Polydopamine-encapsulated zinc peroxide nanoparticles to target the metabolism-redox circuit against tumor adaptability for mild photothermal therapy.

Regulating the metabolism-redox circuit of cancer cells has emerged as an attractive strategy to improve the therapeutic outcome, while often confronting the glaring issue of resistance due to the multiple adaptive responses of tumor cells. This study presents a simple yet efficient approach to regulate this circuit simultaneously against tumor adaptability by utilizing polydopamine-encapsulated zinc peroxide nanoparticles (ZnO2@PDA NPs). The nanoparticles could deliver large amounts of Zn2+ and H2O2 into tumor cells to unfold an intracellular self-amplifying loop for breaking the balance in zinc and redox homeostasis by H2O2-mediated endogenous Zn2+ release from metallothioneins due to its oxidation by H2O2 and Zn2+-induced in situ H2O2 production by disturbing mitochondrial respiration, ultimately disrupting tumor adaptability to exogenous stimuli. The elevated levels of Zn2+ and H2O2 also inhibited adenosine triphosphate (ATP) generation from glycolysis and mitochondrial respiration to disrupt energy adaptability. Furthermore, insufficient ATP supply could reduce glutathione and heat shock protein expression, thereby sensitizing oxidative stress and enabling PDA-mediated mild photothermal therapy (PTT). Consequently, this trinity nanoplatform, which integrated dual-starvation therapy, amplified oxidative stress, and mild PTT, demonstrated outstanding therapeutic effects and a facile strategy.

Neuromelanin-sensitive magnetic resonance imaging: Possibilities and promises as an imaging biomarker for Parkinson's disease.

Parkinson's disease (PD) is an age-related progressive neurodegenerative disorder characterized by both motor and non-motor symptoms resulting from the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and noradrenergic neurons in the locus coeruleus (LC). The current diagnosis of PD primarily relies on motor symptoms, often leading to diagnoses in advanced stages, where a significant portion of SNpc dopamine neurons has already succumbed. Therefore, the identification of imaging biomarkers for early-stage PD diagnosis and disease progression monitoring is imperative. Recent studies propose that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) holds promise as an imaging biomarker. In this review, we summarize the latest findings concerning NM-MRI characteristics at various stages in patients with PD and those with atypical parkinsonism. In conclusion, alterations in neuromelanin within the LC are associated with non-motor symptoms and prove to be a reliable imaging biomarker in the prodromal phase of PD. Furthermore, NM-MRI demonstrates efficacy in differentiating progressive supranuclear palsy (PSP) from PD and multiple system atrophy with predominant parkinsonism. The spatial patterns of changes in the SNpc can be indicative of PD progression and aid in distinguishing between PSP and synucleinopathies. We recommend that patients with PD and individuals at risk for PD undergo regular NM-MRI examinations. This technology holds the potential for widespread use in PD diagnosis.

Arterial Stiffness and Obesity as Predictors of Diabetes: Longitudinal Cohort Study.

BACKGROUND: Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. OBJECTIVE: This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. METHODS: This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. RESULTS: Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. CONCLUSIONS: This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders.

SALW-Net: a lightweight convolutional neural network based on self-adjusting loss function for spine MR image segmentation.

Segmentation of intervertebral discs and vertebrae from spine magnetic resonance (MR) images is essential to aid diagnosis algorithms for lumbar disc herniation. Convolutional neural networks (CNN) are effective methods, but often require high computational costs. Designing a lightweight CNN is more suitable for medical sites lacking high-computing power devices, yet due to the unbalanced pixel distribution in spine MR images, the segmentation is often sub-optimal. To address this issue, a lightweight spine segmentation CNN based on a self-adjusting loss function, which is named SALW-Net, is proposed in this study. For SALW-Net, the self-adjusting loss function could dynamically adjust the loss weights of the two branches according to the differences in segmentation results and labels during the training; thus, the ability for learning unbalanced pixels is enhanced. Two separate datasets are used to evaluate the proposed SALW-Net. Specifically, the proposed SALW-Net has fewer parameter numbers than U-net (only 2%) but achieves higher evaluation scores than that of U-net (the average DSC score of SALW-Net is 0.8781, and that of U-net is 0.8482). In addition, the practicality validation for SALW-Net is also proceeding, including deploying the model on a lightweight device and producing an aid diagnosis algorithm based on segmentation results. This means our SALW-Net has clinical application potential for assisted diagnosis in low computational power scenarios.

