Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.

This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet). Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale-cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6 months; 2.48, 1.69 and 4.81, 3.44 within 12 months; 2.67, 1.86 and 5.81, 3.83 within 24 months; 3.02, 2.03 and 5.09, 3.43 within 36 months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36 months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications.

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Mitochondria-specific near-infrared photoactivation of peroxynitrite upconversion luminescent nanogenerator for precision cancer gas therapy.

Gas therapy is emerging as a highly promising therapeutic strategy for cancer treatment. However, there are limitations, including the lack of targeted subcellular organelle accuracy and spatiotemporal release precision, associated with gas therapy. In this study, we developed a series of photoactivatable nitric oxide (NO) donors NRh-R-NO (R = Me, Et, Bn, iPr, and Ph) based on an N-nitrosated upconversion luminescent rhodamine scaffold. Under the irradiation of 808 nm light, only NRh-Ph-NO could effectively release NO and NRh-Ph with a significant turn-on frequency upconversion luminescence (FUCL) signal at 740 nm, ascribed to lower N-N bond dissociation energy. We also investigated the involved multistage near-infrared-controlled cascade release of gas therapy, including the NO released from NRh-Ph-NO along with one NRh-Ph molecule generation, the superoxide anion O2⋅- produced by the photodynamic therapy (PDT) effect of NRh-Ph, and highly toxic peroxynitrite anion (ONOO‒) generated from the co-existence of NO and O2⋅-. After mild nano-modification, the nanogenerator (NRh-Ph-NO NPs) empowered with superior biocompatibility could target mitochondria. Under an 808 nm laser irradiation, NRh-Ph-NO NPs could induce NO/ROS to generate RNS, causing a decrease in the mitochondrial membrane potential and initiating apoptosis by caspase-3 activation, which further induced tumor immunogenic cell death (ICD). In vivo therapeutic results of NRh-Ph-NO NPs showed augmented RNS-potentiated gas therapy, demonstrating excellent biocompatibility and effective tumor inhibition guided by real-time FUCL imaging. Collectively, this versatile strategy defines the targeted RNS-mediated cancer therapy.

Capecitabine-based chemotherapy in early-stage triple-negative breast cancer: a meta-analysis.

Introduction: The efficacy and safety of adjuvant capecitabine in early-stage triple-negative breast cancer remains undefined. A meta-analysis was conducted to elucidate whether capecitabine-based regimens could improve survival in early-stage triple-negative breast cancer (TNBC). Methods: The current study searched Medline, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov proceedings up to 2023.9. Disease-free survival (DFS), overall survival (OS), and grade 3-4 adverse events (AEs) were assessed. Extracted or calculated hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were pooled. Results: The capecitabine-based regimens showed significant advantages in DFS (HR = 0.81, 95% CI: 0.73-0.90; P <.001) and OS (HR = 0.75, 95% CI: 0.65-0.87; P <.001) from 12 randomized controlled trials (RCTs) with 5,390 unselected participants. Subgroup analysis of DFS showed analogous results derived from patients with lymph node negative (HR = 0.68, 95% CI: 0.50-0.92; P = .006) and capecitabine duration no less than six cycles (HR = 0.73; 95% CI: 0.62-0.86; P <.001). Improvement of DFS in the addition group (HR = 0.77, 95% CI: 0.68-0.87; P <.001) and adjuvant setting (HR = 0.79, 95% CI: 0.70-0.89; P <.001) was observed. As to safety profile, capecitabine was associated with more frequent stomatitis (OR = 5.05, 95% CI: 1.45-17.65, P = .011), diarrhea (OR = 6.11, 95% CI: 2.12-17.56; P =.001), and hand-foot syndrome (OR = 31.82, 95% CI: 3.23-313.65, P = .003). Conclusions: Adjuvant capecitabine-based chemotherapy provided superior DFS and OS to early-stage TNBC. The benefits to DFS in selected patients with lymph node negative and the addition and extended duration of capecitabine were demonstrated.

Global incidence and death estimates of chronic kidney disease due to hypertension from 1990 to 2019, an ecological analysis of the global burden of diseases 2019 study.

BACKGROUND: The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study's objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively. METHOD: Age-standardized incidence rates (ASIR) and death rates (ASDR) were collected from the GBD 2019. Trend analysis was conducted by Joinpoint regression of ASRs from 1990 to 2019. Spatial autocorrelation analysis was performed to obtain spatial patterns. The association between SDI and burden of CKD due to hypertension was estimated using a Pearson correlation analysis. RESULTS: The global ASIR and ASDR due to hypertension-related CKD were 19.45 (95% CI, 17.85 to 21.09) and 5.88 (95% CI, 4.95 to 6.82) per 100 K population in 2019, representing increases of 17.89% and 13.29% compared to 1990, respectively. The elderly population and males were found the highest ASIR and ASDR. The high SDI region had the highest ASIRs, while low SDI regions experienced the highest ASDRs. Joinpoint regression found both global ASIR and ASDR showed increasing trends, with the highest increases observed in middle- and high-SDI regions, respectively. The SDI exhibited a positive association with ASIRs but displayed an inverse V-shaped correlation with the average annual percentage change (AAPC) of ASIRs. Spatial autocorrelation analysis revel significant positive spatial autocorrelation for the AAPC of ASDRs and ASIRs, from 1990 to 2019. CONCLUSIONS: Results met the objectives, and demonstrated a rising global burden of hypertension-related CKD. Factors such as aging, gender, and regional variations should be considered when designing control measures and developing healthcare systems to effectively address the burden of this complex condition.

Quantization via Distillation and Contrastive Learning.

Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This process necessitates a delicate balance between model size and performance. In this work, we explore knowledge distillation (KD) as a promising approach for improving quantization performance by transferring knowledge from high-precision networks to low-precision counterparts. We specifically investigate feature-level information loss during distillation and emphasize the importance of feature-level network quantization perception. We propose a novel quantization method that combines feature-level distillation and contrastive learning to extract and preserve more valuable information during the quantization process. Furthermore, we utilize the hyperbolic tangent function to estimate gradients with respect to the rounding function, which smoothens the training procedure. Our extensive experimental results demonstrate that the proposed approach achieves competitive model performance with the quantized network compared to its full-precision counterpart, thus validating its efficacy and potential for real-world applications.

Automatic recognition of cephalometric landmarks via multi-scale sampling strategy.

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

Classification of Alzheimer's disease using robust TabNet neural networks on genetic data.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.

Background: Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex. Methods: In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced. Results: The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019). Conclusions: The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

A combined deformable model and medical transformer algorithm for medical image segmentation.

Deep learning-based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance.

TNFα-induced IDH1 hyperacetylation reprograms redox homeostasis and promotes the chemotherapeutic sensitivity.

The heterogeneity and drug resistance of colorectal cancer (CRC) often lead to treatment failure. Isocitrate dehydrogenase 1 (IDH1), a rate-limiting enzyme in the tricarboxylic acid cycle, regulates the intracellular redox environment and mediates tumor cell resistance to chemotherapeutic drugs. The aim of this study was to elucidate the mechanism underlying the involvement of IDH1 acetylation in the development of CRC drug resistance under induction of TNFα. We found TNFα disrupted the interaction between SIRT1 and IDH1 and increased the level of acetylation at K115 of IDH1. Hyperacetylation of K115 was accompanied by protein ubiquitination, which increased its susceptibility to degradation compared to IDH1 K115R. TNFα-mediated hyperacetylation of K115 sensitized the CRC cells to 5FU and reduced the NADPH/NADP ratio to that of intracellular ROS. Furthermore, TNFα and 5FU inhibited CRC tumor growth in vivo, while the K115R-expressing tumor tissues developed 5FU resistance. In human CRC tissues, K115 acetylation was positively correlated with TNFα infiltration, and K115 hyperacetylation was associated with favorable prognosis compared to chemotherapy-induced deacetylation. Therefore, TNFα-induced hyperacetylation at the K115 site of IDH1 promotes antitumor redox homeostasis in CRC cells, and can be used as a marker to predict the response of CRC patients to chemotherapy.

Coupling analysis between functional and structural brain networks in Alzheimer's disease.

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.

Small Is Beautiful: Compressing Deep Neural Networks for Partial Domain Adaptation.

Domain adaptation is a promising way to ease the costly data labeling process in the era of deep learning (DL). A practical situation is partial domain adaptation (PDA), where the label space of the target domain is a subset of that in the source domain. Although existing methods yield appealing performance in PDA tasks, it is highly presumable that computation overhead exists in deep PDA models since the target is only a subtask of the original problem. In this work, PDA and model compression are seamlessly integrated into a unified training process. The cross-domain distribution divergence is reduced by minimizing a soft-weighted maximum mean discrepancy (SWMMD), which is differentiable and functions as regularization during network training. We use gradient statistics to compress the overparameterized model to identify and prune redundant channels based on the corresponding scaling factors in batch normalization (BN) layers. The experimental results demonstrate that our method can achieve comparable classification performance to state-of-the-art methods on various PDA tasks, with a significant reduction in model size and computation overhead.

