Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss

Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large data repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on dense embedding vectors that represent image features to allow quantitative similarity assessments. Triplet learning has emerged as a powerful approach to recover embeddings in CBIR, albeit traditional loss functions ignore the dynamic relationship between opponent image classes. Here, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. OCAM uses a variable margin value that is updated continually during the course of training to maintain optimally discriminative representations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against baselines.Comment: 10 pages, 6 figure

Reporting hormone receptor expression in breast carcinomas: which method has the highest prognostic power and what should be the optimal cut-off value?

Background . Hormone receptor (HR) expression is a critical marker that plays a role in the treatment and management of breast cancer. Even if patients receive hormone treatment with a hormone positivity rate of over 1%, it is controversial at what level of positivity they benefit from treatment and contribute positively to their prognosis. Methods . We retrospectively examined the estrogen receptor (ER) / progesterone receptor (PR) expression status, clinicopathological findings, and survival data of 386 patients who underwent surgery for breast cancer. ER/PR expressions of the patients were evaluated according to Allred, H-score and were also grouped according to staining percentages. Separate cut-off values were determined for each of these evaluation methods, and the prognostic power of these methods was investigated using receiver operating characteristic analysis. Results . The prognostic power of all methods was found to be similar in terms of predicting survival. According to the staining percentage of the patients, survival was excellent if the ER value was >80% and the PR value was >1%. Conclusions . All recommended methods for reporting HRs have similar prognostic power. However, in patients with high percentage staining for ER using these methods, the prognosis is excellent. As a result, we predict that if the percentage of ER staining is low, changing the treatment management of patients may be considered clinically

Examining the contact problem of a functionally graded layer supported by an elastic half-plane with the analytical and numerical methods

This study offers a comparative study of the analytical and numerical methods for investigating a contact problem. The contact problem comprises a functionally graded layer supported by a half-plane and loaded with a distributed load from the top surface. First, the analytical and numerical solutions to the problem are acquired by utilizing a theory of elasticity and finite element method, respectively. The problem is transformed into a system of integral equations in which the contact stress is an unknown function. The solution of the integral equation was achieved with Gauss–Jacobi integration formulation. The finite element model of the problem is created using ANSYS software, and the two-dimensional analysis of the problem is performed. Results were obtained from the samples for different material properties and loading conditions. The distributed load width and non-homogeneity parameters significantly impact on contact mechanics. The results indicate that the contact area and the contact stress obtained from finite element method (FEM) are close to the analytical results. As a result, acceptable error rates were obtained. Finally, this study provides evidence of a good agreement between the two methods

The Effect of Mixture Parameters on the Surface Properties of Roller Compacted Concrete (RCC) Pavements

In Turkey, the use of RCC pavements is increasing in urban and rural roads. However, a detailed study examining the effect of RCC mixture parameters on the pavement surface properties that affect road driving comfort and safety is not available in literature. In this study, in order to cover that gap in literature 12 RCC mixtures prepared with different cement dosages, aggregate gradations and water amounts were compacted by "Superpave-Gyratory-Compactor" at different levels. Later, the surface characteristics were evaluated with British pendulum and sand patch tests. It was concluded that cement dosage, water content and gradation have an effect not only in terms of strength but also in terms of pavement surface properties, and recommendations were made for RCC mixture optimization.Ülkemizde SSB kaplamaların şehir içi ve köy yollarında kullanımı gittikçe artmaktadır. Fakat yol sürüş konforunu ve güvenliğini etkileyen kaplama yüzey özelliklerine, karışım parametrelerinin etkisini inceleyen detaylı bir çalışma uluslararası literatürde mevcut değildir. Bu eksikliği gidermeye yönelik yapılan bu çalışmada, farklı çimento dozajları, agrega gradasyonları ve su oranları ile hazırlanan 12 SSB karışımı, “Superpave-Yoğurmalı-Presi” ile farklı seviyelerde sıkıştırılıp yüzey özellikleri, İngiliz pandülü ve kum yama testleriyle değerlendirilmiştir. Yapılan istatistiksel analizlerde, yoğurma sayısının etkisi görülmezken; SSB çimento dozajı, su muhtevası ve gradasyonun yalnızca mukavemet yönünden değil aynı zamanda yüzey özellikleri bakımından da etkili olduğu sonucuna varılmış ve SSB karışım optimizasyonu için öneriler getirilmiştir

Does Post-COVID-19 Erectile Dysfunction Improve over Time?

BACKGROUND Some studies have shown that there may be an increase in the frequency of erectile dysfunction after COVID-19. However, no long-term study has investigated whether this is permanent or temporary. In this study, we aimed to examine whether there was an increase in the frequency of erectile dysfunction among individuals with a history of COVID-19, and, if there was, whether their condition improved over time. MATERIALS AND METHODS In this study, a total of 125 healthy male healthcare workers, 95 with and 30 without a history of COVID-19, were evaluated in terms of erectile function. Four study groups were formed. The first three groups consisted of individuals with a history of COVID-19 confirmed by the polymerase chain reaction (PCR) test at different times, who recovered from the disease (time elapsed since COVID-19 positivity: 12 months for Group 3). The individuals in Group 4 did not have a history of COVID-19 diagnosis. In order to evaluate the erectile function of the participants, they were asked to complete the five-item International Index of Erectile Function questionnaire (IIEF-5). Then, statistical analyses were performed to evaluate whether there was a difference between the groups in terms of the IIEF-5 scores. RESULTS There was a statistically significant difference between the groups in terms of the IIEF-5 scores (p 0.999, p = 0.204, and p = 0.592, respectively). CONCLUSION There may be deterioration in erectile function after COVID-19; however, this tends to improve over time, especially from the first year after active infection. Given that vascular, hormonal, and/or psychogenic factors may lead to the development of erectile dysfunction after COVID-19, we consider that in order to easily manage this process, it is important to determine the underlying cause, initiate appropriate treatment, and inform couples that this situation can be temporary

