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Abstract Harm avoidance (HA) is a Cloninger personality trait that describes behavioural inhibition to avoid aversive stimuli. It serves as a predisposing factor that contributes to the development of mental disorders such as anxiety and major depressive disorder. Neuroimaging research has identified some brain anatomical and functional correlates of HA, but reported findings are inconsistent. We therefore conducted a multimodal meta-analysis of whole-brain structural and resting-state functional neuroimaging studies to identify the most stable neural substrate of HA. Included were a total of 10 structural voxel-based morphometry studies (11 datasets) and 13 functional positron emission tomography or single photon emission computed tomography studies (16 datasets) involving 3053 healthy participants without any psychiatric or neurological disorders evaluated for HA using the Three-Dimensional Personality Questionnaire (TPQ) or the Temperament and Character Inventory (TCI). The meta-analysis revealed brain volumetric correlates of HA in parietal and temporal cortices, and resting-state functional correlates in prefrontal, temporal and parietal gray matter. Volumetric and functional correlates co-occurred in the left superior frontal gyrus and left middle frontal gyrus, and were dissociated in the left rectus gyrus. Our meta-analysis is the first study to give a comprehensive picture of the structural and functional correlates of HA, a contribution that may help bridge the grievous gap between the neurobiology of HA and the pathogenesis, prevention and treatment of HA-related mental disorders.

Inefficient drug penetration hurdled by the stroma in the tumor tissue leads to a diminished therapeutic effect for drugs and a reduced infiltration level of immune cells. Herein, we constructed a PEGylated dendritic epirubicin (Epi) prodrug (Epi-P4D) to regulate the metabolism of cancer-associated fibroblasts (CAFs), thus enhancing Epi penetration into both multicellular tumor spheroids (MTSs) and tumor tissues in mouse colon cancer (CT26), mouse breast cancer (4T1) and human breast cancer (MDA-MB-231) models. Enhanced cytotoxicity against CT26 MTSs and remarkable antitumor efficacy of Epi-P4D were ascribed to reduced fibronectin, α-SMA, and collagen secretion. Besides, thinning of the tumor tissue stroma and efficient eradication of tumor cells promoted the immunogenic cell death effect for dendritic cell (DC) maturation and subsequent immune activation, including elevating the CD4+ T cell population, reducing CD4+ and CD8+ T cell hyperactivation and exhaustion, and amplifying the natural killer (NK) cell proportion and effectively activating them. As a result, this dendritic nanomedicine thinned the stroma of tumor tissues to enhance drug penetration and facilitate immune cell infiltration for elevated antitumor efficacy.

Abstract Transcranial ultrasound stimulation (TUS) has been clinically applied as a neuromodulation tool. Particularly, the phase array ultrasound can be applied in TUS to non-invasively focus on the cortex or deep brain. However, the vital phase distortion of the ultrasound induced by the skull limits its clinical application. In the current study, we aimed to develop a hybrid method that combines the ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences with the prDeep technique to achieve focusing ventral intermediate thalamic nucleus (VIM). The time-reversal (TR) approach of the UTE numerical acoustic model of the skull combined with the prDeep algorithm was used to reduce the number of iterations. The skull acoustic model simulation therapy process was establish to valid this method’s prediction and focus performance, and the classical TR method were considered as the gold standard (GS). Our approach could restore 75% of the GS intensity in 25 iteration steps, with a superior the noise immunity. Our findings demonstrate that the phase aberration caused by the skull can be estimated using phase retrieval techniques to achieve a fast and accurate transcranial focus. The method has excellent adaptability and anti-noise capacity for satisfying complex and changeable scenarios.

Despite the great potential of anti-PD-L1 antibodies for immunotherapy, their low response rate due to an immunosuppressive tumor microenvironment has hampered their application. To address this issue, we constructed a cell membrane-coated nanosystem (mB4S) to reverse an immunosuppressive microenvironment to an immuno-supportive one for strengthening the anti-tumor effect. In this system, Epirubicin (EPI) as an immunogenic cell death (ICD) inducer was coupled to a branched glycopolymer via hydrazone bonds and diABZI as a stimulator of interferon genes (STING) agonist was encapsulated into mB4S. After internalization of mB4S, EPI was acidic-responsively released to induce ICD, which was characterized by an increased level of calreticulin (CRT) exposure and enhanced ATP secretion. Meanwhile, diABZI effectively activated the STING pathway. Treatment with mB4S in combination with an anti-PD-L1 antibody elicited potent immune responses by increasing the ratio of matured dendritic cells (DCs) and CD8+ T cells, promoting cytokines secretion, up-regulating M1-like tumor-associated macrophages (TAMs) and down-regulating immunosuppressive myeloid-derived suppressor cells (MDSCs). Therefore, this nanosystem for co-delivery of an ICD inducer and a STING agonist achieved promotion of DCs maturation and CD8+ T cells infiltration, creating an immuno-supportive microenvironment, thus potentiating the therapy effect of the anti-PD-L1 antibody in both 4T1 breast and CT26 colon tumor mice.

BackgroundAntipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia.MethodsStructural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17).ResultsML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences.ConclusionOur study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy.

Summary: Background: Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case–control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. Methods: Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. Findings: Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. Interpretation: These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. Funding: National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Abstract Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication – a necessary step for precise medicine.

Abstract Background Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders with overlapping behavioral features and genetic etiology. While brain cortical thickness (CTh) alterations have been reported in ASD and ADHD separately, the degree to which ASD and ADHD are associated with common and distinct patterns of CTh changes is unclear. Methods We searched PubMed, Web of Science, Embase, and Science Direct from inception to 8 December 2023 and included studies of cortical thickness comparing youth (age less than 18) with ASD or ADHD with typically developing controls (TDC). We conducted a comparative meta-analysis of vertex-based studies to identify common and distinct CTh alterations in ASD and ADHD. Results Twelve ASD datasets involving 458 individuals with ASD and 10 ADHD datasets involving 383 individuals with ADHD were included in the analysis. Compared to TDC, ASD showed increased CTh in bilateral superior frontal gyrus, left middle temporal gyrus, and right superior parietal lobule (SPL) and decreased CTh in right temporoparietal junction (TPJ). ADHD showed decreased CTh in bilateral precentral gyri, right postcentral gyrus, and right TPJ relative to TDC. Conjunction analysis showed both disorders shared reduced TPJ CTh located in default mode network (DMN). Comparative analyses indicated ASD had greater CTh in right SPL and TPJ located in dorsal attention network and thinner CTh in right TPJ located in ventral attention network than ADHD. Conclusions These results suggest shared thinner TPJ located in DMN is an overlapping neurobiological feature of ASD and ADHD. This alteration together with SPL alterations might be related to altered biological motion processing in ASD, while abnormalities in sensorimotor systems may contribute to behavioral control problems in ADHD. The disorder-specific thinner TPJ located in disparate attention networks provides novel insight into distinct symptoms of attentional deficits associated with the two neurodevelopmental disorders. Trial registration PROSPERO CRD42022370620. Registered on November 9, 2022.

Abstract Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.

Effective tumor treatment depends on optimizing drug penetration and accumulation in tumor tissue while minimizing systemic toxicity. Nanomedicine has emerged as a key solution that addresses the rapid clearance of free drugs, but achieving deep drug penetration into solid tumors remains elusive. This review discusses various strategies to enhance drug penetration, including manipulation of the tumor microenvironment, exploitation of both external and internal stimuli, pioneering nanocarrier surface engineering, and development of innovative tactics for active tumor penetration. One outstanding strategy is organelle-affinitive transfer, which exploits the unique properties of specific tumor cell organelles and heralds a potentially transformative approach to active transcellular transfer for deep tumor penetration. Rigorous models are essential to evaluate the efficacy of these strategies. The patient-derived xenograft (PDX) model is gaining traction as a bridge between laboratory discovery and clinical application. However, the journey from bench to bedside for nanomedicines is fraught with challenges. Future efforts should prioritize deepening our understanding of nanoparticle-tumor interactions, re-evaluating the EPR effect, and exploring novel nanoparticle transport mechanisms.

Abstract Investigation of the choroid plexus in schizophrenia has seen growing interest due to its role in the interaction between neuroinflammation and brain dysfunction. Most previous studies included treated and long-term ill patients, while antipsychotics and illness course might both affect the choroid plexus. Here, we recruited first-episode antipsychotic-naïve schizophrenia patients, performed high-resolution structural brain scan and manually extracted choroid plexus volume. Choroid plexus volume was compared between patients and healthy controls after controlling for age, sex and intracranial volume. Age and sex effects were examined on choroid plexus volume in patient and healthy control groups respectively. In patients, we also examined the correlation of choroid plexus volume with volume measures of cortical and subcortical gray matter, white matter, lateral ventricular as well as symptom severity and cognitive function. Schizophrenia patients showed significantly enlarged choroid plexus volume compared with healthy controls. Choroid plexus volume was positively correlated with age in only patient group and we found significantly larger choroid plexus volumes in males than females in both patient and healthy control groups, while the sex effects did not differ between groups. Choroid plexus volume was only found correlated with lateral ventricular volume among the brain volume measures. No significant correlation between choroid plexus volume and clinical ratings or cognitive performance was observed. Without potential confounding effects of pharmacotherapy or illness course, our findings indicated the enlargement of choroid plexus in schizophrenia might be an enduring trait for schizophrenia.

Abstract Severe mental health problems with the representation of negative affect symptoms (NAS) have been increasingly reported during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the multivariate patterns of brain functional connectome predicting COVID-19-related NAS. This cohort study encompassed a group of university students to undergo neuroimaging scans before the pandemic, and we re-contacted participants for 1-year follow-up COVID-related NAS evaluations during the pandemic. Regularized canonical correlation analysis was used to identify connectome-based dimensions of NAS to compute pairs of canonical variates. The predictive ability of identified functional connectome to NAS dimensional scores was examined with a nested cross-validation. Two dimensions (i.e. mode stress and mode anxiety) were related to distinct patterns of brain functional connectome (r 2 = 0.911, P FDR = 0.048; r 2 = 0.901, P FDR = 0.037, respectively). Mode anxiety was characterized by high loadings in connectivity between affective network (AFN) and visual network (VN), while connectivity of the default mode network with dorsal attention network (DAN) were remarkably prominent in mode stress. Connectivity patterns within the DAN and between DAN and VN, ventral attention network, and AFN was common for both dimensions. The identified functional connectome can reliably predict mode stress (r = 0.37, MAE = 5.1, p

Abstract. Background:. Patients with schizophrenia (SCZ) and major depressive disorder (MDD) share significant clinical overlap, although it remains unknown to what extent this overlap reflects shared neural profiles. To identify the shared and specific abnormalities in SCZ and MDD, we performed a whole-brain voxel-based meta-analysis using magnetization transfer imaging, a technique that characterizes the macromolecular structural integrity of brain tissue in terms of the magnetization transfer ratio (MTR). Methods:. A systematic search based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted in PubMed, EMBASE, International Scientific Index (ISI) Web of Science, and MEDLINE for relevant studies up to March 2022. Two researchers independently screened the articles. Rigorous scrutiny and data extraction were performed for the studies that met the inclusion criteria. Voxel-wise meta-analyses were conducted using anisotropic effect size-signed differential mapping with a unified template. Meta-regression was used to explore the potential effects of demographic and clinical characteristics. Results:. A total of 15 studies with 17 datasets describing 365 SCZ patients, 224 MDD patients, and 550 healthy controls (HCs) were identified. The conjunction analysis showed that both disorders shared higher MTR than HC in the left cerebellum (P=0.0006) and left fusiform gyrus (P=0.0004). Additionally, SCZ patients showed disorder-specific lower MTR in the anterior cingulate/paracingulate gyrus, right superior temporal gyrus, and right superior frontal gyrus, and higher MTR in the left thalamus, precuneus/cuneus, posterior cingulate gyrus, and paracentral lobule; and MDD patients showed higher MTR in the left middle occipital region. Meta-regression showed no statistical significance in either group. Conclusions:. The results revealed a structural neural basis shared between SCZ and MDD patients, emphasizing the importance of shared neural substrates across psychopathology. Meanwhile, distinct disease-specific characteristics could have implications for future differential diagnosis and targeted treatment.

Abstract Although attention-deficit/hyperactivity disorder (ADHD) and a family history of bipolar I disorder (BD) are associated with increased risk for developing BD, their neuroanatomical substrates remain poorly understood. This study compared cortical and subcortical gray matter morphology in psychostimulant-free ADHD youth with and without a first-degree relative with BD and typically developing healthy controls. ADHD youth (ages 10-18 years) with (‘high-risk’, HR) or without (‘low-risk’, LR) a first-degree relative with BD and healthy comparison youth (HC) were enrolled. High-resolution 3D T1-weighted images were acquired using a Philips 3.0 T MR scanner. The FreeSurfer image analysis suite was used to measure cortical thickness, surface area, and subcortical volumes. A general linear model evaluated group differences in MRI features with age and sex as covariates, and exploratory correlational analyses evaluated associations with symptom ratings. A total of n = 142 youth (mean age: 14.16 ± 2.54 years, 35.9% female) were included in the analysis (HC, n = 48; LR, n = 49; HR, n = 45). The HR group exhibited a more severe symptom profile, including higher mania and dysregulation scores, compared to the LR group. For subcortical volumes, the HR group exhibited smaller bilateral thalamic, hippocampal, and left caudate nucleus volumes compared to both LR and HC, and smaller right caudate nucleus compared with LR. No differences were found between LR and HC groups. For cortical surface area, the HR group exhibited lower parietal and temporal surface area compared with HC and LR, and lower orbitofrontal and superior frontal surface area compared to LR. The HR group exhibited lower left anterior cingulate surface area compared with HC. LR participants exhibited greater right pars opercularis surface area compared with the HC. Some cortical alterations correlated with symptom severity ratings. These findings suggest that ADHD in youth with a BD family history is associated with a more a severe symptom profile and a neuroanatomical phenotype that distinguishes it from ADHD without a BD family history.

IntroductionMajor depressive disorder (MDD) is partially inheritable while its mechanism is still uncertain.MethodsThis cross-sectional study focused on gene pathways as a whole rather than polymorphisms of single genes. Deep sequencing and gene enrichment analysis based on pathways in Reactome database were obtained to reveal gene mutations.ResultsA total of 117 patients with MDD and 78 healthy controls were enrolled. The Digestion and Dietary Carbohydrate pathway (Carbohydrate pathway) was determined to contain 100% mutations in patients with MDD and 0 mutation in matched healthy controls.DiscussionFindings revealed in the current study enable a better understanding of gene pathways mutations status in MDD patients, indicating a possible genetic mechanism of MDD development and a potential diagnostic or therapeutic target.

Abstract Nanozyme catalytic therapy for cancer treatments has become one of the heated topics, and the therapeutic efficacy is highly correlated with their catalytic efficiency. In this work, three copper‐doped CeO2 supports with various structures as well as crystal facets are developed to realize dual enzyme‐mimic catalytic activities, that is superoxide dismutase (SOD) to reduce superoxide radicals to H2O2 and peroxidase (POD) to transform H2O2 to ∙OH. The wire‐shaped CeO2/Cu‐W has the richest surface oxygen vacancies, and a low level of oxygen vacancy (Vo) formation energy, which allows for the elimination of intracellular reactive oxygen spieces (ROS) and continuous transformation to ∙OH with cascade reaction. Moreover, the wire‐shaped CeO2/Cu‐W displays the highest toxic ∙OH production capacity in an acidic intracellular environment, inducing breast cancer cell death and pro‐apoptotic autophagy. Therefore, wire‐shaped CeO2/Cu nanoparticles as an artificial enzyme system can have great potential in the intervention of intracellular ROS in cancer cells, achieving efficacious nanocatalytic therapy.

Brain metastasis is the leading cause of death in most cancer patients; thus, anti‐brain metastasis is a crucial step in cancer treatment. The unique microenvironment and pathophysiological characteristics of brain metastases hamper the development of effective treatment methods, while the advances in nanomedicine demonstrate its immense potential for addressing this challenge. In recent years, due to breakthroughs in nanotechnology, functional and complex nanomedicine is prepared and it has been extensively explored to improve the treatment of brain metastasis. Compared to traditional treatment methods, nanomedicine can combine multiple traditional treatment methods into one single medicinal format and target unique characteristics of brain metastases to enhance the treatment efficacy. This review summarizes the main obstacles in the field of brain metastasis treatment and clinical/preclinical studies and surveys the use of a variety of nanomedicine candidates as antibrain metastasis strategies for different types of brain metastases.

Abstract Vicarious traumatization (VT), a negative reaction to witnessing others’ trauma, has been experienced by some people during the COVID-19 pandemic, and can lead to mental health problems. This study aimed to identify functional brain markers of COVID-specific VT and explore the psychological mechanism underlying the brain-VT link. One hundred healthy participants underwent resting-state functional magnetic resonance imaging before the pandemic (October 2019–January 2020) and completed VT measurement during the pandemic (February–April 2020). Whole-brain correlation analysis based on global functional connectivity density (FCD) mapping revealed that VT was negatively correlated with FCD in the right inferior temporal gyrus (ITG) (i.e., the lower FCD in ITG, the worse the VT), identified by mapping onto known large-scale networks as part of the default-mode network (DMN). Resting-state functional connectivity (RSFC) analysis using ITG as seed found that VT was predicted by lower functional connectivity between ITG and other DMN regions including left medial prefrontal cortex, left orbitofrontal cortex, right superior frontal gyrus, right inferior parietal lobule and bilateral precuneus (i.e., the lower the ITG-DMN connectivity, the worse the VT). Mediation analyses suggested that psychological resilience served as a mediator in these associations of ITG FCD and ITG-DMN RSFC with VT. Our results provide novel evidence on the brain basis of VT and emphasize psychological resilience as an important link from DMN functional connectivity to COVID-specific-VT. This may facilitate public health interventions by helping identify individuals at risk of stress- and trauma-related psychopathologies.

Abstract Objective Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. Methods Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Results The results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. Conclusions We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.

Abstract Combined chemotherapy and targeted therapy holds immense potential in the management of advanced gastric cancer (GC). GC tissues exhibit an elevated expression level of protein kinase B (AKT), which contributes to disease progression and poor chemotherapeutic responsiveness. Inhibition of AKT expression through an AKT inhibitor, capivasertib (CAP), to enhance cytotoxicity of paclitaxel (PTX) toward GC cells is demonstrated in this study. A cathepsin B‐responsive polymeric nanoparticle prodrug system is employed for co‐delivery of PTX and CAP, resulting in a polymeric nano‐drug BPGP@CAP. The release of PTX and CAP is triggered in an environment with overexpressed cathepsin B upon lysosomal uptake of BPGP@CAP. A synergistic therapeutic effect of PTX and CAP on killing GC cells is confirmed by in vitro and in vivo experiments. Mechanistic investigations suggested that CAP may inhibit AKT expression, leading to suppression of the phosphoinositide 3‐kinase (PI3K)/AKT signaling pathway. Encouragingly, CAP can synergize with PTX to exert potent antitumor effects against GC after they are co‐delivered via a polymeric drug delivery system, and this delivery system helped reduce their toxic side effects, which provides an effective therapeutic strategy for treating GC.

Background: Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development. Methods: 110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations. Results: In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables. Conclusions: The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

To improve the response rate of immune checkpoint inhibitors such as anti-PD-L1 antibody in immunosuppressive cancers like triple-negative breast cancer (TNBC), induction of immunogenic cell death (ICD) at tumor sites can increase the antigenicity and adjuvanticity to activate the immune microenvironment so that tumors become sensitive to the intervention of immune checkpoint inhibitors. Herein, a self-amplified biomimetic nanosystem, mEHGZ, was constructed by encapsulation of epirubicin (EPI), glucose oxidase (Gox) and hemin in ZIF-8 nanoparticles and coating of the nanoparticles with calreticulin (CRT) over-expressed tumor cell membrane. EPI acts as an ICD inducer, Gox and hemin medicate the cascade generation of reactive oxygen species (ROS) to strengthen the ICD effect, and CRT-rich membrane as “eat me” signal promote presentation of the released antigens by dendritic cells (DCs) to invoke the tumor-immunity cycle. The biomimetic delivery system displays an amplified ICD effect via Gox oxidation, hydroxyl radical generation and glutathione (GSH) depletion. The induced potent ICD effect promotes DCs maturation and cytotoxic T lymphocytes (CTLs) infiltration, reversing an immunosuppressive tumor microenvironment to an immunoresponsive one. Treatment with the nanosystem in combination with anti-PD-L1 antibody results in distinctive inhibition of tumor growth and lung metastasis, supporting that a potent ICD effect can significantly boost the therapeutic efficacy of the anti-PD-L1 antibody. This self-amplified biomimetic nanoplatform offers a promising means of raising the response rate of immune checkpoint inhibitors.

Abstract Repetitive transcranial magnetic stimulation (rTMS) is increasingly used to treat neuropsychiatric disorders. Inhibitory and excitatory regimens have been both adopted but the exact mechanism of action remains unclear, and investigating their differential effects on laminar diffusion profiles of neocortex may add important evidence. Twenty healthy participants were randomly assigned to receive a low‐frequency/inhibitory or high‐frequency/excitatory rTMS targeting the left dorsolateral prefrontal cortex (DLPFC). With the brand‐new submillimeter diffusion tensor imaging of whole brain and specialized surface‐based laminar analysis, fractional anisotropy (FA) and mean diffusion (MD) profiles of cortical layers at different cortical depths were characterized before/after rTMS. Inhibitory and excitatory rTMS both showed impacts on diffusion metrics of somatosensory, limbic, and sensory regions, but different patterns of changes were observed—increased FA with inhibitory rTMS, whereas decreased FA with excitatory rTMS. More importantly, laminar analysis indicated laminar specificity of changes in somatosensory regions during different rTMS patterns—inhibitory rTMS affected the superficial layers contralateral to the DLPFC, while excitatory rTMS led to changes in the intermediate/deep layers bilateral to the DLPFC. These findings provide novel insights into acute neurobiological effects on diffusion profiles of rTMS that may add critical evidence relevant to different protocols of rTMS on neocortex.

Abstract Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Nanoprobes that offer both fluorescence imaging (FI) and magnetic resonance imaging (MRI) can provide supplementary information and hold synergistic advantages. However, synthesis of such dual-modality imaging probes that simultaneously exhibit tunability of functional groups, high stability, great biocompatibility and desired dual-modality imaging results remains challenging. In this study, we used an amphiphilic block polymer from (ethylene glycol) methyl ether methacrylate (OEGMA) and N-(2-hydroxypropyl) methacrylamide (HPMA) derivatives as a carrier to conjugate a MR contrast agent, Gd-DOTA, and a two-photon fluorophore with an aggregation-induced emission (AIE) effect, TPBP, to construct a MR/two-photon fluorescence dual-modality contrast agent, Gd-DOTA-TPBP. Incorporation of gadolinium in the hydrophilic chain segment of the OEGMA-based carrier resulted in a high r1 value for Gd-DOTA-TPBP, revealing a great MR imaging resolution. The contrast agent specifically accumulated in the tumor region, allowing a long enhancement duration for vascular and tumor contrast-enhanced MR imaging. Meanwhile, coupling TPBP with AIE properties to the hydrophobic chain segment of the carrier not only improved its water solubility and reduced its cytotoxicity, but also significantly enhanced its imaging performance in an aqueous phase. Gd-DOTA-TPBP was also demonstrated to act as an excellent fluorescence probe for two-photon-excited bioimaging with higher resolution and greater sensitivity than MRI. Since high-resolution, complementary MRI/FI dual-modal images were acquired at both cellular and tissue levels in tumor-bearing mice after application of Gd-DOTA-TPBP, it has great potential in the early phase of disease diagnosis.

