Platelets Strongly Induce Hepatocyte Proliferation with IGF-1 and HGF In Vitro

Background. It is well known that platelets have athrombotic effect. However, platelets play an importantrole not only in hemostasis but also in woundhealing and tissue regeneration. Platelets have beenreported to accumulate in the liver and promote liverregeneration after an extended hepatectomy, but themechanism is unclear. The present study was designedto clarify the mechanism by which plateletshave a direct proliferative effect on hepatocytes invitro.Materials and methods. Hepatocytes obtained frommale BALB/c mice by collagenase digestion and immortalizedhepatocytes (TLR2) were used. To elucidatethe mechanism of the proliferative effect of platelets,DNA synthesis of hepatocytes was measuredunder various conditions and the related cellular signalswere analyzed. Chromatographic analysis wasalso performed to clarify which elements of plateletshave mitogenic activity.Results. DNA synthesis significantly increased in thehepatocytes cultured with platelets (P < 0.001). However,when the platelets and hepatocytes were separated,the platelets did not have a proliferative effect.Whole disrupted platelets, the supernatant fraction,and fresh isolated platelets had a similar proliferativeeffect, while the membrane fraction did not. After theaddition of platelets, both Akt and extracellularsignal-regulated kinases ERK1/2 were activated, butextracellular signal-regulated kinase STAT3 was not activated. Some mitogenic fractions were obtainedfrom the platelet extracts by gel exclusion chromatography;the fractions were rich in hepatocyte growthfactor and IGF-1.Conclusions. Direct contact between platelets andhepatocytes was necessary for the proliferative effect.The direct contact initiated signal transduction involvedin growth factor activation. Hepatocyte growthfactor, vascular endothelial growth factor, and insulin-like growth factor-1, rather than platelet-derivedgrowth factor, mainly contributed to hepatocyteproliferation

Foxn1−β5t転写制御軸は胸腺でのCD8陽性T細胞生成を制御する

The thymus is an organ that produces functionally competent T cells that protect us from pathogens and malignancies. Foxn1 is a transcription factor that is essential for thymus organogenesis; however, the direct target for Foxn1 to actuate thymic T-cell production is unknown. Here we show that a Foxn1-binding cis-regulatory element promotes the transcription of β5t, which has an essential role in cortical thymic epithelial cells to induce positive selection of functionally competent CD8+ T cells. A point mutation in this genome element results in a defect in β5t expression and CD8+ T-cell production in mice. The results reveal a Foxn1-β5t transcriptional axis that governs CD8+ T-cell production in the thymus

Clinical Study Clinicopathological Factors Affecting Survival and Recurrence after Initial Hepatectomy in Non-B Non-C Hepatocellular Carcinoma Patients with Comparison to Hepatitis B or C Virus

We evaluated clinicopathological factors affecting survival and recurrence after initial hepatectomy in non-B non-C (NBNC) hepatocellular carcinoma (HCC) patients with comparison to hepatitis B or C virus, paying attention to relationship between alcohol consumption and histopathological findings. The medical records on the 201HCC patients who underwent initial hepatectomy between January 2000 and April 2013 were retrospectively reviewed. NBNC patients had higher prevalence of hypertension (47.4%), diabetes mellitus (35.5%), alcohol consumption (&gt;20 g/day) (61.8%), and preserved liver function than hepatitis B or C patients. The 5-year survival rate of NBNC patients (74.1%) was significantly better than hepatitis B (49.1%) or C (65.0%) patients (NBNC versus B, = 0.031). Among the NBNC patients, there was no relationship between alcohol consumption and clinicopathological findings including nonalcoholic fatty liver disease activity score (NAS). However, the 5-year OS and RFS rates in the alcoholunrelated NBNC patients tend to be better than in the alcohol-related. By multivariate analysis, independent factors for OS in NBNC patients were Child-Pugh B/C, intrahepatic metastasis (im), and extrahepatic recurrence. NBNC patients, who were highly associated with lifestyle-related disease and preserved liver function, had significantly better prognosis compared to hepatitis B/C patients; however, there was no association between alcohol consumption and histopathological findings

