Mycobacterium tuberculosis for 30 years. However, knowledge about mycobacterial CRISPR/Cas systems remains limited. It is believed that Type III-A Cas systems are exclusive to Mycobacterium canettii and the M. tuberculosis complex (MTBC) of organisms and that very few of the >200 diverse species of non-tuberculous mycobacteria (NTM) possess any CRISPR/Cas system. This study sought unreported CRISPR/Cas loci across NTM to better understand mycobacterial evolution, particularly in species phylogenetically near the MTBC. An analysis of available mycobacterial genomes revealed that Cas systems are widespread across Mycobacteriaceae and that some species contain multiple types. The phylogeny of Cas loci shows scattered presence in many NTM, with variation even within species, suggesting gains/losses of these loci occur frequently. Cas Type III-A systems were identified in pathogenic Mycobacterium heckeshornense and the geological environmental isolate Mycobacterium SM1. In summary, mycobacterial CRISPR/Cas systems are numerous, Type III-A systems are unreliable as markers for MTBC evolution, and mycobacterial horizontal gene transfer appears to be a frequent source of genetic variation.Bacterial CRISPR/Cas systems target foreign genetic elements such as phages and regulate gene expression by some pathogens, even in the host. The system is a marker for evolutionary history and has been used for inferences in  Mycobacterium tuberculosis complex (MTBC), Mycobacterium avium complex members cause widespread losses in animal agriculture, numerous non-tuberculous mycobacteria cause disease in humans, mycobacterial biofilms pose challenges for water treatment and hospital infection control, and some species are merely innocuous saprophytes. With such diversity, it is perhaps surprising that mycobacterial genomes share much in common. Between Mycolicibacterium smegmatis, a distant and non-pathogenic saprophyte, and M. tuberculosis (MTB), M. avium, Mycolicibacterium leprae, and Mycolicibacterium abscessus—four highly divergent species in the Mycobacterium genus—there are over 1,000 genes with more than 50% amino acid identity  species, Mycobacterium heckeshornense, this genetic locus' believed exclusivity to the MTBC clade is lost. Additionally, species proposed to represent evolutionary intermediates between environmental mycobacteria and professional pathogens, Mycobacterium riyadhense, Mycobacterium shinjukuense, and M. canettii . Questionable matches in each database come with scoring metrics, with most “Evidence Level 1” hits through CRISPR-Cas++'s CRISPRCasFinder being false positives. Potential loci of interest were collected from both databases, though CRISPROne's inclusion of draft genome sequences yielded far more data. Searches were performed with default parameters, except where otherwise noted. Secondary searches of these databases utilized the input of contigs or chromosomes downloaded from NCBI's repository to screen for homologs not included in the CRISPRone and CRISPRCasFinder pre-built databases  and subsequently Mycobacteriales   and Cas5 (Cas I-C), as well as the MK13 Locus I-G Cas1 (which contains a characteristic Cas4 fusion in I-G complexes) sequences. These queries were performed against the WGS dataset filtered by Mycobacteriales (tax ID: 85007) using default parameters. However, the allowed return sequences were increased to 500 to ensure that homologous hits were not prematurely cut off by NCBI filters. Hits were selected from this list for the investigation to determine the likelihood they were real positives and to type any putative CRISPR system(s) identified. Searches were also conducted using PLfams models (genus-specific protein families) through BV-BRC to identify matches in other genomes and assess the genomic context of homologs in other genomes.After the putative detection of two different classes of CRISPR/Cas complexes in some members of the CRISPR repeat and spacer sequences were downloaded from CRISPRCasFinder. Owing to the intermittent availability of the CRISPR-Cas++ webserver's CRISPR Repeats and Spacers BLAST tool, the dataset was downloaded from the website, and custom BLAST databases were built on Michigan State University's High-Performance Computing Center supercomputing cluster to query spacers against for conservation using “makeblastdb” in the BLAST+ package (v2.7.1)  and text labels were applied. The locus maps are only representative and are not drawn to scale. Each locus was visualized using the BV-BRC Compare Region Viewer option set to 20,000 nt.Mycobacterium species outside the M. tuberculosis complex already known to host a system in either database query. One broadly conserved element that was similar to type-I systems' Cas3 protein was returned but found to be very similar to the non-Cas RNA helicase TatC and subsequently discounted as a false positive.As previously published .M. avium str. 104 in 1983 from a patient in California, USA , significant hits were obtained with near-complete coverage only in M. avium subsp. hominissuis strains 101, ATCC 700898, and GM44. Other hits were found in Mycobacterium fortuitum and abscessus, but they only matched a fragment of the original query (7%−30%). In the case of M. abscessus subspecies massiliense strain 618 (contig FVWY01000006.1), a partial hit indicated the presence of a different 9-repeat orphan CRISPR locus as idenetified by CRISPRone and CRISPRCasFinder. This locus only showed partial similarity to the locus found in a few M. avium strains. Upon comparing the DR sequences and spacer sequences, there was little shared homology, suggesting that these hits are unlikely to be related.cas-like genes were initially identified in the draft genome of M. kansasii complex member strain Mycobacterium innocens MK13 using CRISPRone. The first complex, comprising 10,974 bp of the 15,118 bp contig UPHQ01000292.1, contained seven cas-associated gene homologs in sequence, with a CRISPR locus immediately downstream containing 46 predicted spacers and 47 repeats. As a draft sequence, this was not searchable directly through CRISPRCas++'s CRISPRCasFinder, so the contig UPHQ01000292.1 was downloaded from NCBI and uploaded separately for annotation. CRISPRCasFinder predicts seven generic cas-associated genes and, alternatively, four cas Type IC–associated genes, along with the same major CRISPR locus of 46 spacers and 47 repeats, showing 0% spacer conservation and 92.61% repeat conservation  yielded some significant hits  with the locus I-C DR consensus yielded 22 hits. Similarly, when the same strategy was applied using the locus I-G, 24 hits (M. persicum AFPC-000227 with four separate contigs). Hits for I-C were observed in M. persicum (10/15), M. innocens (4/15), and one from Mycobacterium attenuatum (1/15). Expanding the search to Actinobacteria (tax ID: 201174) only added one additional hit in Mycobacterium sp. SM1, an environmental isolate from a mud volcano in Italy , M. riyadhense (4/20), M. innocens (4/20), Mycobacterium ostraviense (2/20), and Mycobacterium gastri (2/20). Expansion of this DR to Actinobacteria yielded no additional hits. The secondary locus identified on Mycobacterium SM1 contigs was annotated by CRISPROne and CRISPRCasFinder as a type III-A CRISPR/Cas system.To explore whether related species have also acquired or maintained these CRISPR loci, a BLAST search of the more stringent NR database with DR sequences from ant hits . Before  24 hits . Some hiM. canettii (tax ID: 78331) using the seven DR sequences from locus I–C. The search was performed with low-complexity filtering turned off. Modest-confidence hits were obtained in M. canettii strain STB-K, where multiple contigs matched for five out of the seven DR sequences. One specific contig (JAHVHL010000002.1) has four closely located matches and was subsequently analyzed using CRISPRCasFinder, along with the other two major contigs. This analysis identified a homologous I-C CRISPR/Cas region with 53 spacers. However, in this genome, a second CRISPR locus with 28 spacers was identified. The DR sequences within this locus exhibited 99.4% conservation. It is worth nothing that this second CRISPR locus was located ~240,000 bp away from the nearest predicted cas genes and was positioned directly at the end of the contig . The repeat consensus was unique among those located proximal to the cas locus, and an identical match to this alternate DR sequence was found at the start of another contig in the assembly . Searching the contigs again using the CRISPRCasFinder with the “unordered” option enabled allowed for the identification of cas gene hits that do not include a complete and ordered locus. This search revealed genes starting at position 1,023 bp, including an untyped cas2, a cas 1 gene belonging to a specific type ID, and a sequence of four genes forming a “type IU.” A literature search indicated that a resequencing study conducted by Blouin et al. in 2014 had previously identified the presence of two systems in STB-K. However, in their classification, one system was classified as Type I-C, while the other was labeled as a Type I-C variant. Moreover, it is worth noting that at the time of their publication, type I-G systems had not been characterized  and in M. tuberculosis variant tuberculosis. High-similarity homologs showed 99%+ query coverage against Type I-G Cas1, and partial similarity groups showed roughly 50%−70% query coverage. Several hits did not belong to these categories, such as 99% coverage/47%−60% identity for Mycolicibacterium hassiacum, Prescottella subtropica, Gordonia paraffinivorans, and others. Of this set, M. heckeshornense featured two sets of hits, one with 68% coverage and one with 31% coverage. In the case of M. heckeshornense strain DSM 44428, the contigs JACKTA010000047.1 and JACKTA010000078.1 were downloaded from NCBI in FASTA format. These contigs were then searched using CrisprCas++, and although contig 47 is short , it was predicted to contain a CRISPR locus with 36 spacers, as well as the presence of cas2 and a partial cas1 gene. Contig 78  was found to contain a predicted complex close to the end of the locus. However, this complex contains some predicted cas genes of Type I-U, according to CRISPRCasFinder. When using CRISPROne, the same system was identified for contig 47, but the complex on contig 78 was not considered credible enough to make a definite call. The second M. heckeshornense WGS dataset (strain RLE) had the same two hits, and interestingly, the two contigs bearing these hits were nearly the same lengths . Unfortunately, SRA data for the two M. heckeshornense assemblies are publicly unavailable, and reconstruction of these possible loci is not possible now. However, the complete genome of M. heckeshornense JCM 15655 was searched next, and a locus disrupted by a transposase and with the structure of [csb1-csb2-cas3-hypothetical protein- < IS1380>-cas1-cas2] was extracted for sequence comparison. A second locus was not evident in this genome. An additional genome, M. heckeshornense JMUB5695, was also identified to contain a high-confidence Cas Type III-A locus, significantly similar to the complex inside the MTBC and some M. canettii strains  and a partial similarity group (~26%−40% AA identity). High-similarity homologs were identified in  strains . The samM. hassiacum returned two hits in CRISPRCasFinder. This genome was reported to contain a CRISPR array by Singh et al.  was upstream of a reported CRISPR repeat region . This locus featured homologs of csf1, csf4, csf2, and csf3, which were found ~10 kb upstream of a small, low-evidence 3 spacer CRISPR locus. Such a finding may very well be noise, but it does share similarities with wildly divergent, plasmid-borne Type 4 Cas systems , Mycobacterium sp. YC-RL4 plasmid pMYC1 (CP015597.1), and the chromosomal sequence of Mycobacterium rhodesiae NBB3. Although confidence in these sequences making up a complete Type IV system was low, these systems are known to be highly divergent even within subtypes. The challenge lies in their identification caused by the lack of reliable signature genes. Therefore, these sequences are still being included in the analysis with appropriate caution. It is essential to exercise prudence when interpreting the results without in vitro confirmation of their functional implications.Mycobacterium riyadhense MR-246 appeared both in the Locus IG Cas1 TBLASTN search as well as the search using the MK13 DR sequence, with both hits independently finding the same region in the chromosome (CAJMWP010000001.1). CRISPRCasFinder and CRISPRone independently identified a 111-spacer CRISPR array with 97% DR conservation and 0% spacer conservation, although only CRISPRone identified upstream cas genes. The subtype on this chromosome was reported as I-U (I-G), but the locus was missing a cas2 gene call. While re-running CRISPRCasFinder and enabling the “Unordered” option to allow for hits outside a longer locus, it also returned the same hits as those by CRISPROne, but it was also missing a cas2 gene call, which presumably led this software to initially discard the other five genes upstream of the CRISPR array. NCBI's ORF Finder output from the full locus  identified a 95aa open-reading frame directly downstream of cas1 and upstream of the CRISPR array, and a BLASTP search of this protein against the UniProtKB database identified 17 moderate confidence alignments  against Cas2 proteins. No other hits were reported. NCBI's Conserved Domain Database (CDD) search of query protein sequences against a database of known domains returned a hit on a Cas2 domain in this sequence . Given that the divergence of the cas2-like ORF in M. riyadhense led to CRISPRCasFinder missing the entire locus and CRISPROne identifying 5/6 genes plus the CRISPR array but not the cas2 homolog, the entire nucleotide sequence of the locus from ORF Finder was also used for an unfiltered BLASTN search of the nt database and a broad scan of the WGS database filtered by hits in Actinomycetota. The NR database returned a hit with 16,914 nt of coverage and only a single non-identical nucleotide from M. riyadhense strain NTM, as well as high confidence hits  in M. ostraviense str. FDAARGOS 1613 and M. canettii strains CIPT 140070010, 140070008, and 140070017.Mycobacterium lacus JCM 15657 presented with a striking 146-repeat CRISPR locus, and subsequent investigation showed unannotated upstream cas2 and cas1 genes, but the rest of the locus appeared unusual, with GenBank annotation noting frameshifts present. A BLASTP search of the sequence upstream of Cas1 hit numerous IS3 family transposases scattered throughout Mycobacterium lacus, kansasii, and persicum strains, as well as in other genera. It appears that this may have been a functional system broken by the insertion of this transposase in the past, and a scan of similar disruptions finds an IS3 family transposase where Cas1 and Cas2 would be found in the CRISPR/Cas locus of M. tuberculosis strain 36918. This disruption appears widespread across Beijing-like strains  M. avium isolates at least minimizes the likelihood that it is a sequencing error. However, the closest match outside these immediate relatives of M. avium isolates appears to be a different orphan CRISPR in M. abscessus subspecies massiliense. Regardless, the origins of these orphan CRISPR loci remain unknown. No further evidence of any cas or cas-like genes was identified in any M. avium complex species, which appears to be devoid of these loci except for this orphaned CRISPR element in a single strain.The CRISPR locus identified in M. kansasii complex of organisms. M. innocens MK13's locus I-C showed homologs within the M. kansasii complex, with most concentrated in M. persicum, a recently characterized opportunistic pathogen in the complex originally isolated from sputum in multiple cases of human disease in Iran , and Cas2.Existing literature can tell us much about the composition of the complexes identified in this study. Cas5 is involved in pre-crRNA processing into crRNA units  combined with the short sequence length of Cas2 proteins  between M. riyadhense MR-246 and M. riyadhense NTM raise questions, and BioSample records  indicate that the samples were collected in 2016 and 2018, respectively, from human disease cases in Saudi Arabia. An additional 2,000 nt on each side of the M. riyadhense NTM locus aligned to MR-246 was extracted, and a BLAST search was run against other riyadhense isolates to assess how much variation occurs between other clinical isolates and to assess if additional CRISPR spacers had been acquired in the newer 2018 NTM isolates using CRISPROne. The same number of spacers were identified, and differing numbers were observed in other riyadhense isolates. This high similarity may indicate that M. riyadhense isolates MR-246 and NTM originated from a common point of exposure, but this is beyond the scope of this investigation. Mycobacterium riyadhense has been proposed as an intermediate between environmental and professionally pathogenic mycobacteria in the MTBC . Little is currently known about M. sp. SM1. However, it is annotated as having been discovered in an Italian mud volcano , no evidence of any Cas proteins, other CRISPR arrays, or even variation in the MAH CRISPR locus was found across any member of the M. avium complex. Through BV-BRC, a search of 493 M. avium genomes for Cas domains returned only a single partial hit for cas2 on a 521bp contig from a study characterizing hypervariable genomic islands from German MAH strains  can be found in the article/SS obtained funding, helped design the study, interpreted the data, and edited the manuscript. EB performed the work, analyzed genomes, organized data for visualization, and wrote the manuscript drafts. All authors contributed to the article and approved the submitted version.


Dear Editor,Severe fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease caused by severe fever with thrombocytopenia syndrome virus (SFTSV) and is characterized by rapid onset, high mortality and widespread distribution. The fatality rate can be as high as 10%–30%, which is significantly higher than that of the hemorrhagic fever caused by Hantavirus in the same area . HoweverPhenuiviridae family within the Bunyavirales order , and belongs to the es order . As is kes order . The smaes order . These sIn this study, we first used traditional virus purification methods to purify the cultured virus see . Howeverin situ single particle analysis (isSPA) methods. Sub-particles around the 3-fold, 2-fold and 5-fold axes were re-extracted for BBR. Within the local refinement on each region, we obtained a reconstruction of the 3-fold axis region with a resolution of 5.6 Å and a reconstruction of the 2-fold axis region with a resolution of 5.9 Å. While the 5-fold axis region exhibited the most significant conformational heterogeneity, probably also because this region is an internal genomic release site  = 12 icosahedral particle and a diameter of approximately 110 nm  domain of one or both viral glycoproteins is presumed to be involved in the assembly and budding processes. Recent studies have demonstrated that the CT domain can interact directly with nucleoproteins, thereby facilitating the packaging of the RNPs into particles to prevent the release of empty particles . HoweverThe glycoprotein shell densities have some tubular shapes corresponding to helices present in previous studies . We specBoth the CT domain of Gn and the CT domain of Gc are anchored to the viral membrane by two transmembrane (TM) helices. Approximately 20 residues in our density map formed a helix clamp at the base of the adjacent stem region, and the helices were connected with the stem region by flexible loops  and S6. In our study, we present molecular insights into the structures of glycoproteins in pre-fusion and how they assemble on authentic viruses. We also analyzed the detailed availability of epitopes. The structure of SFTSV is the current largest solved structure at near-atomic resolution among the complete living virions of enveloped viruses. It provides a complete and detailed picture to understand the assembly and the biology of bunyavirus. Furthermore, this study provides a structural basis for the rational design of vaccines or antiviral drugs.pwad019_suppl_Supplementary_MaterialClick here for additional data file.


