Treatment of high cervical arteriovenous fistulas in the craniocervical junction region

The craniocervical junction (CCJ) is a complex region. Rarely, arteriovenous fistulas (AVFs) can occur in the CCJ region. Currently, it is accepted that CCJ AVFs should only refer to AVFs at the C1-C2 levels. It is reasonable to assume that high cervical CCJ AVFs are being referred to when discussing CCJ AVFs. High cervical CCJ AVFs can be divided into the following four types: dural AVF, radicular AVF, epidural AVF and perimedullary AVF. Until now, it was difficult to understand high cervical CCJ AVFs and provide a proper treatment for them. Therefore, an updated review of high cervical CCJ AVFs is necessary. In this review, the following issues are discussed: the definition of high cervical CCJ AVFs, vessel anatomy of the CCJ region, angioarchitecture of high cervical CCJ AVFs, treatment options, prognoses and complications. Based on the review and our experience, we found that the four types of high cervical CCJ AVFs share similar clinical and imaging characteristics. Patients may present with intracranial hemorrhage or congestive myelopathy. Treatment, including open surgery and endovascular treatment (EVT), can be used for symptomatic AVFs. Most high cervical CCJ AVFs can be effectively treated with open surgery. EVT remains challenging due to a high rate of incomplete obliteration and complications, and it can only be performed in superselective AVFs with simple angioarchitecture. Appropriate treatment can lead to a good prognosis.

Formulation and Evaluation on Synergetic Anti-Hepatoma Effect of a Chemically Stable and Release-Controlled Nanoself-Assembly with Natural Monomers

Introduction

Hepatoma is the leading cause of death among liver diseases worldwide. Modern pharmacological studies suggest that some natural monomeric compounds have a significant effect on inhibiting tumor growth. However, poor stability and solubility, and side effects are the main factors limiting the clinical application of natural monomeric compounds.

Methods

In this paper, drug-co-loaded nanoself-assemblies were selected as a delivery system to improve the chemical stability and solubility of Tanshinone II A and Glycyrrhetinic acid, and to produce a synergetic anti-hepatoma effect.

Results

The study suggested that the drug co-loaded nanoself-assemblies showed high drug loading capacity, good physical and chemical stability, and controlled release. In vitro cell experiments verified that the drug-co-loaded nanoself-assemblies could increase the cellular uptake and cell inhibitory activity. In vivo studies verified that the drug co-loaded nanoself-assemblies could prolong the MRT_0-∞, increase accumulation in tumor and liver tissues, and show strong synergistic anti-tumor effect and good bio-safety in H22 tumor-bearing mice.

Conclusion

This work indicates that natural monomeric compounds co-loaded nanoself-assemblies would be a potential strategy for the treatment of hepatoma.

A Photothermally Enhanced Vancomycin-Coated Liquid Metal Antimicrobial Agent with Targeting Capability

The targeted antimicrobial efficacy of Vancomycin decreases significantly over time due to bacterial resistance, whereas Ga-based liquid metals, which are less prone to inducing bacterial resistance, face challenges in achieving targeted antimicrobial effects. To tackle these issues, a highly efficient antimicrobial agent with targeting properties has been developed by combining Ga-based liquid metals and Vancomycin. Moreover, the performance of this antimicrobial agent can be greatly enhanced through the use of near-infrared light. Microscopic observations reveal that Vancomycin can be effectively encapsulated on the surface of liquid metal, facilitated by the presence of the oxide layer. The resulting core–shell structured antimicrobial agent demonstrates notable targeted antimicrobial effects against S. aureus. Antibacterial tests indicate that Vancomycin effectively improves the antibacterial properties of pure liquid metal. Additionally, this study unveils the excellent photothermal conversion capabilities of liquid metal, enabling the antimicrobial agent exposed to 808nm near-infrared light to exhibit significantly strengthened bactericidal performance. In this scenario, the antimicrobial agent can achieve nearly 100% effectiveness. This work enriches the investigation of integrating Ga-based antimicrobial agents with traditional antibiotics, showcasing promising antibacterial effects and establishing the groundwork for subsequent clinical applications.

The role of microbiome-host interactions in the development of Alzheimer´s disease

Alzheimer`s disease (AD) is the most prevalent cause of dementia. It is often assumed that AD is caused by an aggregation of extracellular beta-amyloid and intracellular tau-protein, supported by a recent study showing reduced brain amyloid levels and reduced cognitive decline under treatment with a beta-amyloid-binding antibody. Confirmation of the importance of amyloid as a therapeutic target notwithstanding, the underlying causes of beta-amyloid aggregation in the human brain, however, remain to be elucidated. Multiple lines of evidence point towards an important role of infectious agents and/or inflammatory conditions in the etiology of AD. Various microorganisms have been detected in the cerebrospinal fluid and brains of AD-patients and have thus been hypothesized to be linked to the development of AD, including Porphyromonas gingivalis (PG) and Spirochaetes. Intriguingly, these microorganisms are also found in the oral cavity under normal physiological conditions, which is often affected by multiple pathologies like caries or tooth loss in AD patients. Oral cavity pathologies are mostly accompanied by a compositional shift in the community of oral microbiota, mainly affecting commensal microorganisms and referred to as 'dysbiosis'. Oral dysbiosis seems to be at least partly mediated by key pathogens such as PG, and it is associated with a pro-inflammatory state that promotes the destruction of connective tissue in the mouth, possibly enabling the translocation of pathogenic microbiota from the oral cavity to the nervous system. It has therefore been hypothesized that dysbiosis of the oral microbiome may contribute to the development of AD. In this review, we discuss the infectious hypothesis of AD in the light of the oral microbiome and microbiome-host interactions, which may contribute to or even cause the development of AD. We discuss technical challenges relating to the detection of microorganisms in relevant body fluids and approaches for avoiding false-positives, and introduce the antibacterial protein lactoferrin as a potential link between the dysbiotic microbiome and the host inflammatory reaction.