Knowledge, Attitudes and Behaviors of Geriatric People About Periodic Health Examination

Amaç: Periyodik sağlık muayenesi (PSM); bireylerin anamnez, fizik muayene, tetkik ve bağışıklamagerekliliklerinin yaşa, cinsiyete ve risk gruplarına göre belli aralıklarla değerlendirilmesidir. PSMkoruyucu hekimliğin parçasıdır ve her yaş grubunda olduğu gibi geriyatrik yaş grubunda da önemibüyüktür. Çalışmamızda geriatrik (65 yaş ve üstü) kişilerin PSM hakkında bilgi, tutum ve davranışlarınıdeğerlendirmeyi amaçladık.Materyal ve Metot: Bu çalışma Haziran? Ağustos 2017 tarihleri arasında 65 yaş ve üstü olup çalışmayakatılmayı kabul eden ve herhangi bir nedenle Sağlık Bilimleri Üniversitesi Şişli Hamidiye Etfal Eğitim veAraştırma Hastanesi Aile Hekimliği Polikliniği’ne başvuran 201 kişiyle yapılmıştır. Sosyodemografikverilerin yanı sıra 65 yaş PSM kapsamında hastalara sorgulanması gereken bulgular; yapılması gerekenmuayene, aşı, tahlil, tetkik ve verilmesi gereken proflaksilerle ilgili; tarafımızca hazırlanan bilgi formuyüz yüze sorgulama yöntemi ile uygulandı. P değeri 0,05 kabul edildi.Bulgular: Çalışmaya 201 kişi katılmıştı ve %52,2’si kadındı. Katılımcıların %90’ında kronik bir hastalıkbulunuyordu ve %96’sı son bir yıl içinde bir sağlık kuruluşuna başvurmuştu. %93,5’i son bir yıldatansiyon ölçümü yapılmış ve en çok (%56,2) evde otomatik cihazla ölçülmüştü. Kan tahlillerini düzenliyaptıran katılımcı oranı %85’ti. Katılımcılardan sadece %42,3’ünün tahlilleri aynı hekim tarafından takipedilmekteydi. Katılımcıların %57,2’si aspirin; %74,6’sı kalsiyum?D vitamini profilaksisi kullanmıyordu.Erkeklerde Kalsiyum?D vitamini kullanımı anlamlı olarak düşüktü. Katılımcılardan %69,2’si grip aşısını;%92’si Pnömokok aşısını yaptırmamıştı. Aşı yaptırmayanların %55,7’si bilgilendirilmediğini belirtmişti.Doktorların bilgilendirdiği 57 (%28,3) kişinin çoğu (n=41; %71,9) grip aşısını yaptırmıştı. Katılımcıların%78,1’i kanser taramaları konusunda bilgilendirilmediklerini belirtmişlerdir. Sonuç: Çalışmamızda geriatrik bireylerin tahlil yaptırma oranlarının yüksek ancak aynı hekimtarafından takip edilme oranlarının düşük olduğunu ve PSM uygulamalarını yeteri kadaryaptırmadıklarını saptadık. Özellikle aşılama ve kanser taramalarında bireylerin en sık yaptırmamanedeni bilgilendirilmeme idi. Bilgi ve farkındalık arttırmaya yönelik çalışılmalar yapılması ve bireyleredüzenli takibin öneminin anlatılmasıyla PSM’ye katılım oranlarının artacağını düşünmekteyiz.Objectives: Periodic health examination (PHE) is an evaluation of the history, physical examination, tests and immunization requirements of individuals according to age, gender and risk groups. PHE is a part of preventive health care services and it is also important in geriatric age group as it is in every age group. In our study, we aimed to evaluate the knowledge, attitudes and behaviors of geriatric people (over 65 years old) about the PHE Materials and Methods: This study was conducted with 201 people over 65 years old who accepted to participate in the study and who admitted to the family medicine outpatient clinic of SBU Şişli Hamidiye Etfal Education and Research Hospital for any reason between June?August 2017. The questionnaires were performed by doctors with face?to?face interview technique with the participants. In addition to socio?demographic factors, we questioned participants’ knowledge, attitudes and behaviors about investigations, treatments, immunizations, counseling and screenings that should be performed at their age. A p value of p ? 0.05 was considered to be statistically significant. Results: Among 201 people that participated in the study 52.2% of them were women. 90% of the participants had a chronic disease and 96% had applied to a health center in the last year. 93.5% of the participants had their blood pressure checked in the last year and most of them (%56,2) were measured with automatic device at home. 85% of the participants had their blood tests performed regularly. Only 42.3% of the participants were followed?up by the same physician. Most of the participants did not take aspirin (%57.2) and calcium?vitamin D (%74,6) prophylaxis. Calcium?vitamin D use in men were significantly lower. Respectively 69.2% and 92% of them didn’t have influenza and pneumococcal vaccines. 55.7% of the participants who didn’t have the vaccines stated that they were not informed. Out of 57 people who were informed by the doctors, 41 of them had a influenza vaccine.78.1% of the participants stated that they were not informed about cancer screening. Conclusion: In our study, we determined that geriatric individuals mostly had their tests done but have low percentage of being followed by the same physician and do not have their PHE’s sufficiently. The particular reason for not having immunizations and cancer screenings were not being informed by physicians. We think studies for increasing knowledge, awareness and explaining the importance of regular follow?up to individuals will increase participation rates in PHE

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales