Rational Design of Biomaterials to Potentiate Cancer Thermal Therapy.

Cancer thermal therapy, also known as hyperthermia therapy, has long been exploited to eradicate mass lesions that are now defined as cancer. With the development of corresponding technologies and equipment, local hyperthermia therapies such as radiofrequency ablation, microwave ablation, and high-intensity focused ultrasound, have has been validated to effectively ablate tumors in modern clinical practice. However, they still face many shortcomings, including nonspecific damages to adjacent normal tissues and incomplete ablation particularly for large tumors, restricting their wide clinical usage. Attributed to their versatile physiochemical properties, biomaterials have been specially designed to potentiate local hyperthermia treatments according to their unique working principles. Meanwhile, biomaterial-based delivery systems are able to bridge hyperthermia therapies with other types of treatment strategies such as chemotherapy, radiotherapy and immunotherapy. Therefore, in this review, we discuss recent progress in the development of functional biomaterials to reinforce local hyperthermia by functioning as thermal sensitizers to endow more efficient tumor-localized thermal ablation and/or as delivery vehicles to synergize with other therapeutic modalities for combined cancer treatments. Thereafter, we provide a critical perspective on the further development of biomaterial-assisted local hyperthermia toward clinical applications.

Early Gray Matter Structural Covariance Predicts Longitudinal Gain in Arithmetic Ability in Children.

Previous neuroimaging studies on arithmetic development have mainly focused on functional activation or functional connectivity between brain regions. It remains largely unknown how brain structures support arithmetic development. The present study investigated whether early gray matter structural covariance contributes to later gain in arithmetic ability in children. We used a public longitudinal sample comprising 63 typically developing children. The participants received structural magnetic resonance imaging scanning when they were 11 years old and were tested with a multiplication task at 11 years old (time 1) and 13 years old (time 2), respectively. Mean gray matter volumes were extracted from eight brain regions of interest to anchor salience network (SN), frontal-parietal network (FPN), motor network (MN), and default mode network (DMN) at time 1. We found that longitudinal gain in arithmetic ability was associated with stronger structural covariance of the SN seed with frontal and parietal regions and stronger structural covariance of the FPN seed with insula, but weaker structural covariance of the FPN seed with motor and temporal regions, weaker structural covariance of the MN seed with frontal and motor regions, and weaker structural covariance of the DMN seed with temporal region. However, we did not detect correlation between longitudinal gain in arithmetic ability and behavioral measure or regional gray matter volume at time 1. Our study provides novel evidence for a specific contribution of gray matter structural covariance to longitudinal gain in arithmetic ability in childhood.

Long-term abacus training gains in children are predicted by medial temporal lobe anatomy and circuitry.

Abacus-based mental calculation (AMC) is a widely used educational tool for enhancing math learning, offering an accessible and cost-effective method for classroom implementation. Despite its universal appeal, the neurocognitive mechanisms that drive the efficacy of AMC training remain poorly understood. Notably, although abacus training relies heavily on the rapid recall of number positions and sequences, the role of memory systems in driving long-term AMC learning remains unknown. Here, we sought to address this gap by investigating the role of the medial temporal lobe (MTL) memory system in predicting long-term AMC training gains in second-grade children, who were longitudinally assessed up to fifth grade. Leveraging multimodal neuroimaging data, we tested the hypothesis that MTL systems, known for their involvement in associative memory, are instrumental in facilitating AMC-induced improvements in math skills. We found that gray matter volume in bilateral MTL, along with functional connectivity between the MTL and frontal and ventral temporal-occipital cortices, significantly predicted learning gains. Intriguingly, greater gray matter volume but weaker connectivity of the posterior parietal cortex predicted better learning outcomes, offering a more nuanced view of brain systems at play in AMC training. Our findings not only underscore the critical role of the MTL memory system in AMC training but also illuminate the neurobiological factors contributing to individual differences in cognitive skill acquisition. A video abstract of this article can be viewed at https://youtu.be/StVooNRc7T8. RESEARCH HIGHLIGHTS: We investigated the role of medial temporal lobe (MTL) memory system in driving children's math learning following abacus-based mental calculation (AMC) training. AMC training improved math skills in elementary school children across their second and fifth grade. MTL structural integrity and functional connectivity with prefrontal and ventral temporal-occipital cortices predicted long-term AMC training-related gains.