R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) over time (m) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: (1) inferring the conditions under which virus infections die out for a specific strain given R0; (2) explaining the occurrence of second waves of infection and developing preventive measures; and (3) understanding the competitive spread of two viruses within a region and devising control strategies. The findings highlight the potential of this simple mathematical framework in comprehensively addressing these challenges. The theoretical insights derived from this model can guide the evaluation of infection wave severity and the formulation of effective strategies for controlling and mitigating epidemic outbreaks.A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number ( The ongoing COVID-19 pandemic has posed several unresolved questions. Firstly, our aim is to understand the reason behind the waveform pattern observed in the daily number of infections (DNI) and to predict the scale and duration of an infection wave, including the cumulative number of infections (CNI) and the time until infection ends. Secondly, we investigate why the second wave of infection typically follows the first wave and how to predict its occurrence. Lastly, we explore how virus spreading in a region is influenced by the emergence of new strains and develop methods to control their spread. Existing epidemic models have limitations in addressing these problems comprehensively. Since the 1920s, differential equations were used to model the population distribution of disease spread, including susceptible, infected, and recovered/dead pools ,4,5. RecR0), which represents the average number of infections caused by an initial infectious person in a completely susceptible population [R0 is a measure of the transmission potential of a particular infectious disease. Second, virus infections undergo a series of events due to the presence of population immunity or intervention that makes the instantaneous reproduction number, tR, decreasing step by step, described by an attenuation parameter k (k < 1) [k is influenced by social contacts and policy measures, where looser contacts and measures result in higher values of k. It is worth noting that public health measures in a given region usually undergo significant changes over a longer period of time compared to the duration of a virus wave [https://ourworldindata.org/covid-stringency-index (accessed on 13 August 2023). Therefore, it is reasonable to assume that the attenuation parameter k is approximately constant during a single wave of virus spread. Based on two parameters, R0 and k, we can deduce the general formula for the daily number of infections (DNI) and the cumulative number of infections (CNI) over m steps. The CNI is denoted by F. These formulas are in good agreement with experiments and provide a generalized framework to simulate single and multiple virus infections. By introducing F1 and F2 we can also study the competitive spread of two viruses in a region, including scenarios with the introduction of a new virus strain. It is worth noting that the loosening of public health measures and/or the emergence of new viruses can lead to subsequent waves of virus transmission.As the Chinese idiom goes, “the greatest truths are the simplest”. Starting from the principle of natural selection—the interaction and compromise between the virus and its host (human)—we propose a mathematical model based on insights from experimental data. The model is based on two fundamental assumptions. First, each viral strain has a basic reproduction number (pulation . R0 is a (k < 1) ,10. The rus wave . The strhttps://covid19.who.int/data, accessed on 12 May 2023) for the UK and from the public database  for Hong Kong. Since Gauss discovered the least square method and successfully applied it to astronomical observation, the ordinary least square is recognized as one of the best methods for curvilinear regression. In the present study, we used the least square simulation of observational data of the virus infection to determine the parameters in each COVID-19 pandemic. The goodness of the nonlinear least square fitting (NLLSF) is tested by R2, i.e.,iy are the observation values, 2 and the p-value of Prob(F-statistic) are calculated for each simulation. The F-statistic is defined asn–number of samples, ν–number of parameters) that can be calculated from the experimental data. The p-value is decided by the percentile of the Fisher’s F-distribution Fνn−ν−1,(z), namely,The data of daily virus infections of COVID-19 were taken from WHO  for each simulation are given in the figures.The goodness RR0. For the virus to spread, it must continually re-infect the rest of the susceptible population. However, countermeasures by humans must aim to limit these subsequent infections. Essentially, this implies that the interaction and compromise between the virus and host (humans) necessitates the j-th reproduction number jR to be smaller than the (j − 1)-th reproduction jR−1, as expressed in the equationThe potential for infection of a given virus strain is determined by the basic reproduction number known as jR changes with j is twofold. Firstly, as the virus continues to spread, the susceptible population decreases, leading to a decrease in the transmission potential. Secondly, ongoing public health measures are being implemented to control the spread of the virus, further contributing to the reduction in the instantaneous reproduction number. The attenuation constant ‘k’ in this scenario is influenced by social contact rates and policy measures. After m rounds of infection spread, the total number of infections in a specific area can be represented asThe reason F describes the cumulative number of infections (CNI), which is an increasing function of m. Assuming the total number of infected individuals to be N, solving the equation F = N will produce ‘m’, the transmission number of the virus strain. The parameter m grows with time t. Assuming each transmission takes place every q days, we havet0 is a time shift parameter linked to the start of infection (the time of m = 1). Equations (1) and (2) provide the cumulative number of infections (CNI) for each branch of the epidemic. If various generations of different branches exist simultaneously, the total CNI is simply the sum of the CNIs of each branch. The CNI can be observed experimentally. Inserting (2) into (1), we obtain a formula of CNI vs. t represented by four parameters R0, k, q, and t0 that can be used to simulate the change of a cumulative number of infections with time in a region. As seen in k is reasonable. It is important to note that our model not only allows for simulation of COVID-19 pandemics but also enables predictions of virus spread over longer periods by utilizing parameter data derived from a particular phase of the outbreak.This function Applying Equations (1) and (2), the daily number of infections (DNI) is derived:R0 > 1 (for most infectious viruses) and k < 1. Zero DNI arrives when dF/dm = 0, which requires m to be large enough thatFrom Equation (3), it is easy to understand why DNI first rises then falls, considering that This is a condition for the end of an infection wave.k and m for the given R0 (Utilizing Equation (1), we plotted the change of CNIs with variables given R0 . Figure F increases with m and approaches a stable value when m > mst for each k < kth. We denote F as Nth. Calculating for mst = 15 in kth and the corresponding CNI Nth for each given R0 (each virus strain). On the other hand, CNI attaining a stable value means the DNI is approaching zero. By defining Ekm = (1/kth)mst−1)/2 can be written as R0 < Ekm. The threshold kth, the corresponding CNI Nth, and the parameter Ekm are listed in R0 < Ekm to hold well across all data.From dF/dm = 0 means the virus infection is dying out. The above result shows the higher the basic reproduction number R0, the stricter the public health measure is required to be to increase Ekm to satisfy Equation (4) and terminate the wave of virus infection effectively. From Nth, of many virus strains (except Omicron with R0 = 18.6) are lower than 105 if an appropriate k (lower than kth) is introduced.The daily increasing number R0, if F  exceeds a threshold Nmax, then the spread of this strain would lead to a wave of COVID-19 infection in a region with a population larger than Nmax. In order to end this spread, the necessary condition would beFor the virus strain of given k for strains with R0. The critical value of k is denoted as kcr. Taking Nmax = 107, the values of kcr are also listed in kth is the threshold value of k required for ending the spread in the 15th generation, but the parameter kcr is that value for ending the spread in an arbitrary generation. The latter constraint is looser than the former. Therefore, kcr is larger than kth.Equation (5) provides a constraint on R0 values have higher kcr and kth values. As a result, they can spread in regions with smaller populations under looser public health measures. This theoretical prediction can explain why strains like SARS-CoV in 2003 only spread in restricted regions and soon disappeared globally. On the contrary, virus strains with high R0 values, such as Omicron, have lower kcr and kth values. To satisfy Equations (4) and (5), the parameter k needs to meet very strict constraints. In cases where social management measures are not stringent enough, the number of infected individuals will quickly surpass the Nmax limit, triggering a global pandemic wave, possibly culminating in a type of coexistence between viruses and humans.Virus strains with low dF/dm = 0 on the CNI diagram (m and the condition in Equation (4) satisfied, i.e., R0 < Ekm = (1/kth)7. Additionally, the necessary condition for a pandemic to die out in a population of Nmax is expressed by Equation (5), imposing a limitation on k, namely k < kcr.In summary, the termination of an infection wave is determined by the condition  diagram , which rEarly models based on previous pandemics such as SARS, MERS, and the 2009 H1N1 outbreak can effectively predict the occurrence of the first wave of a disease. However, their predictive power decreases when it comes to anticipating the possibility of a second wave . This raF versus m) for a given R0 value. These curves differ from each other by the parameter k. When the number of daily new infections (DNI) approaches zero, any fluctuation in k significantly influences the spread of the virus. Therefore, changes in public health measures can induce variations in virus spread along different curves. Generally, as public health measures are relaxed, the parameter k increases, causing the virus to transition from one curve of F to another steeper curve. This signifies the onset of a new wave of virus spread. For instance, in R0 of 18.6) is plotted. Assuming the initial spread is along the curve with k equal to 0.613, a stable state is reached at m = 15. At this point, the k value increases to 0.722. In response to this change, the virus begins spreading along a new curve with k = 0.722, starting from m = 4, as the value of F is equal to the original CNI value, F. This example explains how the second wave of virus infection occurs. However, in cases where the parameter k decreases when the DNI approaches zero, the curve of F will transition to a flatter one. This indicates that the first wave of