Fate mapping of Spp1 expression reveals age-dependent plasticity of disease-associated microglia-like cells after brain injury.

Microglial reactivity to injury and disease is emerging as a heterogeneous, dynamic, and crucial determinant in neurological disorders. However, the plasticity and fate of disease-associated microglia (DAM) remain largely unknown. We established a lineage tracing system, leveraging the expression dynamics of secreted phosphoprotein 1（Spp1） to label and track DAM-like microglia during brain injury and recovery. Fate mapping of Spp1+ microglia during stroke in juvenile mice revealed an irreversible state of DAM-like microglia that were ultimately eliminated from the injured brain. By contrast, DAM-like microglia in the neonatal stroke models exhibited high plasticity, regaining a homeostatic signature and integrating into the microglial network after recovery. Furthermore, neonatal injury had a lasting impact on microglia, rendering them intrinsically sensitized to subsequent immune challenges. Therefore, our findings highlight the plasticity and innate immune memory of neonatal microglia, shedding light on the fate of DAM-like microglia in various neuropathological conditions.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Protease SfpB plays an important role in cell membrane stability and immune system evasion in Streptococcus agalactiae.

Bacteria possess the ability to develop diverse and ingenious strategies to outwit the host immune system, and proteases are one of the many weapons employed by bacteria. This study sought to identify S. agalactiae additional serine protease and determine its role in virulence. The S. agalactiae THN0901 genome features one S8 family serine peptidase B (SfpB), acting as a secreted and externally exposed entity. A S8 family serine peptidase mutant strain (ΔsfpB) and complement strain (CΔsfpB) were generated through homologous recombination. Compared to the wild-type strain THN0901, the absorption of EtBr dyes was significantly reduced (P < 0.01) in ΔsfpB, implying an altered cell membrane permeability. In addition, the ΔsfpB strain had a significantly lower survival rate in macrophages (P < 0.01) and a 61.85 % lower adhesion ability to the EPC cells (P < 0.01) compared to THN0901. In the in vivo colonization experiment using tilapia as a model, 210 fish were selected and injected with different bacterial strains at a concentration of 3 × 106 CFU/tail. At 6, 12, 24, 48, 72 and 96 h post-injection, three fish were randomly selected from each group and their brain, liver, spleen, and kidney tissues were isolated. Subsequently, it was demonstrated that the ΔsfpB strain exhibited a markedly diminished capacity for colonization in tilapia. Additionally, the cumulative mortality of ΔsfpB in fish after intraperitoneal injection was reduced by 19.92-23.85 %. In conclusion, the findings in this study have demonstrated that the SfpB plays a significant role in S. agalactiae cell membrane stability and immune evasion. The immune evasion is fundamental for the development and transmission of invasive diseases, the serine protease SfpB may be a promising candidate for the development of antimicrobial agents to reduce the transmission of S. agalactiae.

A systematic review comparing the efficacy of 980 nm vs. 1470 nm wavelengths in laser hemorrhoidoplasty.

BACKGROUND: Laser Hemorrhoidoplasty (LHP) is a minimally invasive surgical option for the management of hemorrhoidal disease that has been increasingly adopted by surgeons over the last decade. Two wavelengths; 980 nm and 1470 nm have been employed in LHP. However, no data exist comparing the effects of these two wavelengths for this indication. This systematic review investigates both wavelengths for the management of hemorrhoids via the LHP procedure. METHODS: This systematic analysis and meta-analysis was performed following the PICOS and PRISMA guidelines. A systematic research of MEDLINE, Scopus, Clinicaltrials.gov, Embase, Cochrane Central Register of Controlled Trials, CENTRAL and Google Scholar databases from inception until March 2024 was performed. RESULTS: Overall, 19 studies including seven randomized control trials (RCT) and 12 non-randomized control trials with a total of 2492 patients were included in this systematic review and meta-analysis. The duration of LHP with both wavelengths was significantly shorter compared to open hemorrhoidectomy, postoperative pain and the rate of postoperative complications were significantly lower following LHP. There was no statistically significant difference in the rate of recurrence between LHP with the 980-nm wavelength and open hemorrhoidectomy. However, LHP with 1470-nm wavelength resulted in significantly higher recurrence rate compared to hemorrhoidectomy. CONCLUSION: Although no direct studies have compared the two wavelengths used in LHP, the outcomes of LHP seem to be independent of the wavelength used. Both wavelengths, when correctly used provide similar results, which are mostly better compared to open hemorrhoidectomy in terms of postoperative complications and postoperative pain, but not in terms of recurrence, where at least for the 1470-nm wavelength, LHP seems to show a higher recurrence rate when compared to open hemorrhoidectomy. Although a direct comparison of both wavelengths was not possible, technical issues regarding number of shots and energy per pile represent relevant parameters for recurrence after LHP.