Abstract Background The function of the insula has been increasingly mentioned in neurocircuitry models of obsessive-compulsive disorder (OCD) for its role in affective processing and regulating anxiety and its wide interactions with the classic cortico-striato-thalamo-cortical circuit. However, the insular resting-state functional connectivity patterns in OCD remain unclear. Therefore, we aimed to investigate characteristic intrinsic connectivity alterations of the insula in OCD and their associations with clinical features. Methods We obtained resting-state functional magnetic resonance imaging data from 85 drug-free OCD patients and 85 age- and sex-matched healthy controls (HCs). We performed a general linear model to compare the whole-brain intrinsic functional connectivity maps of the bilateral insula between the OCD and HC groups. In addition, we further explored the relationship between the intrinsic functional connectivity alterations of the insula and clinical features using Pearson or Spearman correlation analysis. Results Compared with HCs, patients with OCD exhibited increased intrinsic connectivity between the bilateral insula and bilateral precuneus gyrus extending to the inferior parietal lobule and supplementary motor area. Decreased intrinsic connectivity was only found between the right insula and bilateral lingual gyrus in OCD patients relative to HC subjects, which was negatively correlated with the severity of depression symptoms in the OCD group. Conclusion In the current study, we identified impaired insular intrinsic connectivity in OCD patients and the dysconnectivity of the right insula and bilateral lingual gyrus associated with the depressive severity of OCD patients. These findings provide neuroimaging evidence for the involvement of the insula in OCD and suggest its potential role in the depressive symptoms of OCD.

Resting-state fMRI analysis of patients with obsessive-compulsive disorder (OCD) compared to controls suggests a specific role of amygdala subregions in neurocircuitry models of OCD.

Abstract As two common mental disorders during the period of adolescence that extend to early adulthood, attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUDs) have considerable diagnostic co-occurrence and shared neuropsychological impairments. Our study aimed to identify overlapping and distinct brain structural abnormalities associated with ADHD and SUDs among adolescents and young adults. A systematic literature search on voxel-based morphometry (VBM) studies of ADHD and SUDs was conducted in PubMed and Web of Science. Data were extracted and analyzed to identify brain abnormalities using Seed-based d-Mapping software. Data-driven functional decoding was conducted to identify the psychophysiological functioning associated with brain alterations. 13 and 14 VBM studies for ADHD (619 patients and 483 controls) and SUDs (516 patients and 413 controls), respectively, were included. Patterns of decreased gray matter volume (GMV) were found in the left precentral gyrus, bilateral superior frontal gyri, and left inferior frontal gyrus in the ADHD group compared to the control group. In contrast, individuals with SUDs, relative to controls, were characterized by increased GMV in the left putamen and insula. Comparative analysis indicated larger regional GMV in the right inferior parietal lobule and smaller volumes in the left putamen and left precentral gyrus in the ADHD group than in the SUDs group. Dissociable brain structural abnormalities in adolescents and young adults with ADHD and SUDs potentially implicate different pathogeneses and provide a reference for differential diagnosis and early detection for shared symptomology and comorbidity.

Abstract Background Juvenile myoclonic epilepsy (JME) is the most common syndrome of idiopathic generalized epilepsy. Although resting-state functional magnetic resonance imaging (rs-fMRI) studies have found thalamocortical circuit dysfunction in patients with JME, the pathophysiological mechanism of JME remains unclear. In this study, we used three complementary parameters of rs-fMRI to investigate aberrant brain activity in JME patients in comparison to that of healthy controls. Methods Rs-fMRI and clinical data were acquired from 49 patients with JME undergoing monotherapy and 44 age- and sex-matched healthy controls. After fMRI data preprocessing, the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated and compared between the two groups. Correlation analysis was conducted to explore the relationship between local brain abnormalities and clinical features in JME patients. Results Compared with the controls, the JME patients exhibited significantly decreased fALFF, ReHo and DC in the cerebellum, inferior parietal lobe, and visual cortex (including the fusiform and the lingual and middle occipital gyri), and increased DC in the right orbitofrontal cortex. In the JME patients, there were no regions with reduced ReHo compared to the controls. No significant correlation was observed between regional abnormalities of fALFF, ReHo or DC, and clinical features. Conclusions We demonstrated a wide range of abnormal functional activity in the brains of patients with JME, including the prefrontal cortex, visual cortex, default mode network, and cerebellum. The results suggest dysfunctions of the cerebello-cerebral circuits, which provide a clue on the potential pathogenesis of JME.

Abstract An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.

Abstract Magnetization transfer imaging (MTI) may provide more sensitivity and mechanistic understanding of neuropathological changes associated with schizophrenia than volumetric MRI. This study aims to identify brain magnetization transfer ratio (MTR) changes in antipsychotic-naïve first-episode schizophrenia (FES), and to correlate MTR findings with clinical symptom severity. A total of 143 individuals with antipsychotic-naïve FES and 147 healthy controls (HCs) were included and underwent 3.0 T brain MTI between August 2005 and July 2014. Voxelwise analysis was performed to test for MTR differences with family-wise error corrections. Relationships of these differences to symptom severity were assessed using partial correlations. Exploratory analyses using a support vector machine (SVM) classifier were conducted to discriminate FES from HCs using MTR maps. Model performance was examined using a 10-fold stratified cross-validation. Compared with HCs, individuals with FES exhibited higher MTR values in left thalamus, precuneus, cuneus, and paracentral lobule, that were positively correlated with schizophrenia symptom severity [precuneus (r = 0.34, P = 0.0004), cuneus (r = 0.33, P = 0.0006) and paracentral lobule (r = 0.37, P = 0.001)]. Whole-brain MTR maps identified individuals with FES with overall accuracy 75.5% (219 of 290 individuals) based on SVM approach. In antipsychotic-naïve FES, clinically relevant biophysical abnormalities detected by MTI mainly in the left parieto-occipital regions are informative about local brain pathology, and have potential as diagnostic markers.

Neuromodulation technology has provided novel therapeutic approaches for diseases caused by neural circuit dysfunction. Transcranial focused ultrasound (FU) is an emerging neuromodulation approach that combines noninvasiveness with relatively sharp focus, even in deep brain regions. It has numerous advantages such as high precision and good safety in neuromodulation, allowing for modulation of both peripheral and central nervous systems. To ensure accurate treatment targeting in FU neuromodulation, a magnetic resonance acoustic radiation force imaging (MR-ARFI) sequence is crucial for the visualization of the focal point. Currently, the commonly used 2D Spin Echo ARFI (2D SE-ARFI) sequence suffers from the long acquisition time, while the echo planar imaging ARFI (EPI-ARFI) sequence with a shorter acquisition time is vulnerable to the magnetic field inhomogeneities. To address these problems, we proposed a spatiotemporal-encoded acoustic radiation force imaging sequence (i.e., SE-SPEN-ARFI, shortened to SPEN-ARFI) in this study. The displacement at the focal spot obtained was highly consistent with that of the SE-ARFI sequence. Our research shows that SPEN-ARFI allows for rapid image acquisition and has less image distortions even under great field inhomogeneities. Therefore, a SPEN-ARFI sequence is a practical alternative for the treatment planning in ultrasound neuromodulation.

BackgroundThe retrograde endocannabinoid (eCB) pathway is closely associated with the etiology of major depressive disorder (MDD) at both pathophysiological and genetic levels. This study aimed to investigate the potential role of genetic mutations in the eCB pathway and underlying mechanisms in Han Chinese patients with MDD.MethodsA total of 96 drug-naïve patients with first-episode MDD and 62 healthy controls (HCs) were recruited. Whole-exome sequencing was performed to identify the gene mutation profiles in patients with MDD. Results were filtered to focus on low-frequency variants and rare mutations (minor allele frequencies

Summary: Background: Identifying individuals at risk for severe mental illness (SMI) is crucial for prevention and early intervention strategies. While MRI shows potential for case identification even before illness onset, no practical model for mental health risk monitoring has been developed. This study aims to develop an initial version of an efficient and practical model for mental health screening among at-risk populations. Methods: A deep learning model known as Multiple Instance Learning (MIL) was adopted to train and test a SMI detection model with clinical MRI scans of 14,915 patients with SMI (age 32.98 ± 12.01 years, 9102 women) and 4538 healthy controls (age 40.60 ± 10.95 years, 2424 women) in the primary dataset. Validation analysis was conducted in an independent dataset with 290 patients (age 28.08 ± 10.95 years, 169 women) and 310 healthy participants (age 33.55 ± 11.09 years, 165 women). Another three machine learning models of ResNet, DenseNet and EfficientNet were used for comparison. We also recruited 148 individuals receiving high-stress medical school education to characterize the potential real-world utility of the MIL model in detecting risk of mental illness. Findings: Similar performance of successful differentiation of individuals with SMI and healthy controls was observed for the MIL model (AUC: 0.82) and other models (ResNet, DenseNet, EfficientNet, 0.83, 0.81, and 0.80 respectively). MIL had better generalization in the validation test than other models (AUC: 0.82 vs 0.59, 0.66 and 0.59), and less drop-off in performance from 3.0T to 1.5T scanners. The MIL model did better in predicting clinician ratings of distress than self-ratings with questionnaires (84% vs 22%) in the medical student sample. Brain regions that contributed to SMI identification were mainly neocortical, including right precuneus, bilateral temporal regions, left precentral/postcentral gyrus, bilateral medial prefrontal cortex and right cerebellum. Interpretation: Our digital model based on brief clinical MRI protocols identified individual SMI patients with good accuracy and high sensitivity, suggesting that with incremental improvements the approach may offer potentially useful aid for early identification and intervention to prevent illness onset in vulnerable at-risk populations. Funding: This study was supported by the National Natural Science Foundation of China, National Key Technologies R&D Program of China, and Sichuan Science and Technology Program.

BackgroundAlthough the specific role of the uncinate fasciculus (UF) in emotional processing in patients with obsessive–compulsive disorder (OCD) has been investigated, the exact focal abnormalities in the UF have not been identified. The aim of the current study was to identify focal abnormalities in the white matter (WM) microstructure of the UF and to determine the associations between clinical features and structural neural substrates.MethodsIn total, 71 drug-naïve patients with OCD and 81 age- and sex-matched healthy controls (HCs) were included. Automated fiber quantification (AFQ), a tract-based quantitative approach, was adopted to measure alterations in diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD), along the trajectory of the UF. Additionally, we utilized partial correlation analyses to explore the relationship between the altered diffusion parameters and clinical characteristics.ResultsOCD patients showed significantly higher FA and lower RD at the level of the temporal and insular portions in the left UF than HCs. In the insular segments of the left UF, increased FA was positively correlated with the Hamilton Anxiety Scale (HAMA) score, while decreased RD was negatively correlated with the duration of illness.ConclusionWe observed specific focal abnormalities in the left UF in adult patients with OCD. Correlations with measures of anxiety and duration of illness underscore the functional importance of the insular portion of left UF disturbance in OCD patients.

Abstract Background Conventional MR imaging has limited value in identifying H3 K27M mutations. We aimed to investigate the capacity of quantitative MR imaging variables in identifying the H3 K27M mutation status of diffuse midline glioma. Materials and Methods Twenty‐three patients with H3 K27M mutation and thirty‐two wild‐type patients were recruited in this retrospective study, all of whom underwent multimodal MR imaging. Clinical data and quantitative MR imaging variables were explored by subgroup analysis stratified by age (juveniles and adults). Then, a logistic model for all patients was constructed to identify potential variables for predicting K27M mutation status. Besides, a retrospective validation set including 13 patients was recruited. The C‐index and F1 score were used to evaluate the performance of the prediction model. Results It turned out that patients with H3 K27M mutation were younger in the adult subgroup. In the mutation group, some relative apparent diffusion coefficient (rADC) histogram parameters and myo‐inositol/creatine plus phosphocreatine (Ins/tCr) ratio were lower than in the wild‐type group of both juveniles and adults (p

