Combined quantitative amyloid-ß PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study.

RATIONALE AND OBJECTIVES: Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-ß and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aß PET features and whether combining Aß PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD. MATERIAL AND METHODS: We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aß features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification. RESULTS: The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aß PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aß PET features contributed more to differentiating AD from others. CONCLUSION: Our study demonstrated the discriminative ability of Aß PET features in differentiating AD from OHC and MCI. A combination of Aß PET and structural MRI features can improve the RF model performance.

Expression of regional brain amyloid-ß deposition with [18F]Flutemetamol in Centiloid scale -a multi-site study.

PURPOSE: The Centiloid project helps calibrate the quantitative amyloid-ß (Aß) load into a unified Centiloid (CL) scale that allows data comparison across multi-site. How the smaller regional amyloid converted into CL has not been attempted. We first aimed to express regional Aß deposition in CL using [18F]Flutemetamol and evaluate regional Aß deposition in CL with that in standardized uptake value ratio (SUVr). Second, we aimed to determine the presence or absence of focal Aß deposition by measuring regional CL in equivocal cases showing negative global CL. METHODS: Following the Centiloid project pipeline, Level-1 replication, Level-2 calibration, and quality control were completed to generate corresponding Centiloid conversion equations to convert SUVr into Centiloid at regional levels. In equivocal cases, the regional CL was compared with visual inspection to evaluate regional Aß positivity. RESULTS: 14 out of 16 regional conversions from [18F]Flutemetamol SUVr to Centiloid successfully passed the quality control, showing good reliability and relative variance, especially precuneus/posterior cingulate and prefrontal regions with good stability for Centiloid scaling. The absence of focal Aß deposition could be detected by measuring regional CL, showing a high agreement rate with visual inspection. The regional Aß positivity in the bilateral anterior cingulate cortex was most prevalent in equivocal cases. CONCLUSION: The expression of regional brain Aß deposition in CL with [18F]Flutemetamol has been attempted in this study. Equivocal cases had focal Aß deposition that can be detected by measuring regional CL.

Using a deep learning prior for accelerating hyperpolarized 13C MRSI on synthetic cancer datasets.

PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets. METHODS: A two-site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k-space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated. RESULTS: For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1-norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%. CONCLUSION: The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method.

Social class, schadenfreude, and children's prosocial behavior in moral contexts.

Previous research has shown mixed results regarding the relationship between social class and children's prosocial behavior. The current study aims to further our understanding of these findings by exploring the relationship between social class and children's prosocial behavior in a moral context. Study 1 (N = 833) found that when a target child pursued a morally negative goal and subsequently experienced misfortune, children from higher social class, compared to those from lower social class, experienced greater schadenfreude and exhibited less prosocial behavior. The relation between social class and prosocial behavior was mediated by schadenfreude. Study 2 (N = 389) investigated whether the greater schadenfreude experienced by children from higher social class was due to a weaker empathic response to misfortune or a stronger sense of deservingness. The results revealed a sequential mediation effect of social class on prosocial behavior through deservingness and schadenfreude. These findings provide insight into the impact of social class on the development of children's moral judgment, emotions, and behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Resting-State Functional Magnetic Resonance Imaging as an Indicator of Neuropsychological Changes in Type 1 Narcolepsy.

