Association of Rare NOTCH3 Variants With Prevalent and Incident Stroke and Dementia in the General Population.

BACKGROUND: It is uncertain whether rare NOTCH3 variants are associated with stroke and dementia in the general population and whether they lead to alterations in cognitive function. This study aims to determine the associations of rare NOTCH3 variants with prevalent and incident stroke and dementia, as well as cognitive function changes. METHODS AND RESULTS: In the prospective community-based Shunyi Study, a total of 1007 participants were included in the baseline analysis. For the follow-up analysis, 1007 participants were included in the stroke analysis, and 870 participants in the dementia analysis. All participants underwent baseline brain magnetic resonance imaging, carotid ultrasound, and whole exome sequencing. Rare NOTCH3 variants were defined as variants with minor allele frequency <1%. A total of 137 rare NOTCH3 carriers were enrolled in the baseline study. At baseline, rare NOTCH3 variant carriers had higher rates of stroke (8.8% versus 5.6%) and dementia (2.9% versus 0.8%) compared with noncarriers. After adjustment for associated risk factors, the epidermal growth factor-like repeats (EGFr)-involving rare NOTCH3 variants were associated with a higher risk of prevalent stroke (odds ratio [OR], 2.697 [95% CI, 1.266-5.745]; P=0.040) and dementia (OR, 8.498 [95% CI, 1.727-41.812]; P=0.032). After 5 years of follow-up, we did not find that the rare NOTCH3 variants increased the risk of incident stroke and dementia. There was no statistical difference in the change in longitudinal cognitive scale scores. CONCLUSIONS: Rare NOTCH3 EGFr-involving variants are genetic risk factors for stroke and dementia in the general Chinese population.

NOTCH localizes to mitochondria through the TBC1D15-FIS1 interaction and is stabilized via blockade of E3 ligase and CDK8 recruitment to reprogram tumor-initiating cells.

The P53-destabilizing TBC1D15-NOTCH protein interaction promotes self-renewal of tumor-initiating stem-like cells (TICs); however, the mechanisms governing the regulation of this pathway have not been fully elucidated. Here, we show that TBC1D15 stabilizes NOTCH and c-JUN through blockade of E3 ligase and CDK8 recruitment to phosphodegron sequences. Chromatin immunoprecipitation (ChIP-seq) analysis was performed to determine whether TBC1D15-dependent NOTCH1 binding occurs in TICs or non-TICs. The TIC population was isolated to evaluate TBC1D15-dependent NOTCH1 stabilization mechanisms. The tumor incidence in hepatocyte-specific triple knockout (Alb::CreERT2;Tbc1d15Flox/Flox;Notch1Flox/Flox;Notch2Flox/Flox;HCV-NS5A) Transgenic (Tg) mice and wild-type mice was compared after being fed an alcohol-containing Western diet (WD) for 12 months. The NOTCH1-TBC1D15-FIS1 interaction resulted in recruitment of mitochondria to the perinuclear region. TBC1D15 bound to full-length NUMB and to NUMB isoform 5, which lacks three Ser phosphorylation sites, and relocalized NUMB5 to mitochondria. TBC1D15 binding to NOTCH1 blocked CDK8- and CDK19-mediated phosphorylation of the NOTCH1 PEST phosphodegron to block FBW7 recruitment to Thr-2512 of NOTCH1. ChIP-seq analysis revealed that TBC1D15 and NOTCH1 regulated the expression of genes involved in mitochondrial metabolism-related pathways required for the maintenance of TICs. TBC1D15 inhibited CDK8-mediated phosphorylation to stabilize NOTCH1 and protect it from degradation The NUMB-binding oncoprotein TBC1D15 rescued NOTCH1 from NUMB-mediated ubiquitin-dependent degradation and recruited NOTCH1 to the mitochondrial outer membrane for the generation and expansion of liver TICs. A NOTCH-TBC1D15 inhibitor was found to inhibit NOTCH-dependent pathways and exhibited potent therapeutic effects in PDX mouse models. This unique targeting of the NOTCH-TBC1D15 interaction not only normalized the perinuclear localization of mitochondria but also promoted potent cytotoxic effects against TICs to eradicate patient-derived xenografts through NOTCH-dependent pathways.

