Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms

Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient. We report a machine learning model based on over 100 clinical and imaging features that predict the outcome of wide-neck bifurcation aneurysm treatment with an intrasaccular embolization device. We combine clinical features with a diverse set of common and novel imaging measurements within a random forest model. We also develop neural network segmentation algorithms in 2D and 3D to contour the sac in angiographic images and automatically calculate the imaging features. These deliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive model classifies complete vs. partial occlusion outcomes with an accuracy of 75.31%, and weighted F1-score of 0.74.

Association of TYRP1 with hypoxia and its correlation with patient outcome in uveal melanoma

PURPOSE

Molecular mechanisms of uveal melanoma development in association with high pigmentation are unclear. Tyrosinase Related Protein (TYRP1) is not only one of the important melanogenesis marker that contributes to melanin synthesis, but can also prevents the melanocyte death. The induction of melanogenesis leads to induction of HIF-1α which can affect the behavior of melanoma cells and its surrounding environment. The aim of our study was to determine the expression of TYRP1 and HIF-1α at the protein and RNA level and determine its prognostic significance.

METHODS

In the present study, the expression of TYRP1 and HIF-1α was investigated on 61 formalin-fixed paraffin-embedded choroidal melanoma samples by immunohistochemistry. Fresh 50 samples were validated by real-time PCR. Results were correlated with clinicopathological parameters and Kaplan-Meier was performed to determine the prognostic significance.

RESULTS

High immunoexpression of TYRP1 and HIF-1α was present in 61 and 54% of patients, respectively. Both TYRP1 and HIF-1α correlated well with high pigmentation and BAP1 (BRCA1 Associated Protein-1) loss (p < 0.05) at IHC level as well as transcriptional level. There was reduced metastatic free survival in patients with necrosis and this was statistically significant (p = 0.010).

CONCLUSION

Our findings indicate that TYRP1 can be used as a potential biomarker in the development of targeted therapy in UM. Further studies on melanogenesis markers associated with TYRP1 could provide us a better understanding in this field.

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist's dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) based framework with a customized reconstruction loss function for image reconstruction, followed by a classification module to classify each image patch as tumor versus non-tumor. The resulting patch-based prediction results are spatially combined to generate the final segmentation result for each WSI. Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images. Our proposed model presents superior performance to other benchmark models with extensive experiments, suggesting its efficacy for viable tumor area segmentation with liver whole-slide images.

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

IMPORTANCE

Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care.

OBJECTIVE

To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020.

MAIN OUTCOMES AND MEASURES

The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means.

RESULTS

The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.

Constitutive expression of c-REL in uveal melanoma patients: correlation with clinicopathological parameters and patient outcome

PURPOSE

Uveal melanoma (UM) is the most common intraocular cancer with a high mortality rate that requires new research in the field of prevention and treatment. c-REL is a member of the nuclear factor κB (NF-κB) transcription factor family and an emerging regulator of tumorigenesis. Therefore, the objective of the study is to evaluate the constitutive expression of c-REL in uveal melanoma patients and its prognostic significance.

METHODS

Detection of c-REL expression was carried out by immunohistochemistry in all 75 patients, and qRT-PCR performed on 58 fresh cases of uveal melanoma along with IL-6 status. Immunoblot was performed to validate immunohistochemistry results. Expression of c-REL protein correlated with clinicopathological parameters and overall survival of patients.

RESULTS

Immunohistochemistry results revealed nuclear expression of the c-REL protein (56%) in our cases. Out of 75 cases, 31 cases showed nuclear expression, and 11 cases had cytoplasmic expression. qRT-PCR showed upregulation of the REL gene in 56.89% cases at the transcriptional level. There was a statistically significant difference in the overall survival of patients with c-REL nuclear immunopositivity (p = 0.0048). On multivariate analysis, scleral invasion and c-REL nuclear expression found to be an independent prognostic factor (p < 0.05) CONCLUSIONS: To the best of our knowledge, this was the first study reporting the expression of the c-REL protein in uveal melanoma. Strong nuclear immunoexpression of c-Rel suggests NFκB pathway activation which might be involved in the progression of the disease. Differential expression of c-REL protein may be used as an attractive target for the development of anticancer strategies.

0583 Obesity-Associated Sleep Hypoventilation Syndrome and Adverse Post-Operative Bariatric Surgery Outcomes

Introduction

Although obesity hypoventilation syndrome (OHS) is associated with right ventricular dysfunction and increased mortality, its contribution to post-bariatric surgery risk remains unclear due to non-systematic OHS assessments. We hypothesize that patients with obesity-associated sleep hypoventilation (OASH) have increased adverse post-bariatric surgery outcomes than those without.

Methods

Patients undergoing polysomnography (PSG) prior to bariatric surgery at the Cleveland Clinic from 2011-2018 were retrospectively examined. OASH was defined by body mass index (BMI) ≥30kg/m2 and either PSG-based end-tidal CO2 ≥45mmHg or serum bicarbonate ≥27mEq/L. The following were considered individually and as a composite outcome: ICU stay, re-intubation, tracheostomy, discharge disposition or 30-day readmission. All-cause mortality was also examined. Outcomes were compared using two-sample t-test or Wilcoxon rank sum test and Chi-square or Fisher exact test. A multivariable logistic regression model included age, sex, BMI, apnea hypopnea index(AHI) and diabetes to examine OAHS and the composite outcome. All-cause mortality was compared using Kaplan-Meier estimation and hazard ratios from Cox proportional hazards models. SAS software (version 9.4) was used with overall significance level of 0.05.

Results

The sample comprised 1665 patients: age 45.2±12 years, 20.4% male, BMI=48.7±9 kg/m2, and 63.6% Caucasian. OASH prevalence was 68.5%. OAHS patients were older and more likely to be male with higher BMI, AHI and HbA1c. Although some individual outcomes were higher in OASH vs. non-OASH, findings were not statistically significant: re-intubation (1.5%vs.1.3%, p=0.81) and 30-day readmission (13.8% vs.11.3%, p=0.16). The composite outcome remained significantly associated with OAHS in the multivariable model: OR=1.36, 95%CI:1.005,1.845. Mortality was 2% in OASH and not significantly higher than non-OAHS (HR=1.39, 95%CI:0.56,3.42).

Conclusion

In this largest sample to date of systematically phenotyped OASH in patients undergoing bariatric surgery, we identify increased post-operative morbidity in those with OASH. Further study is needed to identify whether peri-operative treatment of OASH improves surgical outcomes.

Support

 

Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative

A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework. Double-echo steady state MRIs used in this paper originated from the OA Initiative study. Trained on 34 MRIs with varying degrees of OA, the performance of the learning-based method tested on 108 MRIs showed significant reduction in segmentation errors ( ) compared with the conventional gradient-based and single-stage RF-learned costs. The 3-D LOGISMOS was extended to longitudinal-3-D (4-D) to simultaneously segment multiple follow-up visits of the same patient. As such, data from all time-points of the temporal sequence contribute information to a single optimal solution that utilizes both spatial 3-D and temporal contexts. 4-D LOGISMOS validation on 108 MRIs from baseline, and 12 month follow-up scans of 54 patients showed significant reduction in segmentation errors ( ) compared with 3-D. Finally, the potential of 4-D LOGISMOS was further explored on the same 54 patients using five annual follow-up scans demonstrating a significant improvement of measuring cartilage thickness ( ) compared with the sequential 3-D approach.

