Objective To investigate the operative procedure and the clinical results of the island flap based on the vascular chain of the cutaneous branch of dorsal metacarpal artery for repairing finger soft tissue defect. Methods Between January 2008 and March 2012, 28 cases of tissue defect of fingers (32 fingers) were repaired with the island flaps based on the vascular chain of the cutaneous branch of dorsal metacarpal artery. There were 20 males (23 fingers) and 8 females (9 fingers), with an average age of 29.5 years (range, 14-67 years). The injury causes included 14 cases of crush injury, 6 cases of pressing injury, 5 cases of cutting injury, and 3 cases of avulsion injury. The locations included 10 index fingers, 13 long fingers, 6 ring fingers, and 3 little fingers. There were 9 defects of proximal segment, 12 defects of middle segment, and 11 defects of distal segment. The area of defect ranged from 1.0 cm × 0.8 cm to 5.2 cm × 3.5 cm. The disease duration was 1 hour to 15 days. The area of flaps ranged from 1.2 cm × 1.0 cm to 5.5 cm × 3.8 cm. The donors were closed by suture or were repaired with skin graft. Results Tense blister occurred in 3 cases, which was cured after dressing change; the other flaps survived. Wound obtained primary healing. Twenty-five patients (27 fingers) were followed up 6-25 months (mean, 16.8 months). The flaps had soft texture and satisfactory appearance. Two point discrimination was 6-9 mm (mean, 7.7 mm) at 6 months after operation. The total active movement of fingers was 105-230° (mean, 204.6°). The results were excellent in 17 fingers, good in 8 fingers, and fair in 2 fingers with an excellent and good rate of 92.6%. Conclusion The island flap based on the vascular chain of the cutaneous branch of dorsal metacarpal artery has the advantages of the deverting point from the dorsal point to the palm, the extended vessel pedicle, and expanded operation indications, so it is not necessary to cut the dorsal metacarpal artery. It can be used to repair finger tissue defect.
For the increasing number of patients with depression, this paper proposes an artificial intelligence method to effectively identify depression through voice signals, with the aim of improving the efficiency of diagnosis and treatment. Firstly, a pre-training model called wav2vec 2.0 is fine-tuned to encode and contextualize the speech, thereby obtaining high-quality voice features. This model is applied to the publicly available dataset - the distress analysis interview corpus-wizard of OZ (DAIC-WOZ). The results demonstrate a precision rate of 93.96%, a recall rate of 94.87%, and an F1 score of 94.41% for the binary classification task of depression recognition, resulting in an overall classification accuracy of 96.48%. For the four-class classification task evaluating the severity of depression, the precision rates are all above 92.59%, the recall rates are all above 92.89%, the F1 scores are all above 93.12%, and the overall classification accuracy is 94.80%. The research findings indicate that the proposed method effectively enhances classification accuracy in scenarios with limited data, exhibiting strong performance in depression identification and severity evaluation. In the future, this method has the potential to serve as a valuable supportive tool for depression diagnosis.
ObjectiveTo systematically review the value of radiomics in the diagnosis of glioblastoma. MethodsPubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect studies on radiomics in the grading of gliomas or the differentiation diagnosis from inception to May 30th, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias and the quality of the included studies. Meta-analysis was then performed using Meta-Disc 1.4 software and RevMan 5.3 software. ResultsA total of 37 studies involving 2 746 subjects were included. The results of meta-analysis showed that the pooled sensitivity, specificity, and diagnostic odds ratio for the diagnosis of glioblastoma by radiomics were 0.91 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.90), and 78.00 (95%CI 50.81 to 119.72), respectively. The area under the summary receiver operating characteristic (SROC) curve was 0.95. The key radiomic features for correct diagnosis of glioblastoma included intensity features and texture features of the lesions. ConclusionThe current evidence shows that radiomics provides good diagnostic accuracy for glioblastoma. Due to the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.
The cleft lip and palate (CLP) is one of the most common craniofacial malformations in humans. We collected functional magnetic resonance data of 23 CLP patients before rehabilitation training (Bclp) and 23 CLP patients after rehabilitation training (Aclp), who were performing Chinese character pronunciation tasks, and performed brain activation analysis to explore the changes of brain mechanism in CLP patients after articulation disorder rehabilitation training. The study found that Aclp group had significant activation in the motor cortex, Broca area, Wernicke area and cerebellum. While the Bclp group had weak activation in the motor cortex with a small activation range. By comparing the differences and co-activated brain regions between the two groups, we found that rehabilitation training increased the activity level of negatively activated brain areas (cerebellum, left motor area, Wernicke area, etc.) to a positive level. At the same time, the activity level of weakly activated brain areas (right motor area, Broca area, etc.) was also increased. Rehabilitation training promoted the activity level of articulation-related brain regions. So that the activation intensity of articulation-related brain regions can be used as a quantifiable objective evaluation index to evaluate the effect of rehabilitation training, which is of great significance for the formulation of rehabilitation training programs.
ObjectiveTo investigate the effect of PDCA circulation management on pain, psychology and prognosis of patients with thoracic aortic aneurysm in the perioperative period.Methods The clinical data of seventy-six patients with thoracic aortic aneurysm who received perioperative nursing based on PDCA circulation management from April 2016 to March 2017 were retrospective analyzed and these patients were selected as the study group, including 44 males, 32 females, aged 23–65 (47.27±5.87) years. At the same time, 72 patients with thoracic aortic aneurysm who received routine perioperative nursing from April 2015 to March 2016 were selected as the control group, including 41 males, 31 females, aged 24–67 (48.30±5.26) years. The nursing effects of the two groups were compared and analyzed.ResultsThe operation time (t=11.342, P<0.05) and hospitalization time (t=5.986, P<0.05) of the study group were significantly shorter than those of the control group. The visual analogue scale (VAS) scores of the two groups had no significant difference before nursing (t=0.914, P=0.361), but the VAS scores in the study group after nursing were obviously lower than those in the control group (t=5.475, P<0.05). The self-rating depression scale (SDS, t=1.026, P=0.307) and self-rating anxiety scale (SAS) scores (t=7.866, P<0.05) of the two groups had no significant difference before nursing, while the SDS (t=7.657, P<0.05) and SAS (t=7.866, P<0.05) scores in the study group after nursing were obviously lower than those in the control group. The incidence of adverse reactions in the study group was significantly lower than that in the control group (χ2=4.292, P=0.038).ConclusionPDCA circulation management used in patients with thoracic aortic aneurysm in the perioperative period can effectively relieve patients' pain, depression and anxiety, reduce the incidence of adverse reactions, and the prognosis is good.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.