MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging

Growth status and menarcheal age among adolescent school girls in Wannune, Benue State, Nigeria

<p>Abstract</p> <p>Background</p> <p>Menarcheal age is a sensitive indicator of environmental conditions during childhood. The aim of study is to determine the age at menarche and growth status in adolescents in a rural area of Tarka, Wannune, Nigeria.</p> <p>Methods</p> <p>Data on 722 female students (aged 12-18 years) were collected in February 2009. Height and weight were measured. Body mass index (BMI; kg m^-2) was used as an index of relative weight.</p> <p>Results</p> <p>Mean and median menarcheal age calculated by probit analysis were 13.02 (SD 3.0) (95% CI: 13.02-13.07), and age 13.00 (SD 2.8) (95% CI: 12.98-13.04), respectively. Girls who reach menarche are significantly heavier and taller with higher BMIs than those of their pre-menarcheal peers.</p> <p>Conclusion</p> <p>The age of menarche is probably still declining in Nigeria. Although BMI is an important factor in the onset of menstruation, some other unmeasured environmental variables may be implicated in this population.</p

Genotyping and antibiotic resistance of thermophilic Campylobacter isolated from chicken and pig meat in Vietnam

Background Campylobacter species are recognized as the most common cause of foodborne bacterial gastroenteritis in humans. In this study nine Campylobacter strains isolated from chicken meat and pork in Hanoi, Vietnam, were characterized using molecular methods and tested for antibiotic resistance. Results The nine isolates (eight C. jejuni and one C. coli) were identified by multiplex PCR, and tested for the presence or absence of 29 gene loci associated with virulence, lipooligosaccharide (LOS) biosynthesis and further functions. flaA typing, multilocus sequence typing and microarray assay investigation showed a high degree of genetic diversity among these isolates. In all isolates motility genes (flaA, flaB, flhA, fliM), colonization associated genes (cadF, docB), toxin production genes (cdtA, cdtB, secD, secF), and the LOS biosynthesis gene pglB were detected. Eight gene loci (fliY, virB11, Cje1278, Cj1434c, Cj1138, Cj1438c, Cj1440c, Cj1136) could not be detected by PCR. A differing presence of the gene loci ciaB (22.2 %), Cje1280 (77.8 %), docC (66.7 %), and cgtB (55.6 %) was found. iamA, cdtC, and the type 6 secretion system were present in all C. jejuni isolates but not in C. coli. flaA typing resulted in five different genotypes within C. jejuni, MLST classified the isolates into seven sequence types (ST-5155, ST-6736, ST-2837, ST-4395, ST-5799, ST-4099 and ST-860). The microarray assay analysis showed a high genetic diversity within Vietnamese Campylobacter isolates which resulted in eight different types for C. jejuni. Antibiotic susceptibility profiles showed that all isolates were sensitive to gentamicin and most isolates (88.8 %) were sensitive to chloramphenicol, erythromycin and streptomycin. Resistance rates to nalidixic acid, tetracycline and ciprofloxacin were 88.9, 77.8 and 66.7 %, respectively. Conclusions To the best of our knowledge, this study is the first report that shows high genetic diversity and remarkable antibiotic resistance of Campylobacter strains isolated from meat in Vietnam which can be considered of high public health significance. These preliminary data show that large scale screenings are justified to assess the relevance of Campylobacter infections on human health in Vietnam

