Prenatal Ultrasound Diagnosis of Vasa Previa With Careful Intraoperative Management: A Case Report

Vasa previa is a rare but potentially life-threatening condition to the fetus. Timely antenatal diagnosis and delivery by cesarean section (CS) can lead to a favorable outcome. Here, we report a case of recurrent pregnancy loss (G3A2) with vasa previa, which was diagnosed prenatally by ultrasound. She was admitted at her 31st week with bleeding per vaginum (PV) provisionally diagnosed as antepartum hemorrhage (APH) and managed conservatively as placenta previa. Follow-up ultrasonography (USG) revealed vasa previa at 33 weeks. The fetus was delivered by lower segment cesarean section (LSCS) after careful separation of the membranes and avoiding damage to the vessels as there was velamentous insertion of cord with the lower margin of the placenta in the lower segment. The baby was cared for in the neonatal intensive care unit due to prematurity and discharged after six days. This case report highlights the importance of prenatal ultrasound in diagnosing vasa previa and planning an elective cesarean section with caution intraoperatively for the safe delivery of the baby.

FACIAL EMOTION DETECTION OF THERMAL AND DIGITAL IMAGES BASED ON MACHINE LEARNING TECHNIQUES

The aim of the study is (i) to determine temperature distribution for various emotions from the facial thermal images; (ii) to extract statistical features from the facial region using GLCM feature extraction technique and to classify the emotions using machine learning classifiers such as SVM and Naïve Bayes; (iii) to develop the custom CNN model for the classification of various emotions and compare its performance with machine learning classifiers. Fifty normal subjects were considered for the study to analyze the facial emotions using thermal and digital images. The four different emotions, such as happy, angry, neutral and sad, were obtained with a total image of 200 thermal and 200 digital images. Ten statistical features were extracted using the GLCM method from both thermal and digital images and fed into the machine learning classifiers. After data augmentation, the images are fed into the custom CNN model for the classification of various emotions. The SVM classifier produced an accuracy of 80% in thermal images and 76.5% in digital images compared to the Naive Bayes classifier. The developed CNN model improved the classification accuracy to 94.3% and 90.3% for thermal and digital image, respectively, for the multi-class classification of facial emotions. The CNN model implemented using thermal images provided better classification accuracy than digital images in facial emotion recognition. Hence, it was proved that thermal imaging techniques resulted in better performance in predicting facial emotion than digital images.

ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence

Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Single-channel Electrooculogram (EOG) is proposed for detecting diabetic retinopathy. The Corneal-retinal potential of the eyes plays a vital role in the acquisition of Electrooculography. Diabetes is the most prevalent disease and for one out of three people with diabetes above 40 years, diabetic retinopathy occurs. It is necessary for the early detection of diabetic retinopathy as it is one of the primary reasons for blindness in the population. The potential difference between cornea and retina leads to the acquisition of EOG signal. The proposed study aims to design a low-cost miniaturized hardware circuit to obtain EOG signal using second order filters without compromising in accuracy of the outcome signal and to classify the signal into normal and diabetic retinopathy subjects by extracting the statistical features like kurtosis, mean, median absolute deviation, standard deviation, and range from software filtered EOG signal. Among the classifiers used, Support vector machine (SVM) shows a higher accuracy of 93.33%. The sensitivity, specificity and Area Under Curve (AUC) values of SVM are 96.43%, 90.625%, 0.93% is considered as more favorable outcome for the proposed method and it supports the developed prototype and processing methodology. The novelty of the research is based on proposing and exploring a non-invasive methodology for Diabetic retinopathy diagnosis based on EOG signal. Thus, the designed hardware is simple in operation and cost effective, provides an affordable and non-invasive diagnostic tool for diabetic retinopathy patients

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

Study of a retinal layer model to generate a spike waveform for a color deficient and strabismus individual

The physiological modeling of retinal layers to provide an insight into how the incoming image is converted into its equivalent spike train that can be decoded by the human brain is a key issue. Most of the retinal layer models concentrate mainly on image compression, edge detection and image reconstruction. A retinal layer model to generate spike waveform corresponding to the visual information is not covered much in the literature. The aim of this study was to develop a mathematical model of retinal layers that has complex neural structures, that can detect the incoming signal and transform the signal into the equivalent spike train. The proposed retinal layer model includes a photoreceptor, an outer plexiform (OPL), an inner plexiform (IPL) and ganglion cell layers exhibiting the properties of compression, luminance and spatial temporal filtering in the processing of visual information. The photoreceptor layer enhances the contrast visibility in the dark region and maintains the same in the bright regions. The OPL is modeled to enhance the contour of the image. The finer detail of the image is extracted by mathematically modeling the IPL. The ganglion cell layer is modeled using the Hodgkin-Huxley model to generate the spike train for the incoming information. The spike train was generated for color deficient individuals namely protanopia, deuteranopia, tritanopia and for individuals suffering from night blindness. Simulation results showed a spike train was generated only for a certain threshold stimulus value. The differences in spike pattern for a normal and visually impaired individual were studied. This may lead to a methodology for earlier diagnosis.

SEGMENTATION OF LIVER TUMOR USING FAST GREEDY SNAKE ALGORITHM

Back Ground: Liver tumors are a type of growth found in the liver which can be categorized as malignant or benign. It is also called as hepatic tumors. Early stage detection of tumor could be treated at a faster phase; if it is left undiagnosed it may lead to several complications. Traditional method adopted for diagnosis can be time consuming, error-prone and also requires an experts study. Hence a non invasive diagnostic method is required which overcomes the flaws of conventional method. Liver segmentation from CT images in post processing techniques not only is an essential prerequisite, but, by playing an important role in confirming liver function, pathological, and anatomical studies, is also a key technique for diagnosis of liver disease. Hence in the proposed study Fast greedy snakes algorithm in abdominal CT images were used for segmenting tumor portion.

Aim & Objectives: The aim and objectives of study is: (i) to segment tumor region in the liver image using Fast Greedy Snakes Algorithm (FGSA); (ii) to extract the GLCM features from the segmented region; (iii) to classify the normal and abnormal liver image using neural network classifier.

Methodology: The study involved a total of 30 normal and 30 abnormal Images from database. In the proposed study automated segmentation was performed using Fast Greedy Snakes (FGS) Algorithm and the features were extracted using GLCM method. Classification of normal and abnormal images was carried out using Back propagation Neural Network classifier.

Result: The proposed FGS algorithm provides accurate segmentation in liver images. Statistical features like mean, kurtosis, correlation and Entropy showed a higher value for the normal image than liver tumor image. On the other hand, features like Skewness, Homogeneity, contrast, Energy and standard deviation showed a comparatively higher value for a liver tumor image than the normal. Statistical features such as Mean, Contrast, Homogeneity and standard deviation are statistically significant at [Formula: see text]. Features like correlation, entropy and energy exhibits significance at [Formula: see text]. The feature extracted values provided significant difference between the normal and abnormal liver images. The neural network classifier yields the sensitivity of 95.8%, sensitivity of 81.4% and achieved the overall accuracy of 92%.

Conclusion: A most accurate, reliable and fast automated method was implemented to segment the liver tumor image using Fast Greedy snakes algorithm. Hence the proposed algorithm resulted in effective segmentation and the classifier could classify the normal and abnormal images with greater accuracy.

