Nattokinase attenuates endothelial inflammation through the activation of SRF and THBS1.

Natto contains a potent fibrinolytic enzyme called nattokinase (NK), which has thrombolytic, antihypertensive, antiatherosclerotic and lipid-lowering effects. Although NK has been recognized for its beneficial effect on humans with atherosclerotic cardiovascular disease (ASCVD), the underlying mechanisms involved in vascular inflammation-atherosclerosis development remain largely unknown. The current study aimed to explore the effects of NK on gene regulation, autophagy, necroptosis and inflammasome in vascular inflammation. The transcriptional profiles of NK in endothelial cells (ECs) by RNA sequencing (RNA-seq) revealed that NK affected THBS1, SRF and SREBF1 mRNA expression. In Q-PCR analysis, SRF and THBS1 were upregulated but SREBF1 was unaffected in ECs treated with NK. NK treatment induced autophagy and inhibited NLRP3 inflammasome and necroptosis in ECs. Furthermore, the inhibition of SRF or THBS1 by siRNA suppressed autophagy and enhanced the NLRP3 inflammasome and necroptosis. In a mouse model, NK reduced vascular inflammation by activating autophagy and inhibiting NLRP3 inflammasome and necroptosis. Our findings provide the first evidence that NK upregulates SRF and THBS1 genes, subsequently increasing autophagy and decreasing necroptosis and NLRP3 inflammasome formation to reduce vascular inflammation. Therefore, NK could serve as nutraceuticals or adjuvant therapies to reduce vascular inflammation and possible atherosclerosis progression.

S100A8/A9 predicts response to PIM kinase and PD-1/PD-L1 inhibition in triple-negative breast cancer mouse models.

BACKGROUND: Understanding why some triple-negative breast cancer (TNBC) patients respond poorly to existing therapies while others respond well remains a challenge. This study aims to understand the potential underlying mechanisms distinguishing early-stage TNBC tumors that respond to clinical intervention from non-responders, as well as to identify clinically viable therapeutic strategies, specifically for TNBC patients who may not benefit from existing therapies. METHODS: We conducted retrospective bioinformatics analysis of historical gene expression datasets to identify a group of genes whose expression levels in early-stage tumors predict poor clinical outcomes in TNBC. In vitro small-molecule screening, genetic manipulation, and drug treatment in syngeneic mouse models of TNBC were utilized to investigate potential therapeutic strategies and elucidate mechanisms of drug action. RESULTS: Our bioinformatics analysis reveals a robust association between increased expression of immunosuppressive cytokine S100A8/A9 in early-stage tumors and subsequent disease progression in TNBC. A targeted small-molecule screen identifies PIM kinase inhibitors as capable of decreasing S100A8/A9 expression in multiple cell types, including TNBC and immunosuppressive myeloid cells. Combining PIM inhibition and immune checkpoint blockade induces significant antitumor responses, especially in otherwise resistant S100A8/A9-high PD-1/PD-L1-positive tumors. Notably, serum S100A8/A9 levels mirror those of tumor S100A8/A9 in a syngeneic mouse model of TNBC. CONCLUSIONS: Our data propose S100A8/A9 as a potential predictive and pharmacodynamic biomarker in clinical trials evaluating combination therapy targeting PIM and immune checkpoints in TNBC. This work encourages the development of S100A8/A9-based liquid biopsy tests for treatment guidance.

Breast cancer is a complex disease, and not all patients respond well to existing treatments. In this study, we sought to understand why some patients with a specific type of breast cancer called triple-negative breast cancer respond poorly to current therapies. We also aimed to identify new treatments for these patients. We analyzed genetic data from breast cancer patients and identified a factor called S100A8/A9, which is linked to poor outcomes in early-stage cancer. We tested drugs that can reduce the levels of this factor in tumors and found promising results, especially when combined with another treatment called immunotherapy. Our findings suggest that S100A8/A9 could help predict how patients will respond to treatments, potentially leading to better therapies in the future.

Pharmacokinetics, pharmacodynamics, and toxicity of a PD-1-targeted IL-15 in cynomolgus monkeys.

