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BACKGROUND AND OBJECTIVES: The storage temperature of immunohaematological reagents generally ranges from 2 to 8°C, and they should be utilised at room temperature. This study aimed to analyse the stability of immunohaematological reagents used in ABO and RhD typing. METHODS: The evaluation encompassed the potency, specificity, and integrity of anti-A, anti-B, anti-D, RhD control sera, and A1 and B red blood cells (RBC) reagents after long (8 h) and short (4 h) daily periods of exposure to room temperature (20-24°C), 5 days a week for 4 weeks. Additionally, the A1 and B RBC reagents were exposed daily for 11 h and 30 min at room temperature, including 30 more minutes at room temperature with simultaneous homogenisation through equipment. For the control, an aliquot of each reagent was constantly stored at refrigeration temperature, while another was exposed to room temperature for 12 h daily. Tests conducted included reaction intensity, titration, and avidity for antisera, reaction intensity, free haemoglobin determination, and electrical conductivity for the RBC reagents. RESULTS: The antisera maintained the reaction intensity. The titre and avidity of the antisera satisfied the minimum Brazilian requirements after different exposure periods. A higher free haemoglobin concentration was noted in the RBC reagents subjected to room temperature and simultaneous homogenisation, although this did not affect the potency and specificity. The electrical conductivity average of the RBC reagent remained consistent. CONCLUSION: The findings indicate that the immunohaematological reagents from a specific manufacturer are stable under the tested temperature, ensuring the quality of the results under these conditions.

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Objective: To present the protocol and methods for the prospective longitudinal assessments-including clinical and digital phenotyping approaches-of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study, which comprises Brazilian adolescents stratified at baseline by risk of developing depression or presence of depression. Method: Of 7,720 screened adolescents aged 14 to 16 years, we recruited 150 participants (75 boys, 75 girls) based on a composite risk score: 50 with low risk for developing depression (LR), 50 with high risk for developing depression (HR), and 50 with an active untreated major depressive episode (MDD). Three annual follow-up assessments were conducted, involving clinical measures (parent- and adolescent-reported questionnaires and psychiatrist assessments), active and passive data sensing via smartphones, and neurobiological measures (neuroimaging and biological material samples). Retention rates were 96% (Wave 1), 94% (Wave 2), and 88% (Wave 3), with no significant differences by sex or group (p > .05). Participants highlighted their familiarity with the research team and assessment process as a motivator for sustained engagement. Discussion: This protocol relied on novel aspects, such as the use of a WhatsApp bot, which is particularly pertinent for low- to-middle-income countries, and the collection of information from diverse sources in a longitudinal design, encompassing clinical data, self-reports, parental reports, Global Positioning System (GPS) data, and ecological momentary assessments. The study engaged adolescents over an extensive period and demonstrated the feasibility of conducting a prospective follow-up study with a risk-enriched cohort of adolescents in a middle-income country, integrating mobile technology with traditional methodologies to enhance longitudinal data collection.

This article details the study protocol and methods used in the longitudinal assessment of 150 Brazilian teenagers with depression and at risk for depression as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo). Over 3 years, the authors collected clinical and digital data using innovative mobile technology, including a WhatsApp bot. Most adolescents participated in all the study phases, showing feasibility of prospective follow-up in a middle-income country. This approach allowed for a deeper understanding of depression in young populations, particularly in areas where mental health research is scarce.

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BACKGROUND: Congenital hypopituitarism (CH) and its associated syndromes, septo-optic dysplasia (SOD) and holoprosencephaly (HPE), are midline defects that cause significant morbidity for affected people. Variants in 67 genes are associated with CH, but a vast majority of CH cases lack a genetic diagnosis. Whole exome and whole genome sequencing of CH patients identifies sequence variants in genes known to cause CH, and in new candidate genes, but many of these are variants of uncertain significance (VUS). METHODS: The International Mouse Phenotyping Consortium (IMPC) is an effort to establish gene function by knocking-out all genes in the mouse genome and generating corresponding phenotype data. We used mouse embryonic imaging data generated by the Deciphering Mechanisms of Developmental Disorders (DMDD) project to screen 209 embryonic lethal and sub-viable knockout mouse lines for pituitary malformations. RESULTS: Of the 209 knockout mouse lines, we identified 51 that have embryonic pituitary malformations. These genes not only represent new candidates for CH, but also reveal new molecular pathways not previously associated with pituitary organogenesis. We used this list of candidate genes to mine whole exome sequencing data of a cohort of patients with CH, and we identified variants in two unrelated cases for two genes, MORC2 and SETD5, with CH and other syndromic features. CONCLUSIONS: The screening and analysis of IMPC phenotyping data provide proof-of-principle that recessive lethal mouse mutants generated by the knockout mouse project are an excellent source of candidate genes for congenital hypopituitarism in children.