HATRICK-Synchronous Triple Primary Tumors of Thyroid

Multiple primary cancer (MPC) has an incidence of 1.8% and is defined as having two or more cancers in a single patient. Synchronous tumors are defined as ≥ 2 primary tumors occurring within 6 months of diagnosis of the first primary tumor. We present a case of a 27-year-old female patient who presented with a painless, gradually progressive right-sided neck swelling for the last 1 year with no systemic complaints. Examination revealed a 4 × 3-cm, firm, smooth surfaced swelling on right lobe of thyroid. USG neck showed a hypoechoic solid nodule on the right lobe and the left lobe was normal. FNAC showed features of adenomatous colloid nodule, Bethesda II. Right hemithyroidectomy specimen revealed evidence of triple tumors-not otherwise-specified (NOS) tumor, papillary carcinoma thyroid, and medullary carcinoma thyroid, which was confirmed with positivity on IHC with synaptophysin, CEA, and chromogranin. Concurrent appearance of NOS, PTC, and medullary carcinoma thyroid in the very same patient is extremely rare and has not been previously reported in the English literature.

NON-INVASIVE BLOOD GLUCOSE ANALYSIS BASED ON GALVANIC SKIN RESPONSE FOR DIABETIC PATIENTS

Diabetes is a chronic disease due to the lack of production of hormone insulin by the beta cells in the islets of Langerhans. Many diabetic patients often draw a small amount of blood to measure the glucose level every day. This vital information is needed to control their daily food intake. One such method could cause infection and discomfort to the patient. Non-invasive glucose measurement techniques overcome these challenges to monitor blood glucose level continuously. The aim and objective of this study are as follows: (i) to correlate the skin resistance based on Galvanic skin response (GSR) and blood glucose level for diabetic and non-diabetic subject and (ii) to estimate the blood glucose value based on GSR voltage and resistance using stepwise linear regression model. About 50 diabetic and 50 non-diabetic subjects were included in this study. Blood glucose level is recorded using the minimally invasive device called accu-chek for all the subjects. GSR resistance and GSR voltage were recorded using the designed instrumentation setup. In diabetic subjects, the measured blood glucose level shows negative correlation with the GSR voltage ([Formula: see text], [Formula: see text]) and GSR resistance ([Formula: see text], [Formula: see text]). The estimated blood glucose level can be predicted with good sensitivity (94%) and accuracy (92%) using age and GSR voltage, or by the combination of age and GSR resistance in the evaluation of diabetic subjects.

Computer-based automated analysis of X-ray and thermal imaging of knee region in evaluation of rheumatoid arthritis

The aim and objectives of the study are as follows: (1) to perform automated segmentation of knee X-ray images using fast greedy snake algorithm and feature extraction using gray level co-occurrence matrix method, (2) to implement automated segmentation of knee thermal image using RGB segmentation method and (3) to compare the features extracted from the segmented knee region of X-ray and thermal images in rheumatoid arthritis patients using a biochemical method as standard. In all, 30 rheumatoid arthritis patients and 30 age- and sex-matched healthy volunteers were included in the study. X-ray and thermography images of knee regions were acquired, and biochemical tests were carried out subsequently. The X-ray images were segmented using fast greedy snake algorithm, and feature extractions were performed using gray level co-occurrence matrix method. The thermal image was segmented using RGB-based segmentation method and statistical features were extracted. Statistical features extracted after segmentation from X-ray and thermal imaging of knee region were correlated with the standard biochemical parameters. The erythrocyte sedimentation rate shows statistically significant correlations (p < 0.01) with the X-ray parameters such as joint space width and % combined cortical thickness. The skin surface temperature measured from knee region of thermal imaging was highly correlated with erythrocyte sedimentation rate. Among all the extracted features namely mean, variance, energy, homogeneity and difference entropy depict statistically significant percentage differences between the rheumatoid arthritis and healthy subjects. From this study, it was observed that thermal infrared imaging technique serves as a potential tool in the evaluation of rheumatoid arthritis at an earlier stage compared to radiography. Hence, it was predicted that thermal imaging method has a competency in the diagnosis of rheumatoid arthritis by automated segmentation methods.

