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Regarding diagnosis, treatment, monitoring, and prognosis, nanomedicine will play a critical role in customized medicine in the future. Nanomedicine allows for better targeting, more accurate disease mapping, and fewer side effects by delivering drugs straight to the sick cells. The advantageous alterations that nanotechnology makes to the physiochemical, mechanical, magnetic, electrical, and optical properties of computing materials enable the development of new and innovative products. Food security, processing, coloring, nutritional absorption, flavor, nutrition, delivery, disease detection, food functioning, environmental protection, and cost-effective storage and distribution are a few of the important links between nanotechnology and food systems. Nanomaterials are used in biofuel production, wastewater treatment, biosensor pollution removal tools, photocatalysts, biomedical imaging and cancer treatments, and wearable chemical and environmental sensors, as shown by a number of academic publications. A few of the main challenges facing the application of nanotechnology are scalability, increased production rates, managing unwanted byproducts, repeatability and quality control of nanomaterials, and enhanced manufacturing rates.

Increasing evidence suggests that human movement, including international travel and mass forced population displacement, acts as a catalyst for global antimicrobial resistance (AMR) transmission. Immigration increases the likelihood of antibiotic-resistant bacterial strains being imported into developed countries, particularly from several developing countries with high antimicrobial resistance. On the other hand, self-medication, easy access to over-the-counter drugs, promiscuous use, and lax oversight all contribute to an increase in antibiotic use in most lower-middle- and middle-income countries. It leads to antimicrobial resistance and an increased health burden, particularly in high-income countries. Third-party funding agencies are working to reduce the global antimicrobial resistance burden. Understanding the complex system of antibiotic overuse, third-party funding, and cross-border transmission of antimicrobial-resistant strains is a critical task for public health policymakers. In this study, a modeling framework is developed using a combination of epidemiological and game theory methods. This framework aims to describe the phenomenon of migration between countries, specifically focusing on the movement between developing and developed nations. Additionally, the framework incorporates the analysis of self-medication behavior within the developing population. Simulations of the model are conducted, followed by a sensitivity analysis, in order to investigate the influence of various parameters on the optimal funding amount. Our findings indicate that the total cost due to treatment of infection and funding is a convex function of the amount of third-party assistance. This means that there is an optimal level of funding in place to keep the total cost of antimicrobial resistance as low as possible. The sensitivity analyses in relation to various epidemiological and behavioral parameters indicate that the optimal fund is highly dependent on the other parameters of the system. Third-party assistance may be required, as predicted, to reduce the amount of self-medication, but it is only beneficial up to a certain point. These findings result in useful policy recommendations as well as intriguing new research directions.

A deterministic model for the transmission of two-strain typhoid fever is developed. The aim is to predict the influence of the typhoid conjugate vaccine on the prevalence of antimicrobial-resistant typhoid infection. Two mechanisms of antimicrobial resistance are incorporated in the model, namely treatment-induced/acquired resistance and transmission of resistant S. Typhi strain. The mathematical analysis is performed using both analytical and numerical approaches. The results show that the disease-free state is locally asymptotically stable if the effective reproduction number ${{ℛ}}_e$ is less than unity. Further analysis reveals that the model exhibits vaccine-induced backward bifurcation, suggesting that ${{ℛ}}_e<1$ is not sufficient for disease elimination. Furthermore, numerical simulations have shown that the existence of endemic equilibrium depends on the sign of ${{ℛ}}_e^r-{{ℛ}}_e^s$. Here, ${{ℛ}}_e^r$ and ${{ℛ}}_e^s$ denote the effective reproduction numbers for the resistant and sensitive strains, respectively. When $\varphi >0$, where $\varphi$ is the rate of treatment-induced/acquired resistance, the resistant strain will cause the extinction of the sensitive strain if ${{ℛ}}_e^r>{{ℛ}}_e^s$. However, the two strains co-exist if ${{ℛ}}_e^r\le {{ℛ}}_e^s$. When $\varphi =0$, the co-existence equilibrium exists if ${{ℛ}}_e^s={{ℛ}}_e^r$; otherwise, the strain with a higher reproduction number would cause the other strain to become extinct, showing the competitive exclusion of the two competing strains. Moreover, the sensitivities of various parameters on the fraction of AMR infections are demonstrated. Also, the prevalence of typhoid decreases with the increase in the number of vaccinated individuals, but vaccination alone is unlikely to control the AMR typhoid infection. The study suggests that more preventive interventions need to be implemented to effectively control antimicrobial-resistant typhoid infections.

The increase in antibiotic resistance continues to pose a major public health risk leading to a more intense focus on ways to limit and even reduce this threat. One such effort is the push for twenty new classes of antibiotics by the year 2020. Most of the current antibiotics used today are derivations of antibiotics first introduced forty to fifty years ago. In this paper, we develop mathematical models to simulate the difference between implementing a next generation antibiotic versus a new class antibiotic within a hospital setting. Using these models, we simulate the short term and long term effects of using the new antibiotic to combat existing levels of antimicrobial resistance. In addition to analyzing the difference in antibiotic classes, we also analyze the effects of the method of administration of the new antibiotic. Simulations suggest a need in the long term for the development of new classes of antibiotics administered in a very structured, targeted manner.

