Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.

Background: Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. Methods: In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. Results: From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Conclusions: Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.

Cervical cancer segmentation based on medical images: a literature review.

Background and Objective: Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images' segmentation, a comprehensive review within the field is lacking. The purpose of this paper is to comprehensively review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process. Methods: As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: "cervical cancer images", "segmentation", and "outline". The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators. Key Content and Findings: This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods. Conclusions: The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.

[Spatio-temporal Evolution and Driving Factors of Ecological Environment Quality in the Huaihe River Basin Based on RSEI].

Clarifying the spatio-temporal evolution of the ecological environment quality of a watershed and its response to the natural environment and human factors are crucial for policy implementation in the ecological environment of the watershed. Using the Google earth engine（GEE） to establish a remote sensing ecological index （RSEI）, the spatio-temporal changes in the ecological environment quality of the Huaihe River Basin from 2002 to 2022 were evaluated combined with trend analysis, variation coefficient, and Hurst index. The main driving factors of spatial differentiation of RSEI were explored using the geographic detector. The results showed that： ① In the past 21 years, RSEI of the Huaihe River Basin had generally improved, but it showed a gradual upward-downward trend. Overall, the area of poor and less poor grades decreased, the area of medium grades increased, and the area of good and excellent grades increased. The improved area accounted for 55.93%, and the degraded area accounted for 22.01%. ② In terms of spatial distribution, RSEI gradually deteriorated from east to west （except in the northwest and southwest marginal mountainous areas）. The stability was better in the east and worse in the western and central areas. In the future, the ecological quality change in the basin was prone to be anti-sustainable and mainly improved. ③ Factor detection results showed that the spatial differentiation of RSEI in the basin was mainly driven by vegetation factors, followed by altitude. The interaction between two factors enhanced the driving force for RSEI spatial differentiation, in which the interaction between vegetation factor and elevation had the strongest driving force for RSEI spatial differentiation, reaching 86.3%.

Solution plasma-cobalt hydroxide-enabled nitrogen fixation.

Solution plasma-Co(OH)2 interaction significantly boosts nitrogen fixation and achieves a high concentration of NOx- at 9.42 mmol L-1. This surpasses the nitrogen content requirement of 7.67 mmol L-1 for commercial nutrient solutions, offering a sustainable approach for nitrogen fixation from nitrogen, water and electricity.

Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling.

In environmental biofilms, antibiotic-resistant bacteria facilitate the persistence of susceptible counterparts under antibiotic stresses, contributing to increased community-level resistance. However, there is a lack of quantitative understanding of this protective effect and its influential factors, hindering accurate risk assessment of biofilm resistance in diverse environment. This study isolated an opportunistic Escherichia coli pathogen from soil, and engineered it with plasmids conferring antibiotic resistance. Protective effects of the ampicillin resistant strain (AmpR) on their susceptible counterparts (AmpS) were observed in ampicillin-stress colony biofilms. The concentration of ampicillin delineated protective effects into 3 zones: continuous protection (<1 MIC of AmpS), initial AmpS/R dependent (1-8 MIC of AmpS), and ineffective (>8 MIC of AmpS). Intriguingly, Zone 2 exhibited a surprising "less is more" phenomenon tuned by the initial AmpS/R ratio, where biofilm with an initially lower AmpR (1:50 vs 50:1) harbored 30-90 % more AmpR after 24 h growth under antibiotic stress. Compared to AmpS, AmpR displayed superiority in adhesion, antibiotic degradation, motility, and quorum sensing, allowing them to preferentially colonize biofilm edge and areas with higher ampicillin. An agent-based model incorporating protective effects successfully simulated tempo-spatial dynamics of AmpR and AmpS influenced by antibiotic stress and initial AmpS/R. This study provides a holistic view on the pervasive but poorly understood protective effects in biofilm, enabling development of better risk assessment and precisely targeted control strategies of biofilm resistance in diverse environment.

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain.

Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.

Background: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods: We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results: We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions: Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images.

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

Transcriptomic profiling of long non-coding RNAs and messenger RNAs in the liver of mice during Toxoplasma gondii infection.

BACKGROUND: Toxoplasma gondii is an intracellular protozoan parasite that can infect a wide range of warm-blooded animals, including humans. It poses significant health risks, particularly in immunocompromised individuals and during pregnancy, leading to severe disease manifestations. The liver, being a crucial organ involved in immune response and metabolic regulation, plays a critical role in the host's defense against T. gondii infection. METHODS: In this study, we utilized RNA sequencing to investigate the expression profiles of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in the liver of mice infected with T. gondii. By employing this method, we obtained a comprehensive overview of the alterations in gene expression occurring in the liver during infection. RESULTS: By comparing the infected groups to the control groups, we identified numerous differentially expressed lncRNAs DElncRNAs and DEmRNAs at two stages of infection. Specifically, at the acute infection stage, we found 628 DElncRNAs, and 6346 DEmRNAs. At the chronic infection stage, we identified 385 DElncRNAs and 2513 DEmRNAs. Furthermore, we identified 1959 commonly expressed DEmRNAs, including IL27, Nos2, and Cxcr2, across two infection stages. Enrichment and co-location analyses revealed pathways linked to immune and inflammatory responses during T. gondii infection. Notably, through co-location analysis, our analysis revealed several DElncRNAs, including Gm29156, Gm29157, and Gm28644, which are potentially implicated in the progression of liver inflammation induced by T. gondii. Additionally, functional enrichment analysis disclosed stage-specific characteristics of liver inflammation and immune response, alongside changes in metabolic regulation and immunosuppression pathways. CONCLUSIONS: Our findings provide valuable insights into the expression patterns of lncRNAs and mRNAs in the liver at different stages of T. gondii infection. We identified potential regulatory factors and pathways implicated in liver inflammation, thereby enhancing our understanding of the molecular mechanisms underlying liver inflammation and immune responses during T. gondii infection. These findings could contribute to the development of targeted therapeutic strategies for liver inflammation in the context of T. gondii infection.

Microascaceae from the Marine Environment, with Descriptions of Six New Species.

Most reported members of Microascaceae that have been reported originate from the terrestrial environment, where they act as saprobes or plant pathogens. However, our understanding of their species diversity and distribution in the marine environment remains vastly limited, with only 22 species in nine genera having been reported so far. A survey of the fungal diversity in intertidal areas of China's mainland has revealed the discovery of several Microascaceae strains from 14 marine algae and 15 sediment samples. Based on morphological characteristics and LSU-ITS-tef1-tub2 multilocus phylogeny using Bayesian inference and maximum likelihood methods, 48 strains were identified as 18 species belonging to six genera. Among these, six new species were discovered: Gamsia sedimenticola, Microascus algicola, M. gennadii, Scedosporium ellipsosporium, S. shenzhenensis, and S. sphaerospermum. Additionally, the worldwide distribution of the species within this family across various marine habitats was briefly reviewed and discussed. Our study expands the knowledge of species diversity and distribution of Microascaceae in the marine environment.

Physical activity diminished adverse associations of obesity with lipid metabolism in the population of rural regions of China.

The interactive effects of obesity and physical inactivity on lipid metabolism and prevalent dyslipidemia are scarcely reported in rural regions. 39029 subjects were obtained from the Henan Rural Cohort, and their metabolic equivalents (METs) of physical activity (PA) were computed. Independent associations of the obesity indices and PA with either lipid indices or prevalent dyslipidemia were analyzed by generalized linear models, and additive effects of obesity and PA on prevalent dyslipidemia were further quantified. Each obesity index was positively associated with total cholesterol, triglyceride, low-density lipoprotein or prevalent dyslipidemia but negatively associated with high-density lipoprotein, whereas the opposite association of PA with either each lipid index or prevalent dyslipidemia was observed. Joint association of PA and each obesity index with each lipid index and prevalent dyslipidemia was observed. Furthermore, the association of each obesity index in association with each lipid index was attenuated by increased PA levels.

Aryl-Modified Pentamethyl Cyanine Dyes at the C2' Position: A Tunable Platform for Activatable Photosensitizers.

Pentamethyl cyanine dyes are promising fluorophores for fluorescence sensing and imaging. However, advanced biomedical applications require enhanced control of their excited-state properties. Herein, a synthetic approach for attaching aryl substituents at the C2' position of the thio-pentamethine cyanine (TCy5) dye structure is reported for the first time. C2'-aryl substitution enables the regulation of both the twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET) mechanisms to be regulated in the excited state. Modulation of these mechanisms allows the design of a nitroreductase-activatable TCy5 fluorophore for hypoxic tumor photodynamic therapy and fluorescence imaging. These C2'-aryl TCy5 dyes provide a tunable platform for engineering cyanine dyes tailored to sophisticated biological applications, such as photodynamic therapy.

Preference of juvenile tiger puffer for light spectrum and tank colours based on different body size and breeding background.

As important environmental factors, the light spectra and tank colours have not received sufficient attention. Most fishes have the ability to perceive environment, distinguish colours, and exhibit preferences or aversions towards different environments, which can provide a reference for the design of their rearing environment. Tiger puffer (Takifugu rubripes) is an important mariculture species in China and East Asia, but its preference for illumination spectra and tank colours is unclear. This study focuses on the preferences of juvenile tiger puffers for different spectra and tank background colours in different rearing backgrounds and body sizes. The experiments were conducted in a preference testing device, and the behavioural videos were recorded and analysed using a motion behaviour tracking system (EthoVision XT 12). The results show that the puffers showed preference for short-wavelength lights ((i.e., cyan, green, etc.), avoidance of long-wavelength light (i.e., red) and less stay time in the full light spectrum and dark. For tank colours, the puffers showed a preference for light background colours (i.e., white), and avoidance of deep background colours (i.e., dark, red, etc.). Fish body sizes and original breeding environment could significantly affect the selective preference of juvenile puffer (P < 0.05). Large puffers preferred green tank colour than small ones, while small ones preferred grey and red. The puffers reared in green light and grey tank for 3 months preferred green light spectrum and green tank colour compared with those reared in full spectrum and grey tank, while the fish reared in full spectrum preferred grey tank colour and area without light. It was also found that the movement rate of juvenile puffers was affected by the light spectra and tank colours and was positively correlated with light wavelength (P < 0.05). Therefore, for tiger puffer breeding, short-wavelength light spectrums (cyan, green, etc.) and light-coloured tank backgrounds (white) are recommended. Long-wavelength Light-emitting diodes and dark tank colours should be avoided in breeding. This study would provide a reference basis for fish light spectra and background colour preference studies, as well as for the improvement of breeding welfare and production efficiency of juvenile tiger puffer.

Cascaded-TOARNet: A cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation.

BACKGROUND: Accurate and automated segmentation of thoracic organs-at-risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs. PURPOSE: To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT. METHODS: A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded-TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result. RESULTS: We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto-Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset. CONCLUSIONS: This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning.

China's air quality improvement strategy may already be having a positive effect: evidence based on health risk assessment.

Aiming to investigate the health risk impact of PM2.5 pollution on a heavily populated province of China. The exposure response function was used to assess the health risk of PM2.5 pollution. Results shows that the total number of premature deaths and diseases related to PM2.5 pollution in Shandong might reach 159.8 thousand people based on the new WHO (2021) standards. The health effects of PM2.5 pollution were more severe in men than in women. Five of the 16 cities in Shandong had higher health risks caused by PM2.5 pollution, including LinYi, HeZe, JiNing, JiNan, and WeiFang. PM2.5 pollution resulted in nearly 7.4 billions dollars in healthy economic cost, which accounted for 0.57% of GDP in Shandong in 2021. HeZe, LiaoCheng, ZaoZhuang, and LinYi were the cities where the health economic loss was more than 1% of the local GDP, accounted for 1.30, 1.26, 1.08, and 1.04%. Although the more rigorous assessment criteria, the baseline concentration was lowered by 30 µg/m3 compared to our previous study, there was no significant increase in health risks and economic losses. China's air quality improvement strategy may already be having a positive effect.

Effectiveness of an online management platform (Joint Cloud) versus standard process for patients undergoing total knee arthroplasty: study protocol for a prospective randomised controlled trial.

INTRODUCTION: Osteoarthritis (OA) is one of the main causes of mobility impairment in the elderly worldwide. Therefore, total knee arthroplasty (TKA) is often performed and is one of the most successful surgery and has resulted in substantial quality-of-life gains for people with end-stage arthritis. There is still room for improvement in the standard treatment process in the preoperative, intraoperative and postoperative period of TKA. Telerehabilitation has the potential to become a positive alternative to face-to-face rehabilitation nowadays. But it remains unclear how well telemedicine interventions cover the entire surgical pathway (preoperation, intraoperation, postoperation). This study aims to explore the effectiveness of Joint Cloud (JC, an online management platform) compared with existing standard process in regulating functional recovery, pain management, muscle strength changes and other health-related outcomes in patients undergoing total knee arthroplasty preoperation, intraoperation and postoperation. METHODS AND ANALYSIS: A randomised controlled trial was designed to compare the online management platform (JC) with standard process (SP) in patients undergoing TKA. A total of 186 TKA patients will be randomly assigned to the intervention (n=93) or control (n=93) group. Patients in the intervention group will receive access to the 'JC' mini-program. This mini-program provides popular science information (eg, information about OA and TKA), functional exercise information and communication channels. Patients evaluate their condition and functional level through standardised digital questionnaires. The control group of patients will not accept any functions of this mini-program. The primary outcome is knee functional recovery, and the secondary outcomes are pain management, isometric knee extensor muscle strength, patient satisfaction and cost-benefit analysis. Assessments will be performed 1 month and 3 days before surgery (T0) and 1 month and 3 months after surgery. Data analysis will be performed according to the intent-to-treat (ITT) principle. Repeated measures of linear mixed models and parametric and non-parametric testing will be used for statistical analysis. ETHICS AND DISSEMINATION: The study was reviewed and approved by the Tianjin Hospital Medical Ethics Review Committee on 10 February 2023 (2022YLS155). Test data are considered highly sensitive but are available upon request. The findings will be disseminated in peer-reviewed publications. TRIAL REGISTRATION NUMBER: ChiCTR2300068486.

Deep Learning Mammography Classification with a Small Set of Data.

BACKGROUND: Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors' ability to analyze these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized man's involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast. Such abnormalities may be benign or malignant tumors. OBJECTIVE: The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using mammograms. MATERIALS AND METHODS: The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify breast cancer as malignant or benign. RESULTS: The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss, 99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%. CONCLUSION: CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific patient has cancer.

A Dual GLP-1/GIP Receptor Agonist Is More Effective than Liraglutide in the A53T Mouse Model of Parkinson's Disease.

Parkinson's disease (PD) is a complex syndrome with many elements, such as chronic inflammation, oxidative stress, mitochondrial dysfunction, loss of dopaminergic neurons, build-up of alpha-synuclein (α-syn) in cells, and energy depletion in neurons, that drive the disease. We and others have shown that treatment with mimetics of the growth factor glucagon-like peptide 1 (GLP-1) can normalize energy utilization, neuronal survival, and dopamine levels and reduce inflammation. Liraglutide is a GLP-1 analogue that recently showed protective effects in phase 2 clinical trials in PD patients and in Alzheimer disease patients. We have developed a novel dual GLP-1/GIP receptor agonist that can cross the blood-brain barrier and showed good protective effects in animal models of PD. Here, we test liraglutide against the dual GLP-1/GIP agonist DA5-CH (KP405) in the A53T tg mouse model of PD which expresses a human-mutated gene of α-synuclein. Drug treatment reduced impairments in three different motor tests, reduced levels of α-syn in the substantia nigra, reduced the inflammation response and proinflammatory cytokine levels in the substantia nigra and striatum, and normalized biomarker levels of autophagy and mitochondrial activities in A53T mice. DA5-CH was superior in almost all parameters measured and therefore may be a better drug treatment for PD than liraglutide.

CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord.

Ageing is a critical factor in spinal-cord-associated disorders1, yet the ageing-specific mechanisms underlying this relationship remain poorly understood. Here, to address this knowledge gap, we combined single-nucleus RNA-sequencing analysis with behavioural and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord ageing. As an underlying mechanism, we identified a neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons, and they have the ability to trigger senescence, partly by activating SMAD signalling. We further validated the driving role of secreted CHIT1 on MN senescence using multimodal experiments both in vivo, using the NHP spinal cord as a model, and in vitro, using a sophisticated system modelling the human motor-neuron-microenvironment interplay. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.