Intelligent Pharmacy最新文献

Background

The growing significance of social media in commercial enterprises is bringing this theme to the attention of decision-makers. These days, businesses use Facebook, Twitter, and YouTube as part of their marketing strategies. This encourages communication between consumers and marketers. Similar communication tactics are used in the pharmaceutical sector. However, because this is a healthcare-related industry, there are a lot of rules that apply to it, especially to its marketing department.

Purpose

The purpose of this study is to assess the pharmaceutical industry's online presence on social media sites like Facebook, Twitter, and YouTube, as well as to describe the various digital engagement tactics that are employed.

Conclusion

The study's conclusions indicate that not all pharmaceutical businesses use social media, and that certain platforms are more popular than others. It's interesting to note that different social media platforms underwent different digital engagement techniques, and that the level of involvement was unrelated to the size of the companies. This study offers insights into the social media organization of pharmaceutical businesses and ostensibly supplies a framework and technique for further research in this area. Furthermore, a few of the constraints found offer guidance for future research directions.

To the Editor, Antibiotics are a class of drug used to treat or prevent infections caused by bacteria; they function by either eradicating the organism or stopping its growth. Penicillin, cephalosporins, macrolides, fluoroquinolones, and urinary anti-infectives are examples of common antibiotics. To effectively treat the illness, it's critical to take antibiotics as directed by a physician and to finish the entire course of treatment. Antibiotic resistance is a serious issue in Bangladesh as a result of subpar healthcare practices, antibiotic abuse, and overuse. Antibiotic resistance is the result of bacteria changing and becoming resistant to an antibiotic's effects. Moreover, one of Bangladesh's biggest challenges is the fight against antibiotic resistance. Therefore, the purpose of this letter is to raise awareness of the antibiotic resistance in Bangladesh.

India's vast and diverse population strains its healthcare system. Amidst these complexities, Artificial Intelligence (AI) emerges as a beacon of hope. This transformative technology promises to revolutionize healthcare, starting with early disease detection and accurate diagnoses. AI, driven by vast medical data, paints a deeper picture of individual health. By analyzing health patterns, it can detect hidden cancers and tuberculosis early, saving lives through proactive treatment. AI's power extends beyond individual diagnoses. It can scan populations, identifying risk factors and predicting outbreaks before they erupt. This foresight allows for targeted resource allocation and preventive measures, mitigating outbreak impact. AI can even personalize healthcare, shaping treatment plans based on a patient's unique lifestyle and medical history. This maximizes treatment efficacy, minimizes adverse reactions, and improves patient’s well-being. Imagine AI as a trusted medical advisor, suggesting the most effective treatment options for each individual. However, AI's promise comes with challenges. Data privacy, reliable infrastructure, and biased algorithms need effective solutions. India, with its strong tech ecosystem and commitment to innovation, is well-positioned to tackle these challenges. By investing in AI research, strengthening data infrastructure, and establishing ethical frameworks, India can unlock AI's immense potential to revolutionize its healthcare landscape. This will be a dividend for millions, ensuring India's healthcare system transforms with the brushstrokes of AI, leading to a healthier and more affordable future for all.

Fused deposition modelling (FDM) and laser-based additive manufacturing (LBAM) are the essential technologies of 3D Printing under the technological platform of additive manufacturing (AM). This process involves layering tiny layers of a chosen material until the desired three-dimensional shape is achieved. FDM and LBAM have been commercialised and are also being deployed in a variety of medical fields. These technologies are worthwhile in reducing expenditures, increasing precision, and lowering operating and post-operative hazards, and the most crucial part is customisation. FDM is witnessing significant growth as an AM technology primarily because of its exceptional ability to construct functional parts with complex geometries. This study aims to investigate the effect of different process parameters such as build orientation, layer thickness, raster angle, air gap, printing speed, infill density, and extrusion temperature on the mechanical properties of FDM printed parts. This paper explores FDM and LBAM, the technological developments that have various applications in the medical field. Using a laser beam to fuse or melt successive layers of wire or powder material together to form three-dimensional objects is known as LBAM. It is one adaptable manufacturing process that is widely used to create metallic components with improved characteristics. By implementing FDM or LBAM technologies, surgeons can provide patients with precise and better information. The patient's adaption period for customised prostheses/implants is shorter, less painful, and less stressful. Where regular implants are often insufficient for some patients with complex circumstances, the ability to quickly manufacture personalised implants by using these technologies is quite helpful. This paper provides readers with an insight into the capabilities of FDM and LBAM in the medical field.

