Actinobacteria. While Ms1 RNA was identified in Mycobacterium, it is not clear whether Ms1 RNA is present also in other Actinobacteria species. Here, using a computational search based on secondary structure similarities combined with a linguistic gene synteny approach, we identified Ms1 RNA in Streptomyces. In S. coelicolor, Ms1 RNA overlaps with the previously annotated scr3559 sRNA with an unknown function. We experimentally confirmed that Ms1 RNA/scr3559 associates with the RNAP core without the primary sigma factor HrdB in vivo. Subsequently, we applied the computational approach to other Actinobacteria and identified Ms1 RNA candidates in 824 Actinobacteria species, revealing Ms1 RNA as a widespread class of RNAP binding sRNAs, and demonstrating the ability of our multifactorial computational approach to identify weakly conserved sRNAs in evolutionarily distant genomes.Bacteria employ small non-coding RNAs (sRNAs) to regulate gene expression. Ms1 is an sRNA that binds to the RNA polymerase (RNAP) core and affects the intracellular level of this essential enzyme. Ms1 is structurally related to 6S RNA that binds to a different form of RNAP, the holoenzyme bearing the primary sigma factor. 6S RNAs are widespread in the bacterial kingdom except for the industrially and medicinally important  All bacteria contain this housekeeping or primary σ factor, termed σ70 in Escherichia coli, σA in Bacillus subtilis and in Mycobacterium smegmatis  are important regulators of gene expression in bacteria. A majority of sRNAs act by base-pairing to target mRNAs and change mRNA stability or translation but a minor group of sRNAs directly regulates proteins through RNA-protein interaction . The besmegmatis , or HrdBptomyces . In addinitalium  to almoselicolor .E. coli (70 holoenzyme (Eσ70) (6S RNA was first described in E. coli  where ite (Eσ70)  and regue (Eσ70) . 6S RNA e (Eσ70) . Transcre (Eσ70) .70 , industrially important producers of amino acids (Corynebacterium glutamicum) and antibiotics (Streptomyces), bacteria involved in symbiotic nitrogen fixation (Frankia), bacteria utilized for bioremediation (Rhodococcus), probiotic bacteria (Bifidobacterium), and numerous other genera .The intracellular levels of 6S RNAs are high, similar to those of essential non-coding RNAs, such as rRNAs, tRNAs, RNAse P, tmRNA or SRP RNA. Furthermore, 6S RNAs have been found in many bacterial species and are widespread in the bacterial kingdom  with oneActinobacteria, 6S RNAs had been undetected for a long time. In Mycobacterium smegmatis, we identified a putative 6S RNA candidate by the computational suboptimal secondary structure approach    and their secondary structures differ as well—in addition to the central bubble, Ms1 has two additional short hairpins at its 3′ and 5′ ends . An Ms1 23 sRNA) . Other Mocardia) . CurrentStreptomyces coelicolor, a putative 6S RNA (the gene was named ssrS) was identified  and thus it is unclear if the putative 6S RNA sequence is expressed in vivo.In entified , but thed 6S RNA . In addid 6S RNA . Moreove RNA-seq  which isS. coelicolor, which was named “6S-like scr3559 RNA.” It is expressed from the sco3558-sco3559 genomic locus but from the minus strand (the previously published 6S RNA sequence was expressed from the plus strand), with its transcription start site located at position 3,934,888 in the genome  of RNA genes to identify Ms1 candidate genes. First, we applied this approach to Streptomyces coelicolor. Our search identified a Ms1 candidate—the scr3559 sRNA. We validated this result experimentally, demonstrating that scr3559 RNA binds the RNAP core without the primary σ factor HrdB in vivo and, thus, is an Ms1 but not a 6S RNA homolog. Subsequently, we applied our linguistic search to the phylum of Actinobacteria, revealing that Ms1 is present in most orders of Actinobacteria. Ms1 is thus a new type of regulatory RNA associated with RNA polymerase, in addition to 6S RNA, B2 RNA (regulates RNA polymerase II in humans) (To elucidate whether Ms1 RNA is conserved in the phylogenetic group of  humans) , or svRN humans) .Streptomyces well annotated genomes were used (GenBank assembly IDs shown in parenthesis following organism name): S. coelicolor A3(2) (GCA_000203835.1_ASM20383v1), S. ambofaciens ATCC 23877 (GCA_001267885.1_ASM126788v1), S. nodosus (GCA_000819545.1_ASM81954 v1), S. reticuli (GCA_001511815.1_TUE45), S. avermitilis MA 4680 (GCA_000009765.2_ASM976v2), S. griseus NBRC (GCA_000010605.1_ASM1060v1), S. scabies 87 22 (GCA_000091305.1_ ASM9130v1), S. cattleya NRRL8057 (GCA_000237305.1_ASM 23730v1), S. lincolnensis .For the search, 10 selected The search was performed in the following steps:(i)Streptomyces genomes, according to GenBank genomic annotations. IGRs in both DNA strands were searched, hence, an IGR in one strand may overlap with an open reading frame encoded by the other strand.Intergenic regions (IGRs) were identified in the 10 selected (ii)The IGR sequences were sampled with approximate nucleotide lengths of 180 and 300 nucleotides that are specific for 6S and Ms1 RNAs, respectively. The sampling was done in a sliding window, moving sequence window by 5 nucleotides a step through IGR sequences.(iii)P = 1000, W = 1 and X = 10.10 suboptimal secondary structures were predicted for each sampled sequence by UNAfold  with par(iv)B. subtilis 6S RNA The average of three best scores for each sampled sequence was compared to the structural similarity thresholds of 80 and 135 for 6S RNA and Ms1 RNA, respectively. The thresholds were chosen such that we were able to identify known 6S and Ms1 RNAs by the search. To be considered as a candidate for one of the searched for sRNAs, the candidate sequence had to have the averaged similarity score of three most similar suboptimal structures better than the thresholds.–20, and (2) conserved genomic position in the ten searched Streptomyces genomes.To increase reliability of the search, the candidates had to fulfill the following additional criteria derived from genomic properties of known 6S and Ms1 RNAs that were: (1) existence of similar sequences in evolutionarily close species identified by BLAST with BLAST E-values < 10Streptomyces species listed in the previous paragraph. As sRNAs in general are conserved in evolutionarily related species, here in Streptomyces species, the constructed sequences had to be identified by BLAST in multiple Streptomyces species to ensure that they were constructed correctly. Using the superposition of constructed sequences according to their conservation identified by BLAST, we reconstructed probable sRNA sequences in Streptomyces coelicolor. The probable sequences are shown in Streptomyces sRNAs.The full-length sequences were predicted using the approximate length of the transcripts detected by Northern blotting and the genomic positions of oligonucleotide sequences used for sRNA verification by Northern blotting . The seqThe search was based on text phrases in genomic annotations of the genes flanking potential Ms1 RNA genes and sequence similarity search based on BLAST for verification. The search was possible as sRNAs including Ms1 RNA are known to be conserved in evolutionarily close bacteria . The sea(i)Synteny analysis. Specific words in annotations of 5′ and 3′ end flanking genes of known and newly discovered IGR containing Ms1 RNAs are identified. Among them, most repeating specific words are identified. This step is explained in detail in the following section.(ii)Synteny phrases. Text phrases, comprising of at least one most repeating specific word, that are specific for genomic annotation of genes flanking Ms1 RNAs are generated. Synteny phrases used in this work are shown in (iii)Actinobacteria genera which had >4 annotated genomes and in which Ms1 RNA has not yet been discovered. The search is implemented as a sequence of grep LINUX searches, followed by additional text processing implemented in MATLAB computational environment.Text search for Ms1 synteny phrases in genomic annotations of (iv)Synteny hit. An IGR with Ms1 synteny containing a putative Ms1 RNA gene.(v)Evolutionary conservation of the synteny hit. The hit was considered as evolutionarily conserved when there existed IGRs with similar sequence and Ms1 synteny for at least one of the flanking genes.The IGRs with similar sequences were identified by BLAST of the sequence of the synteny hit in NCBI’s nt database with sensitive setting for cross-species exploration Flanking genes annotations of both synteny hit and IGRs homologous to it in new, related species. They were analyzed to get their specific words [step (ii)] and to update old synteny phrases [step (iii)].Annotations of flanking genes were first split into single words and non-specific words were removed. The non-specific words were : ‘hypothetical’, ‘protein’, ‘type’, ‘family’, ‘domain’, ‘putative’, ‘precursor’, ‘component’, ‘subunit’, ‘subfamily’, ‘conserved’, ‘chain’, ‘or’, ‘superfamily’, ‘unknown’, ‘function’, ‘of’, ‘like’, ‘containing’, ‘II’, ‘and’, ‘short’, ‘dependent’, ‘probable’, ‘associated’, ‘to’, ‘that’, ‘the’, ‘predicted’, ‘uncharacterized’, ‘production’, ‘proteases’, ‘fold’, ‘in’, ‘by’, ‘universal’, ‘pathway’, ‘involved’, ‘related’, ‘general’, ‘group’, ‘sequence’, ‘class’, ‘cluster’, ‘accepting’, and ‘determining’.We also removed the so-called questionable non-specific words whose (non)specificity depended on the analyzed context. They were: ‘box’, ‘enzyme’, ‘factor’, ‘secreted’, ‘release’, ‘neighborhood’, ‘solute’, ‘accessory’, ‘peptide’, ‘biosynthesis’, and ‘substrate’.Specific words were left. They characterized specific aspects of gene function. An example is the ‘Fic/Doc family protein’ annotation contained two non-specific words, ‘family’ and ‘protein’, and one specific word ‘Fic/Doc’.M. smegmatis Ms1 RNA identified by sequence similarity by BLAST in 498 species of 8 genera—Mycobacterium, Mycolicibacterium, Rhodococcus, Nocardia, Gordonia, Mycobacteroides, Hoyosella, and Tsukamurella  were grown at 37°C in Middlebrook 7H9 medium with 0.2% glycerol and 0.05% Tween 80, and harvested in exponential (OD600 ∼0.5) or early stationary phase . S. coelicolor A3(2) spore stock expressing HA-tagged HrdB was thawed and inoculated to 2YT medium. Germination was carried out at 30°C for 5 h as described previously , inoculated into Na-glutamate medium supplemented with trace element solution and TMS1 as described previously  at different time points after germination. Rhodococcus erythropolis CCM259 (LK1556) was cultivated at 26°C in 2x TY medium to exponential phase (OD600 ∼2) or stationary phase , Corynebacterium glutamicum ATCC 13032 (LK1557) was grown in 2xTY at 30°C and harvested in exponential (OD600 ∼1) or stationary phase . Bacillus subtilis 168 strain was grown in LB medium at 37°C to exponential phase (OD600 ∼0.3) or stationary phase (OD600 ∼4).eviously . The gereviously , cultivas CCM259  (LK1556)Each frozen cell pellet was resuspended in 240 μl TE (pH 8.0) plus 60 μl LETS buffer  and 600 μl acidic phenol (pH∼3):chloroform (1:1). Lysates were sonicated in a fume hood, centrifuged, the aqueous phase extracted three more times with acidic phenol (pH∼3): chloroform and precipitated with ethanol. RNA was dissolved in water, treated with DNase  and visualized on a 7 M urea 7% polyacrylamide gel by staining with GelRed (Labmark).RNAs were resolved on a 7% polyacrylamide gel and transferred onto an Amersham Hybond-N membrane according to the protocol described in AL645882.The protocol was adopted from The protocol was adopted from Streptomyces coelicolor cells expressing HA-tagged HrdB were pelleted and resuspended in lysis buffer , 0.5 mM phenylmethylsulfonyl fluoride (PMSF), supplemented with Calbiochem Protease Inhibitor Cocktail Set III protease inhibitors), sonicated 15 × 10 s with 1 min pauses on ice and centrifuged. 500 μg (protein) of lysates were incubated for 16–18 h at 4°C with 20 μl of Protein G plus agarose beads (Santa Cruz) coated with 5 μg mouse monoclonal anti-β subunit of RNAP antibody [clone 8RB13] (BioLegend), 2.5 μg of anti-σ70 antibody [clone 2G10] (BioLegend), 1.25 μg of anti-HA antibody [clone HA-7] (Sigma-Aldrich) or 5 μg of mouse non-specific IgG (Sigma-Aldrich) used as a negative control, respectively. The captured complexes were washed 4 times using 20 mM Tris–HCl pH 7.9, 150 mM KCl, 1 mM MgCl2, finally resuspended in 300 μl 20 mM Tris–HCl pH 7.9, 150 mM KCl, 1 mM MgCl2 and divided into two parts. One third of the beads were incubated in SDS sample buffer for 5 min at 95°C and eluted proteins were detected by Coomassie staining and Western blotting. The remaining two thirds of the beads were resuspended in 200 μl 1% SDS, 150 mM KCl, 20 mM Tris–HCl pH 7.9, 1 mM MgCl2 and incubated on a rotating platform with 200 μl acidic phenol (pH∼3):chloroform (1:1) for 15 min. Eluted RNA was precipitated with ethanol, dissolved in water and DNase treated . RNA was reverse transcribed into cDNA  using random hexamers and amplified by quantitative reverse transcription PCR (RT-qPCR) in a LightCycler 480 System (Roche Applied Science) in duplicate reactions containing LightCycler 480 SYBR Green I Master and 0.5 μM primers (each). Primers were designed with Primer3 software and their sequences are in the Supplementary primer list. Negative controls (no template reactions and reactions with RNA as a template to control for contamination with genomic DNA) were run in each experiment, the quality of the PCR products was determined by dissociation curve analysis, and the efficiency of the primers determined by standard curves. The relative amounts of co-immunoprecipitated RNAs were quantified on the basis of the threshold cycle (Ct) for each PCR product that was normalized to input values according to the formula 2∧[Ct(immunoprec)–Ct(input)].Proteins were detected by Western blotting using a rat monoclonal antibody recognizing the HA tag conjugated with HRP [clone BMG-3F10] or a mouse monoclonal antibody recognizing β subunit of RNA polymerase [clone 8RB13] in a combination with secondary antibody conjugated with HRP.Streptomyces genus.First, we conducted a computational homology search for putative 6S RNA/Ms1 RNA in the Streptomyces . Therefore, prior to the experimental validation, we extended the homology search with linguistic synteny analysis approach.Using this computational search, we identified 12 candidate genes for 6S/Ms1 RNAs in yces see . Interesyces see  displayermicutes , making The linguistic approach was based on search for text phrases that were specific to Ms1 RNA synteny and could be found in Ms1 flanking genes annotations. Note that meaning of the term ‘synteny’ in this work is ‘conserved genomic context’. Synteny annotations are annotations of conserved flanking genes of Ms1 RNAs. The phrases were used to identify new IGRs containing putative Ms1 RNAs in other species.Enterobacteriaceae (Enterobacteriaceae (γ-Proteobacteria)  . Althougrmicutes . TherefoMycobacterium, Rhodococcus, and Nocardia were conserved  in annotation of the other flanking gene and the length of IGR between flanking genes larger than 300 nucleotides. This IGR was considered as an IGR containing a putative Streptomyces Ms1 RNA.Intergenic regions with flanking genes with Ms1 RNA-specific synteny phrases (synteny hits) were found in numerous Streptomyces species, analogously to Ms1 RNA from M. smegmatis conserved in other Mycobacteria. Therefore, the sequence of the S. actuosus Ms1 IGR was BLASTed against the NCBI nt database, which produced ∼250 BLAST hits in 188 different Streptomyces species  (ENA ID AL645882.2) were identified at genomic locus 3934559: 3934978. To find out where within this IGR a putative Ms1 RNA was, the 419 nt sequence was sampled with 200–300 nt subsequences in 5 nt steps for which suboptimal secondary structures were predicted using UNAfold  potential 6S/Ms1 RNA candidate genes in S. coelicolor .Next, we used Northern analysis to determine expression of these putative sRNA, probing their expression from both strands. The analysis revealed that several of them were expressed, to various degrees, in exponential and stationary phases in elicolor , 2C. Expsco1791) and it was found in two forms (90 and 120 nt). Additionally, for Str1 we detected a short transcript (100 nt) from the opposite strand, a fragment of the sco1791 (1260 nt) mRNA.Str1 sRNA is an antisense RNA as1791,  to the tsco1761 gene and similarly to Str1, Str3, is also an antisense RNA  excluded them as potential 6S/Ms1 RNAs. 6S RNA/Ms1 RNA must adopt specific secondary structures and one of the shortest known 6S RNAs is from Aquifex aeolicus, which is ∼160 nt long , the candidate with the same synteny as 6S-1 RNA in ssrS gene  or ssrS (putative 6S RNA) is expressed from the 0 nt RNA . This co0 nt RNA . Althoug0 nt RNA , the maj0 nt RNA  or 227 b0 nt RNA .S. coelicolor Ms1 (Str13) RNA - predicted Ms1 starts 126–141 nt downstream from 5′ end of IGR. This 5′ RACE result matches the 5′ end of scr3559 as determined by RNA-seq  is not expressed in detectable amounts in these growth phases.To determine whether scr3559, along with ssrS/6S RNA is perhaps expressed, we used three different probes for Northern blot analysis. Probe “A” should specifically hybridize to ssrS/6S RNA, probe “B” to both sr3559 and ssrS/6S RNA, and probe “C” only to the sr3559 . We detesco3558-sco3559 genomic locus starts at position 3,934,693 and differs from the ssrS/6S RNA sequence that was used to experimentally test the interaction of the putative 6S RNA with the RNAP-HrdB holoenzyme in vitro  that predicted 78 suboptimal structures. The structures were clustered into 5 clusters based on their mutual structure similarity to find structurally representative folds. The clusters represented structural variations of a typical Ms1 fold. The most representative fold was identified in a cluster with most mutually similar suboptimal structures that contained typical Ms1-like structures which resembled Ms1 from M. smegmatis  and Str13 (scr3559) satisfied the criteria for potential 6S/Ms1 candidates and we selected them for further analysis.S. coelicolor. As the commercially available antibody against the primary σ70 (clone 2G10) interacted with HrdB only weakly (S. coelicolor hrdB gene (To answer whether Str11 (scr1505) and/or Str13 (scr3559) are homologs of 6S RNAs or Ms1, we wanted to immunoprecipitate the primary σ factor, HrdB, and RNAP from rdB gene  and immurdB gene . We subsrdB gene .Str11 (scr1505) associated neither with the RNAP core nor with the RNAP-HrdB holoenzyme, similar to four control RNAs that also did not interact with RNAP: 16S rRNA, sco3552, sco3710 encoding membrane proteins, and sco2013 encoding response regulator PdtaR.S. coelicolor  was bound to the RNAP core at 42 h post-germination in elicolor . scr3559NAP core . As we nNAP core , lane 7 NAP core , lane 3, to 42 h  while thmegmatis . Str13 (S. coelicolor and we propose to rename Str13 (scr3559) as Ms1.Therefore, we concluded that Str11 (scr1505) is neither 6S RNA nor Ms1 but a sRNA of unknown function. To the contrary, Str13 (scr3559) is a bona fide homolog of Ms1 in Mycobacterium smegmatis and Rhodococcus erythropolis, Ms1 is an abundant RNA in stationary phase, prominently visible in the gel, similarly to 6S RNA in Bacillus subtilis , there are no prominent bands in the ∼200–300 nt range, suggesting that it might not contain an Ms1 RNA or its expression is below the detection limit of the staining. Therefore, we decided to extend the linguistic search to the whole group of Actinobacteria to reveal how widespread Ms1 RNA is within other Actinobacteria species.In subtilis . In StreS. coelicolor Ms1 RNA using the linguistic search, we applied it to other Actinobacteria. A flowchart of the search procedure is shown in After we identified Mycobacterium, Rhodococcus, Nocardia, Gordonia, Mycobacteroides, Hoyosella, and Tsukamurella . Note that sequences of the synteny hits, i.e., IGRs containing putative Ms1 RNAs, may be dissimilar to each other and therefore could not be identified by sequence similarity searches.For the search, Taxonomy , were usStreptomyces actuosus, we identified IGRs containing Ms1 in 158 species from 46 Actinobacteria genera other than Streptomyces , Brevibacterium , Corynebacterium , Actinomyces , and Bifidobacterium  we found no IGRs that could contain Ms1 RNA , identifying this sRNA as a major class of protein-interacting RNAs. Ms1 RNA associates with the RNAP core as previously demonstrated in Mycobacteria  mycelium, then secondary mycelium is formed and eventually spores  with the direction of transcription oriented toward it .A comparison of genome sizes of the main Ms1 RNAs  reveals Frankiales, Geodermatophilales, and Glycomycetales, where neither the Ms1 synteny nor Ms1 RNA were detected, genome sizes were comparable to Actinobacteria orders with identified Ms1 RNAs. Frankia  genome sizes vary between 4.3 and 10 Mb and this variability is due to the degree of host dependence. Frankia are N2-fixing filamentous plant symbiotic bacteria that can either survive independently in the soil or be entirely dependent on their host plants . Glycomycetales are aerobic bacteria that produce branched vegetative mycelia and aerial mycelia with chains of square-ended conidia  colonies with the individual cubic cells and extracellular matrix forming cauliflower-like aggregates and have been reported to be highly resistant to stresses such as gamma-radiation, UV, and desiccation  the length of the sequence can vary substantially between species or genera, and (2) genomic position of the sequence can be only approximated. In the presented work, the length difference between the known S. coelicolor). The annotation helped us to identify the IGRs that might contain Ms1 RNA even in extremely distant species as represented here by the Actinobacteria genera with predicted Ms1 RNA, in which flanking nucleotide/amino acid sequences might be dissimilar.To overcome this problem, we employed a genomic synteny search. We adopted a linguistics approach based on similarity of genome annotations rather than similarity of nucleotide or amino acid sequences of flanking genes. Ms1 RNA has conserved synteny with a highly specific phrase in one of its flanking genes, the ‘HAD IB hydrolase’. This phrase is relatively rare and occurs only a few times in well annotated genomes , it then proceeded in an iteratively progressive manner within the genus and also in evolutionarily close genera using sequence similarity of the synteny hits. The obtained information was subsequently applied to other genera, expanding the list of identified candidates. Even in this simple form, the computational text search was able to identify Ms1 homologs in The original contributions presented in the study are included in the article/OM, MK, DK, and MJ validated sRNAs expressions. MŠ and JH performed 5′ and 3′ RACE. VVH and MŠ performed immunoprecipitation experiments. PH identified proteins by mass spectrometry. JP and MSc performed the computations. MSc, LK, JHn, and JP wrote the manuscript. JHn and JP designed the study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Following baseline measurements , the volunteers performed a single bout of HIFT. At the end of the session, participants were randomly assigned to one of three distinct groups: control (CONT), foam rolling (FR), or static stretching (SS). At the 24 h time-point, a second experimental session was conducted to obtain the post-test values. The level of significance was set at p < 0.05. Regarding power performance, none of the three groups reached pretest levels at 24 h point of the intervention. However, the CONT group still showed a greater magnitude of effect at the 24 h time-point . Flexibility presented the same recovery pattern as power performance . At 24 h, all groups presented an impaired performance in the COD t-test . The FR protocol resulted in superior recovery perceptions (pre × 24 h TQR = ES = 0.32 p ≥ 0.05). The results of the present study indicate that the use of FR and SS exercises may not be indicated when aiming to restore neuromuscular performance following a single bout of HIFT. The use of the FR technique during the cooldown phase of a HIFT session may be helpful in improving an individual’s perception of recovery.The purpose of this study was to investigate the effects of the foam rolling technique and static stretching on perceptual and neuromuscular parameters following a bout of high-intensity functional training (HIFT), which consisted of 100 pull-ups, 100 push-ups, 100 sit-ups, and 100 air squats (Angie benchmark) in recreationally trained men ( According to the brand, functional movements are performed in a wave of contraction from the core to an extremity which is directly related to motor control and postural stability concepts. Several positive adaptations have been observed with HIFT in a short time frame, such as improved body composition, resting heart rate, muscular endurance, strength, flexibility, bone mineral density, and skill-specific performance [High-intensity functional training (HIFT) can be described as a multimodal training routine primarily composed of functional movements performed at high exertion levels ,2,3. Preformance .Despite being an efficient training modality, the absence of predefined resting periods, its circuit-style format, and the involvement of bodyweight or free-weight exercises may impose a considerable degree of physiological stress during each training session ,6 that cIn this context, performance parameters have been used to assess fatigue and recovery noninvasively, such as the loss of muscle power measured through a countermovement jump (CMJ) as well as increased time for a change in direction test ,9,10. InFoam rolling uses different tools, such as polypropylene rollers and balls, to apply a compressive force to soft tissue, but its mechanisms are a matter of constant debate . Currentt-test (COD t-test) as quantitative measurements.This study had an experimental approach in which the sample was selected by convenience and used a predefined “benchmark” named “Angie” for a HIFT session which consisted of 100 pull-ups, 100 push-ups, 100 air squats, and 100 sit-ups performed as fast as possible . One of t-test were performed, and the HIFT session was conducted. All the evaluations were repeated immediately following the training session (“post”), and participants were randomly assigned to the recovery protocols. Participants returned 24 hours later (“24 h”) for retesting.Familiarization of the qualitative measurements was conducted online through video or voice call for a week before the first testing session, and familiarization of the quantitative measurements was conducted on the first day of testing consisting of two maximum “pretest” attempts in each of the evaluations after the warm-up with their respective resting interval. Following familiarization with the evaluations, testing would begin. Baseline assessments (“pre”) for FS, VAS, and TQR were taken prior to the warm-up protocol, which consisted of five minutes of running with change-of-direction sprints and five minutes of mobility drills. Following the warm-up, the SR, CMJ, and COD f equal to 0.25, alpha equal to 0.05, and power equal to 0.80 reaching a total sample size of 36 individuals [Sample size calculation was conducted using GPower 3.1 software  considering an effect size Exclusion criteria were adopted during an anamnesis. Participants were instructed to maintain their regular dietary and sleep regimens. However, they were requested to refrain from all other recovery modalities that could blunt the results of the present investigation during the conduction of the study. In addition, participants refrained from any other kind of physical activity during the proposed timeframe. All participants were informed of the benefits and risks of the investigation prior to signing an institutionally approved informed consent document to participate in this study. This study was conducted according to the Declaration of Helsinki, and the Institutional Review Board of the corresponding university approved the experimental protocol (45373121.5.0000.5257).The FS is an instrument composed of eleven answering possibilities to the question “How are you feeling right now?” to evaluate the affective response to exercise . VAS wasThe TQR is a scale structured according to the Borg Scale and was initially applied so that the participants could describe their general recovery perception following a HIFT session . For theThis test was conducted to evaluate lumbar and hamstrings flexibility and was performed according to procedures previously described elsewhere. During testing, subjects remained seated on the floor with extended knees and slightly separated legs with their feet pressed against the board. Arms were extended forward with the hands placed palms down on the upper surface of the scale. In this position, the subject bobs forward four times and holds the position of maximum reach on the fourth count. Two trials were conducted with a 30 s rest interval between trials. The mean value was used for statistical analysis .Data collection regarding vertical jump performance was conducted using the MyJump App, previously validated for IOS cameras ,17,18. PTwo testing attempts with a one-minute rest interval between them were conducted. Attempts were invalidated if the subject did not perform the test as instructed. When permitted, participants sprinted forward 9.14 m to touch the first cone. They then side-shuffled 4.57 m to the left and touched the second cone. Next, they side-shuffled 9.14 m to the right and touched a third cone, then 4.57 m to the left back to the point where the first cone was, touching it again. Finally, participants backpedaled 9.14 m. There was no permission to look back or sideways. Submaximal and maximal “pretest” attempts were performed to minimize the “learning effect” ,20. The Participants performed a 45 s bout of foam rolling followed by a 15 s rest twice, alternating left and right sides over the following muscle areas: hamstrings, gluteus, quadriceps, pectoralis, and latissimus dorsi. A total of 20 min of self-massage was completed . The griParticipants performed a 45 s bout of static stretching followed by a 15 s rest twice, alternating left and right sides over the following muscle areas: hamstrings, gluteus, quadriceps, pectoralis, and latissimus dorsi. A total of 20 min of static stretching was completed. Participants stretched to the point of mild discomfort .Participants were instructed to sit quietly on a bench for 20 min.To ensure that the groups had enough homogeneity for comparisons, internal training load in arbitrary units was calculated through the session rating perceived exertion method (RPE × session duration in minutes) (sRPE) . PerceivData were entered into IBM SPSS Statistics 20 software for analysis . Normality tests were conducted on all the quantitative variables. The assumption of normality was not rejected through the Shapiro–Wilk test, and histogram analysis confirmed the normality of the data. A 3 × 3 (group × time) repeated-measures analysis of variance (ANOVA) with Bonferroni´s post hoc adjustment for multiple comparisons was used. Mean and standard deviation values were used to describe normally distributed data. For the qualitative variables , a Friedman test was conducted to assess differences between groups and treatments.p ≤ 0.05), and effect sizes were used for the interpretation of results. Cohen´s d was used for parametric data , and Cohen´s guidelines for “r” were also used for nonparametric data  [When Friedman´s test presented a statistical difference, pairwise comparisons adjusted for Bonferroni were automatically conducted. The median and interquartile range (IQR) was used to describe qualitative and nonparametric data. In addition, to confirm training load homogeneity, groups were considered independent of each other, and a Kruskal–Wallis test by rank was conducted after the typically assumed data