Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods.

BACKGROUND & AIM: Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples. METHODS & MATERIALS: Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy. RESULTS: The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively. CONCLUSION: The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Can employees' trust their supervisor? The role of high-performance work systems and stewardship climate on employee voice.

The focus of this research is to investigate the factors that influence employee voice behaviour by examining the integration of high-performance work systems, stewardship climate, and trust in supervisor. Drawing on social exchange theory and leader-member exchange, this study investigates the positive relationship between trust in supervisor, high-performance work systems, stewardship climate and employee voice. Data were collected in three stages from 376 Nigerian telecommunications customer-contact employees. Partial Least Squares-Structural Equation Modelling was used to test the dataset. The findings indicate that high-performance work systems have a favourable association with employee voice, while stewardship climate has an adverse correlation with employee voice. Moreover, trust in supervisor is found to mediate and enhance the favourable relationship between high-performance work systems, stewardship climate, and employee voice. The relevance of this study to service industries, management research, and its practical implications is discussed.

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy.

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model. This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study.

BACKGROUND: Fabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid's molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. RESULTS: The Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy. CONCLUSION: ATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

Plasma metabolomics profiles and breast cancer risk.

BACKGROUND: Breast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors. METHODS: We conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features. RESULTS: We found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%. CONCLUSIONS: If replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Beyond the hype: A TAM-based analysis of blockchain adoption drivers in construction industry.

In complementing our existing knowledge of blockchain technology adoption in the construction industry, this research investigated how the Technology Acceptance Model (TAM) applies to adopting blockchain technology among Saudi Arabian Construction companies. This study utilized cross-sectional data collection, causal research design, a quantitative research approach, and a simple random sampling technique to collect data from 248 Saudi Arabian construction companies. Partial least squares structural equation modelling (PLS-SEM) was utilized to analyze the data. The results showed that attitude toward adopting Blockchain (ATT) and perceived ease of use (PEU) are good predictors of behavioural intention to adopt Blockchain. The findings showed that when construction companies perceive the ease of use of Blockchain, they intend to adopt the technology. Attitude toward adopting Blockchain, directly and indirectly, influences behavioural intention to adopt the Technology. However, top management support only directly leads to intention once the companies perceive the usefulness of blockchain technology. This paper complements the existing literature on adopting Blockchain in the Saudi Arabian construction industry. The study provides insight into the influence of top management support, attitude toward adoption, and perceived ease of use in the blockchain adoption process.

Metabolomics analysis of Cucumis melo var. flexuosus organs in correlation to its anti-inflammatory activity aided by chemometrics.

Snake melon (Cucumis melo var. flexuosus, CM) is a gourd with health-promoting nutritional traits and unexplored phytochemicals. This study aims to comprehensively investigate the phytoconstituents in the fruits, leaves, roots, seeds, and stems of CM, using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry. Consequently, 118 metabolites were identified, encompassing phenolic compounds, flavonoids, megastigmanes, lignans, cucurbitacins, and fatty acids. Multivariate data analysis revealed differences in the metabolite composition of CM organs and correlated these variations with the potential in-vitro anti-inflammatory properties assessed against RAW 264.7 macrophages through the down-regulation of cyclo-oxygenase-Ⅱ, nuclear factor-kappa B, and tumor necrosis factor-α. The results indicated that leaf and seed extracts showed the highest anti-inflammatory activity due to their enrichment in several flavonoids, phenolic glycosides, and a megastigmane. These findings emphasize the health benefits of CM organs as potential functional foods and functional food by-products, serving as a natural source for developing new anti-inflammatory agents.

The driving mechanism of diverse land use types on dissolved organic matter characteristics of typical urban streams from Wuhan city.

Urban streams play a crucial role in water network connectivity, drainage and urban landscaping, and receive abundant allochthonous dissolved organic matter (DOM), which is derived from nature and human activities. However, the influence of diverse land use types on the spatiotemporal distribution and characteristics of DOM in typical urban streams is still not fully understood. Therefore, the water sample collection and data analysis campaign were carried out in Wuhan in summer and winter. By using parallel factor analysis (PARAFAC) model, two humic-like substances and one protein-like component were eventually identified, and the specific DOM charcteristics of urban streams in industrial area illustrated the impact of allochthonous DOM caused by anthropogenic activities on their properties. The characteristics of DOM presents spatiotemporal distribution differences, and the fluorescence intensity of DOM in summer was significantly higher than that in winter, mainly due to the variation of allochthonous input from precipitation and runoff. Significant differences of the DOM concentration and composition in urban streams under different vegetation coverage were found, indicating that extreme human disturbance and high vegetation coverage can both greatly change DOM characteristics in urban streams. Redundancy analysis (RDA) revealed an indirect driving effect of land use on DOM, and the influence was considerably stronger in summer. The partial least squares structural equation modelling (PLS-SEM) analysis showed that land use can directly affect DOM content of urban streams (-0.147), with anthropogenic land use playing a positive role and natural land use the reverse, and indirectly change DOM concentration by influencing DOM origin (0.128), nutrients (0.022) and heavy metals (0.021). Moreover, human social and economic structure in anthropogenic land use can affect DOM components and sources of urban streams. This study revealed the driving mechanism of land use impact on DOM characteristics and improve our understanding of DOM geochemical cycling in urban streams.

Water quality and microbial interaction network topological parameters were the key factors affecting litter decomposition under wetland restoration.

Wetland restoration has a significantly impacts biogeochemical cycles. However, the specific mechanisms and potential microbial processes by which wetland restoration influences litter decomposition remain unclear. This knowledge gap hinders our ability to accurately predict ecological processes within wetland ecosystems across various restoration timeframes. Therefore, we conducted an in situ experiment was conducted in wetlands in Northeast China using the litterbag method, involving a restoration time gradient. Natural wetlands served as controls, with wetlands restored for 17 years (R17), 3 years (R3), 2 years (R2), and 1 year (R1). The study focused on two typical wetland plants: reed (Phragmites australis) and sedge (Cyperus rotundus), to investigate the effects of wetland restoration on microbial community, as well as the decomposition of litter. During the experiment, the decomposition rates (k-values) of both types of litter were lower in restored wetlands than natural wetlands. Fungal diversity varied significantly between restored and natural wetlands. In the short term (R3), the community structures of bacteria and fungi in restored wetlands resembled those of natural wetlands. However, significant differences persisted in long-term restored wetlands (R17). Partial Least Squares Path Modeling (PLS-PM) revealed that bacterial network cohesion (network average density, network transitivity) and wetland restoration time are the primary drivers of Reed and Sedge litter decomposition. Overall, wetland restoration enhances litter decomposition by altering the physicochemical properties of water and the characteristics of the microbial interaction network, suggesting that wetland restoration can accelerate the material cycling processes within wetland ecosystems.

Effects of Changes in Pectin Constitution on Optical Properties and Firmness of Peach Flesh during Storage.

Understanding the fundamental light-sample interaction process is a crucial step toward the development of vibrational spectroscopy to determine fruit texture (i.e., firmness). This study aimed to investigate the effect of pectin constitution, including total pectin, water-soluble pectin, protopectin contents, and protopectin index (PI), on the optical properties and firmness of 'Baifeng' and 'Xiahui 8' peach flesh at the different softening degrees during postharvest storage of 6 days at 20 °C. The firmness of 'Baifeng' and 'Xiahui 8' peaches significantly (p < 0.05) changed with a decreasing rate from 90.3% to 92.2%. Peach firmness of these two cultivars correlated well with PI contents (r > 0.912) and showed good internal correlations with optical scattering properties. The light absorption coefficient (µa) and reduced scattering coefficient (µ's) at 600-1600 nm were measured using a single integrating sphere system combined with an inversion algorithm. This relationship of µa and µ's with peach firmness and pectin constitution was first analyzed. Notably, the specific µ's at 660 nm, 950 nm, 1203 nm, and 1453 nm showed a satisfactory prediction of peach firmness and PI of 'Xiahui 8' (R2 ≥ 0.926) and 'Baifeng' peaches (R2 ≥ 0.764), respectively. Furthermore, the prediction models were established based on partial least squares regression coupled with optical properties, and considerable prediction performances were obtained for tissue firmness (Rp2 ≥ 0.863) and PI based on µ's (Rp2 ≥ 0.802). Consequently, these results further verified that the spectroscopic prediction model for peach firmness could be related to the high correlations between PI in tissues and their optical scattering properties. Future research interests could include the development of optical absorption and scattering sensors for rapid and efficient determination of peach firmness.

The Performance of Partial Least Squares Methods in Virtual Nanosensor Array-Multiple Metal Ions Sensing Based on Multispectral Fluorescence of Quantum Dots.

