Artificial intelligence in the clinical pharmacy service in a public hospital in Belo Horizonte/MG

Objective: to evaluate aspects related to the analysis of prescriptions by clinical pharmacists and the rate of medication-related errors after the implementation of an AI tool for the analysis of medical prescriptions in a large public teaching hospital in the city of Belo Horizonte/MG. Method: This is an observational study in which the results of the analysis of medical prescriptions performed in two periods were verified: the first (denoted BEFORE), period previously to the use of the AI tool (NoHarm.ai), in the months of March to September 2021; the second (named AFTER), comprises the same period in 2022, already in use of the AI tool. Results: In the BEFORE period, it was found that the rate of prescriptions evaluated was 0.6%, with an error rate of 13% and an average of 85 pharmaceutical interventions/month, which resulted in average savings of direct medication costs of R$1020.76/month. In the AFTER period, there was a 49$ 7848.39/month. Conclusion: The use of an AI tool contributed substantially to the pharmaceutical analysis of medical prescriptions with an average increase of 50% in the prescriptions evaluated, a 43-fold reduction in the number of errors and generated almost triple the number of pharmaceutical interventions after the implementation of the tool, in addition to the direct savings obtained with these interventions that increased sevenfold. The results of this study show that the use of an AI tool probable save of financial resources, increased productivity of the Clinical Pharmacy Service, and increased safety related to the medication use

CD44v6 high membranous expression is a predictive marker of therapy response in gastric cancer patients

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).In gastric cancer (GC), biomarkers that define prognosis and predict treatment response remain scarce. We hypothesized that the extent of CD44v6 membranous tumor expression could predict prognosis and therapy response in GC patients. Two GC surgical cohorts, from Portugal and South Korea (n = 964), were characterized for the extension of CD44v6 membranous immuno-expression, clinicopathological features, patient survival, and therapy response. The value of CD44v6 expression in predicting response to treatment and its impact on prognosis was determined. High CD44v6 expression was associated with invasive features (perineural invasion and depth of invasion) in both cohorts and with worse survival in the Portuguese GC cohort (HR 1.461; 95% confidence interval 1.002-2.131). Patients with high CD44v6 tumor expression benefited from conventional chemotherapy in addition to surgery (p < 0.05), particularly those with heterogeneous CD44v6-positive and -negative populations (CD44v6_3+) (p < 0.007 and p < 0.009). Our study is the first to identify CD44v6 high membranous expression as a potential predictive marker of response to conventional treatment, but it does not clarify CD44v6 prognostic value in GC. Importantly, our data support selection of GC patients with high CD44v6-expressing tumors for conventional chemotherapy in addition to surgery. These findings will allow better stratification of GC patients for treatment, potentially improving their overall survival.This work was funded by FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020–Operacional Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT–Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Inovação in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274). This work was also financed by the projects NORTE-01-0145-FEDER-000003 and NORTE-01-0145-FEDER-000029, supported by the Norte Portugal Regional Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF); project POCI-01-0145-FEDER-016390 and SAICTPAC/0022/2015, funded by ERDF, POCI, and FCT; project PTDC/CTM-NAN/120958/2010, from FCT; and by project PTDC/BTM-TEC/30164/2017 funded by ERDF funds through the COMPETE 2020–POCI, Portugal 2020, and by FCT. Salary support to G.M.A. by PTDC/BTM-TEC/30164/2017 project; C.P. was supported by the grant SFRH/BD/113031/2015 from FCT.info:eu-repo/semantics/publishedVersio

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications