Most Viewed Articles

Background: The health professional appears to be concerned given the number of questions surrounding the patient’s post-transplant survival, patient selection method, and the continuous search for scores that reflect their reality and reconcile the results they desire with the patient’s expectations. It is from these considerations that the present work was born with the purpose of understanding the reality of patients hospitalized for hepatocellular carcinoma and what adherence to the Model For End-Stage Liver Disease (MELD) criteria meant in Brazil. Methods: This study is a discrete, univariate time series on emergency hospitalizations that occurred between 2000 and 2018, in the city of São Paulo, Brazil. Python version 3.11 was the software used for statistical treatment and analysis of the time series. Results: There were 6887 hospitalizations for malignant neoplasm of liver and intrahepatic bile ducts, in the period 2000-2006, of which 2898 were elective and 3915 (56.85%) were urgent in the period. There was an increase of 63.97% in the number of emergency hospitalizations in relation to the period 2000-2006 and 2007-2013. To highlight, in 2013 there were 1270 emergency admissions, which represented an increase of 109.22% in relation to the number of emergency admissions in 2006. The contingency correlation analysis demonstrated a significant association between the variables in which the chi-square value was 82.18, the p-value was 1.24 x 10-19, and the critical value was 3.84 with one degree of freedom. There was a 123.65% increase in the number of hospitalizations for chronic hepatitis, not elsewhere classified, between the periods 2000-2006 and 2007-2013. The chi-square value of the contingency association was 221.22, with a p-value of 4.90 x 10-50, a critical value of 9.488, with a significance level of 5% and one degree of freedom. The study showed an increase in hospitalizations of 21.88% for alcoholic liver disease between the periods 2000-2006 and 2007-2013. In the period 2000-2006, there were 21330 hospitalizations, 19224 of which were urgent (90.13%). In the period 2007-2013, there were 25997 hospitalizations, of which 22,802 (87.71%) occurred urgently. The chi-square value was 68.95, the p-value was 1 x 10-16, and the critical value was 3.84, with one degree of freedom. Conclusion: The MELD score, as it is sensitive to the patient’s severity, selects those who are at an advanced stage of the disease for liver transplantation. In this scenario, patients do not necessarily have access to outpatient services and begin to attend them regularly when MELD identifies the advanced stage of the disease, opening a gap between prevention, treatment, and rehabilitation.

The bio-energy released by the hydrolysis of adenosine triphosphate, which relate to plenty of life activities and is transported in a solution, and its theory of transport are first stated and built in helix protein molecule. In order to confirm and verify the correctness of the transported theory we here systematically summarized and reviewed a great number of experimental investigation and evidences obtained by us and other researchers in past 30 years, involving the real existences of the solution and its features and lifetimes. In this survey we outlined and presented concretely the features of infrared spectra of absorption, Raman spectra and specific heat of the molecular crystal-acetanilide collagen, bivine serum albumin, myoglobin proteins and E.Coli. cell as well as the lifetimes of the solution in acetanilide and myoglobin measured by using pump-probe techniques and free-electron laser experiment, in which we give not only experimental data but also their comparisons with theoretical results. These experimental data and evidences provided here are enough to verify and affirm the true existences of the new solution, which can complete itself functions of bio-energy transport in the lifetime, and the correctness of the new theory of bio-energy transport in the acetanilide and protein molecule. Thus we can affirm the correctness of theory of the bio-energy transport in helix protein molecule, which can greatly promote the development of molecular biology.

Herbarium records provide a valuable historical database for assessing plant phenology shifts in the context of global climate change. The herbarium specimens, collected from diverse locations and periods, offer comprehensive data illustrating how many plants are altering their blooming times in response to global climate change. The appropriate use and analysis of long-term herbarium records offer an additional dimension for the study of plant phenology through the application of advanced experimental methodologies such as bioinformatics and satellite imagery, statistics, and Artificial Intelligence (AI) which, coupled with field observations, will improve ecosystems evaluation. These efforts can significantly contribute to conservation strategies and climate change mitigation and further support the synchronization of scientific inputs for evaluating the impacts of climate change and its ecological implications.

