Predicting multiple sclerosis severity with multimodal deep neural networks

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.

aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients

BACKGROUND

The incidence of stroke and stroke-related hemiparesis has been steadily increasing and is projected to become a serious social, financial, and physical burden on the aging population. Limited access to outpatient rehabilitation for these stroke survivors further deepens the healthcare issue and estranges the stroke patient demographic in rural areas. However, new advances in motion detection deep learning enable the use of handheld smartphone cameras for body tracking, offering unparalleled levels of accessibility.

METHODS

In this study we want to develop an automated method for evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. We pair this technology with a series of machine learning models, including different neural network structures and an eXtreme Gradient Boosting model, to score 16 of 33 (49%) Fugl-Meyer item activities.

RESULTS

In this observational study, 45 acute stroke patients completed at least 1 recorded Fugl-Meyer assessment for the training of the auto-scorers, which yielded average accuracies ranging from 78.1% to 82.7% item-wise.

CONCLUSION

In this study, an automated method was developed for the evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. This novel method is demonstrated with potential to conduct telehealth rehabilitation evaluations and assessments with accuracy and availability.

Generative Adversarial Network-Enhanced Ultra-Low-Dose [18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations

BACKGROUND AND PURPOSE

With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images.

MATERIALS AND METHODS

Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts.

RESULTS

The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts.

CONCLUSIONS

The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.

Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

BACKGROUND

Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD

Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation.

RESULTS

The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard.

CONCLUSIONS

The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.

A comprehensive artificial intelligence framework for dental diagnosis and charting

Background

The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses.

Methods

The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings.

Results

The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations.

Conclusions

The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02514-6.

Advances in non-invasive biosensing measures to monitor wound healing progression

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.

Secure human action recognition by encrypted neural network inference

Advanced computer vision technology can provide near real-time home monitoring to support “aging in place” by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place” by detecting falls and symptoms related to seizures and stroke. In this paper, the authors propose a strategy that uses homomorphic encryption, which guarantees information confidentiality while retaining action detection.

A multitask deep learning approach for pulmonary embolism detection and identification

Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists’workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists’sensitivities ranging from 0.67 to 0.87 with specificities of 0.89–0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis.

Therapeutic potential of stem cell and melatonin on the reduction of CCl4-induced liver fibrosis in experimental mice model

Liver fibrosis is initial stage of any chronic liver disease and its end stage is develops into cirrhosis. Chronic liver diseases are a crucial global health issue and the cause of approximately 2 million deaths per year worldwide. Cirrhosis is currently the 11th most common cause of death globally. Mesenchymal stem cell (MSCs) treatment is the best way to treat acute and chronic liver disease. The aim of this study is to improve the therapeutic potential of MSCs combined with melatonin (MLT) to overcome CCl4-induced liver fibrosis and also investigate the individual impact of melatonin and MSCs against CCl4-induced liver impairment in animal model. Female BALB/c mice were used as CCL4-induced liver fibrotic animal model. Five groups of animal model were made; negative control, Positive control, CCl4+MSCs treated group, CCl4+MLT treated group and CCl4+MSCs+MLT treated group. Cultured MSCs from mice bone marrow were transplanted to CCl4-induced liver injured mice model, individually as well as together with melatonin. Two weeks after MSCs and MLT administration, all groups of mice were sacrificed for examination. Morphological and Histopathological results showed that combined therapy of MSCs+MLT showed substantial beneficial impact on CCl4-induced liver injured model, compared with MSCs and MLT individually. Biochemically, considerable reduction was observed in serum bilirubin and ALT levels of MLT+MSC treated mice, compared to other groups. PCR results shown down-regulation of Bax and up-regulation of Bcl-xl and Albumin, confirm a significant therapeutic effect of MSCs+MLT on CCI4-induced liver fibrosis. From the results, it is concluded that combined therapy of MSCs and MLT show strong therapeutic effect on CCL4-induced liver fibrosis, compared with MSCs and MLT individually.

Use of the deep learning approach to measure alveolar bone level

AIM

The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis.

MATERIALS AND METHODS

A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners.

RESULTS

The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners ( p = .65 ). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85.

CONCLUSIONS

The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.

Machine learning to predict sports-related concussion recovery using clinical data

OBJECTIVES

Sport-related concussions (SRCs) are a concern for high school athletes. Understanding factors contributing to SRC recovery time may improve clinical management. However, the complexity of the many clinical measures of concussion data precludes many traditional methods. This study aimed to answer the question, what is the utility of modeling clinical concussion data using machine-learning algorithms for predicting SRC recovery time and protracted recovery?

METHODS

This was a retrospective case series of participants aged 8 to 18 years with a diagnosis of SRC. A 6-part measure was administered to assess pre-injury risk factors, initial injury severity, and post-concussion symptoms, including the Vestibular Ocular Motor Screening (VOMS) measure, King-Devick Test and C3 Logix Trails Test data. These measures were used to predict recovery time (days from injury to full medical clearance) and binary protracted recovery (recovery time > 21 days) according to several sex-stratified machine-learning models. The ability of the models to discriminate protracted recovery was compared to a human-driven model according to the area under the receiver operating characteristic curve (AUC).

