Liver as a new target organ in Alzheimer's disease: insight from cholesterol metabolism and its role in amyloid-beta clearance.

Alzheimer's disease, the primary cause of dementia, is characterized by neuropathologies, such as amyloid plaques, synaptic and neuronal degeneration, and neurofibrillary tangles. Although amyloid plaques are the primary characteristic of Alzheimer's disease in the central nervous system and peripheral organs, targeting amyloid-beta clearance in the central nervous system has shown limited clinical efficacy in Alzheimer's disease treatment. Metabolic abnormalities are commonly observed in patients with Alzheimer's disease. The liver is the primary peripheral organ involved in amyloid-beta metabolism, playing a crucial role in the pathophysiology of Alzheimer's disease. Notably, impaired cholesterol metabolism in the liver may exacerbate the development of Alzheimer's disease. In this review, we explore the underlying causes of Alzheimer's disease and elucidate the role of the liver in amyloid-beta clearance and cholesterol metabolism. Furthermore, we propose that restoring normal cholesterol metabolism in the liver could represent a promising therapeutic strategy for addressing Alzheimer's disease.

Metabolic shifts during coffee consumption refresh the immune response: insight from comprehensive multiomics analysis.

Coffee, a widely consumed beverage, has shown benefits for human health but lacks sufficient basic and clinical evidence to fully understand its impacts and mechanisms. Here, we conducted a cross-sectional observational study of coffee consumption and a 1-month clinical trial in humans. We found that coffee consumption significantly reshaped the immune system and metabolism, including reduced levels of inflammatory factors and a reduced frequency of senescent T cells. The frequency of senescent T cells and the levels of the senescence-associated secretory phenotype were lower in both long-term coffee consumers and new coffee consumers than in coffee nondrinking subjects, suggesting that coffee has anti-immunosenescence effects. Moreover, coffee consumption downregulated the activities of the The Janus kinase/signal transduction and activator of transcription (JAK/STAT) and mitogen-activated protein kinases (MAPK) signaling pathways and reduced systemic proinflammatory cytokine levels. Mechanistically, coffee-associated metabolites, such as 1-methylxanthine, 3-methylxanthine, paraxanthine, and ceramide, reduced the frequency of senescent CD4+CD57+ T cells in vitro. Finally, in vivo, coffee intake alleviated inflammation and immunosenescence in imiquimod-induced psoriasis-like mice. Our results provide novel evidence of the anti-inflammatory and anti-immunosenescence effects of coffee, suggesting that coffee consumption could be considered a healthy habit.

Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study.

PURPOSE: To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD: About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS: In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS: Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.

A cross-sectional study on the correlation of image quality, effective dose, and body composition with thyroid, chest, and abdominal computed tomography scans.

Background: The rapid increase in the use of radiodiagnostic examinations in China, especially computed tomography (CT) scans, has led to these examinations being the largest artificial source of per capita effective dose (ED). This study conducted a retrospective analysis of the correlation between image quality, ED, and body composition in 540 cases that underwent thyroid, chest, or abdominal CT scans. The aim of this analysis was to evaluate the correlation between the parameters of CT scans and body composition in common positions of CT examination (thyroid, chest, and abdomen) and ultimately inform potential measures for reducing radiation exposure. Methods: This study included 540 patients admitted to Fudan University Shanghai Cancer Center from January 2015 to December 2019 who underwent both thyroid or chest or abdominal CT scan and body composition examination. Average CT values and standard deviation (SD) values were collected for the homogeneous areas of the thyroid, chest, or abdomen, and the average CT values and SD values of adjacent subcutaneous fat tissue were measured in the same region of interest (ROI). All data were measured three times, and the average was taken to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for each area. The dose-length product (DLP) was recorded, and the ED was calculated with the following: formula ED = k × DLP. Dual-energy X-ray was used to determine body composition and obtain indicators such as percentage of spinal and thigh muscle. Pearson correlation coefficient was used to analyze the correlations between body composition indicators, height, weight, body mass index (BMI), and ED. Results: The correlation coefficients between the SNR of abdominal CT scan and weight, BMI, and body surface area (BSA) were -0.470 (P=0.001), -0.485 (P=0.001), and -0.437 (P=0.002), representing a moderate correlation strength with statistically significant differences. The correlation coefficients between the ED of chest CT scans and weight, BMI, spinal fat percentage, and BSA were 0.488 (P=0.001), 0.473 (P=0.002), 0.422 (P=0.001), and 0.461 (P=0.003), respectively, indicating a moderate correlation strength with statistical differences. There was a weak statistically significant correlation between the SNR, CNR, and ED of the other scans with each physical and body composition index (P=0.023). Conclusions: There were varying degrees of correlation between CT image quality and ED and physical and body composition indices, which may inform novel solutions for reducing radiation exposure.

Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.

BACKGROUND: To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS: One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION: This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis.

PURPOSE: To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. METHODS: This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. RESULTS: kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). CONCLUSION: The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Local Tumor Progression Predictive Model Based on MRI for Colorectal Cancer Liver Metastases after Radiofrequency Ablation.

PURPOSE: To investigate the post-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) characteristics in patients with liver metastases from colorectal cancer and to build a predictive model for local tumor progression based on these imaging markers. MATERIALS AND METHODS: A cohort of 73 patients with 110 colorectal cancer liver metastases (CRCLM) who underwent RFA and MRI one month post-ablation was included in image signs analysis and predictive model training. Using a newly developed MRI appearance scoring criteria, MR Image Appearance Scoring at One Month after RFA (MRIAS 1MO), the semi-quantitative analysis of MRI findings within the ablation zone were conducted independently by two radiologists. The intraclass correlation coefficient (ICC) was calculated to evaluate measurement reliability. Differences in MRIAS 1MO scores were compared using Mann-Whitney U test, focusing on local tumor response outcomes. Using local tumor progression (LTP) as the primary end point, MRIAS 1MO scores and other lesion morphological and clinical characteristics were included to establish predictive model. Predication accuracy was subsequently evaluated using calibration curve, time-dependent concordance index (C index) curve, and LTP-free survival (LTPFS) curve. Another cohort comprising 60 patients with 76 CRCLMs provided additional MRIAS 1MO scores and clinical data associated with LTP. We evaluated the performance of the established predictive model using calibration curve, time-dependent C index curve, and LTPFS curve. RESULTS: The MRIAS 1MO criteria exhibited strong measurement reliability. The ICC values of T1S (scores from T1WI), T2S (scores form T2WI) and NCES (scores by adding T1S to T2S) MRIS (the overall scores) were 0.825, 0.779, 0.826 and 0.873, respectively. Lesions with LTP showed significantly higher median values for the overall MRIAS 1MO score (MRIS) compared to lesions without LTP (16 vs. 12, p < 0.001). MRIS and lesion diameter were independent prognostic factors of LTP and were included in predictive model (hazard ratio: MRIS over 13.5:4.275, lesion diameter larger than 30 mm: 2.056). The predictive model demonstrated an overall C index of 0.721 and risk stratification using the predictive model resulted in significantly different LPTFS times. In the validation cohort, the C index were 0.825, 0.794 and 0.764 at six, twelve and twenty-four months, respectively. Patients classified as high-risk in the validation cohort had a median LTPFS time of 10.0 months, while the median LTPFS time was not reached in the low-risk group. CONCLUSIONS: The semi-quantitative MRIAS 1MO criteria, used for post-RFA MRI appearance analysis, exhibited strong measurement reliability. Prediction models established based on overall MRIAS 1MO score (MRIS) and lesion diameter had good predictive performance for LTP in patients undergoing RFA for CRCLM treatment.

Investigation on the correlation factors of positive Streptococcus pneumoniae antibody and IgG antibody level of Streptococcus pneumoniae in the elderly over 60 years old in Shenzhen.

BACKGROUND: Pneumococcal Polysaccharide Vaccine (PPV-23), designed to protect against the most common serotype of Streptococcus pneumoniae, is intended to protect the elderly and other high-risk groups. However, the immunogenicity of all 23 pneumococcal polysaccharide vaccines in older adults has not been thoroughly studied. OBJECTIVE: The purpose of this study is to look into the factors that influence the effect of the pneumonia vaccine on the elderly over 60 years old in Shenzhen, as well as their IgG antibody level against Streptococcus pneumoniae. METHODS: To determine the immune effectiveness of pneumococcal vaccination in older adults over 60 years old, we used the 3rd generation enzyme-linked immunosorbent assay to detect the antibody level of older adults to all 23 pneumococcal polysaccharide vaccines following pneumococcal immunization. RESULTS: Vaccination, the number of physical examinations, pneumonia knowledge, and the pneumonia vaccination policy of the elderly in Shenzhen were all positively correlated with Streptococcus pneumoniae antibody positivity. The distribution of subtypes did not differ between elderly adults (over 65) and younger adults (under 65). The GMCs of IgG antibodies to PPS were significantly lower in males than in females for types 7f, 18c and 19a. At the same time, we found that people with chronic respiratory disease have lower type 9n than people without chronic respiratory disease. Other chronic diseases, such as hypertension and diabetes, had no difference in subtype distribution. CONCLUSION: There was a statistically significant difference in antibody positivity rates for older people with more frequent medical check-ups in Shenzhen, indicating that publicity is playing a role. The effects of age, gender, and chronic diseases on naturally acquired anti-PPS IgG differ.

