperature environments and their ability to resist acute cold stress (ACS), 240 one-day-old broilers were selected and randomly divided into three groups. The control treatment (CC) group was raised at the conventional feeding temperature from 1-43 days (d), the cold stimulation treatment (CS) group was kept at 3 °C below the temperature of CC at 1 d intervals for 3 and 6 h from 15 to 35 d, namely, CS3 and CS6, respectively. Then, all broilers were kept at 20 °C from 36 to 43 d. ACS was then carried out at 44 d, and the ambient temperature was dropped to 10 °C for 6 h. The study investigated the production performance, as well as levels of intestinal barrier genes (including Claudin-1, E-cadherin, Occludin, ZO-1, ZO-2 and Mucin2), secretory IgA in duodenum and jejunum, and immunoglobulins (IgA and IgG) in serum. The results showed that IMCS could increase the daily weight gain and decrease the feed conversion ratio. During IMCS, the expression levels of intestinal barrier genes were up-regulated and the content of secretory IgA was increased. When IMCS ceased for one week, the level of immunoglobulins in serum stabilized, and the expression levels of Occludin, ZO-2 and Mucin2 still maintained high levels. After ACS, broilers that received IMCS training maintained high levels of intestinal barrier genes and secretory IgA.

Data generation for connected and automated vehicle tests using deep learning models

For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs' tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and a hybrid model of variational autoencoder and generative adversarial network (VAE-GAN) for trajectory data generation. These models are capable of learning a compressed representation of the observed data space, and generating data by sampling in the latent space and then mapping back to the original space. The real data and the generated data are applied in the car-following model of CAVs with cooperative adaptive cruise control (CACC) to evaluate safety performance using the time-to-collision (TTC) index. The results indicate that the generated data of the two generative models have reasonable differences while maintaining a certain similarity with the real samples. When real and generated trajectory data are applied to the car-following model of CAVs, the generated trajectory data increases the number of new critical fragments whose TTC is smaller than the threshold. The WGAN-GP model performs better than the VAE-GAN model according to the ratio of critical fragments. Findings of this study provide useful insights for CAVs' tests and safety performance improvement.

A graph-based genome and pan-genome variation of the model plant Setaria

Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

Explore traffic conflict risks considering motion constraint degree in the diverging area of toll plazas

In the diverging area of toll plazas, the absence of lane markings, the gradual widening of lanes, and the crossing of vehicles with different tolling methods increase the likelihood of collisions. This study proposed a concept of motion constraint degree to investigate traffic conflict risks in the toll plaza diverging area. On the basis of the motion constraint degree, a two-step method was developed, in which all potentially influencing factors were separated into two parts. The first part was used to analyze the association between the motion constraint degree and some factors, while the remaining factors were utilized for risk regression/prediction together with the motion constraint degree. The random parameters logit model was applied for regression analysis and four prevalent machine learning models were employed for risk prediction. Results indicate that (1) the proposed approach considering motion constraint degree outperforms the conventional direct method, no matter for conflict risk regression or prediction; (2) the motion constraint degree is not monotonically correlated with the risk level of vehicles; (3) due to the layout of the toll plaza, ETC vehicles are less likely to be at risk in the diverging area; and (4) lane-changing behaviors in the restricted space increase the conflict risk.

Efficacy and Safety of Pulse Magnetic Therapy System in Insomnia Disorder: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial

OBJECTIVE

This study's objective is to assess the efficacy and safety of Pulsed Magnetic Therapy System (PMTS) in improving insomnia disorder.

METHODS

Participants with insomnia disorder were randomly assigned to receive either PMTS or sham treatment for four weeks (n= 153; PMTS: 76, sham: 77). Primary outcomes are the Insomnia Severity Index (ISI) scores at week 0 (baseline), 1, 2, 3, 4 (treatment), and 5 (follow-up). Secondary outcomes are the Pittsburgh Sleep Quality Index at baseline and week 4, and weekly sleep diary-derived values for sleep latency, sleep efficiency, real sleep time, waking after sleep onset, and sleep duration.

RESULTS

The ISI scores of the PMTS group and the sham group were 7.13±0.50, 11.07±0.51 at week 4, respectively. There was a significant group×time interaction for ISI (F3.214, 485.271=24.25, p<0.001, ηp 2=0.138). Only the PMTS group experienced continuous improvement throughout the study; in contrast, the sham group only experienced a modest improvement after the first week of therapy. At the end of the treatment and one week after it, the response of the PMTS group were 69.7% (95% confidence interval [CI]: 58.6%-79.0%), 75.0% (95% CI: 64.1%-83.4%), respectively, which were higher than the response of the sham group (p<0.001). For each of the secondary outcomes, similar group×time interactions were discovered. The effects of the treatment persisted for at least a week.

