Disease detection in high-dimensional low sample size medical data

Deep learning has brought a new age of advancements for Artificial Intelligence. Data-orient modelling paired with computational power brings amazing performance to targeted tasks in computer vision. However, contrary to the hopes of practitioners, real-world data often fails to meet the size (n) or dimensionality (d) ideal for deep learning: high dimensionality is prevalent, and sample sizes are often small. This high-dimensional low-sample size scenario (HLDS) is prevalent and more extreme in medical datasets, where the dimensionality can be not only bigger d > n (in normal HDLS setting) but much bigger than the available sample size, d ≫ n. This scenario presents a major obstacle between research and application. This thesis presents our research on disease detection in HDLS medical data from the approaches of (1) combination of multiple learners, (2) the use of less data and annotations, and (3) learning with existing basis. The first component explores combining information from a set of supervised models. In this premise, a novel committee learning method is proposed that reformulates ensemble learning as a multiple-instance learning problem, which can be solved with attention-pooling mechanisms. The method offers performance benefits to HDLS datasets and broadly applies to committee learning. The second component explores the utilisation of weakly annotated data. An empirical framework is proposed for localising disease regions and generating pseudo data for enhancement. Its performance is demonstrated for anomaly localisation and enhancement of a range of segmentation models. The third component explores a theoretical framework for understanding learning from HDLS data. Under this framework, empirical verification is provided for theoretical properties, leading to developments in simple linear and complex neuromorphic methods for semi-supervised, continual and few-shot learning. Overall, we tackled the extreme HDLS scenario of multiple medical datasets from three perspectives: committee learning, weak-supervised learning and continual/few-shot learning.</p

Disease detection in high-dimensional low sample size medical data

Deep learning has brought a new age of advancements for Artificial Intelligence. Data-orient modelling paired with computational power brings amazing performance to targeted tasks in computer vision. However, contrary to the hopes of practitioners, real-world data often fails to meet the size (n) or dimensionality (d) ideal for deep learning: high dimensionality is prevalent, and sample sizes are often small. This high-dimensional low-sample size scenario (HLDS) is prevalent and more extreme in medical datasets, where the dimensionality can be not only bigger d > n (in normal HDLS setting) but much bigger than the available sample size, d ≫ n. This scenario presents a major obstacle between research and application. This thesis presents our research on disease detection in HDLS medical data from the approaches of (1) combination of multiple learners, (2) the use of less data and annotations, and (3) learning with existing basis. The first component explores combining information from a set of supervised models. In this premise, a novel committee learning method is proposed that reformulates ensemble learning as a multiple-instance learning problem, which can be solved with attention-pooling mechanisms. The method offers performance benefits to HDLS datasets and broadly applies to committee learning. The second component explores the utilisation of weakly annotated data. An empirical framework is proposed for localising disease regions and generating pseudo data for enhancement. Its performance is demonstrated for anomaly localisation and enhancement of a range of segmentation models. The third component explores a theoretical framework for understanding learning from HDLS data. Under this framework, empirical verification is provided for theoretical properties, leading to developments in simple linear and complex neuromorphic methods for semi-supervised, continual and few-shot learning. Overall, we tackled the extreme HDLS scenario of multiple medical datasets from three perspectives: committee learning, weak-supervised learning and continual/few-shot learning.</p

Single-incision versus multiport video-assisted thoracoscopic surgery in the treatment of lung cancer: a systematic review and meta-analysis

<p><b>Objectives:</b> Recent studies compared single-incision thoracoscopic surgery (SITS) with more widely used conventional multiport video-assisted thoracoscopic surgery in the treatment of lung cancer. To establish the safety and feasible of SITS in the treatment of lung cancer, we conducted this systematic review and meta-analysis.</p> <p><b>Methods:</b> Eleven studies were identified from the databases of PubMed, Cochrane Library, SpringerLink, and ScienceDirect. The randomized controlled trials (RCTs) and non-randomized studies evaluated the outcomes of SITS compared with multiport video-assisted thoracoscopic surgery in the treatment of lung cancer were included for analysis. Odds ratio (OR, used to compare dichotomous variables) and weight mean difference (WMD, used to compare continuous variables) were calculated with 95% confidence intervals (CIs) based on intention-to-treat analysis.</p> <p><b>Results:</b> Eleven studies including 1314 patients were included for analysis. Our analysis showed that the operative time, blood loss amount, mean duration of chest tube, lymph nodes retrieved were similar between two approaches, the SITS pulmonary resection might be associated with shorter hospital stay (<i>p</i> = .008) and lower complication rate (<i>p</i> = .009) when compared with conventional multiport video-assisted thoracoscopic surgery approaches.</p> <p><b>Conclusions:</b> In selected patients SITS is safe, feasible and may be considered an alternative to multiport VATS.</p