The decreased astrocyte-microglia interaction reflects the early characteristics of Alzheimer's disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease often associated with olfactory dysfunction. Aß is a typical AD hall marker, but Aß-induced molecular alterations in olfactory memory remain unclear. In this study, we used a 5xFAD mouse model to investigate Aß-induced olfactory changes. Results showed that 4-month-old 5xFAD have olfactory memory impairment accompanied by piriform cortex neuron activity decline and no sound or working memory impairment. In addition, synapse and glia functional alteration is consistent across different ages at the proteomic level. Microglia and astrocyte specific proteins showed strong interactions in the conserved co-expression network module. Moreover, this interaction declines only in mild cognitive impairment patients in human postmortem brain proteomic data. This suggests that astrocytes-microglia interaction may play a leading role in the early stage of Aß-induced olfactory memory impairment, and the decreasing of their synergy may accelerate the neurodegeneration.

Mechanistic Insights and Potential Therapeutic Implications of NRF2 in Diabetic Encephalopathy.

Diabetic encephalopathy (DE) is a complication of diabetes, especially type 2 diabetes (T2D), characterized by damage in the central nervous system and cognitive impairment, which has gained global attention. Despite the extensive research aimed at enhancing our understanding of DE, the underlying mechanism of occurrence and development of DE has not been established. Mounting evidence has demonstrated a close correlation between DE and various factors, such as Alzheimer's disease-like pathological changes, insulin resistance, inflammation, and oxidative stress. Of interest, nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor with antioxidant properties that is crucial in maintaining redox homeostasis and regulating inflammatory responses. The activation and regulatory mechanisms of NRF2 are a relatively complex process. NRF2 is involved in the regulation of multiple metabolic pathways and confers neuroprotective functions. Multiple studies have provided evidence demonstrating the significant involvement of NRF2 as a critical transcription factor in the progression of DE. Additionally, various molecules capable of activating NRF2 expression have shown potential in ameliorating DE. Therefore, it is intriguing to consider NRF2 as a potential target for the treatment of DE. In this review, we aim to shed light on the role and the possible underlying mechanism of NRF2 in DE. Furthermore, we provide an overview of the current research landscape and address the challenges associated with using NRF2 activators as potential treatment options for DE.

Quercetin: A promising therapy for diabetic encephalopathy through inhibition of hippocampal ferroptosis.

BACKGROUND: The pathophysiology of diabetic encephalopathy (DE), a significant diabetes-related pathological complication of the central nervous system, is poorly understood. Ferroptosis is an iron-dependent regulated necrotic cell death process that mediates the development of neurodegenerative and diabetes-related lesions. Quercetin (QE) exerts anti-ferroptotic effects in various diseases. However, the roles of ferroptosis in DE and the potential anti-ferroptotic mechanisms of QE are unclear. PURPOSE: This study aimed to investigate if quercetin can ameliorate DE by inhibiting ferroptosis and to elucidate the potential anti-ferroptotic mechanisms of QE, thus providing a new perspective on the pathogenesis and prevention of DE. METHODS: The spontaneously type 2 diabetic Goto-Kakizak rats and high glucose (HG)-induced PC12 cells were used as animal and in vitro models, respectively. The Morris water maze test was performed to evaluate the cognition of rats. Pathological damage was examined using hematoxylin and eosin staining. Mitochondrial damage was assessed using transmission electron microscopy. Lipid peroxidation was evaluated by examining the levels of malondialdehyde, superoxide dismutase, and glutathione. Additionally, the contents of iron ions were quantified. Immunofluorescence and western blotting were carried out to poke the protein levels. Network pharmacology analysis was conducted to construct a protein-protein interaction network for the therapeutic targets of QE in DE. Additionally, molecular docking and cellular thermal shift assay was performed to examine the target of QE. RESULTS: QE alleviated cognitive impairment, decreased lipid peroxidation and iron deposition in the hippocampus, and upregulated the Nrf2/HO-1 signaling pathway. HG-induced ferroptosis in PC12 cells resulted in decreased cell viability accompanied by lipid peroxidation and iron deposition. QE mitigated HG-induced ferroptosis by upregulating the Nrf2/HO-1 pathway, which was partially suppressed upon Nrf2 inhibition. Network pharmacology analysis further indicated that the Nrf2/HO-1 signaling pathway is a key target of QE. Molecular docking experiments revealed that QE binds to KEAP1 through four hydrogen bonds. Moreover, QE altered the thermostability of KEAP1. CONCLUSION: These results indicated that QE inhibits ferroptosis in the hippocampal neurons by binding to KEAP1 and subsequently upregulating the Nrf2/HO-1 signaling pathway.

