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Abstract This study aims to develop and validate different radiomics models based on thoracic and upper lumbar spine in chest low-dose computed tomography (LDCT) to predict low bone mineral density (BMD) using quantitative computed tomography (QCT) as standard of reference. A total of 905 participants underwent chest LDCT and paired QCT BMD examination were retrospectively included from August 2018 and June 2019. The patients with low BMD (n = 388) and the normal (n = 517) were randomly divided into a training set (n = 622) and a validation set (n = 283). Radiomics features (RFs) were extracted from the single and consecutive vertebrae in chest LDCT images to construct the single vertebra RFs models, mixed RFs models and Radscore models, respectively. The performance of these models was evaluated by the area under the curve (AUC) of receiver operator characteristic curve, using QCT as standard of reference. The Radscore models, mixed RFs models, and single vertebra RFs models yielded the AUC values ranging from 0.809 to 0.906, 0.792 to 0.883, and 0.731 to 0.884 for predicting low BMD in the validation set, respectively. For predicting low BMD, the Radscore model of L1-L2 vertebrae yielded the highest AUC of 0.906, and of T1-T3 yielded the lowest AUC of 0.809 (P 0.1). The Radscore model of L1-L2 have potential to serve as an important tool for predicting and screening low BMD from normal in chest LDCT images.

Abstract Hypertension is a primary risk factor for the onset of cardiocerebrovascular diseases, leading to increased mortality. In the early stages of hypertension, changes in the diameter of the retinal arteries and veins are closely observed. Therefore, the automatic segmentation of these arteries and veins in retinal fundus images is crucial for hypertension monitoring. However, current deep learning based methods still fail to generate semantically consistent segmentation result, especially for narrow blood vessel segments. Moreover, the pixels near the vessel edge are prone to be missclassified by current methods, struggling to obtain edge-preserving segmentation results. These critical issues severely hinder downstream applications, which require accurate measurement of artery/vein diameter. First, to alleviate the issue of lacking semantic consistency for vessel segments, we propose a long-range spatial dependency modeling module to learn to model the long-range spatial dependency. On this basis, a multi-level edge guided spatial aggregation module is further presented to enhance the ability to accurately classify the edge pixels, generating edge-preserving results. Extensive experimental results on the widely used DRIVE dataset and our constructed dataset (HFC-50) show the superiority of the proposed model over state-of-the-art methods. Our method achieves balanced accuracy of 95.7% on DRIVE dataset, outperforming all state-of-art methods. Finally, in order to directly demonstrate the benefit of more accurate retinal artery/vein vessel segmentation for the measurement of the ratio of artery and vein, we quantitatively evaluate the computed artery/vein ratio for hypertension patients, with the artery/vein segmentation results generated by our proposed method.

Abstract To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of 54 patients with CP and 71 healthy controls was randomly separated into training and validation groups, using a 7:3 ratio. Radiomics features were extracted from grey-matter and white-matter in the filtered mwp0* image. The Mann-Whitney U test, Spearman’s rank correlation test, and least absolute shrinkage and selection operator (LASSO) were utilized to select features. An XGBoost model was created based on these features, and Shapley Additive exPlanations (SHAP) was used for personalization and visual interpretation. A nomogram was developed for the diagnosis of FMS subgroups, utilizing radiomics scores and clinical predictors. The efficacy of the nomogram was evaluated using the area under the receiver operating characteristic curve, while decision curve analysis was employed to evaluate its clinical efficacy. The XGBoost model displays stability in the training validation group, indicating lower overfitting of CP model. The nomogram model combined with the rad-score has a greater ability to distinguish between typical and sub-clinical than the clinical factor model alone. We developed and validated a CP diagnosis model by XGBoost and realized model visualization through SHAP. The rad-score obtained by machine learning was used to build a nomogram model that combines clinical scales to distinguish patients with typical and sub-clinical fibromyalgia.

Abstract Zearalenone (ZEA) is a mycotoxin produced by Fusarium fungi that has been shown to have adverse effects on human and animal health, particularly on the fertility of females. As a saponin derived from the medicinal plant Centella asiatica, asiaticoside (AS) has multiple bioactivities. This study aimed to investigate the protective effects of AS on ZEA-induced uterine injury and the underlying mechanism. In the present study, we demonstrated that AS could rescue ZEA-induced uterine histopathological damage and modulate the secretion of sex hormones, including progesterone (P4), luteinizing hormone (LH), and estradiol (E2), in ZEA-treated mice. Moreover, AS alleviated ZEA-induced damage to endometrial barrier function by upregulating the expression of tight junction proteins (ZO-1, occludin, and claudin-3). Further mechanistic investigations indicated that ZEA reduces the antioxidant capacity of uterine tissues, whereas AS improves the antioxidant capacity through activating the Nrf2 signaling pathway. Most notably, the protective effect of AS was blocked in Nrf2 gene knockout (Nrf2−/−) mice. Moreover, the p38/ERK MAPK pathway has been implicated in regulating ZEA toxicity and the beneficial effect of AS. Additionally, an Nrf2 inhibitor (ML385) weaken the suppressive effect of AS on the oxidative stress and MAPK pathway. AS also inhibits ZEA-induced apoptosis in uterine tissues via the PI3K/Akt signaling pathway. However, when the PI3K small molecule inhibitor LY294002 was co-administered, the ability of AS to suppress the expression of apoptosis-related proteins and inhibit ZEA-induced apoptosis decreased. Collectively, these findings reveal the involvement of multiple pathways and targets in the protective effect of AS against ZEA-induced uterine injury, providing a new perspective for the application of AS and the development of a ZEA antidote.

Abstract Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5–20.43% accuracy, 3.6–23.56% F1-score, 3.5–25.71% AUROC and 8.1–36.98% specificity, respectively.

Pharmacological treatment of diabetes mellitus-induced erectile dysfunction (DMED) has become increasingly challenging due to the limited efficacy of phosphodiesterase type 5 inhibitors (PDE5i). As the global prevalence of DM continues, there is a critical need for novel therapeutic strategies to address DMED. In our previous studies, we found that Glutathione peroxidase 4 (GPX4), a ferroptosis inhibitor, can ameliorate DMED in diabetic rats. However, the specific role of GPX4 in corpus cavernosum smooth muscle cells (CCSMCs) and its regulatory mechanisms remain unclear. In this study, we established primary cultures of CCSMCs and systematically analyzed the role of GPX4 under high-glucose conditions. To further elucidate the upstream regulatory pathways of GPX4, we employed immunoprecipitation coupled with mass spectrometry (IP-MS) to identify potential interacting proteins. Additionally, co-immunoprecipitation (Co-IP) and cycloheximide (CHX) chase assays were conducted to explore the regulatory dynamics and post-translational stability of GPX4. Under high-glucose conditions, the expression of GPX4 in CCSMCs is significantly downregulated, leading to an increase in intracellular oxidative stress and heightened levels of ferroptosis, accompanied by dysfunction in smooth muscle cell relaxation. Furthermore, the CHX chase assay revealed that high glucose accelerates GPX4 protein degradation via the ubiquitin–proteasome pathway. Subsequent IP-MS identified NEDD4, an E3 ubiquitin ligase, as a potential interacting partner of GPX4. Further validation demonstrated that NEDD4 modulates the ubiquitination process of GPX4, thereby influencing its stability and expression. In conclusion, we identified NEDD4 as a key regulator of GPX4 stability through ubiquitin-mediated proteasomal degradation. These findings suggest potential therapeutic strategies targeting the NEDD4-GPX4 axis to alleviate DMED pathology.