was rejected . The lev is 0.5) .p = 0.431; chi-square = 1.684, df = 2 arbitrary units CONT = 182 (70); FR = 175 (92); SS = 154 (65). There was no significant difference in training load (sRPE) between groups, p = 0.043). Individuals allocated to the FR group had a worse affective response to the proposed training session, but this difference was not relevant to the point of causing a statistically significant difference between groups. Additionally, for the “SS” group, the Friedman test showed a significant difference between time-points (p = 0.049), but this difference was diluted following adjustment for pairwise comparisons. For the DOMS perception, a statistically significant difference was found between “pre” and “24 h” time-points for the “SS” group (ES: 0.18).Outcomes for FS, VAS, and TQR are presented as medians and interquartile ranges in t-test are presented as means and standard deviations in p = 0.420). The results of the ANOVA presented a significant effect of time (p = −0.001). Post hoc Bonferroni analysis showed statistically significant differences between the “pre” versus “post” (p = 0.002) and “pre” versus “24 h” (p = 0.005) time-points. Group * Time interaction showed differences for the ‘FR” group between the “post” versus “24 h” (p = 0.02 ES = 0.21), and for the “SS” group between the “pre” versus “post” (p = 0.003 ES = −0.38), and “post” versus “24 h” (p = 0.018 ES = 0.19) time-points. For the CMJ, no statistically significant differences were found between groups for any time-points (p = 0.755). The results of the ANOVA presented a significant effect of time (p = −0.001). Post hoc Bonferroni analysis showed statistically significant differences between the “pre” versus “post” (p = 0.001) and “post” versus “24 h” (p = 0.041) time-points. Outcomes for SR, CMJ, and COD p = 0.023 ES = 0.90), for the “SS” group between the “pre” versus “post” (p = 0.023 ES = 0.31), and “post” versus “24 h” (p = 0.007 ES = 0.22) time-points. For the COD-T test, no statistically significant differences were found between groups for any time-points (p = 0.887). The results of the ANOVA presented a significant effect of time (p = 0.005). Post hoc Bonferroni analysis showed statistically significant differences between the “post” versus “24 h” (p = 0.011) time-points. Group*Time interaction showed significant differences for the “SS” group between moments “post” versus “24 h” (p = 0.014 ES = 0.56) time-points.Group*Time interaction showed differences for the “CONT” group between the “pre” versus “post”  depends on various factors such as duration and intensity of the proposed exercise, muscle length, and muscle groups involved during the activity, which influences the extent of the initial muscle damage. Even though DOMS cannot be proposed as a direct marker of EIMD, it is considered a common symptom of muscle damage . However, the CONT group showed a greater magnitude of effect 24 h postintervention (ES = 0.51) versus both experimental groups . FR was shown to acutely worsen fatigue indices (less resistance to fatigue) when the pace was not controlled ,29, whicPrevious research observed an acute deleterious effect of SS on ankle force sense acuity. This may consequently impair motor balance and control, which is critical for the adequate performance of functional activities . In addit-test  that may be related to the presence of DOMS based on the visual analog scale (CONT = 0.43 FR = 0.34 SS = 0.41) [t-test may rely more heavily on important characteristics for linear speed as the stretch-shortening cycle [Twenty-four hours following testing, the three groups presented an impaired performance on the COD  = 0.41) . In addi = 0.41) ,36. The ng cycle ,36.The fact that the proposed HIFT session did not promote a deleterious effect on the ability to change direction and showed a possible improvement in performance in all three groups  may be explained by a motor-learning effect even though a submaximal and two maximal attempts were conducted aiming to minimize this event ,37. ThisThe authors highlight that the protocol proposed by MacDonald et al.  uses thePerceptual measures have been used to monitor training load in different sports settings ,41. In tDOMS seems to reach its peak 24–72 h postexercise. Considering that HIFT practitioners may perform the activity daily, a full investigation in a 72 h protocol is recommended. Regarding the use of SS, a recent meta-analysis has considered data as scarce, heterogeneous, and not supportive of existing guidelines that reinforce the use of static stretching as a recovery modality, making it difficult to support or contradict the use of SS as a postexercise recovery tool . Still, Preceding research classified HIFT sessions as a training concept derived from the combination of high-intensity interval training and traditional resistance training . CompareThe proposed HIFT session primarily consisted of upper body exercises, while the proposed neuromuscular tests were lower body. The recovery process involved several systems, and the fatigue imposed by the training session caused changes in several body structures, including the central nervous system (CNS). It is suggested that central fatigue promotes an inability of the CNS to optimally conduct the skeletal and muscular system, which highlights a more global approach to the recovery process . FurtherThe present study had some limitations. During HIFT sessions, participants were instructed to perform “as fast as possible” or “as many as possible”, and resting periods were not defined, which may have exposed participants to different amounts and variations in the physiological stimulus. However, the lack of rigorous control of training variables guarantees greater ecological validity and maintains the main characteristics of this type of training. Second, participants were not highly familiarized with the foam rolling technique, which may have imposed greater effort and discomfort since the pain threshold might be lower in certain individuals causing the pressure of foam rolling to be highly painful. Lastly, the sample was composed only of male participants with extensive HIFT experience, which prevents the extrapolation of results for novice and female individuals. In summary, the results of the present investigation indicate that the use of foam rolling and static stretching exercises may not be indicated when aiming to restore neuromuscular performance following a single bout of HIFT. However, the study shows that using the FR technique during the cooldown phase of a HIFT session may be useful for improving an individual´s perception of recovery, while using the SS may provoke a greater effective response. Therefore, considering the recovery from an acute HIFT session, neither of the proposed modalities seems more efficient than passive recovery as participants appeared to attain similar short-term recovery.


Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic disease of the liver, and the global prevalence of NAFLD among adults is estimated to be 23%–25%  Estes e. A recenLiver biopsy is the gold standard for diagnosing NAFLD. However, due to its invasiveness, potential bleeding risk, and large sampling error caused by the uneven distribution of liver parenchymal lesions, liver biopsy cannot be well applied in clinical practice . For theMachine learning is a branch of artificial intelligence that allows researchers to use complex data and develop self-trained strategies to predict the characteristics of new samples . The algIn our study, NAFLD and normal sample datasets were systematically retrieved and obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened out through the robust rank aggregation (RRA) method. To explore the DEG function and main metabolic and signal transduction pathways, we used functional enrichment and protein‒protein interaction (PPI) analysis. We modelled three machine learning models to obtain the diagnostic biomarkers . The prehttps://www.ncbi.nlm.nih.gov/geo/). The GSE135251 dataset contains 206 NAFLD samples and 10 normal samples (Homo sapiens).The messenger RNA (mRNA) expression matrix and the related clinical information of NAFLD and normal samples in the GSE135251 and GSE126848 datasets were obtained from the GEO database ( samples . The GSE samples . The seqp < 0.05. The robust rank aggregation (RRA) method can minimize the deviation and error between two datasets and combine them into independent datasets . The screening conditions were as follows: log2|FC| > 1, datasets . Therefohttps://www.string-db.org/), and then the PPI network was constructed by Cyto-scape (version 3.8.2), with a confidence interval of 0.4. At the same time, the m-code plug-in was used to find the key modules and DEGs in the PPI network by setting degree cut-off = 2, node score cut-off = 0.2, k-core = 2, max, depth = 100.The protein interaction among key DEGs was explored by the search tool for the retrieval of interacting genes/proteins database  and “ggplot2” R package (Version 3.3.3) were used to visualize the enrichment results.To further explore the targeted pathways and functions of key DEGs, the “cluster-Profiler” R package (Version 3.18.0) was used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Based on the expression levels of key DEGs and the grouping information of the samples, in which the sample grouping was used as the response variable and key DEGs were used as the explanatory variable, the “caret” R package (version 6.0-86) was used to build three models: RF, SVM, and GLM. Then, the explain function of the “dalex” R package (version 2.3.0) was used to interpret and analyse the three models, the plot function was used to visualize the performance distribution of the models, and a cumulative residual distribution map and box plot distribution map were drawn to obtain the optimal model. Moreover, the relative importance of different variables in different models for model prediction was analysed. The key DEGs that had a great influence on the predicted value of the response variable were selected as diagnostic markers.We further constructed a nomogram through the “rms” R package based on the diagnostic biomarkers to facilitate the clinical judgement of the risk of NAFLD. Then, a calibration curve was drawn to verify the nomogram. In addition, to more intuitively evaluate the clinical effect of the nomogram model, this study used the “rmda” R package to draw a decision curve analysis (DCA) curve and a clinical impact curve on the basis of the DCA curve.To study the difference in immune infiltration between patients with NAFLD and normal samples, the proportion of 22 immune cells in all samples in the GSE126848 dataset was calculated by the single-sample gene set enrichment analysis (ssGSEA) algorithm using the “GSVA” R package (version 1.38.2). Then, the difference in immune cells between normal and NAFLD samples was compared by the rank-sum test. Finally, the Pearson correlation between diagnostic genes and differential immune cells was analysed.https://dgidb.genome.wustl.edu/) to obtain potential drugs or molecular compounds that can treat NAFLD. Cytoscape (version 3.8.2) software was used to construct the relationship pair network between diagnostic markers and molecular compounds.Finally, we searched diagnostic biomarkers from the DGIdb . Total RNA was extracted from the peripheral blood of all samples with TRIzol reagent (cat. 356281) provided by the Ambion company. Then, a Nanodrop 2000fc-3100  was used to quantify the concentration and purity of the RNA solution. A sweScript RT I First-Strand cDNA Synthesis All-in-One™ First-Strand cDNA Synthesis Kit (CAT-G33330-50) provided by the Service-bio company was used for the reverse transcription reaction. PCR was performed using the 2x Universal Blue SYBR Green qPCR Master Mix (CAT.