viral infection will end soon, and no second wave will occur.As shown in R0 value of the virus. For instance, as k changes from 0.85 to 0.9, the CNI (at m = 15) for high R0 viruses increases hundreds of times, whereas it only increases tens of times for low R0 viruses. Therefore, our model predicts that multiwave infections are more likely to occur with viruses that have high R0 values.By examining F not only between different curves with a given R0 but also between different R0 values, providing more opportunities for the occurrence of a second wave during virus spread.In the aforementioned discussions, we have assumed that no virus mutation occurs and that only one type of virus is spreading. In reality, changes in public health measures may be accompanied by virus mutations. In this case, the change in public health measures would cause the jump of k can simultaneously induce a change in q, as the eigen-time (inherent time) m depends on q (Equation (2)). In the case of a single wave, the parameter m can be used to represent time dependence, and q can be simply set to 1 . However, when studying two continuous waves, the dependence on q should be clearly indicated due to the different eigen-times m. We have mentioned that the dependence of F on the change in k results in a jump from one curve to another. Meanwhile, the dependence of F on the change in q only affects the lengthening or shortening of the abscissa of the graph without altering the shape of the curve. When the public health measures change and a jump between curves occurs, the abscissa of the graph of the second wave simultaneously lengthens or shortens.Another crucial point to consider is that the change in q value. These predictions align with experimental data. For example, in the UK from May to September 2021, public health measures were relaxed, and a second wave of infection followed the first wave .The occurrence of the second wave of infection is more likely when public health measures are relaxed. This change in the second wave is accompanied by a change in the rst wave A. Similarst wave B. (The dk (given a specific R0) is determined by the jump between different curves on the graph F versus m, referred to as k-transformation. The dependence of the CNI on the duration of m is obtained by stretching the abscissa m on the graph, known as q-transformation. The k-transformation and q-transformation, occurring when the first wave is nearing its end, are the causes of a continuous second wave. The change in k is attributed to the modification of public health measures, while the modification of q is due to the change in physical time within a unit of m. The changes in k and q provide an explanation for the experimental data on continuous multiwave infections.In summary, the dependence of the CNI on m1 and m2) between these viruses and their relationship with the physical time t. Consequently, additional parameters q and t0 (as given in Equation (2)) must be taken into account. By utilizing the four parameters R0, k, q, and t0, the CNI F can be expressed as:Viral infections often involve the simultaneous presence of two or more viruses. To accurately simulate the cross-spread of two viruses, it is necessary to consider the differences in eigen-times  and F2 intersect at tcr,When the CNIs of two virus strains F1 to F2. As it is challenging to directly solve the intersection equation (Equation (7)), we introduce a function:This represents a transition in the population of virus strains from D21 can be formulated as a simple quadratic function of timeBy utilizing Equations (2), (3), (6) and (8), D21 exists, we define:In order to determine if a real root of D21. This form, known as the discriminant function, serves as a tool to identify the occurrence of tcr and the domain of its existence. The prediction rules can be summarized as follows:The value of Δ determines the existence of the real root in the quadratic form t axis at st and mt (mt > st),Rule 1: When Δ > 0, the quadratic form intersects with the t < ts in the first domain, ts < t < tm in the second domain, and t > tm in the third domain.These values partition the time into three distinct domains: ta = qm0 − t0 is defined as the initial time where m0 >> 1, indicating m1,2 = /c1,2 >> 1. If D21 > 0, there will be no intersection of F1(t) and F2(t) in the domain between ta and ts when the initial values of F at ta satisfy F1(ta) < F2(ta), but there may be one tcr (the number of tcr is either 1 or 0) in the domain when F1(ta) > F2(ta). If D21 < 0, there will be no intersection of F1(t) and F2(t) in the domain when the initial values satisfy F1(ta) > F2(ta), but there may be one tcr (the number of tcr is either 1 or 0) when F1(ta) < F2(ta).Rule 2: In the first domain, D21 > 0 there will be no intersection of F1(t) and F2(t) in the domain when the F-values at t = ts satisfy F1(ts) < F2(ts), but there may be one tcr (the number of tcr is either 1 or 0) when F1(ts) > F2(ts). If D21 < 0 there will be no intersection of F1(t) and F2(t) in the domain when the F-values at t = ts satisfy F1(ts) > F2(ts), but there may be one tcr  when F1(ts) < F2(ts). The F-values at t = ts are determined by F1(t) and F2(t) in the first domain.Rule 3: In the second domain, if D21 > 0 there will be no intersection of F1(t) and F2(t) in the domain when the F-values at t = tm satisfy F1(tm) < F2(tm), but there may be one tcr (the number of tcr is either 1 or 0) when F1(tm) > F2(tm). If D21 < 0 there will be no intersection of F1(t) and F2(t) in the domain when the F-values at t = tm satisfy F1(tm) > F2(tm), but there may be one tcr  when F1(tm) < F2(tm). The F-values at t = tm are determined by F1(t) and F2(t) in the second domain.Rule 4: In the third domain, if tcr in a given domain because the symbol of D21 is definite in any domain. The magnitude of the domain is an important factor to predict the occurrence of a tcr. For example, in the second domain the magnitude is tm − ts = Δ1/2/(2|α|), and the necessary condition for the occurrence of tcr is a large enough (tm − ts) or Δ.Rule 5: There can be at most one D21 does not intersect with t axis. In this case, there is only one domain, and the rule is the same as that in the first domain given by Rule 2.Rule 6: When Δ < 0, the quadratic form R0 value of strain 1 changes) on the intersection of two strains is shown in the figure. In ta = qm0 − t0 as the initial time. To study the cross-spread in the time interval between m = 1 and ta where dF/dt in D21 is difficult to be defined, one should use the F(t)-ladder method as follows:In the first domain we have introduced a1, b1a12, b13a13, b16a14, b110a15, b115a16, b121a17, b128a18, b136a19, etc., and a2, b2a22, b23a23, b26a24, b210a25, b215a26, b221a27, b228a28, b236a29, etc., respectively (Equation (1)). Strain 1 spreads from the 1st step a1 at t = c1 − d1 on ladder 1 and strain 2 spreads from the 1st step a2 at t = c2 − d2 on ladder 2. The CNI and arrival time t of the two strains on their respective ladders are listed in c1 = 7, d1 = 15, c2 = 5, d2 = 10. The earlier arrival times, in turn, are t = c1 − d1 = −8, c2 − d2 = −5, 2c1 − d1 = −1, 2c2 − d2 = 0, 3c2 − d2 = 5, 3c1 − d1 = 6, etc. By using a1, b1, a2, b2 are given, compare the CNI values on two ladders and obtain the information on the cross-spread of two strains.The step size of CNIs of the first few steps in ladders 1 and 2 are given by The epidemics that occurred in the UK from November 2020 to February 2022 provide a clear demonstration of the cross-spread phenomenon involving multiple viruses. This process can be divided into five stages, namely: (A) the alpha epidemic, (B) the delta invasion and cross-spread of two strains, (C) the delta dominant stage, (D) the omicron invasion and cross-spread of delta and omicron, and (E) the omicron dominant stage. In this section, we will specifically focus on studying the cross-spread of two strains during stages B and D.a1 = R0(1) = 3.9. The spread of the delta strain during stage C has been illustrated in a2 = R0(2) = 5.1 based on this data. By using R0(1) and R0(2) as inputs, we simulated the cross-spread of the two strains, as depicted in ia, ib, ic, id , obtained from tcr occurs at t = 71 in the region where t > tm (tm = 44.5), which is consistent with the prediction outlined in Rule 4.Firstly, let us examine the cross-spread between the alpha strain (designated as strain (1) and the delta strain (designated as strain (2) during stage B. The spread of the alpha strain during stage A has been represented in tcr appears at t = 42 in the region where t > tm (tm = 28.5), which is in agreement with the prediction made by Rule 4.Similarly, we investigated the cross-spread between the delta strain (strain 1) and the omicron strain (strain 2) during stage D. The results of this analysis are shown in t of stages B and D by several days. Remarkably, we found that the same values of k1, k2, q1, q2, t0(1) were obtained, and only t0(2) varied while still maintaining the invariant t + t0(2).In both simulations, we assumed April 1 was the initial time for stage B, and November 11 was the initial time for stage D. Due to the uncertainty surrounding the exact timing of the delta invasion in stage B and the omicron invasion in stage D, we shifted the initial times R0, k, q, and t0. R0 describes the inherent infectious ability of the virus, k represents the strictness of social management, q represents the time needed for one step of infection, and t0 is an additional parameter that aligns with the starting date of the experimental data. By incorporating these four parameters, our four-parameter simulation accurately fits all existing COVID-19 epidemic data and will contribute to the prediction of future outbreaks. Moreover, the mechanism of Gompertz growth has been elucidated by our model.The traditional SIR-type epidemic models depict the exponential growth of the number of infected individuals. However, empirical data have demonstrated that COVID-19 outbreaks do not exhibit exponential growth, but rather follow a three-parameter Gompertz growth function ,14. A netcr to manifest under the following conditions: (1) when Δ > 0, there are three domains and in this case there will be no occurrence of tcr within a domain if the period of this domain is short enough  or if the symbol of D21 within a domain does not align with the symbol of F1 − F2 at the initial time of this domain; (2) when Δ < 0, there is only one domain and in this case there will be no tcr if the period of virus spread is short enough or if the symbol of D21 does not align with the symbol of F1 − F2 at the initial time.The SARS-CoV-2 virus continuously undergoes mutations, giving rise to new strains. This ongoing mutation process is the reason why the pandemic has persisted for more than three years. As a result of natural selection, these new mutant strains possess higher infectious ability but lower lethality rates. Although the lethality rate of the new strains may be lower, it still results in a significant number of deaths. Therefore, since mutation occurrence is inevitable and costly, the key focus should be on reducing the epidemic probability of mutants. While humans cannot prevent the virus from mutating, they can hinder the mutant strains from dominating the competition. According to six prediction rules on the cross-spreading of two strains, it is highly unlikely for the intersection point tcr is the winter epidemic in China in 2022. Among