CONCLUSION

PMTS is safe and effective in improving insomnia disorders.

A temporal analysis of crash injury severities in multivehicle crashes involving distracted and non-distracted driving on tollways

Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.

Single-cell sequencing reveals the immune microenvironment landscape related to anti-PD-1 resistance in metastatic colorectal cancer with high microsatellite instability

BACKGROUND

The objective response rate of microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) patients with first-line anti-programmed cell death protein-1 (PD-1) monotherapy is only 40-45%. Single-cell RNA sequencing (scRNA-seq) enables unbiased analysis of the full variety of cells comprising the tumor microenvironment. Thus, we used scRNA-seq to assess differences among microenvironment components between therapy-resistant and therapy-sensitive groups in MSI-H/mismatch repair-deficient (dMMR) mCRC. Resistance-related cell types and genes identified by this analysis were subsequently verified in clinical samples and mouse models to further reveal the molecular mechanism of anti-PD-1 resistance in MSI-H or dMMR mCRC.

METHODS

The response of primary and metastatic lesions to first-line anti-PD-1 monotherapy was evaluated by radiology. Cells from primary lesions of patients with MSI-H/dMMR mCRC were analyzed using scRNA-seq. To identify the marker genes in each cluster, distinct cell clusters were identified and subjected to subcluster analysis. Then, a protein‒protein interaction network was constructed to identify key genes. Immunohistochemistry and immunofluorescence were applied to verify key genes and cell marker molecules in clinical samples. Immunohistochemistry, quantitative real-time PCR, and western blotting were performed to examine the expression of IL-1β and MMP9. Moreover, quantitative analysis and sorting of myeloid-derived suppressor cells (MDSCs) and CD8⁺ T cells were performed using flow cytometry.

RESULTS

Tumor responses in 23 patients with MSI-H/dMMR mCRC were evaluated by radiology. The objective response rate was 43.48%, and the disease control rate was 69.57%. ScRNA-seq analysis showed that, compared with the treatment-resistant group, the treatment-sensitive group accumulated more CD8⁺ T cells. Experiments with both clinical samples and mice indicated that infiltration of IL-1β-driven MDSCs and inactivation of CD8⁺ T cells contribute to anti-PD-1 resistance in MSI-H/dMMR CRC.

CONCLUSIONS

CD8⁺ T cells and IL-1β were identified as the cell type and gene, respectively, with the highest correlation with anti-PD-1 resistance. Infiltration of IL-1β-driven MDSCs was a significant factor in anti-PD-1 resistance in CRC. IL-1β antagonists are expected to be developed as a new treatment for anti-PD-1 inhibitor resistance.

[Preparation, properties and antibacterial applications of medical nano-metals and their oxides: a review]

Antibiotics are playing an increasingly important role in clinical antibacterial applications. However, their abuse has also brought toxic and side effects, drug-resistant pathogens, decreased immunity and other problems. New antibacterial schemes in clinic are urgently needed. In recent years, nano-metals and their oxides have attracted wide attention due to their broad-spectrum antibacterial activity. Nano-silver, nano-copper, nano-zinc and their oxides are gradually applied in biomedical field. In this study, the classification and basic properties of nano-metallic materials such as conductivity, superplasticity, catalysis, and antibacterial activities were firstly introduced. Secondly, the common preparation techniques, including physical, chemical and biological methods, were summarized. Subsequently, four main antibacterial mechanisms, such as cell membrane, oxidative stress, DNA destruction and cell respiration reduction, were summarized. Finally, the effect of size, shape, concentration and surface chemical characteristics of nano-metals and their oxides on antibacterial effectiveness and the research status of biological safety such as cytotoxicity, genotoxicity and reproductive toxicity were reviewed. At present, although nano-metals and their oxides have been applied in medical antibacterial, cancer treatment and other clinical fields, some issues such as the development of green preparation technology, the understanding of antibacterial mechanism, the improvement of biosafety, and the expansion of application fields, require further exploration.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Exploring electronic energy level structure and excited electronic states of β-carotene using DFT

