An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma.

The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643-0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

Risk of Venous Thromboembolism by Cancer Type: A Network Meta-Analysis.

Patients with cancer have an increased risk of venous thromboembolism (VTE). Comparing tumor-specific VTE risk is complicated by factors such as surgery, disease stage, and chemotherapy. Network meta-analysis (NMA) using cancer types as network nodes enabled us to estimate VTE rates by leveraging comparisons across cancer types while adjusting for baseline VTE risk in individual studies. This study was conducted to estimate the risk of VTE by cancer type and factors influencing VTE risk. The Embase, MEDLINE, and Cochrane Library repositories were systematically searched to identify clinical trials and observational studies published from 2005 to 2022 that assessed the risk of primary cancer-related VTE among two or more distinct cancer types. Studies with similar cancer populations and study methods reporting VTE occurring within 1 year of diagnosis were included in the NMA. Relative VTE rates across cancer types were estimated with random-effects Bayesian NMAs. Absolute VTE rates were calculated from these estimates using the average VTE incidence in lung cancer (the most frequently reported type) as the "anchor." From 2,603 records reviewed, 30 studies were included in this NMA. The general network described 3,948,752 patients and 18 cancer types: 3.1% experienced VTE within 1 year of diagnosis, with cancer-specific rates ranging from 0.7 to 7.4%. Consistent with existing VTE risk prediction tools, pancreatic cancer was associated with higher-than-average VTE risk. Other cancer types with high VTE risk were brain and ovarian cancers. The relative rankings of VTE risk for certain cancers changed based on disease stage and/or receipt of chemotherapy or surgery.

Exposure risk to rural Residents: Insights into particulate and gas phase pesticides in the Indoor-Outdoor nexus.

Rural residents are exposed to both particulate and gaseous pesticides in the indoor-outdoor nexus in their daily routine. However, previous personal exposure assessment mostly focuses on single aspects of the exposure, such as indoor or gaseous exposure, leading to severe cognition bias to evaluate the exposure risks. In this study, residential dust and silicone wristbands (including stationary and personal wearing ones) were used to screen pesticides in different phases and unfold the hidden characteristics of personal exposure via indoor-outdoor nexus in intensive agricultural area. Mento-Carlo Simulation was performed to assess the probabilistic exposure risk by transforming adsorbed pesticides from wristbands into air concentration, which explores a new approach to integrate particulate (dust) and gaseous (silicone wristbands) pesticide exposures in indoor and outdoor environment. The results showed that particulate pesticides were more concentrated in indoor, whereas significantly higher concentrations were detected in stationary outdoor wristbands (p < 0.05). Carbendazim and chlorpyrifos were the most frequently detected pesticides in dust and stationary wristbands. Higher pesticide concentration was found in personal wristbands worn by farmers, with the maximum value of 2048 ng g-1 for difenoconazole. Based on the probabilistic risk assessment, around 7.1 % of farmers and 2.6 % of bystanders in local populations were potentially suffering from chronic health issues. One third of pesticide exposures originated mainly from occupational sources while the rest derived from remoting dissipation. Unexpectedly, 43 % of bystanders suffered the same levels of exposure as farmers under the co-existence of occupational and non-occupational exposures. Differed compositions of pesticides were found between environmental samples and personal pesticide exposure patterns, highlighting the need for holistic personal exposure measurements.

Spatial-temporal distribution and potential risk of pesticides in ambient air in the North China Plain.

