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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.

Homozygous deletion (HD) of CDKN2A is one of the most promising biomarkers for predicting poor prognosis of IDH-mutant diffuse gliomas. The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) recommendations propose that IDH-mutant lower-grade astrocytomas with CDKN2A/B HD be classified as grade IV tumors. Loss of methylthioadenosine phosphorylase (MTAP) immunohistochemistry staining has been proposed as a surrogate of CDKN2A HD in various tumors but its performance has not been fully investigated in diffuse glioma. This study determined whether MTAP immunoreactivity could serve as a proxy for CDKN2A HD in adult-type diffuse glioma, thereby contributing to stratifying patient outcome. MTAP immunohistochemistry staining using clone EPR6893 was scored in 178 diffuse glioma specimens consisting of 77 IDH-mutant astrocytomas, 13 IDH-mutant oligodendrogliomas, and 88 IDH-wildtype glioblastomas. The use of MTAP immunohistochemical deficiency to predict CDKN2A HD was good for IDH-mutant astrocytomas (sensitivity, 88%; specificity, 98%) and IDH-wildtype glioblastomas (sensitivity, 89%; specificity, 100%), but poor for IDH-mutant oligodendrogliomas (sensitivity, 67%; specificity, 57%). Both CDKN2A HD and MTAP immunohistochemical deficiency were significant adverse prognostic factors of overall survival for IDH-mutant astrocytoma (P < 0.001 each), but neither were prognostically significant for oligodendroglioma or IDH-wildtype glioblastoma. IDH-mutant lower-grade astrocytoma with CDKN2A HD and deficient MTAP immunoreactivity exhibited overlapping unfavorable outcome with IDH-mutant glioblastoma. MTAP immunostaining was easily interpreted in 61% of the cases tested, but scoring required greater care in the remaining cases. An alternative MTAP antibody clone (2G4) produced identical scoring results in all but 1 case, and a slightly larger proportion (66%) of cases were considered easy to interpret compared to using EPR6893. In summary, loss of MTAP immunoreactivity could serve as a reasonable predictive surrogate for CDKN2A HD in IDH-mutant astrocytomas and IDH-wildtype glioblastomas and could provide significant prognostic value for IDH-mutant astrocytoma, comparable to CDKN2A HD.

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.

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI‐equipped software as a medical device (AI‐SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data‐driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI‐based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology. This review article investigates the current state of artificial intelligence (AI) in the field of cancer research. After first providing a historical perspective on AI and its introduction into medicine, this article described recent and current advances in AI‐based approaches and devices for medical imaging and omics analysis, particularly in the field of oncology, highlighting both approved systems and systems under development. Finally, this article critically reviewed the current state of AI research in medicine and described the main limitations and potential approaches to overcome them.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

Superabsorbent polymers (SAPs) are used as a soil amendment for retaining water, but suitable methods for the application of SAPs have not yet been developed. Here, we characterized a variety of soil–SAP mixtures prepared using four different types of SAP in terms of their water absorption and release characteristics. The teabag method was applied to characterize the soil–SAP mixtures, except for measurements of the matric potential. The results showed that the variations in water absorbency among the four SAPs in isolation became insignificant when they were mixed with sandy soils. The rates of water released from the soil–SAP mixtures under heated conditions were mitigated with decreasing water content, which prolonged the time until desiccation of the mixtures. The water absorbency of the SAPs significantly decreased in salt solutions (KCl and CaCl2), but their absorbency mostly recovered following immersion in tap water. The soil–dry SAP mixtures retained a larger amount of water than the soil–gel SAP mixtures. Swollen SAPs predominantly retained water in the range of −0.98 to −3.92 kPa, suggesting that SAP induces a transition from gravitational water to readily plant-available water by swelling itself. SAPs barely increased the amount of plant-available water in a potential range of −3.92 to −98.1 kPa, but significantly increased the soil water at <−98.1 kPa. The soil water content increased with an increasing SAP application rate, whereas the proportion of plant-available water declined. Our findings indicated that the performance of SAPs depends on the pore space and a saline environment in the soil and that low SAP application rates are suitable for maximizing the water available to plants in sandy soils.