Repetitive Transcranial Magnetic Stimulation Combined with Sling Exercise Modulates the Motor Cortex in Patients with Chronic Low Back Pain.

The study aims to explore the effects of combining repetitive transcranial magnetic stimulation (rTMS) with sling exercise (SE) intervention in patients with chronic low back pain (CLBP). This approach aims to directly stimulate brain circuits and indirectly activate trunk muscles to influence motor cortex plasticity. However, the impact of this combined intervention on motor cortex organization and clinical symptom improvement is still unclear, as well as whether it is more effective than either intervention alone. To investigate this, patients with CLBP were randomly assigned to three groups: SE/rTMS, rTMS alone, and SE alone. Motor cortical organization, numerical pain rating scale (NPRS), Oswestry Disability Index (ODI), and postural balance stability were measured before and after a 2-week intervention. The results showed statistically significant differences in the representative location of multifidus on the left hemispheres, as well as in NPRS and ODI scores, in the combined SE/rTMS group after the intervention. When compared to the other two groups, the combined SE/rTMS group demonstrated significantly different motor cortical organization, sway area, and path range from the rTMS alone group, but not from the SE alone group. These findings highlight the potential benefits of a combined SE/rTMS intervention in terms of clinical outcomes and neuroadaptive changes compared to rTMS alone. However, there was no significant difference between the combined intervention and SE alone. Therefore, our research does not support the use of rTMS as a standalone treatment for CLBP. Our study contributed to optimizing treatment strategies for individuals suffering from CLBP.

The Impact of Sleep Quality on Cognitive Function in Patients with Chronic Subjective Tinnitus.

BACKGROUND: The aim of this study was to explore the impact of sleep quality on cognitive function in patients with chronic subjective tinnitus. METHODS: The Pittsburgh Sleep Quality Index (PSQI) and the Montreal Cognitive Assessment Scale (MoCA) were used to assess sleep quality and cognitive function in patients with chronic subjective tinnitus, sleep disorder patients (SD), and normal controls (NC). The tinnitus evaluation questionnaire (TEQ) and tinnitus loudness were used to assess the severity in patients with chronic subjective tinnitus. Tinnitus patients were divided into two groups based on PSQI results: "tinnitus with sleep disorder (TwSD)" and "tinnitus without sleep disorder (TnSD)." The MoCA scores in TwSD and TnSD groups were compared with those in SD and NC groups, and the correlation between PSQI, TEQ, tinnitus loudness, and MoCA scores in subjective tinnitus patients were analyzed. RESULTS: Whether TwSD group or TnSD group, the MoCA score was significantly lower than those in the NC group and SD group. Meanwhile, there was no significant difference between TwSD and TnSD groups in MoCA score, and PSQI, TEQ, and tinnitus loudness were not significantly correlated with MoCA. CONCLUSION: Subjective tinnitus may be an independent risk factor for cognitive impairment. The underlying neural mechanisms between subjective tinnitus, sleep disorders, and cognitive impairment need to be further explored and clarified.

Neighborhood evaluator for efficient super-resolution reconstruction of 2D medical images.

BACKGROUND: Deep learning-based super-resolution (SR) algorithms aim to reconstruct low-resolution (LR) images into high-fidelity high-resolution (HR) images by learning the low- and high-frequency information. Experts' diagnostic requirements are fulfilled in medical application scenarios through the high-quality reconstruction of LR digital medical images. PURPOSE: Medical image SR algorithms should satisfy the requirements of arbitrary resolution and high efficiency in applications. However, there is currently no relevant study available. Several SR research on natural images have accomplished the reconstruction of resolutions without limitations. However, these methodologies provide challenges in meeting medical applications due to the large scale of the model, which significantly limits efficiency. Hence, we suggest a highly effective method for reconstructing medical images at any desired resolution. METHODS: Statistical features of medical images exhibit greater continuity in the region of neighboring pixels than natural images. Hence, the process of reconstructing medical images is comparatively less challenging. Utilizing this property, we develop a neighborhood evaluator to represent the continuity of the neighborhood while controlling the network's depth. RESULTS: The suggested method has superior performance across seven scales of reconstruction, as evidenced by experiments conducted on panoramic radiographs and two external public datasets. Furthermore, the proposed network significantly decreases the parameter count by over 20× and the computational workload by over 10× compared to prior researches. On large-scale reconstruction, the inference speed can be enhanced by over 5×. CONCLUSION: The novel proposed SR strategy for medical images performs efficient reconstruction at arbitrary resolution, marking a significant breakthrough in the field. The given scheme facilitates the implementation of SR in mobile medical platforms.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Fate mapping of Spp1 expression reveals age-dependent plasticity of disease-associated microglia-like cells after brain injury.

Microglial reactivity to injury and disease is emerging as a heterogeneous, dynamic, and crucial determinant in neurological disorders. However, the plasticity and fate of disease-associated microglia (DAM) remain largely unknown. We established a lineage tracing system, leveraging the expression dynamics of secreted phosphoprotein 1（Spp1） to label and track DAM-like microglia during brain injury and recovery. Fate mapping of Spp1+ microglia during stroke in juvenile mice revealed an irreversible state of DAM-like microglia that were ultimately eliminated from the injured brain. By contrast, DAM-like microglia in the neonatal stroke models exhibited high plasticity, regaining a homeostatic signature and integrating into the microglial network after recovery. Furthermore, neonatal injury had a lasting impact on microglia, rendering them intrinsically sensitized to subsequent immune challenges. Therefore, our findings highlight the plasticity and innate immune memory of neonatal microglia, shedding light on the fate of DAM-like microglia in various neuropathological conditions.

Enhancing Comprehensive Performance via Capturing and Scattering the Carriers inside PESU-Based Nanocomposite Film Capacitors.

Film capacitors have become key electronic components for electrical energy storage installations and high-power electronic systems. Nonetheless, high-temperature and high-electric-field environments would cause a surge of the energy loss, placing a fundamental challenge for film capacitors applied in harsh environments. Here, we constructed a composite film, combining poly(ether sulfone) (PESU) with excellent thermal stability and large-band-gap filler boron nitride nanosheets (BNNSs). The introduction of BNNSs would form deep/shallow traps inside the dielectric polymer matrix, effectively affecting charge migration. Via density functional theory (DFT) calculation, the higher highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of the BNNS than the matrix facilitate scattering electrons and attracting holes. The resultant composite obtains the desired discharged energy densities (Ud) of 5.89 and 3.86 J/cm3 accompanied by an efficiency above 90% at 150 and 200 °C, respectively, surpassing those of existing dielectric materials at the high-temperature conditions. The paper provides a promising composite dielectric material for high-performance film capacitors capable of operating in harsh environments.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Professionally led support groups for people living with advanced or metastatic cancer: a systematic scoping review of effectiveness and factors critical to implementation success within real-world healthcare and community settings.

PURPOSE: To examine the effectiveness of professionally led support groups for people with advanced or metastatic cancer, and identify factors critical to implementation success within real-world settings. METHODS: Databases (MEDLINE; PsychINFO; CINAHL) and grey literature were searched for empirical publications and evaluations. Articles were screened for eligibility and data systematically extracted, charted and summarised using a modified scoping review methodology. Implementation factors were mapped using Proctor's implementation framework and the Consolidated Framework for Implementation Research 2.0. RESULTS: A total of 1691 publications were identified; 19 were eligible for inclusion (8 randomised controlled trials, 7 qualitative studies, 2 cohort studies, 2 mixed methods studies). Most (n=18) studies focused on tumour-specific support groups. Evidence supported professionally led support groups in reducing mood disturbances (n=5), distress (i.e. traumatic stress, depression) (n=4) and pain (n=2). Other benefits included social connectedness (n=6), addressing existential distress (n=5), information and knowledge (n=6), empowerment and sense of control (n=2), relationships with families (n=2) and communication with health professionals (n=2). Thirteen studies identified factors predicting successful adoption, implementation or sustainment, including acceptability (n=12; 63%), feasibility (n=6; 32%) and appropriateness (n=1; 5%). Key determinants of successful implementation included group leaders' skills/experience, mode of operation, travelling distance, group composition and membership and resourcing. CONCLUSIONS: Professionally led tumour-specific support groups demonstrate effectiveness in reducing mood disturbances, distress and pain among patients. Successful implementation hinges on factors such as leadership expertise, operational methods and resource allocation. IMPLICATIONS FOR CANCER SURVIVORS: Professionally led support groups may fill an important gap in supportive care for people with advanced or metastatic cancer.

Establishment of a gill cell line from yellowfin seabream (Acanthopagrus latus) for studying Amyloodinium ocellatum infection of fish.

Amyloodinium ocellatum is among the most devastating protozoan parasites, causing huge economic losses in the mariculture industry. However, the pathogenesis of amyloodiniosis remains unknown, hindering the development of targeted anti-parasitic drugs. The A. ocellatum in vitro model is an indispensable tool for investigating the pathogenic mechanism of amyloodiniosis at the cellular and molecular levels. The present work developed a new cell line, ALG, from the gill of yellowfin seabream (Acanthopagrus latus). The cell line was routinely cultured at 28°C in Dulbecco's modified Eagle medium (DMEM) supplemented with 15% fetal bovine serum (FBS). ALG cells were adherent and exhibited an epithelioid morphology; the cells were stably passed over 30 generations and successfully cryopreserved. The cell line derived from A. latus was identified based on partial sequence amplification and sequencing of cytochrome B (Cyt b). The ALG was seeded onto transwell inserts and found to be a platform for in vitro infection of A. ocellatum, with a 37.23 ± 5.75% infection rate. Furthermore, scanning electron microscopy (SEM) revealed that A. ocellatum parasitizes cell monolayers via rhizoids. A. ocellatum infection increased the expression of apoptosis and inflammation-related genes, including caspase 3 (Casp 3), interleukin 1 (IL-1), interleukin 10 (IL-10), tumour necrosis factor-alpha (TNF-α), in vivo or in vitro. These results demonstrated that the in vitro gill cell monolayer successfully recapitulated in vivo A. latus host responses to A. ocellatum infection. The ALG cell line holds great promise as a valuable tool for investigating parasite-host interactions in vitro.

Can factor substitution reduce the shadow price of air pollution embodied in international trade? A worldwide perspective.

The calculation of trade-embodied air pollution (TEAP) and its economic losses can be reasonably used to assess the impact of transboundary air pollution. However, these air pollutants, which are associated with international trade, can be easily ignored due to their concealment. Based on this, the global multiregional input‒output model (MRIO) is used to quantify the volume of five air pollutants that are embodied in the trade of 20 countries from 2000 to 2016. Then, the shadow price of trade-embodied air pollution (SPTEAP) and the elasticity of factor substitution (EFS) are both calculated by applying the translog production function. Finally, impulse response analysis is used to study the dynamic impact of EFS on the SPTEAP. The main conclusions are as follows: (1) All countries experienced a mass transfer of TEAP, among which China and the USA are the developing and developed countries with the largest amount of TEAP transfers, respectively. (2) The SPTEAP and EFS vary greatly among countries, and these values are generally higher in developed countries than in developing countries. The relationship between the three EFSs can be expressed as [Formula: see text] in all countries, thus indicating that improving the technological level of a country is the best solution for reducing the TEAP in that country while incurring the lowest cost and the least difficulty. (3) Over the long run, the increase in [Formula: see text] and [Formula: see text] reduces the SPTEAP. Conversely, an increase in [Formula: see text] increases the SPTEAP. Therefore, policymakers should weigh these three factors according to the fluctuation of the SPTEAP and constantly adjust the allocation structure and ratio of these factors to maximize the benefits of transboundary air pollution governance.

Promoting the application of pediatric radiomics via an integrated medical engineering approach.

Radiomics is widely used in adult tumors but has been rarely applied to the field of pediatrics. Promoting the application of radiomics in pediatric diseases, especially in the early diagnosis and stratified treatment of tumors, is of great value to the realization of the WHO 2030 "Global Initiative for Childhood Cancer." This paper discusses the general characteristics of radiomics, the particularity of its application to pediatric diseases, and the current status and prospects of pediatric radiomics. Radiomics is a data-driven science, and the combination of medicine and engineering plays a decisive role in improving data quality, data diversity, and sample size. Compared with adult radiomics, pediatric radiomics is significantly different in data type, disease spectrum, disease staging, and progression. Some progress has been made in the identification, classification, stratification, survival prediction, and prognosis of tumor diseases. In the future, big data applications from multiple centers and cross-talent training should be strengthened to improve the benefits for clinical workers and children.

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.

BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy. MATERIALS AND METHODS: We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model. RESULTS: The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods. CONCLUSIONS: The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.

Semi-supervised Medical Image Segmentation with Multiscale Contrastive Learning and Cross-Supervision.

We propose a semi-supervised segmentation method based on multiscale contrastive learning to solve the problem of shortage of annotations in medical image segmentation tasks. We apply perturbations to the input image and encoded features and make the output as consistent as possible by cross-supervision, which is a way to improve the generalizability of the model. Two scales of contrastive learning, patch-level and pixel-level, are employed to enhance the intra-class compactness and inter-class separability of the features. We evaluate the proposed model using three public datasets for brain tumor,left atrial, and cellular nuclei segmentation. The experiments showed that our model outperforms state-of-the-art methods.Clinical relevance- The proposed method can be used for medical image segmentation with limited annotated data and achieve comparable performance to the fully annotated situation. Such an approach can be easily extended to other clinical applications.

Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.

BACKGROUND: Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS: MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS: The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS: Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Validation of the Identification of Medication Adherence Barriers Questionnaire (IMAB-Q); a Behavioural Science-Underpinned Tool for Identifying Non-Adherence and Diagnosing an Individual's Barriers to Adherence.

Purpose: To validate the Identification of Medication Adherence Barriers Questionnaire (IMAB-Q) as a tool to guide practitioners to identify patients who require support to take their medicines as prescribed, their key barriers to adherence and select relevant behaviour change techniques. Patients and Methods: Adults prescribed medication for cardiovascular disease prevention were recruited from nine community pharmacies in England. Participants completed the IMAB-Q comprising 30 items representing potential barriers to adherence developed from our previous mixed methods study (scoping review and focus groups) underpinned by the Theoretical Domains Framework. Participants also self-reported their adherence on a visual analogue scale (VAS) ranging from perfect adherence (100) to non-adherence (1). A subgroup of 30 participants completed the IMAB-Q twice to investigate test-retest reliability using weighted Kappa. Mokken scaling was used to investigate IMAB-Q structure. Spearman correlation was used to investigate IMAB-Q criterion validity compared to the VAS score. Results: From 1407 invitations, 608 valid responses were received. Respondents had a mean (SD) age of 70.12 (9.9) years and were prescribed a median (IQ) 4 (3, 6) medicines. Worry about unwanted effects (n = 212, 34.5%) and negative emotions evoked by medicine taking (n = 99, 16.1%) were most frequently reported. Mokken scaling did not organise related IMAB-Q items according to the TDF domains (scalability coefficient H = 0.3 to 0.6). Lower VAS self-reported adherence correlated with greater IMAB-Q reported barriers (rho = -0.14, p = 0.001). Test-retest reliability of IMAB-Q items ranged from kappa co-efficient 0.9 to 0.3 (p < 0.05). Conclusion: The IMAB-Q is valid and reliable for identifying people not adhering and their barriers to adherence. Each IMAB-Q item is linked to a TDF domain which in turn is linked to relevant behaviour change techniques. The IMAB-Q can therefore guide patients and practitioners to select strategies tailored to a patient's identified barriers.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.

BACKGROUND: Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs). METHODS: A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set. RESULTS: The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis. CONCLUSION: Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

The Clinical Trials Assistance Pilot: reducing the financial burden of cancer clinical trials for patients in regional New South Wales.

Objective This study investigated whether the provision of financial assistance to patients living in regional New South Wales influenced patients' decisions to participate in a cancer clinical trial (cancer treatment or supportive care) and resulted in improved psychosocial outcomes. Methods Administrative data were collected from participants, including demographics, travel distances and the value of financial support provided. Qualitative interviews were then conducted with a subset of consenting patients who received financial assistance for a clinical trial. Results Sixty-four patients with cancer received financial support for a clinical trial, 27 (42%) of whom were interviewed. Participants whose distance to a trial site was over 400 km received almost three times as much financial support (M = A$3194.20, s.d. = A$1597.60) as participants whose distance to a trial site was between 50 and 100 km (M = A$1116.29, s.d. = $A1311.23). Half of participants indicated that receiving financial assistance influenced their decision to participate in a clinical trial, and most indicated the support alleviated the financial burden of clinical trial participation. Conclusions The provision of financial assistance to patients living in regional areas may reduce inequities in cancer clinical trial participation and improve psychosocial outcomes.

Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network.

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.