Search Results (6,652)

The Mycobacterium tuberculosis complex (MTBC) represents one of the most significant bacterial pathogen groups affecting both animals and humans worldwide. This review provides a comprehensive analysis of MTBC species distribution across different animal hosts and evaluates current laboratory diagnostic methodologies for pathogen detection and identification. The complex comprises seven primary species: Mycobacterium bovis, M. caprae, M. tuberculosis, M. microti, M. canettii, M. africanum, and M. pinnipedii, each exhibiting distinct host preferences, geographical distributions, and pathogenic characteristics. Despite sharing >99% genetic homology, these species demonstrate variable biochemical properties, morphological features, and pathogenicity profiles across mammalian species. Current diagnostic approaches encompass both traditional culture-based methods and advanced molecular techniques, including whole genome sequencing. This review emphasises the critical importance of rapid, accurate detection methods for effective tuberculosis surveillance and control programmes in veterinary and public health contexts. Full article

Universal Stress Protein (USP) plays crucial roles in plant stress adaptation, yet their evolutionary dynamics, regulatory mechanisms, and functional diversification in rice (Oryza sativa) remain poorly understood. This study aimed to conduct a genome-wide identification and characterization of the OsUSP gene family to elucidate its role in abiotic stress responses using integrated bioinformatics approaches. Here, we identified 46 OsUSP genes that are unevenly distributed across 11 rice chromosomes and exhibit significant divergence in protein length, molecular weight, and subcellular localization. Phylogenetic analysis classified OsUSPs into three subfamilies, with conserved motif and domain architectures within groups but distinct structural variations across subfamilies. Evolutionary analysis revealed strong collinearity between rice and other monocots, which suggests functional conservation in grasses, whereas limited synteny with dicots indicates lineage-specific divergence. Cis-regulatory element analysis showed enrichment in ABA, MeJA, drought, and hypoxia response motifs, implicating OsUSPs in hormonal and stress signaling. Expression profiling indicated tissue-specific patterns, with subfamily III genes broadly expressed, while subfamily II members were anther-enriched. Stress response profiling revealed that 24 OsUSPs were significantly induced, while LOC_Os02g54590 and LOC_Os05g37970 emerged as particularly notable due to their broad-spectrum responsiveness, being upregulated under all tested stress conditions. Protein–protein interaction (PPI) analysis indicated that OsUSP proteins potentially interact with Leo1/TPR-domain proteins and are involved in stress response and phosphorylation signaling pathways. This study yields key insights into OsUSP-mediated stress adaptation in rice and pinpoints promising candidate genes to facilitate the breeding of climate-resilient rice. Full article

Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings. Full article

Despite advances in vaccination and treatment, the emergence of Long COVID cases has highlighted the continued public health concern posed by the disease. Studies on the early prediction of COVID-19 severity and the identification of associated biomarkers are decisive for preventing Long COVID. The objective is to utilise laboratory test data from patients diagnosed with COVID-19 and apply machine learning techniques to predict disease severity and identify associated biomarkers. From a university hospital in southern Brazil, we processed biochemical and haematological data from patients with COVID-19 (non-severe = non-ICU admission; severe = ICU admission). The data were used to train 15 machine learning algorithms to predict patient prognosis. The Light Gradient Boosting Machine (LGBM) model demonstrated the most effective performance in predicting the prognosis of patients with COVID-19, with accuracy, sensitivity, specificity, and precision values between 80 and 88%. Biomarkers associated with disease severity included Platelets, Creatinine, Erythrocytes, C-reactive protein, Lymphocytes, Albumin, Glucose, Urea, and Sodium. The results of this study demonstrate that machine learning, particularly LGBM, is an effective method for predicting the severity of COVID-19. Identifying specific biomarkers associated with disease severity is crucial for the early intervention and prevention of Long COVID, thereby improving clinical outcomes and patient management. LGBM maintained its performance across different age groups. Full article