RESULTS

For 293 males (mean age 14.0 years) and 362 females (mean age 13.7 years), the median (interquartile range) time to recover from an SRC was 26 (18-39) and 21 (14-31) days, respectively. Among 9 machine-learning models trained, the gradient boosting on decision-tree algorithms achieved the best performance to predict recovery time and protracted recovery in males and females. The models' performance improved when VOMS data were used in conjunction with the King-Devick Test and C3 Logix Trails Test data. For males and females, the AUC was 0.84 and 0.78 versus 0.74 and 0.73, respectively, for statistical models for predicting protracted recovery.

CONCLUSIONS

Machine-learning models were able to manage the complexity of the vestibular-ocular motor system data. These results demonstrate the clinical utility of machine-learning models to inform prognostic evaluation for SRC recovery time and protracted recovery.

Abstract TP203: Predictors Of Alzheimer's Disease And Related Dementias Among Older Adults

Introduction: Alzheimer’s disease and related dementias (ADRD) are a leading cause of disability and premature death in the U.S. The aim of this study was to identify demographic and clinical predictors (age, sex race/ ethnicity, marital status, education level, history of heart disease, myocardial infarction, stroke, hypertension, diabetes, anxiety and depression symptoms, subjective memory rating, clock drawing results) of ADRD in a cohort of adults over age 65.

Methods: We obtained data for 2011 to 2020 from the National Health and Aging Trends Study (NHATS). NHATS contains a nationally representative sample of Medicare beneficiaries aged 65 years and older. We included participants with complete data (N=6161).

Results: Logistic regression analysis (see Table 1) suggested that the odds of having ADRD were 2.24 times higher in participants who had a stroke than in those who had not (p<0.001). The odds of having ADRD were 45% less among Black individuals than among White individuals (p=0.02). Participants ≥75 years old had higher odds of ADRD than those <70 years old (p<0.001). Participants who experienced anxiety symptoms “nearly every day” were more likely to have ADRD than those who did not experience anxiety symptoms (p=0.04). Those rating their memory as fair or poor were more likely to have ADRD (p<0.001). Those with clock drawing results that were reasonably accurate or accurate were less likely to have ADRD than those with clocks that were unrecognizable (p=.03). The area under receiver operator curve for the regression model was 0.887.

Conclusions: Our study findings that participants with a history of stroke, anxiety symptoms, and a poor memory self-rating were at higher risk for ADRD and those with accurate clock drawing tests were at lower risk for ADRD are consistent with prior studies. However, our finding that Blacks were at lower risk for ADRD is inconsistent with prior studies which suggests that additional research is needed.

Metabolic Imaging Using Hyperpolarization for Assessment of Premalignancy

There is an unmet need for noninvasive surrogate markers that can help identify premalignant lesions across different tumor types. Here we describe the methodology and technical details of protocols employed for in vivo ¹³C pyruvate metabolic imaging experiments. The goal of the method described is to identify and understand metabolic changes, to enable detection of pancreatic premalignant lesions, as a proof of concept of the high sensitivity of this imaging modality.

Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer

BACKGROUND

There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI).

OBJECTIVE

Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date.

METHODS

A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis.

RESULTS

Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR-related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future.

CONCLUSIONS

Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

PCR-RFLP Based genetic diversity of Plasmodium vivax genotypes in district Mardan, Pakistan

Plasmodium vivax is the most common human malaria parasite in Asian countries including Pakistan. Present study was designed to explore the genetic diversity of plasmodium vivax genotypes based on Pvmsp-3α and Pvmsp-3βgenes using allelic specific nested PCR and RFLP assays markers from field isolates in district Mardan, Pakistan. Blood samples of 200 P. vivax malarial patients were collected after taking their written informed consent. Genetic diversity in nested PCR products was determined by Restriction Fragment Length Polymorphism (RFLP) utilizing Alu1 and PstI restriction enzymes for alpha and beta gene products digestion, respectively. For analysis the genetic diversity of the sub allelic variants of Pvmsp3α and Pvmsp3β genes, Chi-Square test was performed by utilizing Minitab programming software 18. The P value 0.05 was considered as statistically significant. For Pvmsp-3α genes after gel electrophoresis of digested products, four distinct genotypes were obtained from total of 50 samples; type A: 35 (70%) (1.5-2.0 kb), 12 of type B (24%) (1.5-1.7 kb), 2 of type C (4%) (0.5-1.5) and one for type D (2%) (0.5-0.65 kb) which could be characterized into 9 allelic pattern (A1-A4, B1-B3, C1, D), in which A3 remained the most predominant. For Pvmsp-3βgenes, three distinct genotypes were obtained from 50 samples; 40(80%) of type A (1.5-2.5 kb), 9 (18%) of type B (1.0-1.5kb) and 1(2%) of type C (0.65 kb) which could be characterized into 6 allelic patterns (A1-A3, B1-B2, and C1). Most dominant one in Type A was A1 alleles which were noted (46%), while in Type B, the most dominant were B1 (10%).This study is the first ever report of molecular epidemiology and genetic variation in Pvmsp-3α and Pvmsp-3β genes of P. vivax isolates by using PCR/RFLP from District Mardan and showed a remarkable level of genetic diversity in the studied genes of circulating parasites in the study area. The results of this study will contribute in future studies about the genetic structure of parasite and vaccine development against the malaria.

Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates

OBJECTIVE

Study the impact of local policies on near-future hospitalization and mortality rates.

MATERIALS AND METHODS

We introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g., work from home) and high-contact (e.g., work on-site) subpopulations while sharing parameters to control their respective R₀(t) over time. We test our model on data of daily reported hospitalizations and cumulative mortality of COVID-19 in Harris County, Texas, from May 1, 2020, until October 4, 2020, collected from multiple sources (USA FACTS, U.S. Bureau of Labor Statistics, Southeast Texas Regional Advisory Council COVID-19 report, TMC daily news, and Johns Hopkins University county-level mortality reporting).

RESULTS

We evaluated our model's forecasting accuracy in Harris County, TX (the most populated county in the Greater Houston area) during Phase-I and Phase-II reopening. Not only does our model outperform other competing models, but it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches.

DISCUSSION

Mortality and hospitalization rates are significantly impacted by local quarantine and reopening policies. Existing models do not directly account for the effect of these policies on infection, hospitalization, and death rates in an explicit and explainable manner. Our work is an attempt to improve prediction of these trends by incorporating this information into the model, thus supporting decision-making.

CONCLUSION

Our work is a timely effort to attempt to model the dynamics of pandemics under the influence of local policies.

Detection of Leukocortical Lesions in Multiple Sclerosis and Their Association with Physical and Cognitive Impairment: A Comparison of Conventional and Synthetic Phase-Sensitive Inversion Recovery MRI

BACKGROUND AND PURPOSE

Cortical lesions are common in multiple sclerosis and are included in the latest diagnostic criteria. The limited sensitivity of cortical MS lesions on conventional MR imaging can be improved by phase-sensitive inversion recovery. Synthetic MR imaging could provide phase-sensitive inversion recovery without additional scanning, but the use of synthetic phase-sensitive inversion recovery remains to be validated. We aimed to compare the ability and clinical value of detecting leukocortical lesions with conventional and synthetic phase-sensitive inversion recovery in MS.

MATERIALS AND METHODS

Twenty-one patients with MS prospectively underwent conventional and synthetic phase-sensitive inversion recovery, 3D T1-weighted, and T2 FLAIR imaging. Two neuroradiologists independently performed blinded phase-sensitive inversion recovery lesion assessments; a consensus rating with all sequences was considered the criterion standard. Lesion volumes were segmented. All participants underwent standardized cognitive and physical examinations and Fatigue Severity Scale assessment. Results were analyzed with multiple linear regressions.

RESULTS

Interrater and criterion standard agreement for leukocortical lesions was excellent for both conventional and synthetic phase-sensitive inversion recovery (intraclass correlation coefficient = 0.79-0.97). Leukocortical lesion volumes for both sequences were associated with lower information-processing speed (P ≤ .01) and verbal fluency (P ≤ .02). Both phase-sensitive inversion recovery sequences showed a positive effect on the association when combining volumes of leukocortical lesions and white matter lesions with information-processing speed (P ≤ .005) and verbal fluency (P ≤ .03). No associations were found between leukocortical lesion volumes and physical disability or fatigue.

CONCLUSIONS

Synthetic and conventional phase-sensitive inversion recovery have a sensitivity similar to that of leukocortical MS lesions. The detected leukocortical lesions are associated with cognitive dysfunction and thus provide clinically relevant information, which encourages assessment of cortical MS involvement at conventional field strengths.

MRI of the Swallow Tail Sign: A Useful Marker in the Diagnosis of Lewy Body Dementia?

BACKGROUND AND PURPOSE

There are, to date, no MR imaging diagnostic markers for Lewy body dementia. Nigrosome 1, containing dopaminergic cells, in the substantia nigra pars compacta is hyperintense on SWI and has been called the swallow tail sign, disappearing with Parkinson disease. We aimed to study the swallow tail sign and its clinical applicability in Lewy body dementia and hypothesized that the sign would be likewise applicable in Lewy body dementia.

MATERIALS AND METHODS

This was a retrospective cross-sectional multicenter study including 97 patients (mean age, 65 ± 10 years; 46% women), consisting of the following: controls (n = 21) and those with Lewy body dementia (n = 19), Alzheimer disease (n = 20), frontotemporal lobe dementia (n = 20), and mild cognitive impairment (n = 17). All patients underwent brain MR imaging, with susceptibility-weighted imaging at 1.5T (n = 46) and 3T (n = 51). The swallow tail sign was assessed independently by 2 neuroradiologists.