tens of millions of SARS-CoV-2 cases in one city, no new mutant strain emerged or triumphed over the competition, with the exception of the original omicron strand. This suggests that the pandemic was effectively terminated within a short period, lasting only one month.An example to highlight the phenomenon of no occurrence of In summary, the strategies proposed by our model to control an epidemic have two main aspects. First, it is critical to prevent the occurrence of a second wave. Second, one should aim to avoid competition among mutant strains. In the latter scenario, minimizing the duration of virus spread is an efficient approach.GA and GB, respectively. Generally, if GA is lower than GB, the RBD will primarily assume the inactive conformation A. Conversely, in order to initiate the infectious process, the equilibrium should shift towards the open conformation, implying that GB should be lower than GA [G in a given conformation is related to the partition function Z, which can be expressed as follows:The conformational equilibrium between the open and closed conformations of the receptor binding domain (RBD) of the spike (S) protein can be analyzed using first-principle techniques for a susceptible individual. The RBD can exist in either the open or closed position, known as the up or down conformation, respectively. The population of conformational states can be determined based on the free energy change during conformational transitions of the S protein. Let us denote the closed conformation as state A and the open conformation as state B. The Gibbs free energies of states A and B are represented by  than GA . The frenE represents the energy level of a given conformation,Here VA,B represents two minima of conformational potential, respectively, and (n + 1/2)ħωA,B represents the corresponding vibrational energy around the minimum of the conformational potential. By the summation of Boltzmann factor over vibration states one hasCT represent the phase transition temperature, which can be determined by ΔG = GB − GA = 0. From Equations (12) and (14) we obtain a simplified equation for CTG > 0 for T > CT, ΔG < 0 for T < CT. Therefore, if the environmental temperature decreases to T < CT, the conformational transition from the closed conformation to the open conformation occurs rapidly. This explains why the virus has higher transmission rates during the winter and the entrance to host cells is prioritized. Conversely, the summer season provides the most favorable conditions for virus elimination. In addition to temperature, the conformational equilibrium is influenced by humidity. The virus can be modeled as a charged sphere, and through the application of electrostatics principles to salty solutions, one can derive an expression for the potential at the surface of the charged sphere, where the dielectric constant ε is incorporated [ω2 should be replaced by ω2/ε. Considering water has a dielectric constant of ε = 80, the frequency parameter takes a reduced value of ω/9 in the presence of a fully salty solution rather than ω in a vacuum. Consequently, this provides a quantitative estimation of the virus’s infection potential, which is strongly dependent on humidity.Let rporated . This imR0 is influenced by temperature and humidity. The point will be discussed in detail in the future work.The above discussions on the susceptible individual can be extended to the population level, providing evidence that (1)R0, which quantifies its transmission potential, and the instantaneous reproduction number (tR or jR) decreases gradually due to factors such as population immunity and interventions, which can be represented by an attenuation constant k (k < 1).We propose a logically simple model for analyzing the spread of the COVID-19 virus. The model is based on two fundamental assumptions: each viral strain possesses a basic reproduction number (2)m) are deduced based on the aforementioned two assumptions. By utilizing the explicit relation m(t) where t is the physical time, our simulations with the four parameters  demonstrate excellent agreement with all experimental data.The daily number of infections (DNI) and the cumulative number of infections (CNI) versus time ((3)m ) provide valuable information regarding the conditions required for the decline of a viral infection wave, as well as an explanation for the occurrence of continuous second waves of infection and its preventive measures.Insights obtained from typical plots of CNI vs. (4)The persistence of the SARS-CoV-2 pandemic for more than three years can be attributed to frequent mutations. We thoroughly examine the cross-spread of two strains within a region and lay a theoretical foundation for designing strategies to avoid competition among mutant strains or hindering the mutant strains from dominating the competition.


Ilicis chinensis folium extract (ICFE) is a powder extracted and processed with Ilex chinensis Sims (ICS) which has numerous bioactivities and is conventionally used in traditional Chinese medicine. Nonetheless, there has been no definitive study evaluating ICFE’s application as a feed supplement for broilers. This research sought to determine the chemical composition and evaluate how dietary ICFE supplementation affects the growth performance, serum metrics, intestinal structure, and antioxidant capacity of broilers.A total of 360 one-day-old broiler chicks were assigned to four treatments  of dietary supplementation with ICFE at 0, 250, 500, and 1,000 mg /kg for 42 days.P < 0.05) than the controls. Additionally, compared to the controls, broilers receiving low, medium, or high dosages of ICFE exhibited higher average daily gains (P < 0.05) throughout the starter stage and overall. Organ indices showed no significant variation, suggesting that ICFE was non-toxic. ICFE supplementation increased the height of villi in the duodenum and jejunum, reduced crypt depth, and increased the villus/crypt ratio in the duodenum (P < 0.05). Serum concentrations of IL-4 and IgA were increased in ICFE-supplemented broilers. The serum malondialdehyde concentration was reduced, whereas superoxide dismutase activity and total antioxidant capacity increased through supplementation with ICFE.Ten polyphenolic compounds and two triterpenoid glycosides were detected by HPLC. In the grower stage and overall, broilers supplemented with 500 and 1,000 mg/kg ICFE exhibited a higher ADFI (ICFE supplementation can improve intestinal morphology, antioxidant capacity, and growth performance of broilers. Hence, ICFE is a promising and safe alternative to antibiotics in broilers, and 500 mg/kg appears to be the optimal dose. Abuse of feed antibiotics has given rise to bacterial resistance, which poses a threat to human health. With the increasing demand for better meat quality and physical health, many countries have imposed a ban on antibiotic growth promoters (AGPs). Natural alternatives to antibiotics including probiotics, herbs, botanicals, organic acids, essential oils, and enzymes are the most common feed additives that are increasingly used in poultry industry, apart from AGPs.Traditional Chinese medicine (TCM), which relies on medicinal herbs, has long been recognized as an essential source in promoting and sustaining health in humans and animals –4. PlantIlex chinensis Sims (ICS) belongs to the Aquifoliaceae family and is used to treat lung heat, cough, sore throat, heat strangury, diarrhea, and empyrosis. Previous studies on ICS have revealed several particular and structurally diverse metabolites, such as triterpenoids, phenolic acids, flavonoids, cyanogenic glucosides, and hemiterpene glucosides [Ilicis chinensis folium extract (ICFE) is a powdered extract from ICS. However, studies on the effects of ICS and ICFE on animal production are lacking. We hypothesized that enhancing broiler diets with ICFE would lead to better growth performance through enhanced antioxidant capacity and improved intestinal morphology. We thus assessed the content of active ingredients in ICFE and examined how ICFE supplement influenced intestinal morphology, growth performance, and antioxidant capacity in broiler chickens.ucosides , 8. The ucosides , 10. Manucosides –13. Ilic18 column  was used and the column temperature was adjusted to 30 °C, the flow rate was 1.0 mL/min, and the injection volume was 10 µL. Acetonitrile (A) and acetic acid dissolved in water at a concentration of 0.1% (B) were the mobile phase. The following conditions were used for the gradient program: 95% (B) in 0–20 min, 95–60% (B) in 20–50 min, 60% (B) in 50–60 min, 60–95% (B) in 60–61 min, 95% (B) in 61–65 min. The data were analyzed using the Shimadzu LabSolutions Automated Software System.A powdered form of ICFE synthesized in the laboratory was included in the diets of broilers. High-performance liquid chromatography (HPLC) was used to r determine the chemical composition. Detection and quantification were achieved using an LC-20AD system controller , a CTO-20 A column heater, a DGU-20A5R degasser, a LC-20AT pump, a SIL-20 A autosampler, and diode array detector set at 254 nm. Agilent ZORBAX SB-CA total of 360 Lohmann commercial broilers aged one day were procured from a commercial chick producer  and individually weighed and randomly allotted to one of four treatments for a 42-day feeding study.Nine replicas  were used per treatment, and each cage contained 10 broilers. Mashed feed and water were available to the broilers at all times. The broilers were kept in a climate-controlled facility with an 18/ h light/dark cycle and relative humidity of 45–65%. The temperature during the first seven days of the experiment was 34 °C and 25 °C for the remaining time. Vaccinations were administered according to routine immunization procedures; one-day-old broilers were injected with inactivated Newcastle disease vaccine and bursa Fabricius vaccine, and 14-day-old broilers were injected with avian influenza vaccine. ICFE was added to a corn-soybean meal base diet for broilers at the following levels: 0 (control group), 250, 500, and 1,000 mg/kg, correspondingly. The diet of each group was prepared to contain the nutrients as per the recommendation of the NRC (1994). The ingredients and nutrient content of the corn-soybean meal-based basal diet (the starter and the grower diets) are shown in Table After fasting for 12 h on days 21 and 42 of the trial, a random selection of 18 chicks (2 per cage) was done from each group in the morning. Each bird’s wing vein was punctured to collect 5 mL blood which was placed in an anticoagulant-free Vacutainer tube. Serum was separated through 15 min of centrifugation at 4000 rpm after being incubation at 37 ℃ for 2 h and the serum samples were then stored at -80 ℃ till subsequent usage.After blood sampling, the birds were anesthetized through intravenous injection with sodium pentobarbital   and wereEach broiler’s heart, liver, and kidneys were harvested, and their weights were recorded. The organ weight (mg) was divided by the pre-slaughter broiler weight (g) to produce the organ index (mg/g), according to the following formula: organ index = (organ weight/broiler weight) × 100。.The harvested small intestine samples were fixed in 4% % PFA solution for 24–48 h, dehydrated using an ascending concentration series of alcohol, paraffin-embedded, and sectioned to 5 μm thickness before staining with hematoxylin and eosin. In each section, ten whole crypt-villus