Multiple Human Population Movements and Cultural Dispersal Events Shaped the Landscape of Chinese Paternal Heritage.

Large-scale genomic projects and ancient DNA innovations have ushered in a new paradigm for exploring human evolutionary history. However, the genetic legacy of spatiotemporally diverse ancient Eurasians within Chinese paternal lineages remains unresolved. Here, we report an integrated Y-chromosome genomic database encompassing 15,563 individuals from both modern and ancient Eurasians, including 919 newly reported individuals, to investigate the Chinese paternal genomic diversity. The high-resolution, time-stamped phylogeny reveals multiple diversification events and extensive expansions in the early and middle Neolithic. We identify four major ancient population movements, each associated with technological innovations that have shaped the Chinese paternal landscape. First, the expansion of early East Asians and millet farmers from the Yellow River Basin predominantly carrying O2/D subclades significantly influenced the formation of the Sino-Tibetan people and facilitated the permanent settlement of the Tibetan Plateau. Second, the dispersal of rice farmers from the Yangtze River Valley carrying O1 and certain O2 sublineages reshapes the genetic makeup of southern Han Chinese, as well as the Tai-Kadai, Austronesian, Hmong-Mien, and Austroasiatic people. Third, the Neolithic Siberian Q/C paternal lineages originated and proliferated among hunter-gatherers on the Mongolian Plateau and the Amur River Basin, leaving a significant imprint on the gene pools of northern China. Fourth, the J/G/R paternal lineages derived from western Eurasia, which were initially spread by Yamnaya-related steppe pastoralists, maintain their presence primarily in northwestern China. Overall, our research provides comprehensive genetic evidence elucidating the significant impact of interactions with culturally distinct ancient Eurasians on the patterns of paternal diversity in modern Chinese populations.

Sub-millimeter resolution and high-precision φ-OFDR using a complex-domain denoising method.

Phase noise is one of the main obstacles to achieve high spatial resolution, high precision, and large measurement range in φ-OFDR. Here, we proposed a complex-domain denoising method to achieve unwrapping of phase signals. In this method, the wrapped phase was used to construct a complex signal, and then both real and imaginary parts are denoised by using a wavelet packet. The two sets of denoised signals are reconstructed into a complex form, allowing to obtain an unwrapped phase. Additionally, the spatial position correction algorithm addresses the phase decoherence from strain accumulation. Finally, a high numerical aperture optical fiber is used to enhance the Rayleigh scattering intensity by 15 dB. The comprehensive approach yields remarkable results: a sensing resolution of 0.89 mm, a root mean square error of 1.5 µÎµ, and a maximum strain sensing capability of 2050 µÎµ.

A Rivest-Shamir-Adleman-Based Robust and Effective Three-Factor User Authentication Protocol for Healthcare Use in Wireless Body Area Networks.

In healthcare, wireless body area networks (WBANs) can be used to constantly collect patient body data and assist in real-time medical services for patients from physicians. In such security- and privacy-critical systems, the user authentication mechanism can be fundamentally expected to prevent illegal access and privacy leakage occurrences issued by hacker intrusion. Currently, a significant quantity of new WBAN-oriented authentication protocols have been designed to verify user identity and ensure that body data are accessed only with a session key. However, those newly published protocols still unavoidably affect session key security and user privacy due to the lack of forward secrecy, mutual authentication, user anonymity, etc. To solve this problem, this paper designs a robust user authentication protocol. By checking the integrity of the message sent by the other party, the communication entity verifies the other party's identity validity. Compared with existing protocols, the presented protocol enhances security and privacy while maintaining the efficiency of computation.

Unveiling 2,000 years of differentiation among Tungusic-speaking populations: a revised phylogeny of the paternal founder lineage C2a-M48-SK1061.

Previous studies demonstrated Y chromosome haplogroup C2a-M48-SK1061 is the only founding paternal lineage of all Tungusic-speaking populations. To infer the differentiation history of these populations, we studied more sequences and constructed downstream structure of haplogroup C2a-M48-SK1061 with better resolution. In this study, we generated 100 new sequences and co-analyzed 140 sequences of C2a-M48-SK1061 to reconstruct a highly revised phylogenetic tree with age estimates. We also performed the analysis of the geographical distribution and spatial autocorrelation of sub-branches. Dozens of new sub-branches were discovered, many sub-branches were nearly unique for Ewenki, Evens, Oroqen, Xibe, Manchu, Daur, and Mongolian. The topology of these unique sub-branches is the key evidence for understanding the complex evolutionary relationship between different Tungusic-speaking populations. The revised phylogeny provided a clear pattern for the differentiation history of haplogroup C2a-M48-SK1061 in the past 2,000 years. This study showed that the divergence pattern of founder lineage is essential to understanding the differentiation history of populations.

Modulation of two-dimensional palladium nanozyme activity to enhance chemodynamic/photothermal combined therapy for melanoma.

Nanozymes are artificial enzymes that mimic natural enzyme-like activities and exhibit tremendous potential for tumor chemodynamic therapy. However, the development of novel nanozymes with superior catalytic activities for nanotheranostics remains a formidable challenge. Herein, we report a facile synthesis of monodisperse palladium nanosheets (Pd nanosheets) and their assembly on graphene oxide (GO) that enhances the catalytic activities of Pd nanoparticles. Simultaneously, the obtained nanocomposites (rGO-Pd) could be applied as a smart near-infrared (NIR) light-responsive nanotheranostic for near infrared imaging-guided chemodynamic/photothermal combined therapy. Notably, rGO-Pd exhibited high peroxidase mimicking activities, which could catalyze the conversion of intratumoral H2O2 to ˙OH. Impressively, the reactive oxygen species (ROS) generation of rGO-Pd was further remarkably enhanced by the endogenous acidity of the tumor microenvironment and the exogenous NIR light-responsive photothermal effect. These collective properties of the rGO-Pd nanozyme enabled it to be a ROS generation accelerator for photothermally enhanced tumor chemodynamic therapy. Thus, the as-developed rGO-Pd may represent a promising new type of high-performance nanozyme for multifunctional nanotheranostics toward cancer.

The formation of proto-austronesians: insights from a revised phylogeography of the paternal founder lineage.

OBJECTIVES: Previous studies suggested that the Y-chromosome haplogroups O2-N6-B451-AM01756 and O1a-M119 are two founder lineages of proto-Austronesians at about five thousand years ago. The objective of this study was to investigate the formation of proto-Austronesians from the perspective of the paternal gene pool. MATERIALS AND METHODS: In this study, we developed a highly evised phylogenetic tree with age estimates for haplogroup O2-N6 and early branches of O1a-M119 (M110, F1036, and F819). In addition, we also explored the geographical distribution of eight sub-branches of O2-N6 and O1a-M119, and spatial autocorrelation analysis was conducted for each sub-branch. RESULTS: The paternal lineage combination of proto-Austronesians is a small subset of a diverse gene pool of populations from the mainland of East Asia. The distribution map and results of the spatial autocorrelation analysis suggested that the eastern coastal region of northern China is likely the source of lineage O2-N6 while the coastal region of southeastern China is likely the diffusion center of early branches of O1a-M119. We developed an evolutionary diagram for Austronesians and their ancestors in the past 18,000 years. DISCUSSION: We proposed that the millet farming community in North China is the common ancestor group of the Austronesians and the Han people, while the diverse ancient people in the southeast coastal areas of East Asia form the common ancestor group of the Austronesians and the East Asian mainland population. The demographic history of multiple ancestral groups of the most recent common ancestor group itself in the more ancient period is helpful to understand the deep roots of the genetic components and cultural traditions of Austronesians.

Mitogenome evidence shows two radiation events and dispersals of matrilineal ancestry from northern coastal China to the Americas and Japan.

Although it is widely recognized that the ancestors of Native Americans (NAs) primarily came from Siberia, the link between mitochondrial DNA (mtDNA) lineage D4h3a (typical of NAs) and D4h3b (found so far only in East China and Thailand) raises the possibility that the ancestral sources for early NAs were more variegated than hypothesized. Here, we analyze 216 contemporary (including 106 newly sequenced) D4h mitogenomes and 39 previously reported ancient D4h data. The results reveal two radiation events of D4h in northern coastal China, one during the Last Glacial Maximum and the other within the last deglaciation, which facilitated the dispersals of D4h sub-branches to different areas including the Americas and the Japanese archipelago. The coastal distributions of the NA (D4h3a) and Japanese lineages (D4h1a and D4h2), in combination with the Paleolithic archaeological similarities among Northern China, the Americas, and Japan, lend support to the coastal dispersal scenario of early NAs.

Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis.

Background: The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. Objectives: We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. Design: A multicenter, diagnostic retrospective study. Methods: We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former's generalization performance. Results: On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. Conclusions: The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. Registration: This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773).

Why was this study done? The development of an auxiliary diagnostic tool can reduce the workload of endoscopists and achieve rapid assessment of ulcerative colitis (UC) severity. What did the researchers do? We developed and validated a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. What did the researchers find? The model that was developed in this study achieved high accuracy, fidelity, and stability, and demonstrated potential application in clinical practice. What do the findings mean? Deep learning could effectively assist endoscopists in evaluating the severity of UC in patients using endoscopic images.

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron.

We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system.

The impacts of bronze age in the gene pool of Chinese: Insights from phylogeographics of Y-chromosomal haplogroup N1a2a-F1101.

Objectives: Previous studies of archaeology and history suggested that the rise and prosperity of Bronze Age culture in East Asia had made essential contribution to the formation of early state and civilization in this region. However, the impacts in perspective of genetics remain ambiguous. Previous genetic researches indicated the Y-chromosome Q1a1a-M120 and N1a2a-F1101 may be the two most important paternal lineages among the Bronze Age people in ancient northwest China. Here, we investigated the 9,000-years history of haplogroup N1a2a-F1101 with revised phylogenetic tree and spatial autocorrelation analysis. Materials and Methods: In this study, 229 sequences of N1a2a-F1101 were analyzed. We developed a highly-revised phylogenetic tree with age estimates for N1a2a-F1101. In addition, we also explored the geographical distribution of sub-lineages of N1a2a-F1101, and spatial autocorrelation analysis was conducted for each sub-branch. Results: The initial differentiation location of N1a2a-F1101 and its most closely related branch, N1a2b-P43, a major lineage of Uralic-speaking populations in northern Eurasia, is likely the west part of northeast China. After ~4 thousand years of bottleneck effect period, haplgroup N1a2a-F1101 experienced continuous expansion during the Chalcolithic age (~ 4.5 kya to 4 kya) and Bronze age (~ 4 kya to 2.5 kya) in northern China. Ancient DNA evidence supported that this haplogroup is the lineage of ruling family of Zhou Dynasty (~ 3 kya-2.2 kya) of ancient China. Discussion: In general, we proposed that the Bronze Age people in the border area between the eastern Eurasian steppe and northern China not only played a key role in promoting the early state and civilization of China, but also left significant traces in the gene pool of Chinese people.

Cladding mode characteristics simulation of an excessively tilted fiber grating coated with gold nanoshells.

The cladding mode characteristics simulation of an excessively tilted fiber grating (ExTFG) coated with gold nanoshells was conducted in this study. First, the effective refractive indices of the core and cladding mode before coating were obtained by solving the eigenvalue equation of the three-layer waveguide structure, and the coupling characteristics were briefly analyzed. Then HE1,m and EH1,m modes were selected as the research objects, and the spectral characteristics of ExTFG coated with gold nanoshells were simulated by the finite element method. The simulated refractive index sensitivity of HE1,29 and EH1,29 modes is 160.16 and 185.03 nm/RIU, respectively. Compared with the non-localized surface plasmon resonance (LSPR) effect, it increased by 10.76 nm/RIU (7.2%) and 19.53 nm/RIU (11.8%), respectively. Thus, the LSPR effect was verified to be beneficial to improve the refractive index sensitivity of ExTFG.

Intrinsically Bioactive Manganese-Eumelanin Nanocomposites Mediated Antioxidation and Anti-Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury.

Overproduced reactive oxygen species and the induced oxidative stress and neuroinflammation often result in secondary injury, which is associated with unfavorable prognosis in traumatic brain injury (TBI). Unfortunately, current medications cannot effectively ameliorate the secondary injury at traumatic sites. Here, it is reported that intrinsically bioactive multifunctional nanocomposites (ANG-MnEMNPs-Cur, AMEC) mediate antioxidation and anti-neuroinflammation for targeted TBI theranostics, which are engineered by loading the neuroprotective agent curcumin on angiopep-2 functionalized and manganese doped eumelanin-like nanoparticles. After intravenous delivery, efficient AMEC accumulation is observed in lesions of TBI mice models established by controlled cortical impact method, evidenced by T1 -T2 magnetic resonance and photoacoustic dual-modal imaging. Therapeutically, AMEC effectively alleviates neuroinflammation, protects blood-brain barrier integrity, relieves brain edema, reduces brain tissue loss, and improves the cognition of TBI mice. Mechanistically, following the penetration into the traumatic tissues via angiopep-2 mediated targeting effect, the efficacy of AMEC is synergistically improved by combined functional moieties of curcumin and eumelanin. This is achieved by the alleviation of oxidative stress, inhibition of neuroinflammation via M1-to-M2 macrophage reprogramming, and promotion of neuronal regeneration. The as-developed AMEC with well-defined mechanisms of action may represent a promising targeted theranostics strategy for TBI and other neuroinflammation-associated intracranial diseases.

