Efficacy of Biophotonics as a Therapeutic Adjunct in Resolving Microbial and Viral Infectious Illnesses (preprint)

Discovery and development of effective strategies to treat emerging and often drug resistant strains of viral and microbial pathogens presents one of the greatest challenges of the 21st century. Despite nearly a century of progress in developments in antimicrobial and antiviral therapies, infectious diseases still account for a substantial proportion of deaths worldwide, including over 100 million globally in the recent pandemic to date. In recent decades, the plague of antimicrobial resistance also represents an additional and alarming signal for both human and animal healthcare and signals a renewed sense of urgency in addressing this issue. This has made the search for novel classes of antibiotics, antivirals and therapeutics found to be capable of bypassing the microbial and viral resistance mechanisms necessary to in order to replenish our current arsenal of antimicrobial and antiviral drugs and update our therapeutic regimens. In addition, the tremendous impact exerted by viral infections and related pathologies on our lives during the recent decade has forced scientists to acknowledge the opportunities and challenges associated with tackling infectious diseases by developing effective antiviral agents endowed with novel mechanisms of action. The discovery of new antimicrobial/antiviral agents, as well as the repurposing of existing drugs and therapeutic options will be crucial to fight the ever-increasing resistance of “superbugs”, pathogenic fungi, viruses, and parasites. Proponents of modern biophoton therapy proposed in this paper can trace its origins to the Russian scientist Alexander Gurwitsch, who a century ago observed ultra-weak emissions of light emerging from an onion root and soon found additional living tissues to be emanating similar energetic phenomena, thus energizing research which continues to the present day and projected toward the development of useful applications of the new-found biological photonic phenomena. The purpose of the present paper is to introduce the past experiences, potential applications and highly successful outcomes of biophotonic therapy as an option to treat infectious diseases including HIV, Hepatitis C, MRSA, and others, with emphasis on efficacy of using biophotonic therapies to treat and ameliorate the drug resistant variants of the above infectious agents, including potential applications for the current COVID-19 pandemic and in preparation for the emergence of the next epidemic or pandemic.

The Historical and Epidemiological Events that Preceded and Accompanied the Adjustment of the Description of a Pandemic on the WHO Website in 2009 and 2011 (preprint)

Background: An analysis of the pages of the WHO website on the influenza pandemic revealed changes in the use of the term "pandemic" that occurred in 2009 and 2011. Materials and methods: Since the cause of the change in the description of a pandemic on the WHO website in 2009 and 2011 is not clear, analyses of the literature related to the epidemics and pandemics, WHO documents, WHO website, and articles published in journals and the Internet have been done.Results: Until early May 2009, the description of the pandemic focused on "enormous numbers of cases and deaths." On May 6, 2009, a new description of the pandemic was published, focusing on the prevalence of the disease, but in 2011 it reverted to the initial one without any comments. From the perspective of the WHO document issued in 2009, the declaration of a swine flu pandemic in June 2009 seemed justified. However, considering the previous pandemic history, common sense, and the consequences of declaring a pandemic for a disease not accompanied by a high number of cases and high mortality, it was a premature move.Conclusion: Since the primary factor hindering the development of a pandemic is the effectiveness of treating infectious diseases, but not a definition of a pandemic, to minimize the likelihood of a new pandemic, it is necessary to improve the quality of special medical education and to study and adapt to modern conditions all effective drugs and methods used in the past.

Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand (preprint)

Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.

Evaluating ChatGPT Efficacy in Navigating the Spanish Medical Residency Entrance Examination (MIR): A New Horizon for AI in Clinical Medicine (preprint)

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to the emergence of increasingly sophisticated large language models (LLMs) enabling their use in healthcare. The study assesses the performance of two LLMs: the GPT-3.5 and GPT-4 models in passing the medical examination for access to medical specialist training in Spain MIR. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. We studied the 2022 Spanish MIR examination after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM ChatGPT4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length and question sequence d performance. GPT-4 outperformed GPT -3.5, scoring 86.81% in Spanish (p<0.001). English translations had a slightly enhanced performance. Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, with specialties like Pharmacology, ICU, and Infectious Diseases showing lower performance. The error analysis revealed that while a 13.2% error rate existed, gravest categories like "error requiring intervention to sustain life" and "error resulting in death" had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, varying its capability to discriminate knoweldge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implication on patient safety.

Chicken Interleukin-18 as a Vaccine Adjuvant in a Recombinant Fowlpoxvirus Vaccine for the Protection against Infectious Bursal Disease Virus (preprint)

In mammals, the role of interleukin-18 (IL-18) in the immune response is to drive inflammatory and, normally therefore, anti-viral responses. IL-18 also shows promise as a vaccine adjuvant in mammals. Chicken IL-18 (chIL-18) has been cloned. The aim of this study was to investigate the potential of chIL-18 to act as a vaccine adjuvant in the context of a live recombinant Fowlpox virus vaccine (fpIBD1) against Infectious bursal disease virus (IBDV). fpIBD1 protects against mortality, but not against damage to the bursa of Fabricius caused by IBDV infection. The Fowlpox virus genome itself contains several candidate immunomodulatory genes, including potential IL-18 binding proteins (IL-18bp). We have knocked out (Δ) the potential IL-18bp genes in fpIBD1 and inserted (::) the cDNA encoding chIL-18 into fpIBD1 in the non-essential ORF030, generating five new viral constructs –fpIBD1::chIL-18, fpIBD1ΔORF073, fpIBD1ΔORF073::chIL-18, fpIBD1ΔORF214 and fpIBD1ΔORF214::chIL-18. The subsequent protection from challenge with virulent IBDV, as measured by viral load and bursal damage, given by these altered fpIBD1 strains, was compared to that given by the original fpIBD1. The results show that chIL-18 can act as a vaccine adjuvant, giving complete protection against challenge, with no detectable virus or damage in the bursa of Fabricius.

Development of Therapeutic Monoclonal Antibodies for the Emerging Arbovirus Infections (preprint)

Antibody-based passive immunotherapy has been used effectively in the therapy and prophylaxis of infectious diseases. Outbreaks of the emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to the rapid spread and urge actions and treatment of the infected individuals to combat them. Preparedness in advance developing antivirals and studies related to the epidemiology could protect us from damages and losses. Immunotherapy based on monoclonal antibodies (mAbs) has shown to be very specific to combat infectious diseases and several illnesses. Recent advances in the mAb discovery techniques allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and outstanding strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for the dengue, Zika, chikungunya and West Nile virus are presented, as well as the protective effect verified in animal model studies.

Effectiveness of Canadian travel restrictions in reducing burden of SARS-CoV-2 variants of concern (preprint)

Evaluating travel restriction effectiveness in mitigating infectious disease burden is critical for informing public health policy. Here, we quantify where and when variants of SARS-CoV-2 were introduced into Canada to evaluate the extent to which travel restrictions averted viral introductions and COVID-19 case burden. Our results suggest that, across SARS-CoV-2 variants of concern subject to travel restrictions, at least 281 introductions were prevented, accounting for an averted burden of approximately 44,064 cases. This corresponds to approximately 441 averted hospitalizations, 24 averted deaths, and cost savings to Canadian health care systems of approximately $11.2 million Canadian dollars. Travel restrictions were found to be most effective when implemented rapidly during exponential case growth in the focal source and when global circulation was limited. Our analyses reveal that COVID-19 travel restrictions mitigated case burdens and highlight their value in future pandemic response.

Compartmentalized SARS-CoV-2 replication in upper versus lower respiratory tract after intranasal inoculation or aerosol exposure (preprint)

Non-human primate models are essential for the development of vaccines and antivirals against infectious diseases. Rhesus macaques are a widely utilized infection model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We compared cellular tropism and virus replication in rhesus macaques inoculated with SARS-CoV-2 via the intranasal route, or via exposure to aerosols. Intranasal inoculation results in replication in the upper respiratory tract and limited lower respiratory tract involvement, whereas exposure to aerosols results in infection throughout the respiratory tract. In comparison to multi-route inoculation, the intranasal and aerosol inoculation routes result in reduced SARS-CoV-2 replication in the respiratory tract.

