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The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).

Background Mitomycin-C (MMC) is the most commonly used chemotherapeutic agent for hyperthermic intraperitoneal chemotherapy (HIPEC) after cytoreductive surgery (CRS). However, MMC has a side effect of myelosuppression. This study aimed to evaluate the clinical manifestations and impact of MMC-induced neutropenia after CRS and HIPEC in colorectal cancer patients. Methods A total of 124 colorectal cancer patients who underwent CRS with HIPEC between March 2015 and January 2019 were evaluated. Patients with malignancies of non-colorectal origin, hospital stay longer than 60 days, peritoneal cancer index > 30, and complete cytoreduction score > 2 were excluded. MMC 35 mg/m2 was administered for 90 min at 41–43 °C. The patients were divided into three groups: no neutropenia, mild neutropenia (grade 1–2), and severe neutropenia (grade 3–4). Results In total, mild and severe neutropenia occurred in 30 (24.2%) and 48 (38.7%) patients, respectively. Age and body surface area were significantly different among the neutropenia groups. Severe neutropenia developed significantly earlier than mild neutropenia (6.9 days vs. 10.4 days, p < 0.001) and also lasted significantly longer (4.6 days vs. 2.5 days, p = 0.005). The rate of major postoperative complications was significantly higher in the severe neutropenia group than in the no and mild neutropenia groups (8.3% vs. 6.7% vs. 6.5%, p = 0.015) Conclusions Severe neutropenia starts earlier and lasts longer than mild neutropenia after CRS and HIPEC using an MMC triple method. The higher rate of major postoperative complications in patients with severe neutropenia highlights the importance of postoperative management during the neutropenia period.

OBJECTIVE To prospectively assess the efficacy of a biodegradable collagen matrix (ologen) in dogs with uncontrolled glaucoma receiving an Ahmed glaucoma valve (AGV) implant. ANIMAL STUDIED Five client-owned dogs with glaucoma (five eyes). PROCEDURES Five eyes treated for uncontrolled glaucoma underwent AGV implantation with ologen. Ologen was placed on the AGV plate and tube with a scleral flap. Complete ophthalmological examinations were performed preoperatively and at 1 and 3 days, 1 and 2 weeks, and 1, 2, 3, and 6 months postoperatively. Surgical outcomes were assessed based on the intraocular pressure (IOP), vision, frequency of anti-glaucoma eye drops, and bleb morphology; complications, if any, were recorded. The number of dogs with an IOP

Typical treatment for recurrent pancreatic cancer is potent chemotherapy or chemoradiotherapy. However, recent clinical investigations suggest a potential therapeutic role of local resection in recurrent pancreatic cancer. Based on accumulating surgical experience of minimally invasive pancreatectomy, we report cases of two patients who underwent successful laparoscopic re-resection of isolated local recurrence following laparoscopic radical pancreatectomy for pancreatic cancer.

The general purpose of stock price prediction is to help stock analysts design a strategy to increase stock returns. We present the conceptual framework of an emotion-based stock prediction system ...

Drawing on coping and sensemaking theory, this study proposes a theoretical integration framework that links individuals’ psychological responses and adaptive behaviors with collective interests re...

Regeneration is widespread across the animal kingdom but varies vastly across phylogeny and even ontogeny. Adult mammalian regeneration in most organs and appendages is limited, while vertebrates such as zebrafish and salamanders are able to regenerate various organs and body parts. Here, we focus on the regeneration of appendages, spinal cord, and heart – organs and body parts that are highly regenerative among fish and amphibian species but limited in adult mammals. We then describe potential genetic, epigenetic, and post-transcriptional similarities among these different forms of regeneration across vertebrates and discuss several theories for diminished regenerative capacity throughout evolution.

Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to determine whether convolutional neural networks (CNNs) could assess erythema, papulation, excoriation, and lichenification severity at a level of competence comparable to dermatologists. We created a standard dataset of 8,000 clinical images showing AD. Each component of the EASI was scored from 0 to 3 by three dermatologists. We trained four CNNs (ResNet V1, ResNet V2, GoogLeNet, and VGG-Net) with the image dataset and determined which CNN was the most suitable for erythema, papulation, excoriation, and lichenification scoring. The brightness of the images in each dataset was adjusted to − 80% to + 80% of the original brightness (i.e., 9 levels by 20%) to investigate if the CNNs accurately measured scores if image brightness levels were changed. Compared to the dermatologists’ scoring, accuracy rates of the CNNs were 99.17% for erythema, 93.17% for papulation, 96.00% for excoriation, and 97.17% for lichenification. CNNs trained with brightness-adjusted images achieved a high accuracy without the need to standardize camera settings. These results suggested that CNNs perform at level of competence comparable to dermatologists for scoring erythema, papulation, excoriation, and lichenification severity.

Atopic dermatitis (AD) is a chronic inflammatory skin disease related with abnormal immune response and epidermal barrier function impairment. The aim of our study was to estimate the association between AD and infectious disorders in real world. We analyzed population-based data from 2010 to 2013 using National Health Insurance Service. Sequential pattern mining (SPM) was used to identify comorbid infectious diseases and onset duration of the comorbidities. Patients with AD were at higher risk for molluscum contagiosum (adjusted odd ratio (aOR) 5.273), impetigo (aOR 2.852), chicken pox (aOR 2.251) and otitis media (aOR 1.748), eczema herpeticum (aOR 1.292), viral wart (aOR 1.105). Our study suggests that AD is associated with increased risk of infectious disorders. In particular, care should be taken for early diagnosis of comorbidity and appropriate management.

As Internet media develops, not only individuals but governments have digital identities (digital identity includes images made by digital media), in addition, as Internet media develops, chances are high that images will be determined by digital identity. Therefore, companies are meeting customer needs through digital image management and data analysis. This is also necessary for government organizations like the Ministry of National Defence (MND). At least we can prevent the damage caused by fake news and can see the Internet’s response to defense policy. Therefore, we invented an automatic search and analysis program for real-time internet documents. Also, through experiments with the policy of MND, we studied the significance and possibility of the project.

We present the conceptual framework of a soccer winlose prediction system (SWLPS) focused on passing distribution data (which is a representative characteristic of soccer) using social network analysis (SNA) and gradient boosting (GB). The general purpose of soccer predictions is to help the field supervisor design a strategy to win subsequent games using the derived information to improve and expand the coaching process. To implement and evaluate the proposed SWLPS, actual network indicators and predicted network indicators are generated using passing distribution data and SNA. The winlose prediction is conducted using the GB machine learning technique. The performance of the SWLPS is analyzed through comparison with various machine learning techniques (i.e., support vector machine (SVM), neural network (NN), decision tree (DT), case-based reasoning (CBR), and logistic regression (LR)). The experimental results and analyses demonstrate that the network indicators generated through SNA can represent soccer team performance and that an accurate winlose prediction system can be developed using GB technique. This study proposes a conceptual framework for soccer winlose prediction system.The proposed predicting system employs a social network analysis to generate input variables.A gradient boosting is utilized to simulate predictions based on network indicators.Application to the Champions League is used to validate the proposed system.

