ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier.

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-trained classifier model, ESM2-GRU, for ACP prediction to make it easier to predict ACPs, gain a better understanding of the structural and functional differences of anti-cancer peptides, and optimize the design for the development of more effective anti-cancer treatment strategies. The model is made up of the ESM2 pre-trained model, a bidirectional GRU recurrent neural network, and a fully connected layer. ACP sequences are first fed into the ESM2 model, which then expands the dimensions before feeding the findings back into the bidirectional GRU recurrent neural network. Finally, the fully connected layer generates the ultimate output. Experimental validation demonstrates that the ESM2-GRU model greatly improves classification performance on the benchmark dataset ACP606, with AUC, ACC, and MCC values of 0.975, 0.852, and 0.738, respectively. This exceptional prediction potential helps to identify specific types of anti-cancer peptides, improving their targeting and selectivity and, therefore, furthering the development of tailored medicine and treatments.

The anti-urolithiasis activity and safety of strangury-relieving herbs: A comparative study based on fruit fly kidney stone model.

ETHNOPHARMACOLOGICAL RELEVANCE: Urolithiasis is one of the oldest and most widespread urological diseases suffered globally. In the long history of Traditional Chinese Medicine, there're numerous herbs documented with strangury-relieving properties playing crucial roles in treating various urological disorders, including dysuria, hematuria, and renal colic, etc., which may be caused by urolithiasis. Exploring these herbs may reveal safer, more effective, and cost-efficient drugs and therapies for urolithiasis. AIM OF THE STUDY: This study aims to assess the anti-urolithiasis efficacy and safety of 46 Chinese traditional and folk herbal drugs using the fruit fly (Drosophila melanogaster) kidney stone model, in order to identify the most valuable ethnomedicinal materials. MATERIALS AND METHODS: Water extract and 50% ethanol extract of each herb were prepared respectively. 0.2% (w/w) sodium oxalate was chosen as appropriate lithogenic agent through fruit fly life span study. Male fruit-flies within three days of emergence were aged for an additional three days, then were randomly divided into experimental groups, model group and control groups (n = 20). The flies in blank control group, model group and positive control group were fed with standard food, standard food containing 0.2% sodium oxalate, standard food containing 0.2% sodium oxalate and 3% (w/w) Garcinia cambogia extract, respectively. Meanwhile, flies in the experimental groups were raised on standard food containing 0.2% sodium oxalate and 3% (w/w) herbal extract. The anti-urolithiasis capability of the extracts was evaluated using stone area ratio (the stone area divided by the area of the Malpighian tubule) and stone-clearing rate. Additionally, the 7-day mortality rate was employed as an indicator of safety. RESULTS: Out of the 46 herbs, 24 exhibited significant anti-urolithiasis effects in their water extracts. Among them, Herba Nephrolepidis, Herba Humuli, Herba Desmodii Styracifolii, Cortex Plumeriae Rubrae, and Herba Mimosae Pudicae showed us a low 7-day mortality rate of fruit-flies as well. However, only a limited number of herbal extracts (8 out of 46) showed obvious anti-urolithiasis activity in their 50% ethanol extracts. CONCLUSION: Highly potential anti-urolithiasis candidates were discovered from strangury-relieving herbs recorded in classical Traditional Chinese Medicine works, highlighting the significant value of traditional and folk ethnopharmacological knowledge.

Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study.

Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.

Combined measurement of serum zinc with PSA ameliorates prostate cancer screening efficiency via support vector machine algorithms.

Background: Early screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn2+ levels in PCa patients reportedly plays a crucial role in early screening of PCa. Accordingly, we combined 4 indices comprising the serum levels of total PSA (tPSA), free PSA (fPSA), Zn2+ and demographic information (especially age) in order to ameliorate the efficacies of PCa screening with support vector machine (SVM) algorithms. Methods: A total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients' data included 4 variables and serum Zn2+ was quantified via a self-invented Zn2+ responsive AIE-based fluorescent probe as previously published. tPSA and fPSA were routinely determined by a chemiluminescent method. Mathematical simulations were conducted to establish a SVM model for the combined diagnostics with the four variables. Moreover, ROC and its characteristic AUC were also employed to evaluate the classification efficacy of the model. Sigmoid function was utilized to estimate corresponding probabilities of classifying the clinical subjects as per 5 grades, which were incorporated into our established prostate index (PI) stratification system. Results: In SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn2+] or age alone was 64%, 62%, 55% and 59%, respectively. We further established an integrated prostate index (PI) stratification system with 5 grades and a software package to support clinicians in predicting PCa, with the accuracy of our risk stratification system being 83.3%, 91.6% and 83.3% in predicting normal, benign and PCa cases in corresponding groups. Follow-up findings especially MRI results and PI-RADS scores supported the reliability of this stratification platform as well. Conclusion: Findings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-ß deposition in Alzheimer's disease.

PURPOSE: Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-ß (Aß) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aß PET imaging in clinical AD cohort. METHODS: We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aß ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aß PET imaging. RESULTS: TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aß deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aß deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003). CONCLUSION: By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aß deposition and cognitive dysfunction.

LIPH contributes to glycolytic phenotype in pancreatic ductal adenocarcinoma by activating LPA/LPAR axis and maintaining ALDOA stability.

BACKGROUND: LIPH, a membrane-associated phosphatidic acid-selective phospholipase A1a, can produce LPA (Lysophosphatidic acid) from PA (Phosphatidic acid) on the outer leaflet of the plasma membrane. It is well known that LIPH dysfunction contributes to lipid metabolism disorder. Previous study shows that LIPH was found to be a potential gene related to poor prognosis with pancreatic ductal adenocarcinoma (PDAC). However, the biological functions of LIPH in PDAC remain unclear. METHODS: Cell viability assays were used to evaluate whether LIPH affected cell proliferation. RNA sequencing and immunoprecipitation showed that LIPH participates in tumor glycolysis by stimulating LPA/LPAR axis and maintaining aldolase A (ALDOA) stability in the cytosol. Subcutaneous, orthotopic xenograft models and patient-derived xenograft PDAC model were used to evaluate a newly developed Gemcitabine-based therapy. RESULTS: LIPH was significantly upregulated in PDAC and was related to later pathological stage and poor prognosis. LIPH downregulation in PDAC cells inhibited colony formation and proliferation. Mechanistically, LIPH triggered PI3K/AKT/HIF1A signaling via LPA/LPAR axis. LIPH also promoted glycolysis and de novo synthesis of glycerolipids by maintaining ALDOA stability in the cytosol. Xenograft models show that PDAC with high LIPH expression levels was sensitive to gemcitabine/ki16425/aldometanib therapy without causing discernible side effects. CONCLUSION: LIPH directly bridges PDAC cells and tumor microenvironment to facilitate aberrant aerobic glycolysis via activating LPA/LPAR axis and maintaining ALDOA stability, which provides an actionable gemcitabine-based combination therapy with limited side effects.

Increased Uptake in Microcystic Serous Cystadenoma Mimicking Pancreatic Neuroendocrine Tumor on 68 Ga-DOTATATE PET/MRI.

ABSTRACT: A 2.6-cm solid cystic lesion in the pancreatic head was found in a 51-year-old woman on CT. A pancreatic neuroendocrine tumor was suspected, and a 68 Ga-DOTATATE PET/MRI was performed, which showed increased tracer uptake in the lesion. However, postsurgical pathologic examination indicated a pancreatic serous cystadenoma. Here, we reported a case of microcystic pancreatic serous cystadenoma that could be misdiagnosed as a pancreatic neuroendocrine tumor on a 68 Ga-DOTATATE PET/MRI.

Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients.

BACKGROUND: Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). METHODS: A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). RESULTS: A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). CONCLUSION: The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. TRIAL REGISTRATION: Retrospectively registered.

The value of 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance whole-body scans and local enhancement scans in the preoperative staging and resectability assessment of pancreatic adenocarcinoma.

