The total distance covered was largely greater in national competitions (4506.7 ± 647.9 m) compared with international competitions (2190.3 ± 1950.5 m) (effect size (ES) = 1.2); however, the running pace did not present any significant difference between the international or national level (ES = 0.06). In regard to gender, the total distance covered was moderately greater in female competitions (4549.1 ± 758.6 m) compared with male competitions (3332.6 ± 1257.7 m) (ES = 0.9), and the running pace was largely greater in female competitions (110.5 ± 7.2 m∙min−1) compared with male competitions (78.4 ± 19.7 m∙min−1) (ES = 1.6). In relation to playing position, backs and wings covered a moderately greater total distance (ES = 0.7 and 0.6) and slightly more meters per minute (ES = 0.4 and 0.2) than pivots. Moreover, the technical activity profile differed between playing positions. Backs performed moderately more throws than pivots and wings (ES = 1.2 and 0.9), pivots exhibited largely more body contact than backs and wings, and wings performed moderately more fast breaks (6.7 ± 3.0) than backs (2.2 ± 2.3) (ES = 1.8). Therefore, this research study provides practical applications for handball coaches and strength and conditioning professionals with respect to designing and implementing more individualised training programmes to maximise performance and reduce injury risk.An understanding of physical demands during official competitions is essential to achieving the highest performance in handball. The aim of this systematic review was to summarise the available scientific evidence associated with physical demands during official competitions in elite handball according to playing positions, competition level and gender. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 17 studies were selected after a systematic search and selection process of three digital databases: PubMed, Web of Science and Sport Discus. The quality of the selected studies was evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology checklist; the average score was 18.47 points. The sample consisted of 1175 handball players, of whom 1042 were men (88.68%) and 133 were women (11.32%). The results show that an elite handball player covered on average 3664.4 ± 1121.6 m during a match. The average running pace was 84.8 ± 17.2 m∙min Handball is a professional and Olympic sport played by two teams of seven players  on a court of 40 × 20 m. The game is characterised by fast-paced offensive and defensive actions and frequent body contact, with the ultimate objective of scoring more goals than the opponent at the end of two 30 min periods ,2. ThereCoaches must optimise different factors to achieve the maximum performance  . Thus, kThe study of physical demands in handball historically has been carried out mainly via the analysis of the external and internal load ,6,7. TheTM, CatapultTM and KinexonTM) developed a local positioning system (LPS) with ultra-wideband technology (UWB)  ) have beScientific evidence on the sample characteristics (A), methods (B), external load  and internal load (D) is shown in n = 1042) were men and 11.32% (n = 133) were women. The mean age of the total sample was 25.54 ± 3.28. Regarding the competitive level, six studies were developed in international competitions (n = 901), ten studies were conducted in national leagues (n = 260) and one study was conducted in a youth elite competition (n = 14). Finally, nine studies reported the players’ competitive experience, and five studies no specified the playing positions. Only three studies reported information about body fat.In relation to the data records, 269 HR samples and 50 BLC samples were included, providing information about the players’ internal load. On the other hand, 3244 TMA records in 342 matches and 2480 LPS records in 121 matches were collected with the aim of understanding the players´ external load. Regarding the technology used, ten studies (58.82%) were developed with TMA, and six studies (35.29%) were developed with LPS.One of the main findings was that an elite handball player covered an average of 3664.4 ± 1121.6 m during a match. However, the results obtained for TDC were heterogeneous (range 1346.6 ± 3852.5 to 6943.9 ± 364.4 m). Moreover, in terms of competition level, TDC was largely greater in national competitions (4506.7 ± 647.9 m) compared with international competitions (2190.3 ± 1950.5 m) (ES = 1.2). In relation to the gender, TDC was moderately greater in female competitions (4549.1 ± 758.6 m) compared with male competitions (3332.6 ± 1257.7 m) (ES = 0.9) a,b.TDC differed according to playing positions. Backs (ES = 0.7) and wings (ES = 0.6) covered a moderately greater total distance than pivots .Regarding the locomotor categories, handball players spent more than 50% of total playing time walking or standing still; in contrast, they spent less than 10% of the total playing time running at high intensity and less than 5% for sprinting. Furthermore, in relation to gender, some studies ,43 indic−1 during a match. In terms of competition level, running pace did not present any significant difference between international and national levels (ES = 0.06). In relation to gender, the running pace was largely greater in female competitions (110.5 ± 7.2 m∙min−1) compared to male competitions (78.4 ± 19.7 m∙min−1) (ES = 1.6)  a,b.Running pace presented small differences between playing positions. Backs (ES = 0.4) and wings (ES = 0.2) covered slightly more meters per minute than pivots .−2). Wings, backs and pivots performed a similar number of accelerations and decelerations [Font et al.  found therations . Howevererations . Wings  than pivots and wings  . On the other hand, pivots exhibited largely more body contact  than backs and wings. Finally, wings and pivots performed moderately more fast breaks  than backs (2.2 ± 2.3)  .In addition, the number of actions per match presented some differences and similarities between male and female players. Male players performed largely more fast breaks (6.9 ± 3.3)  than femThe quality analysis (‘STROBE’ checklist) yielded the following results : (a) then = 1175), TMA records (n = 3244) and LPS register (n = 2480), as well as a strict evaluation of the published data. However, the final sample comprised a small number of female players (n = 133), and no studies were carried out at the Olympic Games. On the other hand, a reduced number of HR (n = 269) and BLC (n = 50) samples were collected.To our knowledge, the present study represents the most comprehensive and exhaustive systematic review for analysing physical demands during official competitions in elite male and female handball. The main strengths of this research lie in the qualities of external validity and generalisability of the results from the analysis of a large sample of handball players (n = 14), and 3 were categorized as ‘high quality’ (n = 3) according to the STROBE checklist.Furthermore, from a methodological perspective, this systematic review presents a robust design based on the search process carried out in three databases and limited to unambiguous inclusion and exclusion criteria. Moreover, 14 of the 17 included studies were categorised as ‘very high quality’  compared with international competitions (2190.3 ± 1950.5 m). These results may indicate a larger use of rotations and thus less playing time in national team players, because this type of competition has a high density of games in a relatively short period of time . In rega−1 during a match. In regard to the phase of the game, Manchado et al. [−1 and 96.53 ± 22.57 m∙min−1, respectively) than in defence  . This fact could indicate that specialist players (offense or defence) should have a different training programme and should focus on developing the ability to accelerate and to decelerate in short distances, to move in limited spaces and to bear heavy body contact [The running pace was measured only in 5 of the 17 studies included in this systematic review, although this variable is especially relevant in sports with multiple substitutions such as handball, because each player spends a different amount of time on the court. Thus, we should normalise the distance covered according to the time the players spend on the court to obtain a true reflection of real demands ,11,50. To et al.  indicate contact .−1) [−1) [−1) compared with male competitions (78.4 ± 19.7 m∙min−1). Finally, the running pace presented small differences between playing positions. For example, Font et al. [−1 and 115.3 ± 6.2 m∙min−1, respectively) and backs  compared with pivots . Likewise, Cardinale et al. [−1) and wings (79.7 ± 9.7 m∙min−1) compared with backs (77.9 ± 10.7 m∙min−1) and pivots (74.0 ± 9.8 m∙min−1). On the other hand, Manchado et al. [−1) and pivots (91.17 ± 42.67 m∙min−1) compared with backs (88.7 ± 32.8 m∙min−1) and wings (85.1 ± 32.9 m∙min−1).In terms of competition level, the running pace did not present any significant difference between the international or national levels. However, competition level affects running pace in men’s competitions. International men’s competitions had a higher running pace (84.1 ± 23.3 m∙min−1) ,11,12 th−1) [−1) . This dit et al.  and Belkt et al.  found moSeveral studies ,43,47,48In regard to gender, Michalsik et al. ,43 found−2). Therefore, the ability to accelerate, decelerate and change direction properly is mandatory for successful performance in handball [Data regarding accelerations and decelerations during the handball matches were scarce; specifically, in our systematic review, only two studies provided information about accelerometery. Font et al.  found thhandball . Accordihandball ,61.Font et al.  indicateAs with accelerometery, PlayerLoad data were scarce. We found only four studies that provided information about PlayerLoad. All of the studies indicated that PlayerLoad was similar for wings, backs and pivots ,15,41, eThe halves of the match had an impact on external load outcomes. Specifically, the time spent in high-intensity running during the match decreased in the second half ,43,47. MIn this line, it is well documented that exercise intensity decreases from the first to the second half of the match, suggesting that neuromuscular fatigue, muscle damage and inflammatory responses may occur during the game ,47,50,67Finally, only two studies indicated that there were no differences between top-ranked and lower-ranked teams ,12 or beIt is known that internal load can provide important information about energy systems’ contribution during the game. Accordingly, Povoas et al.  indicate−1 during a game. Moreover, some studies [On the other side, the few existing studies on the contribution of aerobic system in handball were based on HR estimations ,47,48,49Playing position and gender had an impact on the HR response. We found that goalkeepers have the lowest HR values during a game (from 70 ± 11.0% to 78.4 ± 5.9% Hrmax) compared with the other positions . On the n = 269) and BLC (n = 50) samples were included in this systematic review. Therefore, more research is needed in this regard.Despite the importance of the internal load data, we found only seven studies that provided information about that. For this reason, a reduced number of HR  or sprint interval training (SIT), and game-based intermittent endurance exercises, such as small-sided games (SSG) ,74. MoreOn the other hand, backs perform more throws than wings and pivots; therefore, coaches should develop injury prevention programmes to protect the shoulder against overuse injuries, specifically rotator cuff muscle and tendon injuries ,75,76. MFinally, pivots receive and give up more contacts (tackles and screenings). This fact suggests that they need more muscle hypertrophy and maximal strength than wings or backs ,66. More−1) [−1) [In regard to gender, male players performed largely more fast breaks (6.9 ± 3.3) than female players (5.2 ± 2.8) ,46. In t−1)  compared−1) [−1) ,11,12,15n = 133). (ii) A reduced number of HR and BLC samples were collected. (iii) The external load was analysed with different methodologies and technology (TMA or LPS). (iv) A lack of clarification and consensus across different researchers and manufacturers´ tracking systems on which the standardise speed ranges or thresholds for locomotive categories  are based upon may make it difficult to generalise the findings to other populations. (v) PlayerLoad data differed depending on the device used, because each trademark uses a different algorithm to calculate this variable. Moreover, PlayerLoad calculation methods present many inconsistencies and a lack of clear and complete information. This fact represents a disadvantage for comparing and generalising the scarce existing data in handball. (vi) The is a lack of information about the management of the players´ rotation by coaches, which is directly related to the physical load of the player. (vii) The physical demands analysis is based mainly on player displacements, but some technical actions, such as blocks or screenings, represent a high load and do not produce a movement or an acceleration. Therefore, some tracking systems do not allow registering the load produced by these types of actions.This systematic review has some limitations: (i) The final sample was composed of a small number of female players  individualise training programmes according to playing position, gender and competitive level by handball coaches and strength and conditioning professionals; (ii) improve the players’ strength  and endurance to repeat high-intensity actions during the entire match; (iii) design and implement injury prevention programmes according to the technical activity profile of each playing position ; (iv) incorporate different type of interventions to prevent ACL injuries : strength training, landing and COD technique training with disturbances and opposition, deceleration training and endurance training to minimise the appearance of fatigue and core training; (v) develop different players´ rotation strategies to avoid excessive physiological loading and prevent fatigue during the match; and (vi) incorporate tracking systems to control and prescribe physical load during training practice and competition.Further studies will be needed to deepen our knowledge in the following topics: (i) physical demands in elite female handball; (ii) physical demands of the goalkeeper; (iii) physical demands during the most demanding scenarios or passages of handball games, because the average values could underestimate the real physical demands; (iv) relationship between technical–tactical situations and physical demands; (v) effects of contextual factors  on external load; and (vi) create physical profiles according to playing positions, gender and competition level to improve talent identification and development systems. Finally, it would be helpful to standardise LPS in order to have a valid and reliable technology to track players’ performance.This systematic review confirms the differences in relation to physical demands depending on playing positions, competition level and gender. First, competition level affected the total distance covered; specifically, we found that the total distance covered was largely greater in national competitions compared with international competitions; however, the running pace did not present any significant difference between the international and national levels. Regarding gender, the total distance covered was moderately greater in female competitions compared with male competitions, and the running pace was largely greater in female competitions compared with male competitions. In relation to playing position, backs and wings covered a moderately greater total distance and slightly more meters per minute than pivots. Therefore, this research provides practical applications for handball coaches and strength and conditioning professionals in designing and implementing more individualised training programmes to maximise performance and reduce injury risks.