Physiological modeling for detecting degree of perception of a color-deficient person

Physiological modeling of retina plays a vital role in the development of high-performance image processing methods to produce better visual perception. People with normal vision have an ability to discern different colors. The situation is different in the case of people with color blindness. The aim of this work is to develop a human visual system model for detecting the level of perception of people with red, green and blue deficiency by considering properties like luminance, spatial and temporal frequencies. Simulation results show that in the photoreceptor, outer plexiform and inner plexiform layers, the energy and intensity level of the red, green and blue component for a normal person is proved to be significantly higher than for dichromats. The proposed method explains with appropriate results that red and blue color blindness people could not perceive red and blue color completely.

Proliferating cell nuclear antigen expression and the progression of cervical intraepithelial neoplasia

Cell proliferation is an important biological aspect of a tumor cell population which can affect clinical outcome. In addition to other well established clinical and histopathological prognostic criteria? cell kinetic data have significant predictive value. This study evaluates the proliferative activity of benign, premalignant and malignant cervical tissue by analyzing the expression of the proliferating cell nuclear antigen (PCNA). PCNA is a 36 kD nuclear protein associated with the cell cycle and is directly involved in DNA synthesis during cell proliferation. A total of 122 subjects were included in the study. This included 30 benign tissue samples, 30 low grade lesions (CIN 1), 30 high grade lesions (CIN 2/3) and 32 invasive squamous carcinomas. There was significant difference in PCNA index between benign and high grade lesions as well as benign and invasive cancer. The percentage of PCNA positive cells were significantly higher in invasive carcinoma when compared with non malignant lesions. Moreover, there was also good correlation between increasing histological abnormality and PCNA expression. These results suggest that cell proliferation index as detected by PCNA expression may be useful in the evaluation of alterations in cell kinetics of various grades of cervical lesions. Such data could also possibly help explain the biological behaviour of these lesions and be useful in planning of radiotherapy for invasive cervical cancer.

Actinomycosis in urachal remnants: A rare cause of pseudotumor

Actinomycosis is a chronic inflammatory condition caused by Actinomyces israeli, a gram positive anaerobic bacterium. It can have a variety of clinical manifestations and can mimic a malignancy. We present one such case of urachal actinomycosis that mimicked a tumor. A 28-year-old man presented with abdominal pain of 20 days duration. Per abdominal palpation revealed a firm mass with ill-defined borders in the suprapubic region. Computed tomography and magnetic resonance imaging scans of the pelvis showed an irregular lesion in the urinary bladder extending to the umbilicus, giving the impression of urachal remnants with inflammation. Peroperatively, an irregular, hard mass measuring 6 × 5 cm, involving the anterior and posterior bladder walls, the appendix, the terminal ileum and sigmoid colon, was seen, which was suspicious for a malignancy. Frozen sections from the mass showed extensive inflammation and a florid fibroblastic proliferation, giving the impression of an inflammatory pseudotumor. The tissue was extensively sampled for paraffin sections and only one of them revealed a colony of Gram, PAS and GMS- positive organisms, conclusive for Actinomycosis. It is important to be aware of this uncommon, yet significant, presentation of a common infectious disease in order to avoid misdiagnosis and over-treatment as a malignancy.

Histopathologic profile of alopecia areata in Indian patients

Background:

Alopecia Areata (AA) is a “non-scarring” alopecia that has an autoimmune basis. Though clinically distinctive, problems arise in diagnosis depending on the temporal stage of the disease at presentation; some of them progress to scarring alopecia and predicting its prognosis is difficult. Histological changes depend on the disease stage and site of the biopsy.

Objectives:

To describe the spectrum of histologic features in AA.

Materials and Methods:

A prospective and retrospective study of H and E sections of all biopsies signed out as AA between 2001 and 2009 (20 cases) was undertaken.

Results:

The diagnosis was made on vertical sections in all cases. The total number of hair follicles ranged from 1 to 24 with an average of 7 and comprised mainly terminal follicles. Vellus follicles were scanty. Anagen to non-anagen ratio was 1:1.62. Miniaturization of follicles was noted in five (25%) cases. Peribulbar inflammation was seen in all the cases with a dominance of lymphocytes. Perifollicular fibrosis was noted in 12 (60%) and pigment casts in 5 (25%) cases. Scarring was seen in two cases. In these cases, a diagnosis of AA was rendered on the basis of even spacing of the fibrotic units and remnants of the catagenic basement membrane within the scars. The epidermis and interfollicular dermis were normal in all the cases.

Conclusion:

The most consistent features of AA are an increase in non-anagen terminal follicles and peribulbar lymphocytic infiltrate. The etiology can be determined even in cases that have progressed to scarring.

Clinicopathologic analysis of 21 cases of nevus sebaceus: a retrospective study

BACKGROUND

Nevus sebaceus (NS), otherwise designated as 'organoid nevus', involves proliferative changes of the sebaceous glands, sweat glands, and the hair follicles. It displays a range of appearances, depending on the lesion's age.

AIMS

To study the histopathological features of NS and correlate these with clinical findings.

METHODS

All skin biopsy specimens over a 12-year period from 1995 to 2007 which had a diagnosis of NS were included. Clinical data with follow-up notes and histopathology were reviewed.

RESULTS

Half of the cases had a verrucous clinical appearance, while the rest presented as papules, plaques, or patches. All the cases showed immature hair follicles, and 24% of cases showed immature sebaceous glands. Normal terminal hair follicles were characteristically absent in the lesion. Nineteen percent of the cases showed dilated apocrine glands, and 14% showed hyperplasia of eccrine glands. Epidermal changes in the form of acanthosis, papillomatosis, and hyperkeratosis were seen in 86% of cases. Dilated keratin-filled infundibula were observed in 24% of cases. One case was associated with a squamous cell carcinoma.

CONCLUSIONS

Nevus sebaceus is a cutaneous hamartoma, consisting of various elements indigenous to the organ. Normal terminal hair follicles are characteristically absent in the lesion although the same may be seen in rest of the epidermis, a feature of diagnostic importance, not usually highlighted in literature. The divergent differentiation observed in NS is consistent with the common embryologic origin of the folliculosebaceous-apocrine unit and should not mislead the pathologist.

Follicular mycosis fungoides - A report of four Indian cases

BACKGROUND

Follicular Mycosis Fungoides (FMF) is an under-recognized disease in India. Its clinical mimics include Hansen's disease and Sarcoidosis.

AIMS

To describe the clinical and pathological features of FMF.

MATERIALS AND METHODS

All cases of FMF between January and December 2007 were retrieved. Cases of conventional epidermotropic MF with a minor follicular component were excluded. Slides were reviewed by two observers. The following criteria were assessed: degree and density of folliculotropism of lymphocytes, location of folliculotropism (infundibular / isthmic / bulbar), follicular mucin, eosinophils, granulomas, and conventional epidermotropism. Each feature was assigned a semi-quantitative grade.

RESULTS

There were four cases of FMF, with an equal gender distribution and a mean age of 17.5 years. All lesions were on the face. They presented as: hypopigmented patches (2) and erythematous plaques (2). Alopecia was seen in two cases. The clinical diagnosis was Hansen's disease in all four, with a differential of Alopecia mucinosa / Sarcoidosis in two cases.The histological features seen were: disproportionate folliculotropism, lymphocyte tagging with haloes, follicular mucin, and nucleomegaly / convolution in all four cases, prominent eosinophils (2), epithelioid granulomas (1), eccrine infiltration (4), parakeratosis at the follicular ostia (2), and sebaceotropism (1). The infiltrate was bulbar (4) and isthmic (2). The rest of the epidermis showed no hint of conventional MF.

CONCLUSION

The preferential features for FMF were involvement of face, dominant folliculotropism, nuclear atypia and convolution, and follicular mucin. Presence of granulomas and eosinophils necessitated exclusion of infectious causes. The absence of findings of MF in the rest of the epidermis should not deter pathologists from rendering this diagnosis.

