Assessing the effectiveness of fenbendazole supramolecular complexes against schistosomiasis, monieziosis and parabronemosis in sheep.

Helminth infections pose a significant economic threat to livestock production, causing productivity declines and, in severe cases, mortality. Conventional anthelmintics, exemplified by fenbendazole, face challenges related to low solubility and the necessity for high doses. This study explores the potential of supramolecular complexes, created through mechanochemical modifications, to address these limitations. The study focuses on two key anthelmintics, praziquantel (PZQ) and fenbendazole (FBZ), employing mechanochemical techniques to enhance their solubility and efficacy. Solid dispersions (SD) of PZQ with polymers and dioctyl sulfosuccine sodium (DSS) and fenbendazole with licorice extract (ES) and DSS were prepared. The helminthicidal activity of these complexes was assessed through helminthological dissections of sheep infected with Schistosoma turkestanicum, moniesiasis, and parabronemosis. In the assessment of supramolecular complex of FBZ (SMCF) at doses ranging from 1.0 to 3.0 mg/kg for the active substance (AS), optimal efficacy was observed with the fenbendazole formulation containing arabinogalactan and polyvinylpyrrolidone at a 3.0 mg/kg dosage. At this concentration, the formulation demonstrated a remarkable 100% efficacy in treating spontaneous monieziosis in sheep, caused by Moniezia expansa (Rudolphi, 1810) and M. benedenii (Moniez, 1879). Furthermore, the SMCF, administered at doses of 1.0, 2.0, and 3.0 mg/kg, exhibited efficacy rates of 42.8%, 85.7%, and 100%, respectively, against the causative agent of parabronemosis (Parabronema skrjabini Rassowska, 1924). Mechanochemical modifications, yielding supramolecular complexes of PZQ and FBZ, present a breakthrough in anthelmintic development. These complexes address solubility issues and significantly reduce required doses, offering a practical solution for combating helminth infections in livestock. The study underscores the potential of supramolecular formulations for revolutionizing helminthiasis management, thereby enhancing the overall health and productivity of livestock.

Differential diagnosis of theileriosis through blood smear examination and polymerase chain reaction in small ruminants from Pakistan.

Background: Ovine and caprine theileriosis is a tick-borne hemoprotozoan disease, caused by Theileria spp., responsible for heavy economic losses in terms of high mortality and morbidity rates. Diagnosis of ovine theileriosis is primarily based on clinical symptoms, microscopic screening of stained blood smears, and lymph node biopsy smears, but the limitations of these detection methods against Theileria spp. infection limits their specificity. Aim: To overcome these limitations, the current study reports the differential diagnosis of theileriosis through a blood smear examination and polymerase chain reaction (PCR) in small ruminants from Pakistan. Methods: The study was conducted on 1,200 apparently healthy small ruminants (737 sheep and 463 goats). First, blood smears were screened for the presence of Theileria piroplasms in red blood cells. Second, PCR amplification based on 18S rRNA gene was performed by using primers specific to Theileria spp. Results: Out of the 1,200 samples of examined blood smears, 100 animals (8.33%) were found positive for Theileria species, which showed intra-erythrocytic bodies in the form of dot and comma shapes. Amplification of the isolated DNA from randomly collected blood samples of 737 sheep and 463 goats showed that an amplicon size of 1,098 bp was positive for Theileria spp. In total, 315 out of the 1,200 small ruminants examined in this study were found positive for Theileria spp. DNA through PCR amplification. Notably, out of the 885 blood samples negative by PCR amplification, only 15 blood samples were found positive by the blood smear test. Conversely, 230 blood samples that tested negative in the smear technique produced a specific band through PCR amplification. Overall, the sensitivity and specificity rates were 26.98% and 98.31% for the blood smear method and 73.01% and 100% for the PCR assay, respectively. Conclusion: Our finding suggests that PCR is the gold standard method compared to the conventional method of smear examination for the diagnosis of ovine and caprine theileriosis in Pakistan.

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning.

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence.This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient's individual requirements. The abundance of today's healthcare data, dubbed "big data," provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise.The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Absence of GSTT1 and polymorphisms in GSTP1 and TP53 are associated with the incidence of acne vulgaris.

