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  1. Biochemical, Toxicological, and in Silico Aspects of Trillium govanianum Wall. ex D.Don (Trilliaceae): A Rich Source of Natural Bioactive Compounds
    Khan KM, Nadeem MF, Mannan A, Chohan TA, Islam M, Ansari SA, et al.
    Chem Biodivers, 2024 Jan;21(1):e202301375.
    PMID: 38031244 DOI: 10.1002/cbdv.202301375
    Trillium govanianum is a high-value medicinal herb, having multifunctional traditional and culinary uses. The present investigation was carried out to evaluate the phytochemical, biological and toxicological parameters of the T. govanianum Wall. ex D. Don (Family: Trilliaceae) roots collected from Azad Kashmir, Pakistan. Phytochemical profiling was achieved by determining total bioactive contents (total phenolic and flavonoid contents) and UHPLC-MS analysis. For biological evaluation, antioxidant activities (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation assays) and enzyme inhibition activities (against AChE, BChE, glucosidase, amylase, and tyrosinase) were performed. Moreover, cytotoxicity was assessed against three human carcinoma cell lines (MDA-MB-231, CaSki, and DU-145). The tested extract was found to contain higher total phenolics (7.56 mg GAE/g dry extract) as compared to flavonoid contents (0.45 mg RE/g dry extract). Likewise, for the antioxidant activity, higher CUPRAC activity was noted with 39.84 mg TE/g dry extract values. In the case of enzyme assays, higher activity was pointed out against the cholinesterase, glucosidase and tyrosinase enzymes. The plant extract displayed significant cytotoxicity against the cell lines examined. Moreover, the in-silico studies highlighted the interaction between the important phytochemicals and tested enzymes. To conclude, the assessed biological activity and the existence of bioactive phytochemicals in the studied plant extract may pave the way for the development of novel pharmaceuticals.
  2. Retained Fractured Fragment of A Central Venous Catheter: A Minimally Invasive Approach to Safe Retrieval
    Hamad M, Rajan R, Kosai N, Sutton P, Das S, Harunarashid H
    Ethiop J Health Sci, 2016 Jan;26(1):85-8.
    PMID: 26949321
    BACKGROUND: Complication following fracture of a central venous catheter can be catastrophic to both the patient and the attending doctor. Catheter fracture has been attributed to several factors namely prolong mechanical force acting on the catheter, and forceful removal or insertion of the catheter.

    CASE DETAILS: In the present case, the fracture was suspected during the process of removal. The tip of the catheter was notably missing, and an emergency chest radiograph confirmed our diagnosis of a retained fracture of central venous catheter. The retained portion was removed by the interventional radiologist using an endovascular loop snare and delivered through a femoral vein venotomy performed by the surgeon.

    CONCLUSION: Endovascular approach to retrieval of retained fractured catheters has helped tremendously to reduce associated morbidity and the need for major surgery. The role of surgery has become limited to instances of failed endovascular retrieval and in remote geographical locations devoid of such specialty.

  3. Fulltext Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts
    Hassan MK, Syed Ariffin SH, Ghazali NE, Hamad M, Hamdan M, Hamdi M, et al.
    Sensors (Basel), 2022 May 09;22(9).
    PMID: 35591282 DOI: 10.3390/s22093592
    Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.
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