The intensive use of pesticides in the North China Plain (NCP) has resulted in widespread contamination of pesticides in the local atmosphere, posing risks to air quality and human health. However, the occurrence and distribution of atmospheric pesticides in the NCP as well as their risk assessment have not been well investigated. In this study, 300 monthly samples were collected using passive air samplers with polyurethane foam at ten rural sites with different crop systems in Quzhou county, the NCP, from June 2021 to May 2022. The pesticides were quantified using mass-spectrometric techniques. Our results revealed that chlorpyrifos, carbendazim, and atrazine were the most frequently found pesticides in the air samples, with detection frequencies of ≥ 87 % across the samples. The average concentrations of atmospheric pesticides during spring (7.47 pg m-3) and summer (16.05 pg m-3) were significantly higher than those during autumn (2.04 pg m-3) and winter (1.71 pg m-3), attributable to the intensified application of pesticides during the warmer seasons. Additionally, cash crop sites exhibited higher concentrations (10.26 pg m-3) of atmospheric pesticides compared to grain crop (5.59 pg m-3) and greenhouse sites (3.81 pg m-3), primarily due to more frequent pesticides spraying events in cash crop fields. These findings indicate a distinct spatial-temporal distribution pattern of atmospheric pesticides influenced by both seasons and crop systems. Furthermore, the model-based inhalation risk assessment indicates that inhalation exposure to atmospheric pesticides is unlikely to pose a significant public concern.

Experts consensus on the procedure of dental operative microscope in endodontics and operative dentistry.

The dental operative microscope has been widely employed in the field of dentistry, particularly in endodontics and operative dentistry, resulting in significant advancements in the effectiveness of root canal therapy, endodontic surgery, and dental restoration. However, the improper use of this microscope continues to be common in clinical settings, primarily due to operators' insufficient understanding and proficiency in both the features and established operating procedures of this equipment. In October 2019, Professor Jingping Liang, Vice Chairman of the Society of Cariology and Endodontology, Chinese Stomatological Association, organized a consensus meeting with Chinese experts in endodontics and operative dentistry. The objective of this meeting was to establish a standard operation procedure for the dental operative microscope. Subsequently, a consensus was reached and officially issued. Over the span of about four years, the content of this consensus has been further developed and improved through practical experience.

Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain Tumors.

Background Cerebral blood volume (CBV) maps derived from dynamic susceptibility contrast-enhanced (DSC) MRI are useful but not commonly available in clinical scenarios. Purpose To test image-to-image translation techniques for generating CBV maps from standard MRI sequences of brain tumors using the bookend technique DSC MRI as ground-truth references. Materials and Methods A total of 756 MRI examinations, including quantitative CBV maps produced from bookend DSC MRI, were included in this retrospective study. Two algorithms, the feature-consistency generative adversarial network (GAN) and three-dimensional encoder-decoder network with only mean absolute error loss, were trained to synthesize CBV maps. The performance of the two algorithms was evaluated quantitatively using the structural similarity index (SSIM) and qualitatively by two neuroradiologists using a four-point Likert scale. The clinical value of combining synthetic CBV maps and standard MRI scans of brain tumors was assessed in several clinical scenarios (tumor grading, prognosis prediction, differential diagnosis) using multicenter data sets (four external and one internal). Differences in diagnostic and predictive accuracy were tested using the z test. Results The three-dimensional encoder-decoder network with T1-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient maps as the input achieved the highest synthetic performance (SSIM, 86.29% ± 4.30). The mean qualitative score of the synthesized CBV maps by neuroradiologists was 2.63. Combining synthetic CBV with standard MRI improved the accuracy of grading gliomas (standard MRI scans area under the receiver operating characteristic curve [AUC], 0.707; standard MRI scans with CBV maps AUC, 0.857; z = 15.17; P < .001), prediction of prognosis in gliomas (standard MRI scans AUC, 0.654; standard MRI scans with CBV maps AUC, 0.793; z = 9.62; P < .001), and differential diagnosis between tumor recurrence and treatment response in gliomas (standard MRI scans AUC, 0.778; standard MRI scans with CBV maps AUC, 0.853; z = 4.86; P < .001) and brain metastases (standard MRI scans AUC, 0.749; standard MRI scans with CBV maps AUC, 0.857; z = 6.13; P < .001). Conclusion GAN image-to-image translation techniques produced accurate synthetic CBV maps from standard MRI scans, which could be used for improving the clinical evaluation of brain tumors. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Branstetter in this issue.

CLDN18.2 and 4-1BB bispecific antibody givastomig exerts antitumor activity through CLDN18.2-expressing tumor-directed T-cell activation.

BACKGROUND: Claudin18.2 (CLDN18.2) is a tight junction protein that has been identified as a clinically proven target in gastric cancer. Stimulation of 4-1BB with agonistic antibodies is also a promising strategy for immunotherapy and 4-1BB+ T cells were reported to be present within the tumor microenvironment of patients with gastric cancer. However, hepatotoxicity-mediated by 4-1BB activation was observed in clinical trials of agonistic anti-4-1BB monoclonal antibodies. METHODS: To specifically activate the 4-1BB+ T cells in tumor and avoid the on-target liver toxicity, we developed a novel CLDN18.2×4-1BB bispecific antibody (termed 'givastomig' or 'ABL111'; also known as TJ-CD4B or TJ033721) that was designed to activate 4-1BB signaling in a CLDN18.2 engagement-dependent manner. RESULTS: 4-1BB+ T cells were observed to be coexisted with CLDN18.2+ tumor cells in proximity by multiplex immunohistochemical staining of tumor tissues from patients with gastric cancer (n=60). Givastomig/ABL111 could bind to cell lines expressing various levels of CLDN18.2 with a high affinity and induce 4-1BB activation in vitro only in the context of CLDN18.2 binding. The magnitude of T-cell activation by givastomig/ABL111 treatment was closely correlated with the CLDN18.2 expression level of tumor cells from gastric cancer patient-derived xenograft model. Mechanistically, givastomig/ABL111 treatment could upregulate the expression of a panel of pro-inflammatory and interferon-γ-responsive genes in human peripheral blood mononuclear cells when co-cultured with CLDN18.2+ tumor cells. Furthermore, in humanized 4-1BB transgenic mice inoculated with human CLDN18.2-expressing tumor cells, givastomig/ABL111 induced a localized immune activation in tumor as evident by the increased ratio of CD8+/regulatory T cell, leading to the superior antitumor activity and long-lasting memory response against tumor rechallenge. Givastomig/ABL111 was well tolerated, with no systemic immune response and hepatotoxicity in monkeys. CONCLUSIONS: Givastomig/ABL111 is a novel CLDN18.2×4-1BB bispecific antibody which has the potential to treat patients with gastric cancer with a wide range of CLDN18.2 expression level through the restricted activation of 4-1BB+ T cells in tumor microenvironment to avoid the risk of liver toxicity and systemic immune response.

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study.

Objectives: The objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images. Methods: There were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve. Results: There were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models. Conclusions: The 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma. Key points: There was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.

Ecological risk assessment of pesticides on soil biota: An integrated field-modelling approach.

Pesticide residues in soils can cause negative impacts on soil health as well as soil biota. However, research related to the toxicity and exposure risks of pesticides to soil biota are scarce, especially in the North China Plain (NCP) where pesticides are intensively applied. In this study, the occurrence and distribution of 15 commonly used pesticides in 41 fields in Quzhou county in the NCP were determined during the growing season in 2020. The ecological risks of pesticides to the soil biota, including earthworms, enchytraeids, springtails, mites and nitrogen mineralization microorganisms, were assessed using toxicity exposure ratios (TERs) and risk quotient (RQ) methods. Based on pesticide detection rates and RQs, pesticide hazards were ranked using the Hasse diagram. The results showed that pesticides were concentrated in the 0-2 cm soil depth. Chlorantraniliprole was the most frequently detected pesticide with a detection rate of 37%, while the highest concentration of 1.85 mg kg-1 was found for carbendazim in apple orchards. Chlorpyrifos, carbendazim and imidacloprid posed a chronic exposure risk to E. fetida, F. candida and E. crypticus with the TERs exceeding the trigger value. Pesticide mixtures posed ecological risks to soil biota in 70% of the investigated sites. 47.5% of samples were ranked as high-risk, with the maximum RQ exceeding 490. According to the Hasse diagram, abamectin, tebuconazole, chlorantraniliprole and chlorpyrifos were ranked as the most hazardous pesticides for soil biota in the study region, indicating that alternative methods of pest management need to be considered. Therefore, practical risk mitigation solutions are recommended, in which the use of hazardous pesticides would be replaced with low-risk pesticides with similar functions from the Hasse diagram, or with biopesticides.

Trends and factors associated with outpatient anticoagulant treatment initiation among VTE patients with active cancer.

BACKGROUND: Patients with venous thromboembolism (VTE) and cancer are at higher risk of recurrent VTE and mortality. Clinical guidelines recommend anticoagulant treatment for these patients. This study assessed trends in outpatient anticoagulant treatment and factors associated with this treatment initiation in outpatient setting among this high-risk patient population. OBJECTIVE: To study trends and factors associated with anticoagulant treatment initiation among patients with VTE and cancer. METHODS: VTE cancer patients age ≥65 were identified from the SEER-Medicare database from 01JAN2014-31DEC2019. Patients were enrolled for ≥6 months prior to their first VTE (i.e. index event) and without evidence of other reasons for anticoagulation (i.e., atrial fibrillation). Patients were also required to be enrolled for ≥30 days after index. Cancer status was identified from SEER or Medicare database in the 6 months pre- through 30 days post-VTE. Patients were classified into treated or untreated cohorts depending on whether they initiated outpatient anticoagulant treatment within 30 days post-index. The trends of treated vs. untreated were evaluated by quarter. Logistic regression was used to identify demographic-, VTE-, cancer- and comorbid-related factors associated with anticoagulant treatment initiation. RESULTS: A total of 28,468 VTE-cancer patients met all study criteria. Of these, ~46 % initiated outpatient anticoagulant treatment within 30 days, and ~54 % did not. The above rates were stable from 2014 to 2019. Factors such as VTE diagnosis in inpatient setting, pulmonary embolism (PE) diagnosis, and pancreatic cancer were associated with increased odds whereas bleeding history and some comorbid factors were associated with decreased odds of initiating anticoagulant treatment. CONCLUSION: Over half of VTE patients with cancer did not initiate outpatient anticoagulant treatment within the first 30-days after VTE diagnosis. This trend was stable from 2014 to 2019. A range of cancer-, VTE-, and comorbid-related factors were associated with the likelihood of the treatment initiation.

Harnessing Reconstructed Macrophage Modulation of Infiltration-Excluded Immune Microenvironments To Delineate Glioma Infiltrative Region.

High invasiveness of glioma produces residual glioma cells in the brain parenchyma after surgery and ultimately causes recurrence. Precise delineation of glioma infiltrative region is critical for an accurate complete resection, which is challenging. The glioma-infiltrating area constitutes infiltration-excluded immune microenvironments (I-E TIMEs), which recruits endogenous or adoptive macrophages to the invasive edge of glioma. Thus, combined with immune cell tracing technology, we provided a novel strategy for the preoperative precise definition of the glioma infiltration boundary, even satellite-like infiltration stoves. Herein, the biomimetic probe was constructed by internalizing fluorophore labeled PEGylated KMnF3 nanoparticles into bone-marrow-derived macrophages using magnetic resonance imaging (MRI)/fluorescence imaging (FI). The biomimetic probe was able to cross the blood-brain barrier and home to the orthotopic glioma infiltrates including satellite stove under MRI and FI tracing, which was validated using hematoxylin and eosin staining, indicating its excellent performance in distinguishing the margins between the glioma cell and normal tissues. This study guides the precise definition of glioma infiltration boundaries at the cellular level, including the observation of any residual glioma cells after surgery. Thus, it has the potential to guide surgery to maximize resection and predict recurrence in the clinic.

Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model.