Abstract Background The purpose of this study was to investigate retinal layers changes in patients with age-related macular degeneration (AMD) treated with anti-vascular endothelial growth factor (anti-VEGF) agents and to evaluate if these changes may affect treatment response. Methods This study included 496 patients with AMD or PCV who were treated with anti-VEGF agents and followed up for at least 6 months. A comprehensive analysis of retinal layers affecting visual acuity was conducted. To eliminate the fact that the average thickness calculated may lead to differences tending to converge towards the mean, we proposed that the retinal layer was divided into different regions and the thickness of the retinal layer was analyzed at the same time. The labeled data will be publicly available for further research. Results Compared to baseline, significant improvement in visual acuity was observed in patients at the 6-month follow-up. Statistically significant reduction in central retinal thickness and separate retinal layer thickness was also observed (p

BackgroundProstate cancer is viewed as the second most common cancer in men worldwide. In our study, we used bibliometric analysis to construct a visual map of the relationship between prostate cancer and exosomes with the intent of uncovering research trends and current hotspots in this field.MethodWe searched the Web of Science Core Collection for all publications in the prostate cancer associated with exosome field came out since 2010. With the assistance of bibliometric analysis software such as VOSviewer and CiteSpace, we conducted data extraction and analysis for countries/regions, institutions, authors, journals, references and keywords.ResultsA bibliometric analysis of 990 publications was performed. Since 2010, the published quantity and cited frequency of the prostate cancer-associated exosome field have revealed an increasing tendency. In this field, we visualized the research trends by the means of analyzing the references and keywords. We obtained the statistical data: the total citations of publications have increased to 55,462, the average citation per article has reached 55.3 times, and the H-index has amounted to 110. Our findings supported that USA, China and Italy rank the top countries with both the maximum publications and strongest cooperations. Harvard Medical School, Cedars Sinai Med Ctr, Johns Hopkins University, are top institutions in the center of research as they are held to be. Thery C, Skog J and Taylor DD are the leading and outstanding professors and researchers. And top journals like Prostate, Plos One and Journal of Extracellular Vesicle expressed keen interests in this field. Based on our analysis and research, we believe that this field is attracting more and more attention and will focus on tumor bone metastasis, drug delivery, and tumor suppressor.ConclusionIn the past 12 years, researchers have dedicated their efforts to prostate cancer associated exosome. On the basis of previous studies, scientists are showing increasingly solicitude for the role of exosome in prostate cancer progression and potential therapy such as drug delivery.

IntroductionSince the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field.MethodDocuments regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords.ResultsWe analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion.ConclusionAI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field.

IntroductionAccurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.MethodsThe proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.ResultsExperimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.DiscussionOur method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

IntroductionThere have been few mid-term follow-up studies comparing arthroscopic and open Broström-Gould repair of the anterior talofibular ligament (ATFL). The purpose of this study was to evaluate the mid-term therapeutic effectiveness of arthroscopic ATFL repair with open Broström-Gould repair for chronic lateral ankle instability.MethodsWe retrospectively reviewed the database of patients with chronic lateral ankle instability undergoing repair of the ATFL from June 2014 to June 2018. The choice of surgical approach will depend on computer-generated randomization. In total, 49 patients underwent the arthroscopic Brostrom-Gould technique (group AB), while the other 50 patients underwent the open Broström-Gould technique (group OB). The surgery duration, hospitalization time, postoperative complications, the preoperative/postoperative manual anterior drawer test (ADT), Visual analog scale (VAS) scores, American Orthopaedic Foot & Ankle Society (AOFAS) scores, Karlsson-Peterson (K-P) scores, and Tegner activity scores were collected for comparative analysis during the follow-up period of 48 months.ResultsAt the final follow-up, the clinical outcomes, including ADT, VAS, AOFAS, K-P, and Tegner activity scores, were significantly improved after either arthroscopic or open treatment. Specifically, the AOFAS and K-P scores in the group AB were significantly higher than those in the group OB at 6 months post-surgery (P

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.