Carotenoids are a class of natural pigments that play a fundamental role in photosynthesis and optoelectronics. However, the complexity of their energy level structure and electronic states has prevented a clear interpretation of their photophysics and photochemistry. The mediating nonradiative decay of the bright S₂ state to the dark S₁ state of carotenoids involves a population of bridging intermediate state. Herein, time-dependent DFT was used to study the energy level and electronic excitation process of β-carotene. A π-π* transition and π electron delocalization of electron excitation could be inferred based on the difference in the electron cloud distribution of the HOMO and LUMO orbitals. Through the electronic transition contribution in the UV-vis spectra and the electron density difference between the ground state and the excited state, the electronic energy level structure and possible dark state were analyzed. On this basis, the electronic excitation process of β-carotene was theoretically studied by combining electron-hole analysis and transition density matrix (TDM). There was a charge transfer from the β-ionone ring to the long-chain in the (S₀) → (S₂), (S₀) → (S₄) and (S₀) → (S₅).

High-resolution mapping of the rainfall runoff pollution: case study of Shiwuli River watershed, China

Rainfall runoff is the key factor of water quality deterioration in highly urbanized area, which is characterized by intensive human activities and frequent extreme weather events. Urban landscape system is composed of highly diverse and heterogeneous land patches, which makes the effective management of urban runoff pollution difficult. Therefore, high-resolution land-use data is imperative for the identification and analysis of spatial-temporal characteristics of urban runoff pollution. In this study, Shiwuli River watershed, a rapidly urbanizing area in China, is selected as the study area. We first interpret nine kinds of land-use types with a high-resolution remote sensing data of 2 m [Formula: see text] 2 m. Then, a localized Soil Conservation Service model based on field observation and rainfall experiments is applied to map the spatial-temporal pattern of runoff pollution. The results indicate that the COD, NH₃-N, TP, and TN load generated by the runoff in the watershed accounted for 23.4%, 3.7%, 8.2%, and 9.0% of the total pollution load in 2016, respectively. Furthermore, the spatial-temporal pattern of the assessed runoff pollution was mainly subject to the distribution of rainfall and land-use patterns. We suggest that the sponge city construction combined with surface pollution control is an effective way to reduce the runoff pollution. This study highlights the necessity to identify spatial-temporal hotspots in developing precise pollution control measures, which provides valuable information for pollution control policy-making in Shiwuli River watershed and could serve as a reference for other river watersheds undergoing rapid urbanization.

Hydrogen-Bond Dynamics and Water Structure in Aqueous Ethylene Glycol Solution via Two-Dimensional Raman Correlation Spectroscopy

The hydrogen-bond (H-bond) dynamics and water structural transitions in aqueous ethylene glycol (EG) solution were investigated on the basis of concentration- and temperature-dependent two-dimensional Raman correlation spectroscopy (2D Raman-COS). At room temperature, EG-induced enhancement of the water structure when the EG/water molar ratio is less than 1:28 resulted from the hydrophobic effect around the methylene groups of EG. The decrease in the temperature caused an enhancement of the Raman peak at about 3200 cm^-1, representing an increase in the orderliness of water molecules. Further analysis of the water-specific structures by 2D Raman-COS reveals that the strong H-bond structure preferentially responds to external perturbations and induces a weak H-bond structural transition in water. Finally, EG-induced water structural transitions were calculated by the density functional theory (DFT). Hopefully, 2D Raman-COS combined with DFT calculations would advance the study of solute-induced water structural transitions in water-organic chemistry.

Intermolecular energy transfer-enhanced super-broadband stimulated Raman scattering in cyclohexane-benzene mixtures

Supercontinuum radiation has found numerous applications in diverse fields encompassing spectroscopy, pulse compression, and tunable laser sources. Anomalous enhanced stimulated Raman scattering (SRS) of cyclohexane-benzene mixtures was obtained in this study. SRS of the pure solvent, the multi-order Stokes of the strongest fundamental vibration modes, and energy transfer in intra-molecular modes were observed. SRS of the mixture revealed that the cross-pumping effect was generated between the C-H stretching (v₂) mode of cyclohexane and the C=C ring skeleton (v₁) mode of benzene, thereby producing the intermolecular secondary stimulated Raman emission and the appearance of two super-broadband radiations at 664.36-673.9 nm and 704.62-729.22 nm. The results suggest that the energy transfer of intermolecular vibrational modes, where the strongest vibrational mode excites other vibrational modes, is a simple approach for generating supercontinuum coherent radiation.