Application of a Diabetic Foot Smart APP in the measurement of diabetic foot ulcers.

AIMS: In the early stage, we developed an intelligent measurement APP for diabetic foot ulcers, named Diabetic Foot Smart APP. This study aimed to validate the APP in the measurement of ulcer area for diabetic foot ulcer (DFU). METHODS: We selected 150 DFU images to measure the ulcer areas using three assessment tools: the Smart APP software package, the ruler method, and the gold standard Image J software, and compared the measurement results and measurement time of the three tools. The intra-rater and inter-rater reliability were described by Pearson correlation coefficient, intra-group correlation coefficient, and coefficient of variation. RESULTS: The Image J software showed a median ulcer area of 4.02 cm2, with a mean measurement time of 66.37 ± 7.95 s. The ruler method showed a median ulcer area of 5.14 cm2, with a mean measurement time of 171.47 ± 46.43 s. The APP software showed a median ulcer area of 3.70 cm2, with a mean measurement time of 38.25 ± 6.81 s. There were significant differences between the ruler method and the golden standard Image J software (Z = -4.123, p < 0.05), but no significant difference between the APP software and the Image J software (Z = 1.103, p > 0.05). The APP software also showed good inter-rater reliability and intra-rater reliability, with both reaching 0.99. CONCLUSION: The Diabetic Foot Smart APP is a fast and reliable measurement tool with high measurement accuracy that can be easily used in clinical practice for the measurement of ulcer areas of DFU. TRIAL REGISTRATION: Chinese clinical trial registration number: ChiCTR2100047210.

Knowledge, attitudes, and practice of Endocrinology healthcare workers regarding screening for pre-ulcerative diabetic foot lesions.

OBJECTIVE: To investigate knowledge, attitude and practice of screening pre-ulcerative lesions among endocrinology healthcare workers. METHODS: A new questionnaire was developed and distributed online and 1004 valid questionnaires were returned. T-test, ANOVA, Pearson correlation analysis, and multiple linear regression were used for statistical analysis. RESULTS: A total of 1100 questionnaires were returned, and 96 were excluded. The scores of endocrinology healthcare workers' knowledge, attitude, and practice for screening for pre-ulcerative lesions were 45.46 ± 16.26, 92.11 ± 10.50, and 72.27 ± 17.63 respectively. 60.2% participants had been trained to screen for pre-ulcerative lesions, but 39.8% had not been trained. 31.8% of healthcare professionals claimed that their hospital did not have a screening project for pre-ulcer diabetic foot lesions. Positive relationships were found between knowledge and practice and between attitude and practice. Multiple linear regression analysis showed that: level II hospital and tertiary hospital were the main factors influencing the knowledge scores; Undergraduate and participating in relevant training were the main factors influencing the attitude scores; participating in relevant training, hospital conducts relevant projects, and patient cooperation, and working hours were the main factors influencing the practice score. CONCLUSIONS: Endocrinology healthcare workers need more knowledge regarding pre-ulcerative lesions, and their screening practices need to be strengthened. Increased education and training for pre-ulcerative lesion screening should be implemented among healthcare workers in endocrinology departments.

Comprehensive Analysis of bHLH Transcription Factors in Ipomoea aquatica and Its Response to Anthocyanin Biosynthesis.

