Wounds are a consequence of disruption in the structure, integrity, or function of the skin or tissue. Once a wound is formed following mechanical or chemical damage, the process of wound healing is initiated, which involves a series of chemical signaling and cellular mechanisms that lead to regeneration and/or repair. Disruption in the healing process may result in complications; therefore, interventions to accelerate wound healing are essential. In addition to mechanical support provided by sutures and traditional wound dressings, therapeutic agents play a major role in accelerating wound healing. The medicines known to improve the rate and extent of wound healing include antibacterial, anti-inflammatory, and proliferation enhancing agents. Nonetheless, the development of these agents into eluting nanofibers presents the possibility of fabricating wound dressings and sutures that provide mechanical support with the added advantage of local delivery of therapeutic agents to the site of injury. Herein, the process of wound healing, complications of wound healing, and current practices in wound healing acceleration are highlighted. Furthermore, the potential role of drug-eluting nanofibers in wound management is discussed, and lastly, the economic implications of wounds as well as future perspectives in applying fiber electrospinning in the design of wound dressings and sutures are considered and reported.

Aims Sickle cell disease (SCD) is an autosomal recessive inherited condition that presents with a number of clinical manifestations that include musculoskeletal manifestations (MM). MM may present differently in different individuals and settings and the predictors are not well known. Herein, we aimed at determining the predictors of MM in patients with SCD at the University Teaching Hospital, Lusaka, Zambia. Methods An unmatched case-control study was conducted between January and May 2019 in children below the age of 16 years. In all, 57 cases and 114 controls were obtained by systematic sampling method. A structured questionnaire was used to collect data. The different MM were identified, staged, and classified according to the Standard Orthopaedic Classification Systems using radiological and laboratory investigations. The data was entered in Epidata version 3.1 and exported to STATA 15 for analysis. Multiple logistic regression was used to determine predictors and predictive margins were used to determine the probability of MM. Results The cases were older median age 9.5 (interquartile range (IQR) 7 to 12) years compared to controls 7 (IQR 4 to 11) years; p = 0.003. After multivariate logistic regression, increase in age (adjusted odds ratio (AOR) = 1.2, 95% confidence interval (CI) 1.04 to 1.45; p = 0.043), increase in the frequency of vaso-occlusive crisis (VOC) (AOR = 1.3, 95% CI 1.09 to 1.52; p = 0.009) and increase in percentage of haemoglobin S (HbS) (AOR = 1.18, 95% CI 1.09 to 1.29; p < 0.001) were significant predictors of MM. Predictive margins showed that for a 16-year-old the average probability of having MM would be 51 percentage points higher than that of a two-year-old. Conclusion Increase in age, frequency of VOC, and an increase in the percentage of HbS were significant predictors of MM. These predictors maybe useful to clinicians in determining children who are at risk. Cite this article: Bone Joint Open 2020;1-6:175–181.

Aims Sickle cell disease (SCD) is an autosomal recessive inherited condition that presents with a number of clinical manifestations that include musculoskeletal manifestations (MM). MM may present differently in different individuals and settings and the predictors are not well known. Herein, we aimed at determining the predictors of MM in patients with SCD at the University Teaching Hospital, Lusaka, Zambia. Methods An unmatched case-control study was conducted between January and May 2019 in children below the age of 16 years. In all, 57 cases and 114 controls were obtained by systematic sampling method. A structured questionnaire was used to collect data. The different MM were identified, staged, and classified according to the Standard Orthopaedic Classification Systems using radiological and laboratory investigations. The data was entered in Epidata version 3.1 and exported to STATA 15 for analysis. Multiple logistic regression was used to determine predictors and predictive margins were used to determine the probability of MM. Results The cases were older median age 9.5 (interquartile range (IQR) 7 to 12) years compared to controls 7 (IQR 4 to 11) years; p = 0.003. After multivariate logistic regression, increase in age (adjusted odds ratio (AOR) = 1.2, 95% confidence interval (CI) 1.04 to 1.45; p = 0.043), increase in the frequency of vaso-occlusive crisis (VOC) (AOR = 1.3, 95% CI 1.09 to 1.52; p = 0.009) and increase in percentage of haemoglobin S (HbS) (AOR = 1.18, 95% CI 1.09 to 1.29; p < 0.001) were significant predictors of MM. Predictive margins showed that for a 16-year-old the average probability of having MM would be 51 percentage points higher than that of a two-year-old. Conclusion Increase in age, frequency of VOC, and an increase in the percentage of HbS were significant predictors of MM. These predictors maybe useful to clinicians in determining children who are at risk. Cite this article: Bone Joint Open 2020;1-6:175–181.