In recent times, bacterial Antimicrobial Resistance (AMR) analyses becomes a hot study topic. The AMR comprises information related to the antibiotic product name, class name, subclass name, type, subtype, gene type, etc., which can fight against the illness. However, the tagging language used to determine the data is of free context. These contexts often contain ambiguous data, which leads to a hugely challenging issue in retrieving, organizing, merging, and finding the relevant data. Manually reading this text and labelling is not time-consuming. Hence, topic modeling overcomes these challenges and provides efficient results in categorizing the topic and in determining the data. In this view, this research work designs an ensemble of artificial intelligence for categorizing the AMR gene data and determine the relationship between the antibiotics. The proposed model includes a weighted voting based ensemble model by the incorporation of Latent Dirichlet Allocation (LDA) and Hierarchical Recurrent Neural Networks (HRNN), shows the novelty of the work. It is used for determining the amount of “topics” that cluster utilizing a multidimensional scaling approach. In addition, the proposed model involves the data pre-processing stage to get rid of stop words, punctuations, lower casing, etc. Moreover, an explanatory data analysis uses word cloud which assures the proper functionality and to proceed with the model training process. Besides, three approaches namely perplexity, Harmonic mean, and Random initialization of K are employed to determine the number of topics. For experimental validation, an openly accessible Bacterial AMR reference gene database is employed. The experimental results reported that the perplexity provided the optimal number of topics from the AMR gene data of more than 6500 samples. Therefore, the proposed model helps to find the appropriate antibiotic for bacterial and viral spread and discover how to increase the proper antibiotic in human bodies

The following topics are under this section:

• Life of a Scientist at Singapore-MIT Alliance for Research and Technology (SMART)

The following topics are under this section:

• Preterm Births? Diet not the only answer
• The key in diagnosis and treatment
• Tackling Antimicrobial Resistance
• Clinical research studies pave the way for better healthcare
• Up in the Clouds with Artificial Intelligence and Healthcare

For the month of January 2022, APBN looks at some discoveries and innovation in pharmacology. In Features, Dr Harish Dave, Co-Founder and Chief Medical Officer of AUM Biosciences, discusses today's shifting paradigm in oncology drug development towards highly selective, minimally toxic, and patient-centric treatments, while Jade Pallett, Chief Technology Officer for Zoono UK & Europe, sheds light on how antimicrobial coatings outdo traditional methods of disinfection. Then, we have A/Prof Alexandra Sharland, Dr Nicole Mifsud, and Eric Son to elucidate how understanding antigen-specificity of host T cells can reduce organ transplantation rejection. Finally, in Spotlights, we have two interviews – one where we speak to Mr Willson Deng, CEO of Arcstone, on the role of digital technology in supporting MedTech manufacturing, and the other with Liu Qun, Head of IQVIA, China, where we learn more about the Chinese biopharmaceutical market.

The fight against pathogenic microorganisms has in recent decades been met with fierce setbacks owing to the antimicrobial resistance phenomenon. Conventional antimicrobials have thus weakened in their effectiveness against microbes, calling for the development of innovative strategies to combat the emerging global health crisis. A promising therapy for filling this gap is photodynamic antimicrobial chemotherapy which destroys microorganisms by making use of the combined action of a photosensitizer, light, and oxygen. The modality inactivates a wide range of pathogens, including bacteria, fungi, protozoa, and viruses. Of greater interest in photodynamic antimicrobial chemotherapy is the ability to destroy resistant strains of microbes without encouraging selection for resistance genes, thus immensely contributing to the fight against antimicrobial resistance. Different PSs vary in their microbial inactivation efficacies. Therefore, various structural modification approaches are being adopted to improve their respective activities. In this paper, recent studies focusing on strategies employed to improve the effectiveness and efficacies of PSs used in photodynamic antimicrobial chemotherapy are reviewed.

In the face of increasing antimicrobial resistance (AMR) and the looming threat of a return to a pre-antibiotics era, the exploration of alternative antimicrobial strategies becomes imperative. Among these, Photodynamic Antimicrobial Chemotherapy (PACT) stands out as a promising solution due to its multi-target principle, diverging from the key-lock mechanism inherent in conventional antibiotics. However, the hydrophobic nature, non-targeted distribution, stability issues, and limited tissue penetration of photosensitizers (PSs), particularly the extensively studied porphyrin-based compounds, pose structural limitations impacting their bioavailability and effective delivery to target sites. Therefore, various delivery strategies are adopted to improve their effectiveness in PACT. This includes attaching the PS to a variety of delivery vehicles such as nanoparticles, and liposomes, or encapsulating them in nanostructures, such as micelles or dendrimers. This helps protect the PS from degradation, improve their solubility and bioavailability, and enhance their photophysical and photochemical properties, ultimately improving their effectiveness in PACT. In this paper, recent studies focusing on strategies used to improve the delivery of porphyrin-based PS in PACT are reviewed.

Urinary tract infection (UTI) is frequently diagnosed in dogs and cats. To realize common regional uropathogens and their antimicrobial resistance plays an important role in both human and veterinary medicine. An empirical antibiotic was administrated while waiting for the results of bacterial culture and antimicrobial susceptibility testing. Eighty-five bacterial isolates were obtained from 74 cats with clinical signs of UTI. Escherichia coli, Enterococcus species, Staphylococcus species and Klebsiella species were the most common bacterial isolates in our hospital. Compared to the previous researches, the susceptibilities of recommended empirical antibiotics exhibited higher resistant rate in our study. Lincomycin was the most resistant antibiotic within the test panel to that almost 98% uropathogens were resistant. The resistant rates of cephalexin, cephalothin, penicillin and amoxicillin were about 90%. Therefore, the selection of empirical antibiotics should be re-evaluated with time in Taiwan.

Background: Community Acquired Methicillin Resistant Staphylococcus aureus (CA-MRSA) rates have been increasing worldwide and contribute to a growing “global health security threat” as reported by the WHO. Our group previously reported an overall rate of 7% in CA-MRSA upper extremity infections between 2004–2009 at the Auckland Regional Hand Unit. This fell below the Center for Disease Control (CDC) recommendation for empiric antimicrobial cover once local rates exceed 10–15%. We examined prevalence and characteristics of CA-MRSA upper extremity infections in our region over a subsequent 5-year period.

Methods: One thousand two hundred and fifty-two patients with upper extremity infections requiring operative management between 2011 and 2015 inclusive were included in this study. Associated clinical characteristics were recorded including ethnicity, cultured organisms, antibiotic sensitivities, infection rate, and treatment practice.

Results: One hundred and fifty (12%) of patients had culture positive CA-MRSA upper extremity infections. There was an increasing annual trend. Of note, rates of CA-MRSA in the Maori and Pacific Island ethnic subpopulations exceeded 15% in 2014 and 2015. Susceptibilities, associated factors and patient demographics are reported.

Conclusions: Our unit enjoys significantly lower rates of CA-MRSA upper extremity infections than has been reported internationally. However, trends are increasing relative to our prior 6-year report, and the threshold for empiric treatment has been met within the Maori and Pacific Island ethnic subpopulations. This evolving threat is also highlighted by increasing cases of multi-drug resistant CA-MRSA. Evolving regional guidelines for empiric coverage of CA-MRSA among high-risk ethnic subpopulations identified by this study are underway.

The development of cross infections arising from bacteria transmission on frequently touched facilities has led to an urgent need to promptly disinfect these surfaces, such as hand railings, door handles and elevator buttons. Conventional antimicrobial disinfectants are not ideal as they contribute to the growing antimicrobial resistance crisis. In recent years, the discovery that the wings of insects such as the Clanger cicada (Psaltoda claripennis) possess naturally occurring antimicrobial properties has led to a growing interest to synthetically recreate these surfaces. The use of a physical contact killing mechanism on such nanotextured surfaces is a promising strategy for curbing the proliferation of bacteria, as it is unlikely to contribute to the formation of antimicrobial resistance. Here, I highlight the key advantages of using these antimicrobial nanotextured materials and how they could play a role in safeguarding public health security, especially during the current COVID-19 pandemic.

Non-point source microbial pollution damages the overall health of marine ecosystems. Beyond their direct effects, contaminating microbes can introduce antimicrobial resistance and virulence genes into resident species by horizontal gene transfer, a process that may be enhanced by co-pollutants such as heavy metals. In this work, a number of heavy metal- and antimicrobial-resistant (AMR) strains of E. coli were isolated from water and sediment samples collected in the Causeway Bay, Hong Kong (22.2833 N, 114.1847 E) and their complete genomes obtained by hybrid assembly using both Illumina MiSeq and Oxford Nanopore MinION sequencing platforms. Genomic analysis showed an abundance of plasmids and mobile elements bearing heavy metal- and AMR genes, such as the mer operon and tetR-tetA efflux system, with these features frequently co-located. In addition, NCBIBLAST–using isolates collected from in-land soil, freshwater, and animal faeces in Hong Kong, as well as the NCBI database – showed that these genes, while common amongst human and animal-derived Enterobacteriaceae, could also be shown present in a number of strains of obligate halophilic Vibrio spp.. Previous water chemistry analysis at this location showed high levels of heavy metal contamination (98 ppm Pb, 0.12 ppm Hg, and 0.4 ppm Cd), which has been shown to promote AMR transfer by selecting for co- resistance. Further isolation and characterisation of marine bacterial species from Hong Kong coastal waters will help to determine the extent of this process.