units were selected in a random manner . The distance measured vertically from the villus tip or crypt base to the villus-crypt junction was used to determine villus height (VH) and crypt depth (CD), correspondingly . The hisUsing an automated biochemical analyzer , we determined the serum content of low-density lipoprotein (LDL), total cholesterol (TC), albumin (ALB), triglyceride (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein (HDL), and total protein (TP).Serum levels of IgG, IgA, tumor necrosis factor α (TNF-α), IgM, interleukin-2 (IL-2), IL-4, and IL-8 were assessed through enzyme-linked immunoassays (ELISA) (double antibody sandwich method).Serum levels of malondialdehyde (MDA) and glutathione peroxidase (GSH-Px) activities, superoxide dismutase (SOD) activities, and total antioxidant capacity (T-AOC) were determined using respective commercial test kits  following the manufacturer’s instructions.P < 0.05.Statistical analyses were executed using SPSS 22.0  utilizing one-way ANOVA and Duncan’s multiple range test for multiple comparisons. Duncan’s multiple comparison technique was employed to compare the means and test differences between treatments. Statistical significance is reported at Due to the complexity of the chemical constitution of ICFE, the twelve investigated components in ICFE were determined by the external standard method Table . Figure P < 0.05). Broilers receiving 250,500, and 1,000 mg/kg ICFE showed substantially higher ADG in the starter stage and overall (P < 0.05), compared to that of the controls. No significant effects on the FCR were observed in any treatment. Thus, broilers receiving 500 or 1,000 mg/kg ICFE exhibited better feed efficiency.The effects of dietary ICFE supplementation on the growth performance of broilers at various ages are shown in Table P > 0.05).The effects of ICFE supplementation on the organ indices are shown in Table P < 0.05, Table P < 0.05). CD was generally lower in the ICFE-supplemented broilers than in the controls (P < 0.05), and VH in the jejunum was greater in the ICFE treatments than in the controls but not statistically significant. In the ileum, supplementation with 250 mg/kg ICFE significantly increased VH and reduced CD, compared to the controls.Broilers receiving 250 and 500 mg/kg ICFE supplementation showed greater VH in the duodenum compared with that of the controls, and this difference was more pronounced . None of the ICFE treatments significantly affected the IL-8, TNF-α, IgG, and IgM levels in the serum.Effects of ICFE supplementation on antibody and cytokine concentrations in the serum of broilers are presented in Table P > 0.05, each). ICFE at 500 and 1,000 mg/kg reduced the serum MDA levels, and GSH-Px activities were reduced in all ICFE supplementation treatments.The effects ICFE supplementation on the activities of SOD, GSH-Px, and T-AOC, and the MDA levels in the serum of broilers at 42 days of age are shown in Table Amaranthus hybridus, Astragalus, Glycyrrhiza and Camellia sinensis which enhance growth performance, strengthen the immune system, increase antioxidant activity, and help prevent infections [Broiler chickens and pigs may benefit from diets supplemented with Chinese herbal concoctions such as fections –18.Reverse-phase HPLC coupled to a diode array detector (RP-HPLC-DAD) was applied to characterize the components of ICFE. The following compounds were observed: ferulic acid, chlorogenic acid, caffeic acid, caffeic acid ethyl ester, chicoric acid, gallic acid, protocatechuic acid, p-hydroxybenzoic acid, protocatechuic aldehyde, caftaric acid, pedunculoside, and syringin. pedunculoside Fig. . The conThe effects of ICFE supplementation on the growth performance of broilers have never been investigated, to our knowledge. Our results showed that supplementation with ICFE increased ABW, ADFI, and ADG through elevated VH and VH/CD values in the jejunum and duodenum and helped digestive and absorption processes, which resulted in better intestinal health. Increased villi height facilitates better small intestinal absorption, and reduced digestion and absorption ability is associated with increasing crypt depth, and when the VH/CD is high, digestion and absorption are enhanced, suggesting that the ability to digest and absorb nutrients in the gut is favorably linked to the VH/CD ratio , 20. As As expected, dietary supplementation of ICFE did not damage the heart, liver, and spleen in the present study. There were no gross pathological changes in the internal organs of the broiler chickens in each ICFE group relative to the controls which indicated low toxicity of adding ICFE for a long-term.Ilex paraguariensis can exert substantial hypolipidemic effects, lowering the serum levels of TG and LDC-cholesterol (LDL-C), and TC increases as a result of a high-cholesterol diet [Dietary ICFE supplementation decreased the ALT, AST, LDL, TP, TC and ALB levels of broilers in the current study, compared to those of the controls. rol diet , which irol diet . Triterprol diet , anti-arrol diet . Additiorol diet . Thus, tIlex pubescens can downregulate the expression levels of inflammatory cytokines and substances including COX-2, IL-6, MIP-1α, IL-1β, MIP-1, and TNF-α while increasing the abundances of anti-inflammatory cytokines such as IL-10 and TGF-β [Serum immunoglobulins include key markers of the immunological state of an animal , 27. A pnd TGF-β . Proinflnd TGF-β . ConsistIlex paraguariensis which are associated with antioxidant functions [The results of the current study indicated that ICFE can promote antioxidant activity according to the levels of various antioxidant markers in the serum, such as SOD and T-AOC. To exert their role as antioxidants, SOD and T-AOC may scavenge newly generated reactive oxygen species (ROS). SOD is responsible for the initial breakdown of superoxide into hydrogen peroxide, after which an enzyme cascade including GSH-Px completes the final conversion to water . MDA is unctions . Elevateunctions .We evaluated the potential of ICFE as an alternative to in-feed antibiotics and demonstrated that supplementing broiler chicken diets with ICFE may remarkably enhance overall period ABW and ADG. To our knowledge, this study is the first to show that ICFE is a beneficial and effective feed supplement for enhancing the growth performance of broiler chickens. Our findings provide a better understanding of the mechanism of growth-promoting effects of ICFE supplementation. We recommend ICFE supplementation in diets of broiler chicks at 500 mg/kg because of its unique properties and positive effects on poultry production.


Vitis D2 collection in Stellenbosch represents the most comprehensive collection of Vitis species, hybrids, and cultivars in South Africa. We collected leaf petiole material from 229 accessions from this collection. Our metaviromic analyses revealed a total of 406 complete/near complete genomes of various betaflexiviruses. Among these, we identified the presence of grapevine rupestris stem pitting-associated virus and grapevine viruses A, B, E, F, H (GVH), I (GVI), and M (GVM). Notably, this study marks the first report of GVH, GVI, and GVM in South Africa, which were confirmed via RT-PCR. This research significantly contributes to our understanding of viral diversity and introductions in South African viticulture and emphasizes the need for vigilant monitoring and management of viral infections. Our findings lay the groundwork for strategies that mitigate the impact of viruses on South Africa’s wine industry, which generates an annual revenue of approximately 500 million USD.South Africa is associated with a centuries-old viticultural industry, accompanied by a diverse range of wine and table grape cultivars and an extensive history of pervasive introductions of vine material and associated viruses. The  Vitis vinifera L. (grapevine) is one of the most important perennial crops and has an extensive history of domestication and global trade. Grapevines are vegetatively propagated through vine cuttings that promote virus accumulation, which may cause stunted growth, reduced yield, poor fruit quality, and even vine death. Recent advancements in high-throughput sequencing (HTS) technologies have revealed unprecedented levels of viral diversity, with close to 90 known viruses infecting grapevine [rapevine . UnderstBetaflexiviridae are common in grapevine and include vitiviruses (Genus: Vitivirus), foveaviruses (Genus: Foveavirus), trichoviruses (Genus: Trichovirus), and carlaviruses (Genus: Carlavirus). Vitiviruses are positive-sense (+ssRNA) viruses with ~7500 nucleotides (nt) genomes that typically express five gene products, including replication-associated proteins (RAPs), the “22-kDa” transmission-associated protein, movement protein, coat protein (CP), and a nucleic acid-binding protein that functions as a suppressor of RNA silencing [Vitivirus is also one of the most diverse of the grapevine-infecting genera with thirteen accepted/proposed species, that includes Grapevine virus A (GVA) [Grapevine virus B (GVB) [Grapevine virus D (GVD) [Grapevine virus E (GVE) [Grapevine virus F (GVF) [Grapevine virus G (GVG) [Grapevine virus H (GVH) [Grapevine virus I [Grapevine virus J [Grapevine virus K [Members of the family  A (GVA) , Grapevi B (GVB) , Grapevi E (GVE) , Grapevi G (GVG) , Grapevi H (GVH) , Grapevi virus I , Grapevi virus J , Grapevi virus K , grapevi virus K , grapevi virus K , grapevi virus K . Foveavirus) is relatively common since GRSPaV is one of the most widespread and diverse grapevine-infecting viruses [Vitis species, hybrids, and cultivars are analyzed to provide new insights into the diversity of grapevine betaflexiviruses and represents the first report of GVH, GVI, and GVM in the country.Grapevine vitiviruses are common in viral populations of South African vineyards . GVA, GV viruses . GRSPaV  viruses . GVA, GV viruses ,27—a gra viruses . In Sout viruses  and GVB  viruses . Except  viruses . The ful viruses , with th viruses . Here, aVitis cultivar collection at the Agricultural Research Council’s Nietvoorbij Campus, Stellenbosch, South Africa. Cultivars were planted in replicates within the vineyard. Petiole samples were collected from these cultivar replicates (1 to 5)  , combinePetioles were removed and 0.5 g of each sample was macerated in extraction bags  prior to total RNA isolation using the cetyltrimethylammonium bromide protocol of White et al. . RNA quaInitial quality control of raw sequence reads was performed using FastQC  prior to demultiplexing into individual sample datasets with Je software . TrimminThe derived amino acid sequences of the RAP of each virus were aligned against the cognate sequences of other extant variants of the same species obtained from GenBank, using BioEdit 7.2.5 . GRSPaV TM Reverse Transcriptase and GoTaq® Taq polymerase , according to manufacturer’s instructions. Amplicons were visualized on 1.5% agarose gels following electrophoresis. The amplicons from 45-07 Red Globe, 04-13 Alphonse Lavallee, 05-04 Bacchus, and 24-15 Optenhorst were chosen to represent GVF, GVH, GVI, and GVM, respectively, and subjected to Sanger sequencing .RT-PCR targeting the coat protein coding region  was usedVitis cultivar collection took place at ARC’s Nietvoorbij Campus, Stellenbosch. Samples comprised 212 V. vinifera cultivars, including primarily wine grape, but also some table grape cultivars. Additionally, seventeen V. labrusca