Is an excretory urogram mandatory in patients with small to medium-sized renal and ureteric stones treated by extra corporeal shock wave lithotripsy?

Background: An intravenous urogram (IVU) has traditionally been considered mandatory before treating renal and ureteric stones by extracorporeal shock wave lithotripsy (ESWL). This study was designed to see whether there is a difference in complications and the need for ancillary procedures in patients managed by ESWL for renal and ureteric calculi, according to preoperative imaging technique. Methods: This retrospective study compared 133 patients undergoing ESWL from January 2001 to July 2002. Patients were divided into three groups according to the preoperative imaging technique used: i) IVU; ii) non-contrast enhanced helical computed tomography (UHCT); and iii) ultrasound (US) + X-ray kidney, ureter and bladder (KUB). The groups were matched in terms of age and gender, as well as location, side and size of stones. Results: There was no statistically significantly difference for number of ESWL sessions, number of shock waves and use of ancillary procedures between the three groups. The stone-free rate was 98% for the IVU and UHCT groups, and 97% for the US + X-ray KUB group. Conclusions: The complication rate and need for ancillary procedures was comparable across the three groups. Patients imaged by UHCT or US + X-ray KUB prior to ESWL for uncomplicated renal and ureteric stones do not require IVU

Tailored metabolic workup for urolithiasis – the debate continues

Abstract Urolithiasis is one of the commonest afflictions of the urinary tract. Stones are of various chemical compositions, some share some common etiology; but most are specific to the structure and composition of stone. In view of highly recurrent nature of this condition, it is logical to have strategies for prevention. However, due to multiple factors most patients receive no or fragmented information on prevention. The current controversy is to the extent of metabolic workup in adult first time stone former. This requires longitudinal studies to show benefit in prevention strategies. Patients at high risk can have recurrence in weeks to years, depending upon the composition and attending risk factor. They should be targeted with concentric and tailored prevention protocols. The major urological guidelines (EAU and AUA) recommend basic stone workup for all patients. However, indication for detailed workup are less well documented, so one potential solution is to tailor metaphylaxis strategies for individual patient

Statistical Analysis of Balanced Brain and IQ Applications

EEG signal research had been studied massively in such balanced brain and IQ applications. This paper focuses on correlation between balanced brain and Intelligence Quotient (IQ) applications. At first, the raw EEG signals from both applications need to pre-process to remove artefact and unwanted frequency. Then, the EEG signals will go through statistical processes which are Scatterplot and Correlation test. As a result, there is correlation between the balanced brain and IQ application with strong and significant Pearson correlation

Pairwise classification using combination of statistical descriptors with spectral analysis features for recognizing walking activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

Patient\u27s outcome of bladder cancer managed by radical cystectomy with lymphadenectomy at a university hospital

Objective: To study the impact of tumour staging and nodal metastases in predicting 5- year\u27s survival after radical cystectomy and bilateral pelvic lymphadenectomy for primary bladder cancer. Methods: During the period 1995 to 2005, 58 patients underwent radical cystectomy and bilateral pelvic lymphadenectomy and urinary diversion at a University hospital. Patients were identified using medical indexing coding system (ICD 9CM) using standard key words. The patient records were analyzed and follow up data updated. Disease specific survival, death or recurrence was taken as end point.Results: Out of 58 patients, 50 (86%) were males and 8 (14%) females with a mean age of 61 +/- 13.1 years (range from 27 to 87 years). Of 58 patients, 11 (23%) were excluded from the study because of in adequate follow up. The mean follow up was 5.7 years (range, 7 months to 11 years). The overall 5 years survival was 55% with disease specific survival being 66%. Patients with pathological stage TO at cystectomy have 87% 5 years disease specific survival compared to 60%, in patients with pT4 (p = 0.705). The 5-year survival for node positive patients was 16%, compared to 60% for node negative patients (p \u3c 0.01). Conclusions: Radical cystectomy and bilateral pelvic lymphadenectomy is the standard treatment for muscle invasive and high grade T1 cancers, and as salvage for recurrent cancers. Lymphadenectomy has a potential therapeutic benefit. The pathological stage at cystectomy and nodal status are predictors of 5 years survival

Intracytoplasmic sperm injection outcome using ejaculated sperm and retrieved sperm in azoospermic men.

Introduction:We aimed to determine pregnancy and miscarriage rates following intracytoplasmic sperm injection (ICSI) cycles using retrieved epididymal and testicular sperm in azoospermic men and ejaculated sperm in oligospermic and normospermic men. Materials AndMethods: This retrospective study was carried out on 517 couples who underwent ICSI. They included 96 couples with azoospermia and 421 with oligospermia or normal sperm count in the male partner. Of the men with azoospermia, 69 underwent percutaneous epididymal aspiration (PESA) and 47 underwent testicular sperm extraction (TESE). In the 421 men with oligospermia or normal sperm count, ejaculated sperm was used for ICSI. The differences in the outcomes of ICSI using PESA or TESE and ejaculated sperm were evaluated. The main outcome measures were pregnancy and miscarriage rates.Results: No significant differences were seen in pregnancy and miscarriage rates with surgically retrieved and ejaculated sperm. The pregnancy rates (including frozen embryo transfer) were 43.5%, 36.2%, and 41.4% in couples with PESA, TESE, and ejaculated sperm, respectively (P = .93). The miscarriage rates were 16.7%, 23.5%, and 12.1%, respectively (P = .37).Conclusion: Intracytoplasmic sperm injection in combination with PESA and TESE is an effective method and can successfully be performed to treat men with azoospermia. The outcomes with these procedures are comparable to ICSI using ejaculated sperm

New Records of the Spider Fauna From Sarawak, Malaysia

SHORT COMMUNICATION New Records of the Spider Fauna from Sarawak, Malaysi

Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method