-G3326-05) kit provided by Service-bio. The PCR conditions were as follows: 95°C predenaturation for 1 min and then 40 cycles. Each cycle included denaturation at 95°C for 20 s, annealing at 55°C for 20 s, and extension at 72°C for 30 s. GAPDH was used as an internal reference for gene detection. The forward primer for GAPDH was “CCCATCACCATCTTCCAGG”. The reverse primer for GAPDH was “CATCACGCCACAGTTTCCC”. The forward primer for CEBPD was “GCCCCCGCCATGTAC”. The reverse primer for CEBPD was “GCCCGCCTTGTGATT”. The forward primer for H4C11 was “GCGGGGTGCTGAAGGTGTT”. The reverse primer for H4C11 was “GCTTGGCGTGCTCTGTATA”. The forward primer for CEBPB was “TGGGACCCAGCATGTCTC”. The reverse primer for CEBPB was “CAGTTCTTGCCCCCGTAG”. The forward primer for GATA3 was “CACCTCTTCACCTTCCCG”. The reverse primer for GATA3 was “TTGCCCCACAGTTCACAC”. The forward primer for KLF4 was “GAGGAGCCCAAGCCAAAG”. The reverse primer for KLF4 was “CAGCCGTCCCAGTCACAG”. A A total of 9,005 DEGs between NAFLD and normal samples, among which 47 genes were upregulated and 8,958 genes were downregulated in NAFLD samples, were screened in the GSE126848 dataset. In the GSE135251 dataset, 1,489 upregulated and 300 downregulated genes in NAFLD samples compared with normal samples were identified.The volcano plots of the DEGs are shown in To explore the interactions among the 147 key DEGs, a PPI network of 147 genes was constructed. After removing the discrete proteins, 87 nodes and 360 edges were obtained. Cytoscape was used to visualize the interactive relationship network, as shown in Next, we further explored the targeted pathways and functions of the 147 key DEGs. As shown in To further screen diagnostic markers from the genes in module 1, three machine algorithms were used to construct three models separately. The RF model was the most suitable model because it had the smallest sample residual . MoreoveTo better predict the risk of NAFLD by using CEBPD, H4C11, CEBPB, GATA3, and KLF4, a nomogram was constructed . The nomp < 0.05)  . Further < 0.05) .To explore potentially targeted therapeutic drugs that may be the most suitable for targeting diagnostic biomarkers, we retrieved 5 markers from the DGIdb database. Finally, we found two genes with related drugs. No drugs were found for the CEBPD, H4C11 and CEBPB genes. The potential therapeutic drugs predicted by GATA3 were pegaspargase, asparaginase, thioguanine, leucovorin, prednisone, mercaptopurine, cytarabine, vincrisine, daunorubicin, cyclophosphamide, dexamethasone, and methotrexate. The potential therapeutic drug predicted by KLF4 was APTO-253. The top three drugs were APTO-253, pegaspargase and asparaginase .To further verify the expression of biomarkers, we used qRT‒PCR to compare the gene expression levels of CEBPD, H4C11, CEBPB, KLF4, and GATA3 in the peripheral blood of normal controls and NAFLD patients. The qRT‒PCR results showed significant downregulation of the expression of CEBPD, H4C11, CEBPB, KLF4, and GATA3 in NAFLD patients .NAFLD is a liver disease associated with obesity, insulin resistance, type 2 diabetes mellitus (T2DM), hypertension, hyperlipidaemia, and metabolic syndrome . The patIn our study, the diagnostic biomarkers identified by transcriptomic analysis were differentially expressed in 13 types of immune cells. CEBPB, CEBPD, GATA3, KLF4, and H4C11 were the genes identified by our transcriptomics analysis of the NAFLD samples, which were primarily involved in immune cells and had been identified as a target by several disease studies.CEBPB and CEBPD are CCAAT/enhancer-binding protein beta and delta, important transcription factors regulating the expression of genes involved in immune and inflammatory responses. CEBPB and CEBPD have been confirmed to have transcriptional activity in the inflammatory response, and the current work showed that their downregulation was associated with the loss of immune-related signals . CEBPB a+ T-cells. It is also critical for the development, differentiation, and function of other CD4+ T-cell subsets, as well as CD8+ cells. GATA3 controls the function of both adaptive and innate immune cells. Recent findings conclude that although GATA3 allows Th17 cell differentiation, it acts as an inhibitor of Th17-mediated pathology, through IL-4-dependent and IL-4-independent pathways , which have been shown to increase the incidence of fatty liver disease during or after treatment. Studies have shown that some drugs activate PPARα, leading to lipolysis and fatty acid oxidation in adipose tissue and increasing the circulating fatty acid level and their transfer to the liver, resulting in disorders of PPARγ and ApoB, further insulin resistance and hepatic steatosis  Ben-Yak for the. In addiIn conclusion, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes can help us more deeply understand the pathogenesis of NAFLD.


As one of the important applications of Internet of Health Things (IoHT) technology in the field of healthcare, wireless body area network (WBAN) has been widely used in medical therapy, and it can not only monitor and record physiological information but also transmit the data collected by sensor devices to the server in time. However, due to the unreliability and vulnerability of wireless network communication, as well as the limited storage and computing resources of sensor nodes in WBAN, a lot of authentication protocols for WBAN have been devised. In 2021, Alzahrani et al. designed an anonymous medical monitoring protocol, which uses lightweight cryptographic primitives for WBAN. However, we find that their protocol is defenseless to off-line identity guessing attacks, known-key attacks, and stolen-verifier attacks and has no perfect forward secrecy. Therefore, a patient monitoring protocol for WBAN in IoHT is proposed. We use security proof under the random oracle model (ROM) and automatic verification tool ProVerif to demonstrate that our protocol is secure. According to comparisons with related protocols, our protocol can achieve both high computational efficiency and security. Wireless body area network (WBAN) exists as a transmission network for body monitoring. It has intellectual network appliances, such as personal wireless terminals, wearable devices, and wireless sensors. Individuals can use network devices to build personalized health networks based on WBAN, and they are substantial participants in the Internet of Health Things (IoHT) application. WBAN is widely used in patient monitoring, physiological parameter measurement, and so on. The measured data are transmitted by the sensor to the devices with a forwarding function in real time using wireless network transmission and then stored in the database of the remote server –3. UsingThe communication and interaction of WBAN are based on an open wireless channel, so it is inevitable to face a series of challenges. Attackers can eavesdrop, tamper, intercept publicly transmitted information, and use the obtained information to launch attacks and obtain patients' privacy. This poses a great threat to the medical IoHT and patient privacy , 6. In aIn recent years, a lot of anonymous medical key agreements have been proposed. An innovative dynamic ID-based key agreement in telecare medical information system (TMIS) was presented by Chen et al. . HoweverWu et al.  designedWe reviewed Alzahrani et al.'s  protocolA patient monitoring protocol is proposed to realize the security and lightweight requirements of WBAN systemsUsing the automated verification tool ProVerif and formal security proof in ROM, we demonstrate the proposed protocol is secureOur protocol is relatively pragmatic and secure by performance comparisonAccording to the summary of the existing literature –33, we fThe remaining section is constructed as follows: the system model and preliminaries are given in In this section, we present the system model and attack model. Concurrently, we describe the physically unclonable function (PUF).AR can capture messages transmitted via open channels and may eavesdrop, replace, replay, or intercept the data in these messagesAR can obtain verifier table stored in MS, but cannot obtain its secret keySNj and RN and then retrieve all data stored in their memoryAR can capture We adopt Dolev–Yao threat model  and assuPresuming the attacker (AR) maintains the following capacities:As a hardware security technology, a physically unclonable function (PUF) can be regarded as the “digital fingerprint” of the chip . It usesAll notations in our paper are illustrated in We briefly review Alzahrani et al.'s  anonymouKMS for MSSA generates a long-term master secret key KMSSubsequently, MS reserves the master secret key r, PR1, PR2, and an identity idj for the sensor node SNj and reserves tuple <idj, PR1, PR2> in the memory of MSSA selects three random integers xNj=r ⊕ KMS, yNj =  idj⊕hSA computes idj, xNj, yNj, PR1, PR2> in the memory of SNjSA reserves tuple <idj, PR1, PR2, idR>Finally, the verification table of MS is <SNj and MS are as follows:SNj creates a current timestamp T1 and computes the validation Vidj=h, where idj is SNj's identity, xNj=r ⊕ KMS, yNj=idj ⊕ h, PR2 denotes a random integer, and the current timestamp is denoted as T1.SNj submits Message1 tuple <xNj, yNj, Vidj, T1> to RN.idR and forwards the Message2 tuple <xNj, yNj, Vidj, T1, idR> to MS.RN appends its identity idR and finishes the session if no record is found in its memory. Otherwise, MS creates the current timestamp T2 and checks if |T2 − T1| ≤ ΔT, and if not, finishes the session. Otherwise, MS computes r∗=xNj ⊕ KMS, idj∗=yNj ⊕ h. MS checks the validity of the identity idj∗, if so, MS extracts the tuple <idj∗, PR1, PR2> from its memory, computes Vidj∗=h, and checks Vidj∗ ? = Vidj. If so, MS generates random nonce m and rnew and computes s=idj∗ ⊕ yNj, j=idj∗ ⊕ xNj, v=m ⊕ s, xNjnew=rnew ⊕ KMS, yNjnew=idj∗ ⊕ h, g=h, u=xNjnew ⊕ g, n=yNjnew ⊕ g, Δ=h, and the session key KSH=h. Afterwards, MS sends the Message3 tuple <v, u, Δ, n, idR> to RN. MS displaces PR1 with PR2 and PR2 with KSH.MS scans the identity idR and forwards the Message 4 tuple <v, u, Δ, n> to SNj.RN removes its identity SNj computes s∗=idj ⊕ yNj, m∗=v ⊕ s∗, j∗=idj ⊕ xNj, g∗=h, xNjnew+=u ⊕ g∗, yNjnew+=n ⊕ g∗, Δ∗=h. Afterwards, SNj checks Δ∗ ?