Toxicity of Malaysian Medicinal Plant Extracts Against Sitophilus oryzae and Rhyzopertha dominica

The insecticidal activities of extracts from 22 Malaysian medicinal plant extracts from 8 botanical families were tested against rice weevil: Sitophilus oryzae (L.) and lesser grain borer: Rhyzopertha dominica (F.). The extracts were obtained using hexane, methanol, and dichloromethane to extract potential biopesticides from dried leaves. The toxicity levels were examined periodically based on antifeedant activity and contact toxicity assays using treated grain assay. Hexane extracts of Alpinia conchigera, Alpinia scabra, Curcuma mangga, Curcuma purpurascens, Goniothalamus tapisoides, Piper sarmentosum , and methanol extracts of Curcuma aeruginosa, C. mangga , and Mitragyna speciosa were the most potent extracts against S. oryzae and R. dominica with lethal concentration (LC50) values of ≤ 0.42 mg/mL and ≤ 0.49 mg/mL, respectively. The contact toxicity test results showed that methanol extracts of C. aeruginosa and C. mangga , dichloromethane extracts of Cryptocarya nigra , and hexane extracts of C. mangga, and C. purpurascens resulted in 100% mortality of both pests within 28 days exposure of 5 mg/cm2 concentration

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

A Methodology Of Real-Time Data Fusion For Localized Big Data Analytics

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper

Signaling overhead reduction techniques in device-to-device communications: Paradigm for 5G and beyond

Device-to-Device (D2D) communications have recently attracted researchers, attention because of their numerous applications in industry verticals. It enables communications among devices without or with the partial involvement of a central system. To initiate a D2D communications device discovery and radio resource allocation is a critical task when devices have high mobility. Maintaining the quality-of-service and continuous connectivity requires a signaling burden. An efficient mobility management procedure is necessary to discover the neighboring devices in D2D communications systems. The Discovery of a massive number of devices requires an effective radio resource management procedure that causes signaling overhead. In 5G and beyond communication system, two mobility management methods exist; device discovery and beaconing. Since device density and traffic increases exponentially with high mobility, hence device discovery and beaconing increase the signaling overhead and energy consumption in power-limited devices. Thus, signaling overhead research needs much attention in 5G and beyond systems to meet the service requirements like accuracy, latency, and battery life. Therefore, the challenges and the techniques related to signaling overhead in D2D communications are presented

A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper

An Insight into Molecular Targets of Breast Cancer Brain Metastasis

Brain metastasis is one of the major reasons of death in breast cancer (BC) patients, significantly affecting the quality of life, physical activity, and interdependence on several individuals. There is no clear evidence in scientific literature that depicts an exact mechanism relating to brain metastasis in BC patients. The tendency to develop breast cancer brain metastases (BCBMs) differs by the BC subtype, varying from almost half with triple-negative breast cancer (TNBC) (HER2− ER− PR−), one-third with HER2+ (human epidermal growth factor receptor 2-positive, and around one-tenth with luminal subclass (ER+ (estrogen positive) or PR+ (progesterone positive)) breast cancer. This review focuses on the molecular pathways as possible therapeutic targets of BCBMs and their potent drugs under different stages of clinical trial. In view of increased numbers of clinical trials and systemic studies, the scientific community is hopeful of unraveling the underlying mechanisms of BCBMs that will help in designing an effective treatment regimen with multiple molecular targets

Formulation, Optimization and Evaluation of Cytarabine-Loaded Iron Oxide Nanoparticles: From In Vitro to In Vivo Evaluation of Anticancer Activity

Innovative drug delivery systems based on iron oxide nanoparticles (INPs) has generated a lot of interest worldwide and have prime biomedical benefits in anticancer therapy. There are still issues reported regarding the stability, absorption, and toxicity of iron oxide nanoparticles (INPs) when administered due to its rapid surface oxidation and agglomeration with blood proteins. To solve this problem, we have synthesized trehalose-coated stabilized iron oxide nanoparticles (TINPs) by a co-precipitation technique. The surface coating of INPs with trehalose helps to improve the stability, prevents protein binding, and increase absorption uptake inside the body. Developed TINPs was then loaded with anticancer drug cytarabine by chemical crosslinking encapsulation method using suitable solvent. Engineered cytarabine-loaded trehalose-coated stabilized iron oxide nanoparticles (CY-TINPs) were optimized for particle size, zeta potential (−13.03 mV), and solid-state characterization such as differential scanning calorimetry (DSC), X-ray powder diffraction (XRD), and transmission electron microscope (TEM) studies. The particle size of 50 nm was achieved for developed CY-TINPs. The developed CY-TINPs was further evaluated for in vitro cell line investigations which confirmed potential cytotoxic activity. Developed CY-TINPs show remarkable enhancement in in vivo pharmacokinetic parameters Cmax as 425.26 ± 2.11 and AUC0–72 as 11,546.64 ± 139.82 as compared to pure drug. Compared to traditional drug delivery, the CY-TINPs formulation can effectively delay release, improve bioavailability, and boost cytotoxic activity against tumors

The Antifungal Activity of Ag/CHI NPs against <i>Rhizoctonia solani</i> Linked with Tomato Plant Health

Pathogenic infestations are significant threats to vegetable yield, and have become an urgent problem to be solved. Rhizoctonia solani is one of the worst fungi affecting tomato crops, reducing yield in some regions. It is a known fact that plants have their own defense against such infestations; however, it is unclear whether any exogenous material can help plants against infestation. Therefore, we performed greenhouse experiments to evaluate the impacts of R. solani on 15- and 30-day old tomato plants after fungal infestation, and estimated the antifungal activity of nanoparticles (NPs) against the pathogen. We observed severe pathogenic impacts on the above-ground tissues of tomato plants which would affect plant physiology and crop production. Pathogenic infection reduced total chlorophyll and anthocyanin contents, which subsequently disturbed plant physiology. Further, total phenolic contents (TPC), total flavonoid contents (TFC), and malondialdehyde (MDA) contents were significantly increased in pathogen treatments. Constitutively, enhanced activities were estimated for catalase (CAT), superoxide dismutase (SOD), and ascorbate peroxidase (APX) in response to reactive oxygen species (ROS)in pathogen-treated plants. Moreover, pathogenesis-related genes, namely, chitinase, plant glutathione S-transferase (GST), phenylalanine ammonia-lyase (PAL1), pathogenesis-related protein (PR12), and pathogenesis-related protein (PR1) were evaluated, with significant differences between treated and control plants. In vitro and greenhouse antifungal activity of silver nanoparticles (Ag NPs), chitosan nanoparticles, and Ag NPs/CHI NPs composites and plant health was studied using transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) spectrophotometry. We found astonishing results, namely, that Ag and CHI have antifungal activities against R. solani. Overall, plant health was much improved following treatment with Ag NPs/CHI NPs composites. In order to manage R. solani pathogenicity and improve tomato health, Ag/CHI NPs composites could be used infield as well as on commercial levels based on recommendations. However, there is an urgent need to first evaluate whether these NP composites have any secondary impacts on human health or the environment