RATIONALE AND OBJECTIVES: To explore indicators of neuropsychological changes in patients with type 1 narcolepsy (NT1) using resting-state functional magnetic resonance imaging (rs-fMRI). MATERIALS AND METHODS: Thirty-four NT1 patients and 34 age- and sex-matched healthy volunteers were recruited for neuropsychiatric assessments and rs-fMRI data acquisition. Fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and related brain functional connectivity (FC) were calculated for the two groups and compared using a two-sample t test with cluster-level FDR correction. Moreover, partial correlation analysis was performed between these functional values of changed brain regions and clinical scales. RESULTS: Compared to those of healthy controls, spontaneous functional activities were significantly weakened in patients with NT1 in regions such as the left/right posterior cerebellum lobe, left inferior temporal gyrus, and left dorsolateral superior frontal gyrus, whereas those in regions such as the left middle occipital gyrus, right inferior occipital gyrus, and left/right lingual gyrus were significantly strengthened. Furthermore, NT1 patients displayed significantly changed FCs between the left/right anterior cingulate gyrus (ACG) and regions such as the left/right cerebellum, left middle occipital gyrus, and left inferior frontal gyrus in the operculum. In partial correlation analysis, the functions in the left dorsolateral superior frontal gyrus were significantly related to the Trail Making Tests (TMT) score. Moreover, the FC between the left ACG and left inferior frontal gyrus in the operculum was highly correlated with anxiety and depression features, including the Hamilton Anxiety Scale (HAMA) score and Hamilton Depression Rating Scale (HAMD-17) score. CONCLUSION: Patients with NT1 exhibited abnormalities in the anterior cingulate cortex, frontal-parietal cortex, hippocampus, and left/right posterior cerebellum lobe. The deactivation of the left frontal-temporal cortex is stronger, which is involved in the cognitive decline and mental disorders in these patients. Damage to the ACG may affect its FC with other regions and cause cognition and emotion dysregulation, perhaps by impairing patients' visual pathways and frontal-temporal-parietal networks. Hence, these could be important biomarkers for their neuropsychological changes.

Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. PURPOSE: To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. STUDY TYPE: Retrospective. SUBJECTS: A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. FIELD STRENGTH/SEQUENCE: 3 T/QSM. ASSESSMENT: The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. STATISTICAL TESTS: The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. RESULTS: Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. DATA CONCLUSION: We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Cross modality generative learning framework for anatomical transitive Magnetic Resonance Imaging (MRI) from Electrical Impedance Tomography (EIT) image.

This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation. The initial EIT image was generated at three different frequencies (70 kHz, 140 kHz, and 200 kHz) using a 16-electrode belt. Wrist T1-weighted images were acquired on a 1.5T MRI. A total of 19 normal volunteers were imaged using both EIT and MRI, which resulted in 713 paired EIT and MRI images. The cascaded CycleGAN, end-to-end CycleGAN, and Pix2Pix models were trained and tested on the same cohort. The proposed method achieved the highest accuracy in bone detection, with 0.97 for the proposed cascaded CycleGAN, 0.68 for end-to-end CycleGAN, and 0.70 for the Pix2Pix model. Visual inspection showed that the proposed method reduced bone-related errors in the MRI-style anatomical reference compared with end-to-end CycleGAN and Pix2Pix. Multifrequency EIT inputs reduced the testing normalized root mean squared error of MRI-style anatomical reference from 67.9% ± 12.7% to 61.4% ± 8.8% compared with that of single-frequency EIT. The mean conductivity values of fat and bone from regularized EIT were 0.0435 ± 0.0379 S/m and 0.0183 ± 0.0154 S/m, respectively, when the anatomical prior was employed. These results demonstrate that the proposed framework is able to generate MRI-style anatomical references from EIT images with a good degree of accuracy.

The Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer's Disease Spectrum.

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer's disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included. Bilateral hippocampal volume, mean diffusivity (MD), and mean kurtosis (MK) were obtained. We detected that in AD vs. NCs, the right hippocampal volume showed the most prominent AUC value (AUC = 0.977); in MCI vs. NCs, the right hippocampal MD was the most sensitive discriminator (AUC = 0.819); in SCD vs. NCs, the left hippocampal MK was the most sensitive biomarker (AUC = 0.775). These findings suggest that, in the predementia stage (SCD and MCI), hippocampal microstructural changes are predominant, and the best discriminators are microstructural measurements (left hippocampal MK for SCD and right hippocampal MD for MCI); while in the dementia stage (AD), hippocampal macrostructural alterations are superior, and the best indicator is the macrostructural index (right hippocampal volume).

Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management.

PURPOSE: The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS: Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS: RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS: RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.

Deep learning-regularized, single-step quantitative susceptibility mapping quantification.

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.

In vitro study about prevention of vascular reocclusion by low intensity ultrasonic irradiation.