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins during or after protein biosynthesis. In the protein biosynthesis process, the ribosomal mRNA is translated into polypeptide chains, which may further undergo PTM to form the product of mature protein [1]. PTM is a common biological mechanism of both eukaryotic and prokaryotic organisms, which regulates the protein functions, the proteolytic cleavage of regulatory subunits or the degradation of entire proteins and affects all aspects of cellular life. The PTM of a protein can also determine the cell signaling state, turnover, localization, and interactions with other proteins [2]. Therefore, the analysis of proteins and their PTMs are particularly important for the study of heart disease, cancer, neurodegenerative diseases and diabetes [3,4]. Although the characterization of PTMs gets invaluable insight into the cellular functions in etiological processes, there are still challenges. Technically, the major challenges in studying PTMs are the development of specific detection and purification methods.

Laccase catalyzes oxidation of lignin and aromatic compound with similar structure to this one. Their low substrate specificity results on degradation of similar phenolic compounds. In this context, Molecular Docking was performed with different ligands suggesting potential bio-degradation. Binding active-sites prediction of fungal laccase (access number uniprotkb: A0A166P2X0), from Ganoderma weberianum was performed using machine learning algorithm based on Deep Convolutional Neural Networks (DeepSite-CNNs chemoinformatic tool). Herein, ligands like 2,4 - dichlorophenol, benzidine, sulfisoxazole, trimethoprim and tetracycline were analyzed and two additional reference controls which were 2,2 – azinobis 3 – ethylbenzothiazoline – 6 - sulfonic acid (ABTS) and 2,6 - dimetoxyphenol (2,6 DMP) were used in comparison with the other former mentioned ligands based on high laccase affinity. The five ligands were carried out because their potential biotechnological interest: the antibiotics sulfisoxazole, trimethoprim and tetracycline, and xenobiotics 2,4 - dichlorophenol and benzidine. Molecular docking experiments returned Gibbs free energy of binding (FEB or affinity) for laccase-ligand complexes. The best docking binding-interaction from each laccase-ligand conformation complexes suggest great ability of these ligands to interact with the laccase active-binding site. Herein, FEB values (kcal/mol) were obtained with higher affinity values for reference controls like 2,6 - dimethoxyphenol with -4.8 Kcal/mol and ABTS with -7.1 Kcal/mol. Furthermore, the FEB values were -4.7, -6.5, -6.8, -5.2 and -6.5 Kcal/mol, for 2,4 - dichlorophenol, benzidine, sulfisoxazole, tetracycline and trimethoprim respectively with high prevalence of hydrophobic interaction with functional laccase binding residues. Lastly, this study presents for first time at the bioinformatics field a molecular docking approach for the prediction of potential substrate of laccase from Ganoderma weberianum towards biotechnological application.

Purpose of the study:  To evaluate the performance of Best Practice Recommendation (BPR) compliance in reporting abdominal aortic aneurysm findings on imaging, comparing the results before and after its deployment.Methods: Best Practice Recommendations for AAA were deployed in 2020 at a large radiology practice site. Reports between January 2018 through October 2022 were reviewed, representing studies read prior to and subsequent to the implementation of the reporting standards. Cases of abdominal aortic aneurysms ≥ 2.6 cm were counted by year. Adherence to the BPR for each year was calculated as [total number of confirmed cases of ≥ 2.6 cm AAAs with compliant reports] * 100 / [the total number of confirmed ≥ 2.6 cm AAAs]. A secondary analysis was performed to determine whether there was a statistically significant difference in the proportion of BPR-compliant reports for AAA cases before (from 2018 to 2019) and after (from 2020 to 2022) BPR deployment using a chi-square test. Results: From January 2018 to December 2022, there were 8,693 reports referencing AAA. After excluding cases of suspected AAA (N = 2,131), confirmed AAAs with indeterminate sizes (N = 103), and confirmed AAAs with sizes < 2.6 cm (N = 85), the number of AAA cases ≥ 2.6 cm in size was 6,374. Concordance with the BPR standards for the remaining cases with sizes ≥ 2.6 cm were 1.6% and 4.1% in 2018 and 2019, respectively. Post-implementation of BPRs, there was a substantial improvement in guideline adherence to 32.1%, 84.3%, and 83.6% in 2020, 2021, and 2022, respectively. In general, the proportion of BPR-compliant reports of AAA cases in the pre-deployment (3.6%) period statistically differs (p - value < 0.0001) from those in the post-deployment period (73.9%)Conclusion: Adherence to reporting standards increased after the BPR deployment in 2020. The inclusion of management recommendations in the radiology report when AAA is identified is a simple and cost-effective way of improving outcomes for patients with AAAs through appropriate follow-up treatment.

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Background: Human 30kb coronaviruses entered through the ACE-2 receptors causing fibrosis of the lungs and causing six million deaths worldwide. Here, we have investigated the mutations, deletions and insertions of the recent JN.1 omicron coronaviruses to demonstrate that coronaviruses have reached the pre-elimination stage. Methods: We multi-aligned the genomes of recent JN.1 variants using NCBI Virus Portal and CLUSTAL-Omega. The spike proteins are multi-aligned using MultAlin software and CLUSTAL-Omega.Results: The 17MPLF spike insertion was confirmed to compensate 24LPP, 31S, 69HV, 145Y, 211N and 483V deletions. The 49nt deletions in the 3’-UTR were found in 4997 JN.1 sequences although 26nt deletion was initiated previously in JN.1 as well as BA.5, BF.7, BQ.1 and XBB.1.5 omicron viruses. We first compare 3-D structures of spike proteins with or without 17MPLF four amino acids insertion and nine amino acids deletions using SWISS MODELLING. The JN.1 viruses caused a more stable trimeric spike involving Thr342, Lys436, Lys440, His441, Ser442, Gly443, Tyr445, Lys479, Ser489, Tyr490, Arg493, Pro494, Thr495, and Gln501 amino acids to interact with ACE-2 receptors. The FLiRT spike mutations were found in most KP.2 variants and other changes occurred at the NH2 terminus.Conclusion: We claimed that pre-death changes were initiated in JN.1 COVID-19 lineages and computer simulation showed that the Howard spike with 17MPLF spike insertion appeared more stable than the Oppentrons-spike without 17MPLF insertion. Surely, conflicts of COVID-19 spike sequences must be resolved.

To expand the spectrum of used radiopharmaceuticals, it is proposed to obtain a positron-emitting isotope of technetium 94mTc. The intention of this work is to research the possibility of producing various technetium isotopes on a medical cyclotron. For this purpose, we carried out a series of irradiations of an aqueous solution of molybdenum of natural isotopic composition with protons of 11 MeV energy. After technetium isolation, results were analyzed on a γ-spectrometer. 511 keV gamma-ray line was obtained.

This study was conducted in Nyala, South Darfur State, Sudan during August-September 2015 to study the seroprevalence of ovine and caprine theileriosis and to identify the tick infesting sheep and goats. For this purpose, total of 150 samples (ticks, blood smear and sera) were collected from sheep (n=61) and goats (n=89) of different age groups, breed and both sex. Three age groups were included: less than one-year-old, one-two years-old and more than two-year-old. Two diagnostic techniques were used, blood smears, and indirect fluorescent antibody test (IFAT). Out of 150 samples, 9 (6%) were positive for Theileria spp. piroplasms in blood smears, and 81 (54%) were positive for Theileria lestoquardi antibodies. Out of 61 sheep, 3 (4.9%) were positive for Theileria spp. piroplasms, and 25 (41%) were positive for T. lestoquardi antibodies. Out of 89 goats, 6 (6.7%) were positive for Theileria spp. piroplasms, and 56 (62.9%) were positive for T. lestoquardi antibodies. The highest prevalence of T. lestoquardi was recorded among sheep and goats more than two-year-old. Two genera and 4 species of ticks were infested sheep and goats. These included Rhipicephalus evertsi evertsi, R. s. sanguineus, Amblyomma variegatum and A. lepidum. The study concluded that the malignant ovine theileriosis is endemic in Nyala town.