An unusual penile spindle cell malignancy

Primary non-epithelial malignancies of the penis are rare. We report a patient with an unusual presentation of an unexpected spindle cell malignancy at this site. A 35- year-old man presented with a mass in the shaft of the penis with inguinal lymphadenopathy. A malignant spindle cell neoplasm was seen involving the corpora, consistent with leiomyosarcoma. Immunohistochemically, the tumour was positive for S100, HMB-45 and negative for SMA. A final diagnosis of amelanotic spindle cell melanoma was made. This case is being presented to reiterate the importance of including melanoma in the differential diagnosis of spindle cell sarcomas at uncommon sites.

Direct immunofluorescence in cutaneous vesiculobullous lesions

Direct Immunofluorescence (DIF) is invaluable in the diagnosis of cutaneous vesiculobullous lesions (VBL). It is limited by technical factors and disease nature. 1) To record the sensitivity of DIF in VBL 2) To correlate DIF with clinical, histologic findings and analyse discrepancies.

MATERIAL AND METHODS

A retrospective study of 100 DIFs on suspected VBL of skin. DIF, histology and clinical data were reviewed. 73/100 cases showed DIF patterns concordant with clinical/histologic diagnosis. The sensitivity of DIF was 88% in Pemphigus group (39/ 44), 82% in Bullous Pemphigoid (BP) (23/28), and 20% in Dermatitis Herpetiformis (DH) (1/5).18 cases of histologically proven VBL were negative and of these, 4 had no epidermis. The remaining 9 cases were discordant with clinical/histologic features, including 4 BP and 5 DH, whose histology was non-specific and will be discussed in detail. One case of DH showed an aberrant vasculitic pattern. DIF is of great value in the diagnosis of VBL, specially in clinical/histologic dilemmas. In DH, neither biopsy nor DIF were very useful and response to therapy was the standard. Sampling errors contributed to false negative results. Proper selection of cases and judicious use are mandatory to optimize its' utility.

Application of poly ethylene glycol hydrogel to overcome latex urinary catheter related problems

Urinary catheterization is a routine procedure in an intensive care unit (ICU) for monitoring the urine output of critically ill patients. The catheters which are most often used to help with urinary incontinence and retention also face problems like blockage, leakage and infection. These problems are due to proteins that adhere to the catheter surface and quickly build up on each other forming a protein layer. As the layers build up they can crystallize, providing the major source of blockage and leakage. Current strategies to avoid these problems include coating a catheter with silver alloy to reduce bacteria on the catheter surface. However, silver alloy coatings can lead to increased silver resistance for bacteria. Since silver is already used as an antibacterial agent in many places in a hospital, it is even more possible that resistance can develop. An alternative solution is presented involving coating latex, a common urinary catheter material with a micro layer (5-100 microns) of polyethylene glycol. This hydrogel is applied using an interfacial photopolymerization process with ethyl eosin as the photoinitiator. A 25 ppm concentration of ethyl eosin provided the strongest gel to surface adhesion and significantly lowered protein adhesion when compared to an uncoated latex substrate.

Intestinal neuronal dysplasia type A

Intestinal neuronal dysplasia is a rare cause of bowel obstruction in neonates and infants, the diagnosis of which poses a formidable challenge to both clinicians and pathologists alike. The importance of this entity lies not only in the fact that it mimics Hirschsprung's disease, but also in that untreated cases, particularly of type A, may prove fatal. The authors describe one such case of intestinal neuronal dysplasia of type A, which was diagnosed at autopsy.

Colonic carcinomas masquerading as Crohn's colitis

When colonic carcinomas present with acute abdomen, the operating surgeon and the pathologist face a plethora of diagnostic and therapeutic problems. In this retrospective study of 92 cases of carcinoma colon, 4 presented with acute intestinal obstruction of which three had a turbulent post operative period and died. The resected colonic segment showed on gross examination cobblestone appearance characteristic of Crohn's disease but microscopically was ischemic with the stricture site showing features of an infiltrating poorly differentiated adenocarcinoma. We have made an attempt to study the various pathologic features and analyse their significance with reference to prognosis.