RESULTS

Interrater agreement was moderate (κ = 0.4) between raters. An abnormal swallow tail sign was most common in Lewy body dementia (63%; 95% CI, 41%-85%; P < .001) and had a predictive value only in Lewy body dementia with an odds ratio of 9 (95% CI, 3-28; P < .001). The consensus rating for Lewy body dementia showed a sensitivity of 63%, a specificity of 79%, a negative predictive value of 89%, and an accuracy of 76%; values were higher on 3T compared with 1.5T. The usefulness of the swallow tail sign was rater-dependent with the highest sensitivity equaling 100%.

CONCLUSIONS

The swallow tail sign has diagnostic potential in Lewy body dementia and may be a complement in the diagnostic work-up of this condition.

Evaluation of serum hepcidin and iron levels in patients with PCOS: a case-control study

PURPOSE

Polycystic ovary syndrome (PCOS) is the most common cause of chronic anovulation with a prevalence of 5-10% in women of reproductive age. The etiology of this disease is not well known, and hepcidin is one of the factors affecting the pathogenesis of the disease. The aim of this study was to evaluate plasma levels of hepcidin in patients with PCOS and its correlation with serum iron level.

METHODS

In this case-control study, plasma levels of hepcidin, IL-6, and ferritin using ELISA method and serum iron levels using a spectrophotometric method were tested on 56 women with PCOS (case group) and 41 healthy subjects (control group). The results were analyzed using t test, General Linear Model, Binary logistic regression, and linear regression tests.

RESULTS

The mean hepcidin levels were 1.97 ± 0.53 and 2.40 ± 0.25 pg/ml in the case and control groups, respectively. The t-test results showed significant difference between the two groups (p = 0.0001). The mean serum iron levels were 72.89 ± 28.97 and 70.62 ± 31.18 g/dl in the case and control groups, respectively. The t test analysis indicated no significant difference between the two groups. The serum ferritin and iron levels had no significant relation with serum hepcidin level in two groups.

CONCLUSION

Despite the differences in the serum levels of hepcidin between the two groups, no significant relation was observed between serum iron levels and hepcidin level in this group of patients. This implies the need for more comprehensive studies on gene expression in hepcidin and iron pathways using real-time and Western techniques to investigate more precisely serum hepcidin level and its relationship with the factors mentioned.

Retention of Gadolinium-Based Contrast Agents in Multiple Sclerosis: Retrospective Analysis of an 18-Year Longitudinal Study

BACKGROUND AND PURPOSE

Gadolinium-based contrast agents have been associated with lasting high T1-weighted signal intensity in the dentate nucleus and globus pallidus, with histopathologically confirmed gadolinium retention. We aimed to longitudinally investigate the relationship of multiple gadolinium-based contrast agent administrations to the Signal Intensity Index in the dentate nucleus and globus pallidus and any associations with cognitive function in multiple sclerosis.

MATERIALS AND METHODS

The Signal Intensity Index in the dentate nucleus and globus pallidus was retrospectively evaluated on T1-weighted MR imaging in an 18-year longitudinal cohort study of 23 patients with MS receiving multiple gadolinium-based contrast agent administrations and 23 healthy age- and sex-matched controls. Participants also underwent comprehensive neuropsychological testing.

RESULTS

Patients with MS had a higher Signal Intensity Index in the dentate nucleus (P < .001), but not in the globus pallidus (P = .19), compared with non-gadolinium-based contrast agent-exposed healthy controls by an unpaired t test. Increasing numbers of gadolinium-based contrast agent administrations were associated with an increased Signal Intensity Index in the dentate nucleus (β = 0.45, P < .001) and globus pallidus (β = 0.60, P < .001). This association remained stable with corrections for the age, disease duration, and physical disability for both the dentate nucleus (β = 0.43, P = .001) and globus pallidus (β = 0.58, P < .001). An increased Signal Intensity Index in the dentate nucleus among patients with MS was associated with lower verbal fluency scores, which remained significant after correction for several aspects of disease severity (β = -0.40 P = .013).

CONCLUSIONS

Our data corroborate previous reports of lasting gadolinium retention in brain tissues. An increased Signal Intensity Index in the dentate nucleus and globus pallidus was associated with lower verbal fluency, which does not prove causality but encourages further studies on cognition and gadolinium-based contrast agent administration.

The effect of foetal movement counting on maternal anxiety: A randomised, controlled trial

This study assessed the effect of foetal movement counting on maternal anxiety. In a randomised, controlled trial, 208 primigravida women with a singleton pregnancy were allocated either to perform daily foetal movement counting (n100) from 28 weeks ’ gestation or to follow standard Iranian antenatal care (n108). Maternal anxiety was measured by Spielberger State-Trait Anxiety Inventory. Statistical analysis was performed by SPSS (16.0). There was no significant difference in mean state and trait anxiety scores at 28 weeks ’ gestation; the difference was significant at 37 weeks ’ gestation in state anxiety score between the intervention and control groups (35.34 9.98 vs. 38.25 9.63, respectively, P0.001). A significant difference was found in mean trait anxiety score at 37 weeks ’ gestation between the two groups (35.88 8.19 vs. 39.15 9.25, respectively, P 0.008). Women who performed foetal movement counting from 28 to 37 weeks ’ gestation reported less anxiety compared with those in control group.