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions.

Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images.

Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

LncRNA RP11-10E18.7 cooperates with lncRNA RP11-481C4.2 to affect the overall survival of breast cancer patients: a TCGA-based retrospective study.

Background: As either oncogenes or tumor suppressor genes, long non-coding RNAs (lncRNAs) have a major role in both tumorigenesis and progression of human cancers, including breast cancer (BC). However, the statistical correlation between the lncRNA-lncRNA interaction and prognosis of BC remains unclear. Methods: We analyzed the fragments per kilobase per million (FPKM) lncRNA expression data in tumor tissue samples from 890 female patients with BC in The Cancer Genome Atlas (TCGA) between May 2021 and October 2022. The Cox proportional hazards model adjusted for age, race, clinical stage, neoadjuvant therapy, estrogen receptor (ER), and progesterone receptor (PR) was adopted to evaluate the lncRNA-lncRNA interaction regarding overall survival (OS) of BC. The multiple comparison was corrected by Bonferroni method. Results: RP11-10E18.7×RP11-481C4.2 was significantly associated with OS of BC patients [hazard ratio (HR)interaction =1.04, 95% confidence interval (CI): 1.03-1.06, P=3.35×10-9]. Then, gene-gene interaction analysis was performed for genes co-expressed with lncRNAs. FOXA1×U2SURP (HRinteraction =1.49, 95% CI: 1.28-1.73, P=2.16×10-7) was found to have a similar interactive pattern to RP11-10E18.7×RP11-481C4.2. after classifying the patients by intersection (3.47), we observed that the effect of FOXA1 opposite in patients with different U2SURP expression level (HRhigh vs. low =0.58, 95% CI: 0.34-0.99, P=0.046 in low expression of U2SURP; HRhigh vs. low =1.56, 95% CI: 1.18-2.87, P=0.029 in high expression of U2SURP). Conclusions: Our comprehensive study identified RP11-10E18.7×RP11-481C4.2 as a potential biomarker of BC prognosis. The results play an essential role in the impact of lncRNA-lncRNA interaction on BC survival. Our findings elucidated potential molecular mechanisms of BC progression under complex association patterns and provided potential dynamic and reversible therapeutic targets for BC patients.

Protocol for generation of humanized HCC mouse model and cancer-driver mutations using CRISPR-Cas9.

We detail procedures for generating a humanized mouse model of hepatocellular carcinoma (HCC) recapitulating genetic mutations associated with metabolic liver diseases (MLD). We humanized liver parenchymal, non-parenchymal, and hematopoietic cells. We employed CRISPR-Cas9-based ARID1A knockout and constitutively active CTNNB1 knockin combined with an alcohol Western diet to generate cancer-driver mutations commonly found in MLD-HCC patients. This HCC model facilitates the study of tumor-promoting gene-environment interactions. For complete details on the use and execution of this protocol, please refer to Yeh et al.1.

Pembrolizumab for the treatment of progressive multifocal leukoencephalopathy in China.

The aim of this study is to analyze the clinical characteristics and outcomes of Chinese patients with progressive multifocal leukoencephalopathy (PML) who were treated with programmed cell death protein 1 (PD1) blockade therapies. We retrospectively analyzed patients who were admitted to our hospital between October 1, 2020, and October 1, 2022, diagnosed with PML and treated with PD1 blockade therapies. Four patients with PML who were treated with PD1 blockade therapies were identified. All patients were male, and their ages ranged from 19 to 54 years old. One patient (Case 2) exhibited mild pleocytosis, while three patients (Cases 2-4) had markedly reduced T lymphocyte cell counts prior to treatment. The time interval between symptom onset and treatment initiation ranged from six to 54 weeks. All patients received pembrolizumab treatment, with a total of two to four doses administered. Three patients who responded to pembrolizumab treatment showed clinical improvement starting around 8 weeks after the initiation of therapy. Although one patient did not show clinical improvement, they ultimately survived until the last follow-up. None of the patients in this study exhibited immune-related adverse events or immune reconstitution inflammatory syndrome. PD1 blockade appears to be a promising novel therapeutic option for PML; additional prospective studies are necessary to confirm its efficacy.