=Δ. If so, SNj computes the session key KSH=h. SNj displaces xNj and yNj, with xNjnew+ and yNjnew+, and stores them in its memory. Finally, SNj displaces PR1 with PR2 and PR2 with KSH.The communications between SNj and MS. AR intercepts the first round of xNj−1, yNj−1, and the second round of xNj−2, yNj−2, where xNj−2 and yNj−2 are the first round of xNj−1new+ and yNj−1new+. AR computes Δ∗=h, where m∗=v ⊕ s∗, s∗=idj ⊕ yNj. Only idj in Δ∗ is unknown, and AR guesses idj to verify if Δ∗ ? = Δ. If so, AR obtains idj successfully. Otherwise, guesses idj again.Supposing an adversary (AR) can eavesdrop on the conversation between SNj will miss it. The insecurity is that MS has updated xNj, yNj, PR1, PR2, but SNj has not. This will make every subsequent authentication process between SNj and MS fail.If AR intercepts Message4 and drops it, the idj, PR1, PR2, idR> of MS is stolen, AR can obtain all the data in it. AR eavesdrops on the communication between SNj and MS, intercepts Message1 tuple <xNj, yNj, Vidj, T1>, Message 4 tuple <v, u, Δ, n>, computes s∗=idj ⊕ yNj, m∗=v ⊕ s∗, and j∗=idj ⊕ xNj, and computes the session key KSH=h. That is, AR can obtain the session key.If the verifier table <PR1−3 and PR2−3 of the third round. According to identity guessing attacks, AR obtains the SN's identity idj. In the third round of protocol execution, AR intercepts message 1 and message 4 and computes s∗=idj ⊕ yNj−3, m∗=v ⊕ s∗, g∗=h, xNj−3new+=u ⊕ g∗, yNj−3new+=n ⊕ g∗, KSH=h. Therefore, the session key of the subsequent round will be obtained by the AR.If the session keys of two consecutive rounds are leaked, AR will get KMS and short-term secret key PR1 and PR2 of the Alzahrani et al.'s  − 1|=|2 Pr [suc(A)] − 1|. 𝒫 is secure when Adv𝒫A < η, where η is sufficiently small.The random bit P, aP, and bP, computing abP is computationally infeasible in probabilistic polynomial time (PPT). P is the generator point, a, b ∈ Zp. Subsequently, the advantage of solving CDHP is AdvACDHP=Pr   , AdvACDHP < η.CDHP: the CDHP is specified that given A tends to break the proposed scheme 𝒫 in PPT. The queries Execute, Send, and Hash are executed qE, qS, and qH times, respectively. Query Test is allowed to be executed at most once. lh is the bit-length of the hash operation's the output. n=2lt, where lt is the average length of other transcripts. The advantage of breaking 𝒫 by A in PPT can be expressed as follows:Suppose the adversary 𝒫, we define various games Gamei(0 < i < 3). The event SuccessAi(0 < i < 3) corresponding to Gamei means that A completes his/her goal in Gamei.0: which simulates the real attack, at the first, the probability of A cracking 𝒫 isGame1: which simulates that A launches Execute and Test queries to verify the output according to the transcripts {Message1, Message2, Message3, Message4}. Among the transcripts, {A1, Δ, A3, T2} are related to the session key. However, A cannot figure out the relation between them the transcripts and the output of Test because of the random numbers. Therefore, we haveGame2: In this game, we simulate A computes the session key KSH through the messages transmitted openly. KSH=h, which is based on CDHP. The advantage of calculating KSH by A is AdvACDHP. Therefore, we haveGame3: This game simulates A performs Corrupt(INSSNi) to acquire the reserved information {idj, xNj, yNj, STj, Chaj, Tj} in SNj and try to calculate Δ∗=h to testify the KSH's correctness, where xNj∗new=μ ⊕ h, yNj∗new=λ ⊕ h, and MHj=h(PUF(Chaj)) ⊕ STj. A has to break PUF to obtain MHj. The probability of breaking PUF is AdvAPUF. Therefore, we haveGame4: which simulates Execute and Send queries are executed by A to launch the collision attacks. In line with the birthday paradox's definition, the possibility of a hash collision is qH2/2lh+1. Meanwhile, the collision probability of other transcripts is (qS+qE)2/2n. Hence, we haveGameTo simulate the attacks on r ∈ , the probability of guessing r is 1/2, which is equal to guessing the session key. That is,The random bit Combining  with 6)6), we go can be eTHS=0.068ms (millisecond), TEA=2.501ms, TSE=0.56ms [THS is hash operation, TEA represents ECC operation, and TSE is symmetric key encryption. As HS+3THS=8THS(0.544ms). Accordingly, the schemes [HS+4THS=10THS(0.544ms), and scheme [HS+5TEA+3TSE(14.525ms), and ours is 18THS+6TEA(16.230ms). Because our protocol is safer than others and achieves perfect forward secrecy, so ours achieve both high computational efficiency and security.We study and compare security and performance efficiency between ours with others. According to the comparison of the security attributes which are given in E=0.56ms , where T schemes , 30 bothd scheme  needs 5TAccording to , outputsWe first point out that Alzahrani et al.'s protocol can't defend against stolen-verifier attacks, desynchronization attacks, known-key attacks, and off-line identity guessing attacks and has no perfect forward secrecy. After that, we design a patient monitoring scheme based on ECC for WBAN in IoHT. We use verification tool ProVerif and formal security proof to demonstrate the security of our scheme. Through comparative analysis, our protocol is safer and more efficient to suit the lightweight and secrecy in medical scenarios. In the future, we will research more pragmatic and anonymous authentication protocol for more complex WBAN scenarios.


Modern society grew rapidly over the last few decades and this led to an alarming increase in air pollutants and a worsening of the human health, especially in relation to the respiratory system. Indeed, chronic respiratory diseases were the third main cause of death in 2017, with over 3 million of deaths. Furthermore, the pollution has considerable consequences both for burden medical expenses and environmental. However, the mechanisms linking pollutants to the onset of these diseases remain unclear. Thus, in this study we addressed this problem through the United Kingdom BioBank database, analyzing 170 genome-wide association studies (103 related to respiratory diseases and 67 related to pollutants). We analyzed the genetic correlations and causal relationships of these traits, leveraging the summary statistics and bioinformatics packages such as Linkage Disequilibrium Score Regression and Latent Causal Variable. We obtained 158 significant genetic correlations and subsequently we analyzed them through the Latent Causal Variable analysis, obtaining 20 significant causal relationships. The most significant were between "Workplace full of chemicals or other fumes: Sometimes" and “Condition that has ever been diagnosed by a doctor: Asthma” and between “Workplace very dusty: Sometimes” and “Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis”. Finally, we identified single nucleotide polymorphisms independently associated with sveral pollutants to analyze the genes and pathways that could be involved in the onset of the aforementioned respiratory system disorders and that could be useful clinical target. This study highlighted how crucial are the air condition of the working environments and the type of transport used in the onset of respiratory-related morbidity. Based on that, we also suggested some interventions, in order to improve quality life and develop new and eco-friendly society and life style, such as improving indoor air circulation, the use of public transport and urban reforestation. In 2017, over 544 million people in the world had a chronic respiratory disease, representing an increase of 39.8% compared with 1990, with the highest prevalence among both females and males, in high-income countries , nowadayIt is possible to categorize air pollutants into two large groups: i) gaseous and ii) particulate (accessed on 02 March 2022) , 9. i) ATo date, the genetic mechanisms shared between being exposed to pollutants and the onset of respiratory diseases are not completely clear and for this reason we investigated the possible relationships between these aspects. Due to the complexity of the genetic architecture of several respiratory diseases we used genome-wide association studies (GWAS), that investigate the connections between millions of single nucleotide polymorphisms (SNPs) present in the whole genome and a wide variety of phenotypes, in order to identify the most significant associations . SpecifiAfter the selection of SNPs based on Minor Allele Frequency (MAF) and minor allele count and after the munge procedure, we calculated the heritability and the z-score of heritability for each of 170 phenotypes relative to respiratory system disorders and air pollutants with the Linkage Disequilibrium Score regression package (LDSC). The overall SNP-based heritability for the pollutant phenotypes ranged from 0.0059 to 0.0443, whereas the corresponding heritabilities for the respiratory disorders ranged from 0.0075 to 0.1758. After selecting phenotypes with z-score heritability > 4, we obtained a list of 43 phenotypes out of 170 of interest (20 phenotypes related to respiratory system and 23 related to pollution). https://dhcw.nhs.wales/information-services/information-standards/clinical-classifications-and-terminology-standards/about-clinical-classifications-and-coding/icd-10/). The 20 pathological phenotypes are: “Age asthma diagnosed”, “Age asthma diagnosed by doctor”, “Age hay fever or allergic rhinitis diagnosed by doctor”, “Condition that has ever been diagnosed by a doctor: Asthma”, “Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis”, “Condition that has ever been diagnosed by a doctor: Hay fever allergic rhinitis or eczema”, “Condition that has ever been diagnosed by a doctor: None of the above”, “Breathing problems during period of job”, “Bring up phlegm or sputum or mucus on most days”, “Cough on most days”, “Diagnoses main (ICD10) J44: Other chronic obstructive pulmonary disease”, “Diagnoses main ICD10 R07: Pain in throat and chest”, “Diseases of the respiratory system”, “Doctor diagnosed asthma”, “Doctor diagnosed hay fever or allergic