Machine learning in a graph framework for subcortical segmentation

Automated and reliable segmentation of subcortical structures from human brain magnetic resonance images is of great importance for volumetric and shape analyses in quantitative neuroimaging studies. However, poor boundary contrast and variable shape of these structures make the automated segmentation a tough task. We propose a 3D graph-based machine learning method, called LOGISMOS-RF, to segment the caudate and the putamen from brain MRI scans in a robust and accurate way. An atlas-based tissue classification and bias-field correction method is applied to the images to generate an initial segmentation for each structure. Then a 3D graph framework is utilized to construct a geometric graph for each initial segmentation. A locally trained random forest classifier is used to assign a cost to each graph node. The max-flow algorithm is applied to solve the segmentation problem. Evaluation was performed on a dataset of T1-weighted MRI's of 62 subjects, with 42 images used for training and 20 images for testing. For comparison, FreeSurfer and FSL approaches were also evaluated using the same dataset. Dice overlap coefficients and surface-to-surfaces distances between the automated segmentation and expert manual segmentations indicate the results of our method are statistically significantly more accurate than the other two methods, for both the caudate (Dice: 0.89 ± 0.03) and the putamen (0.89 ± 0.03).

Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative

We present a fully automated learning-based approach for segmenting knee cartilage in presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random forest classifiers. The first is a neighborhood approximation forest, the output probability map of which is utilized as a feature set for the second random forest (RF) classifier. The output probabilities of the hierarchical approach are used as cost functions in a Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS). In this work, we highlight a novel post-processing interaction called just-enough interaction (JEI) which enables quick and accurate generation of a large set of training examples. Disjoint sets of 15 and 13 subjects were used for training and tested on another disjoint set of 53 knee datasets. All images were acquired using double echo steady state (DESS) MRI sequence and are from the osteoarthritis initiative (OAI) database. Segmentation performance using the learning-based cost function showed significant reduction in segmentation errors (p < 0.05) in comparison with conventional gradient-based cost functions.

Postoperative pain relief and functional outcome following total knee arthroplasty - a prospective comparative audit of three analgesic regimes

Pain control plays a key role in joint-replacement -surgery. As a surgeon the challenge is to reduce pain to an acceptable level in the post-operative period. The aim of the study was to assess the efficacy of bolus -local anaesthesia, infusion in to the surgical site and nerve blocks with femoral nerve catheter and its functional outcome. A prospective audit of 114 patients undergoing total knee arthroplasty were carried out.The patients were divided in to three groups : Group 1 (n = 27) received a bolus injection of 20 ml 0.25% levobupivacaine + 10 ml 0.25%bupivacaine + adrenaline + 30 ml saline. Group 2 (n = 39) received a bolus injection as on group 1 with 240 ml 0.25% bupivacaine infusion and 5 ml/hour using a Pain Buster pump. Group 3 (n = 48) received 30 ml 0.125% levobupivacaine to femoral (3-in-1) block with 30 ml 0.25% levobupivacaine to -sciatic nerve and introduction of a femoral nerve catheter. All patients were prescribed paracetamol 1 g QDS, Oxycontin 20 mg BD and Ibuprofen post -operatively. Pain was assessed with a Visual Analog Scale (VAS). The incidence of PONV was measured by PONY intensity score. The mean post-op VAS score for Group 3 was 4. The demand of oxynorm and NSAID were minimal in Group 3. The mean in patient stay for Group 3 was 3.1 days. The PONV intensity score was > 50 for 9 (36%) in Group 1, 15 (40%) in Group 2 and 9 (20%) in Group 3. There was loss of 20-30 degrees of flexion movements in Group 3 in the first 4 to 6 days post-op. Our study demonstrated that Regime 3 with the use of nerve blocks and femoral nerve catheter has given the maximum pain relief and good functional outcome following total knee replacement.

