Search Results (10)

Body stalk anomaly (BSA) is a rare and usually fatal congenital disorder involving severe malformations of the body wall, limbs, spine, and internal organs. This study presents the first documented cases of BSA in seven dogs, offering new insights into how the disorder manifests in animals. The affected fetuses consistently exhibited major anomalies, including large abdominal wall defects, structural spinal abnormalities, and a variety of limb malformations ranging from partial agenesis and meromelia to phocomelia and complete amelia. Structural urogenital anomalies and orofacial clefts were also observed, aligning with similar findings in BSA cases reported in pigs and cats. These findings support the hypothesis of a multifactorial etiology involving early embryonic disruptions, such as abnormal folding of the embryo, rupture of the amniotic membrane, and vascular compromise. The frequent occurrence of abdominal wall defects alongside umbilical cord abnormalities further suggests a shared developmental pathway. This study also highlights the value of veterinary cases in comparative embryology and the need to assess congenital anomalies as part of a broader malformation complex. By expanding the phenotypic spectrum of BSA in domestic animals, this work contributes to a deeper understanding of its pathogenesis and emphasizes the importance of further research into the genetic and environmental factors involved. Such efforts could lead to improved classification and diagnosis of complex congenital malformations, as well as facilitate cross-species comparisons. Full article

Background/Objectives: Porcine circovirus type 2d (PCV2d) is the predominant genotype associated with porcine circovirus-associated disease (PCVAD), leading to significant economic losses. In South Korea, current vaccine lot-release testing relies on a T/C-ratio-based guinea pig assay, which lacks scientific justification and methodological robustness. This study aimed to develop and validate a statistically defined in-house ELISA using rabbit-derived polyclonal antibodies against PCV2d for the standardized evaluation of immunogenicity. Methods: Polyclonal IgG was generated by immunizing a rabbit with inactivated PCV2d, and it was purified through Protein A chromatography. Guinea pigs (n = 18) were immunized with IMMUNIS^® DMVac, an inactivated PCV2d vaccine candidate developed by WOOGENE B&G, at different doses. In-house ELISA parameters were optimized (antigen coating, blocking agent, and substrate incubation), and analytical performance was evaluated by ROC, linearity, reproducibility, and specificity. Sera from guinea pigs and pigs were analyzed under validated conditions. Results: The optimal performance was achieved using 10⁵ genomic copies/mL of the antigen coating and a 5% BSA blocking agent. The assay showed strong diagnostic accuracy (AUC = 0.97), reproducibility (CVs < 5%), and linearity (R² = 0.9890). Specificity tests with PCV2a, PCV2b, and PRRSV showed minimal cross-reactivity (<7%). The cross-species comparison revealed a positive correlation (R² = 0.1815) and acceptable agreement (bias = −0.21) between guinea pig and porcine sera. The validated cut-off (S/P = 0.4) enabled accurate classification across both species and aligned well with commercial kits. Conclusions: The in-house ELISA offers a robust, reproducible, and scientifically validated platform for immunogenicity verification, supporting its application in Korea’s national lot-release system. Homologous competition assays with PCV2d are planned to further confirm antigen specificity. Full article

Body stalk anomaly (BSA) is a complex congenital condition characterized by defects in the body wall, skeletal abnormalities, and an absent or abnormal umbilical cord (UC). A classification system for BSA, based on wall and skeletal abnormalities, is proposed and includes eight types observed in porcine models. These classifications consider combinations of thoracoabdominoschisis, abdominoschisis, anal atresia, genitourinary anomalies, and skeletal defects, including extreme retroflexion, scoliosis, vertebral agenesis, hemivertebrae, and thoracic or limb anomalies. While previous studies often treat limb-body wall complex (LBWC) as a separate condition, this study includes cases with co-occurring limb and spinal defects in a new classification, spinal-limb-body wall complex (SPLBWC). Additional skeletal classifications—spine-body wall complex (SPBWC), sternal-body wall complex (STBWC), and sternal-spinal-body wall complex (SSBWC)—are introduced to account for variations in structural anomalies. Nonstructural skeletal anomalies such as deformities, amputations, and arthrogryposis are excluded from the structural classifications. This comprehensive system, based on existing human and porcine models, provides a framework for the systematic categorization of BSA variations. Such an approach supports accurate diagnosis, enhances understanding of developmental defects, and improves clinical management and research outcomes in both veterinary and human medicine. Full article

The valorization of biological aggregates through the extraction of hydrogel-forming polymers can enhance the economics and sustainability of various processes in which bacteria are involved in organic waste transformation, such as wastewater treatment. Achieving these goals requires the development of a method capable of detecting the presence of gel-forming polymers in complex mixtures containing biopolymers that are most often unknown and uncharacterized. A miniaturized screening method capable of detecting gelation via ionic crosslinking using only 1 to 3 mg of the tested samples (commercial molecules or extracellular polymeric substances, EPSs) is proposed. The method consists of calculating a percentage of reactivity (%R) through UV-vis spectra and determining the percentage of gel volume (%Vg) formed after the addition of calcium. Both factors were combined to give a gelling factor (GF), and the test was applied to pure commercial molecules (BSA, DNA, alginate (ALV), and a mixture of them), allowing the classification of the following solutions according to their gel-forming capacity: GF_(ALV) > GF_(ALV+DNA) > GF_{(BSA+ALV+DNA)} > GF_(BSA+ALV) > GF_(DNA) > GF_(BSA+DNA) > GF_(BSA). As a relevant tool for screening hydrogel-forming solutions, the method was applied to the EPS extracted from aerobic granular sludge. The EPS (0.5% w/v) had a GF of 0.16 ± 0.03, equivalent to approximately half of the GF of ALV (0.38 ± 0.02 at 0.5% w/v). The developed test pushes the limits of the existing gel-detection techniques because it allows for quicker, less consuming, and more informative gelation detection through the use of simple methods that do not require sophisticated equipment. Full article

Liquid–liquid phase separation (LLPS) underlies the formation of membrane-free organelles in eukaryotic cells and plays an important role in the development of some diseases. The phase boundary of metastable liquid–liquid phase separation as well as the cloud point temperature of some globular proteins characterize the phase behavior of proteins and have been widely studied theoretically and experimentally. In the present study, we used a regression and classification neural network to deal with the phase behavior of lysozyme and bovine serum albumin (BSA). We predicted the cloud point temperature and solubility of a lysozyme solution containing sodium chloride by regression and the reentrant phase behavior of BSA in YCl₃ solution containing a surfactant dodecyl dimethyl amine oxide (DDAO) by classification. Specifically, our network model is capable of predicting (a) the solubility of lysozyme in the range: pH 4.0–5.4, temperature 0–25 °C, and NaCl concentration 2–7% (w/v); (b) the cloud point temperature of lysozyme in the range: pH 4.0–4.8, NaCl concentration 2–7%, and lysozyme concentration 0–400 mg/mL; and (c) the phase behavior of BSA in the range: DDAO 1–60 mM, BSA 30–100 mg/mL, and YCl₃ 1–20 mM. We experimentally tested the model at some prediction points with a high accuracy, which means that deep neural networks can be applicable in qualitative and quantitive analysis of liquid–liquid phase separation. Full article

The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer $B\_HBO$ are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer $B\_HBO$ are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that $B\_HBO$ is superior or equivalent to the other algorithms used in the literature. Full article

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions. Full article

Modern wireless networks such as 5G require multiband MIMO-supported Base Station Antennas. As a result, antennas have multiple ports to support a range of frequency bands leading to multiple arrays within one compact antenna enclosure. The close proximity of the arrays results in significant scattering degrading pattern performance of each band while coupling between arrays leads to degradation in return loss and port-to-port isolations. Different design techniques are adopted in the literature to overcome such challenges. This paper provides a classification of challenges in BSA design and a cohesive list of design techniques adopted in the literature to overcome such challenges. Full article

The purpose of this study is to propose a ranking system for the severity of psoriasis. The consensus method of selecting the indices to include and the classification of real patient profiles by an expert panel to create a gold standard of severity were used. The performance of potential cut-offs was evaluated to create a ranking algorithm. The combined use of PASI, BSA, and sPGA may allow the classification of the severity of psoriatic patients. The final algorithm identifies severe patients in a single step (2 out 3 are met: PASI ≥ 11 or BSA ≥ 10 or sPGA ≥ 3), while two steps are required for mild ((2 out 3 are met: PASI ≤ 3 or BSA ≤ 5 or sPGA ≤ 2) and DLQI < 5) and moderate forms (the patient does not meet 2 out 3 (PASI ≥ 11 or BSA ≥ 10 or sPGA ≥ 3) but has a DLQI ≥ 5. A ranking algorithm is presented, consisting of different measures of disease which classifies psoriatic patients into three categories: mild, moderate, and severe. Full article

Cefazolin is traditionally active against Escherichia coli, Klebsiella species, and Proteus mirabilis (EKP) isolates. The Clinical and Laboratory Standards Institute (CLSI) has twice updated cefazolin susceptibility breakpoints for EKP since 2010, but its role in the definitive treatment of cefazolin-susceptible EKP bacteremia remains debated. To assess its efficacy as a definitive agent, the 8-year cohort study consisted of 941 adults with monomicrobial cefazolin-susceptible EKP bacteremia, based on the CLSI criteria issued in 2019, was retrospectively established in a medical center. Based on the definitive antimicrobial prescription, eligible patients were categorized into the cefazolin (399 patients, 42.4%) and broader-spectrum antibiotic (BSA) (542, 57.6%) groups. Initially, fewer proportions of patients with fatal comorbidities (the McCabe classification) and the critical illness (a Pitt bacteremia score ≥4) at the onset and day 3 of the bacteremia episode were found in the cefazolin group, compared to the BSA group. After propensity-score matching, no significant difference of patient proportions between the cefazolin (345 patients) and BSA (345) groups was observed, in terms of the elderly, types and severity of comorbidities, bacteremia severity at the onset and day 3, major bacteremia sources, and the 15-day and 30-day crude mortality. In early outcomes, lengths of time to defervescence, intravenous (IV) antimicrobial administration, and hospitalization were similar in the two matched groups; lower costs of IV antimicrobial administration were observed in the cefazolin group. Notably, for late outcomes, lower proportions of post-treatment infections caused by antimicrobial-resistant pathogens (ARPs) and post-treatment mortality rates were evidenced in the cefazolin group. Conclusively, cefazolin is definitively efficacious and cost-effective for adults with community-onset cefazolin-susceptible EKP bacteremia in this one-center study, compared to BSAs. However, a prospective multicenter study should be conducted for external validation with other communities. Full article