CLINICAL PROGNOSTIC FACTORS IN PATIENTS WITH ADVANCED STAGE OF PROSTATE CANCER

Objectives: To determine the prognostic factors that could predict patient outcome in patients with advanced stage prostate cancer. Patients and Methods: In this study we retrospectively evaluated the medical record data of 222 patients with advanced stage prostate cancer treated by hormonal therapy (either castration or total androgen blockade (TAB)). All pre- and post- treatment data records were evaluated with respect to patient age, prostate and tumor size, tumor grade, stage, PSA, alkaline and acid phosphatase and the number of bone lesions. The response to the hormonal treatment was evaluated either early (12 months after treatment) or late (over all follow-up visits until the last visit or death). Descriptive statistics, student T test, multivariate and Kaplan Meier's curve were used for data analysis. Results: Within 12 months of treatment 70% of the cases showed an improvement with a significant decrease of their pre-treatment values after hormonal therapy. Patient age, tumor stage, the number of bone lesions, serum alkaline and acid phosphatase levels in the pre-treatment data were significantly independent predictors of the overall survival outcome (p= 0.0015, 0.002, 0.001, 0.0002 and 0.028, respectively), while the pre-treatment PSA serum level, tumor grade and the type of hormonal treatment used (either castration or TAB) were no predictors of patient outcome (p= 0.18, 0.82 and 0.47, respectively). Importantly, the PSA serum level and the number of bone lesions in the first 12 months of patient follow-up were significant predictors of the overall disease survival status (p=0.001 and 0.028, respectively). The mean follow-up period of alive cases was 39.42 months ranging from 6 – 171 months. Of the 222 cases 110 (51.6%) had overall disease progression during a mean of 59.4 months, while mortality was reported in 118 cases (53.2%) in the course of a mean of 59.9 months.Conclusion: The pre-treatment patient age, tumor stage, serum alkaline and acid phosphatase, as well as the post-treatment PSA level and the number of bone lesions were significant independent predictors of the overall patient outcome inpatients with advanced stage prostate cancer. However, a survival analysis in relation to the treatment type did not reveal a statistically significant difference between the outcomes of castration and TAB. Facteurs Pronostiques Cliniques chez les Patients Atteints de Cancer Prostatique Avancé Objectifs: De déterminer les facteurs pronostiques prédictifs de l'évolution du cancer prostatique chez nos patients atteints de cancer avancé de la prostate. Patients et Méthodes: Dans cette étude nous avons rétrospectivement évalué les données médicales de 222 patients atteints de cancer avancé de la prostate traités par thérapie hormonale (castration ou blocage androgènique total (BAT)). Toutes les données pré et post ont été évaluées en ce qui concerne l'âge des patients, la taille de la prostate et de la tumeur, le score histologique de la tumeur, le stade clinique, le PSA, la phosphatase alkaline et acide et le nombre de lésions osseuses. La réponse au traitement hormonal a été évaluée aussi bien tôt (12 mois après traitement) ou tard (à la dernière visite ou mort). Des statistiques descriptives, les tests T de Student, multivariable et de Kaplan Meier ont été employées pour l'analyse des données. Résultats: Pendant les 12 premiers mois du traitement, 70% des cas ont montré une amélioration avec une régression significative de leurs tumeurs. L'âge des patients, le stade de la tumeur, le nombre de lésions osseuses, les niveaux de phosphatase alcalines et acides sériques préopératoires étaient des facteurs prédictifs de survie indépendants et significatifs (p = 0.0015, 0.002, 0.001, 0.0002 et 0.028, respectivement), tandis que le taux sérique de PSA pré thérapeutique, le grade de la tumeur et le type de traitement hormonal utilisé (castration ou BAT) n'étaient pas significativement prédictifs de l'évolution des patients (p = 0.18, 0.82 et 0.47, respectivement). Essentiellement, le niveau de PSA et le nombre de lésions osseuses pendant les 12 premiers mois de suivi étaient des facteurs prédictifs significatifs du statut global de survie de la maladie (p=0.001 et 0.028, respectivement). La période moyenne de suivi des cas vivants était de 39.42 mois s'étendant de 6 - 171 mois. Parmi les 222 cas 110 cas (51.6%) ont eu une progression de la maladie pendant un intervalle de temps moyen de 59.4 mois, alors que la mortalité était de 118 cas (53.2%) pendant un intervalle moyen de temps de 59.9 mois. Conclusion: L'âge du patient, le stade de la tumeur, le taux sérique de phosphatase alcaline et acide, comme le taux de PSA pré thérapeutique et le nombre de lésions osseuses étaient des facteurs prédictifs indépendants et significatifs de l'évolution du cancer chez les patients présentant un cancer avancé de la prostate. Cependant, une analyse de survie par rapport au type de traitement n'a pas indiqué une différence statistiquement significative entre les résultats de la castration et le BAT. (Af J Urology: 2003 9(2):94-101