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

The management of Meloidogyne incognita often depends on chemical nematicides, which pose environmental and health risks. This study investigated the potential of bacterial strains isolated from uncultivated native soil as biocontrol agents and plant growth-promoting rhizobacteria (PGPR) in tomato plants artificially infected with this nematode. Fifteen strains were screened in vitro for nematicidal and ovicidal activity, and four promising strains (307, GB16, GB24, and GB29) were selected for greenhouse trials. All strains reduced the nematode reproduction factor and the number of nematodes/g of root. Strains 307 and GB24 showed the highest reductions, 61.39 and 57.24%, respectively. Despite some positive physiological trends, Bacillus spp. did not promote a significant increase in plant growth. Metabolomic analysis revealed that the strains produced a wide range of primary metabolites with potential nematicidal activity. All strains also secreted proteases and chitinases, enzymes linked to nematode cuticle degradation. Preliminary identification based on the 16S rRNA gene and phylogenetic analysis grouped the four strains into the Bacillus subtilis group (strains GB16, GB29 and 307) or Bacillus cereus group (strain GB24); however, genome sequencing will be required in future studies. Overall, strains 307 and GB24 demonstrated strong biocontrol potential, supporting their use as sustainable and complementary alternatives to chemical nematicides. Full article

We report the study of seven commercially available rosehip oils (Rosa canina L.) using GC-MS, colorimetry (CIELab), UV-VIS, FTIR, and 3D EEM fluorescence spectroscopy, including using a smartphone spectrometer. GC-MS revealed two groups of oil samples with different chemical constituents: ω-6-dominant with 45–51% α-linolenic acid (samples S1, S2, and S5–S7) and ω-3-dominant with 47–49% α-linolenic, 7.3–19.1% oleic, 1.9–2.8% palmitic, 1.0–1.8% stearic, and 0.1–0.72% arachidic acid (S3, S4). In S1 PUFA content was found to be ~75% with ω-6/ω-3 ≈ 2:1. Favorable lipid indices of AI 0.0197–0.0302, TI 0.0208–0.0304, and h/H 33.0–50.6 were observed. The highest h/H (50.55) was observed in S5 and the lowest TI (0.0208) in S3. FTIR showed characteristic lines at ~3021, 2929/2853, 1749, and ~1370 cm⁻¹, and PCA yielded 60–80% variation and separated S1 from the rest of the samples, while the clusters grouped S5 and S6. The smartphone spectrometer also reproduced the individual differences in sample volumes ≤ 1 µL under 355–395 nm UV excitation. The non-destructive optical markers reflect the fatty acid profile and allow fast low-cost identification and quality control. An integrated control method including routine optical screening, periodic CG-MS verification, and chemometric models to trace oxidation and counterfeiting is suggested. Full article

This article presents a novel statistical approach for analyzing occupational accident data from the ESAW database, aiming to improve the evaluation and prediction of accident severity among specific groups of employees. The proposed method combines univariate and multivariate analytical techniques (effect size measures and classification tree methods: CHAID and CART) to identify employee groups that are both statistically robust and meaningfully distinct. The resulting model is based on six key variables describing employee and workplace characteristics, enabling accurate prediction of accident severity within these groups. The model demonstrates high reliability in predicting accident severity, achieving over 80% accuracy in a binary classification (high vs. low risk), making it a valuable tool for risk management and proactive safety planning. The findings have both theoretical and practical implications. Theoretically, the model’s strong predictive performance suggests that accident severity is not random but follows identifiable patterns linked to underlying risk factors that go beyond standard occupational or economic classification. Practically, the model allows for a more detail and effective categorization of work environments into high- and low-risk classes, and can support safety professionals, managers, and policymakers in achieving more precise identification of employee groups that are more prone to severe accidents. Full article

Genetic variants in nutrition-related genes exhibit variable functional consequences; however, systematic characterization across different nutritional domains remains limited. This highlights the need for detailed exploration of variant distribution and functional effects across nutritional gene categories. Therefore, the main objective of this computational study is to delve deeper into the distribution and functional impact of missense variants in nutrition-related genes. We analyzed Genetic polymoRphism variants using Personalized Medicine (GRPM) dataset, focusing on ten groups of nutrition-related genes. Missense variants were characterized using ProtVar for functional/structural impact, Pharos for functional classification, network analysis for pathway identification, and Gene Ontology enrichment for biological process annotation. The analysis of 63,581 Single Nucleotide Polymorphisms (SNP) revealed 27,683 missense variants across 1589 genes. Food intolerance (0.23) and food allergy (0.15) groups showed the highest missense/SNP ratio, while obesity-related genes showed the lowest (0.04). Enzymes predominated in xenobiotic and vitamin metabolism groups, while G-protein-coupled receptors were enriched in eating behavior genes. The vitamin metabolism group had the highest proportion of pathogenic variants. Network analysis identified apolipoproteins as central hubs in metabolic groups and inflammatory proteins in allergy-related groups. These findings offer insights into personalized nutrition approaches and underscore the utility of computational variant analysis in elucidating gene-diet interactions. Full article

Background: Colorectal cancer is the third most diagnosed cancer and a leading cause of cancer-related deaths worldwide. Early detection significantly improves patient outcomes, yet many cases are identified only at late stages. The high molecular and genetic heterogeneity of colorectal cancer presents major challenges in accurate diagnosis, prognosis, and therapeutic stratification. Recent advances in gene expression profiling offer new opportunities to discover genes that play a role in colorectal cancer carcinogenesis and may contribute to early diagnosis, prognosis prediction, and the identification of novel therapeutic targets. Methods: This study involved 142 samples: 84 primary tumor samples, 27 liver metastases, and 31 adjacent non-tumor tissues serving as controls. RNA sequencing was performed on a subset of tissues (12 liver metastases and 3 adjacent non-tumor tissues) using a targeted RNA panel covering 395 cancer-related genes. Data processing and differential gene expression analysis were carried out using the DRAGEN RNA and DRAGEN Differential Expression tools. The expression of six genes involved in hypoxia and epithelial-to-mesenchymal transition (EMT) pathways (SLC16A3, ANXA2, P4HA1, SPP1, KRT19, and LGALS3) identified as significantly differentially expressed was validated across the whole cohort via quantitative real-time PCR. The relative expression levels were determined using the ΔΔct method and log2FC, and compared between different groups based on the sample type; clinical parameters; and mutational status of the genes KRAS, PIK3CA, APC, SMAD4, and TP53. Results: Our results suggest that the expression of all the validated genes is significantly altered in metastases compared to non-tumor control samples (p < 0.05). The most pronounced change occurred for the genes P4HA1 and SPP1, whose expression was significantly increased in metastases compared to non-tumor and primary tumor samples, as well as between clinical stages of CRC (p < 0.001). Furthermore, all genes, except for LGALS3, exhibited significantly altered expression between non-tumor samples and samples in stage I of the disease, suggesting that they play a role in the early stages of carcinogenesis (p < 0.05). Additionally, the results suggest the mutational status of the KRAS gene did not significantly affect the expression of any of the validated genes, indicating that these genes are not involved in the carcinogenesis of KRAS-mutated CRC. Conclusions: Based on our results, the genes P4HA1 and SPP1 appear to play a role in the progression and metastasis of colorectal cancer and are candidate genes for further investigation as potential biomarkers in CRC. Full article

Acanthamoeba is a widespread free-living amoeba known as an opportunistic parasite of humans and other animals. It comprises several species, whose characterisation relies currently on the analysis of 18S rDNA sequences, recognising more than twenty genotypes; however, the distinction between closely related lineages remains unclear. In this study, the spacer region between the mitochondrial large and small subunits of rRNA genes was analysed for its usefulness as a marker for molecular typing. Previous studies have shown that the mitochondrial spacer contains a group of five transfer RNA (tRNA) genes, and that its length and sequence vary considerably between strains. A total of forty-two mitochondrial spacers were examined here, including twenty-five newly recovered sequences, from ten genotypes covering the three morphological groups of Acanthamoeba. The results showed that lineage-specific profiles can be defined for morphological groups 2 and 3 species (MG2 and MG3), with phylogenetic analysis consistent with that of rDNA, allowing for strain identification at the subtype level. In addition, morphological group 1 (MG1) species have a different tRNA gene arrangement distinguishing them from the others. Mitochondrial spacers therefore appear to be promising phylogenetic markers for the molecular typing of Acanthamoeba. Full article

Analysis of selection signatures in the genomes of farm animals enables the detection of genomic regions affected by selection and contributes to the identification of genes underlying adaptive and productive traits. This research aimed to identify loci under selection pressure and to detect candidate genes in Dzhalgin Merino sheep by performing a comparative genomic analysis with the related Australian Merino and Rambouillet breeds. A total of 293 animals were included in the analysis, comprising Dzhalgin Merino (n = 53), Australian Merino (n = 50), Australian Industry Merino (n = 88), and Rambouillet (n = 102). Whole-genome SNP genotyping data for Dzhalgin Merino were generated within this study, while data for Australian Merino, Australian Industry Merino, and Rambouillet were obtained from the SheepHapMap project. For the purposes of analysis, Australian Merino and Australian Industry Merino were combined into a single group (n = 138). To enhance the reliability of the results, three independent methods were employed to detect selection signatures: the fixation index (FST), analysis of linkage disequilibrium variation (varLD), and the cross-population number of segregating sites by length (xp-nSL). The study showed that Dzhalgin Merino have unique genetic signatures potentially associated with adaptation and productivity, which opens up new opportunities for their selection. The identified genes can become the basis for developing new breeding programs aimed at improving both the productive qualities and the adaptive abilities of the breed. Further research should be aimed at a detailed investigation of gene structure within loci under selection pressure and at clarifying the mechanisms by which these genes influence animal phenotypes. A total of 185 genes were identified within genomic regions exhibiting selection signatures. Among these, particular attention was given to EPHA6, MLLT3, ROBO1, KIAA0753, MED31, SLC13A5, and ELAVL4, which are involved in biological processes such as growth, development, and reproduction. The identified genes represent potential targets for breeding programs aimed at increasing productivity and adaptive capacity of the breed. Full article

China has the largest population of pigeons globally, particularly for commercial meat production. Due to insufficient emphasis on bloodline preservation, there is a significant occurrence of breed hybridization, which presents challenges to the differentiation and identification of various pigeon breeds. In this study, a single-nucleotide polymorphism chip was developed to elucidate genomic relationships and genetic diversity among 10 pigeon breeds, encompassing meat, racing, and ornamental varieties. Principal component analysis revealed that this resource population could be classified into three major clusters: homing and Tarim pigeons; the Dianzi (DZ) and Xinjiang Roller (XR) varieties; and commercial meat pigeon breeds, including the Euro-pigeon (EP), Danish King (DK), Silver King (SK), Yellow Carneau (YC), Red Carneau (RC), and Taishen (TS) varieties. Phylogenetic tree analysis indicated that the HP, TR, DZ, and XR varieties clustered into a large group. Of these, the HP and TR groups and the DZ and XR group were closely genetically related. Other meat pigeon varieties clustered into a large group. The genetic relationship between the YC and RC pigeons was intertwined, suggesting that although there were differences in feather color, the genetic backgrounds are similar. The phylogenetic tree results also demonstrated that the DK and SK pigeons had a considerable genetic distance, indicating that although the feather color was similar, the birds belong to two distinct genetic groups. The Pigeon 5 K liquid chip can effectively discriminate among different pigeon populations and provides a method for the identification and evaluation of pigeon germplasm resources, especially for pure breed identification and exploration of new resources. Full article

