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We are pleased to present this Special Issue of Cells, entitled ‘Feature Papers in Stem Cells’ [...]

Minimally invasive surgery is widely used because of its tremendous benefits to the patient. However, there are some challenges that surgeons face in this type of surgery, the most important of which is the narrow field of view. Therefore, we propose an approach to expand the field of view for minimally invasive surgery to enhance surgeons’ experience. It combines multiple views in real-time to produce a dynamic expanded view. The proposed approach extends the monocular Oriented features from an accelerated segment test and Rotated Binary robust independent elementary features—Simultaneous Localization And Mapping (ORB-SLAM) to work with a multi-camera setup. The ORB-SLAM’s three parallel threads, namely tracking, mapping and loop closing, are performed for each camera and new threads are added to calculate the relative cameras’ pose and to construct the expanded view. A new algorithm for estimating the optimal inter-camera correspondence matrix from a set of corresponding 3D map points is presented. This optimal transformation is then used to produce the final view. The proposed approach was evaluated using both human models and in vivo data. The evaluation results of the proposed correspondence matrix estimation algorithm prove its ability to reduce the error and to produce an accurate transformation. The results also show that when other approaches fail, the proposed approach can produce an expanded view. In this work, a real-time dynamic field-of-view expansion approach that can work in all situations regardless of images’ overlap is proposed. It outperforms the previous approaches and can also work at 21 fps.

The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.

Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34, and ResNet50) were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of 99% when the shift from source domain to target domain was small and 81% when that shift was large and outperformed all other competitive approaches.

Background: Recently, crop byproducts are considered a hot topic and can be converted into beneficial products. Cauliflower is well-known for its protective effects against oxidative stress-induced damage. The current study aimed to investigate the chemical profile and the ameliorative effects of cauliflower leaf extract (CL) on gentamicin-induced renal and hepatic injuries in rats., Methods: Cauliflower leaf was extracted with methanol to give the total methanol extract (TME) followed by the determination of total phenolic contents (TPC). Rats were divided into five groups; Group I was assigned as the control group, while the other groups were injected with gentamicin for ten days. Group II was given distilled water. Rats in groups III and IV were treated with oral CL (200 mg/kg and 400 mg/kg, respectively). Group V received L-cysteine (as a positive control). The functions of the kidneys and liver; oxidative stress and morphological and apoptotic changes of renal and hepatic tissues were assessed., Results: The TME was subjected to chromatographic techniques to yield ferulic acid, vanillic acid, p-coumaric acid and quercetin. TPC was 72.31 mg GAE/g of dried extract. CL treatment dose-dependently ameliorated gentamicin-induced impaired kidney and liver functions and improved the histopathological appearance of both organs. It also reduced gentamicin-induced oxidative stress. CL demonstrated downregulation of mRNA and protein expressions of IL-1β and NF-κB compared to nontreated rats. In silico interaction of the isolated compounds with amino acid residues of IL-1β and NF-κB might explain the current findings., Conclusion: Taken together, this study raises the waste-to-wealth potential of cauliflower to mitigate gentamicin-induced hepatorenal injury and convert the waste agromaterials into valuable products.

In cases of sepsis, the immune system responds with an uncontrolled release of proinflammatory cytokines and reactive oxygen species. The lungs, kidneys, and liver are among the early impacted organs during sepsis and are a direct cause of mortality. The aim of this study was to compare the effects of infliximab (IFX) and celecoxib (CLX) on septic rats that went through a cecal ligation and puncture (CLP) surgery to induce sepsis. This study included four groups: sham, CLP (untreated), and CLP-treated with CLX or IFX. The administration of "low dose" CLX or IFX was performed after 2 h following the induction of sepsis. Twenty-four hours following the induction of sepsis, the rats were sacrificed and blood samples were collected to evaluate kidney, liver, and lung injuries. MDA and NOx content, in addition to SOD activity and GSH levels, were evaluated in the tissue homogenates of each group. Tissue samples were also investigated histopathologically. In a separate experiment, the same groups were employed to evaluate the survival of septic rats in a 7-day observation period. The results of this study showed that treatment with either CLX or IFX ameliorated the three organs' damage compared to septic-untreated rats, decreased oxidative stress, enhanced the antioxidant defense, and reduced serum cytokines. As a result, a higher survival rate resulted: 62.5% and 37.5% after the administration of CLX and IFX, respectively, compared to 0% in the CLP group after 7 days. No significant differences were observed between the two agents in all measured parameters. Histopathological examination confirmed the observed results. In conclusion, CLX and IFX ameliorated lung, kidney, and liver injuries associated with sepsis through anti-inflammatory and antioxidant actions, which correlated to the increase in survival observed with both of them.