Establishment of a gill cell line from yellowfin seabream (Acanthopagrus latus) for studying Amyloodinium ocellatum infection of fish.

Amyloodinium ocellatum is among the most devastating protozoan parasites, causing huge economic losses in the mariculture industry. However, the pathogenesis of amyloodiniosis remains unknown, hindering the development of targeted anti-parasitic drugs. The A. ocellatum in vitro model is an indispensable tool for investigating the pathogenic mechanism of amyloodiniosis at the cellular and molecular levels. The present work developed a new cell line, ALG, from the gill of yellowfin seabream (Acanthopagrus latus). The cell line was routinely cultured at 28°C in Dulbecco's modified Eagle medium (DMEM) supplemented with 15% fetal bovine serum (FBS). ALG cells were adherent and exhibited an epithelioid morphology; the cells were stably passed over 30 generations and successfully cryopreserved. The cell line derived from A. latus was identified based on partial sequence amplification and sequencing of cytochrome B (Cyt b). The ALG was seeded onto transwell inserts and found to be a platform for in vitro infection of A. ocellatum, with a 37.23 ± 5.75% infection rate. Furthermore, scanning electron microscopy (SEM) revealed that A. ocellatum parasitizes cell monolayers via rhizoids. A. ocellatum infection increased the expression of apoptosis and inflammation-related genes, including caspase 3 (Casp 3), interleukin 1 (IL-1), interleukin 10 (IL-10), tumour necrosis factor-alpha (TNF-α), in vivo or in vitro. These results demonstrated that the in vitro gill cell monolayer successfully recapitulated in vivo A. latus host responses to A. ocellatum infection. The ALG cell line holds great promise as a valuable tool for investigating parasite-host interactions in vitro.

A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data.

In this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction and other preprocessing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, Middle East respiratory syndrome and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

The Role of Circular RNAs in Ischemic Stroke.

Ischemic stroke (IS), a devastating condition characterized by intracranial artery stenosis and middle cerebral artery occlusion leading to insufficient oxygen supply to the brain, is a major cause of death and physical disability worldwide. Recent research has demonstrated the critical role of circular RNAs (circRNAs), a class of covalently enclosed noncoding RNAs that are widespread in eukaryotic cells, in regulating various physiological and pathophysiological cellular processes, including cell apoptosis, autophagy, synaptic plasticity, and neuroinflammation. In the past few years, circRNAs have attracted extensive attention in the field of IS research. This review summarizes the current understanding of the mechanisms underlying the involvement of circRNAs in IS development. A better understanding of circRNA-mediated pathogenic mechanisms in IS may pave the way for translating circRNA research into clinical practice, ultimately improving the clinical outcomes of IS patients.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET.

OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric Ki provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning. METHODS: Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and Ki parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images. RESULTS: Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized Ki images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the Ki images obtained in standard dynamic PET practice. CONCLUSIONS: Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET. KEY POINTS: • Compared with conventional static PET, dynamic PET parametric Ki imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

N-Heterocyclic Carbene-Catalyzed Atroposelective Synthesis of Axially Chiral α-Carbolinones via Heterocycle Formation.

An NHC-catalyzed atroposelective synthesis of axially chiral α-carbolinones from α,ß-unsaturated iminoindole derivatives and α-chloroaldehydes was developed. The reaction proceeds through a cascade process including [4 + 2] annulation and then oxidative dehydrogenation with concomitant central-to-axial chirality conversion under mild conditions. The developed method opens a new avenue to efficiently access axially chiral α-carbolinones in moderate to good enantioselectivities.

An effective and considerable treatment of pilonidal sinus disease by laser ablation.

The treatment of sacrococcygeal pilonidal disease (SPD) is still challenging. Although many non-surgical and surgical methods exist, no consensus has been reached on the best treatment. This study aimed to evaluate the efficacy of laser ablation using 1470-nm radial diode laser fiber in treating SPD. We retrospectively studied the data of our 48 patients who operated on this technique between March 2019 and July 2022. All patients were treated with laser ablation using 1470-nm radial diode laser fiber. The healing rate and recurrence rate, demographic and surgical data, postoperative pain, complications (wound infection, wound bleeding), the time of returning to regular work and life, and the time of wound healing were recorded. Postoperative pain was measured based on the visual analog scale (VAS) score. Postoperative follow-up was performed in the outpatient clinic every 1 week for 1 month. Among the 48 patients, 41 males and 7 females, with a mean age of 27.7 years (range 14-42), the healing rate was 100%, and the average healing time was 28.3 ± 5.5 days. Mean operative time was 15.5 ± 3.3 min. The recurrence rate was 2.1%. One patient relapsed 3 months after the operation. The patient underwent laser ablation again, and the sinus tract was closed. The median visual analog scale (VAS) score on the day of operation was 0(0,2). The median VAS score on the first, third, seventh, and fourteenth day after operation was 0(0,2), 0(0,1), 0(0,1), and 0(0,0), respectively. There was no wound infection or bleeding after the operation. The mean time to normal work/life was 7.1 ± 3.2 days. Almost all the patients felt very satisfied with the operation. Laser ablation using 1470-nm radial diode laser fiber is effective in SPD treatment. It is associated with minor wounds and mild postoperative pain. It is a simple, safe, and minimally invasive technique and its clinical application for acute and chronic SPD in the absence of abscess is promising.

Soy protein increases cognitive level in mice by modifying hippocampal nerve growth, oxidative stress, and intestinal microbiota.

BACKGROUND: Three kinds of diet containing chicken protein isolate (CPI), bovine milk protein isolate (BMPI), and soy protein isolate (SPI), respectively, were designed to investigate the influences of proteins on cognitive levels and related mechanisms in mice. RESULTS: A Morris water maze (MWM) test showed that the SPI group had a higher cognitive level than the BMPI group. Immunohistochemical staining and chemical analysis of the hippocampus showed that the SPI group had higher synaptophysin expression, doublecortin-positive cell proportion, superoxide dismutase activity, and lower malondialdehyde content compared with the BMPI group. The same parameters in the CPI group were between those of the BMPI and SPI groups. Microbiome sequencing indicated that the three groups differed significantly at the phylum, genus, and species levels, with higher microbial alpha diversity in the CPI and SPI groups. The association of intestinal microbiota with cognitive improvement was also assessed. The present study suggests that soy protein may increase cognitive function by the gut-brain axis. CONCLUSION: In contrast with CPI and BMPI, SPI had a better effect on improving the cognitive level in mice, which was achieved through the regulation of hippocampal neural growth, oxidative stress, and intestinal microbiota. © 2022 Society of Chemical Industry.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study.

OBJECTIVES: To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. METHODS: In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance. RESULTS: In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram. CONCLUSIONS: DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value. KEY POINTS: • DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

Skin transcriptomic analysis and immune-related gene expression of golden pompano (Trachinotus ovatus) after Amyloodinium ocellatum infection.

Amyloodiniosis is a severe disease of marine and brackish water fish caused by Amyloodinium ocellatum. Golden pompano (Trachinotus ovatus) is often repeatedly infected by A. ocellatum, leading to extensive mortality. However, little is known about the immune response mechanisms of the T. ovatus following reinfection with A. ocellatum. In this study, an extensive analysis at the transcriptome level of T. ovatus skin was carried out at 24 h post-infection by A. ocellatum. During the transcriptomic analysis, 1367 differentially expressed genes (DEGs) in the skin of T. ovatus under A. ocellatum infection and control conditions were obtained. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotated analyses, the DEGs were significantly enriched in the immune-related pathways. To better understand the immune-related gene expression dynamics, a quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was used to assess the primary and secondary infection groups of T. ovatus at different stages (3 h, 12 h, 24 h, 48 h and, 72 h post-infection) of infection with A.ocellatum. The results showed that innate immunity-related genes [interleukin (IL-8), chemokine ligand 3 (CCL3), toll-like receptor 7 (TLR7), and G-type lysosome (LZM g)] and adaptive immunity-related gene [major histocompatibility complex (MHC) alpha antigen I and MHC alpha antigen II] expression levels in the primary and secondary infection groups were significantly increased compared to the control group. The expression of MHC I and MHC II was more rapidly upregulated in the secondary infection group compared with the primary infection group after A.ocellatum infection. However, no significant differences of A.ocellatum load were observed in primary and secondary infection groups. In addition, the serum of the primary infection group had significantly higher concentrations of triglyceride (TG), higher alanine transaminase (ALT), aspartate transaminase (AST), and lactate dehydrogenase (LDH) activities than the control group. This study contributes to understanding the defense mechanisms in fish skin against ectoparasite infection.