Organic molecules in the Sheepbed Mudstone, Gale Crater, Mars

The Sample Analysis at Mars (SAM) instrument on board the Mars Science Laboratory Curiosity rover is designed to conduct inorganic and organic chemical analyses of the atmosphere and the surface regolith and rocks to help evaluate the past and present habitability potential of Mars at Gale Crater. Central to this task is the development of an inventory of any organic molecules present to elucidate processes associated with their origin, diagenesis, concentration, and long-term preservation. This will guide the future search for biosignatures. Here we report the definitive identification of chlorobenzene (150-300 parts per billion by weight (ppbw)) and C₂ to C₄ dichloroalkanes (up to 70 ppbw) with the SAM gas chromatograph mass spectrometer (GCMS) and detection of chlorobenzene in the direct evolved gas analysis (EGA) mode, in multiple portions of the fines from the Cumberland drill hole in the Sheepbed mudstone at Yellowknife Bay. When combined with GCMS and EGA data from multiple scooped and drilled samples, blank runs, and supporting laboratory analog studies, the elevated levels of chlorobenzene and the dichloroalkanes cannot be solely explained by instrument background sources known to be present in SAM. We conclude that these chlorinated hydrocarbons are the reaction products of Martian chlorine and organic carbon derived from Martian sources (e.g., igneous, hydrothermal, atmospheric, or biological) or exogenous sources such as meteorites, comets, or interplanetary dust particles.

KEY POINTS

First in situ evidence of nonterrestrial organics in Martian surface sediments Chlorinated hydrocarbons identified in the Sheepbed mudstone by SAM Organics preserved in sample exposed to ionizing radiation and oxidative condition.

Organic molecules in the Sheepbed Mudstone, Gale Crater, Mars

The Sample Analysis at Mars (SAM) instrument on board the Mars Science Laboratory Curiosity rover is designed to conduct inorganic and organic chemical analyses of the atmosphere and the surface regolith and rocks to help evaluate the past and present habitability potential of Mars at Gale Crater. Central to this task is the development of an inventory of any organic molecules present to elucidate processes associated with their origin, diagenesis, concentration, and long-term preservation. This will guide the future search for biosignatures. Here we report the definitive identification of chlorobenzene (150-300 parts per billion by weight (ppbw)) and C₂ to C₄ dichloroalkanes (up to 70 ppbw) with the SAM gas chromatograph mass spectrometer (GCMS) and detection of chlorobenzene in the direct evolved gas analysis (EGA) mode, in multiple portions of the fines from the Cumberland drill hole in the Sheepbed mudstone at Yellowknife Bay. When combined with GCMS and EGA data from multiple scooped and drilled samples, blank runs, and supporting laboratory analog studies, the elevated levels of chlorobenzene and the dichloroalkanes cannot be solely explained by instrument background sources known to be present in SAM. We conclude that these chlorinated hydrocarbons are the reaction products of Martian chlorine and organic carbon derived from Martian sources (e.g., igneous, hydrothermal, atmospheric, or biological) or exogenous sources such as meteorites, comets, or interplanetary dust particles.

KEY POINTS

First in situ evidence of nonterrestrial organics in Martian surface sediments Chlorinated hydrocarbons identified in the Sheepbed mudstone by SAM Organics preserved in sample exposed to ionizing radiation and oxidative condition.

The effect of total hip and hip resurfacing arthroplasty on vertical ground reaction force and impulse symmetry during a sit-to-stand task

BACKGROUND

The aim of this study was to determine the influence of total hip arthroplasty and hip resurfacing arthroplasty on limb loading symmetry before, and after, hip reconstruction surgery during a sit-to-stand task.

METHODS

Fourteen patients were recruited that were about to receive either a total hip prosthesis (n=7) or a hip resurfacing prosthesis (n=7), as well as matched controls. Patients performed a sit-to-stand movement before, 3 months after, and 12 months after surgery. Peak vertical ground reaction force and impulse were measured for each leg, from which ground reaction force and impulse symmetry ratios were calculated.