Transcriptome analysis reveals how cadmium promotes root development and accumulates in Apocynum venetum, a promising plant for greening cadmium-contaminated soil.

Cadmium (Cd) contamination poses a substantial threat the environment, necessitating effective remediation strategies. Phytoremediation emerges as a cost-efficient and eco-friendly approach for reducing Cd levels in the soil. In this study, the suitability of A. venetum for ameliorating Cd-contaminated soils was evaluated. Mild Cd stress promoted seedling and root growth, with the root being identified as the primary tissue for Cd accumulation. The Cd content of roots ranged from 0.35 to 0.55 mg/g under treatment with 10-50 µM CdCl2·2.5 H2O, and the bioaccumulation factor ranged from 28.78 to 84.43. Transcriptome sequencing revealed 20,292 unigenes, and 7507 nonredundant differentially expressed genes (DEGs) were identified across five comparison groups. DEGs belonging to the "MAPK signaling pathway-plant," "monoterpenoid biosynthesis," and "flavonoid biosynthesis pathway" exhibited higher expression levels in roots compared to stems and leaves. In addition, cytokinin-related DEGs, ROS scavenger genes, such as P450, glutathione-S-transferase (GST), and superoxide dismutase (SOD), and the cell wall biosynthesis-related genes, CSLG and D-GRL, were also upregulated in the root tissue, suggesting that Cd promotes root development. Conversely, certain ABC transporter genes, (e.g, NRAMP5), and some vacuolar iron transporters, predominantly expressed in the roots, displayed a strong correlation with Cd content, revealing the mechanism underlying the compartmentalized storage of Cd in the roots. KEGG enrichment analysis of DEGs showed that the pathways associated with the biosynthesis of flavonoids, lignin, and some terpenoids were significantly enriched in the roots under Cd stress, underscoring the pivotal role of these pathways in Cd detoxification. Our study suggests A. venetum as a potential Cd-contaminated phytoremediation plant and provides insights into the molecular-level mechanisms of root development promotion and accumulation mechanism in response to Cd stress.

Vibration and Noise Analysis and Experimental Study of Rail Conveyor.

The rail conveyor is a new type of energy-saving system for the long-distance transportation of bulk materials. Operating noise is an urgent problem that the current model faces. It will cause noise pollution and affect the health of workers. In this paper, the factors causing vibration and noise are analyzed by modeling the wheel-rail system and the supporting truss structure. Based on the built test platform, the system vibration of the vertical steering wheel, the track support truss, and the track connection were measured, and the vibration characteristics at different positions were analyzed. Based on the established noise and vibration model, the distribution and occurrence rules of system noise under different operating speeds and fastener stiffness conditions were obtained. The experimental results show that the vibration amplitude of the frame near the head of the conveyor is the largest. The amplitude under the condition of 2 m/s running speed at the same position is 4 times that under the condition of 1 m/s. At different welds of the track, the width and depth of the rail gap have a great influence on the vibration impact, which is mainly due to the impact of the uneven impedance at the track gap, and the greater the running speed, the more obvious the vibration impact. The simulation results show the trend of noise generation, the speed of the trolley, and the stiffness of the track fasteners have a positive effect on the generation of noise in the low-frequency region. The research results of this paper will play an important role in the noise and vibration analysis of rail conveyors and help to optimize the structure design of the track transmission system.

A Lightweight Convolutional Neural Network Based on Dynamic Level-Set Loss Function for Spine MR Image Segmentation.

