Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

Background: The unprecedented emergence of the COVID-19 pandemic necessitated the development and global distribution of vaccines, making the understanding of global vaccine acceptance and hesitancy crucial to overcoming barriers to vaccination and achieving widespread immunization. Objective: This umbrella review synthesizes findings from systematic reviews and meta-analyses to provide insights into global perceptions on COVID-19 vaccine acceptance and hesitancy across diverse populations and regions. Methods: We conducted a literature search across major databases to identify systematic reviews and meta-analysis that reported COVID-19 vaccine acceptance and hesitancy. The AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) criteria were used to assess the methodological quality of included systematic reviews. Meta-analysis was performed using STATA 17 with a random effect model. The data synthesis is presented in a table format and via a narrative. Results: Our inclusion criteria were met by 78 meta-analyses published between 2021 and 2023. Our analysis revealed a moderate vaccine acceptance rate of 63% (95% CI 0.60%-0.67%) in the general population, with significant heterogeneity (I2 = 97.59%). Higher acceptance rates were observed among health care workers and individuals with chronic diseases, at 64% (95% CI 0.57%-0.71%) and 69% (95% CI 0.61%-0.76%), respectively. However, lower acceptance was noted among pregnant women, at 48% (95% CI 0.42%-0.53%), and parents consenting for their children, at 61.29% (95% CI 0.56%-0.67%). The pooled vaccine hesitancy rate was 32% (95% CI 0.25%-0.39%) in the general population. The quality assessment revealed 19 high-quality, 38 moderate-quality, 15 low-quality, and 6 critically low-quality meta-analyses. Conclusions: This review revealed the presence of vaccine hesitancy globally, emphasizing the necessity for population-specific, culturally sensitive interventions and clear, credible information dissemination to foster vaccine acceptance. The observed disparities accentuate the need for continuous research to understand evolving vaccine perceptions and to address the unique concerns and needs of diverse populations, thereby aiding in the formulation of effective and inclusive vaccination strategies. Trial Registration: PROSPERO CRD42023468363; https://tinyurl.com/2p9kv9cr

The aim of this study was to fabricate and characterize a pharmaceutical emulgel co-loaded with naproxen/eugenol for transdermal delivery to improve the analgesic and anti-inflammatory effects and to eliminate GIT adverse reactions. Emulgel was prepared using a slow emulsification method and evaluated for physical appearance, thermodynamic stability, viscosity, pH, spreadability, extrudability, in-vitro drug release, drug content, ex-vivo permeation, drug retention studies and in-vivo studies. The emulgel exhibited good physical attributes, being thermodynamically stable with no phase separation, having excellent homogeneity, and pH 5.5 to 6.5. Slight changes in viscosity, spreadability and extrudability with respect to high temperature were observed (p > 0.05). The drug content was 96.69 ± 1.18% and 97.24 ± 1.27% for naproxen and eugenol, respectively. The maximum release of naproxen after 12 h was 85.14 ± 1.11%, whereas eugenol was 86.67 ± 1.23% from emulgel following anomalous non-Fickian mechanism. The maximum % permeation of naproxen across skin was 78.5 ± 1.30, whereas maximum % permeation of eugenol was 83.7 ± 1.33 after 12 h. The skin retention of eugenol and naproxen was 8.52 ± 0.22% and 6.98 ± 0.24%, respectively. The optimized emulgel inhibited the carrageenan induced paw edema. The pain reaction times of optimized emulgel and standard marketed product (Voltral^®) were 11.16 ± 0.17 and 10.36 ± 0.47, respectively, with no statistically significant difference (p > 0.05). This study concluded that transdermal delivery of naproxen-eugenol emulgel synergized the anti-inflammatory and analgesic effects of naproxen and eugenol.

Streptococcus pneumoniae (S. pneumoniae) is a bacterial species often associated with the occurrence of community-acquired pneumonia (CAP). CAP refers to a specific kind of pneumonia that occurs in individuals who acquire the infection outside of a healthcare setting. It represents the leading cause of both death and morbidity on a global scale. Moreover, the declaration of S. pneumoniae as one of the 12 leading pathogens was made by the World Health Organization (WHO) in 2017. Antibiotics like β-lactams, macrolides, and fluoroquinolones are the primary classes of antimicrobial medicines used for the treatment of S. pneumoniae infections. Nevertheless, the efficacy of these antibiotics is diminishing as a result of the establishment of resistance in S. pneumoniae against these antimicrobial agents. In 2019, the WHO declared that antibiotic resistance was among the top 10 hazards to worldwide health. It is believed that penicillin-binding protein genetic alteration causes β-lactam antibiotic resistance. Ribosomal target site alterations and active efflux pumps cause macrolide resistance. Numerous factors, including the accumulation of mutations, enhanced efflux mechanisms, and plasmid gene acquisition, cause fluoroquinolone resistance. Furthermore, despite the advancements in pneumococcal vaccinations and artificial intelligence (AI), it is not feasible for individuals to rely on them indefinitely. The ongoing development of AI for combating antimicrobial resistance necessitates more research and development efforts. A few strategies can be performed to curb this resistance issue, including providing educational initiatives and guidelines, conducting surveillance, and establishing new antibiotics targeting another part of the bacteria. Hence, understanding the resistance mechanism of S. pneumoniae may aid researchers in developing a more efficacious antibiotic in future endeavors.

The world faces multiple public health emergencies simultaneously, such as COVID-19 and Monkeypox (mpox). mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.