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Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.

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The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

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Asthma drug responses may differ due to inflammatory mechanisms triggered by the immune cells in the pulmonary microenvironment. Thus, asthma phenotyping based on the local inflammatory profile may aid in treatment definition and the identification of new therapeutic targets. Here, we investigated protein profiles of induced sputum and serum from asthma patients classified into eosinophilic, neutrophilic, mixed granulocytic, and paucigranulocytic asthma, according to inflammatory phenotypes. Proteomic analyses were performed using an ultra-performance liquid chromatography (ultra-HPLC) system coupled to the Q Exactive Hybrid Quadrupole Orbitrap Mass Spectrometer. Fifty-two (52) proteins showed significant differences in induced sputum among the groups, while only 12 were altered in patients' sera. Five proteins in the induced sputum were able to discriminate all phenotypic groups, while four proteins in the serum could differentiate all except the neutrophilic from the paucigranulocytic inflammatory pattern. This is the first report on comparative proteomics of inflammatory asthma phenotypes in both sputum and serum samples. We have identified a potential five-biomarker panel that may be able to discriminate all four inflammatory phenotypes in sputum. These findings not only provide insights into potential therapeutic targets but also emphasize the potential for personalized treatment approaches in asthma management.

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Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device-LysipheN-assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.

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Despite being one of the main components of anxiety and playing a pivotal role in how an individual perceives and copes with anxiogenic situations or responds to a given treatment, trait anxiety is paradoxically omitted in most animal models of anxiety. This is problematic and particularly more concerning in models that are used to screen drugs and other treatments for specific anxiety disorders and to investigate their neurobiological mechanisms. Our group has been engaged in the search for specific anxiety-related traits in animal models of anxiety. We developed two new lines of rats with strong phenotypic divergence for high (Carioca High-conditioned Freezing [CHF]) and low (Carioca Low-conditioned Freezing [CLF]) trait anxiety as expressed in the contextual fear conditioning paradigm. Here, we summarize key behavioral, pharmacological, physiological, and neurobiological differences in one these lines, the CHF rat line, relative to randomized-cross controls and discuss how far they represent a valid and reliable animal model of generalized anxiety disorder and so high trait anxiety.

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The condition known as 22q11.2 deletion syndrome (MIM #188400) is a rare disease with a highly variable clinical presentation including more than 180 features; specific guidelines for screening individuals have been used to support clinical suspicion before confirmatory tests by Brazil's Craniofacial Project. Of the 2568 patients listed in the Brazilian Database on Craniofacial Anomalies, 43 individuals negative for the 22q11.2 deletion syndrome were further investigated through whole-exome sequencing. Three patients (6.7%) presented with heterozygous pathogenic variants in the KMT2A gene, including a novel variant (c.6158+1del) and two that had been previously reported (c.173dup and c.3241C>T); reverse phenotyping concluded that all three patients presented features of Wiedemann-Steiner syndrome, such as neurodevelopmental disorders and dysmorphic facial features (n = 3), hyperactivity and anxiety (n = 2), thick eyebrows and lower-limb hypertrichosis (n = 2), congenital heart disease (n = 1), short stature (n = 1), and velopharyngeal insufficiency (n = 2). Overlapping features between 22q11.2 deletion syndrome and Wiedemann-Steiner syndrome comprised neuropsychiatric disorders and dysmorphic characteristics involving the eyes and nose region; velopharyngeal insufficiency was seen in two patients and is an unreported finding in WDSTS. Therefore, we suggest that both conditions should be included in each other's differential diagnoses.

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BACKGROUND: Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA). PURPOSE: Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population. METHODS: Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors. RESULTS: Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as ß values. CONCLUSIONS: There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.

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Abstract Phosphorus (P) use efficiency is crucial for sorghum production. P acquisition efficiency is the most important component of P use efficiency. The early-stage evaluation of plant development is a useful tool for identifying P-efficient genotypes. This study aimed to identify sorghum hybrids that are efficient in P use efficiency and assess the genetic diversity among hybrids based on traits related to P acquisition efficiency. Thus, 38 sorghum hybrids and two inbred lines (checks) were evaluated under low and high P in a paper pouch system with nutrient solution. Biomass and root traits related to P efficiency were measured. There was no interaction between genotypes and P levels concerning all evaluated traits. The biomass and root traits, except root diameter, presented smaller means under low P than high P. Efficient and inefficient hybrids under each P level were identified. The genetic diversity assessment grouped these genotypes in different clusters. The hybrids AG1090, MSK326, AG1060, 1G100, AS 4639, DKB 540, and DKB 590 were superior under low-P and high-P. Hybrids SC121, 1236020 e 1167017 presented the lowest means than all other hybrids, under both conditions. The evaluated hybrids showed phenotypic diversity for traits related to P acquisition, such as root length and root surface area, which can be useful for establishing selection strategies for sorghum breeding programs and increasing P use efficiency.

Resumo A eficiência do uso do fósforo (P) é fundamental para a produção de sorgo. A avaliação no estágio inicial do desenvolvimento da planta é uma ferramenta útil para a identificação de genótipos eficientes de P. Este trabalho teve como objetivo identificar híbridos de sorgo que sejam eficientes ao uso de P e avaliar a diversidade genética entre os híbridos com base em características relacionadas à eficiência de aquisição de P. Assim, 38 híbridos de sorgo e duas linhagens (testemunhas) foram avaliados sob baixo e alto P em sistema de pastas de papel com solução nutritiva. Características de biomassa e de raiz relacionadas à eficiência de P foram mensuradas. Não houve interação entre genótipos e níveis de P em todas as características avaliadas. As características de biomassa e raiz, exceto o diâmetro da raiz, apresentaram médias menores sob baixo P em comparação com alto P. Híbridos eficientes e ineficientes sob cada nível de P foram identificados e agrupados quanto à diversidade genética. Os híbridos AG1090, MSK326, AG1060, 1G100, AS 4639, DKB 540 e DKB 590 foram superiores sob baixo-P e alto-P. Os híbridos SC121, 1236020 e 1167017 apresentaram as menores médias que todos os outros híbridos, em ambas condições. Os híbridos avaliados apresentaram diversidade fenotípica para características relacionadas à aquisição de P, como comprimento e área superficial da raiz, o que pode ser útil para estabelecer estratégias de seleção para programas de melhoramento de sorgo e aumentar a eficiência de uso do P.

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Phosphorus (P) use efficiency is crucial for sorghum production. P acquisition efficiency is the most important component of P use efficiency. The early-stage evaluation of plant development is a useful tool for identifying P-efficient genotypes. This study aimed to identify sorghum hybrids that are efficient in P use efficiency and assess the genetic diversity among hybrids based on traits related to P acquisition efficiency. Thus, 38 sorghum hybrids and two inbred lines (checks) were evaluated under low and high P in a paper pouch system with nutrient solution. Biomass and root traits related to P efficiency were measured. There was no interaction between genotypes and P levels concerning all evaluated traits. The biomass and root traits, except root diameter, presented smaller means under low P than high P. Efficient and inefficient hybrids under each P level were identified. The genetic diversity assessment grouped these genotypes in different clusters. The hybrids AG1090, MSK326, AG1060, 1G100, AS 4639, DKB 540, and DKB 590 were superior under low-P and high-P. Hybrids SC121, 1236020 e 1167017 presented the lowest means than all other hybrids, under both conditions. The evaluated hybrids showed phenotypic diversity for traits related to P acquisition, such as root length and root surface area, which can be useful for establishing selection strategies for sorghum breeding programs and increasing P use efficiency.

A eficiência do uso do fósforo (P) é fundamental para a produção de sorgo. A avaliação no estágio inicial do desenvolvimento da planta é uma ferramenta útil para a identificação de genótipos eficientes de P. Este trabalho teve como objetivo identificar híbridos de sorgo que sejam eficientes ao uso de P e avaliar a diversidade genética entre os híbridos com base em características relacionadas à eficiência de aquisição de P. Assim, 38 híbridos de sorgo e duas linhagens (testemunhas) foram avaliados sob baixo e alto P em sistema de pastas de papel com solução nutritiva. Características de biomassa e de raiz relacionadas à eficiência de P foram mensuradas. Não houve interação entre genótipos e níveis de P em todas as características avaliadas. As características de biomassa e raiz, exceto o diâmetro da raiz, apresentaram médias menores sob baixo P em comparação com alto P. Híbridos eficientes e ineficientes sob cada nível de P foram identificados e agrupados quanto à diversidade genética. Os híbridos AG1090, MSK326, AG1060, 1G100, AS 4639, DKB 540 e DKB 590 foram superiores sob baixo-P e alto-P. Os híbridos SC121, 1236020 e 1167017 apresentaram as menores médias que todos os outros híbridos, em ambas condições. Os híbridos avaliados apresentaram diversidade fenotípica para características relacionadas à aquisição de P, como comprimento e área superficial da raiz, o que pode ser útil para estabelecer estratégias de seleção para programas de melhoramento de sorgo e aumentar a eficiência de uso do P.

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This study sought to analyze whether an accurate diagnosis of the type and subtype of hepatic Glycogen Storage Diseases (GSDs) could be performed based on general clinical and biochemical aspects via comparing the proposed diagnostic hypotheses with the molecular results. Twelve physicians with experience in hepatic GSDs reviewed 45 real cases comprising a standardized summary of clinical and laboratory data. There was no relation between the hit rate and the time since graduation, the time of experience in GSD, and the number of patients treated during their careers. The average assertiveness was 47%, with GSD Ia and Ib being the best-identified types, while no expert correctly identified GSD IXc. Underage investigation for later manifestations, incomplete clinical description, and complementary analysis, the overvaluation of a specific clinical finding ("false positive") or the discarding of the diagnosis in the absence of it ("false negative"), as well as the lack of knowledge of the rarest GSD types, may have impacted the accuracy of the assessment. This study emphasized that characteristics considered as determinants in identifying the specific types or subtypes of GSD are not exclusive, thus becoming factors that may have induced the evaluators to misdiagnose.

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Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

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Background: Atopic dermatitis (AD) is a chronic skin condition that millions of people around the world live with each day. Performing research studies into identifying the causes and treatment for this disease has great potential to provide benefit for these individuals. However, AD clinical trial recruitment is a non-trivial task due to variance in diagnostic precision and phenotypic definitions leveraged by different clinicians as well as time spent finding, recruiting, and enrolling patients by clinicians to become study subjects. Thus, there is a need for automatic and effective patient phenotyping for cohort recruitment. Objective: Our study aims to present an approach for identifying patients whose electronic health records suggest that they may have AD. Methods: We created a vectorized representation of each patient and trained various supervised machine learning methods to classify when a patient has AD. Each patient is represented by a vector of either probabilities or binary values where each value indicates whether they meet a different criteria for AD diagnosis. Results: The most accurate AD classifier performed with a class-balanced accuracy of 0.8036, a precision of 0.8400, and a recall of 0.7500 when using XGBoost (Extreme Gradient Boosting). Conclusions: Creating an automated approach for identifying patient cohorts has the potential to accelerate, standardize, and automate the process of patient recruitment for AD studies; therefore, reducing clinician burden and informing knowledge discovery of better treatment options for AD.

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Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

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BACKGROUND: Because severe acute respiratory syndrome coronarivus 2 (SARS-CoV-2) leads to severe conditions and thrombus formation, evaluation of the coagulation markers is important in determining the prognosis and phenotyping of patients with COVID-19. METHODS: In a prospective study that included 213 COVID-19 patients admitted to the intensive care unit (ICU) the levels of antithrombin, C-reactive protein (CRP); factors XI, XII, XIII; prothrombin and D-dimer were measured. Spearman's correlation coefficient was used to assess the pairwise correlations between the biomarkers. Hierarchical and non-hierarchical cluster analysis was performed using the levels of biomarkers to identify patients´ phenotypes. Multivariate binary regression was used to determine the association of the patient´s outcome with clinical variables and biomarker levels. RESULTS: The levels of factors XI and XIII were significantly higher in patients with less severe COVID-19, while factor XIII and antithrombin levels were significantly associated with mortality. These coagulation biomarkers were associated with the in-hospital survival of COVID-19 patients over and above the core clinical factors on admission. Hierarchical cluster analysis showed a cluster between factor XIII and antithrombin, and this hierarchical cluster was extended to CRP in the next step. Furthermore, a non-hierarchical K-means cluster analysis was performed, and two phenotypes were identified based on the CRP and antithrombin levels independently of clinical variables and were associated with mortality. CONCLUSION: Coagulation biomarkers were associated with in-hospital survival of COVID-19 patients. Lower levels of factors XI, XII and XIII and prothrombin were associated with disease severity, while higher levels of both CRP and antithrombin clustered with worse prognosis. These results suggest the role of coagulation abnormalities in the development of COVID-19 and open the perspective of identifying subgroups of patients who would benefit more from interventions focused on regulating coagulation.

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Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, ß-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

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Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.

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In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.