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Aims and objectives: To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models. Background: As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Design: Systematic review. Methods: Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Results: Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation. Conclusions: ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. Relevance to Clinical Practice: This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical practice. [ABSTRACT FROM AUTHOR]

Humans and mice have the ability to regenerate the distal digit tip, the terminal phalanx (P3) in response to amputation. What distinguishes P3 regeneration from regenerative failure is formation of the blastema, a proliferative structure that undergoes morphogenesis to regenerate the amputated tissues. P3 regeneration is characterised by the phases of inflammation, tissue histolysis and expansive bone degradation with simultaneous blastema formation, wound closure and finally blastemal differentiation to restore the amputated structures. While each regenerating digit faithfully progresses through all phases of regeneration, phase progression has traditionally been delineated by time, that is, days postamputation (DPA), yet there is widespread variability in the timing of the individual phases. To diminish variability between digits during tissue histolysis and blastema formation, we have established an in‐vivo method using microcomputed tomography (micro CT) scanning to identify five distinct stages of the early regeneration response based on anatomical changes of the digit stump. We report that categorising the initial phases of digit regeneration by stage rather than time greatly diminishes the variability between digits with respect to changes in bone volume and length. Also, stages correlate with the levels of cell proliferation, osteoclast recruitment and osteoprogenitor cell recruitment. Importantly, micro CT staging provides a means to estimate open versus closed digit wounds. We demonstrate two spatially distinct and stage specific bone repair/regeneration responses that occur during P3 regeneration. Collectively, these studies showcase the utility of micro CT imaging to infer the composition of radiolucent soft tissues during P3 blastema formation. Specifically, the staging system identifies the onset of cell proliferation, osteoclastogenesis, osteoprogenitor recruitment, the spatial initiation of de novo bone formation and epidermal closure. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ydbio.2007.03.518 Byline: Ling Yu (a), Hongbing Liu (a)(b), Mingquan Yan (a), Jing Yang (c), Fanxin Long (d), Ken Muneoka (a), YiPing Chen (a)(b) Keywords: Shox2; Runx2; BMP4; Mouse; Limb patterning; Chondrogenesis Abstract: Mutations in the short stature homeobox gene SHOX lead to growth retardation associated with Turner, Leri-Weill dyschondrosteosis, and Langer mesomelic dysplasia syndromes, which marked the shortening of the forearms and lower legs. We report here that in contrast to the SHOX mutations in humans, Shox2 deficiency in mice leads to a virtual elimination of the stylopod in the developing limbs, while the zeugopod and autopod appear relatively normal. This phenotype is consistent with the restriction of the Shox2 expression to the proximal mesenchyme in the limb bud and later to chondrocytes associated with the forming stylopod. In the Shox2.sup.-/- embryo, the mesenchymal condensation for the stylopod initiates normally but the cartilaginous element subsequently fails in growth, chondrogenesis and endochondral ossification. A dramatic down-regulation of Runx2 and Runx3 could account for the lack of chondrocyte hypertrophy, while a down-regulation of Ihh expression may be responsible for a significant reduction in chondrocyte proliferation in the mutant stylopod. We further demonstrate that an enhanced and ectopic Bmp4 expression in the proximal limb of the Shox2 embryo may underlie the down-regulation of Runx2, as ectopically applied exogenous BMP4 represses Runx2 expression in the early limb bud. Moreover, we show that mouse Shox2, similar to human SHOX, can perform opposite roles on gene expression: either as a transcription activator or a repressor in different cell types. Our results establish a key role for Shox2 in regulating the growth of stylopod by controlling chondrocyte maturation via Runx2 and Runx3. Author Affiliation: (a) Department of Cell and Molecular Biology, Tulane University, New Orleans, LA 70117, USA (b) Department of Oral Biology, The Ohio State University Health Sciences Center Columbus, OH 43210, USA (c) Columbus Children's Research Institute, Columbus, OH 43205, USA (d) Department of Medicine, Washington University Medical School, St. Louis, MO 63110, USA Article History: Received 24 January 2007; Revised 22 March 2007; Accepted 23 March 2007