Inactivated COVID-19 vaccination and SARS-CoV-2 infection among Chinese adults in the "living with COVID" era.

The objective of this research was to examine the correlation between the status of inactivated COVID-19 vaccination and self-reported confirmed SARS-CoV-2 infection among adults after China entered the "living with COVID" era. A cross-sectional online survey was conducted among parents or guardians of students attending all 220 kindergartens and 105 primary or secondary schools in Longhua District of Shenzhen, China during March 1 to 9, 2023. The participating schools invited all parents or guardians of their students to complete the online survey. The study focused on a sub-sample of 68,584 participants who were either unvaccinated (n = 2152) or only receiving inactivated COVID-19 vaccination (n = 66,432). Logistic regression was employed for data analysis. Prior to the implementation of the "living with COVID" policy, 83.5% of the participants received three doses of inactivated COVID-19 vaccines; 63.0% reported being infected with the SARS-CoV-2 after the policy change. In a multivariate analysis, participants who had received a third dose within the past 6 months were less likely to be infected with SARS-CoV-2, as compared to those who had not completed the primary vaccination series (4-6 months: AOR: 0.84, 95%CI: 0.77, 0.92; ≤3 months: AOR: 0.82, 95%CI: 0.73, 0.92). Despite the high coverage, our results suggested that three doses of inactivated COVID-19 vaccines did not provide adequate protection against SARS-CoV-2 infection among Chinese adults.

The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing.

Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.

Coverage with the First Dose of Human Papillomavirus Vaccination among Females Aged 9-50 Years in Shenzhen, China: A Surveillance Based on Administrative Health Records in 2023.

China started to offer human papillomavirus (HPV) vaccines to females aged 9-45 years in 2016. However, there was a lack of reports about HPV vaccination coverage in a representative sample of females in China. Therefore, this study aimed to examine the current HPV coverage and associated factors among females aged 9-50 years in Shenzhen, China, based on administrative health records kept by community health centers. A multistage random sampling approach was used. The research team randomly selected 18 community health centers in Shenzhen, and 3118 health records of females aged 9-50 years were then randomly selected from these health centers. Among all participants, 18.7% received at least one dose of HPV vaccination. The highest coverage was observed among females aged 18-26 years (23.4%), followed by those aged 27-35 years (22.0%) and 36-45 years (20.2%). Such coverage was very low among females aged 9-17 years (4.6%) and those aged 46-50 years (3.2%). Among females aged 18 years or above, higher education level, having a family doctor, and permanent residency in Shenzhen were associated with higher HPV vaccination coverage, while older age and being married/divorced were negatively associated with coverage. The HPV vaccination coverage in Shenzhen was 18.7% and there is a strong need for improvement.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS: This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS: The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS: Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.

Objective.Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach.First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N= 654), an independent internal validation cohort (N= 164) and an external validation cohort (N= 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results.For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (allP< 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P> 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P= 0.02) and overall survival (P< 0.005) for validation cohort 2.Significance.The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.

Artificial intelligence in breast imaging: Current situation and clinical challenges.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

Associations between COVID-19 Vaccination Status and Self-Reported SARS-CoV-2 Infection among 8538 Children Aged 3-17 Years during a Massive COVID-19 Outbreak after China Changed Its Zero-COVID-19 Policy: A Cross-Sectional Survey.