Differentiation of COVID-19 from other emergency infectious disease presentations using whole blood transcriptomics then rapid qPCR: a case-control and observational cohort study (preprint)

Background. The overlapping clinical presentations of patients with acute respiratory disease can complicate disease diagnosis. Whilst PCR diagnostic methods to identify SARS-CoV-2 are highly sensitive, they have their shortcomings including false-positive risk and slow turnaround times. Changes in host gene expression can be used to distinguish between disease groups of interest, providing a viable alternative to infectious disease diagnosis. Methods. We interrogated the whole blood gene expression profiles of patients with COVID-19 (n=87), bacterial infections (n=88), viral infections (n=36), and not-infected controls (n=27) to identify a sparse diagnostic signature for distinguishing COVID-19 from other clinically similar infectious and non-infectious conditions. The sparse diagnostic signature underwent validation in a new cohort using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and then underwent further external validation in an independent in silico RNA-seq cohort. Findings. We identified a 10-gene signature (OASL, UBP1, IL1RN, ZNF684, ENTPD7, NFKBIE, CDKN1C, CD44, OTOF, MSR1) that distinguished COVID-19 from other infectious and non-infectious diseases with an AUC of 87.1% (95% CI: 82.6%-91.7%) in the discovery cohort and 88.7% and 93.6% when evaluated in the RT-qPCR validation, and in silico cohorts respectively. Interpretation. Using well-phenotyped samples collected from patients admitted acutely with a spectrum of infectious and non-infectious syndromes, we provide a detailed catalogue of blood gene expression at the time of hospital admission. The findings result in the identification of a 10-gene host diagnostic signature to accurately distinguish COVID-19 from other infection syndromes presenting to hospital. This could be developed into a rapid point-of-care diagnostic test, providing a valuable syndromic diagnostic tool for future early pandemic use.

Evaluation of Smallpox Vaccination Coverage and Attitude towards Monkeypox Vaccination among Healthcare Workers in An Italian University Hospital (preprint)

(1) Background: Mpox was declared an international public health emergency by WHO. Initially, Smallpox vaccine in Italy was offered only to laboratory personnel at direct risk of infection. Currently, Healthcare workers (HCWs) are all considered by European Centre for Disease Prevention and Control (ECDC) to be at low or moderate risk. (2) Methods: To assess smallpox vaccination coverage among HCWs in an Italian hospital with different levels of risk of exposure to Mpox infection and to assess their attitudes toward vaccination, a self-report was administered to HCWs stratified at high and low/moderate risk. (3) Results: Of the 338 HCWs evaluated, 60.36% provided the requested data; among them the percentage of previously vaccinated staff was 38.7%, which corresponds to 23.4% of the total HCWs in the departments considered. Considering those born before 1979 as vaccinated, vaccination coverage increases from 23.4% to 41.4%. The department with the highest percentage of vaccinated is infectious diseases (60.5%); The percentage of HCWs who adhered to vaccination is 23%; only 8.8% of laboratory staff want to vaccinate, compared with 100% of midwives. The department with the highest percentage of vaccination adherence is proctology (85.7%). (4) Conclusions: Given the vaccination adherence rate and the vaccination coverage of the departments analyzed, it would be useful to extend vaccination against Smallpox to frontline HCWs involved in patient care in Italy too.

Clinical Features and Outcomes of Association of Co-infections in Children with LaboratoryConfirmed Influenza, during 2022–2023 Season; a Romanian Perspective (preprint)

The 2022-2023 influenza season in Romania was characterized by high pediatric hospitalization rates, predominated by influenza A subtypes H1N1 and H3N2. Lowered population immunity to influenza after the SARS-CoV-2 pandemic and subsequent stoppage of influenza circulation, particularly in children who had limited pre-pandemic exposures, influenced hospitalization among children immunosuppressed, and patients with concurrent medical conditions who are at increased risk for developing severe forms of influenza. This study focused on the characteristics of influenza issues among paediatric patients, as well as the relationship between different influenza virus types and viral and bacterial coinfections and illness severity in the 2022-2023 season after the SARS-CoV-2 pandemic. We conducted a retrospective clinical analysis on 301 cases of influenza in pediatric inpatients (age ≤ 18 years), hospitalized at the National Institute of Infectious Diseases "Prof. Dr. Matei Balș" IX Pediatric Infectious Diseases Clinical Section between October 2022 and February 2023. The most significant age group was 57.8% representing children between one to four years old and female. The average clinical forms were found in 61.7%, whereas severe versions represented 18.2% of cases. Most of the complications were respiratory (acute interstitial pneumonia, 76.1%), hematological (72.1%), represented by intra-infectious and deficiency anemia, leukopenia, thrombocytopenia), 33.6% were digestive, such as diarrheal disease, liver cytolysis syndrome, and the acute dehydration syndrome associated with electrolyte imbalance (71.4%). Severe complications were associated with a risk of unfavorable evolution: acute respiratory failure and neurological complications (convulsions, encephalitis). No deaths were reported. We noticed that the flu season 2022-2023 was characterized by the association of co-infections (viral, bacterial, fungal and parasitic) more frequent than in previous years (26.2% vs. 16%), which evolved more severely, with prolonged hospitalization and more complications (p<0.05), and time of use of oxygen therapy was statistically significant (p > 0.05); influenza vaccination in this group was zero. In conclusion, coinfections with respiratory viruses increase the severity of the pediatric population's immunity to influenza, especially among young children who are more vulnerable to developing a serious illness. All people above the age of six months should get vaccinated against influenza to prevent the illness and its severe complications.

A mathematical model for multiple COVID-19 waves applied to Kenya (preprint)

The COVID-19 pandemic, which began in December 2019, prompted governments to implement non-pharmaceutical interventions (NPIs) to curb its spread. Despite these efforts and the discovery of vaccines and treatments, the disease continued to circulate globally, evolving into multiple waves, largely driven by emerging COVID-19 variants. Mathematical models have been very useful in understanding the dynamics of the pandemic. Mainly, their focus has been limited to individual waves without easy adaptability to multiple waves. In this study, we propose a compartmental model that can accommodate multiple waves, built on three fundamental concepts. Firstly, we consider the collective impact of all factors affecting COVID-19 and express their influence on the transmission rate through piecewise exponential-cum-constant functions of time. Secondly, we introduce techniques to model the fore sections of observed waves, that change infection curves with negative gradients to those with positive gradients, hence, generating new waves. Lastly, we implement a jump mechanism in the susceptible fraction, enabling further adjustments to align the model with observed infection curve. By applying this model to the Kenyan context, we successfully replicate all COVID-19 waves from March 2020 to January 2023. The identified change points align closely with the emergence of dominant COVID-19 variants, affirming their pivotal role in driving the waves. Furthermore, this adaptable approach can be extended to investigate any new COVID-19 variant or any other periodic infectious diseases, including influenza. Keywords: Mathematical model, COVID-19 pandemic, non-pharmaceutical interventions, delay functions, multiple waves

Biomimetic System Based on Reconstituted Macrophage Membranes for Analyzing and Selection of Higher-Affinity Ligands Specific to Mannose Receptor to Develop the Macrophage-Focused Medicines (preprint)

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

Prediction of COVID-19 infection risk using personal mobile location data only (preprint)

Predicting an individual's risk of infectious disease is a critical technology in infectious disease response. During the COVID-19 pandemic, identifying and isolating individuals at high risk of infection was an essential task for epidemic control. We introduce a new machine learning model that predicts the risk of COVID-19 infection using only individuals' mobile cell tower location information. This model distinguishes the cell tower location information of an individual into residential and non-residential areas and calculates whether the cell tower locations overlapped with other individuals. It then generates various variables from the information of overlapping and predicts the possibility of COVID-19 infection using a machine learning algorithm. The predictive model we developed showed performance comparable to models using individual's clinical information. This predictive model, which can be used to predict infections of diseases with asymptomatic infections such as COVID-19, has the advantage of supplementing the limitations of existing infectious disease prediction models that use symptoms and other information.

