Cadastro Ambiental Rural (CAR) : instrumento agrário que visa o planejamento e monitoramento da propriedade rural

Orientador : Alessandro PanassoloMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Curso de Especialização em MBA em Gestão do AgronegócioInclui referência

Hearing threshold estimation by Auditory Steady State Responses (ASSR) in children

Hearing threshold identification in very young children is always problematic and challenging. Electrophysiological testing such as auditory brainstem responses (ABR) is still considered the most reliable technique for defining the hearing threshold. However, over recent years there has been increasing evidence to support the role of auditory steady-state response (ASSR). Retrospective study. Forty-two children, age range 3-189 months, were evaluated for a total of 83 ears. All patients were affected by sensorineural hearing loss (thresholds ≥ 40 dB HL according to a click-ABR assessment). All patients underwent ABRs, ASSR and pure tone audiometry (PTA), with the latter performed according to the child’s mental and physical development. Subjects were divided into two groups: A and B. The latter performed all hearing investigations at the same time as they were older than subjects in group A, and it was then possible to achieve electrophysiological and PTA tests in close temporal sequence. There was no significant difference between the threshold levels identified at the frequencies tested (0.25, 0.5, 1, 2 and 4 kHz), by PTA, ABR and ASSR between the two groups (Mann Whitney U test, p < 0.05). Moreover, for group A, there was no significant difference between the ASSR and ABR thresholds when the children were very young and the PTA thresholds subsequently identified at a later stage. Our results show that ASSR can be considered an effective procedure and a reliable test, particularly when predicting hearing threshold in very young children at lower frequencies (including 0.5 kHz)

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Integration in pregnancy and breastfeeding: the role of the pharmacist

openDiscussione sull'utilizzo dei principali integratori alimentari assunti durante la gravidanza e l'allattamento ed il ruolo del farmacist

Health lean management and Clinical risk management: first evidences from two cases

The connection between Health Lean Management and Clinical Risk Management is not studied in the literature yet, as this research stream is still at its early stage and require extensive investigations. Giving a contribution in filling in this gap, the purpose of this research is to investigate the significant features that characterize \u201clean and safety\u201d projects. Two projects have been selected from the same hospital in two different fields of application, in order to grasp useful findings from these innovative cases. Organizational aspects (actors, roles, operational mechanisms, procedures), phases followed, tools and practices adopted and the key factors for a successful \u201clean & safety\u201d project implementation are suggested in this paper. Comparing the two cases, evidences about the linkages between HLM and CRM have emerged. The results suggest that HLM be a support for CRM and an integrated methodology should be considered. First indications about how developing this new synergic methodology are provided, and they should stimulate future research, testing and exploiting them, also in other contexts, in order to contribute to the development of safer and sustainable health care system

First evidences from "lean & safety" projects

Purpose - No studies in the academic literature have yet focused on the relation between healthcare lean management (HLM) and clinical risk management (CRM) to enhance multiple aspects of performance although the possibility of implementing \u201clean & safety\u201d projects has been highlighted. The purpose of this research is to investigate the significant features that characterize these projects: organizational aspects, phases and activities, tools, techniques, practices and key factors for successful implementation. Design and methodology - Two significant projects have been selected from the same Italian hospital in two different fields of application. Within and cross-case analyses have been performed to obtain useful findings. Findings - The results suggest that HLM can provide support for CRM and an integrated methodology should be considered. The first indications concerning how this new synergistic methodology may be developed are provided and these should stimulate future research, testing and exploiting the methodology in other contexts. Originality - From the results of this research, guidelines for the implementation of a \u201clean & safety\u201d project could be developed to improve the management of clinical processes pursuing multiple objectives. This study could contribute to the development of safer and more sustainable health care systems for the benefit of the entire community