Oxidative stress and chronic inflammation have a role in developing neurodegenerative diseases such as Parkinson’s disease (PD) and inflammatory movement disorders such as rheumatoid arthritis that affect millions of populations. In searching for antioxidant and anti-inflammatory molecules from natural sources that can counteract neurodegenerative diseases and arthritis, the flavonoid-rich extract of Diplotaxis harra (DHE) was selected based on its in vitro antioxidant and anti-inflammatory activities. DHE could inhibit the inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) expressions in the lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages from 100% to the level of 28.51 ± 18.67 and 30.19 ± 5.00% at 20 μg/mL, respectively. A TLC bioautography of DHE fractions using 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH) led to the isolation of a major antioxidant compound which was identified by X-ray diffraction analysis as isorhamnetin-3-O-β-D-glucoside (IR3G). IR3G also exhibited a potent anti-inflammatory activity, particularly by suppressing the upregulation of iNOS expression, similar to that of dexamethasone (DEX) at 10 μM to the level of 35.96 ± 7.80 and 29.34 ± 6.34%, respectively. Moreover, IR3G displayed a strong neuroprotectivity (>60% at 1.0−4−1.0−3 μM) against 6-hydroxydopamine (6-OHDA)-challenged SHSY5Y neuroblastoma, an in vitro model of dopaminergic neurons for Parkinson’s disease (PD) research. Accordingly, the in vivo anti-Parkinson potentiality was evaluated, where it was found that IR3G successfully reversed the 6-OHDA-induced locomotor deficit in a zebrafish model. A study of molecular docking and molecular dynamic (MD) simulation of IR3G and its aglycone isorhamnetin (IR) against human acetylcholine esterase (AChE), monoamine oxidase B (MAO-B), and Polo-like kinase-2 (PLK2) was performed and further outlined a putative mechanism in modulating neurodegenerative diseases such as PD. The free radical scavenging, anti-inflammatory through anti-iNOS and anti-COX-2 expression, and neuroprotective activities assessed in this study would present partial evidence for the potentiality of D. harra-derived IR3G as a promising natural therapeutic agent against neurodegenerative diseases and inflammatory arthritis. Finally, a biphasic HPTLC method was developed to estimate the biomarker IR3G in D. harra quantitatively.

Rhodotorula yeasts which are known as carotenogenic yeasts have a great industrial value due to their ability to produce carotenoids. In particular, the isolated yeast Rhodotorula sp. (strain ATL72) has been reported to be a promising producer of high concentrations of carotenoids. A combination of central composite design (CCD) and Plackett-Burman (PB) design was used to optimize carotenoids produced by this yeast. The optimum production of carotenoids was completed when the yeast was grown in a production medium composed of 3.7 g/L malt extract, 7.7 g/L fructose, 9 g/L urea, 35 g/L NaCl, and 1 g/L yeast extract at 27.5 °C, pH 6.7, and 180 rpm. Two batch runs in 1 L and 7 L bioreactors were conducted which increased the productivity of carotenoid concentration from 21.5 mg/L after 98 h of incubation at the level of the shake flask to 229.9 mg/L after 47 h of incubation at the level of 7 L bioreactor. The carotenoid pigment was extracted in dimethylsulfoxide (DMSO), acetone, petroleum ether, and sodium chloride, and subsequently identified and characterized using UV-visible scanning, thin layer chromatography, and gas chromatography/mass spectrometry.