Correction of the Estimated Hearing Level of NB Chirp ABR in Normal Hearing Population.

INTRODUCTION: The narrowband chirp (NB Chirp), a frequency-specific sound stimulus signal obtained by limiting the frequency bandwidth based on chirp, is applied to the frequency specified auditory brainstem response (fsABR) increasingly. Although some studies demonstrated that NB Chirp-evoked auditory brainstem response (NB Chirp ABR) causes a better neural response than tone burst-evoked auditory brainstem response and is preferred for fsABR, there is little known about how to better estimate an individual's hearing level through the threshold of NB Chirp ABR. The present study intended to compare the accuracy and deviation of NB Chirp ABR corrected by different approaches in estimating the hearing level of people with normal hearing. METHODS: A total of 66 volunteers with normal hearing were randomly divided into a model group (n = 26), test group 1 (n = 20), and test group 2 (n = 20). The model group was used to calculate the threshold difference between NB Chirp ABR and pure-tone audiometry at 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz, as well as the regression equation, providing a reference for the correction of estimated hearing level of NB Chirp ABR. Test group 1 was used to observe the accuracy and deviation of the "noncorrection (N)," "threshold difference (A1)," and "regression equation (A2)" methods in correcting the estimated hearing level of NB Chirp ABR. Test group 2 was used to replicate the analysis of test group 1 to verify the repeatability of the experimental results. All data were analyzed using SPSS 24.0. RESULTS: Test group 1 and test group 2 had similar results. First, the accuracy of the estimated hearing level of N was significantly higher than that of A1 or A2. Second, compared with "0," the deviation of the estimated hearing level of N was bigger than that of A1 or A2 at 500 Hz and 1,000 Hz, while similar at 2,000 Hz and 4,000 Hz. Finally, there was no significant difference in the deviation of the estimated hearing level between A1 and A2 at 500 Hz and 1,000 Hz. CONCLUSION: Among people with normal hearing, it was necessary to correct NB Chirp ABR at 500 Hz and 1,000 Hz for lower deviation of the estimated hearing level. Both correction approaches based on threshold difference and regression equation can be used.

Quantitative detection of parasitic ciliate Cryptocaryon irritans in seawater with an optimized sample processing method.

The protozoan Cryptocaryon irritans is one of the most important ectoparasites of marine fish, causing 'white spot disease' and mass mortality in aquaculture. To accurately predict disease outbreaks and develop prevention strategies, improved detection methods are required that are sensitive, convenient and rapid. In this study, a pair of specific primers based on the C. irritans 18S rRNA gene was developed and used in a real-time PCR (qPCR) assay. This assay was able to detect five theronts in 1 L of natural seawater. Furthermore, a linear model was established to analyse the log of Ct value and parasite abundance in seawater (y = -2.9623x + 24.2930), and the coefficient of determination (R2 ) value was 0.979. A lysis buffer was optimized for theront DNA extraction and used for storage sample. This method was superior to the commercial water DNA kit, and there was no significant degradation of DNA at room temperature for 24-96 hr. A dilution method was developed to manage qPCR inhibitors and used to investigate natural seawater samples in a net cage farm with diseased fish, and the findings were consistent with the actual situation. This study provides a valuable tool for assisting in the early monitoring and control of cryptocaryoniasis in aquaculture.

Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma.

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways.

OBJECTIVES: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. METHODS: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). RESULTS: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. CONCLUSIONS: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. KEY POINTS: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC.