The poor pharmacokinetic characteristics of most anticancer drugs have limited their clinical effectiveness. The application of nanoparticles as a novel drug delivery system has provided opportunities to tackle the current challenges facing conventional drug delivery systems such as poor pharmacokinetics, lack of specificity to tumor cells, multidrug resistance, and toxicity. This systematic review aims to examine the application of pharmacokinetic studies of nanoparticles loaded in conventional drugs and herb-derived compounds for cancer therapy. The pharmacokinetic parameters of several herbal medicines and chemotherapeutic drugs loaded into nanoparticles were reported. This included area under the curve (AUC) of plasma concentration-time profile, maximum plasma concentration (Cmax), time to maximum plasma concentration (Tmax), volume of distribution (Vd or Vss), elimination half-life (t½), and clearance (CL). The systematic review was conducted using information available in the PubMed and Science Direct databases up to February 2019. The search terms employed were: pharmacokinetics, pharmacokinetic study, nanoparticles, anticancer, traditional medicine, herbal medicine, herb-derived compounds, natural products, and chemotherapy. Overall, nanoparticle carriers not only significantly improved pharmacokinetics but also further enhanced permeability, solubility, stability, specificity, and selectivity of the carried anticancer drugs/herb-derived compounds to target tumor cells. Additionally, they also limited hepatic first-pass metabolism and P-glycoprotein (P-gp) efflux of the carried anticancer drugs/herb-derived compounds. Based on this systematic review, polymeric nanoparticles were the most commonly used nanocarrier to improve the pharmacokinetic parameters. The use of nanoparticles as a novel drug delivery system has the potential to improve both pharmacokinetics and cytotoxicity activity of the loaded drugs/herb-derived compounds for cancer therapy.

Introduction: Several studies suggest increasing rates of antibiotic resistance among adult populations with Urinary tract infections (UTI). Escherichia coli (E. coli), is the predominant bacterium both in the community and in hospital environments causing uropathogenic infections. This study aimed to estimate the common uropathogen bacteria that cause UTI among outpatients as well as to determine the antibiotic resistance pattern of E. coli isolates among outpatients with UTI infections at Shaafi hospital, Mogadishu, Somalia. Methodology: A cross-sectional study was conducted at Shaafi Hospital, Mogadishu, Somalia. A total of 128 samples were collected from outpatients suspected of UTI and tested through bacteriological investigations and antimicrobial susceptibility tests following the Kirby-Bauer agar disc diffusion method. Results: E. coli was isolated in 34 (41%) out of the total 83 samples that showed growth followed by Staphylococcus aureus 22 (26.5%), Pseudomonas aeruginosa, 13 (15.7%), Klebsiella pneumoniae 8 (9.6 %) and Proteus spp. 6 (7.2%). E. coli was highly sensitive to nitrofurantoin, 29 (85.3%), and ciprofloxacin (n = 23, 67.6%), and had the highest resistance rate of ceftriaxone, 33 (97.1%). The odds of having UTI were higher in patients with a history of UTI (Odds ratio OR = 0.211, 95% confidence interval CI: 0.080, 0.553) and history of antibiotic use (OR = 0.322, 95% CI: 0.113, 0.917). Increased resistance rate of E. coli against cephalosporins could be due to its excessive use as empirical therapy. Conclusion: The study indicates that outpatients with UTI could be at high risk of antibiotic resistance, suggesting regular surveillance and monitoring of antibiotics.

Antimicrobial resistance (AMR) is a critical global health challenge, and Somalia is no exception. This update examines the current status of AMR in Somalia, highlighting the prevalent patterns of resistance, contributing factors, and significant health impacts. Despite limited surveillance data, evidence suggests rising resistance to key antibiotics, exacerbated by inadequate healthcare infrastructure, overuse of antimicrobials, and lack of regulatory oversight. The review identifies key challenges, including insufficient diagnostic capabilities, poor infection control practices, and a need for robust stewardship programs. Opportunities for addressing AMR in Somalia are discussed, including strengthening surveillance systems, improving healthcare access, and fostering international collaboration. Future perspectives emphasize the importance of integrating AMR strategies into broader health policies, enhancing public awareness, and investing in research to develop new treatments and prevention methods. Addressing these issues is crucial for mitigating the impact of AMR and improving health outcomes in Somalia.

Background: Premature birth impacts millions of newborns annually. Sixty percent of the world’s preterm births occur in Sub-Saharan Africa and South Asia. Somalia’s premature birth rates and maternal risk factors are poorly studied; hence, this study aims to identify maternal risk factors related to premature births in Mogadishu, Somalia. Methods: This unmatched case-control study was conducted at four maternity hospitals in Mogadishu, Somalia. The cases were newborns with gestational ages of less than 37 weeks; controls were newborns with gestational ages of 37 to 42 weeks. All were live singletons. Cross-tabulation and binary logistic regression were used to analyze the data; a p-value of 0.05 was deemed statistically significant. Result: Of the total of 499 newborns, 70 were cases, and 429 were controls. Adequate prenatal care, maternal urine analysis, tetanus toxoid (TT) vaccination, hemoglobin (Hb) measurement, ultrasound monitoring, intake of iron + folic acid (IFA) supplement, blood pressure (BP) measurement during the current pregnancy, as well as partograph usage during labor all significantly decreased risk of having premature births. A prior history of preterm delivery and preeclampsia, obstetric complications, and female genital mutilation (FGM) significantly increased the risk of preterm births. Conclusion: The utilization of antenatal care services, use of a partograph, obstetric complications, and prior history of premature birth and preeclampsia had a substantial effect on preterm birth rates. This study identifies female genital mutilation (FGM) as a previously unidentified risk factor for preterm birth that needs additional investigation.

Artificial intelligence (AI) is promptly changing the nature of food production and processing to increase safety, efficiency, quality assurance, and sustainability throughout the farm‐to‐fork chain. This review discusses the recent developments in AI in the food manufacturing industry, such as machine learning (ML), deep learning (Dl), computer vision (CV), hyperspectral imaging (HSI), robotics, Internet‐of‐Things (IoT) integration, and natural language processing. These technologies allow detecting contamination and fraud in real‐time, near‐perfect predictive maintenance less 30% unplanned downtime, automated quality checks with up to 96%–100% accuracy, AI‐enabled predictive maintenance (PdM) has demonstrated its effectiveness by decreasing unplanned downtime in industrial case studies through a reduction rate that reaches 30% of total downtime, resource‐efficient smart factories, and creative AI‐driven formulation of products. New paradigms include digital twins, explainable AI, computational gastronomy, and cultured meat production, highlighting the transformative power of AI. Some of the solutions include standardized datasets, lightweight architectures, federated learning, and scalable deployment training programs. When used responsibly, AI can help create food systems that meet regulatory requirements while achieving sustainable development goals and circular economy standards, and all three Sustainable Development Goals, like Zero Hunger (SDG 2), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13).