Adverse drug reaction (ADR) is a pressing health problem, and one of the main reasons for treatment failure with antiepileptic drugs. This has become apparent in the event of severe cutaneous adverse reactions (SCARs), which can be life-threatening. In this review, four hypotheses were identified to describe how the immune system is triggered in the development of SCARs, which predominantly involve the human leukocyte antigen (HLA) proteins. Several genetic variations in HLA genes have been shown to be strongly associated with the susceptibility to developing SCARs when prescribed carbamazepine or phenytoin. These genetic variations were also shown to be prevalent in certain populations. Apart from the HLA genes, other genes proposed to affect the risk of SCARs are genes encoding for CYP450 drug-metabolising enzymes, which are involved in the pharmacokinetics of offending drugs. Genetic variants in CYP2C9 and CYPC19 enzymes were also suggested to modulate the risk of SCARs in some populations. This review summarizes the literature on the manifestation and aetiology of antiepileptic-induced SCARs, updates on pharmacogenetic markers associated with this reaction and the implementation of pre-emptive testing as a preventive strategy for SCARs.

Organic fractions and extracts of willow ( Salix safsaf ) leaves, produced by sequential solvent extraction as well as infusion and decoction, exhibited anticancer potencies in four cancerous cell lines, including breast (MCF-7), colorectal (HCT-116), cervical (HeLa) and liver (HepG2). Results of the MTT assay revealed that chloroform (CHCl 3 ) and ethyl acetate (EtOAc)-soluble fractions exhibited specific anticancer activities as marginal toxicities were observed against two non-cancerous control cell lines (BJ-1 and MCF-12). Ultra-high-resolution mass spectrometry Q-Exactive™ HF Hybrid Quadrupole-Orbitrap™ coupled with liquid chromatography (UHPLC) indicated that both extracts are enriched in features belonging to major phenolic and purine derivatives. Fluorescence-activated cell sorter analysis (FACS), employing annexin V-FITC/PI double staining indicated that the observed cytotoxic potency was mediated via apoptosis. FACS analysis, monitoring the increase in fluorescence signal, associated with oxidation of DCFH to DCF, indicated that the mechanism of apoptosis is independent of reactive oxygen species (ROS). Results of immunoblotting and RT-qPCR assays showed that treatment with organic fractions under investigation resulted in significant up-regulation of pro-apoptotic protein and mRNA markers for Caspase-3, p53 and Bax, whereas it resulted in a significant reduction in amounts of both protein and mRNA of the anti-apoptotic marker Bcl-2. FACS analysis also indicated that pre-treatment and co-treatment of human amniotic epithelial (WISH) cells exposed to the ROS H 2 O 2 with EtOAc fraction provide a cytoprotective and antioxidant capacity against generated oxidative stress. In conclusion, our findings highlight the importance of natural phenolic and flavonoid compounds with unparalleled and unique antioxidant and anticancer properties.

Antibiotic misuse, either by patients or healthcare professionals, is one of the major contributing factors to antimicrobial resistance. In many Middle Eastern countries including Egypt, there are no strict regulations regarding antibiotic dispensing by community pharmacies. In this study, we examined antibiotic dispensing patterns in Egyptian community pharmacies. About 150 community pharmacies were randomly chosen using convenience sampling from the five most populous urban districts of Minia Governorate in Egypt. Two simulated patient (SP) scenarios of viral respiratory tract infection requiring no antibiotic treatment were used to assess the actual antibiotics dispensing practice of. Face-to-face interviews were then conducted to assess the intended dispensing practice. Descriptive statistics were calculated to report the main study outcomes. In 238 visits of both scenarios, 98.3% of service providers dispensed amoxicillin. Although stated otherwise in interviews, most pharmacy providers (63%) dispensed amoxicillin without collecting relevant information from presenting SPs. Findings showed high rates of antibiotic misuse in community pharmacies. Discrepancies between interviews and patient simulation results also suggest a practice‒knowledge gap. Corrective actions, whether legislation, enforcement, education, or awareness campaigns about antibiotic misuse, are urgently needed to improve antibiotic dispensing practices in Egyptian community pharmacies.