A systematic survey of deep learning in breast cancer

In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them

Additional file 1 of Senescent cancer-associated fibroblasts facilitate tumor associated neutrophil recruitment suppressing tumor immunity

Additional file 1: Methods

Dopant-Assisted Positive Photoionization Ion Mobility Spectrometry Coupled with Time-Resolved Thermal Desorption for On-Site Detection of Triacetone Triperoxide and Hexamethylene Trioxide Diamine in Complex Matrices

Peroxide explosives, such as triacetone triperoxide (TATP) and hexamethylene trioxide diamine (HMTD), were often used in the terrorist attacks due to their easy synthesis from readily starting materials. Therefore, an on-site detection method for TATP and HMTD is urgently needed. Herein, we developed a stand-alone dopant-assisted positive photoionization ion mobility spectrometry (DAPP-IMS) coupled with time-resolved thermal desorption introduction for rapid and sensitive detection of TATP and HMTD in complex matrices, such as white solids, soft drinks, and cosmetics. Acetone was chosen as the optimal dopant for better separation between reactant ion peaks and product ion peaks as well as higher sensitivity, and the limits of detection (LODs) of TATP and HMTD standard samples were 23.3 and 0.2 ng, respectively. Explosives on the sampling swab were thermally desorbed and carried into the ionization region dynamically within 10 s, and the maximum released concentration of TATP or HMTD could be time-resolved from the matrix interference owing to the different volatility. Furthermore, with the combination of the fast response thermal desorber (within 0.8 s) and the quick data acquisition software to DAPP-IMS, two-dimensional data related to drift time (TATP: 6.98 ms, <i>K</i>₀ = 2.05 cm² V^–1 s^–1; HMTD: 9.36 ms, <i>K</i>₀ = 1.53 cm² V^–1 s^–1) and desorption time was obtained for TATP and HMTD, which is beneficial for their identification in complex matrices

MiR-132 Suppresses the Migration and Invasion of Lung Cancer Cells <i>via</i> Targeting the EMT Regulator ZEB2

<div><p>MicroRNAs (miRNAs) are small, non-coding RNAs which can function as oncogenes or tumor suppressor genes in human cancers. Emerging evidence reveals that deregulation of miRNAs contributes to the human non-small cell lung cancer (NSCLC). In the present study, we demonstrated that the expression levels of miR-132 were dramatically decreased in examined NSCLC cell lines and clinical NSCLC tissue samples. Then, we found that introduction of miR-132 significantly suppressed the migration and invasion of lung cancer cells in vitro, suggesting that miR-132 may be a novel tumor suppressor. Further studies indicated that the EMT-related transcription factor ZEB2 was one direct target genes of miR-132, evidenced by the direct binding of miR-132 with the 3′ untranslated region (3′ UTR) of ZEB2. Further, miR-132 could decrease the expression of ZEB2 at the levels of mRNA and protein. Notably, the EMT marker E-cadherin or vimentin, a downstream of ZEB2, was also down-regulated or up-regulated upon miR-132 treatment. Additionally, over-expressing or silencing ZEB2 was able to elevate or inhibit the migration and invasion of lung cancer cells, parallel to the effect of miR-132 on the lung cancer cells. Meanwhile, knockdown of ZEB2 reversed the enhanced migration and invasion mediated by anti-miR-132. These results indicate that miR-132 suppresses the migration and invasion of NSCLC cells through targeting ZEB2 involving the EMT process. Thus, our finding provides new insight into the mechanism of NSCLC progression. Therapeutically, miR-132 may serve as a potential target in the treatment of human lung cancer.</p></div

ZEB2 contributes to miR-132- suppressed migration and invasion of NSCLC cells.

