Early Detection
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2022 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Kiyonori Hamatake ◽  
Kazuaki Kojima

Early detection is the key in managing side effects because immune-related adverse events (irAEs) are becoming more serious, and their onset time differs. In our hospital, we conducted an outpatient pharmacist clinic for early detection of irAEs by self-care practice for the cases of immune checkpoint inhibitor administration. As a result of a retrospective survey of 207 cases, the percentage of irAEs found by pharmacist’s suggestion of the outpatient pharmacist clinic increased over time, and a high detection ratio was obtained even for irAEs with a late onset time. The incidence of serious irAEs was higher than that in the immediate post-marketing surveillance, and different factors were considered. Although there were some problems, the outpatient pharmacist clinic had a certain effect.


Author(s):  
David Crosby

AbstractLiquid biopsy approaches are relatively well developed for cancer therapy monitoring and disease relapse, but they also have incredible potential in the cancer early detection and screening field. There are, however, several challenges to overcome before this potential can be met. Research in this area needs to be cohesive and, as a driver of research, Cancer Research UK is in an ideal position to enable this.


2022 ◽  
pp. 014556132110708
Author(s):  
Marco DiBlasi ◽  
Christopher Jayne ◽  
Reilly McNamara ◽  
Catherine Iasiello ◽  
Daryl Colden

Plasmablastic lymphoma (PBL) is an aggressive, rare variant of B-cell lymphoma typically associated with human immunodeficiency virus and other immunocompromised populations. Most commonly found in the oral cavity, PBL can occasionally originate in the sinonasal tract. Diagnosis of PBL is difficult due to overlapping features with other malignancies; however, early detection and treatment are imperative given its aggressive clinical course. When in the sinonasal tract, the diagnostic process can be further complicated if the patient has a history of recurrent nasal polyposis. Described is the case of a 57-year-old immunocompetent male who initially presented with benign nasal polyposis, only to return a year after sinus surgery with a unilateral sinonasal mass consistent with PBL. As literature has yet to characterize this phenomenon, this article presents the first case reported of sinonasal PBL arising in the setting of recurrent nasal polyposis. This case emphasizes the importance of investigating sinonasal masses showing laterality, maintaining a high index of suspicion for malignancy, and keeping close surveillance of the patient after treatment of PBL.


Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 296
Author(s):  
Yujin Kim ◽  
Bo Bin Lee ◽  
Dongho Kim ◽  
Sang-Won Um ◽  
Joungho Han ◽  
...  

This study aimed to understand aberrant methylation of SLITs genes as a biomarker for the early detection and prognosis prediction of non-small cell lung cancer (NSCLC). Methylation levels of SLITs were determined using the Infinium HumanMethylation450 BeadChip or pyrosequencing. Five CpGs at the CpG island of SLIT1, SLIT2 or SLIT3 genes were significantly (Bonferroni corrected p < 0.05) hypermethylated in tumor tissues obtained from 42 NSCLC patients than in matched normal tissues. Methylation levels of these CpGs did not differ significantly between bronchial washings obtained from 76 NSCLC patients and 60 cancer-free patients. However, methylation levels of SLIT2 gene were significantly higher in plasma cell-free DNA of 72 NSCLC patients than in that of 61 cancer-free patients (p = 0.001, Wilcoxon rank sum test). Prediction of NSCLC using SLIT2 methylation was achieved with a sensitivity of 73.7% and a specificity of 61.9% in a plasma test dataset (N = 40). A Cox proportional hazards model showed that SLIT2 hypermethylation in plasma cell-free DNA was significantly associated with poor recurrence-free survival (hazards ratio = 2.19, 95% confidence interval = 1.21–4.36, p = 0.01). The present study suggests that aberrant methylation of SLIT2 in plasma cell-free DNA is a valuable biomarker for the early detection of NSCLC and prediction of recurrence-free survival. However, further research is needed with larger sample size to confirm results.


Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 142
Author(s):  
Georgios Krekoukias ◽  
George A. Koumantakis ◽  
Vasileios S. Nikolaou ◽  
Konstantinos Soultanis

Early detection of scoliosis with school screening and quick, easy, and reliable assessment of its progress are of paramount importance in the management of patients. There have been several tools described, with the most common being the analog scoliometer. Most recently, smartphone applications have entered this area with and without the use of sleeves for the device. There is no research that has evaluated the accuracy of measurements both left and right in either digital or analog devices. In this study, we evaluated the reliability and validity of a new digital scoliometer called the Scolioscope. Thirty subjects were included for the intra-rater reliability study. ICC values >0.9 were calculated both for same-day and between-day measurements. The device was highly accurate with an average difference from the ones set on the sine bar of 0.03° for right-side measurements and 0.18° for the left. These measurements suggest a highly accurate and reliable tool.


2022 ◽  
Vol 12 (2) ◽  
pp. 593
Author(s):  
Muhammad Attique Khan ◽  
Abdullah Alqahtani ◽  
Aimal Khan ◽  
Shtwai Alsubai ◽  
Adel Binbusayyis ◽  
...  

Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.


Viruses ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 83
Author(s):  
Kalhari Bandara Goonewardene ◽  
Chukwunonso Onyilagha ◽  
Melissa Goolia ◽  
Van Phan Le ◽  
Sandra Blome ◽  
...  

African swine fever (ASF) has spread across the globe and has reached closer to North America since being reported in the Dominican Republic and Haiti. As a result, surveillance measures have been heightened and the utility of alternative samples for herd-level monitoring and dead pig sampling have been investigated. Passive surveillance based on the investigation of dead pigs, both domestic and wild, plays a pivotal role in the early detection of an ASF incursion. The World Organization for Animal Health (OIE)-recommended samples for dead pigs are spleen, lymph nodes, bone marrow, lung, tonsil and kidney. However, obtaining these samples requires opening up the carcasses, which is time-consuming, requires skilled labour and often leads to contamination of the premises. As a result, we investigated the suitability of superficial inguinal lymph nodes (SILNs) for surveillance of dead animals. SILNs can be collected in minutes with no to minimum environmental contamination. Here, we demonstrate that the ASF virus (ASFV) genome copy numbers in SILNs highly correlate with those in the spleen and, by sampling SILN, we can detect all pigs that succumb to highly virulent and moderately virulent ASFV strains (100% sensitivity). ASFV was isolated from all positive SILN samples. Thus, sampling SILNs could be useful for routine surveillance of dead pigs on commercial and backyard farms, holding pens and dead on arrival at slaughter houses, as well as during massive die-offs of pigs due to unknown causes.


2022 ◽  
Vol 18 (2) ◽  
pp. 1-17
Author(s):  
Yufei Chen ◽  
Tingtao Li ◽  
Qinming Zhang ◽  
Wei Mao ◽  
Nan Guan ◽  
...  

Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by inconsistent annotation quality. In this article, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues. This approach consists of two main parts: a preprocessing module for data augmentation and a new neural network architecture, ANT-UNet. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 6% to 35% accuracy improvement versus other commonly used segmentation methods. In addition, the proposed architecture is hardware friendly, which can reduce the amount of parameters to one-tenth of the original and achieve 1.7× speed-up.


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