BACKGROUND: Spine MR image segmentation is important foundation for computer-aided diagnostic (CAD) algorithms of spine disorders. Convolutional neural networks segment effectively, but require high computational costs. PURPOSE: To design a lightweight model based on dynamic level-set loss function for high segmentation performance. STUDY TYPE: Retrospective. POPULATION: Four hundred forty-eight subjects (3163 images) from two separate datasets. Dataset-1: 276 subjects/994 images (53.26% female, mean age 49.02 ± 14.09), all for disc degeneration screening, 188 had disc degeneration, 67 had herniated disc. Dataset-2: public dataset with 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration. FIELD STRENGTH/SEQUENCE: T2 weighted turbo spin echo sequences at 3T. ASSESSMENT: Dynamic Level-set Net (DLS-Net) was compared with four mainstream (including U-net++) and four lightweight models, and manual label made by five radiologists (vertebrae, discs, spinal fluid) used as segmentation evaluation standard. Five-fold cross-validation are used for all experiments. Based on segmentation, a CAD algorithm of lumbar disc was designed for assessing DLS-Net's practicality, and the text annotation (normal, bulging, or herniated) from medical history data were used as evaluation standard. STATISTICAL TESTS: All segmentation models were evaluated with DSC, accuracy, precision, and AUC. The pixel numbers of segmented results were compared with manual label using paired t-tests, with P < 0.05 indicating significance. The CAD algorithm was evaluated with accuracy of lumbar disc diagnosis. RESULTS: With only 1.48% parameters of U-net++, DLS-Net achieved similar accuracy in both datasets (Dataset-1: DSC 0.88 vs. 0.89, AUC 0.94 vs. 0.94; Dataset-2: DSC 0.86 vs. 0.86, AUC 0.93 vs. 0.93). The segmentation results of DLS-Net showed no significant differences with manual labels in pixel numbers for discs (Dataset-1: 1603.30 vs. 1588.77, P = 0.22; Dataset-2: 863.61 vs. 886.4, P = 0.14) and vertebrae (Dataset-1: 3984.28 vs. 3961.94, P = 0.38; Dataset-2: 4806.91 vs. 4732.85, P = 0.21). Based on DLS-Net's segmentation results, the CAD algorithm achieved higher accuracy than using non-cropped MR images (87.47% vs. 61.82%). DATA CONCLUSION: The proposed DLS-Net has fewer parameters but achieves similar accuracy to U-net++, helps CAD algorithm achieve higher accuracy, which facilitates wider application. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

An optimized segmentation convolutional neural network with dynamic energy loss function for 3D reconstruction of lumbar spine MR images.

3D reconstruction for lumbar spine based on segmentation of Magnetic Resonance (MR) images is meaningful for diagnosis of degenerative lumbar spine diseases. However, spine MR images with unbalanced pixel distribution often cause the segmentation performance of Convolutional Neural Network (CNN) reduced. Designing a composite loss function for CNN is an effective way to enhance the segmentation capacity, yet composition loss values with fixed weight may still cause underfitting in CNN training. In this study, we designed a composite loss function with a dynamic weight, called Dynamic Energy Loss, for spine MR images segmentation. In our loss function, the weight percentage of different loss values could be dynamically adjusted during training, thus CNN could fast converge in earlier training stage and focus on detail learning in the later stage. Two datasets were used in control experiments, and the U-net CNN model with our proposed loss function achieved superior performance with Dice similarity coefficient values of 0.9484 and 0.8284 respectively, which were also verified by the Pearson correlation, Bland-Altman, and intra-class correlation coefficient analysis. Furthermore, to improve the 3D reconstruction based on the segmentation results, we proposed a filling algorithm to generate contextually related slices by computing the pixel-level difference between adjacent slices of segmented images, which could enhance the structural information of tissues between slices, and improve the performance of 3D lumbar spine model rendering. Our methods could help radiologists to build a 3D lumbar spine graphical model accurately for diagnosis while reducing burden of manual image reading.

LSW-Net: Lightweight Deep Neural Network Based on Small-World properties for Spine MR Image Segmentation.

BACKGROUND: Segmenting spinal tissues from MR images is important for automatic image analysis. Deep neural network-based segmentation methods are efficient, yet have high computational costs. PURPOSE: To design a lightweight model based on small-world properties (LSW-Net) to segment spinal MR images, suitable for low-computing-power embedded devices. STUDY TYPE: Retrospective. POPULATION: A total of 386 subjects (2948 images) from two independent sources. Dataset I: 214 subjects/779 images, all for disk degeneration screening, 147 had disk degeneration, 52 had herniated disc. Dataset II: 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration. 70% images in each dataset for training, 20% for validation, and 10% for testing. FIELD STRENGTH/SEQUENCE: T1- and T2-weighted turbo spin echo sequences at 3 T. ASSESSMENT: Segmentation performance of LSW-Net was compared with four mainstream (including U-net and U-net++) and five lightweight models using five radiologists' manual segmentations (vertebrae, disks, spinal fluid) as reference standard. LSW-Net was also deployed on NVIDIA Jetson nano to compare the pixels number in segmented vertebrae and disks. STATISTICAL TESTS: All models were evaluated with accuracy, precision, Dice similarity coefficient (DSC), and area under the receiver operating characteristic (AUC). Pixel numbers segmented by LSW-Net on the embedded device were compared with manual segmentation using paired t-tests, with P < 0.05 indicating significance. RESULTS: LSW-Net had 98.5% fewer parameters than U-net but achieved similar accuracy in both datasets (dataset I: DSC 0.84 vs. 0.87, AUC 0.92 vs. 0.94; dataset II: DSC 0.82 vs. 0.82, AUC 0.88 vs. 0.88). LSW-Net showed no significant differences in pixel numbers for vertebrae (dataset I: 5893.49 vs. 5752.61, P = 0.21; dataset II: 5073.42 vs. 5137.12, P = 0.56) and disks (dataset I: 1513.07 vs. 1535.69, P = 0.42; dataset II: 1049.74 vs. 1087.88, P = 0.24) segmentation on an embedded device compared to manual segmentation. DATA CONCLUSION: Proposed LSW-Net achieves high accuracy with fewer parameters than U-net and can be deployed on embedded device, facilitating wider application. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 1.

Heterologous overexpression of Apocynum venetum flavonoids synthetase genes improves Arabidopsis thaliana salt tolerance by activating the IAA and JA biosynthesis pathways.

Salt stress is a serious abiotic stress that primarily inhibits plant growth, resulting in severe yield losses. Our previous research found that flavonoids play important roles in A. venetum salt stress tolerance. In response to salt stress, we noted that the flavonoid content was depleted in A. venetum. However, the detailed mechanism is still not clear. In this study, the expression patterns of three flavonoids synthetase genes, AvF3H, AvF3'H, and AvFLS were systemically analyzed under salt stress in A. venetum seedlings. The salt tolerance of transgenic Arabidopsis plants was improved by heterologous overexpression of these synthetase genes. The NBT and DAB staining results as well as H2O2 and O2•- content analysis revealed that under salt stress, ROS molecules were reduced in transgenic plants compared to WT plants, which corresponded to the activation of the antioxidant enzyme system and an increase in total flavonoid content, particularly rutin, eriodictyol, and naringerin in transgenic plants. External application of flavonoids reduced ROS damage in WT plants just like what we observed in the transgenic plants (without the external application). Additionally, our transcriptome analysis demonstrated that auxin and jasmonic acid biosynthesis genes, as well as signaling transduction genes, were primarily activated in transgenic plants under salt stress, leading to activation of the cell wall biosynthesis or modification genes that promote plant growth. As a result, we investigated the mechanism through flavonoids enhance the salt tolerance, offering a theoretical foundation for enhancing salt tolerance in plants.

Preliminary MRI Study of Extracellular Volume Fraction for Identification of Lymphovascular Space Invasion of Cervical Cancer.

BACKGROUND: Lymphovascular space invasion (LVSI) is a risk factor for poor prognosis of cervical cancer. Preoperative identification of LVSI is very difficult. PURPOSE: To evaluate the potential of extracellular volume (ECV) fraction based on T1 mapping in preoperative identification of LVSI in cervical cancer compared with dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Retrospective. SUBJECTS: A total of 79 patients (median age 54 years) with cervical cancer were classified into LVSI group (n = 29) and without LVSI group (n = 50) according to postoperative pathology. FIELD STRENGTH/SEQUENCE: A 3-T, noncontrast and contrast-enhanced T1 mapping performed with volume interpolated breath hold examination (VIBE) sequence, DCE-MRI applied with 3D T1-weighted VIBE sequence. ASSESSMENT: Regions of interest along the medial edge of the lesion were drawn on slices depicting the maximum cross-section of the tumor. The noncontrast and contrast-enhanced T1 value of the tumor and arteries in the same slice were measured, and ECV was calculated from T1 values. The parametric maps (Ktrans , kep , and ve ) derived from DCE-MRI standard Toft's model were evaluated. STATISTICAL TESTS: ECV, Ktrans , kep , and ve between groups with and without LVSI were compared using Student's t-test. The receiver operating characteristic (ROC) curve and DeLong test were used to evaluate and compare the diagnostic performance of ECV, Ktrans , kep , and ve for differentiating LVSI. P < 0.05 was considered statistically significant. RESULTS: The ECV and Ktrans of the LVSI group were significantly higher than that of non-LVSI group (52.86% vs. 36.77%, 0.239 vs. 0.176, respectively), and no significant differences in Kep or ve values were observed (P = 0.071 and P = 0.168, respectively). The ECV fraction showed significantly higher area under ROC curve than Ktrans for differentiating LVSI (0.874 vs. 0.655, respectively). DATA CONCLUSION: ECV measurements based on T1 mapping might improve the discrimination between patients with and without LVSI in cervical cancer, showing better performance for this purpose than DCE-MRI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Progress of radiological­pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high­grade glioma (Review).

