Poor knowledge of university students regarding paracetamol; a wakeup call for public healthcare practitioners

Over-the-counter (OTC) use of paracetamol has drastically increased over the past few decades. Its toxicity is the foremost cause of acute liver failure in the Western world, unfortunately such cases remain undocumented in underdeveloped country like Pakistan. So far very limited studies have been reported in Pakistan, which have tackled issues related to toxicity and knowledge among public regarding OTC medicines at the national level. We assessed the prevalence of self-medication, level of awareness and knowledge regarding OTC medicines (specifically paracetamol), 352 university students through structured interview method. Awareness was scored by a ranking questionnaire. Data was coded and statistically analyzed using SPSS© 21. The survey questionnaire covered the risk perception, prevalence of self-medication and practices regarding OTC medicine use among the university students. Our results offer an indirect assessment of the knowledge among our general population as well as an estimation of misuse related harmful impact of OTC medicines. Moreover, it point out a major knowledge gap, low risk perception and significant prevalence of self-medication with paracetamol among our population, illustrating an increased potential of its adverse effects through overuse or misuse. These finding reveal a substantial need for educational intervention around OTC medicines. Serves as an eye opener for healthcare practitioners who should be proactive in commencing health awareness programs as well as superintending the irrational OTC drug use among public

Some categorical aspects of BCH-algebras

We show that the category BCH of BCH-algebras and BCH-homomorphisms is complete. We also show that it has coequalizers, kernel pairs, and an image factorization system. It is also proved that onto homomorphisms and coequalizers, and monomorphisms and one-to-one homomorphisms coincide, respectively, in BCH. It is shown that MBCI is a coreflexive subcategory of BCH. Regular homomorphisms have been defined and their properties are studied. An open problem has been posed

On some classes of BCH-algebras

The concept of a BCH-algebra is a generalization of the concept of a BCI-algebra. It is shown that weakly commutative BCH-algebras are weakly commutative BCI-algebras. Moreover, the concepts of weakly positive implicative and weakly implicative BCH-algebras are defined and it is shown that every weakly implicative BCH-algebra is a weakly positive implicative BCH-algebra. The weakly positive implicative BCH-algebras are characterized with the help of their self maps. Two open problems are posed

Sodium stibogluconate loaded nano-deformable liposomes for topical treatment of leishmaniasis: macrophage as a target cell

Topical drug delivery against cutaneous leishmaniasis (CL) signifies an effective alternate for improving the availability and reducing the toxicity associated with the parenteral administration of conventional sodium stibogluconate (SSG) injection. The basic aim of the study was to develop nano-deformable liposomes (NDLs) for the dermal delivery of SSG against CL. NDLs were formulated by a modified thin film hydration method and optimized via Box–Behnken statistical design. The physicochemical properties of SSG-NDLs were established in terms of vesicle size (195.1 nm), polydispersity index (0.158), zeta potential (−32.8 mV), and entrapment efficiency (35.26%). Moreover, deformability index, in vitro release, and macrophage uptake studies were also accomplished. SSG-NDLs were entrapped within Carbopol gel network for the ease of skin application. The ex vivo skin permeation study revealed that SSG-NDLs gel provided 10-fold higher skin retention towards the deeper skin layers, attained without use of classical permeation enhancers. Moreover, in vivo skin irritation and histopathological studies verified safety of the topically applied formulation. Interestingly, the cytotoxic potential of SSG-NDLs (1.3 mg/ml) was higher than plain SSG (1.65 mg/ml). The anti-leishmanial activity on intramacrophage amastigote model of Leishmania tropica showed that IC50 value of the SSG-NDLs was ∼ fourfold lower than the plain drug solution with marked increase in the selectivity index. The in vivo results displayed higher anti-leishmanial activity by efficiently healing lesion and successfully reducing parasite burden. Concisely, the outcomes indicated that the targeted delivery of SSG could be accomplished by using topically applied NDLs for the effective treatment of CL

Particle and Gel Characterization of Irinotecan-Loaded Double-Reverse Thermosensitive Hydrogel

The irinotecan-loaded double-reverse thermosensitive hydrogel (DRTH) is a dispersed system of irinotecan-loaded solid lipid nanoparticles (SLN) in a thermosensitive hydrogel. To optimise the particle and gel properties of DRTHs for rectal administration of irinotecan, SLNs and DRTHs were prepared with tricaprin, triethanolamine, Tween 80, and Span 20. Among the SLNs tested, an SLN composed of 1 g irinotecan, 0.5 g lipid mixture, and 0.5 g combined surfactant gave the highest entrapment efficiency and smallest particle size. A DRTH composed of (poloxamer 407/poloxamer 188/combined surfactant/SLN dispersion/H2O (10/15/17/4/54%)) showed easy administration, fast gelling, and strong gel-forming in the body

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab