Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.

Purpose: To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer. Materials and methods: 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA. Results: Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net. Conclusion: The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Integration of metabolomics and network pharmacology to reveal the protective mechanism underlying Wogonoside in acute myocardial ischemia rats.

ETHNOPHARMACOLOGICAL RELEVANCE: In traditional medicine, both Scutellaria baicalensis Georgi (SBG) and the traditional formulas composed of it have been used to treat a wide range of diseases, including cancer and cardiovascular. Wogonoside (Wog) is the biologically active flavonoid compound extracted from the root of SBG, with potential cardiovascular protective effects. However, the mechanisms underlying the protective effect of Wog on acute myocardial ischemia (AMI) have not yet been clearly elucidated. AIM OF THE STUDY: To explore the protective mechanism of Wog on AMI rats by comprehensively integrating traditional pharmacodynamics, metabolomics, and network pharmacology. METHODS: The rat was pretreatment with Wog at a dose of 20 mg/kg/d and 40 mg/kg/d once daily for 10 days and then ligated the left anterior descending coronary artery of rats to establish the AMI rat model. Electrocardiogram (ECG), cardiac enzyme levels, heart weight index (HWI), Triphenyltetrazolium chloride (TTC) staining, and histopathological analyses were adopted to evaluate the protective effect of Wog on AMI rats. Moreover, a serum metabolomic-based UHPLC-Q-Orbitrap MS approach was performed to find metabolic biomarkers and metabolic pathways, and network pharmacology analysis was applied to predict targets and pathways of Wog in treating AMI. Then, the network pharmacology and metabolomic results were integrated to elucidate the mechanism of Wog in treating AMI. Finally, RT- PCR was used to detect the mRNA expression levels of PTGS1, PTGS2, ALOX5, and ALOX15 to validate the result of integrated metabolomics and network analysis. RESULTS: Pharmacodynamic studies suggest that Wog could effectively prevent the ST-segment of electrocardiogram elevation, reduce the myocardial infarct size, heart weight index, and cardiac enzyme levels, and alleviate cardiac histological damage in AMI rats. Metabolomics analysis showed that the disturbances of metabolic profile in AMI rats were partly corrected by Wog and the cardio-protection effects on AMI rats involved 32 differential metabolic biomarkers and 4 metabolic pathways. In addition, the integrated analysis of network pharmacology and metabolomics showed that 7 metabolic biomarkers, 6 targets, and 6 crucial pathways were the main mechanism for the therapeutic application of Wog for AMI. Moreover, the results of RT-PCR showed that PTGS1, PTGS2, ALOX5, and ALOX15 mRNA expression levels were reduced after treatment with Wog. CONCLUSION: Wog exerts cardio-protection effects on AMI rats via the regulation of multiple metabolic biomarkers, multiple targets, and multiple pathways, our current study will provide strong scientific evidence supporting the therapeutic application of Wog for AMI.

Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints.

Deep learning methods using multimodal imagings have been proposed for the diagnosis of Alzheimer's disease (AD) and its early stages (SMC, subjective memory complaints), which may help to slow the progression of the disease through early intervention. However, current fusion methods for multimodal imagings are generally coarse and may lead to suboptimal results through the use of shared extractors or simple downscaling stitching. Another issue with diagnosing brain diseases is that they often affect multiple areas of the brain, making it important to consider potential connections throughout the brain. However, traditional convolutional neural networks (CNNs) may struggle with this issue due to their limited local receptive fields. To address this, many researchers have turned to transformer networks, which can provide global information about the brain but can be computationally intensive and perform poorly on small datasets. In this work, we propose a novel lightweight network called MENet that adaptively recalibrates the multiscale long-range receptive field to localize discriminative brain regions in a computationally efficient manner. Based on this, the network extracts the intensity and location responses between structural magnetic resonance imagings (sMRI) and 18-Fluoro-Deoxy-Glucose Positron Emission computed Tomography (FDG-PET) as an enhancement fusion for AD and SMC diagnosis. Our method is evaluated on the publicly available ADNI datasets and achieves 97.67% accuracy in AD diagnosis tasks and 81.63% accuracy in SMC diagnosis tasks using sMRI and FDG-PET. These results achieve state-of-the-art (SOTA) performance in both tasks. To the best of our knowledge, this is one of the first deep learning research methods for SMC diagnosis with FDG-PET.

BMNet: A New Region-Based Metric Learning Method for Early Alzheimer's Disease Identification With FDG-PET Images.

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validation experiments are performed for multiple two-class classifications. Experiments show that most metrics are improved after adding the bilinear pooling module and metric losses to the Baseline model respectively. Specifically, in the classification task between EMCI and LMCI, the specificity improves 6.38% after adding the triple metric loss, and the negative predictive value (NPV) improves 3.45% after using the bilinear pooling module. In addition, the accuracy of classification between EMCI and LMCI achieves 79.64% using imbalanced FDG-PET images, which illustrates that the proposed method yields a state-of-the-art result of the classification accuracy between EMCI and LMCI based on PET images.

A study on the correlation between family dynamic factors and depression in adolescents.

Objectives: To evaluate the relationship between systemic family dynamics and adolescent depression. Methods: An offline survey was distributed to 4,109 students in grades 6-12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10-18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics. Results: Family dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948-0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960-0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760-0.796) and 0.710 (95% CI: 0.690-0.730), respectively. The diagnostic models for depression performed well. Conclusion: Poor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression.

Duration of untreated psychosis and clinical outcomes of first-episode schizophrenia: a 4-year follow-up study.

BACKGROUND: The relationship between the duration of untreated psychosis and long-term clinical outcomes remains uncertain. OBJECTIVE: Prospectively assess the relationship of the duration of untreated psychosis on clinical outcomes in a sample of individuals with first-onset schizophrenia treated at the Pudong Mental Health Center from January 2007 to December 2008. METHODS: Information about general health, psychotic symptoms and social functioning were collected using the Brief Psychiatric Rating Scale (BPRS), Treatment Emergent Symptom Scale (TESS), Morningside Rehabilitation Status Scale (MRSS), and Social Disability Screening Schedule (SDSS) at baseline and in June 2010 and June 2012. RESULTS: The 43 individuals with first-episode schizophrenia participating in the study were divided into short (<24 weeks) and long (>24weeks) duration of untreated psychosis (DUP) groups. The mean (sd) duration of follow-up was 1197 (401) days in the short DUP group and 1412 (306) days in the long DUP group (t=9.98, p=0.055). Despite less prominent psychotic symptoms at the time of first diagnosis among patients who had a long DUP compared to those with a short DUP (BPRS mean scores, 42.5 [8.4] v. 50.0 [10.6], t=2.42, p=0.0210) and a similar number of clinical relapses (based on positive symptoms assessed by the BPRS), patients with a long DUP were more likely to require hospitalization at the time of first diagnosis (52% [11/21] v. 9% [2/22], χ(2) =9.55, p=0.002) and more likely to require re-hospitalization during the first two years of treatment (67% [14/21] v. 32% [7/22], χ(2) =5.22, p=0.022). Moreover, after four years of routine treatment, despite a similar severity of positive symptoms, patients who had had a long DUP prior to initiating treatment had significantly poorer social functioning than those who had had a short DUP (SDSS mean scores, 7.0 [5.2] v. 3.4 [4.9], t=2.20, p=0.035). CONCLUSIONS: These findings show that despite having a similar level of psychotic symptoms - as measured by the BPRS - compared to patients with a short DUP, patients with schizophrenia who have a long DUP prior to initial treatment have poorer long-term social functioning. This confirms the clinical importance of the early recognition and treatment of individuals with chronic psychotic conditions.