BACKGROUNDS: Acne vulgaris is a chronic inflammatory skin disease of the pilosebaceous unit affecting most teenagers and numerous adults throughout the world. The present study was designed to assess the association of the presence or absence of GSTM1, GSTT1, and single nucleotide polymorphisms rs1695 in GSTP1 and rs1042522 in TP53 gene with acne vulgaris. METHODS: The cross-sectional case-control study was conducted at the Institute of Zoology from May 2020 to March 2021 and included acne vulgaris patients (N = 100) and controls (N = 100) enrolled in Dera Ghazi Khan district, Pakistan. Multiplex and tetra-primer amplification refractory mutation system-polymerase chain reactions were applied to investigate the genotype in analyzed genes. The association of rs1695 and rs1042522 with acne vulgaris was studied either individually or in various combinations with GATM1 and T1. RESULTS: A significant association of absence of GSTT1 and mutant genotype at rs1695 (GG) and at rs1042522 (CC) in GSTP1 and TP53, respectively, was found to be associated with acne vulgaris in enrolled subjects. Subjects aged 10-25 years and smokers were more susceptible to acne vulgaris. CONCLUSION: Our results suggest that genotypes of glutathione S-transferases (GSTs) and TP53 are involved in protection against oxidative stress and may influence disease progression in acne vulgaris.

Acaricidal, Larvacidal, and Repellent Activity of Elettaria cardamomum Essential Oil against Hyalomma anatolicum Ticks Infesting Saudi Arabian Cattle.

Background: In this experimental study, we aimed to assess the acaricidal effects of Elettaria cardamomum L. essential oil (ECEO) against Hyalomma anatolicum tick in cattle from Saudi Arabia. Methods: Gas chromatography-mass spectrometry (GC-MS) was performed to identify the chemical composition of ECEO. The acaricidal, larvicidal, and repellent activity of ECEO against H. anatolicum was studied through the adult immersion test (AIT), the larval packet test (LPT), the vertical movement behavior of tick's larvae technique, anti-acetylcholinesterase (AChE) activity, and oxidative enzyme activity. Results: By GC/MS, the most compounds were 1,8-cineole (34.3%), α-terpinyl acetate (23.3%), and α-pinene (17.7%), respectively. ECEO significantly (p < 0.001) increased the mortality rate as a dose-dependent response. After ECEO Treatment, number of eggs, egg weight, and hatchability significantly declined as a dose-dependent response. ECEO at concentrations of 5 µL/mL and above completely killed the larva. The LC50 and LC90 values for ECEO were 1.46 and 2.68 µL/mL, respectively. ECEO at concentrations of 10, 20, and 40 µL/mL showed 100% repellency activity up to 60, 120, and 360 min incubation, respectively. ECEO, especially at ½ LC50 and LC50, significantly inhibited GST and AChE activities of H. anatolicum larvae compared to the control group. Conclusions: We found promising adulticidal, larvicidal, and repellent effects of ECEO against H. anatolicum as a vector of theileriosis in Saudi Arabia. We also found that ECEO displayed these activities through inhibiting AChE and GST. Nevertheless, additional investigations are required to confirm the accurate mechanisms and the relevance of ECEO in practical application.

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

BACKGROUND: Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. OBJECTIVE: To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. METHODS: We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. RESULTS: Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. CONCLUSION: An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

BACKGROUND: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. METHODS: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. RESULTS: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). CONCLUSIONS: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Lipoma arborescens affecting multiple joints.

Lipoma arborescens is a rare benign intra-articular lesion of unknown etiology that usually involves the suprapatellar pouch of the knee joint. Clinically, the most common finding is a slow-growing painless swelling, accompanied by intermittent effusion of the joint. We report a case of a multifocal lipoma arborescens localized in the knees and the hips in a 24-year-old man, initially mimicking an inflammatory arthropathy. The diagnosis of lipoma arborescens was made by magnetic resonance imaging of the hips and the knees. Under arthroscopic guidance, the synovial biopsy of the right knee disclosed the specific histological signs of lipoma arborescens. As far as we know, this is the third case of multifocal lipoma arborescens reported in the English literature.

Imaging features of primary pulmonary liposarcoma.

Primary liposarcoma of the lung is extremely rare. We report a 28-year-old pregnant woman who complained of dyspnoea during the third trimester. Chest radiography, thoracic ultrasound, CT and MRI showed a huge heterogeneous tumour involving all the left lung and the mediastinum. The tumour was composed of soft tissue, and fatty and cystic components with calcifications. Diagnosis was made on core biopsy under CT guidance. Surgical excision was performed but unfortunately the patient died during the operation.