PF-07209960 is a novel bispecific fusion protein composed of an anti-PD-1 antibody and engineered IL-15 cytokine mutein with reduced binding affinity to its receptors. The pharmacokinetics (PK), pharmacodynamics (PD), and toxicity of PF-07209960 were evaluated following once every other week subcutaneous (SC) or intravenous (IV) administration to cynomolgus monkeys in a repeat-dose PKPD (0.01-0.3 mg/kg/dose) and GLP toxicity study (0.1-3 mg/kg/dose). PF-07209960 showed dose dependent pharmacokinetics with a terminal T1/2 of 8 and 13 hours following IV administration at 0.03 and 0.1 mg/kg, respectively. The clearance is faster than a typical IgG1 antibody. Slightly faster clearance was also observed following the second dose, likely due to increased target pool and formation of anti-drug antibodies (ADA). Despite a high incidence rate of ADA (92%) observed in GLP toxicity study, PD-1 receptor occupancy, IL-15 signaling (STAT5 phosphorylation) and T cell expansion were comparable following the first and second doses. Activation and proliferation of T cells were observed with largest increase in cell numbers found in gamma delta T cells, followed by CD4+ and CD8+ T cells, and then NK cells. Release of cytokines IL-6, IFNγ, and IL-10 were detected, which peaked at 72 hours postdose. There was PF-07209960-related mortality at ≥1 mg/kg. At scheduled necropsy, microscopic findings were generalized mononuclear infiltration in various tissues. Both the no observed adverse effect level (NOAEL) and the highest non severely toxic dose (HNSTD) were determined to be 0.3 mg/kg/dose, which corresponded to mean Cmax and AUC48 values of 1.15 µg/mL and 37.9 µg*h/mL, respectively.

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might help to improve clinical practice efficiency. PURPOSE: To develop an approach for detecting bAVM and segmenting its nidus on Time-of-flight magnetic resonance angiography using deep learning methods. STUDY TYPE: Retrospective. SUBJECTS: 221 bAVM patients aged 7-79 underwent radiosurgery from 2003 to 2020. They were split into 177 training, 22 validation, and 22 test data. FIELD STRENGTH/SEQUENCE: 1.5 T, Time-of-flight magnetic resonance angiography based on 3D gradient echo. ASSESSMENT: The YOLOv5 and YOLOv8 algorithms were utilized to detect bAVM lesions and the U-Net and U-Net++ models to segment the nidus from the bounding boxes. The mean average precision, F1, precision, and recall were used to assess the model performance on the bAVM detection. To evaluate the model's performance on nidus segmentation, the Dice coefficient and balanced average Hausdorff distance (rbAHD) were employed. STATISTICAL TESTS: The Student's t-test was used to test the cross-validation results (P < 0.05). The Wilcoxon rank test was applied to compare the median for the reference values and the model inference results (P < 0.05). RESULTS: The detection results demonstrated that the model with pretraining and augmentation performed optimally. The U-Net++ with random dilation mechanism resulted in higher Dice and lower rbAHD, compared to that without that mechanism, across varying dilated bounding box conditions (P < 0.05). When combining detection and segmentation, the Dice and rbAHD were statistically different from the references calculated using the detected bounding boxes (P < 0.05). For the detected lesions in the test dataset, it showed the highest Dice of 0.82 and the lowest rbAHD of 5.3%. DATA CONCLUSION: This study showed that pretraining and data augmentation improved YOLO detection performance. Properly limiting lesion ranges allows for adequate bAVM segmentation. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk.

OBJECTIVE: Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk. SUBJECTS: In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort. RESULTS: In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy. CONCLUSIONS: Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.

Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

S100A8/A9 predicts triple-negative breast cancer response to PIM kinase and PD-1/PD-L1 inhibition.

It remains elusive why some triple-negative breast cancer (TNBC) patients respond poorly to existing therapies while others respond well. Our retrospective analysis of historical gene expression datasets reveals that increased expression of immunosuppressive cytokine S100A8/A9 in early-stage tumors is robustly associated with subsequent disease progression in TNBC. Although it has recently gained recognition as a potential anticancer target, S100A8/A9 has not been integrated into clinical study designs evaluating molecularly targeted therapies. Our small molecule screen has identified PIM kinase inhibitors as capable of decreasing S100A8/A9 expression in multiple cell types, including TNBC and immunosuppressive myeloid cells. Furthermore, combining PIM inhibition and immune checkpoint blockade induces significant antitumor responses, especially in otherwise resistant S100A8/A9-high PD-1/PD-L1-positive tumors. Importantly, serum S100A8/A9 levels mirror those of tumor S100A8/A9 in a syngeneic mouse model of TNBC. Thus, our data suggest that S100A8/A9 could be a predictive and pharmacodynamic biomarker in clinical trials evaluating combination therapy targeting PIM and immune checkpoints in TNBC and encourage the development of S100A8/A9-based liquid biopsy tests.