<p>(A) The expression of ZEB2 was examined by Western blot in NSCLC cells. (B) The relative mRNA levels of ZEB2 were detected in 45 paired NSCLC primary tumor tissues and their lymph node metastasis counterparts. ZEB2 expression in those tissues was compared by way of Wilcoxon signed-rank test (***<i>P</i><0.001, Student’s t- test). (C) Inverse correlation between miR-132 and ZEB2 expression in NSCLC tissues. ZEB2 expression was analyzed by qRT-PCR and normalized to GAPDH. The miR-132 expression was detected by qRT-PCR analysis and normalized to U6 expression. Statistical analysis was performed using Pearson’s correlation coefficient (r = −0.68, ***<i>P</i><0.001). (D) The cell invasion was detected by Boyden chamber assay in NL9980 or 95C cells transfected with pCMV or pCMV-ZEB2 vectors, respectively. (E, F) The effect of ZEB2 knockdown on the cell migration or invasion was assessed by wound healing or Boyden chamber assay, respectively (**<i>P</i><0.01, Student’s <i>t</i>- test). Additionally, the silencing efficiency of ZEB2 by siRNA was examined by Western blot. (G, H) The wound healing or Boyden chamber assay was used to detect the migration or invasion ability of NL9980 cells with different treatments, respectively (*<i>P</i><0.05, **<i>P</i><0.01, Student’s <i>t</i>- test). Additionally, the silencing efficiency of ZEB2 by siRNA was examined by Western blot.</p

MiR-132 directly inhibits the expression of ZEB2 through its 3′UTR and regulates the EMT of NSCLC cells.

<p>(A) The miR-132 binding site predicted in the 3′UTR of ZEB2 mRNA. (B) Mutant was generated at the seed region of ZEB2 3′ UTR as indicated by the underline. A 3′ UTR fragment of ZEB2 mRNA containing wild-type or mutant of the miR-132 binding sequence was cloned into the downstream of the luciferase gene in pMIR vector. (C) L9981 cells were transfected with pMIR reporter vectors containing either wild-type or mutant ZEB2 3′UTR (indicated as pMIR-ZEB2-3′ UTR-wt and pMIR-ZEB2-3′ UTR-mut) with either pcDNA3.1 (indicated as NC) or pcDNA3.1-miR-132 vector (indicated as miR-132). Luciferase activity was determined 48 h after transfection. (D) ZEB2 mRNA was detected by qRT-PCR in cell lines transfected with pcDNA3.1 (indicated as NC) or pcDNA3.1-miR-132 vector (indicated as miR-132). (E, F) The protein levels of ZEB2, E-cadherin or vimentin was examined by Western blot in cells transfected with different plasmids. Figure F shows the relative gray values of each band (normalized to β-actin). Protein bands from three independent Western blot assays were quantified using Quantity One software (Bio-Rad, USA). Data are reported as mean ±SD (**<i>P</i><0.01, Student’s <i>t</i>- test).</p

Comprehensive Lipidome-Wide Profiling Reveals Dynamic Changes of Tea Lipids during Manufacturing Process of Black Tea

As important biomolecules in <i>Camellia sinensis</i> L., lipids undergo substantial changes during black tea manufacture, which is considered to contribute to tea sensory quality. However, limited by analytical capacity, detailed lipid composition and its dynamic changes during black tea manufacture remain unclear. Herein, we performed tea lipidome profiling using high resolution liquid chromatography coupled to mass spectrometry (LC-MS), which allows simultaneous and robust analysis of 192 individual lipid species in black tea, covering 17 (sub)­classes. Furthermore, dynamic changes of tea lipids during black tea manufacture were investigated. Significant alterations of lipid pattern were revealed, involved with chlorophyll degradation, metabolic pathways of glycoglycerolipids, and other extraplastidial membrane lipids. To our knowledge, this report presented most comprehensive coverage of lipid species in black tea. This study provides a global and in-depth metabolic map of tea lipidome during black tea manufacture