Abstract Background Recent neuroimaging studies revealed dysregulated neurodevelopmental, or/and neurodegenerative trajectories of both structural and functional connections in schizophrenia. However, how the alterations in the brain’s structural connectivity lead to dynamic function changes in schizophrenia with age remains poorly understood. Methods Combining structural magnetic resonance imaging and a network control theory approach, the white matter network controllability metric (average controllability) was mapped from age 16 to 60 years in 175 drug-naïve schizophrenia patients and 155 matched healthy controls. Results Compared with controls, the schizophrenia patients demonstrated the lack of age-related decrease on average controllability of default mode network (DMN), as well as the right precuneus (a hub region of DMN), suggesting abnormal maturational development process in schizophrenia. Interestingly, the schizophrenia patients demonstrated an accelerated age-related decline of average controllability in the subcortical network, supporting the neurodegenerative model. In addition, compared with controls, the lack of age-related increase on average controllability of the left inferior parietal gyrus in schizophrenia patients also suggested a different pathway of brain development. Conclusions By applying the control theory approach, the present study revealed age-related changes in the ability of white matter pathways to control functional activity states in schizophrenia. The findings supported both the developmental and degenerative hypotheses of schizophrenia, and suggested a particularly high vulnerability of the DMN and subcortical network possibly reflecting an illness-related early marker for the disorder.

Abstract Although functional and structural abnormalities in brain regions involved in the neurobiology of fear and anxiety have been observed in patients with social anxiety disorder (SAD), the findings have been heterogeneous due to small sample sizes, demographic confounders, and methodological differences. Besides, multimodal neuroimaging studies on structural-functional deficits and couplings are rather scarce. Herein, we aimed to explore functional network anomalies in brain regions with structural deficits and the effects of structure-function couplings on the SAD diagnosis. High-resolution structural magnetic resonance imaging (MRI) and resting-state functional MRI images were obtained from 49 non-comorbid patients with SAD and 53 demography-matched healthy controls. Whole-brain voxel-based morphometry analysis was conducted to investigate structural alterations, which were subsequently used as seeds for the resting-state functional connectivity analysis. In addition, correlation and mediation analyses were performed to probe the potential roles of structural-functional deficits in SAD diagnosis. SAD patients had significant gray matter volume reductions in the bilateral putamen, right thalamus, and left parahippocampus. Besides, patients with SAD demonstrated widespread resting-state dysconnectivity in cortico-striato-thalamo-cerebellar circuitry. Moreover, dysconnectivity of the putamen with the cerebellum and the right thalamus with the middle temporal gyrus/supplementary motor area partially mediated the effects of putamen/thalamus atrophy on the SAD diagnosis. Our findings provide preliminary evidence for the involvement of structural and functional deficits in cortico-striato-thalamo-cerebellar circuitry in SAD, and may contribute to clarifying the underlying mechanisms of structure-function couplings for SAD. Therefore, they could offer insights into the neurobiological substrates of SAD.

Precisely delivering combinational therapeutic agents has become a crucial challenge for anti-tumor treatment. In this study, a novel redox-responsive polymeric prodrug (molecular weight, MW: 93.5 kDa) was produced by reversible addition–fragmentation chain transfer (RAFT) polymerization. The amphiphilic block polymer-doxorubicin (DOX) prodrug was employed to deliver a hydrophobic photosensitizer (PS), chlorin e6 (Ce6), and the as-prepared nanoscale system [NPs(Ce6)] was investigated as a chemo-photodynamic anti-cancer agent. The glutathione (GSH)-cleavable disulfide bond was inserted into the backbone of the polymer for biodegradation inside tumor cells, and DOX conjugated onto the polymer with a disulfide bond was successfully released intracellularly. NPs(Ce6) released DOX and Ce6 with their original molecular structures and degraded into segments with low MWs of 41.2 kDa in the presence of GSH. NPs(Ce6) showed a chemo-photodynamic therapeutic effect to kill 4T1 murine breast cancer cells, which was confirmed from a collapsed cell morphology, a lifted level in the intracellular reactive oxygen species, a reduced viability and induced apoptosis. Moreover, ex vivo fluorescence images indicated that NPs(Ce6) retained in the tumor, and exhibited a remarkable in vivo anticancer efficacy. The combinational therapy showed a significantly increased tumor growth inhibition (TGI, 58.53%). Therefore, the redox-responsive, amphiphilic block polymeric prodrug could have a great potential as a chemo-photodynamic anti-cancer agent.

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in the pathogenesis of MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

BackgroundThe house-tree-person (HTP) drawing test has received growing attention from researchers as a common projective test. However, the methods used to select and interpret drawing indicators still lack uniformity.ObjectiveThis study aims to integrate drawing indicators into the process of screening for or classifying mental disorders by conducting a systematic review and meta-analysis of the application of the HTP test.MethodsA search of the following electronic databases was performed in May 2022: PubMed, Web of Science, Embase, EBSCO, CNKI, VIP, and Wanfang. Screening and checking of the literature were performed independently by two researchers. The empirical studies published on the use of the HTP test in mental disorders and studies providing specific data on the occurrence frequency of drawing characteristics were analyzed. A total of 30 studies were included in the meta-analysis, including 665 independent effect sizes and 6,295 participants. The strength of the association between drawing characteristics of the HTP test and the prevalence of mental disorders was measured by the ratio (OR) with a 95% CI. Publication bias was assessed using a funnel plot, Rosenthal’s fail-safe number (Nfs), and the trim and fill method.ResultsThe results revealed 50 drawing characteristics that appeared at least three times in previous studies, of which 39 were able to significantly predict mental disorders. The HTP test can be divided into the following four dimensions: house, tree, person, and the whole. These dimensions reflect the structure, size, and other characteristics of the picture. The results showed that the greatest predictor of mental disorders was the whole (OR = 4.20, p < 0.001), followed by the house (OR = 3.95, p < 0.001), the tree (OR = 2.70, p < 0.001), and the person (OR = 2.16, p < 0.001). The valid predictors can be categorized into the following four types: item absence, bizarre or twisted, excessive details, and small or simplified. The subgroup analysis showed that the affective-specific indicators included no motion, leaning house, and decorated roof; thought-specific indicators included excessive separation among items, no window, loss of facial features, and inappropriate body proportions; and common indicators of mental disorders included no additional decoration, simplified drawing, very small house, two-dimensional house, and very small tree.ConclusionThese findings can promote the standardization of the HTP test and provide a theoretical reference for the screening and clinical diagnosis of mental disorders.

Cancer immunotherapy is an effective antitumor approach through activating immune systems to eradicate tumors by immunotherapeutics. However, direct administration of “naked” immunotherapeutic agents (such as nucleic acids, cytokines, adjuvants or antigens without delivery vehicles) often results in: (1) an unsatisfactory efficacy due to suboptimal pharmacokinetics; (2) strong toxic and side effects due to low targeting (or off-target) efficiency. To overcome these shortcomings, a series of polysaccharide-based nanoparticles have been developed to carry immunotherapeutics to enhance antitumor immune responses with reduced toxicity and side effects. Polysaccharides are a family of natural polymers that hold unique physicochemical and biological properties, as they could interact with immune system to stimulate an enhanced immune response. Their structures offer versatility in synthesizing multifunctional nanocomposites, which could be chemically modified to achieve high stability and bioavailability for delivering therapeutics into tumor tissues. This review aims to highlight recent advances in polysaccharide-based nanomedicines for cancer immunotherapy and propose new perspectives on the use of polysaccharide-based immunotherapeutics.

Abstract Background Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD. Methods We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC. Results Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model’s performance. Conclusions Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

Abstract Background The corpus callosum (CC) deficits have been well documented in chronic schizophrenia. However, the long-term impacts of antipsychotic monotherapies on callosal anatomy remain unclear. This cross-sectional study sought to explore micro- and macro-structural characteristics of the CC in never-treated patients and those with long-term mono-antipsychotic treatment. Methods The study included 23 clozapine-treated schizophrenia patients (CT-SCZ), 19 risperidone-treated schizophrenia patients (RT-SCZ), 23 never-treated schizophrenia patients (NT-SCZ), and 35 healthy controls (HCs). High resolution structural images and diffusion tensor imaging (DTI) data for each participant were obtained via a 3.0 T MR scanner. FreeSurfer was used to examine the volumes and fractional anisotropy (FA) values of the CC for each participant. Results There were significant deficits in the total and sub-regional CC volume and white matter integrity in NT-SCZ in comparison with healthy subjects. Compared with NT-SCZ, both CT-SCZ and RT-SCZ showed significantly increased FA values in the anterior CC region, while only RT-SCZ showed significantly increased volume in the mid-anterior CC region. Moreover, the volume of the mid-anterior CC region was significantly smaller in CT-SCZ compared to HCs. No correlations of clinical symptoms with callosal metrics were observed in schizophrenia patients. Conclusions Our findings provide insight into micro- and macro-structural characteristics of the CC in chronic schizophrenia patients with or without antipsychotics. These results suggest that the pathology itself is responsible for cerebral abnormalities in schizophrenia and that chronic exposure to antipsychotics may have an impact on white matter structure of schizophrenia patients, especially in those with risperidone treatment.

Abstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C p (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

Abstract Glioblastoma (GBM), one of the most common primary intracranial malignant tumours, is very difficult to be completely excised by surgery due to its irregular shape. Here, we use an MRI/NIR fluorescence dual-modal imaging nanoprobe that includes superparamagnetic iron oxide nanoparticles (SPIONs) modified with indocyanine (Cy7) molecules and peptides (ANG or DANG) to locate malignant gliomas and guide accurate excision. Both peptides/Cy7-SPIONs probes displayed excellent tumour-homing properties and barrier penetrating abilities in vitro, and both could mediate precise aggregation of the nanoprobes at gliomas sites in in vivo magnetic resonance imaging (MRI) and ex vivo near-infrared (NIR) fluorescence imaging. However, compared with ANG/Cy7-SPIONs probes, DANG/Cy7-SPIONs probes exhibited better enhanced MR imaging effects. Combining all these features together, this MRI/NIR fluorescence imaging dual-modal nanoprobes modified with retro-enantio isomers of the peptide have the potential to accurately display GBMs preoperatively for precise imaging and intraoperatively for real-time imaging.

Abstract Background Cognitive impairment is a common non-motor symptom in patients with Parkinson’s disease (PD). Mild cognitive impairment (MCI) is also prevalent in nondemented PD patients, even in newly diagnosed PD patients. The possible impacts of MCI on brain function activities for PD patients need more investigation, and the potential of emerging technologies for detecting underlying pathophysiology of cognitive signs in PD can be further improved. Method Forty-seven newly diagnosed drug-naïve PD patients (28 PD-MCI patients and 19 PD patients with cognitively unimpaired (PD-CU)) and 28 healthy controls (HCs) underwent resting-state functional MRI. The connectivity patterns of specific networks were investigated through the independent component analysis among PD-MCI, PD-CU and HCs groups. Results The independent component analysis revealed significantly decreased functional connectivity (FC) of the default mode network, visual network and sensorimotor network in the PD-MCI subgroup compared with the HC group. Furthermore, FC of the default mode network was positively correlated with memory scores from the brief visuospatial memory test-revised, and FC of the visual network was positively correlated with visuospatial scores from the clock copying test in the PD-MCI group. In all patients with PD, FC of the sensorimotor network negatively correlated with motor severity scores from the Unified PD Rating Scale (UPDRS) part III. On the other hand, the potential damage was more likely to occur in FC between the sensorimotor network and limbic network, and between the ventral attention network and visual network in all PD patients. Conclusions Newly diagnosed drug-naïve PD-MCI patients showed characteristic damage of FC within the default mode network, visual network and sensorimotor network, and all PD patients presented impaired FC between the sensorimotor network and limbic network, and FC between the ventral attention network and visual network. These network-wide functional aberrations may underline the pathophysiology of PD.