Social Presence, Negative Emotions, and Self-Protective Behavioral Intentions of Nonsmokers in Response to Secondhand Smoking in Virtual Reality: Quasi-Experimental Design.

BACKGROUND: The application of virtual reality (VR) in health care has grown rapidly in China, where approximately half of the population is directly exposed to secondhand smoke (SHS). As VR headsets have become increasingly popular and short video platforms have incorporated 360° videos in China, new formats and opportunities for health campaigns about SHS have emerged. OBJECTIVE: In a simulated environment of exposure to SHS, this study aims to explore the emotional and behavioral responses to enhanced social presence brought about by VR in contrast to flat-screen videos. It also aims to examine whether and to what extent video modality (360° video vs flat-screen video) and contextual cues (high threat vs low threat) influence psychometric and intentional variables among viewers. METHODS: A total of 245 undergraduate and graduate students who were nonsmokers and from a large university in China participated in this study between October 2020 and January 2021. This study created 4 different versions of a SHS experience in a café with a 2 (360° video on a head-mounted display vs flat-screen display) × 2 (high threat vs low threat) experimental design. It developed and tested a path model examining the effects of experience modality and threat levels on social presence, emotions (anger and disgust), and eventually behavioral intentions (staying away and asking for help). RESULTS: We found that both video modality (P<.001) and threat level (P=.005) significantly influenced social presence, whereas the interaction of video modality and threat level did not have a statistically significant effect on social presence (P=.55). Negative emotions mediated the relationships between social presence and SHS-related self-protective behaviors. Specifically, anger positively predicted the intention to ask smokers to stop smoking through the waitress (P<.001). Disgust and fear both positively predicted the intention to stay away from the SHS environment (P<.001 for disgust; P=.002 for fear). CONCLUSIONS: This study explored the potential mediating mechanisms that influence individuals' responses to the risks of SHS in public areas. The results demonstrated that social presence and negative emotions are 2 important mediators that underlie the relationship between video modality and behavioral intention regarding SHS in a VR setting. These findings suggest that an immersive environment could be a better stimulator of anti-SHS emotions and behaviors than flat-screen videos.

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens.

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

Arteriolosclerosis differs from venular collagenosis in relation to cerebrovascular parenchymal damages: an autopsy-based study.

BACKGROUND AND PURPOSE: Cerebrovascular parenchymal damage is prevalent in ageing brains; however, its vascular aetiology has not been fully elucidated. In addition to the underlying role of sclerotic arterioles, the correlation between collagenised venules has not been clarified. Here, we aimed to investigate the associations between microvascular injuries, including arteriolosclerosis and venular collagenosis, and related parenchymal damages in ageing brains, to investigate the underlying correlations. METHODS: We evaluated arteriolosclerosis and venular collagenosis in 7 regions from 27 autopsy cases with no history of stroke or brain tumour. The correlations between the ratio of arteriolosclerosis, venular collagenosis and the severity of cerebrovascular parenchymal damage, including lacunes, microinfarcts, myelin loss, and parenchymal and perivascular haemosiderin deposits, were assessed. RESULTS: Arteriolosclerosis and venular collagenosis became more evident with age. Arteriolosclerosis was associated with lacunes (p=0.004) and brain parenchymal haemosiderin deposits in the superior frontal cortex (p=0.024) but not with leukoaraiosis severity. Venular collagenosis was not associated with the number of lacunes or haemosiderin, while white matter generally became paler with severe venular collagenosis in the periventricular (ß=-0.430, p=0.028) and deep white matter (ß=-0.437, p=0.025). CONCLUSION: Our findings imply an important role for venular lesions in relation to microvessel-related parenchymal damage which is different from that for arteriolosclerosis. Different underlying mechanisms of both cerebral arterioles and venules require further investigation.

