ABSTRACT
Elasticity, featured by a recoverable strain, refers to the ability that materials can return to their original shapes after deformation. Typically, the elastic strains of most metals are well-known 0.2%. In shape memory alloys and high entropy alloys, the elastic strains can be several percent, as called superelasticity, which are all triggered by external stresses. A superelasticity induced by magnetic field, termed as magneto-superelasticity, is extremely important for contactless work of materials and for developing brand-new large stroke actuators and high efficiency energy transducers. In magnetic shape memory alloys, the twin boundary motion driven by magnetic field can output a strain of several percent. However, this strain is unrecoverable when removing the magnetic field and hence it is not magneto-superelasticity. Here, a giant magneto-superelasticity of 5% in a Ni34Co8Cu8Mn36Ga14 single crystal is reported by introducing arrays of ordered dislocations to form preferentially oriented martensitic variants during the magnetically induced reverse martensitic transformation. This work provides an opportunity to achieve high performance in functional materials by defect engineering.
ABSTRACT
Background: Hysteroscopy is considered the gold standard for diagnosing intrauterine pathology. Traditional hysteroscopy requires the placement of a vaginal speculum and cervical forceps, which are large in diameter, causing discomfort and pain to the patient and even causing vagal reflexes. Aims: To investigate the impact and clinical value of vaginoscopy versus conventional hysteroscopy on pain, complications, and patient satisfaction in patients with endometrial polyps and to analyse the advantages of clinical application of vaginoscopy examination. Materials and Methods: One hundred and twenty-five patients with endometrial polyps treated in our hospital from May 2021 to December 2021 were selected for this study and divided into 52 cases in the hysteroscopy group and 73 cases in the vaginoscopy group according to the random remainder grouping method. Conventional hysteroscopy was used, and in the vaginoscopy group, vaginoscopy was performed. The impact of pain, complications, patient satisfaction, and clinical value of the two groups was observed and compared. Results: The time taken for the examination varied between the different hysteroscopic methods, with the hysteroscopy group taking the longest time compared to the vaginoscopy group (P < 0.01). The VAS scores immediately after the examination and 30 minutes after the examination were both significantly higher in the hysteroscopy group than in the vaginoscopy group (P < 0.01). The difference in NPY, PGE2, and 5-HT after the pain-causing mediator intervention was significantly better in the vaginoscopy group than in the hysteroscopy group. The difference in the incidence of complications such as abortion syndrome, cervical laceration, uterine perforation, and haemorrhage after treatment was significantly lower in the vaginoscopy group than in the hysteroscopy group. In the vaginoscopy group, the satisfaction rate was 91% significantly higher than that of the hysteroscopy group (P < 0.05). Conclusion: The vaginoscopy technique shortens the examination and treatment time, reduces patient pain, improves patient compliance, reduces the use of preintervention drugs and anaesthetics, and reduces complications.
Subject(s)
Hysteroscopy , Polyps , Female , Humans , Hysteroscopy/adverse effects , Hysteroscopy/methods , Pain/etiology , Patient Satisfaction , Pregnancy , VaginaABSTRACT
BACKGROUND: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. METHODS: We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. RESULTS: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. CONCLUSIONS: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.