Differential expression of GABAA receptor subunits δ and α6 mediates tonic inhibition in parvalbumin and somatostatin interneurons in the mouse hippocampus.

Inhibitory γ-aminobutyric acid (GABA)-ergic interneurons mediate inhibition in neuronal circuitry and support normal brain function. Consequently, dysregulation of inhibition is implicated in various brain disorders. Parvalbumin (PV) and somatostatin (SST) interneurons, the two major types of GABAergic inhibitory interneurons in the hippocampus, exhibit distinct morpho-physiological properties and coordinate information processing and memory formation. However, the molecular mechanisms underlying the specialized properties of PV and SST interneurons remain unclear. This study aimed to compare the transcriptomic differences between these two classes of interneurons in the hippocampus using the ribosome tagging approach. The results revealed distinct expressions of genes such as voltage-gated ion channels and GABAA receptor subunits between PV and SST interneurons. Gabrd and Gabra6 were identified as contributors to the contrasting tonic GABAergic inhibition observed in PV and SST interneurons. Moreover, some of the differentially expressed genes were associated with schizophrenia and epilepsy. In conclusion, our results provide molecular insights into the distinct roles of PV and SST interneurons in health and disease.

Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan.

Air pollution, particularly ambient fine particulate matter (PM2.5) pollution, poses a significant risk to public health, underscoring the importance of comprehending the long-term impact on health burden and expenditure at national and subnational levels. Therefore, this study aims to quantify the disease burden and healthcare expenditure associated with PM2.5 exposure in Taiwan and assess the potential benefits of reducing pollution levels. Using a comparative risk assessment framework that integrates an auto-aggressive integrated moving average model, we evaluated the avoidable burden of cardiopulmonary diseases (including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus) and related healthcare expenditure under different air quality target scenarios, including status quo and target scenarios of 15, 10, and 5 µg/m3 reduction in PM2.5 concentration. Our findings indicate that reducing PM2.5 exposure has the potential to significantly alleviate the burden of multiple diseases. Comparing the estimated attributable disease burden and healthcare expenditure between reference and target scenarios from 2022 to 2050, the avoidable disability-adjusted life years were 0.61, 1.83, and 3.19 million for the 15, 10, and 5 µg/m3 target scenarios, respectively. Correspondingly, avoidable healthcare expenditure ranged from US$ 0.63 to 3.67 billion. We also highlighted the unequal allocation of resources and the need for policy interventions to address health disparities due to air pollution. Notably, in the 5 µg/m3 target scenario, Kaohsiung City stands to benefit the most, with 527,368 disability-adjusted life years avoided and US$ 0.53 billion saved from 2022 to 2050. Our findings suggest that adopting stricter emission targets can effectively reduce the health burden and associated healthcare expenditure in Taiwan. Overall, this study provides policymakers in Taiwan with valuable insights for mitigating the negative effects of air pollution by establishing a comprehensive framework for evaluating the co-benefits of air pollution reduction on healthcare expenditure and disease burden.

Cardiovascular Disease Burden Attributable to High Body Mass Index in Taiwan.

Background: Studies on disease burden in Taiwan are lacking. We aimed to quantify the burden of cardiovascular disease (CVD) attributable to high body mass index (BMI) in Taiwan. Methods: Using a comparative risk assessment approach from the Global Burden of Disease study, we estimated the population attributable fraction (PAF), attributable CVD burden, and disability-adjusted life years (DALYs) according to sex, age, and area of residence in Taiwan. The BMI distribution for the population was obtained from the National Health Interview Survey in 2013. CVD was defined as an ischemic heart disease or stroke. Results: The attributable PAF for CVD from high BMI was 18.0% (19.6% in men and 15.6% in women), and it was highest (42.7%) in those aged 25-30 years. Adults aged 60-65 years had the highest absolute DALYs (11,546). The average relative age-standardized attributable burden was 314 DALYs per 100,000 person-years, and it was highest in those aged 75-80 years (1,407 DALYs per 100,000 person-years). Those living in Taitung County had the highest PAF of 21.9% and the highest age-standardized attributable burden (412 DALYs). Conclusions: In Taiwan, an 18% reduction in CVDs could be achieved if obesity/overweight was prevented. Prevention was most effective in early adulthood. The absolute CVD burden from obesity/overweight was highest in middle-aged men, and the relative burden was highest in older adults. Resource allocation in targeted populations and specific areas to eliminate CVD and health inequities is urgently required.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Neurobiological Markers for Predicting Treatment Response in Patients with Bipolar Disorder.