rhinitis”, “Medication related adverse effects (Asthma or COPD)”, “Self-reported: asthma”, “Self-reported: emphysema or chronic bronchitis”, “Self-reported: hay fever or allergic rhinitis”, “Other pulmonary diagnosis”. Afterward, we tested these 43 phenotypes through the LDSC package, in order to investigate the genetic correlations between each pollutant and the respiratory disorders. We obtained 158 statistically significant correlations with False Discovery Rate (FDR) < 0.05. These involve 21 pollutants such as: “A gas fire that you use regularly in winter time”, “An open solid fuel fire that you use regularly in winter time”, “Home area population density urban or rural England or Wales: Urban less sparse”, “Nitrogen dioxide air pollution 2010”, “Nitrogen oxides air pollution 2010”, “Particulate matter air pollution pm2 5 2010”, “Time spent driving”, “Transport type for commuting to job workplace: Car or motor vehicle”, “Transport type for commuting to job workplace: Cycle”, “Transport type for commuting to job workplace: Public transport”, “Transport type for commuting to job workplace: Walk”, “Types of transport used (excluding work): Car or motor vehicle”, “Types of transport used (excluding work): Cycle”, “Types of transport used (excluding work): Public transport”, “Types of transport used (excluding work): Walk”, “Worked with materials containing asbestos: Sometimes”, “Worked with paints thinners or glues: Sometimes”, “Workplace full of chemical or other fumes: Sometimes”, “Workplace had a lot of diesel exhaust: Sometimes”, “Workplace very dusty: Often”, “Workplace very dusty: Sometimes” and 20 pathological phenotypes . It has a genetic correlation = 0.2553, standard error (SE) = 0.1038 and FDR = 0.046.“Home area population density urban or rural: England or Wales: Urban less sparse” and 4 respiratory disorders , with genetic correlations from 0.2157, SE = 0.0658 and FDR = 0.005 (“Condition that has ever been diagnosed by a doctor: Asthma”), to 0.3506, SE = 0.1399 and FDR = 0.0422 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”).“Nitrogen dioxide air pollution 2010” and 1 disorder . It has a genetic correlation = 0.2927, SE = 0.1098 and FDR = 0.0292.“Nitrogen oxides air pollution 2010” and 2 pathological phenotypes  with respectively a genetic correlation = 0.3002, SE = 0.1132 and FDR = 0.0297 and genetic correlation = 0.3288, SE = 0.1033, FDR = 0.007.“Particulate matter air pollution (pm2.5) 2010” and 8 respiratory disorders  with genetic correlations from 0.134, SE = 0.0493 and FDR = 0.0256 (“Self-reported: asthma”), to 0.4063, SE = 0.1075 and FDR = 0.0012 (“Bring up phlegm or sputum or mucus on most days”).“Time spent driving” and 2 pathological phenotypes  with genetic correlation = 0.1707, SE = 0.0601 and FDR = 0.018 for both.“Transport type for commuting to job workplace: Car or motor vehicle”and 8 respiratory disorders  with genetic correlations from 0.1289, SE = 0.0485, FDR = 0.029625 (“Condition that has ever been diagnosed by a doctor: Asthma”), to 0.4572, SE = 0.1057, FDR = 0.00015 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”).“Transport type for commuting to job workplace: Public transport” and 3 respiratory disorders  with genetic correlation from 0.1567, SE = 0.0411, FDR = 0.0006 , to 0.236, SE = 0.0928, FDR = 0.0388 .“Transport type for commuting to job workplace: Walk” and 2 pathological phenotypes  with genetic correlation from 0.217, SE = 0.0546, FDR = 0.0005 , to 0.2714, SE = 0.0785, FDR = 0.002783505 .“Types of transport used (excluding work): Public transport” and 1 pathological phenotype  with genetic correlation = 0.1076, SE = 0.0314 and FDR = 0.0032.“Types of transport used (excluding work): Walk” and 2 respiratory disorders  with genetic correlation from 0.1743, SE = 0.0327, FDR = 2.334273e-06 , to 0.2101, SE = 0.047, FDR = 9.379565e-05 .“Worked with materials containing asbestos: Sometimes” and 7 respiratory disorders  with genetic correlations from 0.3198, SE = 0.1066 and FDR = 0.0125 (“Cough on most days”), to 0.4586, SE = 0.1405 and FDR = 0.0057 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”).“Worked with paints thinners or glues: Sometimes” and 10 pathological phenotypes  with genetic correlations values from 0.1413, SE = 0.0561, FDR = 0.0411 (“Condition that has ever been diagnosed by a doctor: Asthma”), to 0.5807, SE = 0.149, FDR = 0.0006 (“Bring up phlegm or sputum or mucus on most days”).“Workplace full of chemical or other fumes: Sometimes” and 11 respiratory disorders  with genetic correlations from 0.2111, SE = 0.0608, FDR = 0.0027 (“Self-reported: asthma”), to 0.667, SE = 0.1558, FDR = 0.0001 (“Bring up phlegm or sputum or mucus on most days”).“Workplace had a lot of diesel exhaust: Sometimes” and 13 pathological phenotypes ”, “Self-reported: asthma”, “Self-reported: emphysema or chronic bronchitis”, “Other pulmonary diagnosis” with genetic correlations from 0.1797, SE = 0.0659, FDR = 0.025 (“Self-reported: asthma”), to 0.5988, SE = 0.1464, FDR = 0.0003 (“Bring up phlegm or sputum or mucus on most days”).“Workplace very dusty: Often” and 13 pathological phenotypes , to 0.6129, SE = 0.1081, FDR = 4.542e-07 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”).“Workplace very dusty: Sometimes” and 13 respiratory disorders ”, “Self-reported: asthma”, “Self-reported: emphysema or chronic bronchitis”, “Other pulmonary diagnosis”) with genetic correlation from 0.2855, SE = 0.0783, FDR = 0.0018 (“Self-reported: asthma”), to 0.7579, SE = 0.1803, FDR = 0.0002 (“Bring up phlegm or sputum or mucus on most days”).We found a negative genetic correlation between:“An open solid fuel fire that you use regularly in winter time” and 10 pathological phenotypes  with genetic correlations from -0.4312, SE = 0.09 and FDR = 2.173e-05 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”), to -0.1701, SE = 0.0429, FDR = 0.0005 (“Self-reported: asthma”).“Time spent driving” and 1 inverse correlation . It has a genetic correlation = -0.1367, SE = 0.0427 and FDR = 0.007“Transport type for commuting to job workplace: Car or motor vehicle”and 2 inverse correlation  with respectively genetic correlation = -0.2791, SE = 0.1045, FDR = 0.029  and genetic correlation = -0.1807, SE = 0.0621, and FDR = 0.015.“Transport type for commuting to job workplace: Cycle” and 10 respiratory disorders  with genetic correlations from -0.4823, SE = 0.0608, FDR = 5.724e-13 (“Diagnoses main ICD10 R07: Pain in throat and chest”), to -0.1094, SE = 0.0415, FDR = 0.031 (“Self-reported: asthma”).“Transport type for commuting to job workplace: Public transport” and 6 respiratory disorders  with genetic correlations from -0.5054, SE = 0.0746 and FDR = 1.107e-09 , to -0.2801, SE = 0.0886 and FDR = 0.0079 (“Self-reported: emphysema or chronic bronchitis”).“Transport type for commuting to job workplace: Walk” and 5 respiratory disorders  with genetic correlations values from -0.3907, SE = 0.125 and FDR = 0.0087 (“Diagnoses main ICD10 J44: Other chronic obstructive pulmonary disease”), to -0.2472, SE = 0.0853, FDR = 0.01628571 (“Diagnoses main ICD10 R07: Pain in throat and chest”).“Types of transport used (excluding work): Car or motor vehicle” and 1 pathological phenotype (“Self-reported: emphysema or chronic bronchitis”) with a genetic correlation = -0.1992, SE = 0.0654, FDR = 0.0109.“Types of transport used (excluding work): Cycle” and 10 pathological phenotypes ”, “Self-reported: asthma”, “Self-reported: emphysema or chronic bronchitis”, “Other pulmonary diagnosis” with genetic correlations from -0.4286, SE = 0.0498, FDR = 4.36e-15 (“Diagnoses main ICD10 R07: Pain in throat and chest”), to -0.1296, SE = 0.0393 and FDR = 0.0052 (“Self-reported: asthma”).“Types of transport used (excluding work): Public transport” and 3 respiratory disorders  with genetic correlations values from -0.2324, SE = 0.0658 and FDR = 0.0023 (“Diseases of the respiratory system” and “Other pulmonary diagnosis”), to -0.1671, SE = 0.0483, FDR = 0.0027 (“Diagnoses main ICD10 R07: Pain in throat and chest”).“Types of transport used (excluding work): Walk” and 8 pathological phenotypes  with genetic correlation from -0.3274, SE = 0.046 and FDR = 1.498e-10 (“Diagnoses main ICD10 R07: Pain in throat and chest”), to -0.0853, SE = 0.0332, FDR = 0.036 (“Self-reported: asthma”).“Workplace very dusty: Sometimes” and 1 pathological phenotype (“Condition that has ever been diagnosed by a doctor: None of the above”) with genetic correlation = -0.1614, SE = 0.0647 and FDR = 0.0427.Below  we briefBased on these results, subsequently we selected the 158 summary statistics of the phenotypes with the most significant associations, and we submitted them to the Latent Causal Variable (LCV) package, in order to test whether the significant genetic correlation results were due to causal effects rather than shared genetic mechanisms. After the FDR correction, we observed that 20 causal relationships were statistically significant .We calculated genetic causality proportion (gcp) for each air pollutant and respiratory disorder.“Transport type for commuting to job workplace: Car or motor vehicle” had a nearly-full genetic causality (gpc > 0.60) with the onset of “Diseases of the respiratory system” and “Other pulmonary diagnosis”, with gcp = 0.77 and FDR = 7.03e-07 for both.The pollutant “Transport type for commuting to job workplace: Walk” has partial (gcp < 0.60), but significant genetic causality in the onset of “Self-reported: hay fever or allergic rhinitis”, with gcp = 0.56 and FDR = 4.00e-12.The pollutant “Worked with materials containing asbestos: Sometimes” showed a nearly-full genetic causality with the onset of “Cough on most days”, with a gcp = 0.66 and FDR = 0.0001.We observed that some pollutant phenotypes such as "Workplace full of chemicals or other fumes: Sometimes" have partial (gcp < 0.60), but significant genetic causality in the onset of some respiratory disorders .A nearly-full genetic causality was found between “Workplace very dusty: Sometimes” and in the onset of some respiratory disorders . Our results did not identify causal relationships between pollutants such as “Nitrogen dioxide air pollution 2010”, “Nitrogen oxides air pollution 2010” and “Particulate matter air pollution (pm2.5) 2010” and the onset of respiratory complications.Afterward, in order to investigate the genetic traits involved in the complex interactions between these phenotypes, we selected the summary statistics of 3 pollutants with a significant causal relationship  and we identified through PLINK software the SNPs index of each polluting agent. Subsequently, we submitted the list of SNPs index to Biomart and EnsDb.Hsapiens.v79, thus we could identify the variants and the genes that were being affected. As expected, most of the variants identified are related to intronic regions. For the pollutants “Transport type for commuting to job workplace: Car or motor vehicle”, “Workplace full of chemicals or other fumes: Sometimes” and “Workplace very dusty: Sometimes” we identified 23, 9 and 7 genes, respectively that we show below .Finally, we submitted the list of genes to Reactome software, in order to identify pathways involved and that could be clinical targets. Therefore, we obtained a list of pathways that we briefly report below , indicatHHAT, related to rs12410877, an intronic variant [For “Transport type for commuting to job workplace: Car or motor vehicle”  the most statistically significant pathway is the one involving "HHAT G278V non-palmitoyl Hh-Np," p value = 0.008, gene involved: hedgehog acyltransferase  [Related to "Workplace full of chemicals or other fumes: Sometimes"  the most statistically significant pathway involved is “NTRK2 activates RAC1”, p value = 0.004, gene involved: dedicator of cytokinesis 3 (variant) .REV1, related to rs115051693, an intronic variant) [Regarding “Workplace very dusty: Sometimes”  the most statistically significant pathway is “Translation synthesis by REV1”, p value = 0.010, gene involved: REV1 DNA directed polymerase (variant) .Previous GWAS, and observational studies, often use longitudinal approaches to assess the effects of pollutants on participants’ health, collecting questionnaires and medical assessments data and then cross-checking them with parameters like the annual community-level average of pollutants such as: ozone, nitrogen dioxide, and particulate matter air pollution (pm10), or particulate matter air pollution (pm2.5), over the period under study and in the geographic area of interest. Afterward, the results are further corroborated by statistical analyses, such as multilevel Poisson regression model, or Hierarchical multivariate regression model –22.However, one of the limitations of these studies is that the researchers often focus on investigating correlations that exist between the traits examined, or between the phenotypes of interest and the presence of specific SNPs, but not on causal effects. Through a computational approach, we addressed this topic, leveraging the summary statistics present in UKBB related to air pollutants and respiratory disorders and thus investigating the genetic correlations, the underlying causal relationships leading to the onset of these disorders and finally the genetic traits such as the most involved genes and pathways. The results obtained from the LDSC analysis in the present study are confirmed by the literature in relation to low health quality due to life in polluted environments . Indeed,Interestingly, our results suggest that the pollutants most widely recognized as being associated with the onset and exacerbation of respiratory diseases ) do not seem to have a causal effect on the aforementioned diseases. Indeed, our LCV findings show that “Transport type for commuting to job workplace: Car or motor vehicle” and the conditions of the work place  seem to be the main contributors to the onset of several disorders, such as “Diseases of the respiratory system”, “Other pulmonary diagnosis”, “Condition that has ever been diagnosed by a doctor: Asthma”, “Self-reported: asthma“, “Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis”, “Diagnoses main ICD10 R07: Pain in throat and chest”, respectively .Automobile exhaust is known to contain carcinogens that can lead to respiratory disorders, such as lung cancer . TherefoAmong the causality effects, the ones between "Workplace full of chemicals or other fumes: Sometimes" and “Condition that has ever been diagnosed by a doctor: Asthma” and between “Workplace very dusty: Sometimes” and “Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis” are the most statistically significant. These results are expected, indeed previously the National Institute for Occupational Safety and Health reported that between 2011 and 2016 nearly 17% of all adult onset asthma cases were related to the workplace. In addition, the median prevalence of work-exacerbated asthma was 22%, but some studies have suggested that this could be 58% . FurtherHHAT. However, the involvement of this pathway is expected, since its aberrations are related to several cancers [HHAT overexpression is often found in many tumors  [DOCK3 that mediates the activation of Rac family small GTPase 1 (RAC1) downstream of BDNF-induced signaling by neurotrophic receptor tyrosine kinase 2 (NTRK2) [DOCK3 has been shown to be involved in regulation of cytoskeletal organization, cell–cell interactions, and function as a guanine nucleotide exchange factor [DOCK3 is usually expressed in the brain. Although a previous study suggested that DOCK3 could have a role in lung cancer [REV1 gene. The role of this gene is unclear in human. However, it encodes a protein with similarity to the S. cerevisiae protein Rev1, which has a key role in protein-protein interactions. It is assumed that in humans the Rev1-like protein acts as a recruiter for DNA polymerases involved in the synthesis of damaged DNA transions [REV1 transcripts was related with a significant decrease in the diversity of carcinogen-induced lung cancers and complete suppression of tumor development in 27% of the carcinogen-exposed mice models [REV1 should be investigated more, related to respiratory disorders, in order to better understand its role and to obtain an efficient clinical target.Our results showed that the most statistically significant pathway related to “Transport type for commuting to job workplace: Car or motor vehicle”  is "HHAT G278V non-palmitoyl Hh-Np,"  morphogen pathway). This is known to have a central role in the embryonic development and stem cell preservation . An impo cancers . Indeed,rcinoma) . Therefo (NTRK2) . This pa (NTRK2) . Specifie factor , 32. DOCg cancer , to dateransions . This geransions . Althouge models . TherefoAccordingly, more focused efforts should be made to improve work and home environment air quality and the type of vehicle used to get to work. Based on this, the problem of pollution has to be addressed at indoor and outdoor level.At indoor level, in order to improve people’s health conditions, we suggest increasing the ventilation rate in the work places and houses. Indeed, previous studies have shown how an improvement in indoor air given by better ventilation increases productivity and prevents pathological conditions such as rhinitis and the risk of allergic symptoms , 35, 36.At outdoor level, we suggest using public transport to commuting to work, since we did not find a direct causal relationship between this kind of transport and the onset of respiratory diseases. In addition, in order to reduce the production of outdoor pollutants and prevent workers from driving on particularly polluted roads, future efforts could be aimed at encouraging the use of electric or low-emission cars.https://www.fao.org/forestry/urbanforestry/en/), to reduce pollutants concentration.In line with this scenario, our genetic correlation analysis showed a positive correlation between “Home area population density urban or rural England or Wales: Urban less sparse” and 4 respiratory disorders, suggesting urban reforestation  and several respiratory disorders, genetic causality analysis did not yield the same result. Consequently, it is assumed that the relationship between these pollutants and the occurrence of respiratory disorders is due to shared genetic mechanisms rather than a causal relationship , 38. FurIn conclusion, new strategies should be considered to prevent the onset of respiratory disorders and ease the burden of medical expenses that have increased in recent years precisely because of the increased concentration of pollutants . Althoughttp://www.nealelab.is/uk-biobank/, accessed on 4 February 2022). This database provides data for over 13 million SNPs, referring to a cohort of approximately 500000 participants of English European descent, aged between 40 and 69  [Our analyses started with data selection from the UKBB . This package is a tool that refers to GWAS summary statistics for estimating heritability, namely the proportion of phenotypic variance attributable to variance in genetic factors , and genAfterward, we performed the genetic correlation analysis between the selected phenotypes related to the pollution and the selected traits related to the respiratory diseases. This is a method that uses the linkage disequilibrium (LD) mechanism through the genome to estimate the distribution of effect sizes for each SNP as a function of their LD score, thus identifying the score and the kind of correlations between phenotypes. For the purpose of selecting only the most significant genetic correlation, we performed the p values correction by the FDR method, using the software Seed-based d Mapping, formerly "Signed Differential Mapping"  . AfterwaIn this step, we submitted the data of the most significant genetic correlations to the LCV package . This paAfterward, we focused on several representative pollutants with a significant causal relationship with respiratory diseases and extracted the list of their SNPs from each of their summary statistics. Subsequently, we submitted these lists to the PLINK v.1.90b package to remove the highly correlated SNPs and thus obtain the most descriptive SNPs. Indeed, variants may be in LD . For this purpose, we computed the LD-clumping analysis using the clumping procedure provided by PLINK v.1.90b  and usinFinally, in order to investigate the genes effected by the aforementioned SNPs index, we submitted the lists of these variants to two R packages: EnsDb.Hsapiens.v79   and BiomS1 TableAn optimal value of Lambda is between 0.95 and 1.05. If Lambda RG is lower than 0.95, it could be due a bias (in an underpowered dataset). Otherwise, if it is greater than 1.05 it could be for two reasons: the dataset does not follow a normal distribution or due polygenicity. The case of polygenicity can be identified through the intercept. Indeed, a value of intercept close to 1 justifies the polygenicity hypothesis. SE: standard error; GC: genetic correlation.(XLSX)Click here for additional data file.S2 TableThe genetic correlation expresses two aspects: the percentage with which pollutants and respiratory disorders are correlated and the type of correlation . GC (SE): genetic correlation standard error. FDR: false discovery rate p value.(XLSX)Click here for additional data file.S3 TableIt provides the complete list of the phenotypes analyzed in this work.(XLSX)Click here for additional data file.