The design of chemical sensors and probes is usually based on selective receptors for individual analytes, however, many analytical tasks are dedicated to multi-analyte sensing or recognizing properties of the sample related to more than one analyte. While it is possible to simultaneously use multiple sensors/receptors in such cases, multi-responsive probes could be an attractive alternative. In this work, we use thiomalic acid-capped CdTe quantum dots as a multiple-response receptor for the detection and quantification of six heavy metal cations: Ag(I), Cd(II), Co(II), Cu(II), Ni(II), and Pb(II) at micromolar concentration levels. Multiplexing is realized via multispectral fluorescence (so-called virtual sensor array). For such a sensing strategy, the effective decoding of the excitation-emission spectrum is essential. Herein, we show how various parameters of chemometric analysis by the Partial Least Squares method, such as preprocessing type and data structure, influence the performance of discrimination and quantification of the heavy metals. The established models are characterized by respective performance metrics (accuracy, sensitivity, precision, specificity/RMSE, a, b, R2) determined for both train and test sets in replicates, to obtain reliable and repeatable results.

Urine-based SERS and multivariate statistical analysis for identification of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer.

Bladder cancer (BC) is an epidemiological urologic malignancy that continues to increase each year. Early diagnosis and prognosis monitoring is always significant in clinical practice, especially in distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC), due to the various depths of tumor invasion related to different therapeutic schedules and recurrence rates. Common diagnostic approaches are too invasive or generally inefficient in accuracy and specificity. In this work, a totally non-invasive and cost-effective method is established by investigating urine samples using surface-enhanced Raman spectroscopy (SERS) and multivariate statistical analysis. The comparison of urine SERS spectra shows the intensities of characteristic peaks for DNA/RNA, hypoxanthine, albumin, D-( +)-galactosamine, fatty acids, and some amino acids are distinguishable in BC occurrence and invasion progression. A PLS-LDA-based two-step binary classification scheme is performed on urine SERS spectra and the diagnostic accuracies were 97.7% and 96.3% for healthy individuals versus BC patients and NMIBC versus MIBC patients, respectively. Moreover, the impact of urine SERS spectral lengths in reaching high-precision recognition of BC is investigated. The results show that the Raman peaks at 803, 893, 1139, 1375, and 1466 cm-1 play an essential role in correctly categorizing healthy control, NMIBC, and MIBC patients, and SERS spectra ranges from 400 to 1600 cm-1 are enough for this identification task. These findings provide a sensitive, label-free, rapid, and totally non-invasive way for assessment of invasion depth of BC to its early diagnosis and prognosis monitoring, as well as valuable insights for selecting reasonable spectral range to enhance the measurement efficiency especially in large-scale sample datasets.

Comparison between Traditional and Novel NMR Methods for the Analysis of Sicilian Monovarietal Extra Virgin Olive Oils: Metabolic Profile Is Influenced by Micro-Pedoclimatic Zones.

Nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice monovarietal extra virgin olive oils (EVOOs), grown in two zones that are different in altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet-developed data-processing called MARA-NMR (Multiple Assignment Recovered Analysis by Nuclear Magnetic Resonance). This method, taking around one-hour of experimental time per sample, faces the possible quantification of different class of compounds at different concentration ranges, which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices, and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-device analytical strategies. Monovarietal EVOO's composition is mainly affected by pedoclimatic conditions, in turn relying upon the nutritional properties, quality, and authenticity. Data collection, analysis, and statistical processing are discussed, touching on the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

Classification of Mass Spectral Data to Assist in the Identification of Novel Synthetic Cannabinoids.

Detection and characterization of newly synthesized cannabinoids (NSCs) is challenging due to the lack of availability of reference standards and chemical data. In this study, a binary classification system was developed and validated using partial least square discriminant analysis (PLS-DA) by utilizing readily available mass spectral data of known drugs to assist in the identification of previously unknown NCSs. First, a binary classification model was developed to discriminate cannabinoids and cannabinoid-related compounds from other drug classes. Then, a classification model was developed to discriminate classical (THC-related) from synthetic cannabinoids. Additional models were developed based on the most abundant functional groups including core groups such as indole, indazole, azaindole, and naphthoylpyrrole, as well as head and tail groups including 4-fluorobenzyl (FUB) and 5-Fluoropentyl (5-F). The predictive ability of these models was tested via both cross-validation and external validation. The results show that all models developed are highly accurate. Additionally, latent variables (LVs) of each model provide useful mass to charge (m/z) for discrimination between classes, which further facilitates the identification of different functional groups of previously unknown drug molecules.

Understanding metaverse adoption in education: The extended UTAUMT model.

The metaverse, an immersive virtual environment enabling users to engage with digital experiences, has the potential to revolutionize education. However, research pertaining to this area is still in its early stages. This study investigates the variables that influence the acceptance of educational metaverses and the intention to use them. It also presents an expanded model called the Unified Theory of Acceptance and Use of Metaverse Technology (UTAUMT) to provide guidance to educators and decision-makers. The study involved 253 Vietnamese teachers and students who had experience with metaverse, selected through purposive sampling. The UTAUMT provides a comprehensive framework that encompasses various factors, including metaverse performance expectancy (MPE), metaverse effort expectancy (MEE), metaverse social influence (MSI), metaverse hedonic motivation (MHM), metaverse price value (MPC), metaverse self-efficacy (MSE), and metaverse facilitating conditions (MFC). These factors were assessed using a self-administered questionnaire. The findings indicate that MEE, MSI, MFC, MSE, and MBI have a considerable impact on the educational metaverse MUB. The extended UTAUMT model makes a theoretical contribution by including metaverse-specific elements into the technology acceptance framework. The managerial implications focus on the integration of the metaverse, training of users, and providing support to enhance the adoption rate. This study explores the elements that contribute to the adoption of metaverse technology in education and contributes to the existing literature on the acceptance of metaverse technology.

Sustaining the mobile medical units to bring equity in healthcare: a PLS-SEM approach.

BACKGROUND: Equitable access to healthcare for rural, tribal, and underprivileged people has been an emerging area of interest for researchers, academicians, and policymakers worldwide. Improving equitable access to healthcare requires innovative interventions. This calls for clarifying which operational model of a service innovation needs to be strengthened to achieve transformative change and bring sustainability to public health interventions. The current study aimed to identify the components of an operational model of mobile medical units (MMUs) as an innovative intervention to provide equitable access to healthcare. METHODS: The study empirically examined the impact of scalability, affordability, replicability (SAR), and immunization performance on the sustainability of MMUs to develop a framework for primary healthcare in the future. Data were collected via a survey answered by 207 healthcare professionals from six states in India. Partial least squares structural equation modeling (PLS-SEM) was conducted to empirically determine the interrelationships among various constructs. RESULTS: The standardized path coefficients revealed that three factors (SAR) significantly influenced immunization performance as independent variables. Comparing the three hypothesized relationships demonstrates that replicability has the most substantial impact, followed by scalability and affordability. Immunization performance was found to have a significant direct effect on sustainability. For evaluating sustainability, MMUs constitute an essential component and an enabler of a sustainable healthcare system and universal health coverage. CONCLUSION: This study equips policymakers and public health professionals with the critical components of the MMU operational model leading toward sustainability. The research framework provides reliable grounds for examining the impact of scalability, affordability, and replicability on immunization coverage as the primary public healthcare outcome.

How are companies going to business digitalization? An explanation based on the implementation of e-commerce with data of SMEs.

The data presented in this article is based on a questionnaire survey regarding e-commerce and business digitalization of SMEs in Mexico answered by the CEOs of the companies. A valid sample of 4121 Mexican SMEs was collected covering many industries, such as primary sector, extractive industries, manufacturing industries, energy, water, recycling, construction, trade, services, and others. The data includes information about the implementation of e-commerce, business digitalization, operational efficiency, corporate performance, and other demographic indicators of the sampling firms. The link between e-commerce and corporate performance is still underexplored in SMEs, even more so between companies from Latin America, being Mexico a perfect example to explore how different SMEs adapt and thrive due to the rapid growth of their e-commerce and the diversity of their business sectors (Santos-Jaén et al., 2023 [2]). Data analysis was conducted using SPSS and Smart PLS. The data are useful as the data can be reproduced, reused and reanalyzed paying special attention to group-specific effects. This data article also opens up better research opportunities going forward through collaboration with other researchers.

Case Report: A case of immune hemolytic anemia after liver transplantation: passenger lymphocyte syndrome is the culprit.

Passenger lymphocyte syndrome (PLS) is most commonly observed after solid organ transplantation with minor ABO blood group incompatibility. It consists of a set of clinical symptoms brought on by the remaining lymphocytes of the donor organ developing antibodies against the recipient's antigens. Here, we describe a typical case of PLS in a type A+ recipient receiving a liver transplant from a type O+ donor. She suffered from jaundice, abnormally decreased hemoglobin level, and severe hemolytic anemia without bleeding. During hemolysis, we detected a positive direct antiglobulin test (DAT), and the thermal elution test revealed the presence of IgG anti-A antibodies in her serum. When immunosuppressive agents and blood transfusion were used together, cross-matched O+ washing red blood cells led to an expected outcome without side effects.