Hepatocellular carcinoma (HCC) represents the third cause of cancer-related death in the world, and identification of new prognostic factors and/or therapeutic targets remains a major issue. Recently, immune checkpoint inhibitors (ICIs) have opened promising therapeutic options for this disease. Using a large series of transcriptomic data of clinically annotated HCC samples, we provide evidence that the TIGIT/DNAM-1 axis, and notably the PVRIG molecule, might be an interesting therapeutic candidate for HCC tumors.PVRIG, and analyzed the clinical characteristics, the disease-free and overall survivals, and biological pathways associated with PVRIG High tumors. Among all members of the TIGIT/DNAM-1 axis, PVRIG expression was higher in tumors than in normal liver, was heterogeneous across tumors, and was the only member with independent prognostic value for better survival. PVRIG High tumors were characterized by a higher lymphocytic infiltrate and enriched for signatures associated with tertiary lymphoid structures and better anti-tumor immune response. These results suggest that patients with PVRIG High tumors might be good candidates for immune therapy involving ICIs, notably ICIs targeting the TIGIT/DNAM-1 axis. Further functional and clinical validation is urgently required.Hepatocellular carcinoma (HCC) is a frequent and deadly cancer in need of new treatments. Immunotherapy has shown promising results in several solid tumors. The TIGIT/DNAM-1 axis gathers targets for new immune checkpoint inhibitors (ICIs). Here, we aimed at highlighting the potential of this axis as a new therapeutic option for HCC. For this, we built a large transcriptomic database of 683 HCC samples, clinically annotated, and 319 normal liver tissues. We interrogated this database for the transcriptomic expression of each member of the TIGIT/DNAM-1 axis and tested their prognostic value for survival. We then focused on the most discriminant one for these criteria, i.e.,  Hepatocellular carcinoma (HCC) represents the third cause of cancer-related death in the world ,2,3. ItsImmunotherapies are part of these new therapeutic options in solid tumors . They ma+ T cells, Natural killer (NK) cells, and γδ T cells), and four ligands, namely NECTIN-1 (PVRL1/CD111), NECTIN-2 (PVRL2/CD112), NECTIN-3 (PVRL3/CD113), and PVR (CD155), which can be expressed on antigen-presenting cells or tumor cells ) and the 5-year OS was 52.7% (95% CI [47.8–58]). The median OS was 80.7 months (range [0–120.7]) in the PVRIG High group and 53.3 months (range [0–114.3]) in the PVRIG Low group (p < 0.001) with a higher 5-year OS rate in the PVRIG High group than in the PVRIG Low group (p = 1.2 × 10−4)  B.PVRIG groups (p = 1.4 × 10−4), the TNM/UICC classification (p = 1.5 × 10−12), HBV infection status (p = 1.5 × 10−5), and the Hoshida’s subtypes classification (p = 4.2 × 10−4)   . In mult × 10−4) .PVRIG in HCC pathogenesis, we applied a supervised analysis to search for the genes differentially expressed between the PVRIG Low and PVRIG High groups. We first worked with a learning set  and Low (n = 121) groups). The supervised analysis identified 181 differentially expressed genes, including 15 genes downregulated and 166 genes upregulated in the PVRIG High group , a bile acid biosynthetic process (p = 3.6 × 10−2), and a tricarboxylic acid cycle (p = 4.9 × 10−2). This number of genes was however very small, and these data should be interpreted with caution. Inversely, the functional enrichment analysis of the 166 genes upregulated in PVRIG High tumors revealed pathways related to an immune response (p = 1.6 × 10−35), a T cell co-stimulation (p = 1.1 × 10−13), an inflammatory response (p = 5.1 × 10−13), a chemokine-mediated signaling (p = 6.7 × 10−13), a signal transduction (p = 3.1 × 10−12), and a defense in response to virus (p = 3.6 × 10−10)  . Immune PVRIG High tumors, we determined if there was a difference in the quantity of immune or stromal cells infiltrating PVRIG Low and PVRIG High tumors. For this we used the TILs score tool, which provides not only scores for tumor cells’ purity but also for stromal, lymphoid, and myeloid cells’ infiltration within the analyzed tumor. PVRIG High samples showed the highest score for lymphoid cells, followed by myeloid cells and stromal infiltrates . No difference was observed between PVRIG Low and PVRIG High tumors regarding tumor cells. This identified PVRIG High tumors as being more infiltrated by immune cells, notably lymphocytes, than PVRIG Low tumors . The top fifteen subsets infiltrating PVRIG High tumors were the lymphocytes , the dendritic cells  and the macrophages (p = 4.5 × 10−12). Notably, PVRIG High tumors showed a higher infiltration with cytotoxic cell subsets   . This prp = 9.8 × 10−27) [p = 1.9 × 10−26) [p = 1.2 × 10−25) [p = 7.7 × 10−25) [PVRIG High tumors. All these scores/signatures are biologically and clinically relevant of cytotoxic activity from adaptive immune effector cells [PVRIG High tumors might be good responders to ICIs, thanks to pre-infiltrated primed effectors cells. Of note, the antigen APM score [p = 2.6 × 10−6) and in favor of a mounted acquired anti-tumor or anti-viral immune response, but this signature was not among the top functional features associated with PVRIG High tumors  , TIS (p × 10−26) , TLS sig× 10−25) , and imm× 10−25)  in PVRIGor cells ,33,34,42or cells ,44,45. APM score  was alsoh tumors .PVRIG High tumors  were detected [IFNG, IL12A and IL12B, IL18, TNF, GNLY, KLRC2, KLRC3,…) suggested that the cytotoxic potential of infiltrated immune cells was not totally unleashed despite expression of immune cell activation markers   .PVRIG High tumors were enriched with both CAF-S1 and CAF-S4 subsets  [PVRIG High tumors. The iCAF subset was slightly predominant, especially the IFNy-iCAF (p = 6.3 × 10−21), the IL-iCAF (p = 6.3 × 10−15), and the detox-iCAF (p = 5.8 × 10−11) . These subsets are pro-tumoral cells  . Similar× 10−11) . These sal cells D.PVRIG High tumors, concomitant with a mixed anti-tumoral iCAF-like and pro-tumoral myCAF-like stroma.Altogether, these results suggest that cytotoxic immune cells are infiltrating PVRIG is an independent biomarker predictive for favorable clinical outcome. PVRIG expression was lower in HCC samples than in normal hepatic tissues. Its expression was heterogeneous across tumors and allowed us to search for correlations between PVRIG expression levels and clinicopathological variables. Using PVRIG median as a cutoff enabled us to interrogate our database, avoiding time-consuming immunohistochemistry development and analyses, while providing opportunities to work on a relatively large series of samples. We were then able to search for association with the expression of other genes and multigene signatures to investigate pathways associated with PVRIG gene expression levels more in depth.Based on the gene expression analysis of the TIGIT/DNAM-1 axis in a large cohort of HCC clinical samples, we showed that PVRIG expression as a new potential target for immune therapy in HCC. PVRIG High expression has indeed been associated with improved DFS and OS in HCC patients [PVRIG was the most important variable [To our knowledge, this is the third study highlighting patients , and an variable .+ T cell and NK cells [PVRIG is an immune checkpoint receptor with inhibitory function for cytotoxic CD8NK cells ,51. In mNK cells .PVRIG expression and immune variables has never been explored further, and this is the first report characterizing the tumor immune microenvironment and biological pathways in PVRIG High tumors. The HBV and HCV infection status being equally distributed in PVRIG High or Low tumors, the background immune response due to the viral infection can be considered as equivalent. Compared to PVRIG Low tumors, several immune variables were enriched in PVRIG High tumors and suggested: (i) higher infiltration of immune cells; (ii) higher infiltration of both adaptive and innate immune cells, notably B cells, γδ T cells, T cells with a Th1 profile, cytotoxic CD8+ T cells, and activated NK CD56dim cells; and (iii) higher cytotoxic activity. Altogether, these results suggested that PVRIG High tumors have a higher potential for anti-tumor immune response than PVRIG Low tumors. Nonetheless, despite this apparent anti-tumoral environment, the local immune cells did not prevent HCC occurrence and progression. The high expression of NECTIN-2, the ligand of PVRIG, which has also been reported by others in HCC tumors [PVRIG High tumors was also in line with the hypothesis of a poor anti-tumoral response.To our knowledge, the correlation between C tumors , might bC tumors ,54,55. TPVRIG High expression, which can trigger a strong immunosuppressive signal, with better disease-free and overall survivals. We and others have previously reported similar results with expression of PD-L1 [IDO1 inhibitory molecules [IDO1, and we did not detect strong IFN-γ production either, but there was evidence for general IFN and cytotoxic immune responses. One hypothesis might be that the PVRIG/NECTIN-2 axis is a new negative feedback loop, either proper to the tumor liver microenvironment or associated with a specific context of immune response. Indeed, the TIGIT-DNAM-1 axis, with all its receptors and ligands expressed in normal tissues, is much more complex than the PD-1/PD-L1 axis.An apparent contradiction in our results lies in the association of of PD-L1 ,56,57 anolecules . In thesolecules . Here, wPVRIG High tumors. They receive an activation signal and become activated, but they also