Background: In addition to contrast-enhanced multiphase computed tomography (CT) and magnetic resonance imaging (MRI), integrated positron emission tomography (PET)/magnetic resonance (MR) is increasingly being used for the preoperative evaluation of pancreatic cancer. The purpose of this study was to explore the value of hybrid 18F-fluorodeoxyglucose (18F-FDG) PET/MR imaging in preoperative assessment and treatment decision-making. Methods: A retrospective data collection (of imaging, clinical, and pathological information) was conducted on patients who underwent 18F-FDG PET/MR with clinically diagnosed or suspected pancreatic cancer from March 2018 to March 2022 in Ruijin Hospital. The data of 76 patients were initially included, with 1 of the 76 patients eventually being excluded due to a misdiagnosis of inflammatory disease. Of the 75 patients, 38 underwent pancreatic tumor resection and 10 underwent laparoscopic exploration. The accuracy of 18F-FDG PET/MR for pancreatic cancer staging and the assessment of pancreatic resectability was evaluated based on pathological results, intraoperative findings, and documented final clinical stages of illness. The adjustments to patient treatment plans were also analyzed before and after 18F-FDG PET/MR examination. Results: The accuracy of clinical tumor node metastasis (TNM) staging of pancreatic cancer by 18F-FDG PET/MR was 73.3% (55/75). The area under the curve (AUC) of 18F-FDG PET/MR for diagnosing the advanced stage (III-IV) versus the nonadvanced stage (I-II) of disease was 0.922 [95% confidence interval (CI): 0.852-0.993]. The treatment regimen of 20.0% (15/75) of patients was impacted. The accuracy of the evaluation of the resectability of pancreatic cancer with 18F-FDG PET/MR was 91.9% (34/37). With the surgical and pathological results being used as a reference, the overall accuracy of preoperative 18F-FDG PET/MR for T staging was 62.2%, and the AUC for diagnosing T4 versus T1-3 was 0.872 (95% CI: 0.660-1.000). Conclusions: 18F-FDG PET/MR performs well in diagnosing advanced pancreatic cancer and thus may impact the treatment decisions for a considerable number of patients. 18F-FDG PET/MR has a high level of accuracy in evaluating the resectability of pancreatic cancer before surgery.

DRD2 expression based on 18F-fallypride PET/MR predicts the dopamine agonist resistance of prolactinomas: a pilot study.

PURPOSE: The dopamine agonists (DA) have been used widely to treat prolactinomas. However, it is difficult to predict whether the patient will be responsive to DA treatment. METHODS: We aimed to investigate whether the in vivo expression of DRD2 based on 18F-fallypride PET/MR could predict the therapeutic effect of DA on prolactinomas. Seven patients with prolactinomas completed 18F-fallypride PET/MR. Among them, three patients underwent surgery and further tumor immunohistochemistry. Imaging findings and immunohistochemical staining were compared with treatment outcomes. RESULTS: 18F-fallypride PET/MR was visually positive in 7 of 7 patients, and DRD2 target specificity could be confirmed by immunohistochemical staining. A significantly lower tracer standard uptake value (SUV) could be detected in the resistant patients (n = 3) than in the sensitive patients (n = 4; SUVmean, 4.67 ± 1.32 vs. 13.57 ± 2.42, p < 0.05). DRD2 expression determined by 18F-fallypride PET/MR corresponded with the DA treatment response. CONCLUSION: 18F-fallypride PET/MR may be a promising technique for predicting DA response in patients with prolactinoma.

Aerobic glycolysis imaging of epileptic foci during the inter-ictal period.

BACKGROUND: In drug-resistant epilepsy, surgical resection of the epileptic focus can end seizures. However, success is dependent on the ability to identify foci locations and, unfortunately, current methods like electrophysiology and positron emission tomography can give contradictory results. During seizures, glucose is metabolized at epileptic foci through aerobic glycolysis, which can be imaged through the oxygen-glucose index (OGI) biomarker. However, inter-ictal (between seizures) OGI changes have not been studied, which has limited its application. METHODS: 18 healthy controls and 24 inter-ictal, temporal lobe epilepsy patients underwent simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. We used [18F]fluorodeoxyglucose-PET (FDG-PET) to detect cerebral glucose metabolism, and calibrated functional MRI to acquire relative oxygen consumption. With these data, we calculated relative OGI maps. FINDINGS: While bilaterally symmetrical in healthy controls, we observed, in patients during the inter-ictal period, higher OGI ipsilateral to the epileptic focus than contralateral. While traditional FDG-PET results and temporal lobe OGI results usually both agreed with invasive electrophysiology, in cases where FDG-PET disagreed with electrophysiology, temporal lobe OGI agreed with electrophysiology, and vice-versa. INTERPRETATION: As either our novel epilepsy biomarker or traditional approaches located foci in every case, our work provides promising insights into metabolic changes in epilepsy. Our method allows single-session OGI measurement which can be useful in other diseases. FUNDING: This work was supported by ShanghaiTech University, the Shanghai Municipal Government, the National Natural Science Foundation of China Grant (No. 81950410637) and Shanghai Municipal Key Clinical Specialty (No. shslczdzk03403). F. H. and P. H. were supported by USA National Institute of Health grants (R01 NS-100106, R01 MH-067528).Z. W. was supported by the Key-Area Research and Development Program of Guangdong Province (2019B030335001), National Natural Science Foundation of China (No. 82151303), and National Key R&D Program of China (No. 2021ZD0204002).

[Grading of pancreatic neuroendocrine neoplasms with imaging: an update].

Imaging evaluation of pancreatic neuroendocrine neoplasms is developing with the updating of pathological grading. Tumor size, blood perfusion and apparent diffusion coefficient(ADC)value are the most commonly used imaging indicators for the evaluation of the malignancy of pancreatic neuroendocrine neoplasms, as well as other imaging characteristics representing the tumor invasiveness. Dual-probe imaging with SSR-PET and FDG-PET can further improve the accuracy of diagnosis and classification of pancreatic neuroendocrine neoplasms, and provide further information for the tumor treatment and prognosis. Quantitative analysis including texture analysis and radiomics is also a new topic in the research of pancreatic neuroendocrine neoplasms. The combination of morphology, functional imaging and quantitative analysis may contribute to the non-invasive evaluation of the malignancy of pancreatic neuroendocrine neoplasms.

Combined quantitative T2 mapping and [18F]FDG PET could improve lateralization of mesial temporal lobe epilepsy.

OBJECTIVES: To investigate whether quantitative T2 mapping is complementary to [18F]FDG PET in epileptogenic zone detection, thus improving the lateralization accuracy for drug-resistant mesial temporal lobe epilepsy (MTLE) using hybrid PET/MR. METHODS: We acquired routine structural MRI, T2-weighted FLAIR, whole brain T2 mapping, and [18F]FDG PET in 46 MTLE patients and healthy controls on a hybrid PET/MR scanner, followed with computing voxel-based z-score maps of patients in reference to healthy controls. Asymmetry indexes of the hippocampus were calculated for each imaging modality, which then enter logistic regression models as univariate or multivariate for lateralization. Stereoelectroencephalography (SEEG) recordings and clinical decisions were collected as gold standard. RESULTS: Routine structural MRI and T2w-FLAIR lateralized 47.8% (22/46) of MTLE patients, and FDG PET lateralized 84.8% (39/46). T2 mapping combined with [18F]FDG PET improved the lateralization accuracy by correctly lateralizing 95.6% (44/46) of MTLE patients. The asymmetry indexes of hippocampal T2 relaxometry and PET exhibit complementary tendency in detecting individual laterality, especially for MR-negative patients. In the quantitative analysis of z-score maps, the ipsilateral hippocampus had significantly lower SUVR (LTLE, p < 0.001; RTLE, p < 0.001) and higher T2 value (LTLE, p < 0.001; RTLE, p = 0.001) compared to the contralateral hippocampus. In logistic regression models, PET/T2 combination resulted in the highest AUC of 0.943 in predicting lateralization for MR-negative patients, followed by PET (AUC = 0.857) and T2 (AUC = 0.843). CONCLUSIONS: The combination of quantitative T2 mapping and [18F]FDG PET could improve lateralization for temporal lobe epilepsy. KEY POINTS: • Quantitative T2 mapping and18F-FDG PET are complementary in the characterization of hippocampal alterations of MR-negative temporal lobe epilepsy patients. • The combination of quantitative T2 and18F-FDG PET obtained from hybrid PET/MR could improve lateralization for temporal lobe epilepsy.

Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study.

The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer's disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.

Model-Driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-Transformation.

Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both manually annotate and automatically segment cancers. Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. Specifically, a spiral-transformation algorithm with uniform sampling was developed to map 3D images onto 2D planes while preserving the spatial relationship between textures, thus addressing the challenge in effectively applying 3D contextual information in a 2D model. This study is the first to introduce spiral transformation in a segmentation task to provide effective data augmentation, alleviating the issue of small sample size. Moreover, a transformation-weight-corrected module was embedded into the deep learning model to unify the entire framework. It can achieve 2D segmentation and corresponding 3D rebuilding constraint to overcome non-unique 3D rebuilding results due to the uniform and dense sampling. A smooth regularization based on rebuilding prior knowledge was also designed to optimize segmentation results. The extensive experiments showed that the proposed method achieved a promising segmentation performance on multi-parametric MRIs, where T2, T1, ADC, DWI images obtained the DSC of 65.6%, 64.0%, 64.5%, 65.3%, respectively. This method can provide a novel paradigm to efficiently apply 3D information and augment sample sizes in the development of artificial intelligence for cancer segmentation. Our source codes will be released at https://github.com/SJTUBME-QianLab/ Spiral-Segmentation.

Utilizing GCN and Meta-Learning Strategy in Unsupervised Domain Adaptation for Pancreatic Cancer Segmentation.

Automated pancreatic cancer segmentation is highly crucial for computer-assisted diagnosis. The general practice is to label images from selected modalities since it is expensive to label all modalities. This practice brought about a significant interest in learning the knowledge transfer from the labeled modalities to unlabeled ones. However, the imaging parameter inconsistency between modalities leads to a domain shift, limiting the transfer learning performance. Therefore, we propose an unsupervised domain adaptation segmentation framework for pancreatic cancer based on GCN and meta-learning strategy. Our model first transforms the source image into a target-like visual appearance through the synergistic collaboration between image and feature adaptation. Specifically, we employ encoders incorporating adversarial learning to separate domain-invariant features from domain-specific ones to achieve visual appearance translation. Then, the meta-learning strategy with good generalization capabilities is exploited to strike a reasonable balance in the training of the source and transformed images. Thus, the model acquires more correlated features and improve the adaptability to the target images. Moreover, a GCN is introduced to supervise the high-dimensional abstract features directly related to the segmentation outcomes, and hence ensure the integrity of key structural features. Extensive experiments on four multi-parameter pancreatic-cancer magnetic resonance imaging datasets demonstrate improved performance in all adaptation directions, confirming our model's effectiveness for unlabeled pancreatic cancer images. The results are promising for reducing the burden of annotation and improving the performance of computer-aided diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/UDAseg, once this manuscript is accepted for publication.

Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer's Disease.

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer's disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

Amyloid-Beta Influences Memory via Functional Connectivity During Memory Retrieval in Alzheimer's Disease.

Objective: Amnesia in Alzheimer's disease (AD) appears early and could be caused by encoding deficiency, consolidation dysfunction, and/or impairment in the retrieval of stored memory information. The relationship between AD pathology biomarker ß-amyloid and memory dysfunction is unclear. Method: The memory task functional MRI and amyloid PET were simultaneously performed to investigate the relationship between memory performance, memory phase-related functional connectivity, and cortical ß-amyloid deposition. We clustered functional networks during memory maintenance and compared network connectivity between groups in each memory phase. Mediation analysis was performed to investigate the mediator between ß-amyloid and related cognitive performance. Results: Alzheimer's disease was primarily characterized by decreased functional connectivity in a data-driven network composed of an a priori default mode network, limbic network, and frontoparietal network during the memory maintenance (0.205 vs. 0.236, p = 0.04) and retrieval phase (0.159 vs. 0.183, p = 0.017). Within the network, AD had more regions with reduced connectivity during the retrieval than the maintenance and encoding phases (chi-square p = 0.01 and < 0.001). Furthermore, the global cortical ß-amyloid negatively correlated with network connectivity during the memory retrieval phase (R = - 0.247, p = 0.032), with this relationship mediating the effect of cortical ß-amyloid on memory performance (average causal mediation effect = - 0.05, p = 0.035). Conclusion: We demonstrated that AD had decreased connectivity in specific networks during the memory retrieval phase. Impaired functional connectivity during memory retrieval mediated the adverse effect of ß-amyloid on memory. These findings help to elucidate the involvement of cortical ß-amyloid (Aß) in the memory performance in the early stages of AD.

Differentiation of Hepatocellular Carcinoma from Hepatic Hemangioma and Focal Nodular Hyperplasia using Computed Tomographic Spectral Imaging.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy. This study was designed to investigate the value of computed tomography (CT) spectral imaging in differentiating HCC from hepatic hemangioma (HH) and focal nodular hyperplasia (FNH). METHODS: This was a retrospective study of 51 patients who underwent spectral multiple-phase CT at 40-140 keV during the arterial phase (AP) and portal venous phase (PP). Slopes of the spectral curves, iodine density, water density derived from iodine- and water-based material decomposition images, iodine uptake ratio (IUR), normalized iodine concentration, and the ratio of iodine concentration in liver lesions between AP and PP were measured or calculated. RESULTS: As energy level decreased, the CT values of HCC (n=31), HH (n=17), and FNH (n=7) increased in both AP and PP. There were significant differences in IUR in the AP, IUR in the PP, normalized iodine concentration in the AP, slope in the AP, and slope in the PP among HCC, HH, and FNH. The CT values in AP, IUR in the AP and PP, normalized iodine concentration in the AP, slope in the AP and PP had high sensitivity and specificity in differentiating HH and HCC from FNH. Quantitative CT spectral data had higher sensitivity and specificity than conventional qualitative CT image analysis during the combined phases. CONCLUSIONS: Mean CT values at low energy (40-90 keV) and quantitative analysis of CT spectral data (IUR in the AP) could be helpful in the differentiation of HCC, HH, and FNH.