To optimize and characterize xanthan gum multi-particulate formulation for colon targeting, which increases the residence time at the absorbing surface of the colon. Xanthan gum was dispersed in cold water containing drug and was permitted to expand for 2 ​h. Sodium alginate was blended well in 10 ​ml of water. Xanthan gum solution containing the drug was added to sodium alginate solution and 0.3 ​ml of glutaraldehyde was added to the dispersion, with constant stirring. Then, polymer-drug solution was added dropwise into 5% w/v calcium chloride solution with continuous stirring, producing microspheres filtered by Whatman filter paper and dried at 30 ​°C–40 ​°C. Microspheres were performed by chemical cross-linking with glutaraldehyde, which increased the maximum drug entrapment efficiency up to 73.63 ​± ​0.65% with an increasing concentration of xanthan gum polymer 0.7% w/v for the optimized F6 batch. Better results were found by increasing the polymer concentration along with the glutaraldehyde concentration. The kinetics of drug release for the F6 batch was considered as an optimized batch because the regression value was found to be 0.997 in the peppas model. The accelerated stability study on the optimized F6 batch performed to learn whether the drug has any change during its period of usability. The polysaccharide remains intact in the stomach and intestine and the drug was released in the colon with low toxicity and biodegradability. The present studies showed that optimizing and characterizing xanthan gum multi-particulate formulation for colon targeting gives metronidazole the most effective and controlled delivery.

Background

Cancer is the second leading cause of death worldwide. Although great advancements have been made in the treatment and control of cancer progression, significant deficiencies and room for improvement remain. Several undesired side effects sometimes occur during chemotherapy. Natural therapies, such as the use of plant-derived products in cancer treatment, may reduce adverse side effects.

Methods

Currently, a few plant products are being used to treat cancer. However, a myriad of plant products exist that have shown very promising anti-cancer properties in vitro but have yet to be evaluated in humans. Further study is required to determine the efficacy of these plant products in treating cancers in humans.

Results

This review will focus on the various traditional medicinal plants and their chemical compounds that have, in recent years, shown promise as anticancer agents and will outline their potential mechanism of action.

Conclusions

The current manuscript discusses natural products currently in clinical use, and under clinical trials, for cancer chemotherapy and chemoprevention. Future research focusing on natural anticancer agents can open a new horizon in cancer treatment, which will play a great role in enhancing the survival rate of cancer patients.

Artificial intelligence (AI) has immense potential to revolutionize pharmacy operations by simplifying procedures, improving efficiency, and expediting pharmaceutical research. Nevertheless, obstacles such as steep expenses, absence of faith in AI, worries about unemployment, threats to privacy, and the incapacity to substitute human decision-making have impeded acceptance. This text discusses the future of AI in the field of pharmacy, obstacles that are preventing its usage, and methods to make its integration easier. The expansion of large data in healthcare offers chances for AI to obtain understanding, but examining and implementing information still presents difficulties. Significant obstacles such as costly implementation, safety concerns, restrictions on data exchange by regulations, and absence of interpersonal interaction need to be resolved. Methods to facilitate acceptance involve upgrading medical instruction to center around AI, involving interested parties, allocating resources for research and development, creating safeguarded machine learning methods, and carefully incorporating AI to enhance, rather than replace, pharmacy personnel. Although additional effort is required to establish confidence in AI and address genuine worries, specific actions can tap into AI's capacity to enhance effectiveness, lower expenses, expedite drug exploration, and improve healthcare for patients. Responsible and moral adoption requires tackling obstacles through cooperation among interested parties and gradual incorporation centered on enhancing human workforce, rather than substituting them.

