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Abstract In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm−1 and 1285 cm−1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist’s interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.

Abstract The universe is a vast store of organic abiotic carbon that could potentially drive heterotrophy on habitable planets. Meteorites are one of the transporters of this carbon to planetary surfaces. Meteoritic material was accumulating on early Earth when life emerged and proliferated. Yet it is not known if this organic carbon from space was accessible to life. In this research, an anaerobic microbial community was grown with the CM2 carbonaceous chondrite Aguas Zarcas as the sole carbon, energy and nutrient source. Using a reversed 13C-stable isotope labelling experiment in combination with optical photothermal infrared (O-PTIR) spectroscopy of single cells, this paper demonstrates the direct transfer of carbon from meteorite into microbial biomass. This implies that meteoritic organics could have been used as a carbon source on early Earth and other habitable planets, and supports the potential for a heterotrophic metabolism in early living systems.

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C–D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C–D signature peak at 2,163 cm−1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.

Azo compounds such as the Sudan dyes I–IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I–IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I–IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q2) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10−6, 5.35 × 10−5, 9.40 × 10−6 and 1.84 × 10−6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (214 combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q2 for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples.

We report the use of a novel technology based on optical photothermal infrared (O-PTIR) spectroscopy for obtaining simultaneous infrared and Raman spectra from the same location of the sample allowing us to study bacterial metabolism by monitoring the incorporation of 13C- and 15N-labeled compounds. Infrared data obtained from bulk populations and single cells via O-PTIR spectroscopy were compared to conventional Fourier transform infrared (FTIR) spectroscopy in order to evaluate the reproducibility of the results achieved by all three approaches. Raman spectra acquired were concomitant with infrared data from bulk populations as well as infrared spectra collected from single cells, and were subjected to principal component analysis in order to evaluate any specific separation resulting from the isotopic incorporation. Similar clustering patterns were observed in infrared data acquired from single cells via O-PTIR spectroscopy as well as from bulk populations via FTIR and O-PTIR spectroscopies, indicating full incorporation of heavy isotopes by the bacteria. Satisfactory discrimination between unlabeled (viz. 12C14N), 13C14N- and 13C15N-labeled bacteria was also obtained using Raman spectra from bulk populations. In this report, we also discuss the limitations of O-PTIR technology to acquire Raman data from single bacterial cells (with typical dimensions of 1 × 2 µm) as well as spectral artifacts induced by thermal damage when analyzing very small amounts of biomass (a bacterium tipically weighs ~ 1 pg).

Um problema recorrente quando ensina-se conceitos matemáticos é falta de engajamento, por parte dos alunos, quanto ao conteúdo trabalhado. Como consequência, a teoria trabalhada não é absorvida por uma parte dos alunos e assim quando executada a etapa de fixação da teoria através de exercícios, por exemplo, percebe-se grande desmotivação, e em muitos casos, as tentativas de fixação da aprendizagem acabam sendo frustradas. Por outro lado, estamos diante de um rápido avanço no surgimento de novas tecnologias, que não vem sendo acompanhado pelos sistemas educacionais ao longo de todo o planeta, embora já exista uma grande convergência entre vários setores de alguns países para tal realidade. Uma tecnologia que vem ganhando cada vez mais espaço dentro dos sistemas de ensino, é a robótica. Em particular, dentro do processo de ensino e aprendizagem dos conceitos matemáticos, a robótica possui grande relevância. Desta forma, este trabalho apresenta uma maneira de combinar robótica com conceitos matemáticos, a fim de proporcionar o engajamento dos estudantes e consequentemente melhorar o desempenho dos alunos em relação a aprendizagem matemática. Mais precisamente, este trabalho apresenta uma proposta didática para o ensino de funções exponenciais, através da plataforma de simulação CoppeliaSim, utilizando o robô móvel Lumibot. A proposta é composta por cenários virtuais que ilustram problemas do mundo real, e um código em linguagem Python que pode ser editado por pessoas que não possuem um conhecimento aprofundado sobre programação. Ou seja, nesta pesquisa construímos cenários e criamos o algoritmo que dá sentido aos cenários, produzindo uma sequência didática que auxilia os professores a utilizarem o conceito de funções exponenciais, de tal modo a permitir que os estudantes associem a noção de gráfico desta função a resoluções de problemas reais.

Abstract Background Sarcopenia is a geriatric syndrome associated with negative health outcomes and the use of viable alternative screening tools may help in the diagnosis of this condition. This study aimed to analyze the association of sarcopenia with anthropometric indicators among community-dwelling older adults and to identify cut-off points for such indicators as a discriminant criterion for predicting sarcopenia. Methods This was a cross-sectional study conducted on community-dwelling older adults ≥60 years old (n = 411) of both sexes from Macapá, Amapá, Brazil. Socioeconomic, clinical and anthropometric data (arm circumference - AC, waist circumference - WC, calf circumference - CC and body mass index – BMI) were collected using a structured form. Sarcopenia was identified according to the EWGSOP 2 consensus. The association between anthropometric indicators and sarcopenia was performed using logistic regression and cut-off points established from the ROC Curve. Statistical significance was defined as p ≤ 0.05. Results Adjusted analysis indicated an independent and inverse association between sarcopenia and the anthropometric indicators: AC (odds ratio, OR: 0.63; 95% confidence interval, 95%CI: 0.53–0.76), CC (OR: 0.73; 95%CI: 0.62–0.85), WC (OR: 0.93; 95%CI: 0.90–0.97) and BMI (OR: 0.64; 95%CI: 0.53–0.76). The following cut-off points for older men and women represented the discriminant criterion for the presence of sarcopenia: WC (≤97 and ≤ 86 cm), CC (≤33 and ≤ 31 cm), AC (≤27 cm) and BMI (≤24.8 kg/m2 and ≤ 24.5 kg/m2) (area under the ROC curve superior to 0.70). BMI and AC were the indicators with the highest ability to discriminate older adults of both sexes with sarcopenia. Conclusions An increase of one unit of the indicators can reduce the probability of occurrence of sarcopenia. All indicators were considered to discriminate the occurrence of sarcopenia, with emphasis on BMI and AC, and could be used to screen for this condition among community-dwelling older adults.

Resumo Os efeitos da transformação digital vêm promovendo uma revolução na produção rural no Brasil nos últimos anos. Nesse contexto, espera-se que tal transformação reflita-se na performance e sustentabilidade do agronegócio. Este estudo propõe avaliar como se avolumou a produção científica sobre a temática da transformação digital, atinente ao agronegócio frutícola, no período 2010-2021, mediante um levantamento sistemático bibliográfico realizado na base eletrônica de dados da Web of Science. A pesquisa caracteriza-se como quali-quanti, com abordagem exploratória e descritiva. Os resultados, logrados com a tabulação dos dados, apontam, dentre outros achados, que a partir do ano de 2017 há uma tendência contínua de crescimento no número de publicações relacionadas à temática aqui pesquisada. Quanto ao mapeamento científico sob uma estrutura longitudinal, observou-se a comunicação entre os clusters Inovação e Fruticultura entre os anos de 2013 a 2017 e posteriormente, dos clusters Fruticultura e Transformação Digital entre os anos de 2017 e 2021. Tais resultados comprovam a ascensão da temática transformação digital como mecanismo inovador para o ecossistema empresarial do agronegócio frutícola.