OBJECTIVES: Automatic bone lesions detection and classifications present a critical challenge and are essential to support radiologists in making an accurate diagnosis of bone lesions. In this paper, we aimed to develop a novel deep learning model called You Only Look Once (YOLO) to handle detecting and classifying bone lesions on full-field radiographs with limited manual intervention. METHODS: In this retrospective study, we used 1085 bone tumor radiographs and 345 normal bone radiographs from two centers between January 2009 and December 2020 to train and test our YOLO deep learning (DL) model. The trained model detected bone lesions and then classified these radiographs into normal, benign, intermediate, or malignant types. The intersection over union (IoU) was used to assess the model's performance in the detection task. Confusion matrices and Cohen's kappa scores were used for evaluating classification performance. Two radiologists compared diagnostic performance with the trained model using the external validation set. RESULTS: In the detection task, the model achieved accuracies of 86.36% and 85.37% in the internal and external validation sets, respectively. In the DL model, radiologist 1 and radiologist 2 achieved Cohen's kappa scores of 0.8187, 0.7927, and 0.9077 for four-way classification in the external validation set, respectively. The YOLO DL model illustrated a significantly higher accuracy for intermediate bone tumor classification than radiologist 1 (95.73% vs 88.08%, p = 0.004). CONCLUSIONS: The developed YOLO DL model could be used to assist radiologists at all stages of bone lesion detection and classification in full-field bone radiographs. KEY POINTS: • YOLO DL model can automatically detect bone neoplasms from full-field radiographs in one shot and then simultaneously classify radiographs into normal, benign, intermediate, or malignant. • The dataset used in this retrospective study includes normal bone radiographs. • YOLO can detect even some challenging cases with small volumes.

MRI comparative study of diffuse midline glioma, H3 K27-altered and glioma in the midline without H3 K27-altered.

PURPOSE: The MRI features of Diffuse midline glioma, H3 K27-altered and glioma in the midline without H3 K27-altered were compared and analyzed, and the changes in the apparent diffusion coefficient (ADC) of the two groups were quantitatively analyzed. METHODS: The MRI images of 35 patients with Diffuse midline gliomas, H3 K27-altered and gliomas in the midline without H3 K27-altered were analyzed retrospectively. The location, edge, signal, peritumoral edema and enhancement characteristics of the lesions were observed, and the changes in ADC values were analyzed. RESULTS: In the H3 K27-altered group, 85.7% (12/14) of the tumors were located in the thalamus and brainstem compared with 28.6% (6/21) in the no H3 K27-altered group. In the H3 K27-altered group, for tumors only located in the midline area, only 14.3% (1/7) had irregular shapes and unclear boundaries, while for tumors also invaded the extramidline tissues 85.7% (6/7) had irregular shapes and unclear boundaries.The"basilar artery wrapped sign" was found in 6 patients with tumors located in the pons in the H3 K27-altered group, but none in the no H3 K27-altered group had this sign. In the H3 K27-altered group, only 14.3% (1/7) of the tumors confined to the midline area had small cystic degeneration and necrosis, while for tumors also invaded the extramidline tissues, 100% (7/7) of the tumors had cystic degeneration and necrosis, and the cystic degeneration and necrosis only located in the extramidline region of the tumor in 6 cases.A total of 78.6% (11/14) of tumors in the H3 K27-altered group showed mild to moderate enhancement, while 47.6% (10/21) of tumors in the no H3 K27-altered group showed mild to moderate enhancement. The average peritumoral edema index was 1.13 in the H3 K27-altered group and 1.75 in the no H3 K27-altered group. The average ADC value of tumor in the H3 K27-altered group was 7.83 × 10- 4 mm2/s, and the ratio to normal brain tissue was 0.844, while the values in the no H3 K27-altered group were 13.5 × 10- 4 mm2/s and 1.75, respectively. CONCLUSION: Compared with gliomas in the midline without H3 K27-altered, The MRI findings and ADC value of Diffuse midline gliomas, H3K27-altered have some characteristics, which can help improve the diagnosis and differential diagnosis.

Transcriptional patterns reveal tumor histologic heterogeneity and immunotherapy response in lung adenocarcinoma.