cultivars, non-vinifera species, and interspecific hybrids were also included  samples were associated with GVA and produced complete genomes. This is similar to the positivity rate described among commercial vineyards in South Africa . Genome Vitivirus genus [As with most of the viruses discussed in this study, GVA is a member of the us genus . Like maus genus , howeverus genus . GVA is us genus  and in Sus genus . GVA is us genus . GVA divus genus  and the us genus . HoweverGVB is part of the RW complex and is most commonly associated with GCB symptoms  and grafV. flexuisa, which clustered with sequences from Croatia, Japan, Greece, and China , only behind GRSPaV at 45%. The phylogenetic analysis of the RdRP of the variants from this study showed that the majority group within a single phylogroup together with other variants from Portugal and USA, except for the variant from 44-05 which formed a unique phylogroup . This inGVH is a recently described vitivirus, initially discovered in Portugal . Since tGVI is a recently discovered vitivirus  and is most closely related to GVE . The onlVitis vinifera cultivars and one with V. champinii. GVM is reported here for the first time, in South Africa and on V. vinifera. The phylogeny shows that the ten V. vinifera GVM variants group within a phylogroup, distinct from that formed by the Texan variant TX-WAT (V. champinii also forms an isolated phylogroup, suggesting that it is highly divergent. The complete genome ANI shared between this variant and TX-WAT was 70% (Vitivirus genus [V. champinii variant of GVM may represent a putatively novel vitivirus species, its detection from only a single sample suggests that additional variants need to be identified before this can be confirmed, especially given that the sequence is on the edge of the demarcation threshold. A putatively novel vitivirus, called GVM, was recently detected and described by Alabi et al.  from thet TX-WAT . The var was 70% . The aveus genus . While tMultiple betaflexivirus infections were identified in 139 cultivars from this study, based on the recovery of complete/near complete genomes. Up to six different betaflexiviruses were identified within single accessions, and 50 unique combinations were observed , with GRThis study is one of the most comprehensive investigations into the diversity of betaflexiviruses in grapevines and provides significant advancements in our understanding of this family’s diversity within the South African context. It also marks the first formal reports of GVH, GVI, and GVM in the country. While the exact disease etiologies of these viruses are still unclear, their potential roles in the expression of RW symptoms should not be disregarded when considering grapevine phytosanitation. Similar to many grapevine germplasm collections worldwide, the D2 vineyard at Nietvoorbij clearly consists of materials collected over a long period from various global sources, and as such includes a diverse range of viruses. Previous studies by Read et al. ,22 and MAmpelographic collections hold significant value as they often embody a history of extensive cultivar introductions. These collections can serve as sentinels for the early detection of phytopathogens and provide materials for the development of detection techniques. In addition to this, grapevine biodiversity is currently facing significant threats and undergoing a widespread contraction, partly due to the “globalization of wine” phenomenon . This trMetaviromics studies, like the one conducted here, will remain instrumental in successfully implementing grapevine phytosanitary measures in grape-growing regions. By expanding our knowledge of the viral landscape, these studies contribute to the development of effective virus elimination strategies, which are crucial for preserving grapevine health and biodiversity.


Transnasal flexible laryngoscopy is considered an aerosol generating procedure. A negative pressure face shield (NPFS) was developed to control aerosol from the patient during laryngoscopy. The purpose of this study was to determine the effectiveness of the NPFS at controlling virus aerosol compared to a standard disposable plastic face shield. The face shields were placed on a simulated patient coughing machine. MS2 bacteriophage was used as a surrogate for SARS-CoV-2 and was aerosolized using the coughing machine. The aerosolized virus was sampled on the inside and outside of the face shields. The virus aerosol concentration was not significantly different between the inside and outside of the traditional plastic face shield (p = 0.12). However, the particle concentrations across all particle sizes measured were significantly decreased outside the face shield. The virus and particle concentrations were significantly decreased (p < 0.01) outside the NPFS operating at a flow rate of 38.6 L per minute (LPM). When the NPFS was operated at 10 LPM, virus concentrations were not significantly different (p = 0.09) across the face shield. However, the number particle concentrations across all particle sizes measured were significantly different (p < 0.05). Aerosol generating-procedures are medical procedures that can increase transmission risk for respiratory pathogens because they generate aerosols2. There is some debate over which specific procedures are considered aerosol generating however, there is a consensus that special care should be taken by the HCW when performing the procedures involving the upper airway in order to ensure adequate respiratory protection2. High risk aerosol generating procedure include endotracheal intubation and extubation, high frequency oscillatory ventilation, cardio-pulmonary resuscitation (CPR), bronchoscopy and bronchoalveolar lavage, laryngoscopy, nasopharyngeal washing, aspirate and scoping, and sputum induction3. During the pandemic, some elective surgeries have been rescheduled or cancelled due to overflowing capacity in the hospital 4. During the COVID-19 pandemic, the use of transnasal laryngoscopy was markedly attenuated due to concern about inducing bioaerosol spread—particularly by inducing a cough. HCWs are exposed to virus aerosols and other pathogens from the equipment used during the procedure as well as when a patient coughs which typically occurs when the surgical equipment is removed from the nose or mouth5. HCWs in otolaryngology clinics have been found to be at a higher risk of exposure to SARS-CoV-2 than other specialties and therefore special care should be taken to control their exposure6.Health care workers (HCWs) experience inhalation exposure to viruses while performing aerosol generating procedures6. PPE is considered not as effective of an exposure control measure because the HCW is required to correctly wear the protection device and they are still exposed to the hazard7. Therefore, PPE should be used as a last resort to control for a hazard or used in conjunction with another control measure. However, PPE use is required during healthcare since the HCW can be exposed when treating patients with viral related illness. To control for hazards during patient care through the pandemic, engineering controls were analyzed8. Ventilation is a type of engineering control where the focus is on the amount of air changes per hour in a room to control exposure to SARS-CoV-28. However, providers still had close contact with patients and PPE was required and recommended6.A commonly used control measure in otolaryngology clinics during these aerosol generating procedures is personal protective equipment (PPE)9. For example, a patient that is undergoing laryngoscopy cannot wear a traditional plastic facemask, which could be modified to be worn during the procedure. These traditional face shields have been reported to be ineffective in eliminating virus aerosols10. Modifications could include two holes in the front of the face shield so the provider could still use a laryngoscope in the patient’s nose and/or mouth.PPE worn by patients varies by their health status and procedure being done in the hospital11. The NPFS is a hard acrylic shield that has flanged sides and is supported by a heavy-duty clamp and stand . A suction pump is connected to the side of the NPFS using tubing to evacuate air away from the face at approximately 38.6 LPM (the maximum flow rate of the pump). The NPFS combines PPE and engineering control technology to improve biocontainment. On the front of the NPFS, there are two ports for the scope to access the nose or mouth. The shield is placed in front of the patient with their nose is approximately 5 cm away from the front of the shield. A sterilization wrap  is secured to the face shield and then draped over the patients head to create a closed environment.A NPFS was designed to be more protective to the provider than a typical face shield or no PPE at all when worn by a patient during laryngoscopyAnalyzed viable virus concentrations and particle concentrations across a disposable plastic face shield that is placed on a simulated patient.Analyzed viable virus concentrations and particle concentrations across the NPFS that is placed on a simulated patient.Compared the viable virus concentrations and particle concentrations across the disposable plastic face shield and the NPFS.We need to critically examine the effectiveness of the NPFS, on reducing virus aerosols to HCWs during patient care. The coughing machine is designed to simulate a coughing patient during a laryngoscopy procedure. The simulated coughing patient was outfitted with the two face shields and the virus and particle concentrations will be measured on the inside and outside of the face shields. This approach will allow the comparison of virus concentrations observed in the HCWs breathing zone between the two face shield designs. To meet this aim, we:A power analysis was conducted a priori to determine the sample size needed to result in 80% power to detect the true mean of the population, the recommended level, when using a two-tailed t-test with an alpha (α) set at the 0.05 R (R Core Team (2020) ). The determined sample size was 16.7 for each group; therefore, 17 aerosolization trials were targeted for each face shield experiment.11. The NPFS was connected to a pump that is evacuating the air away from the patients face. Two flow rates of the face shield were tested, 10 LPM and 38.6 LPM. The 38.6 LPM was selected since it is the average flow rate used during patient care and 10 LPM was selected as a potential lower flow rate.A coughing machine was designed to simulate a patient that is coughing viral aerosol particles (article currently under review). In summary, the coughing machine aerosolized MS2, a surrogate virus for SARS-CoV-2. The MS2 was suspended in liquid broth (Appendix Virus and particle concentrations were sampled on the inside and outside of the two face shields placed on the simulated coughing patients. SKC Biosamplers (Biosamplers)  were used to sample the MS2 (virus concentration) and GRIMM 11D Optical Particle Counters (OPCs)  were used to sample the particle concentration. The inlets of the Biosamplers and OPCs were placed on the inside and outside of the patient’s disposable plastic face shield  × 3.7 m × 2.4 m). All ventilation to the room was turned off, there was limited traffic, and doors and windows were shut. This approach allowed the sampling room to be used as an undisturbed chamber. The room also maintained a consistent relative humidity and temperature