Bronchial balloon dilatation combined with cryotherapy for tuberculous cicatricial central airway stenosis, with Adobe Photoshop for the degree measurement: A multicenter, retrospective study.

Objective: To explore bronchial balloon dilation (BD) combined with cryotherapy and Photoshop (PS)-base image processing technology in the interventional treatment of tuberculous cicatricial central airway stenosis (CCAS). Methods: This multicenter, retrospective study analyzed the clinical data of patients with CCAS from six hospitals in Henan, China between June 2019 and October 2022. Results: A total of 307 patients were included, including 152 (62 males) treated with BD alone and 155 (54 males) treated with BD combined with cryotherapy. One month after treatment, compared with the BD group, the total response rate [133 (85.8) vs. 105 (69.1), P < 0.001] and occurrence of restenosis at 1-monthaftertreatment [22 (14.2 %) vs. 47 (30.9 %), P < 0.001] in BD & cryotherapy group were significantly higher; furthermore, the inner diameter of the airway stenosis (8.1 ± 0.9 mm vs. 6.4 ± 1.5 mm, P < 0.001), the cross-sectional area of the airway stenosis (51.6 ± 7.8 mm2 vs. 33.1 ± 11.6 mm2, P < 0.001), FEV1 (5.26 ± 0.42 L vs. 4.32 ± 0.31 L, P < 0.001), and PEF (5.72 ± 0.36 L/s vs. 4.56 ± 0.42 L/s, P < 0.001) in BD & cryotherapy group showed significantly more improvements. Moreover, Pearson's correlation analysis showed significantly strong positive correlation (r = 0.818，P < 0.001) between the cross-sectional area of airway stenosis measured by PS-base image processing technology and the inner diameter of airway stenosis measured by chest CT. Conclusion: BD combined with cryotherapy for the interventional treatment of CCAS might be superior to BD alone. PS-base image processing technology might be used as a supplement strategy for measuring the degree of central airway stenosis.

Comprehensive use of a high-frequency electric knife, balloon dilatation, and cryotherapy for tuberculous central tracheobronchial cicatricial constriction.

BACKGROUND: To examine the benefits of interventional therapy for cicatricial constriction using a high-frequency electric knife, saccular dilatation, and cryotherapy. METHODS: This case series included patients with central tracheobronchial cicatricial constriction admitted to the Department of Tuberculosis of Henan Provincial Chest Hospital from July 2018 to March 2021 and treated with bronchoscopic interventional therapies based on systemic anti-tuberculosis treatment. RESULTS: 96 patients were included, in whom 443 interventional therapies were performed. The total mid-(3 months) and long-term (12 months) effective rates were both 100%. The internal diameter of tracheobronchial stenosis increased after the operation, and the difference was statistically significant (all < 0.05). After interventional treatment, patients' symptoms of choking sensation in the chest and shortness of breath were relieved. Respiratory function was obviously improved. The ratios of hemorrhage, granulation hyperplasia, chest pain, and postoperative fever were 58.2%, 42.6%, 31.3%, and 26.7%, respectively. No focal transmission and progression of tuberculosis occurred, and no serious complications were observed. CONCLUSION: The use of a high-frequency electric knife, saccular dilatation, and/or cryotherapy according to the pathological stage of the tracheobronchial cicatricial constriction is feasible, with good mid- and long-term curative effects and few complications.

Deep learning for non-parameterized MEMS structural design.

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 103 times and 2.6 × 104 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.

Parallel optical random bit generator.

We present an optical approach for high-speed parallel random bit generation based on stochastic pulse-to-pulse fluctuation in the supercontinuum (SC). Through spectrally demultiplexing the SC pulse sequence into different wavelength channels, we simultaneously extract multiple independent fast random bit streams from each SC pulse subsequence via associated comparators in parallel. Proof-of-concept experiments demonstrate that using our method, four 10 Gb/s random bit streams are obtained from a SC pulse source with verified randomness. Moreover, this method also provides a promising strategy to fabricate ultrafast random bit generators with Tb/s throughput capacity just by increasing additional wavelength channels.

Decreased Serum C3 Levels in Immunoglobulin A (IgA) Nephropathy with Chronic Kidney Disease: A Propensity Score Matching Study.

BACKGROUND The effects of low serum C3 levels and the activation of the complement system on the development and the prognosis of IgAN are unclear. The present study aimed to determine whether decreased levels of complement C3 influence the prognosis of IgAN patients with chronic kidney disease. MATERIAL AND METHODS We enrolled a total of 1564 patients with primary IgAN diagnosed by renal biopsy at the Chinese PLA General Hospital from January 2011 to March 2015. The endpoint was end-stage renal disease (ESRD) or a doubling of the baseline serum creatinine (D-SCr) level. All patients were using 1: 1 propensity score matching (PSM), and the baseline values were not significantly different between these 2 groups (P>0.05). RESULTS During a follow-up period, 14 patients in the group with decreased C3 levels reached the endpoint, with 12 patients with normal C3 levels. There was no significant difference between the 2 groups in achieving D-SCr or ESRD (P=0.676). In multivariate Cox analysis, adjusted for demographic and laboratory examination, the risk of reaching the endpoint was comparable in the 2 groups (HR, 0.70; 95% CI, 0.27-1.78; P=0.449;). Furthermore, the risk of reaching ESRD (HR, 0.83; 95% CI, 0.25-2.75; P=0.757) and D-SCr (HR, 1.45; 95% CI, 0.20-10.60; P=0.718) did not differ between the 2 groups. CONCLUSIONS Decreased serum C3 levels in IgA nephropathy with chronic kidney disease did not play a decisive role in renal progression.