Background: Calcium oxalate kidney stone is one of the common diseases in the urinary system and has a high recurrence rate. Currently, the pathogenesis of kidney stone and the methods to prevent recurrence are still being investigated. Autophagy, as an event of cellular self-repair, has received attention in the field of kidney stone in recent years. In some current studies, autophagy has shown destructiveness and protectiveness in the pathogenesis of kidney stone. The inhibition or promotion of autophagy may be a key target for future kidney stone therapy. This systematic literature review discusses the function of autophagy in kidney stone pathogenesis in the context of current research and synthesizes the evidence analysis to provide a basis for new future therapies.Method: We systematically reviewed the literature during September 2021 according to the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) guidelines. Articles on studying the role of autophagy in the pathogenesis of calcium oxalate kidney stone were extracted from PubMed, MEDLINE, Embase and Scopus, including in vivo versus in vitro experiments. The study topic, language and publication date were not restricted. Two authors (Li and Zhou) searched and screened the literature.Results: We screened 18 articles from the 33 collected articles, of which 6 conducted in vitro cellular studies, four conducted animal studies, eight conducted cellular studies with animal studies, and five studied human specimens. In early studies, the literature generally concluded that autophagy is deleterious in the development of kidney stone. In 2020, the idea of the protectiveness of autophagy associated with kidney stone was first proposed and focused on targeting transcription factor EB. In addition, the interaction of autophagy with other cellular events and the regulation of signaling molecules are focused on in this paper.Conclusion: This systematic review provides advances in research on the role of autophagy in renal calculi. The current studies suggest that both upregulation and downregulation of autophagy may ameliorate injury in kidney stone models. The authors prefer the upregulation of autophagy as a future research direction for kidney stone treatment.

Prostate cancer is the third leading cause of new cancer cases and the second most common tumor type in men globally. LMO3 has been stated to play a vital role in some cancers; however, the prognostic value of LMO3 in PCa remains vague. Here, we utilized various web databases to elucidate in detail the prognostic value and molecular functions of LMO3 in PCa. LMO3 expression was significantly decreased in PCa. Low LMO3 expression was associated with gender, age, and TNM grade and predicted a poor prognosis in PCa patients. Functional enrichment analysis suggested that LMO3 is engaged in the extracellular matrix and immune response. Moreover, LMO3 was positively correlated with immune infiltration levels and numerous immune markers. LMO3 may function as a prospective biomarker of immune infiltration in PCa.

Erectile dysfunction (ED) is a common sexual dysfunction in males, with multifactorial alterations which consist of psychological and organic. Diabetes mellitus (DM) induced erectile dysfunction (DMED) is a disconcerting and critical complication of DM, and remarkably different from non-diabetic ED. The response rate of phosphodiesterase type 5 inhibitor (PDE5i), a milestone for ED therapy, is far from satisfactory in DMED. Unfortunately, the contributing mechanisms of DMED remains vague. Hence, It is urgent to seek for novel prospective biomarkers or targets of DMED. Numerous studies have proved that non-coding RNAs (ncRNAs) play essential roles in the pathogenesis process of DM, which comprise of long non-coding RNAs (lncRNAs) and small non-coding RNAs (sncRNAs) like microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs). However, the implications of ncRNAs in DMED are still understudied. This review highlights the pathophysiology of DMED, summarizes identified mechanisms of ncRNAs associated with DMED and covers the topic of perspectives for ncRNAs in DMED.

Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.

Ketamine is widely used for cancer pain treatment in clinic, and has been shown to inhibit various tumor cells growth. However, the effect of ketamine on ovarian cancer cells growth and the downstream molecules has not been defined. In the present study, we found that ketamine significantly inhibited the proliferation and survival of six ovarian cancer cell lines. Moreover, ketamine induced ovarian cancer cell cycle arrest, apoptosis, and inhibited colony formation capacity. Since lncRNAs have been identified as key regulators of cancer development, we performed bioinformatics analysis of a GEO dataset and found fourteen significantly altered lncRNAs in ovarian cancer patients. We then investigated the effect of ketamine on these lncRNAs, and found that ketamine regulated the expression of lncRNA PVT1. Mechanistically, ketamine regulated P300-mediated H3K27 acetylation activation in the promoter of PVT1. Our RNA immunoprecipitation experiment indicated that PVT1 bound histone methyltransferase enhancer of zeste homolog 2 (EZH2), and regulated the expression of target gene, including p57, and consequently altered ovarian cancer cell biology. Our study revealed that ketamine could be a potential therapeutic strategy for ovarian cancer patients.

ObjectivesTo systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.MethodsA total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models.ResultsAmong nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.ConclusionsThe combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.

Objective Traditional approaches that involve measuring the height and volume of the pituitary by magnetic resonance imaging (MRI) are unreliable. We investigated the use of a more accurate method using texture analysis to evaluate idiopathic central precocious puberty (ICPP) by MRI. Methods In total, 352 texture features of the pituitary were extracted from 12 healthy girls and 18 girls with ICPP. A LASSO regression model and linear regression model were used to create the prediction model. Pearson’s correlation analysis and receiver operating characteristic curves were used to evaluate the predictive performance. Results The radiomics score had a significant linear relationship with the luteinizing hormone concentration and the luteinizing hormone/follicle-stimulating hormone ratio. The radiomics score showed better predictive performance than traditional pituitary measurements. The area under the curve of the radiomics score, pituitary height, and variable combinations was 0.759 (95% confidence interval [CI], 0.583–0.936), 0.681 (95% CI, 0.483–0.878), and 0.829 (95% CI, 0.681–0.976), respectively. Conclusion Combination of the radiomics score with pituitary height measurements allows for better evaluation of the pituitary during diagnostic imaging, indicating satisfactory potential for efficacy assessments.

Malignant middle cerebral artery infarction (mMCAi) is a serious complication of cerebral infarction usually associated with poor patient prognosis. In this retrospective study, we analyzed clinical information as well as non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) data from patients with cerebral infarction in the middle cerebral artery (MCA) territory acquired within 24 h from symptoms onset. Then, we aimed to develop a model based on the radiomics signature to predict the development of mMCAi in cerebral infarction patients. Patients were divided randomly into training (n = 87) and validation (n = 39) sets. A total of 396 texture features were extracted from each NCCT image from the 126 patients. The least absolute shrinkage and selection operator regression analysis was used to reduce the feature dimension and construct an accurate radiomics signature based on the remaining texture features. Subsequently, we developed a model based on the radiomics signature and Alberta Stroke Program Early CT Score (ASPECTS) based on NCCT to predict mMCAi. Our prediction model showed a good predictive performance with an AUC of 0.917 [95% confidence interval (CI), 0.863–0.972] and 0.913 [95% CI, 0.795–1] in the training and validation sets, respectively. Additionally, the decision curve analysis (DCA) validated the clinical efficacy of the combined risk factors of radiomics signature and ASPECTS based on NCCT in the prediction of mMCAi development in patients with acute stroke across a wide range of threshold probabilities. Our research indicates that radiomics signature can be an instrumental tool to predict the risk of mMCAi.

Sialic acids (SA), negatively charged nine-carbon sugars, have long been implicated in cancer metastasis since 1960's but its detailed functional roles remain elusive. We present a computational analysis of transcriptomic data of cancer vs. control tissues of eight types in TCGA, aiming to elucidate the possible reason for the increased production and utilization of SAs in cancer and their possible driving roles in cancer migration. Our analyses have revealed for all cancer types: (1) the synthesis and deployment enzymes of SAs are persistently up-regulated throughout the progression for all but one cancer type; and (2) gangliosides, of which SAs are part, tend to converge to specific types that allow SAs to pack at high densities on cancer cell surface as a cancer advances. Statistical and modeling analyses suggest that (i) a highly plausible reason for the increased syntheses of SAs is to produce net protons, used for neutralizing the OH− persistently generated by elevated intracellular iron metabolism coupled with chronic inflammation in cancer tissues; (ii) the level of SA accumulation on cancer cell surface strongly correlates with the stage of cancer migration, as well as multiple migration-related characteristics such as altered cell-cell adhesion, mechanical stress, cell protrusion, and contraction; and (iii) the pattern of SA deployment correlates with the 5-year survival rate of a cancer type. Overall, our study provides strong evidence for that the continuous accumulation of SAs on cancer cell surface gives rise to increasingly stronger cell-cell repulsion due to their negative charges, leading to cell deformation by electrostatic force-induced mechanical compression, which is known to be able to drive cancer cell migration established by recent studies.

Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building the classification models in aMCI diagnosis.Methods: For this target, we recruited 42 subjects with aMCI and 44 normal controls (NC). The right and left hippocampi were segmented for each subject using an efficient learning-based method. Then, the radiomic analysis was applied to calculate and select the radiomic features. Finally, two logistic regression models were built based on the selected features obtained from the right and left hippocampi.Results: There were 385 features derived after calculation, and four features remained after feature selection from each group of data. The area under the receiver operating characteristic (ROC) curve, specificity, sensitivity, positive predictive value, negative predictive value, precision, recall, and F-score of the classification evaluation index of the right hippocampus logistic regression model were 0.76, 0.71, 0.69, 0.69, 0.71, 0.69, 0.69, and 0.69, and those of the left hippocampus model were 0.79, 0.71, 0.54, 0.64, 0.63, 0.64, 0.54, and 0.58, respectively.Conclusion: Results demonstrate the potential hippocampal radiomic biomarkers are valid for the aMCI diagnosis. The MRI-based radiomic analysis, with further improvement and validation, can be used to identify patients with aMCI and guide the individual treatment.

Alzheimer’s disease (AD) is a neurodegenerative disease with main symptoms of chronic primary memory loss and cognitive impairment. The study aim was to investigate the correlation between intrahippocampal functional connectivity (FC) and MRI radiomic features in AD. A total of 67 AD patients and 44 normal controls (NCs) were enrolled in this study. Using the seed-based method of resting-state functional MRI (rs-fMRI), the whole-brain FC with bilateral hippocampus as seed was performed, and the FC values were extracted from the bilateral hippocampus. We observed that AD patients demonstrated disruptive FC in some brain regions in the left hippocampal functional network, including right gyrus rectus, right anterior cingulate and paracingulate gyri, bilateral precuneus, bilateral angular gyrus, and bilateral middle occipital gyrus. In addition, decreased FC was detected in some brain regions in the right hippocampal functional network, including bilateral anterior cingulate and paracingulate gyri, right dorsolateral superior frontal gyrus, and right precentral gyrus. Bilateral hippocampal radiomics features were calculated and selected using the A.K. software. Finally, Pearson’s correlation analyses were conducted between these selected features and the bilateral hippocampal FC values. The results suggested that two gray level run-length matrix (RLM) radiomic features and one gray level co-occurrence matrix (GLCM) radiomic feature weakly associated with FC values in the left hippocampus. However, there were no significant correlations between radiomic features and FC values in the right hippocampus. These findings present that the AD group showed abnormalities in the bilateral hippocampal functional network. This is a prospective study that revealed the weak correlation between the MRI radiomic features and the intrahippocampal FC in AD patients.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model.Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects.Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively.Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD.

Background Solute-linked carrier 26 gene family 6 (SLC26A6), which is mainly expressed in intestines and kidneys, is a multifunctional anion transporter crucial in the transport of oxalate anions. This study aimed to investigate the role of kidney SLC26A6 in urolithiasis. Methods Patients were divided into two groups: stone formers and nonstone formers. Samples were collected from patients following nephrectomy. Lentivirus with Slc26a6 (lentivirus-Slc26a6) sequence and lentivirus with siRNA-Slc26a6 (lentivirus-siRNA-Slc26a6) sequence were transfected into rats’ kidneys respectively and Slc26a6 expression was detected using Western blot and immunohistochemical analyses. After administering ethylene glycol, oxalate concentration and prevalence of stone formation between the transgenic and control groups were measured using 24-h urine analysis and Von Kossa staining, respectively. Results Immunohistochemical and Western blot analyses indicated that stone formers had a significantly higher level of expression of SLC26A6 in the kidney compared with the control group. After lentivirus infection, the urinary oxalate concentration and rate of stone formation in lentivirus-Slc26a6-tranfected rats increased remarkably, while lentivirus-siRNA-Slc26a6-transfected rats showed few crystals. Conclusion The results showed that high expression levels of renal SLC26A6 may account for kidney stone formation. Downregulating the expression of SLC26A6 in the kidney may be a potential therapeutic target to prevent or treat urolithiasis.