Optical coherence tomography angiography in the evaluation of vascular patterns of ocular surface squamous neoplasia during topical medical treatment

PurposeOptical coherence tomography angiography (OCTA) was utilized to examine changes in ocular surface squamous neoplasia (OSSN) vascular patterns over time in individuals treated with topical medical therapy.MethodsTen individuals with OSSN diagnosed by clinical examination and high resolution (HR)-optical coherence tomography (OCT) were recruited. All individuals received topical immuno- or chemotherapy. OCTA images were obtained and analyzed at three points: presentation, mid-treatment and tumor resolution. Tumor metrics including tumor area (TA), tumor volume (TV), tumor depth (TD), and total tumor density (TTD) were calculated. Vessel area density (VAD) was also quantified within the OSSN, the subepithelium under and adjacent to the OSSN and the subepithelium of the uninvolved, contralateral eye. Vascular network changes were also subjectively evaluated.ResultsTA, TV, TD and TTD all significantly decreased with time (p < 0.001). The mean VAD within the OSSN significantly decreased (p < 0.001) between visits (presentation: 26.52 ± 6.8%, mid-treatment: 7.19 ± 5.88%, tumor resolution: 0.11 ± 0.34%). The mean subepithelial VAD under the OSSN also decreased with time (23.22 ± 11.03%, 20.99 ± 5.99% and 19.58 ± 7.08%), and after resolution the sub-tumor VAD (19.58 ± 7.08%) was comparable to the subepithelial VAD in the contralateral eye (15.47 ± 4.37%, p > 0.05). The mean VAD in the subepithelium adjacent to the OSSN increased with treatment, then decreased significantly between mid-treatment and resolution (23.26 ± 4.54, 28.30 ± 7.43% and 21.68 ± 6.10%, p = 0.009). Qualitatively, the tumor subepithelial vascular network was complex and dense but with tumor resolution appeared less tortuous and similar to the uninvolved eye.ConclusionOCTA provided insight into the pathophysiology of tumor angiogenesis, showing decreased vascular density and normalization of vascular networks associated with tumor resolution.     

Frame Invariance and Scalability of Neural Operators for Partial Differential Equations

Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes. Solving these PDEs often requires prohibitively high computational costs, especially when multiple evaluations must be made for different parameters or conditions. After training, neural operators can provide PDEs solutions significantly faster than traditional PDE solvers. In this work, invariance properties and computational complexity of two neural operators are examined for transport PDE of a scalar quantity. Neural operator based on graph kernel network (GKN) operates on graph-structured data to incorporate nonlocal dependencies. Here we propose a modified formulation of GKN to achieve frame invariance. Vector cloud neural network (VCNN) is an alternate neural operator with embedded frame invariance which operates on point cloud data. GKN-based neural operator demonstrates slightly better predictive performance compared to VCNN. However, GKN requires an excessively high computational cost that increases quadratically with the increasing number of discretized objects as compared to a linear increase for VCNN.     

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

脑血管畸形致脑出血患者手术治疗效果观察

目的分析脑血管畸形致脑出血患者应用手术治疗的临床疗效。方法纳入本院2018年1月—2020年1月收治的60例脑血管畸形致脑出血患者,随机分组(每组样本容量共计30例),参照组给予保守治疗,观察组给予手术治疗,对比两组临床疗效、治疗前、治疗1月后NIHSS评分、住院时间。结果临床总有效率观察组(93.33%)明显高于参照组(63.33%),观察组治疗1月后NIHSS评分明显低于参照组,观察组住院时间明显比参照组短,差异均具有统计学意义(P<0.05)。结论脑血管畸形致脑出血患者采纳手术治疗,可有效减轻患者神经功能受损情况,缩短治疗时间。     

Exploration and validation of the effects of robust co-expressed immune-related genes on immune infiltration patterns and prognosis in laryngeal cancer

ObjectivesLaryngeal cancer is a common malignant tumor that originates from the larynx, yet its molecular mechanisms have not been thoroughly explored. The purpose of this study was to identify and evaluate immune-related genes in laryngeal cancer through gene co-expression networks, which may serve as biomarkers for its immunotherapy.MethodsWe applied ESTIMATE to evaluate the immune-infiltration landscape of tumor microenvironment. The co-expression networks were constructed by weighted gene co expression network analysis (WGCNA) and compared with the existing human immune related genes (IRGs) to determine the co-expressed IRGs. GSVA combined with CIBERSORT and ssGSEA illustrated the correlation of hub genes and immune infiltration patterns. TIDE algorithm and Subclass mapping evaluated the function of hub genes in predicting immune function and immunotherapeutic sensitivity. The pRRophetic was employed in the sensitivity prediction of chemotherapeutic drugs.ResultsA total of 23 co-expressed IRGs were identified and showed robust expression characteristics. These genes were significantly related to immune infiltration patterns, immune function and sensitivity prediction of immunotherapy and chemotherapeutic drugs for laryngeal cancer patients. Genetic alteration in somatic mutation level and related pathways were also revealed.ConclusionThe 23 co-expressed IRGs may act as immunotherapeutic biomarkers and potential therapeutic targets for laryngeal cancer with certain expression robustness. The molecular mechanisms deserve further investigation, which will guide clinical treatment in the future.