For the treatment of acute ischemic stroke, the current standard of care is thrombolysis by the administration of intravenous (IV) recombinant tissue-type plasminogen activator (rt-PA). Although this approach is proven to be effective, reocclusion within 24 hours occurs in about 20% of patients who receive recanalization by rt-PA. In addition, the administration of anticoagulants within 24 hours after IV rt-PA increases the risk of intracranial hemorrhage; therefore, treatment with anticoagulants is contraindicated in this population. To address the need for an approach to sustain the effects of thrombolysis prevent blood vessel reocclusion without the use of anticoagulants, this study proposes a novel method using a low-intensity ultrasound (US) irradiation. An in vitro thrombus-growth model, in a latex rubber container was developed to study the effect of thrombus-growth suppression by US irradiation at 500 kHz in a 37°C water bath. The US acoustic intensity was set at or below 0.72 W/cm2, which is the maximum allowed for noninvasive acoustic irradiation. Low-intensity US irradiation of the thrombus-growth model resulted in a remarkable suppression of thrombus growth (100.22 ± 10.1 mg vs. 50.22 ± 5.3 mg, p < 0.0001), and the clot-growth inhibition depended logarithmically on acoustic intensity. Thrombus growth can be suppressed by low-intensity US irradiation, opening a new way to combat vascular reocclusion after rt-PA treatment of acute ischemic stroke.

Motion-resolved and free-breathing liver MRF.

PURPOSE: To develop a motion-resolved and free-breathing liver magnetic resonance fingerprinting (MRF) protocol. METHODS: The deformation maps were obtained from the first singular image of MRF data. The reconstruction method enforced the consistency of the MRF data with the deformation maps by adding the deformation maps to the encoding matrix. A sliding window reconstruction was inherently assumed, with a window size of 60 repetition times (TRs) and a step size of 30 TRs. L1 wavelet regularization was applied to reduce the undersampling artifact. MRF was tested on four healthy volunteers with parameters: 13 s/slice, 0.39 s/frame, and 33 time frames/slice. RESULTS: For measuring the accuracy of the deformation map, the typical normalized root mean square error (NRMSE) of the first singular image after motion correction was 0.19. In the sagittal scan, the liver T1 and T2 were 808.7±96.8 ms and 52.7±11.6 ms, respectively. They agreed with our previously reported values, i.e., T1 = 759 ms and T2 = 51 ms at 3 T, using free-breathing liver MRF. Compared to breath-hold MRF, the NRMSEs for T1 and T2 maps (without considering vessel pixels) from the proposed method were 0.13 and 0.18, respectively. CONCLUSION: We demonstrated a motion-resolved MRF with a nominal frame rate of 2.5 Hz for free-breathing liver imaging.

Childhood psychological maltreatment and internet gaming addiction in Chinese adolescents: Mediation roles of maladaptive emotion regulation strategies and psychosocial problems.

BACKGROUND: Internet gaming addiction (IGA) is a global concern, especially among young children. There have been some suggestions that childhood psychological maltreatment influences the development of IGA, but evidence for this has thus far been lacking. OBJECTIVE: The goal of this study was to investigate the association between childhood psychological maltreatment and IGA in adolescents and the mediation roles of maladaptive emotion regulation strategies and psychosocial problems (depression and social anxiety). METHODS: This study recruited 1280 (girls = 690) middle school students with a mean age of 16.09 ± 0.98 years old. All participants undertook a standardized assessment of childhood psychological maltreatment, maladaptive emotion regulation strategies, psychosocial problems (depression and social anxiety), and IGA. RESULTS: We examined whether the effect of childhood psychological maltreatment on IGA was mediated by maladaptive emotion regulation strategies and psychosocial problems (depression and social anxiety). Both parallel and sequential mediation analysis showed that maladaptive emotion regulation strategies and depression mediated the relationship between childhood psychological maltreatment and IGA. CONCLUSIONS: Childhood psychological maltreatment is positively associated with IGA in adolescents. Maladaptive emotion regulation strategies and depression both significantly mediated the relationship between childhood psychological maltreatment and IGA.

A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

PURPOSE: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification. METHODS: The proposed deep learning QSM pipeline consisted of two projections onto convex set (POCS) models designed to decouple trainable network components with the spherical mean value (SMV) filters and dipole kernel in the data-driven optimization. They were a background field removal network (named POCSnet1) and a dipole inversion network (named POCSnet2). Both POCSnet1 and POCSnet2 were the unrolled V-Net with iterative data-driven optimization to enforce the data fidelity. For training POCSnet1, we simulated phantom data with random geometric shapes as the background susceptibility sources. For training POCSnet2, we used geometric shapes to mimic the QSM. The evaluation was performed on synthetic data, a public COSMOS (N = 1), and clinical data from a Parkinson's disease cohort (N = 71) and small-vessel disease cohort (N = 26). For comparison, DLL2, FINE, and autoQSM, were implemented and tested under the same experimental setting. RESULTS: On COSMOS, results from POCSnet1 were more similar to that of the V-SHARP method with NRMSE = 23.7% and SSIM = 0.995, compared with the NRMSE = 62.7% and SSIM = 0.975 for SHARQnet, a naïve V-Net model. On COSMOS, the NRMSE and HFEN for POCSnet2 were 58.1% and 56.7%; while for DLL2, FINE, and autoQSM, they were 62.0% and 61.2%, 69.8% and 67.5%, and 87.5% and 85.3%, respectively. On the Parkinson's disease cohort, our results were consistent with those obtained from VSHARP+STAR-QSM with biases <3% and outperformed the SHARQnet+DeepQSM that had biases of 7% to 10%. The sensitivity of cerebral microbleed detection using our pipeline was 100%, compared with 92% by SHARQnet+DeepQSM. CONCLUSION: Data-driven optimization improved the accuracy of QSM quantification compared with that of naïve V-Net models.

The influence of serum sodium concentration on prognosis in patients with urothelial carcinoma treated by radical cystectomy.

Serum sodium concentration has been found to be associated with poor survival in many solid tumors. This study investigated the effect of basal serum sodium concentration on prognostic in patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC). MIBC patients with histologically proven urothelial carcinoma treated by RC were retrospectively reviewed. According to the optimal cutoff value, we divided the patients into 2 groups: high-serum sodium concentration group (≥140 mmol/L, n = 39) and low-serum sodium concentration group (<140 mmol/L, n = 32). Overall survival (OS) was estimated with the Kaplan-Meier method and the significance was examined by the log-rank test. Multivariable Cox regression for OS was performed for lymphatic metastasis, hypertension, diabetes mellitus, and tumor size. A total of 71 MIBC patients (60 males and 11 females) were included who underwent cystectomy between 2014 and 2018. The patients' ages at the time of operation ranged from 44 to 86 years (mean, 66.66 years). Patients' serum sodium concentration <140 mmol/L had shorter median OS (1224 days (HR: 2.454 [95% CI, 1.083-5.561; P = .031]). In multivariate analysis, lower serum sodium concentration was significantly associated with worse OS after adjusted (adjusted HR: 2.422 [95% CI, 1.055-5.561; P = .037]). Serum sodium concentration <140 mmol/L was independently associated with a poorer prognosis in patients with MIBC used who underwent RC.

γ-Secretase inhibitors for breast cancer and hepatocellular carcinoma: From mechanism to treatment.

The γ-secretase complex is a key hydrolase for many type 1 transmembrane proteins. It is very important for activation of the Notch receptor and regulation of target-gene transcription. Abnormal activation and expression of the Notch pathway are closely related to the occurrence and development of many tumor types, including breast cancer and liver cancer. In this review, we elaborated on the basic situation of γ-secretase complex and the biological function and role of γ-secretase in APP and Notch signal pathway are described in detail. Subsequently, all currently known γ-secretase inhibitors and γ-secretase modulators are listed and their mechanism of action, value of IC50, chemical structure and current research stage are summarized. Next, the selection presented the treatment progress of γ-secretase inhibitors in breast cancer and hepatocellular carcinoma in the past five years. Finally, the mechanism of action of γ-secretase-mediated breast cancer and hepatocellular carcinoma and the advantages and disadvantages of γ-secretase inhibitors are discussed, and the concept of further research is proposed.

Underdogs make an alliance: The co-experience of rejection promotes cooperation.

Social rejection research has largely focused on the consequences of rejection when individuals experience rejection alone. Yet little is known about the reaction of those co-experiencing rejection. We tested the hypothesis that the co-experience of rejection increases cooperation between the co-experiencers. Three experiments provided supporting evidence for the hypothesis. The participants cooperated more when they co-experienced rejection than when they experienced rejection alone. The need to belong mediated the relationship between those co-experiencing rejection and cooperation. These findings shed light on the factors that initiate the formation of small groups, especially deviant ones.