Squamous cell carcinoma of the renal pelvis is a rare neoplasm, often unsuspected clinically due to its rarity and ambiguous clinical and radiological features, and hence patients present at advanced stages resulting in poor prognosis. We report here four cases of incidentally diagnosed primary renal squamous cell carcinoma, treated at our hospital over a short span of one year, and review the relevant literature. Mean age of the patients (3 males, 1 female) was 60 years. All suffered from staghorn stones. Interestingly, renal carcinoma was unsuspected clinically in all patients. In one case, a computerised tomography scan showed a suspicious nodule. All underwent nephrectomy for nonfunctioning kidney. In just two cases, tumor was identified on gross examination, while the other two only showed thickened pelvis. Our series emphasises the need for pelvicalyceal biopsy during treatment for long-standing nephrolithiasis, and thorough sampling of the renal pelvis in nephrectomy specimen of such patients.
Isolated spinal involvement of juvenile xanthogranuloma (JXG) is extremely rare. There are only seven prior published cases of spinal JXG, of which only one has been reported in an adult. We report here the eighth case of spinal JXG and the second in an adult. The patient, a 22-year-old female, presented with progressive upper backache. Radiological examination revealed a well-defined osteolytic hypointense mass in the T7 vertebral body, with a large soft tissue paraspinal extension causing cord compression. Complete resection of the mass was performed, with resolution of symptoms. Histology showed a histiocytic tumour with numerous Touton, foreign body and osteoclastic giant cells, immunopositive for CD68 and vimentin and negative for S100 and CD1a, corresponding to a diagnosis of JXG. Literature regarding spinal JXG is reviewed and discussed.
Introduction: Diabetic ketoacidosis is one of the most severe acute complications of diabetes mellitus characterised by hyperglycemia, hyperketonemia, and metabolic acidosis. Prompt diagnosis and treatment of diabetic ketoacidosis can decrease severity, hospital stay, and possible mortality. This study aimed to find out the prevalence of diabetic ketoacidosis among diabetic patients admitted to the department of medicine of a tertiary care centre.
Methods: This descriptive cross-sectional study was conducted at a tertiary care centre. Data from 1 March 2022 to 1 December 2022 were collected between 1 January 2023 and 1 February 2023 from the hospital records. The ethical approval was taken from the Institutional Review Committee of the same institute (Reference number: 466/2079/80). All the diabetic patients admitted to the Department of Medicine during our study duration were enrolled for the study. Diabetic patients who left against medical advice and those with incomplete data were excluded from the study. Data were collected from the medical record section. Convenience sampling method was done. Point estimate and 95% Confidence Interval were calculated.
Results: Among 200 diabetic patients, the prevalence of diabetic ketoacidosis was 7 (3.5%) (3.47-3.53, 95% Confidence Interval) among which 1 (14.29%) patients had type I diabetes mellitus and 6 (85.71%) had type II diabetes mellitus patients and the mean HbA1C level was 9.77%.
Conclusions: The prevalence of diabetic ketoacidosis among diabetes mellitus patients admitted to the department of medicine of a tertiary care centre was found to be higher than in other studies done in similar settings.
Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. The constant development of computer tools lead to the requirement of easier interface between the man and the computer. Handwritten character recognition may for instance be applied to Zip-Code recognition, automatic printed form acquisition, or cheques reading. The importance to these applications has led to intense research for several years in the field of off-line handwritten character recognition. „Hindi‟ the national language of India (written in Devanagri script) is world‟s third most popular language after Chinese and English. Hindi handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. Recognition of handwritten Hindi characters by computer machine is complicated task as compared to typed characters, which can be easily recognized by the computer. This paper presents a scheme to recognize Hindi number numeral with the help of neural network.
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