Preparation of cyclodextrin nanoparticles and evaluation of its effect on the capacitation of bovine spermatozoa used in the in vitro fertilization

This study was conducted to produce nanosized cyclodextrin (NCD) and assess its effect on bovine spermatozoa during In vitro fertilization (IVF) to optimize the capacitation media for successful IVF. Therefore, Four cyclodextrin formulations were prepared and characterized. Data analysis revealed the best formula (F2) showed a smallest particle size (15 nm), zeta potential (-37 mv), and higher yield percentages (95%) was selected for spem capacitation. Motile spermatozoa were separated from frozen-thawed semen by a swim-up procedure and capacitated in IVF-TALP medium with different formulae of NCD or CD or without treatments (control) and incubated for 3hours(hr) at 38°C and evaluated every one (hr) interval. Data analysis revealed that the formulation of cyclodextrin nanoparticles (F2) after (2hr) incubation in the media gave best effect on sperm capacitation and acrosme reaction (AR) and effect of sperm treated with NCD on fertilization rate was evaluated. The results showed that the proportion of Oocytes fertilized was increased significantly in F2 (60%) than in the control (35%), and cyclodextrin group (50%) groups (p<0.05). It could be inferred from this investigation that cyclodextrin nanoparticles can be used for biomedical interventions in bovine spermatozoa. NCD improve sperm motility, viability, and (AR), also fertilization rate of sperm treated with NCD increase. So NCD gave positive effect on sperm functions during IVF.

Isolation and Characterization of Thermophilic Enzymes Producing Microorganisms for Potential Therapeutic and Industrial Use

The need of extremophile enzymes is increased. Such enzymes had found their utility in bio-industries such as leather, food, animal feed, textiles, and in bioconversions and bioremediation. Screening of microorganisms producing enzymes from different areas of soil led to the isolation of 38 isolates, the isolates were plate-screened for their ability to produce extracellular enzymes. The promising strains were selected and screened for their enzyme thermostability and screened quantitatively for potential industrial and therapeutic applications. Tolerance of selected microorganisms was investigated to a varied range of pH, salinity, and enzyme activity over a range of temperature. The genotypic identification of 16S rDNA sequence of the promising strains revealed that our strains were Streptomyces mutabilis, Streptomyces ghanaensis, Streptomyces rochei and Enterobacter cloacae. The isolated microorganisms quantified as an effective producer of industrially important enzymes amylase, cellulase, esterase, casienase and therapeutic enzyme L-asparaginase. All enzymes produced from the four isolates show enzyme activity and stability at different high temperature (60 °C, 80 °C, 100 °C). The amylase shows optimum activity at 37 °C, while the other four enzymes show optimum activity at different high temperature (60 °C, 80 °C). The study shows that Streptomyces mutabilis produce acidophilic enzyme amylase, Streptomyces ghanaensis produce acidophilic enzyme cellulase and neutrophilic enzyme esterase, Enterobacter cloacae and Streptomyces rochei produce alkalophilic enzymes (L-asparaginase, caseinase) respectively. Enzymes show highest enzyme activity at high NaCl concentration (5 and 7.5%)

Antibacterial, Antifungal, and Anticancer Effects of Camel Milk Exosomes: An In Vitro Study

Camel milk (CM) has potent antibacterial and antifungal effects and camel milk exosomes (CM-EXO) have been shown to inhibit the proliferation of a large variety of cancer cells including HepaRG, MCF7, Hl60, and PANC1. However, little is known regarding the effects of CM-EXO on bacteria, fungi, HepG2, CaCo2, and Vero cells. Therefore, this study aimed to evaluate the antibacterial, antifungal, and anticancer effects of CM-EXO. EXOs were isolated from CM by ultracentrifugation and characterized by transmission electron microscope and flow cytometry. Unlike CM, CM-EXO (6 mg/mL) had no bactericidal effects on Gram-positive bacteria (Staphylococcus aureus, Micrococcus luteus, and Enterococcus feacalis) but they had bacteriostatic effects, especially against Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa, and Proteus mirabilis), and fungistatic effects on Candida albicans. HepG2, CaCo2, and Vero cells were respectively treated with CM-EXOs at low (6.17, 3.60, 75.35 μg/mL), moderate (12.34, 7.20, 150.70 μg/mL), and high (24.68, 14.40, 301.40 μg/mL) doses and the results revealed that CM-EXOs triggered apoptosis in HepG2 and CaCo2 cells, but not in normal Vero cells, as revealed by high Bax expression and caspase 3 activities and lower expression of Bcl2. Interestingly, CM-EXOs also induced the elevation of intracellular reactive oxygen species and downregulated the expression of antioxidant-related genes (NrF2 and HO-1) in cancer cells but not in normal cells. CM-EXOs have antibacterial and antifungal effects as well as a selective anticancer effect against HepG2 and CaCo2 cells with a higher safety margin on normal cells

Ameliorative Effects of Camel Milk and Its Exosomes on Diabetic Nephropathy in Rats

Contradictory results were obtained regarding the effects of extracellular vesicles such as exosomes (EXOs) on diabetes and diabetic nephropathy (DN). Some studies showed that EXOs, including milk EXOs, were involved in the pathogenesis of DN, whereas other studies revealed ameliorative effects. Compared to other animals, camel milk had unique components that lower blood glucose levels. However, little is known regarding the effect of camel milk and its EXOs on DN. Thus, the present study was conducted to evaluate this effect on a rat model of DN induced by streptozotocin. Treatment with camel milk and/or its EXOs ameliorated DN as evidenced by (1) reduced levels of kidney function parameters (urea, creatinine, retinol-binding protein (RBP), and urinary proteins), (2) restored redox balance (decreased lipid peroxide malondialdehyde (MDA) and increased the activity of antioxidants enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx)), (3) downregulated expression of DN-related genes (transforming growth factor-beta 1 (TGFβ1), intercellular adhesion molecules 1 (ICAM1), and transformation specific 1 (ETS1), integrin subunit beta 2 (ITGβ2), tissue inhibitors of matrix metalloproteinase 2 (TIMP2), and kidney injury molecule-1 (KIM1)), and (4) decreased renal damage histological score. These results concluded that the treatment with camel milk and/or its EXOs could ameliorate DN with a better effect for the combined therapy

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model

Objectives: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. Setting: This is a retrospective study conducted at the family medicine department, Cairo University. Methods: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. Results: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. Conclusion: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources

NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists. In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers. We show how suggested annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing. We systematically examine interrater agreement and describe modifications to the MaskRCNN model to improve cell mapping. We also describe a technique we call Decision Tree Approximation of Learned Embeddings (DTALE) that leverages nucleus segmentations and morphologic features to improve the transparency of nucleus classification models. The annotation data produced in this study are freely available for algorithm development and benchmarking at: https://sites.google.com/view/nucls

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially