This study aims to retrospectively analyze anatomical parameters for safe LCP performance and provide a technical note on its execution. We retrospectively analyzed 200 head computed tomography (CT) scans to identify the optimal LCP point (P), defined by the intersection of the anterior margin of the C1 posterior arch and the midpoint between the inferior C1 posterior arch and superior C2 lamina. Anatomical distances (D1, D2, D3) from this point to relevant landmarks were measured. Statistical analyses were performed using the Mann-Whitney U test and Student’s t-test to compare groups. A detailed technical note on LCP execution, including patient positioning, anatomical considerations, and procedural steps, is provided, augmented by an illustrative case. The mean age of the 200 patients (108 females, 92 males) was 63.67 ± 11.79 years. Mean distances were D1: 7.04 ± 2.09 mm, D2: 14.59 ± 4.94 mm, and D3: 12.17 ± 4.79 mm. No statistically significant differences in puncture distances were observed between sexes or age groups (≤ 60 vs. >60 years), except for D3 in women, where those over 60 years exhibited a significantly greater mean distance compared to younger women (p = 0.047). The illustrative case describes a 61-year-old male who developed a surgical site infection and required CSF analysis, for which LCP was successfully performed due to contraindication for LP, yielding clear CSF with negative cultures. LCP is a safe and effective alternative for CSF collection in scenarios where LP is contraindicated. Our anatomical findings, supported by the illustrative case, delineate consistent landmarks and suggest minimal anatomical variation that might necessitate slight adjustments, particularly for D3 in older women. The detailed technical approach combined with anatomical precision enhances the safety and efficacy of LCP, making it a valuable tool in neurosurgical practice.
Composite indicators simplify the understanding of multidimensional phenomena such as sustainability and economic performance. However, composite indicators can be constructed using different methods, generating uncertainty about which one to use. This study presents the Python tool S-CI-MaxS, with a Streamlit interface, which offers several functionalities. Automatic identification of the polarity and normalization function of subindicators. Construction of composite indicator by Principal Component Analysis, Benefit of the Doubt, Equal Weights, and Entropy methods. The novel maximum stability method that minimizes uncertainty in composite indicator classification while ensuring consistency between sub-indicator weights and expert opinion. Providing information demonstrating the method's superiority over more popular methods. These advantages of S-CI-MaxS are demonstrated in two case studies: 1.) "Affordable and Clean Energy Index" for Latin American countries; and 2.) "Photovoltaic Generation Potential Index" of Brazilian municipalities.
Technological advancements are transforming the healthcare sector, bringing solutions such as IoT and artificial intelligence, which enable faster diagnostics, near-real-time monitoring, and personalized care. However, challenges such as scalability, security, privacy, and interoperability still persist. This work proposes a distributed data management architecture for a Smart Healthcare environment, which integrates edge-fog-cloud computing, IoT, containers, and blockchain, and Apache Kafka for near real-time data streaming. The adoption of edge computing enables decentralized processing closer to data sources, reducing latency and improving responsiveness, while fog computing facilitates intermediate data aggregation and pre-processing, enhancing scalability and efficiency before reaching the cloud. The solution uses the Hyperledger Fabric blockchain network from the Amazon AWS cloud provider and the InterPlanetary File System (IPFS) content-addressable distributed storage system. The architecture is evaluated against a baseline solution, ensuring the integrity and auditability of information, and demonstrating the feasibility of its application in medical systems. The results demonstrate that the proposed approach significantly improves data access time, enhances system reliability, and security. The findings suggest that combining edge-fog-cloud layers is essential for scalable and secure healthcare data management, optimizing computational resources while ensuring data integrity and auditability. Furthermore, this research highlights future directions for Smart Healthcare, emphasizing emerging trends and opportunities to enhance interoperability and distributed technologies in digital health systems.
The objective of this study was to assess the responses of sorghum hybrids to drought and select the most significant morphophysiological indicators to differentiate the hybrids grown under water deficit and well-watered conditions. Two field experiments were conducted simultaneously, one under well-watered and the other under water deficit conditions after the pre-flowering stage, evaluating four contrasting grain sorghum hybrids (DKB 540, BRS 310, BRS 332, and 50A10), in a randomized block experimental design with four replications. Means were subjected to individual and joint analysis of variance, and the effects of water conditions and sorghum hybrids were compared using the F-test (p < 0.05) and the Tukey's test (p <= 0.05), respectively. Multivariate canonical variable analysis and Pearson correlation were also applied. Water deficit significantly reduced grain yield in 23.9%. The higher grain yields of the evaluated sorghum hybrids are associated with the higher relative chlorophyll content, photosynthetically active leaf area, panicle diameter and mass, grain mass per panicle, threshing index, and 100-grain mass, and the lower number of lodged plants. Grain mass per panicle, 100-grain mass, panicle mass, number of lodged plants, relative chlorophyll content, and leaf area are the most important indicators for explaining drought tolerance variations in grain sorghum hybrids grown under water deficit and well-watered conditions.
The objective of this study was to evaluate the agronomic performance and select biomass sorghum genotypes for growing in different regions of Brazil based on adaptability and stability analysis using the GGE biplot method. The 25 genotypes evaluated were from trials of value for cultivation and use (VCU) of biomass sorghum of the Brazilian Agricultural Research Corporation (Embrapa Maize and Sorghum) Breeding Program, conducted in eight locations across Brazil (Sobral, CE; Jaguari & uacute;na and Narandiba, SP; Nova Porteirinha and Sete Lagoas, MG; Planaltina, DF; Vilhena, RO; and Terra Rica, PR) during the 2021-2022 crop season. A randomized block experimental design with three replications was used. The following traits of were subjected to joint analysis of variance: plant height, flowering, and fresh and dry matter yields. The confirmation of genotype-by-environment interaction (GxE) was followed by adaptability and stability analysis using the GGE biplot method for all traits. The adjusted means were used to obtain the mean clustering using the Scott-Knott test (p < 0.05). Biomass sorghum genotypes showed a longer growth cycle, taller plants, and higher biomass yield than forage sorghum genotypes. The experimental sorghum hybrids 202129B014 and 202129B016 and the commercial hybrid BRS 716 can be recommended for fresh and dry matter production in all tested environments due to their high adaptability and stability.