Tumoral heterogeneity has proven to be a leading cause of difference in prognosis and acquired drug resistance. High intratumor heterogeneity often means poor clinical response and prognosis. Histopathological subtypes suggest tumor heterogeneity evolved during the progression of lung adenocarcinoma, but the exploration of its molecular mechanisms remains limited. In this work, we first verified that transcriptional patterns of a set of differentially expressed genes profoundly revealed the histologic progression of lung adenocarcinoma. Next, a predictive model based on the transcriptional patterns was established to accurately distinguish histologic subtypes. Two crucial genes were identified and used to construct a tumor heterogeneous scoring model (L2SITH) to stratify patients, and we found that patients with low heterogeneity score had better prognosis. Low L2SITH scores implied low tumor purity and beneficial tumor microenvironment. Moreover, L2SITH effectively identified cohorts with better responses to anti-PD-1 immunotherapy.

A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images.

OBJECTIVES: The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1 weighted imaging (T1WI) images. METHODS: 188 patients with meningioma were analyzed retrospectively. There were 94 high-grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low-grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer-Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and decision curve analysis curve. RESULTS: The sex, size and surrounding invasion were used to build clinical model. The area under the receiver operator characteristic curve (AUC) of clinical model was 0.870 (95% CI: 0.782-0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802-0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904-1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p＜0.05). The decision curve analysis curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature. CONCLUSION: The radiomics nomogram based on enhanced T1 weighted imaging images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma. ADVANCES IN KNOWLEDGE: 1. We first constructed a radiomic nomogram to predict the meningioma grade.2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.

Contrastive learning-based computational histopathology predict differential expression of cancer driver genes.

MOTIVATION: Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. RESULTS: In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expression from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological features in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expression. Interestingly, we found the genes with higher fold change can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attention scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSIs.

Metabolomics and Cytokine Analysis for Identification of Schizophrenia with Auditory Hallucination.

PURPOSE: To investigate the metabolic profile and biomarkers of schizophrenia with auditory hallucinations (AHs). METHODS: A total of 18 schizophrenic patients with the symptom of pure AHs (pAHs), 28 without AH (nAHs) and 43 age-matched healthy persons (Con) were enrolled in this study. Participants in pAHs and nAHs groups had relapsed into exacerbations of psychosis after self-discontinuing antipsychotics for at least one month; blood samples were drawn prior to restarting anti-psychotic treatment. Participants with history of recreational substance use were excluded. Positive and Negative Syndrome Scale (PANSS) and Auditory Hallucinations Rating Scale (AHRS) were used to assess the clinical mental state of all samples. Enzyme-linked immunosorbent assay (ELISA) was used to estimate the level of cytokines, and metabolomics analysis to identify potential biomarkers and pathways in the three groups. Graphpad 8.0 software was used to calculate the area under the receiver operating characteristic (ROC) curve. The relationship between metabolites and cytokines were determined using correlation analysis. RESULTS: Questionnaire scores showed significant differences in the positive symptom scale and PANSS total between nAHs and pAHs groups. Four cytokines (BDNF, IL-2, NGF-ß and TNF-α) differed significantly among the three groups. Six molecules in the nAHs group (phenylalanine, hippurate, serine, glutamate, valine and cystine) and four in the pAHs group (phenylalanine, serine, glutamate and cystine) were identified as potential biomarkers. In addition, phenylalanine was shown as a potential independent diagnostic biomarker for pAHs. Correlation analysis revealed that cystine and serine were significantly negatively correlated with IL-2 in the pAHs group. CONCLUSIONS: This study revealed the metabolic profile of patients with schizophrenia with AHs and provided new information to support the diagnosis. The identification of unique biomarkers would contribute to objective and reliable diagnoses of patients with schizophrenia with AH.

Effects of γ-aminobutyric acid on freshness and processing properties of eggs during storage.

Effects of γ-aminobutyric acid (GABA) on egg storage properties were investigated by comparing freshness and processing properties between eggs treated with different GABA concentrations and untreated controls. GABA treatment delayed storage-associated increases of albumen pH value and surface hydrophobicity and decreases of protein index, yolk index, Haugh unit (HU) value, albumen height, solubility, gel hardness, and apparent viscosity. Highest HU, yolk index, and emulsion stability values and peak storage performance were observed after injection of eggs with 0.05 mL of GABA (0.3 g/mL). Even after 25 days of storage, GABA-treated eggs exhibited freshness resembling that of fresh eggs, indicating that GABA treatment extended shelf life by 10 days relative to controls. Peak solubility, emulsifying activity, emulsifying stability, foaming capacity, and foaming stability values of 89.74%, 0.72, 14.18, 43.35, and 45.57, respectively, for GABA-treated eggs exceeded corresponding control group values, thus demonstrating that GABA treatment of eggs slowed storage-related deterioration of freshness and processing quality.

