Role of Scanning Electron Microscopy and X-Ray Microanalysis in the Identification of Urinary Crystals

Urinary crystals can be identified by using analytical electron microscopic techniques of scanning electron microscopy and energy dispersive x-ray microanalysis. Crystal habit can be recognised by scanning electron microscopy and their chemical nature by elemental analysis. With a conventional detector the lightest element that can routinely be detected is sodium, but with a windowless or thin window detector even carbon can be detected. Thus almost all the commonly occurring urinary crystals including uric acid can be analysed by energy dispersive x-ray microanalysis

Histochemistry of Colloidal Iron Stained Crystal Associated Material in Urinary Stones and Experimentally Induced Intrarenal Deposits in Rats

Organic material associated with the calcium oxalate crystals in urinary stones and experimentally induced nephrolithiasis was stained with colloidal iron and analysed by energy dispersive x-ray microanalysis using standard techniques. Iron was positively identified in the stained specimens indicating that some of the organic material is an acidic mucosubstance. The results also indicate that some of the organic material of urinary stones may originate in the kidneys

Retention of Calcium Oxalate Crystals in Renal Tubules

Crystal retention within the renal tubules is essential for nephrolithiasis and the development of urinary stone disease. We studied the mechanisms involved in this process by inducing calcium oxalate crystal deposition within the rat renal tubules and examining them using various microscopic techniques. Crystals appeared to be retained either by attachment to the tubular epithelium or by aggregating with other crystals thus becoming large enough to be retained by their collective size

Presence of Calcium Oxalate Crystals in the Mammalian Thyroid Gland

Birefringent crystals of calcium oxalate have been previously identified in the colloid of human thyroid glands. We found such crystals in 19/20 adult thyroids at autopsy, in 4/ 20 infants at autopsy, and, using frozen sections, in 19/20 thyroids partially or totally removed at surgery. These crystals were soluble in hydrochloric acid, insoluble in acetic acid, and contained only calcium by energy dispersive X-ray microanalysis, confirming their calcium oxalate character. Similar crystals were found in equine and ovine thyroids

Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data

Objective: To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple, cost-effective and provides a non-contact approach to tissue differentiation. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%, meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated. Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery

Rhinocerebral zygomycosis in Pakistan: clinical spectrum, management, and outcome

OBJECTIVE: To study the disease spectrum and salient management features of 36 patients with histopathologically-confirmed rhinocerebral zygomycosis seen at our academic center over a 16-year period. METHODS: Retrospective review of patients admitted to the Aga Khan University Hospital in Karachi, Pakistan from January 1991 to December 2006 with histopathologically-confirmed zygomycosis of the head and neck. RESULTS: Mean patient age was 40 +/- 5.0 years (range, 34-63 years), and 23 (64%) patients were male. Thirty-two (89%) patients were referred from clinical services other than otolaryngology. Underlying predisposing conditions included diabetes mellitus (21 patients), haematologic diseases (9), and renal failure (6). Twenty (55%) patients had limited sinonasal disease, ten (28%) had orbital involvement, and six (17%) had intracranial extension. All patients underwent rigid nasal endoscopy and biopsy, and black necrotic tissue was seen in 22 (61%) instances warranting endoscopic or open surgical debridement. Four of 6 patients undergoing open surgery required orbital exenteration. Overall patient survival was 56% (20/36 patients). Diabetic patients had improved survival (17/21, or 81%) compared to patients with haematologic disorders (3/9, or 33%) (p = 0.001). All six patients with intracerebral disease died. Eighteen of the 22 (82%) patients treated with surgery plus amphotericin B survived vs. two of 14 (14%) receiving amphotericin B alone (p \u3c 0.001). CONCLUSIONS: In rhinocerebral zygomycosis, an aggressive, multidisciplinary, diagnostic and therapeutic approach that utilizes CT or MRI staging, and combines endoscopic or open surgical debridement with amphotericin B-based antifungal therapy offers the best chance of recovery

Low-temperature thermochronology of central and northwestern Pamir gneiss domes

Abstract HKT-ISTP 2013 A

CHRONIC TRANSPLANT REJECTION: PROBLEMS, DISCOVERIES, SOLUTIONS

poster abstractOrgan transplantation has become an increasingly important remedy in helping extend the lives of patients with organ failures or deficien-cies. Although the survival rates of organ recipients have dramatically increased in the short term (1-5 years), long-term (5+ years) survival rates have not improved significantly. Additionally, increasingly un-healthy lifestyles have contributed to a dramatic increase in the need for organ transplants, while organ supply has only slightly increased, resulting in a constantly increasing gap between organ demand and organ supply. Dr. Raymond Johnson, an IU Health Physician, discov-ered a new T cell subset that is resistant to medications routinely used to prevent transplant rejection. This discovery is important because it can be used to develop mechanism-specific diagnostic blood tests for chronic rejection and, potentially, new drugs to treat chronic trans-plant rejection. However, innovations developed in faculty labs often face multiple hurdles in reaching the market place. As participants in the Innovation-to-Technology Central (ITEC) program, our unique multidisciplinary team of students investigated Dr. Raymond Johnson’s discovery by conducting literature research and expert interviews on organ transplantation and rejection and pharmaceutical drugs used in preventing acute transplant rejection. Through our research and our interviews, we were able to further document the dire need for meth-ods for increasing survival rates of transplanted organs. Most im-portantly, we have conducted preliminary market research and devel-oped several commercialization strategy recommendations based on comparable innovation analysis and precedent biotechnology start-up strategies. We anticipate that our research will provide Dr. Johnson with new information and perspectives to help seek venture capitalists to invest in his research, which holds the promise to change the lives of thousands of transplant recipients each year. Funding provided by IUPUI’s Innovation-to-Enterprise Central (ITEC)

An investigative study into the sensitivity of different partial discharge φ-q-n pattern resolution sizes on statistical neural network pattern classification

This paper investigates the sensitivity of statistical fingerprints to different phase resolution (PR) and amplitude bins (AB) sizes of partial discharge (PD) φ-q-n (phase-amplitude-number) patterns. In particular, this paper compares the capability of the ensemble neural network (ENN) and the single neural network (SNN) in recognizing and distinguishing different resolution sizes of φ-q-n discharge patterns. The training fingerprints for both the SNN and ENN comprise statistical fingerprints from different φ-q-n measurements. The result shows that there exists statistical distinction for different PR and AB sizes on some of the statistical fingerprints. Additionally, the ENN and SNN outputs change depending on training and testing with different PR and AB sizes. Furthermore, the ENN appears to be more sensitive in recognizing and discriminating the resolution changes when compared with the SNN. Finally, the results are assessed for practical implementation in the power industry and benefits to practitioners in the field are highlighted