FINDINGS

Before surgery, hip resurfacing patients showed a small asymmetry which was not different to normal for ground reaction force (0.88(0.28) vs. 1.00(0.11); p=0.311) or impulse (0.87(0.29) vs. 0.99(0.09); p=0.324) symmetry ratios. Total hip patients offloaded their affected hip by 30% in terms of impulse symmetry ratio (0.71(0.36) vs. 0.99(0.23); p=0.018). At 3 months following surgery asymmetries were seen that were different to normal in both hip resurfacing patients for ground reaction force (0.77(0.16); p=0.007), and total hip patients for ground reaction force (0.70(0.15); p=0.018) and impulse (0.72(0.16); p=0.011) symmetry ratios. By 12 months after surgery total hip patients regained a symmetrical loading pattern for both ground reaction force (0.95(0.06); p=0.676) and impulse (1.00(0.06); p=0.702) symmetry ratios. Hip resurfacing patients, however, performed the task by overloading their operated hip, with impulse symmetry ratio being larger than normal (1.16(0.16); p=0.035).

INTERPRETATION

Physiotherapists should appreciate the need for early recovery of limb loading symmetry as well as subsequent differences in the responses observed with different prostheses.

Phenotypic evaluation of severely damaged ocular surface after reconstruction by cultured limbal epithelial cell transplantation

OBJECTIVE

To report the clinical outcome of autologous cultured limbal epithelial cell transplantation (CLECT) followed by deep anterior lamellar keratoplasty (DALK) in paediatric eyes and to correlate the clinical outcome with the phenotype of rejuvenated corneal epithelium.

METHODS

Four patients with total limbal stem cell deficiency (LSCD) underwent autologous CLECT. Cultivated cell sheets were transplanted onto the damaged ocular surface followed by DALK surgery. Excised corneal buttons were subjected to histopathological analysis. Data recorded included age, sex, laterality, nature of injury, follow-up period, severity of stem cell deficiency, visual acuity, Schirmer's test and impression cytology.

RESULTS

At a mean follow-up period of 19.5 ± 7.4 (range 9-26) months after CLECT, all 4 eyes showed epithelialized and clinically stabilized ocular surface. Manual DALK was performed in all 4 eyes, with a mean follow-up of 9.75 ± 4.5 (range 5-15) months. All eyes exhibited smooth and clear corneal epithelium with improved visual acuity. Excised corneal buttons demonstrated organized corneal epithelial morphology and showed expression of cornea-specific CK3/12 marker.

CONCLUSION

Restoration of severely damaged ocular surface following chemical injury by using 2-stage meticulous approaches offers a new modality for the treatment of severe LSCD. Transplantation of cultivated autologous limbal epithelial cell sheet followed by DALK surgery can efficiently restore the corneal phenotype with improved vision.

Computational Modeling Deduced Three Dimensional Structure of Cry1Ab16 Toxin from Bacillus thuringiensis AC11

The first theoretical structural model of newly reported Cry1Ab16 δ-endotoxin produced by Bacillus thuringiensis AC11 was predicted using homology modeling technique. Cry1Ab16 resembles the Cry1Aa protein structure by sharing a common three domains structure responsible in pore forming and specificity determination along with few structural deviations. The main differences between the two is in the length of loops, absence of α7b, α9a, α10b, α11a and presence of additional β12b, α13 components while α10a is spatially located at downstream position in Cry1Ab16. A better understanding of the 3D structure shall be helpful in the design of domain swapping and mutagenesis experiments aimed at improving toxicity.

Tuberculous lymphadenopathy: a multicentre operational study of 6-month thrice weekly directly observed treatment

SETTING

Eight operational locations for the Revised National Tuberculosis Control Programme in six Indian states.

OBJECTIVE

To assess the 6-month efficacy of an intermittent thrice-weekly directly observed treatment (DOT) regimen for tuberculous peripheral adenopathy and the need for prolongation of treatment to 9 months for non-resolution of lymphadenopathy.

DESIGN

Patients aged >5 years with tuberculous lymphadenopathy were included in the study. Patients were evaluated for resolution at repeat visits following treatment. Those with poor resolution at 6 months were randomised to extended treatment up to 9 months or observation without additional treatment.

RESULTS

Resolution of lymphadenopathy was observed at the end of 6 months in 517/551 (93.8%) patients. There was a significant difference in response among patients with and those without the presence of systemic symptoms. There was no association between treatment response and number, size, site, consistency and matting of lymphadenopathy. No differences in response were seen in the remaining 34 patients with or without extended treatment.