제4차 산업혁명의 등장과 경제성장으로 인한 ‘국민 삶의 질 향상’ 요구 증대로 인해 의료서비스의 질과 의료비용에 대한 국민들의 요구수준이 향상되고 있으며, 이로 인해 인공지능이 의료현장에 도입되고 있다. 하지만 인공지능이 의료분야에 활용된 사례를 살펴보면 ‘삶의 질’에 직접적인 영향을 끼치는 만성피부질환에 활용된 사례는 부족한 실정이며, 만성피부질환 중 대표적 질병인 아토피피부염은 정성적 진단 방법으로 인해 진단의 객관성을 확보할 수 없다는 한계가 존재한다. 본 연구에서는 아토피피부염의 객관적 중증도 평가 방법을 마련하여 아토피피부염 환자의 삶의 질을 향상시키고자 다음과 같은 연구를 수행하였다. 첫째, 가톨릭대학교 의과대학 성모병원의 데이터베이스로부터 아토피피부염 환자의 이미지 데이터를 수집했으며, 수집된 이미지 데이터에 대한 정제 및 라벨링 작업을 수행하여 모델 학습과 검증에 적합한 데이터를 확보했다. 둘째, 지능형 아토피피부염 중증도 진단 모형에 적합한 이미지 인식 알고리즘을 파악하기 위해 다양한 CNN 알고리즘들을 병변별 학습용 데이터로 학습시키고, 검증용 데이터를 활용하여 해당 모델의 이미지 인식 정확도를 측정했다. 실증분석 결과 홍반(Erythema)의 경우 ‘ResNet V1 101’, 긁은 정도(Excoriation)의 경우 ‘ResNet V2 50’이 90% 이상의 정확도를 기록하였으며, 태선화(Lichenification)의 경우 학습용 데이터 부족의 한계로 인해 두 병변보다 낮은 89%의 정확도를 보였다. 해당 결과를 통해 이미지 인식 알고리즘이 단순한 사물 인식 분야뿐만 아니라 전문적 지식이 요구되는 분야에도 높은 성능을 나타낸다는 것을 실증적으로 입증했으며, 본 연구는 실제 아토피피부염 환자의 이미지 데이터를 활용했다는 측면에서 실제 임상환경에서 활용성이 높을 것으로 사료된다.

본 연구의 목적은 여성의 사회진출이 활발해진 현재의 시대양상에 맞춰 여성 직장인들의 주요 고민을 통해 여성 이직의 원인을 개인관련 요인과 직무관련 요인으로 분류하여 현 노동시장에서 여성 인력이 실감하는 직무 스트레스와 이직 원인, 이직 준비과정에서 느끼는 심리적 반응 및 행동을 탐색하는 것이다. 이를 위해, 여성전용 커뮤니티에 작성된 게시글들을 수집하였고, 텍스트 마이닝 기법 중 토픽분석을 수행하여 직장여성들의 주요 고민을 유형별로 분류하고 시간에 따른 고민들의 변화 추이와 연관성 분석을 통해 이직원인을 다각도로 탐색하였다. 분석결과 직장여성의 주요 고민은 개인관련 요인과 직무관련 요인 두 가지로 구분되었으며, 세부 토픽으로는 관계, 일 자체, 돈, 나이와 성별, 결혼에 의한 고민과 같이 다섯 가지 유형으로 확인되었다. 구체적인 직장여성들의 이직원인으로 직무관련요인 보다 나이, 성별 그리고 결혼과 같은 개인관련 요인이 더 영향을 미치는 것으로 파악되었다. 또한 여성들의 우선순위가 바뀌어 결혼이 늦어진다는 점과 결혼을 하게 되면 업무에 지장을 받는다는 점을 결혼과 일에 의한 고민의 연관성을 통해 확인할 수 있었다. 연구결과는 현재 직장여성들의 이직원인은 여성으로서 겪는 차별에서 기인한다는 점을 시사하며, 각종 정부정책들이 직장여성에게 유의미하지 않다는 기존의 연구와 일맥상통한다.

This paper describes the conceptual framework of a relative value (RV)-based trading system focused on the data characteristics of the foreign exchange futures market using a correlation and rough set analysis. RV trading is an investment strategy that can generate potential profits based on the RV of two securities, regardless of market direction. We select pairs with a positive correlation, negative correlation, or no correlation based on the correlation coefficients between foreign exchange futures contracts. To implement and experiment with the proposed system, trading rules are generated using a rough set analysis that employs technical indicators derived from the RVs of the pairs. The performance of the proposed trading system is analyzed using the momentum and buy-and-hold trading strategies as benchmarks. The experimental results and analyses demonstrate that the level of the correlation of the pairs must be considered when developing stable and profitable RV trading systems in a foreign exchange futures market. This study proposes a conceptual framework for a relative value trading system.The proposed trading system employs a correlation analysis to form pairs.A rough set analysis is utilized to generate trading rules.Application to the foreign exchange futures market is used to validate the proposed system.