receive negative signals from PVRIG, which is strongly engaged with NECTIN-2, which might limit anti-tumor activation. In these tumors, precluding the PVRIG/NECTIN-2 interaction might unleash a cytotoxic anti-tumor immune response  its originality, (ii) a relatively large size of series, and (iii) the biological and clinical relevance of PVRIG expression. It also includes a few limitations: (i) its retrospective nature and associated biases, and (ii) the mRNA assessment, rather than protein, analysis on bulk tissue samples. Of course, analyses in larger series are warranted to confirm our observation, as well as functional analyses on HCC preclinical models to validate PVRIG’s role in HCC progression. However, and considering the inhibitory role of PVRIG, our results highlight the potential value of anti-PVRIG immunotherapies to enhance the anti-tumoral immune local response in HCC. Our data suggest that the microenvironment of PVRIG High samples  might be more favorable for ICIs efficiency than that of PVRIG Low samples. However, the link between the target expression level and tumor response to ICIs remains unclear in many cancers. For example, in the CheckMate-040 trial dedicated to patients with HCC [PVRIG expression and to test if it can predict the therapeutic response. Overall, our study also highlights the complexity behind immune checkpoints function, revealing important roles for other potential candidates, such as here, with the members of the TIGIT/DNAM-1 axis.We showed that with HCC ,19, the


Mines Safety and Inspection Regulations 1995  criteria, L ˃ 5 µm and D ≤ 1 µm. The majority of the countable particles were consistent with α-spodumene cleavage fragments. These results suggest elongated α-spodumene particles may pose a health risk. It is recommended the precautionary principle be applied to respirable α-spodumene particles and the identification and control of dust hazards in spodumene extraction, handling and processing industries be implemented.Health risks from exposure to lithium-bearing spodumene cleavage fragments are unknown. While asbestiform fibres can lead to fibrosis, mesothelioma and lung cancer, controversy remains whether non-asbestiform cleavage fragments, having equivalent dimensions, elicit similar pathologic responses. The mineralogy of respirable particles from two alpha (α)-spodumene concentrate grades  were characterised using semi-quantitative X-ray diffraction (XRD). Particles were measured using scanning electron microscopy (SEM) and the dimensions  applied to regulatory counting criteria for asbestiform fibres. Application of the current World Health Organization (WHO) and National Occupational Health and Safety Commission (NOHSC) counting criteria, L ˃ 5 µm, D ˂ 3 µm, AR ˃ 3:1, to 10 SEM images of each grade identified 47 countable particles in the chemical and 37 in the technical concentrate test samples. Of these particles, 17 and 16 in the chemical and technical test samples, respectively, satisfied the more rigorous, previously used  Lithium, the lightest metal, is vital in the modern world due to its unique physical and chemical properties and high electrochemical reactivity . With di2O6) occurs in coarse-grained pegmatite deposits and co-exists with quartz, feldspar, and micas [2O]) ) ,13. Spodas [2O])  with Ausas [2O]) .Spodumene is a complex single-chain aluminium silicate and a member of the pyroxene group . In natuMechanical pressure applied to α-spodumene throughout comminution causes the crystal to fracture along the preferential side {110} and basal {001} planes of weakest bond strength ,17,18. ACleavage fragments differ from asbestos fibres. Asbestos is the commercial term applied to the six regulated fibrous asbestiform minerals of chrysotile, crocidolite, amosite, anthophyllite asbestos, tremolite asbestos, actinolite asbestos . AsbestiIn contrast, cleavage fragments have a multidirectional crystalline growth pattern  and are The physical dimensions, length (L), diameter (D) and aspect ratio (AR) (AR: L/D) of mineral fibres is a critical factor in pulmonary toxicity, pathogenicity, carcinogenicity, and inflammation ,25,26,27Despite more recent research recognising other determinants including size and shape, chemical composition, surface chemistry and surface charge, and durability in the lung (biopersistence) all influencing the toxicity of mineral fibres ,30, geomIndeed, the concentration of fibres that fulfil specific dimensional metrics is used as the basis for regulatory threshold levels for asbestiform fibre exposure . For regMines Safety and Inspection Regulations 1995 (WA) (s.9.33.3) [Work Health and Safety (Mines) Regulations 2022 (WA) [Mines Safety and Inspection Regulations 1995 (WA) remain beneficial for the proper assessment of any health risks associated with exposure to respirable mineral particles [Many authors propose more rigorous dimensional counting criteria be implemented to better discriminate asbestiform fibres from potentially non-toxic cleavage fragments ,36,37. V.9.33.3) . However022 (WA) . NeverthWhile the causal relationship between inhalation exposure to asbestiform fibres and adverse health outcomes is well documented, controversy still remains as to whether non-asbestiform elongated mineral particles (EMP) or cleavage fragments, having equivalent dimensions, elicit a similar pathologic response . There iWith the rapid growth in global demand for lithium and lithium-rich compounds over the last decade lithium recovery from spodumene has garnered much interest from industry . SeveralThere is no data available in published literature on the potential toxicity or pathogenicity of α-spodumene particles in vitro or in exposed humans or experimental animals. In addition, α-spodumene is insoluble in water and dilute acids . DissoluMines Safety and Inspection Regulations 1995 (WA) [This study aims to determine the dimensional characteristics  of cleavage fragments generated from typical comminution (crushing/grinding) processes and apply regulatory counting criteria in accordance with WHO (1997) and NOHSC (2005) ,34, L ˃ 995 (WA) . Geometr995 (WA) ,27, path995 (WA)  and infl995 (WA) ,54 of miTwo types of test materials, chemical and technical grade α-spodumene concentrate, were collected from a spodumene mining and processing facility in WA, Australia. The α-spodumene ore was recovered from a single pegmatite deposit.Approximately 1.5 kilograms (kg) of each concentrate grade was collected directly from their respective processing conveyors following comminution and beneficiation, to remove major impurities, also known as gangue materials. Both grades of crushed α-spodumene concentrate test samples were observed as white to grey in colour and resembled damp, fine sand. The concentrates had a high water content due to the wet comminution and heavy liquid separation and floatation techniques used for beneficiation.2O content found across and within spodumene deposits which may influence cleavage fragment morphology.The two test samples were selected as being representative of relatively high-grade α-spodumene concentrate accompanied by typical gangue materials. The two sample grades were selected for the variance in Li2O content of approximately 6.0% and a maximum ferric oxide (Fe2O3) content of 1.0%. Chemical grade concentrate is mainly used in the manufacture of lithium-ion batteries, lubricants, refrigeration, metal alloys, optical glass, pharmaceuticals and polymers. In contrast, technical grade α-spodumene concentrate typically has a higher purity of Li2O (˃7.0%), significantly lower Fe2O3 content and a lesser number of gangue materials. Technical grade concentrate finds use as an additive in tableware, container and specialty glass products. Both the chemical and technical grade α-spodumene concentrate test samples were treated separately.In general, chemical grade α-spodumene concentrate has an average LiTo analyse the composition and identify the concentration of α-spodumene in the test materials a 2.7 grams (g) sub-sample was removed from each grade and micronised in a McCrone Micronising Mill. Sub-samples were subjected to light grinding such that 90% were sieved to pass ≤ 20 µm and dried at 40 °C overnight. The mineralogy was determined using semi-quantitative X-ray diffraction (XRD) analysis with a corundum spike. XRD data were processed using Eva 4.3 search match software. Crystalline mineral compounds were calculated using the normalised reference intensity ratio (RIR) method at a wavelength (λ) of 1.78901 Angstrom (Å). It should be noted that XRD scan only gives peaks for crystalline materials while amorphous material adds to the background.In preparation for undertaking SEM analysis, 10 g of each spodumene grade were passed through horizontal elutriation to extract respirable particles with an EAD ≤ 4 µm. A small portion of the elutriated particles was removed from each grade and mounted on top of separate double-sided carbon tabs. Samples were not carbon coated. Carl Zeiss EVO50 scanning electron microscope (SEM) fitted with Oxford INCA X-Max energy dispersive spectrometer (EDS) was used to identify EMP. SEM operating conditions were 20 kilo electron volts (KeV) working at a magnification of ×2000. Backscattered electron images (BEI) were captured, and each image frame individually examined. Every countable particle in the images was manually measured using the digital calipers in the Oxford INCA software to determine length and diameter. The AR of the countable particles was then calculated.Guidance Note on the Membrane Filter Method for Estimating Airborne Asbestos Fibres, particles with dimensions that satisfy counting criteria by WHO (1997) and NOHSC (2005), L ˃ 5 µm, D ˂ 3 µm, AR ˃ 3:1, and the previously used counting criteria stipulated by Mines Safety and Inspection Regulations 1995 (WA), L ˃ 5 µm and D ≤ 1 µm, were determined.Following NOHSC:3003 (2005) The mineralogy of crystalline particles identified in the test materials by XRD results are summarised in Due to low concentrations full characterisation of the amphibole and sodium magnesium aluminium oxide present in both concentrate grades could not be determined with certainty. Generic amphibole and sodium magnesium aluminium oxide XRD patterns were selected to complete the minerology.Spodumene was confirmed as the most abundant mineral in the chemical grade concentrate accounting for 42%, followed by quartz at a significantly lower concentration at 14%. Both spodumene and quartz observed good ICDD match probability. The remainder of the chemical grade concentrate sample was completed by nontronite, feldspars , mica (muscovite), clay minerals (clinochlore and kaolinite) and holmquistite and beryl in medium, low, or trace accessory phases.In the technical grade sample, nontronite was the most dominate mineral phase at 52%, followed by spodumene and quartz at 25% and 12%, respectively. The remainder of the technical grade concentrate was completed by muscovite, amphibole and clay minerals as minor or trace accessory phases. No asbestiform minerals were identified in either of the concentrate test sample grades.A total of 20 images were collected from both grades of prepared concentrate, ten for chemical grade and ten for technical grade. For comparison, Mines Safety and Inspection Regulations 1995 (WA) criteria. Using the WHO (1997) and NOHSC (2005) criteria, in the 10 images examined of the technical grade concentrate 37 particles were countable. Of these particles, 16 were countable by the Mines Safety and Inspection Regulations 1995 (WA) criteria. In the 10 images examined of chemical grade concentrate, 47 particles were identified as satisfying counting criteria according to WHO (1997) and NOHSC (2005). Of these 47 particles, 17 were countable by the Following the WHO (1997) and NOHSC (2005) counting criteria, measurement data from the SEM images identified countable particles in the chemical grade concentrate sample exhibited a mean (M) L 7.9 µm (M = 7.9) (range 5–20) µm, mean D 1.1 µm (M = 1.1) (range 0.2–2.2) µm and a mean AR 9:1 (M = 9:1) (range 3:1–32:1). Using the same counting criteria, countable particles in the technical grade demonstrated a mean L 7.6 µm (M = 7.6) (range 4.2–18.1) µm, mean D 1.1 µm (M = 1.1) (range 0.1–2.5) µm, mean AR 10:1 (M = 10:1) (range 3:1–65:1).Mines Safety and Inspection Regulations 1995 (WA) counting criteria, SEM measurements determined that countable particles in the chemical grade sample exhibited a mean L 7.5 µm (M = 7.5) (range 5–12.7) µm, mean D 0.6 µm (M = 0.6) (range 0.2–1) µm, and mean AR 14:1 (M = 14:1) (range 7:1–32:1) µm. The same counting criteria applied to the technical grade sample demonstrated a mean L 7.1 µm (M = 7.1) (range 5–18) µm, mean D 0.6 µm (M = 0.6) (range 0.1–1) µm and a mean AR 17:1 (M = 17:1) (range 7:1–65:1). A complete summary of dimensional (µm) statistics for the countable particles is shown in Following application of the Mines Safety and Inspection Regulations 1995 (WA) criteria. Details of the counting criteria outcomes are shown in This study has identified that the typical comminution used in α-spodumene processing readily generates cleavage fragments that satisfy the widely used WHO (1997) and NOHSC (2005) regulatory counting criteria and the previously used Despite both chemical and technical grade concentrates having completed beneficiation, XRD study results revealed a complex mineralogy as shown in Due to pronounced cleavage planes, α-spodumene cleavage fragments typically appear long, flat or columnar in shape with stepped, bladed or prismatic surfaces ,18,55 whIn general, crystalised nontronite forms globular or spheroidal aggregates composed of intergrown fibre-like or lath-shaped particles . DependiAs shown in SEM images  of the t2O purity and lower Fe2O3 content in technical grade α-spodumene concentrate does not influence the dimensions of cleavage fragments. However, a single countable particle in the technical grade concentrate sample was measured with a significantly greater maximum AR, more than two-fold, when compared to the maximum AR in the chemical grade sample. An explanation for the difference in maximum AR may be due to the larger concentration of nontronite in the technical grade sample. Nontronite can produce particles with a fibre-like morphology reaching lengths between 10–100 µm and 1 µm in width depending on the deposit [Comparison of measurement results revealed countable particles in the two grades exhibited similar size ranges as recorded in  deposit ,60. The A scatterplot of the distribution of L and D (µm) measurements of countable particles that satisfied the two different counting criteria in both α-spodumene concentrate grade test samples is shown in For several decades extensive research has been dedicated to understanding the mechanisms of disease induction following the inhalation of naturally occurring mineral fibres ,70,71,72As proposed in the widely used “Stanton Hypothesis”, needle-shaped particles with a L > 8 µm and D ≤ 0.25 µm are the optimal dimensions for the induction of intrapleural tumours . Based oMeasurement data identified no countable particles in either the chemical or technical grade concentrate samples satisfied the optimal physical parameters detailed by the “Stanton Hypothesis”. However, application of the WHO (1997) and NOHSC (2005) counting criteria, determined that 70.2% of countable particles in the chemical grade sample and 75.7% of countable particles in the technical grade samples met with the dimensional parameters, L ˃ 4 µm and D ˂ 1.5 µm, as reported by Stanton et al. (1981) as correlating with a relatively high probability of carcinogenicity.Lippman (1990) and Donaldson et al. (1992) support that fibre length is a critical determinant of pathogenicity ,74. AccoTo expand on the importance of length in fibre pathogenicity, recent research by Padmore et al. (2017) reported that length is a critical factor in fibre-induced cytotoxicity as well as increased inflammatory response in AM . In the Despite α-spodumene not occurring in fibrous habit, the measurement data shown in in vivo and/or in vitro mouse and rat bioassays following exposure to natural and synthetic fibres including but not limited to JM-100 glass fibres  [Further studies have investigated fibre length as a determinant of toxicity using , 33 µm) , short  , glass m, 33 µm) , glass m, 33 µm) , and sil, 33 µm) . In addi, 33 µm)  and huma, 33 µm) . However, 33 µm) .More recent research has attempted to model and predict the carcinogenic potency of elongated amphibole particles based on dimensional characteristics and fibrous versus non-fibrous mineral growth habit ,27. WyliRather than length, Korchevskiy and Wylie (2021) proposes that width is a better predictor of mesothelioma and lung cancer potency . Using eApplication of the dimensions, L > 5 µm, width ≤ 0.25 µm, AR ≥ 3:1 for predictive cancer potency, as described by Wylie et al. (2020) and the Korchevskiy and Wylie (2021) width criteria of ≤0.25 µm and ˂0.28 µm to the measurement data confirms that 4.3% of overall countable particles in the chemical grade sample and 8.1% in the technical grade sample satisfied these metrics. However, Korchevskiy and Wylie (2021) reported a lower or non-existent cancer potency from the simulated and raw datasets of 5058 non-asbestiform cleavage fragments compared to the asbestiform varieties.When compared to asbestiform mineral fibres, cleavage fragments are typically shorter and thicker, break along cleavage planes and lack strength and flexibility . HoweverIn contrast, evidence indicating a carcinogenic risk from exposure to cleavage fragments is provided by Allen et al. (2014) . In the Case  states that ”For regulatory and health assessment purposes… there is no evidence that potentially affected cells can distinguish between ‘asbestiform’ and ‘non-asbestiform’ fibres having equivalent dimensions” . NIOSH (Results from XRD study  demonstr3) with an action level at 0.025 mg/m3 [Irrespective of the toxic potential of α-spodumene cleavage fragments, at sufficient airborne concentrations the mineral dust generated by the extraction, handling, and processing of α-spodumene may cause permanent, disabling or fatal respiratory disease due to respirable crystalline silica content. In Australia and the US the 8 h time weighed average (TWA) workplace exposure standard (WES) for respirable crystalline silica dust is 0.05 milligrams per cubic metre .In addition, respirable crystalline quartz occurs in α-spodumene concentrate in significant quantities. Occupational exposure to respirable crystalline silica dust is documented to cause permanent and fatal respiratory disease. It is further recommended that the identification and control of dust hazards in spodumene extraction, handling and processing industries be implemented to ensure the risk of mineral dust exposure is reduced and maintained ALARP. It is proposed that the robust measures required for the control of respirable crystalline silica should also mitigate any adverse health risks resulting from exposure to respirable α-spodumene cleavage fragments.