CONCLUSION

The operational efficacy of 6-month thrice-weekly DOT for peripheral tubercular lymphadenopathy was satisfactory. There was no evidence of additional benefits of prolonging treatment to 9 months.

Peculiar reduction of graphene oxide into graphene after diffusion in exponentially growing polyelectrolyte multilayers

In the present work, in situ reduction of graphene oxide (GO) into graphene was preformed, after diffusion in exponentially growing polyelectrolyte multilayers, using sodium citrate as the reducing agent. First, the graphene oxide was obtained by treating a commercial grade of Expanded Graphite (EG). Based on XRD and Raman spectroscopy results, a complete exfoliation of graphene nanopellets down to one layer was achieved during the oxidation process. Secondly, the diffusion of GO was carried out in an exponentially growing polyelectrolyte multilayer film made from poly(diallyldimethylammonium chloride) as the polycation and from poly(acrylic acid) as the polyanion. Electrical conductivity of the GO based films was measured during the reduction process as a function of time. The conductivity reached values of the order of 10(-4) S cm(-1), whereas the pristine polyelectrolyte multilayer was highly insulating (∼10(-8) S cm(-1)). The conductivity also reached a maximal value after about 24 h of reduction and decreased for longer reduction duration. Some tentative explanations for this peculiar finding will be given.

Retinoblastoma

Retinoblastoma is the most common intraocular malignancy in children and one of the very few life-threatening ophthalmic conditions. Genetically, the disease may be heritable or non-heritable. It can have unilateral or bilateral involvement and can present either sporadically or with a positive family history. Leukocoria and strabismus are the most common presentations. Diagnosis is made by indirect ophthalmoscopy aided by imaging techniques. Multidisciplinary management is aimed at saving lives, salvaging the globe and maintaining good vision. The use of neoadjuvant chemotherapy and focal treatments, such as cryotherapy, laser photocoagulation, transpupillary thermotherapy, brachytherapy and periocular chemotherapy, form the mainstay of globe preserving treatment in retinoblastoma. In developing countries, retinoblastoma is unfortunately accompanied by a high mortality rate due to delayed diagnosis made at advanced stages of the disease. Early diagnosis and timely management are vital for a good prognosis.

Evaluating most probable number method to count and isolate viable methylotrophs

Nine different receptacles were tested with the MPN method to determine which receptacle was most reliable and economical for MPN counts. Results showed that 96 well PCR plate were the best vessels for this type of analysis and facilitated the isolation of viable Methylotrophs.

Prevalence of bronchial asthma in school children of 6-13 years of age in Shimla city

BACKGROUND

Asthma is a major public health problem worldwide. However, there is limited data on asthma prevalence among school children in Shimla. The present study was undertaken to determine the prevalence of asthma in Shimla city and also to examine the different factors influencing its occurrence.

METHODS

This was a questionnaire-based study (International Study of Asthma and Allergies in Childhood [ISAAC] protocol) carried out in four randomly selected schools in the city of Shimla. The questionnaire was pre-tested in 50 school children. The age group included in the study was 6-13 years. The questionnaire was distributed to all the children (n = 1136). A response rate of 89.5% was achieved. The socio-economic status was determined on the basis of monthly income (cumulative square root value method). The ISAAC questionnaire was given standard scores. Various factors influencing the prevalence of asthma, such as socio-economic status, history of asthma and other allergic disorders in the family, type of cooking fuel used, history of active and passive smoking were elucidated from the history.

RESULTS

Overall prevalence of asthma in our study was found to be 2.3 percent. Boys had a higher prevalence (3.1%) than girls (1.4%). We found a significant association between asthma prevalence and family history of asthma and other atopic manifestations. There was no significant association of socio-economic factors and exposure to smoking with prevalence of asthma.

CONCLUSION

In the present study, the prevalence of bronchial asthma in school children in the age group of 6-13 years is 2.3 percent.