Autoimmune Cerebellar Ataxia: Etiology and Clinical Characteristics of a Case Series from China.

Autoimmune cerebellar ataxia (ACA) is an important and potentially treatable cause of sporadic cerebellar syndrome, but studies with large sample size are limited. This study reported a large ACA series in China and described its etiology and clinical characteristics. We reviewed all ACA patients from our hospital (2013-2021) and analyzed their clinical and paraclinical features, treatment, and outcome. ACA subtypes investigated included paraneoplastic cerebellar degeneration (PCD), primary autoimmune cerebellar ataxia (PACA), anti-glutamate decarboxylase (GAD)-associated cerebellar ataxia, opsoclonus-myoclonus syndrome (OMS), Miller Fisher syndrome (MFS), and ACA-associated with autoimmune encephalitis. A total of 127 patients were identified and 40.9% were male. The median onset age was 47.0 years. Gait ataxia was the most prevalent feature followed by limb ataxia, dizziness, and dysarthria/dysphagia. Extracerebellar manifestations included pyramidal signs (28.3%) and peripheral neuropathy/radiculopathy (15.0%). ACA subtypes were PCD (30.7%), PACA (37.8%), ACA associated with autoimmune encephalitis (12.6%), anti-GAD-associated ACA (8.7%), MFS (7.1%), and OMS (3.1%). Neuronal antibodies were positive in 67.7% of patients. Brain magnetic resonance imaging was unremarkable (55.7%) or showed atrophy (18.3%) or abnormal signal intensity (26.1%, most of which was extracerebellar). Although most patients received immunotherapy, the modified Rankin scale at last follow-up was ≤ 2 in only 47.3% patients. Thirteen patients died and 24 relapsed. Compared with PACA, PCD patients were older and had poorer outcome. This study illustrates the heterogeneity in the clinical features of ACA and suggests the importance of neuronal antibody testing in ACA diagnosis. PCD and PACA are the dominant ACA subtypes, and the former has a less favorable prognosis.

Recurrent headache and visual symptoms in a young man: a rare neuronal intranuclear inclusion disease case report.

BACKGROUND: Neuronal intranuclear inclusion disease (NIID) is a progressive neurodegenerative disease. Patients with NIID may present with heterogeneous clinical symptoms, including episodic encephalopathy, dementia, limb weakness, cerebellar ataxia, and autonomic dysfunction. Among the NIID cases reported in China, patients often have complicated and severe manifestations. Therefore, many clinicians do not consider the disease when the patient presents with relatively minor complaints. CASE PRESENTATION: We present the case of a 39-year-old man showing migraine-aura-like symptoms for the past 3 years. Brain magnetic resonance imaging (MRI) revealed hyperintense signals in the splenium of the corpus callosum and corticomedullary junction on diffusion-weighted imaging (DWI) over time. In addition, brain atrophy that was not concomitant with the patient's age was detected while retrospectively reviewing the patient's imaging results. Genetic analysis and skin biopsy confirmed a diagnosis of NIID. The patient was treated with sibelium, and the symptoms did not recur. DISCUSSION AND CONCLUSIONS: Migraine-aura-like symptoms may be the predominant clinical presentation in young patients with NIID. Persistent high-intensity signals on DWI in the brain and early-onset brain atrophy might be clues for the diagnosis of NIID.

A deep learning-based diagnostic pattern for ultrasound breast imaging: can it reduce unnecessary biopsy?