Incidence of Radiologically Isolated Syndrome: A Population-Based Study

BACKGROUND AND PURPOSE

Incidental MR imaging findings resembling MS in asymptomatic individuals, fulfilling the Okuda criteria, are termed "radiologically isolated syndrome." Those with radiologically isolated syndrome are at high risk of their condition converting to MS. The epidemiology of radiologically isolated syndrome remains largely unknown, and there are no population-based studies, to our knowledge. Our aim was to study the population-based incidence of radiologically isolated syndrome in a high-incidence region for MS and to evaluate the effect on radiologically isolated syndrome incidence when revising the original radiologically isolated syndrome criteria by using the latest radiologic classification for dissemination in space.

MATERIALS AND METHODS

All 2272 brain MR imaging scans in 1907 persons obtained during 2013 in the Swedish county of Västmanland, with a population of 259,000 inhabitants, were blindly evaluated by a senior radiologist and a senior neuroradiologist. The Okuda criteria for radiologically isolated syndrome were applied by using both the Barkhof and Swanton classifications for dissemination in space. Assessments of clinical data were performed by a radiology resident and a senior neurologist.

RESULTS

The cumulative incidence of radiologically isolated syndrome was 2 patients (0.1%), equaling an incidence rate of 0.8 cases per 100,000 person-years, in a region with an incidence rate of MS of 10.2 cases per 100,000 person-years. There was no difference in the radiologically isolated syndrome incidence rate when applying a modified version of the Okuda criteria by using the newer Swanton classification for dissemination in space.

CONCLUSIONS

Radiologically isolated syndrome is uncommon in a high-incidence region for MS. Adapting the Okuda criteria to use the dissemination in space-Swanton classification may be feasible. Future studies on radiologically isolated syndrome may benefit from a collaborative approach to ensure adequate numbers of participants.

Clinical Feasibility of Synthetic MRI in Multiple Sclerosis: A Diagnostic and Volumetric Validation Study

BACKGROUND AND PURPOSE

Quantitative MR imaging techniques are gaining interest as methods of reducing acquisition times while additionally providing robust measurements. This study aimed to implement a synthetic MR imaging method on a new scanner type and to compare its diagnostic accuracy and volumetry with conventional MR imaging in patients with MS and controls.

MATERIALS AND METHODS

Twenty patients with MS and 20 healthy controls were enrolled after ethics approval and written informed consent. Synthetic MR imaging was implemented on a Siemens 3T scanner. Comparable conventional and synthetic proton-density-, T1-, and T2-weighted, and FLAIR images were acquired. Diagnostic accuracy, lesion detection, and artifacts were assessed by blinded neuroradiologic evaluation, and contrast-to-noise ratios, by manual tracing. Volumetry was performed with synthetic MR imaging, FreeSurfer, FMRIB Software Library, and Statistical Parametric Mapping. Repeatability was quantified by using the coefficient of variance.

RESULTS

Synthetic proton-density-, T1-, and T2-weighted images were of sufficient or good quality and were acquired in 7% less time than with conventional MR imaging. Synthetic FLAIR images were degraded by artifacts. Lesion counts and volumes were higher in synthetic MR imaging due to differences in the contrast of dirty-appearing WM but did not affect the radiologic diagnostic classification or lesion topography (P = .50-.77). Synthetic MR imaging provided segmentations with the shortest processing time (16 seconds) and the lowest repeatability error for brain volume (0.14%), intracranial volume (0.12%), brain parenchymal fraction (0.14%), and GM fraction (0.56%).

CONCLUSIONS

Synthetic MR imaging can be an alternative to conventional MR imaging for generating diagnostic proton-density-, T1-, and T2-weighted images in patients with MS and controls while additionally delivering fast and robust volumetric measurements suitable for MS studies.

SWI or T2*: which MRI sequence to use in the detection of cerebral microbleeds? The Karolinska Imaging Dementia Study

BACKGROUND AND PURPOSE

Cerebral microbleeds are thought to have potentially important clinical implications in dementia and stroke. However, the use of both T2* and SWI MR imaging sequences for microbleed detection has complicated the cross-comparison of study results. We aimed to determine the impact of microbleed sequences on microbleed detection and associated clinical parameters.

MATERIALS AND METHODS

Patients from our memory clinic (n = 246; 53% female; mean age, 62) prospectively underwent 3T MR imaging, with conventional thick-section T2*, thick-section SWI, and conventional thin-section SWI. Microbleeds were assessed separately on thick-section SWI, thin-section SWI, and T2* by 3 raters, with varying neuroradiologic experience. Clinical and radiologic parameters from the dementia investigation were analyzed in association with the number of microbleeds in negative binomial regression analyses.

