TRANSETHOSOMES AS BREAKTHROUGH TOOL FOR CONTROLLED TRANSDERMAL DELIVERY OF DEXKETOPROFEN TROMETAMOL: DESIGN, FABRICATION, STATISTICAL OPTIMIZATION, IN VITRO, AND EX VIVO CHARACTERIZATION

Objective: Transethosomes (TEs) have introduced an emerging avenue of interest in vesicular research for transdermal delivery of drugs and can be a proper delivery system for painkillers like NSAIDS. This study aimed to formulate and characterize the potential of TE to enhance the transdermal transport of Dexketoprofen trometamol (DKT) to achieve controlled pain management compared to DKT solution. Methods: Factorial design (23) was adopted to appraise the influence of independent variables, namely, Lipoid S100 and surfactant concentrations and surfactant type (X3) on the % solubilization efficiency (% SE), vesicle size (VS), and % release efficiency (% RE). Thin film hydration was the preferred approach for preparing TEs where vesicle size, zeta potential, polydispersity index, %SE and %RE were investigated. The optimized formula was nominated and subjected to several studies. For the permeation study, optimum TE was incorporated into carbapol gel base for comparison with DKT solution. Also, an accelerated stability study was assessed for optimized formula. Results: All the prepared DKT-loaded TEs revealed acceptable VS, PDI, and ZP. The highest %SE (86.08±1.05 %) and lowest %RE (44.62±1.36 %) were observed in case of F1. The optimized formula (F1) displayed VS of 133.2±1.62 nm, PDI of 0.342±0.03 and ZP of-21.6±2.45 mV. F1 revealed enhanced skin permeation of a 2.6-fold increase compared with DKT solution. Moreover, F1 was stable upon storage and a non-significant change (P>0.05) was observed. Conclusion: DKT was successfully incorporated into vesicle carrier and can signify an alternative option for providing this therapy, bypassing the poor bioavailability and considerable adverse consequences of using the oral route besides improved patient compliance

SLSNet: Skin lesion segmentation using a lightweight generativeadversarial network

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications

Machine learning and computational chemistry to improve biochar fertilizers : a review

Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars are not optimal because mechanisms ruling biochar properties are poorly known. This issue can be solved by recent developments in machine learning and computational chemistry. Here we review phosphorus-loaded biochar with emphasis on computational chemistry, machine learning, organic acids, drawbacks of classical fertilizers, biochar production, phosphorus loading, and mechanisms of phosphorous release. Modeling techniques allow for deciphering the influence of individual variables on biochar, employing various supervised learning models tailored to different biochar types. Computational chemistry provides knowledge on factors that control phosphorus binding, e.g., the type of phosphorus compound, soil constituents, mineral surfaces, binding motifs, water, solution pH, and redox potential. Phosphorus release from biochar is controlled by coexisting anions, pH, adsorbent dosage, initial phosphorus concentration, and temperature. Pyrolysis temperatures below 600 °C enhance functional group retention, while temperatures below 450 °C increase plant-available phosphorus. Lower pH values promote phosphorus release, while higher pH values hinder it. Physical modifications, such as increasing surface area and pore volume, can maximize the adsorption capacity of phosphorus-loaded biochar. Furthermore, the type of organic acid affects phosphorus release, with low molecular weight organic acids being advantageous for soil utilization. Lastly, biochar-based fertilizers release nutrients 2–4 times slower than conventional fertilizers

Sensitive determination of amlodipine besylate using bare/unmodified and DNA-modified screen-printed electrodes in tablets and biological fluids

The screen-printed technique is widely used as an efficient tool for electrochemical analysis in environment, clinical and agri-food areas. Significantly, it has the ability to transfer electrochemical laboratory experiments into the field. In the present work, we report a highly sensitive, simple, low-cost protocol for determination of amlodipine (AML) using bare/unmodified and DNA-modified screen-printed electrodes (SPEs). The immobilization of DNA molecules onto SPE offers promising robust and chemically stable molecular wires, which provides a unique opportunity for charge transfer processes. Consequently, the electroanalytical sensing of AML was explored at bare/unmodified and DNA-modified SPEs in a linear range between 0.066–1.0 μM and 0.066–2.0 μM with the detection limit (3σ) found to be 20.70 nM and 14.94 nM, whilst corresponding sensitivities of: 0.43 A L mol−1 and 4.23 A L mol−1 respectively. Although, the superior electrochemical signature of bare SPEs is evident, the immobilization of DNA onto SPEs enhances the sensitivity 10-times more than the bare SPEs. Furthermore, the optimized electroanalytical protocol using the unmodified SPEs, which requires no pre-treatment and electrode modification step, was then further applied to the determination of AML in real samples

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112