Wound Healing

All speech data were transcribed in ELAN [39 ]. The duration between utterances was extracted automatically in milliseconds (ms). Positive durations correspond to gaps and negative durations correspond to overlaps. For the turn-taking analysis presented here we only considered cases where there was a speaker change. We do not consider whether the speaker change occurs at a TRP or not—in some cases it will, in other cases it will not (for example, where there is an apparent speaker switch for a mid-turn backchannel from another speaker which will split a single turn from a speaker into two utterances, with an intervening backchannel). Of course, this has consequences for the analysis and interpretation, however, the methodology applies equally to both PSz and C groups so differences between them are still relevant and meaningful. See also [40 ] for discussion of related issues.
Self-repairs were annotated using strongly incremental repair detection (STIR; [41 ]) which automatically detects speech repairs on transcripts. STIR is trained on the Switchboard corpus [42 ], and has been shown to be applicable to therapeutic dialogue, with high rates of correlation to human coders in terms of self-repair rate [43 ].
Hand movement was automatically extracted from the raw motion capture data. In order to control for individual variation, for each participant we extracted the movement from each of the three hand/wrist markers, and calculated the mean and standard deviation (s.d.) of movement in any direction by frame in millimetres (mm). For frames with missing markers, if this was fewer than 50 frames (frame rate 60 s⁻¹), we imputed the missing data using a linear trajectory, otherwise left the data as missing. Following the methodology in [33 (link),44 ], to account for individual variation, for each pair of frames we calculated whether the movement between them was greater than the individual’s $\mathrm{mean}\hspace{0.17em}\mathrm{movement}+1\hspace{0.17em}\mathrm{s}.\mathrm{d}.$ , for any of the three markers, and if so marked this as movement. The use of all three wrist and hand markers helps to mitigate the points where single markers dropped out, e.g. owing to occlusion. The hand movement data were imported to ELAN. Visual inspection of the data suggested that using an individual’s $\mathrm{mean}+1\hspace{0.17em}\mathrm{s}.\mathrm{d}.$ is generally a good proxy for hand movement. However, this is not the case where this value was very low (owing to minimal or no movement, or extreme cases of marker drop out) in which case the algorithm was oversensitive to minor non-gestural movements caused by posture shifts, for example. It was also not accurate in cases where the value of the $\mathrm{mean}+1\hspace{0.17em}\mathrm{s}.\mathrm{d}.$ was very high (individuals who gesture a lot), in which case the algorithm was undersensitive to genuine gestures. For this reason, we introduced a lower and upper threshold for the movement values. These were set at 2 mm per frame for the lower bound and 5 mm per frame for the upper bound. These refinements to the movement calculation result in a more reliable and sensitive index of hand movement than has been adopted in previous analyses of this corpus (e.g. [33 (link)]).
It should be borne in mind that although we believe that our automatically derived hand movement measures are a good proxy for gesture, they do not distinguish between gestural hand movement and other hand movement (e.g. scratching, fidgeting). It is also the case that the automatic hand movement annotation captures only the movement phases of a gesture—including preparation and retraction [16 ], and will not pick up any hold phases of gestures, which are known to be interactionally relevant (see e.g. [45 (link)]), particularly in respect to turn-taking.
Analyses were performed in SPSS 28.

The cartilage tissue was immediately fixed in 4% (w/w) paraformaldehyde for 24 h and then transferred to 10% (w/w) disodium ethylenediamine tetraacetate (EDTA-Na₂) to decalcify for 2 to 3 weeks. The samples were dehydrated with gradient concentration alcohol, cleared with xylene, and finally embedded in paraffin wax. Then, the embedded paraffin samples were cut into 5-micron slices, affixed to slides, and stored at room temperature. Before dyeing, the slices were baked at 65°C for 1 h, dewaxed with xylene, and then rehydrated under the condition of reducing the concentration of ethanol. A proper amount of 0.1% trypsin was added to cover the tissue wax for antigen repair, and it was incubated at 37°C for 30 min and washed three times with 1 × PBS. The endogenous peroxidase activity was blocked with 1.5% (w/w) hydrogen peroxide at room temperature for 10 min and then the slices were washed with 1 × PBS. A 5% normal goat serum working solution was added to the tissue wax block and sealed for 15 min at room temperature. Then, anti-GPX1/anti-GPX3/anti-DIO1 (1:50 dilution ratio, bs-11790-1-ap, protein technology)/anti-DIO2 (1:100 dilution ratio, bs-3673R, Bioss)/anti-DIO3 (1:50 dilution ratio, bs-40229) were added to the area, and the tissue wax block was kept at 4°C overnight (and immunoglobulin G was used as a negative control). The slices were washed with 1 × PBS and incubated using the rabbit SP kit (rabbit streptomycin-biotin detection system) (SP-9001, Zhongshan Jinqiao, Guangzhou, China) according to the manufacturer’s instructions. Freshly prepared DAB chromogenic solution (ZLI-9018, Zhongshan Jinqiao, Guangzhou, China) was added for slice staining and rinsed off gently with tap water. Hematoxylin re-staining, hydrochloric acid alcohol differentiation, flushing, and ammonia anti-blue addition were all carried out. Finally, the slices are dehydrated and installed under an alcohol-washed envelope. The two pathologists carried out and interpreted the IHC staining results under an optical microscope without knowing the source of the sample. The three-layer zones of cartilage joints were determined, according to the different morphologies of the cells, and three or more visual fields were randomly selected for statistical analysis of the positive staining rate (Schumacher et al., 1994 (link); Lorenzo et al., 1998 (link); Karlsson and Lindahl, 2009 (link)). In detail, the long axis of the superficial chondrocytes is parallel to the cartilage surface, and the cells are smaller and flatter than those in the middle and deep zones. The middle zone is randomly distributed in the matrix, and the deep zone is perpendicular to the surface, in which the cells have obvious columnar arrangement characteristics. We chose five visual fields of the same size randomly for each zone and counted at a magnification of 50.