The duration of tracheostomy dependence in patients with juvenile-onset recurrent respiratory papillomatosis.

BACKGROUND: Tracheostomy is a vital therapy for juvenile-onset recurrent respiratory papillomatosis (JORRP) to maintain an adequate airway in an emergency, yet the relationship between cannulation duration and prognosis has not been extensively explored. OBJECTIVES: To investigate the predictive influence of the duration of tracheostomy dependence on JORRP remission. MATERIALS AND METHODS: A retrospective review of JORRP patients (n = 77) with tracheostomy treated in Beijing Tongren Hospital was performed. RESULTS: The rate of decannulation was 72.7%. After decannulation for one year, the percentage of distal spread fell from 42.9 to 30.4%. Twenty-six of 77 patients (33.8%) had remission of their disease, 40 (51.9%) continued to have active disease while 11 (14.3%) died during follow-up. The cannulation duration was positively correlated with the overall duration of this disease (r = 0.6). The cut-off point of 34.9 months for cannulation duration indicated the highest predictive value of remission. Duration of cannulation >34.9 months (OR = 0.33) and distal spread (OR = 0.29) decreased odds of remission. CONCLUSION: The study demonstrates that the time span before decannulation indicates the severity of disease and cannulation aggravates the distal spread. Patients with cannulation duration ≤ 34.9 months after tracheostomy are prone to possess a relatively pleasant prognosis.

High recurrence rate in patients with juvenile-onset respiratory papillomatosis and its risk factors.

PURPOSE: To identify the recurrence rate and risk factors for recurrence in patients with juvenile-onset recurrent respiratory papillomatosis (JORRP). METHODS: A retrospective review was performed for all JORRP patients who underwent surgery between 2002 and 2019 at our institution. The demographic characteristics and clinical parameters were recorded. Kaplan-Meier estimates and Cox proportional hazards models were used to analyze the rate of recurrence and its risk factors. RESULTS: Our study included 721 patients. The cumulative recurrence rates at 1, 5, and 10 postoperative years following initial surgery were 74.2%, 90.0%, and 94.3%, respectively. Age at diagnosis younger than 4.5 years (HR = 2.380, 95% CI [1.169-4.846], P = 0.017), high Derkay anatomical score (HR = 1.136, 95% CI [1.043-1.236], P = 0.003) and HPV type 11 infection (HR = 2.947, 95% CI [1.326-6.551], P = 0.008) were independent risk factors for recurrence. Adjuvant therapy with interferon was less likely to recur (HR = 0.237, 95% CI [0.091-0.616], P = 0.003). Additionally, gender, tracheotomy, mode of delivery, parity, expression of Ki-67, HPV vaccination, and surgical treatment method were not independently associated with recurrence (P > 0.05). CONCLUSION: Age at diagnosis younger than 4.5 years, high Derkay anatomical score and HPV type 11 infection were associated with an increased risk for recurrence in patients with JORRP. Adjuvant therapy with interferon may reduce the risk of recurrence.

Tracheotomy as a predictor of remission and demise for juvenile-onset recurrent respiratory papillomatosis.

BACKGROUD: The pros and cons of tracheotomy, as a classic treatment of juvenile-onset recurrent respiratory papillomatosis (JORRP), have gradually been recognized, but the exact impact of tracheotomy on remission and demise is not clear. OBJECTIVES: To investigate the predicting influence of tracheotomy on prognosis for JORRP. MATERIAL AND METHODS: Three hundred forty two patients with JORRP treated in Beijing Tongren Hospital were retrospectively reviewed. The clinical characteristics and prognosis parameters were compared in the group of tracheotomy and non-tracheotomy. RESULTS: The rate of tracheotomy was 24.6% (84/342). Among these patients, 68 (81.0%) developed the tracheal papillomatosis. The onset age of RRP occurred earlier in tracheostomized group, and patients performed tracheotomy needed a greater number of surgeries and developed distal spread more easily (p < .05). The remission rate was significantly lower (35.1 vs. 53.7%) and the mortality higher (13.1 vs. 1.2%) in patients with tracheotomy than non-tracheotomy. Tracheotomy decreased odds of remission (OR = 0.48; 95%CI: 0.28-0.83) and increased odds of demise (OR = 11.98; 95%CI: 3.21-44.65). CONCLUSIONS: The age at diagnosis, the surgical frequency and the medical level of hospital are important factors affecting the occurrence of tracheotomy. Patients who had undergone tracheotomy are prone to possess the low remission rate and high mortality.

Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method.

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.