Análise histórica, sistemática e jurisprudencial da aplicação do artigo 32 da Lei de Propriedade Industrial

O direito de propriedade industrial, que protege patentes, dentre outros bens imateriais, é de grande importância para o desenvolvimento tecnológico das sociedades, com relevância no âmbito internacional, através de acordos e tratados entre Estados. As patentes protegem criações do intelecto humano com aplicabilidade industrial. No Brasil, a concessão de uma patente de invenção, cuja vigência é de 20 anos, deve atender ao disposto na Lei de Propriedade Industrial (LPI), através da atuação do Instituto Nacional de Propriedade Industrial (INPI). Este trabalho monográfico teve como objetivo estudar de que forma o dispositivo legal que regula modificações no quadro reivindicatório de pedidos de patente, o art. 32 da LPI, é aplicado pelo INPI e pelo judiciário. Esse dispositivo é de relevância, pois estabelece o marco temporal e material de mudanças nas reivindicações, que determinam o alcance da proteção que será outorgada e constituem o elemento de definição do direito do titular da patente. Para tanto, foi averiguado de que forma o INPI vem interpretando o art. 32 da LPI ao longo do tempo, particularmente, após a Ação Civil Pública impetrada pelo MPF, em 2003, contra o Instituto, e de que forma o judiciário e a doutrina discutem a aplicabilidade desse dispositivo

Osteoblasts and Bone Marrow Mesenchymal Stromal Cells Control Hematopoietic Stem Cell Migration and Proliferation in 3D In Vitro Model

BACKGROUND: Migration, proliferation, and differentiation of hematopoietic stem cells (HSCs) are dependent upon a complex three-dimensional (3D) bone marrow microenvironment. Although osteoblasts control the HSC pool, the subendosteal niche is complex and its cellular composition and the role of each cell population in HSC fate have not been established. In vivo models are complex and involve subtle species-specific differences, while bidimensional cultures do not reflect the 3D tissue organization. The aim of this study was to investigate in vitro the role of human bone marrow-derived mesenchymal stromal cells (BMSC) and active osteoblasts in control of migration, lodgment, and proliferation of HSCs. METHODOLOGY/PRINCIPAL FINDINGS: A complex mixed multicellular spheroid in vitro model was developed with human BMSC, undifferentiated or induced for one week into osteoblasts. A clear limit between the two stromal cells was established, and deposition of extracellular matrix proteins fibronectin, collagens I and IV, laminin, and osteopontin was similar to the observed in vivo. Noninduced BMSC cultured as spheroid expressed higher levels of mRNA for the chemokine CXCL12, and the growth factors Wnt5a and Kit ligand. Cord blood and bone marrow CD34(+) cells moved in and out the spheroids, and some lodged at the interface of the two stromal cells. Myeloid colony-forming cells were maintained after seven days of coculture with mixed spheroids, and the frequency of cycling CD34(+) cells was decreased. CONCLUSIONS/SIGNIFICANCE: Undifferentiated and one-week osteo-induced BMSC self-assembled in a 3D spheroid and formed a microenvironment that is informative for hematopoietic progenitor cells, allowing their lodgment and controlling their proliferation

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Gene expression profile of BMSC in 2D or 3D cultures.

<p>(A) A representative RT-PCR analysis is shown for osteo-induced BMSC cultured for 4 days as monolayers (2D ind) or as simple osteo-induced spheroids (3D ind), and non-induced BMSC cultured as monolayers (2D) or simple non-induced spheroids (3D). Blots correspond to the transcriptional factor RUNX2, the Notch ligands DELTA1 and JAG1 (Jagged-1), ANGPT1 (Angiopoietin-1), the inhibitor of Wnt pathway, DKK1, and SPP (Osteopontin). (B–E) Semiquantitative analysis is shown for GAPDH, CXCL12 (C), WNT5a (D), and KITLG (E). (n = 3, ± SEM).</p

Time-dependent migration of CD34⁺ cells into spheroids.