Primary central nervous system lymphoma (PCNSL) and high­grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy­based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.

Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects.

The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6~0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development.

Abnormal brain activities in multiple frequency bands in Parkinson's disease with apathy.

Background: Apathy is among the most prevalent and incapacitating non-motor symptoms of Parkinson's disease (PD). PD patients with apathy (PD-A) have been reported to have abnormal spontaneous brain activity mainly in 0.01-0.08 Hz. However, the frequency-dependence of brain activity in PD-A remains unclear. Therefore, this study aimed to examine whether abnormalities in PD-A are associated with specific frequency bands. Materials and methods: Overall, 28 patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 gender-, age-matched healthy controls (HCs) were enrolled. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data, demographic information, and neuropsychological assessments, including apathy, depression, anxiety and cognitive function for every participant. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in the conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) frequency bands based on statistical parametric mapping (SPM12) and RESTplus V1.25. Two-sample t-tests were performed to compare the differences among the three groups. Results: PD-A reduced ALFF in the right anterior cingulate gyri in the slow-5 band and decreased fALFF in the right middle frontal gyrus in the conventional band, compared to patients with PD-NA. However, PerAF, ReHo, and DC could not distinguish PD-A from PD-NA in the three bands. PD-A had higher ALFF and fALFF in the left middle occipital gyrus and lower fALFF in the bilateral insula in the slow-5 band compared to the HCs. Furthermore, abnormal DC value in hippocampus and parahippocampus was observed separately in the conventional band and in the slow-4 band between PD-A and HCs. Moreover, PD-A and PD-NA showed lower ReHo in cerebellum in the three bands compared to the HCs. Conclusion: Our study revealed that PD-A and PD-NA might have different neurophysiological mechanisms. Concurrently, the ALFF in the slow-5 band and fALFF in the conventional band were sensitive in differentiating PD-A from PD-NA. The influence of apathy on the disease can be considered in the future research on PD, with the effects of frequency band taken into account when analyzing spontaneous brain activities in PD-A.

Effect of Titrated Exposure to Non-Traumatic Noise on Unvoiced Speech Recognition in Human Listeners with Normal Audiological Profiles.

Non-traumatic noise exposure has been shown in animal models to impact the processing of envelope cues. However, evidence in human studies has been conflicting, possibly because the measures have not been specifically parameterized based on listeners' exposure profiles. The current study examined young dental-school students, whose exposure to high-frequency non-traumatic dental-drill noise during their course of study is systematic and precisely quantifiable. Twenty-five dental students and twenty-seven non-dental participants were recruited. The listeners were asked to recognize unvoiced sentences that were processed to contain only envelope cues useful for recognition and have been filtered to frequency regions inside or outside the dental noise spectrum. The sentences were presented either in quiet or in one of the noise maskers, including a steady-state noise, a 16-Hz or 32-Hz temporally modulated noise, or a spectrally modulated noise. The dental students showed no difference from the control group in demographic information, audiological screening outcomes, extended high-frequency thresholds, or unvoiced speech in quiet, but consistently performed more poorly for unvoiced speech recognition in modulated noise. The group difference in noise depended on the filtering conditions. The dental group's degraded performances were observed in temporally modulated noise for high-pass filtered condition only and in spectrally modulated noise for low-pass filtered condition only. The current findings provide the most direct evidence to date of a link between non-traumatic noise exposure and supra-threshold envelope processing issues in human listeners despite the normal audiological profiles.

Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer.