Predictive neurobiological markers for prognosis are essential but underemphasized for patients with bipolar disorder (BD), a neuroprogressive disorder. Hence, we developed models for predicting symptom and functioning changes. Sixty-one patients with BD were recruited and assessed using the Young Mania Rating Scale (YMRS), Montgomery−Åsberg Depression Rating Scale (MADRS), Positive and Negative Syndrome Scale (PANSS), UKU Side Effect Rating Scale (UKU), Personal and Social Performance Scale (PSP), and Global Assessment of Functioning scale both at baseline and after 1-year follow-up. The models for predicting the changes in symptom and functioning scores were trained using data on the brain morphology, functional connectivity, and cytokines collected at baseline. The correlation between the predicted and actual changes in the YMRS, MADRS, PANSS, and UKU scores was higher than 0.86 (q < 0.05). Connections from subcortical and cerebellar regions were considered for predicting the changes in the YMRS, MADRS, and UKU scores. Moreover, connections of the motor network were considered for predicting the changes in the YMRS and MADRS scores. The neurobiological markers for predicting treatment-response symptoms and functioning changes were consistent with the neuropathology of BD and with the differences found between treatment responders and nonresponders.

Affective Mobile Language Tutoring System for Supporting Language Learning.

Students often face difficulties and experience negative emotions toward second language learning. The affective tutoring system (ATS) is a next-generation learning approach that can detect the affective status of learning to increase performance. Therefore, for the purposes of this study, an innovative affective mobile language tutoring system (AMLTS) was designed to support Japanese language learning. The effects of AMLTS, along with asynchronous discussion, that were intended to improve performance, were examined using a triangulation method. To investigate the effect on emotion, the proposed AMLTS provides a virtual emotion agent that can interact with users and record emotional events, learning assessments, and the results of the interaction into a database. Learning effectiveness evaluations were conducted via two experiments: prototype evaluation and final evaluation. Sixty-three students, all beginners, were invited to use the AMLTS to learn Japanese. The research results show that the proposed AMLTS affective interaction design significantly improves learner engagement and performance. In the emotion feedback analysis and learning process, AMLTS helped students deepen their understanding of the content, enabled them to clearly understand the content, and to engage in peer interaction and experience positive emotions. In the evaluation of system usability, AMLTS reveals good usability for foreign language acquisition.

Molecular interaction mechanisms of glycol chitosan self-healing hydrogel as a drug delivery system for gemcitabine and doxorubicin.

Glycol chitosan is a derivative of chitosan that has attracted attention in recent years due to its biocompatibility and biodegradability. Due to its unique biological characteristics, it has been widely used in hydrogels and biomaterials. In this study, we explored the loading efficiency of a self-healing hydrogel (GC-DP) comprising glycol chitosan (GC) and telechelic difunctional poly(ethylene glycol) (DF-PEG) for delivering the anticancer drugs gemcitabine and doxorubicin through full atomistic simulations. We also constructed full atomistic models of the two drug delivery systems at three drug concentrations of 10%, 40%, and 80% to understand how the drug concentration affects the loading efficiency and molecular structure of the GC-DP hydrogels. Through the analysis of the results, we show that the GC-DP hydrogel exhibits excellent loading efficiency for both gemcitabine and doxorubicin at all drug concentrations (10%, 40% and 80%). Our results reveal that the main mechanism of interaction between the GC-DP hydrogels and gemcitabine is van der Waals adsorption and that the dominant interactions between the GC-DP hydrogel and doxorubicin are hydrogen bonds for the D10 model and van der Waals adsorption for the D40 and D80 models. Our results provide molecular insights into how drug molecules are carried by hydrogel materials and indicate that the GC-DP hydrogel is a promising candidate for carrying both gemcitabine and doxorubicin, and thus serving as a novel drug carrier for cancer treatment.

An Engineered IL15 Cytokine Mutein Fused to an Anti-PD1 Improves Intratumoral T-cell Function and Antitumor Immunity.

The use of cytokines for immunotherapy shows clinical efficacy but is frequently accompanied by severe adverse events caused by excessive and systemic immune activation. Here, we set out to address these challenges by engineering a fusion protein of a single, potency-reduced, IL15 mutein and a PD1-specific antibody (anti-PD1-IL15m). This immunocytokine was designed to deliver PD1-mediated, avidity-driven IL2/15 receptor stimulation to PD1+ tumor-infiltrating lymphocytes (TIL) while minimally affecting circulating peripheral natural killer (NK) cells and T cells. Treatment of tumor-bearing mice with a mouse cross-reactive fusion, anti-mPD1-IL15m, demonstrated potent antitumor efficacy without exacerbating body weight loss in B16 and MC38 syngeneic tumor models. Moreover, anti-mPD1-IL15m was more efficacious than an IL15 superagonist, an anti-mPD-1, or the combination thereof in the B16 melanoma model. Mechanistically, anti-PD1-IL15m preferentially targeted CD8+ TILs and single-cell RNA-sequencing analyses revealed that anti-mPD1-IL15m treatment induced the expansion of an exhausted CD8+ TIL cluster with high proliferative capacity and effector-like signatures. Antitumor efficacy of anti-mPD1-IL15m was dependent on CD8+ T cells, as depletion of CD8+ cells resulted in the loss of antitumor activity, whereas depletion of NK cells had little impact on efficacy. The impact of anti-hPD1-IL15m on primary human TILs from patients with cancer was also evaluated. Anti-hPD1-IL15m robustly enhanced the proliferation, activation, and cytotoxicity of CD8+ and CD4+ TILs from human primary cancers in vitro, whereas tumor-derived regulatory T cells were largely unaffected. Taken together, our findings showed that anti-PD1-IL15m exhibits a high translational promise with improved efficacy and safety of IL15 for cancer immunotherapy via targeting PD1+ TILs.See related Spotlight by Felices and Miller, p. 1110.

Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics.

The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-state functional magnetic resonance images of 112 patients with BD were obtained, and patients were segregated according to diagnostic subtype (i.e., types I and II) and clinical patterns, including the number of episodes and hospitalizations and history of suicide and psychosis. For each clinical pattern, fewer and more occurrences subgroups and types I and II were classified through nested cross-validation for robust performance, with minimum redundancy and maximum relevance, in feature selection. To assess the proportion of variance in cognitive performance explained by the neurobiological markers, multiple linear regression between verbal memory and the selected features was conducted. Satisfactory performance (mean accuracy, 73.60%) in classifying patients with a high or low number of episodes was attained through functional connectivity, mostly from default-mode and motor networks. Moreover, these neurobiological markers explained 62% of the variance in verbal memory. The number of episodes is a potentially critical aspect of the neuropathology of BD. Neurobiological markers can help identify BD neuroprogression.

Overexpression of Adiponectin Receptor 1 Inhibits Brown and Beige Adipose Tissue Activity in Mice.

Adult humans and mice possess significant classical brown adipose tissues (BAT) and, upon cold-induction, acquire brown-like adipocytes in certain depots of white adipose tissues (WAT), known as beige adipose tissues or WAT browning/beiging. Activating thermogenic classical BAT or WAT beiging to generate heat limits diet-induced obesity or type-2 diabetes in mice. Adiponectin is a beneficial adipokine resisting diabetes, and causing "healthy obese" by increasing WAT expansion to limit lipotoxicity in other metabolic tissues during high-fat feeding. However, the role of its receptors, especially adiponectin receptor 1 (AdipoR1), on cold-induced thermogenesis in vivo in BAT and in WAT beiging is still elusive. Here, we established a cold-induction procedure in transgenic mice over-expressing AdipoR1 and applied a live 3-D [18F] fluorodeoxyglucose-PET/CT (18F-FDG PET/CT) scanning to measure BAT activity by determining glucose uptake in cold-acclimated transgenic mice. Results showed that cold-acclimated mice over-expressing AdipoR1 had diminished cold-induced glucose uptake, enlarged adipocyte size in BAT and in browned WAT, and reduced surface BAT/body temperature in vivo. Furthermore, decreased gene expression, related to thermogenic Ucp1, BAT-specific markers, BAT-enriched mitochondrial markers, lipolysis and fatty acid oxidation, and increased expression of whitening genes in BAT or in browned subcutaneous inguinal WAT of AdipoR1 mice are congruent with results of PET/CT scanning and surface body temperature in vivo. Moreover, differentiated brown-like beige adipocytes isolated from pre-adipocytes in subcutaneous WAT of transgenic AdipoR1 mice also had similar effects of lowered expression of thermogenic Ucp1, BAT selective markers, and BAT mitochondrial markers. Therefore, this study combines in vitro and in vivo results with live 3-D scanning and reveals one of the many facets of the adiponectin receptors in regulating energy homeostasis, especially in the involvement of cold-induced thermogenesis.

Altered nociception in Alzheimer disease is associated with striatal-enriched protein tyrosine phosphatase signaling.

ABSTRACT: Alzheimer disease (AD) is the most common form of dementia, accounting for approximately 60% of cases. In addition to memory loss, changes in pain sensitivity are found in a substantial proportion of patients with AD. However, the mechanism of nociception deficits in AD is still unclear. Here, we hypothesize that the nociception abnormality in AD is due to the aberrant activation of striatal-enriched protein tyrosine phosphatase (STEP) signaling, which modulates proteins related to nociception transduction. Our results indicated that the transgenic mice carrying human amyloid precursor protein (APP) gene had lower sensitivity to mechanical and thermal stimulation than the wild-type group at the ages of 6, 9, and 12 months. These APP mice exhibited elevated STEP activity and decreased phosphorylation of proteins involved in nociception transduction in hippocampi. The pharmacological inhibition of STEP activity using TC-2153 further reversed nociception and cognitive deficits in the APP mice. Moreover, the phosphorylation of nociception-related proteins in the APP mice was also rescued after STEP inhibitor treatment, indicating the key role of STEP in nociception alteration. In summary, this study identifies a mechanism for the reduced nociceptive sensitivity in an AD mouse model that could serve as a therapeutic target to improve the quality of life for patients with AD.

Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder.

BACKGROUND: A number of mental illness is often re-diagnosed to be bipolar disorder (BD). Furthermore, the prefronto-limbic-striatal regions seem to be associated with the main dysconnectivity of BD. Functional connectivity is potentially an appropriate objective neurobiological marker that can assist with BD diagnosis. METHODS: Health controls (HC; n = 173) and patients with BD who had been diagnosed by experienced physicians (n = 192) were separated into 10-folds, namely, a ninefold training set and a onefold testing set. The classification involved feature selection of the training set using minimum redundancy/maximum relevance. Support vector machine was used for training. The classification was repeated 10 times until each fold had been used as the testing set. RESULTS: The mean accuracy of the 10 testing sets was 76.25%, and the area under the curve was 0.840. The selected functional within-network/between-network connectivity was mainly in the subcortical/cerebellar regions and the frontoparietal network. Furthermore, similarity within the BD patients, calculated by the cosine distance between two functional connectivity matrices, was smaller than between groups before feature selection and greater than between groups after the feature selection. LIMITATIONS: The major limitations were that all the BD patients were receiving medication and that no independent dataset was included. CONCLUSION: Our approach effectively separates a relatively large group of BD patients from HCs. This was done by selecting functional connectivity, which was more similar within BD patients, and also seems to be related to the neuropathological factors associated with BD.

Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution of standard VS MR scan protocols and because some patients have non-homogeneous cystic areas within their tumors. In this study, we retrospectively collected multi-parametric MR images from 516 patients with VS; these were extracted from the Gamma Knife radiosurgery planning system and consisted of T1-weighted (T1W), T2-weighted (T2W), and T1W with contrast (T1W + C) images. We developed an end-to-end deep-learning-based method via an automatic preprocessing pipeline. A two-pathway U-Net model involving two sizes of convolution kernel (i.e., 3 × 3 × 1 and 1 × 1 × 3) was used to extract the in-plane and through-plane features of the anisotropic MR images. A single-pathway model that adopted the same architecture as the two-pathway model, but used a kernel size of 3 × 3 × 3, was also developed for comparison purposes. In addition, we used multi-parametric MR images with different image contrasts as the model training input in order to effectively segment tumors with solid as well as cystic parts. The results of the automatic segmentation demonstrated that (1) the two-pathway model outperformed single-pathway model in terms of dice scores (0.90 ± 0.05 versus 0.87 ± 0.07); both of them having been trained using the T1W, T1W + C and T2W anisotropic MR images, (2) the optimal single-parametric two-pathway model (dice score: 0.88 ± 0.06) was then trained using the T1W + C images, and (3) the two-pathway models trained using bi-parametric (T1W + C and T2W) and tri-parametric (T1W, T2W, and T1W + C) images outperformed the model trained using the single-parametric (T1W + C) images (dice scores: 0.89 ± 0.05 and 0.90 ± 0.05, respectively, larger than 0.88 ± 0.06) because it showed improved segmentation of the non-homogeneous parts of the tumors. The proposed two-pathway U-Net model outperformed the single-pathway U-Net model when segmenting VS using anisotropic MR images. The multi-parametric models effectively improved on the defective segmentation obtained using the single-parametric models by separating the non-homogeneous tumors into their solid and cystic parts.