Drugs against malaria are losing their effectiveness because of emerging drug resistance. This underscores the need for novel therapeutic options for malaria with mechanism of actions distinct from current antimalarials. To identify novel pharmacophores against malaria we have screened compounds containing structural features of natural products that are pharmacologically relevant. This screening has identified a 4-nitro styrylquinoline (SQ) compound with submicromolar antiplasmodial activity and excellent selectivity. SQ exhibits a cellular action distinct from current antimalarials, acting early on malaria parasite's intraerythrocytic life cycle including merozoite invasion. The compound is a fast-acting parasitocidal agent and also exhibits curative property in the rodent malaria model when administered orally. In this report, we describe the synthesis, preliminary structure-function analysis, and the parasite developmental stage specific action of the SQ scaffold., Graphical abstract Image 1

The purpose of this methodological study was to develop a convolutional neural network (CNN), which is a recently developed deep-learning-based image recognition method, to determine corneal ulcer severity in dogs. The CNN model was trained with images for which corneal ulcer severity (normal, superficial, and deep) were previously classified by veterinary ophthalmologists’ diagnostic evaluations of corneal photographs from patients who visited the Veterinary Medical Teaching Hospital (VMTH) at Konkuk University and 3 different veterinary ophthalmology specialty hospitals in Korea. The original images (depicting normal corneas (36) and corneas with superficial (47) ulcers, deep (47) ulcers), flipped images (total 520), rotated images (total 520), and both flipped and rotated images (total 1,040) were labeled, learned and evaluated with GoogLeNet, ResNet, and VGGNet models, and the severity of each corneal ulcer image was determined. To accomplish this task, models based on TensorFlow, an open-source software library developed by Google, were used, and the labeled images were converted into TensorFlow record (TFRecord) format. The models were fine-tuned using a CNN model trained on the ImageNet dataset and then used to predict severity. Most of the models achieved accuracies of over 90% when classifying superficial and deep corneal ulcers, and ResNet and VGGNet achieved accuracies over 90% for classifying normal corneas, corneas with superficial ulcers, and corneas with deep ulcers. This study proposes a method to effectively determine corneal ulcer severity in dogs by using a CNN and concludes that multiple image classification models can be used in the veterinary field.

Onychomycosis (OM) is a common nail disease. Although controversial, vascular diseases are considered independent predictors of OM and vice versa. Sequential pattern mining (SPM) has not been previously used for statistical analysis in dermatology, but it is an efficient method for identifying frequent association rules in multiple sequential data sets. The aim of our study was to identify the relationship between OM and vascular diseases in the real world through a population-based study using SPM. We obtained population-based data recorded from 2002 to 2013 by the Health Insurance Research and Assessment Agency. Cases of vascular-related disease and OM were identified using the diagnostic codes of the International Classification of Diseases 10th Revision, version 2010. SPM measures were based on comorbidity and duration values. We estimated 3-year risk for progression from OM to vascular disease and vice versa using logistic regression. Patients with varicose veins and peripheral vascular disease had higher OM comorbidity (comorbidity: 1.26% and 0.69%, respectively) than did those with other vascular diseases. Patients diagnosed with varicose veins and peripheral vascular disease were diagnosed with OM after 25.50 and 55.10 days, respectively, which was a shorter duration than that observed for other diseases. Patients with OM were at higher risk for peripheral vascular disease (adjusted odds ratio (aOR) 1.199 [95% confidence interval (CI) 1.151–1.249]) and varicose veins (aOR 1.150 [95% CI 1.063–1.245]). Patients with peripheral vascular disease (aOR 1.128 [95% CI 1.081–1.177]) were at higher risk for OM, while patients with varicose veins had no significant risk for OM. Careful consideration of varicose veins or peripheral vascular disease is required for proper management of comorbidities in patients with OM.