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study.

Background: The tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual's TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using 18F-FDG PET/CT radiomics and clinical characteristics. Methods: The RNA-seq data of 1145 NSCLC patients from The Cancer Genome Atlas (TCGA) cohort were analyzed. Then, 221 NSCLC patients from Daping Hospital (DPH) cohort received18F-FDG PET/CT scans before treatment and CD8 expression of the tumor samples were tested. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images and develop a radiomics signature. The models were established by radiomics, clinical features, and radiomics-clinical combination, respectively, the performance of which was calculated by receiver operating curves (ROCs) and compared by DeLong test. Moreover, based on radiomics score (Rad-score) and clinical features, a nomogram was established. Finally, we applied the combined model to evaluate TIME phenotypes of NSCLC patients in The Cancer Imaging Archive (TCIA) cohort (n = 39). Results: TCGA data showed CD8 expression could represent the TIME profiles in NSCLC. In DPH cohort, PET/CT radiomics model outperformed CT model (AUC: 0.907 vs. 0.861, P = 0.0314) to predict CD8 expression. Further, PET/CT radiomics-clinical combined model (AUC = 0.932) outperformed PET/CT radiomics model (AUC = 0.907, P = 0.0326) or clinical model (AUC = 0.868, P = 0.0036) to predict CD8 expression. In the TCIA cohort, the predicted CD8-high group had significantly higher immune scores and more activated immune pathways than the predicted CD8-low group (P = 0.0421). Conclusion: Our study indicates that 18F-FDG PET/CT radiomics-clinical combined model could be a clinically practical method to non-invasively detect the tumor immune status in NSCLCs.

MicorRNA-195 links long non-coding RNA SEMA3B antisense RNA 1 (head to head) and cyclin D1 to regulate the proliferation of glioblastoma cells.

Long non-coding RNA (lncRNA) SEMA3B antisense RNA 1 (head to head) (SEMA3B-AS1) is a recently identified tumor suppressor in gastric cancer. However, its role in glioblastoma (GBM) is unclear. This study was conducted to explore the role of SEMA3B-AS1 in GBM. In this study, the expression of SEMA3B-AS1, cyclin D1 and miR-195 were determined by RT-qPCR. Gene interactions were evaluated by dual-luciferase assay and overexpression experiments. BrdU assay was performed to monitor cell proliferation. We observed downregulation of SEMA3B-AS1 in GBM. The expression of SEMA3B-AS1 was inversely correlated with the expression of cyclin D1 but positively correlated with the expression of miR-195. In GBM cells, overexpression of SEMA3B-AS1 and miR-195 caused reduced expression levels of cyclin D1. MiR-195 inhibitor reduced the effects of overexpression of SEMA3B-AS1 on the expression of cyclin D1. Moreover, overexpression of SEMA3B-AS1 increased the expression levels of miR-195. Cell proliferation data showed that, SEMA3B-AS1 and miR-195 decreased cell proliferation, while overexpression of cyclin D1 increased GBM cell proliferation. In addition, miR-195 inhibitor inhibited the role of overexpression of SEMA3B-AS1 in cancer cell proliferation. Moreover, miR-195 interacted with cyclin D1, but not SEMA3B-AS1. Furthermore, SEMA3B-AS1 decreased the methylation of the promoter region of miR-195. Therefore, we concluded that miR-195 links lncRNA SEMA3B-AS1 and cyclin D1 to regulate the proliferation of GBM cells.

The Role of Gut Microbiota and Genetic Susceptibility in the Pathogenesis of Pancreatitis.