Rationally designed multimeric nanovaccines using icosahedral DNA origami for molecularly controlled display of SARS-CoV-2 receptor binding domain (preprint)

Multivalent antigen display on nanoparticles can enhance the immunogenicity of nanovaccines targeting viral moieties, such as the receptor binding domain (RBD) of SARS-CoV-2. However, particle morphology and size of current nanovaccines are significantly different from those of SARS-CoV-2. Additionally, surface antigen patterns are not controllable to enable the optimization of B cell activation. Herein, we employed an icosahedral DNA origami (ICO) as a display particle for SARS-CoV-2 RBD nanovaccines. The morphology and diameter of the particles were close to those of the virus. The surface addressability of the DNA origami permitted facile modification of the ICO surface with numerous RBD antigen clusters (ICO-RBD) to form various antigen patterns. Using an in vitro screening system, we demonstrate that the antigen spacing, antigen copies within clusters and cluster number parameters of the surface antigen pattern all impact the ability of the nanovaccines to activate B cells. Importantly, the optimized ICO-RBD nanovaccines evoked stronger and more enduring humoral and T cell immune responses in mouse models compared to soluble RBD antigens. Our vaccines activated similar humoral immunity and slightly stronger cellular immunity compared to mRNA vaccines. These results provide reference principles for the rational design of nanovaccines and exemplify the utility of DNA origami as a display platform for vaccines against infectious disease.

Psychological and Social Aspects of Vaccination Hesitancy – Implications for Travel Medicine in the Aftermath of the COVID-19 Crisis. A Narrative Review (preprint)

Vaccines are an important tool of preventive medicine. Organized vaccination programs have saved numerous people from serious infectious diseases. Surprisingly, there is a considerable portion of the population who oppose vaccinations. In particular, the existence of anti-vaccination perceptions among travelers to countries with endemic diseases is a major public health concern. Although hesitancy towards vaccinations is not a novel phenomenon, it came back to the forefront during the fight against the COVID-19 pandemic. This review explored the etiology of anti-vaccination beliefs among travelers and drew conclusions about their impact on public health and society in general. For this purpose, a purposeful search for data on the causative factors of vaccine hesitancy and their impact on people’s health was conducted. A descriptive analysis of the findings and conclusions about possible implications in health policy and clinical practice are presented. Fear of side effects, lack of credence in the necessity of vaccines, and mistrust of medical authorities are important causative factors. Their interplay shapes hesitancy towards vaccines. However, anti-vaccination beliefs can also be an aspect of a more general unconventional stance of life. Health care professionals and organizations must be ready to tackle vaccine hesitancy by making the necessary interventions. Correcting misconceptions about vaccinations is a prerequisite for ensuring personal and public health, especially in the context of a pandemic or epidemic. Moreover, ensuring the efficacy and safety of vaccines, especially in cases of modern technology applications, is a fundamental factor in addressing people’s concerns about vaccines. For this purpose, medical authorities and organizations must provide accurate and clear information on vaccines so as to eliminate misinformation. Furthermore, clinicians should cultivate their communication skills in order to convey the appropriate messages to prospective recipients of vaccinations.

Prediction Model for the Effects of Tourism on the Spread of HIV and AIDS (preprint)

To assess the impact of tourism on the incidence of HIV and AIDS using Malaysian epidemiological data over the period of 1986-2011 with additional consideration for newborns infected with HIV. A population-level mathematical model was used to investigate: i) the role of tourism in the spread of HIV and measures used to reduce HIV spread in Malaysia; ii) whether the stability of infectious disease transmission is dependent on the flow of visiting tourists. We first derived an equation for the reproduction number (R0) threshold to quantify the contagiousness of HIV in Malaysia. Sensitivity analyses were used to determine the effect of various parameters on HIV transmission with respect to the increase in tourism. Our findings suggest that a stable disease-free state is sustainable based on the low value of R0 was 0.0017. This result is encouraging from a public health perspective. Approximately 14% of outbound tourists who leave the country return infected with HIV and the difference between the rate at which tourists move to the susceptible category and the rate at which tourists leave the susceptible is category is 12%. Estimated parameters for the influx of tourist rates, δ=1.1540x10-3[1.1477x10-3 - 1.15954x10-3], δ1=7.7901x10-4[7.7867x10-4 - 7.79418x10-4], and δ2=1.4030x10-8[-7.2287x10-7-7.5096x10-7], significantly impacted the spread of HIV in Malaysia. Some significant adjustments were made to the expected parameters. The methods used are helpful to public health analyses and provide a framework for epidemiological modeling of HIV spread among tourists. The trend and magnitude of tourist inflows may be determinants in the incidence of HIV and AIDS in Malaysia.

Association of Polymeric Immunoglobulin Receptor Gene’s Single Nucleotide Polymorphisms with the Growth and Disease-Resistant in Chicken F2 Resource Population (preprint)

Polymeric immunoglobulin receptor (pIgR) plays an important role in mediating mucosal defenses, but the association between its single nucleotide polymorphisms (SNPs) and traits (such as growth and disease-resistant) in birds is scarcely known. In this research, we aimed to detect the single nucleotide polymorphisms of the pIgR gene in the chicken F2 resource population and discern the possible associations between pIgR SNPs and chicken growth, disease-resistant, respectively. Six-SNPs (3, 9, 15, 16, 19, and 21) in chicken (Gallus gallus) were significantly associated with disease-resistant in the pIgR gene (P < 0.05). The major allele genotype with SNP 9 and SNP 19 occurred more frequently with high Newcastle Disease Virus (NDV) antibody rates; the major allele genotype with the SNP 3 was predominant in those with significantly lower NDV antibody rates (P < 0.05); heterozygous with the SNP 15 and SNP 21 occurred more frequently with high avian leukemia virus (ALV) antibody rates; the TT genotypes with the SNP 16 was predominant in those with low infectious bursal disease (IBD) antibody rates (P < 0.05). Besides, SNP 12 showed significant associations with body weights (BW) and shank lengths (SL) (P < 0.05). Genotyping revealed that the C allele occurred more frequently in breeds with high growth rates and the T allele was predominant in those with low growth rates at 8, 10, and 12w of age (P < 0.05). This polymorphic site may serve as a useful target for the marker assisted selection of growth and disease-resistant traits in chicken.

Antibiotic Potentiation as a Promising Strategy to Combat Macrolide Resistance in Bacterial Pathogens (preprint)

Antibiotics were once called miracle drugs which hit the market with astounding impact as it was considered the ultimate cure for infectious diseases in the mid-20th century. However, today nearly all bacteria that afflict humankind have become resistant to these wonder drugs once developed to stop them, imperilling the foundation of modern medicine. During the COVID pandemic, there was a surge in macrolide use to treat secondary infections and this persistent use of macrolide antibiotics has provoked the emergence of macrolide resistance. In view of the current dearth of new antibiotics in the pipeline, it is essential to find an alternative way to combat drug resistance. Antibiotic potentiators or adjuvants are non-antibacterial active molecules that, when combined with antibiotics, increase their activity. Thus, potentiating the existing antibiotics is one of the promising approaches to tackle and minimize the impact of antimicrobial resistance. Several natural and synthetic compounds have demonstrated effectiveness in potentiating macrolide antibiotics against MDR pathogens. The present review summarizes the different resistance mechanisms adapted by bacteria to resist macrolides and further emphasizes the major macrolide potentiators identified which could serve to revive the antibiotic and can be used for the reversal of macrolide resistance.

Improving estimates of epidemiological quantities by combining reported cases with wastewater data: a statistical framework with applications to COVID-19 in Aotearoa New Zealand (preprint)

Background: Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by human behaviours. Here, we investigated how these two data sources can be combined to inform estimates of the instantaneous reproduction number, R, and track changes in the CAR over time. Methods: We constructed a state-space model that we solved using sequential Monte Carlo methods. The observed data are the levels of SARS-CoV-2 in wastewater and reported case incidence. The hidden states that we estimate are R and CAR. Model parameters are estimated using the particle marginal Metropolis Hastings algorithm. Findings: We analysed data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaked at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaked around 12 March 2022. Accounting for reduced CAR, we estimate that New Zealand's second Omicron wave in July 2022 was similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 was approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. The CAR in subsequent waves around November 2022 and April 2023 was estimated to be comparable to that in the second Omicron wave. Interpretation: This work on wastewater-based epidemiology (WBE) can be used to give insight into key epidemiological quantities. Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time, which may be increasingly useful as intensive pandemic surveillance programmes are wound down.