Traditional solid nanoparticle aerogels have been unable to meet the requirements of practical application due to their inherent brittleness and poor infrared shielding performance. Herein, combining vacuum impregnation and high-temperature pyrolysis, a novel micro/nano-composite fibrous aerogel was prepared via in situ synthesis of silicon carbide nanowires (SiC NWS) in mullite fiber (MF) preform. During this process, uniformly distributed SiC NWS in the MF preform serve as an enhancement phase and also act as an infrared shielding agent to reduce radiation heat transfer, which can significantly improve the mechanical properties of the mullite fiber/silicon carbide nanowire composite aerogels (MF/SiC NWS). The fabricated MF/SiC NWS exhibited excellent thermal stability (1400 °C), high compressive strength (~0.47 MPa), and outstanding infrared shielding performance (infrared transmittance reduced by ~70%). These superior properties make them appealing for their potential in practical application as high-temperature thermal insulators. Cer2 , ZrO2 [22 [2 ), SiC na2 [2 ,22, Si3N aerogel ,24, BN f aerogel ,26,27, eIn recent years, a new kind of ceramic aerogel with fibers as matrix and nanostructured materials as reinforcement is being developed ,28,29,302 coating [However, in high-temperature environments, the heat transfer of materials is mainly accomplished via thermal radiation. According to Planck’s law, the higher the temperature, the greater the proportion of thermal radiation in heat transfer. The wavelength of thermal radiation applied to insulation materials is mainly concentrated at 1–8 μm , but the coating , SiC coa coating , etc.) a coating ,38,39. X coating  preparedIn the range of 1–8 μm, SiC is capable of acting as an excellent infrared shielding material to improve the high-temperature thermal insulation of mullite fiber aerogel owing to its high infrared reflection and absorption capacity ,40. SimiIn this study, a mullite fiber/silicon carbide nanowire composite aerogel (MF/SiC NWS) was prepared using in situ synthesized silicon carbide nanowires (SiC NWS) in a mullite fiber (MF) preform, applying a combination of sol–gel, vacuum impregnation, and high-temperature pyrolysis techniques. The microstructure, mechanical, and thermal properties of MF/SiC NWS were studied. Moreover, the growth mechanisms of SiC NWS and heat insulation mechanisms of MF/SiC NWS were analyzed.3BO3, AR grade, Tianjin Guangfu Fine Chemical Research Institute Co., Ltd., Tianjin, China) and silica sol  were selected as high-temperature binders. Soluble starch  was selected as the low-temperature binder. Firstly, as-prepared mullite fiber, soluble starch, H3BO3, and silica sol were mixed in 50 mL deionized water, with a weight ratio of 0.3:1:9:10, by magnetic stirring for 20 min, yielding a uniform fiber/binder mixture slurry. Then, the obtained mixture slurry was prepared into a wet billet using vacuum filtration and molding. Finally, the wet billet was dried at 90 °C for 4 h, followed by sintering at 1200 °C for 2 h, with a heating rate of 5 °C·min−1 in the air, further attaining MF preform. The bulk density of MF preform is 0.20 ± 0.02 g·cm−3, with a diameter and height of 40 mm and 10 mm, respectively.The first step was to prepare MF preform. Mullite fiber s were prepared from mullite fiber cotton  by shearing and slag removal with a high-speed blender. Boracic acid  in deionized water, followed by a water bath at 40 °C for 0.5 h. Subsequently, glucose solution and SiO2 precursor sol were mixed with the molar ratio of C: Si = 4:1, by magnetic stirring for 0.5 h, yielding precursor sol with a SiO2 concentration of 0.6 mol L−1. For direct comparison, precursor sols with SiO2 concentrations of 0.4, 0.6, 0.8, and 1.0 mol L−1 were also prepared by varying the TEOS content in the solution. After that, MF preform was entirely immersed into the as-prepared precursor sol under vacuum at 50 °C for 2 h and then dried at 60 °C to obtain MF/dry gel. Finally, MF/dry gel was pyrolyzed at 1400 °C for 2, 3, and 4 h in the airtight alumina crucible in Ar to prepare MF/SiC NWS. The bulk density of MF/SiC NWS is 0.22 ± 0.02 g·cm−3, with a diameter and height of 40 mm and 10 mm, respectively.MF/SiC NWS were fabricated via vacuum impregnation and high-temperature pyrolysis with TEOS as silicon source and glucose as carbon source. In a typical process, TEOS , deionized water, and EtOH with a molar ratio of 1:16:4 were mixed by stirring for 2 h to obtain a homogenous solution. Then, HNO−1. Compression measurements of MF preform and MF/SiC NWS were measured using a universal testing machine with a loading speed of 0.05 mm·min−1 . FTIR (Nicolet Avatar 360) and a UV–Vis–NIR spectrophotometer  were used to determine the infrared transmittance of the samples. The Thermograph of the MF/SiC NWS was recorded by an infrared thermal imager .Bulk density was calculated using ρ = m/V . Pore-size distribution was analyzed using mercury intrusion porosimetry (Auto Pore V 9600). Morphologies of the samples were characterized via field-emission scanning electron microscopy . The crystalline structure and phase compositions of SiC NWS were examined via field-emission transmission electron microscopy  with an energy-dispersive spectrometer (EDS). A thermal analyzer  was used to record the curves of thermogravimetry (TG) of MF/SiC NWS from room temperature to 1500 °C in the air at a heating rate of 10 °C·min2 sol is beneficial for obtaining uniformly mixed sol 2 nanostructure reacted with SiO gas to form silicon carbide crystal nuclei, providing growth sites for the growth of SiC NWS [The formation of SiC NWS consists of two stages—namely, nucleation and growth. At the nucleation stage, C atoms on the surface of C-coated SiO SiC NWS :SiO (g) 2@C composites remained in the sample. When the CO gas reached a high degree of saturation, CO and SiO generated a new SiC crystal nucleus, as expressed in Equation (3) [The continuous growth of SiC NWS was through the gas-phase reaction between CO and SiO. In the heat preservation stage, since the sample was prepared in a relatively closed crucible, most of the SiO and CO gases generated by the reaction of SiOtion (3) . The new2 using Equation (3) continued to react with the remaining C to produce CO (Equation (4)), which continued to participate in the growth of SiC NWS [The generated CO SiC NWS .CO2 (g)2 might be formed on the surface of SiC NWS, following Equation (5), which was consistent with TEM images.During the cooling phase, the Gibbs free energy of Equation (5) decreases with the decline in temperature. When the SiO gas was dominant, Equation (5) was favorable . Therefo2 [Connecting pores in MF preform facilitated the diffusion of gas products such as SiO, CO, and CO2 , therebyThe mechanical properties of the thermal insulation material are one of the most vital factors that determine their practical applications. For pure MF preform, high porosity and brittleness lead to relatively low compressive strength. Therefore, as shown in The pore-size distribution of MF/SiC NWS was characterized using mercury intrusion porosimeter and SEM image analysis c–e. Acco−1·K−1 at room temperature, which was slightly higher than that of MF preform (0.067 W·m−1·K−1). By contrast, the thermal conductivity of MF/SiC NWS decreased by 0.050 W·m−1·K−1 (~30%), compared with that of MF preform at 1100 °C. This demonstrated the role of the SiC NWS in improving thermal insulation properties at high temperatures. To verify this, infrared transmittance of MF preform and MF/SiC NWS between 0.85 and 25 μm was measured. In the band of 1–2.5 μm, the results showed that the infrared transmittance of MF/SiC NWS was about 10–20%, and the infrared transmittance of MF preform was about 70–90%, which suggests the infrared transmittance decreased by about 70% after in situ synthesis of SiC NWS . However, in a high-temperature environment, the heat transfer of materials is mainly accomplished by thermal radiation. The wavelength of thermal radiation is mainly concentrated at 1–8 μm at high temperatures [Generally speaking, the heat transfer of materials mainly consists of solid and gas thermal conduction, thermal radiation, and thermal convection. Thermal convection is negligible in the fiber/nanowire aerogel, because the pores inside the material are micron-sized, further leading that the temperature difference in the pores is very small and almost no airflow can be formed . At roomeratures ; in thiseratures . TherefoThe excellent thermal insulation performance was further confirmed by simulating the high-temperature combustion environment in practical applications. Butane flames were used to simulate extreme high-temperature environments, and an infrared thermal imager was used to record the temperature changes on the back of samples over time. A detailed description of the device is shown in Thermal stability is also an important criterion for evaluating the application safety of thermal protective materials. To demonstrate the thermal stability of MF/SiC NWS, thermogravimetric analysis (TGA) was conducted. As shown in In this study, MF/SiC NWS were prepared by in situ synthesizing SiC NWS in MF preform using a combination of vacuum impregnation and high-temperature pyrolysis. SiC NWS were capable of withstanding compressive stresses, acting as a buffer against deformation. Consequently, MF/SiC NWS exhibited excellent mechanical properties, with the compressive strength of 0.47 MPa at 10% strain, which was over 370% higher than that of MF preform (0.10 MPa). Meanwhile, SiC NWS were uniformly distributed in MF preform, significantly decreasing the pore size and forming hierarchical pore structures, as well as exhibiting a remarkable infrared shielding effect. The infrared transmittance of MF/SiC NWS was reduced by ~70%, compared with MF preform, and MF/SiC NWS exhibited exceptional thermal insulation and thermal stability at 1400 °C. This research provides a new idea for future design and preparation of thermal protection materials suitable for harsh environments.


The application of NIR to optogenetic systems is in great demand due to its superior properties enabling in vivo deep tissue penetration. Irradiation of NIR to tissue samples or cells rapidly generates heat locally. The resultant elevation in temperature affects cells at the molecular level because of the activation of the heat shock pathway and ROS generation. Nevertheless, few reports have presented detailed comparisons of the effects of the temperature change rate on signaling pathway biomolecules, especially those of rapid heat changes. Aiming at broadening the understanding of temperature sensitivity, we investigated seven insulin signaling pathway biomolecules  in three cell lines  at temperatures between 25 and 45 °C. The results show that, except for INSR, pAkt(T308), and FoxO1, biomolecules are sensitive to rapid temperature changes at temperatures higher than 42 °C, at which they are significantly phosphorylated. At 25 °C, around a 50% reduction in phosphorylation occurred. Moreover, p70S6K is sensitive over time. It dephosphorylates quickly (5 min) and then phosphorylates over time. Our findings extend the temperature range to 45 °C, while providing additional time course information about the signaling pathway biomolecule response necessary to advance NIR optogenetic research. Genetically engineered protein research has received vast attention, flourishing and advancing in recent decades. Optogenetic approaches, including those using fluorescent proteins (FPs), biosensors, optogenetic tools, and reporters, offer high spatiotemporal accuracy. They have inspired numerous innovative ideas to expand our understanding of organism functions. Nevertheless, the conventional usage of blue light hinders its application to living animals because of light scattering and absorption by hemoglobin, water, and melanin inside the tissues. In contrast, at the near-infrared (NIR) window (NIR-I of 600–900 nm and NIR-II of 900–1800 nm), the absorption of light by hemoglobin and water is minimal . Therefo2+ channel, the development of APC-modified nanoCRISPR to activate CRISPR-Cas9 optogenetically, and RpBph-P1-based protein targeting control and transcription activation in cells [Bacterial phytochromes (BphPs) with heme catabolic intermediate biliverdin (BV) as their chromophore are widely adopted NIR FPs that have developed into dimer systems with variations such as iRFP670, iRFP713, and iRFP720 . Other Nin cells ,15,16,17in cells ,18,19,20The irradiation of NIR to tissue or cell samples of interest rapidly generates heat locally through photothermal interaction. Head tissue modeling studies have predicted local temperature increases of up to 10 °C, depending on laser spot sizes and intensities . DespiteSignaling pathways are one of the important optogenetic applications. They offer great potential for applying NIR to enhance in vivo penetration depths into tissues of living animals. Some earlier studies have examined the rapid temperature changes caused by NIR and their effects on biomolecules in specific signaling pathways. Recent research often lacks sufficient information about experimental temperature conditions. Elucidating the temperature sensitivity of biomolecules is, therefore, important (1) to eliminate temperature-related factors that lead obtained results away from the truth, (2) to increase awareness of temperature sensitivity to facilitate evidence-based experiment design for additional studies, and (3) to inspire future research of detailed mechanisms in this area.As described herein, aiming at broadening the understanding of temperature sensitivity of signaling pathway biomolecules, and taking the insulin signaling pathway as an example, we investigated temperature change rates, especially rapid temperature changes and their effects on key biomolecules. Three cell lines that originate from different mammalians (mouse myoblast cell line (C2C12), human liver cancer cell line (HepG2), and rat hepatoma cell line (Fao)) that are used widely for insulin research were selected to examine the phosphorylation changes of key biomolecules including Insulin receptor (INSR), Insulin receptor substrate 1 (IRS1), Extracellular signal-regulated kinase 1/2 (ERK1/2), Akt phosphorylated at S473 (pAkt(S473)), Akt phosphorylated at T308 (pAkt(T308)), p70 Ribosomal S6 kinase (p70S6K), GSK3β, and Forkhead box protein O1 (FoxO1). Cells that underwent rapid stimulation within 5 min to reach the target temperature (25–45 °C) were collected. They showed a significant increase in IRS1, pAkt(S473), GSK3β, and ERK1/2 phosphorylation from 42 °C, whereas dephosphorylation occurs at 25 °C. In all cell lines, p70S6K behaviors were both temperature-sensitive and time-sensitive where a decreasing trend with a rapid temperature increase was observed. By contrast, INSR, pAkt(T308), and FoxO1 were not sensitive to temperature change. This study examined the biomolecule-specific temperature sensitivity in the insulin signaling pathway.®20, albumin from bovine serum globulin (BSA), paraformaldehyde (PFA), and insulin . Gelatin from cold-water fish  and Triton X-100 .The chemical reagents used in this paper include: Tris-(hydroxymethyl)-aminomethane and hydrochloric acid buffer solution (Tris HCl), sodium chloride (NaCl), sodium dodecyl sulfate (SDS), ethylenediaminetetraacetic acid (EDTA), glycerol, 2-mercaptoethanol, hydrochloric acid (HCl), methanol, sodium deoxycholate, bromophenol blue, sucrose, ammonium peroxodisulfate, glycine, TweenSpecific antibodies for pAkt(Ser473), pAkt(Thr308), Akt, pINSR, INSRβ, p-p70-S6K, S6K, pFoxO1(S256), FoxO1, pGSK3β, GSK3α/β, p-ERK1/2(T202/Y204), ERK1/2, and Hsp70 were obtained from . pIRS1 and IRS1 were acquired from . β-Actin was procured from (Sigma-Aldrich). Peroxidase-conjugated secondary antibodies Anti-Rabbit IgG  and Anti-Mouse IgG  were purchased from . Anti-Rabbit IgG Secondary Antibody  was obtained from .2.Mouse myoblast cell line (C2C12) , Manassas, VA, USA) was maintained in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum (Sigma-Aldrich), 100 unit/mL of penicillin , and 2 mM glutamine . Human liver cancer cell line (HepG2)  cells were maintained in Eagle’s minimal essential medium  supplemented with 10% fetal bovine serum (Sigma-Aldrich), 100 unit/mL of penicillin (Gibco™), and 1% sodium pyruvate . Rat hepatoma cell line (Fao)  was maintained in Kaighn′s modified Ham′s F12 (Sigma-Aldrich) supplemented with 10% fetal bovine serum (Sigma-Aldrich), 100 unit/mL of penicillin (Gibco™), 2 mM glutamine , and 45 mg/L of ascorbic acid (Sigma-Aldrich). After seeding onto a 10 cm dish , cells were maintained at 37 °C with 5% COCells were seeded on a 35 mm dish (Corning) before the experiment. At approximately 50–60% confluency, they were washed with PBS twice and were replaced with a cell culture medium containing 0.1% FBS for serum starvation for 24 h. Cells were then stimulated accordingly using a tabletop incubator . The incubator temperature was adjusted from 25 °C to 60 °C to allow the cells in different stimulation conditions to reach the targeted temperature at the indicated temperature within 5 min (cells were collected immediately when reaching the target temperature within 5 min). For the constant-temperature experiment, cells were maintained at 25 °C and 45 °C for different timepoints up to 60 min. The temperature changes during the experiment were measured using a thin-fiber temperature sensor (SFS-E-100-ASP) positioned at the bottom of the dish closest to the cells. The sensor was connected with a digital thermometer . Rapid temperature changes were measured every 1 min while gradual temperature changes were measured at the sampling timepoint.v/v) NP-40, 0.5% (w/v) sodium deoxycholate, 1.0 mM EDTA, 0.1% (w/v) SDS, and 0.01% (w/v) sodium azide) with PhosSTOP™  and cOmplete™ (Roche) were added along with 2× sampling buffer  immediately to cells for cell lysis. After the collected samples were sonicated  for 3 min, they were boiled at 95 °C for 5 min. The BCA protein assay  was used to ascertain the protein concentration.After stimulation, cells were placed immediately on a metal block positioned on ice and were washed with ice-cold PBS twice. Equal volumes of RIPA buffer (50 mM Tris HCl (pH 7.4), 150 mM NaCl, 1.0%  to detect bands with an imager  and software .An equal quantity of proteins (8 μg/lane) was loaded to SDS-polyacrylamide gel  for electrophoresis. Ponceau  staining was performed on each membrane to ensure equal protein loading in addition to housekeeping protein (β-Actin). The gels were transferred onto a nitrocellulose membrane and blocked with 2% ECL prime blocking reagent  or 5% Skim milk  in Tris-buffered saline containing Tween-20  for 1 h at room temperature. Subsequently, the membranes were blotted with corresponding antibodies at 4 °C overnight. Then, membranes were incubated with corresponding peroxidase-conjugated secondary antibodies for 1 h at RT. Membranes were then developed using ECL−1, 568 nm laser excitation, and 405 nm laser for Hoechst 33342, excitation.Cells seeded on a 24-well glass bottom plate were subjected to the same stimulation conditions as those of the Western blot sample. The collected cells were set immediately on a metal block positioned on ice and were washed five times with ice-cold PBS. Then, 4% PFA was added to cells to fix them at room temperature (RT) for 20 min. The cells were then washed with PBS three times and were incubated with 0.2% Triton X-100 in PBS(+) for 15 min at RT. After cells were washed with PBS(+) supplemented with 0.1% Tween-20 (PBST), they were blocked with gelatin from cold-water fish for 1 h at RT. Then, cells were washed twice with PBST and were stained with Hoechst 33342  for 10 min at RT. After rabbit anti-Akt antibody was dissolved in 2% BSA (in PBST) with 1:200 dilution, it was incubated at RT for 1 h. After washing with PBST another three times, secondary antibodies dissolved in 2% BSA (in PBST) were added to cells for shaking at RT for 1 h. After the final wash with PBST three times , cells were put to immediate microscopic observation using a confocal microscope  with a 10× or 60× lens and imaging acquisition condition: 10 μs pixelThe blot image was analyzed using ImageJ ver. 2.3.0  and ImageQuant TL 1D Version 8.1 (Cytiva). Data were normalized using antibodies against housekeeping protein (β-Actin). GraphPad Prism software ver. 9.0 was used to perform statistical analysis to report the mean and 95% confidence interval (CI) for each testing condition. One-way analysis of variance (ANOVA) followed by Dunnett’s test was conducted using temperature or time as the variance to compare the difference between test groups.All the images in this article were processed (representation Western blot images and immunofluorescence) using ImageJ ver. 2.3.0.The insulin signaling pathway , which fp = 0.0002 for C2C12, p < 0.0001 for HepG2, and p = 0.0021 for Fao). In addition, GSK3β exhibited a two-fold increase  for all three cell lines. The results show that IRS1 phosphorylation was significant, with less magnitude for C2C12 (p = 0.0002) and HepG2 (p = 0.0032) cells. For Fao cells, IRS1 phosphorylate showed a higher mean value at 45 °C than at 36 °C, but the difference was not statistically significant. In addition, pAkt(T308) failed to detect visible changes until 42 °C where a more noticeable increase in phosphorylation was observed at 45 °C. Conversely, p70S6K phosphorylation decreased significantly from 38 °C (p < 0.0001) with the rising temperature. However, FoxO1, which Akt also mediates, was not sensitive to the rapid temperature change, showing no statistically significant phosphorylation change. An exception was HepG2, which detected a 1.5-fold increase in FoxO1 phosphorylation (p = 0.0002). It is noteworthy that ERK1/2, which belongs to the SHC-Ras-MAPK pathway, was calculated to have a three-fold increase at 45 °C for both ERK 1 and ERK 2 (p < 0.0001 at 45 °C), but ERK1/2 in C2C12 cells were not sensitive to the temperature change. Furthermore, Hsp70 was also detected to examine the activation of the heat-shock response pathway by the temperature change , whereas both GSK3β and ERK1/2 were around four times more phosphorylated (p < 0.0001). Similarly to the result reported in To further understand the effect of the temperature change rate on phosphorylation with a gradual changing rate, cells were positioned in a 45 °C incubator from 5 min to 30 min. The real-time temperature measurement A recordep = 0.0002). This finding is related to the higher standard deviation of results. Nevertheless, the 95% confidence interval range at 25 °C remained higher than 36 °C, demonstrating the higher phosphorylation level at 25 °C. Furthermore, ERK1/2 (p = 0.0021 for HepG2 and p = 0.0032 for Fao) dephosphorylation with cooling was significant, except for C2C12 cells. Altogether, ERK1/2 was found to have no significant temperature-dependent phosphorylation change from 25 °C to 45 °C in C2C12 cells.The environment temperature often differs from the NIR heating condition in the testing sample. The cooling event occurs immediately after heating. As a result, rapid cooling was investigated independently in this study. Cells underwent a rapid temperature decrease from 36 °C to 25 °C within 5 min A. The rep < 0.0001), and C2C12 (p < 0.0001) and Fao (p = 0.0002), respectively, showed 50% and 80% reductions. At 10 min, the temperature reached approximately 25 °C and stabilized for up to 60 min. Compared to the results in p < 0.0001) and quickly declined to around threefold after 10 min (p = 0.0032). FoxO1 failed to display a trend in phosphorylation with time. The data deviation was greater than other biomolecules. In contrast, ERK1/2 were dephosphorylated in HepG2 (p < 0.0001 with 70%–90% reduction) and Fao (p < 0.0001 with 70% reduction) cells, but less dephosphorylated in C2C12 cells (p = 0.0021 with 50% reduction).The rate of temperature change affects biomolecule behavior. Cells were positioned at 25 °C for up to 60 min and were collected at different times to expand our knowledge of the gradual-cooling-related phosphorylation change. In This study investigated key biomolecules from insulin signaling pathways to evaluate their sensitivity to different rates of temperature change. The results demonstrated that, at temperatures higher than 42 °C, Akt and MAPK signaling molecules are significantly phosphorylated, but at 25 °C, they are dephosphorylated to around 50%. In addition, INSR, pAkt(T308), and FoxO1 are otherwise not sensitive to temperature fluctuation. Both the gradual and rapid temperature changes induce similar trends of phosphorylation change for biomolecules that are sensitive to temperature change. However, the magnitudes of the changes differ where the gradual temperature change engenders a greater phosphorylation change. For instance, in HepG2 cells, IRS1 dephosphorylation is significant in the condition in which the temperature is decreased gradually to 25 °C in 10 min. A rapid reduction to 25 °C within 5 min shows a reduction in the phosphorylation level, but it is not significant. Similarly, in the heating event, pAkt(S473) phosphorylates at 45 °C with a three-fold increase during rapid heating, but with a ten-fold increase for gradual heating. The results indicate that these biomolecules are less sensitive to rapid temperature changes between 32 °C and 40 °C. These findings accord with observations reported by Oehler-Janne et al. They highlighted that the pAkt(S473) is especially temperature-sensitive, whereas pAkt(T308) is stable . After eA comparison of cell lines shows that most biomolecules behave similarly across tissues (liver and muscle). Despite that finding, the magnitude of the phosphorylation changes upon different stimulations. For example, IRS1 phosphorylation in the heating condition is observed more in C2C12 cells (2–4-fold change with different heating rates) than in the other two cell lines . Unlike other cell lines, ERK1/2 in C2C12 cells is less temperature-sensitive throughout various testing conditions. Ultimately, the C2C12 cell line is of different tissue (muscle), whereas the other two cell lines are liver-originated. Additional studies must be conducted to investigate the underlying reasons for these different temperature sensitivities.One major signaling pathway that is activated after insulin stimulation is the IRS1-PI3K-Akt pathway. The key biomolecules that are commonly used as indicators to evaluate this activation were examined in this experiment: IRS1, Akt, GSK3β, p70S6K, and FoxO1. The findings indicate a temperature-related increase in phosphorylation of IRS1, pAkt(S473), and GSK3β. From the immunofluorescence image, the Akt distribution in the rapid heating condition was observed to differ from the insulin activation. Akt is known to be recruited to the plasma membrane upon activation via the PI3K pathway ,37. SimiContrary to the general trend of the increase in phosphorylation with temperature, p70S6K is dephosphorylated under rapid heating conditions as well as during gradual heating at 45 °C within 5 min . Our resIn gradual heating conditions, most biomolecules are phosphorylated over time. The peak phosphorylation for IRS1, GSK3β, p70S6K, and FoxO1 was beyond the investigated 30 min duration. Future experiments with a longer duration will be able to elucidate the peak phosphorylation time points. In the gradual cooling experiment, some biomolecules displayed a transient increase in phosphorylation  at the initial stage within 5 min, although this transient increase was not statistically significant. Thereafter, most biomolecules experienced a gradual increase in phosphorylation from 30 min with prolonged exposure to 25 °C where cell apoptosis accompanied by Akt activation would be initiated.In short, this study has identified temperature-sensitive biomolecules in the insulin signaling pathways and their functioning thresholds. The variation in temperature change rates is expected to result in different activity statuses, among which p70S6K dephosphorylation observed in heating is markedly time-dependent. Moreover, pAkt(T308) is less temperature-sensitive than pAkt(S473). The results reveal that future studies should position the cells within 32 °C to 40 °C. Beyond that range, the effects of biomolecule activities should be considered from a technical perspective. Therefore, we would like to raise awareness of the temperature sensitivity of biomolecules and urge future NIR research designs to include temperature considerations.The rapid growth in NIR optogenetic research in recent years highlights the necessity for deep-tissue in vivo optogenetic manipulation. Effects on signaling biomolecules exerted by rapid NIR-associated heating has not been studied adequately. We sought to investigate and to provide a pioneering insight into this area by examining the temperature-related phosphorylation of seven key biomolecules from the insulin signaling pathway in three selected cell lines originating from different mammalian species. Western blot results were quantified and analyzed statistically. A temperature sensitivity profile between 25 °C and 45 °C was built to show effects of the respective exposures to rapid and gradual temperature changes. The findings underscore the necessity for temperature monitoring in NIR-related research, especially when studying the signaling pathways that use phosphorylation as a key indicator for activity confirmation. These findings also expand our understanding of the thermal sensitivity of essential biomolecules in the insulin signaling pathway. Signaling pathway activation is a dynamic process for which multiple factors must be considered. Other pathways such as HSP are activated concurrently. Their respective effects require additional detailed analyses. Aiming at raising awareness of temperature-related issues, this work has also shed light to guide additional research to assess NIR thermal effects in other signaling pathways and to explore cross-effects of different signaling pathways in different cell lines. The information obtained by this study is expected to support the advancement of NIR optogenetic research.


Plasma was collected at baseline, 2, 6, 12, and 24 h. After protein extraction, data-independent SWATH-MS was applied to inspect protein abundance at baseline, 2, 6, 12, and 24 h. Cluster analysis revealed protein patterns emerging over the study observation time, more pronounced by the factor of time than different injury models of ARDS. A protein signature consisting of 33 proteins differentiated among Ph1/2 with high diagnostic accuracy. Applying network analysis, proteins involved in the inflammatory and defense response, complement and coagulation cascade, oxygen binding, and regulation of lipid metabolism were activated over time. Five proteins, namely LUM, CA2, KNG1, AGT, and IGJ, were more expressed in Ph2.Despite decades of comprehensive research, Acute Respiratory Distress Syndrome (ARDS) remains a disease with high mortality and morbidity worldwide. The discovery of inflammatory subphenotypes in human ARDS provides a new approach to study the disease. In two different ovine ARDS lung injury models, one induced by additional endotoxin infusion (phenotype 2), mimicking some key features as described in the human hyperinflammatory group, we aim to describe protein expression among the two different ovine models. Nine animals on mechanical ventilation were included in this study and were randomized into (a) phenotype 1,  More than five decades since its first description in 1967 ,3. ARDS Recent post-hoc analyses of large clinical ARDS trials within the National Health Lung and Blood Institute (NHLBI) network revealed evidence for distinct ARDS subgroups, defined by specific clinical and biological features: a hypoinflammatory (P1) and a hyperinflammatory subphenotype (P2) ,11,12,13Whilst promising, the current data based on subphenotypes are all retrospective in nature, and, most importantly, at this time, little is known about the biological processes over time driving the disease, and there is no tool set available for the early identification of the respective subphenotype at bedside. There have been several efforts in the quest for biomarkers in ARDS in the past ,20,21, sOur group previously assessed different models of lung injury in an ovine model, and demonstrated that one specific ARDS induction method mimics the key features observed in the human ARDS P2 subphenotypes . Given tWe therefore aim to describe quantitative protein abundance among two different ovine ARDS models: phenotype 1 (Ph1) and phenotype 2 (Ph2).Baseline characteristics of studied animals did not differ among Ph1 and Ph2 and are shown in Inflammatory cytokines (interleukin-6 (IL), -8, -10) displayed an early peak in Ph2 around 2 h. Furthermore, while IL-8 and IL-10 then assimilated to Ph1 levels over the observation period, IL-6 remained elevated in Ph2 animals .The Lung Injury Score (LIS) was 0.32 (0.28–0.37) among Ph1 and 0.35 (0.30–0.41) among Ph2 animals .Principal component analysis (PCA) was performed to visualize differences and similarities among Ph1 and Ph2. It resulted in distinctive patterns, with PC1  explaining 31%, PC2 19%, and PC3 10% of the variability among samples. Correlation of all samples is shown in After quality control and log-normalization, 198 proteins remained for analysis. In total, 45 proteins were detectable in every sample, 91 proteins in >75%, 120 in >50%, and 160 proteins in >25% of samples.Differential abundance between Ph2 and Ph1 at each time point is shown In the partial least squares discriminant analysis (PLS-DA), the best number of components was determined to be three and the optimal number of proteins to keep per component was manually selected as 20, 3, and 10 according to The final class error rate among phenotypes as expressed in terms of maximal distance (max. dist.) was generally low; for Ph1, it resulted in 29.7% , 15.8% , and 12.3% , and for Ph2, in 23.9% (C1), 18.3% (C2), and 19.1% (C3).p < 0.0001) for distinction between Ph1 and Ph2. Individual loadings of components 1 to 3 are reported in The PLS-DA components resulted in an AUC of 0.916 (C1), 0.992 (C2), and 1.0 (C3)  showed a difference between Ph1 and Ph2 for immunoglobulin J chain, hypothetical protein JEQ12_001510, heparin cofactor 2, angiotensinogen (upregulated in Ph2), and immunoglobulin lambda-1 light chain isoform X47 (downregulated in Ph2) A. A signp < 0.1 among Ph1/2, over time and/or for phenotype:time interaction) was done with a peptide BLAST . The average local clustering coefficient was 0.591 and the protein–protein interaction (PPI) enrichment The most dominant biological process found to be activated was the complement and coagulation cascade  < 0.001%) displaying antithrombin-III precursor (SERPINC1), thrombin (F2), kininogen-1 isoform X2 (KNG1), inter-alpha-trypsin inhibitor heavy chain H2 (ITHI2), complement C4-like isoform X1 (ENSOARP00000002890), and ENSOARP00000000771 (uncharacterized protein). The same proteins except F2, but with the addition of apolipoprotein CIII (APOC3), apolipoprotein A-II (APOA2), and a serpin family protein (ENSOARP00000016410), are involved in the negative regulation of catalytic activity (FDR 0.0015%). Oxygen binding and carrier activity (FDR both <0.03%) was represented by hemoglobin subunit alpha (ENSOARP00000011736) and hemoglobin subunit beta (HBB). Other dominant biological processes include the inflammatory , ENSOARP00000002890, ENSOARP00000000771) and the defense response , APOA2). APOA2 and APOC3 participate in the (negative) regulation of cholesterol and lipid metabolism (FDR 0.012%) .This is a comprehensive animal model of ovine ARDS phenotypes, in which the novelty is an observation time of up to 24 h and targeted sampling for protein assessment to discover a protein signature for differentiation among the two phenotypes.The discovery of ARDS subphenotypes in the heterogenous critical care population is a promising new approach to decreasing ARDS mortality with phenotype-specific treatment, yet it is supported only by retrospectively collated evidence and the underlying biological processes are poorly understood. In addition, a biomarker signature that can easily and reliably differentiate among the subphenotypes at bedside is still missing. The similarities of the ovine and human inflammatory pathways and the possibility to induce an ovine lung injury, mimicking the features of the human hyperinflammatory ARDS subphenotype, gave us the opportunity to develop a highly controlled study to further explore the distinct biological processes in ovine ARDS.We report five main findings: firstly, in the unsupervised cluster analysis, there were patterns in proteins emerging over the study observation time, resulting from time-specific biological processes. Second, protein patterns were slightly more pronounced by the factor of time than different injury models of ARDS. Third, among the 198 proteins analyzed using supervised clustering, it was apparent that a signature consisting of 33 proteins was able to differentiate best among Ph1/2 with a low error rate and good diagnostic accuracy. Fourth, network analysis showed that specific proteins involved in the inflammatory and defense response, complement and coagulation cascade, oxygen binding, and regulation of lipid metabolism were activated over time. Fifth, five proteins, LUM, CA2, AGT, KNG1, and IGJ, were more expressed in Ph2.The finding that protein patterns among time points were more pronounced than among ARDS phenotypes is relatively surprising, as our previous results clearly show that the two ovine phenotypes are different in their biological responses and cytokine profiles . The valThe STRING interaction enrichment parameters indicate that the input proteins in the pathway analysis show interactions that are clearly not random but instead at least partially biologically connected. The most dominant active biological processes in both ovine ARDS phenotypes were the complement and coagulation cascade, oxygen binding and carrier activity, negative regulation of catalytic activity, inflammatory and defense response, as well as the regulation of cholesterol and lipid metabolism. These findings are in line with previously identified key biological processes in the disease: in particular, the defense response, inflammation, and coagulation processes are largely intertwined  in a preSix proteins were clearly more expressed in Ph2 than Ph1, in the LMM and in the graphical display; five could be identified in the pathway analysis. LUM belongs to the proteoglycan family and is involved in collagen fibrillogenesis within the extracellular matrix . In an aImportant features of ARDS were first described in animal models, well before its first official description in humans by Ashbaugh in 1967 ,40. Due Strengths of this study include the use of an established and reliable model of ovine ARDS phenotypes ,27, a coWe report several limitations. Firstly, we did not perform albumin depletion; therefore, the discovery rate of low-abundance proteins may be lower than expected. However, the total amount of identified proteins is in line with previous reports ,45,46,47Animal studies were conducted at the Queensland University of Technology (QUT) Medical Engineering Facility (MERF) in Brisbane. Animal ethics was approved by the QUT Office of Research Ethics and Integrity (No. 18-606).ARDS was induced according to the definition and guidance of Acute Lung Injury in experimental animal models as provided by the American Thoracic Society (ATS) ,49.Animal experiments were approved by the QUT Office of Research Ethics and Integrity (No. 18-606), in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and the Animal care and Protection Act 2001 (QLD).n = 5) and Ph2 (n = 4). Randomization was performed using a random number generator.This study is a secondary analysis of control animals of Ph1 and Ph2 ARDS from a blinded, randomized, controlled preclinical trial in an ovine model. A total of 9 female non-pregnant Merino-Dorset crossbreed ewes, aged 1–3 years, mean weight 50 ± 5 kg, were included in this analysis and randomly assigned to one of the two groups: Ph1 (2/FiO2 ratio (PF) of <150 mmHg was reached, and (b) in Ph2: aforementioned oleic acid IV until PF ratio <150 followed by 0.5 µg/kg of lipopolysaccharide , dissolved in 50 mL of normal saline and infused over 1 h. A schematic of the experimental timeline is provided in General anesthesia was induced with a combination of midazolam and propofol intravenously, and the animal was endotracheally intubated. Animals were further instrumented with a jugular central venous line (CVL) and sheath for Swan Ganz catheter, femoral arterial line, nasogastric tube, urinary catheter, and bilateral pleural drains. Surgical tracheostomy was performed in all animals and lung-protective ventilation was applied as according to the EXPRESS trial . After cIntra-experimental monitoring, management, and data collection have been reported in detail before .g for 15 min at 4 °C, and then plasma was aliquoted and stored at −80 °C until batch protein extraction.Arterial blood samples for protein analysis were collected in EDTA blood tubes at baseline, 2, 6, 12, and 24 h. Samples were centrifuged twice at 3000× Blood samples for full blood count  and biochemistry  were collected at baseline and every 12 h following T0. In-house ELISAs  were useFor histological assessment, lung tissue was collected in 10% neutral buffered formalin for 24 h and then embedded in paraffin. After sectioning to 5 µm thickness, samples were stained with hematoxylin and eosin. All slides were assessed by a blinded, independent veterinary pathologist using the LIS, as recommended by the ATS for experimental ARDS in animal models .TM BCA protein assay kit  as per the manufacturer’s protocol. In brief, 12.5 µL of sample was added to 12.5 µL of 1× phosphate-buffered saline (PBS), mixed with 200 µL of working BCA reagent in a 96-well microplate, and incubated for 30 min at 37 °C. The absorbance was read at 562 nm on a plate reader and the concentration was calculated against a standard curve ranging from 20 to 2000 µg/mL. Then, 100 µg of total protein was processed for each sample using the FASP method to generate tryptic peptides [® 500–10 kDa MWCO column; Sartorius AG, Gottingen, Germany) and spun at 14,000× g at 4 °C for 15 min. Detergent removal by buffer exchange was performed in four successive washes with 8 M urea in 0.1 M Tris-HCl pH 8.5 (200 µL/wash) with a 15 min spin at 14,000× g at 4 °C. Afterwards, proteins were reduced using 100 uL of 5 mM DDT in 100 uL of wash buffer and incubated at 56 °C for 30 min, followed by alkylation with 25 mM iodoacetamide (IAA) for 30 min in the dark. Excess IAA was quenched by the addition of 1 µL of 1 M DTT and columns were spun at 14,000× g for 15 min. Protein digestion was achieved by adding proteomics-grade trypsin  at a 1:25 enzyme to protein ratio in 100 µL of 50 mM ammonium bicarbonate and by incubating at 37 °C overnight. Peptides were recovered by centrifugation and an additional wash with 50 µL of 0.5 M NaCl .Total protein concentrations of plasma samples were quantified by the Piercepeptides . Plasma Digested peptides were then subjected to Zip Tip clean-up . In short, the Zip Tips were activated by passing through 10µL of 80% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA) and equilibrated with 10 µL of 1% ACN, 0.1% TFA. The acidified samples were loaded to the Zip Tip, followed by three subsequent washes with 10 µL of 1% ACN, 0.1%TFA. Samples were eluted with 10 µL of 80% ACN, 0.1% TFA. The eluted peptides were dried using a Speed Vac vacuum concentrator and reconstituted in 0.1% TFA to a final concentration of 5 µg peptide/100 µL 0.1% TFA. Samples were then frozen at −80 °C until mass spectrometric analysis.Liquid chromatography–mass spectrometry (LC–MS/MS) analysis was performed using a Prominence nanoLC system (Shimadzu) and 5600 Triple-TOF mass spectrometer with a Nanospray III interface . First, 0.5–2 μg peptides were desalted on an Agilent C18 trap  at a flow rate of 30 μL/min for 3 min, and then separated on a Vydac EVEREST reversed-phase C18 high-performance liquid chromatography (HPLC) column  at a flow rate of 1 μL/min . Separatm/z of 350–1800 was performed for 0.5 s, followed by data-dependent acquisition (DDA) of MS/MS with automated CE selection of the top 20 peptides at m/z of 400–1250 for 0.5 s per spectrum. SWATH-MS was done with LC conditions as before, but with an MS-TOF scan at an m/z of 350–1800 for 0.05 s, followed by high-sensitivity information-independent acquisition with 26 m/z isolation windows, with 1 m/z window overlap each for 0.1 s across an  m/z range of 400–1250.An MS-TOF scan at http://www.uniprot.org as of 8 October 2021, 162`326 sequences; accessed on 8 October 2021) with standard settings: sample type: identification; alkylation: iodoacetamide; digestion: trypsin; instrument: TripleTOF 5600; special factors: none; ID focus: biological modifications; search effort: thorough ID; detected protein threshold: >0.05 (10.0%). Determined by a confidence of >99% and a global FDR rate of <1%, the spectral library contained 5907 peptides corresponding to 226 proteins.A spectral library was generated in ProteinPilot  using the DDA files, and peptides were identified, searching the UniProt database  and transition ion, peptide, and protein peak areas were extracted and exported for further analysis. Data were reformatted for statistical analysis as previously described .Peak protein intensity areas were further processed with MSstats: after log2 normalization, quality control was performed. Features with one or two measurements across runs were removed, and log2 intensities under the cutoff of 3.2678 and NA values were considered as censored missing values. There were 198 proteins consisting of 1–12 peptides per protein and 1–6 features per peptide available for further analysis.Proteins that were expressed in at least 4/5 Ph1 and 3/4 Ph2 samples at each assessed time point were analyzed for differential abundance among Ph2 and Ph1 and compared with a Wilcoxon test.Clusters among subphenotypes and time points were visualized using principal component analysis (PCA). To detect a protein signature for differentiating between Ph1 and Ph2, a partial least squares discriminant analysis (PLS-DA) was performed, a supervised clustering method for predictive and descriptive modeling as well as for discriminative variable selection.For proteins that were detected in at least three time points in each animal, linear-mixed-effects models (LMM) ,56 were https://www.string-db.org/) was consulted to analyze protein–protein interaction (PPI) networks. This database displays known and predicted protein interactions based on direct and indirect  associations.The STRING database , using the packages “MSstats”, “mixOmics”, “ggvenn”, “lmerTest”, and “stargazer”.Among the two phenotypes of ovine ARDS, patterns of proteins known to be involved in biological processes associated with ARDS emerge over time. Additionally, it is possible to derive a characteristic protein signature to differentiate between the two phenotypes.