The basic helix-loop-helix (bHLH) proteins compose one of the largest transcription factor (TF) families in plants, which play a vital role in regulating plant biological processes including growth and development, stress response, and secondary metabolite biosynthesis. Ipomoea aquatica is one of the most important nutrient-rich vegetables. Compared to the common green-stemmed I. aquatica, purple-stemmed I. aquatica has extremely high contents of anthocyanins. However, the information on bHLH genes in I. aquatica and their role in regulating anthocyanin accumulation is still unclear. In this study, we confirmed a total of 157 bHLH genes in the I. aquatica genome, which were classified into 23 subgroups according to their phylogenetic relationship with the bHLH of Arabidopsis thaliana (AtbHLH). Of these, 129 IabHLH genes were unevenly distributed across 15 chromosomes, while 28 IabHLH genes were spread on the scaffolds. Subcellular localization prediction revealed that most IabHLH proteins were localized in the nucleus, while some were in the chloroplast, extracellular space, and endomembrane system. Sequence analysis revealed conserved motif distribution and similar patterns of gene structure within IabHLH genes of the same subfamily. Analysis of gene duplication events indicated that DSD and WGD played a vital role in the IabHLH gene family expansion. Transcriptome analysis showed that the expression levels of 13 IabHLH genes were significantly different between the two varieties. Of these, the IabHLH027 had the highest expression fold change, and its expression level was dramatically higher in purple-stemmed I. aquatica than that in green-stemmed I. aquatica. All upregulated DEGs in purple-stemmed I. aquatica exhibited the same expression trends in both qRT-PCR and RNA-seq. Three downregulated genes including IabHLH142, IabHLH057, and IabHLH043 determined by RNA-seq had opposite expression trends of those detected by qRT-PCR. Analysis of the cis-acting elements in the promoter region of 13 differentially expressed genes indicated that light-responsive elements were the most, followed by phytohormone-responsive elements and stress-responsive elements, while plant growth and development-responsive elements were the least. Taken together, this work provides valuable clues for further exploring IabHLH function and facilitating the breeding of anthocyanin-rich functional varieties of I. aquatica.

Health Economic Indicators in Patients With Diabetic Foot: Outpatient Multidisciplinary Management.

The purpose of this study is to investigate the influence of an outpatient multidisciplinary diagnosis and treatment model on the health economic indices of diabetic foot patients. We included 142 diabetic foot patients who received treatment in 2 target hospitals from January to April 2018 in this prospective cohort study. According to their exposure factors, the patients were divided into a MDT group and a control group, with 71 patients in each group. The patients' baseline data were collected. The follow-up period was 12 months; all patients were followed up to April 30, 2019. Health economic indicators were collected when the patients were discharged from the hospital. The prognosis of each group was followed every month. If a wound healed, a major amputation occurred, or the patient died within 12 months, the follow-up was stopped. A total of 129 patients were followed, and their baseline data were comparable. During the follow-up period, the healing rate of the 2 groups was significantly different (P = .034). The healing rate of Wagner grade 4 patients was significantly better than Wagner grade 2 and grade 3 patients (P = .001). Health economic indicators demonstrated significant differences in bed waiting time (P = .038), transfer time (P = .001), surgery waiting time (P = .003), length of hospital stay (P = .047), and hospitalization expenses (P = .011). In conclusion, an outpatient multidisciplinary diagnosis and treatment model for diabetic foot can support cost-effective patient management.

Construction and verification of an intelligent measurement model for diabetic foot ulcer. / ä¸€ç§ç³–å°¿ç— è¶³æºƒç–¡æ™ºèƒ½æµ‹é‡æ¨¡åž‹çš„æž„å»ºä¸ŽéªŒè¯.

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS: The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.

Development and validation of a brief diabetic foot ulceration risk checklist among diabetic patients: a multicenter longitudinal study in China.

The study aims to develop and assess and validate a brief diabetic foot ulceration risk checklist among diabetic patients through a longitudinal study. Patients who had diabetes mellitus and had no foot ulceration and severe systematic disorders were recruited from eleven tertiary hospitals in nine provinces or municipalities of China. Internal consistency reliability, construct validity, concurrent validity, item property, and measurement invariance of the tool were assessed. The predictive capability of the tool was validated by the follow-up data using the receiver operating characteristic curve. At baseline, 477 valid cases were collected. Twelve items were remained after initial selection. Cronbach's alpha was 0.56. Confirmatory factor analysis showed that the model had acceptable goodness-of-fit yet local dependency between two items. Item response theory showed that most items had acceptable discrimination and difficulty parameters. Differential item functioning showed that tool had measurement invariance. 278 were followed up one year after the baseline. Follow-up showed that one-year incidence of ulceration among the patients was 3.6%, and the area under the receiver operating characteristic curve was 0.77 (95% confidence interval: 0.61-0.93). The cut-off point of the tool was 4, when sensitivity and specificity were 0.62 and 0.75 respectively. The checklist has good psychometric properties according to mixed evidences from classical and modern test theory, and has good predictive capability.