Biomphalaria pfeifferi snails serve as the major intermediate host for intestinal schistosomiasis in Sudan. The genetic structure and infection status of 163 B. pfeifferi collected from six localities in Gezira State, Sudan (East Gezira, Greater Wadmedani, Hasahisa, North Umelgura, South Gezira, and Managil) were characterized. Cytochrome oxidase subunit I (COI) and 16S ribosomal RNA (16S rRNA) mitochondrial genes were used for B. pfeifferi molecular identification and genetic diversity investigation. Schistosoma mansoni infection was detected using the traditional cercarial shedding and molecular methods (Sm^F/R primers). Five COI haplotypes and ten 16S haplotypes were identified, with haplotype diversity of 0.50 for COI and 0.11 for 16S. High evolutionary divergence was observed between groups (Fst = 0.94) for the COI, and low genetic divergence (Fst = 0.04) for the 16S, indicating genetic divergence among Sudanese B. pfeifferi, with the 16S showing lower divergence than the COI, consistent with a post-bottleneck population expansion. Cercarial shedding detected an overall infection prevalence of 3.6% (8/219), with only two snails from Hasahisa shedding S. mansoni cercariae. The Sm^F/R primers revealed a higher infection prevalence of 7.4% (12/163), with all S. mansoni positive samples found at the Hasahisa site. Findings highlight the value of molecular diagnostic tools for accurate surveillance and emphasize the need for site-specific control strategies. Full article

Difference in Differences (DiD) is a useful statistical technique employed by researchers to estimate the effects of exogenous events on the outcome of some response variables in random samples of treated units (i.e., units exposed to the event) ideally drawn from an infinite population. The term “effect” should be understood as the discrepancy between the post-event realisation of the response and the hypothetical realisation of that same outcome for the same treated units in the absence of the event. This theoretical discrepancy is clearly unobservable. To circumvent the implicit missing variable problem, DiD methods utilise the realisations of the response variable observed in comparable random samples of untreated units. The latter are samples of units drawn from the same population, but they are not exposed to the event under investigation. They function as the control or comparison group and serve as proxies for the non-existent untreated realisations of the responses in treated units during post-treatment periods. In summary, the DiD model posits that, in the absence of intervention and under specific conditions, treated units would exhibit behaviours that are indistinguishable from those of control or untreated units during the post-treatment periods. For the purpose of estimation, the method employs a combination of before–after and treatment–control group comparisons. The event that affects the response variables is referred to as “treatment.” However, it could also be referred to as “causal factor” to emphasise that, in the DiD approach, the objective is not to estimate a mere statistical association among variables. This review introduces the DiD techniques for researchers in economics, public policy, health research, management, environmental analysis, and other fields. It commences with the rudimentary methods employed to estimate the so-called Average Treatment Effect upon Treated (ATET) in a two-period and two-group case and subsequently addresses numerous issues that arise in a multi-unit and multi-period context. A particular focus is placed on the statistical assumptions necessary for a precise delineation of the identification process of the cause–effect relationship in the multi-period case. These assumptions include the parallel trend hypothesis, the no-anticipation assumption, and the SUTVA assumption. In the multi-period case, both the homogeneous and heterogeneous scenarios are taken into consideration. The homogeneous scenario refers to the situation in which the treated units are initially treated in the same periods. In contrast, the heterogeneous scenario involves the treatment of treated units in different periods. A portion of the presentation will be allocated to the developments associated with the DiD techniques that can be employed in the context of data clustering or spatio-temporal dependence. The present review includes a concise exposition of some policy-oriented papers that incorporate applications of DiD. The areas of focus encompass income taxation, migration, regulation, and environmental management. Full article