Breast cancer is the most common malignant tumour in women worldwide, as well as the leading cause of death from malignant tumours. All across the world, the incidence of breast cancer is steadily rising. Although numerous drugs acting through various mechanisms of action are available in the market as conventional formulations for the treatment of breast cancer, they face significant challenges in terms of bioavailability, dosing, and associated adverse effects, which severely limit their therapeutic efficacy. Several studies have shown that nanocarriers can significantly improve the drug's bioavailability, reducing the need for frequent dosing and reducing the toxicity linked to high drug doses. The current review provides insight into the challenges associated with conventional breast cancer formulations and the need for oral nanoparticulate systems to overcome problems associated with conventional formulations. This review focuses on various topics, such as an in-depth analysis of potential anticancer drugs that have used nanocarrier technology to treat breast cancer successfully.

The current prominent virus that induces severe acute respiratory syndrome is SARS-CoV-2. The incidence of COVID-19 cases is increasing, necessitating the immediate development of effective treatments. Our objective was to employ an in-silico approach to evaluate the effectiveness of conventional compounds against COVID-19's nucleoprotein and envelope protein. A docking simulation was performed on 9 compounds as SARS-coronavirus inhibitors using AMDock software. Anti-covid-19 activities were further evaluated for the compounds. Based on docking results, the binding affinity of "N-(4-carbamoylphenyl)-8-cyclopropyl-7-(naphthalen-1-ylmethyl)-5-oxo-2,3-dihydro-5H-thiazolo[3,2-a]pyridine-3-carboxamide,” also called compound 36 in this research, was found to be −8.8 ​kcal/mol for the modeled envelope protein and −7.3 ​kcal/mol for the template envelope protein, while −10.1 ​kcal/mol for the modeled nucleocapsid proteins (NP) and −8.7 ​kcal/mol for the template nucleocapsid proteins (NP) of SARS-coronavirus, respectively. The ligand and control drug (ritonavir) with high docking scores were subjected to pharmacological screening, molecular dynamic simulations, and Molecular Mechanics-generalized Born Surface Area (MM/GBSA) calculations. Furthermore, the jobs of pharmacokinetics were assessed, and the outcomes acquired show that the proposed compound 36 includes great oral bioavailability and a capacity to diffuse through various organic boundaries. The protein-ligand complexes were subjected to dynamic simulation analyses with a re-enactment time of 10 ns, likewise, their free binding energy was inspected operating the MM/GBSA approach. The docking (MD simulation) results acquired emphasize the pivotal residues answerable for the protein-ligand interaction, giving an understanding of the method of association. The MD simulation analysis verifies the structural stability of the selected complexes during the MD trajectory, with minor changes detected. The MM/GBSA data show that compound 36 has the lowest free energy of −12.498 ​kcal/mol for EP and −57.5185 ​kcal/mol for NP proteins of SARS-coronavirus, confirming the molecular docking result. As a result, the identified chemical can be used to develop a new family of antiviral medications against SARS-coronavirus-2.

To create novel treatments and treat complex diseases, the pharmaceutical sector is essential. Drug discovery, however, is a time-consuming, pricey, and dangerous endeavor. Artificial intelligence (AI) has become a potent instrument that has transformed several industries, including healthcare, in recent years. This summary gives a general overview of how AI is expediting the creation of novel medicines, revolutionizing the pharmaceutical sector, and enabling drug discovery. The pharmaceutical sector is experiencing a drug discovery revolution because of AI. The drug discovery process is changing at different phases because of AI approaches like machine learning and deep learning. This abstract demonstrates how AI facilitates drug development through target identification, lead compound optimization, drug design, drug repurposing, and clinical trial enhancement. AI integration has the potential to hasten the creation of novel treatments, save costs, and improve patient outcomes. To fully realize the potential of AI in pharmaceutical research and development, issues relating to data accessibility, algorithm interpretability, and laws must be resolved.