White matter microstructural impairments in amyotrophic lateral sclerosis: A mean apparent propagator MRI study.

BACKGROUND: White matter (WM) impairment is a hallmark of amyotrophic lateral sclerosis (ALS). This study evaluated the capacity of mean apparent propagator magnetic resonance imaging (MAP-MRI) for detecting ALS-related WM alterations. METHODS: Diffusion images were obtained from 52 ALS patients and 51 controls. MAP-derived indices [return-to-origin/-axis/-plane probability (RTOP/RTAP/RTPP) and non-Gaussianity (NG)/perpendicular/parallel NG (NG⊥/NG||)] were computed. Measures from diffusion tensor/kurtosis imaging (DTI/DKI) and neurite orientation dispersion and density imaging (NODDI) were also obtained. Voxel-wise analysis (VBA) was performed to determine differences in these parameters. Relationship between MAP parameters and disease severity (assessed by the revised ALS Functional Rating Scale (ALSFRS-R)) was evaluated by Pearson's correlation analysis in a voxel-wise way. ALS patients were further divided into two subgroups: 29 with limb-only involvement and 23 with both bulbar and limb involvement. Subgroup analysis was then conducted to investigate diffusion parameter differences related to bulbar impairment. RESULTS: The VBA (with threshold of P < 0.05 after family-wise error correction (FWE)) showed that ALS patients had significantly decreased RTOP/RTAP/RTPP and NG/ NG⊥/NG|| in a set of WM areas, including the bilateral precentral gyrus, corona radiata, posterior limb of internal capsule, midbrain, middle corpus callosum, anterior corpus callosum, parahippocampal gyrus, and medulla. MAP-MRI had the capacity to capture WM damage in ALS, which was higher than DTI and similar to DKI/NODDI. RTOP/RTAP/NG/NG⊥/NG|| parameters, especially in the bilateral posterior limb of internal capsule and middle corpus callosum, were significantly correlated with ALSFRS-R (with threshold of FWE-corrected P < 0.05). The VBA (with FWE-corrected P < 0.05) revealed the significant RTAP reduction in subgroup with both bulbar and limb involvement, compared with those with limb-only involvement. CONCLUSIONS: Microstructural impairments in corticospinal tract and corpus callosum represent the consistent characteristic of ALS. MAP-MRI could provide alternative measures depicting ALS-related WM alterations, complementary to the common diffusion imaging methods.

Brain network analyses of diffusion tensor imaging for brain aging.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

γ-Glutamyl transpeptidase-activatable near-infrared nanoassembly for tumor fluorescence imaging-guided photothermal therapy.

Rationale: Precise treatment of tumors is attracting increasing attention. Molecular probes simultaneously demonstrating the diagnostic signal and pharmacological effect in response to tumor microenvironment are highly desired. γ-glutamyl transpeptidase (GGT) is a biomarker with significantly up-regulated expression in the tumor area. We developed a GGT responsive near-infrared (NIR) nanoassembly for tumor-specific fluorescence imaging-guided photothermal therapy. Methods: The GGT responsive NIR probe was constructed by conjugating GGT-specific substrate γ-glutamic acid (γ-Glu) with cyanine fluorophore (NRh-NH2) via amide reaction. The resulting NRh-G spontaneously assembled into nanoparticles (NRh-G-NPs) around 50 nm. The NPs were characterized and the properties evaluated in the presence or absence of GGT. Subsequently, we studied fluorescence imaging and photothermal therapy of NRh-G-NPs in vitro and in vivo. Results: NRh-G-NPs, upon specific reaction with GGT, turned into NRh-NH2-NPs, showing a ~180-fold fluorescence enhancement and excellent photothermal effect recovery. NRh-G-NPs could selectively light up U87MG tumor cells while their fluorescence was weak in L02 human normal liver cells. The NPs also showed excellent tumor cell ablation upon laser irradiation. After intravenous injection into tumor-bearing mice, NRh-G-NPs could arrive in the tumor area and specifically light up the tumor. Following laser irradiation, the tumor could be completely erased with no tumor reoccurrence for up to 40 days. Conclusions: NRh-G-NPs were specifically responsive to GGT overexpressed in U87MG tumor cells and selectively lit up the tumor for imaging-guided therapy. Besides, the recovery of photothermal property in the tumor area could improve cancer therapy precision and decreased side effects in normal tissues.

Classification of white blood cells using weighted optimized deformable convolutional neural networks.

BACKGROUND: Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms. METHODS: In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification. Our algorithm is characterized as two-module transfer learning and deformable convolutional (DC) layers for the betterment of robustness. To validate the performance, our method was compared with classical MLs of VGG16, VGG19, Inception-V3, ResNet-50, support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT) and random forest (RF) on our undisclosed WBC data set and public BCCD data set. RESULTS: TWO-DCNN achieved the best performance with the precisions (PREs) of 95.7%, 94.5% and 91.6%, recalls (RECs) of 95.7%, 94.5% and 91.6%, F1-scores (F1s) of 95.7%, 94.5% and 91.6%, area under curves (AUCs) of 0.98, 0.97 and 0.95 for low-resolution and noisy undisclosed data sets, BCCD data set, respectively. CONCLUSIONS: With accurate feature extraction and optimized network weights, the proposed TWO-DCNN showed the best performance in WBC classification for low-resolution and noisy data sets. It could be used as an alternative method for clinical applications.

Azo-Based Hypoxia-Responsive Self-Assembly Near-Infrared Fluorescent Nanoprobe for In Vivo Real-Time Bioimaging of Tumors.

Hypoxia is an obvious characteristic of cancer, especially solid tumors. which may give rise to the expansion of invasion and metastasis. Exploring near-infrared (NIR) nanoprobes that could accurately evaluate the degree of hypoxia will contribute to the assessment of the degree of malignant neoplasms, so as to adopt more accurate and individualized treatment options Here, we have developed a self-assembled NIR organic nanoprobe to specifically and authoritatively detect the oxygen concentration in vivo and in vitro to evaluate the level of hypoxia. The organic nanoprobe mainly contains two motifs: a fluorophore moiety NRh-NH2 for NIR fluorescence imaging and hypoxia-sensitive moiety Azonaphthalene derivatives for quenching NIR emissions, detecting oxygen in hypoxic regions and improving the hydrophilicity. The nanoprobes were used for detection of oxygen in a variety of living cells under different conditions and real-time bioimaging of neoplasms in live mice. This design strategy provides ideas for the development of nanoprobes for the diagnosis of tumors and other hypoxia-related diseases.

Near-infrared fluorescent chemodosimeter for real-time in vivo evaluation of H2S-release efficiency of prodrug.

Hydrogen sulfide (H2S) is a significant gasotransmitter. A deficiency in H2S might contribute to some serious diseases. The development of H2S drugs has received a great deal of attention. There is a pressing need for an effective method to evaluate H2S release efficiency, especially in in vivo evaluation. In this study, a highly sensitive and selective near-infrared (NIR) fluorescent chemodosimeter (NRh-N3) was synthesized for detecting H2S. Importantly, NRh-N3 was shown to be capable of visually monitoring H2S-release from the prodrug in vitro and in vivo.

Functional connectivity changes of nucleus Accumbens Shell portion in left mesial temporal lobe epilepsy patients.

Growing evidence has supported that the nucleus accumbens (NAc), especially its shell portion, has been involved in epileptogenesis. However, relevant studies on vivo human brain are quite limited. In this study, we investigated left mesial temporal lobe epilepsy (MTLE) related function connectivity (FC) changes of NAc subregions using resting-state functional magnetic resonance imaging. We calculated functional connectivity from two NAc subregions to both whole brain and 16 related targets. Two-sample t-test (Alphasim multiple comparisons corrected) was performed to identify the effect of the disease on each seed's whole brain network. Repeated-measures ANOVA and Post hoc pairwise t test (Bonferroni corrections) were performed to visualize the seed to target FC group differences in each subdivision. In whole brain FC networks, neither the left or right core show different FC changes. The left shell showed decreased FC with a cluster located around the right inferior frontal gyrus. The right shell portion showed increased FC with a cluster located around the left inferior temporal gyrus. The seed to targets results showed that the left shell of LTLE group exhibited lower FC with left posterior-parahippocampal gyrus and right caudate, putamen, thalamus, paracingulate gyrus but higher FC with right subcallosal cortex. The right core of LTLE group exhibited higher FC with right frontal pole and the right shell exhibited lower FC with left thalamus and left anterior-parahippocampal gyrus. This is the first study to investigate the functional connectivity changes of NAc subdivisions of epilepsy in vivo human brain. Our results showed that the left MTLE related FC changes on NAc are mainly on shell portion rather than core. The decrease FC between the left shell and right frontal area and the decrease FC between the right shell and left temporal area suggested they serve vital roles for MTLE.