Nutrient storage in the forest floor is regulated through litter decomposition and nutrient cycling. Stoichiometry of nutrients can provide characterization of the forest floor. To quantify nutrient ...storage in the forest floor and to determine stoichiometry among different forest types, available data on nutrients were meta-analyzed. The data on nutrients—nitrogen, phosphorus, potassium, calcium, and magnesium—were collected from published reports and original data on Japanese forests. The relationship between nutrient storage and forest floor mass was also examined. Japanese cypress and cedar plantations had small N and P storage in the forest floor with high C:N and C:P ratios, whereas subalpine conifers had large N and P storage in the forest floor with low C:N and C:P ratios; cedar plantations showed large Ca-specific storage in the forest floor. The stoichiometry of the forest floor varied between different forest types, namely C:N:P ratios were 942:19:1 for cedar and cypress plantations, 625:19:1 for broad-leaved forests, and 412:13:1 for subalpine conifers and fir plantations. N storage was closely correlated; however, P and other mineral storages were weakly correlated with the forest floor mass. Nutrient storage and stoichiometry can provide a better perspective for the management of forest ecosystem.
Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of ...determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.
In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical ...devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
Glioblastoma is one of the most devastating human malignancies for which a novel efficient treatment is urgently required. This pre–clinical study shows that eribulin, a specific inhibitor of ...telomerase reverse transcriptase (TERT)‐RNA‐dependent RNA polymerase, is an effective anticancer agent against glioblastoma. Eribulin inhibited the growth of 4 TERT promoter mutation‐harboring glioblastoma cell lines in vitro at subnanomolar concentrations. In addition, it suppressed the growth of glioblastoma cells transplanted subcutaneously or intracerebrally into mice, and significantly prolonged the survival of mice harboring brain tumors at a clinically equivalent dose. A pharmacokinetics study showed that eribulin quickly penetrated brain tumors and remained at a high concentration even when it was washed away from plasma, kidney or liver 24 hours after intravenous injection. Moreover, a matrix‐assisted laser desorption/ionization mass spectrometry imaging analysis revealed that intraperitoneally injected eribulin penetrated the brain tumor and was distributed evenly within the tumor mass at 1 hour after the injection whereas only very low levels of eribulin were detected in surrounding normal brain. Eribulin is an FDA‐approved drug for refractory breast cancer and can be safely repositioned for treatment of glioblastoma patients. Thus, our results suggest that eribulin may serve as a novel therapeutic option for glioblastoma. Based on these data, an investigator‐initiated registration‐directed clinical trial to evaluate the safety and efficacy of eribulin in patients with recurrent GBM (UMIN000030359) has been initiated.
Eribulin inhibited the growth of TERT promoter mutation‐harboring glioblastoma cell lines in vitro. In addition, it suppressed the growth of glioblastoma cells transplanted subcutaneously or intracerebrally into mice, and significantly prolonged the survival of mice harboring brain tumors at a clinically equivalent dose. Thus, our results suggest that eribulin may serve as a novel therapeutic option for glioblastoma.
Air pollution and atmospheric deposition have adverse effects on tree and forest health. We reviewed studies on tree and forest decline in Northeast and Southeast Asia, Siberia, and the Russian Far ...East (hereafter referred to as East Asia). This included studies published in domestic journals and languages. We identified information about the locations, causes, periods, and tree species exhibiting decline. Past air pollution was also reviewed. Most East Asian countries show declining trends in SO2 concentration in recent years, although Mongolia and Russia show increasing trends. Ozone (O3) concentrations are stable or gradually increasing in the East Asia region, with high maxima. Wet nitrogen (N) deposition was high in China and tropical countries, but low in Russia. The decline of trees and forests primarily occurred in the mid-latitudes of Japan, Korea, China, and Russia. Long-term large N deposition resulted in the N saturation phenomenon in Japan and China, but no clear forest health response was observed. Thereafter, forest decline symptoms, suspected to be caused by O3, were observed in Japan and China. In East Russia, tree decline occurred around industrial centers in Siberia. Haze events have been increasing in tropical and boreal forests, and particulate matter inhibits photosynthesis. In recent years, chronically high O3 concentrations, in conjunction with climate change, are likely have adverse effects on tree physiology. The effects of air pollution and related factors on tree decline are summarized. Recently, the effects of air pollution on tree decline have not been apparent under the changing climate, however, monitoring air pollution is indispensable for identifying the cause of tree decline. Further economic growth is projected in Southeast Asia and therefore, the monitoring network should be expanded to tropical and boreal forest zones. Countermeasures such as restoring urban trees and rural forests are important for ensuring future ecosystem services.
Display omitted
•Tree and forest declines have followed industrialization in each country.•The causes of tree and forest decline have changed, depending on the time period.•Recent chronic effects of ozone and changing climate deteriorate tree health.•The apparent effect of air pollution was not found in recent tree decline.•An outline of future monitoring and countermeasures is proposed.
•We propose a content-based image retrieval framework to support comparative diagnostic reading.•The feature decomposing network decomposes medical images into normal and abnormal anatomy codes.•By ...utilizing either normal or abnormal anatomy codes, our algorithm can retrieve images according to any semantic components.•Synthetic feature vectors from two different query images can retrieve images with intended features without corresponding example images.
Display omitted
In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results.
Meningioma is the most common intracranial tumor, with generally favorable patient prognosis. However, patients with malignant meningioma typically experience recurrence, undergo multiple surgical ...resections, and ultimately have a poor prognosis. Thus far, effective chemotherapy for malignant meningiomas has not been established. We recently reported the efficacy of eribulin (Halaven) for glioblastoma with a telomerase reverse transcriptase (TERT) promoter mutation. This study investigated the anti–tumor effect of eribulin against TERT promoter mutation‐harboring human malignant meningioma cell lines in vitro and in vivo. Two meningioma cell lines, IOMM‐Lee and HKBMM, were used in this study. The strong inhibition of cell proliferation by eribulin via cell cycle arrest was demonstrated through viability assay and flow cytometry. Apoptotic cell death in malignant meningioma cell lines was determined through vital dye assay and immunoblotting. Moreover, a wound healing assay revealed the suppression of tumor cell migration after eribulin exposure. Intraperitoneal administration of eribulin significantly prolonged the survival of orthotopic xenograft mouse models of both malignant meningioma cell lines implanted in the subdural space (P < .0001). Immunohistochemistry confirmed apoptosis in brain tumor tissue treated with eribulin. Overall, these results suggest that eribulin is a potential therapeutic agent for malignant meningiomas.
Induction of apoptosis by eribulin was consistent with cell‐based assays and animal models, demonstrating a potent survival advantage in orthotopic malignant meningioma xenograft mice. Therefore, eribulin may serve as a potential agent for improving clinical outcomes in patients with notoriously aggressive malignant meningiomas.
Glioblastoma (GBM) is the most common, but extremely malignant, brain tumor; thus, the development of novel therapeutic strategies for GBMs is imperative. Many tyrosine kinase inhibitors (TKIs) have ...been approved for various cancers, yet none has demonstrated clinical benefit against GBM. Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase (RTK) that is confirmed only during the embryonic development period in humans. In addition, various ALK gene alterations are known to act as powerful oncogenes and therapeutic targets in various tumors. The antitumor activity of various TKIs was tested against three human GBM cell lines (U87MG, LN229, and GSC23), which expressed substantially low ALK levels; second‐generation ALK inhibitors, alectinib and ceritinib, effectively induced GBM cell death. In addition, treatment with either alectinib or ceritinib modulated the activation of various molecules downstream of RTK signaling and induced caspase‐dependent/‐independent cell death mainly by inhibiting signal transducer and activator of transcription 3 activation in human GBM cells. In addition, alectinib and ceritinib also showed antitumor activity against a U87MG cell line with acquired temozolomide resistance. Finally, oral administration of alectinib and ceritinib prolonged the survival of mice harboring intracerebral GBM xenografts compared with controls. These results suggested that treatment with the second‐generation ALK inhibitors, alectinib and ceritinib, might serve as a potent therapeutic strategy against GBM.
Anaplastic lymphoma kinase (ALK) inhibitors, alectinib and ceritinib, demonstrated antitumor activity for glioblastoma (GBM) cells which expressed substantially low ALK levels in vitro and in vivo. Treatment with either alectinib or ceritinib induced cell death mainly by inhibiting signal transducer and activator of transcription 3 activation in GBM cells. Alectinib and ceritinib might serve as potent therapeutic agents against GBM.
We herein report a case of encephalitis in a 42-year-old woman with hepatocellular carcinoma following atezolizumab plus bevacizumab therapy. After two weeks of treatment, she was admitted for a high ...fever, impaired consciousness, and convulsive seizure refractory to diazepam. Magnetic resonance imaging revealed a hyperintense splenial lesion. A cerebrospinal fluid test excluded malignancy and infection. These findings were highly suggestive of a diagnosis of encephalitis due to atezolizumab, an immune-related adverse event. Steroid pulse therapy improved the fever and seizure. However, her incomplete right-sided paralysis and aphasia persisted. This is the first case report of encephalitis caused by atezolizumab plus bevacizumab therapy for hepatocellular carcinoma.
Primary central nervous system lymphoma (PCNSL) responds relatively quickly to chemotherapy or radiotherapy. However, determination of a complete response after treatment is often difficult because ...of extremely light residual contrast enhancement on magnetic resonance images due to the effects of microhemorrhages and scar tissue formation. These small enhancing lesions define an unconfirmed complete response. The aim of this study was to investigate the usefulness of carbon-11-labeled methionine (11C-Met) positron-emission tomography (PET) for determining the treatment response of PCNSL.
Data for 36 patients who were treated for PCNSL between 2011 and 2015 and underwent magnetic resonance imaging and 11C-Met PET were reviewed. Magnetic resonance imaging findings were classified as complete response, unconfirmed complete response, and tumor mass (a composite of partial response, stable disease and progressive disease). PET images were evaluated, standardized uptake values were quantified, and the tumor-to-normal tissue count ratio (TNR) was calculated. Receiver operating characteristic curves were generated to determine the optimal cutoff TNRs.
The optimal TNRs for differentiating complete response and unconfirmed complete response from tumor mass were 1.83 (area under the curve, 0.951) and 1.80 (area under the curve, 0.932), respectively. The corresponding sensitivity and specificity values for the diagnosis of tumor mass were 82.4 and 100%, respectively, in the complete response group and 85.3 and 85%, respectively, in the unconfirmed complete response group.
A TNR of ≥1.80 can aid in the detection of active PCNSL using 11C-Met PET. Thus, 11C-Met-PET may be a useful tool for accurate evaluation of the treatment efficacy in PCNSL.
PDF
