Each year, the number of cases of strokes and deaths due to this is increasing around the world. This could be due to work stress, lifestyles, unhealthy food habits, and several other reasons. Currently, there are several traditional methods like thrombolysis and mechanical thrombectomy for managing strokes. The current approach has several limitations, like delayed diagnosis, limited therapeutic delivery, and risks of secondary injuries. So, there is a need for some effective and reliable methods for the management of strokes, which could help in early diagnosis followed by the treatment of strokes. Nanotechnology has played an immense role in managing strokes, and recently, it has emerged as a transformative solution offering innovative diagnostic tools and therapeutic strategies. Nanoparticles (NPs) belonging to several classes, including metallic (metallic and metal oxide), organic (lipids, liposome), and carbon, can cross the blood–brain barrier and may exhibit immense potential for managing various strokes. Moreover, these NPs have exhibited promise in improving imaging specificity and therapeutic delivery by precise drug delivery and real-time monitoring of treatment efficacy. Nanomaterials like cerium oxide (CeO₂) and liposome-encapsulated agents have neuroprotective properties that reduce oxidative stress and promote neuroregeneration. In the present article, the authors have emphasized the significant advancements in the nanomedicine management of stroke, including NPs-based drug delivery systems, neuroprotective and neuroregenerative therapies, and multimodal imaging advancements.

Oral delivery, the most common method of therapeutic administration, has two significant obstacles: drug solubility and permeability. The challenges of current oral medicine delivery are being tackled through an emerging method that uses structures called polymeric micelles. In the present study, polymeric micelles were developed using conjugates of linoleic acid–carboxymethyl chitosan (LA-CMCS) for the oral delivery of paclitaxel (PCL). The developed micelles were evaluated by particle size, zeta potential, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). When PCL was contained within micelles, its solubility increased by almost 13.65 times (around 60 µg/mL). The micelles’ zeta potentials were −29 mV, their polydispersity indices were 0.023, and their particle diameters were 93 nm. Micelles showed PCL loading and entrapment efficiencies of 67% and 61%, respectively. The sustained release qualities of the PCL release data from micelles were good. In comparison to the pure PCL suspension, the permeability of the PCL from micelles was 2.2 times higher. The pharmacokinetic data revealed that PCL with LA-CMCS micelles had a relative bioavailability of 239.17%, which was much greater than the PCL in the suspension. The oral bioavailability of PCL was effectively increased by LA-CMCS micelles according to an in vivo study on animals. The polymer choice, maybe through improved permeability, plays an essential role when assessing oral bioavailability enhancement and solubility improvement (13.65 times). The outcomes demonstrated that PCL’s solubility and pharmacokinetics were improved in the micelles of the LA-CMCS conjugate.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory impairment and cognitive dysfunctions. It has been shown that hypoglycemia can adversely affect AD neuropathology. It is well-known that chronic hyperglycemia in type 2 diabetes (T2D) is regarded as a potential risk factor for the development and progression of AD. However, the effect of recurrent hypoglycemia on the pathogenesis of AD was not deeply discussed, and how recurrent hypoglycemia affects AD at cellular and molecular levels was not intensely interpreted by the previous studies. The underlying mechanisms for hypoglycaemia-induced AD are diverse such as endothelial dysfunction, thrombosis, and neuronal injury that causing tau protein hyperphosphorylation and the accumulation of amyloid beta (Aβ) in the brain neurons. Of note, the glucagon hormone, which controls blood glucose, can also regulate the cognitive functions. Glucagon increases blood glucose by antagonizing the metabolic effect of insulin. Therefore, glucagon, through attenuation of hypoglycemia, may prevent AD neuropathology. Glucagon/GLP-1 has been shown to promote synaptogenesis, hippocampal synaptic plasticity, and learning and memory, while attenuating amyloid and tau pathologies. Therefore, activation of glucagon receptors in the brain may reduce AD neuropathology. A recent glucagon receptor agonist dasiglucagon which used in the management of hypoglycemia may be effective in preventing hypoglycemia and AD neuropathology. This review aims to discuss the potential role of dasiglucagon in treating hypoglycemia in AD, and how this drug reduce AD neuropathology.

Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.

The population models allow for a better understanding of the dynamical interactions with the environment and hence can provide a way for understanding the population changes. They are helpful in studying the biological invasions, environmental conservation and many other applications. These models become more complicated when accounting for the stochastic and/or random variations due to different sources. In the current work, a spectral technique is suggested to analyze the stochastic population model with random parameters. The model contains mixed sources of uncertainties, noise and uncertain parameters. The suggested algorithm uses the spectral decompositions for both types of randomness. The spectral techniques have the advantages of high rates of convergence. A deterministic system is derived using the statistical properties of the random bases. The classical analytical and/or numerical techniques can be used to analyze the deterministic system and obtain the solution statistics. The technique presented in the current work is applicable to many complex systems with both stochastic and random parameters. It has the advantage of separating the contributions due to different sources of uncertainty. Hence, the sensitivity index of any uncertain parameter can be evaluated. This is a clear advantage compared with other techniques used in the literature.

Background: Inappropriate complementary feeding during the first two years of life significantly impacts children’s health, increasing risks of malnutrition and illness. Methods: This study investigates factors influencing early feeding patterns among 600 mothers of children aged 9–23 months in selected hospitals in Punjab, Pakistan. Using a structured questionnaire, data were collected and analyzed, with associations measured by odds ratios (ORs) and 95% confidence intervals (CIs). Results: The results showed the key indicators of inappropriate complementary feeding among young children, including timely complementary feeding, minimum meal frequency, dietary diversity, and acceptable diet. The rates for these factors were found to be 60.3%, 32.7%, 24.6%, and 48.5%, respectively. The study identified several significant factors influencing these practices. Key predictors of inappropriate feeding included the order of birth, the mother’s employment status, parental education, the number of children, household income, maternal knowledge, and maternal health. Conclusion: The findings underscore that maternal education, employment, and health significantly influence complementary feeding. Targeted interventions and education programs are essential to support healthy feeding behaviors, especially for mothers facing challenges related to education, work, or health conditions. Addressing these practices can improve child health outcomes, contributing to economic growth and a healthier future for Pakistan’s youngest population.