This study aimed to investigate the associations between COVID-19 vaccination status and self-reported SARS-CoV-2 infection among children and adolescents aged 3-17 years during a massive COVID-19 outbreak after China changed its zero COVID policy. A cross-sectional online survey was conducted between 1 and 9 March 2023. Participants were the parents of children studying in kindergartens, primary schools, or secondary schools in Shenzhen. Convenient sampling was used. All kindergartens, primary schools, and secondary schools in the Longhua District of Shenzhen invited the parents of children and adolescents attending the schools. Interested parents completed an online survey. Multivariate logistic regression was fitted. Among 8538 participants, 40.9% self-reported that their children had SARS-CoV-2 infection after 7 December 2022, where 92.9% of them received two doses of the COVID-19 vaccines, and 74.6% received their second dose for more than six months. In multivariate analysis, children who received their second dose of the COVID-19 vaccination for no more than three months had a lower SARS-CoV-2 infection rate compared to unvaccinated children (<1 month: AOR: 0.17, 95% CI: 0.07, 0.44; 1-3 months: AOR: 0.54, 95% CI: 0.41, 0.75). The duration of protection conferred by the primary COVID-19 vaccination series was relatively short among children. A booster dose should be considered for children.

Distinct signatures of gut microbiota and metabolites in different types of diabetes: a population-based cross-sectional study.

Background: Patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) present intestinal disturbances. Recent epidemiological data have showed that, worldwide, over half of newly diagnosed T1D patients were adults. However, the gut microbial alterations in adult-onset T1D are unclear. We aimed to identify the signatures of gut microbiota and metabolites in patients with adult-onset T1D systematically, comparing with T2D patients and healthy controls (HCs). Methods: This study enrolled 218 subjects from February 2019 to April 2022 (discovery cohort: 36 HCs, 51 patients with adult-onset T1D and 56 patients with T2D; validation cohort: 28 HCs, 27 patients with adult-onset T1D and 20 patients with T2D). Gut microbial profiles of the study subjects were investigated by metagenomic sequencing, and their faecal and serum metabolites were measured with targeted metabolomics. The study was registered on ClinicalTrials.gov (NCT05252728). Findings: Patients with adult-onset T1D had significant differences in the composition of bacteria and their metabolites, characterized by notable depletion of short-chain fatty acid-producing bacteria, especially Eubacterium rectale. This was associated with a severe loss of phenolic acids and their derivatives, including gallic acid (associated with glucose metabolism) and 3,4-dihydroxyhydrocinnamic acid (linked with glucose metabolism and pancreatic beta cell autoimmunity). A predictive model based on six bacteria and six metabolites simultaneously discriminated adult-onset T1D from T2D and HCs with high accuracy. Interestingly, bacterial-viral or bacterial-fungal trans-kingdom relationships, especially positive correlations between bacteriophages and beneficial bacteria, were significantly reduced in adult-onset T1D compared to HCs. Interpretation: Adult-onset T1D patients exhibit unique changes in host-microbiota-metabolite interactions. Gut microbiota and metabolite-based algorithms could be used as additional tools for differential diagnosis of different types of diabetes and beyond. Funding: National Key Research and Development Program of China, the National Natural Science Foundation of China.

Exploring the mechanisms of action of Zengye decoction (ZYD) against Sjogren's syndrome (SS) using network pharmacology and animal experiment.

CONTEXT: Zengye decoction (ZYD) has been considered to have a curative effect on Sjogren's syndrome (SS). However, its therapeutic mechanisms remain obscure. OBJECTIVES: This research explores the mechanisms of ZYD against SS. MATERIALS AND METHODS: The active compounds and targets of ZYD were searched in the TCMSP and BATMAN-TCM databases. SS-related targets were obtained from the GeneCards database. The GO and KEGG enrichment analyses elucidated the molecular mechanisms. Animal experiments were performed using 8 C57BL/6 mice that served as the control group (physiological saline treatment) and 16 NOD mice randomly divided into the model group (physiological saline treatment) and the ZYD group (ZYD treatment) for 8 weeks to verify the therapeutic effects of ZYD on SS. RESULTS: Twenty-nine active compounds with 313 targets of ZYD and 1038 SS-related targets were screened. Thirty-two common targets were identified. ß-Sitosterol and stigmasterol might be important components. GO analysis suggested that the action of ZYD against SS mainly involved oxidative stress, apoptotic processes, and tumor necrosis factor receptor superfamily binding, etc. KEGG analysis indicated the most significant signaling pathway was apoptosis-multiple species. Animal experiments showed that ZYD improved lymphocytic infiltration of the submandibular glands (SMGs), reduced the serum levels of TNF-α, IL-1ß, IL-6, and IL-17, upregulated the expression of Bcl-2, and downregulated the expression of Bax and Caspase-3 in the model mice. DISCUSSION AND CONCLUSION: ZYD has anti-inflammatory and anti-apoptotic effects on SS, which provides a theoretical basis for the treatment of SS with ZYD.