<p>At defined intervals, cells in the supernatant were collected and spheroids were harvested and trypsinized. (A) The proportion of CD34⁺ cells (R2) was determined by flow cytometry, and calculated as percentages of CD34⁺ cells inside the spheroids in relation to the total plated. (B) Dot plot of control spheroids without hematopoietic cells. (C) To distinguish migration from proliferation of cells inside the spheroids, hematopoietic cells were removed after 24 hours of co-culture, and the spheroids were maintained in culture for up to 48 hours. The number of CD34⁺ events inside the washed spheroids (closed symbols) was compared to the number of CD34⁺ events inside no washed spheroids (open symbols). (D) Time-dependent migration of CB CD34⁺ cells into simple non-induced (full line, dots), simple osteo-induced (dotted line, triangles), and mixed (dotted line, circles) spheroids. (E) The migratory profile of BM (closed squares) and CB (open squares) CD34⁺ cells in mixed spheroids is shown. Data are mean ± SEM.</p

Extracellular matrix distribution and cytoskeleton organization in simple non-induced and mixed spheroids.

<p>(A) Confocal microscopy of paraffin sections stained with H&E showing complex cellular interactions in the center of simple non-induced spheroids. (B–D) Picrosirius staining of simple non-induced (B) and mixed (C–D) spheroids showing, by optical (B–C) and confocal (D) microscopy, collagen fiber deposition restricted to the inner region of mixed spheroids. (E–J) Expression of ECM protein in mixed spheroids. Immunofluorescence staining (in green) for collagen I (E), laminin (F), collagen IV (G), osteopontin (H), and fibronectin (I–J). Osteo-induced BMSC were labeled with CM-DiI (red). A negative control is shown as an insert in (E). (K) Fibronectin expression in simple non-induced spheroids is shown for comparison. (L–O) Expression of α-SMA (L, N) and actin polymerization (M, O, phalloidin staining) in non-induced BMSC. Note the formation of stress fibers in monolayers (L–M) that are absent in 3D cultures (N–O). Nuclei were stained with DAPI (blue). (P–R) α-SMA expression (green) in mixed spheroids. Osteo-induced BMSC were labeled with CM-DiI (red in P, R). Numbers above scale bars represent the value (in micrometers) of each scale bar.</p

Migration of CD34⁺ cells in mixed spheroids is dynamic.

<p>CB and BM CD34⁺ cells were co-cultured with simple or mixed spheroids for 24 hours. The supernatant was removed and the spheroids were washed and maintained in culture for more 48 hours. Phase contrast microscopy of CB CD34⁺ cells emigrating from mixed spheroids at 24 hours (A) and 48 hours (B). Number above scale bar represents the value (in micrometers) of both scale bars. Percentage of CB and BM CD34⁺ cells that migrated out from mixed and simple osteo-induced spheroids was determined by flow cytometry (E). Unlabeled cells were co-cultured with mixed spheroids for 24 hours and then the supernatants were removed and the spheroids were washed. CFSE labeled CD34⁺ cells were added to these spheroids and the co-cultures were maintained for an additional 48 hours. Representative FACS analysis showing CD34⁺ cells that were CFSE⁻ or CFSE⁺ in supernatants (C) and spheroids (D) after 48 hours of co-culture. (F) Histogram showing the percentage of CD34⁺ cells that were positive or negative for CFSE in the supernatant or spheroids after 24 (open bar) and 48 hours (grey bar). Data represent average percentages (± SEM) of CFSE⁺ and CFSE⁻ among CD34⁺ cells in one experiment with duplicates. Similar results were obtained in a second independent experiment.</p

CD34⁺ cells localize at the vicinity of osteo-induced BMSC in mixed spheroids.

<p>(A) Semithin section of simple non-induced spheroids after 48 hours of co-culture with CB CD34⁺ cells, showing numerous hematopoietic cells homogeneously distributed throughout the spheroids, even at the center. Methylene Blue. (B) Ultrastructure of the co-cultures, showing a CD34⁺ cell in contact with stromal cell projections. (C–D) Mixed spheroids co-cultured for 72 hours with CB CD34⁺ cells. Hematopoietic cells (arrows) were aligned at the interface of the two stromal cell layers (C, arrowhead) or at the vicinity of osteoid tissue (* in D). H–E. (E–F) Clusters of CD34⁺ cells (inserts) are seen at the vicinity of the osteo-induced BMSC after 48 hours. Immunohistochemistry. (H–J) Confocal microscopy of mixed spheroids co-cultured for 72 hours with CFSE labeled CB CD34⁺ cells (green, H, J). Osteo-induced BMSC were labeled with CM-DiI (red, I–J) and nuclei, that was not confocalized, were stained with DAPI, (blue, G, J). Note that hematopoietic cells are located in close proximity to osteo-induced CM-Dil⁺ BMSC but actually at the transitional region between the two cell populations. Numbers above scale bars represent the value (in micrometers) of each scale bar. (Bars in inserts  = 30 µm).</p