and was measured using a direct reading instrument  throughout all trials. Post sampling, the chamber-like room, and the coughing machine were cleaned with sodium hypochlorite wipes and 70% ethanol.E. coli (107 PFU/mL) (ATCC15597) and 100 µL of the serial dilution sample was added to the 3 mL of top agar, this was then mixed and poured over the solidified bottom agar plates. Each serial dilution was plated in triplicate. The plates were left to solidify for 20 min and then sealed with a thermoplastic film . The sealed plates were placed upside down in a 37 °C incubator for 14–24 h. After the incubation period, the MS2 plaques formed. The serial dilution plate that produced between 20 and 250 plaques were then counted. The average across the triplicate measure was taken. Blank samples were used throughout the sampling chamber and ventilation hood to ensure no contamination.A plaque assay was used to analyze the concentration and viability of the Biosampler liquid media. The sample, 20 mL of PBS that was run through the Biosampler, was be concentrated using an Amicon Ultra 15 Centrifugal Filters  which is spun in the centrifuge at 4000 rotations per minute (RPM) in one-minute intervals until the remaining sample volume is less than 1000 µL. The concentrated sample volume was measured and recorded. A serial dilution was then conducted using 100 µL of the concentrated sample and 900 µL of MS2 broth , NPFS at “low flow rate” or 10 LPM (n = 6), and NPFS at a “high flow rate” or 38.6 LPM (n = 18). The censored data for the virus concentration was replaced with the LOD/2. Arithmetic means and standard deviations were computed across the trials for the inside and outside of the face shield. A paired t-test was performed to compare the two groups.12. Descriptive statistics were calculated, specifically the geometric mean and geometric standard deviation. A box plot showing the median first quartile, third quartile, standard deviations and outliers was also made for each condition. For each variable, outliers were identified as values greater than 1.5 interquartile range (IQR) third quartile or 1.5 IQR lesser than the first quartile.The OPCs were connected to two computers which each ran the OPC Software . The particle data were exported to Microsoft Excel  for analysis. The inside and outside face shield particle data for all the trials in one condition was compiled for each particle size. The aerosol particle data were determined to be log-normally distributed and were log-transformed. The variance was tested and based off the results, an appropriate t-test was selected and then conducted for comparing the inside and outside of the face shield for each particle size. The paired t-test was conducted using an unadjusted alpha of 0.05 as well as an alpha that was adjusted for False Discovery Rate using the Benjamini Hochberg procedure3. This concentration was verified after the aerosolization-coughing event ensure consistency within each trial. The airborne concentration inside the face shield, near the breathing zone of the patient, when the patient was wearing the disposable plastic face shield had a mean (n = 16) of 1.27E+09 PFU/m3 (Standard Deviation (SD) = 8.15E+08) (Table 3 (SD = 9.11E+07) (Table 3 (SD = 8.17E+05) (Table 3 (SD = 6.44 E+08) (Table 3 (SD = 2.57E+08) . We analyzed viable virus and particle concentrations across a disposable plastic face shield modified for laryngoscopy procedures, a high flow NPFS and a low flow NPFS placed on a patient during a simulated cough. The measurements taken on the inside of the face shield were used to see if there was any reduction in MS2, the surrogate for SARS-CoV-2, concentration exposure to the outside of the face shield where the HCWs would be exposed. To see if there was a difference between the inside and outside of the face shields, we performed statistical analysis to compare the virus and particle concentrations found across the face shield. The goal of this experiment was to analyze the effectiveness of the face shield at reducing virus exposure so that we can improve control measures for protecting HCWs during high-risk procedures.13. To date the effectiveness in controlling bioaerosol had not been tested. However, combining PPE and engineering controls is more effective than PPE alone7. In addition, studies have shown that PPE needs to be updated and engineered to meet the needs of HCWs and the NPFS does this14.The NPFS is a novel control device identified as effective in permitting effective transnasal laryngoscopy for diagnosis and procedures with excellent patient toleranceWhen the simulated coughing patient was wearing a disposable plastic face shield during a coughing event, we did not observe a statistically significant difference in airborne virus concentration between the inside and outside of the face shield (p = 0.12). We did observe a statistically significant decrease (p < 0.05) in particle number concentrations between the inside and outside of the face shield for all particle sizes measured.When the NPFS was operated at a high flow during a coughing event, we did observe a statistically significant decrease in airborne virus concentration outside of the face shield (p < 0.05). Specifically, no virus was measured on the outside of the NPFS, where the HCW would be exposed. We also observed a statistically significant decrease (p < 0.05) in particle number concentrations outside of the face shield for all particle sizes measured. The inner quartile ranges and number of outliers were reduced when operating the NPFS’s compared to the disposable plastic face shield.When the NPFS operated at a low flow during a coughing event, we did not observe a statistically significant difference in airborne virus concentration between the inside and outside of the face shield (p = 0.09). We did observe a statistically significant difference (p < 0.05) in particle number concentrations between the inside and outside of the face shield for all particle sizes.The virus concentration was significantly reduced for the high flow NPFS and was reported at 0. We believe that our sampled amount was in fact zero because the particle concentration did not increase on the outside of the face shield when the coughing trials were run. Our coughing machine may have affected virus viability, which would in turn impact the concentrations of the plaque assay. The virus concentrations on the inside and outside of the typical disposable plastic face shield and the low flow NPFS were not significantly different. This means that the HCW is still potentially exposed to viable virus aerosols when those control methods are used. However, while not significantly reduced, it is important to note that any reduction in concentration reduces exposure of virus to the HCW and is therefore important. While the reduction may not eliminate the risk of transmission it may reduce risk below the threshold needed for infection. Further work should be done to quantify the concentration of airborne virus needed for infection. There were a few notable limitations in this study. The experiment was performed in a chamber like room with no room ventilation. In health care environments, ventilation is present which may result in different observations. Specifically, in the otolaryngology surgical clinic, rooms may be under negative pressure, which may allow for additional protection for the HCW.We did not correct for background aerosol concentration in any of these experiments. Our experimental approach was designed using paired comparisons  so this limitation should have little if any impact on our observations of face shield effectiveness. The background concentrations should be the same for the inside and outside of the two face shields including the NPFS since it was pulling air from just outside the face shield. Further, we measured live virus concentrations across the face shield rather than solely relying on airborne particle concentrations and the results from the live virus and particle concentrations were consistent across all face shield scenarios further validating this decision. The virus aerosol concentration aerosolized may have varied across trials. To control for this error, we measured virus plaque concentrations in the stock solution before each experiment. Furthermore, our comparisons and statistical analysis was paired which would reduce the impact of this error on our experiment. In addition, there may have been variance in our virus concentration estimates due to using a plaque assay as our method of quantification. To mitigate this limitation, we used a triplicate measure when performing the plaque assay and took the numerical mean. In the future a more sensitive analysis, like digital droplet polymerase chain reaction (ddPCR) could be utilized. We chose not to utilize ddPCR because we were interested only in the viable virus concentration in order to analyze infectious particles a HCW would be exposed to. However, ddPCR could be very informative to negate the limitation that the coughing machine was inactivating the virus which would make the plaque assay not a viable analysis method.15. However, most studies have analyzed how effective face shields are at protecting HCWs from infectious patients when the HCW wears the face shield, making our work novel16. In addition, by using the GRIMM 11-D OPC we introduce an inherent limitation in that the smallest bin size that it can accurately detect is 0.253 µm. We hope that this limitation is mitigated by the MS2 being laden in liquid media and therefore creating a droplet during the coughing event however, there is still a limitation that the smallest airborne virus particles were not analyzed. We also further tried to validate our approach by analyzing virus concentration in addition to particle concentration. While we chose the OPC for its sensitivity, flow rate, and detection limit further work should be done which analyzes smaller particle sizes.The particle concentrations were significantly different for all three tested face shields. This implies that the new design of face shields reduced some aerosol from the coughing plume. Some studies have found that face shields can reduce exposureThe sample sizes across the three different face shield scenarios varied. The lowest sample size was for the low flow NPFS with a total sample size of six. We chose not to continue to pursue the lower flow rate and meet the sample size needed to reach a power of 0.8 because the there was still viable virus being measured on the outside of the face shield. We chose to focus on the higher flow rate and the disposable plastic face shield since that flow rate has been used in clinical practice.The mannequin head of the coughing machine is not the same size as an average head so the typical disposable plastic face shield did not fit the same as it would. Since the head was smaller, the face shield wrapped further around the head and our results were probably overestimating the effectiveness of the control measure. In conclusion, we recommend that the high flow NPFS be considered for use during higher-risk aerosol generating endoscopy procedures to limit the potential transmission of virus to HCWs. We recommend that future studies be conducted with the NPFS on patients because the added effect of aerosol generation from the procedures was not analyzed, only the aerosol generated from the patient coughing.Supplementary Information.