Standalone RF Self-Interference Cancellation System for In-Vivo Simultaneous Transmit and Receive (STAR) MRI

Demonstrated is a standalone RF self-interference canceller for simultaneous transmit and receive (STAR) magnetic resonance imaging (MRI) at 1.5T. Standalone STAR cancels the leakage signal directly coupled between transmit and receive RF coils. A cancellation signal, introduced by tapping the input of a transmit coil with a power divider, is manipulated with voltage-controlled attenuators and phase shifters to match the leakage signal in amplitude, 180° out of phase, to exhibit high isolation between the transmitter and receiver. The cancellation signal is initially generated by a voltage-controlled oscillator (VCO); therefore, it does not require any external RF or synchronization signals from the MRI console for calibration. The system employs a field programmable gate array (FPGA) with an on-board analog to digital converter (ADC) to calibrate the cancellation signal by tapping the receive signal, which contains the leakage signal. Once calibrated, the VCO is disabled and the transmit signal path switches to the MRI console for STAR MR imaging. To compensate for the changes of parameters in RF sequences after the automatic calibration and to further improve isolation, a wireless user board that uses an ESP32 microcontroller was built to communicate with the FPGA for final fine-tuning of the output state. The standalone STAR system achieved 74.2 dB of isolation with a 94 second calibration time. With such high isolation, in-vivo MR images were obtained with approximately 40 mW of RF peak power.

Multi-Targeted and On-Demand Non-Coding RNA Regulation Nanoplatform against Metastasis and Recurrence of Triple-Negative Breast Cancer

Dysregulation of microRNAs (miRs) is the hallmark of triple-negative breast cancer (TNBC), which is closely involved with its growth, metastasis, and recurrence. Dysregulated miRs are promising targets for TNBC therapy, however, targeted and accurate regulation of multiple disordered miRs in tumors is still a great challenge. Here, a multi-targeting and on-demand non-coding RNA regulation nanoplatform (MTOR) is reported to precisely regulate disordered miRs, leading to dramatical suppression of TNBC growth, metastasis, and recurrence. With the assistance of long blood circulation, ligands of urokinase-type plasminogen activator peptide and hyaluronan located in multi-functional shells enable MTOR to actively target TNBC cells and breast cancer stem cell-like cells (BrCSCs). After entering TNBC cells and BrCSCs, MTOR is subjected to lysosomal hyaluronidase-induced shell detachment, leading to an explosion of the TAT-enriched core, thereby enhancing nuclear targeting. Subsequently, MTOR could precisely and simultaneously downregulate microRNA-21 expression and upregulate microRNA-205 expression in TNBC. In subcutaneous xenograft, orthotopic xenograft, pulmonary metastasis, and recurrence TNBC mouse models, MTOR shows remarkably synergetic effects on the inhibition of tumor growth, metastasis, and recurrence due to its on-demand regulation of disordered miRs. This MTOR system opens a new avenue for on-demand regulation of disordered miRs against growth, metastasis, and recurrence of TNBC.

Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI

BACKGROUND

Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear.

PURPOSE

To investigate the potential of the proposed attention-based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast-enhanced MRI (DCE-MRI).

STUDY TYPE

Retrospective.

POPULATION

A total of 941 breast cancer patients who underwent DCE-MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts.

FIELD STRENGTH/SEQUENCE

A 3.0 T MR scanner, DCE-MRI sequence.

ASSESSMENT

A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor-ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan-Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.).

STATISTICAL TESTS

Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification.

RESULTS

The optimal RCNet model, that is, RCNet-tumor+ALN , achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet-tumor+ALN , the radiologists' performance was improved (external test cohort, P < 0.05).

DATA CONCLUSION

DCE-MRI-based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography

Background

Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.

Patients and Methods

620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results

The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively).

Conclusion

Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Dihydroxy-Metabolites of Dihomo-γ-linolenic Acid Drive Ferroptosis-Mediated Neurodegeneration

Even after decades of research, the mechanism of neurodegeneration remains understudied, hindering the discovery of effective treatments for neurodegenerative diseases. Recent reports suggest that ferroptosis could be a novel therapeutic target for neurodegenerative diseases. While polyunsaturated fatty acid (PUFA) plays an important role in neurodegeneration and ferroptosis, how PUFAs may trigger these processes remains largely unknown. PUFA metabolites from cytochrome P450 and epoxide hydrolase metabolic pathways may modulate neurodegeneration. Here, we test the hypothesis that specific PUFAs regulate neurodegeneration through the action of their downstream metabolites by affecting ferroptosis. We find that the PUFA dihomo-γ-linolenic acid (DGLA) specifically induces ferroptosis-mediated neurodegeneration in dopaminergic neurons. Using synthetic chemical probes, targeted metabolomics, and genetic mutants, we show that DGLA triggers neurodegeneration upon conversion to dihydroxyeicosadienoic acid through the action of CYP-EH (CYP, cytochrome P450; EH, epoxide hydrolase), representing a new class of lipid metabolites that induce neurodegeneration via ferroptosis.

Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors

PURPOSE

To assess the usefulness of extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT (CECT) for diagnosing anterior mediastinal tumors.

METHOD

This study included 161 histologically confirmed anterior mediastinal tumors (55 low-risk thymomas, 57 high-risk thymomas, 32 thymic carcinomas, and 17 malignant lymphomas) that were assessed by pretreatment CECT. ECV fraction was calculated using measurements obtained within the lesion and the aorta on unenhanced and equilibrium phase CECT. ECV fraction was compared among anterior mediastinal tumors using one-way ANOVA or t-test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of ECV fraction to differentiate thymic carcinomas/lymphomas from thymomas.

RESULTS

ECV fraction differed significantly among the anterior mediastinal tumors (p < 0.001). ECV fraction of thymic carcinomas was significantly higher than those of low-risk thymomas, high-risk thymomas, and lymphomas (p < 0.001, p < 0.001, and p = 0.006, respectively). ECV fraction of lymphomas was significantly higher than that of low-risk thymomas (p < 0.001). ECV fraction was significantly higher in thymic carcinomas/lymphomas than in thymomas (40.1 % vs. 27.7 %, p < 0.001). The optimal cutoff value to differentiate thymic carcinomas/lymphomas from thymomas was 38.5 % (AUC, 0.805; 95 %CI, 0.736-0.863).

CONCLUSIONS

ECV fraction derived from equilibrium CECT is helpful in diagnosing anterior mediastinal tumors. High ECV fraction is indicative of thymic carcinomas/lymphomas, particularly thymic carcinomas.

Investigation of brain iron levels in Chinese patients with Alzheimer's disease

Introduction

We aimed (i) to explore the diagnostic value of deep gray matter magnetic susceptibility in Alzheimer's disease (AD) in China and (ii) to analyze its correlation with neuropsychiatric scales. Moreover, we conducted subgroup analysis based on the presence of the APOE-ε4 gene to improve the diagnosis of AD.

Methods

From the prospective studies of the China Aging and Neurodegenerative Initiative (CANDI), a total of 93 subjects who could undergo complete quantitative magnetic susceptibility imaging and APOE-ε4 gene detection were selected. Differences in quantitative susceptibility mapping (QSM) values between and within groups, including AD patients, individuals with mild cognitive impairment (MCI), and healthy controls (HCs), both APOE-ε4 carriers and non-carriers, were analyzed.

Results

In primary analysis, the magnetic susceptibility values of the bilateral caudate nucleus and right putamen in the AD group and of the right caudate nucleus in the MCI group were significantly higher than those in the HCs group (P < 0.05). In APOE-ε4 non-carriers, there were significant differences in more regions between the AD, MCI, and HCs groups, such as the left putamen and the right globus pallidus (P < 0.05). In subgroup analysis, the correlation between QSM values in some brain regions and neuropsychiatric scales was even stronger.

Discussion

Exploration of the correlation between deep gray matter iron levels and AD may provide insight into the pathogenesis of AD and facilitate early diagnosis in elderly Chinese. Further subgroup analysis based on the presence of the APOE-ε4 gene may further improve the diagnostic efficiency and sensitivity.

msQSM: Morphology-based Self-supervised Deep Learning for Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

Recent progress of oxidative stress associated biomarker detection

Oxidative stress denotes the imbalance between the generation of reactive oxygen species (ROS) and antioxidant defenses in living organisms, participating in various pathophysiological processes and mediating the occurrence of diseases. Typically, the excessive production of ROS under oxidative stress elicits oxidative modification of biomacromolecules, including lipids, proteins and nucleic acids, leading to cell dysfunction and damage. Therefore, the analysis and detection of oxidative stress-associated biomarkers are of considerable importance to accurately reflect and evaluate the oxidative stress status. This review comprehensively elucidates the recent advances and applications of imaging probes for tracking and detecting oxidative stress-related biomarkers such as lipid peroxidation, and protein and DNA oxidation. The existing challenges and future development directions in this field are also discussed.

Tumor infiltrating lymphocytes and radiological picture of the tumor

Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.

Surface Oxidation and Wetting Synergistic Effect of Liquid Metals

Various functions of liquid metals are closely related to their surface performances, among which oxidation and wetting are the two most important surface processes. The two processes of liquid metals are inseparable in most practical applications; however, the coupling of oxidation and wetting of liquid metals has received little attention. Here, we demonstrate the synergistic effect of oxidation and wetting of liquid metals through establishing a liquid system containing the copper ion acid solution. By modulating the concentrations of copper ions and hydrogen ions, three different modes of the liquid metal surface are presented, where the oxidation process and the wetting process are in a competitive relationship. Whichever of the two processes is dominant can determine the stability of copper particles produced on the surface of liquid metals, that is, affect whether the "phagocytosis" process can occur. It is revealed that the magnitude of current density on the surface of liquid metals, caused by galvanic corrosion behavior between liquid metals and copper particles, is the key factor influencing the dominance of different surface processes of liquid metals. Utilizing the synergistic effect, we prepare a liquid metal film with adjustable reflectivity, in which surface states can be changed repeatedly between the bright state and the darken state by simple solution immersion. The liquid metal film with different surface states can show obvious difference in optical performance, which has application potential in color camouflage. Understanding the surface synergistic effect will facilitate further exploration of the abundant exotic liquid metal interface phenomena.

Reactive oxygen species in biological media are they friend or foe? Major In vivo and In vitro sensing challenges

The role of Reactive Oxygen Species (ROS) on biological media has been shifting over the years, as the knowledge on the complex mechanism that lies in underneath their production and overall results has been growing. It has been known for some time that these species are associated with a number of health conditions. However, they also participate in the immunoactivation cascade process, and can have an active role in theranostics. Macrophages, for example, react to the presence of pathogens through ROS production, potentially allowing the development of new therapeutic strategies. However, their short lifetime and limited spatial distribution of ROS have been limiting factors to the development and understanding of this phenomenon. Even though, ROS have shown successful theranostic applications, e.g., photodynamic therapy, their wide applicability has been hampered by the lack of effective tools for monitoring these processes in real time. Thus the development of innovative sensing strategies for in vivo monitoring of the balance between ROS concentration and the resultant immune response is of the utmost relevance. Such knowledge could lead to major breakthroughs towards the development of more effective treatments for neurodegenerative diseases. Within this review we will present the current understanding on the interaction mechanisms of ROS with biological systems and their overall effect. Additionally, the most promising sensing tools developed so far, for both in vivo and in vitro tracking will be presented along with their main limitations and advantages. This review focuses on the four main ROS that have been studied these are: singlet oxygen species, hydrogen peroxide, hydroxyl radical and superoxide anion.

Banks of the Superior Temporal Sulcus in Alzheimer's Disease: A Pilot Quantitative Susceptibility Mapping Study

BACKGROUND

Brain iron homeostasis is disrupted in neurodegeneration and areas of iron overload partially overlap with regions of amyloid and tau burden in Alzheimer's disease (AD). Previous studies demonstrated alterations in brain iron accumulation in AD using quantitative susceptibility mapping (QSM).

OBJECTIVE

Here, we investigate brain alterations of QSM values in AD and non-AD patients as compared to healthy controls (HC) in the superior temporal sulcus and its banks (BANKSSTS), one of the top AD-affected regions.

METHODS

Thirty-four patients who underwent brain MRI including a multi-echo gradient-echo sequence were subdivided into AD (n = 19) and non-AD (n = 15) groups according to their clinical profile, CSF (Aβ 42/40) and/or amyloid-PET status. Ten HC were also included. QSM values were extracted from left and right BANKSSTS and compared among groups. Correlation and binomial regression analyses between QSM values and CSF-AD biomarkers were conducted.

RESULTS

QSM in left BANKSSTS was significantly different among groups (p = 0.003, H = 11.40), being higher in AD. QSM values in left BANKSSTS were correlated with Aβ 42 (rho -0.55, p = 0.005), Aβ 42/40 (rho -0.66, p <  0.001), pTau (rho 0.63, p <  0.001), tTau (rho 0.56, p = 0.005), tTau/Aβ 42 (rho 0.68, p <  0.001) and pTau/Aβ 42 (rho 0.71, p <  0.001). No correlations between QSM values and amyloid-PET SUVR in the left BANKSSTS were found. QSM values in left BANKSSTS showed good accuracy in discriminating AD (AUC = 0.80, CI95 % [0.66-0.93]). Higher QSM values were independent predictors of Aβ 42 (B = 0.63, p = 0.032), Aβ 42/40 (B = 0.81, p = 0.028), pTau (B = 0.96, p = 0.046), tTau (B = 0.55, p = 0.027), and tTau/Aβ 42 (B = 1.13, p = 0.042) positivity.