PURPOSE: To establish and validate a radiomics model based on multi-sequence magnetic resonance (MR) images for preoperative prediction of immunoscore in rectal cancer. MATERIALS AND METHODS: This retrospective study included 133 patients with pathologically confirmed rectal cancer after surgical resection who underwent MR examination before treatment within two weeks. All patients were randomly divided into training cohort (n = 92) and validation (n = 41) cohort according to a ratio of 7:3. The volumes of interest were manually delineated in the T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) images, from which a total of 804 radiomics features were extracted. Thereafter, we used Spearman correlation analysis and gradient boosting decision tree (GBDT) algorithm to select the strongest features, and the radiomics scores were established using multivariate logistic regression algorithm, including two single-mode models and two dual-mode models. The predictive performance and the clinical usefulness of the model were assessed by the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS: Integrated model A based on T2WI and ADC images showed a better predictive performance, which yielded an AUC of 0.770 (95% CI 0.673-0.867) in the training cohort and 0.768 (95% CI 0.619-0.917) in the validation cohort. Calibration curve showed good agreement between predicted results of the model and actual events, and DCA indicated good clinical usefulness. Moreover, stratification analysis proved that the integrated model A had strong robustness. CONCLUSIONS: Integrated model A based on T2WI and ADC images has the potential to be used as a non-invasive tool for preoperative prediction of immunoscore in rectal cancer. It may be useful in evaluating prognosis and guiding individualized immunotherapy of patients.

Development of Nickel Selenide@polydopamine Nanocomposites for Magnetic Resonance Imaging Guided NIR-II Photothermal Therapy.

The penetration depth of near-infrared laser has greatly restricted the development of most photothermal agents. Recently, photothermal agents in the second near-infrared (NIR-II) window have drawn great attention as they can overcome above barrier. Herein, a novel "all in one" NIR-II responsive nanoplatform (nickel selenide @polydopamine nanocomposites, NiSe@PDA NCs) based on in situ coating the polydopamine (PDA) on the surface of biomineralized nickel selenide nanoparticles (NiSe NPs) for dual-model imaging-guided photothermal therapy is reported. Under the illumination of NIR-II laser (1064 nm), the photothermal conversion efficiency of NiSe@PDA NCs can reach 48.4%, which is higher than that of single NiSe NPs due to the enhanced molar extinction coefficient. In addition, because of the paramagnetic effect of NiSe NPs, the constructed NiSe@PDA NCs can be acted as T1 contrast agent for magnetic resonance imaging (MRI). Most importantly, the MRI contrast effect is enhanced with the coating of PDA layer due to the loose structure of PDA. Ultimately, both in vitro and in vivo experiments demonstrate that the developed NCs can achieve efficient MRI-guided photothermal therapy for treating malignant tumor. Therefore, the designed NiSe@PDA NCs with excellent features show great potential for clinical MRI-guided cancer therapy.

Corrigendum: Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer.

[This corrects the article DOI: 10.3389/fonc.2021.697497.].

A preliminary study on speech recognition in noise training for children with hearing loss.

PURPOSE: The current study is a preliminary study to examine whether children with hearing loss would benefit from a speech recognition in noise training. METHODS: Twenty-five children who wore hearing aids, cochlear implants, or bimodal devices from 4 to 12 years old participated in the study (experimental, n = 16; control, n = 9). The experimental group received a speech-in-noise training that took sixteen 15-min sessions spanning 8 to 12 weeks. The task involves recognizing monosyllabic target words and sentence keywords with various contextual cues in a multi-talker babble. The target stimuli were spoken by two females and fixed at 65 dB SPL throughout the training while the masker varied adaptively. Pre- and post-training tests measured the speech recognition thresholds of monosyllabic words and sentences spoken by two males in the babble noise. The test targets were presented at 55, 65, and 80 dB SPL. RESULTS: The experimental group improved for word and sentence recognition in noise after training (Mean Difference = 2.4-2.5 dB, 2.7-4.2 dB, respectively). Training benefits were observed at trained (65 dB SPL) and untrained levels (55 and 80 dB SPL). The amount of post-training improvement was comparable between children using hearing aids and cochlear implants. CONCLUSIONS: This preliminary study showed that children with hearing loss could benefit from a speech recognition in noise training that may fit into the children's school schedules. Training at a conversational level (65 dB SPL) transfers the benefit to levels 10-15 dB softer or louder. Training with female target talkers transfers the benefit to male target talkers. Overall, speech in noise training brings practical benefits for school-age children with hearing loss.

Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer.

Background: Immunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer. Purpose: To develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI. Assessment: The patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging. Statistical Tests: Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysis. Results: The radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794-0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777-1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794-0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777-1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845-0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use. Data Conclusion: Radiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.