RESULTS

Prevalence and number of microbleeds were higher on thick-/thin-section SWI (20/21%) compared with T2*(17%). There was no difference in microbleed prevalence/number between thick- and thin-section SWI. Interrater agreement was excellent for all raters and sequences. Univariate comparisons of clinical parameters between patients with and without microbleeds yielded no difference across sequences. In the regression analysis, only minor differences in clinical associations with the number of microbleeds were noted across sequences.

CONCLUSIONS

Due to the increased detection of microbleeds, we recommend SWI as the sequence of choice in microbleed detection. Microbleeds and their association with clinical parameters are robust to the effects of varying MR imaging sequences, suggesting that comparison of results across studies is possible, despite differing microbleed sequences.

Anatoxin-a producing Tychonema (Cyanobacteria) in European waterbodies

In order to identify the cyanobacterial species responsible of anatoxin-a (ATX) production in Lake Garda (Northern Italy), an intensive isolation and culturing of filamentous cyanobacteria were established since 2014 from environmental samples. In this work, we report a detailed account of the strategy adopted, which led to the discovery of a new unexpected producer of ATX, Tychonema bourrellyi. So far, this species is the first documented example of cultured Oscillatoriales able to produce ATX isolated from pelagic freshwater ecosystems. The isolated filaments were identified adopting a polyphasic approach, which included microscopic species identification, genetic characterisation and phylogenetic analyses based on 16S rRNA genes. The taxonomic identification was further confirmed by the high (>99%) rbcLX sequence similarities of the T. bourrellyi strains of Lake Garda with those deposited in DNA sequence databases. More than half of the isolates were shown to produce a significant amount of ATX, with cell quota ranging between 0.1 and 2.6 μg mm(-3), and 0.01 and 0.35 pg cell(-1). The toxic isolates were tested positive for anaC of the anatoxin-a synthetase (ana) gene cluster. These findings were confirmed with the discovery of one ATX producing T. bourrellyi strain isolated in Norway. This strain and a further non-ATX producing Norwegian Tychonema bornetii strain tested positive for the presence of the anaF gene of the ana gene cluster. Conversely, none of the Italian and Norwegian Tychonema strains were positive for microcystins (MCs), which was also confirmed by the absence of mcyE PCR products in all the samples analysed. This work suggests that the only reliable strategy to identify cyanotoxins producers should be based on the isolation of strains and their identification with a polyphasic approach associated to a concurrent metabolomic profiling.

Cerebral microbleeds: different prevalence, topography, and risk factors depending on dementia diagnosis—the Karolinska Imaging Dementia Study

BACKGROUND AND PURPOSE

Cerebral microbleeds are thought to represent cerebral amyloid angiopathy when in lobar regions of the brain and hypertensive arteriopathy when in deep and infratentorial locations. By studying cerebral microbleeds, their topography, and risk factors, we aimed to gain an insight into the vascular and amyloid pathology of dementia diagnoses and increase the understanding of cerebral microbleeds in dementia.

MATERIALS AND METHODS

We analyzed 1504 patients (53% women; mean age, 63 ± 10 years; 10 different dementia diagnoses) in this study. All patients underwent MR imaging as part of the dementia investigation, and all their clinical parameters were recorded.

RESULTS

Among the 1504 patients with dementia, 22% had cerebral microbleeds. Cerebral microbleed topography was predominantly lobar (P = .01) and occipital (P = .007) in Alzheimer disease. Patients with cerebral microbleeds were significantly older (P < .001), were more frequently male (P < .001), had lower cognitive scores (P = .006), and more often had hypertension (P < .001). Risk factors for cerebral microbleeds varied depending on the dementia diagnosis. Odds ratios for having cerebral microbleeds increased with the number of risk factors (hypertension, hyperlipidemia, diabetes, male sex, and age 65 and older) in the whole patient group and increased differently in the separate dementia diagnoses.

CONCLUSIONS

Prevalence, topography, and risk factors of cerebral microbleeds vary depending on the dementia diagnosis and reflect the inherent pathology of different dementia diagnoses. Because cerebral microbleeds are seen as possible predictors of intracerebral hemorrhage, their increasing prevalence with an increasing number of risk factors, as shown in our study, may require taking the number of risk factors into account when deciding on anticoagulant therapy in dementia.

Developing a real-time quantitative loop-mediated isothermal amplification assay as a rapid and accurate method for detection of Brucellosis

AIM

The aim of this study was designing a LAMP method for the rapid detection of Brucella and development of a sensitive quantitative-LAMP (Q-LAMP) assay for quantification of brucellosis.

METHODS AND RESULTS

In this study for the LAMP detection of the causative agent of brucellosis, we used specifically designed primers to target the omp25 conserved gene of Brucella spp. The sensitivity of the LAMP method was evaluated by preparing serial tenfold dilution of omp25 gene containing plasmid followed by performing the LAMP reaction. To improve the assay as a quantitative test, LAMP products in the serial dilution were evaluated by Loopamp real-time turbidimeter system and then standard curve was generated by plotting time threshold values against log of copy number. The assay specificity was evaluated using Brucella genomic DNA and a panel containing genomes of 11 gram-positive and gram-negative organisms. The LAMP assay was highly specific and no amplification products were observed from the non-Brucella organisms. The test sensitivity for visual detection of turbidity or fluorescent colour change and also agarose gel electrophoresis was 560 ng and 5·6 ng, respectively. The lower limit of detection was 17 copies of the gene that could be detected in 50 min.

CONCLUSIONS

The results of this study indicated that the LAMP assay is a simple, rapid, sensitive and specific technique for detection of Brucella spp. that may improve diagnostic potential in clinical laboratories.

SIGNIFICANCE AND IMPACT OF THE STUDY

The LAMP assay because of the simplicity and low cost can be preferred to other molecular methods in the diagnosis of infectious diseases.

Prevalence of intestinal parasites among children referred to Children's Medical Center during 18 years (1991-2008), Tehran, Iran

Epidemiological studies show that parasitic inections are among the most common infections and one of the biggest health problems of the society worldwide. Children at school age have the highest morbidity compared with other ages. Therefore, by treating these children, the disease burden in the total population is reduced. In this study, prevalence of parasitic infection in children referred to Children's Medical Center was compared in different years. In this retrospective cross-sectional study, the subjects were children under 13 years who were referred to Children's Medical Center Laboratory during 18 years (1991-2008) and underwent stool exam by any reason. The specimens were evaluated by different common methods of stool parasitology. In suspected cases, parasites was cultured in specific medium and stained as needed. Required data were obtained from the laboratory files and analysed according to study's purpose. Subjects were 124 366 children. Among them, 0.78% of cases had parasitic infections and 60.54% cases were male. Parasitic infections were related to protozoa in 95.33% cases and intestinal worms in 4.87%. Of them, 50.352% were pathogenic protozoa. The most parasitic infection was Giardia lamblia. Among intestinal worms, the highest prevalence was related to Hymenolepis nana (40.7%). A comparison between the first 10 years and the next 8 years of the study showed that the prevalence of intestinal parasites were 8% and 1% (P < 0.001) and the rate for protozoal infection were 14.9% and 4.3%, respectively (P < 0.001). The prevalence of intestinal parasites was reduced during recent years; however, more attempts should be performed to make it lower.

Establishment of a Modified in Vitro Cultivation of Protoscoleces to Adult Echinococcus granulosus; an Important Way for New Investigations on Hydatidosis

BACKGROUND

Echinococcus granulosus, a zoonotic cestode parasite, causative agent of hydatid cyst is endemic in many parts of the world including the Middle East. Study on different aspects of this parasite is very important and valuable. However, working with adult worms which their habitat situated in the small intestine of canids, is dangerous and risky. Achieving such risky situation needs a controlled condition which is cultivation of the organisms in the laboratory. In this regard, cultivation of E. granulosus protoscoleces leading to adult worms was established in the laboratory for the first time in Iran.

METHODS

Under aseptic conditions a number of protoscoleces were cultivated in diphasic S.10E.H medium using CO2 incubator to produce adult worms.

RESULTS

Different forms of parasites including pre-segmentation stages (PS1 - PS4) and segmentation stages (S5-S8) and developing stages in segmented worms (S10-S11) were observed and evaluated in these medium. Finally adult worms contained four proglottids with a large and distinct genital pore were observed 50-55 days post cultivation. These parasites do not produce fertile eggs and conclusively do not have risk of hydatid disease transmission to the researchers.

CONCLUSION

The mentioned method for producing E. granulosus adult worms can open a new window for researches and facilitate working on different aspects of hydatidosis especially for diagnosis, protection and treatment studies.

The prevalence of iron deficiency anaemia in female medical students in Tehran

INTRODUCTION

Female adolescents and adults are among the population groups who are most affected by iron deficiency. Thus, the aim of this study was to investigate the prevalence of iron deficiency and iron deficiency anaemia in female students aged 18 to 25 years old from the Tehran University of Medical Sciences, Iran.

METHODS

295 female university students participated in the study. The mean corpuscular volume (MCV) and haemoglobin (Hb), serum ferritin, serum iron and total iron binding capacity (TIBC) levels were measured. Iron deficiency anaemia was defined as a situation where Hb is less than 12 g/dL, MCV is less than 78 microm3, ferritin is less than 12 ng/ml or transferin saturation (TS) (iron/TIBC x 100) is less than 15 percent, Iron deficiency (ID) was defined as a situation where Hb is greater than or equal to 12 g/dL, MCV is greater than or equal to 74 microm3, ferritin is less than 12 ng/ml or TS is less than 15 percent.

RESULTS

The complete data was available for 237 students. The prevalence of ID was 40.9 percent and that of IDA was 3.8 percent. Normal iron status was found in 49.8 percent of the subjects. The remaining (5.5 percent) had other kinds of anaemia or required confirmatory tests.

CONCLUSION

ID is common among 18 to 25-year-old Iranian female university students. Iron supplementation is thus required for the target group.

Effects of Co-Administration of an iNOS Inhibitor with a Broad-Spectrum Reactive Species Scavenger in Rat Renal Ischemia/Reperfusion Injury

BACKGROUND

It is generally believed that reactive oxygen species (ROS) formation and nitric oxide (NO) generation by the inducible isoform of nitric oxide synthase (iNOS) are the key mediators of ischemia-reperfusion (IR)-induced damage to the kidney. The present study was designed to investigate the effects of ROS and NOS inhibition in prevention of renal IR injury. MnTBAP (Manganese (III) meso-tetrakis (4-benzoic acid) porphyrin), a broad-spectrum reactive species scavenger was administered to inhibit ROS formation and L-Nil (N6-(1-iminoethyl)-L-lysine hydrochloride) was used for iNOS inhibition.

METHODS

Ischemic acute renal failure (ARF) was induced by 40-min clamping of the renal arteries followed by a 6-hour reperfusion. Rats were administered saline, MnTBAP (10 mg/kg i.v.), L-Nil (3 mg/kg i.v. bolus followed by infusion of 1 mg/kg/h) or co-administration of MnTBAP and L-Nil. Plasma creatinine (Cr) and BUN levels as well as fractional excretion of Na+ (FE(Na+)) and urinary N-acetyl-beta-D-glucosaminidase (NAG) activities were measured. Renal damages were evaluated by light microscopy.

RESULTS

MnTBAP, L-Nil and their co-administration significantly improved renal functional and histological indices. Co-administration of the mentioned drugs did not demonstrate significant difference with the administration of either drug alone.

CONCLUSION

These results suggest that the significant portion of ROS and iNOS nephrotoxicities in this model of ARF may be mediated by peroxynitrite (ONOO-). These results emphasize the multifactorial nature of ARF and the need for a multidrug therapy in the future.

Experimental design, analysis of variance and slide quality assessment in gene expression arrays

A microarray experiment is a sequence of complicated molecular biology procedures relying on various laboratory tools, instrumentation and experimenter's skills. This paper discusses statistical models for distinguishing small changes in gene expression from the noise in the system. It describes methods for assigning statistical confidence to gene expression values derived from a single array slide. Some of the theory is discussed in the context of practical applications via software usage.

High throughput screening of gene expression signatures

This paper focuses on microarray image analysis and discusses a completely automated approach to image processing, which eliminates human intervention. A system for automated image processing is described, which is capable of processing image files in a batch-mode thus allowing high-throughput of microarray image analysis. Grid-placement and spot finding are achieved without operator's help. The software eliminates noise signals from the data analysis process and minimizes operator's involvement in the procedure.

Case report. An unusual case of cutaneous sporotrichosis and its response to weekly fluconazole

Cutaneous sporotrichosis is clinically divided into two main types: lymphocutaneous and fixed plaque type. Our report represents an unusual case with both types simultaneously. Fluconazole 150 mg once weekly was used as the treatment. The fixed type lesions responded very well and were healed after 4 months, but the lymphocutaneous lesions were not controlled even after 6 months of the treatment.

Translation-, rotation-, scale-, and distortion-invariant object recognition through self-organization

The task of visual object recognition is often complicated by the fact that a single 3-D object can undergo a number of transformations which can substantially alter its projection onto a 2-D surface, such as the retina. Such transformations include translation of the object in the visual field, changes in the size of the object, its orientation in the 2-D plane and the viewing perspective. For a general pattern recognition system to detect and recognize and object after such transformations, it must be able to associate widely differing patterns with the same object label. In this paper, a novel self-organizing model, called the Multiple Elastic Modules (MEM), is presented which attempts to solve this problem by searching a multi-dimensional space, where each axis is defined by one of the transformations (e.g. scale, translation, rotation, etc.). A particular object of a specific size, orientation and spatial location is mapped onto a single point in this space. Of course, distortions and minor variations in an object's image will expand this point to a small localized area in this multi-dimensional space. Such a powerful representation scheme comes at a cost of high computational demand due to the combinatorially large search space. The MEM approach to solving this problem efficiently partitions the solution space to search the most promising areas for the correct match. Simulation results are presented on detecting a stick-figure object under translation, distortion, scale, and rotation transformations in a cluttered background.

Stereotactic biopsy of brain tumours

Computerized tomography assisted Stereotactic biopsy technique using Leksell stereotactic frame was performed on 27 patients with small, multiple and deep seated brain tumours. There were 19 men and 8 women with an age range from 17 to 65 years. Histological diagnosis of 18 glial tumours, 9 non-glial tumours (5 colloid cysts, 4 metastatic lesions) was obtained. There was no mortality and minimal morbidity of 3.7%, histological diagnosis provided the information regarding differentiation from infectious and vascular lesions and grading of malignancy leading to logical guidance for therapeutic management of each lesion, confirming the value of stereotactic biopsy in brain tumours.