CONCLUSION

Our preliminary data support the potential role of increased QSM values in the left BANKSSTS as an auxiliary imaging biomarker in AD diagnosis.

Rational Design of MMP-Independent Near-Infrared Fluorescent Probes for Accurately Monitoring Mitochondrial Viscosity

Mitochondrial viscosity affects metabolite diffusion and mitochondrial metabolism and is associated with many diseases. However, the accuracy of mitochondria-targeting fluorescent probes in measuring viscosity is unsatisfactory because these probes can diffuse from mitochondria during mitophagy with a decreased mitochondrial membrane potential (MMP). To avoid this problem, by incorporating different alkyl side chains into dihydroxanthene fluorophores (denoted as DHX), we developed six near-infrared (NIR) probes for the accurate detection of mitochondrial viscosity, and the sensitivity to viscosity and the mitochondrial targeting and anchoring capability of these probes increased by increasing the alkyl chain length. Among them, DHX-V-C₁₂ had a highly selective response to viscosity variations with minimum interference from polarity, pH, and other biologically relevant species. Furthermore, DHX-V-C₁₂ was used to monitor the mitochondrial viscosity changes of HeLa cells treated by ionophores (nystatin, monensin) or under starvation conditions. We hope that this mitochondrial targeting and anchoring strategy based on increasing the alkyl chain length will be a general strategy for the accurate detection of mitochondrial analytes, enabling the accurate study of mitochondrial functions.

Peripheral Primitive Neuroectodermal Tumor: A Rare Case in Pediatrics

Primitive neuroectodermal tumors (PNETs) are a type of malignant tumors made up of small neuroectodermal-derived round cells that affect soft tissue and bone, with a wide range of clinical symptoms and histological commonalities depending on the site of the tumor. PNETs account for 4% of all pediatric and adolescent cancers. Here we report a case of a peripheral primitive neuroectodermal tumor in a five-year-old boy. Two days before admission, he complained of multiple attacks of vomiting and one episode of hematemesis, associated with subjective fever, abdominal pain, and distention. He also complained of weight loss and bruises on his face and lower extremities for the last four weeks. Upon physical examination, there was hepatomegaly to the right iliac fossa. Abdominal ultrasound showed that the liver is hugely enlarged with heterogeneous echo texture and smooth borders. A computed tomography scan with contrast showed hepatomegaly to the right iliac fossa region with no focal lesion. Bone marrow aspiration and bone marrow biopsy showed heavy infiltration by monomorphic cells. Moreover, liver biopsy was done for this patient, and it showed metastatic undifferentiated neuroblastoma. Before the liver biopsy results, the patient deteriorated rapidly and dead. Therefore, peripheral primitive neuroectodermal tumors (pPNETs) should be considered in the differential diagnosis of liver masses in young patients to early diagnosis and treatment, and to increase the survival rate.

Fluorescence microscopic platforms imaging mitochondrial abnormalities in neurodegenerative diseases

Neurodegenerative diseases (NDs) are progressive disorders that cause the degeneration of neurons. Mitochondrial dysfunction is a common symptom in NDs and plays a crucial role in neuronal loss. Mitochondrial abnormalities can be observed in the early stages of NDs and evolve throughout disease progression. Visualizing mitochondrial abnormalities can help understand ND progression and develop new therapeutic strategies. Fluorescence microscopy is a powerful tool for dynamically imaging mitochondria due to its high sensitivity and spatiotemporal resolution. This review discusses the relationship between mitochondrial dysfunction and ND progression, potential biomarkers for imaging dysfunctional mitochondria, advances in fluorescence microscopy for detecting organelles, the performance of fluorescence probes in visualizing ND-associated mitochondria, and the challenges and opportunities for developing new generations of fluorescence imaging platforms for monitoring mitochondria in NDs.

A liquid metal-polydopamine composite for cell culture and electro-stimulation

Gallium (Ga) is a low melting point metal in the liquid state in the biological environment which presents a unique combination of fluidity, softness, and metallic electrical and thermal properties. In this work, liquid Ga is proposed as a biocompatible electrode material for cell culture by electro-stimulation since the cytotoxicity of Ga is generally considered low and some Ga compounds have been reported to exhibit anti-bacterial and anti-inflammatory activities. Complementarily, polydopamine (PDA) was coated on liquid Ga to increase the attachment capability of cells on the liquid Ga electrode and provide enhanced biocompatibility. The liquid Ga layer could be readily painted at room temperature on a solid inert substrate, followed by the formation of a nanoscale PDA coating layer resulting in a conformable and biocompatible composite electrode. The PDA layer was shown to coordinate with Ga³⁺, which is sourced from liquid Ga, providing electrical conductivity in the cell culture medium. The PDA-Ga³⁺ composite acted as a conductive substrate for advanced electro-stimulation for cell culture methods of representative animal fibroblasts. The cell proliferation was observed to increase by ∼143% as compared to a standard glass coverslip at a low potential of 0.1 V of direct coupling stimulation. This novel PDA-Ga³⁺ composite has potential applications in cell culture and regenerative medicine.

Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

Distinct CSF biomarker-associated DNA methylation in Alzheimer's disease and cognitively normal subjects

BACKGROUND

Growing evidence has demonstrated that DNA methylation (DNAm) plays an important role in Alzheimer's disease (AD) and that DNAm differences can be detected in the blood of AD subjects. Most studies have correlated blood DNAm with the clinical diagnosis of AD in living individuals. However, as the pathophysiological process of AD can begin many years before the onset of clinical symptoms, there is often disagreement between neuropathology in the brain and clinical phenotypes. Therefore, blood DNAm associated with AD neuropathology, rather than with clinical data, would provide more relevant information on AD pathogenesis.

METHODS

We performed a comprehensive analysis to identify blood DNAm associated with cerebrospinal fluid (CSF) pathological biomarkers for AD. Our study included matched samples of whole blood DNA methylation, CSF Aβ42, phosphorylated tau181 (pTau181), and total tau (tTau) biomarkers data, measured on the same subjects and at the same clinical visits from a total of 202 subjects (123 CN or cognitively normal, 79 AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. To validate our findings, we also examined the association between premortem blood DNAm and postmortem brain neuropathology measured on a group of 69 subjects in the London dataset.

RESULTS

We identified a number of novel associations between blood DNAm and CSF biomarkers, demonstrating that changes in pathological processes in the CSF are reflected in the blood epigenome. Overall, the CSF biomarker-associated DNAm is relatively distinct in CN and AD subjects, highlighting the importance of analyzing omics data measured on cognitively normal subjects (which includes preclinical AD subjects) to identify diagnostic biomarkers, and considering disease stages in the development and testing of AD treatment strategies. Moreover, our analysis revealed biological processes associated with early brain impairment relevant to AD are marked by DNAm in the blood, and blood DNAm at several CpGs in the DMR on HOXA5 gene are associated with pTau181 in the CSF, as well as tau-pathology and DNAm in the brain, nominating DNAm at this locus as a promising candidate AD biomarker.

CONCLUSIONS

Our study provides a valuable resource for future mechanistic and biomarker studies of DNAm in AD.

A novel beneficial role of humanin on intestinal apoptosis and dysmotility in a rat model of ischemia reperfusion injury

A prevalent clinical problem including sepsis, shock, necrotizing enterocolitis, and mesenteric thrombosis is intestinal ischemia/reperfusion (I/R) injury. Humanin (HN), a recently identified mitochondrial polypeptide, exhibits antioxidative and antiapoptotic properties. This work aimed to study the role of HN in a model of experimental intestinal I/R injury and its effect on associated dysmotility. A total of 36 male adult albino rats were allocated into 3 equal groups. Sham group: merely a laparotomy was done. I/R group: for 1 h, clamping of the superior mesenteric artery was done, and then reperfusion was allowed for 2 h later. HN-I/R group: rats underwent ischemia and reperfusion, and 30 min before the reperfusion, they received an intraperitoneal injection of 252 μg/kg of HN. Small intestinal motility was evaluated, and jejunal samples were got for biochemical and histological analysis. I/R group showed elevation of intestinal NO, MDA, TNF- α, and IL-6 and decline of GPx and SOD levels. Furthermore, histologically, there were destructed jejunal villi especially their tips and increased tissue expression of caspase-3 and i-NOS, in addition to reduced small intestinal motility. Compared to I/R group, HN-I/R group exhibited decrease intestinal levels of NO, MDA, TNF- α, and IL-6 and increase GPx and SOD. Moreover, there was noticeable improvement of the histopathologic features and decreased caspase-3 and iNOS immunoreactivity, beside enhanced small intestinal motility. HN alleviates inflammation, apoptosis, and intestinal dysmotility encouraged by I/R. Additionally, I/R-induced apoptosis and motility alterations depend partly on the production of nitric oxide.

The role of venous anatomy in guiding treatment approach for dural arteriovenous fistulas of the craniocervical junction; case series & systematic review

BACKGROUND

Dural arteriovenous fistulas (DAVF) of the craniocervical junction (CCF) are an uncommon entity with the following venous drainage pattern: inferior, superior and mixed. Patients may present with subarachnoid hemorrhage, myelopathy or brainstem dysfunction. CCJ DAVF can be treated with microsurgery or with transarterial and transvenous embolization, depending on the venous drainage pattern. We present our institutional experience of treating CCJ DAVFs along with a systematic review of the literature.

METHODS

Six patients with CCJ DAVF were treated at our institution over five years. Data was collected using electronic medical record review. Systematic review was performed on CCJ DAVF using the PubMed database from 1990 to 2021. We characterized venous drainage patterns, treatment choices, and outcomes to create a classification system.

RESULTS

50 case reports, consisting of 115 patients, were included in our review. 61 (53.0 %) patients had inferior drainage while 32 (27.8 %) patients had superior drainage and 22 (19.2 %) patients had mixed venous drainage. Patients with inferior drainage had the fistulous connection at the foramen magnum while patients with superior drainage had a fistulous connection at C1-C2 (p value = 0.026). Patients with inferior drainage were more likely to present with myelopathy while patients with superior drainage presented with hemorrhage (p value = 0.000).

CONCLUSIONS

Classifying the venous drainage pattern is essential in making treatment decision. Transvenous embolization works best with large superior venous drainage. If endovascular treatment is not an option, then surgical clipping can achieve successful cure. Transarterial embolization is a reasonable option in cases with a large arterial feeder.

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation

Automatic segmentation of coronary arteries provides vital assistance to enable accurate and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task of coronary artery segmentation (CAS) remains highly challenging due to the large-scale variations exhibited by coronary arteries, their complicated anatomical structures and morphologies, as well as the low contrast between vessels and their background. To comprehensively tackle these challenges, we propose a novel multi-attention, multi-scale 3D deep network for CAS, which we call CAS-Net. Specifically, we first propose an attention-guided feature fusion (AGFF) module to efficiently fuse adjacent hierarchical features in the encoding and decoding stages to capture more effectively latent semantic information. Then, we propose a scale-aware feature enhancement (SAFE) module, aiming to dynamically adjust the receptive fields to extract more expressive features effectively, thereby enhancing the feature representation capability of the network. Furthermore, we employ the multi-scale feature aggregation (MSFA) module to learn a more distinctive semantic representation for refining the vessel maps. In addition, considering that the limited training data annotated with a quality golden standard are also a significant factor restricting the development of CAS, we construct a new dataset containing 119 cases consisting of coronary computed tomographic angiography (CCTA) volumes and annotated coronary arteries. Extensive experiments on our self-collected dataset and three publicly available datasets demonstrate that the proposed method has good segmentation performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. Compared with U-Net3D, the proposed method significantly improves the Dice similarity coefficient (DSC) by at least 4% on each dataset, due to the synergistic effect among the three core modules, AGFF, SAFE, and MSFA. Our implementation is released at https://github.com/Cassie-CV/CAS-Net.

Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy

Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. Code is available at https://github.com/ladderlab-xjtu/DyAdapt.

Liquid metal integrated PU/CNT fibrous membrane for human health monitoring

Wearable flexible sensors are widely used in several applications such as physiological monitoring, electronic skin, and telemedicine. Typically, flexible sensors that are made of elastomeric thin-films lack sufficient permeability, which leads to skin inflammation, and more importantly, affects signal detection and consequently, reduces the sensitivity of the sensor. In this study, we designed a flexible nanofibrous membrane with a high air permeability (6.10 mm/s), which could be effectively used to monitor human motion signals and physiological signals. More specifically, a flexible membrane with a point (liquid metal nanoparticles)-line (carbon nanotubes)-plane (liquid metal thin-film) multiscale conductive structure was fabricated by combining liquid metal (LM) and carbon nanotubes (CNTs) with a polyurethane (PU) nanofibrous membrane. Interestingly, the excellent conductivity and fluidity of the liquid metal enhanced the sensitivity and stability of the membrane. More precisely, the gauge factor (GF) values of the membrane is 3.0 at 50% strain and 14.0 at 400% strain, which corresponds to a high strain sensitivity within the whole range of deformation. Additionally, the proposed membrane has good mechanical properties with an elongation at a break of 490% and a tensile strength of 12 MPa. Furthermore, the flexible membrane exhibits good biocompatibility and can efficiently monitor human health signals, thereby indicating potential for application in the field of wearable electronic devices.