Using texture analyzer to characterize pecan and olive oil tactile properties, compare to viscometer analysis, and link to fatty acid profile and total polyphenols.

This study aimed to characterize mechanical properties of five pecan oils and one olive oil using a texture analyzer compared to a rotational viscometer; the results were linked to fatty acid profile and total polyphenol content. The seven texture parameters (firmness, consistency, cohesiveness, viscosity index, and stickiness at 5 s, stickiness at 30 s, and delta stickiness) showed significant difference (p ≤.05) among the six oils. Overall, olive oil had higher texture analysis values and significantly higher rotational viscosity than pecan oils. Chemically, C18 fatty acids accounted for approximately 90% of the total fatty acids in the five pecan oils. Olive oil had a higher amount of long-chain, unsaturated fatty acids. Total polyphenols in pecan oils were 8-15 mg gallic acid equivalent (GAE)/100 g, while olive oil contained 27.2 mg GAE/100 g. Correlation analysis demonstrated a significant, positive relationship between "consistency" texture and rotational viscosity measurement. Mechanical properties (seven texture parameters and rotational viscosity) were partially correlated to fatty acid profile, though no universal pattern was identified.

[Effects of astragaloside IV on delaying kidney aging and its mechanisms].

OBJECTIVE: To investigate the mechanisms of Astragaloside Ⅳ on inhibiting apoptosis and delaying kidney aging in rats by regulating SIRT1/p53 signaling pathway. METHODS: The aging model was established by subcutaneous injection of D-galactose 200 mg/(kg·d). SPF-grade healthy male SD rats were randomly divided into 4 groups: normal control group (intragastric infusion of 5 ml/(kg·d) normal saline), aging model group (intragastric infusion of 5 ml/(kg·d) normal saline), Astragaloside IV group (intragastric infusion of 40 mg/(kg·d) Astragaloside IV),and SRT1720 group( intragastric infusion of 20 mg/(kg·d) SRT1720), with 10 rats in each group. After 8 weeks, the serum samples of rats were collected to detect the levels of renal function (creatinine and urea nitrogen) and senescent associated secretory phenotype (TGF-ß and IL-6) by ELISA. The renal tissues of rats were obtained for HE and Masson staining. The protein and mRNA expressions of SIRT1, p53, Bcl-2, Bax, p21 and pRb were detected by Western blot and RT-PCR. RESULTS: Serum creatinine and urea nitrogen levels in the aging model group were higher than those in the normal group, but there was no significant difference in each group (P＞0.05). The serum levels of TGF-ß and IL-6 in the aging model group were higher than those in the normal group (P＜0.05), and which in the Astragaloside IV group and SRT1720 group were lower than those in the model group (P＜0.05). There was no significant differences between Astragaloside IV group and SRT1720 group (P＞0.05). The results of pathological staining of renal tissues showed that, compared with the normal group, the renal tubules dilated, local atrophy, infiltration of inflammatory cells and proliferation of collagen fibers were observed in the aging model group. Compared with the aging model group, the pathological changes were alleviated in Astragaloside IV group and SRT1720 group. The results of Western blot and RT-PCR showed that, compared with the normal group, the protein and mRNA expressions of SIRT1 and pRb in the renal tissue of the aging group were decreased, the protein expression of Bcl-2 was decreased(P＜0.05), and the protein and mRNA expressions of p53 and p21 were increased, the protein expression of Bax was increased(P＜0.05). Compared with the aging group, Astragaloside IV and SRT1720 improved the above-mentioned indexes (P＜0.05). CONCLUSION: Astragaloside IV can delay kidney aging by regulating the SIRT1/p53 signaling pathway.