Pancreatitis is one of the most common inflammatory diseases of the pancreas caused by autodigestion induced by excessive premature protease activation. However, recognition of novel pathophysiological mechanisms remains a still challenge. Both genetic and environmental factors contribute to the pathogenesis of pancreatitis, and the gut microbiota is a potential source of an environmental effect. In recent years, several new frontiers in gut microbiota and genetic risk assessment research have emerged and improved the understanding of the disease. These investigations showed that the disease progression of pancreatitis could be regulated by the gut microbiome, either through a translocation influence or in a host immune response manner. Meanwhile, the onset of the disease is also associated with the heritage of a pathogenic mutation, and the disease progression could be modified by genetic risk factors. In this review, we focused on the recent advances in the role of gut microbiota in the pathogenesis of pancreatitis, and the genetic susceptibility in pancreatitis.

Highly sensitive vibration sensor based on the dispersion turning point microfiber Mach-Zehnder interferometer.

In the present work, we introduced a highly sensitive vibration sensor, which is based on the dispersion turning point (DTP) microfiber Mach-Zehnder interferometer. The axial strain and vibration sensing characteristics of the microfiber Mach-Zehnder interferometer were investigated. First, we theoretically analyzed the spectrum evolution characteristics of the microfiber Mach-Zehnder interferometer caused by axial strain. Second, the microfiber with different diameters was fabricated using the electrode discharge and fused taper method, and the axial strain experiments were conducted; the maximum sensitivity of the DTP microfiber with a diameter of ∼2.2 µm reached -45.55 pm/µÉ› at ∼1550 nm. Finally, based on the axial strain principle of the microfiber, we designed a highly sensitive vibration sensor using a DTP microfiber integrated into a rectangular through-hole cantilever beam. The 30-3500 Hz vibration signal monitoring could be realized, the maximum signal-to-noise ratio (SNR) was ∼75 dB at 52 Hz, and the acceleration sensitivity reached as high as 0.764 V/g at 45Hz. These results suggested the high performance of the microfiber in axial strain and micro-vibration sensing fields.

Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study.

BACKGROUND: Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease. METHODS: Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models. RESULTS: 305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set. CONCLUSIONS: A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).

Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

BACKGROUND: Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. METHODS: In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models. RESULTS: A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k-nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector-machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed. CONCLUSION: The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung.

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features. Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA. Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

18F-FDG PET Combined With MR Spectroscopy Elucidates the Progressive Metabolic Cerebral Alterations After Blast-Induced Mild Traumatic Brain Injury in Rats.

A majority of blast-induced mild traumatic brain injury (mTBI) patients experience persistent neurological dysfunction with no findings on conventional structural MR imaging. It is urgent to develop advanced imaging modalities to detect and understand the pathophysiology of blast-induced mTBI. Fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) could detect neuronal function and activity of the injured brain, while MR spectroscopy provides complementary information and assesses metabolic irregularities following injury. This study aims to investigate the effectiveness of combining 18F-FDG PET with MR spectroscopy to evaluate acute and subacute metabolic cerebral alterations caused by blast-induced mTBI. Thirty-two adult male Sprague-Dawley rats were exposed to a single blast (mTBI group) and 32 rats were not exposed to the blast (sham group), followed by 18F-FDG PET, MRI, and histological evaluation at baseline, 1-3 h, 1 day, and 7 days post-injury in three separate cohorts. 18F-FDG uptake showed a transient increase in the amygdala and somatosensory cortex, followed by a gradual return to baseline from day 1 to 7 days post-injury and a continuous rise in the motor cortex. In contrast, decreased 18F-FDG uptake was seen in the midbrain structures (inferior and superior colliculus). Analysis of MR spectroscopy showed that inflammation marker myo-inositol (Ins), oxidative stress marker glutamine + glutamate (Glx), and hypoxia marker lactate (Lac) levels markedly elevated over time in the somatosensory cortex, while the major osmolyte taurine (Tau) level immediately increased at 1-3 h and 1 day, and then returned to sham level on 7 days post-injury, which could be due to the disruption of the blood-brain barrier. Increased 18F-FDG uptake and elevated Ins and Glx levels over time were confirmed by histology analysis which showed increased microglial activation and gliosis in the frontal cortex. These results suggest that 18F-FDG PET and MR spectroscopy can be used together to reflect more comprehensive neuropathological alterations in vivo, which could improve our understanding of the complex alterations in the brain after blast-induced mTBI.