Injectable Chitosan-Methoxy Polyethylene Glycol Hybrid Hydrogel Untangling the Wound Healing Behavior: In Vitro and In Vivo Evaluation.

Wound healing, particularly for difficult-to-treat wounds, presents a serious threat and may lead to complications. Currently available dressings lack mucoadhesion, safety, efficacy, and, most importantly, patient compliance. Herein, we developed a unique, simple, and inexpensive injectable chitosan-methoxy polyethylene glycol (chitosan-mPEG) hybrid hydrogel with tunable physicochemical and mechanical properties for wound healing. The detailed physicochemical and rheological characterization of the chitosan-mPEG hydrogel has revealed chemical interaction between available -NH2 groups of chitosan and -COOH groups of mPEG acid, which, to our perspective, enhanced the mechanical and wound healing properties of hybrid chitosan and mPEG hydrogel compared to solo chitosan or PEG hydrogel. By introducing mPEG, the wound healing ability of hydrogel is synergistically improved due to its antibacterial feature, together with chitosan's innate role in hemostasis and wound closure. The detailed hemostasis and wound closure potential of the chitosan-mPEG hydrogel were investigated in a rat model, which confirmed a significant acceleration in wound healing and ultimately wound closure. In conclusion, the developed chitosan-mPEG hydrogel met all the required specifications and could be developed as a promising material for hemostasis, especially wound management, and as an excellent candidate for wound healing application.

COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins.

Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics. Experimental procedures involving wet-lab experiments to reveal glycoproteins are very time-consuming, laborious, and highly costly. However, costly and tedious experimental procedures can be assisted by ranking the most probable glycoproteins through computational methods with improved accuracy. In this study, we have proposed a novel machine learning-based predictive model for glycoproteins identification. Our proposed model is based on sequence-derived structural descriptors (SDSD) that fill the gap of unavailability of protein 3D structures and lack of accuracy in sequence information alone. Through a series of simulation studies, we have shown that our proposed model gives state-of-the-art generalization performance verified through various machine learning-centric and biologically relevant techniques and metrics. Through data mining in this study, we have also identified the role of descriptors in determining glycoproteins. Python-based standalone code together with a webserver implementation of our proposed model (COYOTE: identifiCation Of glYcoprOteins Through sEquences) is available at the URL: https://sites.google.com/view/wajidarshad/software.

Out-of-Pocket Costs of Complementary Medicine Following Cancer and the Financial Impact in a Setting With Universal Health Coverage: Findings From a Prospective Cohort Study.

PURPOSE: To determine household spending patterns on complementary medicine following cancer and the financial impact in a setting with universal health coverage. METHODS: Country-specific data from a multinational prospective cohort study, Association of Southeast Asian Nations Costs in Oncology Study, comprising 1,249 cancer survivors were included. Household costs of complementary medicine (healthcare practices or products that are not considered as part of conventional medicine) throughout the first year after cancer diagnosis were measured using cost diaries. Study outcomes comprised (1) shares of household expenditures on complementary medicine from total out-of-pocket costs and health costs that were respectively incurred in relation to cancer, (2) incidence of financial catastrophe (out-of-pocket costs related to cancer ≥ 30% of annual household income), and (3) economic hardship (inability to pay for essential household items or services). RESULTS: One third of patients reported out-of-pocket household expenditures on complementary medicine in the immediate year after cancer diagnosis, accounting to 20% of the total out-of-pocket costs and 35% of the health costs. Risk of financial catastrophe was higher in households reporting out-of-pocket expenditures on complementary medicine (adjusted odds ratio: 1.39 [95% CI, 1.05 to 1.86]). Corresponding odds ratio within patients from low-income households showed that they were substantially more vulnerable: 2.28 (95% CI, 1.41 to 3.68). Expenditures on complementary medicine were, however, not associated with economic hardship in the immediate year after cancer diagnosis. CONCLUSION: In settings with universal health coverage, integration of subsidized evidence-based complementary medicine into mainstream cancer care may alleviate catastrophic expenditures. However, this must go hand in hand with interventions to reduce the use of nonevidence-based complementary therapies following cancer.

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology.

Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.

Phytochemical, antimicrobial and time-kill kinetics potentials of Euphorbia nivulia Buch.-Ham.: A Cholistan desert medicinal plant.

Euphorbia nivulia a locally occurring plant species possesses antiseptic, analgesic and anti-inflammatory properties and is ethnopharmacologically used in various ailments like skin, ear disorders, boils, and worm infestation. Preliminary phytochemical screening showed presence of flavonoids, polyphenolics, glycosides, alkaloids, tannins and triterpenoids in (70% aqueous-ethanolic) Euphorbia nivulia crude extract (En cr) and its four fractions, i.e., hexane fraction (En hex), butanol fraction (En bt), chloroform fraction (En ch), and aqueous fraction (En aq). In current study, Agar well diffusion and time-kill kinetic assays were performed for antimicrobial activity. 300 mg/ml concentration showed maximum inhibitory zone. Highest zone of inhibition (15.5mm) was demonstrated by En ch fraction against Proteus mirabilis. Staphyllococcus aureus was the most sensitive bacteria against whom all fractions except En aq fraction were active. Maximum MIC (15.3 mg/ml) was shown by En ch fraction against Proteus mirabilis. Similarly, En ch fraction showed (15.1 mg/ml) remarkable MIC against Candida albicans. Significant higher antibacterial and antifungal activity was revealed in high concentration. Time-kill kinetics studies revealed bacteriostatic action. Noteworthy antimicrobial activity may be due to bioactive compounds of extract which may be a potential antibacterial and antifungal agent.

Evaluation of economically feasible, natural plant extract-based microbiological media for producing biomass of the dry rot biocontrol strain Pseudomonas fluorescens P22Y05 in liquid culture.

The production of microbial biomass in liquid media often represents an indispensable step in the research and development of bacterial and fungal strains. Costs of commercially prepared nutrient media or purified media components, however, can represent a significant hurdle to conducting research in locations where obtaining these products is difficult. A less expensive option for providing components essential to microbial growth in liquid culture is the use of extracts of fresh or dried plant products obtained by using hot water extraction techniques. A total of 13 plant extract-based media were prepared from a variety of plant fruits, pods or seeds of plant species including Allium cepa (red onion bulb), Phaseolus vulgaris (green bean pods), and Lens culinaris (lentil seeds). In shake flask tests, cell production by potato dry rot antagonist Pseudomonas fluorescens P22Y05 in plant extract-based media was generally statistically indistinguishable from that in commercially produced tryptic soy broth and nutrient broth as measured by optical density and colony forming units/ml produced (P ≤ 0.05, Fisher's protected LSD). The efficacy of biomass produced in the best plant extract-based media or commercial media was equivalent in reducing Fusarium dry rot by 50-96% compared to controls. In studies using a high-throughput microbioreactor, logarithmic growth of P22Y05 in plant extract-based media initiated in 3-5 h in most cases but specific growth rate and the time of maximum OD varied as did the maximum pH obtained in media. Nutrient analysis of selected media before and after cell growth indicated that nitrogen in the form of NH4 accumulated in culture supernatants, possibly due to unbalanced growth conditions brought on by a scarcity of simple sugars in the media tested. The potential of plant extract-based media to economically produce biomass of microbes active in reducing plant disease is considerable and deserves further research.