The dynamics of inter-organisational adversarial relationships in patenting

Much research has used patent data, deriving various useful insight into future innovation, technological advancement, collaborative knowledge creation, and so on. These existing studies consider patents as a positive sign of invention. However, inter-organisational relationships are fundamentally adversarial/rivalry, and patenting is companies’ core competitive strategic action. This essential aspect has not received much attention in patent network science, and data science in general. This study illuminates patent opposition as a sign of inter-organisational rivalry. A company can oppose a patent to challenge its validity within a certain period after grant. If an opposition is successful, the opposed patent is revoked and cannot take effect. Therefore, companies oppose rivals’ patents clearly intending to hinder their innovation activities. In this study, we constructed and analyzed the network in which the nodes represent companies (rather than patents), and the directed edges represent oppositions that occurred between 1980 and 2018. Data were collected from Orbis Intellectual Property Database [1]. We also added undirected ‘collaboration’ edges representing joint patent ownerships between companies. In social network analysis, negative ties and their interactions with positive ones have attracted increasing interest [2]. The difficulty here lies in obtaining data capturing such ties. Our data directly captures rivalry and collaborative relationships among companies, providing a great opportunity to study their emerging mechanisms and mutual interactions. Here, it must be noted that rivalry may be considerably different from other types of negative feelings (e.g., disliking), since a company consider others as rivals when it admits (and is threatened by) their high value. Indeed, we found that the opposition network exhibits heavy-tailed, power-law-like degree distribution and assortative mixing, differentiating it from other negative-tie networks reported in the existing studies*. We also conducted a temporal network motif analysis, with both opposition and collaboration edges taken into account. The results identified the structurally imbalanced triadic motifs and the temporal patterns of the occurrence of triads formed by a mixture of both types of edges*. Furthermore, we investigated when, and in which situations, companies oppose (or get opposed by) others. Figure 1(a) shows the distribution of proximity [4] between the patent portfolios of company pairs with no opposition edge between them (red) and those with opposition edges (blue). Indeed, oppositions occur when companies’ technological fields largely overlap (i.e., proximity close to 1). Figure 1(b) shows the distribution of proximity between the technological fields of patents that a given company opposed, and its own patent portfolio. The majority of companies oppose a patent that appeared in their core technological fields (proximity close to 1). However, there are a considerable number of companies opposing patents that appeared in the field where they have few (or no) patents (proximity close to 0). We found that these companies tend to publish patents in that field later on. That is, patent opposition may be a good predictor of companies’ strategic directions. (Notes: *: Some part of these results has been published in [5]. The rest of the results are not included in the publication.

Technology network focusing on “small” firms

The “technology space”, or “technology network”, is a network in which each technological field is a node and the weights of the edges connecting the nodes reflect the proximity of the fields. The technological network has been recognized for its usefulness in understanding the processes of technological development and convergence, and for predicting future developments. The proximity between any given technological field is measured by the co-occurrence of IPC codes within a firm's patent portfolio. By mapping a firm's patent activities on the technology network, it is possible to predict the fields in which the company should newly advance according to its technology portfolio [1]. Ref. [2] compared different methodologies for the prediction of firm’s future submission of patents in new sectors. While these studies provide us with very useful insights into technology development, they do not provide sufficient analysis or strategic planning suggestions at a firm level. They focused only on major firms, but the strategies for technology portfolio building of small firms are not necessarily the same as those of major firms. (Note that here, “small” means that a firm has a small technology portfolio – i.e., with only a small number of patents.) Nowadays, small firms such as start-ups with severely limited resources are the key to major technological innovation. In this study, we compare two technology networks: one constructed by capturing the proximity between technological field in the usual way (hereafter, RCA-based network), and one in which technology portfolios of small firms are also reflected in the proximity (hereafter, non-RCA-based network). The proximity between a given pair of technology fields in the conventional measure takes into account RCA (Revealed Comparative Advantage), which means that those firms with small portfolios or evenly diversified portfolios are neglected. Therefore, we used another major to construct the non-RCA-based network, which is simply not using RCA. The data we used were on patents filed between 2000 and 2020, extracted from Orbis IP, one of the largest patent databases. Panel A in the figure shows the annual change in the total number of firms whose portfolio information is included in the proximity calculations. It indicates that the portfolios of the majority of firms are actually not reflected in the conventional RCA-based network. That is appropriate when one wants to look at technology developments, but not when one wants to see where in the network new technologies may emerge (often driven by small firms) and how small firms should expand their portfolios. By comparing the two networks, we could understand which combinations of technological fields are underrepresented in the conventional RCA-based network (see Panel B). Furthermore, we classified patterns of temporal changes in proximities between technological fields. Our analysis captured multiple pattens such as small firms achieving a combination of rare technological fields and then major firms beginning to focus on that combination, or small firms expanding their portfolios into fields that major firms never even looked at in the first place