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction

Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks.

Methods

We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph).

Results

The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing.

Discussion

Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git.

CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study

Purpose

This study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC).

Methods

This retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan–Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness.

Results

Overall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model.

Conclusion

For patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.

Exploring Whether Iron Sequestration within the CNS of Patients with Alzheimer’s Disease Causes a Functional Iron Deficiency That Advances Neurodegeneration

The involvement of iron in the pathogenesis of Alzheimer’s disease (AD) may be multifaceted. Besides potentially inducing oxidative damage, the bioavailability of iron may be limited within the central nervous system, creating a functionally iron-deficient state. By comparing staining results from baseline and modified iron histochemical protocols, iron was found to be more tightly bound within cortical sections from patients with high levels of AD pathology compared to subjects with a diagnosis of something other than AD. To begin examining whether the bound iron could cause a functional iron deficiency, a protein-coding gene expression dataset of initial, middle, and advanced stages of AD from olfactory bulb tissue was analyzed for iron-related processes with an emphasis on anemia-related changes in initial AD to capture early pathogenic events. Indeed, anemia-related processes had statistically significant alterations, and the significance of these changes exceeded those for AD-related processes. Other changes in patients with initial AD included the expressions of transcripts with iron-responsive elements and for genes encoding proteins for iron transport and mitochondrial-related processes. In the latter category, there was a decreased expression for the gene encoding pitrilysin metallopeptidase 1 (PITRM1). Other studies have shown that PITRM1 has an altered activity in patients with AD and is associated with pathological changes in this disease. Analysis of a gene expression dataset from PITRM1-deficient or sufficient organoids also revealed statistically significant changes in anemia-like processes. These findings, together with supporting evidence from the literature, raise the possibility that a pathogenic mechanism of AD could be a functional deficiency of iron contributing to neurodegeneration.

Natural Polymeric Composites Derived from Animals, Plants, and Microbes for Vaccine Delivery and Adjuvant Applications: A Review

A key element in ensuring successful immunization is the efficient delivery of vaccines. However, poor immunogenicity and adverse inflammatory immunogenic reactions make the establishment of an efficient vaccine delivery method a challenging task. The delivery of vaccines has been performed via a variety of delivery methods, including natural-polymer-based carriers that are relatively biocompatible and have low toxicity. The incorporation of adjuvants or antigens into biomaterial-based immunizations has demonstrated better immune response than formulations that just contain the antigen. This system may enable antigen-mediated immunogenicity and shelter and transport the cargo vaccine or antigen to the appropriate target organ. In this regard, this work reviews the recent applications of natural polymer composites from different sources, such as animals, plants, and microbes, in vaccine delivery systems.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Dual-drug codelivery nanosystems: An emerging approach for overcoming cancer multidrug resistance

Multidrug resistance (MDR) promotes tumor recurrence and metastasis and heavily reduces anticancer efficiency, which has become a primary reason for the failure of clinical chemotherapy. The mechanisms of MDR are so complex that conventional chemotherapy usually fails to achieve an ideal therapeutic effect and even accelerates the occurrence of MDR. In contrast, the combination of chemotherapy with dual-drug has significant advantages in tumor therapy. A novel dual-drug codelivery nanosystem, which combines dual-drug administration with nanotechnology, can overcome the application limitation of free drugs. Both the characteristics of nanoparticles and the synergistic effect of dual drugs contribute to circumventing various drug-resistant mechanisms in tumor cells. Therefore, developing dual-drug codelivery nanosystems with different multidrug-resistant mechanisms has an important reference value for reversing MDR and enhancing the clinical antitumor effect. In this review, the advantages, principles, and common codelivery nanocarriers in the application of dual-drug codelivery systems are summarized. The molecular mechanisms of MDR and the dual-drug codelivery nanosystems designed based on different mechanisms are mainly introduced. Meanwhile, the development prospects and challenges of codelivery nanosystems are also discussed, which provide guidelines to exploit optimized combined chemotherapy strategies in the future.

The signal intensity variation of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) as a potential biomarker for patients' disability: A feasibility study

Introduction

Although many lesion-based MRI biomarkers in multiple sclerosis (MS) patients were investigated, none of the previous studies dealt with the signal intensity variations (SIVs) of MS lesions. In this study, the SIVs of MS lesions on direct myelin imaging and standard clinical sequences as possible MRI biomarkers for disability in MS patients were assessed.

Methods

Twenty seven MS patients were included in this prospective study. IR-UTE, FLAIR, and MPRAGE sequences were employed on a 3T scanner. Regions of interest (ROIs) were manually drawn within the MS lesions, and the cerebrospinal fluid (CSF) and signal intensity ratios (SIR) were calculated from the derived values. Variations coefficients were determined from the standard deviations (Coeff 1) and the absolute differences (Coeff 2) of the SIRs. Disability grade was assessed by the expanded disability status scale (EDSS). Cortical/gray matter, subcortical, infratentorial, and spinal lesions were excluded.

Results

The mean diameter of the lesions was 7.8 ± 1.97 mm, while the mean EDSS score was 4.5 ± 1.73. We found moderate correlations between the EDSS and Coeff 1 and 2 on IR-UTE and MPRAGE images. Accordingly, Pearson's correlations on IR-UTE were R = 0.51 (p = 0.007) and R = 0.49 (p = 0.01) for Coeff 1 and 2, respectively. For MPRAGE, Pearson's correlations were R = 0.5 (p = 0.008) and R = 0.48 (p = 0.012) for Coeff 1 and 2, respectively. For FLAIR, only poor correlations could be found.

Conclusion

The SIVs of MS lesions on IR-UTE and MPRAGE images, assessed by Coeff 1 and 2, could be used as novel potential MRI biomarkers for patients' disability.