Background: Early studies have demonstrated the potential of deep learning in bringing revolutionary changes in medical analysis. However, it is unknown which deep learning based diagnostic pattern is more effective for differentiating malignant and benign breast lesions (BLs) and can assist radiologists to reduce unnecessary biopsies. Methods: A total of 506 malignant BLs and 557 benign BLs were enrolled in this study after excluding incomplete ultrasound images. 396 malignant BLs and 447 benign BLs were included in the training cohort while 110 malignant and 110 benign BLs were included in the validation cohort. All BLs in the training and validation cohort were biopsy-proven. The most common convolutional neural networks (VGG-16 and VGG-19) were applied to identify malignant and benign BLs using grey-scale ultrasound images. Two radiologists determined the malignant (suggestion for biopsy) and benign (suggestion for follow-up) BLs with a 2-step reading session. The first step was based on conventional ultrasound (US) images alone to make a biopsy or follow-up decision. The second step was to take deep learning results into account for the decision adjustment. If a deep learning result of a first-classified benign BL was above the cut-off value, then it was re-classified as malignant. Results: In terms of area under the curve (AUC), the VGG-19 model yielded the best diagnostic performance in both training [0.939, 95% confidence interval (CI): 0.924-0.954] and testing dataset (0.959, 95% CI: 0.937-0.982). With the aid of deep learning models, the AUC of radiologists improved from 0.805 (95% CI: 0.744-0.865) to 0.827 (95% CI: 0.771-0.875, VGG-16) and 0.914 (95% CI: 0.871-0.957, VGG-19). The unnecessary biopsies decreased from 10.0% (11/110) to 8.2% (9/110) (assisted by VGG-16) and 0.9% (1/110) (assisted by VGG-19). Conclusions: The application of deep learning patterns in breast US may improve the diagnostic performance of radiologists by offering a second opinion. And thus, the assist of deep learning algorithm can considerably reduce the unnecessary biopsy rate in the clinical management of breast lesions.

Inflammatory biomarkers and cerebral small vessel disease: a community-based cohort study.

BACKGROUND AND PURPOSE: Although inflammation has been proposed to be a candidate risk factor for cerebral small vessel disease (CSVD), previous findings remain largely inconclusive and vary according to disease status and study designs. The present study aimed to investigate possible associations between inflammatory biomarkers and MRI markers of CSVD. METHODS: A group of 15 serum inflammatory biomarkers representing a variety of those putatively involved in the inflammatory cascade was grouped and assessed in a cross-sectional study involving 960 stroke-free subjects. The biomarker panel was grouped as follows: systemic inflammation (high-sensitivity C reactive protein (hsCRP), interleukin 6 and tumour necrosis factor α), endothelial-related inflammation (E-selectin, P-selectin, intercellular adhesion molecule 1, vascular cell adhesion molecule 1 (VCAM-1), CD40 ligand, lipoprotein-associated phospholipase A2, chitinase-3-like-1 protein and total homocysteine (tHCY)) and media-related inflammation (matrix metalloproteinases 2, 3 and 9, and osteopontin). The association(s) between different inflammatory groups and white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs), enlarged perivascular space (PVS) and the number of deep medullary veins (DMVs) were investigated. RESULTS: High levels of serum endothelial-related inflammatory biomarkers were associated with both increased WMH volume (R2=0.435, p=0.015) and the presence of lacunes (R2=0.254, p=0.027). Backward stepwise elimination of individual inflammatory biomarkers for endothelial-related biomarkers revealed that VCAM-1 was significant for WMH (ß=0.063, p=0.005) and tHCY was significant for lacunes (ß=0.069, p<0.001). There was no association between any group of inflammatory biomarkers and CMBs or PVS. Systemic inflammatory biomarkers were associated with fewer DMVs (R2=0.032, p=0.006), and backward stepwise elimination of individual systemic-related inflammatory biomarkers revealed that hsCRP (ß=-0.162, p=0.007) was significant. CONCLUSION: WMH and lacunes were associated with endothelial-related inflammatory biomarkers, and fewer DMVs were associated with systemic inflammation, thus suggesting different underlying inflammatory processes and mechanisms.

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study.

BACKGROUND: This pilot study aims at exploiting machine learning techniques to extract color Doppler ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification. METHODS: A total of 674 patients with 712 thyroid nodules (TNs) (512 from internal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS-Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and gray-scale ultrasound (US) features defined by the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the gray-scale US features alone (TUS-Net), and that of radiologists. RESULTS: The TDUS-Net (0.898, 95% CI: 0.868-0.922) achieved a higher area under the curve (AUC) than that of TUS-Net (0.881, 95% CI: 0.850-0.908) in the internal tests. Compared with radiologists, TDUS-Net (AUC: 0.925, 95% CI: 0.880-0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749-0.862) in the external tests. CONCLUSIONS: Applying a machine learning model by combining both gray-scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying TNs.

Distinct lesion features and underlying mechanisms in patients with acute multiple infarcts in multiple cerebral territories.

Objective: To determine the etiology spectrum and lesion distribution patterns of patients with acute multiple infarcts in multiple cerebral territories (AMIMCT) and provide guidance for treatment and prevention strategies in these patients. Methods: Patients with acute ischemic stroke diagnosed using diffusion-weighted imaging (DWI) were consecutively included in this study between June 2012 and Apr 2022. AMIMCT was defined as non-contiguous focal lesions located in more than one cerebral territory with acute neurological deficits. We retrospectively analyzed the clinical and imaging characteristics, etiology spectra and underlying mechanisms in patients with and without AMIMCT. Infarct lesion patterns on DWI and their relevance to etiology were further discussed. Results: A total of 1,213 patients were enrolled, of whom 145 (12%) were diagnosed with AMIMCT. Patients with AMIMCT tended to be younger (P = 0.016), more often female (P = 0.001), and exhibited less common conventional vascular risk factors (P < 0.05) compared to those without AMIMCT. The constitution of the Trial of Org 10,172 in Acute Stroke Treatment classification was significantly different between patients with and without AMIMCT (P = 0.000), with a higher proportion of stroke of other determined causes (67.6% vs. 12.4%). For detailed etiologies, autoimmune or hematologic diseases were the most common (26.2%) etiologies of AMIMCT, followed by periprocedural infarcts (15.2%), cardioembolism (12.4%), tumor (12.4%), large artery atherosclerosis (10.3%), and sudden drop in blood pressure (8.3%). Hypercoagulability and systemic hypoperfusion are common underlying mechanisms of AMIMCT. Distinctive lesion distribution patterns were found associated with stroke etiologies and mechanisms in AMIMCT. Most of patients with large artery atherosclerosis (73.3%), autoimmune/hematologic diseases (57.9%) manifested the disease as multiple infarct lesions located in bilateral supratentorial regions. However, 66.7% of cardioembolism and 83.8% of cardiovascular surgery related stroke presented with both supratentorial and infratentorial infarct lesions. Conclusion: The etiologies and mechanisms of patients with AMIMCT were more complex than those without AMIMCT. The distribution characteristics of infarct lesions might have important implications for the identification of etiology and mechanism in the future, which could further guide and optimize clinical diagnostic strategies.

FOXP3+ regulatory T cells and age-related diseases.

Regulatory T (Treg) cells are critical for the maintenance of immune homeostasis. Dysregulation of Treg cells has been implicated in the pathogenesis of autoimmunity and chronic inflammation, while aging is characterized by an accumulation of inflammatory markers in the peripheral blood, a phenomenon known as 'inflammaging'. The relationship between Treg cells and age-related diseases remains to be further studied. Increasing evidence revealed that Treg cells' dysfunction occurs in aged patients, suggesting that immune therapies targeting Treg cells may be a promising approach to treat diseases such as cancers and autoimmune diseases. Furthermore, drugs targeting Treg cells show encouraging results and contribute to CD8+ T-cell-mediated cytotoxic killing of tumor and infected cells. In general, a better understanding of Treg cell function may help us to develop new immune therapies against aging. In this review, we discuss potential therapeutic strategies to modify immune responses of relevance for aging to prevent and treat age-related diseases, as well as the challenges posed by the translation of novel immune therapies into clinical practice.

Neoantigen-reactive T cell: An emerging role in adoptive cellular immunotherapy.

Adoptive cellular immunotherapy harnessing the intrinsic immune system for precise treatment has exhibited preliminary success against malignant tumors. As one of the emerging roles in adoptive cellular immunotherapy, neoantigen-reactive T cell (NRT) focuses on the antigens expressed only by tumor cells. It exclusively obliterates tumor and spares normal tissues, achieving more satisfying effects. However, the development of NRT immunotherapy remains in a relatively primitive stage. Current challenges include identification of NRTs and maintenance of adoptive cell efficacy in vivo. The possible side effects and other limitations of this treatment also hinder its application. Here, we present an overview of NRT immunotherapy and discuss the progress and challenges as well as the prospects in this promising field.

Thyroid ultrasound image classification using a convolutional neural network.

BACKGROUND: Ultrasound (US) is widely used in the clinical diagnosis of thyroid nodules. Artificial intelligence-powered US is becoming an important issue in the research community. This study aimed to develop an improved deep learning model-based algorithm to classify benign and malignant thyroid nodules (TNs) using thyroid US images. METHODS: In total, 592 patients with 600 TNs were included in the internal training, validation, and testing data set; 187 patients with 200 TNs were recruited for the external test data set. We developed a Visual Geometry Group (VGG)-16T model, based on the VGG-16 architecture, but with additional batch normalization (BN) and dropout layers in addition to the fully connected layers. We conducted a 10-fold cross-validation to analyze the performance of the VGG-16T model using a data set of gray-scale US images from 5 different brands of US machines. RESULTS: For the internal data set, the VGG-16T model had 87.43% sensitivity, 85.43% specificity, and 86.43% accuracy. For the external data set, the VGG-16T model achieved an area under the curve (AUC) of 0.829 [95% confidence interval (CI): 0.770-0.879], a radiologist with 15 years' working experience achieved an AUC of 0.705 (95% CI: 0.659-0.801), a radiologist with 10 years' experience achieved an AUC of 0.725 (95% CI: 0.653-0.797), and a radiologist with 5 years' experience achieved an AUC of 0.660 (95% CI: 0.584-0.736). CONCLUSIONS: The VGG-16T model had high specificity, sensitivity, and accuracy in differentiating between malignant and benign TNs. Its diagnostic performance was superior to that of experienced radiologists. Thus, the proposed improved deep-learning model can assist radiologists to diagnose thyroid cancer.

MXene-assisted organic electrochemical transistor biosensor with multiple spiral interdigitated electrodes for sensitive quantification of fPSA/tPSA.

BACKGROUND: The ratio of fPSA/tPSA in the "grey zone" of tPSA with the concentration range between 4 ng/ml and 10 ng/ml is significant for diagnosis of prostate cancer, and highly efficiency quantification of the ratio of fPSA/tPSA remain elusive mainly because of their extremely low concentration in patients' peripheral blood with high biosample complexity. METHODS: We presented an interdigitated spiral-based MXene-assisted organic electrochemical transistors (isMOECTs) biosensor for highly sensitive determination of fPSA/tPSA. The combination of MXene and the interdigitated multiple spiral architecture synergistically assisted the amplification of amperometric signal of biosensor with dual functionalizations of anti-tPSA and anti-fPSA. RESULTS: The ultrasensitivity of the biosensor was enhanced by tunable multiple spiral architecture and MXene nanomaterials; and the sensor exhibited improved detection limit of tPSA and fPSA down to 0.01 pg/ml and acceptable performance of selectivity, repeatability and stability. Moreover, the isMOECTs displayed area under the curve (AUC) value of 0.8138, confirming the potential applications of isMOECTs in clinics. CONCLUSIONS: The merits of isMOECTs biosensor demonstrated the reliability of MXene-assisted organic electrochemical transistor biosensor with multiple interdigitated spiral for ultrasensitive quantification of fPSA/tPSA, suggesting potential current and future point-of-care testing applications.