Application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system for undergraduate medical students.

BACKGROUND: Process-based teaching is a new education model. SPARK case database is a free medical imaging case database. This manuscript aimed to explore the application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system. METHODS: 117 third year medical students were included. They were divided into Group A, B, C and D according to the curriculum arrangement. Group A and B attended the experimental class at the same time, A was the experimental group, B was the control group. Group C and D attended experimental classes at the same time, C was the experimental group, D was the control group. The experimental group used SPARK case database, while the control group used traditional teaching model for learning. The four groups of students were respectively tested after the theoretical class, before the experimental class, after the experimental class, and one week after the experimental class to compare the results. Finally, all students used SPARK case database to study, and were tested one month after the experimental class to compare their differences. RESULTS: The scores after the theoretical class of Group A and B were (100.0 ± 25.4), (101.0 ± 23.8)(t=-0.160, P > 0.05), Group C and D were (94.7 ± 23.7), (92.1 ± 18.6)(t = 0.467, P > 0.05). The scores of Group A and B before and after the experimental class and one week after the experimental class were respectively (84.1 ± 17.4), (72.1 ± 21.3)(t = 2.363, P < 0.05), (107.6 ± 14.3), (102.1 ± 18.0)(t = 1.292, P > 0.05), (89.7 ± 24.3), (66.6 ± 23.2)(t = 3.706, P < 0.05). The scores of Group C and D were (94.0 ± 17.3), (72.8 ± 25.5)(t = 3.755, P < 0.05), (107.3 ± 20.3), (93.1 ± 20.9)(t = 2.652, P < 0.05), (100.3 ± 19.7), (77.2 ± 24.0)(t = 4.039, P < 0.05). The scores of Group A and B for one month after the experimental class were (86.6 ± 28.8), (84.5 ± 24.0)(t = 0.297, P > 0.05), and Group C and D were (95.7 ± 20.3), (91.7 ± 23.0)(t = 0.699, P > 0.05). CONCLUSIONS: The process-based teaching based on SPARK case database could improve the radiology practice ability of the musculoskeletal system of students.

Inhaled Lipid Nanoparticles Alleviate Established Pulmonary Fibrosis.

Pulmonary fibrosis, a sequela of lung injury resulting from severe infection such as severe acute respiratory syndrome-like coronavirus (SARS-CoV-2) infection, is a kind of life-threatening lung disease with limited therapeutic options. Herein, inhalable liposomes encapsulating metformin, a first-line antidiabetic drug that has been reported to effectively reverse pulmonary fibrosis by modulating multiple metabolic pathways, and nintedanib, a well-known antifibrotic drug that has been widely used in the clinic, are developed for pulmonary fibrosis treatment. The composition of liposomes made of neutral, cationic or anionic lipids, and poly(ethylene glycol) (PEG) is optimized by evaluating their retention in the lung after inhalation. Neutral liposomes with suitable PEG shielding are found to be ideal delivery carriers for metformin and nintedanib with significantly prolonged retention in the lung. Moreover, repeated noninvasive aerosol inhalation delivery of metformin and nintedanib loaded liposomes can effectively diminish the development of fibrosis and improve pulmonary function in bleomycin-induced pulmonary fibrosis by promoting myofibroblast deactivation and apoptosis, inhibiting transforming growth factor 1 (TGFß1) action, suppressing collagen formation, and inducing lipogenic differentiation. Therefore, this work presents a versatile platform with promising clinical translation potential for the noninvasive inhalation delivery of drugs for respiratory disease treatment.

Low-grade adenosquamous carcinoma of the breast: a clinical, morphological and immunohistochemical analysis of 25 patients.

AIMS: Due to its rarity and non-specific clinical and pathological features, low-grade adenosquamous carcinoma (LGASC) of the breast continues to pose diagnostic challenges. Unlike other triple-negative breast carcinomas, LGASC tends to have an indolent clinical behaviour. It is essential to recognise this lesion for accurate diagnosis and appropriate management. METHODS AND RESULTS: Twenty-five cases of LGASC were identified in our archives and collaborating institutes. Cases of LGASC with dominant coexisting other type carcinomas were excluded. We studied the clinical presentation, morphological features, patterns of the commonly used immunohistochemical stains and follow-up. In our cohort, LGASC was commonly located at the outer aspect of the breast and associated with intraductal papilloma. The morphology of LGASC is characterised by infiltrating small glands and nests with variable squamous differentiation. We also found cuffing desmoplastic (fibrolamellar) stromal change in 75% of patients and peripheral lymphocytic aggregates in 87.5% of patients. P63 and smooth muscle myosin (SMM) were the most common myoepithelial markers used to assist in diagnosis. P63 often stained peripheral tumour cells surrounding invasive glands (circumferential staining in 80% of the cases), mimicking myoepithelial cells. It also stained the small nests with squamous differentiation. However, SMM was negative in 63% of the cases. The vast majority of our cases were triple-negative; only a few had focal and weak expressions of ER and PR. One patient who did not have excision developed lymph node metastasis. Most patients underwent excision or mastectomy with negative margins as surgical treatment; there were no recurrences or metastases in these patients with clinical follow-ups up to 108 months. CONCLUSIONS: LGASC has some unique, although not entirely specific, morphological features and immunohistochemical staining patterns. Fibrolamellar stromal change, peripheral lymphocytic aggregates and variable staining of p63 and SMM are valuable features to facilitate the diagnosis.

Correlation of Apparent Diffusion Coefficient With Proliferation and Apoptotic Indexes in a Murine Model of Fibrosarcoma: Comparison of Four Methods for MRI Region of Interest Positioning.

BACKGROUND: Diffusion-weighted imaging (DWI) has demonstrated great potential in predicting the expression of tumor cell proliferation and apoptosis indexes. PURPOSE: To evaluate the impact of four region of interest (ROI) methods on interobserver variability and apparent diffusion coefficient (ADC) values and to examine the correlation of ADC values with Ki-67, Bcl-2, and P53 labeling indexes (LIs) in a murine model of fibrosarcoma. STUDY TYPE: Prospective, animal model. ANIMAL MODEL: A total of 22 female BALB/c mice bearing intramuscular fibrosarcoma xenografts. FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted fast spin-echo (FSE), T2-weighted fast relaxation fast spin-echo, and DWI PROPELLER FSE sequences. ASSESSMENT: Four radiologists measured ADC values using four ROI methods (oval, freehand, small-sample, and whole-volume). Immunohistochemical assessment of Ki-67, Bcl-2, and P53 LIs was performed. STATISTICAL TESTS: Interclass correlation coefficient (ICC), one-way analysis of variance followed by LSD-t post hoc analysis, and Pearson correlation test were performed. The statistical threshold was defined as a P-value of <0.05. RESULTS: All ROI methods for ADC measurements showed excellent interobserver agreement (ICC range, 0.832-0.986). The ADC values demonstrated significant differences among the four ROI methods. The ADC values for oval, freehand, small-sample, and whole-volume ROI methods showed a moderately negative correlation with Ki-67 (r = -0.623; r = -0.629; r = -0.642, and r = -0.431) and Bcl-2 (r = -0.590; r = -0.597; r = -0.659, and r = -0.425) LIs, but no correlation with P53 LI (r = 0.364, P = 0.104; r = 0.350, P = 0.120; r = 0.379, P = 0.091; r = 0.390, P = 0.080). DATA CONCLUSION: The ADC value can be used to evaluate cell proliferation and apoptosis indexes in a murine model of fibrosarcoma, employing the small-sample ROI as a reliable method. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Herbal active small molecule as an immunomodulator for potential application on resistance of common carp against SVCV infection.

Herbal immunomodulators are an important part of prevention and control on viral diseases in aquaculture because of their propensity to improve immunity in fish. The present study was conducted to evaluate the immunomodulatory effect and antiviral activity of a synthesized derivative (serial number: LML1022) against spring viremia of carp virus (SVCV) infection in vitro and in vivo. The antiviral data suggested that LML1022 at 100 µM significantly inhibited the virus replication in epithelioma papulosum cyprini (EPC) cells, and may completely inhibit the infectivity of SVCV virion particles to fish cells by affecting the viral internalization. The results in the related stability of water environments also demonstrated that LML1022 had an inhibitory half-life of 2.3 d at 15 °C, which would facilitate rapid degradation of LML1022 in aquaculture application. For in vivo study, the survival rate of SVCV-infected common carp was increased 30% at least under continuous oral injection of LML1022 at 2.0 mg/kg for 7 d treatment. Additionally, pretreatment of LML1022 on fish prior to SVCV infection also obviously reduced the viral loads in vivo as well as an improved survival rate, showing that LML1022 was potential as an immunomodulator. As an immune response, LML1022 significantly upregulated the immune-related gene expression including IFN-γ2b, IFN-I, ISG15 and Mx1, indicating that its dietary administration may improve the resistance of common carp against SVCV infection.

Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography.

PURPOSES: To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). MATERIALS AND METHODS: Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. RESULTS: A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were - 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). CONCLUSIONS: The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error.

Development of clinical and magnetic resonance imaging-based radiomics nomograms for the differentiation of nodular fasciitis from soft tissue sarcoma.

BACKGROUND: Accurate differentiation of nodular fasciitis (NF) from soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis and treatment of patients. PURPOSE: To develop and evaluate radiomics nomograms based on clinical factors and magnetic resonance imaging (MRI) for the preoperative differentiation of NF from STS. MATERIAL AND METHODS: This retrospective study analyzed the MRI data of 27 patients with pathologically diagnosed NF and 58 patients with STS who were randomly divided into training (n = 62) and validation (n = 23) groups. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI. Radiomics analysis was applied to fat-suppressed T1-weighted (T1W-FS) images, fat-suppressed T2-weighted (T2W-FS) images, and contrast-enhanced T1-weighted (CE-T1W) images. The radiomics nomograms incorporating the radiomics signatures, clinical factors, and semantic features of MRI were developed. ROC curves and AUCs were carried out to compare the performance of the clinical factors, radiomics signatures, and clinical radiomics nomograms. RESULTS: Tumor location, size, heterogeneous signal intensity on T2W-FS imaging, heterogeneous signal intensity on CE-T1W imaging, margin definitions on CE-T1W imaging, and septa were independent predictors for differentiating NF from STS (P < 0.05). The performance of the radiomics signatures based on T2W-FS imaging (AUC = 0.961) and CE-T1W imaging (AUC = 0.938) was better than that based on T1W-FS imaging (AUC = 0.833). The radiomics nomograms had AUCs of 0.949, which demonstrated good clinical utility and calibration. CONCLUSION: The non-invasive clinical radiomics nomograms exhibited good performance in the differentiation of NF from STS, and they have clinical application in the preoperative diagnosis of diseases.

Resource Allocation for a Secure SWIPT Network Based on a Quantitative Energy Harvesting Mechanism.

Simultaneous wireless information and power transfer (SWIPT) technology can effectively extend the lifecycle of energy-constrained networks. In order to improve the energy harvesting (EH) efficiency and network performance in secure SWIPT networks, this paper studies the resource allocation problem based on the quantitative EH mechanism in the secure SWIPT network. Based on a quantitative EH mechanism and nonlinear EH model, a quantified power-splitting (QPS) receiver architecture is designed. This architecture is applied in the multiuser multi-input single-output secure SWIPT network. With the goal of maximizing the network throughput, the optimization problem model is established under the conditions of meeting the legal user's signal-to-interference-plus-noise ratio (SINR), EH requirements, the total transmit power of the base station, and the security SINR threshold constraints. Due to the coupling of variables, the problem is a nonconvex optimization problem. To deal with the nonconvex optimization problem, a hierarchical optimization method is adopted. Firstly, an optimization algorithm based on the optimal received power of EH circuit is proposed, and a power mapping table is constructed through the optimization algorithm, from which the optimal power ratio to meet the user's EH requirements is obtained; then, the nonconvex problem is transformed into a convex problem by using variable substitution, semidefinite relaxation, dichotomous optimization, etc. The simulation results show that compared with the power splitting receiver architecture, the input power threshold range of the QPS receiver architecture is larger, which can avoid the EH circuit falling into the saturated working area and maintain high network throughput.

The longitudinal development of large-scale functional brain networks for arithmetic ability from childhood to adolescence.

Arithmetic ability is an important high-level cognitive function that requires interaction among multiple brain regions. Previous studies on arithmetic development have focused on task-induced activation in isolated brain regions or functional connectivity among particular seed regions. However, it remains largely unknown whether and how functional connectivity among large-scale brain modules contributes to arithmetic development. In the present study, we used a longitudinal sample of task-based functional magnetic resonance imaging (fMRI) data comprising 63 typically developing children, with two testing points being about 2 years apart. With graph theory, we examined the longitudinal development of large-scale brain modules for a multiplication task in younger (mean age 9.88 at time 1) and older children (mean age 12.34 at time 1), respectively. The results showed that the default-mode (DMN) and frontal-parietal networks (FPN) became increasingly segregated over time. Specifically, intra-connectivity within the DMN and FPN increased significantly with age, and inter-connectivity between the DMN and visual network decreased significantly with age. Such developmental changes were mainly observed in the younger children but not in the older children. Moreover, the change in network segregation of the DMN was positively correlated with longitudinal gain in arithmetic performance in the younger children, and individual difference in network segregation of the FPN was positively correlated with arithmetic performance at Time 2 in the older children. Taken together, the present results highlight the development of the functional architecture in large-scale brain networks from childhood to adolescence, which may provide insights into potential neural mechanisms underlying arithmetic development.

Utility of Advanced DWI in the Detection of Spinal Cord Microstructural Alterations and Assessment of Neurologic Function in Cervical Spondylotic Myelopathy Patients.

BACKGROUND: Diffusion-weighted imaging (DWI) can quantify the microstructural changes in the spinal cord. It might be a substitute for T2 increased signal intensity (ISI) for cervical spondylotic myelopathy (CSM) evaluation and prognosis. PURPOSE: The purpose of the study is to investigate the relationship between DWI metrics and neurologic function of patients with CSM. STUDY TYPE: Retrospective. POPULATION: Forty-eight patients with CSM (18.8% females) and 36 healthy controls (HCs, 25.0% females). FIELD STRENGTH/SEQUENCE: 3 T; spin-echo echo-planar imaging-DWI; turbo spin-echo T1/T2; multi-echo gradient echo T2*. ASSESSMENT: For patients, conventional MRI indicators (presence and grades of T2 ISI), DWI indicators (neurite orientation dispersion and density imaging [NODDI]-derived isotropic volume fraction [ISOVF], intracellular volume fraction, and orientation dispersion index [ODI], diffusion tensor imaging [DTI]-derived fractional anisotropy [FA] and mean diffusivity [MD], and diffusion kurtosis imaging [DKI]-derived FA, MD, and mean kurtosis), clinical conditions, and modified Japanese Orthopaedic Association (mJOA) were recorded before the surgery. Neurologic function improvement was measured by the 3-month follow-up recovery rate (RR). For HCs, DWI, and mJOA were measured as baseline comparison. STATISTICAL TESTS: Continuous (categorical) variables were compared between patients and HCs using Student's t-tests or Mann-Whitney U tests (chi-square or Fisher exact tests). The relationships between DWI metrics/conventional MRI findings, and the pre-operative mJOA/RR were assessed using correlation and multivariate analysis. P < 0.05 was considered statistically significant. RESULTS: Among patients, grades of T2 ISI were not correlated with pre-surgical mJOA/RR (P = 0.717  and 0.175, respectively). NODDI ODI correlated with pre-operative mJOA (r = -0.31). DTI FA, DKI FA, and NODDI ISOVF were correlated with the recovery rate (r = 0.31, 0.41, and -0.34, respectively). In multivariate analysis, NODDI ODI (DTI FA, DKI FA, NODDI ISOVF) significantly contributed to the pre-operative mJOA (RR) after adjusting for age. DATA CONCLUSION: DTI FA, DKI FA, and NODDI ISOVF are predictors for prognosis in patients with CSM. NODDI ODI can be used to evaluate CSM severity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 5.

Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T2*-weighted radiomic-based extra trees models.

OBJECTIVES: Conventional MRI may not be ideal for predicting cervical spondylotic myelopathy (CSM) prognosis. In this study, we used radiomics in predicting postoperative recovery in CSM. We aimed to develop and validate radiomic feature-based extra trees models. METHODS: There were 151 patients with CSM who underwent preoperative T2-/ T2*-weighted imaging (WI) and surgery. They were divided into good/poor outcome groups based on the recovery rate. Datasets from multiple scanners were randomised into training and internal validation sets, while the dataset from an independent scanner was used for external validation. Radiomic features were extracted from the transverse spinal cord at the maximum compressed level. Threshold selection algorithm, collinearity removal, and tree-based feature selection were applied sequentially in the training set to obtain the optimal radiomic features. The classification of intramedullary increased signal on T2/T2*WI and compression ratio of the spinal cord on T2*WI were selected as the conventional MRI features. Clinical features were age, preoperative mJOA, and symptom duration. Four models were constructed: radiological, radiomic, clinical-radiological, and clinical-radiomic. An AUC significantly > 0.5 was considered meaningful predictive performance based on the DeLong test. The mean decrease in impurity was used to measure feature importance. p < 0.05 was considered statistically significant. RESULTS: On internal and external validations, AUCs of the radiomic and clinical-radiomic models, and radiological and clinical-radiological models ranged from 0.71 to 0.81 (significantly > 0.5) and 0.40 to 0.55, respectively. Wavelet-LL first-order variance was the most important feature in the radiomic model. CONCLUSION: Radiomic features, especially wavelet-LL first-order variance, contribute to meaningful predictive models for CSM prognosis. KEY POINTS: • Conventional MRI features may not be ideal in predicting prognosis. • Radiomics provides greater predictive efficiency in the recovery from cervical spondylotic myelopathy.

Bayesian transformation models with partly interval-censored data.

In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.

Adaptive Reconfiguration of Intrinsic Community Structure in Children with 5-Year Abacus Training.

Human learning can be understood as a network phenomenon, underpinned by the adaptive reconfiguration of modular organization. However, the plasticity of community structure (CS) in resting-state network induced by cognitive intervention has never been investigated. Here, we explored the individual difference of intrinsic CS between children with 5-year abacus-based mental calculation (AMC) training (35 subjects) and their peers without prior experience in AMC (31 subjects). Using permutation-based analysis between subjects in the two groups, we found the significant alteration of intrinsic CS, with training-attenuated individual difference. The alteration of CS focused on selective subsets of cortical regions ("core areas"), predominantly affiliated to the visual, somatomotor, and default-mode subsystems. These subsystems exhibited training-promoted cohesion with attenuated interaction between them, from the perspective of individuals' CS. Moreover, the cohesion of visual network could predict training-improved math ability in the AMC group, but not in the control group. Finally, the whole network displayed enhanced segregation in the AMC group, including higher modularity index, more provincial hubs, lower participation coefficient, and fewer between-module links, largely due to the segregation of "core areas." Collectively, our findings suggested that the intrinsic CS could get reconfigured toward more localized processing and segregated architecture after long-term cognitive training.

Sustained high atmospheric Hg level in Beijing during wet seasons suggests that anthropogenic pollution is continuing: Identification of potential sources.

Gaseous elemental Hg (GEM), particulate bound Hg (PBM), and gaseous oxidized Hg (GOM) were monitored at an urban site in Beijing, China during wet seasons (July-November) of 2021. The mean (± standard deviation) GEM, PBM, and GOM concentrations were 3.45 ± 1.27 ng m-3, 48.2 ± 88.6 pg m-3, and 13.7 ± 55.0 pg m-3, respectively. GEM level was stable (generally 3.0-4.0 ng m-3) and the average concentration was about twice that of the background level in Beijing, while the occasionally very high PBM and GOM concentrations (>1000 pg m-3) suggest pollution events. Moreover, GEM, CO, and NO2 exhibit a conspicuous similar diurnal trend with lower values during daytime compared to nighttime under the combined influence of anthropogenic emissions and meteorological factors, and the significantly positive relationship between them indicates that they had similar or common sources. However, the diurnal pattern of reactive Hg (i.e., RM = PBM + GOM) was not pronounced. Both cluster and potential source contribution function analyses show that southern Beijing, Tianjin, as well as central and east Hebei provinces were the dominant source regions for elevated GEM at this monitoring site. The dominant reason for the elevated GEM level (generally >3.5 ng m-3) during pollution event is that majority of air masses originated from the southern polluted regions of this sampling site and traveled at low heights, while the long-range transport of upper clean air masses and continuous high traveling heights were attributed to the low GEM level (<2.0 ng m-3) during clean event. Positive matrix factorization results reveal that regional transport of coal fired air pollutants and local vehicle emissions were the dominant contributors to elevated GEM level, while RM mainly originated from local sources.

Equipping Cancer Cell Membrane Vesicles with Functional DNA as a Targeted Vaccine for Cancer Immunotherapy.

By inducing tumor-specific immune responses, tumor vaccines have recently aroused great research interest. Herein, we design a targeted nanovaccine by equipping cell membrane vesicles (CMVs) harvested from tumor cells with functional DNA including CpG oligonucleotide, an agonist for toll-like receptor 9, as well as an aptamer targeting the dendritic cell (DC)-specific intercellular adhesion molecule (ICAM)-3 grabbing nonintegrin (DC-SIGN) receptor overexpressed on DCs. Such DNA-modified CMVs could target DCs and further stimulate their maturation. Notably, our nanovaccines could trigger robust antitumor immune responses to effective delay the tumor growth. Moreover, the combination of CMV-based nanovaccines with an immune checkpoint blockade could result in improved therapeutic responses by eliminating the majority of the tumors as well as long-term immune memory to prevent tumor recurrence. Therefore, by simply assembling functional DNA on CMVs harvested from tumor cells, we propose a general platform of DC-targeted personalized cancer vaccines for effective and specific cancer immunotherapy.

Individualized video recommendation modulates functional connectivity between large scale networks.

With the emergence of AI-powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long-term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large-scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal-parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN-FPN-Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN-VAN-dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.

BACKGROUND: Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM. PURPOSE: To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients. STUDY TYPE: Retrospective. POPULATION: Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]). FIELD STRENGTH/SEQUENCE: A 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI). ASSESSMENT: Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T1 WI/T2 WI/two-sequence MRI (T1 WI and FS-T2 WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non-HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k-nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. STATISTICAL TESTS: Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method. RESULTS: The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82; P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05). CONCLUSION: The LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two-sequence MRI showed good performance in differentiating HRC and non-HRC statuses in MM. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.