Dynamic multi-site graph convolutional network for autism spectrum disorder identification

Multi-site learning has attracted increasing interests in autism spectrum disorder (ASD) identification tasks by its efficacy on capturing data heterogeneity of neuroimaging taken from different medical sites. However, existing multi-site graph convolutional network (MSGCN) often ignores the correlations between different sites, and may obtain suboptimal identification results. Moreover, current feature extraction methods characterizing temporal variations of functional magnetic resonance imaging (fMRI) signals require the time series to be of the same length and cannot be directly applied to multi-site fMRI datasets. To address these problems, we propose a dual graph based dynamic multi-site graph convolutional network (DG-DMSGCN) for multi-site ASD identification. First, a sliding-window dual-graph convolutional network (SW-DGCN) is introduced for feature extraction, simultaneously capturing temporal and spatial features of fMRI data with different series lengths. Then we aggregate the features extracted from multiple medical sites through a novel dynamic multi-site graph convolutional network (DMSGCN), which effectively considers the correlations between different sites and is beneficial to improve identification performance. We evaluate the proposed DG-DMSGCN on public ABIDE I dataset containing data from 17 medical sites. The promising results obtained by our framework outperforms the state-of-the-art methods with increase in identification accuracy, indicating that it has a potential clinical prospect for practical ASD diagnosis. Our codes are available on https://github.com/Junling-Du/DG-DMSGCN.

Brain White Matter Structural Alteration in Hemifacial Spasm: A Diffusion Tensor Imaging Study

OBJECTIVE

To analyze the changes in the white matter structure of the whole brain in hemifacial spasm (HFS) patients by using the tract-based spatial statistics (TBSS) method.

MATERIALS AND METHODS

29 HFS patients without anxiety and depression and 29 healthy controls with matching age, sex, and education were selected. All subjects received a 3.0T magnetic resonance (MR) brain diffusion tensor imaging scan. Tract-based spatial statistics method was used to analyze the changes in white matter structure in the whole brain and obtained the cerebral white matter fibrous areas exhibiting significant intergroup differences. The fractional anisotropy (FA), mean diffusivity, axial diffusivity, and radial diffusivity of these areas were abstracted. Analyzed the correlation between these diffusion metrics and clinical variables (disease duration, spasm severity).

RESULTS

Compared with the healthy controls group, the HFS group exhibited significantly lower FA in the forceps minor, bilateral anterior thalamic radiation, and right superior longitudinal fasciculus ( P <0.05, threshold-free cluster enhancement corrected). Cohen grading scale of HFS patients was negatively correlated with FA of forceps minor.

CONCLUSION

Based on TBSS analysis, the injury of white matter fiber tracts in HFS patients was found, including forceps minor, bilateral anterior thalamic radiation, and right superior longitudinal fasciculus. The changes of FA values in forceps minor were negatively correlated with the Cohen grading scale, suggesting that the alteration of white matter fiber in the genu-of-corpus-callosum-cortex circuit plays an important role in the neuro-pathological mechanism of HFS. Combined with previous research, it is also necessary to further explore the change of the superior longitudinal fasciculus in the future.

Actively Triggerable Metals via Liquid Metal Embrittlement for Biomedical Applications

Actively triggerable materials, which break down upon introduction of an exogenous stimulus, enable precise control over the lifetime of biomedical technologies, as well as adaptation to unforeseen circumstances, such as changes to an established treatment plan. Yet, most actively triggerable materials are low-strength polymers and hydrogels with limited long-term durability. By contrast, metals possess advantageous functional properties, including high mechanical strength and conductivity, that are desirable across several applications within biomedicine. To realize actively triggerable metals, a mechanism called liquid metal embrittlement is leveraged, in which certain liquid metals penetrate the grain boundaries of certain solid metals and cause them to dramatically weaken or disintegrate. In this work, it is demonstrated that eutectic gallium indium (EGaIn), a biocompatible alloy of gallium, can be formulated to reproducibly trigger the breakdown of aluminum within different physiologically relevant environments. The breakdown behavior of aluminum after triggering can further be readily controlled by manipulating its grain structure. Finally, three possible use cases of biomedical devices constructed from actively triggerable metals are demonstrated.

Can quantitative parameters of spectral computed tomography predict lymphatic metastasis in lung cancer? A systematic review and meta-analysis

BACKGROUND AND PURPOSE

This study evaluated the use of quantitative spectral computed tomography (CT) parameters to identify lymph node metastasis (LM) in lung cancer.

MATERIALS AND METHODS

Literature about LM in lung cancer diagnosed using spectral CT up to September 2022 was retrieved from the PubMed, EMBASE, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure, and Wanfang databases. The literature was strictly screened according to the inclusion and exclusion criteria. Data were extracted, quality assessment was performed, and heterogeneity was evaluated. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (+LR), -LR, and diagnostic odds ratio (DOR) for normalized iodine concentration (NIC) and spectral attenuation curve (λHU) were calculated. The subject receiver operating characteristic (SROC) curves were used, and the area under the curve (AUC) was calculated.

RESULTS

Eleven studies, including 1,290 cases, without obvious publication bias were enrolled. In eight articles, the pooled AUC of NIC in the arterial phase (AP) was 0.84 (SEN=0.85, SPE=0.74, +LR=3.3, -LR=0.20, DOR=16) while that of NIC in the venous phase (VP) was 0.82 (SEN=0.78, SPE=0.72). Additionally, the pooled AUC for λHU (AP) was 0.87 (SEN=0.74, SPE=0.84, +LR=4.5, -LR=0.31, DOR=15) and that for λHU (VP) was 0.81 (SEN=0.62, SPE=0.81). Lymph node (LN) short-axis diameter was ranked last, with a pooled AUC of 0.81 (SEN=0.69, SPE=0.79).

CONCLUSION

Spectral CT is a suitable noninvasive and cost-effective method for determining LM in lung cancer. Additionally, NIC and λHU in the AP have good discrimination ability than short-axis diameter, providing a valuable basis and reference for preoperative evaluation. (registration number INPLASY202290096).

Carbon monoxide therapy: a promising strategy for cancer

Cancer is one of the acute life-threatening diseases endangering the whole of humanity. The treatment modalities for cancer are various. However, in most cases, a single treatment choice provides multiple side effects, poor targeting, and ineffective treatment. In recent years, the physiological regulatory function of carbon monoxide (CO) in the cancer process has been reported gradually, and CO-related nano-drugs have been explored. It shows better application prospects in cancer treatment and provides new ideas for treatment. The present review introduces the pathophysiological role of CO. The recent advances in cancer therapy, such as CO-mediated gas therapy, combined application of CO chemotherapy, photodynamic therapy (PDT), photothermal therapy (PTT), and immunotherapy, are described. Current challenges and future developments in CO-based treatment are also discussed. This review provides comprehensive information on recent advances in CO therapy and also some valuable guidance for promoting the progress of gas therapy nanomedicine.

Molecular insights of Hippo signaling in the chick developing lung

Hippo signaling pathway and its effector YAP have been recognized as an essential growth regulator during embryonic development. Hippo has been studied in different contexts; nevertheless, its role during chick lung branching morphogenesis remains unknown. Therefore, this work aims to determine Hippo role during early pulmonary organogenesis in the avian animal model. The current study describes the spatial distribution of Hippo signaling members in the embryonic chick lung by in situ hybridization. Overall, their expression is comparable to their mammalian counterparts. Moreover, the expression levels of phosphorylated-YAP (pYAP) and total YAP revealed that Hippo signaling is active in the embryonic chick lung. Furthermore, the presence of pYAP in the cytoplasm demonstrated that the Hippo machinery distribution is maintained in this tissue. In vitro studies were performed to assess the role of the Hippo signaling pathway in lung branching. Lung explants treated with a YAP/TEAD complex inhibitor (verteporfin) displayed a significant reduction in lung size and branching and decreased expression of ctgf (Hippo target gene) compared to the control. This approach also revealed that Hippo seems to modulate the expression of key molecular players involved in lung branching morphogenesis (sox2, sox9, axin2, and gli1). Conversely, when treated with dobutamine, an upstream regulator that promotes YAP phosphorylation, explant morphology was not severely affected. Overall, our data indicate that Hippo machinery is present and active in the early stages of avian pulmonary branching and that YAP is likely involved in the regulation of lung growth.

Distinct CSF biomarker-associated DNA methylation in Alzheimer's disease and cognitively normal subjects

Background Growing evidence has demonstrated that DNA methylation (DNAm) plays an important role in Alzheimer's disease (AD) and that DNAm differences can be detected in the blood of AD subjects. Most studies have correlated blood DNAm with the clinical diagnosis of AD in living individuals. However, as the pathophysiological process of AD can begin many years before the onset of clinical symptoms, there is often disagreement between neuropathology in the brain and clinical phenotypes. Therefore, blood DNAm associated with AD neuropathology, rather than with clinical data, would provide more relevant information on AD pathogenesis. Methods We performed a comprehensive analysis to identify blood DNAm associated with cerebrospinal fluid (CSF) pathological biomarkers for AD. Our study included matched samples of whole blood DNA methylation, CSF Aβ 42 , phosphorylated tau 181 (pTau 181 ), and total tau (tTau) biomarkers data, measured on the same subjects and at the same clinical visits from a total of 202 subjects (123 CN or cognitively normal, 79 AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. To validate our findings, we also examined the association between premortem blood DNAm and postmortem brain neuropathology measured on a group of 69 subjects in the London dataset. Results We identified a number of novel associations between blood DNAm and CSF biomarkers, demonstrating that changes in pathological processes in the CSF are reflected in the blood epigenome. Overall, the CSF biomarker-associated DNAm is relatively distinct in CN and AD subjects, highlighting the importance of analyzing omics data measured on cognitively normal subjects (which includes preclinical AD subjects) to identify diagnostic biomarkers, and considering disease stages in the development and testing of AD treatment strategies. Moreover, our analysis revealed biological processes associated with early brain impairment relevant to AD are marked by DNAm in the blood, and blood DNAm at several CpGs in the DMR on HOXA5 gene are associated with pTau 181 in the CSF, as well as tau-pathology and DNAm in the brain, nominating DNAm at this locus as a promising candidate AD biomarker. Conclusions Our study provides a valuable resource for future mechanistic and biomarker studies of DNAm in AD.

Cerebro-cerebellar functional neuroplasticity mediates the effect of electric field on electroconvulsive therapy outcomes

Electroconvulsive therapy (ECT) is the most effective treatment for severe depression and works by applying an electric current through the brain. The applied current generates an electric field (E-field) and seizure activity, changing the brain's functional organization. The E-field, which is determined by electrode placement (right unilateral or bitemporal) and pulse amplitude (600, 700, or 800 milliamperes), is associated with the ECT response. However, the neural mechanisms underlying the relationship between E-field, functional brain changes, and clinical outcomes of ECT are not well understood. Here, we investigated the relationships between whole-brain E-field (Ebrain, the 90th percentile of E-field magnitude in the brain), cerebro-cerebellar functional network connectivity (FNC), and clinical outcomes (cognitive performance and depression severity). A fully automated independent component analysis framework determined the FNC between the cerebro-cerebellar networks. We found a linear relationship between Ebrain and cognitive outcomes. The mediation analysis showed that the cerebellum to middle occipital gyrus (MOG)/posterior cingulate cortex (PCC) FNC mediated the effects of Ebrain on cognitive performance. In addition, there is a mediation effect through the cerebellum to parietal lobule FNC between Ebrain and antidepressant outcomes. The pair-wise t-tests further demonstrated that a larger Ebrain was associated with increased FNC between cerebellum and MOG and decreased FNC between cerebellum and PCC, which were linked with decreased cognitive performance. This study implies that an optimal E-field balancing the antidepressant and cognitive outcomes should be considered in relation to cerebro-cerebellar functional neuroplasticity.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network

For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.

Multi-task deep learning for medical image computing and analysis: A review

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Mapping the myelin bilayer with short-T2 MRI: Methods validation and reference data for healthy human brain

PURPOSE

To explore the properties of short-T₂ signals in human brain, investigate the impact of various experimental procedures on these properties and evaluate the performance of three-component analysis.

METHODS

Eight samples of non-pathological human brain tissue were subjected to different combinations of experimental procedures including D₂ O exchange and frozen storage. Short-T₂ imaging techniques were employed to acquire multi-TE (33-2067 μs) data, to which a three-component complex model was fitted in two steps to recover the properties of the underlying signal components and produce amplitude maps of each component. For validation of the component amplitude maps, the samples underwent immunohistochemical myelin staining.

RESULTS

The signal component representing the myelin bilayer exhibited super-exponential decay with T_2,min of 5.48 μs and a chemical shift of 1.07 ppm, and its amplitude could be successfully mapped in both white and gray matter in all samples. These myelin maps corresponded well to myelin-stained tissue sections. Gray matter signals exhibited somewhat different components than white matter signals, but both tissue types were well represented by the signal model. Frozen tissue storage did not alter the signal components but influenced component amplitudes. D₂ O exchange was necessary to characterize the non-aqueous signal components, but component amplitude mapping could be reliably performed also in the presence of H₂ O signals.

CONCLUSIONS

The myelin mapping approach explored here produced reasonable and stable results for all samples. The extensive tissue and methodological investigations performed in this work form a basis for signal interpretation in future studies both ex vivo and in vivo.